Spatial Uncertainties in Fluoride Levels and Health Risks in Endemic Fluorotic

Groundwater for Sustainable Development 14 (2021) 100618

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Groundwater for Sustainable Development

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Research paper Spatial uncertainties in fluoridelevels and health risks in endemic fluorotic regions of northern Tanzania

Julian Ijumulana a,b,c,*, Fanuel Ligate a,b,d, Regina Irunde a,b,e, Prosun Bhattacharya a,g, Jyoti Prakash Maity f, Arslan Ahmad g,h,i, Felix Mtalo b a KTH-International Groundwater Arsenic Research Group, Department of Sustainable Development, Environmental Science and Engineering, KTH Royal Institute of Technology, Teknikringen 10B, SE-100 44 Stockholm, Sweden b Department of Water Resources Engineering, College of Engineering and Technology, University of Dar Es Salaam, Dar Es Salaam, Tanzania c Department of Transportation and Geotechnical Engineering, College of Engineering and Technology, University of Dar Es Salaam, Dar Es Salaam, Tanzania d Department of Chemistry, Mkwawa College of Education, University of Dar Es Salaam, Tanzania e Department of Chemistry, College of Natural and Applied Sciences, University of Dar Es Salaam, Tanzania f Department of Earth and Environmental Sciences, National Chung Cheng University, 168 University Road, Min-Hsiung, Chiayi County, 62102, Taiwan g KWR Water Cycle Research Institute, Groningenhaven 7, 3433 PE Nieuwegein, The Netherlands h SIBELCO Ankerpoort NV, Op de Bos 300, 6223 EP Maastricht, the Netherlands i Department of Environmental Technology, Wageningen University and Research (WUR), Wageningen, The Netherlands

ARTICLE INFO ABSTRACT

Keywords: Spatial uncertainty caused by large-scale variation in fluoride(F ) occurrence remains a setback for water supply Groundwater authorities in the F belts of the world. It is estimated that approximately 80 million people in the East African Fluoride contamination Rift Valley (EARV) regions and volcanic areas exhibit a wide variety of fluorosissymptoms due to drinking water Probability kriging with F‾ concentrations higher than 1.5 mg/L (WHO guideline limit). In this study, we combined geostatistical Dental caries techniques, spatial statistical methods, and geographical information systems (GIS) to (i) map the probable Fluorosis places with F‾< 0.5 mg/L and F‾> 1.5, 4.0 and 10.0 mg/L using probability kriging (PK) method, (ii) estimate Northern Tanzania the probable total population at high or low F risk levels using univariate local Moran’s I statistic, and (iii) map the spatial distribution of population at high and low F risk levels in Manyara, Arusha and Kilimanjaro regions using GIS. It was predicted that places along the major and minor EARV mountain ranges and around the flanks of major stratovolcanoes were dominated by groundwater sources with extremely low F‾(<<0.5 mg/L). In contrast, places within EARV graben were dominated by groundwater sources with F‾> 1.5 mg/L. About 1 million people (~20% of the total population) living around Mt. Kilimanjaro in Rombo, Moshi, and Mwanga districts are at high dental caries risk. Furthermore, it was estimated that about 2 million people (~41% of the total population) in Siha, Hai, Arusha City, Hanang’, Arusha, Simanjiro, and Meru districts are at high risk of dental, skeletal, and crippling fluorosis. Fluorosis, especially dental and crippling fluorosis, is an increasing disease burden at the community level due to prolonged consumption of F contaminated water within EARV graben. The major findingsof the present study are very crucial for authority to minimize the uncertainty caused by high spatial variability in geogenic F occurrence.

Abbreviations: m a.m.s. l, Meters above mean sea level; CSR, Completely spatial randomness; DAFWAT, Development of affordable adsorbent systems for arsenic and fluoride removal in the drinking water sources in Tanzania; DNA, Deoxyribonucleic acid; F‾, Fluoride; FRL, Fluoride risk level; GDEM, Global digital elevation model; EARV, East African Rift Valley; GIS, Geographical Information Systems; IK, Indicator kriging; ISE, Ion Selective Electrode; IQ, Intelligent quotient; MoW, Ministry of Water; NBS, National Bureau of Statistics; NDRS, Ngurdoto Defluoridation Research Station; NFD, National fluoride database; OLS, Ordinary least squares; PBWO, Pangani Basin Water Office; PK, Probability kriging; Prob. […], Probability of exceedance; Sida, Swedish International Development Cooperation Agency; SOP, Standard operating procedures; SRS, Spatial reference system; SSA, Sub-Sahara Africa; TISAB, Total Ionic Strength Adjustor Buffer; URT, United Republic of Tanzania; WGS84, World Geodetic System of 1984; WHO, World Health Organization. * Corresponding author. KTH-International Groundwater Arsenic Research Group, Department of Sustainable Development, Environmental Science and Engi neering, KTH Royal Institute of Technology, Teknikringen 10B, SE-100 44, Stockholm, Sweden. E-mail address: [email protected] (J. Ijumulana). https://doi.org/10.1016/j.gsd.2021.100618 Received 23 February 2021; Received in revised form 3 June 2021; Accepted 3 June 2021 Available online 11 June 2021 2352-801X/© 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). J. Ijumulana et al. Groundwater for Sustainable Development 14 (2021) 100618

Abbreviations MoW Ministry of Water NBS National Bureau of Statistics m a.m.s. l Meters above mean sea level NDRS Ngurdoto Defluoridation Research Station CSR Completely spatial randomness NFD National fluoride database DAFWAT Development of affordable adsorbent systems for arsenic OLS Ordinary least squares and fluoride removal in the drinking water sources in PBWO Pangani Basin Water Office Tanzania. PK Probability kriging DNA Deoxyribonucleic acid Prob. […] Probability of exceedance F‾ Fluoride Sida Swedish International Development Cooperation Agency FRL Fluoride risk level SOP Standard operating procedures GDEM Global digital elevation model SRS Spatial reference system EARV East African Rift Valley SSA Sub-Sahara Africa GIS Geographical Information Systems TISAB Total Ionic Strength Adjustor Buffer IK Indicator kriging URT United Republic of Tanzania ISE Ion Selective Electrode WGS84 World Geodetic System of 1984 IQ Intelligent quotient WHO World Health Organization

