CONTRIBUTION OF INERT WASTE IN DETERIORATING URBAN AMBIENT AIR QUALITY
by
Khalid Iqbal (2010-NUST-TfrPhD-ENV-58)
Institute of Environmental Sciences & Engineering (IESE) School of Civil and Environmental Engineering (SCEE) National University of Sciences & Technology (NUST) Islamabad, Pakistan (44000) (2016)
i
CONTRIBUTION OF INERT WASTE IN DETERIORATING URBAN AMBIENT AIR QUALITY
by
Khalid Iqbal (2010-NUST-TfrPhD-ENV-58)
A thesis submitted in partial fulfillment of the requirements for the degree of
Doctor of Philosophy
in
Environmental Engineering
Institute of Environmental Sciences & Engineering (IESE) School of Civil and Environmental Engineering (SCEE) National University of Sciences & Technology (NUST) Islamabad, Pakistan (44000) (2016)
ii
APPROVAL SHEET
Certified that the contents and form of thesis titled “Contribution of Construction Inert Waste in Deteriorating Urban Ambient Air Quality” submitted by Mr. Khalid Iqbal have been found satisfactory for the requirement of the degree.
Supervisor: ______Professor (Dr. Muhammad Anwar Baig)
Member: ______Associate Professor (Dr. Sher Jamal Khan)
Member: ______Associate Professor (Dr. Muhammad Arshad)
External Examiner: ______Name: Dr Nawazish Ali Designation: Principal Engineer Department: DD&CE-in C’sB & GHQ, Rawalpindi
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DEDICATION
This work is dedicated to my beloved parents and rest of the members of my family and friends! It is their support and love that enabled to complete this task.
iv
DECLARATION
I hereby declare that this dissertation is the outcome of my own efforts and has not been published anywhere else before. The matter quoted in the text has been properly referred and acknowledged.
______Khalid Iqbal (2010-NUST-TfrPhD-ENV-58)
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ACKNOWLEDGEMENTS
Islam prohibits all sorts of mischief in the land. Allah says, “That if anyone slew a person –unless it be for murder or for spreading mischief in the land – it would be as if he slew the whole people.” - (Maida: 32)
Many Islamic experts pointed out that types of mischief include tree felling and all types of pollution, including solid waste, in view of the fact that they cause death.
The Prophet (P. B. U. H) prohibited causing damage and inflicting it on others. He said, “No harm and no inflicting harm”, and “who caused harm, Allah shall inflict harm on him,” - Narrated by Ibne Majja and Abu Dawud.
I would like to express the deepest appreciation to my committee chair, Professor
Dr Muhammad Anwar Baig, Head of Department of Environmental Sciences,
IESE, SCEE, NUST, who has the attitude and the substance of a genius: he continually and convincingly conveyed a spirit of adventure in regard to research and scholarship, and an excitement in regard to teaching. Without his guidance and persistent help this dissertation would not have been possible.
I would like to thank my committee members, Dr Sher Jamal Khan, Dr
Muhammad Arshad and Dr Nawazish Ali, whose work demonstrated to me that concern for global affairs supported by an “engagement” in comparative literature and modern technology should always transcend academia and provide a quest for our times.
May Allah bestow strength and contentment to all these splendid celebrities!
(Aamin)!
Khalid Iqbal
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TABLE OF CONTENTS
APPROVAL SHEET...... iii DEDICATION ...... iv DECLARATION ...... v ACKNOWLEDGEMENTS ...... vi TABLE OF CONTENTS ...... vii LIST OF ABBREVIATIONS / ACRONYMS ...... xii LIST OF TABLES ...... xiii LIST OF THE FIGURES...... xiv ABSTRACT ...... 1 Chapter 1 ...... 4 1. INTRODUCTION ...... 4 1.1. CONSTRUCTION INDUSTRY ...... 4 1.1.1. Role of Construction Activities...... 5 1.1.2. Global Situation of Construction Industry and Employment ...... 5 1.1.3. Economic Impact of Construction Sector in Pakistan ...... 6 1.1.4. Construction Waste...... 6 1.1.5. Economic Aspects of Construction Waste Materials...... 7 1.1.6. Construction Waste Generation ...... 9 1.1.7. Impacts on Environment and Human Health...... 9 1.2. PROBLEM STATEMENT...... 11 1.2.1. Assessment of Construction Waste Generation ...... 11 1.2.2. Physico-chemical Characteristics of SPM...... 12 1.2.3. Prediction of SPM Concentration at Varying Distances ...... 13 1.3. OBJECTIVES ...... 14 1.4. BENEFITS OF THE STUDY...... 15 1.5. SCOPE OF WORK ...... 16 1.5.1. Quantitative and Qualitative Assessment of Construction Waste ...... 16 Chapter 2 ...... 17 2. REVIEW OF THE LITERATURE ...... 17 2.1. CONSTRUCTION WASTE CHARACTERIZATION...... 17 2.1.1. Construction Waste Generation ...... 17 2.1.2. Types of Construction Waste...... 20
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2.1.3. Composition of Construction Waste...... 21 2.1.4. Reasons and Sources of Construction Waste...... 22 2.2. SPM CHARACTERIZATION ...... 23 2.2.1. Methods for Particulate Matter Sampling ...... 23 2.2.2. Concentration of Suspended Particulate Matter ...... 26 2.2.3. Composition of Suspended Particulate Matter...... 29 2.3. ATMOSPHERIC DISPERSION MODELS...... 31 2.3.1. Gaussian Air Pollutant Dispersion Equation ...... 32 2.3.2. Briggs Plume Rise Equations...... 34 2.3.3. Other advanced atmospheric pollution dispersion models ...... 35 2.3.3.1. ADMS 3 ...... 35 2.3.3.2. AERMOD...... 35 2.3.3.3. DISPERSION21 ...... 36 2.3.3.4. ISC3 ...... 36 2.3.3.5. Operational Street Pollution Model (IOSPM) ...... 36 2.4. STATISTICAL MODELS ...... 37 Chapter 3 ...... 41 3. MATERIALS AND METHODS ...... 41 3.1. CONSTRUCTION WASTE MATERIAL ...... 42 3.2. PREDICTION OF SPM CHARACTERISTICS...... 43 3.2.1. Site Selection ...... 43 3.2.2. Time and Duration of Samples Collection ...... 44 3.2.3. Fine Inert Sample Collection ...... 44 3.2.4. Particulate Matter Sampling ...... 45 3.2.5. Physicochemical Analysis of Inert Material ...... 45 3.2.6. pH and Electrical Conductivity ...... 47 3.2.7. Metals Analysis of Inert Material...... 47 3.2.8. Ions Analysis in Inert Material...... 50 3.3. Physicochemical Analysis of Suspended Particulate Matter ...... 51 3.3.1. pH and electrical conductivity ...... 51 3.3.2. Trace Metals Analysis in Particulate Matter...... 52 3.3.3. Ions Analysis in Particulate Matter ...... 52 3.4. Statistical Analysis ...... 52 3.4.1. Dependent and independent variables...... 54 3.4.2. Statistical Data Treatment ...... 54 3.4.3. Confirmatory Tests ...... 54
viii
3.4.4. Regression Models ...... 55 3.4.5. Validation of the Models ...... 55 3.5. SPM MONITORING AT METRO PROJECT SITE ...... 55 3.6. PREDICTION OF SPM CONCENTRATION AT VARYING DISTANCES ...... 57 3.6.1. Site Selection ...... 57 3.6.2. Time and Duration of SPM Samples Collection ...... 58 3.6.2.1. Lahore City ...... 58 3.6.2.2. Gujrat City ...... 58 3.6.2.3. Kharian City...... 58 3.6.3. Particulate Matter Monitoring...... 59 3.6.3.1. Meteorological data ...... 60 3.6.4. Particulate Matter Comparison ...... 60 3.6.5. Statistical Analysis ...... 60 3.6.5.1. Dependent and independent variables...... 61 3.6.5.2. Statistical data treatment ...... 61 3.6.5.3. Confirmatory tests ...... 61 3.6.6. Regression Models ...... 62 3.6.7. Validation of the Models ...... 62 Chapter 4 ...... 63 4. RESULTS AND DISCUSSION...... 63 4.1. CONSTRUCTION WASTE ASSESSMENT ...... 63 4.2. PREDICTION OF SPM CHARACTERISTICS...... 75 4.2.1. Correlations Analysis...... 76 4.2.2. Linear Regression Analysis: ...... 76 4.2.3. Data Normality Tests: ...... 84 4.2.4. Statistical Regression-Based Models ...... 84 4.2.5. Validity of the models...... 88 4.3. SPM MONITORING AT RAWALPINDI ISLAMABAD METRO PROJECT SITE ...... 90 4.4. COMPARISON OF THE SUSPENDED PARTICULATE MATTER CONCENTRATIONS ...... 91 4.4.1. Lahore City ...... 91 4.4.2. Gujrat City ...... 95 4.4.3. Kharian City...... 103 4.5. STATISTICAL MODELS FOR PREDICTION OF PM CONCENTRATIONS AT VARYING DISTANCES ...... 108
ix
4.5.1. Correlation Analysis ...... 108 4.5.2. Linear Regression Analysis ...... 114 4.5.3. Data Normality Tests: ...... 116 4.5.4. Statistical Regression-Based Models ...... 116 4.5.5. Validity of the models...... 119 4.6. GEOGRAPHICAL BOUNDARIES...... 120 4.7. LIMITATIONS ...... 121 Chapter 5 ...... 122 5. CONCLUSIONS AND RECOMMENDATIONS ...... 122 5.1. CONCLUSIONS ...... 122 5.2. RECOMMENDATIONS ...... 123 Chapter 6 ...... 125 6. REFERENCES ...... 125 APPENDIX...... 147 LIST OF PUBLICATIONS ...... 147 ANNEXURE - I...... 148 ANNEXURE – II...... 151 ANNEXURE – III ...... 152 ANNEXURE – IV ...... 153 ANNEXURE VI ...... 155 ANNEXURE – VII ...... 156 ANNEXURE – VIII...... 157 ANNEXURE – IX ...... 158 ANNEXURE - X ...... 159 ANNEXURE - XI ...... 160 ANNEXURE – XII ...... 161 ANNEXURE – XIII...... 162 ANNEXURE – XIV ...... 163 ANNEXURE – XV ...... 164 ANNEXURE – XVII...... 166 ANNEXURE – XVIII ...... 167 ANNEXURE – XIX ...... 168 ANNEXURE – XX ...... 169 ANNEXURE – XXI ...... 170
x
ANNEXURE – XXII...... 171 ANNEXURE – XXIII ...... 172 ANNEXURE – XXIV...... 173 ANNEXURE – XXV ...... 174 ANNEXURE – XXVI...... 175 ANNEXURE – XXVII ...... 176 ANNEXURE – XXVIII ...... 177 ANNEXURE – XXIX...... 178 ANNEXURE – XXX ...... 179
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LIST OF ABBREVIATIONS / ACRONYMS
ADS Asian Dust Storm
ANOVA Analysis of Variance
C&D Construction and Demolition Waste
DETR Department of Environment Transport and the Regions
EC Electrical Conductivity
EPA Environmental Protection Agency
EWC European Waste Catalog
ICW Inert Construction Waste
ISO International Organization of Standardization
MSW Municipal Solid Waste
NEQS National Environmental Quality Standards
PAK-EPA Pakistan Environmental Protection Agency pH Power of Hydrogen
PM Particular matter
POP Plaster of Paris
SPM Suspended particular matter
SPSS Statistical Package for Social Sciences
T&V Theft and vandalism
UK United Kingdom
US EPA United States Environmental Protection Agency
US United States
WGR Waste Generation Rate
xii
LIST OF TABLES
Table 2-1: Particulate matter concentration in various Asian cities ...... 27 Table 4-1: Quantitative assessment of cutting waste at construction sites ...... 65 Table 4-2: Quantitative assessment of Theft & Vandalism Waste at construction sites...... 67 Table 4-3: Quantitative assessment of Transit Waste at construction sites ...... 69 Table 4-4: Quantitative assessment of Applications Waste at construction sites ... 71 Table 4-5: Overall mean percentage of waste categories on construction sites...... 73 Table 4-6: Reasons and source identification for each kind of waste...... 74 Table 4-7: Pearson correlation (two tailed) between various physico-chemical characteristics of inert material and particulate matter ...... 75 Table 4-8: Regression analysis (ANOVA) of physico-chemical characteristics .... 81 Table 4-9: Statistical Regression-based models (y = a + b.x) for determination of various ...... 87 Table 4-10: Validation of the regression based models by comparing estimated and actual values at a new construction site ...... 89 Table 4-11: Pearson correlations (two tailed) between concentrations of ...... 109 Table 4-12: Regression analysis (ANOVA) of particulate matter concentrations 115 Table 4-13: Statistical regression-based models (y = a + b.x) for determination of particulate matter...... 118 Table 4-14: Validation of the regression based models by comparing estimated and actual values at a new construction site ...... 120
xiii
LIST OF THE FIGURES
Figure 3-1: Pattern for inert material sampling from ground at the construction site ...... 46 Figure 4-1: Percentage of respondents in the survey ...... 63 Figure 4-2: Percentage of educational qualification of the respondents ...... 66 Figure 4-3: Comparison of physico-chemical characteristics of fine inert construction waste and suspended particulate matter...... 78 Figure 4-4: Relationship between pH value of SPM and construction...... 79 Figure 4-5: Relationship between electrical conductivity of SPM and ...... 79 Figure 4-6: Relationship between concentration of Al observed in the inert waste dumped and SPM collected samples ...... 80 Figure 4-7: Relationship between concentration of Ca observed in the inert waste dumped and SPM collected samples ...... 80 Figure 4-8: Relationship between concentration of Ni observed in the inert waste dumped and SPM collected samples ...... 81 Figure 4-9: Relationship between concentration of Fe observed in the inert waste dumped and SPM collected samples ...... 82 Figure 4-10: Relationship between concentration of Zn observed in the inert waste dumped and SPM collected samples ...... 82 Figure 4-11: Relationship between concentration of SO4-2 observed in the inert waste dumped and SPM collected samples ...... 83 Figure 4-12: Relationship between concentration of NO3-1 observed in the inert waste dumped and SPM collected samples ...... 83 Figure 4-13: Relationship between concentration of Cl-1 observed in the inert waste dumped and SPM collected samples ...... 84 Figure 4-14: Comparison of SPM Concentrations at five Metro Project Sites...... 90 Figure 4-15: Comparison of SPM concentrations at varying distances at Lahore construction site during 01-07 January 2014...... 92 Figure 4-16: Comparison of PM10 concentrations at Lahore construction site at varying distances during 01-07 January 2014 ...... 92 Figure 4-17: Comparison of PM2.5 concentrations at Lahore construction site at varying distances during 01-07 January 2014 ...... 93 Figure 4-18: Comparison of SPM concentrations at Lahore construction site at varying distances during 11-17 June 2014 ...... 93 Figure 4-19: Comparison of PM10 concentrations at Lahore construction site at varying distances during 11-17 June 2014 ...... 94
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Figure 4-20: Comparison of PM2.5 concentrations at Lahore construction site at varying distances during 11-17 June 2014 ...... 94 Figure 4-21: Comparison of SPM concentrations at Gujrat construction site at varying distances during 19-25 May 2015 ...... 96 Figure 4-22: Comparison of PM10 concentrations at Gujrat construction site at varying distances during 19-25 May 2015 ...... 97 Figure 4-23: Comparison of PM2.5 concentrations at Gujrat construction site at varying distances during 19-25 May 2015 ...... 97 Figure 4-24: Comparison of SPM concentrations Gujrat construction site at varying distances during 13-19 June 2015 ...... 98 Figure 4-25: Comparison of PM10 concentrations at Gujrat construction site at varying distances during 13-19 June 2015 ...... 99 Figure 4-26: Comparison of PM2.5 concentrations at Gujrat construction site at varying distances during 13-19 June 2015 ...... 99 Figure 4-27: Comparison of SPM concentrations at Gujrat construction site at varying distances during 18-24 August 2015 ...... 100 Figure 4-28: Comparison of PM10 concentrations at Gujrat construction site at varying distances during 18-24 August 2015 ...... 100 Figure 4-29: Comparison of PM2.5 concentrations Gujrat construction site at varying distances during 18-24 August 2015 ...... 101 Figure 4-30: Comparison of SPM concentrations at Kharian construction site at varying distances during 26 May to 01 June 2015 ...... 103 Figure 4-31: Comparison of PM10 concentrations at Kharian construction site at varying distances during 26 May to 01 June 2015 ...... 104 Figure 4-32: Comparison of PM2.5 concentrations at Kharian construction site at varying distances during 26 May to 01 June 2015 ...... 104 Figure 4-33: Comparison of SPM concentrations at Kharian construction site at varying distances during 17-23 November 2015...... 105 Figure 4-34: Comparison of PM10 concentrations at Kharian construction site at varying distances during 17-23s November 2015 ...... 106 Figure 4-35: Comparison of PM2.5 concentrations at Kharian construction site at varying distances during 17-23 November 2015...... 106 Figure 4-36: Regression curve between SPM Conc at 3 m and 8 m distance from the source ...... 110 Figure 4-37: Regression curve between SPM Conc at 3 m and 13 m distance from the source ...... 110 Figure 4-38: Regression curve between SPM Conc at 3 m and 18 m distance from the source ...... 111 Figure 4-39: Regression curve between PM10 Conc at 3 m and 8 m distance from the source ...... 111
xv
Figure 4-40: Regression curve between PM10 Conc at 3 m and 13 m distance from the source ...... 112 Figure 4-41: Regression curve between PM10 Conc at 3 m and 18 m distance from the source ...... 112 Figure 4-42: Regression curve between PM2.5 Conc at 3 m and 8 m distance from the source ...... 113 Figure 4-43: Regression curve between PM2.5 Conc at 3 m and 13 m distance from the source ...... 113 Figure 4-44: Regression curve between PM2.5 Conc at 3 m and 18 m distance from the source ...... 114
xvi
ABSTRACT
Developing countries are exhaustively involved in construction activities as visible from their budgets and ground realities. This involves all sorts of earth material resources from soil, rocks to cables and aluminum channels for building a structure. During such activities, a large amount of construction material is wasted.
This waste not only creates hindrance in solid waste management, but gives ugly look and chokes open drains, while particulate matter (PM) is also generated which causes life-threatening health effects. Therefore, waste and PM are imperative to be monitored in any country in order to improve air quality of its cities. Generally monitoring of suspended particulate matter (SPM) needs sophisticated and costly equipment, highly trained manpower and expensive resources including continuous supply of energy, which the countries like Pakistan lacks. In order to overcome such issues, a study aiming at simple, less expensive and cost effective method to assess the amount of construction waste material generated and resulting SPM based on physio-chemical analysis of waste material was conducted. This has been achieved by the estimation of physico-chemical characteristics of SPM only by determining the same characteristics of fine inert material and the prediction of
SPM, PM10 and PM2.5 concentration away from the source of construction waste generation. In order to carry on, a structured questionnaire was distributed among
800 stakeholders, including civil engineers, architects, quantity surveyors and contractors from large, medium and small cities namely: Lahore, Gujranwala,
Sialkot, Gujrat and Kharian, were approached for the assessment of construction waste. For monitoring physico-chemical analysis of left over waste material and its contribution in local air quality from four construction projects/sites in Lahore,
1
Gujrat, Kharian and Rawalpindi/Islamabad (construction of Metero Bus mega project) was investigated during various seasons (2013 – 2015) and at different construction stages of the project. In order to accomplish the objectives, a total of
168 samples including 84 samples of fine inert material and similar number of samples of corresponding SPM, were collected from the selected construction site in Lahore whereas, a total of 1764 samples, 147 of each SPM, PM10 and PM2.5 at 3,
8, 13 and 18m distance from the source of generation at other three construction sites. Analysis included pH, electrical conductivity trace metals (Al, Ca, Ni, Fe &
-2 - - Zn) and ions (SO4 , NO3 & Cl ) of fine inert material and corresponding PM which were used in developing regression-based statistical models to estimate physico-chemical characteristics of SPM.
The study concluded that construction materials wastage accounted for an average of 9.88% due to poor transportation, error in calculations/cutting, improper storage, over ordering and poor material handling. The PM concentration was observed well beyond the permissible limits (NEQS) at all construction sites, except the sites where recommended measures like watering were being adopted to control the PM generation. The statistical analysis showed highly significant correlation and regression between (i) all the physico-chemical parameters of fine inert material and corresponding PM, and (ii) concentrations of SPM, PM10 and PM2.5 at 3, 8, 13 and 18m at all construction sites, and the linear regression model has been proposed and tested to estimate physico-chemical characteristics of SPM from the corresponding characteristics of fine inert material. The residual error percentage difference of less than 20% in case of estimation of physico-chemical
2 characteristics and less than 10% in case of estimation of concentration at varying distances from source of generation signifies the reliability of proposed model.
3
Chapter 1
1. INTRODUCTION
Ambient air quality refers to the quality of outdoor air, measured near ground level, away from direct sources of pollution in our surrounding environment; while inert waste is defined as the waste which is neither biologically or chemically reactive and will not decompose. Sand and, concrete, blocks, bricks, ceramics, pipes, gravel, sand, soil and stones are included in it.
