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

Assessing the impact of COVID-19 lockdown on surface water quality in Ireland using advanced Irish water quality index (IEWQI) model

The COVID-19 pandemic has significantly impacted various aspects of life, including environmental conditions. Surface water quality (WQ) is one area affected by lockdowns imposed to control the virus's spread. Numerous recent studies have revealed the considerable impact of COVID-19 lockdowns on surface WQ. In response, this research aimed to assess the impact of COVID-19 lockdowns on surface water quality in Ireland using an advanced WQ model. To achieve this goal, six years of water quality monitoring data from 2017 to 2022 were collected for nine water quality indicators in Cork Harbour, Ireland, before, during, and after the lockdowns. These indicators include pH, water temperature (TEMP), salinity (SAL), biological oxygen demand (BOD5), dissolved oxygen (DOX), transparency (TRAN), and three nutrient enrichment indicators-dissolved inorganic nitrogen (DIN), molybdate reactive phosphorus (MRP), and total oxidized nitrogen (TON). The results showed that the lockdown had a significant impact on various WQ indicators, particularly pH, TEMP, TON, and BOD5. Over the study period, most indicators were within the permissible limit except for MRP, with the exception of during COVID-19. During the pandemic, TON and DIN decreased, while water transparency significantly improved. In contrast, after COVID-19, WQ at 7% of monitoring sites significantly deteriorated. Overall, WQ in Cork Harbour was categorized as "good," "fair," and "marginal" classes over the study period. Compared to temporal variation, WQ improved at 17% of monitoring sites during the lockdown period in Cork Harbour. However, no significant trend in WQ was observed. Furthermore, the study analyzed the advanced model's performance in assessing the impact of COVID-19 on WQ. The results indicate that the advanced WQ model could be an effective tool for monitoring and evaluating lockdowns' impact on surface water quality. The model can provide valuable information for decision-making and planning to protect aquatic ecosystems.

Thermal power plants pollution assessment based on deep neural networks, remote sensing, and GIS: A real case study in Iran

To investigate the impact of the Bandar Abbas thermal power plant on the waters of the Persian Gulf coast, a combination of satellite images and ground data was utilized to determine the Sea Surface Temperature (SST) as a thermal index, Total Organic Carbon (TOC) and Chemical Oxygen Demand (COD) as biological indices. Additionally, measurements of SO₂, O₃, NO₂, CO₂, CO, and CH₄ values in the atmosphere were taken to determine the plant's impact on air pollution. Temperature values of the water for different months were predicted using Long Short-Term Memory (LSTM), Support Vector Regression (SVR), and Cascade neural networks. The results indicate that the waters near thermal power plants exhibit the highest temperatures in July and September, with temperatures reaching approximately 50 °C. Furthermore, the SST values were found to be strongly correlated with ecological indices. The Multiple Linear Regression (MLR) analysis revealed a strong correlation between the temperature and TOC, COD, and O₂ in water (R_TOC²=0.98), [Formula: see text] , R_COD²=0.87 and O₃, NO₃, CO₂, and CO in the air ( [Formula: see text] ). Finally, the results demonstrate that the LSTM method exhibited high accuracy in predicting the water temperature (R² = 0.98).

Spatiotemporal trends and impact of Covid-19 lockdown on eight sewage contaminants in Brisbane, Australia, from 2012 to 2020

This study aims to investigate the spatiotemporal trends and impact of COVID-19 lockdowns to the profile of physiochemical parameters in the influent of wastewater treatment plants (WWTPs) around Brisbane, Australia. One 24-hr composite influent sample was collected from 10 WWTPs and analyzed for a range of physiochemical parameters per week (i.e., chemical oxygen demand (COD), total organic carbon (TOC), total nitrogen (TN), total phosphorus (TP), ammonia, volatile suspended solid (VSS)) and per month (i.e., Ni and Cr) from 2012 to 2020, including the period of COVID-19 lockdowns in the region. The catchments studied were urban, with a mix of domestic and industrial activities contributing towards the contaminant profile. Statistical analysis identified that industrial and commercial land use, as well as population size had a large impact to the parameter loads and profile. Per capita mass loads of Cr in one catchment were 100 times higher than in others from one industrial point source. TP demonstrated a potential monotonic decrease over time due to practical reduction policies that have been implemented for phosphorous content in household detergents, except for one catchment where trade waste from food manufacturing industries contributed to an overall increase of 6.9%/year TP. The COVID-19 lockdown (March-April 2020) posed different impact on different catchments, either decrease (7-61%) or increase (2-40%) of most parameter loads (e.g., COD, TOC, TN, TP, VSS, Ammonia), which was likely driven by catchment characteristics (i.e., the proportion of residential, commercial, and industrial land uses). This study enhances our understanding of spatiotemporal trend of contaminants in the catchments for further effective source control.

Impact of barrages on assemblage pattern of phytoplankton in tropical river Ganga, India

The occurrence, abundance, and distribution of phytoplankton have been investigated upstream and downstream of three barrages on the river Ganga at Bijnor, Narora, and Kanpur in Uttar Pradesh, India. A total of 104 phytoplankton species belonging to eight phyla (Bacillariophyta, Charophyta, Chlorophyta, Cryptophyta, Cyanophyta, Euglenophyta, Miozoa, and Ochrophyta) were identified during the sampling period. During the summer, monsoon, and post-monsoon seasons, the density of phytoplankton (Ind. L^-1) ranged from 9.6 × 10⁴ to 2.03 × 10⁷, 9.6 × 10⁴ to 4.5 × 10⁵, and 2.2 × 10⁵ to 2.17 × 10⁶, respectively. The species abundance and the relative abundance showed an increasing trend from the first (Bijnor) to the third (Kanpur) barrage, suggesting a gradual decrease in river flow and an increase in residence time. Phytoplankton cell density in Kanpur, however, was unexpectedly higher and showed eutrophic conditions attributable to elevated organic load and surplus nutrients from the land runoff. One-way ANOVA (post-hoc Tukey test) showed statistically significant (p < 0.05) seasonal variation in temperature, transparency, free CO₂, PO₄^3-, and dissolved organic matter. Analysis of Pearson's correlation coefficient suggested a statistically significant correlation (p < 0.05) of mostly phytoplanktonic groups with free CO₂, CO₃^2-, HCO₃^-, Cl^-, specific conductivity, total dissolved solids, total hardness, Mg²⁺, PO₄^3-, and SiO₄^4-. The minimum species diversity was recorded during the monsoon season, while the maximum diversity was reported during the post-monsoon season which might be due to high nutrient load and a high concentration of PO₄^3- post-monsoon. We concluded that aquatic biodiversity and ecological structure could be adversely influenced by a series of obstructed barrages and dams, which influenced the assemblage pattern of phytoplankton communities.