1. Introduction poor quality in groundwater sources is geogenic F which is prevalent within East African Rift Valley (EARV) regions and volcanic areas. It is Groundwater contamination by fluoride (F‾) ion is a major water estimated that approximately 80 million people there exhibit a wide quality challenge around the world (Bundschuh et al., 2017; Maity et al., variety of fluorosissymptoms due to prolonged exposure to F‾ exceeding 2021). In volcanic regions, the presence of elevated concentrations of F‾ the recommended maximum permissible value of 1.5 mg/L (WHO, in groundwater is a result of geochemical processes, such as weathering 2011) for drinking water (Shen and Schafer,¨ 2015; Smedley et al., 2002). of fluorine-bearing volcanic rocks, dissolution of fluorine-bearing min It is further estimated that the population under the greatest levels of erals, evaporation, and geothermal water mixing (Abdelgawad et al., disease burden due to skeletal fluorosis live in Eritrea, Tanzania, 2009; Berger et al., 2016; Brindha and Elango, 2011; Edmunds and Ethiopia, Kenya, and South Africa (Fewtrell et al., 2006). In Tanzania, Smedley, 2013; Iwatsuki et al., 2005; Jacks et al., 2005; Kim and Jeong, the disease burden due to dental caries and fluorosishas been reported 2005; Olaka et al., 2016; Vithanage and Bhattacharya, 2015a,b; Wang sporadically in parts of the Northern Development Zone (NDZ), where et al., 2012). Furthermore, the occurrence of F‾ has also been associated about 90% of the population are reported to be affected by dental with ion exchange and evapotranspiration, especially in semi-arid and fluorosis at varying severity or stages (Mabelya, 1995; Vuhahula et al., arid regions (Jacks et al., 2005, 2009; Rashid et al., 2018). 2009; Yoder et al., 1998). Due to high spatial variability in F occur Fluoride concentrations above 1.5 mg/L in drinking water have rence within these regions (Ijumulana et al., 2020), the actual health risk adverse health effects for humans if ingested for a prolonged period to the population is uncertain and highly speculative. (WHO, 2011; 2017). Dissanayake (1991) and Bretzler and Johnson Assuming the effects of elevated F concentrations across all age (2015) reported prevalence of dental, skeletal, and crippling fluorosis groups in the EARV system, the objective of this study was to estimate among the population exposed to different levels of F concentrations, the probable total population under dental caries and fluorosis risk namely 1.5 < F‾ < 4.0 mg/L, 4.0 < F‾ < 10.0 mg/L, and F‾> 10.0 mg/L levels in three regions: Arusha, Kilimanjaro, and Manyara. Due to high through drinking water. Furthermore, Dissanayake (1991) reported spatial variability in F concentrations, a combination of geospatial significant human health problems in terms of dental caries when methods was adopted. The methods included probability kriging (PK), drinking water even with F‾ <0.5 mg/L is used for a prolonged period. which was used to delineate places with F‾< 0.5 mg/L, F‾> 1.5, 4.0, and There are also non-fluorosis health effects that may occur due to 10.0 mg/L using F‾ concentrations in the National Fluoride Database long-term consumption of drinking water with F‾ levels exceeding the (NFD). The univariate local Moran’s I statistic and GIS were used to maximum permissible limit of 1.5 mg/L (WHO, 2011), e.g. growth decipher communities and total population under high risks in terms of retardation, deoxyribonucleic acid (DNA) structural changes, lowering dental caries and fluorosis in the NDZ of Tanzania. of children’s intelligence quotient (IQ), loss of mobility, and eventually death related to the ingestion of F‾ at a concentration of 250 mg/L over a 1.1. The study area prolonged period (Fawell et al., 2006; Mohanta and Mohanty, 2018). Furthermore, based on calculated hazard indices (Bhattacharya et al., 1.1.1. Climate, population distribution, and drinking water sources 2020; Mridha et al., 2021), fluoride has been reported to affect all age The study was conducted in three regions of NDZ (Fig. 1a) namely, groups with the severity varying significantly between groups, the Arusha, Manyara, Kilimanjaro, and Tanga (Fig. 1b). According to the children being prominently at relatively high risk. national population census of 2012 and projections of 2017 (Unies, Fluoride contaminated groundwater has been reported in many 2017; URT, 2013), approximately 12% of the population lives in NDZ countries. It is estimated that approximately 200 million people, with unequal geographical distribution at the regional level due to distributed in around 28 countries across continents depend on several environmental, geological, and climatic factors. It was projected groundwater sources with F‾ exceeding 1.5 mg/L (Fawell et al., 2006; that approximately 30, 25, 23, and 22% of the population live in Tanga, ¨ Kimambo et al., 2019; Shen and Schafer, 2015). The specific countries Arusha, Kilimanjaro, and Manyara region, respectively. As indicated in where the population depends on F‾ contaminated groundwater sources the quintiles map (Fig. 1c), the population density in each region is have been summarized in Kimambo et al. (2019). Although there is no highly variable, with the highest population concentrated around major confirmed data on the number of people depending on groundwater stratovolcanoes, i.e., Mt. Kilimanjaro, Mt. Meru, and Mt. Hanang’ in with a high concentration of F‾, it is estimated that approximately 272 Kilimanjaro, Arusha, and Manyara regions, respectively. The lowest million people (~40% of the total population) in Sub-Sahara Africa population density is geographically distributed within EARV graben, ◦ ′ depend on groundwater sources of poor quality (Ali et al., 2019; which definesthe northern Tanzania dry belt, extending between 3 20 ◦ ◦ ◦ Kimambo et al., 2019; Kut et al., 2016). One of the well-known causes of and 6 S and between 36 and 38 E (Solomon et al., 2000).

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Fig. 1. Map showing the (a) location of Tanzania in East African continent, (b) administrative regions in the northern development zone and (c) spatial distribution of population density in the studied regions (URT, 2013) and groundwater sources for drinking water.

The study area is characterized by considerable spatial and temporal possible severe health problems among communities that depend solely variability climatic conditions, which are controlled by topography on groundwater sources with F‾ levels above 1.5 mg/L, especially in the (landforms) and the geological setting. The windward side of the stra EARV graben and around the flanks of stratovolcanoes. tovolcanoes and EARV mountain ranges in the western and eastern parts of the studied regions are more humid compared to the places on the 1.1.2. Geomorphological setting, and population density distribution leeward side. On average terms, many places in EARV graben are hot The relief of the study area is highly variable with elevation ranging and semi-arid with annual rainfall ranging between 600 and 800 mm, between 400 and 5900 m above mean sea level (a.m.s.l.). As shown in mainly during October–December and March–May (Kijazi and Reason, Fig. 2a, lowlands lie between 450 and 1200 m. a.m.s.l., where major 2009). Like many places in sub-Saharan Africa (SSA), particularly re water bodies are situated. The location of the water bodies indicates gions in EARV, average monthly temperatures range between 20 and 26 primary discharge areas within the internal drainage basin (IDB) and ◦ C in the studied regions with a dry season of around 4–6 months Pangani drainage basin (PDB). While the highest elevations lie between (MacDonald et al., 2009; New et al., 1999). 2300 and 5900 m a.m.s.l., the highest points (2900–5900 m a.m.s.l.) in The prolonged droughts and scarcity of surface water in the studied this range constitute the peaks of the major stratovolcanoes, including 1. regions promote groundwater as the primary source of water, especially Mt. Kilimanjaro, 2. Mt. Meru, 3. Mt. Hanang’, 4. Mt. Oldoinyo Lengai (an for drinking (García-P´erez et al., 2013; Ijumulana et al., 2020; Mac active volcano), 5. Ngorongoro Crater (Maar), 6. Mt. Gelai, 7. Mt. Donald and Calow, 2009; Malago et al., 2017; Thole, 2013). It is esti Kitumbeine, 8. Mt. Monduli and 9. Mt. Kwaraha. In terms of ground mated that approximately 45 million people in these regions depend on water occurrence in these regions, the major stratovolcanoes play an groundwater hosted in young volcanic rocks, which account for 4% of important role, as they constitute primary recharge areas, depending on the potential hydrogeological environments of SSA (Adelana and Mac the nature of the local geological setting. donald, 2008; Ghiglieri et al., 2010; MacDonald and Davies, 2000). The The relief with an elevation between 1200 and 2900 m a.m.s.l. is geogenic occurrence of elevated F concentrations in groundwater dominated by gentle slopes controlling sporadic and clustered settle systems has rendered many sources unfit for drinking. It is estimated ments. As indicated in Fig. 2b, this range includes places along the slopes that over one-third of the groundwater resources are unfit for drinking of the major rift valley mountain ranges in the eastern and western of the purposes, causing severe adverse health effects, viz. fluorosis in NDZ study areas and around the flanks of the major stratovolcanoes, partic (Chowdhury et al., 2019; Thole, 2013; Vuhahula et al., 2009). ularly Mt. Kilimanjaro (1), Mt. Meru (2), Mt. Monduli (8), and Mt. Furthermore, it has been recently reported that geogenic F‾ Hanang’ (3). At this slope (between 1200 and 2900 m a.m.s.l), the contamination is space dependent, having significant patterns that are population density ranges between 150 and 16,000 persons per square important to decipher for ensuring safe water supply in regions with kilometer. On the other hand, the population is sparsely distributed in naturally contaminated groundwater systems (Ijumulana et al., 2020). lowlands, especially in EARV graben, with population density ranging In the studied regions, potential adverse health effects vary in space with between 1 and 150 persons per square kilometer (Fig. 2b).

