Receptor model-based source apportionment and ecological risk assessment of metals in sediment of river Ganga, India.

Ganga river surface sediment was sampled from 11 locations, which revealed average concentrations (mg/kg) of metals in the order Mn (296.93) > Zn (61.94) > Cr (54.82) > Cu (30.19) > Pb (24.42) > Cd (0.36). Sediment quality guidelines showed metals rarely to occasionally exhibit adverse biological effects. Indices like potential ecological risk, contamination security index, hazard quotients, multiple probable effect concentrations quality, mean probable effects level quotients, mean effects range median quotient suggest nil to a very low level of pollution with low ecological risk. Contamination factor, geo accumulation index, enrichment factor, quantification of contamination revealed that Pb and Cd originated from anthropogenic activities. APCS-MLR model revealed that metals contributed from natural sources (Zn, Mn, Cr; 20.18 %), industrial-agricultural (Cd; 21.35 %); and discharge of paints, Pb batteries, fossil fuel (Pb; 8.49 %). Present findings will serve as an effective guideline for managing and ameliorating pollution in the river system.

Ability of machine learning models to identify preferred habitat traits of a small indigenous fish (Chanda nama) in a large river of peninsular India.

Physical and chemical parameters of river influence the habitat of fish species in aquatic ecosystems. Fish showed a complex relationship with different aquatic factors in river. Machine learning modeling is a useful tool to identify relationships between components of a complex environmental system. We identified the preferred habitat indicators of Chanda nama (a small indigenous fish), in the Krishna River located in peninsular India, using machine learning modeling. Using data on Chanda nama fish distribution (presence/absence) and associated ten physical and chemical parameters of water at 22 sampling sites of the river collected during the year 2001-2002, machine learning models such as random forest, artificial neural network, support vector machine, and k-nearest neighbors were used for classification of Chanda nama distribution in the river. The machine learning model efficiency was evaluated using classification accuracy, Cohen's kappa coefficient, sensitivity, specificity, and receiver-operating-characteristics. Results showed that random forest is the best model with higher classification accuracy (82%), Cohen's kappa coefficient (0.55), sensitivity (0.57), specificity (0.76), and receiver-operating-characteristics (0.72) for prediction of the occurrence of Chanda nama in the Krishna River. Random forest model identified three preferred physicochemical habitat traits such as altitude, temperature, and depth for Chanda nama distribution in Krishna River. Our results will be helpful for researcher and policy maker to understand important physical and chemical variables for sustainable management of a small indigenous fish (Chanda nama) in a large tropical river.

Multidecadal assessment of environmental variables in the river Ganga for pollution monitoring and sustainable management.

The Ganga River is the major source of drinking water for humans over the decades. It is also the ecological niche for millions of relict species, i.e., for a variety of planktons, benthic organisms, fish, and various other aquatic organisms. The blasting population resulted in an enhanced rate of pollution in the river system emanating from various anthropogenic activities and industrialization in the bank of river Ganga. The study was made in the middle and lower stretch of the river to monitor the decadal changes in the water quality of river Ganga from 1960 to 2019 at six different study sites. In the present study, various water quality parameters such as dissolved oxygen, pH, free carbon dioxide, total alkalinity, conductivity, total dissolved solids (TDS), hardness, chloride, and nitrate have been studied during 2015-2019. The data for 1960 to 2006 were taken from ICAR-CIFRI publications. Based on the studied parameters, National Sanitation Foundation (NSF)-water quality index (WQI) was calculated. In the present study, it was found that the calculated NSF-WQI was 69.24 in 1960-1961 which increased up to 113.39 during 2001-2006. But, with the implementation of various rejuvenating strategies, the WQI of the river got reduced to 106.48 during 2015-2019. This reflected the positive changes in the riverine system. Different water quality parameters such as dissolved oxygen, pH, and hardness were observed mostly within the permissible range as based on the drinking water guidelines for humans and survival of the aquatic organisms as well, except a few location-specific observations.

Water quality assessment in the ecologically stressed lower and estuarine stretches of river Ganga using multivariate statistical tool.

Water quality of the Ganga River system is changing day by day due to multifold increase in population, especially near the banks of river Ganga, and associated exponential amplification of anthropogenic activities also played a remarkable role in it. The ecologically important lower and estuarine stretch of river Ganga comprising 7 different sampling stations, i.e., Jangipur, Berhampore, Balagarh, Tribeni, Godakhali, Diamond Harbour and Fraserganj, were selected for the study as the stretch is enriched with the vast number of floral and faunal diversity. The study was conducted for a period of 5 years, i.e., from 2016 to 2020. In the study, various analytical tools and techniques were used for the assessment of riverine water quality, i.e., for calculation of water quality index (WQI); The National Sanitation Foundation Water Quality Index (NSF-WQI) and the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI) were used for the assessment. Along with WQI various statistical univariate as well as multivariate analytical tools like principal component analysis, correlation, ANOVA, and cluster analysis were also used to achieve the desired outputs. In the study, it has been observed that NSF-WQI varied from 61 to 2552, in which the higher value of NSF-WQI denoted the unsuitability of the water quality concerning the drinking water standards and vice versa. The CCME-WQI represented a similar trend as that of NSF-WQI, as it varied from 18 to 92 in which the lower value denoted degradation in the drinking water quality and vice versa. The study revealed that the Diamond Harbour-Fraserganj stretch is having an undesired level of water quality which were analyzed based on the drinking water guideline values of the Bureau of Indian Standards and that of NSF-WQI and CCME-WQI.

