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 entropy­weighted 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.

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.