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Fig. 2. Map showing (a) topography and population density distribution in Arusha, Manyara and Kilimanjaro regions.

2. Materials and methods powder was completely dissolved and uniformly mixed with the water sample to be analyzed. A detailed description of the data collection 2.1. Spatial data compilation and preparation methods and analysis techniques can be found in the water quality mapping report of the Ministry of Water (Water Quality Service Divi Spatial data used in this study was compiled from different sources sion, 2016). The other spatial data used was F‾ concentrations measured (Table 1). Fluoride concentrations in groundwater were obtained from in situ using the same procedure and instrument during fieldwork the NFD at the Ngurdoto DefluoridationResearch Station (NDRS) of the campaigns of 2016 and 2018. This data was used to validate predicted Ministry of Water. According to Water Quality Service Division (2016) F‾ concentrations using F data of the Ministry of Water (Water Quality report, water samples from each source were collected following stan Service Division, 2016). The last two spatial datasets were collected dard operating procedures (SOP) for water sampling (WHO, 2017). A from the national agencies. While major drainage boundaries were ob 1-Liter clean plastic (polyethylene) bottle was used for water sampling. tained from Pangani Basin Water Office (PBWO), the administrative The sampling bottles were rinsed three times with distilled water and the boundaries and population data were acquired from the National Bureau water to be sampled before water sampling. Determination of F con of Statistics (NBS) of Tanzania. The specificspatial dataset description is centrations was carried out immediately at the sampling point following presented in Table 1. standard analytical methods (APHA, 1989; WHO, 2017; HACH, 2021) Spatial data preparation involved the integration of the data using using an F ion-selective electrode with HQ440D ion meter calibrated various GIS software. As spatial data were on different datum, the using standard F solutions supplied by Hach. In a typical procedure, 25 integration process included defining the data on a common spatial mL of water sample was mixed with Total Ionic Strength Adjustor Buffer reference system (SRS), which was the World Geodetic System of 1984 (TISAB) powder in a beaker and shaken thoroughly to ensure TISAB (WGS84) through on the fly datum transformation models present in

Table 1 Spatial data used in this study.

Spatial data source Spatial data type description

Ngurdoto DefluoridationResearch Station (NDRS) of the Groundwater quality data with fluoride concentrations (mg/L) in different sources including natural springs, boreholes Ministry of Water (MoW) and shallow wells using as primary source for drinking water. Field sampling campaign in northern Tanzania Fluoride concentrations in selected natural springs, boreholes and shallow wells used as primary source of drinking water in fluorotic regions. Pangani Basin Water Office (PBWO) • Major drainage basins boundary National Bureau of Statistics (NBS) of the United • Administrative boundary and population data at regional, district and ward level (https://www.nbs.go.tz/index. Republic of Tanzania (URT) php/en/census-surveys/population-and-housing-census/172-2012-phc-shapefiles-level-one-and-two) Global digital elevation model (GDEM) • Free open source elevation data (30 m resolution) downloaded from https://gisdata.mn.gov/dataset/elev-30m-digit al-elevation-model.

4 J. Ijumulana et al. Groundwater for Sustainable Development 14 (2021) 100618 many GIS software; viz. ArcGIS, GlobalMapper, QGIS, etc. The other conditioning boundary in the GIS environment to estimate the average process was the preparation of a spatial database for the population and value using equation (2): integrate it with the F database (Ijumulana et al., 2020), from which 1 ∑N summary statistics of F concentrations were estimated at the regional p = p k,av N k,i (2) and district level. i=1

2.2. Statistical analysis of F concentrations where, pk,av stands for average probability of exceedance at individual ward k while N stands for total number of prediction grids within the High F concentrations and related health problems are sporadically administrative ward boundary. Since F contamination in the study area well studied at the regional level in three regions by various researchers is space dependent (Ijumulana et al., 2020), the median values at each (Malago et al., 2017; Shorter, 2011; Vuhahula et al., 2009). For this ward level were used instead of the mean value. For wards with small 2 study, the statistical distribution analysis of F concentrations was sys coverage (less than 2.25 km , the prediction grid in this study), the tematically done at regional, district, and ward levels. Since ground logical assignment of the nearest values was assigned, although there water systems are ubiquitous, the water supply process is independent of were few such cases. the political-administrative boundaries indicated in Fig. 1a. For example, the population in some administrative wards depends on 2.5. Spatial cluster and spatial outlier analysis in FRL groundwater sources located outside the administrative boundary. To provide an understanding of F contamination burden, predicted con ’ The global and local Moran s I statistics were used to analyze spatial centrations were used to estimate the population at F risk levels. patterns using estimated FRL in equation (1). The global Moran’s I statistics is widely used in environmental sciences to test the hypothesis 2.3. Geostatistical analysis of completely spatial randomness (CSR) in the observed events on the variable of interest, which are continuous and stochastic (Moran, 1950). Since groundwater sources in the study regions followed the popu In principle, the results of the test static indicate whether the phenom lation distribution, which is influenced by environmental, geological, enon under investigation is clustering, randomly distributed, or and climatic factors, kriging was used to systemize the sampling loca dispersed in space and/or in time-space indicated by I > 0, I = 0, or I < 0, tions. Kriging is widely used in the fieldof structural optimization while respectively. The method has recently been used to study the nature of dealing with stochastic processes (Basudhar and Missoum, 2008; Chiles F‾ contamination in northern Tanzania (Ijumulana et al., 2020). and Delfiner,2009 ; Cressie, 1990; Meng et al., 2018; Hengl, 2009; Stein, The local Moran’s I statistics determines significantspatial patterns. 2012; Zhang et al., 2020). Specifically,probability kriging (PK) was used Spatial patterns referred to here are as definedby Ijumulana et al. (2020) in this study to predict places with F‾ less than 0.5 mg/L and greater and conceptualized by Zhang et al. (2008) in their soil pollution study. A than 1.5 mg/L, which are well known to cause dental caries and fluo detailed description of the working principles of the local Moran’s I rosis, respectively (Dissanayake, 1991). The basic theory of the kriging statistics is provided by Anselin (1995) and Zhang et al. (2008). In this model and the concept of PK are introduced brieflyin the Annex. When study, high-high and low-low spatial patterns refer to the population using PK, the values 0.5 and 1.5 mg/L were used as cut-off points, c, in under high and low F risk levels, respectively. The low-high and equation 12 (Section 2.3.2 of the Annex). The predictions were done at a high-low spatial patterns refer to populations under high or low F risk 1.5 by 1.5-km grid. The selection of the prediction grid was automati levels surrounded by low or high F risk levels, respectively. Under this cally determined based on the minimum-maximum separation distance assumption, the concept of water mixing as part of the remediation of the groundwater sources from which samples were collected. process of F burden can be implemented. The mapping of significant patterns was done at three significancelevels, i.e., α = 0.001, 0.01, and 2.4. Fluoride risk levels estimation at F‾ < 0.5, > 1.5 mg/L 0.05.

