ISSN (Print): ISSN (Online): ISSN (Print): 2616-051X | ISSN (electronic): 2616-0501

NIGERIAN JOURNAL OF ENVIRONMENTAL SCIENCES AND TECHNOLOGY (NIJEST)

https://www.nijest.com Volume 2 | Number 1 | March 2018

NIJEST / Nig. J. of Env. Sci. & Tech. 2 (1), March 2018

www.nijest.com NIGERIAN JOURNAL OF ENVIRONMENTAL SCIENCES AND TECHNOLOGY (NIJEST) https://www.nijest.com

EDITORIAL BOARD EDITOR-IN-CHIEF Prof J. O. Ehiorobo Faculty of Environmental Sciences, University of Benin, Benin City, Nigeria / [email protected]

EDITORS

Prof. L. A. Ezemonye Prof. Olatunde Arayela Department of Animal and Environmental Biology, Department of Architecture, Federal University of University of Benin, Benin City, Nigeria Technology, Akure, Nigeria

Prof. O. C. Izinyon Prof. G. C. Ovuworie Department of Civil Engineering, University of Benin, Department of Production Engineering, University of Benin City, Nigeria Benin, Benin City, Nigeria

Prof. M. N. Ono Prof. C. C. Egolum Department of Surveying and Geoinformatics, Department of Estate Management, Nnamdi Azikwe Nnamdi Azikwe University, Awka University, Awka

Prof. T. C. Hogbo Prof. Vladimir A. Seredovich Department of Quantity Surveying, Federal Siberian State University of Geosystems and Technologies, University of Technology, Minna Novosibirsk, Russia

Prof. F. O. Ekhaise Prof. George W. K. Intsiful Department of Microbiology, University of Benin, Department of Architecture, Kwame Nkrumah University Benin City, Nigeria of Science and Technology, Kumasi, Ghana

Prof. Clinton O. Aigbavboa Prof. Toshiroh Ikegami Department of Construction Management and Department of Urban Studies / School of Policy Studies, Quantity Surveying, University of Johannesburg, Kwansei Gakuin University, Yubinbango Nishinomiya, South Africa Japan

Prof. Samuel Laryea Dr. (Ms) Oluropo Ogundipe School of Construction Economics and Management, Nottingham Geospatial Engineering Department, University of Witwatersrand, Johannesburg, South University of Nottingham, UK Africa

Prof. Stephen Ogunlana Dr. Eugene Levin School of the Built Environment, Heriot Watt Geomatics Engineering Department, Michigan University, UK Technological University, Michigan, USA

Prof. A.N. Aniekwu Prof. P. S. Ogedengbe Department of Architecture, University of Benin, Department of Estate Management, University of Benin, Benin City, Nigeria Benin City, Nigeria

Dr. H.A.P. Audu Dr. Patrick Ogbu Department of Civil Engineering, University of Benin, Department of Quantity Surveying, University of Benin, Benin City, Nigeria Benin City, Nigeria

JOURNAL SECRETARIAT

Journal Secretary Assistant Journal Secretary Prof. Raph Irughe-Ehigiator Dr. Okiemute Roland Ogirigbo Department of Geomatics, University of Benin, Department of Civil Engineering, University of Benin, Benin City, Nigeria Benin City, Nigeria

Nigerian Journal of Environmental of Environmental Sciences and Technology (NIJEST) Journal available online at http://www.nijest.com Vol 2 No. 1 March 2018 ISSN (Print): 2616-051X | ISSN (electronic): 2616-0501

Contents

Article Page Stress and Environmental Health of Women in Different Neighbourhoods of Lagos Metropolis

Nwokoro, I.I.C., Olayinka, D.N. and Okolie, C.J. 1 – 10 Improvement on the Strength of 6063 Aluminium Alloy by Means of Warm Rolling Operation

Adekunle, N.O., Aiyedun, P.O., Kuye, S.I. and Lawal, I.O. 11 – 18 Evaluation of the Corrosion Rate of Aluminium 6063 in Petrol, Kerosene and Water

Adekunle, N.O., Aiyedun, P.O., Kuye, S.I. and Adetunji, R.O. 19 – 27 Change Detection Analysis Using Surveying and Geoinformatics Techniques

Onuigbo, I.C. and Jwat, J.Y. 28 – 38 Hydrogeophysical Survey of Groundwater Development at Okada Community Ovia North - East L.G.A. Edo State

Ehigiator, M. O. 39 – 45 Performance Assessment of Biological Wastewater Treatment at WUPA Wastewater Treatment Plant, Abuja, Nigeria

Chukwu, M.N. and Oranu, C.N. 46 – 55 Determination of Conversion Constant between Lagos Datum and Niger Delta Mean Lower Low Water Datum and their Horizontal and Vertical Accuracy Standards using GNSS Observations

Ehigiator, M.O. and Oladosu, S.O. 56 – 68 Staff Satisfaction with Workplace Facilities in the School of Environmental Technology, Federal University of Technology, Akure, Nigeria

Mbazor, D.N., Ajayi, M.A. and Ige, V.O. 69 – 77 Validation of Global Digital Elevation Models in Lagos State, Nigeria

Arungwa, I.D., Obarafo, E.O. and Okolie, C.J. 78 – 88 Heavy metals in soil and accumulation in medicinal plants at an industrial area in Enyimba city, Abia State, Nigeria

Ogbonna, P.C., Nzegbule, E.C., Obasi, K.O. and Kanu, H. 89 – 95 Soil chemical characteristics in wet and dry season at Iva long wall underground mined site, Nigeria

Ogbonna, P.C., Nzegbule, E.C. and Okorie, P.E. 96 – 107 Linacre Derived Potential Evapotranspiration Method and Effect on Supplementary Irrigation Water Needs of Tomato/Cabbage/Carrot

Emeribe, C.N., Isagba, E.S. and Idehen, O.F. 108 – 117 Aquifer Mapping in the Niger Delta Region: A Case Study of Edo State, Nigeria

Seghosime, A., Ehiorobo, J.O., Izinyon, O.C. and Oriakhi O. 118 – 129 Non-Linear Error Functions Approach to Kinetic Study of Arsenic Removal from Soils using Proteus mirabili and Bacillus subtilis

Atikpo, E., Agori, J.E., Iwema, E.R., Michael, A. and Umukoro, L.O. 130 – 136 Nigerian Journal of Environmental Sciences and Technology (NIJEST) www.nijest.com

ISSN (Print): 2616-051X | ISSN (electronic): 2616-0501

Vol 2, No. 1 March 2018, pp 1 - 10

Stress and Environmental Health of Women in different Neighbourhoods of Lagos Metropolis

Nwokoro, I.I.C. 1, Olayinka, D.N.2,* and Okolie, C.J.2 1Department of Urban & Regional Planning, Faculty of Environmental Sciences, University of Lagos, Nigeria 2Department of Surveying & Geoinformatics, Faculty of Engineering, University of Lagos, Nigeria Corresponding Author: *[email protected]

ABSTRACT

It has been established that women spend more time in the neighbourhood environment, and therefore, are more vulnerable to the observable poor conditions. The focus of this study is on neighbourhood environmental stressors that affect womens’ health in Lagos metropolis. The factors considered include access to clean water, adequate sanitation, drainage conditions, ventilation and hygiene, type of energy for cooking and nutrition. These factors are exacerbated by poverty and differ across different neighbourhoods in Lagos metropolis. 1150 respondents (high – 50; medium – 328; and low - 772) consisting of randomly selected women, aged 18 years and above were selected from all the 17 Local Government Areas (LGAs) in metropolitan Lagos to achieve 100% representation. Focus Group Discussions were held with women from selected different neighbourhoods. A 5-point likert scale was used as a measure of self-reported stress and self-reported health, with higher numbers indicating a greater self-reported stress. From the different survey methods used, results show that women in the low income neighbourhoods are more vulnerable to environmental stressors, and so their health is mostly affected negatively. Women in the other income groups also experience some form of stress but at lower severity levels. Environmental stressors and severity of chronic illness are linked to stress. An improvement in the environmental conditions will reduce the amount of stress experienced by women of different income neighbourhoods.

Keywords: Stress, Environmental Stressors, Metropolitan Lagos, Neighbourhoods, Health

1.0. Introduction

Women are major players in health care service provision through their roles as household managers and carers. In so doing, they spend longer hours in their household environment. All societies are divided along what we can call the fault line of gender (Moore, 1988; Papenek, 1990). This means that women and men are defined as different types of beings, each with their own opportunities, roles and responsibilities. However, WHO (1994) notes that these circumstances cause women to cope with the pressures of modernisation, which often requires them to assume additional duties and responsibilities, plus the burdens of their traditional roles. Thus women tend to have less time and energy. The social role of women may make them more vulnerable to certain hazards or exposures. Examples are the stress of womens’ multiple roles as income providers, home managers, and reproducers; or poor nutritional status which can increase susceptibility to environmental chemicals such as lead and cadmium, and exposure to harmful emissions from smoke while cooking. This was corroborated in the theory of work-family-conflict by Greenhaus and Beutell (1985). The above indices are the major reasons for exploring the interplay between the neighbourhood environment, stress and health of women. Pearlin and Schooler (1978) reported that the concept of stress not only refers to major life events but also encompasses ongoing minor events like electricity failure, maids not turning up, unexpected guests and childrens’ misbehaviour. Similarly, stress can be viewed as a physiological demand placed on the body when one must adapt, cope or adjust (Nevid and Rathus, 2007). Different types of stress include psychological, physical, chemical, environmental, long term and short term stress (Kelly et

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Nigerian Journal of Environmental Sciences and Technology (NIJEST) Vol 2, No. 1 March 2018, pp 1 - 10 al., 1997). Similarly, Djuric et al. (2008) have also noted that chronic stressors (a long term form of stress) associated with health disparities include perceived discrimination, neighbourhood stress, daily stress, family stress, acculturative stress, environmental stress and maternal stress. However, this study focuses on the environmental stress particularly how neighbourhood environmental factors interact with a woman's individual genetic susceptibilities to affect her health over her lifetime. The factors considered include access to clean water, adequate sanitation, drainage conditions, ventilation and hygiene, type of energy for cooking and nutrition. Evidence strongly suggests that the neighbourhood in which people live influences their health, either in addition to or in interaction with individual level characteristics (Vidanaarachchi et al., 2006). There are environmental health challenges in Lagos which include low access to potable water, poor sanitation methods, inadequate drainage provision and poor housing conditions particularly in low income communities. These factors which are exacerbated by poverty differ across different neighbourhoods in Lagos and women and children are more vulnerable to these conditions. Similarly, in the study of prevalence of different neighbourhood environmental stressors and associations between the stressors and self-rated health, nuisance from neighbours and drug users, shortage of water and having poor water/sewage drainage system were associated with self-rated mental health among the women (Perera et al., 2009). This aspect of womens’ health is under-researched in the developing countries like Nigeria. It is therefore important to understand stress, especially as it relates to gender and health. This study investigates how these environmental stress conditions affect the health of women in different income residential neighbourhoods of metropolitan Lagos. It specifically attempts to investigate which environmental stressors are mostly experienced by women, what the most prevalent reported environmentally related diseases are, and how these affect the health status of women in different neighbourhoods of Lagos. Although there are many different concepts of stress in the respective fields of medicine, psychology, and sociology, it is generally understood that stress is aversive in some sense. Psychologists often favour the stress concept proposed by Lazarus (1993) which states that stress is a condition or feeling experienced when a person perceives that demands exceed the personal and social resources the individual is able to mobilize. Kelly et al. (1997) identified the principal components of stress as stressors and the stress response. Stressors can be broadly defined as those events or situations that perturb a person’s psychological and/or physical homeostasis. The authors believe that the division of labour by gender results in differential exposure and vulnerability to stress among women and men; women are in poorer health because their lives are more stressful than that of men and they are more vulnerable to the health consequences of life stressors because of their relative lack of material, personal, and social resources. Some researchers argue that the health effects of stress may be experienced and embodied by women and men in different ways (Umberson et al., 1996). An investigation done by Macintyre et al. (1996) using data from a British regional, longitudinal study found female excess in ill health for depression, high blood pressure, varicose veins, malaise symptoms (such as worrying, nervousness, difficulty in concentrating, tiredness, and sleep problems), as well as selected physical symptoms (such as headaches, fainting, or dizziness). However, environmental stress which is the major focus of this study is constructed from some items that indicate problems with residential neighbourhoods and friends. “The living environment plays a vital role in determining health” (Stafford et al., 2006; Wedan et al., 2008). As also reported by Rao et al. (2007) and Wedan et al. (2008), individuals living in poor neighbourhood environments tend to have higher morbidity and mortality rates compared to those living in environmentally sound neighbourhoods. Accordingly, adverse neighbourhood factors have been shown to be positively associated with coronary heart disease (CHD) (Diez-Roux et al., 1997; Sundquist et al., 2006). The authors further observed that “neighborhood economic deprivation may compromise health-promoting resources” (Diez-Roux et al., 2001). For example, poor and minority neighbourhoods tend to have fewer grocery stores with healthy foods (Morland et al., 2002) and fewer pharmacies with needed medications (Morrison et al., 2000). Finally, poor nutrition can increase susceptibility to environmental pollutants by compromising immune functions (Rios et al., 1993). Figure 1 illustrates a stress-exposure disease framework for environmental health disparities.

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Also, the study carried out by Hazra et al. (2005) titled “effects of household environment on womens’ health in Northeast India” clearly illustrates the morbidity situation mainly attributable to household air pollution. It tells how the factors related to the household environment influence the prevalence of diseases and thus the health of women. However, most researches in Nigeria have focused mainly on occupational stress and health. For example, Mojoyinola (2008) investigated the effects of job stress on health of nurses in Ibadan and found that the highly stressed nurses exhibited personal and work behavioural problems. Similarly, Oluwole et al. (2012) examined the relationships between stress, social support and work/family conflict on the mental health of Nigerian women. The study revealed that there was significant difference between young and old women in the level of stress experienced; as well as between junior and senior staff in the social support experienced between single and married women. This study, therefore, intends to further explore the relationship between environmental stressors and health of women across different neighbourhoods of Lagos metropolis.

Figure 1: Stress-exposure disease framework for environmental health disparities Source: Modified from NIEHS (2004)

2.0. Methodology 2.1. Study area The seventeen Local Government Areas (LGAs) in Lagos metropolis were selected in order to achieve 100% representation. Collectively, these seventeen LGAs had a total population of 4,129,697 females at the 2006 national census (National Bureau of Statistics, 2012). This figure represents 94% of the total female population in the state recorded by the census. For the questionnaire distribution, one LGA each from the most predominant income neighbourhoods in the metropolis was selected. The three LGAs selected include Eti-osa representing the high income area, Lagos Mainland for the medium income area and Alimosho for the low income area. These three LGAs have a combined female population of 934,886 persons. A map of Lagos State showing the metropolis is presented in Figure 2. Lagos State was the former capital of Nigeria and is the country’s centre of commerce. The state has a very diverse and fast-growing population, resulting from heavy and ongoing migration to its cities from all parts of Nigeria as well as neighbouring countries. The metropolitan area of the state lies between Latitudes 6º20′00′′- 6º42′10′′N and Longitudes 3º02′30′′- 3º42′40′′E. It comprises the following LGAs – Agege, Ajeromi/Ifelodun, Alimosho, Amuwo Odofin, Apapa, Eti-Osa, Ifako/Ijaye, Ikeja, Ikorodu, Kosofe, Lagos Island, Lagos Mainland, Mushin, Ojo, Oshodi/Isolo, Shomolu, and Surulere.

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Figure 2: Map of Lagos State showing the LGAs in the metropolis

2.2. Procedure A multi stage sampling method was used to arrive at a selection of 1150 respondents made up of high (50), medium (328) and low (772) income neighbourhoods comprising seventy-two wards within the three selected LGAs. Questionnaires were administered to a random selection of women aged 18 years and above. Focus Group Discussions were also held with women from different neighbourhoods. A 5-point likert scale was used as a measure of self-reported stress and self-reported health, with higher numbers indicating a greater self-reported stress. To facilitate the interpretability of interactive effects, the Cronbach’s alpha, a measure of internal reliability, bound by 0 and 1, with measures closer to 1 representing strong reliability for the items in the research instrument was adopted. Data analysis was by descriptive statistics, chi-square tests, and mean item scores (for the likert scale). For the Focus Group Discussion, one LGA each from the most predominant income neighbourhood was selected. The three LGAs selected include Eti-osa representing the high income area, Lagos Mainland for the medium income area and Alimosho for the low income area. Accordingly, two groups were chosen from the high income area, three groups from the medium income area and four groups from the low income area. Each group consisted of ten women of age 18 years and above drawn from different wards, educational, social and professional backgrounds. Also each group was met four times to ascertain consistency of comments. The choice of the number of groups from each LGA was informed by findings in the literature (e.g. Nwokoro and Agbola, 2011) which showed that women from the low income area are more vulnerable to environmental health problems.

3.0. Results and Discussion The major issues discussed here include the socio-economic characteristics of the women, the environmental stressors and their relationship with the health of women. Results of the likert scale were also discussed. Finally, results of the Focus Group Discussions are presented to corroborate the research questionnaire data. 3.1. Socio-economic characteristics of the women The major factors considered here are the womens’ monthly income and their educational status. Table 1 shows all the socio-economic variables considered in this study. While over 40% of women in the low income neighbourhood earn less than N15,000 ($100) per month, about 75% of the women in the high income areas earn over N45,000 ($300) per month.

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Table 1: Selected Socio-economic characteristics of women Socio-economic Characteristics Low Income (LI) Medium Income (MI) High Income (HI) Income per Month (Naira) < 15,000 40.1 18.4 5.0 15,000 – 30,000 27.2 14.4 7.5 30,001 – 45,000 8.6 18.4 15.0 >45,000 24.1 48.9 72.5 Total 100 100 100 Level of Education No Formal Education 7.8 4.9 2.5 Primary School 11.6 10.8 5.0 Secondary School 38.7 25.9 17.5 Tertiary Education 36.1 47.5 62.5 Others 5.9 10.8 12.5 Total 100 100 100 No. of respondents (N) 732 305 40 Source: Questionnaire survey (2012)

There is also a significant difference between the earning capacities of income of the women across the income groups. Results of the chi-square tests (π2 = 79.777; P = 0.05) corroborates the relationship between the income earned and residential neighbourhoods of women. Women in the high income group attained the highest levels of education more than those in the low income group. There is a significant difference in the levels of educational attainment of the various income groups as shown in Table 1 and results of the chi-square tests (π2 = 49.238; P = 0.05). This means that the different income groups attained different levels of education. With the above results, it can be concluded that the socio–economic group of women is a function of income and educational status. 3.2. Environmental stressors and health of the women Having identified the environmental stressors affecting the women in different neighbourhoods, an investigation of the relationship between these stressors and the health of the women was done using the most reported environmentally related diseases as shown in Table 2. The results revealed that the low income group experienced all the diseases most, followed by the medium income group and the least by the high income group. All the diseases examined are also stress inducing. It can be concluded that the environmental stressors are associated with the health of the women but at different degrees.

Table 2: Most frequently reported experienced environmental diseases in women (occurring more than 4 times in 1 year) Types of Environmental Diseases (%) Low Income (LI) Medium Income (MI) High Income (HI) Malaria 44.5 33.8 17.5 Diarrhoea 70.5 54.4 35.0 Sleeplessness 37.4 51.8 87.5 Fatigue 25.1 36.4 85.0 Respiratory Infection 77.5 49.8 42.5 Total 100 100 100 Note : Multiple responses possible Source: Questionnaire survey (2012)

Figure 3 shows the distribution of these most frequently reported experienced environmental diseases presented graphically on a map of the Focus LGAs. To further explore the relationship between stress, environmental factors and health of the women in Lagos, a 5-point likert scale was used to elicit answers from questions on the most stressful environmental stressor in the past one year on one hand, and the effect of these environmental stressors on their health on the other. Responses ranged from 1 (not stressful) to 5 (most stressful) across the different neighbourhoods. This was further subjected to analysis using the mean item score, which led to the ranking of the environmental factors according to

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Nigerian Journal of Environmental Sciences and Technology (NIJEST) Vol 2, No. 1 March 2018, pp 1 - 10 the most stressful (items with the highest score) to the least stressful. These are indicated as self- reported stress and self-reported health in Tables 3 and 4 respectively.

Figure 3: Distribution of the most frequently reported experienced environmental diseases shown graphically on a map of the Focus LGAs

These results are corroborated by findings from earlier studies. For example, Nwokoro and Agbola (2011) while examining the environment and health inequalities of women in different neighbourhoods of metropolitan Lagos reported that the women across all income levels experienced some diseases associated with poor environmental conditions. Their findings also showed that the health status of women in the high and medium income communities were higher than that of the women in the low- income communities (slums).

Table 3: Self-reported stress (most stressful environmental stressor in the past one year) Environmental factors Mean Item Score Ranking Low Income Water quality and supply 0.881 1 Ventilation and hygiene 0.79 2 Cooking source of energy 0.755 3 Drainage facilities 0.696 4 Nutrition (food) 0.648 5 Medium Income Ventilation and hygiene 0.712 1 Cooking source of energy 0.704 2 Water quality and supply 0.685 3 Drainage facilities 0.657 4 Nutrition (food) 0.525 5 High Income Ventilation and hygiene 0.575 1 Cooking source of energy 0.57 2 Nutrition (food) 0.55 3 Water quality and supply 0.535 4 Drainage facilities 0.415 5 Source: Questionnaire survey (2012)

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Results from Table 3 show that the most significant environmental stressor reported by the low income group is poor water quality and supply with a mean item score of 0.881. Other significant environmental stressors reported by this group of women are poor ventilation and hygiene and sources of energy for cooking which ranked second and third respectively. The practical implication of this result is that current conditions of environmental factors in the low income neighbourhood are very poor and there is an urgent need for improvement. On the other hand, the middle income group reported poor ventilation and hygiene and poor sources of energy for cooking as the most stressful environmental factors. These were significant with mean item scores of 0.718 and 0.704, and ranked first and second respectively, closely followed by poor sources of water. Results of this group of women appears better in terms of stress level, but the environmental conditions still need to be improved on. The results of the high income group of women do not show any of the environmental factors having a significant stress impact. However, ventilation and hygiene ranked highest with a mean item score of 0.575. Again, this corroborates the results of the questionnaire survey indicating that this group of women have access to good sources of water, drainage facilities and, sources of energy for cooking.

Table 4: Self-reported health (how much have these environmental stressors affected your health in the past one year)

Environmental factors Mean Item score Ranking

Low Income Water quality and supply 0.861 1 Nutrition (food) 0.811 2 Ventilation and hygiene 0.809 3 Cooking source of energy 0.76 4 Drainage facilities 0.681 5 Medium Income Ventilation and hygiene 0.718 1 Cooking source of energy 0.704 2 Water quality and supply 0.685 3 Drainage facilities 0.633 4 Nutrition (food) 0.622 5 High Income Drainage facilities 0.685 1 Water quality and supply 0.665 2 Nutrition (food) 0.63 3 Ventilation and hygiene 0.51 4 Cooking source of energy 0.505 5 Source: Questionnaire survey (2012) Similarly, results of ranking of the self-reported health by different groups of women are shown in Table 4. For the low income group, poor sources of water (0.861), poor nutrition (0.811) and poor ventilation and hygiene (0.809) were reported as the most significant environmental factors that affected their health in the past one year. Poor sources of energy for cooking was also reported as having some effect on their health. These also agree with previous results which identified these factors as the most reported environmental stressors in the past one year for this group of women. However, the most reported stressful environmental factors to the health of the women in the medium income group are poor ventilation and hygiene (0.718) and poor sources of energy for cooking (0.704), followed by poor sources of water. These were the same factors reported by the women in this group as causing them the highest level of stress. Again, the environmental stressors as reported by the high income group appear to have the least effect on the health of this group. Although poor drainage facilities and poor sources of water ranked high on the scale, their mean item scores are not as high as those of the low and medium income groups. It can be concluded that women in all income groups experience stress associated with environmental factors which also affect their health but these occur at varying degrees.

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The Focus Group Discussions identified stress as a major cause of ill health among women across different income neighbourhoods although reported at various levels. Women from all the three groups said that stress, in addition to poor environmental conditions, affects their health. This is in consonance with the findings by Nwokoro and Okusipe (2002) that deteriorating environmental conditions (poor access to safe water, waste management disposal methods, affordable healthcare) are the major contributory factors to poor health and quality of life in Lagos metropolis. The women further said that although the condition of the environment can affect their health, there are other associated factors of which stress is the most prominent. As already discussed in the literature review, stress can take on many different forms, and can contribute to symptoms of illness. Common symptoms of stress include headache, sleep disorders, difficulty in concentration, short temper, upset stomach, job dissatisfaction, low morale, depression, and anxiety. In the focus groups, women noted that certain socio-economic factors may contribute to the symptoms of stress. For example, individuals with less education may be at a higher risk of stress. The unemployed and those with little access to healthcare are also more likely to experience stress. When one’s livelihood is at stake, the simple act of survival can be stressful. Accordingly, the Focus Group Discussion participants explained that their daily activities had no room for rest. In spite of their busy schedule as workers and children minders, they are still expected to take care of their husbands. The interesting aspect of this is that stress cuts across all the income groups, although reported at different levels. As the women agreed, this is caused by their changing roles in society.

This is well captured by participant 7, a middle aged woman in the low income group who stated that: “Stress is a major cause of ill-health for me. In the morning before 5am, I wake up, bath the children and prepare them for school, cook for the family, and then go to market where I hawk. I do not have a house help to assist in all these chores and my husband does not care to help. I get home late to continue caring for the family.”

The major difference between the three Focus Group Discussion reports of women from different income groups is the ability and resources to manage the stress. The professional women in the high income group are able to afford the services of house helps which to an extent reduces their stress and consequently lessens the impact on their health. Here lies the inequality in their health status.

4.0. Conclusions

This study has explored the socio-economic as well as the environmental conditions of women in different income neighbourhoods of Lagos metropolis. It further x-rayed the relationship between stress, environmental factors and the health of these women. From the different survey methods used, results show that women in the low income neighbourhoods are more vulnerable to environmental stressors, and as such their health is mostly affected negatively. Women in the other income groups also experience some form of stress but at lower severity levels. Environmental stressors and severity of chronic illness are linked to stress. An improvement in the environmental conditions will reduce the amount of stress experienced by women of different income neighbourhoods.

Acknowledgements The authors are grateful to the African Population Health Research Centre (APHRC), Kenya for their support in hosting the lead researcher on this study.

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Djuric, Z., Bird, C.E., Furumoto-Dawson, A., Rauscher, G.H., Ruffin, M.T., Raymond, I.V., Stowe, P., Tucker, K.L., and Masi, C.M. (2008). Biomarkers of psychological stress in health disparities research. The Open Biomarkers Journal, 1, pp. 7-19.

Greenhaus, J., and Beutell, N. (1985). Sources of conflict between work and family roles. Academy of Management Revie, 10, pp. 76-88. Hazra, A., Datta, S., and Guha, M. (2005). Effect of household environment on women’s health in Northeast India. International Institute for Population Sciences. Mumbai. Kelly, S., Hertzman, C., and Daniels, M. (1997). Searching for biological pathway between stress and health. Annual Review of Public Health, 18, pp. 437-62 Lazarus, R.S. (1993). Why we should think of stress as a subset of emotion. In: Leo Golderberger and Shlomo Breznitz (Eds). The Handbook of Stress The Free Press, New York, USA. Macintyre, S., Hunt, K., and Sweeting, H. (1996). Gender differences in health: are things really as simple as they seem? Social Science & Medicine, 42, pp. 617–624. Mojoyinola, J.K. (2008). Effects of job stress on health, personal and work behaviour of nurses in public hospitals in Ibadan Metropolis, Nigeria. Kamla-Raj (Ethno-Med), 2(2), pp. 143-148. Moore, H. (1988). Feminism and Anthropology. Polity Press, Oxford. Morland, K, Wing, S., and Diez-Roux, A. (2002). Neighborhood characteristics associated with the location of food stores and food service places. Am J Prev Med, 22, pp. 23–29. Morrison, R.S., Wallenstein, S., Natale, D.K., Senzel, R.S., and Huang, L.L. (2000). We don’t carry that—failure of pharmacies in predominantly non-white neighbourhoods to stock opioid analgesics. N Engl J Med, 3426, pp. 1023–1026. National Bureau of Statistics (2012). Annual abstract of statistics, 2012. Nwokoro, I.I.C., and Agbola, B.S. (2001). Environment and health inequalities of women in different neighbourhoods of Metropolitan Lagos, Nigeria. In Maantay, J., & McLafferty, S. (Eds) Geospatial Analysis of Environmental Health – Geo technologies and the Environment, USA, Springe, pp. 283-302. Nwokoro, I.I.C., and Okusipe, M.D. (2002). Urban health and urban infrastructure: A spatial analysis of low-income communities in Lagos metropolis. In Amole, D., Ajayi, A., & Okewole, A (Eds): The City in Nigeria. Faculty of Environmental Management, Obafemi Awolowo University, Ile-Ife, Nigeria, pp. 378-383. Nevid, J.S., and Rathus, S.A. (2007). Your Health. In: Mason, O. H: Thomson Custom Solutions. Oluwole, DA., Hammed, A.T., and Awaebe, J.I. (2012). Patterns of stress, social support, and mental health among Nigerian women. Advancing Women in Leadership Journal, March 2012, Kindle Edition. Papenek, H. (1990). To each less than she needs, from each more than she can do; allocations, entitlements and value. Tinker, I. [Ed] Persistent Inequalities; Women and World Development. Oxford University Press, Oxford. Pearlin, L., and Schooler, C. (1978). The structure of coping. J. Hlth. Soc.Behav., 19, pp. 2- 21. Perera, B., Qstbye, T., and Jayawardana, C. (2009). Neighborhood environment and self-rated health among adults in southern Sri Lanka .Int. J. Environ. Res. Public Health, 6, pp. 2102-2112. Rao, M., Prasad, S., Adshead, F., and Tissera, H. (2007). The built environment and health. BMJ, 370, pp. 1111–1113. Rios, R., Poje, G.V., and Detels, R. (1993). Susceptibility to environmental pollutants among minorities. Toxicol Ind Health, 9, pp. 797–820.

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Stafford, M., and McCarthy, M. (2006). Neighborhoods, housing and health. In: Marmot M, Wilkinson RG, (Eds). Social determinants of health. 2nd ed. Oxford University Press; Oxford, UK, pp. 297–317. Sundquist, K., Theobald, H., Yang, M., Li, X., Johansson, S.E., and Sundquist, J. (2006). Neighborhood violent crime and unemployment increase the risk of coronary heart disease: a multilevel study in an urban setting. Soc Sci Med, 62(8), pp. 2061-2071. Umberson, D., Chen, M.D., House, J.S., Hopkins, K., and Slaten, E. (1996). Gender differences in relationships and psychological well-being. American Sociological Review, 61, pp. 837–857. Vidanaarachchi, C.K., Yuen, S.T., and Pilapitiya, S. (2006). Municipal solid waste management in the southern province of Sri Lanka; problems, issues and challenges. Waste Manage, 26, pp. 920–930. Wedan, M.M., Carpiano, R.M., and Robert, S.A. (2008). Subjective and objective neighbourhood characteristics and adults health. Soc. Sci. Med, 66, pp. 1256–1270. World Health Organization (1994). Assessment of fracture risk and its application to screening for post-menopausal women. Technical Paper Series No. 843. WHO Scientific Study Group, Geneva.

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ISSN (Print): 2616-051X | ISSN (electronic): 2616-0501

Vol 2, No. 1 March 2018, pp 11 - 18

Improvement on the Strength of 6063 Aluminium Alloy by Means of Warm Rolling Operation

1, 1 1 1 Adekunle N.O. *, Aiyedun P.O. , Kuye S.I. and Lawal I.O. 1Department of Mechanical Engineering, Federal University of Agriculture, Abeokuta, Nigeria Corresponding Author: *[email protected]

ABSTRACT

This paper presents the effect of varying processing variables on the superplastic ductility of Al 6063 alloy. The three variables investigated were total strain, reheating time and strain rate during warm rolling (by varying the reduction per pass). The alloy samples were warm rolled at strain rates varying from 1.0 x 10-5s-1 to 4.1 x 10-1s-1 to a total true strain of 3, at 4.4 – 7.5% reduction per pass for 3 hours reheating time between passes. Tensile test and microstructural analysis were carried out on the samples. The results showed that at lowest strain rate (4.1 x 10-3s-1), greatest ductility, fracture stress and fracture energy were achieved with 4.0% reduction pass. The material warm rolled to a total true strain of 2.9 exhibited much higher ductility. Longer reheating time revealed coarser grains, with peak ductility occurring at slower strain rates.

Keywords: Hot Work, Tensile Strength, Total Strain, Strain Rate, Reheating Time

1.0. Introduction

The demand for aluminium has increased rapidly, due to its unique properties that have made it one of the most versatile materials being used for engineering and construction (Chee and Mohamad, 2009). These properties include: durability, light weight, extrudability, good surface finish and corrosion resistant, at present aluminium and its alloys are used as an alternative for other metals (De Silva and Pereral, 2012).

The excellent properties of 6xxx-group alloys have made it suitable for application in the building, aircraft, and automotive industries (Mrówka-Nowotnik and Sieniawski, 2005).Through proper combination of solution heat treatment, quenching, cold working and artificial aging, very high strengths can be obtained (Esezobor and Adeosun, 2006).

The most commonly accepted characteristics of a superplastic material are: a fine, equiaxed grain structure with high angle boundaries; a deformable second phase if present; low strain rates at elevated temperatures equal to 0.5 – 0.7 Tm; resistance to cavitation; and a thermally stable structure (Stengall,1999). Several thermomechanical treatments have been done in which the interplay of precipitates or constituent particles with deformation to refine grain size, enhance strength, toughness and improve ductility (Sherby and Wadsworth, 2007). Thermomechanical treatment is the combination of plastic deformation and heat treatment to provide high strength properties. It can either be intermediate thermomechanical heat treatment whose deformation is applied to give very fine recrystallized grains prior to solution treatment or final thermomechanical heat treatment whose deformation is applied after solution treatment and may involve cold or warm working before, during or after ageing (Oliveira and Nice, 2004). The benefits that will be gained by controlling a 6063 aluminium alloy additions, combined with the appropriate thermomechanical process, will lead to grain size control which will be a requirement for superplastic behaviour (Sherby and Wadsworth, 2007). The effect of homogenization treatment conditions on cold deformation of aluminium AA2014 and AA6063 has been studied. These aluminium alloys were homogenized at different temperature range for 8h at strain rate of 2 x 10-2s-1. The secondary phases, which were large and distributed on grain boundaries became small and were spread through the grains by homogenization treatment followed by the cold deformation. The homogenization treatment improved the degree of the cold

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Nigerian Journal of Environmental Sciences and Technology (NIJEST) Vol 2, No. 1 March 2018, pp 11 - 18 deformation (Totik et al., 2003). It has been observed that when aluminium alloy 6063 was processed by upset forging and cold rolling at ambient temperature, the maximum ultimate tensile strength (UTS) and hardness (HRN) increase as the range of reduction from processing increases from 0 to 50 percent. However, the ductility decreases correspondingly, which is indicative of a low strain- hardening exponent (Balogun et al., 2007).

This present experimental study was designed to find out the influence of total strain, reheating time and strain rate during warm rolling on mechanical properties of structural aluminium 6063 alloys.

2.0. Materials and Methods

2.1. Materials and equipment Materials and equipment used for this work were aluminium ingot, universal testing machine, oil-fired crucible furnace, optical microscope, roll mill and metal analyser.

2.2. Warm rolling operation Rectangular test samples of dimensions 350 mm x 30 mm x 12 mm, were cast from AA6063 aluminium alloy ingot and melted in an oil fired crucible furnace. The melted ingot was sectioned into slabs, 2032 mm long with a cross section of 254 mm square and were solution treated above the solvus line at 615°C for about 5 hours and at 625°C for 16 hours using the procedure developed by Johnson (2005). The temperatures were monitored using two thermocouples placed on the slab surface.

All rolling was conducted utilizing a rolling mill having a maximum roll opening of 450mm. The slabs were warm rolled within 16 hours of upset forging into sheets in accordance with the techniques reported by Mills, (2004).

When the thermocouple indicated the desired temperature (625°C), the rolling was commenced prior to the initial pass and the heating time was approximately 11 minutes. In order to maintain isothermal conditions, the slabs were reheated between passes and in the later stages of rolling, with reheating time of 3 hours. Due to the fact that the rolls were not heated on time during the rolling sequence, slabs were held under 15 seconds for each pass. The billet was heated to 625°C for 30 minutes to isothermal conditions prior to the first rolling pass. This was done to prevent cracking of forged slabs due to uneven heating during the rolling process. To achieve the isothermal condition the slabs were placed on a large steel plate, which acted as a heat sink in a preheated furnace. For every rolling condition used in this work, the recovery temperature was maintained at 625°C. The temperature at which the material was stabilized just prior to rolling became the variable of interest.

A total of 5 different processes were achieved (see Table 1) by varying the percentage reduction per pass, the interpass reheating time and total true final strain. The warm rolling processing samples were lettered from A to E progressing from the least severe light reduction schedule to the most severe heavy reduction schedule. Reduction per pass of 7.5% was referred to as heavy reduction schedule while that of 4.4% was referred to as light reduction schedule.

Table 1: Warm rolling operation used for the experiment Processes Sample A Sample B Sample C Sample D Sample E For (M) For (M) For (N) For (N) For (N) % Reduction per pass 4.4 4.4 4.4 7.5 7.5 Reheating time between passes 3.0 3.0 4.0 3.0 3.0 Total true strain 3.0 3.0 2.9 2.9 2.9

After rolling, tensile samples were produced according ASTM standard. The tensile test samples were prepared such that the test axis was parallel to the rolling direction. The tensile strength test was carried out at a speed of 30.00 mm/min using 15 samples for product M and N.

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2.3. Microstructural examination Warm rolled samples were prepared for photomicrographic examination by sequential grinding using emery paper grades 80, 220, 320 and 600 micron in succession. Etching of the samples for 20 seconds was done using a mixture of dilute nitric acid (68%), hydrofluoric acid (30%), and sodium hydroxide (2%). Photomicrographs of samples were taken at x400 magnification using a CEISS ICM 405 optical microscope.

3.0. Results and Discussion

3.1. Chemical analysis The percentage chemical composition of the constituents of the alloy samples used for both products M and N are shown in Table 2.

Table 2: Chemical composition of alloy samples M and N Product Mg Si Mn Cu Zn Ti Fe Na B Sn Pb Al M 0.380 0.574 0.066 0.102 0.672 0.014 0.442 0.015 0.00 0.00 0.00 97.7 N 0.358 0.617 0.058 0.095 0.204 0.031 0.664 0.004 0.00 0.00 0.00 97.9

3.2. Tensile test Figure 1 shows the variation of ultimate tensile strength (UTS) with the strain rates varying from 1.0 x 10-5s-1 to 4.1 x 10-1s-1 for samples A and B of product M and samples C, D and E of product N. It was observed that Sample E had the highest UTS value of 330 MPa at the strain rate of 1.0 x 10-5s-1, followed by sample C at the strain rate of 1.0 x 10-5s-1. Sample A comes after sample C at the strain rate of 1.0 x 10-5s-1 followed by sample D at the strain rate of 1.0 x 10-5s-1. The sample A had the least UTS value of 120 MPa at the strain rate of 1.0 x 10-5s-1. This describes the maximum stress that the samples can handle before breakage at the same strain rate.

Figure 1 shows the variation of ultimate tensile strength (UTS) with strain rates from 1.0 x 10-5s-1 for samples A, B, C, D and E. It can be seen from the figure that the highest UTS is associated with sample E followed by sample C with A, B, and D trailing behind.

Figure 1: Ultimate tensile strength versus strain rates for tensile test conducted at 625°C for aluminium 6063 alloy

The graph showing the variation of yield stress versus strain rates for samples A, B, C, D and E are shown in Figure 2. It can be observed that all the samples had very low yield stress between 1.0 x 10- 5 and 1.0 x 10-4s-1. This later rose with samples C and E having the highest yield stress compared with A, B and D. At the same strain rates considered for all the samples, samples E and C had the highest

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UTS. This describes the minimum stress under which the samples deform permanently at the same strain rate.

Figure 2: Yield stress versus strain rate for tensile test at 625oC for 6063 aluminium alloy

Variation of fracture stress with strain rates for samples A, B, C, D and E are shown in Figure 3. It can be seen that the highest fracture stress is associated with sample E, closely followed by sample C with samples A, B and D having the lowest values.