Variation of Aulacoseira granulata as an eco-pollution indicator in subtropical large river Ganga in India: a multivariate analytical approach.

Aulacoseira granulata (Ehrenberg) Simonsen 1979 are considered as the eco-variable species which varies in density and diversity along with their morphological traits with the interference of environmental changes, so it is considered as one of the major ecological indicators of the water quality of lotic as well as lentic aquatic ecosystems. To assess major environmental factors which contribute to A. granulata bloom in the riverine system, a study was carried out from 2018 to 2019 comprising four different seasons at 11 sampling sites of river Ganga in the middle and lower stretch of river Ganga comprising freshwater and estuarine zones. For the analysis, different univariate, as well as multivariate, analytical tools such as principal component analysis (PCA) and water pollution index (WPI) were used. In the finding, it was observed that the average abundance of A. granulata was found maximum during the winter season. Among all the studied sites, the maximum average abundance was at Balagarh (71,576 cell l-1) and minimum at Diamond Harbour (68 cell l-1). The environmental factors such as dissolved oxygen, depth, and altitude showed a significant influence on the growth of A. granulata, while the water temperature negatively influenced the growth rate of A. granulata. The WPI showed a significantly negative correlation with cell length. Finally, the study concludes that the blooming of A. granulata is highly influenced by varied environmental conditions along the river Ganga, suggesting possible eutrophication. Therefore, a certain minimum flow and depth especially during the lean season have to be maintained for the sustenance of planktonic biota in the river Ganga.

Microplastics removal efficiency of drinking water treatment plant with pulse clarifier.

Microplastics are recognized as ubiquitous pollutants in aquatic environments; however, very little study is done on their occurrence and fate at drinking water treatment plants (DWTPs). Though, the toxic effect of microplastics on human health is not yet well established; there is global concern about their possible ill effect on the human. Hence, the present study evaluates the occurrence of microplastics at different treatment stages of a typical DWTP with pulse clarification and its removal efficiency. In the test DWTP, raw water, sourced from river Ganga, was found to contain microplastics 17.88 items/L. Cumulative microplastic removal at key treatment stages viz. pulse clarification and sand filtration was found to be 63% and 85%, respectively. The study also revealed higher microplastic abundance on the sand filter bed due to the screening effect. The most frequently occurring microplastics were fibers and films/fragments with polyethylene terephthalate and polyethylene as a major chemical type. The t-distributed stochastic neighbor embedding machine learning algorithm revealed a strong association between microplastic abundance with turbidity, phosphate and nitrate. The test DWTP with a pulse clarification system was having comparable microplastics removal efficiency with previously reported advanced DWTPs.

Occurrence, fate and removal of microplastics as heavy metal vector in natural wastewater treatment wetland system.

Microplastics pollution in aquatic ecosystems is of great concern; however, systemic investigations are still lacking in freshwater wetland systems used for wastewater treatment. The present study discusses such freshwater wetland system in Eastern India to understand its microplastics transport mechanism, heavy metals association and microplastics removal efficiency. Microplastics (63 µm - 5 mm) were heavily found in surface water and sediments of treatment ponds (7.87 to 20.39 items/L and 2124.84 to 6886.76 items/kg) and associated wastewater canals (30.46 to 137.72 items/L and 1108.78 to 34612.87 items/kg). A high content of toxic metals (As, Cd, Cr, Cu, Ni, Pb and Zn) were found on the microplastics with polyethylene terephthalate and polyethylene as major plastics types which were also found in fishes and macroinvertebrates of treatment ponds. Machine learning algorithm revealed a close association between microplastics content in fishes and surface water, indicating risk associated with floating microplastics to the aquatic biota. The study also revealed that microplastics were acting as heavy metals vector and potentially causing fish contamination. Surface water microplastics removing efficiency of the treatment ponds was estimated to be 53%. The study bespeaks about transport of microplastics through wastewater canals and their retention in treatment ponds emphasizing sustainability maintenance of natural wastewater treatment systems especially considering microplastics contamination to the aquatic biota of freshwater wetland systems.

Impact assessment of barge trafficking on phytoplankton abundance and Chl a concentration, in River Ganga, India.