The presence of different concentrations of F‾ in drinking water af 2.6. Software used in this study fects the population depending on the contaminated source (Brindha and Elango, 2011; Dissanayake, 1991; Mohanta and Mohanty, 2018; Different software (both open source and proprietary) were used in Bhattacharya et al., 2020; Mridha et al., 2021). For this study, we this study. They include spatial statistical and GIS software. Open source definedF risk level (FRL) as follows based on the risk mapping concepts spatial statistical software included GeoDa Version 1.14.0 and R-Studio recently developed by Quino Lima et al. (2020): Version February 1, 5042. GIS software included ArcGIS 9.6.1 and QGIS FRL = pknk (1) version 2.18.23. GeoDa and R-Studio were used to map spatial patterns at local scales, while ArcGIS and QGIS were used in spatial data prep where pk and nk are the probability of exceedance and total population aration and generation of final maps presented in this paper. in the local administrative unit, k, respectively. The former was esti mated based on 0.5 and 1.5 mg/L thresholds and equation 12 (Section 3. Results and discussion 2.3.2 of the Annex (Carr and Mao, 1993; Okx et al., 1993; Sullivan, 1984)) while the latter was obtained from the Census population data 3.1. Geographical distribution of fluoride in groundwater in the northern base (URT, 2013). By local administrative unit in this study, we refer to zone of Tanzania ward level, which is the second and third administrative level in urban and rural settings in Tanzania, respectively. 3.1.1. Geographical distribution of fluoride in groundwater at the regional * To estimate pk at ward level, k, Z (x0) , raster data were converted to level vector format. Thereafter, the respective ward boundary was used as a The geographical distribution of F concentrations in terms of

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Fig. 3. Geographical distribution of F concentrations in groundwater sources at regional level. potential health effects in the Kilimanjaro, Manyara, and Arusha regions vulnerable with 88 and 80% of groundwater sources having F‾ above are presented in Fig. 3. Despite Kilimanjaro region (N = 297) having 1.5 mg/L, respectively. The probability of dental, skeletal, and crippling substantial groundwater sources with F‾ levels below WHO (2011) fluorosis prevalence was 0.19, 0.25, and 0.43, respectively in Siha dis guideline limit for drinking water (1.5 mg/L), over 55% of sources had trict. In Hai district, the probability of dental, skeletal, and crippling F‾<0.5 mg/L, representing potential health risks in terms of dental fluorosis prevalence was 0.04, 0.35, and 0.41, respectively. Despite caries. The safe range for drinking water (0.5 = F‾<1.5 mg/L) was some districts, viz. Rombo, Moshi, Mwanga, Same, and Moshi munici represented by only 22% of the total water sources in the NFD, contrary pality, had substantial safe groundwater sources for drinking purposes, to the 70% presently known at the national level in the fluoroticregions some of the sources there presented the likelihood of dental caries of Tanzania (Mjengera and Mkongo, 2003). Furthermore, the proba prevalence, as 88, 85, 69, 55, and 33% of the groundwater sources had bility of developing dental fluorosis (1.5≤ F‾ < 4.0 mg/L), skeletal F‾ <0.5 mg/L, respectively. Furthermore, the probability of safe sources fluorosis (4.0 = F‾ < 10.0 mg/L) and crippling fluorosis (F‾ ≥ 10.0 for drinking water provision (0.5 = F‾<1.5 mg/L) varied between dis mg/L) in this region based on the existing drinking water sources was tricts. The probability was lowest (p = 0.06) in Rombo, Hai, and Siha estimated to be 0.05, 0.08 and 0.10, respectively. districts, while in ascending order it was 0.11, 0.30, 0.35, and 0.50 in For Manyara (N = 499) and Arusha (N = 405) regions, the safe range Moshi district, Mwanga and, Same districts and Moshi municipality, for drinking purposes was presented by 31 and 30% of the existing respectively (Fig. 4a). groundwater sources, respectively. This is contrary to what is currently Although 30% of the groundwater samples in the Arusha region known about contaminated groundwater sources in these regions represented safe sources of water for drinking purposes, the probability (Thole, 2013). The probability of health problems in terms of fluorosis of accessing a safe source varied from one district to another. In was higher in Arusha region, with 55% of the sources having F‾ above ascending order, the probability was 0.16, 0.20, 0.22, 0.26, 0.34, 0.51, 1.5 mg/L, thus causing potential dental fluorosis (Prob.[1.5≤ F‾<4.0 and 0.52 in Arusha City, Arusha, Karatu, Meru, Ngorongoro, Monduli, mg/L] = 0.25), skeletal fluorosis (Prob.[4.0 = F‾<4.0 mg/L] = 0.17), and Longido districts, respectively (Fig. 4b). In terms of potential health and crippling fluorosis (Prob.[F‾≥10.0 mg/L] = 0.13). For Manyara risk levels emanating from drinking F contaminated water, the overall region, the probability was relatively moderate with 43% of ground probability of fluorosisprevalence in Arusha region was 0.55 but varied water sources having F‾ above 1.5 mg/L, with the probability of dental, between districts. In ascending order, the probability of dental fluorosis skeletal, and crippling fluorosis of 0.26, 0.14, and 0.03, respectively. prevalence varied from 0.09 through 0.10, 0.14, 0.22, 0.29, 0.30 to 0.42 in Karatu, Longido, Monduli, Ngorongoro, Meru, Arusha, and Arusha 3.1.2. Geographical distribution of fluoride in groundwater and potential City, respectively. For skeletal fluorosis, it varied from 0.00 through health risks at the district level 0.02, 0.09, 0.10, 0.19, 0.32, to 0.34 in Karatu, Ngorongoro, Monduli, The groundwater samples were grouped according to the district Longido, Meru, Arusha City, and Arusha, respectively. In terms of administrative boundary and the probability of dental caries and fluo crippling fluorosis, the probability was 0.03 for Monduli and Longido rosis estimated at the district level in each region. Fig. 4 presents the districts and varied from 0.00 through 0.02, 0.05, 0.12 to 0.25 in Karatu, geographical distribution of F‾ in terms of health risk implication due to Ngorongoro, Arusha (City), Arusha, and Meru districts, respectively. drinking F contaminated water. The probability of health burden In Manyara region (Fig. 4c), 31% of the groundwater samples pre varied between districts, with Siha and Hai districts being the most sented safe sources of drinking water. However, the probability varied

6 J. Ijumulana et al. Groundwater for Sustainable Development 14 (2021) 100618

Fig. 4. Geographical distribution of F concentrations in groundwater sources in (a) Kilimanjaro, (b) Arusha and (c) Manyara regions.