Figure 3: Fracture stress versus strain rate for tensile test conducted at 625oC for 6063 aluminium alloy

The graph showing the fracture energy versus strain rate for the samples being considered are shown in Figure 4. It can be observed that samples E again had the highest values of fracture energy, closely followed by sample C while those for samples A, B and D were relatively small.

Figure 4: Fracture energy versus strain rate for tensile stress conducted at 625oC for 6063 aluminium alloy

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The graph showing the variation of elongation with strain rate is given by Figure 5 for samples A, B, C, D and E. It can be observed from the figure that sample E had the highest percentage elongation, closely followed as usual by sample C, with samples A, B and D trailing behind. This describes the maximum elongation of the gage length divided by the original length at the same strain rate.

Figure 5: Percentage elongation versus strain rate for tensile test conducted at 625oC for 6063 aluminium alloy

Figure 5 shows maximum elongations occurring at strain rates of 1.0 x 10-5s-1 and 1.0 x 10-4s-1. This agrees with Grider, 2006. Figure 5 clearly shows that at lower strain rates a lower strength is realized and the same was observed for the other processes investigated. In comparing process A (light reduction) with process B (heavy reduction), shown in figure 5, process A is stronger than the same alloy rolled to the same total true strain of 3.0 using process B.

3.3. Microstructural analysis The micrographs of the processes A-E, deformed at room temperature are shown in Plates A-J. These plates revealed fine grain structures due to grain boundary sliding. The microstructure revealed the response of the aluminium alloy when undergoing various processes during the investigation of the influence of total strain, strain rate and reheating time during warm rolling on the superplastic ductility of 6063 aluminium alloy.

Processes A and B achieved elongations of 232 and 408%, though they had similar grain size. These microscopy results suggest a refined structure has evolved; however, if continuous re-crystallization is occurring, the short reheating may not have allowed sufficient time for a structure capable of sustaining grain boundary sliding to develop.

Process C, shown in Plate F reveals that grain coarsening is beginning to appear. These grains are longer because of the longer reheat times used in Process C. An elongation of 424% was strained with this specimen.

Process D has a banded grain structure with a second phase uniformly distributed throughout the structure. Plate G and H shows the effect of Process D on the material. This specimen reveals a very fine beta phase that is not as uniformly distributed as in the other processes.

Process E is illustrated in Plates I and J, and shows a coarser second phase that is also more uniformly distributed than in Process E.

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Product M:

Plate A: As-received (x400) Plate B: Process A tensile tested at room temp. (x400)

Plate C: After warm rolling using Process B Plate D: After warm rolling using Process (x400) in transverse direction B (x400) in longitudinal direction

Product N:

Plate E: As-received (x400) Plate F: Process C tensile tested at room temp. (x400)

Plate G: After warm rolling using Process D Plate H: After warm rolling using Process (x400) in transverse direction D (x400) in longitudinal direction

Plate I: Hot working at 625°C using Process Plate J: Taken near fracture point of E (x400) Process E (x400)

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3.4. Warm rolling

3.4.1. Total strain (2.9 vs 3.0) Thermomechanical Process A and B, as shown in Table 1 involved warm rolling to nominal true strain of 2.9, while Processes C, D and E represented a nominal true strain of 3.0. Overall, the more severely worked material (Processes Band C) exhibited much higher warm-temperature ductility than the material strained to 3.0. As shown in Figure 4, Processes B and C resulted in peak ductility of 5.55% and 5.8%, respectively; while Processes D and E showed peak ductility of 3.7% and 8.0% respectively. Process A yielded the lowest ductility of 3.0%.

The cause of low ductility was believed at first to be from damaged particles resulting from heavy reduction per pass during the warm rolling phase of Process A. These cracked particles would become void initiation sites which could cause premature fracture and hence low ductility. Increased reduction per pass and short reheat times likely resulted in a higher dislocation density, possibly less precipitate and lessened extent of continuous recrystallization, i.e. insufficient misorientation between adjacent grains for boundary sliding. Figure 3 shows that the material used in Process A is stronger and less ductile. Further study is required in this area.

3.4.2. Strain rate (Light reduction versus Heavy reduction) Process A and B were warm rolled to a total nominal strain of 3.0 with Process A being the light reduction schedule and Process B the heavy reduction schedule. Figure 1 shows that at lower strain rates (1.0 x 10-5s-1) Process B appears to be strain hardening to a greater extent. This suggests increased coarsening. At higher strain rates (4.1 x 10-1s-1), Process A strain harden at higher rates than B. The heavily reduced material (Process B) at lower strain rate is strain hardening faster than Process A because more stored energy is present, causing the grains to grow out faster and coarsen. This could be one reason why Process A has low ductility. In the case of Process C, D and E the same results were observed except that overall flow stress values were higher than for Process A and B.

3.4.3. Reheating time (2 hours versus 3 hours) Process B was warm rolled with a 2 hours reheat time per pass and Process C was warm rolled with a 3 hours reheat time per pass. Longer reheating times as compared to shorter reheating time revealed coarser grains, peak ductility occurring at higher strain rates, and similar m values. Figure 1, clearly shows higher m values for Process A, B and D at the higher strain rates of 4.1 x 10-1s-1, 1.0 x 10-2s-1 and 4.1 x 10-1s-1 respectively. Process C and Process E had their highest m values at strain rates of 1.0 x 10-2s-1 and 4.1 x 10-1s-1 respectively.

4.0. Conclusion

The study investigated the effects of hot working on 6063 aluminium alloy. The study conducted revealed that hot working of 6063 aluminium alloy impacts significant effects on its mechanical properties. Longer reheating times as compared to shorter reheating time revealed coarser grains, peak ductility occurring at higher strain rates, and similar values. Based on the results obtained, the study confirmed that the greatest superplastic ductility were achieved in material experiencing the largest total strain, lowest strain rate and most prolonged reheating time during warm rolling.

References Balogun, S., Esezobor, D. & Adeosun, S. (2007). Effects of Deformation Processing on the Mechanical Properties of Aluminium Alloy 6063. Metallurgical and Materials Transactions A, 38(7), pp. 1570-1574.

Chee Fai Tan & Mohamad R. Said (2009). Effect of Hardness Test on Precipitation Hardening Aluminium Alloy 6061-T6. Chiang Mai J. Sci., 36(3), pp. 276-286.

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De Silva, G. I. P. & Pereral, W. C. (2012). Improvement of the Mechanical properties of Aluminium 6063 T5 Extrudates by Varying the Aging Condition Cost-effectively. In: Procs SAITM Research Symposium on Engineering Advancements, pp. 62-64.

Esezobor D. E. & Adeosun S. O. (2006). Improvement on the Strength 6063 Aluminium Alloy by Means of Solution Heat. Treatment Materials Science and Technology (MS&T) PROCESSING

Grider, W.J. (2006). The Effect of Thermomechanical Processing Variables on Ductility of a High- Mg, Al-Mq-Zr Alloy. M.S. Thesis, Naval Postgraduate School, Monterey, California.

Johnson, R.B. (2005). Effect of Thermomechanical Processing on the Elevated Temperature Behaviour of Lithium-Containing High Mg, Al-Mg Alloys. M.S. Thesis, Naval Postgraduate School, Monterey, California.

Mills, J.G. (2004). Tensile Deformation Behaviour of Aluminium Alloys at Warm Forming Temperatures. Materials Science and Engineering A, 352(1-2), pp. 279-286.

Mrówka-Nowotnik G. & Sieniawski, J. (2005). Influence of Heat Treatment on the Microstructure and Mechanical Properties of 6005 and 6082 Aluminium Alloys. In: Procs 13th Scientific International Conference on Achievements in Mechanical and Materials Engineering, 447–450.

Oliveira, C. V. & Nice, M. (2004). Investigation of Mechanical Properties and Grain Structure of 5xxx Aluminium Alloys under Precisely Controlled Annealed Conditions. International Journal of Scientific and Research Publications, 3(1), pp. 1-4.

Sherby, O. D. & Wadsworth (2007). Development and Characterization of Fine-Grain Superplastic Materials Deformation. Processing and Structure, pp. 354-384.

Stengall, M.J. (1999). Cavitation in Superplasticity Superplastic Forming of Structure Alloys. In: Procs TMS-AIME, 321-336.

Totik, Y., Sadeler, R., Kaymaz, I. & Gavgali, M. (2003). The Effect of Homogenization Treatment on Cold Deformations of AA 2014 and AA 6063 Alloys. Journal of Materials Processing Technology, 147, pp. 60–64.

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ISSN (Print): 2616-051X | ISSN (electronic): 2616-0501

Vol 2, No. 1 March 2018, pp 19 - 27

Evaluation of the Corrosion Rate of Aluminium 6063 in Petrol, Kerosene and Water

1, 1 1 1 Adekunle N.O. *, Aiyedun P.O. , Kuye S.I. and Adetunji, R.O. 1Department of Mechanical Engineering, Federal University of Agriculture, Abeokuta, Nigeria Corresponding Author: *[email protected]

ABSTRACT

The unparalleled combinations of properties of aluminium and its alloy makes aluminium one of the most versatile, economical, and attractive metallic materials for a broad range of uses. This study was carried out to evaluate the influence of kerosene, petrol and water on the corrosion performance of three samples of aluminium 6063 alloy (A and B sourced locally while sample C is an imported material). The corrosion rate was determined both by weight loss and Potentiostatics method, and the microstructure of the samples after five weeks of immersion also was examined with the aid of an optical microscope. Samples A, B and C were immersed in the media for five (5) weeks duration. The cumulative corrosion rate was measured at the end of each week. The average corrosion rates using the weight loss method for A in petrol, kerosene and distilled water were 3.100E-4, 4.905 E-4 and 6.205 E-4; for B were 4.367 E-4, 2.703 E-4 and 2.147 E-4; and those of C were 4.550 E-4, 2.257 E-4 and 1.633 E-4 respectively. Similarly, the average corrosion rate using Potentiostatics method of A in petrol, kerosene and distilled water were 0.586E-3, 0.643E-3 and 0.454E-3; for B were 0.206E-3, 0.197E-3 and 0.298E-3; and those of C were 0.183E-3, 0.232E-3 and 0.407E-3 respectively. The average result showed that Sample A had the highest corrosion rate. The media which corroded the samples the most was petrol.

Keywords: Corrosion, Aluminium, Petrol, Kerosene, Water

1.0. Introduction

Aluminium alloys are second only to steels in use as structural metals (Davis, 2001). The three main properties on which the application of aluminium is based are its low density of approximately 2.7 g/cm3, high mechanical strength achieved by suitable alloying and heat treatments, and its relatively high corrosion resistance especially in its pure state. Other valuable properties include high thermal and electrical conductance, reflectivity, high ductility and resultant low working cost, magnetic neutrality, high scrap-value, and non-poisonous and colourless nature of its corrosion products which facilitates its use in the chemical and food-processing industries (Sheasby and Pinner, 2001). It has been stated that corrosion can be fast or slow depending on the material and environmental factors (Syed, 2006).

The corrosion resistance of aluminium alloy is the result of their ability to form a natural oxide film on the surface in different media (Al-Karafi, 1996; El-Shafei et al., 2004; Belkhaouda et al., 2010). Aluminium has a natural corrosion protection from its oxide layer, but may corrode if exposed to aggressive environments (Kciuk et al., 2010). The behaviour of aluminium and its alloys in aqueous environments depends on several parameters such as the surface properties of the material, nature, temperature, pH and the composition of the aggressive solution (Belkhaouda et al., 2010). Aluminium alloys have excellent corrosion resistance to a wide variety of exposure conditions. Usually they corrode by pitting rather than by uniform corrosion (Melchers, 2015).

Copper additions, which augment strength in many of these alloys, are limited to small amounts to minimize effects on corrosion resistance. At copper levels higher than 0.5% some intergranular corrosion can occur in some tempers (e.g. T4 and T6). However, this intergranular corrosion does not result in susceptibility to exfoliation. When the magnesium and silicon contents in a 6xxx alloy are

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balanced (in proportion to form only Mg2Si), corrosion by intergranular penetration is slight in most commercial environments. If the alloy contains silicon beyond that needed to form Mg2Si or contains a high level of cathodic impurities, susceptibility to intergranular corrosion increases (Davis, 2001).

Nuhu (2012) investigated the response of aluminium specimens immersed in saline water and one molar aqueous solution of acetic and sulphuric acids towards corrosion inhibitive actions of oil locally extracted from seeds of watermelon. Reasonable corrosion and inhibition occurred in specimens immersed in solutions of sulphuric and acetic acid while weight losses in specimens immersed in saline water were very minimal and beyond detectable limits of weighing apparatus used.

Seri and Kudo (2009) investigated the corrosion behaviour of aluminium alloy in ethanol. It was seen that aluminium alkoxide (aluminium tri-ethoxide) as corrosion product will produce hydrogen peroxide on the way of chain process of productions of ethyl chloride, diethyether and ethyether peroxide. It is also pointed out that the hydrogen peroxide will play a strong cathodic role in further pitting corrosion process. The aim of this paper is to study corrosion rate of structural aluminium alloys 6063 in different media.

2.0. Materials and Methods

2.1. Materials Three samples of aluminium 6063 alloy were used for the study. Samples A and B were sourced locally while Sample C was an imported material. The media used were petrol, kerosene and distilled water. Other items used in carrying out the experiments were plastic containers, paper tape, mentholated spirit and plastic bowls.

2.2. Equipment The following equipment were used in carrying out this work: 1. Spectrometer (Model SP-9268A). This was used for chemical analysis of the alloy samples. 2. Sensitive weighing balance (Model FX700CT) 3. Metallurgical Microscope (Model XZJ-L2030B) 4. Potentiostate (Model 1287A)

2.3. Test Methods 2.3.1. Weight loss method The three alloy samples were cut into 15 coupons (4cm x 4cm) totaling 45 pieces and were weighed (Figures 1 and 2). The plastic containers were first washed with detergent, rinsed in distilled water and allowed to dry for hours. They were then filled with petrol, kerosene and distilled water. The samples were completely immersed in petrol, kerosene and distilled water. Proper cleaning of each sample was ensured after removal using water and mentholated spirit. The samples were weighed (Figure 3) at 168, 336, 504, 672 and 840 hours (a span of 5 weeks) to determine the weight loss. The data collected from the experiment were analysed.

The corrosion rate was determined using Equation (1) developed by Callister and Rethwisch (2009):

퐾푊 (1) 퐶 = 푅 휌퐴푇

Where, W Weight loss in mg 휌 Density of the aluminium alloy in g/cm3 T Immersion time in hrs K Rate constant (K = 87.6 for mm/yr)

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Figure 1: Preparation of samples for the weight loss experiment

Figure 2: Weighing of the samples Figure 3: Weight loss experimental setup

2.3.2. Potentiostatics experimental setup and process Corrosion analysis involves the particular surface of interest, and so other surfaces which were not required for the corrosion analysis were isolated. These were done using synthetic epoxy. Connecting wires were placed at one side of the sample and covered with a tape. Epoxy was prepared and placed all over the surface which was not needed for the corrosion analysis. The epoxy was then allowed to cure. This required a day or two to solidify. Continuity of the connection was checked to ensure proper connection between the wires and samples.

Preparation of electrochemical cell: The basic electro-chemical cell involves a working electrode, reference electrode, counter electrode and solution media. Since the Working electrode (WE) is the electrode in an electrochemical system on which the reaction of interest is occurring. The working electrodes were:  Aluminium from the different sources: Samples (A), (B), (C)  The reference electrode used was silver – silver chloride  The counter electrode used was graphite electrode The entire electrode was then dipped into the different media for each experiment to form the electrochemical cell.

Determination of the open circuit potential: The equilibrium potential assumed by the metal in the absence of electrical connections to the metal is called the Open Circuit Potential, Eoc. The terms Eoc (Open Circuit Potential) and Ecorr (Corrosion Potential) are usually interchangeable, but Eoc is preferred. This is done by the use of a multimeter to measure the Eoc value for the given electrochemical cell at a measured pH value. It is very important to allow sufficient time for the Eoc to stabilize before beginning the electrochemical experiment. A stable Eoc is taken to indicate that the system being studied has reached "steady state", i.e., the various

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Nigerian Journal of Environmental Sciences and Technology (NIJEST) Vol 2, No. 1 March 2018, pp 19 - 27 corrosion reactions have assumed a constant rate. Some corrosion reactions reach steady state in a few minutes, while others may need several hours.

A graph plot of Potential versus logarithm of current was drawn using Origin 6.1. Polynomial line of best fit was drawn which obeys the Butler-Volmer equation for the cathodic corrosion occurring. The Eoc potential was also plotted on the graph. Using Tafel analysis, an extrapolation of the linear portion of the cathodic curve to the corrosion potential to indicate the corrosion current (Icorr) at which corrosion is occurring. The value of either the anodic or cathodic current at Ioc is called the Corrosion Current.

Monitoring of the electrochemical cell using the potentiostat: The different electrodes were connected to the potentiostat. This potentiostat was connected to a computer-controlled system. The corrosion monitoring was done in real time in which the values were determined from the computer Graphics User Interface (GUI), where a plot of voltage versus time and current versus time were displayed. The different reading for the current and voltage were then exported to excel for further analysis.

Potential scan rate: This determines how slow or fast a potential window can be scanned. This is most important for experiments that require high scan rate. The experiment was run at a scan rate of 20 mV/s. This was done to ensure that the potentiostat would detect a quantitative amount of corrosion i.e. the potentiostat does not run too fast and also does not run too slow.

Corrosion rate analysis for potentiostatics: The corrosion rate for potentiostatics method was determined using Equation (2) according to ASTM G (1999):

퐾 × 푖 × 퐸푊 (2) 퐶 (푚푝푦) = 푖 푐표푟푟 푅 푑퐴

Where, Ki 3.272 m/(amp-cm-year) EW Equivalent weight d Density in kg/m3 A Cross sectional area of sample exposed to the medium in mm2 Icorr Corrosion current determined for data analysis in amps

2.3.3. Microstructural examination The metallographic observations of the surface morphology of the samples after corrosion investigations let to estimate the kind and the stage of corrosive damages. Conducted investigations showed, that analyzed aluminium alloys after investigations in the media have diverse character of the damages of the surface.

Photomicrographs of samples after five weeks of immersion were taken using Metallurgical Microscope. The photomicrographs were done without etching to reveal the surface property at a magnification of x400 of the corroded material.

3.0. Results and Discussion

Table 1 shows the average value of the chemical compositions of samples A, B and C. It can be seen that the three samples have the same proportion of Silicon, Manganese and Boron. Sample A has the highest content of Titanium, Iron, Tin and Lead and least content of Zinc, while sample B has the highest content of Copper, and sample C has the highest content of Magnesium and Zinc.

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Table 1: Average values of the chemical wt % composition of the samples Element Mg Si Mn Cu Zn Ti Fe Na B Sn Pb Al Sample A 0.3578 0.6165 0.0598 0.0949 0.2044 0.0305 0.6644 0.0036 0.0003 0.0036 0.007 <97.89 Sample B 0.3875 0.5674 0.0659 >0.1012 0.6760 0.0139 <0.4318 0.0110 0.0003 0.0027 0.0041 <97.67 Sample C 0.4092 0.5969 0.0636 0.0984 0.6864 0.0139 <0.4078 0.0056 0.0003 0.001 0.0045 <97.67

The measured corrosion rate of Samples A, B and C immersed in petrol for a period of five (5) weeks using weight loss method is illustrated in Figure 4. The cumulative CR was determined at the end of each week. It showed that the CR measured at the end of first, second, third, fourth and fifth weeks for Sample A are 2.385, 1.789, 5.962, 1.789 and 3.577 respectively. For Sample B we have 8.479, 1.060, 6.36, 2.120 and 3.816 respectively; and for Sample C we have 6.360, 5.299, 4.946, 3.180 and 2.967.

4 9 - 8 7 6 5 CR of Sample A 4 CR of Sample B 3 CR of Sample C Corrosion Rate,(mm/yr)x10 Corrosion 2 1 0 0 200 400 600 800 1000 Time of Immersion, (hrs)

Figure 4: Corrosion rate in petrol

Figure 5 illustrate the measured corrosion rate of Sample A, B and C immersed in kerosene for a period of five (5) weeks using weight loss method. The cumulative CR measured at the end of first, second, third, fourth and fifth weeks for Sample A are 2.385, 5.366, 1.153, 6.558 and 9.062. Also for Sample B we have 6.360, 4.240, 0.156, 2.650 and 0110. Similarly, for Sample C we have 0.148, 5.299, 0.116, 5.299 and 0.419.

10

9 8 7

6

4 - 5 CR of Sample A 10 10 4 3 CR of Sample B 2 CR of Sample C corrosion rate, (mm/yr) corrosionrate, 1 0 0 200 400 600 800 1000 Time of immersion, (hrs)

Figure 5: Corrosion rate in kerosene

The measured corrosion rate of Samples A, B and C immersed in distilled water for a period of five (5) weeks using weight loss method was illustrated in Figure 6. The cumulative CR was determined at the end of each week, for Sample A are 4.770, 6.558, 9.142, 5.068, 5.485 and 5.485 Also for Sample B we have 8.418, 0.180, 0.141, 0.3371 and 1.696. Similarly, for Sample C we have 0.636, 0.201, 0.120, 4.240 and 2.968 in respect order.

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Nigerian Journal of Environmental Sciences and Technology (NIJEST) Vol 2, No. 1 March 2018, pp 19 - 27

10 8

6

4 CR of Sample A - 4

x10 CR of Sample B 2 CR of Sample C 0 Corrosion Rate, (mm/yr) Rate, Corrosion 0 200 400 600 800 1000 -2 Time of Immersion, (hrs)

Figure 6: Corrosion rate in distilled water

Table 2 shows that sample A had the highest mean corrosion rate of (3.100 x 10-4mm/yr) followed by sample B (4.367 x 10-4mm/yr) and sample C (4.550 x 10-4mm/yr), respectively in Petrol. It can be seen that sample C had the highest mean corrosion rate (2.257 x 10-4mm/yr) in kerosene, followed by samples B (2.703 x 10-4mm/yr) and A (4.905 x 10-4mm/yr) respectively. Moreover, sample C had the highest mean corrosion rate (1.633 x 10-4mm/yr) in distilled water, followed by samples B (2.147 x 10-4mm/yr) and A (6.205 x 10-4mm/yr) respectively.

Table 2: Mean values of corrosion rate of each sample in the media using weight loss method Media Mean corrosion rate (mm/yr) × 10−4 Sample A Sample B Sample C Petrol 3.100 4.367 4.550 Kerosene 4.905 2.703 2.257 Water 6.205 2.147 1.633

Tables 3 to 5 show the corrosion potential (Eoc), corrosion current (Icorr) and the corrosion rate for the three samples immersed in petrol, kerosene and distilled water, as determined using the potentiostatic method.

Table 3: Corrossion Potential (Eoc) of the samples in the different media using Potentiostatic method Samples A B C Kerosene 13.4mV -3.3mV 0.1mV Petrol -2.5mV -2.6mV -18.3mV Distilled water -0.1V 0.4V 0.56V

Table 4: Corrosion current (Icorr) for the different samples in the different media using Potentiostatic method Samples A B C Kerosene 4.53mA 13.6mA 3.78mA Petrol 4.72mA 12.4mA 2.98mA Distilled water 6.85mA 9.6mA 6.63mA

Table 5: Corrosion Rate (mpy) for the samples in media using Potentiostatic method Samples A B C Kerosene 0.643E-3 0.197E-3 0.232E-3 Petrol 0.586E-3 0.206E-3 0.183E-3 Distilled water 0.454E-3 0.298E-3 0.407E-3

The photomicrographs of the samples immersed in the media for five weeks are displayed in plates 1- 9. Plates 1, 2 and 3 show photomicrograph of samples A, B and C in petrol after 5 weeks of immersion at a magnification of x 400.

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Plate 1 Plate 2 Plate 3

Plates 4, 5 and 6 show photomicrograph of samples A, B and C in kerosene after 5 weeks of immersion at a magnification of x400.

Plate 4 Plate 5 Plate 6

Plates 7, 8 and 9 show photomicrograph of samples A, B and C in distilled water after 5 weeks of immersion at a magnification of x400.

Plate 7 Plate 8 Plate 9

The increased corrosion resistance of pure aluminium is mainly attributed towards the formation of stable oxide layer on its surface. The addition of Silicon and Magnesium as alloying elements in case of 6063 aluminium could lead to the discontinuities on the oxide film, thereby increasing the number of sites where corrosion can be initiated. This increases the corrosion rate of 6063 aluminium (Padmalatha, 2013; Pardo et al., 2003; Trowsdale et al., 1996) as obtained in this study.

However, Larsen et al. (2010) found that copper is significant in increasing rate of corrosion in 6063 aluminium. The percentage of copper in Sample B was the highest but Sample A had the highest percentage by weight of iron and tin in the three samples which explained its higher corrosion rate compared to other samples. Pure aluminium consists of 0.120 % Silicon, 0.270 % Iron with 99.61% of aluminium. The 6063 aluminium consists of the items as stated in Table 1. It is noted that none of the samples had aluminium of up to 98%. This explained the increased rate of corrosion that was observed.

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4.0. Conclusion

Two different corrosion test methods were evaluated in this report to evaluate their effectiveness in assessing corrosion performance of the samples. In addition, comparisons between the results were made. Based on the results, it was observed that sample A had the highest corrosion rate in the kerosene and distilled water while sample C had the highest in petrol. To store kerosene and distil water, sample A should therefore be used while sample C should be used to store petrol.

References Al-Karafi, F. M. & Badawy, W. A. (1996). Stability of anodically passivated Al, Al-Cu, AI-6061 and AI-7075 in nitric acid and nitric acid solutions containing chloride. Indian Journal of Chemical Technology, 3(4), pp. 212-218.

Belkhaouda, M., Bazzi, L., Salghi,R., Jbara, O., Benlhachmi, A., Hammouti, B. & Douglad, J. (2010). Effect of the heat treatment on the behavior of the corrosion and passivation of 3003 aluminium alloy in synthetic solution. Journal of Material and Environmental Science, 1(1), pp. 25-33.

Callister, D. W. Jr. & Rethwisch, G. D. (2009). Material Science and Engineering, 8th Edition, John Wiley and Sons Inc. Chapter 17, pp. 682-683.

Davis J. R. (2001). Alloying: Understanding the Basics. ASTM International, 351-416. Kciuk, M., Kurc, A., & Szewczenko, J. (2010). Structure and Corrosion Resistance of Aluminium Almg2.5; AlMg5mn and AlZn5Mg1 Alloys. Journal of Achievements in Materials and Manufacturing Engineering, 41(1-2), pp. 74-81.

El-Shafei, A. A., Abd El-Maksoud, S. A., & Fouda, A. S. (2004). The role of indole and its derivatives in the pitting corrosion of Al in neutral chloride solution. Corrosion Science, 46(3), pp. 579 -590.

Larsen, H. M., Walmsley, C. J., Lunder, O. & Nisancioglu K. (2010). Effect of Excess Silicon and Small Copper Content on Intergranular Corrosion Of 6000-Series Aluminium Alloys. Journal of the Electrochemical Society, 157 (2), pp. C61-C68.

Melchers, E. R. (2015). Time Dependent Development of Aluminium Pitting Corrosion. Advances in Materials Science and Engineering, Article ID 215712, 10 pages. http://dx.doi.org/10.1155/2015/215712.

Nuhu, A. A. (2012). Inhibition Characteristics of Watermelon Oil on Aluminium in Acids and Saline Water. AU Journal of Technology, 15(4), pp. 265-272.

Padmalatha, P. D. (2013). Studies of Corrosion of Aluminium and 6063 Aluminium Alloy in Phosphoric Acid Medium. International Journal of ChemTech Research, 5(6), pp. 2690-2705.

Pardo, A., Merino, M. C., Merino, S., Lopez, M. D., Viejo, F. & Carboneras, M. (2003). Influence of reinforcement grade and matrix composition on corrosion resistance of cast aluminium matrix composites (A3xx.x/SiCp) in a humid environment. Material Corrosions, 54, pp. 311-317.

Seri, O. & Kido,Y. (2009). Corrosion Phenomenon and Its Analysis of 6063 Aluminium Alloy in Ethyl Alcohol. Materials Transactions, 50(6), pp. 1433 -1439.

Sheasby, P.G. & Pinner, R. (2001). The Surface Treatment and Finishing of Aluminium and Its Alloys, 6th Edition, ASTM International. Available at: www.asminternational.org

Syed, S. (2006). Atmospheric Corrosion of Materials. Emirates Journal for Engineering Research, 11 (1), pp. 1-24.

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Trowsdale, A.J., Noble, B., Harris, S. J., Gibbins, I. S. R., Thompson, G. E., & Wood, G. C. (1996). The influence of silicon carbide reinforcement on the pitting behaviour of aluminium. Corrosion Science, 2, pp. 177-191.

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Nigerian Journal of Environmental Sciences and Technology (NIJEST) www.nijest.com

ISSN (Print): 2616-051X | ISSN (electronic): 2616-0501

Vol 2, No. 1 March 2018, pp 28 - 38

Change Detection Analysis Using Surveying and Geoinformatics Techniques

1, 1 Onuigbo I.C. * and Jwat, J.Y. 1Department of Surveying and Geoinformatics, Federal University of Technology, Minna, Nigeria Corresponding Author: *[email protected]

ABSTRACT

The study was on change detection using Surveying and Geoinformatics techniques. For effective research study, Landsat satellite images and Quickbird imagery of Minna were acquired for three periods, 2000, 2005 and 2012. The research work demonstrated the possibility of using Surveying and Geoinformatics in capturing spatial-temporal data. The result of the research work shows a rapid growth in built-up land between 2000 and 2005, while the periods between 2005 and 2012 witnessed a reduction in this class. It was also observed that change by 2020 may likely follow the trend in 2005 – 2012 all things being equal. Built up area may increase to 11026.456 hectares, which represent 11% change. The study has shown clearly the extent to which MSS imagery and Landsat images together with extensive ground- truthing can provide information necessary for land use and land cover mapping. Attempt was made to capture as accurate as possible four land use and land cover classes as they change through time.

Keywords: Surveying, Geoinformatics, Analysis, Land use, Land cover

1.0. Introduction

Studies have shown that there are only few landscapes on the earth that are still in their natural state. Due to anthropogenic activities, the earth’s surface is being significantly altered in some manner and man’s presence on the earth and his use of land has had a profound effect upon the natural environment thus resulting into an observable pattern in the land use and land cover over time. In situations of rapid and often unrecorded land use change, observations of the earth from space provide objective information of human utilization of the landscape.

According to Meyer (1999), every parcel of land on the earth’s surface is unique in the cover it possesses. Land use and land cover are distinct yet closely linked characteristics of the earth’s surface. Globally, land cover today is altered principally by direct human use: by agriculture and livestock raising, forest harvesting and management and urban and suburban construction and development.

Conventional ground methods of land use mapping are labor intensive, time consuming and are done relatively infrequently. These maps soon become outdated with the passage of time, particularly in a rapid changing environment. In fact according to Olorunfemi (1983), monitoring changes and time series analysis is quite difficult with traditional method of surveying. In recent years, satellite remote sensing techniques have been developed, which have proved to be of immense value for preparing accurate land use and land cover maps and monitoring changes at regular intervals of time. To date, the most successful attempt in developing a general purpose classification scheme compatible with remote sensing data has been by Anderson et al. (1976) referred to as USGS classification scheme.

Ever since the launch of the first remote sensing satellite (Landsat-1) in 1972, land use and land cover studies were carried out on different scales for different users. For instance, waste land mapping of India was carried out on 1:1 million scales by NRSA using 1980 – 82 Landsat multi spectral scanner data. About 16.2% of waste lands were estimated based on the study.

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Change detection is the process of identifying differences in the state of an object or phenomenon by observing it at different times (Singh, 1989). Change detection is an important process in monitoring and managing natural resources and urban development because it provides quantitative analysis of the spatial distribution of the population of interest.

Macleod and Congalton (1998) listed four aspects of change detection which are important when monitoring natural resources: i. Detecting the changes that have occurred ii. Identifying the nature of the change iii. Measuring the area extent of the change iv. Assessing the spatial pattern of the change

The basis of using remote sensing data for change detection is that changes in land cover result in changes in radiance values which can be remotely sensed. Techniques to perform change detection with satellite imagery have become numerous as a result of increasing versatility in manipulating digital data and increasing computer power. A wide variety of digital change detection techniques have been developed over the last two decades (Singh, 1989; Coppin and Bauer, 1996).

It has been noted over time through series of studies that Landsat Thematic Mapper is adequate for general extensive synoptic coverage of large areas. As a result, this reduces the need for expensive and time consuming ground surveys conducted for validation of data.

An analysis of land use and land cover changes using the combination of MSS Landsat and land use map of Indonesia reveals that land use and land cover change were evaluated by using remote sensing to calculate the index of changes which was done by the superimposition of land use and land cover images of 1972, 1984 and land use maps of 1990 (Dimyati et al. 1995). This was done to analyze the pattern of change in the area, which was rather difficult with the traditional method of surveying as noted by Olorunfemi (1983), when he was using aerial photographic approach to monitor urban land use in developing countries with Ilorin in Nigeria as the case study.

Daniel et al.(2002), in their comparison of land use and land cover change detection methods, made use of 5 methods viz; traditional post – classification cross tabulation, cross correlation analysis, neural networks, knowledge – based expert systems, and image segmentation and object – oriented classification. A combination of direct T1 and T2 change detection as well as post classification analysis was employed. Nine land use and land cover classes were selected for analysis. They observed that there are merits to each of the five methods examined, and that, at the point of their research, no single approach can solve the land use change detection problem.

Coppin and Bauer (1996) stated that detecting and characterizing change over time is the natural first step toward identifying the driver of the change and understanding the change mechanisms. Satellite remote sensing has long been used as a means of detecting and classifying changes in the condition of the land surface over time. Satellite sensors are well-suited to this task because they provide consistent and repeatable measurements at a spatial scale which is appropriate for capturing the effects of processes that cause change, including natural (e.g. fires, insect attacks) and anthropogenic (e.g. deforestation, urbanization, farming) disturbance. They also stated that the ability of any system to detect change depends on its capacity to account for variability at one scale, example, seasonal variations, which identifying change at another multi-year trends, only a limited number of time series change detection methods have been developed. The present status would not have been achieved without close interaction between various fields such as utility networks, cadastral mapping, topographic mapping, thematic cartography, surveying and photogrammetry, remote sensing, image processing, computer science, rural and urban planning, earth science, and geography. Image differencing is a more automated technique for detecting change. The method involves the subtraction of a recent image (time1) from an earlier image (time2), to detect radiance change between two dates resulting from a change in land cover.

Minna, the Niger State capital has witnessed remarkable expansion, growth and development such as buildings, road construction, deforestation and many other anthropogenic activities since its inception in 1976. This has therefore resulted in increased land consumption and a modification and alterations

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Nigerian Journal of Environmental Sciences and Technology (NIJEST) Vol 2, No. 1 March 2018, pp 28 - 38 in the status of her land use land cover over time without any detailed and comprehensive attempt to evaluate this status as it changes over time with a view to detecting the land consumption rate and also make attempt to predict same and the possible changes that may occur in this status so that planners can have a basic tool for planning. Therefore, attempt was made in this study to determine the status of land use changes over a period of time between 2000 and 2012 with a view to detecting the land consumption rate and the changes that have taken place so as to predict possible changes that might take place in the next 20 years.

2.0. Materials and Methods

2.1. Study Area Nyikamgbe a suburb of Minna, lies approximately on Latitude 90 39’ 40” N to Latitude 90 35’ 14”E and Longitude 60 31’ 26“N to Longitude 60 31’ 26”E (see Figure 1) on a geological base of undifferentiated basement complex of mainly gneiss. The land in Minna is essentially used for agricultural purpose. Within Minna, crops like maize, yam, melon, groundnuts are produced. Vegetable gardens are also maintained near some households. The largest percentage of the land is used for farming activities.

Figure1: Location map of Minna, Niger State in Nigeria Source: Ministry of Land and Housing Minna, 2013

2.2. Data Acquired and Sources For the study, Landsat satellite images and Quickbird imagery of Minna were acquired for three periods: 2000, 2005 and 2012, and analyzed according to the steps shown in Figure 2. It is also important to state that Minna and its environs which were carved out using the local government boundary map and Nigerian Administrative map was also obtained from Niger State Ministry of Town Planning. These were brought to Universal Transverse Marcator projection in Zone 31.

2.3. Description of Data The sets of data used for the study area are Landsat and Quickbird Imagery covering the study area. Landsat Image data have potential applications for monitoring the conditions of the earth's land surface. The images can be used to map anthropogenic and natural changes on the earth over periods of several months to several years. Landsat Enhanced Thematic Mapper (ETM) was also obtained from Global Land Cover Facility on the internet. The Landsat ETM satellite has seven (7) bands like

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Nigerian Journal of Environmental Sciences and Technology (NIJEST) Vol 2, No. 1 March 2018, pp 28 - 38 the Landsat TM but carries an additional panchromatic band (see Table 1). Landsat ETM has a spatial resolution of 30m and the panchromatic band has a 15m spatial resolution. The ETM has a temporal resolution of 16 days.

Figure 2: Flow model of analysis

Table 1: The wavebands of Landsat TM Bands Wavelength (μm) Spectral location 1 0.45 – 0.52 Blue 2 0.52 – 0.60 Green 3 0.63 – 0.69 Red 4 0.76 – 0.90 NR 5 1.55 – 1.25 MR 6 10.4 – 12.5 Far 7 2.08 – 2.35 IR 8 0.52 – 0.90 Panchromatic

2.4. Description of Landsat TM Imageries Utilized The Landsat TM imagery used is of scale 1: 2500. It sensed in seven bands. The spatial resolution is 30 meters (in the panchromatic mode). It has a swath width of 185 km, meaning that each scene or image covers a distance of 185km.

2.5. Geo-referencing One of the major tasks of image geometric operation is to rectify an image to a given map projection system. The process is called geocoding or georeferencing. The geo-referencing properties of both

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Nigerian Journal of Environmental Sciences and Technology (NIJEST) Vol 2, No. 1 March 2018, pp 28 - 38 images are the same, while image thinning was applied to the 2000 imagery which has a low resolution using a factor of two to modify its properties and resolution to conform to the other two.

Image thinning was carried out through contract. Contract generalizes an image by reducing the number of rows and columns while simultaneously decreasing the cell resolution. Contraction may take place by pixel thinning or pixel aggregation with the contracting factors in X and Y being independently defined. With pixel thinning, every nth pixel is kept while the remaining is thrown away.

2.6. Software Used Basically, five softwares were used for this project via: 1. ArcView 3.2a – this was used for displaying and subsequent processing and enhancement of the image. It was also used for the carving out of Nyikamgbe from Minna imagery using both the admin and local government maps 2. ArcGIS – This was also used to compliment the display and processing of the data 3. Idrisi32 – This was used for the development of land use land cover classes and subsequently for change detection analysis of the study area. 4. Microsoft word – was used basically for the presentation of the research 5. SPSS was used for time series analysis

2.7. Development of a Classification Scheme Based on the prior knowledge of the study area for over 12 years and a brief reconnaissance survey with additional information from previous research in the study area, a classification scheme was developed for the study area after Anderson, et al. (1976). The classification scheme developed gives a rather broad classification where the land use land cover was identified by a single digit (see Table 2).

Table 2: Land use land cover classification scheme Code Land use land cover categories 1 Farmland 2 Built-up land 3 Water bodies 4 Wet vegetated land

2.8. Analysis Three main methods of data analysis were adopted in this study: 1. Calculation of the Area in hectares and percentage of the resulting land use/land cover types for each study year and subsequently comparing the results. 2. Markov Chain and Cellular Automata Analysis for predicting change 3. Maximum Likelihood Classification

The three methods above were used for identifying change in the land use types. The comparison of the land use land cover statistics assisted in identifying the percentage change, trend and rate of change between 2000 and 2012. In achieving this, the first task was to develop a table showing the area in hectares and the percentage change for each year, measured against each land use land cover type. Percentage change to determine the trend of change can then be calculated by dividing observed change by sum of changes multiplied by 100, as shown in the expression below:

Observed change (1) Trend (Percentage change) = × 100 Sum of change

In obtaining annual rate of change, the percentage change is divided by 100 and multiplied by the number of study year (12years). Going by the second method (Markov Chain Analysis and Cellular Automata Analysis), Markov Chain Analysis is a convenient tool for modeling land use change when changes and processes in the landscape are difficult to describe. A Markovian process is one in which

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Nigerian Journal of Environmental Sciences and Technology (NIJEST) Vol 2, No. 1 March 2018, pp 28 - 38 the future state of a system can be modeled purely on the basis of the immediately preceding state. Markovian chain analysis will describe land use change from one period to another and use this as the basis to project future changes. This is achieved by developing a transition probability matrix of land use change from time one to time two, which shows the nature of change while still serving as the basis for projecting to a later time period. The transition probability may be accurate on a per category basis, but there is no knowledge of the spatial distribution of occurrences within each land use category. Hence, Cellular Automata (CA) was used to add spatial character to the model.

