Impact assessment on water quality in the polluted stretch using a cluster analysis during pre- and COVID-19 lockdown of Tawi river basin, Jammu, North India: an environment resiliency

Spatio-temporal evaluation of surface water quality of Tawi watershed in the Himalayan region of Jammu (J&K, UT) using algal pollution indices: a geospatial approach

In the present work, an investigation was performed based on the genera and species stated in Palmer pollution index to show the extent of organic pollution in the surface water of the Tawi watershed in the Jammu province of the Union Territory of Jammu and Kashmir using algal pollution indices. Sampling was carried out for two seasons, pre-monsoon (PRM) and post-monsoon (POM), at 16 locations distributed over the entire Tawi watershed. The physico-chemical variables like water temperature, pH, electrical conductivity, TDS, total alkalinity, total hardness, DO, BOD, COD, nitrate, and phosphate were analyzed. The seasonal distribution of the pollution-tolerant algal genera and species was recorded and the algal pollution index for both genus (AGP index) and species (ASP index) was also calculated. The concentration of BOD, COD, and nitrate in the sampled river water was found to be higher during the PRM season as compared to the POM season. The lower stretch of the watershed (Jammu Sub-Watershed) falls in class IV-V as per the polluted river stretch priority ranking based on BOD levels as BOD levels are >3 mg/L in the downstream locations during both seasons. A total of 23 algal taxa belonging to 8 families, Chlorophyceae (4 algal genera), Cyanophyceae (2 algal genera), Bacillariophyceae (7 algal genera), Zygnematophyceae (3 algal genera), Trebouxiophyceae (2 algal genera), Ulvophyceae (1 algal genus), Mediophyceae (1 algal genus), and Euglenophyceae (3 algal genera), have been reported in the Tawi watershed. The results of the Palmer indices showed a lack of organic pollution in the upstream, varying pollution levels in the midstream, and partially high to very high organic pollution levels in the downstream of the watershed. Comparative temporal analysis of the distribution of pollution-tolerant algal genera and species showed more organic pollution during PRM. Navicula and Cymbella were found to be the most abundant genera in almost all the stations, whereas Ulothrix, Cocconeis, Anacystis, and Crucigenia were the least recorded genera in the entire watershed. The results will enhance the understanding of the health status of the watershed, and provide database for watershed vulnerability assessment for sustainability and watershed management with spatio-temporal improvement.

Air-water exchange and risk assessment of phthalic acid esters during the early phase of COVID-19 pandemic in tropical riverine catchments of India

Given the increased load of waste plastic in the solid waste stream after the outbreak of the COVID-19 pandemic, we investigated the fate of selected plastic additives along open burning dumps, industrial and residential transects in tropical riverine catchments of India. Polyurethane foam disk passive air samples, surface water and community stored water (CSW) samples were collected along the Adyar River (AR), Cooum River (CR) and canals in Chennai and Daman Ganga River (DG) in Vapi. Among the quantified phthalic acid esters (PAEs), a widely used plastic additive, di(2-ethylhexyl) phthalate (DEHP), was ubiquitous across all the transects. More open drains and leaching of littered single-use plastic items can be the reason for significantly higher (p < 0.05) levels of PAEs in CR over other rivers with a dominance of di-n-butyl phthalate (DnBP). Prevalence of open burning of dumped plastic waste was the possible primary emission source of PAEs in these riverine catchments. Excluding highly soluble dimethyl phthalate (DMP), air-water exchange processes reflected the secondary emission of all the PAEs from the surface water along the open burning sites. Despite the cleansing effect of the oceanic air mass from the Bay of Bengal and the Indian Ocean, the average atmospheric PAE level was two-fold higher in Chennai than Vapi. Even though Vapi is a coastal city along the Arabian Sea, it was impacted by inland air masses during the sampling event. Open burning dumpsites showed a five-fold increase in atmospheric priority PAEs in Chennai city after the outbreak of the COVID-19 pandemic. DnBP was the major contributor to estrogenicity in CSW and DG, and also posed maximum risk for fishes in the open burning transect of these tropical rivers.

The Effect of the COVID-19 Pandemic on Environmental Health (Two Sides of the Same Coin): A Systematic Review

BACKGROUND

The outbreak of the COVID-19 pandemic in late 2019 has led to many changes such as reduced human activities and effects on the environment. There is no big picture of the effects of pandemics on the environment using related evidence.

OBJECTIVES

This study was conducted to investigate the effect of the COVID-19 pandemic on environmental health.

METHODS

A systematic search of English language studies was performed in major electronic databases; Web of Science, PubMed, Scopus, and Google scholar web search engine from December 2019 to February 2022. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standard guidelines were used to follow up the review process. finally 58 articles entered the review procedure.

RESULTS

The results of indicate a significant reduction of air pollutants and improved air quality. It improved the water quality of some rivers, canals, and seas during the lockdown of the COVID-19 pandemic. The effects of this disease on the environment cannot be fully described yet.

CONCLUSION

In the short term, the amount of air, water, and coastal pollution has been reduced. few studies have examined the effects of pandemics on the environment in the long run, which paves the way for more researches.