7 J. Ijumulana et al. Groundwater for Sustainable Development 14 (2021) 100618 among the districts. In ascending order, the probability varied from 0.17 Manyara regions, respectively. The presence of groundwater sources through 0.23, 0.30, 0.32, 0.38 to 0.51 in Hanang’, Simanjiro, Mbulu, with extremely low F‾ concentrations around elevated terrain may be Kiteto, and Babati districts, and Babati Urban, respectively. In terms of due to less contact time between recharge water and F bearing volcanic fluorosis prevalence, the probability of dental, skeletal, and crippling rocks (Ghiglieri et al., 2010). fluorosis varied from one district to another. For dental fluorosis, it In contrast, the probability of F‾ <0.5 mg/L was extremely low (p < varied from 0.10, through 0.17, 0.18, 0.31, 0.38, and 0.39 in Babati, and 0.30) in places within EARV graben along the administrative boundary Kiteto districts, Babati Urban, Mbulu, Simanjiro, and Hanang’ districts, between Arusha and Manyara regions. This is a typical indication of the respectively. On the other hand, no potential health risks in terms of sources and mobility of F‾ in the aqueous environment, which is contact skeletal fluorosiswere found in Babati Urban and Mbulu district, while it time between volcanic rocks and recharge water from precipitation varied from 0.02 through 0.16, 0.27 to 0.29 in Kiteto, Babati, Hanang’, (Ghiglieri et al., 2010). Similarly, places with F‾ <0.5 mg/L were and Simanjiro districts, respectively. It was further envisioned that distributed along the regional administrative boundary between Sin crippling fluorosis is a probable health problem in some parts of the gida, Simiyu, and Arusha regions in the western part of the study area. Manyara region. No potential health risks exist in Babati Urban, Mbulu, The administrative boundaries in these regions are elevated braking and Kiteto districts. Although relatively small, the probability of crip slopes constituting rift faults with steep slopes, where the hydraulic pling fluorosisprevalence was 0.06 for Babati and Simanjiro districts, as gradients are largely resulting in high groundwater flows.The presence well as 0.02 in Hanang’ district. In a strict sense, the overall risk level of of large hydraulic gradients results in less contact time between recharge fluorosisin Manyara region was 0.43, and varied between districts from water from precipitation and F -bearing rocks leading to reduced 0.18 through 0.20, 0.31, and 0.68 to 0.72 in Babati Urban, Mbulu, and dissolution processes of F -bearing minerals, mainly titanite, amphi Babati districts, Kiteto, and Simanjiro districts, respectively. bole, hornblende, and biotite (Ijumulana et al., 2020). The probability of encountering groundwater sources with F‾ above the maximum recommended value for drinking water (WHO, 2011; 3.2. Spatial distribution of fluoride concentrations unsafe for human 2017) varied from one place to another, and the potential health prob consumption in groundwater from kilimanjaro, Manyara, and Arusha lems in terms of fluorosis were a regional problem in the studied areas regions (Fig. 5b). The locations with extreme F‾ concentrations (probability of exceedance between 0.60 and 1.00) were along the southern and eastern Using the probability kriging method (Equations 12, 13, and 14 in boundary of the Arusha region and around the flanks of major strato the Annex), the maps (Fig. 5) were generated. Fig. 5a shows the spatial volcanoes, especially Mt. Meru and Mt. Hanang’ in the north-eastern ‾ < – distribution of places with F 0.5 mg/L, whereas Fig. 5b d represents and south-western parts of the study area, respectively. Specific places ‾ > places with F 1.5, 4.0 and 10.0 mg/L, respectively. Places with with extreme F‾ are summarized in Table 1. The risk of F is a regional ‾ << extremely low F ( 0.5 mg/L) levels (probability between 0.80 and problem affecting specific locations on a local scale. In the Arusha re 1.00) were geographically distributed along the major and minor rift gion, it is estimated that approximately 62 administrative wards (57%) mountain ranges in the western and eastern part of the study regions, are affected by extreme concentrations of F‾. However, the probability respectively; around major stratovolcanoes, especially Mt. Kilimanjaro of having this problem varied from one district to another. In descending (1), Mt. Meru (2), and Mt. Hanang’ (4) in Kilimanjaro, Arusha and

Fig. 5. Spatial distribution of F concentrations (a) < 0.5 mg/L, (b) > 1.5 mg/L, (c) > 4.0 mg/L and (d) > 10.0 mg/L in Kilimanjaro, Manyara and Arusha regions.

8 J. Ijumulana et al. Groundwater for Sustainable Development 14 (2021) 100618 order, the most affected wards were within Arusha district, Arusha City, In Kilimanjaro region, 20 out of 107 wards (19%) depend on Meru, Monduli, Ngorongoro, and Longido districts in which 95, 89, 71, groundwater sources with extreme concentrations of F‾. At the district 40, 19, and 19% of the ward population depend on groundwater sources level, this percentage varies from one ward to another. In Siha district, with extreme F‾ concentrations, respectively. groundwater sources in all administrative wards were affected by Fig. 5c and d show locations with a probability of F‾ exceeding 4.0 extreme F‾ concentrations, followed by Hai district in which 5 out of 14 and 10.0 mg/L, respectively. The probability of F‾ > 4.0 mg/L was high wards (36%) depend on groundwater sources with extreme F‾ concen (ranging between 0.60 and 1.00) around Mt. Meru (2), especially in the trations. The rest of the districts demonstrated few wards whose popu graben between Mt. Kilimanjaro (1) and Mt. Meru (2). Other locations lation depends on groundwater sources with extreme F‾ concentrations were distributed around Mt. Hanang’ (4) and south-east of Lake as indicated in Table 2. Manyara, which lies within EARV graben. Lake Manyara, being one of For Manyara region, the population in 62 out of 122 wards (~51%) the groundwater discharge areas in the Internal drainage basin, contains were estimated to depend on groundwater sources with extreme F‾ water with high levels of F‾ mobilized through dissolution processes of concentrations. However, the risk levels varied between districts. The F‾ bearing minerals and the presence of phosphate minerals at the population in Hanang’ district demonstrated the highest risk levels Minjingu area in the south-east of the lake. Furthermore, places with F‾ (84%), followed by Simanjiro, Babati, Mbulu, Babati urban, and Kiteto exceeding 10.0 mg/L were distributed around Mt. Meru, especially in districts with 76, 43, 38, 38%, and 21% risk levels, respectively. the graben between Mt. Meru and Mt. Kilimanjaro.