CA - Markov uses the output from the Markov Chain Analysis particularly Transition Area file to apply a contiguity filter to “grow out” land use from time two to a later time period. Overlay operations which is the last method of the three, identifies the actual location and magnitude of change although this was limited to the built-up land. Boolean logic was applied to the result through the reclass module of Idrisi32 which assisted in mapping out separately areas of change for which magnitude was later calculated for.

The Land consumption rate and absorption coefficient formula are as given below:

A (2) L.C.R = P

Where: A Areal extent of the city in hectares P Population

A − A (3) L.A.C = 2 1 P2 − P1

Where: A1 and A2 Areal extents (in hectares) for the early and later years P1 and P2 Population figure for the early and later years respectively (Yeates and Garner, 1976) L.C.R Measure of compactness which indicates a progressive spatial expansion of a city. L.A.C Measure of change in consumption of new urban land by each unit increase in urban population

3.0. Results and Discussion

3.1. Land use land cover of Nyikamgbe Village 3.1.1. Landsat Imagery Analysis Landsat TM of the study area was used, the interpretation of land use category vector map covering the study area shown in Figure 3.

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Figure 3: Landsat TM classification of Minna 2000

Ground truthing was carried out, using physical features as sample points. The interpreted image showed that vegetation covers about 87% of the area which made up the largest land use category. The built-up land comprises of mainly residential buildings which covers about 10% of the area of study.

3.1.2. Quick bird Imagery analysis 2005 The interpretation of imagery of 2005 for land use category covering the study area is shown in Figure 4. The classification map of 2005 shows the land use map of the study area. This period witnessed a great change in land use category. Most especially the built-up land, a striped residential land and commercial land was constructed which occupied 217 ha. However, the ground truthing showed that commercial services were present in the clustered residential area ranging from, shops, and mini supermarkets to shopping complexes. Also, present here are mosques, churches and schools.

3.2. Estimating Future Changes The transition probability matrix records the probability that each land cover category will change to the other category. This matrix is produced by the multiplication of each column in the transition probability matrix by the number of cells of corresponding land use in the later image.

For the 4 by 4 matrix table presented below (see Table 3), the rows represent the older land cover categories and the column represents the newer categories. Although this matrix can be used as a direct input for specification of the prior probabilities in maximum likelihood classification of the remotely sensed imagery, it was however used in predicting land use land cover of 2015.

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Figure 4: QuickBird imagery showing land use of study area in 2005

Table 3: Transitional Probability table derived from the land use land cover map of 2005 and 2012 Classes Wet Vegetated Land Farm Land Intensive Built-up Land Water Body Wet Vegetated land 0.1495 0.5553 0.0885 0.0097 Farm land 0.1385 0.5132 0.1735 0.0057 Intensive Built-up land 0.0471 0.3902 0.5029 0.0090 Water body 0.1682 0.4378 0.0633 0.0133

Row categories represent land use land cover classes in 2005 whilst column categories represent 2015 classes. As seen from the table, farm land has a 0.1495 probability of remaining farm land and a 0.5553 of changing to wet vegetated land in 2015. This therefore shows an undesirable change (reduction), with a probability of change which is much higher than stability. Farm land during this period will likely maintain its position as the highest class with a 0.5132 probability of remaining farm land in 2015.Built-up land also has a probability as high as 0.5029 to remain as built-up land in 2015 which signifies stability. On the other hand, the 0.4050 probability of change from wet vegetated land to farm land shows that there might likely be a high level of instability within this period. Water body which is the last class has a 0.0133 probability of remaining as water body and a 0.4378 probability of changing to wet vegetated land; which may not however be a true projection of this class except there is an occurrence of drought in the region.

Table 4 shows the statistics of land use land cover projection for 2015. Comparing the percentage representations of this table and that of Table 5, there exist similarities in the observed distribution particularly in 2005.

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Table 4: Land Use Land Cover Projection for 2015 Land use land Wet vegetated Farm land Intensive built-up Water body cover classes land land Area in Hectares 16583.5458 47432.4759 11026.456 20397.871 Area in Percentage 17 50 11 21

Table 5: Land Cover/Land Use in Percentage Landuse Percentage Intensive Built-up 39.6% Farmland 32.2% Wet vegetated land 6.2% Water body 12%

This may tend to suggest no change in the classes between 2005 and 2015, but a careful look at the area in hectares between these two tables shows a change though meager. Thus in Table 4, farm land still maintains the highest position in the class, followed by water body, wet vegetated land and finally by intensive built-up land.

3.3. Statistical Analysis Statistical analysis was carried out on the data obtained from the study. The results shown in Table 6, shows that more developments in terms of residential, commercial, road network (physical structures) have taken place in 2012 compared to 2005.

Table 6: Results of Statistical Analysis Land Use Quickbird Landsat X = x - x̅ Y = y - y̅ X2 Y2 XY Imagery Imagery Residential 176.61 488.80 53.77 365.96 2891.21 133926.72 19677.67 Commercial 11.18 36.58 -111.66 -86.26 12467.96 7440.79 9631.79 Industrial 4.06 12.60 -118.78 -110.24 14108.69 12152.86 13094.31 Public/semi- 48.83 40.83 -74.01 -82.01 5477.48 6725.64 6069.56 public Open 64.99 58.57 -57.85 -64.27 3430.44 4130.63 3718.02 space/green area Road 42.30 57.95 -80.54 -64.89 6486.69 4210.71 5226.24 network Agriculture 511.90 164.52 389.06 41.68 151367.68 1737.22 16216.02 Total 859.85 859.85 ∑X2 = 196230.15 ∑Y2 = 170324.57 ∑XY = 73633.61

Product moment correlation coefficient (r): ∑ x 859.85 (4) Mean = = = 122.84 N 7

∑ XY − ∑ X ∑ Y (5) r = √[n ∑ X2 − (∑ X)2][n ∑ Y2 − (∑ Y)2]

7 (73633.61) − (859.85) (859.85) r = √[7 (196230.15) − (859.85)2][7 (170324.57) − (859.85)2] r = - 0.42

Bivariat regression (b): ∑ XY − ∑ X ∑ Y (6) b = ∑ X2 − (∑ X)2 7 (73633.61) − (859.85) (859.85) b = 7 (196230.15) − (859.85)2 b = - 0.35

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Therefore: a = y – bx a = 122.84 − (- 0.35×122.84) a = 165.83

Therefore, the least squares regression line Y = a + bX becomes Equation (7) shown below. This equation shows the rate of change of land cover from 2005 to 2012.

Y = 165.83 – 0.35X (7) Where: X size (area) of land in 2005 as obtained from Quick Bird image, 2005 Y prediction of change

4.0. Conclusion and Recommendation

This research work demonstrated the ability of Remote Sensing, GIS and Time Series analysis in capturing spatial-temporal data. Attempt was made to capture as accurate as possible five land use land cover classes as they change through time. Except for the inability to accurately map out water body in 2000 due to the aforementioned limitation, the five classes were distinctly produced for each study year but with more emphasis on built-up land as it is a combination of anthropogenic activities that make up this class; and indeed, it is one that affects the other classes.

However, the result of the work shows a rapid growth in built-up land between 2000 and 2005 while the periods between 2005 and 2012 witnessed a reduction in this class. It was also observed that change by 2020 may likely follow the trend in 2005-2012 all things being equal. Built up may increase to 11026.456 which represent 11% of changes.

The study has shown clearly the extent to which MSS imagery and Landsat images interaction techniques with extensive ground truthing can be of help in providing biophysical information necessary for landuse landcover mapping. Landuse maps could provide baseline information for resource planner and manager and would also assess the changes and effect created by development projects.

Based on the findings, the following recommendations are made: 1. Frequent acquisition of remote sensing data, particularly SPOT and Landsat imageries for multi-temporal study of Minna and environs is recommended with a view to monitoring changes. 2. People should be given positive orientation on their attitude towards land management in relation to the environment. References Anderson et al. (1976). A Land Use and Land Cover Classification System for Use with Remote Sensor Data. Geological Survey Professional Paper No. 964, U.S. Government Printing Office, Washington, D.C. p. 28.

Coppin, P. & Bauer, M. (1996). Digital Change Detection in Forest Ecosystems with Remote Sensing Imagery. Remote Sensing Reviews, 13, pp. 207-234.

Daniel et al. (2002). A comparison of Landuse and Landcover Change Detection Methods. In: Procs ASPRS-ACSM Annual Conference and FIG XXII Congress p. 2.

Dimyati et al. (1995). An Analysis of Land Use/Land Cover Change Using the Combination of MSS Landsat and Land Use Map- A case study of Yogyakarta, Indonesia. International Journal of Remote Sensing, 17(5), pp. 931 – 944.

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Macleod & Congalton (1998). A Quantitative Comparison of Change Detection Algorithms for Monitoring Eelgrass from Remotely Sensed Data. Photogrammetric Engineering & Remote Sensing, 64(3), pp. 207 – 216.

Meyer, W.B. (1999). Past and Present Land-use and Land-cover in the U.S.A. Consequences, pp. 24- 33.

Olorunfemi, J.F. (1983). Monitoring Urban Land – Use in Developed Countries – An aerial photographic approach. Environmental Int., 9, pp. 27 – 32.

Singh, A. (1989). Digital Change Detection Techniques Using Remotely Sensed Data. International Journal of Remote Sensing, 10(6), pp. 989-1003.

Yeates, M and Garner, B. (1976). The North American City. Harper and Row Pub. N. Y.

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ISSN (Print): 2616-051X | ISSN (electronic): 2616-0501

Vol 2, No. 1 March 2018, pp 39 - 45

Hydrogeophysical Survey of Groundwater Development at Okada Community Ovia North - East L.G.A. Edo State

Ehigiator, M. O. Department of Basic Sciences (Geophysics option), Benson Idahosa University, Benin City, Edo State, Nigeria [email protected]

ABSTRACT

Geophysical investigation was conducted at Okada community in ovia North Local Govertment area of Edo state to determine the prospect of aquifer zone. The Petrozenith PZ-02 Terrameter, one of the Electrical Resistivity Equipment was used to conduct a Vertical Electrical Sounding (VES) in the study area. The Garmin Etrex 10 Global Navigation satellite systems (GNSS) was used to acquire Geodetic coordinates of point where VES observations were made. This research was carried out as a pre-drilling Hydro-geophysical survey conducted for the purpose of surveying and studying the proposed water borehole site at Okada Community that has suffered acute water problems for a very long time. There have been series of boreholes drilled in the studied area but all are dry wells. This survey was conducted to investigate the subsurface complexity of the sites in respect of lithology and to recommend the total drill depth based on the prospective aquifer unit so identified. Result of interpretation suggests that the area is underlain with substantive aquiferous formation but at a depth not exceeding 121.60 m (398.95 ft), which is the lower aquifer unit. The value of elevation at point of observation referenced to mean sea level is 94 m.

Keywords: Aquiferous, Shale, Geoelectric, Groundwater, Subsurface, Sandstone

1.0. Introduction

Groundwater is characterised by certain number of parameters which are determined by geophysical methods, resistivity methods, seismic methods and gravity methods (Alile and Ehigiator, 2011). This research was carried out as a pre-drilling Hydro-geophysical survey conducted for the purpose of surveying and studying the proposed water borehole site at Okada Community. There have been series of boreholes drilled in the studied area but all are dry wells. This survey was conducted to investigate the subsurface complexity of the sites in respect of lithology and to recommend the total drill depth based on the prospective aquifer unit so identified.

2.0. Materials and Methods

2.1. Study area Okada Community (Figure 1) is located on a latitude 6° 44' 0'' and longitude 5° 23' 00'' in Ovia North- East L.G.A. of Edo State. The subsurface of this area consists of lignite, clay, claystone, shale, mudstone, coal, sandstone with limestone intercalations belonging to the Imo shale group. Vegetation is made of shrubs and few scattered trees within the rain forest belt of Nigeria.

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(a) (b)

Figure 1: Map of study area (Okada)

2.2. Data acquisition To compliment surface geological mapping, the Schlumberger configuration was used for a total spread (L) of 500 m. A VES station was located in front of the proposed water borehole site. A spread of 250 m (L/2) was covered on the right running towards Okada, and another 250 m (L/2) was run on the left towards Utese Community. Necessary precautions required in geo-electric measurement were duly considered and maintained. The survey lasted between 09.15 hrs to 18.25 hrs under favourable weather condition.

Precautions are taken during the course of electrode spacing. When electrode spacing is small compared to the layer thickness, nearly all current will flow through the upper layer. The resistivities of the lower layers have negligible effect. This is due to the fact that the measured apparent resistivity is the resistivity of the upper layer (Schlumberger, 2011).

2.3. Data processing All field data have been subjected to manual computation and finally to computer processing techniques, applying the IPI2WIN Resistivity Sounding Interpretation software. IPI2WIN is software that is designed to analyze geo-electric measurements on a single piece automatically or semi- automatically to get the smallest error. Results of data processing by the software package are integrated in order to arrive at the realistic composition and layering of the subsurface (William, 2012).

For the VES, the Schlumberger electrode configuration was adopted. A Petrozenith PZ-02 Terrameter was used to take field measurements of resistance (R) from which apparent resistivity (ρa) was calculated by the relation:

휌푎 = 퐾 ∗ 푅 (1) where: R Resistance in ohms

2 2 (퐴퐵⁄ ) − (푀푁⁄ ) × 휋 퐾 = 2 2 (2) 2푀푁

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where: AB Current electrode spacing in meters MN Potential electrode spacing in metres

3.0. Results and Discussion

3.1. Vertical sounding for two horizontal beds The images developed are useful in dealing with sounding on two horizontal layers, as well as profiling over elementary 2D structures (Telford et al., 2012). Its application to the former also provides some simple illustrations of limiting cases of the bed parameters (Schlumberger, 2011).

The potential of a single electrode to the resistivity of the upper layer in terms of the electrode spacing, the depth to the interface, and the resistivity contrast between the two beds can be expressed in Equation (3) below (Koefoed, 1979). Writing this expression in the form of an apparent resistivity, which would be measured by a four-electrode system and considering the measured potential difference between ρ1 and ρ2, we have (SPDC, 2010):

∞ 푚 퐼휌1 1 푘 푉 = [ + 2 ∑ ] 2휋푟 푟 2 (3) 푚=1 √{1 + (2푚푧/푟) }

휋푟3 ∆푉 휋푟3 휕2푉 휌 ≈ − ( ) ≈ − ( ) (4) 푎 (∆푟)2 퐼 퐼 휕푟2

where:

ρa Resistivity r Distance from first electrode V Voltage ∆V Change in voltage I Current

퐼휌1 1 1 1 1 ∆푉 = 푉1 − 푉2 = {[( − ) − ( − )] 2휋 푟1 푟2 푟3 푟4 ∞ 1 1 1 + 2 ∑ 푘푚 ( − − (5) (푟2 + 4푚2푧2)1⁄2 (푟2 + 4푚2푧2)1⁄2 (푟2 + 4푚2푧2)1⁄2 푚−1 1 2 3 1 + 2 2 2 1⁄2)} (푟4 + 4푚 푧 )

Applying Wenner spread; because r1 = r4 = a, r2 = r3 = 2a, Equation (5) is simplified as follows: ∞ ∞ 퐼휌 4푘푚 4푘푚 퐼휌 ∆푉 = 1 [1 + ∑ − ∑ ] = 1 (1 + 4퐷 ) (6) 2휋 {1 + (2푚푧⁄푎)2}1⁄2 {4 + (2푚푧⁄푎)2}1⁄2 2휋 푤 푚−1 푚−1 where: ∞ 1 1 퐷 = ∑ 푘푚 [ − ] 푤 {1 + (2푚푧⁄푎)2}1⁄2 {4 + (2푚푧⁄푎)2}1⁄2 (7) 푚−1

But, 휌푎 = 2휋∆푉푝⁄퐼 = 2휋∆푉푝⁄퐼 (1⁄푎 − 1⁄2푎 − 1⁄2푎 + 1⁄푎) = 2휋푎∆푉⁄퐼 (8)

So that the apparent resistivity is:

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∞ ∞ 4푘푚 4푘푚 휌 = 휌 [1 + ∑ − ∑ ] = 휌 (1 + 4퐷 ) (9) 푎 1 {1 + (2푚푧⁄푎)2}1⁄2 {4 + (2푚푧⁄푎)2}1⁄2 1 푤 푚−1 푚−1

Applying Schlumberger spread; when x = 0, r1 = r4 = L - l, r2 = r3 = L + l, and the potential difference is written by modifying Equation (3) as: ∞ 퐼휌 2푙 푘푚 퐼휌 2푙 푉 = 1 [1 + 2 ∑ ] ≈ 1 (1 + 2퐷′) (10) 휋퐿2 {4 + (2푚푧⁄푟)2} 휋퐿2 푠 푚−1 where: ∞ 푘푚 퐷′ = ∑ 푠 {1+(2푚푧/퐿)2/}3/2 푚=1

The exact expression for apparent resistivity becomes:

∞ ∞ 퐿 + 푙 푘푚 퐿 − 푙 푘푚 휌푎 = 휌1 [1 + ( ) × ∑ − ( ) × ∑ ] 푙 {1+(2푚푧)2/퐿2}1/2 푙 {1+(2푚푧)2/퐿2}1/2 (11) 푚=1 푚=1 = 휌1(1 + 퐷푠)

where: ∞ ∞ 퐿 + 푙 푘푚 퐿 + 푙 푘푚 퐷 = ( ) ∑ − ( ) ∑ 푠 푙 {1+(2푚푧)2/(퐿 − 푙2)}1/2 푙 {1+(2푚푧)2/(퐿 − 푙2)}1/2 푚=1 푚=1

Approximately, we have:

∞ 푘푚 휌 = 휌 [1 + 2 ∑ ] = 휌 (1 + 퐷 ) (12) 푎 1 {1+(2푚푧/퐿)2}3/2 1 푠 푚=1

This result can also be obtained by differentiating Equation (3) with respect to r, multiplying the result by 2 (because there were two current electrodes), and applying Equation (12) to get ρa: 휋퐿2 ∆푉 휌 = ( ) (13) 푎 퐼 ∆푟

Applying double-dipole spread; because r1 = r4 = 2nl, r2 = 2(n – 1)l, r3 = 2(n + 1)l, the exact expression for the potential difference is: 퐼휌 ∆푉 = − 1 2휋(푛 − 1)푛(푛 + 1)푙

∞ 푘푚 × 1 + 푛(푛 + 1) × ∑ + 푛(푛 − 1) 2 1⁄2 푚−1 (2푚푧) [1 + 2] (14) [ {2(푛 − 1)푙}

∞ ∞ 푘푚 푘푚 × ∑ − 2(푛 − 1)(푛 + 1) × ∑ 2 1⁄2 2 1⁄2 푚−1 (2푚푧) 푚−1 2푚푧 [1 + 2] [1 + ( ) ] {2(푛 − 1)푙} 푧푛푙 ]

The apparent resistivity is given by:

휌푎 = 휌1(1 + 퐷푑)

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where (1 + Dd) is the expression inside the large square brackets in Equation (14) above.

If we make n > 1, the preceding result is simplified and we can make use of Equation (10) differentiating twice,

∞ ∞ 휕2푉 퐼휌 푘푚 푘푚 1 2 = 3 1 − ∑ 3⁄2 + 3 ∑ 5⁄2 (15) 휕푟 휋푟 2푚푧 2 2푚푧 2 푚−1 [1 + ( ) ] 푚−1 [1 + ( ) ] [ 푟 푟 ]

And using Equation (9),

∞ ∞ 푘푚 푘푚 ( ) 휌푎 = 휌1 1 − ∑ 3⁄2 + 3 ∑ 5⁄2 = 휌1 1 + 퐷푑 (16) 2푚푧 2 2푚푧 2 푚−1 [1 + ( ) ] 푚−1 [1 + ( ) ] [ 푟 푟 ]

Quantitatively, we can see how the apparent resistivity varies for different electrode spreads. When the electrode spacing is very small, that is r << z, the cases tend to zero, so that we measure the resistivity in the upper formation, this is the surface resistivity (Schlumberger, 2011).

Figure 2a represent the layer resistivity model which is the plot of resistivity with length as obtained from the processed geophysical data using IPI2WIN software. The Resistivity and the cross sessional area is presented in Figure 2b.

Half the current electrode spacing (AB/2), the potential electrode spacing (MN), the derived constant (K) and the Resistivity (Ωm) of the study area are presented in Table 1.

Figure 2a: Layered resistivity model Figure 2b: Measured and modelled

3.2. Discussion Table 2 below is the results obtained using IPI2WIN software revealing seven sublayers at different depths. At depth 0.5 m of thickness 0.5 m, the resistivity was found to be 368 Ωm, while the lithology indicated sandy topsoil. At a depth of 1.21 m of sand thickness 0.706 m, the resistivity was 482 Ωm with clayey subsoil lithology. The third layer is at a depth of 2.91 m of thickness 1.7 m had resistivity of 323 Ωm and of sandy clayey formation. The fourth formation at depth 7.02 m had a thickness of 4.11 m and resistivity of 551 Ωm. The lithologic formation was found to be sandy. The fifth formation at depth 16.9 m had a thickness of 9.92 m, resistivity of 1620 Ωm and shale sandy lithology. The sixth sand at depth 40.9 m has a thickness of 23.9 m and resistivity of 2133 Ωm reflects the shaly lithology. The seventh sand whose is 116 m has a thickness of 75.8 m, resistivity of 1958 Ωm lithology of

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Nigerian Journal of Environmental Sciences and Technology (NIJEST) Vol 2, No. 1 March 2018, pp 39 - 45 prospective sandstone. The eighth sand at depth 255.6 m has a thickness of 139 m, 2038 Ωm resistivity and lithology of shale. The ninth sand which is undefined with an undefined sand thickness has a resistivity of 2053 Ωm and a sandy shale lithology. The resistivity values as reflected from the table increase with depth but with the exception of the third sand layer. The prospective water formation (aquiferous zone) is at a depth of 166.6 m with resistivity of 1958 Ωm. Hydrogeologists (Akinlabi and Oladunjoye, 2008), conducted VES in the study area, the results of their analysis indicate a remarkably inhomogeneity in geological composition. This conclusion agrees with our analysis as presented in Table 2.

Table 1: Resistivity data SP, V, and I =0 Current Electrode spacing (AB/2) Potential electrode spacing (MN) Constant (K) Resistivity (Ωm) 1 0.5 5.8905 19.8370 1.5 0.5 13.744 18.3740 2 0.5 24.74 127.5800 3 0.5 56.156 1.3709 4.5 0.5 126.84 4.2790 7 0.5 307.48 2.2799 10 0.5 627.93 1.2740 14.5 0.5 1321 0.48097 14.5 1 659.73 1.1913 21.5 1 1451 0.70151 21.5 2 724.53 1.8449 32 2 1607 1.0053 47 2 3468 0.38686 47 5 1384 1.6591 70 5 3075 1.1028 100 5 6279 0.61832 100 10 3134 1.2309 150 10 7061 0.68225 150 20 3519 1.357 220 20 7587 0.51213 220 50 3002 1.2323 300 50 6395 0.42139 350 50 7658 0.3513

Table 2: Geological formation Layer Depth (m) Thickness (m) Resistivity (Ωm) Lithology 1 0.50 0.500 368 Sandy topsoil 2 1.21 0.706 482 Clayey sub soil 3 2.91 1.700 323 Sandy clay formation 4 7.02 4.110 551 Sandy formation 5 16.90 9.920 1620 Shale (Sandy) 6 40.90 23.900 2133 Shale 7 121.60 75.800 1958 Sandstone (Prospective aquifer layer) 8 255.60 139 2038 Shale 9 Undefined Undefined 2053 Sandy shale

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4.0. Conclusion/ Recommendations

Result of interpretation suggests that the area is underlain with substantive aquiferous formation but at a depth not exceeding 121.6 m, which is the lower aquifer unit. The depth is obtained by adding (94 m), the value of the elevation obtained at point of observation with GNSS to the formation depth of 116.6 m. From the above results, the following are hereby recommended.

i. Drilling should be done to a depth not exceeding 121.6 m (398.95 ft) to allow for large reservoirs within the lower aquifers unit to be tapped. ii. There should be adequate borehole logging of the samples to enable the proper screening of the aquifer zones which are captured; this should be done by an experienced hydrogeologist. iii. The borehole drilling should be done with a competent drilling rig specifically built for the sedimentary terrain in order to attain this recommended Total Drilled Depth (TDD). iv. Proper water analysis should be done in a credible analytical laboratory to determine the quality of the water so produced from the borehole.

5.0. Acknowledgement The author would like to acknowledge the support the members of staff of Pacific Associates. We are also grateful to the department of Geophysics, Benson Idahosa University for allowing the use of departmental equipment.

References Alile, M.O. and Ehigiator M.O. (2011). Determination of aquifer layer by the application of electrical method of exploration at Ubiaja in Edo Central of Nigeria. Scientific research and Essays, 6(2), pp. 493 – 498.

Akinlabi, I.A and Oladunjoye, M.A. (2008). Geophysical investigation of dam site in a sedimentary terrain. Research Journal of Applied Sciences, 3(7), pp. 484 – 489.

Koefoed, O. (1979). Geosounding principles. Elsevier Publications. Co., Amsterdam, Oxford, New York, pp. 46 – 54.

Schlumberger (2011). Lecture notes on resistivity methods, SPDC, Warri. Available at: http://www.nigerianoil-gas.com/industry profile

Shell Petroleum Development Company (SPDC) (2010). Lecture note on resistivity methods. Available at: http://www.nigerianoil-gas.com/industry profile

Telford, W.M., Geldart, L.P., Sheriff, R. E. and Keys, D.A. (2012). Applied geophysics. Cambridge University Press, London.

William, L. (2012). Fundamentals of geophysics, Cambridge University Press, London. Available at: https://www.textbooks.com.MHG>Geophysics

Zohdy, A. (1989). A new method for the interpretation of Schlumberger and Wenner sounding curves. Geophysics, 54(2), pp. 245-253.

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Nigerian Journal of Environmental Sciences and Technology (NIJEST) www.nijest.com

ISSN (Print): 2616-051X | ISSN (electronic): 2616-0501

Vol 2, No. 1 March 2018, pp 46 - 55

Performance Assessment of Biological Wastewater Treatment at WUPA Wastewater Treatment Plant, Abuja, Nigeria

1, 1 Chukwu M.N. * and Oranu C.N. 1Biology Unit, Department of Pure and Applied Sciences, Faculty of Science, National Open University of Nigeria, Abuja, Nigeria Corresponding Author: *[email protected]

ABSTRACT

Biological treatment of wastewater from Wupa Wastewater Treatment Plant, Abuja was investigated. Wastewater samples were collected from the inlet and effluent point (before and after the ultra violet radiation unit) of the treatment plant. The physicochemical parameters; temperature, pH, conductivity, total dissolved solids (TDS), total suspended solids (TSS) and dissolved oxygen (DO) were measured. The Biological Oxygen Demand (BOD) and Chemical Oxygen Demand (COD) were obtained from samples collected from the influent and effluent points using Standard methods specified by the American Public Health Association (APHA). The water samples were also analysed for the presence of bacterial organisms via Total Coliform Count (TCC), Total Bacteriological Count (TBC) and Faecal Count (FC). At the end of the analysis, the percentage removal efficiency of the ultra violet radiation for TCC, TBC and FC were calculated. Results showed that the mean removal efficiency for TCC, TBC and FC were 99.6% , 89.9% and 98.9% respectively; all within the permissible limit of World Health Organization and Federal Ministry of Environment. There was a reduction of 81.5% in COD, 98.9% in BOD which met the required effluent standards. There was significant differences between the pH, TSS, DO, COD and BOD of the influent and effluent (P<0.05). These results showed that there is an urgent need for appropriate steps to be taken for proper management and sanitation of the wastewater before discharging it to the stream, to ensure total conformity with the approved standards.

Keywords: Influent, Effluent, Biological Oxygen Demand, Physicochemical, Conductivity, Bacteriological count

1.0. Introduction

Wastewater is any water that has been adversely affected in quality by anthropogenic influence. It comprises liquid waste discharged by domestic residences, commercial properties, and industrial and or agricultural wastes, and can encompass a wide range of potential contaminants and concentrations (Nielsen et al., 2004). Waste water that contains urine, faeces, kitchen and laundry waste is referred to as sewage.

At the beginning of the 20th Century, septic tank was introduced as a means of treating domestic sewage from individual households both in suburban and rural areas. Then a few cities and industries recognised that the discharge of sewage directly into streams caused health problems and this led to the development of sewage treatment facilities referred to as waste water treatment plants (Ikupolati, 2005). Wastewater Treatment Plants (WWTPs) are complex systems which include a large number of biological, physicochemical, and biochemical processes (Sotomayor et al., 2001).

The activated sludge process is the most widely applied biological treatment of liquid waste, treating both municipal sewage and a variety of industrial wastewaters (Aguilar-López et al., 2013; Slater, 2006). The microbiological quality of effluent consumable water is a concern to consumers (Wupa dwellers), water suppliers (Wupa Wastewater Treatment Plant), and regulatory and public health authorities alike. Most recent gastrointestinal outbreaks that have been reported throughout the world demonstrated that transmission of pathogens by effluent consumable water remains a significant cause

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Nigerian Journal of Environmental Sciences and Technology (NIJEST) Vol 2, No. 1 March 2018, pp 46 - 55 of illness (Hunter and Syed, 2001). This study was designed to examine the Performance Assessment of biological waste water treatment which is considered as one of the most efficient methods of waste water treatment.

2.0. Materials and Methods

2.1. Study area This research work was carried out in the Quality Control Laboratory of Wupa Waste Water Treatment Plant Abuja, Nigeria. Wupa Wastewater Treatment Plant is located at Cadastral Zone COO Institute & Research District of Abuja FCT, Latitude and Longitude. It is close to Wupa River and it covers an area of 297,900 square meters. WWTP which is one of the largest in the world was constructed to treat sewage generated from phase 1, II, III of Abuja Metropolis. It was designed to handle the waste generated by 700,000 Population Equivalent (PE) and expandable to 1,000,000 PE, thus the Plant can accommodate an average dry weather inflow of 5,500 cubic meter per hour and a wet weather inflow of 9,000 cubic meter per hour. The plant operates on the activated sludge process that relies on microbial population in mixed suspension to achieve the waste water treatment. The plant was constructed to address the growing concern of the disposal of human waste, as Abuja metamorphosed into a global city (Saminu et al., 2017).

2.2. Sampling points Four (4) sampling points from Wupa Wastewater Treatment Plant were selected for the study: A; Inlet-the first channel that receives raw wastewater from different homes, B; Raw sewage (influent)- just as it was discharged into the sewage treatment plant, C; Effluent (before ultra violet rays)-just before it passes through the most important stage of the waste water treatment; the UV and D; Effluent (after ultra violet rays)-just as it passes through the ultra violet ray channel, before it is discharged into the Wupa River.

2.3. Sample collection Grab method of sampling was used at the different sampling points. Sixteen samples were collected from each of the four sampling points. Each 250ml sterile sample bottle was dipped into the wastewater at a depth of 30cm, and placed in the direction of the flow of water. The cork was removed and the sample was taken, leaving space for agitation. The samples were properly labelled, then stored in a cooler and transferred to the laboratory for analysis (Benethan, 2003).

2.4. Determination of physicochemical parameters of the water samples The physicochemical parameters; conductivity, temperature, Hydrogen ion concentration (pH), Dissolved Oxygen (DO), Total Dissolved Solids (TDS) and Total Suspended Solid (TSS) were determined using Electrometric method (APHA, 2005).

2.5. Determination of the Chemical Oxygen Demand (BOD) level of the water samples The Chemical Oxygen Demand level was determined using the closed reflux method by Merck KGaA kit, 64271 Darmstadt, Germany, according to the manufacturer’s instruction.

2.6. Determination of the Biological Oxygen Demand (BOD) level of the water samples The Biological Oxygen Demand Level was determined using the Respirometric method; the standard method recognized by U.S. EPA and a labelled Method 5210B in the Standard Methods for Examining water and waste water (Lenore et al., 2005).

2.7. Bacteriological analysis (count) of the waste water samples Ninety-two (92) McCartney bottles were used; two of which were used as control for dev-lactose and EC Broth media. Dev-lactose media was used to determine Total Coliform Count (TCC) by Fermentation tube technique. Same method was also used in the determination of Fecal Count (FC) using EC broth media (Uzoigwe and Agwa, 2012). Serial dilutions of the samples were done using pour plate method according to Willey et al. (2008).

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2.8. Presumptive and confirmatory test Presumptive and confirmatory test for detection of the presence of coliforms was carried out as described by Chessbrough (2004).

2.9. Statistical Analysis The data were analysed using two way analysis of variance. Further analysis was carried out using Pearson Correlation Coefficient at 5% level of significance (P < 0.05).

3.0. Results and Discussion

3.1. Physicochemical parameters of the water samples 3.1.1. Temperature There was a slight variation in temperature between the influent and effluent (Figure 1). The influent’s temperature decreased as that of the effluent increased for the first three weeks followed by same values for both weeks 4 and 5 (25.3°C and 27.1°C respectively). Thereafter, the temperature of the influent increased as that of the effluent decreased for the remaining period of the work. There was no significant difference between the temperatures of the influent and the effluent (P > 0.05). The similarity in the temperatures of both the influent and effluent was probably because the weather condition was stable during the study period. This is in agreement with the requirement for temperature in accordance to National Guidelines of the Federal Ministry of Environment (2013) that temperature should not be greater than 40°C (Table 1).

29 Influent Effluent 28

27

C) ◦ 26

25

Temperature ( Temperature 24

23

22 1 2 3 4 5 6 7 8 Time (Weeks)

Figure 1: Variations in temperatures (°C) of Influent and Effluent (values shown are Mean ± SE)

Table 1: Effluent limits for various parameters S/N Parameters Results for effluent W.H.O’s limit for F.M.Env limit for (expressed in mean) effluent effluent 1 Temperature (°C) 26.1 <40 <40 2 pH 6.95 6.5-8.5 6-9 3 Conductivity(µS/cm) 267 1250 ------4 Total Suspended Solids(mg/l) 79.13 ------30 5 Total Dissolved Solids(mg/l) 167 1000 2000 6 Dissolved Oxygen(mg/l) 5.55 7-10 ------7 Biological Oxygen Demand(mg/l) 2.75 30 50 8 Chemical Oxygen Demand(mg/l) 24 100 80 9 Total Coliform Count (daily average, 6.5 400MPN/100ml 400MPN/100ml MPN/ml) 10 Fecal Count(daily average, MPN/ml) 6.13 0/100ml 0/100ml 11 Total Bacteria Count(daily average, 29.4 400MPN/100ml 400MPN/100ml MPN/ml) Source: Federal Ministry of Environment (FMEnv) (2013), World Health Organization (WHO) (2003)

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3.1.2. Hydrogen ion concentration (pH) Figure 2 indicated that pH values of influent ranged from 7.162 - 7.466 and those of effluent from 6.728 - 7.239 with a mean of 6.95; the permissible limit of pH by Federal Ministry of Environment (FMEnv, 2013) and World Health Organization (Table 1). There was an inversely proportionality between the pH values of the influent and effluent and significant difference between them (P < 0.05) throughout the study period.

Influent 7.6 Effluent

7.4

7.2

7

pH 6.8

6.6

6.4

6.2 1 2 3 4 5 6 7 8 Time (weeks) Figure 2: Variations in pH of Influent and Effluent (values shown are Mean ± SE)

3.1.3. Conductivity Figure 3 indicated that there was a slight variation between the conductivities of influent and effluent. The conductivities of influent decreased as those of the effluent increased as the experiment progressed except in week 3 where the reverse was the case. There was no significant difference between the conductivities of the influent and those of the effluent (P > 0.05). The conductivity of the influent is usually higher than that of effluent, under normal conditions, but this was not so during the period of study because of the sampling time, pH and temperature changes. High pH increased the ionic concentration of effluent, thus the conductivity of effluent was increased. The mean conductivity of effluent, 267µS/cm is in agreement with World health Organization (WHO) limit for conductivity (1250 µS/cm) (Table 1).

350

300 Influent

Effluent

250 µS/cm) 200

150

Conductivity ( Conductivity 100

50

0 1 2 3 4 5 6 7 8 Time (weeks)

Figure 3: Variations in conductivity (µS/cm) of Influent and Effluent (values shown are Mean ± SE)

3.1.4. Total Dissolved Solid (TDS) Figure 4 showed that there was an inverse proportionality between the values of the TDS of influent and those of the effluent throughout the study period. There was no significant difference between the TDS of the influent and effluent (P>0.05). The TDS values of the effluent (70-292mg/l) agreed with the requirement for TDS values according to the National Guidelines of the Federal Ministry of

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Environment (2013) which states that TDS value of effluent should not be greater than 2000mg/l (Table 1). The overall removal efficiency ultra violet unit for TDS was 15.6%-76.5% while the mean removal efficiency was 36%. This showed that the effluent was fairly safe to be discharged.

400 Influent

350 Effluent

300

250

200

150

100 Total Dissolved Solid(mg/l) Dissolved Total 50

0 1 2 3 4 5 6 7 8 Time (weeks)

Figure 4: Variations in Total Dissolved Solid (mg/l) of Influent and Effluent (values shown are Mean ± SE) 3.1.5. Total Suspended Solids (TSS) There was a large variation between the TSS of influent and effluent (Figure 5). The TSS of influent was indirectly proportional to that of the effluent with a significant difference between them (P < 0.05) throughout the study period. The overall removal efficiency of pollutant in the treatment plant was 31.3 - 98.5% with a mean removal efficiency of 81.5%. This is in agreement with the findings of Healy et al. (2006) who observed TSS removal efficiency of 99%.

1400 Influent

Effluent

1200

1000

800

600

400

200 Total Suspended Solid(mg/l) Suspended Total

0 1 2 3 4 5 6 7 8 -200 Time (Weeks)

Figure 5: Variations in Total Suspended Solids (mg/l) of Influent and Effluent (values shown are Mean ± SE)

3.1.6. Dissolved Oxygen (DO) The DO of the influent was inversely proportional to that of the effluent throughout the study period (Figure 6). The DO concentration of the effluent was obviously higher than that of influent probably because the effluent contained a small quantity of organic matter hence a small quantity of DO was used by microorganisms to break them down. There was thus a large quantity of unused DO in the effluent. There was a significant difference between the DO of influent and that of the effluent (P < 0.05).

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3.2. Chemical Oxygen Demand (COD) Figure 7 indicated that the COD of influent was inversely proportional to that of the effluent throughout the study period. There was a significant difference between the COD of influent and the effluent (P < 0.05). This was probably because the effluent contained small quantities of organic and inorganic loads, thus lower concentration of dissolved oxygen was needed for decomposition of the organic matter. The effluent COD range of 7-96mg/l is within the World Health Organization limit for effluent which is 100mg/l (Table 1). The overall removal efficiency of the Ultra Violet unit for COD is 72.2 - 98.6% and mean removal efficiency of 81.5%.

Influent Effluent 7

6

5

4

3

2 Dissolved Oxygen(mg/l) Dissolved 1

0 1 2 3 4 5 6 7 8 Time (Weeks) Figure 6: Variations in Dissolved Oxygen (mg/l) of Influent and Effluent (values shown are Mean ± SE)

1000

900 Influent

800 Effluent

700

600

500

400

300

200 Chemical OxygenDemand (mg/l) OxygenDemand Chemical 100

0 1 2 3 4 5 6 7 8 -100 Time (Weeks)

Figure 7: Variations in Chemical Oxygen Demand (mg/l) of Influent and Effluent (values shown are Mean ± SE)

3.3. Biological Oxygen Demand (COD) Figure 8 indicated that the BOD of both the influent and effluent increased as the experiment progressed. The BOD of the influent was significantly different from that of the effluent (P < 0.05). This was probably because the effluent contained small quantity of organic load, thus lesser concentration of dissolved oxygen would be needed for the decomposition of organic matter. The effluent range of 2-3mg/l is within the World Health Organization (WHO) limit for effluent BOD (30mg/l) (Table 1). The overall removal efficiency of the UV unit for BOD was 98.2 - 99.6% and

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Nigerian Journal of Environmental Sciences and Technology (NIJEST) Vol 2, No. 1 March 2018, pp 46 - 55 mean removal efficiency 98.9%. This is similar to the result obtained by Devi et al. (2008) who found that the reduction of BOD of wastewater from a coffee processing plant using activated sludge process was 98%.

