Achieving Sustainable Development Goal for Clean Water in India: Influence of Natural and Anthropogenic Factors on Groundwater Microbial Pollution

Linking Land Use Land Cover change to global groundwater storage

Groundwater storage is facing the constant threat of over-exploitation and irreversible depletion, often attributed to agricultural and industrial usage as well as human mismanagement. While several methodologies, varying from well logs to gravity recovery data, have been successfully adopted over the years to track and mitigate groundwater loss, Land Use and Land Cover (LULC) has never been quantified to evaluate groundwater storage and variability. LULC change alters the hydrological connectivity between the surface and subsurface water. Towards this, we employed a decision tree based Machine Learning model to (a) identify hydrological and terrestrial drivers affecting groundwater resources, (b) predict shallow and deep groundwater variability, (c) rank the drivers according to their impact on groundwater distribution, and (d) understand groundwater distribution as a function of LULC change. The model was developed globally, and then extended to basinal scale observations in the Indus, Ganga and Brahmaputra rivers of the Indian subcontinent. Model output has helped to (a) compute the 'infiltration index' associated with each Land Cover, (b) equate cropland expansion among the three basins with shallow and deep groundwater storage and (c) link LULC-groundwater change to crop yield. RCP 2.6 crop yield estimates for the 21^st century proves detrimental to Indian food and freshwater security, given the strong coupling of groundwater-LULC among the three basins and how Land Cover change translates to groundwater storage.

Insight into impact of sewage discharge on microbial dynamics and pathogenicity in river ecosystem

Direct sewage discharge could cause copious numbers of serious and irreversible harm to the environment. This study investigated the impacts of treated and raw sewage on the river ecosystem. Through our analysis, sewage carried various nutrients into the river, leading to changes in the microbial community in the river and reducing the diversity and richness of bacteria. The relative abundances of Hydrogenophaga, Thauera, Planctomyces, Zoogloea, and Pseudomonas boosted from 0.25, 0.01, 0.00, 0.05, and 0.08% to 3.33, 3.43, 0.02, 6.28, and 2.69%, before and after raw sewage discharge, respectively. The gene abundance of pathogenic bacteria significantly increased after raw sewage discharge. For instance, the gene abundance of Vibrio, Helicobacter, Tuberculosis, and Staphylococcus augmented from 4055, 3797, 13,545, 33 reads at Site-1 to 23,556, 13,163, 19,887, 734 reads at Site-2, respectively. In addition, according to the redundancy analysis (RDA), the infectious pathogens were positively related to the environmental parameters, in which COD showed the highest positive correlation with Mycobacterium tuberculosis. Additionally, river self-purification may contribute to improving water quality and reducing pathogenicity. The outcomes of this study showed that direct discharge brought pathogens and changed microbial community structure of the river.

Off-office audit of natural resource assets and water pollution: a quasi-natural experiment in China

Purpose

The purpose of this paper is to analyze the impact of the off-office audit of natural resource assets on the prevention and control of water pollution against a background of big data using a differences-in-differences model.

Design/methodology/approach

This study constructs a differences-in-differences model to evaluate the policy effects of off-office audit based on panel data from 11 cities in Anhui Province, China, from 2011 to 2017, and analyzes the dynamic effect of the audit and intermediary effect of industrial structure.

Findings

The implementation of the audit system can effectively reduce water pollution. Dynamic effect analysis showed that the audit policy can not only improve the quality of water resources but can also have a cumulative effect over time. That is, the prevention and control effect on water pollution is getting stronger and stronger. The results of the robustness test verified the effectiveness of water pollution prevention and control. However, the results of the influence mechanism analysis showed that the mediating effect of the industrial structure was not obvious in the short term.

Practical implications

These findings shed light on the effect of the off-office audit of natural resource assets on the prevention and control of water pollution, and provide a theoretical basis for the formulation of relevant environmental policies. Furthermore, these findings show that the implementation of the audit system can effectively reduce water pollution, which has practical significance for the sustainable development of China's economy against the background of big data.

Originality/value

This study quantitatively analyzes the policy effect of off-office auditing from the perspective of water resources based on a big data background, which differs from the existing research that mainly focuses on basic theoretical analysis.