Microplastics (plastic < 5 mm in size) are now known to contaminate riverine systems but understanding about how their concentrations vary spatially and temporally is limited. This information is critical to help identify key sources and pathways of microplastic and develop management interventions. This study provides the first investigation of microplastic abundance, characteristics and temporal variation along the Ganges river; one of the most important catchments of South Asia. From 10 sites along a 2575 km stretch of the river, 20 water samples (3600 L in total) were filtered (60 samples each from pre- and post-monsoon season). Overall, 140 microplastic particles were identified, with higher concentrations found in the pre-monsoon (71.6%) than in post-monsoon (61.6%) samples. The majority of microplastics were fibres (91%) and the remaining were fragments (9%). We estimate that the Ganges, with the combined flows of the Brahmaputra and Meghna rivers (GBM), could release up to 1–3 billion (10⁹) microplastics into the Bay of Bengal (north-eastern portion of the Indian Ocean) every day. This research provides the first step in understanding microplastic contamination in the Ganges and its contribution to the oceanic microplastic load.

Aquatic pollution from emerging organic contaminants (EOCs) is of key environmental importance in India and globally, particularly due to concerns of antimicrobial resistance, ecotoxicity and drinking water supply vulnerability. Here, using a broad screening approach, we characterize the composition and distribution of EOCs in groundwater in the Gangetic Plain around Patna (Bihar), as an exemplar of a rapidly developing urban area in northern India. A total of 73 EOCs were detected in 51 samples, typically at ng.L⁻¹ to low μg.L⁻¹ concentrations, relating to medical and veterinary, agrochemical, industrial and lifestyle usage. Concentrations were often dominated by the lifestyle chemical and artificial sweetener sucralose. Seventeen identified EOCs are flagged as priority compounds by the European Commission, World Health Organisation and/or World Organisation for Animal Health: namely, herbicides diuron and atrazine; insecticides imidacloprid, thiamethoxam, clothianidin and acetamiprid; the surfactant perfluorooctane sulfonate (and related perfluorobutane sulfonate, perfluorohexane sulfonate, perfluorooctanoic acid and perfluoropentane sulfonate); and medical/veterinary compounds sulfamethoxazole, sulfanilamide, dapson, sulfathiazole, sulfamethazine and diclofenac. The spatial distribution of EOCs varies widely, with concentrations declining with depth, consistent with a strong dominant vertical flow control. Groundwater EOC concentrations in Patna were found to peak within ∼10 km distance from the River Ganges, indicating mainly urban inputs with some local pollution hotspots. A heterogeneous relationship between EOCs and population density likely reflects confounding factors including varying input types and controls (e.g. spatial, temporal), wastewater treatment infrastructure and groundwater abstraction. Strong seasonal agreement in EOC concentrations was observed. Co-existence of limited transformation products with associated parent compounds indicate active microbial degradation processes. This study characterizes key controls on the distribution of groundwater EOCs across the urban to rural transition near Patna, as a rapidly developing Indian city, and contributes to the wider understanding of the vulnerability of shallow groundwater to surface-derived contamination in similar environments.

Many perfluoroalkyl substances (PFAS) are ubiquitous environmental contaminants. They have been widely used in production processes and daily-use products or may result from degradation of precursor compounds in products or the environment. India, with its developing industrialization and population moving from traditional to contemporary lifestyles, represents an interesting case study to investigate PFAS emission and exposure along steep environmental and socioeconomic gradients. This study assesses PFAS concentrations in river and groundwater (used in this region as drinking water) from several locations along the Ganges River and estimates direct emissions, specifically for PFOS and PFOA. 15 PFAS were frequently detected in the river with the highest concentrations observed for PFHxA (0.4–4.7 ng L⁻¹) and PFBS (<MQL – 10.2 ng L⁻¹) among PFCAs and PFSAs, respectively. Prevalence of short-chain PFAS indicates that the effects of PFOA and PFOS substitution are visible in environmental samples from India. The spatial pattern of C5–C7 PFCAs co-varied with that of PFOS suggesting similar emission drivers. PFDA and PFNA had much lower concentrations and covaried with PFOA especially in two hotspots downstream of Kanpur and Patna. PFOS and PFOA emissions to the river varied dramatically along the transect (0.20–190 and 0.03–150 g d⁻¹, respectively). PFOS emission pattern could be explained by the number of urban residents in the subcatchment (rather than total population). Per-capita emissions were lower than in many developed countries. In groundwater, PFBA (<MQL – 9.2 ng L⁻¹) and PFBS (<MQL – 4.9 ng L⁻¹) had the highest concentrations among PFCAs and PFSAs, respectively. Concentrations and trends in groundwater were generally similar to those observed in surface water suggesting the aquifer was contaminated by wastewater receiving river water. Daily PFAS exposure intakes through drinking water were below safety thresholds for oral non-cancer risk in all age groups.