500 Influent

450 Effluent 400 350 300 250 200 150 100

Biological Oxygen (mg/l) Deman$d Oxygen Biological 50 0 1 2 3 4 5 6 7 8 Time (Weeks)

Figure 8: Variations in Biological Oxygen Demand (mg/l) of Influent and Effluent (values shown are Mean ± SE)

3.4. Bacteriological Analysis 3.4.1. Total Coliform Count (TCC) Figure 9 showed the variations of TCC between the inlet, effluent before and after UV. There was a large variation between the inlet, effluent before UV and effluent after UV. The TCC of inlet was indirectly proportional to those of effluent before UV and effluent after UV. The highest level of contamination of wastewater with coliform occurred at the inlet (3220 - 3610 cfu/ml). Minor treatment was observed in the effluent before the Ultra Violet radiation ranging from 2040 - 2450cfu/ml, while the major treatment occurred in the effluent after the Ultra Violet radiation with the lowest coliform count of 1 - 13cfu/ml, this conform with Federal Ministry of Environment limit (400MPN/100ml) (Table 1). The treatment drastically reduced coliform but does guarantee its complete elimination. Overall removal efficiency for coliform ranged from 99.3 - 99.9% with mean removal efficiency of 99.6%.

Inlet 4000 Effluent before UV 3500 Effluent after UV 3000

2500

2000

1500

1000

Total Coliform Coun$t (cfu/ml) Coun$t Coliform Total 500

0 1 2 3 4 5 6 7 8 Time (Weeks)

Figure 9: Variations in TCC (cfu/ml) of Influent, Effluent before and after UV

3.4.2. Total Bacteriological Count (TBC) Figure 10 showed the variations in TBC between the inlet, effluent before and after UV. There was a large variation between the inlet, effluent before UV and effluent after UV. The TBC of inlet was

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Nigerian Journal of Environmental Sciences and Technology (NIJEST) Vol 2, No. 1 March 2018, pp 46 - 55 inversely proportional to that of effluent before and after UV. Contamination was highest at the inlet with Bacteria Count (BC) of 5540 - 5940cfu/ml. Minor treatment took place at the effluent before UV ray with Bacteria Count ranging from 96-3430cfu/ml while major treatment took place at the effluent after UV ray unit with BC of 1 - 54cfu/ml. This conformed to the Federal Environmental Protection Agency limit for TBC (100MPN/ml) (Table 1). Overall efficiency ranged from 43.7 - 99.9% while the mean efficiency removal was 89.9%.

Inlet

6000 Effluent before UV 5000 Effluent after UV

4000

3000

2000

1000

Total BacterIological (cfu/ml) Count BacterIological Total 0 1 2 3 4 5 6 7 8 Time(Weeks)

Figure 10: Variations in TBC (cfu/ml) of Influent, Effluent before and after UV

3.4.3. Faecal Count (FC) Figure 11 showed variations in FC between the inlet, effluent before and after UV. The inlet was consistent throughout the period of study with high values (> 1600). The FC of inlet was inversely proportional to those of effluent before UV and effluent after UV. Inlet’s FC was consistently high (> 1600) throughout the study period. The lowest faecal content in the wastewater was observed after the UV ray unit, where there was little (22) or no faecal count (0) compared to the effluent before UV ray unit (120 - 1600cfu/ml). The occurrence of zero faecal count in the second and fifth week conformed to the World Health Organization’s (WHO, 2009) standard for faecal coliform in domestic water; zero faecal count per 100ml. The effluent after UV ray unit also agreed with the Standard Guidelines for Effluent discharge in Nigeria (400MPN/100ml). The overall removal efficiency of Ultra violet unit for faecal count was 97.9 - 100%, with a mean efficiency of 98.9%.

1800 Inlet 1600 Effluent before UV Effluent after UV 1400 1200 1000 800 600

Faecal Count (Cfu/ml) CountFaecal 400 200 0 1 2 3 4 5 6 7 8 Time(Weeks)

Figure 11: Variations in FC (cfu/ml) of Influent, Effluent before and after UV

3.5. Statistical analysis Table 2 indicated that there were positive correlations between Conductivity and DO (r = 0.172), Conductivity and TSS (r = 0.208), Temperature and pH (r = 0.613), pH and TDS (r = 0.644), DO and TSS (r = 0.364), COD and TDS (r =0.096). However, Temperature showed negative correlations with Conductivity (r = -0.723), DO (r = -0.441), COD (r = - 0.521), TSS (r =-0.502) and TDS (r = -0.438).

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Negative correlations were also observed between Conductivity and COD (r = -0.100), DO and TDS (r = -0.210), DO and COD (r = -0.214), COD and TSS (r =-0.001).

Table 2: Two-Tailed Pearson’s Correlation Coefficient values among the studied physicochemical parameters. Parameters Temperature pH Conductivity DO COD TSS TDS Temperature 1 pH 0.613 1 Conductivity -0.723 -1.588 1 DO -0.441 -0.675 0.172 1 COD -0.521 -0.246 -0.100 -0.214 1 TSS -0.502 -0.002 0.208 0.364 -0.001 1 TDS -0.438 0.644 0.009 -0.210 0.096 1

4.0. Conclusion

Physicochemical parameters’ values except TSS and TDS were within the permissible limits of World Health Organisation (WHO), Federal Environmental Protection Agency (FEPA) and the National Guidelines of Federal Ministry of Environment (FMEnv). Bacteriological analysis (TCC, TBC, FC) results were all within the permissible limit of WHO and FEPA with high mean removal efficiency of 99.6% for TCC, 89.9% for TBC and 98.9% for FC. This treatment plant was thus quite effective in biological treatment of wastewater. However, there is an urgent need for appropriate steps to be taken for proper management and sanitation of the wastewater before discharging it to the stream, in order to ensure to total conformity with the approved standards.

Based on the findings of the investigation, the following recommendations were made: i. Algal removal facilities should be introduced to improve the removal efficiency of Total Dissolved Solids and Total Suspended Solids. ii. Efforts should be geared towards full utilization of the plant capacity so as to maximize its full potentials. iii. There must be continuous monitoring of the efficiency of the wastewater treatment plant so as to enhance biological treatment of wastewater and ensure sustained adherence to permissible standards. iv. In line the success of the Wupa Wastewater Treatment Plant in remediating polluted water mostly from municipal activities, similar facilities should be provided at all State Capitals in Nigeria with a view to reduce the negative impacts of untreated wastewater in the environments

References Aguilar-López, R., López-Pérez, P.A., Penã-Caballero, V. and Maya-Yescas, R. (2013). Regulation of an activate sludge wastewater plant via robust active control design. Int. J. Environ Res, 7, pp. 61–68

American Public Health Association (APHA) (2005). Standard Methods for the Examination of Water and Wastewater, (21st Ed). Washington, D. C., pp. 1368.

Benethan, I. A. (2003). Microbiology with Health care Application. Star Publishing Company, USA, pp. 111-125.

Chessbrough, M., (2004). Medical Laboratory Manual for Tropical Countries, (4th Ed). Cambridge University Press, Cape Town, pp. 143-157.

Devi, R. and Dahiya, R.P. (2008). COD&BOD removal for domestic wastewater generated in decentralized sectors. Bioresour Technology, 99, pp. 344-349.

Federal Ministry of Environmental (FMEnv). (2013). National Environmental Protection Regulations (Effluent Limitation) Regulations. Federal Republic of Nigeria Official Gazette, Lagos, pp. 42-78

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Federal Environmental Protection Agency (FEPA). (2005). National Environmental Protection Regulations (Effluent Limitation) Regulations. Federal Republic of Nigeria, Official Gazette, Lagos, pp. 42-78.

Healy, M.G., Rodgers, M. and Mulqueen, J. (2006). Performance of stratified sand filter in removal of chemical oxygen demand, total suspended solids from high strength wastewater. Journal of Environmental Management, 83, pp. 409-415

Hunter, P. R. and Syed, Q. (2001). Community Surveys of Self-reported diarrhea, can dramatically overestimates in size of outbreaks of Waterborne Cryptosporidioses. Water Science Technology, 43, pp. 27-30.

Ikupolati, A.O. (2005). Sewage Disposal. Microsoft Corporation. Encarta Encyclopedia

Lenore, S., Clesceri, A. D. and Eugene, W. (2005). Standard Methods for Examination of Water & Wastewater Method 5210B. Washington, DC: American Public Health Association, American Water Works Association, and the Water Environment Association.

Nielsen, P. H., Thomsen, I. R. and Nielsen, J. L. (2004). Bacteria Composition of Activated Sludge- Importance for Floc and Sludge Properties. Water Science and Technology, 49, pp. 31-58

Saminu, A., Chukwujama, I.A., Garba, A. and Nnamdi, M.M.(2017). Performance evaluation of Wupa wastewater treatment plant, Abuja. American Journal of Engineering Research (AJER), pp. 87- 88.

Slater, N. (2006. Sequencing batch reactors: cost effective wastewater treatment. In: Alberta Water and Wastewater Operators Association, 32nd Annual Operators Seminar, 16 March 2006, Banff, Alberta

Sotomayor, O.A.Z, Park, S.W, Garcia, C. (2001). A simulation benchmark to evaluate the performance of advanced control techniques in biological wastewater treatment plants. Braz J Chem. Eng, 18, pp. 81–10113.

Uzoigwe, C. I. and Agwa, O. K. (2012). Microbiological quality of water collected from boreholes sited near refuse dumpsites in Port Harcourt, Nigeria. African Journal of Biotechnology, 11(3), pp. 3135-3139.

Willey, J. M., Sherwood, L.M. and Woolverton, C.J. (2008), Prescott Harley and Kleins Microbiology. (7th Ed). Mc-Graw Hill, New York, pp. 1049-1088.

World Health Organization (WHO) (2009). Global Water Supply and Sanitation Assessment. WHO Press, Switzerland, 1, pp. 4-10

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ISSN (Print): 2616-051X | ISSN (electronic): 2616-0501

Vol 2, No. 1 March 2018, pp 56 - 68

Determination of Conversion Constant between Lagos Datum and Niger Delta Mean Lower Low Water Datum and their Horizontal and Vertical Accuracy Standards using GNSS Observations

1 2 Ehigiator, M.O. and Oladosu, S.O. 1Department of Basic Sciences (Geophysics option), Benson Idahosa University, Benin City, Edo State, Nigeria 2Department of Geomatics, Faculty of Environmental Sciences, University of Benin, Benin City, Edo State, Nigeria Corresponding Author: *[email protected]

ABSTRACT

With the use of Global Navigation Satellite System (GNSS) technology, it is now possible to determine the position of points in 3D coordinates systems. Lagos datum is the most common Mean Sea Level used in most parts of Nigeria. In Niger Delta, for instance Warri and its environs, the most commonly used datum for height determination is the Mean Lower Low Water Datum. It then becomes necessary to determine a constant factor for conversion between the two datum when the need arises as both are often encountered during Geomatics Engineering field operations. In this paper, the constant to be applied in converting between both datum was determined. The constant was found to be 17.79m. The horizontal and vertical accuracy standard was also determined as well as the stack maps.

Keywords: GNSS, Accuracy Standard, Observation, Datum, Horizontal, Static Technique

1.0. Introduction

The proliferation of Global Navigation Satellite System (GNSS) receivers in Nigeria has drastically brought a significant reduction in the rigour posed by using conventional survey equipment and techniques in the establishment and densification of control points of high order accuracy standard for mapping and Geographical Information System (GIS) purposes. Not only does GNSS have many advantages over the conventional methods, it also saves time, efforts and can cover large area within the shortest possible time.

GNSS receivers are available in different categories. The three categories according to Pete and Krista (2012) are survey-grade, consumer’s grade and mapping grade. Each grade as noted by them can provide as follows: survey-grade receiver gives sub-centimetres static horizontal position accuracy in open air but sub-meters accuracy in or near forest area (Pirti, 2008); mapping-grade receiver may be able to give sub-meter in static horizontal position accuracy, but usually 2 – 5 m in or near forest area; while the consumer-grade gives the lowest. In terms of open air, GNSS can give static horizontal accuracy of up to 5 m while in forested area it ranges between 5 – 25 m. GNSS is able to measure ranges between points up to 20 – 30 km or more. The differential tropospheric separation is usually ignored for horizontal separations less than approximately 30 km; however, differences in height should be modelled since horizontal gives higher accuracy than vertical. The differential ionospheric separation is usually ignored for separations of less than 30 km, depending on ionospheric conditions. Due to ionospheric uncertainty, it is better to calibrate for the ionosphere using dual-frequency receivers for distances greater than a few km (Geoffrey, 1997).

Sources of errors in GNSS surveying include but are not limited to: (i) The multipath effect, which is caused by reflection of satellite signals (radio waves) on objects in contact. For GPS signals this effect mainly appears in the neighbourhood of large buildings, foliage, terrain, vehicle structure and so on.

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(ii) Atmospheric (tropospheric and Ionospheric) error: Since atmosphere is varied at different places at different times, it causes a delay not possible to be accurately compensated for. Also, radio signals which travel at the speed of light are slower in ionosphere. To mitigate this, inverse square law is applied and the waves are slowed down inversely proportional to the square of their frequency (1/f2) while passing the ionosphere (the ionosphere delays the code but advances the phase). For troposphere, the reasons for the refraction are different concentrations of water vapours, caused by different weather conditions. The error caused by tropospheric effect is smaller than that of ionospheric effect, but cannot be eliminated by calculation. It can only be approximated for by a general calculation model. (iii) Receiver Error: Since the receivers are also not perfect, they can introduce their own errors which usually occur from their clocks or internal noise. Despite the synchronization of the receiver clock with the satellite time during the position determination, the remaining inaccuracy of the time still leads to an error of about 2 m in the position determination. Rounding and calculation errors of the receiver sum up approximately to 1 m (CED, 2012; Ehigiator et al., 2017), in stand-alone positioning.

Lagos 1955 as reported by (Badejo et al., 2014) is a vertical datum first defined in 1955 and is suitable for use in Nigeria. Lagos 1955 origin is Mean Sea Level at Lagos, 1912-1928. Lagos 1955 is a vertical datum for Geodetic survey, topographic mapping and engineering survey. It was defined by information from (Ebong et al. and AVN International, 1991; Uzodinma, 2005). The Lagos Datum is 2.8310 m above Mean Sea Level (British Oceanic Data Centre, 2011). The Lagos datum is the height commonly adopted in Nigeria mapping system. This is not true for areas in Niger Delta where oil and Gas are produced. Height determination is usually referenced to Low Low Water (LLW).

Warri Mean Lower Low Water (WMLLW) is the average of all the lower low water elevations (heights) observed over the official segments over which tidal observations has been made and reduced by taking the mean value. These levels has the maximum high tide recorded in the tide tables for Warri which is of a maximum height of 1.9 m and a minimum height of 0.1 m (Tides and Solunar Charts, 2013). This study was aimed at establishing controls at Warri for mapping and prospecting purposes. Two base stations were set up in Benin on XSU92 and BEM_1000 which are government controls while four other points were coordinated in Warri, Delta State.

1.1. Observation times and baseline lengths The observation time required for an accurate result in post processing depends on several factors such as baseline length, number of satellites, satellite geometry (GDOP) and ionosphere (Leica Geosystems User’s Manual, 2000). Ionospheric disturbances vary with time, day, night, month, year and position on earth’s surface. Generally, the larger the constellation of satellites, the better the available geometry, the lower the positioning dilution of precision (PDOP), and the shorter the length of the session needed to achieve the required accuracy (GPS and GNSS for Geospatial Professionals, 2015). With static observation method, four (4) or more satellites with a baseline length of 15 – 30km should be observed for 1 – 2 hrs by day and 1 hr at night; while four (4) or more satellites with a baseline length of over 30 km should be observed for 2 – 3 hrs by day and 2 hr at night. RICS (2010) also noted in the same vein the following accuracy conditions for GNSS survey; Dual-frequency receivers H – 5 mm + 1 ppm, V – 10mm + 1 ppm, Baseline 20 km for at least 1 hr, 30 km for at least 2 hrs, 50 km for at least 4 hrs, 100 km for at least 6 hrs. The observation times for the baselines are as presented in Table 1 while baseline lengths in kilometres are presented in column 3 of Table 3. This work commenced on the 10th of October, 2017 and ended the same day. Observers were positioned at each stations and communication to start the equipment were made through hand set (communication gadgets) as soon as responses from all stations were perfected, data acquisition commenced immediately.

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Table 1: Station Location and Occupation Time No of Control Stn_ID Status Occupation Time Start Time Stop Time satellites Location acquired BEM_1000 Known 6hrs:14mins 08:32am 14.46pm 12 Benin GPS-ITER_01 Unknown 4hr:47mins 10:35am 13:22pm 11 Warri NP-WW01 Unknown 5hr:47mins 09:55am 13:42pm 09 Warri NP-WW02 Unknown 5hr:51mins 09:49am 13:40pm 08 Warri NP-WW03 Unknown 4hrs:08mins 10:59am 14:08pm 10 Warri XSU92 Known 6hrs:12mins 08:35am 14:12pm 12 Benin

Table 1, shows the control station locations and IDs, their status at the time the project was carried out and the occupation time spent on each station for which the receivers to acquire spatial data.

1.2. Network design The network design goes a long way in affecting the results that are obtained from the survey work. Therefore, its design must take into account the following objectives which were strictly adhered to in this work. The nature and characteristics of the project site made it necessary to establish the controls above flood plain as it is close to an adjoining river. The lengths of each base line have been included in Table 3. Clearance of least 250 m away from a radio transmitter or mobile phone tower was carefully observed during the establishment of the controls and subsequent field work to prevent interference, and finally multipath inducing objects like tall buildings, and large bill boards were avoided in the course of survey execution.

The design of the network took into consideration the listed factors above, which are very crucial to the integrity of data acquired. Figure 1 show the diagram of the designed network. The network was designed in such a way as to avoid both natural and artificial disturbances on the site.

Figure 1: Network design diagram

2.0. Materials and Methods

i. 1 x Hi-Target V30 50 Dual Frequency GNSS (Global Navigation Satellite System) ‘Base Station’ receiver ii. 5 x Hi-Target V30 50 Dual Frequency GNSS (Global Navigation Satellite System) ‘Rover’ receiver iii. 1 x Data Processing Computer and Accessories

On the 15th of October, 2017 data were collected between the hours of 08:32am Nigerian time and 14:12pm. Instruction was passed to the observers to watch out for when the value of standard deviation of acquired satellite values as read from the data logger is reduced to barest minimum before taking measurement.

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Static technique was adopted in the process of data acquisition in which the (receivers) are set on known base stations and the rover (receiver) on the unknown stations both acquiring data simultaneously with regular communication with each other. In static technique post processing is required unlike in RTK technique.

2.1. Data acquisition GNSS observations were carried out using two existing (Government) GNSS 1st order control stations in Benin City. In-situ and stability tests were performed on the two GNSS control stations i.e. BEM_1000 and XSU92 used for this investigation. Hi-Target GNSS receiver was setup on XSU92 and BEM_1000 and the data collected was processed while XSU92 was held fixed. The coordinate of XSU92 obtained was compared with the given coordinates; the results are presented as shown in Table 2 below.

Table 2: Integrity check on BEM_1000 and XSU92, 1st order controls Station Given Coordinates Obtained Coordinates Remark Name N(m) E(m) H(m) N(m) E(m) H(m) XSU92 257998.9930 357763.3315 103.9247 257999.2512 357763.3333 86.1242 ok BEM_1000 244420.3051 356733.3788 54.3573 244420.5072 356733.3593 36.5656 ok

In Table 2, the check on the integrity of the two controls was performed after centering and levelling of the instruments. The results showed that horizontal coordinate values are more accurate than vertical coordinate values.

2.2. Data post-processing The collected survey data in static mode saved in the instrument memory was downloaded to PC with post-processing software (Hi-target Geomatics Office) which was then processed accordingly.

2.3. Datum transformation The term datum is used to describe the reference frame for geodetic computation. The position of a point on the earth surface can be given either in terms of (φ, ʎ, h) or (X, Y, Z) coordinates systems. Whichever system is used has an origin and a relationship for transformation to other systems. The transformation from (φ, ʎ, h) system to (X, Y, Z) system can be achieved using the following relationship (Georgiadou et al., 2001).

The formula to perform the Molodensky Abridge Model for transforming WGS84 to the equivalent Minna datum Geographical coordinates read thus:

'' [tx SinCos t y Sin  tzCos  (af  fa)Sin2 /(MSin1'')] (1a)

'' [tx Sin  t yCos]/(VCosSin1''] (1b)

2 '' [t x SinCos  t y Sin  t zCos  (af  fa)Sin  /(MSin1'')] (1c)

2 aL aL (1 e ) V   1 , M    3 (1d) (1 e2 Sin2) 2 (1 e2 Sin2) 2

where: tx, ty , tz Translations between both datum (in geocentric coordinates) φ, λ, h Geodetic co-ordinates of the local geodetic system ellipsoid ∆φ, ∆λ, ∆h Corrections to transform local datum co-ordinates to WGS84 φ, λ, h ∆X, ∆Y, ∆Z Corrections to transform local datum co-ordinates to WGS84 X, Y, Z ∆a, ∆f (WGS84 minus local) semi-major axis and flattening respectively a Semi-major axis of the local geodetic system ellipsoid f Flattening of the local geodetic system ellipsoid

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M (φ) Radius of curvature in the meridian V (φ) Prime vertical radius of curvature

The datum shift parameters derived by Shell Petroleum Development Company, (2010), are presented thus: Datum Shift Parameters from WGS84 to Minna datum geographical coordinates are:

tx = plus 111.916 ty = plus 87.852 tz = minus 114.499 a f L = 6378249.145 L = 1/293.465 ∆f = minus 0.54750714

Mathematically transformation from one datum to another can be realized by relating the geographic coordinates (φ, ʎ, h) of both datum systems directly or indirectly by relating the geocentric coordinates (X, Y, Z) of the datum.

The transformation from (φ, ʎ, h) system to (X, Y, Z) system can be achieved using the following relationship by SNZGD (2000):

X  N  hcos cos   Y  N  hcos sin  Z  N(1 e 2 )  h sin    (2)

where: N Radius vector of the prime vertical h Point above ellipsoid e Eccentricity.

N and e are given by: e2  2 f  f 2   a  N  (1 e2 sin2 )1/ 2   (3)

Where: a Major axis of the earth, and f Flattening of the reference ellipsoid.

For WGS84, a = 6,378,137 m, f = 1/298.257223563 = 0.003352810665.

These formulas used by the SNZGD (2000) can be used to convert the Cartesian coordinates (X, Y, Z) to the geographic coordinates (φ, ʎ, h) as follows: tan   Y  X  Z(1 f )  e 2 a sin3   tan  2 3  (4a) (1 f )(p  e a cos   2 2  h  p cos  Z sin  a 1 e sin   where a and f are obtained from the ellipsoid under consideration for the geodetic datum respectively.

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2 2  p  X  Y  

2 2  r  p  Z  (4b)  Z  e 2 a tan   (1 f )   p  r 

2.4. Carrier phase method The carrier phase model is presented in equation (5) as follows: Michael, (2005). 1 (t)  [r(t)  I  T ]  f (t  t S )  N   (5)  U  where: φ Partial carrier phase cycle measured by the receiver λ Wavelength f Carrier frequency r Geometric range between the receiver and the satellite I Ionospheric advance T Tropospheric delay, which are all expressed in units of meters. s δtu and δt , are the receiver and satellite clock biases respectively, which are expressed in units of seconds. N Integer ambiguity, which is the total number of full cycles between the receiver and the satellite. The integer ambiguity cannot be measured and has to be estimated, but the integers remain constant as long as the carrier tracking loop maintains lock (Michael, 2005).

2.4.1. Relative positioning using carrier phase The single difference for two receivers simultaneously collected data from one satellite is presented in equations (6) and (7). The differences between equations (6) and (7) eliminate Receiver clock error

(훿푡푢) 1  k (t)  [r k (t)  I k  T k ]  f (t  t k )  N   k (6) u  u u u u ,u Similarly, the same expression can be written for the stationary reference receiver as: 1  k (t)  [r k (t)  I k  T k ]  f (t  t k )  N   k (7) r  r r r r ,r

2.4.2. Double difference The double and triple difference can be used to overcome integer ambiguity and clock bias problems inherent in single difference method. To eliminate the satellite clock bias term, two single difference equations can be subtracted to yield the double difference equation. This involves making use of two satellites (Michael, 2005).

kl k k l l ur  (u r )  (u r ) (8) kl k l ur  ur ur (9) 1  kl  (r k  r k )  f (t  t )  (N k  N l )  ( k   l ) (10) ur  ur ur ur ur ur ur ur ur

1  kl  (r kl )  N kl   kl (11) ur  ur ur ur

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From equation (10), the satellite clock bias terms cancel out which reduces the number of unknowns to the 3 position coordinates and (K − 1) integer unknowns, where K is the number of satellites. The relative position vector can be formed using the difference of the distance from the receivers to the k l th th satellites, rur and rur , and the unit vectors in the direction of the k and l satellites.

2.4.3. Geometry of multiple receivers The geometry of the multiple receivers has been explained by Michael (2005), which has been employed in this work. If (XSU92) which is the base station is represented by A and any other point in the network as presented in Figure 1 as B, or C and subsequent point D or more points. Equation (12) shows a case of using one roaming receiver for double difference relative positioning. To start with, receiver A is the base station receiver, receiver B and C are the roaming receivers, and point D is a point in space whose position is to be determined.

 AB  GAB X AB  N AB (12)

2 1 T   (1A 1A )     (13 11 )T Where: G   A A  AB      K AB 1 T  (1A 1A ) 

For more receivers, Equation (13) can be used to determine the new position.

 GAB 0  N AB    0 G  N   AB   AC   AC    X AB     1 0 0 1 0 0  *   0  (13)  AC      X AC     X CB   0 1 0 0 1 0   0   0 0 1 0 0 1   0  where φAB is a (KAB − 1) × 1 vector in which KAB is the number of satellites that are common between receiver A and B, φAC is an (KAC − 1) × 1 vector in which KAC is the number of satellites that are common between receiver A and C, and X CB is a 3 × 1 vector. This yields a total possible number of equations of (KAB − 1) + (KAC − 1) + 3. The vectors X AB and X AC are also 3 × 1 and therefore, there are 6 unknowns which correspond to the receivers’ positions. Therefore, the number of satellites that are necessary between receiver B and C are KAB + KAC ≥ 5.

 AB  GAB  N AB   0      X AB      X CB (14)  AC  GAC  N AC   GAC 

Equation (14) represents the double difference where the constraint of knowing the vector between the two roaming receivers has been imposed by augmenting the measurements from the additional roaming receiver. For the two roaming receivers with the constraint of knowing the displacement vector X CB is a different manner in which it uses the fact that X AC = X AB − X CB . The estimated location can be transformed into any point D in space as long as the vector from D to B is known. Equation (16) shows the transformation.

X AD  X AB  X DB (15)

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 AB  GAB  N AB   0  GAB  X CB      X AD       *   (16)  AC  GAC  N AC   GAC  GAC  X DB 

The adjustment of network was done alongside the processing earlier mentioned using Hi-target Geomatics Office. This is provided for in the software and output can be in Microsoft Excel file format.

Table 3 shows the baselines IDs, lengths, uncorrected baselines, baseline residuals, correction to uncorrected baselines, the final change or difference in coordinates and heights.

Table 3: Baseline lengths and residuals

Corrected Baseline Northing

Baseline Uncorrected Baseline Baseline Residual Easting (m)

(m) (m)

Height Height

Baseline Baseline length (km) (m)

From To ∆X(m) ∆Y(m) ∆Z(m) Vx(m) Vy(m) Vz(m) ∆X ∆Y ∆Z X Y Z

356733.378 244420.305 XSU92 (control) 0 0 0 0 0 0 0 0 0 0 103.92 8 1 BEM_1 622956.590 376244.813 XSU92 12.2 5290.0024 19984.1984 -70657.6415 0.0045 0.0061 0.0033 5289.9979 19984.1923 -70657.6448 54.36 000 1 8 GPS- BEM_1 642940.801 364031.543 ITER_0 86.6 -1531.1362 913.3610 13501.4345 -0.0570 -0.0022 -0.0009 -1531.0792 913.3632 13501.4354 24.36 00 7 1 1 NP- NP- 630795.540 363978.293 13.6 -6599.4692 -7838.9332 71555.8851 0.0014 -0.0007 0.0085 -6599.4706 -7838.9325 71555.8766 21.27 WW01 WW02 0 1 NP- NP- 630742.566 363915.871 12.3 -1309.5621 12145.2226 898.1741 -0.0008 -0.0002 0.0227 -1309.5613 12145.2228 898.1514 21.07 WW02 WW03 3 5 GPS- NP- 630680.512 357763.331 ITER_0 12.4 -8130.6310 -6925.5537 85057.3321 0.0014 -0.0007 -0.0025 -8130.6324 -6925.5530 85057.3346 21.29 WW03 9 5 1 GPS- 622956.590 356733.378 ITER_0 XSU92 73.6 -6605.0034 -7785.9560 71560.3778 0.0006 0.0001 0.0002 -6605.0040 -7785.9561 71560.3776 103.92 1 8 1

The accuracy standard for each baseline can be derived from linear accuracy relationship as follows:

 2 2 1/ 2  1: Vx Vy  (17)  l   ac 

Vx and Vy are as presented in Table 4 and lac is the baseline length.

By multiplying Equation (17) by 1,000,000 gives the accuracy in parts per million (ppm).

 2 1/ 2  1: Vz  (18)  l   ac 

Vz are also represented in Table 5 and lac represents the base line length. By multiplying Equation (18) by 1,000,000 gives the accuracy in parts per million (ppm).

Table4: Horizontal accuracy Baseline Baseline Residual From To Vx (m) Vy (m) Length (m) Linear Accuracy Std. Std. ppm Dev_N Dev_E (mm) (mm) XSU92 BEM_1000 0.0045 0.0061 12248.558 1615854.128 13.4 17.5 2 BEM_1000 NP-WW01 -0.0570 -0.0022 86562.033 1517502.275 6.2 11.7 0 NP-WW01 NP-WW02 0.0014 -0.0007 13618.621 8700617.805 9.9 15.1 9 NP-WW02 NP-WW03 -0.0008 -0.0002 12301.988 14918351.744 10.0 15.2 15 GPS- 0.0014 -0.0007 12364.648 7899483.842 8 NP-WW03 10.0 15.3 ITER_01 GPS-ITER_01 XSU92 0.0006 0.0001 73619.617 121029904.806 0.0 0.0 121

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Table 4 shows the stations horizontal coordinates residuals, baseline lengths as well as linear accuracy in parts per million. The maximum parts per million accuracy obtained in horizontal was 121 while the minimum was 0.1.

Table 5: Vertical accuracy Baseline Baseline Residual From To Vz (m) Length (m) Vertical Accuracy Std. ppm Dev_U (mm) XSU92 BEM_1000 0.0033 12248.558 3711684.242 44.8 4 BEM_1000 NP-WW01 -0.0009 86562.033 96180036.667 27.1 96 NP-WW01 NP-WW02 0.0085 13618.621 1602190.706 34.9 2 NP-WW02 NP-WW03 0.0227 12301.988 541937.797 35.0 1 GPS- -0.0025 12364.648 4945859.200 35.1 5 NP-WW03 ITER_01 GPS-ITER_01 XSU92 0.0002 73619.617 368098085.000 0.0 368

Table 5 shows the station vertical coordinate residuals, baseline lengths as well as vertical accuracy in part per million. The maximum parts per million accuracy obtained in vertical was 368 while the minimum was 1.

2.5. Relative accuracy standard for national surveys in Nigeria The relative accuracy standard for national surveys as specified by Surveyor Council of Nigeria (SURCON) (2007) for horizontal and vertical control was compared with the accuracy standard obtained and the results are presented in Tables 6 and 7.

Table 6: Horizontal accuracy standard Allowable linear Obtained linear Class misclosure Dist.(m) misclosure Baseline 2 2 2 2 Description x  y ∆x ∆y x  y L L   XSU92 First Order 1:100,000 12248.558 0.0045 0.0061 1:161,585

BEM_1000 First Order 1:100,000 86562.033 -0.0570 -0.0022 1:151,750

NP-WW01 First Order 1:100,000 13618.621 0.0014 -0.0007 1:870,062

NP-WW02 First Order 1:100,000 12301.988 -0.0008 -0.0002 1:149,184

NP-WW03 First Order 1:100,000 12364.648 0.0014 -0.0007 1:789,948

GPS-ITER_01 First Order 1:100,000 73619.617 0.0006 0.0001 1:121,029

Table 7: Vertical accuracy standard Distance St. Dev. of elevation Misclosure Baseline (d) (km) diff. (S) (mm) (b) = (S/√푑) = (mm)/ Class Remark √푘푚) XSU92 12.2 0.0 0.0 II ok BEM_1000 86.6 44.8 4.81 II ok NP-WW01 0.05 27.1 121.20 II ok NP-WW02 0.06 34.9 142.48 II ok NP-WW03 0.12 35.0 101.04 II ok GPS-ITER_01 73.6 35.1 40.91 II ok

3.0. Data Analysis, Results and Map Production

3.1. Comparison of adjusted data with fixed controls Hi-target post processing software allows network adjustment to be performed, first by holding the control point XSU92 fixed which is the one referenced to Lagos datum, again by using the Lower

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Low Water datum at Warri and repeating the process. The outcome of this process is presented in Figure 2.

Figure 2: Difference in heights between the two reference points

From Figure 2, Elevation 1 is Lagos datum reference, while Elevation 2 is for Warri Lower Low Water reference. It also shows the differences obtained when the two reference systems are compared. The differences maintain what can be seen as almost constant values for all the respective points considered. A mean of 17.79 m was obtained when the heights were averaged given rise to the constant factor determined from this work.

3.2. Stacking a contour map over a 3D surface map Plotting of a contour map can be done over a 3D surface map using the following procedure for stacking maps in surfer golden software, (2011) clicking help from the software window and search. Usually a contour map is stacked over a 3D surface of the same grid file, but you can also stack different grid files one over another. If different grid files are used for stacking, the X and Y coordinates for both the grid files should be similar.

Figure 3 is a stack map of the study area at a scale of 1:5000 at contour intervals of 5m with the highest showing a height of 90 m. The 3D model of the area represented was also shown. The reference was to the Lagos Datum defined by the control in Benin, Edo State.

Figure 3: Stack maps of the study area with reference to XSU92.

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Figure 4 is a stack map of the study area at a scale of 1:5000 at contour interval of 5m with the highest showing a height of 90 m. The 3D model of the area represented was also shown. The reference is to the Lower Low Water at Warri Delta State. The two stack maps represented in Figures 3 and 4 are similar pictorially for the two data set represented.

Figure 4: Stack maps of the study area with reference to NP-WW01

4.0. Conclusion/ Recommendations

In this study, a conversion factor for height has been determined by taking the mean of the differences in heights as shown in Figure 1. Horizontal and Vertical accuracy have been evaluated as well as the positional accuracy and standard for GNSS first and second order geodetic coordinates as specified by Surveyors Council of Nigeria (SURCON).

Due to the fact that no satellites under horizon are available, GPS receivers can only receive signals from satellites above ground. This results in a poor geometry for fixing the height component in GPS measurements. In contrast, the horizontal components are fixed by satellites from different azimuths of the sky. It is therefore worthy of note that the VDOP value is always higher than the HDOP value and the accuracy of vertical component is often less precise than horizontal one by 2 to 3 times or even more depending on the satellite geometry (Choi et al., 2007).

Throughout the study, a confidence level of 10.00σ was maintained, significance level for τ test is at 1.00% and ratio of standard error of unit weight is 2.7803. The accuracy of the heights is not as good as that of the horizontal. This is probably due to errors associated with Satellite. For example satellite Constellation, tropospheric delay, Phase Centre Variation (PCV) and Offset, Multipath, Geoid- ellipsoid separation accuracy etc. as can be observed from Tables 4 and 5 considering the standard deviation in E, N and H respectively. The following conclusions have been made:

i. While working in Benin with reference to the XSU92 and NP-WW01 controls, a value of 17.79 m can be an alternative constant to swap between the two reference datum for data collection or as a means of check on observations carried out in that region ii. The accuracy standard for 1st order control is 1:100,000. The obtained standard proves to be better giving an indication that the GNSS equipment deploy for this study is of high accuracy iii. A class II standard accuracy was obtained for vertical standard accuracy iv. The maximum parts per million accuracy obtained in horizontal was 121 while the minimum was 0 as shown in Table 4 v. For vertical, the maximum parts per million accuracy obtained was 368 while the minimum was 1 as presented in Table 5.

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Tide Tables and Solunar Charts for Warri (2013). High tides and low tides, surf reports, sun and moon rising and setting times, lunar phase, fish activity and weather. Available at: http://www.tides4fishing.com/af/nigeria/warri [Accessed 08 March. 2018].

Uzodinma, V. N. (2005). VLBI, SLR and GPS Data in the Nigerian Primary Triangulation Network: What Benefits to Future Research and the National Economy? In: Proceedings of the 1st International Workshop on Geodesy and Geodynamics, Toro, Nigeria, 10th – 11th February 2005.

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ISSN (Print): 2616-051X | ISSN (electronic): 2616-0501

Vol 2, No. 1 March 2018, pp 69 - 77

Staff Satisfaction with Workplace Facilities in the School of Environmental Technology, Federal University of Technology, Akure, Nigeria

1, 1 1 Mbazor D.N. *, Ajayi M.A. and Ige V.O. 1Department of Estate Management, School of Environmental Technology, Federal University of Technology, Akure, Nigeria Corresponding Author: *[email protected]

ABSTRACT

This paper presents the findings of the existing university infrastructural facilities (i.e. water supply, electricity supply, office facilities, faculty buildings, laboratory facilities and toilet/convenience facilities) in a representative sample of staff satisfaction with workplace facilities in the School of Environmental Technology, Federal University of Technology, Akure Nigeria. Published literatures have been analysed to review knowledge areas pertaining to workplace facilities and their contributions to organisational high productivity. Various workplace facilities evaluated in this study have been analysed to identify major areas of challenges for the faculty staffs’ satisfaction. A user satisfaction survey was developed to obtain the staffs’ qualitative feedback on their experience and satisfaction with the facilities provided in the faculty. The findings of the survey were analysed and reported to describe the level of satisfaction with the identified performance requirements for the workplace facilities. The main purpose of conducting the study was to determine whether or not the facilities provided at the workplace by the organisation provides the needed satisfaction to the working staff for optimum performance. This paper is of practical value to employers of labour whether private or public sectors, educational institutions, staff and students whose performance of duties are tied to the available facilities. University administrators involved in the provision of infrastructural facilities in the workplace should ensure that the provision and up-keep of the workplace enhancement requirements presented in this paper are properly addressed in the system.

Keywords: Environment, Facilities, Satisfaction, Staff, Workplace

1.0. Introduction

A workplace environment is the environment where people work together to achieve organizational goals. AbdulGhafoor and Tafique (2015) described environment as “systems, processes, structures and tools and all those things which interact with employees and affect in positive or negative ways on employees performance.” Environment can also be defined as a particular location where task is done or completed. In relating the concept to employment, the work environment involves the physical geographical location as well as the immediate surroundings of the workplace such as a university environment, a construction site, a mechanic workshop or an office building. Workplace environment equally consist of other factors associated with the place of employment such as the quality of the air, noise, illumination levels, structural facilities and fringe benefits associated to employment such as free child care or unlimited coffee, or adequate parking or intangible benefits such as sponsored holidays.

It has been noted that infrastructural facilities are important to the overall organisational performance of universities. Besides the fact that such facilities provide a healthy learning environment for students, they are also expected to engender a safe and conducive working environment for the employees of the universities. Observably, however, most universities in sub-Saharan Africa mostly lag behind in the quality and quantity of infrastructural facilities provided for both staff and students

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(Amole, 2009). A number of studies have evaluated the perception of students on the facilities provided for them in their hostels, whereas not much research has been done on the perception of staff about the facilities provided for them (Adewunmi et al., 2011; Oluwunmi et al., 2012). Secondly, health challenges have been reported among staff of tertiary institutions that are related to their work environment. Furthermore, unconducive work environment leads to low productivity among university staff (Shantakumari et al., 2014; Sudaw et al., 2017).

