Rural-urban spatial inequality in water and sanitation facilities in India: A cross-sectional study from household to national level

Applied Geography 85 (2017) 27e38

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Rural-urban spatial inequality in water and sanitation facilities in India: A cross-sectional study from household to national level Sriroop Chaudhuri*, Mimi Roy Center for Environment, Sustainability and Human Development (CESH), Jindal School of Liberal Arts and Humanities, O.P. Jindal Global University, Sonipat, Haryana 131001, India

a r t i c l e i n f o

a b s t r a c t

Article history: Received 7 December 2016 Received in revised form 8 May 2017 Accepted 21 May 2017

A major obstacle for the developing nations to meeting the United Nation's Sustainable Development Goals (SDG: 2015e2030) for WaSH (Water-Sanitation-Hygiene) is the appalling rural-urban inequality in infrastructural facilities that lead to regional/spatial differences in livelihood. In India, where about 70% of the population dwells in villages, rural-urban inequality can pose steep challenges to the authorities in their motto of ensuring improved water and sanitation for all. Cognizant of the need, the present study aimed to map nationwide rural-urban spatial inequalities for various WaSH infrastructural facilities along a four-tier administrative hierarchy: household-district-state-national. Cross-sectional data for districtwise percentages of rural and urban households having access to (i) latrine facility within premises, (ii) treated tap water, (iii) improved water source, and (iv) at-home water source were obtained from the Census of India database for 2011. A variety of metrices (Bray-Curtis Dissimilarity Index (BCDI), Gini coefficient, Moran’I, LISA) were used to characterize underlying spatial patterns. Rural-urban spatial inequality in 'treated tap water' appeared as the most spatially variable WaSH parameter across the nation. Results indicated that governmental claims of having met the Millennium Development Goal (MDG) for ‘improved’ water source require a thorough reappraisal, especially for rural India, as majority of these so called improved sources thrive on groundwater (hand pumpþtube well), which is heavily contaminated by co-occurrences of multiple pollutants (fluoride, arsenic, nitrate, salinity), which have grave human health effects, and thus questioning the fundamental premise of 'safe water'. About 54% of the rural households in India rely on groundwater sources as compared to <20% urban households. In addition, about 67% of rural, against about 12% urban, households still ‘indulge’ in open defecation practices, which calls for stringent management actions coupled with strategic awareness campaigns. Rural-urban inequality in WaSH facilities appeared most alarming across the central Indian states of Chattisgarh, Bihar, Jharkhand, Odisha, Madhya Pradesh and Rajasthan. Overall, spatial heterogeneity in the rural-urban inequality appeared a daunting challenge for the authorities, urging for spatiallyoptimized policy reforms instead of enacting nationwide uniform policy measures. © 2017 Elsevier Ltd. All rights reserved.

Keywords: WaSH Open defecation Rural-urban differences Gini coefficient Moran's I Agglomerative hierarchical cluster analysis

1. Introduction Recent studies attribute the stark rural-urban differences as a major obstacle to a nation's (especially the developing nations) progress to achieve the Millennium Development Goal (MDGs) in the WaSH sector (Wolf, Bonjour, & Pruss-Ustun, 2013). Globally, over 80% of the population lacking access to improved water sources dwell in rural areas (WHO/UNICEF, 2015). For large parts of

* Corresponding author. E-mail addresses: [email protected] (S. Chaudhuri), [email protected] (M. Roy). http://dx.doi.org/10.1016/j.apgeog.2017.05.003 0143-6228/© 2017 Elsevier Ltd. All rights reserved.

India, there exists a pronounced rural-urban divide in various sociodemographic aspects (Das & Pathak, 2012). Using long-term (1950e2011) governmental data, Hassan (2016) reports about 16% and 17% of rural population lag their urban counterparts, in literacy and infant mortality rates, respectively. Interestingly, both of these parameters are emphatically mentioned in the 8 MDGs (Literacy: MDG 2; Infant mortality: MDG 4) as major global indicators to ensure sustainable human development. To add to the misery, relevant UN database reveals that for both MDGs, there are substantial differences between rural and urban population (especially in the developing nations of Sub-Saharan Africa and South/SouthEast Asia). A recent study revealed alarming rural-urban disparity

