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      Earth Observations Based Assessment of Impact of COVID-19 Lockdown on Surface Water Quality of Buddha Nala, Punjab, India

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      MDPI AG

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          Abstract

          The novel coronavirus disease (COVID-19) halted almost all the industrial scale anthropogenic activities across the globe, resulting in improvements in water and air quality of megacities. Here, using Sentinel-2A data, we quantified impact of COVID-19 lockdown on the water quality parameters in one of the largest perennial creeks i.e., the Buddha Nala located in District Ludhiana in India. This creek has long been considered as a dumping ground for industrial wastes and has resulted in surface and ground water pollution in the entire lower Indus Basin. Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Chlorophyll Index (NDCI), Nitrogen Content Index (NI), Normalized Difference Turbidity Index (NDTI), and Total Suspended Matter (TSM) were compared prior (2019) and during (2020) lockdown in the creek. There was a significant enhancement in NDVI, NDWI, NDCI, and NI values, and reduction in NDTI and TSM values during the lockdown period. When compared with prior year (2019), the values of indices suggested an improvement in water quality and an indicative change in aquatic ecology in the creek. The impact of the COVID-19 lockdown on the improvement in water quality of Buddha Nala was more evident in the upstream and downstream sections than the middle section. This is intriguing since the middle section of the creek was continually impacted by domestic household effluents. The earth observation inspired methodology employed and findings are testament to the discriminatory power to employ remote sensing data and to develop protocols to monitor water quality in regions where routine surveillance of water remains cost prohibitive.

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          The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features

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            Effect of restricted emissions during COVID-19 on air quality in India

            The effectiveness and cost are always top factors for policy-makers to decide control measures and most measures had no pre-test before implementation. Due to the COVID-19 pandemic, human activities are largely restricted in many regions in India since mid-March of 2020, and it is a progressing experiment to testify effectiveness of restricted emissions. In this study, concentrations of six criteria pollutants, PM10, PM2.5, CO, NO2, ozone and SO2 during March 16th to April 14th from 2017 to 2020 in 22 cities covering different regions of India were analysed. Overall, around 43, 31, 10, and 18% decreases in PM2.5, PM10, CO, and NO2 in India were observed during lockdown period compared to previous years. While, there were 17% increase in O3 and negligible changes in SO2. The air quality index (AQI) reduced by 44, 33, 29, 15 and 32% in north, south, east, central and western India, respectively. Correlation between cities especially in northern and eastern regions improved in 2020 compared to previous years, indicating more significant regional transport than previous years. The mean excessive risks of PM reduced by ~52% nationwide due to restricted activities in lockdown period. To eliminate the effects of possible favourable meteorology, the WRF-AERMOD model system was also applied in Delhi-NCR with actual meteorology during the lockdown period and an un-favourable event in early November of 2019 and results show that predicted PM2.5 could increase by only 33% in unfavourable meteorology. This study gives confidence to the regulatory bodies that even during unfavourable meteorology, a significant improvement in air quality could be expected if strict execution of air quality control plans is implemented.
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              Effect of lockdown amid COVID-19 pandemic on air quality of the megacity Delhi, India

              Amid the COVID-19 pandemic, a nationwide lockdown is imposed in India initially for three weeks from 24th March to 14th April 2020 and extended up to 3rd May 2020. Due to the forced restrictions, pollution level in cities across the country drastically slowed down just within few days which magnetize discussions regarding lockdown to be the effectual alternative measures to be implemented for controlling air pollution. The present article eventually worked on this direction to look upon the air quality scenario amidst the lockdown period scientifically with special reference to the megacity Delhi. With the aid of air quality data of seven pollutant parameters (PM10, PM2.5, SO2, NO2, CO, O3 and NH3) for 34 monitoring stations spread over the megacity we have employed National Air Quality Index (NAQI) to show the spatial pattern of air quality in pre and during-lockdown phases. The results demonstrated that during lockdown air quality is significantly improved. Among the selected pollutants, concentrations of PM10 and PM2.5 have witnessed maximum reduction (>50%) in compare to the pre-lockdown phase. In compare to the last year (i.e. 2019) during the said time period the reduction of PM10 and PM2.5 is as high as about 60% and 39% respectively. Among other pollutants, NO2 (−52.68%) and CO (−30.35%) level have also reduced during-lockdown phase. About 40% to 50% improvement in air quality is identified just after four days of commencing lockdown. About 54%, 49%, 43%, 37% and 31% reduction in NAQI have been observed in Central, Eastern, Southern, Western and Northern parts of the megacity. Overall, the study is thought to be a useful supplement to the regulatory bodies since it showed the pollution source control can attenuate the air quality. Temporary such source control in a suitable time interval may heal the environment.
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                Author and article information

                Contributors
                Journal
                WATEGH
                Water
                Water
                MDPI AG
                2073-4441
                May 2021
                May 14 2021
                : 13
                : 10
                : 1363
                Article
                10.3390/w13101363
                5900eae5-51e3-4457-82da-1d49ea8fbc1b
                © 2021

                https://creativecommons.org/licenses/by/4.0/

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