According to an estimate, 90% of the construction waste is the inert waste, which, at constructions sites, mainly contributes in the generation of the particulate matter - one of the major constituents of the ambient air pollution (EPD HK, 2015).
Therefore, it can be concluded that construction industry and processes significantly contribute in generation of particulate matter in the ambient air (Ingrid et al., 2014).
1.1. CONSTRUCTION INDUSTRY
Construction in any country is a complex sector of the economy, which involves a broad range of stakeholders and has wide ranging linkages with other areas of activity such as manufacturing and the use of materials, energy, finance, labor and equipment (Hillebrandt, 1985).
Construction is a process of making and developing buildings, infrastructure and all related activities are combined termed as construction industry. As an industry, it comprises six to nine percent of the gross domestic product (GDP) of developed countries (Chitkara, 1998). Building construction is generally categorized into residential and non-residential
4
(commercial/institutional), while infrastructure is usually called heavy/highway, which includes bridges, highways, large public works, dams and water/wastewater and utility distribution, which also includes power generation, refineries, mills and manufacturing plants (Halpin and Bolivar, 2010). All type of construction is an important activity in terms of infrastructure and economic development, but is believed to be environmentally unfriendly due to generation of construction waste during various phases (Foo et al., 2013; Babatunde and Olusola, 2012).
1.1.1. Role of Construction Activities
The construction activities have a key role in socioeconomic development of any country with great significance to the attainment of national socioeconomic development goals of providing infrastructure, sanctuary and employment.
Besides, the industry creates considerable employment and supply a growth stimulus to other sectors through backward and forward linkages. Therefore, it is essential that this vital activity is nurtured for the healthy growth of the economy
(Khan, 2008).
1.1.2. Global Situation of Construction Industry and Employment
Globally, construction industry is looked upon as one of the largest fragmented industries. An estimate of annual global construction output is closer to
US $ 8.2 trillion in 2013 (HIS Economics, 2013). The construction industry is also a prime source of employment generation offering job opportunities to millions of unskilled, semi-skilled and skilled workforce. Total construction output worldwide was estimated at just over $3,000 billion in 1998. Output is heavily concentrated
(77 per cent) in the high income countries (Western Europe, North America, Japan
5 and Australasia). The contribution of low and middle income countries was only 23
% of total world construction output (ILO Geneva, 2001).
1.1.3. Economic Impact of Construction Sector in Pakistan
As indicated in Pakistan Economic Survey of 2015, the contribution of construction in industrial sector is above 12 %, while it contributes 2.4 % in the
GDP. The sector offers employment opportunities to more than seven percent of the labor force. This subsector is believed to be one of the potential apparatus of the industries. The construction sector has recorded a growth of 7.05 % in 2015-16 against the growth of 7.25 % last year (2014-15). The seven plus growth in this subsector is owing to speedy and quick execution of work on various projects, enhanced investment in small-scaled construction and brisk accomplishment of development schemes and other projects of federal and provincial governments in
Pakistan (Pakistan Economic Survey, 2015)
1.1.4. Construction Waste
The construction waste is the material wasted in any construction process
(Li and Zhang, 2013), which may typically be defined as the difference between the construction materials ordered and applied in real at any construction site. All the construction processes generate the construction waste, which is the mixture bricks or blocks, concrete or crushed stones, sand, cement, wood, metals and others
(Bakshan et al., 2015).
Around 25%-30% of the total waste generated in the European Union (EU) comprises of construction and demolition waste (CDW), which is produced due to construction or total or partial demolition of buildings and civil infrastructure. It consists mainly of concrete, bricks, gypsum, wood, glass, metals, plastic, solvents,
6 asbestos and excavated soil. Materials produced from land levelling are regarded as construction and demolition waste in some countries (EC, 2016).
The European Waste Catalog (EWC) defines construction waste into eight categories such as tiles, bricks, concrete and ceramics; glass, wood and plastic; bituminous mixtures, coal tar and tarred products; dredging spoil, metals, soil and stones; insulation materials and asbestos-containing materials; etc. However, the
“Directive 2008/98/EC of the European Parliament and of the Council of 19
November 2008 on Waste” excludes “uncontaminated soil and other naturally occurring material excavated in the course of construction activities where it is certain that the material will be used for the purposes of construction in its natural state on the site from which it was excavated.”
In Hong Kong, the construction waste is divided into inert and non-inert construction waste (non-ICW) (Lu et al., 2015).
1.1.5. Economic Aspects of Construction Waste Materials
In most parts of the world, construction industry consumes huge amount of natural resources and often generates large quantities of construction waste (Jain,
2012). Activities like construction, renovation or demolition of structures generate a mixture of inert and non-inert materials which are particularly defined as construction wastes. Statistical data shows, construction and demolition (C&D) debris frequently makes up 10–30% of the waste received at many landfill sites around the world (Fishbein, 1998). Pakistani construction industry is one among the largest as far as economic spending, amount of raw materials and natural resources consumed, quantity of products and materials manufactured, employment created and environmental impacts etc. Due to growth in construction industry, it
7 seems appropriate to develop linkage between construction and demolition (C&D) waste generation and the national and global economic growth related issues. At present, there is lack of awareness about resource-efficient construction practices and techniques (Jain, 2012).
The environment and economy related benefits from waste minimization and recycling are mammoth, as it will benefit both the environment and the industry in terms of cost savings and waste management (Muniraja et al., 2015;
Kamran et al., 2015; Chaudhry and Batool, 2014; Noman et al., 2014; Gutherie et al., 1999).
Least priority to waste minimization and management systems results in generation of enormous amount of material waste every year, which is not only detrimental at environmental level but also in economic terms as waste materials have their specific economic values before getting mishandled. It is economically workable to do significant cost savings from the whole process (Jain, 2012) and adoption at large scale can significantly save huge amount of money.
The undue wastage of construction materials and low awareness about waste reduction are common in the construction sites. In most European countries, it has been cost effectively feasible to recycle up to 80–90% of the total amount of construction waste with easy-to-implement and control recycling technologies
(Lauritzen, 1998). Considering enormous increase in amount of waste generation owing to the growth in construction industry can lead to wastage of materials which has its economic value. Currently, existence of regional and national policies, laws and regulations governing reuse and recycle principles for C&D waste lacks in Pakistan.
8
1.1.6. Construction Waste Generation
Quantity and composition of construction waste keep on changing due to dynamic nature of construction activities (Pinto and Agopyan, 1994) and hence cannot be exactly measured with varying construction methods and practices and specificity and phases of the project (Kern et al., 2015). However, various studes have been carried out for determination of waste generated during various projects and phases of construction.
1.1.7. Impacts on Environment and Human Health
Being important source of pollution locally and globally (Pinto and
Agopayan, 1994), construction activities and waste material cause serious environmental disruption and pollution (Bakshan et al., 2015; Wang et al., 2014;
Nugroho et al., 2013; Fatima et al., 2012; Karim et al., 2010; Esin and Cosgun,
2007) and inflict negative impacts of direct and indirect nature on environment
(Cho et al 2010; Tam and Tam, 2008).
The construction waste not only causes problem in solid waste management, but also give ugly look, besides causing water and soil pollution
(Ahmad et al., 2011) and threatening sustainable development in developed and developing states (Li and Zhang, 2013).
In developing and under-developed countries, like Pakistan, usually the construction material i.e., sand, clay, crushed stone and bricks etc, is not only placed openly in front of and/or around the construction sites at roads and streets during the whole construction process, but also the waste generated during the construction is not removed from the scene after the completion of the construction.
9
Due to sweeping, wind blowing, traffic flow and other mechanical disturbances, a part of fine inert makes suspended particular matter (SPM) of varying sizes in the air. Rest of the large-sized inert waste erodes with the passage of time and more and more fine inert is produced, resulting in increased particulate matter in the ambient air around.
Furthermore, during rains, a part of inert waste deposits on roads and around, dries and transforms into particulate matter due to traffic and other mechanical disturbances. Several epidemiological studies have also demonstrated that PM exposure, carrying various metals within, is responsible for life- threatening and serious health effects causing occurrence of acute respiratory infections, lung cancer, and chronic respiratory and cardiovascular diseases (King et al., 2016; Challoner et al., 2015; Beelen et al. 2014; Assimakopoulos et al. 2013;
Heinrich et al 2013; Xu et al., 2012; WHO, 2006; WHO, 2005; Sorensen et al.,
2003; Chiaverini 2002).
The health impacts of PM emissions are not restricted to the construction site, as fine particles (smaller than 2.5 µm in diameter) can travel further than coarser dust (particulate matter of 2.5-10 µm in diameter) and hence can affect the health of people living and working in the surrounding and far away (Ahmad and
Aziz, 2012; Resende, 2007).
Each year, over two million deaths are estimated to occur globally as a direct consequence of air pollution through damage to the lungs and the respiratory system (Shah et al., 2013; Ahmad et al., 2011; Shabbir and Ahmad, 2010). Among these, about 2.1 and 0.47 million are caused by fine particulate matter (PM) and ozone, respectively (Shah et al., 2013; Chuang et al., 2011).
10
Moreover, increased mass of SPM in the ambient air reduces visibility and creates hindrances in managing rest of the MSW around.
1.2. PROBLEM STATEMENT
1.2.1. Assessment of Construction Waste Generation
The construction waste, actually, contributes a major part of waste in each country. But, in under-developed and developing countries, unfortunately, awareness to construction waste, being not priority, is very poor (Nugroho et al.,
2013). Though, this is not considered as good solution, but construction waste in developed countries like the US, Australia, Germany and Finland, is disposed of by dumping at landfills (Bakshan et al., 2015; Nagapan et al., 2012; Faniran and
Caban, 1998). Due to this option, a shortage of waste dumping yards and exhaust of landfill spaces have become a major issues in a number of countries. This situation has forced the researchers to find out an alternate and efficient waste management system. Surplus construction material, which is one of the major causes of construction waste generation, also increases cost of the project significantly, which can be lowered by reducing construction waste by 5%, which could save up to £130 million in the UK (Ajayi et al., 2015).
The need for environmental protection led to the development of guidelines and regulations to improve the management of construction waste with the goal of reducing the amount of waste. In many nations, solid waste management plan is a legislative requirement for construction activities.
Therefore, for a sustainable-built environment, raising the awareness and designing and implementing plans for management and minimization of waste has become essential (Li and Zhang, 2013). The first step in designing and
11 implementing such plans and programs is to estimate and categorize the quantity and composition of construction waste generated. Information about quantification and classification, in fact, provides the actual amount of the waste and hence help in making the adequate decision for the minimization and ultimately the sustainable management (Wu et al., 2014; Jalali, 2007).
In nutshell, minimization of construction waste and management have become a serious and challenging environmental issue in the developing cities all over the world today and hence more and more research is needed in this area to combat the issue (Laurent et. al., 2014).
The enormous amount of construction activity, at the growth rate of 2.4 %, has produced a large amount of inert waste over the past two decades in Pakistan.
Hence, a wide range of pollutants, in the form of PM of varying sizes, carrying different metals, continuously enter the urban environment during construction activities (Waheed et al., 2012).
1.2.2. Physico-chemical Characteristics of SPM
Keeping in view all these significances, advance research needs to be performed to expose the pollution impact on the environment during construction activities. In this connection, determination of concentration and other physico- chemical characteristics of the fine inert on ground and particulate matter generated due to this fine inert waste at and around construction sites are imperative to monitor and control the atmospheric quality of the cities.
The determination of physico-chemical characteristics of fine inert/dust/soil is technically easy and comparatively inexpensive owing to readily available equipment and trained manpower. But, on the other hand, air pollution monitoring
12 for determination of physical and chemical characteristics, needs not only sophisticated and expensive equipment but also highly trained manpower and costly resources, including continuous supply of energy, which the countries like
Pakistan lack and suffering acute shortage. Keeping in view all these issues and problems, there has been need to develop any mechanism to estimate physical and chemical characteristics of particulate matter (PM) only by determining the physico-chemical characteristics of inert/dust/soil at the construction sites.
Therefore, this study aims at determining the physic-chemical characteristics of the inert material/dust/soil and the corresponding particulate matter and finding correlation between both by regression analysis to estimate physico-chemical characteristics of particulate matter (PM) only by determining characteristics of inert material/dust/soil at any construction site.
1.2.3. Prediction of SPM Concentration at Varying Distances
PM generated at the construction sites is not restricted to the construction site. Fine particles (particularly smaller than 2.5 µm in diameter) travel further in the air than coarser dust (particulate matter of 2.5-10 µm in diameter), and hence can also affect the health of people living and working far away (Resende 2007).
Particles less than 10 μm (PM10) reach tracheobronchial and alveolar regions of the respiratory tract and hence have been of prime interest for epidemiology studies.
PM10 comprises of organic carbon, elemental carbon, sulfate, nitrate, and metals.
Coarse particles (2.5–10 μm and PM10-2.5) are formed by mechanical grinding and re-suspension of solid material and are composed of crustal elements, metals from suspended road dust, and organic debris. These variations suggest that PM2.5 and
PM10-2.5 may differ in their impacts on human health (Adar et al., 2014).
13
Therefore, there has been a need to determine the phyico-chemical characteristics of suspended particulate matter at the varying distances from the construction site. Again, due to lack of resources, equipment, shortage of energy in the country and other constraints monitoring and characterization of suspended particulate matter at varying distances is a difficult process. Hence, keeping in view all the issues and problems, there has also again been a need to develop any mechanism to estimate concentration particulate matter (PM) of different sizes at the varying distances from the construction site only by determining the concentration at the source of generation at the construction sites.
1.3. OBJECTIVES
The objectives of the study are:
Quantifying and classifying the construction waste in order to make
this data a tool for waste minimization, environmental protection in
terms of air pollution, particularly emission of suspended particulate
matter, and reducing the project cost
Identification of sources and determining the contribution of inert
waste in generation of suspended particulate matter (SPM)
Developing correlation between physico-chemical characteristics of
the inert material/dust/soil and the corresponding SPM by regression
analysis to estimate characteristics of SPM only by characteristics of
inert material/dust/soil
Developing mechanism to estimate concentration of suspended
particulate matter at the varying distances from the construction site
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only by determining the SPM concentration at the source of generation
at the construction sites
Providing baseline information for managing the construction waste,
establishing national environmental quality standards and making
guidelines for passersby and workers at construction site
1.4. BENEFITS OF THE STUDY
Quantification and classification of the construction waste will help in
assessing the wastage of construction material and overcasting in the
construction project, which will ultimately help in designing strategies
for waste minimization, reducing the cost of the construction projects
and designing solid waste management system
Model/Mechanism developed for determining physico-chemical
characteristics of suspended particulate matter at the construction site
and at the varying distances from the construction sites from the
characteristics of inert material/soil/dust at the construction site will
make the monitoring possible with limited time, energy resources,
equipment and trained manpower .
The study will be a part of efforts to monitor and control the
atmospheric quality of our cities with a view to study the impacts of
rapid and unplanned urbanization.
The correlations determined and statistical model developed will help
monitoring PM in air and help in developing inert waste disposal
National Environmental Quality Standards (NEQS), besides making
guidelines for passersby and workers at construction site
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1.5. SCOPE OF WORK
1.5.1. Quantitative and Qualitative Assessment of Construction Waste
The study was conducted in various cities of Punjab Province of Pakistan at construction sites through questionnaires as far as quantification and classification is concerned.
1.5.2. Physico-chemical Characteristics of Fine Inert Waste and SPM
For monitoring physico-chemical characteristics of fine inert waste and suspended particulate matter and developing statistical models for estimating physico-chemical characteristics of suspended particulate matter at the construction site from the characteristics of inert material/soil/dust, data was collected from a construction site at Model Town Link Road in Lahore, the metropolitan city of
Punjab, Pakistan.
1.5.3. Monitoring and Estimation of SPM Conc at Varying Distances
Data for monitoring concentration of suspended particulate matter at the varying distances from the source of generation at construction sites was collected at construction sites in Lahore, the metropolitan city of Punjab, Pakistan; Gujrat, a district headquarters in Punjab; and Kharian city, a subdivision in District Gujrat.
1.5.4. Monitoring of SPM at Mega Project
Data for monitoring concentration of suspended particulate matter generated at the mega project site, samples were collected at five locations of
Rawalpindi Islamabad Metro Bus Project site.
So, a big, medium and small urban construction locality were selected for conducting the aforesaid study.
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Chapter 2
2. REVIEW OF THE LITERATURE
A lot of efforts are being exercised to determine and study construction waste quantification and classification, physico-chemical characterization of particulate matter in the ambient air and estimation and prediction of various factors and features (dependent) by determining other correlated factors and characters with the help of statistical models, including correlation, simple linear regression.
2.1. CONSTRUCTION WASTE CHARACTERIZATION
The review on relevant research papers and articles on construction waste provide basis of understanding to various concepts. Construction waste characterization includes quantity, types, composition and reasons and resources of construction waste.
2.1.1. Construction Waste Generation
The construction waste is not a priority in many developing states due to poor awareness to construction waste. The construction waste contributes in a major part of waste in every country. The waste is important for the construction manager to manage a site space and also for an environmentalist to manage.
Therefore, the quantity of waste is pivotal to handle the construction waste problems (Nugroho et al., 2013).
Construction waste quantification needs to be done early in the project, but it is difficult to exactly determine the quantity of construction waste at the construction site (Mahayuddin and Zaharuddin, 2013).
17
The construction waste generation trend varies from developed to the developing and underdeveloped countries (Chen and Chang, 2000). There are many factors that contribute in construction waste amount. The total waste generated in any state, region or country is also affected by the economic conditions, local regulations, major disasters and weather (Foo et al., 2013).
Construction waste accounts for a substantial share of 25-30% of total solid waste generated worldwide (Kern et al., 2015; Rodríguez et al., 2015). Globally, building waste production of 2-3 billion tonne per year is estimated (Shirvastava and Chini, 2008). As per statistical data available, construction and demolition waste around the world frequently makes 10 to 30% of the waste at many landfill sites (Rodríguez et al., 2015; Begum et al. 2005). The construction process in the
European Union generates 530 million tones waste annually (European
Commission, 2011) and produces about 33% of the total waste stream (Rodríguez et al., 2015; Eurostat 2010).
A high amount of construction waste, up to 30%, is generated during construction activities (Kern et al., 2015; Rodríguez et al., 2015; Lau et al., 2008).
A study by Sandler and Swingle (2006) revealed approximately 136 million tons of building-related construction and demolition debris generation each year in the US.
In the Netherlands, nearly 1-10% of the amount purchased is wasted for each building material. In UK, around 70 million tons of C&D materials and soil ended up every year (DETR, 2000).The construction waste contributed 16-44 % of the total solid waste generated every year in Australia (McDonald and Smithers, 1998;
Bell, 1998).
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Other countries like Finland and Germany, the construction wastes contribute as much as 15% to the landfills (Faniran and Caban, 1998). In China, construction activities contribute for nearly 40% of the total municipal solid waste generated every year (Wang et al., 2008; Dong et al., 2001). According to another study, the construction activities generate solid waste 30-40% of the total solid waste generated per year in China. In Hong Kong, contribution of construction waste has been reported to be 38% (Hong Kong Polytechnic and the Hong Kong
Construction Association Ltd, 1993); while other studies have reported construction waste in the range of 30- 40% (Wong et al., 2005) and 15-27% (Tam et al., 2007). In 2007, total construction waste produced was reported to be
4,656,037 tons, which accounts for 61% of the total waste (7,669,097 tons) generated that year (CDM, 2010). In two separate studies, the waste produced at construction sites in Brazil have been reported to be almost 28% (Formoso et al.,
2002) and 20-30% of the total weight of materials on site. These results quite matches with results of the studies conducted in other countries, like Germany,
Netherlands, Australia, the United Kingdom and China etc (Bossink and Brouwers,
1996).
The construction waste generated is about 175,000 tonnes annually in
Kuching and almost 100,000 tonnes in Samarahan in Malaysia (The Star, 2006). In
India, construction waste accounts for above 25% of the total solid waste of 48 million tonne generated per year (TIFAC Report, 2000). Hamassaki and Neto
(1994) concluded that about 25% of construction material is wasted during various construction activities.
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A series of surveys conducted by Wakade and Sawant (2010) shows that the quantity of construction waste generated is 5.8 million tons annually in the city of Mumbai, India. In Kuwait, construction work generates about 45 kg/m2, whereas, the demolition work produces waste at an average rate of 1.5 ton/m2 : at the rate of 1.45 ton/m2 for residential; and at the rate of 1.75 ton/m2 for industry.
As many as 30% of the total solid waste generated in Pakistan is estimated to be comprising of construction and demolition waste.
2.1.2. Types of Construction Waste
Construction is responsible for generating a variety of wastes. Ekanayake and Ofori (2000) categorized construction waste into three major classes as material, labour and machinery waste. Construction material waste can also be categorized as cutting waste, application waste, transit waste and theft and vandalism (Muhweziet al., 2012). The waste can also be classified into construction, demolition, civil work and renovation work waste (Li et al., 2005).