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in availability of ‘improved’ water sources such as treated tap water in the rural areas of the country (Chaudhuri & Roy, 2016a). There is an appalling lack of adequate water supply (40 L per capita per day) in the rural areas in India (Chaudhuri & Roy, 2016b). Importance of ensuring ‘appropriate’ WaSH facilities is recognized around the world in view of its tremendous impacts on longterm sustainable human development. Poor WaSH facilities entails an annual loss of about $260 billion in the developing nations (Kumar, 2014). Appalling ignorance for managing/separating excreta is a grieving menace to overall public health and hygiene infrastructure in most parts of rural India, often incurring dismal disease burden (Dhaktode, 2014). Faeco-oral diseases such as dysentery, cholera, giardia, lead to massive human life losses in the developing nations around the world with diarrhea alone claiming about 1.8 million lives annually (Howard, 2002). However, reforms and policy-implementation drives to ‘amend’ the WaSH sector to account for emerging diseases and environmental mishaps still lack due acknowledgement in India (George, 2009). Numerous studies have reported on human health hazards ensuing from unhygienic sanitation practices (Clasen et al., 2014; Coffey et al., 2014; Routray, Schmidt, Boisson, Clasen, & Jenkins, 2015), including diarrhea (Kumar and Das, 2014), stunting (Spears, Ghosh, & Cumming, 2013) and mortality (Patil et al., 2014) in children. Rural-urban disparities in the WaSH sector reigns throughout south Asia: a cluster of nine countries defined by the WHO-UNICEF's Joint Monitoring Program (JMP) for monitoring global progress towards Millennium Development Goals for water and sanitation (WHO/UNICEF, 2015). Among these, Bhutan currently ‘leads’ the tally with about 45 percentage point gap in the ruralurban sanitation sector, followed by India (34%) and Pakistan (32%). Among the nine south Asian countries, India occupies 8th and 6th positions, respectively, for rural and urban sanitation sector, and clearly fails to meet the MDGs for both sectors. Presently over 66% of global population practicing open defecation reside in India, among which 90% dwell in the rural areas (Routray et al., 2015). Even though the rural-urban divide in India has been recognized by several studies (Roy & Mondal, 2015; Shafiquallah, 2011; Narayanamoorthy and Hanjra, 2010), to the best of our knowledge none so far have attempted to characterize it in terms of the WaSH sector to date. In light of the above observation, this study is primarily aimed at highlighting the rural-urban inequalities in water and sanitation facilities (WaSH in a broader notion) using the latest Census (2011) data which is the most authentic crosssectional national statistics available freely. The study is divided into two parts in the main: the first part was devoted to map the rural-urban inequalities, while the second looked into exploring means to elucidate geographical heterogeneity therein, using a four-tier administrative hierarchy from household to national level. In the post-MDG era, addressing geographic inequalities in WaSH coverage emerged as a major challenge to the international authorities to tracking progress towards the Sustainable Development Goal for WaSH (SDG, Target 6): ensure safe, equitable, and affordable water and sanitation for all by 2030 and end open defecation. Keeping this in view, a thorough appraisal of rural-urban inequality is highly sought after by the concerned authorities (both national and international) to assess the existing level of spatial equity in the WaSH sector. Findings of the present study can be used to design/conduct follow-up investigations with exclusive focus on the hotspots (regions with high rural-urban spatial inequality) to explore links between WaSH parameters and socio-economic factors that influence WaSH developmental profile. In addition, it would help evaluating ‘true’ achievements in the rural potable water sector by shedding light on water quality that is seldom taken into account in estimation of 'improved' water sources.

Though implemented specifically for India, due to highly generic nature, methods outlined herein can offer easy-to-use, yet informative and visually dynamic tools to assess spatial inequality across geopolitical units that will help evaluating the SDGs (2015e2030) in the coming years. By the same token, the inherent flexibility of the methods will allow for their application in any other field of study aimed to depict/quantify/map spatial inequality/heterogeneity.