Table 2 Geographical distribution of places with probability of exceedance between 0.60 and 1.00.

Districtregion # of % of Ward names F‾ range (mg/ Wards Wards L)

Arushaa 20 95 Olkokola, Ilkiding’a, Bangata, Sokon II, Oltoroto, Moivo, Kiranyi, Kimnyaki, Oltrumet, Mwandeti, Mussa, Kisongo, 0.22–11.10 Mateves, Bwawani, Nduruma, Mlangarini, Sambasha, Olorieni, Olmotonyi and Oldonyosambu Arusha 17 89 Kati, Kaloleni, Sekei, Kimandolu, Oloirieni, Themi, Lemara, Terrat, Sokoni I, Daraja Mbili, Unga Ltd, Sombetini, 0.34–6.90 Urbana Levolosi, Engutoto, Elerai, Olasiti and Moshono Merua 12 71 Ngarenanyuki, King’ori, Maji ya chai, Kikatiti, Maroroni, Makiba, Mbuguni, Kikwe, Nkoaranga, Poli, Nkoanrua and 0.09–42.00 Songoro Mondulia 6 40 Monduli juu, Maita, Lolkisale, Makuyuni, Meserani and Mswakini 0.38–24.00 Ngorongoroa 4 19 Pinyinyi, Malambo, Endulen and Kakesio 0.63–24.57 Longidoa 3 19 Engikaret, Kimokouwa and Tingatinga 0.15–11.00 Sihak 12 100 Ndumeti, Ngarenairobi, Gararagua, Sanya Juu, Biriri, Makiwaru, Nasai, Livishi, Ivaeny, Kashisha, Karansi and Olkolili 0.29–27.00 Haik 5 36 Masama Kusini, Masama Rundugai, Hai Mjini, Machame Weruweru and KIA 0.64–74.00 Samek 1 3 Ruvu 0.53–20.00 Moshik 1 3 Arusha Chini 0.64 Mwangak 1 5 Lang’ata – Hanang’m 21 84 Balagidalalu, Gehandu, Laghanga, Getanuwas, Hirbadaw, Bassodesh, Bassotu, Gendabi, Mogitu, Giting, Ganana, 0.26–12.00 Lalaji, Endasiwold, Masakta, Masqaroda, Endasak, Gidahababieg, Maskron, Hidet and Nangwa Simanjirom 13 76 Loiborsiret, Emboreet, Terrat, Oljoro No. 5, Shambarai, Mererani, Msitu wa Tembo, Liborsoit, Komolo, Naisinyai, 0.56–22.00 Endiantu, Kitwai and Naberera Babatim 9 43 Nkaiti, Mwada, Mamire, Galapo, Qashi, Dabil, Ufana, Magugu and Endokise 0.13–62.00 Mbulum 12 38 Uhuru, Maretadu, Maghang, Haydom, Yaeda Chini, Endamilay, Masqaroda, Sanu Baray, Imboru, Geterer, Hayderer 0.23–3.80 and Eshkesh Babati 3 38 Babati, Sigino and Maisaka 0.16–2.76 Urbanm Kitetom 4 21 Partimbo, Makame, Ndedo and Njoro 0.24–6.50

Where a, k and m stands for districts in Arusha Kilimanjaro and Manyara regions, respectively.

Fig. 6. Spatial autocorrelation analysis in fluoride risk levels at (a) F‾<0.5 mg/L and (b) F‾>1.5 mg/L in drinking water sources.

9 J. Ijumulana et al. Groundwater for Sustainable Development 14 (2021) 100618

3.3. Fluoride risk level mapping in Arusha, Kilimanjaro, and Manyara Table 3 region Properties of contiguity queen’s spatial weights used to map fluoriderisk levels. Q N Min Max Mean Median % Non-zero As described in sections 2.5 and 2.6, Fig. 6 shows spatial autocor Neighbors Neighbors Neighbors Neighbors relation analysis results using population depending on groundwater sources with F‾ concentrations below 0.5 mg/L and above 1.5 mg/L q1 398 1 14 5.66 6.00 1.42 (Fig. 6a and b). Using queen’s first order spatial weight matrix and q2 398 2 29 12.78 12.00 3.21 q2a 398 3 38 18.44 18.00 4.63 estimated F risk levels (Risk 1 = F‾ <0.5 mg/L and Risk 2 = F‾ >1.5 q3 398 2 51 19.83 18.50 4.98 mg/L), a significantpositive Moran’s I statistic was obtained indicating a q3 398 7 88 38.27 37.00 9.62 clustering of F risk levels in space. However, the Moran’s I statistics q4 398 2 72 27.23 25.00 6.84 = = = a was highly significant (I 0.615, z-value 19.680, pseudo p-value q4 398 13 149 65.50 62.00 16.46 0.001) for F risk level above 1.5 mg/L compared to F risk level below a Lower order neighbors included. 0.5 mg/L (I = 0.515, z-value = 18.190, pseudo p-value = 0.001).

Table 4 Summary statistics of spatial clusters and spatial outliers in fluoride risk level.

Q N NS H–H L-L L-H H-L I Z-value p-value

F‾<0.5

q1 398 248 68 75 3 4 0.6398 20.1826 0.001 q2 398 199 79 91 3 26 0.4126 20.5374 0.001 q2a 398 184 84 109 3 18 0.4804 28.8277 0.001 q3 398 156 80 94 3 65 0.2882 17.9326 0.001 q3a 398 125 85 126 5 57 0.3741 32.4654 0.001 q4 398 140 76 89 10 83 0.1992 14.2496 0.001 q4a 398 72 88 144 10 84 0.2972 34.8876 0.001 F‾>1.5

q1 398 230 107 53 1 7 0.709 23.0890 0.001 q2 398 202 131 52 2 11 0.512 24.5668 0.001 q2a 398 178 148 59 1 12 0.584 34.1504 0.001 q3 398 167 144 59 14 14 0.324 19.5977 0.001 q3a 398 135 169 67 12 15 0.465 41.0592 0.001 q4 398 166 148 47 22 15 0.196 14.1339 0.001 q4a 398 96 188 67 28 19 0.360 42.8233 0.001

a Lower order neighbors included; N = number of wards; NS = not significant patterns; H-H, L-L, L-H and H-L = High-high, Low-low, Low-high and High-low patterns, respectively.

Fig. 7. Spatial distribution of probable dental caries risk levels in Kilimanjaro, Arusha and Manyara region.