Infrastructural facilities has for long been thought of as a crucial element of university campuses. Hassanain (2007) confirmed that universities worldwide have realized the contributions of infrastructural facilities towards achieving their objectives. According to the author, campus facilities operate as an integral component of the university which contributes significantly to the achievement of its overall goals and missions. Donald (1974) explained that infrastructures and facilities such as water, power, waste disposal, transportation or similar services are developed or acquired by public agencies such as universities to enhance their smooth operations and to facilitate the achievement of common social and academic objectives. Amole (2009) observed that most students in Nigeria universities are not satisfied with the facilities provided in both the faculty buildings and halls of residences, resulting to poor academic performances over the years.

The high prevalence of vision related problems among universities staff is an issue of great concern which is affecting the productivity of staffs in their work place. Despite this, research efforts to investigate the real sources of these challenges in the working environment of the staff are sparse. This study, therefore seeks to investigate staffs’ satisfaction with workplace facilities in the School of Environmental Technology, Federal University of Technology Akure (FUTA), Nigeria.

2.0. Literature Review

The physical and social environments where students, particularly those in higher institutions receive training and learning are vital to ensure the derivation of highest level of benefit from education system. Parameters such as space arrangement, size of offices, classrooms, laboratories, number of users in a building, electricity, water, furniture and equipment, colour of paint used, hygiene, aesthetics, safety, heat, internet, sound and lighting conditions can be listed among the physical environment criteria for university facilities. Available research has shown that these conditions not only influence education activities but also have great influence on the social and communication behaviour of the users (Erbil and Sezer, 2016). Collet da Graca et.al (2007) studied environmental comfort conditions in education buildings. Garret (1981) suggests that acoustic, heat and temperature, size of spaces, lighting, and ventilation factors have an impact on the academic achievement of both staff and students. This study was corroborated by (Garret 1981, Wargocki and Wyon, 2007, Erbil and Sezer, 2016). On the other hand, Bako-Biro et al. (2012) noted that poor ventilation rates can have a negative impact on schoolwork performance and health of teachers and students.

Similarly, Edwards (1991) indicate that when physical conditions of school buildings improve the academic achievement of students also improve. Also, Overbaugh (1990) indicates that lecturers have lower efficiency when heating, cooling and ventilation problems exist in classrooms that are small and very crowded. Khedariet al. (2000) carried out studies on indoor air quality and ventilation. Avsar and Gonullu (2005), Elmallawany (1983) have studied on sound insulation, acoustic and noise. Kruger and Dorigo (2008) studied the impact of daylight and natural lighting and their finding revealed that these factors will make a positive impact in improving indoor physical environmental quality in educational facilities. Some researchers observed that it is not enough to just satisfy users but it is more important to ensure that users are extremely satisfied through the provision and effective maintenance of needed facilities in the system (Sivadass and Baker-Prewitt, 2000); Bowen and Chen, 2001).

Scholars have observed that the work place environment contributes to 24% in job satisfaction. It increases productivity level of an individual by 5% and team performance can be increased to 11% through developing good working environment (Arokiasamy, 2013). AbdulGhafoor and Tafique (2015) found that the factors like compensation, rewards, job security and good working environment increase level of commitment and sense of belonging with an organization.

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In a similar manner, Bakotic and Babic (2013) stated that working environment such as office size, power and water supply impacts on job satisfaction. The researcher further noted that employees prefer to work in working environment that is less risky. Also, Ollukkaran and Gunaseelan (2012) in their study found that how well employees engage with their working environment will have positive impact on employees’ performance level. Also, office design and size are source of motivation by employees as observed by Amina and Shela (2009). Roelofsen (2002) in studying the impact of indoor environment, observed that indoor office environment has great impact on job performance and level of performance, which can be increased from 5 to 15 percent because of improving working conditions. Similarly, work place condition also impacts on employees’ stress, of which Vischer (2006) noted that a good working environment will lead to better fit between work space and employees and results in improving behaviour and stress related emotions. It is equally observed that a good working environment changes employees approach towards job performance as according to Berg and Kalleberg (1999) job and overall working environment has substantial effect on workers and could help in balancing work and family life. Goudswaard (2012), in describing factors constituting a healthy working environment, highlighted work life balance, motivation level, psychological conditions, social dialogue, management and leadership coherence, transparency develop a good working environment and a good working environment leads to increased organizational productivity. Haynes (2008) found that the behaviour components of working environment have more impact than the physical components of working environment.

University environment is an environment where teaching and research activities are carried out simultaneously. Creating comfortable facilities in the university environment and maintaining them to ensure efficient performance will provide a supportive setting for a high quality education. Several studies have examined educational facilities but not from the angle of staff satisfaction (Boneh, 1982; Edwards, 1991; Clements-Croome et al., 2008). The number of studies examining university facilities from the point of view of staffs is relatively low. To this extent, this study is poised to seek to examine staffs’ satisfaction with facilities provided in the School of Environmental Technology, Federal University of Technology Akure, thereby contributing to literature in the field knowledge.

The major focus in this research is to point out how satisfactory working environment can be created in the university working environment. The variables selected for study essentially relate to basic infrastructural facilities in a faculty building of university, an aspect of working environment which plays fundamental role in shaping a satisfactory and conducive working environment. The variables have been considered in the context of university environment which depend on the intellectual efforts of the employee for growth and progress of the institution. It is believed that the research results will guide university authorities in considering the influence facilities have on the overall performance of their academic staff in the discharge of their responsibilities.

3.0. Materials and Methods

The goal of this study is determining the parameters that influence work performance in the university building facilities from the viewpoint of both academic and non-academic staffs’ satisfaction.

Primary data were used to conduct the research study. The primary source was through the use of questionnaire. The questionnaire was based on five point Likert scale. The staffs of the School of Environmental Technology were the target audience. Both academic and non-academic were considered in the study. Out of one hundred and eighty eight (188) staffs of the faculty, 100 staffs were purposely chosen and questionnaires were distributed to them across the seven departments in the School of Environmental Technology. Only 84 of the questionnaires were correctly filled and returned.

The cross-sectional descriptive study design was used to examine different variables of a working environment affecting performance and satisfaction of the employees in different departments in the School of Environmental Technology, Federal University of Technology Akure, Nigeria. After collection of primary data, coding was used to translate respondents’ responses and to organize and summarize research data into manageable form. The questionnaire, which is based on a Likert type, was applied to 100 staff users in School of Environmental Technology building. The data collected

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Nigerian Journal of Environmental Sciences and Technology (NIJEST) Vol 2, No. 1 March 2018, pp 69 - 77 were analysed by percentages, relative satisfaction index and weighted mean score, which were calculated using the formulas stated below:

(5푛 + 4푛 + 3푛 + 2푛 + 푛 ) (1) 푊푀푆 = 5 4 3 2 1 푁

(5푛 + 4푛 + 3푛 + 2푛 + 푛 ) (2) 푅푆퐼 = 5 4 3 2 1 5푁

where: WMS weighted mean score RSI relative satisfaction index n5 number of respondents who answer strongly agree n4 number of respondents who answer agree n3 number of respondents who answered undecided n2 number of respondents who answer strongly disagree n1 number of respondents who answer disagree N total number of respondents

4.0. Results and Discussion

4.1. Results Table 1 show the distribution and retrieval of questionnaires from the respondents which consist of both academic and non-academic staff of the School of Environmental Technology, Federal University of Technology Akure, Nigeria. The reason for administering questionnaires to both academic and non-academic staffs is because the two entities constitute the entire staff of the school under study. In all, 69 of the respondents were academic staffs which represent 82.2% of the sampled population while 15 which represent 17.8% of the respondents were the non-academic staffs.

Table 1: Analysis of Administered Questionnaires Number of questionnaire Number of questionnaire retrieved Percentage (%) of response Respondents/ administered Department Academic Non-academic Non-academic Non-academic Academic staff Academic staff staff staff staff staff ARC 11 3 11 2 16.4 13.3 BDG 12 2 7 2 10.4 13.3 ESM 13 3 12 3 17.9 20 IDD 12 2 11 2 16.4 13.3 SGV 12 2 6 2 9.0 13.3 QSV 12 2 8 2 11.9 13.3 URP 12 2 12 2 17.9 13.3 TOTAL 84 16 67 15 100 100 Source: Field survey, 2018

From the 82 respondents and as shown in the Table 2 below, there were 60 men and 22 women who took part in the survey. Their percentages were 73.2 % and 26.8% respectively. As far as their level of education is concerned, 5 had Bachelors or HND certificates, 44 had Master’s degree, 22 had Doctorate degree and 11 were professors. Their percentage distributions were 6.1%, 53.7%, 26.8% and 13.4% respectively. The respondents belonged to different age groups. Twenty seven (27) belonged to age group of 25-35 years, 37 were between 36-45 years and 18 respondents belonged to age group of 46 years and above. On the job experience among the respondents, 19.5% had job experience from 1 to 5 years, 35.4% had experience of 6 to 10years, 18.3% had 11-15years, 20.7% had 16-20 years, 3.7% had 21-25 years, 1.2% had 26 to 30 years and 1.2% of them had job experience of 31 years and above. The respondents belonged to seven (7) different departments in the faculty (see Table 1). These include 13 from Architectural Technology (ARC), 9 from Building Technology (BDG), 15 from Estate Management (ESM), 13 from Industrial Design department (IDD), 10 from Quantity Surveying (QSV), 8 from Surveying and Geo-Informatics (SGV) and 14 from Urban and Regional Planning (URP).

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Table 2: Socio-economic characteristics of the respondents Gender Frequency Percentage Male 60 73.2 Female 22 26.8 Total 82 100 Level of education Bachelors/HND 5 6.1 Masters 44 53.7 Doctorate 22 26.8 Professors 11 13.4 Total 82 100 Age 25 – 35 years 27 32.9 36 – 45 years 37 45.1 46 years and above 18 22.0 Total 82 100 Working experience 1 – 5 years 16 19.5 6 – 10 years 29 35.4 11 – 15 years 15 18.3 16 – 20 years 17 20.7 21 – 25 years 3 3.7 26 – 30 years 1 1.2 31 years and above 1 1.2 Total 82 100 Source: Field survey, 2018

From Table 3, it was observed that of all the factors that enhance work performance in the study area, availability of water supply within the faculty building is the most significant factor that enhances staff work performance in the organization. It has the highest weighted mean score of 4.73 and rank highest in the scale. Closely followed to water supply is the availability of toilets and places of convenience in the work environment which has a weighted mean score of 4.45. The result shows that availability of water supply within the faculty building is the most important factor that can enhance workplace satisfaction among the studied staff in their work environment.

Table 3: Facilities that enhance workplace performance in Universities Weighted S/N Factors SA A N D SD Rank Mean 1 Availability of water supply 68 10 1 2 1 4.73 1 2 Toilet & convenience facilities 54 22 2 3 1 4.45 2 3 Adequacy of lecture& laboratory rooms 23 54 2 1 2 4.15 3

4 Size of staff offices 8 29 12 18 15 2.96 4

5 Office facilities & equipment 4 10 11 18 49 2.17 5 6 Electricity supply 1 3 1 41 36 1.68 6 7 Location of buildings in relation to other uses 0 4 1 12 65 1.12 7 SA – Strongly Agreed; A – Agreed; N – Neutral; D – Disagreed; SD – Strongly disagreed Source: Field survey, 2018

Table 4 below shows the satisfactory level of some selected workplace facilities among the respondents of the study area. The study revealed that the respondents are satisfied with the internet connectivity, aesthetic nature of the workplace and nature of ventilation/indoor air quality in their workplace, which have relative satisfactory indexes of 0.92, 0.78 and 0.75 respectively. The study further revealed that health and safety issues in the workplace is considered less significant as it has only 0.44. The implication of this is that majority of staffs of the School of Environmental Technology, Federal University of Technology Akure (FUTA) do not prioritize the issue of health and safety in the choice of their workplace.

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Table 4: Analysis of respondents’ satisfaction level with workplace S/N Responses VS S N D VD RSI RANK 1. Internet connectivity 62 14 1 4 1 0.92 1 2. Aesthetics of work place 28 34 6 13 1 0.78 2 3. Ventilation/indoor air quality 27 31 2 9 13 0.75 3 4. Health safety of workplace 9 3 5 45 20 0.44 4 VS – Very satisfied; S – Satisfied; N – Neutral; D – Dissatisfied; VD – Very dissatisfied Source: Field survey, 2018

4.2. Discussion From the above results, it can be seen that adequate office space in a workplace leads to increase in staff productivity. The result is in line with Garret (1981), Wargocki and Wyon (2007) and Erbil and Sezer (2016) who opined that size of spaces, lighting, and ventilation factors have an impact on the academic achievement of both staffs and students. Good power and lighting condition were helpful in developing the workplace that enhances staff productivity. The result of this study is in agreement with the study conducted by Kruger and Dorigo (2008) that studied the impact of daylight and natural lighting in a workplace environment and found that proper lighting of the workplace creates optimum comfortable condition for users. Similarly good and hygienic work environment such as functional toilet and convenient facilities are also helpful in developing a favourable working environment that has positive impact on employees’ productivity. The results agree with that of Goudswaard (2012) who described factors constituting a healthy working environment to include adequate hygiene, motivation level, psychological conditions, social dialogue and a good working environment as they lead to increase organizational productivity. The results are also in line with Ajala (2012) who argued that working in an environment is considered to be conducive aids the productivity of workers in such an organization.

5.0. Conclusion

Drawing from the above analysis and findings, a conclusion can be drawn to the effect that workplace environment is helpful in increasing employees’ level of productivity. Factors like adequate office space, good indoor and outdoor air quality, availability of water, power, toilets, and health and safety facilities are helpful in developing a workplace environment that has positive impact on employees’ level of productivity in an organization. The results also send a message to organizations especially universities and other educational institutions that by developing a conducive environment, the level of employees’ productivity can be increased and maintained.

In this regard it is very important to define the issues that staffs are discontent with to establish design criteria for faculty buildings to be designed in the future and to establish main goals for the planning of the university in general. Factors that influence staff satisfaction should be taken into consideration in university building designs for the future.

Given the resources available to the researchers in undertaking this study, only 53.19% of the population was issued questionnaire. The questionnaires actually used in data analyses were 84 representing 44.68% of the original population. This sample size may affect the generalization of the findings of this study. However, for the sample analysed in the course of this work, the findings are valid.

As the employees play important role in the progress of academic and research efforts and a lot of time and resources are required to train and retain the employees and to equip them according to future challenges, developing a conducive working environment is a crucial issue faced by universities. The study has highlighted the importance of good and efficient workplace environment. The findings recommend the organizations to develop strategies which are useful in developing a conducive working environment at the workplace.

Finally the study recommends that universities must observe continuously the dynamic nature of the environment under which people work with a view to enhancing workers satisfaction. Universities

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Avsar, Y. and Gonullu, M. T. (2005). Determination of safe distance between roadway and school buildings to get acceptable school outdoor noise level by using noise barriers. Building and Environment Journal, 40(9), pp. 1255–1260

Bako-Biro, Z., Clements-Croome, D., Kochhar, N., Awbi, H. and Williams, M. (2012). Ventilation rates in schools and pupils’ performance. Building and Environment, 1(48), pp. 215–223

Bakotic, D. and Babić, T. (2013). Relationship between working conditions and job satisfaction: The Case of Croatian Shipbuilding Company. International Journal of Business and Social Science, 4(2), pp. 98 -102

Berg, P. and Kalleberg, A. (1999). The role of the work environment and job characteristics in balancing work and family. Paper presented at an Economic Policy Institute symposium on June 15, 1999. The symposium was funded by grants from the United States Department of Labour and the Alfred P.Sloan foundation.

Boneh, M. (1982). Environmental comfort in educational buildings - influence of windows and other openings. Energy and Buildings, 4(3), pp. 239-243

Bowen, J. T. and Chen, S. L. (2001). The relationship between customer loyalty and customer satisfaction. International Journal of Contemporary Hospitality Management, 13(5), pp. 213-217

Clements-Croome, D. J., Awbi, H. B., Bako-Biro, Z., Kochhar, N. and Williams, M. (2008). Ventilation rates in schools. Building and Environment, 43(3), pp. 362-367

Collet da Graca, V. A., Kowaltowski, D. C. C. K. and Diego Petreche, J. R., (2007). An evaluation method for school building design at the preliminary phase with optimization of aspects of environmental comfort for the school system of the state São Paulo in Brazil. Building and Environment, 42(2), pp. 984-999

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Edwards, M. (1991). Building conditions, parental involvement and student achievement in the D. C. public school system. Unpublished Master Degree Thesis, Georgetown University, Washington, D.C

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Erbil, Y. and Sezer, S. (2016). User satisfaction of environmental quality in university buildings. European Journal of Sustainable Development, 5(3), pp. 476-488

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ISSN (Print): 2616-051X | ISSN (electronic): 2616-0501

Vol 2, No. 1 March 2018, pp 78 - 88

Validation of Global Digital Elevation Models in Lagos State, Nigeria

1, 2 3 Arungwa I.D. *, Obarafo E.O. and Okolie C.J. 1Department of Surveying and Geo-Informatics, Faculty of Engineering, Abia State University, Uturu, Nigeria 2H.D. Surveys, Calabar, Cross River State, Nigeria 3Department of Surveying and Geo-Informatics, Faculty of Engineering, University of Lagos, Lagos, Nigeria Corresponding Author: *[email protected]

ABSTRACT

Satellite-derived Digital Elevation Models (DEM) are fast replacing the classical method of elevation data acquisition by ground survey methods. The availability of free and easily accessible DEMs is no doubt of great significance and importance, and a valuable resource in the quest to accurately model the earth's surface topography. However, the suitability of Digital Elevation Models in simulating the topography of the earth at micro, local and regional scales is still an active area of research. The accuracy of Digital Elevation Models vary from one location to another. As such, it is important to conduct local and regional assessments to inform the global user community on the relative performance of these DEMs. This study evaluates the accuracy of the 30-metre Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Models version 2, the 1-kilometre GTOPO30, the 90-metre Shuttle Radar Topography Mission v4 and the 1-kilometre Shuttle Radar Topography Missionv2.1 Digital Elevation Models by validating with highly accurate GPS check-points over Lagos, Nigeria. With a Root Mean Square Error of 3.75m, the results show that Shuttle Radar Topography Mission v4 has the highest vertical accuracy followed by Shuttle Radar Topography Mission v2.1 (Root Mean Square Error: 5.73m), Advanced Spaceborne Thermal Emission and Reflection Radiometer (Root Mean Square Error: 21.70m), and GTOPO30 which shows the lowest vertical accuracy (Root Mean Square Error: 29.41m). By conducting the accuracy assessment of these products in Lagos, this study informs efforts directed at the exploitation of these Digital Elevation Models for topographic mapping and other scientific and environmental application.

Keywords: Digital Elevation Model, Topography, SRTM, ASTER GDEM, GTOPO30

1.0. Introduction

The availability of free and easily accessible Digital Elevation Models (DEMs) is no doubt of great significance and importance, and a valuable resource in the quest to accurately model earth’s surface topography. According to Isioye and Obarafo (2010), the capacity to understand and model earth surface processes depends on the quality of the topographic data that is available in digital format. In the field of geodesy, Featherstone and Kirby (2000) noted that DEMs play an important role in determining a precise gravimetric geoid, computation of terrain corrections, direct topographical effects on gravity and indirect effects on geoid, and also to generate mean gravity anomalies.

Elkhrachy (2017) submits that global elevation datasets are inevitably subject to errors, mainly due to the methodology adopted in extracting elevation information and the various processing steps the models have undergone. He further noted that errors in DEMs comprise mainly of two components: the horizontal component, often referred as the positional accuracy of the X and Y components; and the vertical component or the accuracy of the attributes. He further pointed out that, the positional and attributive accuracy generally cannot be separated. The error may be due to an incorrect elevation value at the correct position, or a proper elevation for an incorrect position or any combination of these. It is important to note that no matter how rigorous the process of a DEM’s construction is, it will invariably contain systematic or other measurement or estimation problems and may show

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Nigerian Journal of Environmental Sciences and Technology (NIJEST) Vol 2, No. 1 March 2018, pp 78 - 88 uncertainties in the data due to poor representation (Olusina and Okolie, 2018).There are various approaches for ascertaining the extent of error in a DEM. A standard uncertainty measure is the Root Mean Square Error (RMSE) (Rinehart and Coleman, 1988). Another method is to determine if the DEM heights fall between contour elevations by using elevation histograms to show if there is a linear fit between contours (Reichenbach et al., 1993; Carrara et al., 1997). In addition, errors based on grid bias can be found by comparing drainage networks extracted by multiple rotations of the DEM (Charleux-Demargne and Puech, 2000).

Since the accuracy of satellite-derived DEMs vary from one location to another, it is important to conduct localised assessments. This study focuses on the 30-metre Advanced Spaceborne Thermal Emission and Reflection Radiometer Global DEM version 2 (ASTER GDEM2), the 1-kilometre GTOPO30, the 90-metre SRTM v4 and the 1-kilometre SRTM30 v2.1 DEMs. Several researchers have evaluated the accuracy of these DEMs (e.g. Hirano et al.,2003; Gorokhovich and Voustianiouk, 2006; Yastikli et al., 2006; Racoviteanu et al., 2007; Mangoua et al., 2008; Tighe and Chamberlain, 2009; Hirt et al.,2010; Hengl and Reuter, 2011; Arefi and Reinartz, 2011; Rexer and Hirt, 2014; Santillan and Makinano-Santillan, 2016; Elkhrachy, 2017). These accuracy assessments have provided valuable information to the global user community on their performances in different regions of the world. Although a number of studies have been conducted in Nigeria (e.g. Ozah and Kufoniyi, 2008; Isioye and Obarafo, 2010; Ojigi and Dang, 2010; Isioye et al., 2012; Nwilo et al., 2012; Nwilo et al., 2017), the available information in the literature on the relative performance of the ASTER, GTOPO30 and SRTM DEMs in the country is still inadequate. This study contributes to the existing knowledge by conducting an accuracy assessment of these DEM products at a test site in Lagos, Nigeria.

The 1 arc-second/30-metre Advanced Spaceborne Thermal Emission and Reflection Radiometer Global DEM version 2 - ASTER GDEM2, is a significant improvement over the earlier ASTER GDEM version 1 – GDEM1, which was released in 2009 (NASA JPL, 2011; Santillan and Makinano- Santillan, 2016). GDEM1 was found to have an overall accuracy of around 20 metres at the 95% confidence level (ASTER GDEM Validation Team, 2011). It also had several artefacts associated with poor stereo coverage at high latitudes, cloud contamination and water masking issues. However, GDEM2 (released in 2011) has several improvements over GDEM1 such as the use of additional scenes to improve coverage, a smaller correlation kernel to yield higher spatial resolution, and an improved water mask (NASA/METI, 2011). The number of voids and artefacts in GDEM1 was substantially reduced in GDEM2. In Japan, GDEM2 was reported by the ASTER GDEM Validation Team to have a RMSE of 6.1m in flat and open areas, and 15.1m in mountainous areas largely covered by forest (Tachikawa et al., 2011). In the conterminous United States, Gesch et al. (2012) notes that the RMSE for GDEM2 was 8.68m based on a comparison with more than 18,000 independent reference ground control points. The overall global accuracy of GDEM2 at 95% confidence level has been put at 17m (ASTER GDEM Validation Team, 2011). For this study, GDEM2 was obtained from the NASA/USGS Land Processes Distributed Active Archive Centre (LPDAAC) Global Data Explorer.

GTOPO30 is a global DEM with elevations regularly spaced at 30arc-seconds (approximately 1 kilometre) resolution. It covers the full extent of latitude from 90°N-90°S, and the full extent of longitude from 180°W-180°E. It is the result of a collaborative effort led by the staff at the United States Geological Survey (USGS) Earth Resources Observation and Science Data Centre. It was compiled from the following raster and vector sources of elevation information: Digital Terrain Elevation Data (50% of global land area), Digital Chart of the World (29.9% of global land area), USGS 1-degree DEMs (6.7% of global land area), Army Map Service 1:1,000,000 scale maps (1.1% of global land area), International Map of the World 1:1,000,000 scale maps (3.7% of global land area), Peru 1:1,000,000 scale map (0.1% of global land area), New Zealand DEM (0.2% of global land area), and Antarctic Digital Database (8.3% of global land area). The absolute vertical accuracy of GTOPO30 varies by location depending on the site-specific dataset used. Generally, the areas derived from the raster datasets have higher accuracy than those derived from the vector datasets. For example, the full resolution 3-arc second DTED and USGS DEMs have a vertical accuracy of ±30m linear error at the 90% confidence level (Defense Mapping Agency, 1986; U.S. Geological Survey, 1993; GTOPO30 Readme, 2017). According to the GTOPO30 documentation, if this error distribution is assumed to be Gaussian with a mean of zero, the statistical standard deviation of the

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Nigerian Journal of Environmental Sciences and Technology (NIJEST) Vol 2, No. 1 March 2018, pp 78 - 88 errors is equivalent to the RMSE. Under those assumptions, vertical accuracy expressed as ±30m linear error at 90% can also be described as a RMSE of 18metres. The estimated absolute vertical accuracy for the areas of GTOPO30 derived from each source, with the method of estimating the accuracy is given in the GTOPO30 Readme (2017).

The Shuttle Radar Topography Mission (SRTM) is the result of a collaborative effort by the National Aeronautics and Space Administration (NASA), the National Geospatial Intelligence Agency (NGA), the German Space Agency (DLR), and the Italian Space Agency (ASI) (Van Zyl, 2001; Rabus et al., 2003; Foni and Seal, 2004). The mission was launched on 11 February 2000 aboard the Space Shuttle Endeavour. Using radar interferometry, the SRTM DEM was produced for almost the entire globe. There are several resolution outputs available, including a 3 arc-second (version 4) and a 30 arc- second (version 2.1) product for the world. The absolute vertical and horizontal accuracy of the data collected was reported to be ±16m (Rabus et al., 2003; Kellndorfer et al., 2004; Miliaresis and Paraschou, 2005; Kaab, 2005). SRTM DEMs have been shown to suffer from a number of gross, systematic and random errors propagated from the Synthetic Aperture Radar (SAR) imaging system (Koch and Lohmann, 2000; Ozah and Kufoniyi, 2008). Such errors are due to baseline tilt angle, baseline length, platform position, phase and slant range. Although SRTM data produced a number of voids due to lack of contrast in the radar image, a methodology based on spatial filtering was developed to correct this phenomenon (Dowding et al., 2004; Jarvis et al., 2004). SRTM v4 with voids filled in was obtained from the website of the Consultative Group for International Agriculture Research-Consortium for Spatial Information (CGIAR-CSI), while SRTM30 v2.1 was obtained from the USGS Earth Resources Observation and Science Data Centre archive.

2.0. Materials and Methods

2.1. Study area The study area is Lagos State, a low-lying coastal state in south-west Nigeria located between longitudes 2°41'15'' - 4°22'00''E and latitudes 6°20′10′′ - 6°43′20′′N. The state has a relatively stable terrain with minimal terrain undulations. Lagos is bounded in the north and east by Ogun State, in the west by the Republic of Benin and in the south by the Atlantic Ocean. About 40% of the State’s total land area is covered by water and wetlands. The State has many notable features including lagoons and creeks, wetlands, barrier islands, beaches and estuaries, the Iddo Port, Apapa Port etc. (Osei et al., 2006; Odumosu et al., 2015). Figure 1 shows a map of Lagos State. Based on the old Local Government set-up, Lagos State has 20 Local Government Areas (LGAs). The state has a very diverse and fast-growing population, resulting from accelerated migration from all over the country as well as from neighbouring countries.

2.2. Data acquisition ASTER GDEM2, GTOPO30, SRTM v4 and SRTM30 v2.1 DEMs were obtained from their respective online portals. The DEMs are provided in tiles and are referenced to the WGS84 datum. Table 1 shows the characteristics of the DEMs. Also, the three-dimensional coordinates of 581 first and second order GPS controls (check-points) with grid coordinates in Universal Transverse Mercator (UTM) system referenced to WGS84 datum were acquired from the Office of the Surveyor General of Lagos State. The GPS controls serve as check-points to validate the accuracy of the DEMs. The spatial distribution of the GPS control points in Lagos state used for the study is shown in Figure 2. Since the GPS data is of high accuracy, independent and sufficiently precise, it will give reasonable estimates in the accuracy assessment (Obarafo, 2015; Olusina and Okolie, 2018).

2.3. Data processing The data processing consists of two steps: 1. Datum harmonization and height conversion 2. Comparison of DEM data with reference GPS points.

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Figure 1: A map of Lagos State

Table 1: DEM datasets and characteristics Dataset Source Coordinate Geoid Vertical Height system Resolution System Reference Units ASTER GDEM NASA/ 1 arc-second (30m) v2 METI Geodetic WGS84/ GTOPO30 USGS Metres Orthometric 30 arc-seconds (1km) (φ,λ,H) EGM96 SRTM v4 NASA/ 3 arc-seconds (90m) WGS84 SRTM30 v2.1 NGA 30 arc-seconds (1km)

Figure 2: A map showing the location of GPS controls in the study area

2.3.1. Data harmonization and height conversion The DEMs are provided in a geographic coordinate system and so it was necessary to reproject the data in ArcGIS 10.3 to a UTM system. This transformation helped to overcome linear measurement

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Nigerian Journal of Environmental Sciences and Technology (NIJEST) Vol 2, No. 1 March 2018, pp 78 - 88 difficulties and preserve geometric properties of the DEMs. The orthometric equivalent of the GPS ellipsoidal heights was derived using Equation 1 (Elkhrachy, 2017):

퐻 = ℎ − 푁 (1)

where: H Orthometric height h Ellipsoidal height N Geoid height/undulation

The relationship between the orthometric height, ellipsoidal height and the geoid height is shown in Figure 3. N was computed using the GeoidEval online geoid height calculator. GeoidEval computes the geoid height of any given point based on global geoid models (EGM96 or EGM2008). To ensure consistency with the vertical datum of the DEMs, the geoidal heights were computed based on the EGM96 geoid model.

Figure 3: Relationship between orthometric, ellipsoidal and geoid heights (Source: ELTE, 2017)

2.3.2. Comparison of DEM data with reference GPS points In ArcGIS, the GPS points were overlaid on the DEMs. Next, using the ‘extract values to points’ tool on ArcGIS Spatial Analyst, elevations were extracted from the DEMs at points coincident with the GPS data. The DEM point elevations were then subtracted from the GPS point elevations and the differences were tabulated. To evaluate the accuracy of the elevations from the DEMs with respect to the reference GPS control points, the following goodness of fit statistics were computed –elevation differences, Pearson’s coefficient of correlation (R), the coefficient of determination (R2), and Root Mean Square Error (RMSE). Positive elevation differences represent locations where the DEM is below the GPS elevation, while negative differences occur at locations where the DEM is above the GPS elevation. The mean difference indicates if a DEM has an overall vertical positive or negative offset from the true ground level (Gesch et al., 2012; Santillan and Makinano-Santillan, 2016).

3.0. Results and Discussion

After a sequential elimination of outlier points based on physical inspection in the initial comparison, the summary of coincident elevations from the DEMs and reference GPS points are shown in Table 2. The point elevations from the ASTER GDEM2, GTOPO30, SRTM v4 and SRTM30 v2.1 DEMs are denoted by HASTER, HGTOPO30, HSRTMv4 and HSRTM30v2.1 respectively. Across the terrain, the GPS elevations range from 0.17 – 66.89m. The elevation ranges at all sites from the DEMs are ASTER (min: 0m; max: 97m), GTOPO30 (min: 35m; max: 64m), SRTM v4 (min: 0m; max: 67m), and SRTM30 v2.1 (min: 0m; max: 68m). The mean elevations are as follows: ASTER (mean: 28.95m), GTOPO30 (mean: 42.77m), SRTM v4 (mean: 18.17m), and SRTM30 v2.1 (mean: 17.08m). Figure 4(a-d) presents the scatter plots of the DEMs data against the reference GPS points fitted with 95% confidence bounds (shown by the red dotted lines). The highest agreement with the GPS

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reference data is seen in SRTM v4 (R2 = 0.95) followed by SRTM30 v2.1 (R2 = 0.86), ASTER (R2 = 0.40), and GTOPO30 (R2 = 0.35) which shows the lowest agreement.

Table 2: Comparison of elevations from the DEMs and reference GPS points H H H ASTER H (m) GTOPO30 H (m) SRTMv4 H (m) H (m) H (m) (m) GPS (m) GPS (m) GPS SRTM30v2.1 GPS Count 570 580 569 580 Min 0.00 0.17 35.00 0.17 0.00 0.17 0.00 0.17 Max 97.00 66.89 64.00 66.89 67.00 66.89 68.00 66.89

Mean 28.95 16.27 42.77 16.26 18.17 16.41 17.08 16.26

Figure 4: The graphic expression of 95% confidence zone for DEM comparison with reference GPS points (a) ASTER (b) GTOPO30 (c) SRTM v4, and (d) SRTM30 v2.1

Figure 5(a-d) presents a graphical comparison of the magnitude of elevation differences. In the analysis of correlation with the GPS points, Table 3 presents the Pearson’s R for each DEM. It can be seen that there is a significant relationship between the ASTER DEM’s elevations and its residuals. However, there is no clear relationship between the residuals and elevations for all other DEMs. For other DEMs therefore, it cannot be said that the errors in the DEM increase with elevation or otherwise. As a matter of fact, they show relatively weak correlation with elevation (Table 3). For the ASTER GDEM however, the errors show some significant negative correlation with the DEM elevation (R = - 0.755). This shows an increase in error with respect to increasing DEM elevations within the study area.

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Figure 5: Graphical comparison of the magnitude of elevation differences (a) ASTER (b) GTOPO30 (c) SRTM v4 and (d) SRTM30 v2.1

Table 3: Pearson Correlation Analysis ASTER GTOPO30 SRTM v4 SRTM30 v2.1 Pearson’s R -0.755 0.098 -0.062 -0.089

Table 4 shows a summary of the differences between the DEMs and the GPS elevations. The DEM point elevations were subtracted from the elevations of the GPS points to yield the vertical differences in ASTER (ΔHASTER-GPS), GTOPO30 (ΔHGTOPO30-GPS), SRTM v4 (ΔHSRTMv4-GPS), and SRTM30 v2.1 (ΔHSRTM30v2.1-GPS) respectively. A larger percentage of the residuals in Figure 5(a-d) fall below zero implying that all four DEM products overestimated the terrain elevation in the area under study. This tendency to overestimate the true ground elevation is further reflected in the mean differences between the DEMs and GPS points (ASTER: -12.68m, GTOPO30: -26.51m; SRTM v4: -1.76m; SRTM30 v2.1: -0.82m).With a RMSE of 3.75m, SRTM v4 has the highest vertical accuracy followed by SRTM30 v2.1 (RMSE: 5.73m), ASTER GDEM2 (RMSE: 21.70m), and GTOPO30 which shows the lowest vertical accuracy (RMSE: 29.41m).

The abysmal performance of ASTER GDEM, (with mean error and RMSE values of -12.68m and 21.70m respectively) though buttresses the fact that accuracy of DEM vary in space, it however challenges the opinion that accuracy of DEM is a function of resolution - this evidently wasn’t the case between ASTER of 1'' resolution and SRTMs of 3'' and 30'' resolution. The probable reason for this poor performance may not be unconnected to the presence of voids and artefacts in the DEM as reported by Santillan et al. (2016) and relatively limited number of GCP’s that were used in its creation as hinted by Racoviteanu et al. (2007). The research also adds to the body of literature reporting a poor performance of ASTER GDEM (e.g. Santillan et al., 2016; Ioannidis et al., 2014, Racoviteanu et al., 2007).

Another important finding of the research as seen in Figure 5(a) is that errors in ASTER GDEM increases with altitude over the study area. This of course is to the observation of Racoviteanu et al., (2007), in Nevado Coropuna, Peru.

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Table 4: Summary of the elevation differences (residuals) between the DEMs and GPS points ΔH ΔH ΔH ΔH ASTER-GPS GTOPO30-GPS SRTMv4-GPS SRTM30v2.1-GPS (m) (m) (m) (m)

Count 570 580 569 580

Min -82.59 -48.32 -12.59 -28.53

Max 50.60 17.89 22.98 32.61

Mean -12.68 -26.51 -1.76 -0.82

RMSE 21.70 29.41 3.75 5.73

4.0. Conclusion and Recommendations

The results show that the four satellite DEMs (ASTER GDEM2, GTOPO30, SRTMv4 and SRTM30 v2.1) tend to overestimate the true ground elevation. The vertical accuracy for SRTM v4 and SRTM30 v2.1 in this study (3.75m and 5.73m respectively) surpasses the 16m accuracy requirement presented in the original SRTM specification. SRTM v4 is the best fitting DEM in this study. It is also clear from results that SRTM30 v2.1 is fairly comparable to SRTM v4. NASA/METI (2011) gave the overall accuracy of ASTER GDEM2 to be around 17m at the 95% confidence level. However, with a vertical accuracy of 21.70m in this study, GDEM2 does not meet up with this requirement. Although of a higher spatial resolution, the accuracy of GDEM2 does not even surpass or compare favourably with that of SRTM v4 and SRTM30 v2.1. Evidently, the accuracy of a DEM does not necessarily increase with improvements in its spatial resolution. The probable reason for this poor performance may not be unconnected to the presence of artefacts in the DEM. This study has also shown that the errors in the ASTER DEM are amplified with increasing altitude. GTOPO30 was assembled from a wide range of raster and vector datasets. As such, it might be difficult to narrow down the cause of its poor performance. However, it can still be used as a substitute in areas not covered by SRTM, especially in mountainous areas. The overestimation tendency exhibited by all four DEMs within the study area is surely an important finding for users of these products and this must be considered when making decisions concerning their fitness for the purpose of any application.

Higher resolution DEMs such as the 5-metre ALOS World 3D and the 12-metre World DEM with reported higher vertical accuracies have since been available. However, the cost of acquisition still limits their usage by the global user community. As such, freely available DEMs such as ASTER, GTOPO30 and SRTM will continue to be a valuable resource for many scientific and engineering applications such as floodplain mapping, disaster vulnerability assessment, hydrological and geological studies, infrastructure planning, environmental management and gravimetric geoid modelling studies. The results of this study have contributed to the body of literature on performance assessment of satellite-derived digital elevation models. This study recommends the use of SRTM v4 and SRTM30 v2.1 in mapping the earth’s topography particularly within the study area or in other regions with similar terrain characteristics. From the site-specific results here, ASTER GDEM2 and GTOPO30 might still be regarded as research-grade products. Going further, future research can study the relative performance of these DEMs in varying landscapes to determine the impact of above- ground obstructions and land cover, which block satellite pulses from having direct contact with the bare earth.

Acknowledgement The authors are grateful to the Office of the Surveyor General of Lagos State for the provision of coordinates of GPS controls in the State for use in the accuracy assessment.

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ISSN (Print): 2616-051X | ISSN (electronic): 2616-0501

Vol 2, No. 1 March 2018, pp 89 - 95

Heavy metals in soil and accumulation in medicinal plants at an industrial area in Enyimba city, Abia State, Nigeria

1, 1 1 1 Ogbonna, P.C. *, Nzegbule, E.C. , Obasi, K.O. and Kanu, H. 1Department of Environmental Management and Toxicology, Michael Okpara University of Agriculture, Umudike, PMB 7267 Umuahia, Abia State, Nigeria Corresponding Author: *[email protected]

ABSTRACT

The study assessed heavy metals in the soil and subsequent accumulation in plants at an industrial site at Enyimba city, Abia State, Nigeria. Soil and medicinal plant samples were analyzed for zinc (Zn), lead (Pb) and cadmium (Cd). The highest concentration of Zn (142.06 ± 2.91 mg/kg), Pb (18.06 ± 1.30 mg/kg) and Cd (27.055 ± 2.468 mg/kg) were obtained at the sampling points of 2, 7 and 5, respectively. The highest concentrations of Zn (27.09 ± 1.44 mg/kg) and Cd (2.000 ± 0.156 mg/kg) were accumulated by Azadiractha indica while the highest concentration of Pb (4.58 ± 0.51 mg/kg) was accumulated by Mangifera indica. The levels of Zn and Cd in soil were 13.77 ± 1.35 to 142.06 ± 2.91 and 0.695 ± 0.106 to 27.055 ± 2.468, and their concentrations in Azadiractha indica were 5.06 ± 0.35 to 27.09 ± 1.44 and 0.002 ± 0.001 to 2.000 ± 0.156 mg/kg, respectively. The concentrations of Zn and Cd in soil and Cd in Azadiractha indica reflected a state of pollution relative to Dutch criteria for soil and the FAO/WHO Codex Alimentarius Commission for soil and herbal plants.