Impact of barge movement on phytoplankton abundance and biomass was assessed in the lower stretch of river Ganga, popularly known as Bhagirathi-Hooghly river, during April 2016 to March, 2017. Based on the magnitude of tide, intensity of shipping and boating activities, the stretch from Baranagar to Lalbag (278 km), located at latitude (22°38'33.41"N to 24°10'59.75"N) and longitude (88°21'21.29"E to 88°16'5.65"E) was divided into three zones viz. zone-I (Baranagar to Barrackpore), zone II (Triveni to Balagarh) and zone III (Nabadweep to Lalbag). Water samples were collected randomly from six stations covering 22 barge movements at their passage at three different time intervals viz., 30 minutes before 'barge movement', during 'barge movement' and 30 minutes after 'barge movement'. Analysis revealed the presence of 52 phytoplankton taxa belonged to 5 phylum during the study period. The abundance of phytoplankton was highest in zone-I followed by zone III and the zone II. A 44% decrease (1,997 ±1,510 ul-1) in phytoplankton abundance was observed during 'barge movement' with respect to normal condition (3,513 ± 2,239 ul-1) which could be due to propeller turbulence in the passage. Cell damage study revealed 21% damage in phytoplankton cell structure in 'during barge' followed by 'after barge' (10%) condition compared to natural state (6%). Study revealed that phytoplankton biomass (Chlorophyll a) was influenced by 'barge movement' in the sampling stretches and the impact was assessed by one way ANOVA. The effect was found significant at Barrackpore (p <0.01), Triveni (p <0.01), Balagarh (p <0.01) and Lalbag (p <0.01) where as it was insignificant at Baranagar and Nabadweep, which may be due to continuous and existing boat trafficking at Baranagar and Nabadweep. Two way ANOVA computed using 'barge movement' and sampling stations showed significant (p<0.01) effect on magnitude of Chl a concentrations in the sampling locations. Thus, the 'barge movement' influenced phytoplankton abundance and biomass, it had a detrimental effect on phytoplankton cell architecture also. The data set of this work serves as foundation information to understand the ecological implications augmented barge induced environmental disturbances in waterways. This is the first such study which depicts the impact of 'barge movement' on aquatic food chain linkages in Bhagirathi- Hooghly river.

Spatial distribution of meso and microplastics in the sediments of river Ganga at eastern India.

Small plastic debris is one of the most significant emerging pollutants, due to their extreme durability and synthetic nature, possessing a tremendous threat to the aquatic environment. In the present study, sediments of river Ganga at a lower stretch were analyzed for distribution of meso and microplastics at seven different locations viz. Buxar, Patna, Bhagalpur, Nabadwip, Barrackpore, Godakhali and Fraserganj. All the sediments were found to contain mesoplastics (>5 mm) and microplastics (<5 mm) particles with varying degree of the mass fraction (11.48 to 63.79 ng/g sediments), numerical abundance (99.27-409.86 items/kg) and morphotypes. Analysis of the mesoplastics with FT-IR revealed polyethylene terepthalate (39%) as the major contributing plastic debris in the sediments followed by polyethylene (30%). Statistical analysis revealed a strong correlation between microplastics abundance and the pollution traits, BOD and available phosphate, of water and sediment, respectively. This study exhibits the spatial distribution of meso and microplastics in the highly populated locations along the river Ganga emphasizing the attention to be given to this emerging pollutant in the inland river system underlining their role as a transporter of plastic fragments finally to the ocean.

Microplankton dynamics of a coastal lagoon, Chilika: interactive effect of environmental parameters on microplankton groups.

Microplankton population of Asia's largest coastal lagoon Chilika was studied for five major groups, bacillariophyceae, cyanophyceae, chlorophyceae, dinophyceae, rotifera, and tintinninae. The study reported presence of 233 species of microplankton whose average annual abundance was 1631 cells/l. The physicochemical parameters contributing to the spatio-temporal fluctuations in microplankton diversity, abundance, and community structure were identified as salinity, pH, DO, nitrate, and silicate. Salinity, transparency, depth, and silicate most explained the abundance of bacillariophyceae; nitrate, pH, and DO influenced cyanophyceae; salinity, transparency, and chlorophyll concentration influenced chlorophyceae; salinity, depth, and water temperature influenced dinophyceae; salinity, free CO2, and nitrate-influenced rotifers, whereas salinity, pH, DO, and depth influenced tintinnids. Biotic-abiotic relationships revealed particular preference of environmental conditions at species level in groups like bacillariophyceae, cyanophyceae, and dinophyceae. Although the lagoon is shallow, bacillariophyceae-environment interaction showed depth can be a critical factor for species like Aulocoseira sp., Amphipleura sp., and Rhophalodia sp. Species of dinoflagellates like Dinophysis caudata, Noctiluca scintillans, and Protoperidinium proliferated in lower level of silicate. Unlike other cyanophyceae species Streptococcus sp., Chroococcus sp., Diplococcus sp., Aphanocapsa sp., and Gloeocapsa sp. were negatively influenced by nitrate concentration. The study provides better scope for ecological management of the lagoon with respect to conserving biodiversity and hydrological quality of the ecosystem.