2. Materials and methods 2.1. Study area, data types and data analysis There are 29 states in India and seven Union Territories. According to the Census 2011 database, there were 641 districts in India. In 2011, the total rural population accounted for about 70% of the national total. Provisional totals indicate that about 70% of the rural population in India are potentially exposed to a multitude of water-borne diseases, owing largely to lack of appropriate WaSH facilities. Over 69% of rural households in India lacked improved (water closet and/or pit latrine) latrine facilities within premises, as against about 19% urban households (Fig. 1). Cross-sectional data for four WaSH parameters were obtained from the Census of India database (‘H-series’: Household Amenities) for 2011 as district-wise percentages of rural and urban households having access to: (a) treated tap water, (b) improved water, (c) athome water source, (d) latrine facility within premises and (e) open defecation. Throughout the study, water and sanitation has been used synonymously to WaSH (Water-Sanitation-Hygiene). Five methods were used to highlight the rural-urban divide at different levels of administrative hierarchy (household-districtstate-nation) as follows (Fig. 2): i Rural-urban arithmetic difference: computed by subtracting rural percentage of households accessing different WaSH facilities from that of urban: District level

Fig. 1. Nationwide percentages of rural and urban households in India accessing different types of improved water and latrine facilities. (improved implies certain types of facilities that are considered relatively more hygienic than others).

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Fig. 2. Overall rubric of analytical methods followed in the study (rural-urban arithmetic difference is used synonymously to rural-urban inequality).

ii Spatial autocorrelation (Moran's I and LISA): District level iii Bray-Curtis Dissimilarity Index (BCI): Based on (i) and later aggregated by state iv Agglomerative hierarchical cluster analysis (AHC): State level v Gini coefficient: National level Methods (i - ii) is mainly intended to highlight district-wise rural-urban differences for each WaSH parameter, while methods (iii - iv) were used to indicate spatial heterogeneity in the differences therein. Unlike other methods, the Gini coefficient (method ‘v’), however, was computed using the absolute values (districtwise percentages of households), individually for the rural and urban areas, instead of rural-urban differences, and aimed to capture the nation-wide heterogeneity in key WaSH parameters. A key novelty attempted in the present study is the use of household-level information -lowest administrative unit used in policy making around the world- as the backbone of all analyses. It is a bottoms-up approach where district- and/or state-wise results are in effect macroscopic views of this microscopic unit that helps visualize/assess regional spatial inequality at its very root. 2.2. Spatial autocorrelation Moran's I and Local Indicators of Spatial Association (LISA) were computed using district-wise WaSHQI (Water-sanitation-Hygiene Quality Index). The WaSHQI is computed by the rural-urban arithmetic difference in percentages of households practicing open defecation, which is taken as an overall indicator of public health and hygiene. Moran's I was computed using the following

equation:

I¼

n So

Pn Pn i¼1

j¼0

  Wij ðxi  XÞ xj  X

Pn

i¼1 ðxi

 XÞ2

where n ¼ total number of observations (districts) i and j ¼ spatial location of districts with respect to each other Wij ¼ spatial weight matrix So ¼ product sum of the spatial weight matrix xi ¼ value for rural-urban arithmetic difference at location i xj ¼ value for rural-urban arithmetic difference at location j X ¼ mean of value x The product sum of the spatial weight matrix, So was computed as:

So ¼

n X n X

Wij

i¼1 j¼1

Moran's I is essentially an extension of Pearson's productemoment correlation coefficient with the numerator representing a covariance function while the denominator representing sample variance (Moran, 1950). Analogous to Pearson's correlation coefficient, values of Moran's I can vary from þ1 (strong positive spatial autocorrelation between adjacent geographic units) to 1 (strong negative spatial autocorrelation therein) through 0, suggesting random spatial pattern.

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Local Indicator of Spatial Association (LISA) illustrates spatial autocorrelation phenomena with two interrelated maps: one depicts clustering of spatial units (districts), while the other reveals the possible range of statistical significance (0.001 < p < 0.05) associated with each cluster. As the name suggests, LISA represents local scenario of spatial autocorrelation within a defined neighborhood, using following equation:

LISA ¼ Zi

n X

Wij Zj

i¼1

where, Z ¼ standardized variable of interest. For both Moran's I and LISA, a first-order queen contiguity (like queen's move on the chess board) between the adjoining spatial units (districts) was considered to derive the spatial weight matrix. The spatial weight matrix (Wij) was row-standardized and, by convention, equaled to zero. 2.2.1. Bray-Curtis Dissimilarity Index (BCI) and agglomerative hierarchical cluster analysis (HCA) The main purpose of the Bray-Curtis Dissimilarity Index (BCDI) was to compute one composite score reflective of the intra-state rural-urban inequality in WaSH attributes. The BDCI values were computed for each state individually, following the equation:

P  Yurban j jY BCDI ¼ P rural jYrural þ Yurban j where Y ¼ District-wise percentage of households having particular WaSH parameter value. The BCDI is a non-metric (non-Euclidean) index which provides robust and reliable dissimilarity results for a wide range of applications (Bray & Curtis, 1957) and among the most widely used measurements to express relationships/diversity in ecology/environmental sciences (Storkey et al., 2015). In essence, it is a modified Manhattan measurement, where the summed differences between the variables are standardized by the summed variables of the objects. The BCDI ranges from zero to one, with the latter indicating 100% difference between the parameters compared (district-wise rural and urban household percentages). State-wise BCDI values were used to perform agglomerative hierarchical cluster analysis (HCA). Ward's Minimum Variance algorithm was used in conjunction with Squared Euclidean Distance to determine the total sum of squared deviations from the mean of each cluster (Chaudhuri & Ale, 2015). Resultant clusters were mapped to identify state-wise zonal patterns in rural-urban dissimilarity across the nation. 2.2.2. Gini coefficient The Gini coefficient was aimed at obtaining a holistic overview of spatial inequality in available WaSH facilities individually for rural and urban areas, following the equation:

G ¼

n X n   1 X Yj  Yi  2 2n u j¼ 1 i¼ 1

where G ¼ Gini coefficient n ¼ sample size (number of districts) u ¼ average value of the study parameter jYj e Yij ¼ absolute value of the difference between districts Gini coefficient ranges between 0 (perfect equality) and 1 (perfect inequality) (Wagstaff, Paci, & van Doorslaer, 1991), with

following categories for G: < 0.20: good equality; 0.20e0.30: fair equality; 0.30e0.40: reasonable equality; 0.40e0,50: high inequality; > 0.50: stark inequality (Fang, Zhu, & Deng, 2013). 3. Results 3.1. Rural-urban differences (inequality) District-wise assessment of rural-urban differences portrayed alarming lags in the rural WaSH sector throughout the nation (Fig. 3aed). In 604, 524, 634, and 633 districts, rural areas trailed the urban areas for availability of treated tap water, improved water sources, at-home water sources and latrine facilities, respectively. In about 220 districts, mainly occurring in states of Jharkhand (JH), Madhya Pradesh (MP), Rajasthan (RJ) and Uttar Pradesh (UP), over half the total rural households lacked in latrine facilities for over 90% districts (Fig. 3a). For latrine facilities within premises, rural areas trailed their urban counterparts by at least a quarter of households in about 500 districts occurring in the states of Bihar (BR), West Bengal (WB), Odisha (OR), Chattisgarh (CG), Jharkhand (JH), Uttar Pradesh (UP), Madhya Pradesh (MP), Rajasthan (RJ), Andhra Pradesh (AP), Telengana (TS), Tamil Nadu (TN), Karnataka (KA), Gujarat (GJ) (Fig. 3a). Overall, less than a third of the rural households (28.3%) in India had latrine facilities within premises as compared to over 80% for their urban counterparts. Apparently, the scenario was a little gratifying for improved water sources: in about 116 districts (WB in the main, and in isolated pockets in BR, OR, UP, TS, AP and KA), rural areas were ‘apparently’ ahead of the urban areas (Fig. 3b). However, a closer inspection of rural-urban differences in treated tap water (Fig. 3c) and at-home water source (Fig. 3d) brought out grieving lags, for over at least quarter of rural households. In rural RJ, over half the rural households were behind their urban counterparts for over 75% districts for both parameters. 3.2. Open defecation Open defecation is a major debacle to the WaSH sector in rural India. Currently about 67% of rural households in India take to open defecation, as compared to about 12.2% of urban households, further elucidating the dismal rural-urban divide. As of 2011, in 429 districts of the country, distributed across 13 states, over half the rural households practiced open defecation (Fig. 4a). In about 80 districts, in the states of BR, WB, JH, CG, OR, and MP, over 90% of the rural household practiced open defecation. Adding further to the grievance, extent of dilapidation in the sanitation sector in these states was further demonstrated by occurrence of about 123 districts where a third of urban households practiced open defecation. In 18 of these districts, about half the urban households indulged in such unhygienic practices which calls for urgent management action (Fig. 4b). Spatial distribution of district-wise rural-urban inequality in open defecation practices identified 160 districts in the top quartile (>75%) of the spectrum. The majority of these high-inequality districts occurred in RJ, MP, UP, MH, CG, JH, OR, and TN (Fig. 4c). On the other hand, identical number of districts were found in the bottom quartile (<25%) of the spectrum as well, occurring in the northeastern states, Jammu and Kashmir (JK) and Punjab (PN) in the north; and most of west coast (MH, KA, Kerala (KL)). 3.2.1. Open defecation: spatial clusters Assessment of different spatial autocorrelative metrices further corroborated to the zonal pattern in rural-urban inequality in open defecation practices (Fig. 5aec). A statistically significant