Keywords: Medicinal plants, heavy metals, industrial area, Enyimba city, Nigeria

1.0. Introduction

The use of medicinal plants for therapeutic purpose or as a dietary supplement dates back beyond records of history, but have increased substantially in the last decade (WHO, 2002). Medicinal plants are applied as single plants, which action is directed at individual ailments, as plant mixtures, syrups, plants and fruit-plant teas and as spices (Ozarowski and Jaroniewski, 1987). Thus, a large populace of people in developing countries relied heavily on medicinal plants for their primary health care because of the low price and safety of active ingredients in plant materials.

The atmosphere is an important pathway for transport of metals and the major external input of bio- available metals in the environment, which are potential threats to the health and survival of man. This is because urban atmosphere is submitted to large inputs of metals arising from stationary source such as industries. The metals are either deposited directly on plant surfaces such as leaves, flowers, branches, and stems or on soils, which are absorbed from the soil solution into plants via the roots. Therapeutic use of medicinal plants contaminated with heavy metals may lead to hazards of enriching human alimentary canal with toxic levels of metals. For instance, the prevalence of upper gastrointestinal cancer in the Van region of Turkey has been linked to metal pollution in soil, fruits and vegetables (Turkdogan et al., 2003).

Among the medicinal plants commonly seen in home gardens at Enyimba city are Azadiractha indica, Citrus sinensis, Psidium guajava, Mangifera indica, Vernonia amygdalina, Occimum gratissimum, and Carica papaya. These plants extracts has undergone extensive pharmacological screening and found to have several pharmacological activities due to presence of several active ingredients. Notwithstanding this, there is increasing concern in environmental connection with human health. Consequently, there is need to investigate the level of concentrations of metals in medicinal plants around the industrial area in Enyimba city. This will enhance the knowledge of the people on the possible health hazards of using medicinal plants contaminated with heavy metals.

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2.0. Materials and Methods

2.1. Study area The study was carried out at Enyimba city. The Enyimba city is the major industrial and commercial hub of Abia State and it is located in the lowland rainforest zone of Nigeria (Keay, 1959). It lies on latitude 5° 1'N and longitude 7° 35' E and characterized by heavy rainfall and short dry season. The mean annual rainfall is 150 to 186 mm, and annual relative humidity is over 80% while the mean annual temperature exceeds 21°C.

2.2. Sampling collection and analysis A simple factorial experiment in a randomized complete block design was used to carry out the study. Twenty three (3) surface soil samples (0-15 cm) each were collected randomly from seven (7) different sampling positions (1, 2, 3, 4, 5, 6, and 7) at industrial and residential areas and stored in cellophane bags, labelled well, and taken to the laboratory for pre-treatment and analysis. The residential area where there was no industry served as control. Twenty three samples from each sampling positions were bulked separately (e.g. 23 samples of 0-15 cm at sampling position 1), homogenized and air dried in circulating air in an oven at 30°C to constant weight and passed through a 2 mm sieve. The procedure described by MAFF (1981) was used for the digestion of soil samples. Exactly 1 g of sub samples were placed in a 100 ml beaker and 10 ml HNO3 acid and 3 ml HClO4 were added and the solution was heated until fuming. Sample solution was obtained by processing with 4 ml hot E mol/Hec, filtered and diluted with water in a 50 ml standard flask. Triplicate digestion of each sample together with a blank was also carried out and metallic content of digested samples were determined with flame atomic absorption spectrophotometer (UNICAM 919 Model) after calibration.

Fresh leaves were sampled from twenty one (21) stands each of Azadiractha indica, Citrus sinensis, Psidium guajava, Mangifera indica, Vernonia amygdalina, Occimum gratissimum, and Carica papaya from where soil samples were collected at industrial and residential areas. The fresh leaves were collected randomly from the various branches of the medicinal plants (except for Carica papaya that does not have branches). The samples from each plant species were placed in large paper bags, labelled well and transferred to the Laboratory for pre-treatment and analysis. The plant samples were thoroughly rinsed with distilled-deionized water to remove dust and pollen particles and placed in large crucibles and oven dried at 70°C for 72 hrs. The dried samples from each species were bulked together and milled with a Thomas Wiley machine. Sub samples were collected from the milled samples and analysed for heavy metals. The procedure described by Allen et al. (1976) was used for sample digestion. Exactly 0.2 g of each samples were weighed into 100 ml standard flask and 1 ml per chloric acid and 5 ml conc. HNO3 added and digested at 80-90°C on hot plates until white fumes evolved. The digest was allowed to cool and then filtered into 50 ml standard flask with watchman No 1 filter paper. Triplicate digestion of each sample together with a blank was also carried out and metallic content of digested samples was determined with flame atomic absorption spectrophotometer (UNICAM 919 Model) after calibration. Data from the result of laboratory analysis were subjected to analysis of variance (ANOVA) and means were separated with Duncan Multiple Range Test.

2.3. Experimental design and statistical analysis A Single Factor Experiment in a randomized complete block design (RCBD) was used to carry out the experiment. The data generated from laboratory analysis were subjected to analysis of variance (ANOVA) using Statistical Package for Social Sciences (SPSS) version 15.0. Analysis of variance was performed using a One-way ANOVA and Duncan Multiple Range Test (DMRT) was used to test if significance difference existed between mean concentrations in physical parameters and heavy metals in water, fish and sediment from the different sampling stations.

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3.0. Results and Discussion

3.1. Heavy metal in soil The result of the concentrations of heavy metals in soil is summarized in Table 1. The result indicates that the highest and lowest concentrations of metals in soils were observed at the industrial area and the residential area, respectively. The lower concentration of metals at the residential area (i.e. control) is attributed to the absence of industry (ies) in that location. The highest concentrations of Zn (142.06 ± 2.91 mg/kg), Pb (18.06 ± 1.30 mg/kg), and Cd (27.055 ± 2.468 mg/kg) were obtained at the sampling points of 2, 5 and 7, respectively at the industrial area and these values are significantly (P < 0.05) higher than their highest corresponding values (35.59 ± 0.69, 8.99 ± 1.03, and 1.060 ± 0.042 mg/kg) at the residential area, respectively. The high values of Zn, Pb, and Cd at the industrial area are attributed to various industrial activities taken place in the location. Mobilization of metals into the atmosphere as a result of anthropogenic activities is an important process in the geochemical cycling of heavy metals. This is acutely evident in urban areas where various stationary and mobile sources release large quantities of metals into the atmosphere and soil (Bilos et al., 2001).

The highest concentration of Zn, Pb and Cd at the industrial area is 3.99, 2.01 and 25.52 times higher than their highest concentrations at residential area, respectively. According to Logan and Miller (1983), soil is said to be contaminated when concentrations of an element in soils were two-to-three times higher than the control. The soils at the industrial area is considered to be contaminated base on the findings that Cd, Zn and Pb concentrations in the control soil samples are significantly lower compared to their corresponding values at the industrial area. The concentrations of Zn and Cd in soils of the industrial area of Enyimba city, Nigeria were 13.77 ± 1.35 to 142.06 ± 2.91 and 0.695 ± 0.106 to 27.055 ± 2.468, respectively for Zn and Cd which are above the accepted limits (i.e. target value) of 140 mg/kg (Zn) and 0.8 mg/kg (Cd) as described by Dutch criteria for soil (Wikipedia, 2013) and the maximum permitted levels of 60 mg/kg (Zn) and 0.1 mg/kg (Cd) established by the Codex Alimentarius Commission (FAO/WHO, 2001) (Table 2). The concentration of Pb (4.91 ± 1.26 to 18.06 ± 1.30 mg/kg) in soils at the industrial area is below the accepted limits (i.e. target value) of 85 mg/kg (Pb) as described by Dutch criteria for soil (Wikipedia, 2013) and the maximum permitted levels of 50 mg/kg (Pb) established by the Codex Alimentarius Commission (FAO/WHO, 2001) Table 2). The values of Zn range from 9.22 ± 1.14 mg/kg at the residential area to 142.06 ± 2.91 mg/kg at the industrial area, which is relatively lower than 114.0 to 162.0 mg/kg for Zn in soils at industrial sites in Isfahan, Iran (Fallahzade et al., 2013) but higher than 59.85 ± 0.002 to 64.5 ± 0.014 mg/kg for Zn in soil at Kaduna, Nigeria (Ogundele et al., 2015), 0.01 to 1.26 ppm for Zn in soils around Superphosphate factory at Assiut city, Egypt (El-Desoky and Ghallab, 2000) but well below 93.0 to 2841 mg/kg for Zn in soils around a smelter at Slovenia (Glavac et al., 2017). The values of Pb in this study range from 1.02 ± 0.04 mg/kg at the residential area to 18.06 ± 1.30 mg/kg at the industrial area, which is higher than 0.102 to 1.082 ppm for Pb (El-Desoky and Ghallab, 2000) but substantially lower than 45.0 to 4132 mg/kg for Pb (Glavac et al., 2017) and 27.0 ± 0.005 to 238.0 ± 0.003 mg/kg for Pb in soil (Ogundele et al., 2015) while the values of Cd range from 0.275±0.332 at the residential area to 27.055 ± 2.468 mg/kg at the industrial area, which is higher than 0.033 ± 0.003 to 0.100 ± 0.002 mg/kg for Cd in soil (Ogundele et al., 2015) and 0.002 to 0.140 ppm for Cd (El- Desoky and Ghallab, 2000) but well below 0.40 to 74.7 mg/kg for Cd (Glavac et al., 2017). Generally, the concentrations of heavy metals in soil followed a decreasing order: Zn > Cd > Pb.

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Table 1: Heavy metal concentration (mg/kg) in soil Zn Pb Cd Industrial area 58.66c ± 3.34 10.7de ± 0.05 1.160d ± 0.113 142.06a ± 2.91 16.35ab ± 1.20 9.210c ± 1.683 58.66c ± 2.20 4.91g ± 1.26 1.490d ± 0.156 26.57e ± 2.17 11.08d ± 0.37 1.170d ± 0.042 105.30b ± 4.38 16.41ab ± 1.68 27.055a ± 2.468 13.77g ± 1.35 12.97c ± 1.18 0.695e ± 0.106 57.08c ± 0.45 18.06a ± 1.30 14.005b ± 0.290 Residential area 18.55ef ± 2.19 3.07gh ± 0.22 0.550ef ± 0.651 27.41e ± 1.83 1.64i ± 0.31 1.005d ± 0.007 11.76g ± 1.07 1.02ij ± 0.04 0.275f ± 0.332 9.86h ± 1.20 5.68f ± 0.48 1.060d ± 0.042 21.56ef ± 2.21 8.99e ± 1.03 0.790e ± 0.099 9.22h ± 1.14 3.59gh ± 0.53 0.305f ± 0.035 35.59d ± 0.69 5.20fg ± 0.57 0.520ef ± 0.085 a, b, c, d, e, f, g, h, i, j, means in a column with different superscript are significantly different (P < 0.05). Values are mean ± standard deviation of 3 replications

Table 2: Comparison of our result with International Standard (Dutch criteria and FAO/WHO Codex Alimentarius Commission Dutch criteria FAO/WHO 2001 NESREA 2011

(target value) mg/kg Codex Alimentarius Commission (mg/kg) Standard (mg/kg) Zn 140 60 421 Pb 85 50 164 Cd 0.8 0.1 3

3.2. Heavy metal accumulation in plants The result of heavy metal accumulation in medicinal plants is summarized in Table 3. The result shows that samples of medicinal plants collected from the environment of the industrial area were more contaminated with the metals (Cd, Pb and Zn). Some pollution studies in different sites in the world, such as Rome, Naples and Sydney, showed that air, soil or plants adjoined to source of pollutants had higher content of metals than a control area (Imperatoa et al., 2003; Moreno et al., 2003; Birch and Snowdon, 2004; Davila et al., 2006). The result also indicate that the concentrations of metals in the medicinal plants varied with plant species and metal contaminants with Zn being the most and Cd the least accumulated.

The highest concentration of Zn was obtained in Azadiractha indica (27.09 ± 1.44 mg/kg) and the value is significantly (P < 0.05) higher than its corresponding values in Psidium guajava (19.77 ± 1.17 mg/kg), Mangifera indica (15.45 ± 0.92 mg/kg), Vernonia amygdalina (11.43 ± 0.87 mg/kg), Carica papaya (9.36 ± 0.95 mg/kg), Citrus sinensis (7.97 ± 1.33 mg/kg) and Ocimum gratissimum (5.06 ± 0.35 mg/kg) at the industrial area as well as the values recorded for plants at the residential area (Table 3). The highest concentration of Cd was also obtained in Azadiractha indica (2.000 ± 0.156 mg/kg) and the value is significantly (P < 0.05) higher than values observed in Psidium guajava (1.72 ± 0.17 mg/kg), Mangifera indica (1.190 ± 0.099 mg/kg), Vernonia amygdalina (0.055 ± 0.021 mg/kg), Carica papaya (0.008 ± 0.001 mg/kg), Ocimum gratissimum (0.005 ± 0.002 mg/kg) and Citrus sinensis (0.002 ± 0.001 mg/kg) at the industrial area as well as the values recorded for plants at the residential area. The high concentration of Zn and Cd in A. indica may be attributed to inherent ability of the plant (A. indica) to absorb and translocate more Zn and Cd to the aerial plant parts (leaves) than other plants. Some plants can tolerate high heavy metals concentration from soil (McGrath et al., 2001) by binding metals to cell walls, compartmentalizing them in vacuoles or complexing them to certain organic acids or proteins (Reeves and Baker, 2000). The concentration of Zn increased from 1.15 ± 0.09 mg/kg (Ocimum gratissimum) at the residential area to 27.09 ± 1.44 mg/kg at the industrial area (Azadiractha indica). The level of Zn in this study is higher than 2.42 ± 0.2173 to 8.93 ± 0.0264 ppm for Zn in Acacia nilotica, Bacopa monnieri, Commiphora wightii, Ficus religiosa, Glycyrrhiza glabra, Hemidesmus indicus, Salvadora oleoides, Terminalia bellirica, Terminalia chebula and Withania somnifera from north western India (Kulhari et al., 2013) but well below 23.2 to 799.5 mg/kg for Zn in Urtica dioica, Hypericum perforatum, Achillea millefolium, and Plantago lanceolata around a smelter at eight Meza Valley locations, Slovenia (Glavac et al., 2017), 12.65 to 146.67 mg/kg for Zn in Petroselinum crispum, Ocimum basilicum, Salvia officinalis,

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Origanum vulgare, Mentha spicata, Thymus vulgaris, and Matricaria chamomilla in the United Arab Emirates, UAE (Dghaim et al., 2015), and 83.74 to 433.76 for Zn µg/g in G. glabra, O. bracteatum, V. odorata, F. vulgare, C. cyminum, C. sativum, and Z. officinalis from Karachi city, Pakistan (Hina et al., 2011). The level of Zn in our study is below the permissible limit (PL) 50 mg/kg set by Codex Alimentarius Commission, FAO/WHO (2006) for medicinal plants and herbs but if the concentration of the metal in the plants at the industrial area continue to increase, it could pose significant health hazard to the population who consume the medicinal plants grown there.

Table 3: Heavy metal concentration in medicinal plants Location Medicinal plants Zn Pb Cd Industrial area Carica papaya 9.36e ± 0.95 2.56bc ± 0.21 0.008d ± 0.001 Azadiractha indica 27.09a ± 1.44 2.77b ± 0.21 2.000a ± 0.156 Vernonia amygdalina 11.43d ± 0.87 0.76d ± 0.21 0.055d ± 0.021 Citrus sinensis 7.97g ± 1.33 2.40c ± 0.28 0.002d ± 0.001 Psidium guajava 19.77b ± 1.17 2.28cd ± 0.51 1.720b ± 0.170 Ocimum gratissimum 5.06gh ± 0.35 2.55bc ± 0.35 0.005d ± 0.002 Mangifera indica 15.45c ± 0.92 4.58a ± 0.51 1.190c ± 0.099 Residential area Carica papaya 4.05h ± 0.21 0.40de ± 0.14 0.004d ± 0.001 Azadiractha indica 9.00ef ± 0.57 0.25e ± 0.07 0.008d ± 0.001 Vernonia amygdalina 1.66j ± 0.54 0.15f ± 0.07 0.002d ± 0.001 Citrus sinensis 3.21i ± 0.57 0.75d ± 0.21 0.003d ± 0.001 Psidium guajava 6.40gh ± 0.42 1.10d ± 0.28 0.002d ± 0.001 Ocimum gratissimum 1.15j ± 0.09 0.27e ± 0.06 0.001d ± 0.000 Mangifera indica 8.07f ± 0.06 0.86d ± 0.06 0.005d ± 0.002 a, b, c, d, e, f, g, h, i, j, means in a column with different superscript are significantly different (P<0.05). Values are mean ± standard deviation of 3 replications

The concentration of Pb increased from 0.15 ± 0.07 mg/kg (Vernonia amygdalina) at the residential area to 4.58 ± 0.51 mg/kg at the industrial area (Mangifera indica). The level of Pb in this study is higher than 0.25 ± 0.00088 to 2.34 ± 0.0173 ppm (Kulhari et al., 2013) but well below 1.1 to 195.9 mg/kg in Urtica dioica, Hypericum perforatum, Achillea millefolium, and Plantago lanceolata around a smelter at Slovenia (Glavac et al., 2017), 1.0 to 23.52 mg/kg in Petroselinum crispum, Ocimum basilicum, Salvia officinalis, Origanum vulgare, Mentha spicata, Thymus vulgaris, and Matricaria chamomilla in the United Arab Emirates, UAE (Dghaim et al., 2015), 19.50 to 121.3 mg/kg in Plantago lanceolata at polluted areas of Poland (Nadgorska-Socha et al., 2013), and 102.3 mg/kg in Urtica dioica at polluted areas of Macedonia (Gjorgieva et al., 2010). The level of Pb in our study is below the permissible limit (PL) 10 mg/kg set by Codex Alimentarius Commission, FAO/WHO (2006) for medicinal plants and herbs. Notwithstanding this, the use of these medicinal plants on a regular basis can increase the accumulation of Pb in human’s body beyond the permitted limit, and this could pose serious health issues for them. Accumulation and magnification of heavy metals in human tissues through consumption of herbal remedies can cause hazardous impacts on health (Kulhari et al., 2013).

Cadmium concentration in this study range from 0.001 ± 0.00 mg/kg (Ocimum gratissimum) at the residential area to 2.00 ± 1.56 mg/kg at the industrial area (Azadiractha indica). The level of Cd in this study is well below 0.20 to 16 mg/kg in Urtica dioica, Hypericum perforatum, Achillea millefolium, and Plantago lanceolata around a smelter at Slovenia (Glavac et al., 2017), 5.70 to 13.8 mg/kg in Plantago lanceolata at polluted areas of Poland (Nadgorska-Socha et al., 2013) and 7.37 mg/kg in Urtica dioica at polluted areas of Macedonia (Gjorgieva et al., 2010) but higher than 0.1 to 1.11 mg/kg for Cd in Petroselinum crispum, Ocimum basilicum, Salvia officinalis, Origanum vulgare, Mentha spicata, Thymus vulgaris, and Matricaria chamomilla in the United Arab Emirates, UAE (Dghaim et al., 2015). The level of Cd in our study is well above the permissible limit (PL) 0.3 mg/kg set by FAO/WHO (2006) for medicinal plants and herbs. The level of Cd in the samples of medicinal plants is 6.67 times higher than the permissible limit (PL) 0.3 mg/kg set by Codex Alimentarius Commission, FAO/WHO (2006) for medicinal plants and herbs. The safety and benefits of medicinal plants are directly connected to its composition and or quality.

Consequently, the use of medicinal plants grown at the industrial area of Enyimba city can be a major route of entry of Cd in human body at the study area, which can be very deleterious to health. Kidney is the critical target organ in the exposed population and excretion of Cd is very slow and it

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4.0. Conclusion

The study shows that industrial activities are one of the anthropogenic sources of metals in the environment. Comparison with an international (Dutch criteria and FAO/WHO Codex Alimentarius Commission) standard i.e. the international scientific literature, shows that the level of metal contamination in soil and accumulation in plant is high. The Azadiractha indica clearly has an inherent potential to take up metals from the soil compared with the other plants tested and could constitute serious health risk when used by consumers.

Acknowledgements The authors appreciate the Technologists in the Soil Laboratory of National Root Crops Research Institute (NRCRI) Umudike, Abia State, Nigeria for the analysis of the samples tested in this study.

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ISSN (Print): 2616-051X | ISSN (electronic): 2616-0501

Vol 2, No. 1 March 2018, pp 96 - 107

Soil chemical characteristics in wet and dry season at Iva long wall underground mined site, Nigeria

1, 1 2 Ogbonna, P.C. *, Nzegbule, E.C. and Okorie, P.E. 1Department of Environmental Management and Toxicology, Michael Okpara University of Agriculture, Umudike, PMB 7267 Umuahia, Abia State, Nigeria 2Department of Forestry and Environmental Management, Michael Okpara University of Agriculture, Umudike P.M.B. 7267 Umuahia, Abia State, Nigeria Corresponding Author: *[email protected]

ABSTRACT

In a bid to diversify the sources of revenue generation in Nigeria, the Government of the Federation has initiated plans to resuscitate coal mining. This study, therefore, assessed the impact of previous mining activities on soil chemical characteristic of the abandoned site. A single factor experiment was conducted in a randomized complete block design (RCBD) with three replications to obtain information on soil status of Iva mined site. Soil samples were collected randomly from ten different sampling points at 0-10, 10-20, and 20-30 cm soil depth in four cardinal points at north (N), south (S), east (E), west (W), and at the centre (c) of crest, middle slope, and valley of Iva mined site. The samples were analysed for heavy metals, macronutrient, soil pH, and organic matter content. The organic matter values ranged from 0.00 ± 0.00 to 1.14 ± 0.02% in dry season and 0.00 ± 0.00 to 1.04 ± 0.06% in wet season, with higher levels of organic matter in the valley (OM ≤ 1.14 %). Soil pH values ranged from 3.98 to 6.00 in wet season and 3.82 to 5.34 in dry season, with higher levels of acidity in the middle slope (pH ≤ 4.37). The range of values of soil macronutrients (K, Mg, N and P) were higher in wet season than in dry season with higher levels of K, Mg, N and P, at middle slope. Similarly, the values of the concentration of heavy metals (Ni and Pb) in soil were higher in wet season than in dry season with higher levels of Ni and Pb at middle slope. The levels of Ni, Pb and Cd in this study are above their allowable limits in Austria, Germany, France, Netherlands, Sweden, and United Kingdom. The high concentrations of Cd and Ni in soils could be taken up in plants via the roots, thus, exposing both man and herbivores to serious health risks.

Keywords: Chemical characteristics, soil, dry, Iva mine, wet

1.0. Introduction

The demand for coal increased when coke made from bituminous coal began replacing charcoal in the iron ore smelting industries (Brown and Dey, 1975). Coal contains a significant amount of pyrites, and exposure of pyrite to atmospheric oxygen through mining activities causes its oxidation to ferrous sulphate and sulphuric acid in the presence of bacteria. The suphuric acid formed lowers the pH of the soil and water, which affects the populations and activities of organisms inhabiting these environments (Sarma, 2002).

Mining activities have adversely affected the physical environment causing massive damage to landscape and biological communities, and contaminating soil and water with the associated metals (Ademoroti, 1996). Heavy metal (HM) pollution is a world-wide phenomenon that poses serious health hazards to aquatic and terrestrial ecosystems. In Guizhou Province of Southwest China, millions of inhabitants were badly affected with dental and skeletal fluorosis as well as arsenic poisoning due to heavy metals released during the burning of mineralized coal (Finkelman, 2007). More so, fluorine emitted from domestic use of coal was responsible for the fluorosis being suffered by thousands of people in China (Ando et al., 1998).

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Soil chemical properties and soil life have possibilities of being modified by the mining activities. Like any fossil fuel, coal is associated with naturally occurring radioactive materials due to their 238U, 232Th, and 40K content, which certainly has radiological implications not only for the miners but also for the populace in the immediate environment of the mines and the users (Balogun et al., 2003). Presently, moves are being made in Nigeria by the Federal government to resuscitate coal mining (National Mirror 28 Feb. 2013; The Tide 28 Feb. 2013), thus, it is necessary to know the outcome of previous activities on the environment for necessary precaution. There is presently inadequate knowledge on the impact of coal mining on soil chemical characteristics of soils at coal mine sites in Nigeria. This, therefore, warrants the investigation on the soil quality in the vicinity of Iva coalmine in Enugu, Enugu State.

2.0. Materials and Methods

2.1. Study area The study area consisted of very large deposits of sub-bituminous coal, estimated at 1.5 million tons that have been mined since 1916 (Diala, 1984). It lies within latitude 6° 23' and 6° 26' N and longitude 7° 27' and 7° 30'E, and the mean monthly temperature lies between 27 and 29°C (Ekere and Ukoha, 2013).The climate is influenced by South-westerly winds that bring rains from April to October, while the Northeast trade winds are responsible for the harmattan with low humidity from December to February. On the average, about 70% of the total land area of Iva support agriculture, and the food and cash crops produced by farmers include yam, maize, ogbono (Irvingia gabonensis var excelsa), melon, cassava, local beans, oil palm, rice, groundnut, and cocoa. The major streams/rivers in Enugu include the Ekulu, Ogbete and Nyaba rivers. Most streams in the area are not perennial but dry up during the late part of the dry season. Some perennial streams rise from the middle levels of the escarpment near the base of the Ajali sandstone.

2.2. Soil collection and analysis Prior to the sample collection, a reconnaissance survey was carried out to determine the altitude of Iva mined site, which is 259 m. Soil samples were collected in February for dry season and June for wet season from Iva mine and control site. Soil samples were collected randomly from ten different sampling points at 0-10, 10-20, and 20-30 cm soil depth with Dutch soil auger in four cardinal points (i.e. two sampling points each at north (N), south (S), east (E), west (W), and at the centre (C) of crest, middle slope and valley of the Iva mined site. The control sample was collected in a 5 year upland bush fallow about 2 km from the mined sites where there was no visible source of contamination. Samples from each particular soil depth (e.g., 0-10 cm at N, S, E, W, and C) were placed in cellophane bags (about 25 g), well labelled, placed in a wooden box and covered to avoid contamination from external sources. The samples in the wooden box were transferred to the laboratory for pre-treatment and analysis. Samples from the same soil depth were bulked together to give a composite sample which were homogenized and air-dried in a circulating air in the oven at 30°C to a constant weight and passed through a 2 mm sieve.

2.3. Analysis of heavy metal in soil Sub-samples from the composite samples were digested according to the method of Shriadah (1999). To 5 g of each soil sample was added 3 ml of 30% hydrogen peroxide, which was left to stand for 60 mins until vigorous reaction ceased. Then 75 ml of 0.5 M solution of HCl was added and content heated gently at 50°C on hot plate for 2 h. The digest was filtered into 50 mL standard flask. The hydrogen peroxide and hydrogen chloride acid used were of analytical grade and were manufactured by Merck KgaA (Darmstadt, Germany). The concentrations of Ni, Pb, Cd, As, and Fe in the digested samples were determined using flame Atomic Absorption Spectrophotometer (UNICAM 919 model) after calibrating the equipment with different standard concentrations as follows: Cd: 0.5, 1, and 2 ppm, Pb: 1, 5, 10 ppm , Ni: 2, 5, 10 ppm, Fe: 1, 2, 5 ppm, and As: 1, 4, 8 ppm. The calibration curves were prepared from standards by dissolving appropriate amounts of the metal salts in purified nitric acid, diluting with deionized water and storing as stock solutions in a quartz flask. Fresh working solutions were obtained by serial dilution of stock solutions. Quality control was implemented through three replicate samples, reagent blank, spiking and use of international soil reference sample (SRM 989, The Netherlands).

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Figure 1: Map of study area

Measurements were made using the hollow cathode lamps for Cd, Pb, Ni, Fe, and As at the proper wavelength and the slit width were adjusted and other AAS conditions were employed in these determinations. The flame type used for all elements was air-acetylene. Working solutions were prepared by dilution. For the determination, two solutions were prepared for each sample and three separate readings were made for each solution. The mean of these figures were used to calculate the concentrations. Triplicate digestion of each sample was carried out together with blank digest without the plant sample. Distilled-deionized water was prepared by passing distilled water through a Milli-Q reagent grade water system.

2.4. Macro element content For macro-element determination, sieved soil samples were digested according to the wet digestion method of Novozamsky et al. (1983). Ca and Mg in the digest were determined by EDTA titration

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Nigerian Journal of Environmental Sciences and Technology (NIJEST) Vol 2, No. 1 March 2018, pp 96 - 107 method, K and P was determined by flame photometry while total N was extracted by sulphur digestion and determined by the micro-Kjeldahl method (Nelson and Sommers, 1972).

2.5. Determination of soil organic matter Organic matter levels in the air dried soils were estimated indirectly from organic carbon. Organic carbon was determined by oxidation of organic matter with a hot mixture of K2Cr2O7 and H2SO4 using the Walkley and Black procedure (Walkley and Black, 1934). The amount of organic carbon was then determined by titration with 0.05N FeSO4 following the procedure outlined by Nelson and Sommers (1982). The organic matter content was obtained by multiplying the organic carbon content with the factor 1.729 (Nelson and Sommers, 1982).

2.6. Determination of soil pH In soil pH determination, soil samples were weighed and mixed with distilled water in the water to soil ratio of 2.5:1 (Lu, 1999; Nartey et al., 2012) in the laboratory. Thereafter, soil pH was measured using pH meter DEMO 13702.93 manufactured by PHYWE of Germany.

2.7. Experimental design and data analysis A single factor experiment was conducted in a randomized complete block design (RCBD) with 3 replications. Data collected on socio-economic variables was analysed with descriptive analysis while data on impact of mining on soil, plant and soil fauna were subjected to a 2-way analysis of variance (ANOVA) using special package for social sciences (SPSS) v. 15 and means were separated (Steel and Torrie, 1980) at P < 0.05 using Duncan New Multiple Range Test (DNMRT).

3.0. Results and Discussion

3.1. Soil chemical characteristics of the experimental site The results on soil pH, macronutrient, and soil organic matter content in soil at Iva mined site in wet and dry season is summarized in Table 1. The results indicate that the highest and the lowest values of soil pH were obtained at the control site and the mined site, respectively. The higher soil pH values at the control site may be attributed to the release of higher basic cations during organic matter decomposition (Oyedele et al., 2008) since there was no mining activity at the control site. The pH of the control site ranged from 5.90 - 6.55 which is higher than 3.82 - 6.00 recorded at Iva mine site. The Iva mine site was more acidic than the control site. The high acidic nature of the mined site may be attributed to flushes of acid mine drainage at the mine site. Soil pH ranged from 3.98 - 6.00 in wet season and 3.82 - 5.34 in dry season. Consequently, soil acidity was higher in dry season than in wet season, and this may be attributed to the dilution effect of rainfall. Soil pH was strongly acidic at middle slope (3.96) than crest (4.12) and valley (4.68) at Iva mine site. Generally, most metals do not exist in free form in the pH range of 6.0 - 9.0 (Porteus, 1985; Adie and Etim, 2012). The pH for all middle slope samples analysed in this study fell well below this range (Table 1). It therefore implies there would be high leaching of metals from the topsoil of the middle slope towards subsoil and subsequently the aquifer as most of the metals would be dissolved in solution. Lower pH increase the solubility of metals (Gabriella and Anton, 2005) and this may have contributed to the higher concentration of heavy metals in middle slope of Iva mine site (Table 2).

The highest organic matter contents in soil were observed at the control site. The low organic matter in soil at the mined site was as a result of mining activities. Mining involves the removal of topsoil and subsoil which destroys many plant species (Down, 1974), thus, reducing the build-up of organic matter on the site. The results also indicate that organic matter content in soil was higher in dry season than in wet season (Table 1). This is attributed to leaching effect of rainfall in wet season. Erosive activities in the agro-ecology of south-eastern Nigeria have contributed to decline in organic matter in soil (Mbagwu and Obi, 2003). The highest content of organic matter in soil at the mined site was recorded in dry season at 0 - 10 cm valley (1.14 + 0.02%) of Iva mine (Table 1). Organic matter content decreases with soil depth (Agboola and Fagbenro, 1985; Omenihu and Ojimgba, 2008) since the abundance and activity of microorganisms decline with soil depth (Andersen and Domsche, 1989; Ekklund et al., 2001; Taylor et al., 2002; Fang et al., 2005). The magnitude of decline in organic matter content with depth in this study varied amongst sampling locations (crest, middle slope, and

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The highest content of macronutrients in soils was obtained at the control site (Table 1). Abandoned metalliferous mines are sites of extreme edaphic conditions characterized by low levels of essential soil nutrients (Bradshaw and Chadwick, 1980; Gibson and Risser, 1982) and mining activities alter the cycling of aboveground forest organic materials and their incorporation into the soil (Onweremadu et al., 2008). The value of Ca, Mg, K, N and P at the control site was 19.30, 6.37, 12.48, 25.20, and 60.33% higher than the highest values recorded at Iva mine. These results corroborate the finding that macronutrient content in unpolluted soil is higher than the levels observed in metal contaminated soil (Ogbonna and Okezie, 2011). Among all the macronutrient investigated in soils at the mined site, K (343.00 ± 0.28 cmol/kg) had the highest content in soils and this was obtained at 10-20 cm middle slope of Iva mine in wet season (Table 1). The value of K in soil during wet season ranged from 88.35 ± 2.18 to 343.00 ± 0.28 cmol/kg, which is higher than 57.10 ± 0.57 to 212.00 ± 0.57 cmol/kg observed in dry season. The value of Mg (319.40 ± 0.85 cmol/kg) in soil at the mined site was highest at 10-20 cm middle slope of Iva mine in wet season (Table 1). Mg in soil in wet season ranged from 78.00 ± 0.57 to 319.40 ± 0.85 cmol/kg, which is higher than 89.60 ± 1.41 to 251.90 ± 1.41 cmol/kg observed in dry season. N (0.80 ± 0.06 cmol/kg) and P (1.42 ± 0.06 cmol/kg) in soil was highest at 20-30 cm middle slope of Iva mine in wet season. The mineralization of nitrogen and phosphorus from plant residues provides these nutrients (N and P) to the soil (Manzoni et al., 2010). The high content of N and P in 20-30 cm depth as well as Mg and K in 10-20 cm depth at Iva mine may be attributed to leaching effect of rainfall. This is in agreement with the findings of Asiegbu (1989) who reported that nitrogen is in short supply in south-eastern Nigeria because of leaching by rain and low organic matter content of the soil. Since these nutrients (Mg, K, N and P) were observed to have peaked at the middle slope, leaching effect by rainfall may have accounted to the high macronutrient in soil (Table 1). In this study, the value of N in soil in wet season ranged from 0.06 ± 0.03 to 0.80 ± 0.06 cmol/kg, which is higher than 0.09 ± 0.01 to 0.36 ± 0.03 cmol/kg observed in dry season; while the value of P in soil in wet season ranged from 0.20 ± 0.03 to 1.42 ± 0.06, which is higher than 0.14 ± 0.04 to 1.23 ± 0.10 observed in dry season. Ca (1.02 ± 0.14 to 8.12 ± 0.14 cmol/kg) in soil during the dry season is higher than 0.81 ± 0.03 to 6.83 ± 0.10 cmol/kg in wet season. Calcium (0.81 ± 0.03 to 8.12 ± 0.14 cmol/kg) in soil is higher than 1.86-2.10 mg/kg reported by Derome and Nieminen (1998) in their study on metal and macronutrient fluxes in heavy metal polluted scots pine ecosystems in SW Finland. The accumulation of heavy metals in soils can have long-term implications on biological, chemical and physical properties of soil (Nicholson et al., 2003). Generally, the values of N, P, K, Ca and Mg in soil at Iva mine was higher in wet season than in dry season, and this may be attributed to rapid decomposition of organic material and subsequent mineralization of these macro elements in wet season. Indeed, the pattern of result is in the increasing order: N < P < Ca < Mg < K.

Heavy metal concentrations in soils at mined and unmined (control) sites are summarized in Table 2. The comparisons in this study will be on the concentrations of the control soils (background soils) and concentrations in mined sites obtained in a number of research works globally and in other cities in Nigeria (Table 3) and allowable limits of some developed countries in the world (Table 4). The results indicate that the highest and the lowest metal concentrations in soil were observed at the mined site and control site, respectively. Soil concentrations of all metals except arsenic (As) were raised to different degrees in wet and dry season at the Iva mined site. The concentrations of heavy metal at the mined site were significantly raised compared to those at the control site, and significant differences was evidenced amongst the three sampling location (crest, middle slope and valley). The tailings dumped indiscriminately at mines can influence the natural concentrations of heavy metals in soil (Wong et al., 1998; Giachetti and Sabastiani, 2006).