Assessing lake water quality during COVID-19 era using geospatial techniques and artificial neural network model

The present study evaluates the impact of the COVID-19 lockdown on the water quality of a tropical lake (East Kolkata Wetland or EKW, India) along with seasonal change using Landsat 8 and 9 images of the Google Earth Engine (GEE) cloud computing platform. The research focuses on detecting, monitoring, and predicting water quality in the EKW region using eight parameters-normalized suspended material index (NSMI), suspended particular matter (SPM), total phosphorus (TP), electrical conductivity (EC), chlorophyll-α, floating algae index (FAI), turbidity, Secchi disk depth (SDD), and two water quality indices such as Carlson tropic state index (CTSI) and entropy‑weighted water quality index (EWQI). The results demonstrate that SPM, turbidity, EC, TP, and SDD improved while the FAI and chlorophyll-α increased during the lockdown period due to the stagnation of water as well as a reduction in industrial and anthropogenic pollution. Moreover, the prediction of EWQI using an artificial neural network indicates that the overall water quality will improve more if the lockdown period is sustained for another 3 years. The outcomes of the study will help the stakeholders develop effective regulations and strategies for the timely restoration of lake water quality.

Effects of COVID-19 lockdown on water quality, microbial extracellular enzyme activity, and sediment-P release in the Ganga River, India

This study investigates possible improvement in water quality and ecosystem functions in the Ganga River as influenced by COVID-19 lockdown in India. A total of 132 samples were collected during summer-2020 low flow (coinciding COVID-19 lockdown) for water (sub-surface and sediment-water interface) and 132 samples separately for sediment (river bottom and land-water interface) considering 518-km main river stem including three-point sources (one releases urban sewage and the other two add metal-rich industrial effluents) and a pollution-impacted tributary. Parameters such as dissolved oxygen deficit and the concentrations of carbon, nutrients (N and P), and heavy metals were measured in water. Sediment P-release was measured in bottom sediment whereas extracellular enzymes (EE; alkaline phosphatase, FDAase, protease, and β-D-glucosidase) and CO₂ emission were measured at land-water interface to evaluate changes in water quality and ecosystem functions. The data comparisons were made with preceding year (2019) measurements. Sediment-P release and the concentrations of carbon, nutrients, and heavy metals declined significantly (p<0.05) in 2020 compared to those recorded in 2019. Unlike the preceding year, we did not observe benthic hypoxia (DO <2.0 mg L^-1) in 2020 even at the most polluted site. The EE activities, which declined sharply in the year 2019, showed improvement during the 2020. The stability coefficient and correlative evidences also showed a large improvement in the water quality and functional variables. Positive changes in functional attributes indicated a transient recovery when human perturbations withdrawn. The study suggests that timing the ecosystem recovery windows, as observed here, may help taking management decision to design mitigation actions for rivers to recover from anthropogenic perturbations.

Risk Assessment in Monitoring of Water Analysis of a Brazilian River

This study aimed to introduce non-parametric tests and guard bands to assess the compliance of some river water properties with Brazilian environmental regulations. Due to the heterogeneity of the measurands pH, Biochemical Oxygen Demand (BOD), manganese molar concentration, and Escherichia coli, which could be wrongly treated as outliers, as well as the non-Gaussian data, robust methods were used to calculate the measurement uncertainty. Next, based on guard bands, the compliance assessment was evaluated using this previous uncertainty information. For these four measurands, partial overlaps between their uncertainties and the specification limit could generate doubts about compliance. The non-parametric approach for calculating the uncertainty connected to the guard bands concept classified pH and BOD as “conform”, with a risk to the consumer of up to 4.0% and 4.9%, respectively; in contrast, manganese molar concentration and Escherichia coli were “not conform”, with a risk to the consumer of up to 25% and 7.4%, respectively. The methodology proposed was satisfactory because it considered the natural heterogeneity of data with non-Gaussian behavior instead of wrongly excluding outliers. In an unprecedented way, two connected statistical approaches shed light on the measurement uncertainty in compliance assessment of water analysis.

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.

Modeling electricity consumption patterns during the COVID-19 pandemic across six socioeconomic sectors in the State of Qatar

The propagation of the COVID-19 pandemic, and the associated measures taken by many countries to slow down the spread of the disease, has significantly affected all aspects of people's lives, including the global energy sector. This study aims to investigate the impact of the pandemic on the spatial patterns of electricity consumption in six socioeconomic sectors (residential (villa and flat), industrial, commercial, government, and productive farms) in the State of Qatar. The spatiotemporal patterns of electricity consumption have been assessed using various Geographic Information Systems (GIS) and spatial statistical modeling prior and during the pandemic. The results demonstrate variations in electricity consumption within and between the six sectors. The main changes in the electricity consumption within sectors during the pandemic year is during the lockdown phase. Spatially, some sectors are affected by the pandemic, and hence the pattern and the spatial and temporal distribution of electricity consumption has changed during the pandemic year compared to pre-pandemic years. The results also show that there were variations of spatial clustering of electricity consumption among these sectors. Most of the high-high clustering patterns are located in the mid-eastern and northeastern parts of Qatar. The highest variation in electricity consumption between sectors occurred in the productive farms due to its massive development during the pre-pandemic period and were not affected by the pandemic. There is a sharp decline in electricity consumption in both the industrial and commercial sectors during the pandemic year. Other sectors witnessed an increase in electricity consumption during the summer months, which was mainly due to travel restrictions imposed by many countries around the world. This analysis is vital for policymakers to detect the changes in electricity consumption patterns in the context of emergencies such as the pandemic.