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. 2019 Oct 23;9(1):15193.
doi: 10.1038/s41598-019-50875-w.

Impact of sanitation and socio-economy on groundwater fecal pollution and human health towards achieving sustainable development goals across India from ground-observations and satellite-derived nightlight

Abhijit Mukherjee  1   2 , Srimanti Duttagupta  3 , Siddhartha Chattopadhyay  4 , Soumendra Nath Bhanja  5 , Animesh Bhattacharya  3   6 , Swagata Chakraborty  7 , Soumyajit Sarkar  3 , Tilottama Ghosh  8 , Jayanta Bhattacharya  3   9 , Sohini Sahu  10
Affiliations

Impact of sanitation and socio-economy on groundwater fecal pollution and human health towards achieving sustainable development goals across India from ground-observations and satellite-derived nightlight

Abhijit Mukherjee et al. Sci Rep. .

Erratum in

Abstract

Globally, ~1 billion people, mostly residing in Africa and South Asia (e.g. India), still lack access to clean drinking water and sanitation. Resulting, unsafe disposal of fecal waste from open-defecation to nearby drinking water sources severely endanger public health. Until recently, India had a huge open-defecating population, leading declining public health from water-borne diseases like diarrhoea by ingesting polluted water, mostly sourced to groundwater. However, in recent past, sanitation development to achieve Sustainable Development Goals (SDGs) has been encouraged throughout India, but their effect to groundwater quality and human health conditions are yet-unquantified. Here, for the first time, using long term, high-spatial resolution measurements (>1.7 million) across India and analyses, we quantified that over the years, groundwater fecal coliform concentration (2002-2017, -2.56 ± 0.06%/year) and acute diarrheal cases (1990-2016, -3.05 ± 0.01%/year) have significantly reduced, potentially influenced by sanitation development (1990-2017, 2.63 ± 0.01%/year). Enhanced alleviation of groundwater quality and human health have been observed since 2014, with initiation of acceletated constructions of sanitation infrastructures through Clean India (Swachh Bharat) Mission. However, the goal of completely faecal-pollution free, clean drinking water is yet to be achieved. We also evaluated the suitability of using satellite-derived night-time light (NLan, 1992-2013, 4.26 ± 0.05%/year) as potential predictor for such economic development. We observed that in more than 80% of the study region, night-time light demonstrated to be a strong predictor for observed changes in groundwater quality, sanitation development and water-borne disease cases. While sanitation and economic development can improve public health, poor education level and improper human practices can strongly influence on water-borne diseases loads and thus health in parts of India.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Map of study area across India, showing (a) annual linear trend of groundwater fecal coliform anomaly (FCan, 2002–2017) in each of the administrative blocks or equivalents (BLKs, n = 7010 BLKs) within the study period across the study region (shown in the inset map on top left). Linear slope of FCan for the entire study area is −2.56 ± 0.06%/year for the data period. The map also show locations of the detailed study areas A (located in Highly Improved, FCan decrease >90% within the study period), B (Improved, >70–90%), C (Moderately Improved, >50–70%) and D (Less Improved, <50%); annual linear trends of anomalies of (b) acute diarrheal cases (ADan, 1990–2016, −2.93 ± 0.03%/year for entire study area), (c) household sanitation structures (SANan, 1990–2017, 2.63 ± 0.06%/year) and (d) night-time light (NLan, 1992–2013, 4.26 ± 0.05%/year); (e–h) non-linear, Hordick Prescott (HP) trends of the entire and detailed study areas (A, B, C and D) for FCan, ADan, SANan and NLan.
Figure 2
Figure 2
Schematic flowchart of data and methods applied in this study, displaying the parameters that have used e.g. FC, AD, SAN, NL, SANu, Lt (white boxes) and analyzed relationships [(a) through (h)]. The parameters are listed with the numbers of observation and time period used in the study. The relationships also summarize the outcome of the analyses.
Figure 3
Figure 3
Maps of study area showing correlation for synchronous study periods between different parameters. (a) FCan and ADan (2002–2016; r2 = 0.985 for entire area, A: 0.99, B: 0.91, C: 0.72 and D: 0.522, p < 0.01), (b) FCan and SANan (2002–2017; r2 = 0.922 for entire, A: 0.96, B: 0.84, C: 0.67 and D: 0.77, p < 0.01), (c) FCan and NLan (2002–2013; r2 = 0.841 for entire, A: 0.96, B: 0.84, C: 0.67 and D: 0.77, p < 0.01), (d) SANan and ADan (1990–2016; r2 = 0.895 for entire, A: 0.96, B: 0.84, C: 0.67 and D: 0.77, p < 0.01), (e) SANan and NLan (1992–2013; r2 = 0.943 for entire, A: 0.96, B: 0.84, C: 0.67 and D: 0.77, p < 0.01) and (f) ADan and NLan (1992–2013; r2 = 0.425 for entire, A: 0.96, B: 0.84, C: 0.67 and D: 0.77, p < 0.01)].
Figure 4
Figure 4
Temporal trends of (a) Household Sanitation usage (SANu) and (b) Literacy rate (Lt) as percentage of population in the detailed study areas A through D.
Figure 5
Figure 5
Bayesian VAR t-statistics value, showing impact of Sanitation and Economic development (DEV) and Improper Human Practices (IHP) on Water Quality and Health (WQH) for detailed study area A through D. Positive and negative t-statistics value indicates more direct and inverse, significant variance relationships, respectively.
Figure 6
Figure 6
Map of the study area showing the four clusters, showing Cluster I (superset of detailed study area A) blocks (3069 BLKs, i.e. 44% of entire study region) where Water Quality and Health (WQH) alleviation are predominantly influenced by Sanitation and Economic development (DEV), and Improper Human Practices (IHP) has minimal influence, within the study period; Cluster II (superset of area B) blocks (1596 BLKs, 23%), where DEV influence WQH, but IHP has some effect; Cluster III (superset of area C) blocks (631 BLKs, 9%), where IHP influence WQH, but DEV has some effect; and Cluster IV (superset of area D) blocks (1714 BLKs, 24%), where IHP predominant influence on WQH, but DEV has minimal influence.
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