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Table 1: Macronutrient (cmol/kg), soil pH and organic matter (%) content in soil at Iva site in wet and dry season Location Depth Season Ca Mg K N P pH (H2O) OM (%) Wet 6.83e ± 268.90c ± 278.70d ± 0.14fgh ± 0.40lmn ± 4.12jk ± 0.86c ± 0.10 12.87 13.86 0.03 0.42 0.10 0.05 0-10 cm Dry 1.05l ± 180.70ef 118.80ij ± 0.19e-h ± 0.14n ± 4.82m ± 0.96c ± 0.14 ± 1.70 1.84 0.03 0.04 0.08 0.04 Wet 2.84j ± 248.90cd 191.50fgh 0.09h ± 0.50k-n ± 4.20ij ± 0.50de ± 0.14 ± 1.84 ± 0.99 0.01 0.10 0.03 0.13 Crest 10-20 cm Dry 1.02l ± 129.80g ± 97.50jkl ± 0.13fgh ± 0.62i-m ± 4.07kl ± 0.57de ± 0.14 0.28 0.99 0.03 0.06 0.06 0.07 Wet 3.86h ± 259.00cd 230.10ef 0.07h ± 0.36mn ± 4.27hi ± 0.03g ± 0.06 ± 15.56 ± 1.56 0.01 0.08 0.07 0.01 20-30 cm Dry 1.80k ± 130.70g ± 168.10gh 0.26ef ± 0.38lmn ± 3.99kl ± 0.13g ± 0.06 1.56 ± 0.28 0.07 0.13 0.01 0.04 Wet 0.81l ± 239.80d ± 192.60fgh 0.06h ± 1.20fgh ± 4.37h ± 0.06g ± 0.03 13.86 ± 1.13 0.03 0.06 0.08 0.01 0-10 cm Dry 4.15g ± 167.40f ± 112.60ijk 0.09b ± 1.23fg ± 4.06kl ± 0.75d ± 0.07 0.85 ± 1.56 0.01 0.10 0.06 0.01 Wet 5.18f ± 319.40b ± 343.00c ± 0.12fgh ± 0.84h-k ± 4.23ij ± 0.01h ± Middle 0.03 0.85 0.28 0.03 0.11 0.08 0.00 10-20 cm slope Dry 3.36i ± 102.00h ± 92.10jkl ± 0.25efg ± 0.90g-j ± 4.05kl ± 0.12f ± 0.06 0.99 0.42 0.08 0.08 0.05 0.00 Wet 2.90j ± 235.80d ± 258.60de 0.80d ± 1.42f ± 3.98kl ± 0.00h ± 0.21 47.66 ± 5.66 0.06 0.06 0.03 0.00 20-30 cm Dry 2.81j ± 116.00gh 88.30jkl ± 0.12fg ± 0.64i-m ± 3.96l ± 0.00h ± 0.07 ± 1.70 16.12 0.01 0.11 0.14 0.00 Wet 1.63k ± 256.80cd 187.20fgh 0.20e-h ± 0.76i-l ± 6.00c ± 1.04bc ± 0.10 ± 3.11 ± 11.60 0.06 0.13 0.10 0.06 0-10 cm Dry 5.33f ± 124.40gh 90.60jkl ± 0.26ef ± 0.81ijk ± 5.05f ± 1.14b ± 0.10 ± 3.68 0.57 0.07 0.04 0.05 0.02 Wet 1.64k ± 252.00cd 149.10hi 0.16e-h ± 0.49k-n ± 5.70d ± 0.92c ± 0.11 ± 0.57 ± 0.71 0.01 0.16 0.10 0.05 Valley 10-20 cm Dry 5.12f ± 120.40gh 57.10l ± 0.29e ± 0.53j-m ± 5.34e ± 0.89c ± 0.16 ± 0.85 0.57 0.03 0.04 0.10 0.01 Wet 1.64k ± 250.40cd 88.35jkl ± 0.11gh ± 0.82ijk ± 5.45e ± 0.00h ± 0.03 ± 0.85 2.18 0.03 0.10 0.05 0.00 20-30 cm Dry 3.20i ± 106.00gh 63.00kl ± 0.18e-h ± 0.92ghi ± 4.68g ± 0.01h ± 0.08 ± 0.99 0.71 0.06 0.08 0.02 0.00 Wet 9.10b ± 301.10b ± 422.00b ± 0.81d ± 2.94e ± 6.52a ± 20.66a ± 0.08 0.85 0.57 0.08 0.11 0.06 0.08 0-10 cm Dry 12.04a ± 380.00a ± 471.00a ± 1.46b ± 3.21e ± 6.55a ± 26.21a ± 0.08 1.13 4.24 0.10 0.27 0.14 0.34 Wet 7.14d ± 310.00b ± 280.10d ± 0.92cd ± 4.01d ± 6.48a ± 1.06bc ± 0.23 12.73 0.85 0.08 0.16 0.08 0.07 Control 10-20 cm Dry 9.20c ± 367.10b ± 316.00cd 1.20a ± 4.40c ± 6.19b ± 1.15b ± 0.07 4.10 ± 13.86 0.16 0.03 0.03 0.09 Wet 4.03gh ± 198.10e ± 200.00fg 0.44c ± 5.82b ± 6.24b ± 0.00h ± 0.13 0.85 ± 2.83 0.45 0.01 0.04 0.00 20-30 cm Dry 5.16f ± 248.00cd 224.00ef 1.01c ± 6.53a ± 5.90c ± 0.00h ± 0.14 ± 1.56 ± 1.70 0.01 0.14 0.10 0.00 a, b, c, d, e, f, g, h, i, j, k, l, m, n means in a column with different superscript are significantly different (P < 0.05). Values are mean ± standard deviation of 3 replications; OM = organic matter

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Table 2: Heavy metal concentration (mg/kg) in soil at Iva mine in wet and dry season Location Depth Season Ni Pb As Fe Cd Wet 28.01g ± 0.03 55.70d ± 1.13 0.00 ± 0.00 377.30j ± 3.82 5.02a ± 0.11 0-10cm Dry 20.05i ± 0.21 48.25e ± 0.21 0.00 ± 0.00 353.00k ± 1.27 0.11kl ± 0.03 Wet 15.25j ± 0.42 45.30f ± 0.57 0.00 ± 0.00 387.50i ± 3.54 2.87c ± 0.13 Crest 10-20cm Dry 10.05k ± 0.78 22.05m ± 0.35 0.00 ± 0.00 238.60n ± 0.28 0.07kl ± 0.03 Wet 10.00k ± 0.57 38.10h ± 0.28 0.00 ± 0.00 402.80fg ± 2.55 1.01g ± 0.13 20-30cm Dry 6.10l ± 0.42 29.40j ± 0.85 0.00 ± 0.00 218.00o ± 2.83 0.48ij ± 0.13 Wet 46.45d ± 0.16 37.50hi ± 0.28 0.00 ± 0.00 410.30e ± 2.40 0.49ij ± 0.08 0-10cm Dry 21.05i ± 1.34 30.01j ± 1.40 0.00 ± 0.00 238.60n ± 1.56 0.19k ± 0.01 Wet 64.12b ± 0.35 65.04c ± 0.34 0.00 ± 0.00 467.80c ± 3.11 1.64e ± 0.07 Middle slope 10-20cm Dry 40.06e ± 1.33 43.60g ± 1.56 0.00 ± 0.00 392.70hi ± 3.82 0.04kl ± 0.03 Wet 79.00a ± 0.57 81.60a ± 0.57 0.00 ± 0.00 498.20a ± 2.55 1.86d ± 0.23 20-30cm Dry 52.30c ± 0.57 69.70b ± 2.40 0.00 ± 0.00 408.00ef ± 2.83 1.17f ± 0.04 Wet 15.20j ± 0.57 36.65hi ± 0.35 0.00 ± 0.00 328.50l ± 2.12 3.08b ± 0.03 0-10cm Dry 10.06k ± 0.65 23.85l ± 0.21 0.00 ± 0.00 397.70gh ± 0.42 0.01kl ± 0.01 Wet 23.50h ± 0.37 35.90i ± 0.85 0.00 ± 0.00 454.70d ± 0.42 0.74h ± 0.07 Valley 10-20cm Dry 10.05k ± 0.49 27.01k ± 0.01 0.00 ± 0.00 273.70m ± 0.42 0.08kl ± 0.03 Wet 31.07f ± 0.38 26.10k ± 0.28 0.00 ± 0.00 490.00b ± 5.66 0.61hi ± 0.04 20-30cm Dry 23.13h ± 0.33 19.60n ± 0.28 0.00 ± 0.00 381.40j ± 1.98 0.41j ± 0.07 Wet 0.02m ± 0.01 1.48o ± 0.12 0.00 ± 0.00 70.60r ± 0.57 0.01kl ± 0.00 0-10cm Dry 0.02m ± 0.01 0.72o ± 0.04 0.00 ± 0.00 43.00t ± 0.57 0.00l ± 0.00 Wet 0.01m ± 0.00 1.01o ± 0.16 0.00 ± 0.00 82.00q ± 0.71 0.00l ± 0.00 Control 10-20cm Dry 0.00m ± 0.00 0.40o ± 0.06 0.00 ± 0.00 38.50t ± 0.85 0.00l ± 0.00 Wet 0.01m ± 0.01 0.26o ± 0.10 0.00 ± 0.00 101.00p ± 1.41 0.00l ± 0.00 20-30cm Dry 0.00m ± 0.00 0.22o ± 0.08 0.00 ± 0.00 62.00s ± 4.24 0.00l ± 0.00 a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, means in a column with different superscript are significantly different (P<0.05). Values are mean ± standard deviation of 3 replications

Table 3: Comparison of values obtained in this study with previous work at mine sites in Nigeria Levels obtained in Ranges of values Locations in Nigeria Heavy metals Authors this study (mg/kg) (mg/kg) 28.8 - 126.0 Enyigba, Ebonyi State Oti and Nwabue (2013) 0.42 - 0.70 OdoIllesa, Osun State Ekwue et al. 2012 Cd 0.01 - 3.06 26 - 210 Arufu, Benue State Adamu et al. (2011) 0.8 - 6.0 Benue State Adamu and Nganje (2010) 2.0 - 39.5 Ishiagu, Ebonyi State Eze and Chukwu (2011) Ni 4.15 - 79.00 6.34 - 17.4 Osun State Adie and Etim (2012) Fe 186.0 - 522.0 102.17 - 181.08 Osun State Mahboob (2001) 2.12 - 4.8 Enyigba, Ebonyi State Oti and Nwabue (2013) As 0.001 - 0.003 8.0 - 18.0 Benue State Adamu and Nganje (2010) 1.72 - 2.00 OdoIllesa, Osun State Ekwue et al. (2012) 91.7 - 1,116.8 Enyigba, Ebonyi State Oti and Nwabue (2013) Pb 6.11 - 81.60 11.5 - 27.7 Osun State Adie and Etim (2012) 8.00 - 15.00 OdoIllesa, Osun State Ekwue et al. (2012)

Table 4: Allowable limits of heavy metal concentrations in soil (mg/kg) by various countries Heavy metals Levels obtained in this study Austria Germany France Netherlands Sweden UK Cd 0.01 - 3.06 1 - 2 1 2 0.5 0.4 3 Ni 4.15 - 79.00 50 - 70 50 50 15 30 75 Pb 6.11 - 81.60 100 70 100 40 40 300 Fe 186.0 - 522.0 NA NA NA NA NA NA As 0.001 - 0.003 NA NA NA NA NA NA NA= not available Source: ECDGE (2010).

The concentration of Pb (81.60 ± 0.57 mg/kg) was highest in wet season at 20-30 cm middle slope of Iva mine (Table 2) and this value was significantly (P < 0.05) higher than Pb concentrations in control site (1.46 ± 0.23 mg/kg) in wet and dry season. The high concentration of Pb at 20-30 cm middle slope was as a result of low content of organic matter in soil vis-à-vis the leaching of metals into the

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Nigerian Journal of Environmental Sciences and Technology (NIJEST) Vol 2, No. 1 March 2018, pp 96 - 107 sub-layer of the soil during the wet season. Wide dispersion patterns are found in metal concentrations in soils, which may be due to transport from tailings, especially during the wet season (Chon et al., 2001), and flushes of dissolved ions in wet season runoff (acid mine drainage-AMD) are well known (Williams and Melack, 1991) to trigger the metal levels in soil, thus, elevated levels of metallic elements due to oxidation of sulphide minerals are frequently common characteristics of mine tailings (Vega et al., 2004). The concentration of Pb in soil at Iva mine (81.60 ± 0.57 mg/kg) was about 76.91% (1.48 ± 0.12 mg/kg) higher than its (Pb) value at the control site. Pokethitiyook et al. (2008) in their study reported that Pb concentration in soil from the land site was about 10 times higher than that from the pond site at the Bo Ngam lead mine, Thailand. The concentration of Pb (19.60 ± 0.28- 81.60 ± 0.57 mg/kg) in soil at Iva mine is lower than 21 - 484 mg/kg in soils at the Daduk Au-Ag-Pb- Zn mine, Korea (Chon et al., 2001) but higher than 2.5 - 36.3 mg/kg in soils at Zacatecas mine, Mexico (González and González-Chávez, 2006).

Ni (79.00 ± 0.57 mg/kg) and Cd (5.02 ± 0.11 mg/kg) were observed to peak in wet season at 20-30 cm middle slope and 0-10 cm crest of Iva mine, respectively. Liang et al. (2003) opined that transfer through water runoff is the main vehicle of heavy metal transportation in soil. The Ni (79.00 ± 0.57 mg/kg) in soil at Iva mine was about 79.92% (0.02 ± 0.00 mg/kg) higher than its concentrations at control site while Cd in soil (5.02 ± 0.11 mg/kg) at Iva mine was about 61.93% (0.01 ± 0.00 mg/kg) higher than its concentration at control site. The Ni (6.10 ± 0.42-79.00 ± 0.57 mg/kg) in soil at Iva mine is higher than 0.5 - 13.7 mg/kg in soils at Zacatecas mine, Mexico (González and González- Chávez, 2006). The Cd (0.01 ± 0.00-5.02 ± 0.11 mg/kg) in soil at Iva mine is higher than 0.34 - 2.12 mg/kg in China (SEPAC, 1995; Bai et al., 2008) 0.4 - 4.76 mg/kg in soils at the Daduk Au-Ag-Pb-Zn mine, Korea (Chon et al., 2001) and 0.3 - 3.3 mg/kg in soils at Zacatecas mine, Mexico (González and González-Chávez, 2006). The source of Cd in soil may be attributed to wastes such as spoil heaps and tailings deposited at the mine.

Logan and Miller (1983) suggested that soil contamination may be considered when concentrations of an element in soils were two-to-three times greater than the average background level. In this study, soils at the mined site is considered contaminated since Pb, Ni and Cd concentrations in all the background (control) soil samples were significantly lower compared to their corresponding values in Iva mine. In Iva mined site, higher concentrations of the metals in soil occurred mostly at 20-30 cm (Table 2). At Iva mine, the concentrations of metals were higher at the middle slope than crest and valley, and this may be due to the topography of the site coupled with the leaching effect of rainfall due to low organic matter in soil. Large amounts of heavy metals present in tailings and associated soils provide a source for continuing dispersion down slope, and have led to various degree of contamination in soils (Chon et al., 2001).

4.0. Conclusion

The assessment of soil chemical characteristics at Iva mine show that coal mining affected the soil chemical characteristics of the mine site. Coal mining which involved vegetation removal affected negatively the soil organic matter and macro-nutrient content of soils around the coal mine site. It also increased soil acidity and elevated the levels of heavy metals in soil in the immediate environment of the mine site. The concentration of heavy metals in soil is high and its accumulation in plants could result to serious health risk to animals and man that depend on them (plants) for food. Therefore, effective remediation should be carried out at the mine site.

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Nigerian Journal of Environmental Sciences and Technology (NIJEST) www.nijest.com

ISSN (Print): 2616-051X | ISSN (electronic): 2616-0501

Vol 2, No. 1 March 2018, pp 108 - 117

Linacre Derived Potential Evapotranspiration Method and Effect on Supplementary Irrigation Water Needs of Tomato/Cabbage/Carrot

1 2, 3 Emeribe C.N. , Isagba E.S. * and Idehen O.F. 1 National Centre for Energy and Environment, University of Benin, Benin City, Nigeria 2 Department of Civil Engineering, University of Benin, Benin City, Nigeria 3 Department of Geography and Regional Planning, Igbinedion University, Okada, Nigeria Corresponding Author: *[email protected]

ABSTRACT

The study examined the dynamic nature of water balance parameters over Kano town, a semi-arid environment and impact of Linacre derived potential evapotranspiration method on the supplementary irrigation water needs of selected crops. Monthly Rainfall and Temperature data were collected from the Nigerian Meteorological Agency, Lagos for the period 1953-2012. The study observed that there is a steady decline in annual precipitation over Kano from the first decade (1953-1962) to the fifth decade (1993-2002), after which there was a sign of weak recovery in the last decade (2003-2012). For water loss through potential evapotranspiration, there was a steady rise from the first decade (1953-1962) to the fifth decade (1993-2002), and then followed by a sudden decline in the last decade (2003-2012). The total average of water storage on the other hand, first experienced a rise between the first two decades (1953-1962) and (1963-1972), followed by a steady decline, up until the fifth decade (1993-2002) and finally a rise in the last decade (2003-2012). The total average of soil water deficit experienced a steady rise between the first and the fifth decades (1953-1962) to (1993-2002), this was followed by a decline in the last decade (2003-2012). Finally, the total average of water surplus experienced a steady decline between the first and the fifth decades. The observed decline in precipitation, storage, and water surplus, and the rise in water loss from potential evapotranspiration and soil water deficit, suggests that there have been changes in the climatic pattern over Kano and this could be seen in the supplementary irrigation water needs of Tomato/Cabbage/Carrot.

Keywords: Water Balance, Climate Change, Decadal Pattern, Water Resources Management

1.0. Introduction

From a hydrologic viewpoint, precipitation constitutes almost the entire water supply to any region but more importantly in the tropics. However, its water potential can never be assessed from precipitation alone. It is necessary to know whether the precipitation is greater or less than the water need as determined essentially by the maximum amount of evaporation and transpiration, or the evapotranspiration (Thornthwaite, 1948). Where the precipitation is high, the region is wet and where the precipitation is low in comparison with the water need, the region is dry. The mutual comparison of precipitation and potential evapotranspiration for the evaluation of water balance, i.e. for quantitatively assessing the adequacy of precipitation (as water supply) in relation to potential evapotranspiration (as water need) may be effectively performed employing a simple accounting procedure devised by (Thorntrawaite, 1948). In this procedure, based on the hydrological cycle, precipitation is treated as income, potential evapotranspiration as expenditure and the amount of moisture stored in the soil as a sort of reserve available for use to a limited extent for purposes of evapotranspiration during rainless periods. Where the precipitation is exactly the same as the potential evapotranspiration all of the time and water is available for use just as needed, there is neither water deficiency nor excess; the climate of the station is neither wet nor dry. As water deficiency (precipitation less than potential evapotranspiration) becomes large with respect to water need, the

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According to Dettinger and Diaz (2000); Sauchyn and Kulshreshtha (2008) climate change has brought about alterations in various processes and activities that influence the provision of water resources in basins. This calls for proper evaluation of water availability in regions of the world. Equally, the various components of climatic water balance have also been affected hence evaluation of the water budget of a basin, potentially identifies the genesis of climate change in a region of interest. Climate change affects various aspects of the water resources of a basin, it may give rise to either too much, or too little water. In situations where rainfall over a region is affected, the resultant effect manifests in various aspects of human activities, agricultural activity being one of them.

The Northern part of Nigeria where Kano State is located, experiences less rainfall amounts than southern Nigeria (Tarhule and Woo, 1998; Ekpoh, 1999; Hulme, 2001; Dai et al., 2004; Adefolalu 2007; Ekpoh , 2007) resulting in agricultural practices not being dependent on rain-fed agriculture but rather on irrigation. The need to understand the water availability pattern of the region through water balance is thus imperative. In addition, monitoring and controlling water balance in a region is valuable to the efficient management of the water and soil of the region .The knowledge of water balance is useful in deciding the possible methods to minimize loss and to maximize gain and in the utilization of water which is often a limiting factor in crop production. In this study therefore, the climatic water balance of Kano State was examined using the Thornthwaite (1948) model and the Linacre (1977) potential evapotranspiration calculation method, in order to investigate the amount of water resources available in the study area in the past decades (from 1953 – 2012); and to also examine the pattern of hydrological behaviour of the state over the period of six decades.

2.0. Materials and Methods

2.1. Study area The study area is Kano town in Kano State of Nigeria. The town lies within Latitude 10°0'48''N and 12°0'0.30''N and Longitude 7°0'59''E and 9°0'11''E (Figure 1). The area is bounded to the north by Niger republic, to the south by Bauchi State, to the east by Jigawa State, and to the west by Kaduna and Katsina states. The study area covers an area of approximately 42,592 km2 (Mamman et al., 2002).

2.2. Data collection Monthly Rainfall in mm and Temperature in 0C were collected from the Nigerian Meteorological Agency, Lagos for the period 1953-2012. The rainfall and temperature data were divided into decades before computation; from 1953 – 1962, 1963 – 1972, 1973 – 1982, 1983 – 1992, 1993 – 2002, and 2003 – 2012 to create the decadal pattern required for this study. Afterwards, the computation of the potential evapotranspiration was carried out, using the Linacre (1977) method of analysis. Computation of the potential evapotranspiration was executed for each decade employing the total average decadal climatic component values (which include; temperature, precipitation and dew point) for each month. Elevation and latitude remained constant throughout computation. The results for the potential evapotranspiration were therefore presented in decadal form, showing the values for each month. This was followed by climatic water balance computation in decades, using the Thornthwaite (1948) budget method of analysis.

2.3. Data analysis To estimate the values of the potential evapotranspiration of our study area, the Linacre (1977) model was used. This is because the model is suitable for West African climates and is thus superior to the Thornthwaite model over West Africa and can be used as a substitute for the Penman method with confidence (Anyadike 1987). By simplifying the Penman’s formula, Linacre (1977) proposed the formulae for estimating values of evaporation and potential evapotranspiration. This formula is given as:

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500푇푚 + 15(푇 − 푇푑) (1) 푃퐸푇 = 100 − 퐴 80 − 푇

where: Tm Temperature + 0.006(Elevation) A Latitude T Temperature in °C Td Dew point

Figure 1: Map of Kano State, Nigeria

In the absence of data for dew point, estimation method developed by Linacre (1992) was used. The equation is given as:

푇푑 = 푇푚푎푥 + 0.8퐶 − 14 (2)

where: Tmax Temperature in °C 0.8 Constant

With the knowledge of the monthly precipitation, potential evapotranspiration values and soil moisture holding capacity of the study area, other water budget elements such as actual

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Nigerian Journal of Environmental Sciences and Technology (NIJEST) Vol 2, No. 1 March 2018, pp 108 - 117 evapotranspiration, change in storage, soil moisture deficit and surplus were calculated as arithmetical differences either positive or negative, between precipitation and potential evapotranspiration values.

The moisture storage of any soil cannot be directly obtained unless the field capacity of the soil is known. According to Thornthwaite and Mather (1955) the field capacity of a soil is the optimum amount of water a given soil can contain. Information on soil moisture holding capacity is often scarce in developing countries. In the absence of data on soil moisture holding capacity, suitable values are frequently assumed based on vegetation and soil characteristics of the region. In this study field capacity value of 300 was used as have suggested by Anyadike (1992).

2.4. Monthly supplementary irrigation water needs To ascertain if monthly supplementary irrigation for any of the selected crops (Tomato/Cabbage/Carrot) is required, the model designed by FAO (1986) for estimating supplementary irrigation need was used. The equation is given as:

푚표푛푡ℎ−1 퐼푅 = 퐸푇푐푟표푝 − 푃푒 (3)

where: IR Supplementary irrigation in mm ETcrop Consumptive water need in mm Pe Effective rainfall in mm

ETcrop is defined as the depth (or amount) of water needed to meet the water loss through evapotranspiration. It is determined as:

퐸푇푐푟표푝 = 퐸푇0 × 퐾푐 (4)

where: d-1 ET0 Reference crop evapotranspiration mm as well as represents an index of climatic demand. ET0 was determined using the Linacre model.

Because of the sparseness of data on crop coefficient (Kc) in developing countries, empirical estimates are usually employed and the most common is the FAO estimates FAO, (1986). The Kc values as estimated crop initial stage, development stage, mid-season stage and late season stage by FAO (1986) was used. In this study however, the development and mid-season stages have been bridged together as flowering stage, while initial and late season stage is considered as vegetative and harvesting stages respectively. This re-grouping/consideration is in line with three stages of crop growth according to Israelsen and Hansen (1962) classification i.e. vegetative, flowering and harvesting stages. According to the author, about 40- 60% of water consumed by a crop is during the flowering stage while the remaining percentage is shared between the vegetative and harvesting stages.

3.0. Results and Discussion

3.1. Results The result of the average monthly water balance components for each decade is given in Table 1. For the decade (1953 – 1962), total average precipitation was (772.45 mm), with the highest rainfall being recorded in the month of August. The total average of storage was (1,506.69 mm). The study area experienced a period of moisture deficit for up to eight months (January to May) and (October to December) with a total average of (265.51 mm). May to August was periods of soil moisture recharge. The period of soil moisture utilization was from August to October. Water surplus was recorded in August and September in this decade with a total average of (242.7 mm). However, large periods of soil moisture deficits were recorded especially at the beginning and end of the year, disrupting water availability during those periods. Hence, irrigation practices during those periods (January to May with May being the onset of rainfall and October to December with October being the rainfall departure month) would have been necessary, groundwater supply would also have been

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limited or unavailable, as well as the year-round availability and utility of water disrupted during those periods (Table 2).

Table 1: The Water Balance over Kano Town for the Years (1953 – 1962) J F M A M J J A S O N D Total P 0 0 0.89 14.03 38.21 71.76 173.01 278.09 175.13 21.33 0 0 772.45 PET 34.58 37.10 45.94 51.84 52.23 44.25 40.4 38.01 32.63 42.96 40.73 34.59 495.26 ST 0 0 0 0 0 27.51 160.12 300 300 278.37 237.64 203.05 1,506.69 DST 0 0 0 0 0 +27.51 +132.61 +139.88 0 -21.63 -40.73 -34.59 AE 0.3 0 0.89 14.03 38.31 44.25 40.4 38.01 32.63 21.33 0 0 229.85 DET 34.58 37.10 45.05 37.81 14.02 0 0 0 0 21.63 40.73 34.59 265.51

Table 2: Monthly supplementary irrigation water need for Tomato/Cabbage/Carrot in mm, over Kano State (1953 – 1962) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Crop initial Crop development Late season growth stage stage stage

ET0 (mm) 34.58 37.10 45.94 51.84 52.23 44.25 40.4 38.01 32.63 42.96 40.73 34.59

Kc 0.45 0.45 0.9 0.9 0.70 0.7

ETcrop 23.5 19.9 36.4 34.2 22.8 30.1

Pe (mm) 0 0 0 0 12.75 34.2 112.6 176 112.5 2 0 0 IR (mm) 10.75 -14.3 -76.2 -141.8 -89.7 28.1

For the decade (1963 – 1972), total average precipitation was (770.28) with the highest rainfall month as August (Table 3). The total average of storage was (1,611.52 mm). The study area experienced a period of soil moisture deficit of up to eight months (January to May) and (October to December), with a total average of (268.02 mm). May to August was periods of soil moisture recharge, and the period of soil moisture utilization was from August to October. Water surplus was recorded in August and September with a total average of (239.83 mm). In the onset of rain when farmers would begin cultivation, supplementary irrigation was needed (Table 4).

Table 3: The Water Balance over Kano town for the Years (1963 – 1972) Decades J F M A M J J A S O N D Total P 0.3 0 0.28 12.57 38.03 101.16 205.83 262.75 128.73 20.58 0.05 0 770.28 PET 33.59 38.13 53.16 52.16 50.6 45.03 39.38 37.18 39.05 38.91 38.99 34.32 500.5 ST 0 0 0 0 0 56.13 222.58 300 300 281.67 242.73 208.41 1,611.5 DST 0 0 0 0 0 +56.13 +166.45 +77.42 0 -18.33 -38.94 -34.32 AE 0.3 0 0.28 12.57 38.03 45.03 39.38 37.18 39.05 20.58 0.05 0 232.45 DET 33.29 38.13 52.88 39.59 12.57 0 0 0 0 18.33 38.94 34.32 268.02

Table 4: Monthly supplementary irrigation water need for Tomato/Cabbage/Carrot in mm, over Kano State (1963 – 1972) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Crop initial Crop development Late season growth stage stage stage

ET0 (mm) 33.59 38.13 53.16 52.16 50.6 45.03 39.38 37.18 39.05 38.91 38.99 34.32

Kc 0.45 0.45 0.9 0.9 0.70 0.7

ETcrop 22.8 20.3 32.7 33.5 27.3 27.2

Pe (mm) 0 0 0 0 12.8 56 136.5 176.5 80.6 2.2 0 0 IR (mm) 10.0 -35.7 -103.8 -143.3 -53.3 25

For the decade (1973 – 1982) as given in Table 5, total average precipitation was (623.3 mm) with the highest rainfall recorded in the month of August. Storage also recorded a total average of (1,547.55 mm). Soil moisture deficits of up to eight months were recorded in the study area (January to May)

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Table 5: The Water Balance over Kano town for the Years (1973 – 1982 J F M A M J J A S O N D Total P 0.3 0 1.29 10.19 31.54 101.09 165.7 192.46 102.92 18.11 0 0 623.3 PET 34.52 40.52 46.94 52.13 52.03 45.85 41.26 38.21 40.38 43.11 38.37 35.63 508.95 ST 0 0 0 0 0 55.24 179.68 300 300 275 236.63 201 1,547.55 DST 0 0 0 0 0 +50.24 +124.44 +120.32 0 -25 -38.37 35.63 AE 0.3 0 1.29 10.19 31.54 45.85 41.26 38.21 40.38 18.11 0 0 226.83 DET 3.52 40.52 45.65 41.94 20.49 0 0 0 0 25 38.37 35.63 282.12

Table 6: Monthly supplementary irrigation water need for Tomato/Cabbage/Carrot in mm, over Kano State (1973 – 1982) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Crop initial Crop development Late season stage growth stage stage

ET0 (mm) 34.52 40.52 46.94 52.13 52.03 45.85 41.26 38.21 40.38 43.11 38.37 35.63

Kc 0.45 0.45 0.9 0.9 0.70 0.7

ETcrop 23.4 20.6 32.7 37.1 28.3 30.2

Pe (mm) 0 0 0 0 8 55 103 127 55 0 0 0 IR (mm) 15.4 -34.4 -70.3 -89.9 -26.7 30.2

In 1983-1992 decade given in Table 7, precipitation had a total average of (609.74 mm). Storage also recorded a total average of (1,477.6 mm). Soil moisture deficit of up to eight months were also recorded in the study area between (January to May) and (October and December) with a total average of (285.99 mm) (Table 7). May and August were periods of soil moisture recharge and the period of soil moisture utilization was between August and October. Water surplus was observed in the area in August and September at a total average of (77.83 mm). Water resources available for groundwater supply, domestic purposes, rainwater harvesting, agricultural purposes and industrial use were therefore scarce as large periods of water deficit were recorded and as water storage, precipitation and surplus were also on the decline as also seen in the supplementary irrigation needs of selected food crops (Table 8).

Table 7: The Water Balance over Kano town for the Years (1983 – 1992) J F M A M J J A S O N D Total P 0 0 0 2.46 42.03 73.48 163.03 220.84 89.21 17.83 0.89 0 609.74 PET 34.46 39.76 47.27 53.98 52.61 47.30 40.71 39.58 41.14 44.75 41.73 34.64 517.93 ST 0 0 0 0 0 26.18 148.5 300 300 273.08 232.24 199.6 1,477.6 DST 0 0 0 0 0 +26.18 +122.32 +151.5 0 -26.92 -40.84 -32.64 AE 0 0 0 2.46 42.03 47.30 40.71 39.58 41.14 17.83 0.89 0 231.94 DET 34.46 39.76 47.27 51.52 10.58 0 0 0 0 26.92 40.84 34.64 285.99

Table 8: Monthly supplementary irrigation water need for Tomato/Cabbage/Carrot in mm, over Kano State (1983 – 1992) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Crop initial Crop development Late season stage growth stage stage

ET0 (mm) 34.46 39.76 47.27 53.98 52.61 47.30 40.71 39.58 41.14 44.75 41.73 34.64

Kc 0.45 0.45 0.9 0.9 0.70 0.7

ETcrop 23.7 21.3 36.6 35.6 28.8 31.3

Pe (mm) 0 0 0 0 14 32 103 151 47 0 0 0 IR (mm) 9.4 -10.7 -66.4 -115.4 -18.2 31.3

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For the decade (1993 -2002) given in Table 9, the total average of precipitation was (569.26 mm). Also, the total average of storage had reduced to (1425.74 mm) in this decade. Furthermore, the study area experienced a period of soil moisture deficit of up to eight months from (January to May) and (October to December) with a total average of (301.72 mm) (Table 9). May to August was recorded as periods of soil moisture recharge. The period of soil moisture utilization was between August and October. Water surplus was recorded in only one month, (September), and it had a total average of (41 mm) which suggests that there was drought in this decade, as it is a clear drop in the usual amount of months of water surplus recorded in previous decades. From this, it is clear that water resources available for groundwater supply, domestic purposes, rainwater harvesting, agricultural purposes and industrial use were immensely scarce in this decade. In this decade therefore, intensive irrigational and water conservation practices would have been very critical and underground water supply crucially low for the dry season while supplementary irrigation for the onset and rainfall cessation months (Table 10).

Table 9: The Water Balance over Kano town for the Years (1993 – 2002) J F M A M J J A S O N D Total P 0 0.28 4.12 2.91 39.99 83.8 157.58 168.46 105.4 6.72 0 0 569.26 PET 35.15 40.18 47.92 55.06 53.98 48.47 42.52 40.85 42.40 46.25 42.19 35.01 529.98 ST 0 0 0 0 0 35.33 150.39 278 300 260.47 218.28 183.27 1,425.74 DST 0 0 0 0 0 +35.33 +115.06 +127.61 +22 -39.53 -42.19 -35.01 AE 0 0.28 4.12 2.91 39.99 48.47 42.52 40.85 42.40 6.72 0 0 228.76 DET 35.15 39.9 43.8 52.15 13.99 0 0 0 0 39.53 42.19 35.01 301.72

Table 10: Monthly supplementary irrigation water need for Tomato/Cabbage/Carrot in mm, over Kano State (1993 – 2002) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Crop initial Crop development Late season stage growth stage stage

ET0 (mm) 35.15 40.18 47.92 55.06 53.98 48.47 42.52 40.85 42.40 46.25 42.19 35.01

Kc 0.45 0.45 0.9 0.9 0.70 0.7

ETcrop 24.3 21.8 38.3 36.8 29.7 32.4

Pe (mm) 0 0 0 0 14 39 96.6 104.6 56.9 0 0 0 IR (mm) 10.3 -17.2 -58.3 -67.8 -27.2 32.4

For the decade (2003 – 2012), the total average of precipitation surprisingly and unexpectedly had an increase to (703.88 mm). Also, the total average of storage increased to (1,618.5 mm) which is another astounding change in the pattern of hydrological behaviour in Kano Town. Also, the study area experienced a period of soil moisture deficit of seven months between (January to April) and (October to December) months which marks yet another change in the usual pattern of the water deficit occurrence from eight months to seven months (Table 11). Soil moisture recharge was recorded between April and July and soil moisture utilization was recorded between July and October, which marks yet another change in the usual pattern of behaviour over the study area in the past decades. Water surplus was recorded in August and September with an astonishing total average of (151.92 mm). The fact that rainfall amount appreciated during the decade 2003 - 2012 further supports the findings from other authors rainfall conditions improved in the 90s, followed by a total rainfall recovery in 2000s (Yamusa et al., 2015) . Studies by (Nicholson et al., 2000) and (Ati et al., 2009) have also shown that there was a sign of a recovery and a significant increase in annual rainfalls in the 1990s compared to previous decades. This sign of recovery is also seen in the reducing amounts of water required for supplementary irrigation water needs (Table 12). Water resources available for groundwater supply, domestic purposes, rainwater harvesting, agricultural practices and industrial use are therefore now readily available with soil moisture deficit present at the beginning and end of the year. However, water surplus during the year (August and September) suggests periods of water availability and sufficient surface -run-off in the study area.

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Table 11: The Water Balance over Kano town for the Years (2002 – 2012) J F M A M J J A S O N D Total

P 0 0 3.48 7.54 55.67 109.38 213.24 197.81 104.28 12.48 0 0 703.88

PET 35.68 40.14 49.53 55.22 54.18 48.08 42.67 41.13 42.40 41.40 41.92 37.46 529.81

ST 0 0 0 0 1.49 62.79 233.36 300 300 271.08 229.16 220.62 1,618.5

DST 0 0 0 0 +1.49 +61.3 +170.57 +66.64 0 -28.92 -41.92 -8.54

AE 0 0 3.48 7.54 54.18 48.08 42.67 41.13 42.40 12.48 0 0 251.96

DET 35.68 40.14 46.05 47.68 0 0 0 0 0 28.92 41.92 37.46 277.85

Table 12: Monthly supplementary irrigation water need for Tomato/Cabbage/Carrot in mm, over Kano State (2003 – 2012) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Crop initial Crop development Late season stage growth stage stage

ET0 (mm) 35.68 40.14 49.53 55.22 54.18 48.08 42.67 41.13 42.40 41.40 41.92 37.46

Kc 0.45 0.45 0.9 0.9 0.70 0.7

ETcrop 24.4 21.6 38.4 37.0 29.7 28.9

Pe (mm) 0 0 0 0 22.7 63 143 127 56 0 0 0 IR (mm) 1.7 -41.4 -104.6 -90 -26.3 28.3

3.2. Discussion This study showed that in the first (5) decades, water deficit occurred over a period of eight (8) months first between (January to May) and (October to December). However, this pattern changed in the last decade (2003-2012) where deficit occurred during (7) months between (January to April) and (October to December). It can therefore be appreciated that the dry season in the study area starts in October and ends in May, which corresponds to the period of dry season of northern Nigeria which equally spans from the month of October to May. However, this pattern has changed over the last decade, suggesting a change in the climatic behaviour over the basin. The periods of soil moisture recharge over the basin occurred between May and August, while soil moisture utilization occurred between August and October. Except in the last decade, where soil moisture recharge occurred between April and July and soil moisture utilization occurred between July and October. From this, it is seen that water was most available in the study area between the months of August and October, (This period coincides with the period of water surplus which occurred between August and September). Therefore, it can be seen activities that are water-dependent would have sufficient water during the months of August to October; but alternative sources of water would be required during the remaining months of the year, as water was scarce during those other months. The steady increase in water deficit and decline in water availability confirms the Sahelian drought that occurred in Nigeria between the 1960’s and 1980’s and suggests that the drought was at its peak during (the mid 1970’s and early 1980’s). However, the significant change in the availability of water over the study area due to recent change in climate over the last decade, suggests that water resources will not become totally scarce, but may however become readily available in the future as a result of the changing climate especially for the Kaduna, Sokoto and Rima Rivers in addition to few small streams like the Ka, Zamfara Gurara and Mada Rivers which drain Kano parts of the study area. Therefore, the decadal pattern of the climatic water balance of the study area should be of importance to the government and other hydrological institutions in the state, to guide their activities in the development and planning of water resources of the region.

4.0. Conclusion

The major contribution to stream flow to river basins is from rainfall. In this study, it was observed that precipitation, storage, as well as water surplus over the sixty years of study (1953 – 2012) have been on a steady decline up until the fifth decade (1993 – 2002), where it was at its lowest, this was followed by a sudden and remarkable increase in the next decade (2003 – 2012). Furthermore, water

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Anyadike, R.N.C., (1987). The Linacre evaporation formula tested and compared to others in various climates over West-Africa. Agricultural and Forest Meteorology, 39, pp. 111-119.

Anyadike, R.N.C., (1992). Water Balance of the Lake Chad Basin. Buttetine des Institute fundamental d’ Afrique Novire, 47, pp. 13-28.

Ati, O.F., Stigter, C. J., Iguisi, E.O. and Afolanyan, J.O., (2009). Profile of rainfall change and variability in northern Nigeria 1953-2003. Res. J Environ. Earth Sci, 1, pp. 58-63.

Burt, C. M., (1999). Irrigation Water Balance Fundamentals. In: Proceedings of Conference on Benchmarking Irrigation System Performance Using Water measurement and water balances. San Luis Obispo, C.A., March 10. USCID, Denver, pp. 1-13. ITRC paper 99-101

Dai, A., Lamb, P., Trenberth, K. E., Hulme, M., Jones, P. D. and Xie, P., (2004). The recent Sahel drought is real. Int. J. Climatology, 24, pp. 1323-1331.

Dettinger, M.D. and Diaz, H.F., (2000). Global characteristics of streamflow seasonality and variability. J. Hydromet. 1, pp. 289–310.

Ekpoh, I. J., (1999). Rainfall and Peasant Agriculture in Northern Nigeria. Global Journal of Pure and Applied Sciences, 5, pp. 123-128.

Ekpoh, I. J., (2007). Climate and Society in Northern Nigeria: Rainfall variability and farming. The International Journal Series on Tropical issues, 8, pp. 157-162.

Food and Agriculture Organization of the United Nations FAO (1986). Irrigation Management, Training Manual, No 3. Via delle Terme di Caracalla, 00100 Rome, Italy

Hulme, M., (2001). Climate perspectives on Sahelian desiccation; 1973-1998. Global Environmental Change, 11, pp. 19-29.

Israelsen, O.W. and Hansen, V.E., (1962). Irrigation Principles and Practices, John Wiley and Sons. Inc, New York.

Linacre, E.T., (1977). A simple Formula for estimating evaporation rate in various climate, using temperature data alone. Agriculture and Meteorology, 18, pp. 409-424.

Linacre, E.T., (1992). Climate Data and Resources: A Reference and Guide. London, Routledge.

Mamman, A. B., Oyebanji, J. O. and Peters, S. W., (2002). Nigeria, A People United, A Future Assured. Survey of States, Vol. 2 Millennium Ed., Gabumo House, Calabar

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Nicholson, S.E., Some, B. and Kone, B., (2000). An analysis of recent rainfall conditions in west Africa, including the rainy seasons of the 1997 EI Nino and the 1998 La Nina years. Jouurnal of climate, 13, pp. 2628-2640.

Sauchyn, D. and Kulshreshtha, S., (2008). Climate change impacts on Canada’s Prairie Provinces: A summary of our state of knowledge, from “Prairies”. In: From Impacts to Adaptation: Canada in a Changing Climate 2007, Edited by D. Lemmen et al., Government of Canada, Ottawa

Tarhule, A. and Woo, M.K., (1998). Changes in rainfall characteristics in northern Nigeria. Int. J. Climatol., 18, pp. 1261–1271.

Thornthwaite, C.W. and Maither, J.R. (1955). The Water Balance, Publications in Climatology VIII, Drexel Institute of Technology, Centerton, NJ, 104P.

Thornthwaite, C.W., (1948). An Approach towards a Rational Classification of Climate. Geographical review, 38, pp. 55 – 94.

Yamusa, M.A., Abubakar, I.U. and Falaki, A. M., (2015). Rainfall variability and crop production in the north western semi-arid zone of Nigeria. Journal of soil science and environmental management, 6(5), pp. 125-131.

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ISSN (Print): 2616-051X | ISSN (electronic): 2616-0501

Vol 2, No. 1 March 2018, pp 118 - 129

Aquifer Mapping in the Niger Delta Region: A Case Study of Edo State, Nigeria

1, 1 1 1 Seghosime, A. *, Ehiorobo, J.O. , Izinyon, O.C. and Oriakhi, O. 1Department of Civil Engineering, Faculty of Engineering, University of Benin, Benin City, Nigeria Corresponding Author: *[email protected]

ABSTRACT

In Nigeria, potable water is in short supply to the greater population and where available, groundwater accounts for over 90% of the supply. Oil exploration and exploitation activities are carried out in the Niger Delta region of Nigeria and this has affected the environment in this region. However, naturally occurring traces of petroleum products in strata or petroleum losses through spillages can contaminate groundwater, thus aquifer mapping in the Niger Delta Region becomes crucial. The study involves collection, collation and analysis of relevant information and data required for successful development of groundwater in Edo state. Groundwater in Edo State occurs under different conditions in the various aquifers defined by the following geological units, namely: Coastal Plain Sands of the Benin Formation, Ogwashi- Asaba, Bende- Ameki and Imo Shale Group of the Tertiary Deposits, False-bedded Sandstones and the Nkporo Shale Group of the Cretaceous deposits and the Basement Complex Rocks which only contain groundwater in the overburden, faults and joints. From the information collated, groundwater levels are deepest in the Ishan Plateau where it is about 171 metres above mean sea level at Ekpoma. Away from the pleateau, groundwater rises southwards and northwards. At Aduwawa/Ikpoba Hill (Benin City), the groundwater level is 40 metres, at Iguiye (Lagos Road, Benin City) to the west, the groundwater level is 55 metres and at Fugar to the north, the groundwater level is 95 metres. The groundwater flow direction is from the Plateau to all other areas with higher groundwater levels. Therefore, aquifer mapping in the Niger Delta region is necessary, as it will help in assessing the availability and development methods to be adopted.