In yet another classification, contractions waste have been divided into three major categories: (1) inert (soil, sand, rocks, concrete, aggregates, plaster, bricks, masonry blocks, glass, and tiles), (2) non-inert (, wood, paper, drywall, gypsum, metals, plastic, cardboard, packaging), and (3) hazardous (flammable materials like paint and corrosive materials such as acids and bases, explosive materials that undergo violent or chemical reaction when exposed to air or water)
(Bakshan et al., 2015).
Construction waste is also grouped into physical and non-physical waste.
Physical waste is defined as the losses during construction activities or materials damaged that cannot be repaired or used. On the other hand, non-physical wastes is
20 related to cost overrun and delay in construction projects (Nagapan, 2012). This can be interpreted as losses of money and time and not physical (Foo et al., 2013).
Structure and finishing waste have been defined for new building construction (Skoyles and Skoyles, 1987).
2.1.3. Composition of Construction Waste
The composition of construction waste is also required to be determined in the start of the project, but exact composition of the construction waste is difficult to be calculated (Mahayuddin and Zaharuddin, 2013). The composition of construction waste tends to vary from country to country owing to their own construction techniques and material (Chen and Chang, 2000)
The construction is responsible for producing a number of waste components including papers, wood, metal, brick, material packaging, concrete, drywall, roofing, organic material, plastics, cardboard and others (Astrup et al.,
2014; Nagapan et al., 2013; Lau et al. 2008). Among many, the typical components of construction waste include wood, concrete, drywall, metals, roofing and brick
(Tang & Larsen 2004; US EPA, 1998).
A study conducted on 30 construction sites reveals wastage of 12.32%
(concrete), 9.62% (metal), 6.54% (brick), 0.43% (plastic), 69.10% (wood) and 2%
(others) as major waste generated (Faridah et al., 2004).
In another study the concrete was estimated to be the largest part of the construction waste. Further, Tam et al. (2007) and Li et al. (2005) concluded that the concrete is the one of the major sources of construction waste at a construction project. Pinto and Agopyan (1994) stated that rubbish (40-50%), wood waste (20-
30%) and miscellaneous (20-30%) composes the construction waste.
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2.1.4. Reasons and Sources of Construction Waste
Waste production on construction sites have been reported owing to poor or multiple handling, inadequate storage and protection, over-ordering of materials, poor site control, lack of training, bad stock control and damage to materials during delivery (WRAP, 2007; DETR, 2000; cited in Swinburne et.al., 2010).The building material surplus is the biggest contributor to construction waste generation
(Mahayuddin and Zaharuddin, 2013). Moreover, reasons and sources of waste are also found in faulty design, poor material handling, lack of planning, inappropriate procurement, mishandling and other processes.
Attitude and behavior of labour, material management and design coordination (Al-Sari et al.2012; Chen et al., 2002; Teo and Loosemore, 2001), region, structural and functional type, building above ground, height underground and total floor area (Huang et al., 2011) and project size, construction method, building type, human error, technical problem and material storage method,
(Mokhtar et al., 2011) are a few other factors that influence construction waste generation.
Furthermore, lack of experience and inadequate planning (Wan et al., 2009;
Nazech et al., 2008; Osmani et al., 2008), mistakes and errors in design (Wang et al., 2008; Osmani et al., 2008) frequent design changes (Faniran and Caban, 2007) and inadequate monitoring and control (Wan et al., 2009; Osmani et al., 2008) are yet another reasons responsible for generation of construction waste. Sources of construction waste are also classified into five groups which include design, material procurement, material handling, operations and residual (Gavilan and
Bernold, 1994). The design changes and the variability in the number of drawings,
22 along with the redesigning and material alteration, are the major construction waste sources (Esin and Cosgun, 2007). The modification generates about 92 % of waste, while interior modification causes approximately 70% of total waste. Floor, kitchen components and exterior door often undergo modification different from the initial design (Esin and Cosgun, 2007).
Likewise, external factors like theft and vandalism and other key stakeholders such as vendors, developers, architects, owners, designers and contractors influence waste generation in their capacities.
2.2. SPM CHARACTERIZATION
Suspended particulate matter is material suspended in the air, and it can include soil, road dust, soot, smoke, and liquid droplets. SPM can come directly from sources like vehicles, ships, aircraft, unpaved roads, and wood burning.
Larger particles, those with a diameter larger than 2.5 µm (PM2.5), typically come from unpaved roads and windblown dust, but finer particles, those smaller than
PM2.5, typically come from combustion sources: vehicles, ships, etc.
2.2.1. Methods for Particulate Matter Sampling
The sampling of particulate matter can be carried out by different types of equipment.
For identification and characterization of particulate matter concentrations at construction jobsites, Ingrid et al. (2014) used MiniVol Portable Air Sampler which has been jointly developed by the US Environmental Protection Agency (US
EPA) and the Lane Regional Air Pollution Authority for portable air pollution sampling technology. Airmetrics (Springfield, OR, USA) manufactures the
MiniVol™ TAS, which samples ambient air at 5 L/min for particulate matter
23
(PM10, PM2.5 and TSP). Lightweight and portable, the MiniVol™ TAS is ideal for remote areas or locations where no permanent site has been established.
Martínez et al. (2014) collected samples with a high volume Andersen equipment using quartz fiber filters for studying dispersion of atmospheric coarse particulate matter in the San Luis Potosí, an urban area of Mexico. A total of 188 samples were randomly collected at 24-hour running time within the period from May 2003 to April 2004. The filters were stabilized before and after sampling at 23 ± 2 ºC and
40 ± 5% relative humidity.
Respirable Dust Sampler was used to collect suspended particulate matter
(SPM) and respirable particulate matter (RPM) from the open atmosphere, while
Dust Trak was used for PM concentrations for PM1, PM2.5,PM10 (RPM) and suspended particulate matter (SPM) in order to study pollution due to particulate matter from mining activities in India. The relevant meteorological data, that include wind speed, humidity, were also collected (Gautam et al., 2012).
For estimation of suspended particulate matter (SPM) and respirable particulate matter (RPM) in ambient air due to mining in Manavalakurichi, South West Coast of Tamilnadu, India, High Volume Air Sampler, Ecotech model AAS217BL with flow rate of 1.1 m3/min and special grade glass micro-fibre filter, Whatmann EPM
2000 was used for 8 hour duration monitoring. SPM and RPM were analysed gravimetrically (Mini and Manjunatha, 2014).
The Partisol® model 2300 4-channel speciation samplers (Thermo Fisher
Scientific Inc., USA) were used to collect air through an inlet (at a flow rate of 16.7
LPM) that removes particles with aerodynamic diameters greater than 10.0 μm; the remaining particles are collected on the filter.
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Teflon filters (Whatman Grade PTFE Filters of 47 mm diameter) were used for collection and measurement of mass concentration of PM10 gravimetrically following the standard operating procedures (USEPA 1998). Meteorological parameters (temperature, relative humidity, wind speed, and wind direction) were also recorded during the monitoring periods (Behera et al., 2011).
Pandey et al. (2014) collected SPM for eight hour from 09:00 to 17:00 h by drawing air at a flow rate of 1.1 m3/min through Whatman glass Fiber filter (20.4 cm× 25.4 cm) using a high volume sampler (Model APM 415, Envirotech, India) for assessment of air pollution around coal mining area near Jharkhand, India. The difference of weight of filter before and after sampling was used to calculate PM10 concentration and SPM concentration was calculated by adding the concentration of particulate collected through hopper. Eight hourly Monitoring of PM1.0 and
PM2.5 was also done by portable aerosol Spectrometer Model 1.109, Grimm
Technology Inc., USA.
In a study to measure particulate matter, “The Casella” (particulate sampling system instrument), in compliance with ISO-9096 and BS-3405, was used. Cellulosic filter media, with pore size <10 micron, were used in the instrument, for retention of PM10 for definite time intervals (Mumtaz et al., 2014).
For spatial, temporal and size distribution of particulate matter and its chemical constituents in Faisalabad, Pakistan, Ambient PM of different size fractions (TSP, PM10, PM4, PM2.5) was monitored with a MicroDust Pro Real Time
Aerosol Monitor (model HB3275-07, Casella CEL, UK) on a 6-h average basis at each sampling site. This instrument has a detection range of 0.001-2500 mg m–3 with a resolution 0.001 mg m–3 (Javed et al., 2015).
25
Singh and Perwez (2015) collected SPM samples on 24 hourly basis at 14 selected discrete receptors once a week in the study area during three seasons for one year (post-monsoon, winter and summer seasons). The samples were collected by resipirable dust sampler (Envirotech APM 460 NL) (flow rate of 1.1 m3 min–1).
In a study for determining the distribution of respirable suspended particulate matter in ambient air in Joda-Barbil region in Odisha, India, air quality parameters like suspended particulate matter (SPM) and respirable suspended particulate matter (RSPM) were measured using High Volume Air Sampler (Make:
Envirotech, Model: APM-460) maintaining an average flow rate of more than
1.1m3/min, (Glass Fiber Filter Paper) and Electronic Balance adopting Gravimetric method. Sampling was conducted on 24 hourly basis at each station during the study period (Panda et al., 2011).
Characterization of suspended particulate matter primarily accounts for concentration and composition of suspended particulate matter.
2.2.2. Concentration of Suspended Particulate Matter
The rapid infrastructural growth and urbanization have resulted in generation of a large amount suspended particulate matter in the air over the past two decades in Pakistan (Tahir et al., 2015). Hence, a wide range of pollutants in particulate matter has continuously entered the urban environment (Waheed et al.,
2012).
Concentration of suspended particulate matter reported in various cities and countries are as under:
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Table 2-1: Particulate matter concentration in various Asian cities
City/ TSP PM2.5 PM10 PM10 -2.5 Reference Country (μgm−3) (μgm−3) (μgm−3) (μgm−3) Parekh et al., Karachi 668 - - - (2001) Parekh et al., Islamabad 691 - - - (2001) Shaka and Medit cities - 40 - 76 Saliba (2004) Kanpur, Sharma and - 25–200 45–589 - India Shaily (2005) Ghauri et al., Lahore 996 - 368 - (2007)
Ghauri et al., Quetta 778 - 298 - (2007)
Ghauri et al., Karachi 410 - 302 - (2007)
Kolkata, Karar and - - 68-280 - India Gupta (2007) Martet al., Cincinnati - 7-48 - - (2004) Zurich, SL - - 24–25 - Minguillón et al., (2012)
A study was carried out for determining the suspended particulate matter concentrations in ambient air of ten locations. The locations were selectd around the mining and mineral separation activity in Manavalakurichi, southwest coast of
Tamil Nadu, India. The study period was from January 2014 to June 2014. The results showed that SPM varied from 80.2 µg/m3 to 173.0 µg/m3 within permissible limit of 200 µg/m3 (Mini and Manjunatha, 2014).
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In another study, conducted at a mountainous rural site of Tamdao,
Vietnam shoed higher PM2.5 levels during dry season. The average reading was found to be 51 µg/m3, followed by the transitional season, 33 µg/m3, and the lowest in wet season, 25 µg/m3 (Co et al., 2014).
Another study was carried out at three different construction site phases, of earthworks, superstructure and finishing in Salvador, Bahia, Brazil. The results of the study showed the highest TSP concentrations with average concentrations of
462.25 µg/m3, 483.12 µg/m3 and 212.31 µg/m3, at various points (Ingrid et al.,
2014).
In Birmingham (the UK), Coimbra (Portugal) and Lahore (Pakistan), a comparative receptor modeling study, for airborne particulate pollutants, was conducted. In the cities of Birmingham and Coimbra, samples of only PM10, while in Lahore, total suspended particulates (TSP) were collected. A high concentration of TSP in Lahore was indicated. Large differences among the cities were observed with soil dust. It was estimated to contribute 62% of TSP in Lahore, but much less contribution was estimated in case of the cities of Birmingham and Coimbra
(Harrison et al. 1997).
In another study, for monitoring of PM2.5 and PM10 in the city of Lahore from 12 January 2007 to 19 January 2008, showed ambient aerosol characterized with organic carbon (OC), elemental carbon (EC), sulfate, nitrate, chloride, ammonium, sodium, calcium, and potassium, and organic species. The
−3 concentration of PM2.5 and PM10 was recorded as 194±94 μg m and 336 ± 135 μg m−3, respectively (Stone et al., 2010).
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2.2.3. Composition of Suspended Particulate Matter
Composition of suspended particulate matter varies in different studies conducted in various parts of the world. Concentration of metal contents in the PM in Islamabad was reported to be calcium as 4.531 μg m−3, sodium 3.905 μg m−3, iron 2.464 μg m−3, zinc 2.311 μg m−3, potassium 2.086 μg m−3, magnesium 0.962
μg m−3, copper 0.306 μg m−3, antimony 0.157 μg m−3, lead 0.144 μg m−3 and strontium 0.101 μg m−3 (Shah and Shaheen, 2008). In the Midwestern United
States, average urban levels of Fe, Pb and Zn ranged from 0.04–0.07 μg m−3,
0.001–0.005 μg m−3, and 0.006–0.011 μg m−3, while average rural concentrations were 0.03–0.04 μg m−3, 0.002 μg m−3, and 0.006 μg m−3, respectively, in the fine particulate matter (PM2.5) (Kundu and Elizabeth, 2014). Analyzing the chemical data of the PM10 elements like Cl, Ca, Si, Al, Fe and Na have been reported at a residential building construction site in Salvador, Bahia, Brazil (Araújo et al 2014).
Average concentrations for different trace metals in PM10 at various locations in
Dhanbad Region, Jharkhand, India were found to be Fe at 8.5 μg/m3, followed by
Cu (1.43 μg/m3, ), Zn (0.60 μg3), Mn (0.39 μg/m3), Cr (0.28 μg/m3), Cd (0.050
μg/m3), Pb (0.24 μg/m3) and Ni (0.0096 μg/m3). On the average, the decreasing elemental concentration trend was: Fe>Cu>Zn>Mn>Cr> Cd>Pb>Ni (Dubey et al.,
2012). Ten of the eleven metals listed as EPA air toxics (Mn, Cr, Sb, Ni, Pb, As,
Co, Cd, Se, and Be) were detected North Carolina interstate in each PM size fraction (Hays et al 2011).
In the city of Thessaloniki, Greece, crustal elements, such as Ca, Si, Fe, Al were the most abundant, followed by elements related to anthropogenic processes
(K, S and Zn) (Terzi et al 2010). In Madurai city, India, industrial areas had the
29
-2 highest concentrations of heavy metals such as Fe, Zn and Cr and also the SO 4 ions, traffic areas with relatively higher traffic densities in the city endured highest concentrations of Cd and the NO−3 ion. (Bhaskar et al 2009).
In Taipei, mineral dust in PM10 was estimated to be 80% during Asian Dust
Storm (ADS) episodes and 15% in non-ADS periods (Hu et al., (2004)
Chemical characterization of PM2.5 measured at Ohio shows sulfate ion to be the largest component, showing strong seasonal variations with max concentration during the summer (John et al. 2000).
In the Cincinnati, the PM2.5 concentration was reported as 38.2% in case of iron and 68.7% for nickel (Martuzevicius et al., 2004).
In Kuwait, high trace metal concentrations were observed in the sequence of PM10
>; PM2.5 >; PM1.0 respectively (Bu-Olayan and Thomas, 2010).
Analysis of PM2.5 chemical components in North Carolina organic matter as the most abundant component of PM2.5 (45–50% of total mass), sulfate as major soluble ion (30%), followed by ammonia and nitrate (7–11% and 6–9%, respectively).
At all sites, ammonia combined mainly with sulfate, except in winter, when sulfate was relatively low, while nitrate was found high. Examining correlations between PM2.5 and its major chemical components showed that total PM2.5 was well correlated with sulfate, ammonia and organic carbon. The ammonia correlated much better with sulfate than nitrate.
For both rural and urban sites, sulfate had a maximum mass concentration in summer and a minimum in winter. On the other hand nitrate displayed a vice versa trend. A low mass ratio of nitrate to sulfate was seen at all sites, which
30 suggests that stationary source emissions were more important than the vehicle emissions in the studied areas. The equivalent ratio of ammonia to the sum of sulfate and nitrate is < 1 (Viney et al., 2006).
2.3. ATMOSPHERIC DISPERSION MODELS
Atmospheric dispersion models are computer–programmed mathematical simulations for prediction of air pollutants dispersion.
These models estimate the downwind ambient concentration of air pollutants or toxins emitted from various sources by solving the mathematical equations and algorithms.
Moreover, with changes in emission sources, these models can also be used to estimate future concentrations.
These are the most useful for pollutants that are dispersed over large distances. The regression models are also used for pollutants with a very high spatio-temporal variability.
For governmental agencies, which are responsible of environmental protection and air quality management, these dispersion models are very important as far as determination of existing or proposed industrial facilities for compliance of the National Environmental Quality Standards of various countries.
A major and significant application of a roadway dispersion model that resulted from such research was applied to the Spadina Expressway of Canada in
1971 (Fensterstock et al., 1971).
For planning of accidental chemical releases, these models are also used by public safety responders and personnel of the emergency management services.
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The dispersion models vary depending on the mathematics used to develop the model, but all require the input of data that may include:
Meteorological conditions: wind speed and direction, atmospheric turbulence,
ambient air temperature, inversion, cloud cover and solar radiation
Concentration or quantity of pollutant and temperature of the material
Other parameters: height, source location, source type and exit velocity an
temperature and mass flow rate
The location, height and width of any obstructions (such as buildings or other
structures) in the path of the emitted gaseous plume
A post and pre-processor module for the input of meteorological is included in many dispersion modeling programs for graphing the output data and/or plotting the area impacted by the air pollutants on maps.
The isopleths, showing areas of minimal to high concentrations may also include in plots of areas impacted, which usually define areas of the highest health risk.
Moreover, for the public and responders, these isopleths plots are useful in determining protective actions.
2.3.1. Gaussian Air Pollutant Dispersion Equation
The history of air pollution dispersion dates back to the 1930s and earlier.
Bosanquet and Pearson (1936) derived one of the early air pollutant plume dispersion equations.
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This equation neither assumed Gaussian distribution nor did it include any effect of ground reflection of the pollutant plume.
Sir Graham Sutton derived an air pollutant plume dispersion equation in
1947. For the vertical and crosswind dispersion of the plume, this equation included the assumption of Gaussian distribution. It also included the effect of ground reflection of the plume (Sutton, 1947).
Today, there was a huge growth in employing air pollutant plume dispersion calculations between the late 1960s, with the beginning of stringent environmental control regulations.
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A number of computer programs, for calculation of dispersion of air pollutant emissions, were established meanwhile, which were termed "air dispersion models". The complete equation, for Gaussian Dispersion Modeling of
Continuous, Buoyant Air Pollution Plumes, was the basis for most of those models
(Beychok, 2005; Turner, 1994).
2.3.2. Briggs Plume Rise Equations
He = Hs + ΔH
This equation needs the input of H, which is the pollutant plume's centerline height above ground level. The H is the sum of Hs and ΔH. Hs is the actual physical height of the pollutant plume's emission source point, while ΔH is the plume rise due the plume's buoyancy.
To determine ΔH, many models used "the Briggs equations." Briggs first published his plume rise observations and comparisons in 1965 (Briggs, 1965).
At a symposium, in 1968, sponsored by a Dutch organization, CONCAWE,
Briggs compared many of the plume rise models.
Same year, Briggs also wrote the section of the publication edited by Slade
(Slade, 1968), describing the comparative analyses of plume rise models. In 1969, that was followed by his classical critical review of the entire plume rise literature, proposing a set of plume rise equations. These equations have become widely known as "the Briggs equations". Briggs, subsequently, modified his 1969 plume rise equations in 1971 and 1972 (Briggs, 1968, 1969, 1971, 1972).
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2.3.3. Other advanced atmospheric pollution dispersion models
2.3.3.1. ADMS 3
The ADMS 3 (Atmospheric Dispersion Modeling System) is one among advanced model for atmospheric pollution dispersion. This model estimates concentrations emitted from point, line, volume and area sources, besides intermittently from point sources (US EPA, 2016). It was developed by Cambridge
Environmental Research Consultants (CERC) of the UK in collaboration with the
UK Meteorological Office, National Power plc (now INNOGY Holdings plc) and the University of Surrey. The first version of ADMS was released in 1993. The version of the ADMS model discussed on this page is version 3 and was released in
February 1999. It runs on Microsoft Windows. The current release, ADMS 5
Service Pack 1, was released in April 2013 with a number of additional features
(CERC, 2016).
2.3.3.2. AERMOD
The AERMOD is an integrated system. This atmospheric dispersion system includes three modules (Brode, 2006):
A dispersion model meant for short-range (up to 50 kilometers) dispersion of
air pollutant emissions from stationary industrial sources.
A meteorological data preprocessor (AERMET), which accepts surface
meteorological data, upper air soundings, and optionally, data from on-site
instrument towers.
A terrain preprocessor (AERMAP), with major purpose is to provide a physical
relationship between terrain features and the behavior of air pollution plumes.
35
AERMOD also includes PRIME (Plume Rise Model Enhancements) (US
EPA, 2016) which is an algorithm for modeling the effects of downwash created by the pollution plume flowing over nearby buildings.
2.3.3.3. DISPERSION21
DISPERSION21, also called DISPERSION 2.1, is model developed by the air quality research unit at Swedish Meteorological and Hydrological Institute
(SMHI), located in Norrköping (Turner, 1994).