10 J. Ijumulana et al. Groundwater for Sustainable Development 14 (2021) 100618

3.4. Spatial cluster and outlier mapping in fluoride risk levels 20.5374 and pseudo p-value = 0.001 for F‾<0.5 mg/L and I = 0.512, z-value = 24.5668 and pseudo p-value = 0.001 for F‾>1.5 mg/L). The Fluoride risk levels were mapped using univariate Moran’s I statis spatial distribution of probable F health effects in the study regions is tical analysis. Since the univariate Moran’s I statistic is calculated under presented in Figs. 7 and 8, respectively. In both cases, the significant the assumption of normality at the local scale, the F risk levels were spatial patterns were classifiedand mapped at three significantlevels (i. transformed using the Box-Cox transform (Box and Cox, 1964; Zhang e., α = 0.001, 0.01, and 0.05). et al., 2008). Table 3 gives a summary of the properties of the contiguity The probability of disease burden in terms of dental caries was queen’s case (Anselin, 2013) spatial weights that were used to calculate highest in 79 wards around the flanksof Mt. Kilimanjaro (Fig. 7). It was the local Moran’s I statistic at four orders, Q (q1, q2, q3, and q4). The estimated that around 20% of the population depends on groundwater first and second-order (q1and q2, respectively) queen’s contiguity sources for drinking with the median probability for F‾<0.5 mg/L of spatial weights demonstrated high levels of symmetric as indicated by around 0.84 (Table 5). In contrast, 91 wards with approximately 22% of small differences between mean and median neighbors compared to the the population were at low risk in terms of dental caries, as indicated by third (q3) and fourth (q4) orders. Likewise, the firsttwo orders indicated the lowest median probability of 0.13. Mostly, the population in this a smaller number of % non-zero compared to the last two orders. category are Maasai communities, which are sparsely distributed within Furthermore, the experiment to map significant spatial clusters and EARV graben. Around 10% of the population were estimated to live in spatial outliers was done at three-four orders of the queen’s contiguity the low or high-risk levels where approximately 20,671 (in three wards) spatial weights. Table 4 gives summary statistics of spatial clusters and and 406,005 (in 11 wards) were at low-risk level but in the neighbor spatial outliers estimates in F risk levels at F‾<0.5 mg/L. As a rule of hood of high-risk levels and vice versa, respectively. The median prob thumb when using local Moran’s I statistics, the best classification re ability of low-risk levels in the neighborhood of high-risk levels and vice sults are attained at convenient large and converging z-value, as well as versa was 0.67 and 0.25, respectively. the smaller pseudo p-value (Anselin, 1995). In this study, significant The results of FRL analysis at F‾>1.5 mg/L are presented in Fig. 8. spatial clusters and spatial outliers in F risk levels were mapped at q2 Communities in rural and urban settings around Mt. Meru and Mt. with lower-order neighbors excluded (i.e., I = 0.4126, z-value = Hanang’ were at high FRL with a median probability of exceedance of

Fig. 8. Spatial distribution of probable fluorosis risk levels in Kilimanjaro, Arusha and Manyara region.

Table 5 Population clusters under high and low fluoride risk level in terms of dental caries in northern Tanzania.

Spatial pattern #of Wards Population 2012 Mean probability±s.d Median probability

Not significant 199 2,335,546 0.39 ± 0.29 0.16 High-high 79 973,148 0.77 ± 0.20 0.84 Low-low 91 1,042,466 0.20 ± 0.20 0.13 Low-high 3 20,671 0.56 ± 033 0.67 High-low 36 406,005 0.35 ± 0.26 0.25

11 J. Ijumulana et al. Groundwater for Sustainable Development 14 (2021) 100618

Table 6 Population clusters under high and low fluoride risk level in terms of fluorosis in northern Tanzania.

Spatial pattern #of Wards Population 2012 Mean probability±s.d Median probability

Not significant 202 2,145,678 0.20 ± 0.17 0.16 High-high 131 1,958,459 0.62 ± 0.24 0.64 Low-low 52 483,181 0.02 ± 0.03 0.00 Low-high 2 18,329 0.06 ± 0.04 0.06 High-low 11 172,189 0.14 ± 0.09 0.10

approximately 0.64 (Table 6). In these settings, it is estimated that conductivity (EC), and elevation above mean sea level. The high pH ◦ approximately 41% of the population (distributed in 131 wards) is at (8.17), temperature (28.7 C), elevation (~1808 m a.m.s.l), and rela high fluorosis risk emanating from consumption of groundwater with tively low EC (494 μS/cm) at Lemanda spring is an indication that the F‾>1.5 mg/L. In contrast, 52 wards (with ~10% of the population) spring could be originating from the rift system. The relatively low pH ◦ distributed along with the Usambara mountain ranges, which is part of a (7.15), temperature (24.6 C), elevation (~1261 m a.m.s.l) and high EC minor escarpment of EARV, were at a low fluorosisrisk level. However, (947 μS/cm) can be an indication that high levels of F‾ originate from some of the wards were at a very high-risk level in terms of dental caries prolonged contact time between recharge water and volcanic rocks/ as described above. ashes, which enhance processes, viz. dissolution of fluorine-bearing minerals and ion exchange in water solution (Brindha and Elango, 2011; Jacks et al., 2005; Vithanage and Bhattacharya, 2015a). 3.5. Validation of fluoride risk level maps 4. Conclusion The validation of F risk maps was done through field water sam pling and observation on prevailing fluorosisdisease burden in the high- Despite over 80% of safe water demand for various purposes in NDZ high zone of Fig. 8 between 2016 and 2018. Fig. 9 presents a summary of of Tanzania is sufficedby groundwater resources, the occurrence of F‾ in fieldmeasured F‾ concentrations and other physio-chemical parameters elevated or extremely low concentrations has rendered many sources in drinking water sources as well as adverse health effects as an outcome unfit for drinking purposes. The occurrence of F‾ in groundwater sys measured in September 2016. Children and adolescents aged between 4 tems in northern Tanzania is due to stochastic processes resulting in a and 12 years old in Embaseni village of Maji ya Chai ward in Meru large scale of variability in space. In this study, integrated geostatistical district in the south-east flanks of Mt. Meru were visually observed to techniques, spatial statistical methods, and GIS mapping tools were used have developed mild to severe dental fluorosisdue to consumption of F‾ to delineate places at high F risk levels in three regions namely, Kili contaminated (F‾ = 11.4 mg/L) spring water (Fig. 9a). Similarly, the manjaro, Arusha, and Manyara. The F risk levels were estimated at F‾ woman in Fig. 9b was 29 years in Lemanda village of Oldonyosambu < 0.5 mg/L and F‾> 1.5 mg/L, which-correspond to threshold values ward in Arusha district-Arusha region. She developed crippling fluorosis likely to cause dental caries and fluorosis disease burden in human due to consumption of F‾ contaminated spring water (F‾ = 18.7 mg/L) health, respectively. Around 1 million people were estimated to be since her birth. under significant-high dental caries risk in districts around Mt. Kili Based on in-situ measured physio-chemical parameters, the two manjaro in Kilimanjaro region. Likewise, around 2 million people were springs differed significantly in terms of pH, temperature (T), electric

Fig. 9. Potential fluorosis disease burden among (a) children and adolescents, and (b) adults in Embaseni and Lemanda village in Meru and Arusha districts, respectively around Mt. Meru.