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Fig. 3. District-wise rural-urban arithmetic differences in % of households with (a) latrine facility within premises, (b) improved water, (c) treated tap, (d) at-home water source.

(p < 0.001) Moran's I value (þ0.65) coupled with clustering of highhigh and low-low districts, in diagonally opposite quadrants of the Moran's scatterplot implied strong positive spatial association between districts with similar traits in rural-urban WaSH differences (Fig. 5a). In addition, the scatterplot, showed that a vast number of districts occur rather ‘randomly’ where the rural-urban inequality, irrespective of their absolute magnitude, lacked any spatial clustering. Overall, the Moran's scatterplot, provided initial clues into the possible range in spatial variability in the rural-urban divide that warrants special management strategies and targeted policy reforms in days ahead. Being a global statistic, Moran's I, however, failed to ‘map’ the exact geographic locations of the spatial clusters which is more important for the authorities to devise/implement spatiallyoptimized reforms to meet the emerging needs. In this regard, Local Indicators of Spatial Association (LISA) appears more useful as it provides a clear insight into the clustering phenomena by identifying statistically significant (0.05 < p < 0.01) clusters (districts) within a specified local neighborhood as follows:

 High-High (hotspots): Districts with high rural-urban arithmetic differences in percentages of households with particular WaSH trait surrounded by districts having similar values  Low-Low (coldspot): Districts with low rural-urban arithmetic differences surrounded by districts having similar values  High-Low/Low-High: Districts with dissimilar rural-urban differences in spatial contiguity  Not significant: random spatial pattern Following the above rubric, hotspots (high-high) of rural-urban inequality, comprising of 121 districts, were identified in MP and RJ across central and northwestern India; JH and CG in east-central parts; and AP and TN in southern India (Fig. 5b). In addition, the LISA also identified several coldspots (low-low) in the northeastern states (Mizoram (MZ), Manipur (MN), Nagaland (NL)); PN and JK in north and northwest; and KA and MH along the west coast. Simultaneous occurrences of cold-/hotspots in different parts of the nation indicated that not only the rural-urban inequality a debacle to the WaSH sector in India, spatially variable nature of this

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Fig. 4. District-wise % of households practicing open defecation in (a) rural and (b) urban areas, and (c) box-plot of arithmetic rural-urban differences. Districts outlined by white in (a) indicate >90% open defecation.

inequality is a challenge to address with nation-wide uniform policy reforms. Spatial heterogeneity in rural-urban WaSH inequality was further illustrated by the ‘random’ occurrence of a large number of districts (395) scattered across the nation (Fig. 5c). Overall, the results revealed discrete regional traits that merits closer scrutiny of the major drivers that give rise to such heterogeneous mix, coupled with spatially-optimized-policy-implementationschemes, developed by individual state authorities in lieu with the central government, rather than ‘uniform’ nationwide management strategies that seldom acknowledge region-specific challenges and/or needs.

3.3. Spatial heterogeneity in rural-urban inequality 3.3.1. BCDI-HCA The Bray-Curtis Dissimilarity Index (BCDI) was computed by intra-state rural-urban inequality with the aim of bringing out the dissimilarities between states for each WaSH parameter. Based on the BCDI analysis, the agglomerative hierarchical cluster analysis (HCA) was performed to summarize the state-wise dissimilarities to divide the nation into discrete WaSH-clusters: group of states aggregated by similar BCDI values for different WaSH parameters (Fig. 6). In effect, the HCA presented the collective dissimilarity between

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Fig. 5. (a) Global Moran's I, (b) LISA cluster, and (c) statistical significances map for district-wise rural-urban arithmetic differences in open defecation.