The model is widely used in Sweden by local and regional environmental agencies, various industrial users, consultant services offered by SMHI and for educational purposes (Beychok, 2005).
2.3.3.4. ISC3
ISC3 (Industrial Source Complex) model is a popular steady-state Gaussian plume model which can be used to assess pollutant concentrations from a wide variety of sources associated with an industrial complex. This model can account for point, area, line, and volume sources; settling and dry deposition of particles; downwash; separation of point sources and limited terrain adjustment. ISC3 operates in both long-term and short-term modes. The screening version of ISC3 is
SCREEN3 (Beychok, 2005; Turner, 1994).
2.3.3.5. Operational Street Pollution Model (IOSPM)
The OSPM is a model developed for simulating the dispersion of air pollutants in the street canyons. This model was was developed by the National
Environmental Research Institute of Denmark, Department of Atmospheric
Environment, Aarhus University (Beychok, 2005). As a result of re-organisation at
Aarhus University the model has been maintained by the Department of
36
Environmental Science at Aarhus University. For about 20 years, OSPM has been used in many countries for studying traffic pollution, performing analyses of field campaign measurements, studying efficiency of pollution abatement strategies, carrying out exposure assessments and as reference in comparisons to other models. OSPM is generally considered as state-of-the-art in practical street pollution modeling (Turner, 1994)
2.4. STATISTICAL MODELS
Although any statistical models have not been used for estimating physico- chemical characteristics of suspended particulate matter, but simple linear and multiple regressions have been used for estimating dependent variables and parameters by several academic studies all over the world.
Kern et al (2015) developed a statistical model for determination of waste generated at the construction of high-rise buildings mainly by guessing the influence of design and production system. They used multiple regressions for estimating the amount of waste at the construction site. The resultant model produced dependent (amount of waste generated) and independent variables, associated with design and production system. Consequently, the regression model, resulted in an adjusted R2 value of 0.694, which predicts approximately 69% of the factors involved in the generation of waste in similar constructions.
Later, Kolmogorov–Smirnov and Shapiro–Wilk statistical tests were used with the dependent variable (Y) in order to see whether multiple linear regression would be suitable or not for the purpose. This process was later followed by the hypothesis of data normality of the dependent variable.
37
Yilmaz and Kaynar (2011) developed regression models for predicting swell potential of clayey soils in Turkey. They used of RBF and MLP functions of
ANN (artificial neural networks), ANFIS (adaptive neuro-fuzzy inference system) for prediction of S% (swell percent) of soil, and compared with the traditional statistical model of MR (multiple regression). However, it was found that the constructed RBF exhibited a high performance than MLP, ANFIS and MR for predicting S%.
Based on 43 Japanese cases as the training dataset, Dong et al (2011) developed logistic regression model, a widely used statistical approach, for quantitative prediction of the stability and failure probability of a landslide dam in
Taiwan. The study utilized the logistic regression method and the jack-knife technique to identify the important geomorphic variables, including peak flow (or catchment area), dam height, width and length in sequence, affecting the stability of landslide dams and evaluated failure probability of a landslide dam. Together with an estimation of the impact of an outburst flood from a landslide-dammed lake, the failure probability of the landslide dam predicted by the proposed logistic regression model could be useful for evaluating the related risk (Dong et al., 2011).
Nguyen et al (2011), using three very different genres of data simultaneously, i.e, blogs, telephone conversations, and online forum posts and developed linear regression to predict the age of a text’s author with correlation
(0.74) as well as mean absolute error between 4.1 and 6.8 years as complementary and useful measures.
Naseem et al (2010) adopted a novel approach of face reading and recognition by formulating a pattern in terms of linear regression. They used a
38 fundamental concept that patterns from a single-object class lie on a linear subspace and developed a linear model representing a probe image as a linear combination of class-specific galleries.
Mrode and Thompson (2005) predicted animal breeding values using linear models. Kamruzzaman et al (2012) introduced non-destructive prediction and visualization of chemical composition in lamb meat using NIR hyper-spectral imaging and multivariate regression. They acquired hyperspectral images for lamb samples originated from different breeds and different muscles and extracted mean spectra of the samples from the hyperspectral images. Accordingly, they built multivariate calibration models by using partial least squares (PLS) regression for predicting water, fat and protein contents. The models had good prediction abilities
2 for these chemical constituents with determination coefficient (R p) of 0.88, 0.88 and 0.63 with standard error of prediction (SEP) of 0.51%, 0.40% and 0.34%, respectively. The results obtained from this study clearly revealed that NIR hyperspectral imaging in tandem with PLSR modeling can be used for the non- destructive prediction of chemical compositions in lamb meat for the meat industry
(Mrode and Thompson, 2005).
Cosby et al (1984) examined 1448 soil samples in an evaluation of the usefulness of qualitative descriptors as predictors of soil behavior and used analysis of variance and multiple linear regression techniques to derive quantitative expressions for the moments of the parameters as functions of the particle size distributions (percent sand, silt, and clay content) of soils and based upon discriminate analysis, suggested that the co-variation of these parameters can be used to construct a classification scheme based on the behavior of soils which is
39 analogous to the textural classification scheme based on the sand, silt, and clay content of soils.
Air quality was predicted by outdoor air characteristics by two modelling techniques, the Personal-exposure Activity Location Model (PALM), to predict outdoor air quality at a particular building, and Artificial Neural Networks, to model the indoor/outdoor relationship of the building (Challoner et al., 2015).
40
Chapter 3
3. MATERIALS AND METHODS
The quantification and classification of construction waste material was planned with the help of questionnaires to be distributed among construction stakeholders in various districts of Punjab Province of Pakistan, while monitoring of (i) physico-chemical characteristics of fine inert construction material and suspended particulate matter for developing statistical model to estimate physico- chemical characteristics of suspended particulate matter, and (ii) monitoring of concentrations of SPM, PM10 and PM2.5 at the distance of 3, 8, 13 and 18m from the source of generation of particulate matter for estimation of concentration of different sizes of suspended particulate matter at varying distances from source of generation was planned to be conducted in mega, medium and small cities.
Further, for monitoring of suspended particulate matter at mega project in big city, five sites of Rawalpindi-Islamabad Metrobus Project were selected for sampling.
Samples were to be taken for consecutive seven days (one week) from 2013 to 2015 from various sites in different seasons in dry and sunny weeks.
The methodology followed for quantification and classification of construction waste, characterization of physico-chemical characteristics of inert construction waste material at the construction site and concentration of various sized of suspended particulate matter at the varying distances from the construction site and consequently developing statistical model have been explained below.
41
3.1. CONSTRUCTION WASTE MATERIAL
Methodologies adopted for determining data for quantifying and classifying waste generation rates are diverse and usually include: direct observation by the researchers; analyzing records of contractors; survey via telephone and questionnaire; on-site weighing and sorting the waste materials; data acquiring through employees of construction companies; and tape measurement and truck load records. Most of the studies investigated WGRs by differentiating material waste, while others investigated waste by treating the waste stream as a whole. All the studies derived a general rate in terms of percentage (%), volume (m3) or quantity (tons). This research study adopted (Howard, 1970 cited in Muhwezi et.al., 2012) classification of construction materials waste, i.e. cutting waste, application waste, transit waste and theft and vandalism. Cutting waste includes reinforcement bars, roof carcass, roofing sheets, false ceiling, wires and cables and pipes; Theft and Vandalism waste includes cement, sand, clay, crushed stone, wood/timber, wires and cables, pipes, wood preservatives and reinforcement bars;
Transit wastes includes blocks and bricks, window glazing, prefabricated windows, tiles and ceramic sanitary appliances; while application waste includes paint, mortar, concrete and POP/POP ceiling.
For this study, data were collected through structured questionnaire
(Annexure I). The questionnaires were distributed among civil engineers, architects, quantity surveyors and contractors, hailing from various districts of
Punjab province of Pakistan. As many as 800 copies of the questionnaire were administered to construction professionals, contractors and other stakeholders involved at design and construction activities in the study area. 200 questionnaires
42 were distributed among each group of professional (200 x 4 = 800). A total of 411 copies collected were found suitable for the analysis. The data collected were presented in tables and analyzed using frequency distribution, summation, percentage and mean representations. Along with other details such as educational qualification and experience in the relevant field, the respondents were mainly asked to score their judgments about various categories and sub-categories on percentage of ten construction wastage classes as: 00-05%, 5.1-10%, 10.1-15%,
15.1-20%, 20.1-25%, 25.1-30%, 30.1-35%, 35.1-40%, 40.1-45% and 45.1-50%, besides reasons of wastages. The score of each class of wastage [frequency (f)] was multiplied by the mean (x) of each class and summation of fx was divided by the total numbers of responses (questionnaires) i.e. 411, to calculate mean (%) of each construction material in all four categories of wastes. Mean wastages (%) of all four types of wastes was calculated by taking mean of all constituents in each and every category of the waste. Similarly, reasons of wastage were determined by calculating percentages of responses. The same methodology was adopted by
Babatunde and Olusola (2012). Moreover, respondents were also asked to give reason of each sub-category of the waste in the questionnaire. The respondents were also asked to recommend the maximum percentage of wastage of construction material in the construction project.
3.2. PREDICTION OF SPM CHARACTERISTICS
3.2.1. Site Selection
For this study, under-construction plaza having one acre plot size, at the
Model Town Link Road, Lahore, was selected (Annex II-A). From this site collection of samples of fine inert at ground and particulate matter in the
43 surrounding/ambient air for determining correlation/statistical regression/relationship between physicochemical characteristics of fine inert waste and particulate matter in the corresponding air.
3.2.2. Time and Duration of Samples Collection
Samples of both the fine inert and the particulate matter in the air generated from the fine inert were collected for four weeks: 04-10 June 2013 (Week 1), 19-25
October 2013 (Week 2), 25-31 December 2013 (Week 3) and 08-14 February 2014
(Week 4). On each day of the sampling week, three samples each in the morning, noon/afternoon and evening were collected 24 hour/8 hourly basis. In total, 21 samples of inert fine material and as many samples (21) of corresponding particulate matter in the air were collected at the construction site during each week. As a whole, 84 samples of inert fine material and the same number of samples of particulate matter were collected from the construction site for developing relationship between physicochemical characteristics of fine inert material and particulate matter in the air. Samples were collected on calm days with wind speed < 10 km/hr. Samples of both fine inert and PM were collected for four weeks covering all four seasons and various stages of construction
(earthworks, superstructure and finishing).
3.2.3. Fine Inert Sample Collection
Samples of fine inert material/waste were taken with stainless steel utensil from the top surface (0-6 inches) using the pattern as shown in the Figure 1. All the five subsamples were then mixed and grind to make a composite sample of at least
200 grams. The samples were preserved in the polyethylene bags.
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3.2.4. Particulate Matter Sampling
For monitoring of particulate matter, the Casella Particulate Sampling
System (CEL-712 Microdust Pro Real-time Dust Monitor/Instrument, the UK), was used, after making the modifications recommended by its manufacturers under iso-kinetic conditions (Annex-II-B). The instrument was designed to comply with
BS 3405 and ISO-9096 for compliance monitoring,
From the ambient air, the monitor sucks particulate matter at the rate monitored by a calibrated volume-measuring standard gauge. On the scale calibrated in liters and fractions thereof, the volume of the ambient air drawn is indicated. With the help of a stopwatch, which provides measurements in seconds with high precision and accuracy, the total time of ambient air inlet was noted.
Quantitative special filter media was used as the surface to retain the particulate quantitatively during a definite interval of time. To prevent the escaping of fugitive ambient particulate matter being monitored and to ensure the accuracy of measurement, the filter media is placed in the special port with leak-proof assembly.
After monitored interval of time, the pre-weighted filter media is weighed with analytical balance measuring up to 0.01µg. The difference is the weight of the
PM measured during a definite interval of time. The weight of the PM obtained by this way is further calculated into the units of µg/m3 (Szybist et al., 2007).
3.2.5. Physicochemical Analysis of Inert Material
Physico-chemical characterization is the characterization of the properties relating to both physical and chemical behaviour of a substance. Among many
45 others, these properties include pH, electrical conductivity, boiling point, freezing point, size, shape and chemical composition of the substance (Akhtar et al., 2014).
Using the standards methods, samples of both inert fine material collected from ground and particulate matter in the air surrounding/ambient air were analyzed for pH, electrical conductivity (EC) five trace metals including aluminum
(Al), calcium (Ca), nickel (Ni), iron (Fe) and zinc (Zn) and three ions i.e. sulphate
-2 - - (SO4 ), nitrate (NO3 ) and chloride (Cl ).
The physico-chemical properties chosen for this study included pH and electrical conductivity (EC) and concentrations of metals including Aluminum
(Al), Calcium (Ca), Nickel (Ni), Iron (Fe) and Zinc (Zn) and a few ions like sulfate
(SO42−), nitrate (NO3−) and chloride (Cl−) keeping in view the possibility of prevalence at construction site and their impacts on human health. The pH and electrical conductivity affect human skin and cause regulatory and auto-regulatory physiological dysfunctioning and disorder of human body resulting in many medical complexities and issues, while the metals and ions in particulate matter affect respiratory system, cardiovascular system, cause cancer, genotoxicity, neurotoxicity, immunotoxicity, affect eyes, liver, pancreases and glucose metabolism (Nejadkoorki, 2015).
Figure 3-1: Pattern for inert material sampling from ground at the construction site
46
3.2.6. pH and Electrical Conductivity
First 50 gram of inert material/soil/dust was dissolved in de-ionized water to make slurry with the ratio of 1:1. The pH and electrical conductivity (µs/cm) was measured using hand-held pH meter (HANNA Instruments Model # HI 9812,
USA) and EC meter (HANNA Instruments Model # HI 9812, USA), respectively
(Annex III-A) (GTM, 2015).
The meter can operate well at the temperature range of 0-50°C. HI 9812-5 is the complete, versatile and splash-proof portable combination meter. The instrument provides measurements for pH, EC, TDS and temperature ranges, which are easily selectable through a keypad on the front panel.
Conductivity measurements are automatically compensated for temperature changes with a built-in temperature sensor. The temperature coefficient is fixed at
2%/°C.
This meter, HI 9812-5, is a pH/EC/TDS meter designed for simplicity of use in taking pH, µS/cm, ppm and temperature measurements. The pH measuring range of the meter is 0-14, while EC measuring range is 1990 μS/cm.
3.2.7. Metals Analysis of Inert Material
Inert waste samples were analyzed in Atomic Absorption
Spectrophotometer (Perkin Elmer 1210, USA) (Annex III-B) for concentrations (of aluminum (Al), calcium (Ca), nickel (Ni), iron (Fe) and zinc (Zn), following the method of US-EPA (Compendium Method IO-3.2) (CFR Part 50, 2008).
The main principle of atomic absorption spectroscopy is that atoms of different elements absorb and re-emit light in different ways. In this characterization technique, an extremely light-sensitive device called a photometer
47 measures how much light passes through a material and how much is absorbed to identify the elements present.
Different elements absorb different wavelengths of light. These absorbed light waves excite the electrons of an element’s atoms, causing them to jump up to higher energy levels around the nucleus of the atom. In atomic absorption spectroscopy, a beam source emitting a set of known wavelengths or a continuous spectrum is shined up a thin sample or a solution. As the different wavelengths of light pass through the sample, they encounter different elements that either absorb or pass along the light, depending on the characteristic wavelength of the sample atoms. Opposite to the beam source, a sensitive electronic detector of light measures the amplitude or intensity of different wavelengths of light after they pass through the sample. Regions of the spectrum with decreased intensity indicate the absorption of specific wavelengths. These specific wavelengths correspond to specific atoms, which can be identified by comparing these absent wavelengths with the elemental spectra listed in a table or electronic database.
For reducing interferences caused by organic matter and converting associated metals into their free form, the samples were digested. The concentrations of heavy metals were determined by running the samples on Atomic
Absorption Spectrophotometer.
The samples were digested by using nitric acid (HNO3) and hydrochloric acid (HCl) digestion. The nitric acid is used to digest the samples more effectively for both flame photometry and electro-thermal atomic absorption spectrophotometry. Sometimes, there is need to add perchloric acid, hydrocholic acid or sulphuric acid for complete digestion. For metals determination, in short,
48 the combination of the abovementioned two acids is the best option to digest the samples.
3.2.7.1. Nitric Acid (HNO3) and Hydrochloric Acid (HCl) Digestion
The samples collected were burnt in muffle furnace at 500oC to get its ash.
Three milliliter of concentrated nitric acid and one milliliter of hydrocholic acid in
3:1 (aqua regia) was added. The beakers were placed on hot plates by covering with watch glasses at the temperature of 90oC for the period of about one hour.
During heating, it was ensured that sample did not boil and bottom of the beaker was not allowed to go dry.
Samples were allowed to cool at room temperature, fo llowed by addition of
10 ml of de-ionized (DI) water. The samples were filtered through 0.45 micron millipore filter in order to remove any insoluble material to avoid any blockage or malfunctioning of nebulizer. Later, the filtrates were transferred to 50 ml volumetric flasks and volume was adjusted with DI water.
3.2.7.2. Standards Preparation
Standard solutions of (Al), calcium (Ca), nickel (Ni), iron (Fe) and zinc
(Zn) were prepared to determine the concentration of metals in the samples to draw the calibration curves for respective metals. The standards and samples were analyzed on Atomic Absorption Spectrophotometer (Perkin 1210 AA).
3.2.7.3. Atomic Absorption Spectroscopy
In this technique, sample solution is aspirated into flame and sample element is converted into atomic vapors. The flame contains atoms of that element.
Some of them are thermally excited by the flame, but most remain in ground state.
Then ground state atoms are capable of absorbing radiant energy of their own
49 specific resonance wavelength. The radiation of specific resonance wavelength is given off by a specific line source as hollow cathode lamp, of element to be analyzed.
Analytical grade chemicals were used in all this work.
3.2.8. Ions Analysis in Inert Material
The ions were determined using HACH Spectrophotometer DR 2010, USA
(Annex IV). 50 gram of inert material was dissolved in 100 ml of water and filtered through ordinary filter. The filtrate was processed in HACH Spectrophotometer
-2 - DR 2010 for determination of concentrations of sulphate (SO4 ), nitrate (NO 3) and chloride (Cl-) ions (APHA, 2005).
Specifications of the instrument are as under:
i. Wavelength Range: 400 - 900 nm
ii. Wavelength Resolution: 1nm iii. Source Lamp: Halogen Tungsten
iv. Detector: Silicon Photodiode, UV enhanced
v. Data Readout: 4-digit LCD, 1.5-cm Character Height
3.2.8.1. Determination of sulfate ion
After selecting the wavelength to 450nm, the contents of one Sulfa Ver 4
Sulfate reagent powder pillow was added to the sample cell filled with 25ml of sample and swirled to dissolve. After giving the reaction time of five minutes, another sample cell was filled with 25ml of blank sample and reading was adjusted
-2 to 0 mg/L (SO4 ). Finally, the prepared sample cell was pit into the cell holder for reading of sulfate ions concentration in mg/L.
50
3.2.8.2. Determination of nitrate ion
After calibration and selecting the wavelength to 500nm, the contents of one Nitra Ver 5 Nitrate reagent powder pillow was added to the sample cell filled with 25ml of sample and then swirled to dissolve. After giving the reaction time of five minutes, an amber color was developed. This indicated presence of nitrate.
The reading was adjusted to 0 mg/L (NO3-) after another sample cell was filled with 25ml of blank sample. The reading was recorded for nitrate ions of in mg/L after prepared sample cell was placed into the cell holder.
3.2.8.3. Determination of chloride ion
Similarly, after calibration, selecting the wavelength to 455nm, filling the sample cell with 25ml of sample, adding the contents reagent powder pillow and giving the reaction time of five minutes, filling another sample cell with 25ml of blank
- sample, adjusting the reading adjusted to 0 mg/L (NO3 ), the reading was noted for
Nitrate ions concentration in mg/L.
3.3. Physicochemical Analysis of Suspended Particulate Matter
The same physico-chemical characteristics, as that of inert soil/material, were determined using the standards methods.
3.3.1. pH and electrical conductivity
The suspended particulate matter collected was dissolved in de-ionized water to make slurry with the ratio of 1:1. The pH and electrical conductivity
(µS/cm) was measured by dipping the probes of the hand-held pH meter (HANNA
Instruments Model # HI 9812) and EC meter (HANNA Instruments Model # HI
9812), respectively (GTM, 2015) (Annex III-A).
51
3.3.2. Trace Metals Analysis in Particulate Matter
The filter paper with suspended particulate matter was burnt at 700oC for 15 minutes. Later, one gram of ash was dissolved and digested in 4 ml of aqua regia in light heat for the period of one hour. 10 ml of water was added and filtered through
0.45 micron millipore filter. The filtrate was then analyzed in Atomic Absorption
Spectrophotometer (PerkinElmer 1210) for concentrations of aluminum (Al), calcium (Ca), nickel (Ni), iron (Fe) and zinc (Zn), following the method of US-
EPA (Compendium Method IO-3.2) (CFR Part 50, 2008) as described in the
Section 3.2.7 above in this dissertation (Annex III-B).