This study reports the first evidence of the quantification of two dominant perfluorinated compounds (PFCs), namely perfluorooctanesulfate (PFOS) and perfluorooctannoate (PFOA), in surface sediment samples (0–5cm; n=13) from the Ganges (Hugli) River including Sundarban wetland, India using HPLC–MS/MS. The concentrations of PFOA exhibited a wide range of concentrations from <0.5 to 14.09ng/gdry wt, whereas the concentration of PFOS was always below the detection limit of <0.5ng/g drywt. A consistent enrichment of PFOA was recorded in all the five sites of Sundarban (mean value 11.61±1.86) whereas it was of moderate concentration or below the detection level in the seven sites along with the lower stretch of the Ganges (Hugli) River estuary (mean value 5.96ng/gdry wt±5.36). Wastewater and untreated effluents are likely the major causes of accumulation of PFCs in sediments. The present paper could be used as baseline study to assess future monitoring programs of the ecosystem.

In order to examine the influence of discharge from different rivers from peninsular India and urban sewage on intensity and dissemination of heterotrophic, indicator and pathogenic bacteria, a study was carried out during peak discharge period along coastal Bay of Bengal. The coastal Bay received freshwater inputs from the river Ganges while Godavari and Krishna contributed to the south. Contrasting difference in salinity, temperature, nutrients and organic matter was observed between north and south east coast of India. The highest heterotrophic, indicator and pathogenic bacterial abundance was observed in the central coastal Bay that received urban sewage from the major city. Intensity and dissemination of heterotrophic, indicator and pathogenic bacteria displayed linear relation with magnitude of discharge. The coliform load was observed up to 100km from the coast suggesting that marine waters were polluted during the monsoon season and its impact on the ecosystem needs further studies.

Reproduction plays an important role in fish population efficiency and its resiliency to fishing and environment changes. The present study described the comprehensive information on reproductive feature of stinging catfish, Heteropneustes fossilis (Bloch 1794), including size at sexual maturity, spawning season, and fecundity using 622 female individuals sampling by the use of gill net, cast net, and square lift net from January to December 2019 in the Ganges River. We calculated the influences of various environmental parameters which include temperature, dissolved oxygen, pH, and rainfall on the reproductive feature of H. fossilis in the Ganges River. For every specimen, total length (TL), standard length (SL), and body weight (BW) were estimated by measuring board and electronic weighing scale. With ventral dissection of fishes, female gonads were cautiously removed and measured to 0.01 g precision. The gonadosomatic index (GSI), modified gonadosomatic index (MGSI), and Dobriyal index (DI) were used to assess the size at sexual maturity (Lₘ) and spawning season. According to the results of these indices, Lₘ was obtained 15.5 cm in TL. Also, TL₅₀ was determined through logistic function as 15.5 cm in TL. Moreover, the highest GSI, MGSI, and DI values indicated the spawning season as of March–August, with peak in May–June. Total fecundity (FT) varied from 2059 to 59,984 with a mean of 25,028 ± 15,048. Temperature and rainfall was statistically correlated with GSI. In addition, long climatic data series analysis denoted that yearly mean atmospheric temperature is rising in 0.028 °C/year and yearly mean rainfall is declining in 2.98 mm/year which may suggest a potential shift of the spawning period of the species in the future if this trend persists. The results of our study might be more useful in imposing particular management and conservation for H. fossilis in the Ganges River and the surroundings.