the states across the nation and summarized the results into five statistically significant (p < 0.01) clusters. Cluster-wise assessment of the BCDI values indicated (a) cluster 4 (HP, PN and HR) consistently occurred on the lowest end of the BCDI scale for each WaSH parameter while (b) cluster 3 (JH, OR, CG, MP and RJ) on the highest end. Interestingly, states occurring in cluster 3 account for the highest rural population and household density in India and also some of the lowest literacy rates in the country (data not shown). Interestingly, cluster 3 and 4 were comprised of spatially contiguous states while the other clusters were more scattered across the nation (especially cluster 2). Closer scrutiny of clusters established treated tap water (0.05e0.82) and latrine (0.01e0.78) facilities within the highest BCDI ranges while safe water, within the lowest (0.06e0.66), which indicated that even within individual stateclusters, treated tap water was a highly variable parameter and needs urgent management action to ensure uniformity.

3.3.2. Gini coefficient Urban Gini coefficient ranged from 0.04 (improved water) to 0.31 (treated tap water) while between 0.18 (improved water) to 0.54 (treated tap water) for the rural areas indicating pronounced geographic inequality in treated tap water sources through major parts of the nation while the opposite was true for improved water sources (Fig. 7). Fundamentally though, this questions the very basis of classifying improved potable water sector in India as treated tap water should be at the very root of it. According to the inequality class breaks imposed on the Gini coefficient (Fang et al., 2013), urban Gini coefficient appeared having reasonable geographic equity (G < 0.40) for all WaSH parameters. For the rural areas, on the other hand, distribution of athome water sources were marked with high inequality (G > 0.40) while treated tap and latrine facilities appeared starkly inequal (G > 0.50). The latter two also corroborated to the inferences drawn

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Fig. 6. State-wise HCA-clusters for different WaSH parameters. (TT: Treated Tap; SW: Improved Water; ATH: At-home Water Source; LH: Latrine Facility within premises).

by the HCA. The Gini coefficient summarized nationwide spatial inequalities in a holistic comparison between rural and urban areas. Inequality in infrastructural facilities clearly appeared of greater concern in the rural areas of the country calling for urgent measures. Interestingly, the national pattern of inequality closely mimicked the rural trend, which underlined the contribution of rural processes on the national scenario (Fig. 7).

4. Discussion Results of this study obviate the conclusion of 'infrastructural inadequacy' in the rural sanitation sector that needs to be addressed with urgent policy reforms and governmental subsidies to build appropriate and adequate facilities. The ground-truth, however, appears quite puzzling and far from this simple solution and thus deserves a deeper scrutiny of human behavior/attitudes

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Fig. 7. Gini coefficient for different WaSH parameters for rural, urban and national total.

and age-old customs handed down through generations. The question the authorities might want go after is: Is open defecation in rural India a compulsive necessity or largely a choice? In addition to sanitation, some in-depth discussion is also necessary so as to judge the validity of governmental claims to have met the erstwhile MDG target for safe water. 4.1. Open defecation: need or choice? Rampant open defecation is a major ‘failure’ to WaSH reforms/ policy-making in rural India (Boisson et al., 2014; Brocklehurst, 2014; Sinha et al., 2016). A major cause for the ‘failure’ is certainly infrastructural inadequacies in the rural areas. The cause for it is diverse and can often appear perplexing. The governmentsubsidized latrines in the rural areas are often reported to be inappropriate, especially for the women: lacking roofs, doors, walls, buried pits, adequate spatial dimensions, each of which hinge upon the question of ease of latrine usage and more importantly, privacy (Barnard et al., 2013). Concurrent to this is the rural-urban disparity in availability of improved latrine facilities. While 'water closets' (having piped sewer system and/or septic tanks) are available to about 72.6% of urban households in the country, only 19.4% of the rural households have access to it (Fig. 8). In addition, public latrines are available to about 6% of urban households as against only about 2% for their rural counterparts. Often lack of access to water sources around the latrines can deter latrine-use as well (Gius & Subramanium, 2015). In India, preand post-defecation cleansing is of great importance and can often take quite elaborate measures ranging from simple hand/feetwashing to laundering clothes and full-fledged bathing. Under the circumstances, lack of sustainable water sources in the vicinity is a deterrant to latrine usage, especially in rural areas. But apart from constructional design, a key propellant to open defecation practices in the rural areas is a diverse suit of sociocultural dogmas that hinders latrine usage (Routray et al., 2015). For example, having at-home latrine facilities is often considered impure, especially in rural India (in some cases in urban locales as well), from a religious point of view (Banda, Sarkar, Gopal, Govindarajan, & Harijan, 2007). In India numerous idols are worshipped at home on regular basis. Under the circumstances, it is a long-standing belief, especially in the rural areas, having holy idols and latrine facilities under the same roof is against the sanctity of the home and should therefore be avoided as much as possible. Another such issue that builds upon faith and/or social custom is that villagers do not feel ‘comfortable’ with idea of cooking/eating