3.3.3. Ions Analysis in Particulate Matter
For determination of ions, 50 gram of inert material was dissolved in 100 ml of water and filtered through ordinary filter. The filtrate was processed in
HACH Spectrophotometer DR 2010 for determination of concentrations of
-2 - - sulphate (SO4 ), nitrate (NO3 ) and chloride (Cl ) ions (APHA, 2005) as described in Section 3.2.8 above in this dissertation (Annex IV).
All analyses were done in EPA/EPD Certified Apex Environment
Laboratory Lahore and the University of Gujrat, Gujrat.
3.4. Statistical Analysis
To address the research question and achieve the objective of the study, correlation and statistical linear regression analysis was done with SPSS 16
Statistical Package (software).
SPSS 16 Statistics is a widely used software package for statistical analysis.
The software name originally stood for Statistical Package for the Social Sciences
(SPSS). It is also used by market researchers, health researchers, survey
52 companies, government, education researchers, marketing organizations, data miners, and others. In addition to statistical analysis, data management and data documentation are features of the base software.
Statistics included in the base software are:
Descriptive statistics: Frequencies, Descriptives
Bivariate statistics: Means, ANOVA, Correlation
Prediction for numerical outcomes: Linear regression
The SPSS was used to predict/estimate values of response variables
(dependent variables: physico-chemical characteristics of suspended particulate matter in the air) through explanatory variables (independent variable: physico- chemical characteristics of inert matter/material/waste at ground), or assesses the effects of the explanatory variables as predictor of response variables (Stevenson,
2001). Enter method of linear regression analysis was used.
In correlation analysis, correlation coefficient (r) is estimated, which ranges between -1 and +1 and quantifies the direction and strength of the linear association between the two or more variables. The correlation between two variables can be positive (i.e., higher levels of one variable are associated with higher levels of the other) or negative (i.e., higher levels of one variable are associated with lower levels of the other). The sign of the correlation coefficient indicates the direction of the association, while magnitude of the correlation coefficient indicates the strength of the association. Estimation or prediction of future values is not possible through correlation analysis.
Correlation quantifies the degree to which two variables are related, but does not fit a line through the data points. On the other hand, simple linear
53 regression finds the best line that predicts one variable from another. From correlation an index describing the linear relationship between two variables can be obtained; while in regression the relationship between the variables can be predicted and can be used to predict dependent variable from the independent variable.
3.4.1. Dependent and independent variables
Values of pH and electrical conductivity and concentrations of aluminum
-2 (Al), calcium (Ca), nickel (Ni), iron (Fe) and zinc (Zn) metals and sulphate (SO4 ),
- - nitrate (NO3 ) and chloride (Cl ) ions in the particulate matter were taken as dependent variables, while the values of pH and electrical conductivity and concentrations of metals and ions mentioned above in the inert matter/waste were taken as independent variables.
3.4.2. Statistical Data Treatment
The next step of the study consisted of treatment of data collected using linear regression in order to identify the relationship between the dependent variable Y (physico-chemical characteristics of suspended particulate matter) and the independent variables X (physico-chemical characteristics of inert matter). All analyses were made using the SPSS 16 Statistical Package for correlations and regression analysis for calculating regression coefficients, including regression constants and slopes for dependent variables against the predictor (independent variables).
3.4.3. Confirmatory Tests
Before developing regression models for prediction of dependent variables, in addition to significance of the regression, a test of the hypothesis of data
54 normality of the dependent variable was also conducted to check whether simple linear regression can be used for prediction or not.
3.4.4. Regression Models
After regression analysis, regression equations, using constants and slopes, were developed for determining pH and EC, concentrations of five trace metals and three ions in the particulate matter from the corresponding values/concentration of the inert material of waste.
3.4.5. Validation of the Models
Physico-chemical characteristics on both inert material and particulate matter were determined at a new construction site .Moreover, the values/concentrations of physico-chemical characteristics in the inert waste were estimated by using model established with help of regression analysis. The estimated and actual values/concentrations were compared. The percentage differences of actual/established and estimated values of the physicochemical characteristics of suspended particulate matter were calculated to test the validity of the results.
3.5. SPM MONITORING AT METRO PROJECT SITE
Suspended particulate matter monitoring was done at five construction sites of the Metro Bus Project of the twin cities. The construction sites selected are as under (Annex V & VI):
i. Site 1: IJP Road-Tipu Sultan Road Junction, Islamabad
ii. Site 2: IJP Road- 9th Ave Junction, Islamabad iii. Site 3: 9th Ave - Itwar/Pesh Morr, Islamabad
iv. Site 4: Pak Secretariat, Islamabad
55
v. Site 5: Benazir Bhutto Hospital, Murree Road, Rawalpindi
Suspended particulate matter samples were collected with High Volume Air
Sampler HV 500 F SIBATA-Japan (Annex VII & VIII-A). This open faced equipment is designed for suction flow rate of 500 L/min for measuring the dust pollution during the work environment and to carry along. It also has constant flow rate system and is loaded cumulative flow rate and timer. Other specifications include suction pressure of -160hPa (500 L/min), ability to go back to the operating state before power failure if anything happens, temperature range 0-40 degree
Celsius, and weight of 8.5 kg.
The conditioning of the filter papers [Corporation 110 mm Glass Fiber TSP
Filters] (Annex VII-B) was done at room temperature of 20 degree Celsius with the humidity of 50% for the period of 24 hours before sampling. Each filter paper was numbered and weighed. After sample collection, conditioning of the filter papers were done at 20 degree Celsius with 50% relative humidity for the period of 48 hours. Later the filter papers were weighed and concentration of the particulate matter was calculated with the volume of air sucked.
C = m/(Q x T)) (equation 3.1)
Where:
C= particulate concentration (mass/volume); m= net mass collected on the filter or substrate (mass);
Q= volumetric flow rate of the sampler (volume/time);
T= duration of sampling (time).
Samples were collected for the following three different weeks during different season.
56
i. Week 1: 06-12 July 2014
ii. Week 2: 02-08 January 2015 iii. Week 3: 09-15 April 2015
Concentrations of suspended particulate matter during three weeks periods were compared with each other and the NEQS set by the PAK-EPA to determine the contribution of inert waste and particulate matter generated during the construction process in the twin cities.
3.6. PREDICTION OF SPM CONCENTRATION AT
VARYING DISTANCES
3.6.1. Site Selection
Three construction sites:
(i) An under-construction one acre plaza, at Model Town Link Road, Lahore, the metropolitan city and the provincial capital of the Punjab Province of Pakistan
(Annex II-A),
(ii) half acre under-construction plaza at Rehman Shaheed Road, Gujrat, District
Gujrat of Province Punjab (Annex VIII-B), and,
(iii) another ¾ acre under-construction plaza at President Fazal Elahi Road,
Kharian City, a sub-division in District Gujrat of Punjab Province of Pakistan
(Annex IX-A), were selected for collecting samples of suspended particulate matter in the surrounding/ambient air for determining concentrations at varying distances from the construction site along the roads in front of construction sites.
57
3.6.2. Time and Duration of SPM Samples Collection
Samples of SPM, PM10 and SPM2.5 at all construction sites were collected from three (3) meters, eight (8) meters, 13 meters and 18 meters away from the source of particulate matter generation as per following details:
3.6.2.1. Lahore City
Samples were collected for two weeks, once in the winter from 01-07
January 2014 (Week 1) and once in the summer season from 11-17 June 2014
(Week 2).
3.6.2.2. Gujrat City
Samples were collected during various seasons for the period of three weeks from 19-25 May 2015, 13-19 June 2015 and 18-24 August 2015.
3.6.2.3. Kharian City
Samples were taken for two weeks, for seven consecutive days during each week, in different seasons, once in the summer from 26 May 2015 to 01 June 2015 and once in the winter from 17-23 November 2015.
Samples from all sites were collected on calm days with wind speed < 10 km/hr. Samples of both fine inert and PM were collected for four weeks covering different seasons and various stages of construction (earthworks, superstructure and finishing).
After completion of the construction, traffic frequency and concentration of
SPM, PM10 and PM2.5 for the period of one week from 11 October 2016 to 17
October 2016 for finding correlation between traffic frequency and concentration of particulate matter of various sizes.
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3.6.3. Particulate Matter Monitoring
Monitoring of all suspended particulate matter concentrations regarding
SPM, PM2.5, and PM10 size fractions was executed using the Dust Trak™ II
Aerosol Monitor (USA, model 8530), a light-scattering laser photometer, using iso- kinetic conditions (Annex IX-B).
The DustTrak™ II Aerosol Monitor 8530 is a desktop battery-operated, data-logging, light-scattering laser photometer. The equipment gives real-time aerosol mass readings. For low maintenance and improved reliability, the equipment uses a sheath air system to isolate the aerosol in the optics chamber to keep the optics clean. The monitor is suitable not only for the clean office settings but also for the industrial workplaces, construction and environmental sites, besides other outdoor applications. The monitor measures aerosol contaminants like dust, smoke, fumes, and mists.
Benefits and feature of the equipment include: capability to measure concentrations of PM2.5, PM10, TSP or size fractions; manual and programmable data logging functions; aerosol concentration range 0.001 to 400 mg/m3, environmental protected and tamper-proof with environmental enclosure; Cloud
Data Management System for efficient remote monitoring and Heated Inlet Sample
Conditioner to reduce humidity effects.
The monitor can also be used for industrial and occupational hygiene surveys, indoor air quality investigations, outdoor environmental monitoring, baseline trending and screening, engineering control evaluations, remote monitoring, process monitoring, emissions monitoring and aerosol research studies.
59
SPM monitoring was carried out at all the specified sampling points using iso-kinetic conditions. Monitoring was carried out thrice during each day – once in the morning, once at noon and once in the afternoon, in bright sunny days with calm wind velocity conditions ideal for PM monitoring/air pollution monitoring at the height of four feet.
3.6.3.1. Meteorological data
The meteorological data, including temperature, humidity, wind speed, rainfall and atmospheric pressure, were also recorded during sampling of SPM,
PM10 and PM2.5 from all sites of Lahore, Gujrat and Kharian and
Islamabad/Rawalpindi during all fourteen (14) weeks (Annex XX to XXVII).
3.6.4. Particulate Matter Comparison
Concentrations of SPM, PM2.5, and PM10 at the distances of 3, 8, 13 and
18m at each construction site and during the each week were compared not only with each other but also the National Environmental Quality Standards (NEQS) of the Pakistan Environmental Protection Agency (Pak-EPA).
3.6.5. Statistical Analysis
To address the research question and achieve the objective of the study, correlation and statistical linear regression analysis was done with SPSS 16
Statistical Package (software) to predict/estimate concentration of response variables (dependent variables: concentrations of SPM, PM2.5 and PM10 at 8, 13 and 18 m from the source of generation of particulate matter ) through explanatory variables (independent variable: concentrations of SPM, PM2.5 and PM10.0 at 8m from the source of generation of particulate matter), or assesses the explanatory
60 variables as predictor of response variables (Stevenson, 2001). Enter method of linear regression analysis was used.
3.6.5.1. Dependent and independent variables
Concentrations of SPM, PM2.5, and PM10 at 8, 13 and 18 m from the source of generation of particulate matter were taken as dependent variables, while the concentrations of SPM, PM2.5, and PM10 at 3 m from the source of generation of particulate matter were taken as independent variables. For monitoring particulate matter in the air, it is standard to take air samples/PM samples at the distance of three meters from the source of generation of particulate matter. As monitoring particulate matter in the air from 3 meters from the source of generation is the standard, hence three meters was taken as the independent variable.
3.6.5.2. Statistical data treatment
The next step of the study consisted of treatment of data collected using linear regression in order to identify the relationship between the dependent variable Y (Concentrations of SPM, PM2.5, and PM10 at 8, 13 and 18 m from the source of generation of particulate matter) and the independent variables X
(concentrations of SPM, PM2.5, and PM10 at 3 m from the source of generation of particulate matter). All analyses were done using the SPSS 16 Statistical Package for correlations and regression analysis for calculating regression coefficients, including regression constants and slopes for dependent variables against the predictor (independent variables).
3.6.5.3. Confirmatory tests
Before developing regression models for prediction of dependent variables, in addition to significance of the regression, a test of the hypothesis of data
61 normality of the dependent variable was also conducted to check whether simple linear regression can be used for prediction or not (Annex X to XIX).
3.6.6. Regression Models
After regression analysis, regression equations, using constants and slopes, were developed for determining concentrations of SPM, PM2.5, and PM10 at 8, 13 and 18 m from the source of generation of particulate matter from the corresponding concentration of SPM, PM2.5, and PM10 at 3 m distance from the source of generation of particulate matter.
3.6.7. Validation of the Models
Concentrations of SPM, PM2.5, and PM10 from 3, 8, 13 and 18 m were determined at new construction site in Jhelum City. Moreover, concentrations of
SPM, PM2.5, and PM10 at 8, 13 and 18 m were estimated by using determined concentrations of particulate matters at 3m from the source of generation and models established with help of regression analysis. The estimated and actual concentrations were compared. The percentage differences of actual/determined and estimated values of the concentrations were calculated to test the validity of the results.
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Chapter 4
4. RESULTS AND DISCUSSION
Based upon the data, samples and analysis conducted as per material and methods in the previous chapter, results obtained have been described as under:
4.1. CONSTRUCTION WASTE ASSESSMENT
The average years of professional experience of respondents were approximately five years. So, it can be concluded that the respondents were suitable and have acquired adequate relevant experience of the construction industry. Therefore, based on this ascertain, the information provided by these respondents was considered reliable and dependable.
Figure 4-1: Percentage of respondents in the survey
Figure 4-1 demonstrates 35.52% respondents were civil engineers, 23.36% were architects, 21.65% were quantity surveyors and 19.46 % were contractors. Based on this information, it can be stated that civil engineers, followed by contractors, played a major role in this study. As far as academic qualification of respondents is
63 concerned, 56.45% were bachelors and 18.49% was masters’ degree holders
(Figure 4-2). Hence, it can be deduced that most of the respondents were highly educated and information provided by them were reliable. Table 4.1 exhibits the quantitative assessment of cutting waste generated on construction sites. The table shows that pipes had highest percentage of wastages (12%), followed by false ceiling (11.44%). On the other hand, wires and cables and roofing sheets have the least percentage wastages (7.67% and 8.73%). Table 4-6 shows that error in calculation/cutting and poor material handling/operations are the main reasons behind this high percentage (Gavilan and Bernold, 1994; Skoyles and Skoyles,
1987). However, in another study in Nigeria, wastage of reinforcement bars was found to be highest (19.03%), followed by wires and cables (17.26%) and roofing sheets and pipes (both 15.70%). Poor and multiple handling of tools, and inadequate training of the construction workers to handle sophisticated equipment were stated to be reason of wastage (Babatunde and Olusola, 2012;
(Gavilan and Bernold, 1994). Almost same wastage due to cutting (10%) was reported by Katz and Baum (2011).
64
Table 4-1: Quantitative assessment of cutting waste at construction sites
No of Responses against each Types of Cutting Waste [Frequency (f)] Class Mean Reinforcement Roof Roofing False Wires and Interval (x) Pipes bars carcass sheets Ceiling cables 00-05% 2.5 137 103 135 90 119 51 5.1-10% 7.5 109 133 123 121 190 90 10.1-15% 12.5 46 80 71 85 75 153 15.1-20% 17.5 75 55 81 51 23 85 20.1-25% 22.5 23 26 1 38 4 30 25.1-30% 27.5 10 11 0 17 0 2 30.1-35% 32.5 9 2 0 9 0 0 35.1-40% 37.5 2 1 0 0 0 0 Sum fx 4207.50 4207.50 3587.50 4702.50 3152.50 4932.50 Mean (%) 10.24 10.24 8.73 11.44 7.67 12.00
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Table 4-2 shows quantitative assessment of Theft and Vandalism (T&V) during construction. The table indicates that Wood/Timber have highest percentage of wastages of 16.78 %, followed by sand and cement with percentage of 16.03 and
13.80%, respectively; while wood preservatives and pipes have the least percentage wastages of 8.90% and 10.65%, respectively. The main reason for this wastage was found to be improper storage as indicated in Table 4-6. Same reason was stated in another study carried out in Malaysia (Skoyles and Skoyles, 1987). Contrary to this, Babatunde and Olusola (2012) reported that reinforcement bars, timber and cement had the highest percentage of wastages (18.64%, 18.64% and 18.44%, respectively), due to workers’ poor or no educational level and poverty in Nigeria.
Educational Qualification
Below Bachelors 25%
Bachelors Masters 56% 19%
Figure 4-2: Percentage of educational qualification of the respondents
Table 4-3 reveals that blocks & bricks, tiles and window glazing have the highest percentage of wastages of 13.61%, 10.19% and 6.79%, respectively, in the category of Transit Waste. Out of all constituents of the Transit Waste in this study, blocks and bricks, tiles and ceramic appliances contribute in the generation of suspended particulate matter in the surrounding air.
66
Table 4-2: Quantitative assessment of Theft & Vandalism Waste at construction sites
No of Responses against each Types of T&V Waste [Frequency (f)] Class Means Crushed Wood or Wires & Wood Reinforcement Intervals (x) Cement Sand Clay Pipes stone Timber Cables preservatives bars 00-05% 2.5 53 34 48 66 55 67 100 88 73 5.1-10% 7.5 86 63 100 110 50 103 80 203 146 10.1-15% 12.5 69 56 169 92 61 84 149 37 92 15.1-20% 17.5 139 127 36 36 107 85 55 83 49 20.1-25% 22.5 39 101 57 66 67 70 13 0 31 25.1-30% 27.5 18 28 1 28 33 2 9 0 17 30.1-35% 32.5 7 2 0 12 18 0 5 0 2 35.1-40% 37.5 0 0 0 1 20 0 0 0 1 Sum fx 5672.50 6587.50 4922.50 5452.50 6897.50 5107.50 4377.50 3657.50 4552.50 Mean (%) 13.80 16.03 11.98 13.27 16.78 12.43 10.65 8.90 11.08
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Out of all constituents of theft and vandalism waste of our study, cement, sand, clay and crushed stone produce fine inert waste, which ultimately generate suspended particulate matter in the surrounding air.
While ceramic sanitary appliances and prefabricated windows have the least percentage of wastages with 5.41% and 5.63%. As against this study, a survey, conducted by Babatunde and Solomon Olusola (2012), indicated tiles, window glazing and ceramic sanitary with highest wastage of 21.38%, 14.73% and 14.72%, respectively, while prefabricated windows and blocks/bricks with least percentage wastages of 11.58% and 14.15%, respectively. The reason was reported to be deplorable road network in Nigeria.
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Table 4-3: Quantitative assessment of Transit Waste at construction sites
No of Responses against each Types of Transit Waste [Frequency (f)] Class Means Blocks Ceramic Intervals (x) Window Prefabricated & Tiles sanitary glazing windows Bricks appliances 00-05% 2.5 70 115 211 53 219 5.1-10% 7.5 93 208 151 128 146 10.1-15% 12.5 81 30 43 187 45 15.1-20% 17.5 83 30 4 42 1 20.1-25% 22.5 27 2 2 1 0 25.1-30% 27.5 42 0 0 0 0 30.1-35% 32.5 14 0 0 0 0 35.1-40% 37.5 1 0 0 0 0 Sum fx 5592.50 2792.50 2312.50 4187.50 2222.50 Mean (%) 13.61 6.79 5.63 10.19 5.41
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Table 4-4 describes that mortar has the highest percentage of wastage of
9.73%, followed by paint with wastage of 8.36%, whereas, application of
POP/POP ceiling and concrete has the least percentage of wastages of 7.20% and
8.25%, respectively, among the application waste. Reason behind this wastage was found to be over ordering and improper storage as shown in Table 4-6. Same results were given by Skoyles and Skoyles (1987) in their study conducted in
Malaysia. Whereas, in another study conducted in Nigeria, wastage of the POP ceiling was reported as highest (15.70%), followed by wastage of mortar (14.91%), concrete (14.13%) and paint (12.95%), respectively. The reason was stated as multiple handling of tools, and inadequate training of the workers to handle sophisticated equipment (Babatunde and Olusola, 2012).
Among application waste, mortar, concrete and POP contribute in produce fine inert waste, which ultimately generate suspended particulate matter in the surrounding air.
Table 4-5 represents the overall mean percentage of waste categories on construction sites. The table demonstrates that theft and vandalism has the highest average wastage of 12.77% followed by cutting waste with 10.05 % wastage.
Transit waste and application waste have least overall average wastage of 8.32% and 8.39%, respectively. All the respondents were of the view that overall mean percentage of waste at any construction project should not be more than five percent.
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Table 4-4: Quantitative assessment of Applications Waste at construction sites
No of Responses against each Types of Applications Waste Class Means [Frequency (f)] Intervals (x) Morter Concrete Paint POP/POP ceiling (cement+sand) (mortar+stone) 00-05% 2.5 100 60 83 140 5.1-10% 7.5 178 207 211 171 10.1-15% 12.5 95 67 92 86 15.1-20% 17.5 38 60 22 13 20.1-25% 22.5 0 13 3 1 25.1-30% 27.5 0 3 0 0 30.1-35% 32.5 0 1 0 0 35.1-40% 37.5 0 0 0 0 Sum fx 3437.50 3997.50 3392.50 2957.50 Mean (%) 8.36 9.73 8.25 7.20
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Earlier, a study conducted in Nigeria, concluded that theft and vandalism waste had the highest average level of 16.58% followed by cutting waste with 15.44%. Application waste and transit waste had the least overall average wastage of 14.16 % and 14.89% respectively (Babatunde and Olusola,
2012).