12 J. Ijumulana et al. Groundwater for Sustainable Development 14 (2021) 100618 estimated to be under significant-high fluorosis risk in districts within Berger, T., Mathurin, F.A., Drake, H., Astrom, M.E., 2016. Fluoride abundance and EARV graben and around the flanks of Mt. Meru and Mt. Hanang’ in controls in fresh groundwater in Quaternary deposits and bedrock fractures in an area with fluoride-rich granitoid rocks. Sci. Total Environ. 569–570, 948–960. Arusha and Manyara regions, respectively. The probability for dental https://doi.org/10.1016/j.scitotenv.2016.06.002. caries disease burden in Kilimanjaro region was 0.77 ± 0.20, whereas Bhattacharya, P., Adhikari, S., Samal, A.C., Das, R., Dey, D., Deb, A., Ahmed, S., that of fluorosis was 0.62 ± 0.24 in the three regions. Hussein, J., De, A., Das, A., Joardar, M., Panigrahi, A.K., Roychowdhury, T., Santra, S.C., 2020. Health risk assessment of co-occurrence of toxic fluoride and Specific districts with a population under high dental caries risk arsenic in groundwater of Dharmanagar region, North Tripura (India). Groundw. include Rombo, Moshi, and Mwanga. In these districts, the probability of Sustain. Dev. 11, 100430. https://doi.org/10.1016/j.gsd.2020.100430. accessing a safe source for drinking purposes is 0.06, 0.11, and 0.30 in Box, G.E., Cox, D.R., 1964. An analysis of transformations. Royal Statis. Soc. Ser. B 26 (2), 211–243. https://doi.org/10.1111/j.2517-6161.1964.tb00553.x. Rombo, Moshi, and Mwanga, respectively. In terms of fluorosis disease Bretzler, A., Johnson, C.A., 2015. The Geogenic Contamination Handbook: addressing burden, the specific districts with a population under significant risk arsenic and fluoride in drinking water. Appl. Geochem. 63, 642–646. https://doi. include Siha and Hai in Kilimanjaro region, Arusha City, Arusha and org/10.1016/j.apgeochem.2015.08.016. ’ Brindha, K., Elango, L., 2011. Fluoride in groundwater: causes, implications, and Meru in Arusha region, and Simanjiro and Hanang in the Manyara re mitigation measures. In: Monroy, S.D. (Ed.), Fluoride: Properties, Applications, and gion. The probability of accessing safe water in these districts is 0.06 Environmental Management. Nova Science Publishers, New York, pp. 111–136. (Siha and Hai), 0.16 (Arusha City), 0.17 (Hanang’), 0.20 (Arusha), 0.23 Bundschuh, J., Maity, J.P., Mushtaq, S., Vithanage, M., Seneweera, S., Schneider, J., (Simanjiro), and 0.26 (Meru). In these districts, almost all members of Bhattacharya, P., Khan, N.I., Hamawand, I., Guilherme, L.R.G., Reardon-Smith, K., Parvez, F., Morales-Simfors, N., Ghaze, S., Pudmenzky, C., Kouadio, L., Chen, C-Y., the household have developed mild to severe fluorosis which is an 2017. Medical geology in the framework of the sustainable development goals. Sci. increasing disease burden, as observed visually during field campaigns Total Environ. 581-582, 87–104. https://doi.org/10.1016/j.scitotenv.2016.11.208. in Embaseni and Lemanda villages in Meru and Arusha districts, Carr, J.R., Mao, Nh, 1993. A general form of probability kriging for estimation of the indicator and uniform transforms. Math. Geol. 25, 425–438. https://doi:10.1007/B respectively. F00894777. The findings of this study are very important for the local water Chiles, J.-P., Delfiner, P., 2009. Geostatistics: Modeling Spatial Uncertainty, vol. 497. & supply authorities in the studied regions, not only for managing the John Wiley Sons, New York. Chowdhury, A., Adak, M.K., Mukherjee, A., Dhak, P., Khatun, J., Dhak, D., 2019. existing groundwater sources in the naturally contaminated hydro A critical review on geochemical and geological aspects of fluoride belts, fluorosis, geological environments but also in the planning for new sources of safe and natural materials, and other sources for alternatives to fluoride exposure. water for drinking purposes to meet the national 2025 vision on the safe J. Hydrol. 574, 333–359. https://doi.org/10.1016/j.jhydrol.2019.04.033. Cressie, N., 1990. The origins of kriging. Math. Geol. 22 (3), 239–252. https://doi.org/ water supply to all citizens. The findings are also crucial for the re 10.1007/BF00889887. searchers to estimate the severity of dental caries and fluorosis using Dissanayake, C.B., 1991. The fluoride problem in the groundwater of Sri Lanka — appropriate hazard indices among different age groups, with emphasis environmental management and health. Int. J. Environ. Stud. 38 (2–3), 137–155. https://doi.org/10.1080/00207239108710658. on children to avoid later health catastrophe in the aforementioned Edmunds, W.M., Smedley, P.L., 2013. Fluoride in natural waters. In: Selinus, O., districts. Alloway, B., Centeno, J.A., Finkelman, R.B., Fuge, R., Lindh, U., Smedley, P.L. (Eds.), Essentials of Medical Geology. Springer, Dordrecht. The Netherlands, pp. 311–336. https://doi.org/10.1007/978-94-007-4375-5_13. Declaration of competing interest Fawell, J., Bailey, K., Chilton, J., Dahi, E., Fewtrell, L., Magara, Y., 2006. Fluoride in Drinking Water. IWA Publishing, London, UK. Fewtrell, L., Smith, S., Kay, D., Bartram, J., 2006. An attempt to estimate the global The authors declare that they have no known competing financial burden of disease due to fluoride in drinking water. Water and Health 4 (4), interests or personal relationships that could have appeared to influence 533–542. https://doi.org/10.2166/wh.2006.0036. ´ ´˜ the work reported in this paper. García-Perez, A., Irigoyen-Camacho, M.E., Borges-Yanez, A., 2013. Fluorosis and dental caries in Mexican schoolchildren residing in areas with different water fluoride concentrations and receiving fluoridated salt. Caries Res. 47 (4), 299–308. https:// Acknowledgment doi.org/10.1159/000346616. Ghiglieri, G., Balia, R., Oggiano, G., Pittalis, D., 2010. Prospecting for safe (low fluoride) groundwater in the eastern African Rift: the arumeru district (northern Tanzania). The authors extend their sincere thanks to the Swedish International Hydrol. Earth Syst. Sci. 14 (6), 1081–1091. https://doi.org/10.5194/hess-14-1081- Development Cooperation Agency (Sida) for the funding through the 2010. DAFWAT project (Sida Contribution 51170072, Project No. 2235). We Hach Usa, 2021. Fluoride, ISE Method for Drinking Water. In: HQ440D Laboratory Fluoride (F⁻) Ion Meter Package with ISEF121 Ion-Selective Electrode. https://www. deeply appreciate all government agencies for providing spatial data hach.com/. (Accessed 28 May 2021). used in this study. Special appreciation goes to the Director of Water Hengl, T., 2009. A Practical Guide to Geostatistical Mapping. https://d1wqtxts1xzle7.cl Quality Division of the Ministry of Water of the Government of the oudfront.net/40396676/A_Practical_Guide_to_Geostatistical_Mapping.pdf. (Accessed 28 May 2021). United Republic of Tanzania for allowing access to the National Fluoride Ijumulana, J., Ligate, F., Bhattacharya, P., Mtalo, F., Zhang, C., 2020. Spatial analysis Database and the use of F‾ data for this work. Lastly, we thank anony and GIS mapping of regional hotspots and potential health risk of fluoride mous reviewers for constructive comments and suggestions that have concentrations in groundwater of northern Tanzania. Sci. 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