food and defecating under the same roof (Dhaktode, 2014). So effectively, even after having indoor facilities (e.g. governmentsubsidized latrines), villagers still indulge in using outdoor means (Coffey et al., 2014). Interestingly, a chance at social connectivity is another fact that frequently prompts the rural populace to open defecation (Dickinson & Pattanayak, 2012; Routray et al., 2015). Added to this is the grieving lack of awareness about the numerous health hazards ensuing from open defecation: latrine pits are often considered as insect spawning sites (e.g. mosquitos) and thus ‘unhygienic’ to have within premises (Geetha & Kumar, 2014). In rural areas, building safe latrine facilities rarely rank among top priorities to homeowners and are frequently overlooked (Banerjee, Banik, & Dalmia, 2016). Open defecation is, however, not a menace exclusively to the rural areas but as likely in the urban areas as well (Satterthwaite, 2016). In about 150 districts, over a third of the urban households practice open defecation (Fig. 4b). Rural populace who migrate to the urban hubs in search of jobs largely lack houses with appropriate household amenities (e.g. improved water and/or latrine facilities) and thus are compelled to defecate in the open. 4.2. Potable water sources: does improved mean safe? Fig. 8 demonstrates relative reliance of states/UTs on hand pump þ tube well based potable water infrastructure in India, with Bihar (BR) leading the tally, both in rural and urban areas. In BR, WB, CG, UP, MP, PN, AS and JH, combined total of hand pump þ tube well provides potable water sources to over half the rural households (Fig. 8a), while treated tap water furnishes only about 5% of the same. Overall, hand pumps and tube wells exceed that of the treated water by at least 15 percentage points in 13 states in rural India. Over 50% urban households in Bihar rely on hand pumps þ tube well system. In BR, Assam (AS), NL, JH, and UP, these sources exceed that of treated tap water sources even for the urban areas which indicates that hand pump and tube wells form the mainstay of potable water infrastructure in India (Fig. 8b). Hand pumps and tube wells draw from groundwater resources, which have a dismal legacy of water quality impairment issues due to occurrences of multiple pollutants such as nitrate (Trivedi, 2012; Suthar, Bishnoi, Singh, & Patil, 2009), fluoride (Dahariya et al., 2015), arsenic (Guha Mazumder, 2010), and salinity (Lorenzen, Sprenger, Baudron, & Gupta, 2011), each having adverse human health impacts. Groundwater quality impairment due to high levels of fluoride is reported from 20 states in India (Fig. 9), with in some states (RJ, CG, TS, AP, KA, GJ, WB) two-thirds of the districts are ‘affected’ (Kumar & Kumar, 2015).

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Fig. 8. State-wise percentage of households having access to hand pump þ tube wells and treated tap water for (a) rural and (b) urban areas. States are arranged from left to right following decreasing order of percentages of households using hand pump þ tube for water source.

High levels (greater than Maximum Permissible Level, MPL) of nitrate, salinity, arsenic, iron, and chloride in groundwater is reported from about 18, 12, 2, 20, and 10 states in India, respectively, which indicates nationwide impairment of ‘improved’ potable water sources (hand pump þ tube well) that rely on groundwater resources. In addition, agricultural seepage/runoff introduces a variety of pollutants (nitrate, salinity, organic agrochemicals etc.) (Chaudhuri & Ale, 2014; Chaudhuri et al., 2012) to water resources in the rural areas that threatens entire potable water infrastructure. Widespread occurrences of multiple pollutants in groundwater sources across the nation question the fundamental basis of defining ‘improved’ water sources in India. Apparently, the results suggest that the term ‘improved’ in India probably does not take into account the chemical quality of potable water that is linked to human health risks and therefore is not synonymous with the idea of ‘safe’. Moreover, about 65% rural households in India (29% urban) still lack at-home water sources, which imply that a vast cross-section still thrive on external sources for potable need. Ironically, however, the framework for source water protection, especially that for the rural public sources, still lacks holistic appeal as the rural