In this study, the total means wastage was calculated as 9.88%, which is in accordance with the findings of Shen et al. (2005), who reported wastage rate as equivalent to 1–10% of the purchased construction materials and much less than the reported by Yahya and Boussabaine (2006), who found out wastage of about
25% of construction materials during construction activities. The wastage rate in
Nigerian and the UK construction industry were reported as high as 15.32%
(Babatunde and Olusola, 2012) and 10–15% (McGrath and Anderson, 2000), respectively. In another study, surprisingly, 30% of the weight of total construction materials on site has been reported in the UK.
From business and financial viewpoint, the cost of construction waste revealed in this study is too high. Reducing wastage to 5% or less may certainly help in saving billions in case of mega projects and millions in case of small or medium sized construction projects.
Table 4-6 shows that 62.77% respondents believe that the reason for cutting waste is error in calculations and cutting while 24.09% were of the view that poor material handling/operations are the main reasons as stated by Gavilan and Bernold
(1994) and Skoyles and Skoyles (1987) in their findings. Improper storage was declared as the major source of theft and vandalism waste by 69.34% respondents.
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Table 4-5: Overall mean percentage of waste categories on construction sites
Waste Types Mean Wastage (%) Cutting waste 10.05 T & V Waste 12.77 Transit waste 8.32 Application Waste 7.39 Total Waste 9.88
Similarly 78.35% construction stakeholders opined transportation as main cause of transit waste. Therefore, it can be concluded that careful calculations and proper material handling can lead to reduction is cutting waste. Similarly, theft and vandalism waste can be reduced by proper storage of the construction material.
However, respondents indicated multiple reasons for wastage of application waste including over ordering (60.58%), improper storage (14.36%), poor planning
(14.11%) and poor material handling/operations (10.95%)
In general, it may be deduced that all the construction materials have higher percentage wastages due to poor and multiple handling of tools, and inadequate training of the construction workers to handle sophisticated equipment. Theft and vandalism was supposed to be very common among poor, unskilled and uneducated workers.
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Table 4-6: Reasons and source identification for each kind of waste
Sources/reasons of Cutting T&V Transit Application wastage waste (%) Waste (%) waste (%) Waste (%) Faulty or fancy design 13.14 - - - Improper storage - 69.34 - 14.36 Over ordering - 24.57 14.84 60.58 Error in calculations/cutting 62.77 - - - Poor material 24.09 6.08 - 10.95 handling/operations Poor planning - - 6.81 14.11 Transportation - - 78.35 -
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4.2. PREDICTION OF SPM CHARACTERISTICS
The results of the statistical analysis are presented in five parts – (1) comparison of the average values and concentration of physico-chemical characteristics of inert waste material and that of suspended particulate matter
(Figure 4-3), (2) two tailed Pearson correlations of physico-chemical characteristics of inert waste to the corresponding physico-chemical characteristics of suspended particulate matter at 0.01 level (Table 4-7), (3) analysis of variance (ANOVA) of regression analysis (Table 4-8), (4) simple linear regression curves with R2 values
(Figure 4-4 to 4-13), and (5) statistical models for determining values and concentrations of physico-chemical characteristics (y) of suspended particulate matter in the air with determined values/concentrations of physico-chemical characteristics of inert matter at new location (x), regression constant (a) and value of slope (b) of the regression curve, at any new locations (Table 4-9).
Concentration of sulfate in SPM is higher than that of the inert matter. The reason may be due to additional contribution of the sulfate from any other source in air, especially diesel fuel used in the public transport.
Table 4-7: Pearson correlation (two tailed) between various physico-chemical characteristics of inert material and particulate matter
Characteristics Significance S No Correlation P value (Inert vs PM) at 0.01 level 1 pH 0.778 2 EC 0.494
3 Al 0.708
4 Ca 0.792 5 Ni 0.757 0.00 6 Fe 0.813
7 Zn 0.945 Significant -2 8 SO4 - 0.485 -1 9 NO3 0.592 10 Cl-1 0.830
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4.2.1. Correlations Analysis
Table 4-7 exhibits two-tailed Pearson correlations between values/concentrations of physic-chemical characteristics of inert material at ground and suspended particulate material in the surrounding/ambient air. The P values
(0.000 in all cases) show that all the correlations were highly significant at 0.01 level. The correlation ranges from highest 0.945 to lowest 0.484. The highest correlation was found in case of Zn (0.945), followed Cl-1 (0.830) and Fe (0.813),
-2 whereas, lowest correlation was determined in case of SO4 (0.485), followed by
- electrical conductivity (0.494) and NO3 (0.592). All the correlations were positive, except that of sulfate’s. The negative correlation of sulfate is surprising and needs further investigation for digging out the reason behind this unanticipated
-2 - behaviour. However, possible reasons of low correlations for SO4 and NO3 might be due to the effect and contribution of some other sources, e.g. road traffic emissions.
4.2.2. Linear Regression Analysis:
Coefficient of determination (R2) is an important concept in regression analysis and is believed to be one of the parameters to verify and confirm the efficiency and validity of regression model for estimation purpose. The maximum
2 − 2− R value is found in case of Zn (0.892) followed by Cl (0.688), while SO4 , followed by electrical conductivity, exhibit the minimum value of R2 as 0.235 and
0.244, respectively. Usually it is considered that higher the value of R2, the better the model will fit the data and greater will be the explanatory power of the regression. But only R2 is insufficient to decide about the goodness of fit of model.
Smaller R2 value always does not mean model is not good for estimation. In such
76 cases, R2 is interpreted with ANOVA significance for proper model interpretation.
2 2− In this study, R value for SO4 and electrical conductivity are only 24%, but their
ANOVA results are significant and hence only 24% explained variation cannot be neglected and can be considered for estimation of the dependent variables.
Table 4-8 exhibits summary of analysis of variance (ANOVA) of simple linear regression showing R2 values, F values, P values and significance at 0.01 level. These values shows that R2 values of all physico-chemical characteristics were significant at 0.01 level. Figures 4-4 to 4-13 illustrate linear regression curves between determined/observed values/concentrations of physico-chemical characteristics of inert material (independent variable: along x-axis) and corresponding characteristics of suspended particulate matter (depended variable: along y-axis). All the curves/graphs, except Figure 4-9, demonstrated that values/concentrations of physico-chemical characteristics of suspended particulate matter decreased as compared to values/concentrations of physico-chemical characteristics of inert material. However, in case of Figure 4-9, concentration of sulfate in SPM (dependent variable) increased as compared to concentration of sulfate in inert matter (independent variable). Nonetheless, the extent of increase or decrease depends upon slope of the corresponding regression line/curve.
The meteorological data during all four weeks of sample collection have been show in Annex XX, XXI-A and XXII-A. As the wind velocity was calm during all days of the four weeks, therefore, there was no effect of wind velocity and direction on the dispersion of particulate matter. It is pertinent to mention that there was no rain during all days of four weeks during which the samples were taken and hence effect of wind and rain is negligible.
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Chloride (mg/l) [PM]
Chloride (mg/l) [inert]
Nitrate (mg/l) [PM]
Nitrate (mg/l) [inert]
Sulfate (mg/l) [PM]
Sulfate (mg/l) [inert]
Zn (µg/g) [PM]
Zn (µg/g) [inert]
Fe (µg/g) [PM]
Fe (µg/g) [inert] Week 4 Week 3 Ni (µg/g) [PM] Week 2 Week 1 Ni (µg/g) [inert]
Ca (µg/g) [PM]
Ca (µg/g) [inert]
Al (µg/g) [PM]
Al ( µg/g) [inert]
EC (µS/cm) [PM]
EC (µS/cm) [inert]
pH [PM]
pH [inert]
0.0 50.0 100.0 150.0
Figure 4-3: Comparison of physico-chemical characteristics of fine inert construction waste and suspended particulate matter
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Figure 4-4: Relationship between pH value of SPM and construction waste dumped on ground
Figure 4-5: Relationship between electrical conductivity of SPM and construction waste dumped on ground
79
Figure 4-6: Relationship between concentration of Al observed in the inert waste dumped and SPM collected samples
Figure 4-7: Relationship between concentration of Ca observed in the inert waste dumped and SPM collected samples
80
Table 4-8: Regression analysis (ANOVA) of physico-chemical characteristics
Significance S No Characteristics R2 P value at 0.01 level 1 pH 0.606 2 EC 0.244 3 Al 0.501
4 Ca 0.627 Significant 5 Ni 0.572 0.000
6 Fe 0.660
7 Zn 0.892 -2 8 SO4 0.235 -1 9 NO3 0.351 10 Cl-1 0.688
Figure 4-8: Relationship between concentration of Ni observed in the inert waste dumped and SPM collected samples
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Figure 4-9: Relationship between concentration of Fe observed in the inert waste dumped and SPM collected samples
Figure 4-10: Relationship between concentration of Zn observed in the inert waste dumped and SPM collected samples
82
-2 Figure 4-11: Relationship between concentration of SO4 observed in the inert waste dumped and SPM collected samples
-1 Figure 4-12: Relationship between concentration of NO3 observed in the inert waste dumped and SPM collected samples
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Figure 4-13: Relationship between concentration of Cl-1 observed in the inert waste dumped and SPM collected samples
4.2.3. Data Normality Tests:
Though, regression analyses of all the characteristics were found significant, but before developing regression models for prediction of dependent variables, data normality test of the dependent variable were also conducted to check whether simple linear regression can be used for prediction of dependent variable or not.
Data normality test of all dependent variable have been shown in the
Annexure X to XIX. After confirmation of the normality of the dependent variables, regression based models were established.
4.2.4. Statistical Regression-Based Models
Table 4-9 exhibits simple linear regression-based models [Y = a + b (x)] developed for determining the values and concentrations of each physico-chemical
84 characteristics in suspended particulate matter (dependent variables) at any other construction site, by determining only the values and concentrations of corresponding physico-chemical characteristics of inert material (independent variables) at the new site.
In the model [Y = a + b (x)], Y is value/concentration of physico-chemical characteristics of suspended particulate matter (dependent variable: to be estimated at new location), a and b are the values of constant and slope of regression line/curve (both determined for each and every physico-chemical characteristics in regression analysis), and x is the value/concentration of physico-chemical characteristics of inert material (independent variable) determined at the new location.
The constant/intercept itself and alone doesn’t tell anything about the relationship between predictor and response (independent and dependent variable).
The negative value of constant in case of pH, Al, Ca and Zn is simply indicating that the fitted line is passing through x-axis. The intercept/constant is like a matter of extrapolation and extrapolation towards base/x-axis is a meaningless extrapolation.
For example, if house prices are modeled in terms of size of rooms. If you use the raw data, the intercept is a rather meaningless extrapolation – the price of a zero-roomed house; you are extrapolating beyond the observed data! Further, in case, there is no fine inert, the models will not be applicable. So there is no justification in stating that ‘the constant b is the level of ambient air pollutant in case there is no emission from a construction site (inert = 0)’. So, keeping in view
85 all above arguments, it is not justified to say that the value of constant represents the background/ambient air concentration.
Therefore, at any new construction site, employing the statistical regression models given in Table 4-9, values/concentrations of pH and EC and concentrations of aluminum (Al), calcium (Ca), nickel (Ni), iron (Fe) and zinc (Zn) metals and
-2 - - sulfate (SO4 ), nitrate (NO3 ) and chloride (Cl ) ions in suspended particulate matter in air can be estimated only by determining the corresponding values/concentrations of pH and EC and concentrations of aluminum (Al), calcium
-2 - (Ca), nickel (Ni), iron (Fe) and zinc (Zn) metals and sulfate (SO4 ), nitrate (NO3 ) and chloride (Cl-1) ions in the inert matter on ground at any construction site.
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Table 4-9: Statistical Regression-based models (y = a + b.x) for determination of various physico-chemical characteristics of particulate matter
Statistical model for Constant Slope dependent variable in S No Characteristics Unit (a) (b) particulate matter [y = a + b(x)] 1 pH - -1.264 1.136* Y= -1.264 + 1.136 (x) 2 EC (µS/cm) 73.014 0.323* Y= 73.014 + 0.323 (x) 3 Al (µg/g) -3.120 0.996* Y = -3.120 + 0.996 (x) 4 Ca (µg/g) -9.270 0.977* Y= -9.270 + 0.977 (x) 5 Ni (µg/g) 2.031 0.825* Y= 2.031 + 0.825 (x) 6 Fe (µg/g) 30.430 0.881* Y= 30.430+ 0.881 (x) 7 Zn (µg/g) -8.291 0.883* Y= -8.291 + 0.883 (x) -2 8 SO4 (mg/l) 41.574 -0.532* Y= 41.574 + -0.532 (x) - 9 NO3 (mg/l) 3.695 0.593* Y= 3.695 + 0.593 (x) 10 Cl- (mg/l) 6.889 0.810* Y= 6.889 + 0.810 (x) *Significant at 0.01 level
87
4.2.5. Validity of the models
At a new construction site at Model Town Link Road Lahore, Pakistan, physico-chemical characteristics on both inert material and particulate matter were determined. Moreover, the values/concentrations of physico-chemical characteristics in the inert waste were estimated by using model established with help of regression based models.
The estimated and actual values/concentrations were compared in the Table
4-10. The same type of comparison was adopted by Kern at al. (2015) in their study in which they estimated construction waste generation by linear regression
-1 analysis. The percentage difference varied from -16.3 in case of NO3 to 19.8 in case of Al. The minimum difference was found in case of pH (3.8%), followed by
Ca (-4.3%), Fe (8.6%) and Cl-1 (-9.1%).
However, all the differences were less than 20%, which underlines the reliability of the statistical models established for estimating phyico-chemical characteristics.
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Table 4-10: Validation of the regression based models by comparing estimated and actual values at a new construction site
Dependent Dependent Independent variable variable S No Characteristics Unit % Difference variable (x) (Y) (Y) (estimated) (actual) 1 pH - 7.6 7.3696 7.1 3.8 2 EC (µS/cm) 145 119.849 135 -11.2 3 Al (µg/g) 22.5 19.29 16.1 19.8 4 Ca (µg/g) 81 69.867 73 -4.3 5 Ni (µg/g) 11.5 11.5185 12.7 -9.3 6 Fe (µg/g) 180 189.01 174 8.6 7 Zn (µg/g) 60.7 45.3071 40.7 11.3 -2 8 SO4 (mg/l) 31 25.082 29 -13.5 -1 9 NO3 (mg/l) 22 16.741 20 -16.3 10 Cl-1 (mg/l) 24.7 26.896 29.6 -9.1
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4.3. SPM MONITORING AT RAWALPINDI ISLAMABAD
METRO PROJECT SITE
Figure 4-14 illustrates concentration of suspended particulate matter (SPM) the Rawalpindi Islamabad Metro Project Site (Annex V & VI) during three weeks,
ie. 06-12 July 2014, 02-08 January 2015 and 09-15 April 2015.
)
3
Conc(mg/m
Figure 4-14: Comparison of SPM Concentrations at five Metro Project Sites
The figure shows that maximum concentration of suspended particulate matter was found at IJP-Tipu Sultan Road Junction, Islamabad, followed by
Benazir Bhutto Hospital, Rawalpindi, during all three weeks. Whereas, minimum concentration of suspended particulate matter was found at Pakistan Secretariat,
Islamabad. However, it was observed that at all project sites, concentration of suspended particulate matter was well beyond permissible limits of 500 microgram/m3 set by the PAK-EPA.
The meteorological data during all three weeks of sample collection have been shown in Annex XXIII to XXIV. As, during all days of the three weeks, the
90 wind velocity was calm, therefore, there was no effect of wind velocity and direction on the dispersion of particulate matter. Further, there was no rain during all days of three weeks and hence there was no effect of rain on particulate matter generation and dispersion.
4.4. COMPARISON OF THE SUSPENDED PARTICULATE
MATTER CONCENTRATIONS
4.4.1. Lahore City
Figures 4-15 to 4-17 depict comparative analysis of concentrations of suspended particulate matter (SPM), PM10 and PM2.5 at the construction site in
Lahore during the week starting from 01-07 January 2014 (Week 1), while figures
4-18 to 4-20 exhibit concentrations of suspended particulate matter (SPM), PM10 and PM2.5 during the week from 11-17 June 2014 (Week 2) at the distances of 3, 8,
13 and 18m.
During the week from 01-07 January 2014, concentration of SPM remained below the permissible limits of 500 µg/m3 as per NEQS set by the PAK-EPA.
However, during the week from 01-07 January 2014, concentration of PM10 exhibited the random response, some days below and some days above the permissible limits of 150 µg/m3 as per NEQS set by the PAK-EPA. This trend might be due to high rise building in proximity of the monitor.
In case of PM2.5, the concentration, like SPM, also remained below the permissible limits of 35 µg/m3 as per NEQS set by the PAK-EPA.
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600 SPM
500
) 400 3
300
200
3m Concentration (µg/m 8m 100 13m 18m 0 Wednesday Thursday Friday Saturday Sunday Monday Tuesday 01/01/2014 02/01/2014 03/01/2014 04/01/2014 05/01/2014 06/01/2014 07/01/2014
Figure 4-15: Comparison of SPM concentrations at varying distances at Lahore construction site during 01-07 January 2014
250 3m PM10 8m 200
13m
) 3 18m 150
100 Concentration (µg/m
50
0 Wednesday Thursday Friday Saturday Sunday Monday Tuesday 01/01/2014 02/01/2014 03/01/2014 04/01/2014 05/01/2014 06/01/2014 07/01/2014
Figure 4-16: Comparison of PM10 concentrations at Lahore construction site at varying distances during 01-07 January 2014
92
40
PM2.5 35 3m
30
)
3 8m 25 13m 20 18m
15
Concentration (µg/m 10
5
0 Wednesday Thursday Friday Saturday Sunday Monday Tuesday 01/01/2014 02/01/2014 03/01/2014 04/01/2014 05/01/2014 06/01/2014 07/01/2014
Figure 4-17: Comparison of PM2.5 concentrations at Lahore construction site at varying distances during 01-07 January 2014
SPM 600 3m
500 8m
) 3 13m
400 18m
300 Concentration (µg/m 200
100 Wednesday Thursday Friday Saturday Sunday Monday Tuesday 11/06/2014 12/06/2014 13/06/2014 14/06/2014 15/06/2014 16/06/2014 17/06/2014
Figure 4-18: Comparison of SPM concentrations at Lahore construction site at varying distances during 11-17 June 2014
93
200
PM10
)
3 150
100 Concentration (µg/m 3m 50 8m 13m 18m 0 Wednesday Thursday Friday Saturday Sunday Monday Tuesday 11/06/2014 12/06/2014 13/06/2014 14/06/2014 15/06/2014 16/06/2014 17/06/2014
Figure 4-19: Comparison of PM10 concentrations at Lahore construction site at varying distances during 11-17 June 2014
40
PM2.5 35
3m
) 30 3 8m
25 13m
18m 20
Concentration (µg/m 15
10
5 Wednesday Thursday Friday Saturday Sunday Monday Tuesday 11/06/2014 12/06/2014 13/06/2014 14/06/2014 15/06/2014 16/06/2014 17/06/2014
Figure 4-20: Comparison of PM2.5 concentrations at Lahore construction site at varying distances during 11-17 June 2014
94
Similarly, during the week from 11-17 June 2014, concentration of SPM was recorded below the NEQS set by the PAK-EPA. During the same week, concentration of PM10 showed the random and mixed response due to random activity and mechanical disturbance.
However, during the week from 11-17 June 2014, concentration of PM2.5 was also found below the NEQS set by the PAK-EPA.
The meteorological data during all two weeks of sample collection have been shown in Annex XXI-B and XXII-B. The calm wind velocity during all days of the two weeks, caused no effect of wind velocity and direction on the dispersion of particulate matter. Further, as the samples were taken from the one-way wide urban roadside, at both side of which, there were high rising buildings, plazas and towers etc, therefore, there was no tunnel effect of the wind as well. Hence, the dispersion and movement of the particulate matter were only in the direction towards which the traffic was flowing. It is pertinent to mention that there was no rain during all days of two weeks during which the samples were taken and hence there was no effect of rain on particulate matter generation and dispersion.
4.4.2. Gujrat City
Figures 4-21 to 4-29 demonstrate comparison of concentrations of SPM,
PM10 and PM2.5 at the construction site in Gujrat during the week from 19-25 May
2015, 13-19 June 2015 and 18-24 August 2015 at the distances of 3, 8, 13 and 18m from the source of generation of particulate matters.