Keywords: Groundwater, Aquifer, Borehole, Formation, Sediment, Niger Delta, Mapping

1.0. Introduction

As a result of rapid population growth and local development, potable water is in short supply and this has led to the resurgence of groundwater potentials for steady and reliable water (Alabi et al., 2010; Anomohanran, 2011a & 2011b). Water in the zone of saturation is normally referred to as ground water and geology is therefore a controlling factor. Groundwater is very vital as it is a viable source of portable water for domestic use (Peter, 2013). An aquifer is a water saturated geologic unit that will yield water to well or spring at sufficient rate that can serve as practical source of water supply. It is therefore a water-bearing formation or ground water reservoir. In countries like Denmark, groundwater mapping is a high priority; the Danish government initiated the National Groundwater Mapping Programme to achieve a detailed description of Danish aquifers with respect to localization, extension, distribution and interconnection as well as vulnerability against contaminants (Stockmarr and Thomsen, 2012). Naturally occurring traces of petroleum products in strata (from which water is obtained) or petroleum losses through spillages can contaminate groundwater. This makes the aquifer mapping in the Niger Delta more significant as it is a region where oil exploration and exploitation activities are carried out. A study on aquifer mapping and characterization in Anambra State indicated that Nanka formation in Anambra basin has a high level of groundwater potential (Emenike, 2001). Also, aquifer mapping in Onibode area, near Abeokuta South-West of Nigeria shows groundwater potentials (Oyedele, 2001).

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Therefore, the occurrence of groundwater is controlled by geology and hydrogeology; hence groundwater does not occur in desired quantities and qualities anywhere and everywhere (Kogbe, 1989). To successfully locate and drill a water borehole, the favourable conditions must be identified (Egbai, 2012; Peter, 2013; Egbai et al., 2015). This is achieved by the water explorer or hydrogeologist, employing a number of investigative tools. One of the direct tools includes procurement and analysis of existing geological and hydrogeological information and maps. In addition, subsurface information from existing borehole logs and pumping test data are required. The indirect tool involves the use of geophysical surveys, most often the electrical resistivity survey to identify subsurface features. Hence, it is necessary to continue to improve the data base for groundwater development in a country in order to be able to identify a stable and steady ground water sources for water supply. Therefore, this study will focus on aquifer mapping in the Niger Delta Region of Nigeria using Edo State as a case study.

2.0. Materials and Methods

2.1. Study area Edo State lies between latitude 05°44' to 07°34'N and longitude 05°04' and 06°45'E and covers a land area of about 19,635 square kilometres (see Figure 1). The State is bounded to the East and South by Delta State, to the North by Kogi State and to the West by Ondo and Ekiti States. It is located within the rainforest zone of Nigeria with mean annual rainfall in the range of 1500 mm to 2500 mm and the mean monthly temperature varying from 25 °C to 28 °C (Benin Kingdom/Edo State Weather, 2018). Edo State is situated in a zone with relatively high rainfall. The State has two distinct seasons. These are the wet (rainy) season and the dry season. The rainy season occurs between the months of April and October with a short break in August. The dry season on the other hand lasts from October to April with dry harmattan winds between December and February, but with the effect of global warming and climate change, rains have been observed to fall irregularly almost in every month of the year. The terrain consists of hilly or dissected country in the north and dry flat country, around Benin City towards the South. There are also abundant fresh swamp waters flanking the main rivers, particularly the Niger. The highest elevations are at the dissected and hilly terrains in the north with heights of about 300 m and above such as the Igarra and Ososo hills in Edo North. Further south of this zone lies the dissected areas around Auchi and Okpella with elevations decreasing southwards to about 200-100 m down south to about 15 m above mean sea level. In the central region there is also the Ishan Plateau with elevation reaching about 350 m above sea level.

2.2. Test methods The method used for the study included the following; a) Field work which includes: i. Geophysical survey using (Vertical Electrical Soundings, VES) ii. Water borehole drilling. b) Data Processing and Plotting of Groundwater contours.

2.2.1. Electrical Resistivity Sounding (By Vertical Electrical Soundings) Groundwater exploration was carried out in four geological regions (Quaternary Sediments, Tertiary Sediments, Cretaceous Sediments and Basement Complex) and this was done using vertical electrical soundings (VES) method with the aid of ABEM SAS 300 Tetrameters and other field accessories. Geographical coordinates were obtained from Garmin Handheld GPS 72 receiver. The quantitative interpretations of the VES data acquired at the regions were done initially with the aid of the conventional partial curve matching technique subsequently fine-tuned with the aid of computer assisted iteration techniques.

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Figure 1: Map of Edo State (Source: https://www.nigeriagalleria.com/Nigeria/States_Nigeria/Edo/Edo_State.html)

2.2.2. Water borehole drilling Water boreholes were drilled in some selected area of Edo State to obtain boreholes data. The water borehole drilling involved drilling to a diameter of 8 inches using a water bore hole drilling rig (KW30). The drill’s rotary machine (rotates at 30 rpm to ensure straight borings) was powered with a hydraulic pump and then, compressed air drives to down-the-hole-hammer to pulverise the rock. Dust and cuttings were flushed out of the borehole by compressed air.

2.2.3. Plotting of Groundwater Contours for Edo State Contours were plotted using the groundwater levels obtained from the water borehole data in some selected areas of Edo State. The water level at each location was determined and established with reference to the ground elevation.

3.0. Results and Discussion

Table 1 show the classification of groundwater regimes.

Table 1: Classification of groundwater regimes S/N Region Group Nature of Aquifer 1 Recent deposits Niger Alluvium, River Isolated minor sand lenses. Unimportant as Alluvium aquifer in Edo state 2 Quaternary deposits Benin formation Thick, unconsolidated sands with minor clays. Highly permeable and productive aquifer 3 Tertiary deposits (a) Ogwashi- Asaba Thick fine to coarse grained sand with clays and formation (lignite series) lignite. (b) Bende- Ameki Thick, fine to medium and coarse sand with formation sandy clays and shalely limestone Very thick shale with lenticular sands. This is (c) Imo shale essentially an aquiclude 4 Cretaceous False- bedded sandstone. Thick moderately permeable aquifers Deposits Fugar sandstone 5 Basement complex Meta sediments, Isolated aquifers in weathered zones, joints and migmatites, gneisses, and faults. Aquifer has low to moderate yields. granites

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From Table 1, the results indicated that the alluvium deposits does not present important aquifers in Edo State due to the isolated minor sand lenses it contains and that of the basement complex has low to moderate yield.

Table 2 show the location where Electrical Resistivity tests were conducted.

Table 2: Electrical resistivity data in the quaternary sediments S/N Location Depth to top of Saturated Zone (inferred) (m) 1 Oghada 65 2 Uhie 71.20 3 Umelu 75.0 4 Iguododo 96 5 Oghede 83 6 Evboesi 73 7 Evbuekabua 93 8 Evbuekoi 102 9 Ugbineh 20

Electrical resistivity carried out at Ugbineh was used as a case study for Quaternary sediments. The resistivity data are presented in Table 3a and 3b while the curve is in Figure 2.

Table 3a: Resistivity data for Ugbineh Array Apparent Synthetic Difference Spacing (m) Geometric Resistivity AB/2 MN Factor (K) (Roa) (Ohm-m) 1 0.5 5.8905 301.76 1.5 0.5 13.744 204.7 2 0.5 24.74 202.1 3 0.5 56.156 248.93 4.5 0.5 126.84 317.58 7 0.5 307.48 338.75 10 0.5 627.93 327.26 14.5 1 659.73 373.12 21.5 1 1451 453.8 32 2 1607 797.59 47 5 1384 1750 100 10 3134 1400 150 20 3519 1739 220 20 7587 1400 320 50 6395 2493

Table 3b: Geoelectric parameters and inferred lithology for Ugbineh Layer Thickness (m) Depth(m) Resistivity(ohm-m) Lithology 1 0.5897 0.5897 335.4 Lateritic Topsoil 2 0.6154 1.205 109.2 Sandy Sub- soil 3 1.203 2.408 580.2 Sandy Clay 4 2.521 4.93 183.2 Clayey Formation 5 4.794 9.723 634.75842 Clay stone (Dry) 6 10.4 20.12 1456 Sandy Clay 7 61.29 81.41 3188 Sandy Clay 8 57.36 138.77 1453 Sand (Aquifer) 9 Undefined Undefined 537.1 Sandstone (Aquifer)

Results in Table 3a and 3b were interpreted, using sounding curves as shown in Figure 2. Note that AB is the outer (current) electrode spacing and MN is the inner (potential) electrode spacing.

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10000

m) -

1000 Resistivity(ohm

100 1 10 100 1000 Spacing (m)

Figure 2: Resistivity curve for Ugbineh

Nine geo-electric layers were revealed for Ugbineh as indicated in Table 3b with the top of saturated sand layer at 20.12 m deep, while the depth to the top of the aquifer was 138 m and that of the base, undefined. This indicates that the groundwater may tend to be ferruginised (i.e. the groundwater may contain a sizable amount of Fe2+). This result is in accordance with that obtained from the study conducted by Ako and Olorunfemi (1989), which show that one of the peculiarities of deep aquifer is that the groundwater tends to be ferruginised.

Electrical resistivity data in the tertiary sediments are presented in Table 4.

Table 4: Electrical resistivity data in the tertiary sediments S/N Location Resistivity (Ohm-m) 1 Ehor 200 2 Ekpoma 264 3 Ubiaja 220 4 Eguaholor 174 5 Odiguetue 322

Electrical resistivity for Ekpoma was used as a case study for Tertiary sediments. The resistivity data are shown in Tables 5a and 5b.

Table 5a: Resistivity data for Ekpoma (VES1) Synthetic Synthetic Difference Spacing (m) Data Resistivity Potential Difference S/N Resistivity (Ohm- (Ohm-m) (V) AB/2 MN m) 1 2.00 0.500 158.0 154.60 2.11 2 6.00 1.00 158.0 173.1 -9.55 3 8.00 1.00 181.0 178.9 1.10 4 12.00 1.00 204.0 188.2 7.70 5 15.00 2.00 212.0 194.8 8.07 6 25.00 2.00 240.0 223.2 6.99 7 32.00 2.00 241.0 251.4 -4.31 8 40.00 5.00 251.0 291.1 -15.98 9 60.00 5.00 360.0 410.7 -14.10 10 100.0 10.00 656.0 670.7 -2.25 11 150.0 10.00 1056.0 989.7 989.7 12 200.0 10.00 1360.0 1296.1 6.27 13 250.0 15.00 1750 1588.8 9.21 14 300.0 15.00 2100 1866.7 11.10 15 350.0 15.00 2411 2129.3 11.68 16 400 15.00 2836.0 2376.2 16.21 17 450.0 15.00 2513.0 2607.3 -3.75 18 500.0 20.00 2237.0 2822.5 -26.17

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Table 5b: Geoelectric parameters and inferred lithology at Ekopma [VES1] Apparent Resistivity Layer No Thickness (m) Lithology (Ohm-m) 1 150 1.56 Topsoil 2 181 2.83 Sub topsoil 3 195 23.23 Clayey layer 4 29305 236.1 Dry/resistive sandy layer 5 492 - Saturated sand layer

Results in Table 5a and 5b were interpreted, using sounding curve as shown in Figure 3.

(a)

10000

1000

EKPOMA VES Apparent Resistivity (ohmm) Resistivity Apparent

100 1 10 100 1000 Spacing (m)

(b)

100

1,000

10,000

Resistivity (ohmm) Resistivity

100,000 1000 100 10 1

Depth (m)

Figure 3: Resistivity curve for Ekpoma (VES1)

Table 5a and 5b show the interpreted VES data as well as the inferred lithology for Ekopma. This indicated that five geo-electric layers were revealed for Ekpoma with the fifth layer indicating the aquifer (i.e. saturated sand layer).

Table 6 presents the electrical resistivity data in cretaceous sediments.

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Table 6: Electrical resistivity data in cretaceous sediments S/N Location Data Resistivity (Ohm-m) 1 Afuze 129 2 Usen 102 3 Ugbogui 65 4 Uzebba 163 5 Agennobode 98 6 Auchi 150 7 Ovbiomu 150 8 Iyakpi 195 9 Egori 254

In the case of cretaceous sediments, electrical resistivity at Auchi is presented as shown in Tables 7a and 7b.

Table 7a: Resistivity data for Auchi at VES 2 Potential Synthetic Difference Spacing (m) Data Resistivity Synthetic Resistivity No Difference (Ohm-m) (Ohm-m) AB/2 MN (V) 1 1.00 0.200 636.0 608.0 4.33 2 1.47 0.200 572.0 623.2 -8.96 3 2.15 0.200 700.0 668.9 4.43 4 3.16 0.200 800.0 783.3 2.07 5 4.64 0.200 947.0 984.0 -3.91 6 6.81 1.00 1213.0 1228.4 -1.27 7 10.00 1.00 1400.0 1425.9 -1.85 8 14.70 1.00 1600.0 1466.5 8.33 9 21.50 1.00 1262.0 1293.5 -2.49 10 31.50 5.00 961.0 996.2 -3.66 11 46.40 5.00 809.0 792.7 2.00 12 61.80 5.00 788.0 779.8 1.03 13 100.0 10.00 954.0 970.5 -1.73 14 133.0 10.00 1153.0 1153.9 -0.0821 15 178.0 10.00 1304.0 1352.0 -3.68 16 237.0 10.00 1512.0 1507.7 0.279 17 316,0 10.00 1697.0 1566.0 7.70 18 422.0 10.00 1433.0 1474.0 -2.86 19 562.0 10.00 1207.0 1222.8 -1.31

Table 7b: Geo-electric parameters and inferred lithology at Auchi (VES 2) Apparent Resistivity LAYER NO Thickness (m) Lithology (Ohm-m) 1 602 1.38 Topsoil 2 227 0.3 Sub topsoil 3 4073 4.54 Sandstone layer 4 260 7.45 Sandy/clayey layer 5 719 38.98 Sandy/Sandstone layer 6 5231 97.5 Dry sandy/Sandstone 7 155 ˂ 150 Saturated sand layer

Results in Table 7a and 7b are interpreted, using sounding curves as shown in Figure 4.

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10000

1000 AUCHISeries1 VES

Apparent Resistivity (ohmm) Resistivity Apparent 2 per. Mov. Avg. (Series1)

100 1 10 100 1000 Spacing (m)

10000

1000 Resistivity (ohmm) Resistivity

100 1000 100 10 1 Depth (m)

Figure 4: Resistivity curve for Auchi

Interpretation of the resistivity data revealed six geo-electric layers with the top of saturated sandy layer below 150 m deep as indicated in Table 7b.

Electrical resistivity data in some selected basement complex locations are summarized in Table 8. The data presented in Table 8 show the erratic nature of aquifers in the basement complex rocks. Thicknesses of overburden and weathered rocks vary widely from location to location. Data obtained from Oluma-Otuo and Iyeu-Otuo which is barely 2 km apart revealed a great deal of variation. At Ukhuse-Oke, weathered rocks are absent and the overburden provides the only aquifer in the location.

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Table 8: Electrical resistivity data in the basement complex Location Thickness (m) of probable aquifer Inferred Lithology Igarra 19.52 Overburden 33. 14 Weathered Zone Below 52.66 m Fresh basement rock Ikiran-Ile 7.01 Overburden 6.12 Weathered Zone Below 13.13m Fresh basement rock Okpilla 8.09 Overburden 33.50 Weathered Zone Below 41.59 m Fresh basement rock Ugboshi-Ele 9.17 Overburden 8.28 Weathered Zone Below 17.45 m Fresh basement rock Ukhuse – oke 34.22 Overburden 0 Weathered Zone Below 34. 22 m Fresh basement rock Oluma-Otuo 4.0 Overburden 31.0 Weathered Zone Below 35. 0 m Fresh basement rock Iyeu-Otuo 5.75 Overburden 23.5 Weathered Zone Below 29.25 m Fresh basement rock

The results of the borehole drilled in selected location within the state are presented in Table 9. The groundwater levels show the direct relationship between topography of the borehole location and depth to static water level. The groundwater level is deepest in Ishan Plateau and also has the highest altitude. Ekpoma is on an elevation of about 342 m above the sea level while the groundwater is 176 m below ground surface. Away from the Plateau, groundwater rises towards all directions, North, East, West and South. To the North-East of the Plateau, at Idoa, the water level is 96 m below ground surface and to the South-East at Ogua, the water level below ground surface is 120 m. To the South- West at Ehor, the water level is 122 m below ground surface.

In the Benin Formation, groundwater level is low in Urhonigbe (7 m) and high in Ekiadolor (78 m), Aduwawa/Ikpoba Hill (72 m), Iguobazuwa (81m) and Iguiye (70 m). These are areas with the highest elevation in the Benin Formation. In Southern part of the Benin Formation, groundwater level is low in Ologbo (7 m) and a bit high in Abudu (32 m), Iyanomo (17 m) which also follows the pattern of topography. In the Creteceous sediments, groundwater levels are deepest in Ogbona (190 m), Fugar (133 m) and Afuze (58 m). It was observed that in the Cretaceous, some boreholes are either semi-artesian or fully artesian. The boreholes at Sabongidda-Ora are fully artesian where groundwater flows freely out of the borehole to as much as 2-3 m above ground surface. It was also found that a borehole drilled at Sabongidda-Ora/Afuze road junction has been flowing freely since it was drilled (more than 2 years ago). At Uzebba and parts of Auchi, ground waters are semi-artesian.

Figure 5 show groundwater level of Edo State (3-Dimension). The map was plotted using the groundwater levels obtained from the borehole data in some selected areas of Edo State (see Table 9). The occurrence of groundwater in the Basement Complex areas does not lead to the plotting of the groundwater contour map. This is because the aquifers are localized and rarely have hydraulic contact with one another. For this reason, the basement complex areas of the state were not covered by groundwater contours.

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Table 9: Summary of borehole data at selected area in Edo State S/N Borehole Elevation Total Final Water Yeild Draw Specific Locality (ASL) Depth Water Level (m3/h) Down (m) Capacity (m) (m) Level (m) (ASL) (m3/h/m) (m) 1 Aduwawa/ 102 148.3 72 40 136 4.05 33.6 Ikpoba Hill 2 Esigie 90 111.5 56.07 33.9 109.8 3.1 35.4 St. Emmanuel 3 Iyaro 93 114.3 52.6 40.4 71.0 1.87 37.9 4 New Benin 92 143.3 54.5 37.5 - - - 5 Guiness 78 87 43.6 34.4 108.3 3.1 34.9 6 Edo Textile Mill 62 102 55.5 6.5 67.0 7.5 8.9 7 Obayantor 59 85.7 34.3 24.7 45.8 2.8 16.4 8 Iyanomo 31 106.5 17.00 14.00 23.8 4.7 5.01 9 Abudu 59 75.4 31.5 27.5 76.6 3.1 24.7 10 Ologbo 10 98 6.5 3.5 43.1 4.0 10.8 11 Urhonigbe 41 57 7.00 3.4 41.6 4.0 10.3 12 Okada Dry 87 114.0 51.7 35.3 79.5 3.1 25.6 13 Ugoneki 95 95.3 56.7 38.3 17.2 1.6 10.8 14 Iguobazuwa 138 117.8 81.0 57 40.1 3.7 10.8 15 Iguiye 125 110.6 69.8 55.2 41.4 5.0 8.3 16 Ogan 155.7 160.1 101 54.7 30.1 2.5 12 17 Ugo 98 155.7 63.6 34.4 55.1 1.2 46 18 Okhuo - 141.7 18.7 - 45.4 3.1 14.6 19 Ogba 64 81 3.1 60.9 76.7 4.4 17.4 20 Iguovbiobo 109 79 64 45 52.2 1.6 32.6 21 Ehor 260 204.7 122 138 45.8 2.5 18.3 22 Ugieghudu 247 208.7 119 128 - - - 23 Iruekpen 337 - 171 166 - - -

24 Igieduma 259 - 122 137 - - - 25 Ekpoma 342 350 171 171 - - - 26 Evboerhen 214 - 91 123 - - - 27 Idoa 238 138 96 142 - - - 28 Ogua 250 200 120 130 - - - 29 Igbanke 224 220 119.6 104.4 37.2 7.2 5.2 30 Ugboha 146 168.5 86 60 37.9 20 1.9 31 Eguaholor 187 213 112 77 13.2 14.6 0.9 32 Afuze 88 140.8 57.6 30.4 55.1 3.7 14.9 33 Ihievbe - 124.6 43.6 - 9.6 21.8 0.44 34 Auchi 224 203 96.6 126 41.3 31.1 1.3 35 Agenebode 79 169 114 -35 40 7.2 5.5 36 Fugar 218 175 133 95 26.5 1.6 16.6 37 Uzebba - 127 Artesian 76.6 11.8 6.5 38 Sabongidda-Ora 94 141 Artesian 76.6 8.7 8.8 39 Ogbona 249 221 190 59 56.8 12.5 4.5 40 Oben - 94 31 - 20 6.2 3.2 41 Sobe - 148 43 - 15.5 31.7 0.5 42 Ekenwan Army 58 140 40 18 - - - Barracks 43 Amedokhian - 296 199 - - - - (Uromi) 44 Udo 120 1.31 72 48 26.5 6.2 4.3 45 Ekiadolor - 162 78 - - - -

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Figure 5: Groundwater level map of Edo State in 3-Dimension

4.0. Conclusion

The mapping of aquifers in Edo State has led to the collection, collation and analysis of relevant information and data required in the successful development of groundwater in the state. The study has revealed that drilling should not be done on a trial and error basis, but has to be guided by available geological and hydrogeological data. Therefore, it is necessary to continue to improve on the data base for groundwater development in the state. To achieve this, it is recommended that the State Government should compel all groundwater developers to lodge their data with the State Urban Water

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Board. The Benin Owina River Basin Development Authority should also be encouraged to maintain a data bank for groundwater resources.

References Ako B. D. and Olorunfemi, M. O. (1989). Geoelectrical Survey for Groundwater in the Newer Basals of Vom, Plateau State. Journal of Mining and Geol., 10, pp. 23-30.

Alabi, A. A., Bello, R., Ogungbe, A. S. and Oyerinde, H. O. (2010). Determination of Groundwater Potential in Lagos State University, Ojo; using geoelectric methods (Vertical electrical sounding and horizontal profiling). Report Opinion, 24, pp. 68-75. Anomohanran, O. (2011a). Underground Water Exploration of Oleh, Nigeria Using the Electrical Resistivity Method. Scientific Res. Essays, 6, pp. 4295-4300. Anomohanran, O. (2011b). Determination of Groundwater Potential in Asaba, Nigeria Using Surface Geoelectric Sounding. Int. J. Physical Sci., 6, pp. 7651-7656. Benin Kingdom/ Edo State Weather, (2018), [Online], Available: http://www.edoworld.net/Edotourismweather.html [Accessed: 14th September, 2018]. Egbai, J. C. (2012). Geoelectric Evaluation of Groundwater Potential in the Sedimentary Region of Abavo, Delta State and Urhonigbe, Edo State, Nigeria. International Journal of Research and Review in Sciences, 10(3), pp. 491. Egbai, J. C., Efeya, P. and Iserhien-Emekeme, R. E. (2015). Geoelectric Evaluation of Aquifer Vulnerability in Igbanke, Orhionmwon Local Government Area of Edo State, Nigeria. International Journal of Science, Environment and Technology, 4(3), pp. 701-715. Emenike, E. A. (2000). Geophysical Exploration for Groundwater in a Sedimentary Envrionment: A case study from Nanka over Nanka Formation in Anambra Basin, Southeastern, Nigeria. Global Jour. of Pure and Applied Sciences. 7(1), pp. 97-110.

Kogbe, C.A. (1989). Geology of Nigeria, 1st Edition, Rock View (Nig.) Ltd., Plot 1234, Zaramaganda, Km.8, Yakubu Gowon Way, Jos, Nigeria. Map of Edo State (2016), [Online], Available: https://www.nigeriagalleria.com/Nigeria/States_Nigeria/Edo/Edo_State.html [Accessed: 26th September, 2016]. Oyedele, K. F. (2001). Geo-Electric Investigation of Groundwater Resources at Onibode Area, Near Abeokuta South-West Nigeria, pp. 501-504. Peter, O. O. (2013). Groundwater Potential Evaluation and Aquifer Characterization Using Resistivity Method in Southern Obubra, Southeastern Nigeria. International Journal of Environmental Sciences, 4, pp. 96-105. Stockmarr, J. and Thomsen, R. (2012). Water Supple in Denmark. The Danish Action Plan for Promotion of Eco-efficient Technologies – Danish Lessons. Danish Ministry of the Environment.

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ISSN (Print): 2616-051X | ISSN (electronic): 2616-0501

Vol 2, No. 1 March 2018, pp 130 - 136

Non-Linear Error Functions Approach to Kinetic Study of Arsenic Removal from Soils using Proteus mirabili and Bacillus subtilis

1, 1 1 2 1 Atikpo, E. *, Agori, J.E. , Iwema, E.R. , Michael, A and Umukoro, L.O. 1Department of Civil Engineering, Faculty of Engineering, Delta State University, Abraka, Delta State, Nigeria 2Department of Animal and Environmental Biology, Faculty of Sciences, University of Benin, Edo State, Nigeria Corresponding Author: *[email protected]

ABSTRACT

Kinetics of arsenic (As) removal by Proteus mirabilis and Bacillus subtilis were studied using the non-linear error functions approach. The two microorganisms (from soils samples obtained from a contaminated site in Amaonye-Ishiagu in Ebonyi State of Nigeria) were cultured and employed to deplete the metal ion from the contaminated soils. The experimental data were studied with four kinetic models namely pseudo first order, pseudo second order, simple Elovich, and intraparticle diffusion models using some non-linear error functions of root mean square error (RMSE), standard error of experiment (SEE), average relative error (ARE) and normalized standard deviation (NSD). Data from removal by both organisms were best described with pseudo first order model indicated by the ARE of minimum value of -1.8728 for Proteus mirabilis, and -2.1208 for Bacillus subtilis. These showed that the removal mechanism was reaction controlled as chemisorption was the rate limiting step.

Keywords: Arsenic, Bioremediation, Error functions, Kinetic models

1.0. Introduction

The commonest heavy metals seen at contaminated sites are lead (Pb), chromium (Cr), arsenic (As), zinc (Zn), cadmium (Cd), copper (Cu), and mercury (Hg) in their order of abundance (USEPA, 1996). These metals are significant since they have the capabilities to decrease crop production by reason of the risk of biomagnification and bioaccumulation in food chain; and risk of contamination to surface and groundwater (Wuana and Okieimen, 2011).

Heavy metals laden sediment causes many health and ecological related problems. These contaminants have become one of the vital ecological problems of health consequence (Aktan et al., 2013). Exposure to arsenic in early life is connected with increased risk of adverse health effects that can remain till adulthood (Smith et al., 2006; Steinmaus et al., 2014; Bailey et al., 2016). This was the case of the nearly 50-fold raised standardized mortality ratio of people with bronchiectasis in a number of young adults in Chile who got exposed to high levels of As in utero and during childhood compared with mortality rates of the rest population of Chilean (Smith et al., 2006).

Close to every organ in the body of human being is readily accessible to arsenic exposure, with many health consequences like lung disease, diabetes, skin lesions and cancer (Naujokas et al., 2013). Patients of chronic arsenic exposure, show inflammation of the respiratory membrane; cardiomyopathy, skin disease, destruction of red-blood corpuscle; leucopenia; jaundice; renal damage; ataxia; irrational speech, paralysis; poor memory and degeneration of inner ear (Athar and Vohora, 2006).

Bioremediation is regarded as one of the safer, cost effective and ecological suitable technology for treating sites which are polluted with diverse pollutants. Thus, it has become the most effectual management means to control environmental pollution and restore polluted soils to their natural status

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(Kulshreshtha et al., 2014). It involves the use of biological agents like bacteria, algae, fungi and higher plants to eliminate toxic bodies from the environment (Kulshreshtha et al., 2014). Bioremediation is an innovative technology for heavy metals removal from polluted lands by microorganisms through different mechanisms such as biosorption, biotransformation, bioaccumulation and biomineralization (Girma, 2015).

This work was carried out to study biosorption and the kinetics of removal of arsenic ion by Proteus mirabilis and Bacillus subtilis. The kinetics of removal were analyzed using pseudo first order; pseudo second order (Ho et al., 2000); simple Elovich (Chien and Clayton, 1980); and intraparticles diffusion models by concentrating on the non-linear error functions to compensate for the altered error structure and the violated normality assumption during linearization of the mathematical kinetic models. This is arising from the difficulty in establishing the accuracy of kinetic models.

In most studies, linear regression has been used for this purpose because of its simplicity. The linear least-square approach has also been used with linearized kinetic equation with a reliance on the coefficient of determination close to or equal to one (1) in establishing accuracy of fitted models (Ho, 2006). This approach was characterized with alteration of error structures and normality assumption which has led to a later development of the use of non-linear optimization modeling with error functions intervention to restore normalcy to the altered model structure (Kumar and Porkodi, 2007; Passos et al., 2008).

Therefore, selecting the best fitted kinetic model for this work necessitated the use of some non-linear error functions such as root mean square error (RMSE), standard error of experiment (SEE), average relative error (ARE) and normalized standard deviation (NSD).

2.0. Materials and Methods

2.1. Reagents and materials The materials and reagents used for this research work are presented in Table 1.

Table 1: Materials and reagents for the study Materials Reagents 1. Magnetic stirrer, 2. Hot plate, 3. Whatman filter paper, 4. 1. MacConkry agar, 2. Safranin, 3. Kovac’s reagent, 4. Autoclave, 5. Refrigerator, 6. MacCartney bottles, 7. Sodium hydroxide, 5. Nutrient agar, 6. Hydrochloric acid, 7. Incubator, 8. Wire loops, 9. Measuring cylinder, 10. Atomic Oxidase reagent, 8. Triple sugar iron agar, 9. Sulphuric acid, absorption spectrophotometer, 11. Cotton wool, 12. 10. Methylene blue, 11. Peptone water, 12. Ethanol, 13. Inoculating needles, 13. Petri dishes, 14. Microscope, 15. Hydrogen peroxide, 14. Simon citrate ager, 15. Crystal Conical flasks, 16. Beakers, 17. Pipettes, 18. Soil samples violent, 16. Perchloric acid, 17. Potato dextrose agar, 18. Nitric acid, 19. Lugo’s iodine

2.2. Preparation of nutrients The solutions of potato dextrose agar, triple sugar iron agar, nutrient agar, Simon Citrate agar, MacConkey agar and peptone water powder were prepared in accordance with the instruction given by the manufacturers and by following the methods of Cheesebrough (2000). Thirty-nine (39), sixty- five (65), twenty-eight (28), twenty-four (24), fifty-two (52), and fifteen (15) grams of their respective powered were measured into one litre each of distilled water. The mixtures were left for 10 minutes and swirled to allow for dissolution before autoclaving for 15 minutes at temperature of 121°C and pressure of 1.5 psi; and then allowed to cool to 45°C before usage.

2.3. Distinguishing the organisms Bacteriological analysis was conducted in the laboratory on soils sampled from a village forest at Amaonye –Ishiagu located in Ebonyi State of Nigeria. The soil was diluted serially and 0.1 ml each was obtained from dilutions (10-1, 10-3 and 10-5) and dispensed into sterile Petri-dishes containing nutrient agar and MacConkey agar distinctly using the pure plate method (Cowan, 1993 and Baron et al., 1994).

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The distinct plates of inoculums were incubated at temperature 37°C for 24 hours (Cheesebrough, 2000), and the yielded Colonies were enumerated, recorded, sub cultured and characterized with the techniques of (Cheesebrough, 2000; Cowan and Steel, 1990; Holt, 1994).

2.4. Factors screening Certain factors at their optimal values give vent to optimal bioremediation processes. Initial experimental investigation of these factors is a requirement for selecting their optimal values. These critical factors: pH, temperature (°C), organisms’ weights (g), nutrient dosage (ml) and stirring frequency [per week (pw)] were screened in batches and the experiment conducted in triplicate (Lima et al., 2007; Atikpo and Micheal, 2018).

Nutrient dosage of 1, 2, 3, 4, 5, 6, 7, 8, 9 and 10 ml were introduced separately into thirty (30) beakers (50 ml) containing three grams (3 g) of soil samples each. The soils in each beaker were inoculated with 24 days old Proteus mirabilis and Bacillus subtilis respectively; and the soils residual arsenic ion content was evaluated on the 14th day with Atomic Absorption Spectrophotometer (AAS) (GBC SensAA, Model no. A6358) after centrifuge action to eliminate the organisms from soils.

The procedure was repeated for 1, 2, 3, 4, 5, 6, 7, 8 g of organisms’ masses; 15, 20, 25, 30, 35, 40, 45 and 50°C of temperature; 3, 4, 5, 6, 7, 8, 9 and 10 of pH; and 0, 1, 2, 3, 4, 5 and 6 per week (pw); and their respective optimal values were determined.

2.5. Arsenic removal study Three grams (3 g) of soil samples in each beaker (50 ml) were inoculated with each selected bacterium. This was done for all the thirty beakers needed for triplicate evaluation (Lima et al., 2007; Atikpo, 2016) of the residual metal ion. The samples were conditioned with the factors’ optimum values of 1 g, 8 ml, 30°C, 7 and 6 pw for Proteus mirabilis; and 5 g, 8 ml, 30°C, 8 and 6 pw for Bacillus subtilis. The optimum value without a unit is the pH of the process.

Residual metal ion in soils was determined on the 7th, 14th, 21st, 28th and 35th day using AAS (GBC SensAA, Model no. A6358), after eliminating the organisms from the samples using a centrifuge. The ion removed with time in (mg/kg), ion removed at equilibrium in (mg/kg) and the efficiency of removal in (%) were evaluated using Equations (1) and (2) (Badmus et al., 2007; Chen and Wang, 2007).

(퐶 − 퐶 ) (1) q = 표 푡 . 푉 t 푚 (퐶 − 퐶 ) (2) q = 표 푒 . 푉 e 푚 (퐶표 − 퐶푓) (3) ɛ = . 100 퐶표

C0, Ct, Ce, Cf, qt, qe, V and m are the initial metal ion (mg/kg); residual metal ion with time in mg/kg, residual metal ion at equilibrium in mg/kg, final metal ion (mg/kg), volume (m3) of soil used and the mass (g) of organisms used respectively.

The experimental data were analyzed with some error functions to determine the fit of pseudo-first order, pseudo-second order, simple Elovich and intraparticle diffusion models in Equations (4), (5), (6), (7) respectively (Ho et al., 2000; Chien and Clayton, 1980; Mckay and Poots, 1980) for the study of the kinetics of the metal ion removal from the soils. dq (4) t  kq  q  dt e t dq (5) t  k q  q 2 dt 1 e t

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-1 -1 Where qt is removal capacity with time t in mg.kg ; k is rate constant of pseudo first order model (d ); -1 qe is removal capacity at equilibrium in mg.kg ; k1 is rate constant of pseudo second order model (kg. -1 -1 -1 -1 -1 mg d ); α is the initial removal rate in mg.kg ; β is desorption rate constant in mg.kg d ; and K2 is the rate constant for intra-particle diffusion model.

The error functions computation was carried-out with the solver add-in Microsoft Excel. The relationship of experimental data and time was laid with a polynomial function; and the objective function was minimized with solver at a maximum iteration of 100 seconds and a precision of 0.000001.

3.0. Results and Discussion

Microbial recognition analysis generated the required bacteria from the colony of 2.2 x 107 cfu/ml. The bacteria were identified with biochemical tests results of positive oxidase, negative indole, negative citrate, positive catalase, positive glucose, negative H2S, positive lactose, positive motility and gram positive rods (GPR) in gram stain for Bacillus subtilis; and positive motility, positive catalase, negative indole, negative citrate, positive glucose, negative oxidase, positive lactose, positive H2S and gram negative rods (GNR) in gram stain for Proteus mirabilis.

Proteus mirabilis was found to perform effectively and efficiently at optimum values of 1g of organism’s weight, nutrient dosage of 8 ml, temperature of 30oC, pH of 7, and 6 pw of stirring frequency; while the optimum values of factor for performance by bacillus subtilis were found to be 5g of organism’s weight, 8 ml of nutrient dosage, temperature of 30oC, pH of 8, and 6 pw of stirring frequency.

The specific models for describing the removal of the metal by the organisms were carefully studied by fitting the linear forms of the models and engaging the minimized error functions in scrutinizing the kinetic models. Adopting this approach to the study, the metal removal by Proteus mirabilis was evidently influenced by chemisorption. This was indicated by SEE, NSD, RMSE and the ARE.

Fundamentally, chemisorption governed removal mechanism usually have either a fit of pseudo first order model, pseudo second order model, or the simple Elovich model as the best fit model. However, in this study of As removal by Proteus mirabilis, the simple Elovich model was not among the highlighted, performing models indicated by the error functions. The SEE indicated pseudo second order model, the NSD indicated pseudo second order model, the RMSE indicated pseudo second order model, while the ARE indicated pseudo first order model.

The rate limiting step of this removal is the ultimate requirement of the error function analysis because it gives information on the removal process by the specific organism. Comparison of error functions values displayed in Table 2 was instrumental to deciding which model signified the rate limiting step. The minimum from 0.000159 of SEE, 1.9667 of NSD, -1.8728 of ARE and 0.00128 of RMSE was the focal value for the judgement.

As a rule, the model with the minimum error function values gives the best fit and indicate the rate limiting step of the process. In this instant removal case of As by Proteus mirabilis, pseudo first order model indicated by the ARE with minimum value of -1.8728 gave the best fit, and showed that the removal mechanism was reaction controlled.

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Table 2: Summary of Computed Error Functions for Arsenic Removal by Proteus Mirabilis Micro Organism Error First Order Second Order Elovich Intraparticle Best Model Function Kinetics Kinetics Diffusion Selected SEE 0.0754 0.000159 0.000342 0.000242 Pseudo Second Proteus mirabilis Order NSD 6.9233 1.9667 3.3621 2.2240 Pseudo Second Order ARE -1.8728 0.5456 0.9082 0.5937 Pseudo First Order RMSE 0.3868 0.00128 0.00208 0.00135 Pseudo Second Order

Bacillus subtilis exerted removal influence on the metal ion, and the removal mechanism was chemisorption influenced. The three models for chemical process of removal were highlighted by the error functions utilized. SEE highlighted the simple Elovich model, NSD highlighted Pseudo second order model, RMSE highlighted the pseudo second order model, while ARE highlighted the pseudo first order model for removal. The information on Table 3 showed the values of these functions as 0.000110 for SEE, 1.1658 for NSD, -2.1208 for ARE, and 0.000363 for RMSE. The rate limiting step for this removal as indicated by the model with the minimum error function value is chemisorption which emanated from the pseudo first order model. And the removal mechanism was judged to be reaction controlled.

Table 3: Summary of Computed Error Functions for Arsenic Removal by Bacillus Subtilis Micro Organism Error First Order Second Order Elovich Intraparticle Best Model Function Kinetics Kinetics Diffusion Selected SEE 0.0974 2.3908 0.000110 6.4267 Elovich Bacillus substilis NSD 7.6656 1.1658 2.6620 1.4833 Pseudo Second Order ARE -2.1208 0.3401 0.7312 0.4040 Pseudo First Order RMSE 0.5441 0.000363 0.000748 0.000409 Pseudo Second Order

4.0. Conclusion

Kinetics of arsenic removal by Proteus mirabilis and Bacillus subtilis were studied using the non- linear error functions approach. Four models namely pseudo first order, pseudo second order, simple Elovich, and intraparticle diffusion models were evaluated with SEE, NSD, RMSE and the ARE. Data from removal by both organisms were best fitted with pseudo first order model indicated by the ARE of minimum value of -1.8728 for Proteus mirabilis, and -2.1208 for Bacillus subtilis. These showed that the removal mechanism was reaction controlled as chemisorption was the rate limiting step.

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