populace are largely unaware of environmentally ‘undesirable’ activities (e.g. open defecation, ablution, laundry, cleaning of utensits, performing of holy rituals etc) near/at the water sources. Under the circumstances, a perplexing question is: how improved are the improved sources (hand pump/tube well) indeed? Overall, the rural water sector in India warrants in-depth investigation of potable water quality involving region-specific details about the geochemical identity of water resources and human dynamics. Incorporating water quality in the framework of 'improved' water sources and assessing progress towards the SDGs has been stressed upon by several studies (Bain et al., 2012, 2014; Onda, LoBuglio, & Bartram, 2012). Studies around the world heavily criticize the JMP's definition of ‘improved’ source for the low- and middle-income countries, such as India, where potable water quality impairment by microbial contamination is ubiquitous (Bain et al., 2014; Megha, Kavya, & MuruganHarikumar, 2015) but seldom accounted for in the framework. Interestingly, however, accounting for water quality will substantially lower the estimates for improved water sources and in turn governmental claims of having met the MDG target. For example, if treated tap water is taken as the only unambiguous

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Fig. 9. Groundwater pollutants occurring above their maximum permissible levels (MPL).

improved water source then percentages of improved water sources will take an alarming nosedive by about 55% from what is claimed in the official JMP reports for India. 5. Conclusion Meeting the SDG targets for WaSH, in view of raving rural-urban spatial inequality, is probably among the most daunting task faced by the international authorities globally. Though a global concern, it is no less enervating at local-scale (e.g. for district/village administration) as well. At the outset of the SDGs, this has to be appreciated by the authorities, especially that of the developing nations, and work in sync with each other, if needed, to come up with sustainable solutions to ensure nationwide homogeneity in public services systems and advance towards maximum commonwealth. Results brought to light two fundamental issues that aggravate the rural-urban gap: (1) sociocultural dogmas leading to open defecation practices in rural India and (2) the notion of 'improved' water sources, which calls for further region-specific investigations in future. The latter deals with ideas to distinguish between ‘improved’ and ‘safe’ water sources acknowledging water quality issues. Lack of improved sanitation facilities within premises is a major threat to the public health hygiene in rural India. Currently about 70% of rural households, as against only about 19% urban, lack improved latrine facilities within premises. To add to the alarm, about 67% of rural households in India practice open defecation as against about 12% of their urban counterparts. Causes of high percentages of open defecation in the rural areas range from complete

lack of latrines and/or faulty constructions of the same to deep rooted sociocultural taboos against adapting to latrine usage. However, latrine usage is not the sole debacle to the WaSH sector in India. A major task for the concerned authorities in days ahead will be to delve into the true identity of ‘improved’ water sources, especially in the rural areas. Although governmental estimates claim to have met the MDG target, ensuring improved coverage for about 90% and 96% for rural and urban households, respectively, these sources largely rely on groundwater resources, frequently contaminated by multiple pollutants. What is worse, some of these pollutants (e.g. fluoride and arsenic) have demonstrated adverse impacts on human health. However, about 54% rural and about 20% of urban households rely on groundwaterbased sources. Moreover, among all the WaSH parameters considered in the study, highest spatial inequality is observed for treated tap water sources, which questions the sustainability of potable water infrastructure in India as a whole. Under the circumstances, present study urges for a thorough appraisal of the potable water sector to ‘amend’ the estimates for improved water sources and chalk out necessary action plans. Overall, it appeared that the ruralurban inequality is most striking across a vast landscape through central to west India, comprising of states of Chattisgarh, Jharkhand, Odisha, Madhya Pradesh and Rajasthan. The study outlined a highly generic approach to illustrate ruralurban inequality in India, and more importantly, the spatial variability therein, which can be replicated anywhere in the world for similar studies. The four-tier approach can be useful to the authorities to devise spatially-optimized management actions. It appeared that to attain homogeneity in WaSH infrastructural

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