95
800
SPM 750
700
) 3
650
600
550
Concentration (µg/m 3m
500 8m
13m 450 18m 400 Tuesday Wednesday Thursday Friday Saturday Sunday Monday 19/05/2015 20/05/2016 21/05/2015 22/05/2015 23/05/2015 24/05/2015 25/05/2015
Figure 4-21: Comparison of SPM concentrations at Gujrat construction site at varying distances during 19-25 May 2015
The concentration of SPM and PM10 was observed beyond the permissible limits of 500 µg/m3 and 150 µg/m3 during the week 19-25 May 2015, at 3m, 8m,
13m and 18 m distances from the source of generation of particulate matter, with the exception of concentration of SPM at 18 m distance on Friday. On Friday and
Saturday, the concentration was below the permissible limit due to less activities and traffic due to Friday prayers and weekly leave in the city. Though, being a metropolitan city, the mechanical disturbance at and around the construction site in
Lahore was more as compared construction site in Gujrat, the suspended particulate matter generation was less is Lahore than Gujrat owing to watering at construction site, covering of fine inert material with plastic sheets and construction activities during the night time when other mechanical disturbances were less.
96
500
PM10 450 3m
8m
) 400
3 13m
350 18m
300
250 Concentration (µg/m 200
150
100 Tuesday Wednesday Thursday Friday Saturday Sunday Monday 19/05/2015 20/05/2016 21/05/2015 22/05/2015 23/05/2015 24/05/2015 25/05/2015
Figure 4-22: Comparison of PM10 concentrations at Gujrat construction site at varying distances during 19-25 May 2015
80
PM 70 2.5
60
) 3 50
40
30 Concentration (µg/m 20 3m 8m 10 13m 18m 0 Tuesday Wednesday Thursday Friday Saturday Sunday Monday 19/05/2015 20/05/2016 21/05/2015 22/05/2015 23/05/2015 24/05/2015 25/05/2015
Figure 4-23: Comparison of PM2.5 concentrations at Gujrat construction site at varying distances during 19-25 May 2015
97
In case of PM2.5, the concentration was beyond the PAK-EPA NEQS limits at 3m and 8 m distances, while, as the particulate matter travels ahead, it settles and concentration went below the permissible limits of 35 µg/m3 at the distance of 13m and 18 m from the source of generation. During the week 13-19 May 2015, the concentration of SPM was observed beyond the permissible limits of 500 µg/m3 at
3m and 8m distance, while at the distance of 18 m distance, concentration was within permissible limit of PAK-EPA. At the distance of 13 m, the concentration of
SPM was random while comparing with standard. During the same week, concentrations of PM10 at all distances from the source were beyond the standard
3 limit of 150 µg/m . As far as PM2.5 is concerned, concentration at distance of 3m was beyond the PAK-EPA permissible limit of 35 µg/m3, but as it travels farther to
8, 13 and 18 m from the source, the concentration decreased to the permissible limit due to settlement and dispersion of the particulate matter.
SPM
700
) 600 3
500
3m
Concentration (µg/m 400 8m 13m 18m 300 Saturday Sunday Monday Tuesday Wednesday Thursday Friday 13/06/201514/06/2015 15/06/2015 16/06/2015 17/06/2015 18/06/2015 19/06/2015
Figure 4-24: Comparison of SPM concentrations Gujrat construction site at varying distances during 13-19 June 2015
98
PM10 400
3m 350
8m
) 3 13m 300 18m
250
200 Concentration (µg/m
150
100 Saturday Sunday Monday Tuesday Wednesday Thursday Friday 13/06/2015 14/06/2015 15/06/2015 16/06/2015 17/06/2015 18/06/2015 19/06/2015
Figure 4-25: Comparison of PM10 concentrations at Gujrat construction site at varying distances during 13-19 June 2015
70
60 PM2.5 3m 8m
50
) 13m 3
40 18m
30
20 Concentration (µg/m 10
0 Saturday Sunday Monday Tuesday Wednesday Thursday Friday 13/06/2015 14/06/2015 15/06/2015 16/06/2015 17/06/2015 18/06/2015 19/06/2015
Figure 4-26: Comparison of PM2.5 concentrations at Gujrat construction site at varying distances during 13-19 June 2015
99
800
SPM 750
700
) 3 650
600
3m 550 8m
Concentration (µg/m 500 13m
450 18m
400 Tuesday Wednesday Thursday Friday Saturday Sunday Monday 18/08/2015 19/08/2015 20/08/2015 21/08/2015 22/08/2015 23/08/2015 24/08/2015
Figure 4-27: Comparison of SPM concentrations at Gujrat construction site at varying distances during 18-24 August 2015
500 PM10 450
400
) 3 350
300
250 3m
200 Concentration (µg/m 8m 150 13m 100 18m 50
0 Tuesday Wednesday Thursday Friday Saturday Sunday Monday 18/08/2015 19/08/2015 20/08/2015 21/08/2015 22/08/2015 23/08/2015 24/08/2015
Figure 4-28: Comparison of PM10 concentrations at Gujrat construction site at varying distances during 18-24 August 2015
100
90
80 PM2.5
70
) 3 60
50
40
30
3m Concentration (µg/m 20 8m 13m 10 18m 0 Tuesday Wednesday Thursday Friday Saturday Sunday Monday 18/08/2015 19/08/2015 20/08/2015 21/08/2015 22/08/2015 23/08/2015 24/08/2015
Figure 4-29: Comparison of PM2.5 concentrations Gujrat construction site at varying distances during 18-24 August 2015
Figures 4-27 to 4-29 exhibit the concentration of SPM, PM10 and PM2.5, at the varying distances of 3, 8, 13 and 18m from the source of generation of particulate matter during the period from 18 August to 24 August 2015 at the construction site in Gujrat.
The concentrations of all sizes of particulate matter were found above the permissible limits of 500 microgram/m3, 150 microgram/m3 and 35 microgram/m3, in case of SPM, PM10 and PM2.5, respectively, with the exception of concentration of PM2.5 at the distance of 18m, where it was found below the standard limit of 35 microgram/m3.
As shown in Fig 4-15, SPM in Lahore site was recorded high on Saturdays and reaches lowest on Sundays. This might be due to late closing of market and people staying outside for late hours on Saturday night and very little showing up and late coming out on Sundays. Similar trend was observed in Fig 4-18 in June
101
2014. However mixed trend was observed in case of PM10, but the concentration was touching the maximum permissible levels on few days of week.
Different trend was observed in Gujrat city (Fig 4-22 – 4-24) where roads are not much wider and traffic is low on Fridays and Sundays due to two local holidays (offices and schools remain closed on Sunday and markets are mostly closed on Fridays). Concentration of both PM10 and PM2.5 were noticed above the
NEQS. Here, local traffic and local wind (calm) might be affecting dispersion of dust. Moreover, environmental practices are not strictly practiced in Gujrat as compared to Lahore city, where water spray on dust is commonly observed due to
EPD monitoring.
The meteorological data recorded during all three weeks of sample collection have been shown in Annex XXV to XXVI. As the wind velocity was calm during all days of the three weeks, therefore, there was no effect of wind velocity and direction on the dispersion of particulate matter.
Further, as the samples were taken from the wide one-way urban roadside, at both side of which, there were high rising buildings, plazas and towers etc, therefore, there was no tunnel effect of the wind as well.
Hence, the dispersion and movement of the particulate matter were in the direction towards which the traffic was flowing. It is pertinent to mention that there was no rain during all days of three weeks during which the samples were taken and hence there was no effect of rain on particulate matter generation and dispersion.
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4.4.3. Kharian City
Figures 4-30 to 4-35 illustrate trend in change of concentrations of SPM,
PM10 and PM2.5 at the varying distances of 3m, 8m, 13m and 18 m from the source of particulate matter generation during two week from the period from 26 May
2015 to 01 June 2015 and from 17-24 November 2015.
As shown in the Figures 4-30 to 4-32, concentrations of all sizes of the particulate matter was witnessed well beyond the permissible limits at all distances from the source of generation of particulate matter during the week starting from
26 May 2015 to 01 June 2015.
1500
1400 SPM
1300
1200
) 3 1100
1000
900 3m
800 8m Concentration (µg/m 700 13m
600 18m
500
400 Tuesday Wednesday Thursday Friday Saturday Sunday Monday 26/05/2015 27/05/2015 28/05/2015 29/05/2015 30/05/2015 31/05/2015 01/06/2015
Figure 4-30: Comparison of SPM concentrations at Kharian construction site at varying distances during 26 May to 01 June 2015
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1000
900 PM10
800
)
3 700
600
500
400 3m
8m
Concentration (µg/m 300
200 13m
100 18m 0 Tuesday Wednesday Thursday Friday Saturday Sunday Monday 26/05/2015 27/05/2015 28/05/2015 29/05/2015 30/05/2015 31/05/2015 01/06/2015
Figure 4-31: Comparison of PM10 concentrations at Kharian construction site at varying distances during 26 May to 01 June 2015
245
PM2.5
210
)
3 175
140 3m
105 8m
13m Concentration (µg/m 70 18m
35
0 Tuesday Wednesday Thursday Friday Saturday Sunday Monday 26/05/2015 27/05/2015 28/05/2015 29/05/2015 30/05/2015 31/05/2015 01/06/2015
Figure 4-32: Comparison of PM2.5 concentrations at Kharian construction site at varying distances during 26 May to 01 June 2015
104
Figures 4-33 to 4-35 explain the concentrations SPM, PM10 and PM2.5 at the distance of 3m, 8m, 13m and 18m from the source of generation of particulate matter during the week 17-24 November 2015 in Kharian.
From the figures mentioned above, it has been observed that concentration of SPM remained beyond the permissible limits at all four points from the source of generation of particulate matter, except for Friday. However, in case of PM10 and PM2.5, the concentration was found random, sometimes above and sometimes below the NEQS limits set by the PAK-EPA.
It was observed that concentrations of all sizes of particulate matter remain less on Friday, Saturday and Sunday due to less activity and mechanical disturbance owing to Friday prayers and weekends.
900
SPM
800
) 700 3
600
500
3m
Concentration (µg/m 400 8m
300 13m 18m 200 Tuesday Wednesday Thursday Friday Saturday Sunday Monday 17/11/2015 18/11/2015 19/11/2015 20/11/2015 21/11/2015 22/11/2015 23/11/2015
Figure 4-33: Comparison of SPM concentrations at Kharian construction site at varying distances during 17-23 November 2015
105
450
PM 400 10 3m 8m 350
13m
) 3 300 18m
250
200
150 Concentration (µg/m
100
50
0 Tuesday Wednesday Thursday Friday Saturday Sunday Monday 17/11/2015 18/11/2015 19/11/2015 20/11/2015 21/11/2015 22/11/2015 23/11/2015
Figure 4-34: Comparison of PM10 concentrations at Kharian construction site at varying distances during 17-23s November 2015
90
80 PM2.5
70
) 3 60
50
40
30
3m Concentration (µg/m 20 8m 13m 10 18m 0 Tuesday Wednesday Thursday Friday Saturday Sunday Monday 17/11/2015 18/11/2015 19/11/2015 20/11/2015 21/11/2015 22/11/2015 23/11/2015
Figure 4-35: Comparison of PM2.5 concentrations at Kharian construction site at varying distances during 17-23 November 2015
106
As no preventive measures were taken to control suspended particulate matter generation at the construction site in Kharian city as compared to construction site in Lahore, therefore particulate matter at construction site in
Kharian was higher as compared to the construction site in Lahore, where measures were adopted to suppress the generation of particulate matter. Being a smaller city than Gujrat, particulate matter generation at the construction site in Kharian was less as compared to Gujrat, owing to less mechanical disturbance and traffic flow in Kharian. Though, being a metropolitan city, the mechanical disturbance at and around the construction site in Lahore was more as compared construction site in
Gujrat, but the suspended particulate matter generation was less is Lahore than
Gujrat owing to watering at construction site, covering of fine inert material with plastic sheets and construction activities during the night time when other mechanical disturbances were less.
In Annex XXVII, the meteorological data recorded during all two weeks of sample collection have been shown. As the wind velocity was calm during all days of the two weeks, therefore, there was no effect of wind velocity and direction on the dispersion of particulate matter. Further, as the samples were taken from the wide urban roadside, at both side of which, there were high rising buildings, plazas and towers, therefore, there was no tunnel effect of the wind as well. Hence, the dispersion and movement of the particulate matter were in the direction towards which the traffic was flowing. It is pertinent to mention that there was no rain during all days of four weeks during which the samples were taken and hence there was no effect of rain on particulate matter generation and dispersion.
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4.5. STATISTICAL MODELS FOR PREDICTION OF PM
CONCENTRATIONS AT VARYING DISTANCES
The results of the statistical analysis are presented in four parts – (1) two tailed Pearson correlations between concentrations of SPM, PM10 and PM2.5 at the distance of 3, 8, 13 and 18 m from the source of generation of particulate matter at
0.01 level at all construction sites during all weeks. (Table 4-11), (2) simple linear regression curves with R2 values (Fig 4-36 to 4-44), and (3) analysis of variance
(ANOVA) of regression analysis (Table 4-12) and (4) statistical models for determining concentrations of SPM, PM10 and PM2.5 at 8m, 13m and 18m (y) with determined concentrations of SPM, PM10 and PM2.5 at 3m distance from the source of particulate generation. at new location (x), regression constant (a) and value of slope (b) of the regression curve, at any new locations (Table 4-13).
4.5.1. Correlation Analysis
Table 4-11 demonstrates two-tailed Pearson correlations between concentrations of SPM, PM10 and PM2.5 at varying distances of 3, 8, 13 and 18m from the source generation of particulate matter generation at all construction sites during all weeks.
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Table 4-11: Pearson correlations (two tailed) between concentrations of particulate matter at varying distances
3 m 8 m 13 m 18 m Particulate Correlations P value Correlations P value Correlations P value Matter SPM 0.998 0.000 0.994 0.000 0.995 0.000
PM10 0.992 0.000 0.986 0.000 0.978 0.000
PM2.5 0.996 0.000 0.989 0.000 0.978 0.000
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Figure 4-36: Regression line between SPM Conc at 3 m and 8 m distance from the source
Figure 4-37: Regression line between SPM Conc at 3 m and 13 m distance from the source
110
Figure 4-38: Regression line between SPM Conc at 3 m and 18 m distance from the source
Figure 4-39: Regression line between PM10 Conc at 3 m and 8 m distance from the source
111
Figure 4-40: Regression line between PM10 Conc at 3 m and 13 m distance from the source
Figure 4-41: Regression line between PM10 Conc at 3 m and 18 m distance from the source
112
Figure 4-42: Regression line between PM2.5 Conc at 3 m and 8 m distance from the source
Figure 4-43: Regression line between PM2.5 Conc at 3 m and 13 m distance from the source
113
Figure 4-44: Regression line between PM2.5 Conc at 3 m and 18 m distance from the source
The P values (0.000 in all cases) show that all the correlations were highly significant at 0.01 level. The correlation ranges from highest 0.998 (in case of correlations between SPM at 3 m and 8 m distance) to lowest 0.978 (in case of both
PM 10 at 3m and 18 m, and PM2.5 at 3m and 18 m). All the correlation values were positive.
4.5.2. Linear Regression Analysis
Figures 4-39 to 4-47 exhibit linear regression curves between concentrations of SPM, PM10 and PM2.5 at 3m distance from the source of generation of particulate matter (independent variable: along x-axis) and concentrations of SPM, PM10 and PM2.5 at 8, 13 and 18m distance from the source of generation (depended variable: along y-axis).
114
Table 4-12: Regression analysis (ANOVA) of particulate matter concentrations
Significance S No Regression between R2 F value P value at 0.01 level 1 SPM (3m) & SPM (8m) 0.997 4.14 x 104 2 SPM (3m) & SPM (13m) 0.988 1.19 x 104 4 3 SPM (3m) & SPM (18m) 0.989 1.32 x 10 Significant
3 0.000 4 PM10 (3m) & PM10 (8m) 0.984 9.106 x 10 3 5 PM10 (3m) & PM10 (13m) 0.972 5.015 x 10
3 6 PM10 (3m) & PM10 (18m) 0.957 3.228 x 10 3 7 PM2.5 (3m) & PM2.5 (8m) 0.993 2.04 x 10 3 8 PM2.5 (3m) & PM2.5 (13m) 0.978 6.373 x 10 3 9 PM2.5 (3m) & PM2.5 (18m) 0.956 3.153 x 10
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4.5.3. Data Normality Tests:
Though, regression analyses of all the concentrations were found significant, but before developing regression models for prediction of dependent variables, data normality test of the dependent variable were also conducted to check whether simple linear regression can be used for prediction of dependent variable or not. Data normality tests of the dependent variable have been shown in
Annexure VII to XI. After confirmation of the normality of the dependent variables, regression based models were established.
4.5.4. Statistical Regression-Based Models
Table 4-13 exhibits simple linear regression-based models [Y = a + b (x)], developed for determining the concentrations of SPM, PM10 and PM2.5 at the distance of 8m, 13m and 18 m (dependent variables) at any other construction site, by determining only the concentrations of SPM, PM10 and PM2.5 at the distance of
3m from the source of generation of particulate matter.
In the model [Y = a + b (x)], Y is concentration of SPM, PM10 and PM2.5 at the distance of 8, 13and 18 m (dependent variable: to be estimated at new location),
(a) and (b) are the values of constant and slope of regression line/curve (both determined for corresponding concentration of SPM, PM10 and PM2.5, and (x) is the concentration of SPM, PM10 and PM2.5 at the distance of 3m from the source of generation of particulate matter (independent variable) determined at the new location.
Therefore, at any new construction site/location, employing the statistical regression models given in Table 4-12, concentrations of SPM, PM10 and PM2.5 at the distance of 8, 13 and 18 m can be estimated/calculated only by determining the
116 concentrations of SPM, PM10 and PM2.5 at the distance of 3 m. Moreover, concentration of SPM, PM10 and PM2.5 can also be interpolated at any distance from the source of generation with the help of these models.
117
Table 4-13: Statistical regression-based models (y = a + b.x) for determination of particulate matter concentrations at varying distances from source of generation
Statistical model for dependent Concentration to Concentration Constant Slope S No Unit variable in particulate matter be determined to be estimated (a) (b) [y = a + b(x)] 1 SPM (3m) SPM (8m) (µg/m3 -22.724 0.963* Y= -22.724 + 0.963 (x) 3 2 SPM (3m) SPM (13m) (µg/m -77.273 0.976* Y= -77.273 + 0.976 (x) 3 SPM (3m) SPM (18m) (µg/m3 -75.693 0.892* Y = -75.693 + 0.892 (x) 3 4 PM10 (3m) PM10 (8m) (µg/m -12.423 0.937* Y= -12.423 + 0.937 (x) 3 5 PM10 (3m) PM10 (13m) (µg/m -19.159 0.854* Y= -19.159 + 0.854 (x) 3 6 PM10 (3m) PM10 (18m) (µg/m -35.951 0.795* Y= -35.951 + 0.795 (x) 3 7 PM2.5 (3m) PM2.5 (8m) (µg/m -3.788 0.884* Y= -3.788 + 0.884 (x) 3 8 PM2.5 (3m) PM2.5 (13m) (µg/m -8.465 0.821* Y= -8.465 + -0.821 (x) 3 9 PM2.5 (3m) PM2.5 (18m) (µg/m -11.033 0.734* Y= -11.033 + 0.734 (x) *Significant at 0.01 level
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4.5.5. Validity of the models
At a new construction site in District Jhelum, Pakistan, concentrations of
SPM, PM10 and PM2.5 were determined at 3m, 8m, 13 and 18 m from the source of generation of particulate matter.
Moreover, concentrations of particulate matter at 8, 13 and 18 m were also estimated by using models established with the help of regression analysis. The estimated and actual concentrations were compared in the Table 4-14 (Kern at al.,
2015).
The percentage difference varied from -8.6 (in case of concentration of
PM2.5 at 8m distance from source of generation of particulate matter) to 7.5 when concentration of PM2.5 was estimated at 18 m distance.
The minimum difference was found in case of concentration of SPM at 8 m distance (1.5%), followed by concentration of SPM at 13 m (2.7%) and concentration of SPM at 18 m (3.5%). Whereas maximum difference was found in case of concentration of PM2.5 at 8 m (-8.6%), followed by concentration of
PM2.5 at 18 m (7.5%) and concentration of PM10 at 13 m (6.9%).
However, all the differences were less than 10%, which underlines the reliability of the statistical models established for estimating concentrations of different sizes of particulate matters at varying distances from the source of generation at the construction site.
119
Table 4-14: Validation of the regression based models by comparing estimated and actual values at a new construction site
Concentration Particulate Distance 3 % Sr No (µg/m ) Matter (m) Difference Actual Estimated 1 3 678 - - 2 8 621 630 1.5 SPM 3 13 569 584 2.7 4 18 511 529 3.5 5 3 324 - - 6 8 274 291 6.3 PM 7 10 13 241 258 6.9 8 18 231 222 -4.1 9 3 119 - - 10 8 111 101 -8.6 PM 11 2.5 13 85 89 5.0 12 18 71 76 7.5
4.6. GEOGRAPHICAL BOUNDARIES
In one way or the other, a growing number of studies justifiably put emphasize on the importance of regionalization. Results of the studies may change from region to region depending upon the various factors involved in the studies affecting the results. As far as this study is concerned, the basic idea of estimating physico-chemical characteristics of suspended particulate matter from the physico- chemical characteristics of the corresponding soil or fine inert material is logical and rationale and assumed to be workable worldwide. However, generation of particulate matter and its physico-chemical characteristics greatly depends upon local inert material and environment and metrological and climatic conditions.