River water quality is a function of various bio-physicochemical parameters which can be aggregated for calculating the Water Quality Index (WQI). However, it is challenging to model the nonlinearity and uncertain behavior of these parameters. When data is deficient and noisy, it creates missing and conflicting parameters within their complex inter-relationships. It is also essential to model how climatic variations and river discharge affect water quality. The present study proposes a cloud-based efficient and resourceful machine learning (ML) modeling framework using an artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and advanced particle swarm optimization (PSO). The framework assesses the sensitivity of five critical water quality parameters namely biochemical oxygen demand (BOD), dissolved oxygen (DO), pH, temperature, and total coliform toward WQI of the River Ganges in India. Monthly datasets of these parameters, river flow, and climate components (rainfall and temperature) for a nine-year (2011–2019) period have been used to build the models. We also propose collecting the data by placing various monitoring sensors in the river and sending the data to the cloud for analysis. This helps in continuous monitoring and analysis. Results indicate that ANN and ANFIS capture the nonlinearity in the relationship among water quality parameters with a root mean square error (RMSE) of 7.5 × 10⁻⁷ (0.002%) and 1.02 × 10⁻⁵ (0.029%), respectively, while the combined ANN-PSO model gives normalized mean square error (NMSE) of 0.0024. The study demonstrates the role of cloud-based machine learning in developing watershed protection and restoration strategies by analyzing the sensitivity of individual water quality parameters while predicting water quality under changing climate and river discharge.

The Batchwa vacha, Eutropiichthys vacha is commercially important, supporting a viable small- and large-scale fishery throughout the Ganges River, NW Bangladesh. This study provides detail information on reproduction of E. vacha including size at sexual maturity, spawning and peak spawning season, and fecundity based on 734 female specimens through regular monthly sampling using cast net, gill net, and square lift net in the Ganges River during January to December 2016. Also, our study estimated the effects of climate change including temperature and rainfall on reproduction of E. vacha in the Ganges River. For each individual, lengths (total length, TL; standard length, SL) and body weight (BW) were measured with slide caliper and digital balance, respectively. Gonads (ovaries) were collected carefully by ventral dissection of each female specimen and weighed to the nearest 0.01 g accuracy. The gonadosomatic index (GSI % = (GW/BW) ×100), modified gonadosomatic index (MGSI % = (GW/BW − GW) × 100), and Dobriyal index (DI = [Formula: see text]) were calculated to estimate the size at sexual maturity (L₅₀) and spawning season. Based on GSI, MGSI, and DI, the L₅₀ was calculated as 12.5 cm TL for female. The TL₅₀, the TL at which 50% of individuals become mature, was calculated by logistic equation as 12.7 cm. Also, on the basis of higher values of GSI, MGSI, and DI, spawning season was ranged from April to August, with the peak in June–July, signifying the peak spawning season for E. vacha in the Ganges River. The total fecundity (FT) ranged from 4800 to 77,976 (mean ± SD, 31384 ± 23,747) and was highly correlated with TL and BW. Water temperature during the spawning period ranged from 28 to 34 °C, with an average of 31 °C and there was significant correlation between temperature and GSI. Also, the spawning season coincides with the peak rainfall and there was significant correlation between rainfall and GSI. Additionally, analysis of long data series indicated that annual average air temperature is increasing by 0.0258 °C/year, while the annual average rainfall is decreasing by 3.107 mm/year. Finally, the findings of this study would be very effective to impose specific management for E. vacha in the Ganges River and surrounding ecosystems.

Assessment of water quality status of a river with respect to its discharge has become prerequisite to sustainable river basin management. The present paper develops an integrated model for simulating and evaluating strategies for water quality management in a river basin management by controlling point source pollutant loadings and operations of multi-purpose projects. Water Quality Analysis and Simulation Program (WASP version 8.0) has been used for modeling the transport of pollutant loadings and their impact on water quality in the river. The study presents a novel approach of integrating fuzzy set theory with an “advanced eutrophication” model to simulate the transmission and distribution of several interrelated water quality variables and their bio-physiochemical processes in an effective manner in the Ganges river basin, India. After calibration, simulated values are compared with the observed values to validate the model’s robustness. Fuzzy technique of order preference by similarity to ideal solution (F-TOPSIS) has been used to incorporate the uncertainty associated with the water quality simulation results. The model also simulates five different scenarios for pollution reduction, to determine the maximum pollutant loadings during monsoon and dry periods. The final results clearly indicate how modeled reduction in the rate of wastewater discharge has reduced impacts of pollutants in the downstream. Scenarios suggesting a river discharge rate of 1500 m³/s during the lean period, in addition to 25 and 50% reduction in the load rate, are found to be the most effective option to restore quality of river Ganges. Thus, the model serves as an important hydrologic tool to the policy makers by suggesting appropriate remediation action plans.