Therefore, specific regression-based models need to be developed for various geographical areas in different parts of the world having its own and distinctive environmental, meteorological and climatic conditions.
120
4.7. LIMITATIONS
These models are applicable in dry periods only when there is no rainfall.
Obviously, when there will be rainfall, no particulate matter will be generated from the fine inert material even if there is a massive mechanical disturbance at the construction site. Secondly, it will pertinent to mention here that particulate matter monitoring is recommended only in dry and sunny days/weeks only. As these models are developed primarily for estimating physico-chemical characteristics as part of particulate matter monitoring, hence there is no question to apply these models in the rainy days and during wet periods. The urban geometry around the site will also cast impact on the results as it would affect the wind flow.
121
Chapter 5
5. CONCLUSIONS AND RECOMMENDATIONS
5.1. CONCLUSIONS
i. This study identified four major types of construction waste generation,
which includes cutting (10.05%), theft and vandalism 12.77%), transit (8.32%)
and application wastes (7.39%). The study finally concluded that construction
materials wastage accounted for an average of 9.88% at the construction sites in
Punjab province of Pakistan.
ii. The main reasons behind wastage were found to be poor
transportation/network of transportation, error in calculations/cutting, improper
storage, over ordering and poor material handling.
iii. Monitoring of suspended particulate matter generation at construction sites
of small and mega projects in various small and big cities indicated that the
SPM was well beyond the permissible limits of PAK-EPA’s NEQS due to
construction activities, digging and other mechanical disturbances. However, the
particulate matter generation was within permissible limits at those sites where
preventive measures, like watering at construction sites and covering of fine
construction material, were taken to control the generation of particulate matter.
iv. Significant correlation and regression was found at 0.01 level between all
corresponding physico-chemical characteristics of fine inert material and
suspended particulate matter in the ambient/surrounding air. Data normality test
of all the dependent variable and finally the validity of the simple linear
regression based models by comparing the actual and estimated values of
122
dependent variables (with difference less than 20%) indicate that statistical
regression model can be used for estimation and prediction of physico-chemical
characteristics of the suspended particulate matter (dependent variable) by using
the physico-chemical characteristics of fine inert material at any other/new
construction site.
v. Significant correlation and regression was found at 0.01 level between
corresponding concentrations of SPM, PM10 and PM2.5 from 3m, 8m, 13m and
18m from source of generation of particulate matter. Data normality test of all
the dependent variable was performed and actual and estimated values of
dependent variables (with difference less than 10%) were also compared. All the
above indicators show that statistical regression models can be used for
estimation and prediction of concentrations of SPM, PM10 and PM2.5 at 8m, 13m
and 18m, (dependent variable) only by determining the concentrations of of
SPM, PM10 and PM2.5 at 3m distance from the source of generation of
particulate matters. at any other/new construction site.
5.2. RECOMMENDATIONS
i. The study recommends reducing wastage to as low as possible (5% or less)
by strict cheek on identified reasons of construction wastage to minimize
environmental hazards and reduce the costs of projects and make solid waste
management systems manageable.
ii. Improvement in transportation/network of transportation, training of
workers for precision in calculations/cutting, development of proper storage
facilities, control on over ordering and careful construction material handling are
recommended to reduce the wastage of construction material.
123
iii. Watering at the construction and covering with plastic sheets of fine
construction material is recommended to control generation of particulate matter
due to mechanical disturbance at the construction sites.
iv. Instead of placing at open spaces at roads and streets around and in front of
construction site, the construction material should be stored properly in covered
and walled area.
v. Regression-based models developed for estimating physico-chemical
characteristics of suspended particulate matter are recommended to be applied as
easier and low costing method for monitoring ambient air quality at the
construction site in order determine characteristics of suspended particulate
matter.
vi. Regression-based models developed for predicting concentration of
different sizes are also recommended to be applied for determining the distance
from the source of generation where suspended particulate matter would be
below the permissible limits. The same should be conveyed to passersby so that
they could stand at the safer place from the construction site.
vii. The relationship between other physico-chemical characteristics of fine
inert material and that of suspended particulate matter is recommended to
develop statistical models to estimate other characteristics of suspended
particulate matter from the fine inert. viii. It is also recommended to estimate physico-chemical characteristics of
suspended particulate matter at varying distances from the source of generation.
ix. Regression based models are recommended to be tested/validated at more
constructions sites
124
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APPENDIX
LIST OF PUBLICATIONS i. Iqbal, K. and Baig, M.A. (2015). Source identification, classification and quantification of construction waste material. Proceedings of International
Conference on Waste Management and Environment 2015: Paradigm
Transformation in Waste Management towards Green Environment, 20th-22nd
August 2015, Kuala Lumpur, Malaysia. pp: 150-165. ii. Iqbal, K. and Baig, M.A. (2016). Quantitative and qualitative estimation of construction waste material in Punjab Province of Pakistan. American-Eurasian
Journal of Agricultural and Environmental Sciences, 16(4): 770-779. iii. Iqbal, K., Baig, M.A. and Khan, S. J (2017). Estimation of physico- chemical characteristics of suspended particulate matter (SPM) at construction sites: A statistical regression-based model. Accepted for Publication in Journal of the Chemical Society of Pakistan (JCSP), 39 (2).
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ANNEXURE - I
Survey pro forma
Construction (Building) Waste Estimation Questionnaire
Name: ______Designation & Organization: ______Profession: Civil Engineer/Architect/Quantity surveyor/Contractor (tick one only or write ahead) ______Qualification: PhD/MPhil/MS/Bachelor’s/Intermediate/Matric/any diploma/other (please tick or write ahead) ______Experience (in years): ______Phone number: ______
Table 1: Quantitative Assessment of Cutting Waste at Construction Sites
0- 5.1- 10.1- 15.1- 20.1- 25.1- 30.1- 35.1- 45.1- Cutting waste 05% 10% 15% 20% 25% 30% 35% 40% 40.1-45% 50% Above 50% Reinforcement bars Roof carcass Roofing sheets False Ceiling Wires and cables Pipes
148
Table 2: Quantitative Assessment of Theft and Vandalism (T&V) Waste at Construction Sites
0- 5.1- 10.1- 15.1- 20.1- 25.1- 30.1- 35.1- 45.1- T & V Waste 40.1-45% Above 50% 05% 10% 15% 20% 25% 30% 35% 40% 50% Cement Sand Clay Crushed stone Wood/Timber Wires and cables Pipes Wood preservatives Reinforcement bars
Table 3: Quantitative Assessment of Transit Waste at Construction Sites
0- 5.1- 10.1- 15.1- 20.1- 25.1- 30.1- 35.1- 45.1- Transit waste 40.1-45% Above 50% 05% 10% 15% 20% 25% 30% 35% 40% 50% Blocks & Bricks Window glazing Prefabricated windows Tiles Ceramic sanitary appliances
149
Table 4: Quantitative Assessment of Applications Waste at Construction Sites
0- 5.1- 10.1- 15.1- 20.1- 25.1- 30.1- 35.1- 45.1- Application Waste 40.1-45% Above 50% 05% 10% 15% 20% 25% 30% 35% 40% 50% Paint Mortar (cement + sand) Concrete (cement + sand + crushed stone) POP Ceiling
Table 5: Source Identification (tick only one for each kind of waste)
Sources/reasons of wastage Cutting waste T & V Waste Transit waste Application Waste
Faulty or fancy design Improper storage Over ordering Error in calculations/cutting Poor material handling/operations Poor planning Transportation
In case of any confusion while filing up the questionnaire, please contact 0343-6206591
150
ANNEXURE – II
A: Construction site at Model Town Link Road Lahore
B: Casella Particulate Sampling System at Construction site at Model Town Link Road Lahore
151
ANNEXURE – III
A: HANNA Instruments Model # HI 9812 for measuring pH and electrical conductivity of samples
B: AAS (Perkin Elmer 1210) for determining concentrations of Al, Ca, Ni, Fe and Zn samples
152
ANNEXURE – IV
HACH Spectrophotometer DR/2010 for determination of ions in dust samples
153
ANNEXURE V
A: Rawalpindi Islamabad Metrobus Project Layout
B: Rawalpindi Islamabad Metrobus Project Layout- Rawalpindi Area
154
ANNEXURE VI
A: Rawalpindi Islamabad Metrobus Project Layout- Islamabad Area
B: Rawalpindi Islamabad Metrobus Project Layout- Sampling Sites
155
ANNEXURE – VII
A: Collecting SPM sample with High Volume Sampler Sibata HV 500F at Rawalpindi Islamabad Metrobus Project
B: Particulate matter collected at filter paper
156
ANNEXURE – VIII
A: High Volume Sampler Sibata HV 500F installed at Rawalpindi Islamabad Metrobus Project Site
B: Construction site at Rehman Shaheed Road, Gujrat, for collecting samples of SPM, PM10 and PM2.5 at varying distances from source of generation
157
ANNEXURE – IX
A: Construction site at Fazal Elahi Road (Gulyana Road), Kharian, for collecting samples of SPM. PM10 and PM2.5 at varying distances
B: DustTrak™ II Aerosol Monitor 8530 used for collection of SPM, PM10, PM2.5 samples
158
ANNEXURE - X
Data Normality Tests of Physico-Chemical Characteristics
159
ANNEXURE - XI Data Normality Tests of Physico-Chemical Characteristics
160
ANNEXURE – XII Data Normality Tests of Physico-Chemical Characteristics
161
ANNEXURE – XIII Data Normality Tests of Physico-Chemical Characteristics
162
ANNEXURE – XIV Data Normality Tests of Physico-Chemical Characteristics
163
ANNEXURE – XV Data Normality Tests of Particulate Matter at Various Distances from Source
164
ANNEXURE – XVI Data Normality Tests of Particulate Matter at Various Distances from Source
165
ANNEXURE – XVII Data Normality Tests of Particulate Matter at Various Distances from Source
166
ANNEXURE – XVIII Data Normality Tests of Particulate Matter at Various Distances from Source
167
ANNEXURE – XIX Data Normality Tests of Particulate Matter at Various Distances from Source
168
ANNEXURE – XX
Meteorological Data: Lahore City
Lahore: 4-10 June 2013
Temperature Humidity Wind Speed Rain Atmospheric Date/Year (°C) (%) (knot) (mm) Pressure (MBS) 8:00 5:00 8:00 5:00 8:00 5:00 June 2013 Min Max Daily Total AM PM AM PM AM PM 4 28.2 39.6 46 27 2 0 0 978.0 975.0 5 29.5 42.5 43 28 0 2 0 977.0 972.7 6 30.5 44.5 45 21 0 8 0 974.0 971.0 7 25.6 44.0 59 30 6 4 0 976.0 972.0 8 28.2 42.0 67 26 4 6 0 974.6 970.1 9 28.5 43.1 70 30 4 2 0 972.3 969.7 10 30.0 44.0 65 32 2 6 0 973.6 969.0
Lahore: 19-25 October 2013
Temperature Humidity Wind Speed Rain Atmospheric Date/Year (°C) (%) (Knot) (mm) Pressure (MBS) 5:00 5:00 5:00 Oct 2013 Min Max 8:00 AM 8:00 AM Daily Total 8:00 AM PM PM PM 19 18.7 32.5 75 36 0 4 0 987.1 986.0 20 18.5 33.0 75 43 0 2 0 987.9 984.9 21 19.2 32.4 75 42 0 2 0 986.2 983.6 22 18.0 32.0 74 50 0 0 0 987.7 986.0 23 18.2 32.0 78 58 0 0 0 990.6 987.9 24 18.0 30.7 82 50 0 0 0 990.6 987.5 25 19.0 31.0 67 50 0 4 0 988.5 984.9
169
ANNEXURE – XXI
Meteorological Data: Lahore City
Lahore: 25-31 December 2013 - A
Temperature Humidity Wind Speed Rain Atmospheric Date/Year (°C) (%) (Knot) (mm) Pressure (MBS) 5:00 5:00 5:00 Dec 2013 Min Max 8:00 AM 8:00 AM Daily Total 8:00 AM PM PM PM 25 5.5 19.5 73 36 0 4 0 996.0 993.8 26 4.5 19.7 71 40 0 0 0 995.0 992.5 27 3.5 19.7 84 55 0 0 0 993.4 992.7 28 3.0 17.2 84 46 0 0 0 994.1 992.6 29 2.3 18.2 84 41 0 0 0 993.8 992.1 30 2.4 18.2 84 49 0 0 0 992.6 990.4 31 2.5 17.5 80 81 0 4 0 992.6 995.5
Lahore: 01-07 January 2014 - B
Temperature Humidity Wind Speed Rain Atmospheric Date/Year (°C) (%) (Knot) (mm) Pressure (MBS) 5:00 5:00 5:00 Jan 2014 Min Max 8:00 AM 8:00 AM Daily Total 8:00 AM PM PM PM 1 3.5 14.8 78 53 4 4 0 997.7 996.3 2 2.5 16.2 70 43 0 2 0 995.5 993.7 3 2.6 19.0 84 36 0 4 0 993.1 990.9 4 2.5 20.5 84 51 0 0 0 992.3 990.4 5 2.5 19.3 84 47 0 0 0 990.8 987.9 6 3.1 19.0 84 48 0 4 0 990.6 992.4 7 3.0 19.5 72 52 0 0 0 992.6 991.5
170
ANNEXURE – XXII
Meteorological Data: Lahore City
Lahore: 8-14 February 2014 - A
Temperature Humidity Wind Speed Rain Atmospheric Date/Year (°C) (%) (Knot) (mm) Pressure (MBS) 5:00 5:00 5:00 Feb 2014 Min Max 8:00 AM 8:00 AM Daily Total 8:00 AM PM PM PM 8 6.2 16.0 93 56 0 4 0 987.0 985.5 9 5.8 19.0 73 41 0 4 0 986.4 985.3 10 6.2 19.4 75 49 0 0 0 986.3 985.2 11 6.5 19.6 86 50 0 4 0 987.9 988.4 12 6.5 19.5 87 39 4 6 0 988.8 987.5 13 5.5 20.6 67 40 0 4 0 987.9 985.8 14 5.7 21.5 87 61 4 6 0 988.4 984.5
Lahore: 11-17 June 2014 - B
Temperature Humidity Wind Speed Rain Atmospheric Date/Year (°C) (%) (Knot) (mm) Pressure (MBS) 8:00 5:00 8:00 5:00 8:00 5:00 June 2014 Min Max Daily Total AM PM AM PM AM PM 11 30.0 45.7 37 21 4 6 0 972.7 970.1 12 26.0 42.8 43 33 0 6 0 973.8 971.4 13 27.0 42.0 48 36 8 6 0 973.6 970.6 14 27.4 40.0 58 33 4 6 0 972.7 969.7 15 29.5 41.3 59 36 0 4 0 973.7 970.9 16 31.0 42.4 49 31 6 6 0 972.7 969.3 17 30.0 43.3 55 29 0 6 0 971.8 968.8
171
ANNEXURE – XXIII
Meteorological Data: Islamabad/Rawalpindi
Islamabad/Rawalpindi: 06-12 July 2014
Temperature Humidity Wind Speed Rain Atmospheric Date/Year (°C) (%) (Knot) (mm) Pressure (MBS) 5:00 5:00 5:00 July 2014 Min Max 8:00 AM 8:00 AM Daily Total 8:00 AM PM PM PM 6 23.5 38.5 70 34 0 6 0 943.1 941.5 7 22.5 36.5 56 27 0 0 0 942.1 938.7 8 23.5 39.0 53 33 0 2 0 942.2 936.6 9 24.0 41.0 55 33 0 2 0 939.3 937.0 10 23.0 40.5 66 45 0 4 0 941.0 938.6 11 27.5 38.5 57 39 0 2 0 939.2 936.8 12 28.5 40.0 55 41 0 0 0 940.1 936.6
Islamabad/Rawalpindi: 2-8 January 2015
Temperature Humidity Wind Speed Rain Atmospheric Date/Year (°C) (%) (Knot) (mm) Pressure (MBS)
Jan 2015 Min Max 8:00 AM 5:00 PM 8:00 AM 5:00 PM Daily Total 8:00 AM 5:00 PM 2 1.6 21.2 91 45 0 0 0 957.4 954.3 3 2.0 22.0 92 57 0 0 0 956.7 954.5 4 2.0 19.5 92 52 0 2 0 958.8 957.0 5 2.5 19.0 92 53 0 0 0 956.9 954.3 6 2.5 20.0 92 43 0 2 0 955.4 950.9 7 3.5 20.3 100 61 0 0 0 954.5 952.1 8 3.5 16.7 100 70 0 0 0 956.1 955.7
172
ANNEXURE – XXIV
Meteorological Data: Islamabad/Rawalpindi
Islamabad/Rawalpindi: 9-15 April 2015
Temperature Humidity Wind Speed Rain Atmospheric Date/Year (°C) (%) (Knot) (mm) Pressure (MBS) 5:00 5:00 5:00 April, 2015 Min Max 8:00 AM 8:00 AM Daily Total 8:00 AM PM PM PM 9 13.0 27.0 81 42 0 0 0 952.7 950.5 10 13.5 28.4 73 49 0 4 0 950.9 948.3 11 14.3 30.2 81 51 0 0 0 950.3 949.7 12 15.5 31.0 86 39 0 0 0 952.8 952.1 13 15.6 32.0 74 45 0 4 0 954.8 950.9 14 15.8 31.0 78 46 0 4 0 954.8 951.5 15 20.0 31.0 83 84 0 6 0 954.8 952.7
173
ANNEXURE – XXV
Meteorological Data: Gujrat City
Gujrat: 19-25 May 2015
Temperature Humidity Wind Speed Rain Date/Year (°C) (%) (Knot) (mm)
May 2015 Min Max 8:00 AM 5:00 PM 8:00 AM 5:00 PM Daily Total 19 21.5 36.0 64 26 4 4 0.0 20 22.0 38.5 60 21 2 4 0.0 21 20.5 38.5 47 19 0 4 0.0 22 21.0 40.5 52 24 0 6 0.0 23 22.5 41.5 38 20 0 2 0.0 24 23.0 40.5 28 18 0 8 0.0 25 23.0 39.0 48 19 0 4 0.0
Gujrat: 13-19 June 2015
Temperature Humidity Wind Speed Rain Date/Year (°C) (%) (Knot) (mm)
June 2015 Min Max 8:00 AM 5:00 PM 8:00 AM 5:00 PM Daily Total 13 24.0 40.5 61 43 4 6 0 14 23.0 35.0 66 39 6 6 0 15 22.0 35.5 67 54 4 2 0 16 22.5 34.0 58 31 2 4 0 17 24.0 39.5 59 24 0 4 0 18 24.5 41.0 49 26 6 6 0 19 26.0 42.0 36 32 6 8 0
174
ANNEXURE – XXVI
Meteorological Data: Gujrat City
Gujrat: 18-24 August 2015
Temperature Humidity Wind Speed Rain Date/Year (°C) (%) (Knot) (mm)
Aug 2015 Min Max 8:00 AM 5:00 PM 8:00 AM 5:00 PM Daily Total 18 25.0 36.5 72 65 4 4 0.0 19 24.5 36.0 77 65 4 6 0.0 20 25.0 34.5 85 60 4 6 0.0 21 22.5 36.0 84 75 4 4 0.0 22 22.5 32.0 77 61 2 4 0.0 23 24.0 33.0 81 73 4 6 0.0 24 23.0 34.5 88 54 2 4 0.0
175
ANNEXURE – XXVII
Meteorological Data: Kharian City
Kharian: 26 May-01June 2015
Temperature Humidity Wind Speed Rain Date/Year (°C) (%) (Knot) (mm) May/June, 5:00 5:00 Min Max 8:00 AM 8:00 AM Daily Total 2015 PM PM 26 21.5 36.0 64 26 4 4 0.0 27 22.0 38.5 60 21 2 4 0.0 28 20.5 38.5 47 19 0 4 0.0 29 21.0 40.5 52 24 0 6 0.0 30 22.5 41.5 38 20 0 2 0.0 31 23.0 40.5 28 18 0 8 0.0 1 23.0 39.0 48 19 0 4 0.0
Kharian: 17-24 November 2015
Temperature Humidity Wind Speed Rain Date/Year (°C) (%) (Knot) (mm)
Nov, 2015 Min Max 8:00 AM 5:00 PM 8:00 AM 5:00 PM Daily Total 17 24.0 40.5 61 43 4 6 0 18 23.0 35.0 66 39 6 6 0 19 22.0 35.5 67 54 4 2 0 20 22.5 34.0 58 31 2 4 0 21 24.0 39.5 59 24 0 4 0 22 24.5 41.0 49 26 6 6 0 23 26.0 42.0 36 32 6 8 0
176
ANNEXURE – XXVIII
Measuring pH and electrical conductivity in the Laboratory
177
ANNEXURE – XXIX
Construction site at Rehman Shaheed Road, Gujrat City for sampling of SPM, PM10 and PM2.5 at varying distances
Construction site at Fazal Elahi Road Kharian for sampling of SPM, PM10 and PM2.5 at varying distances
178
ANNEXURE – XXX
Construction site at Model Town Link Road Lahore for sampling of SPM, PM10 and PM2.5 at varying distances
179