Assessment of COVID-19 lockdown impact on surface water quality using remote sensing techniques in Raipur, Chhattisgarh, India

Due to improper management, industrialization and urbanization resulted in poorer surface and river water quality flowing through the city. Still, complete lockdown in the country resulted in improved surface water quality. Hence, a study has been performed to analyze these changes held during COVID-19 lockdown using a combination of different parameters derived from spatial data. The study includes analyses of significant water bodies, surface water bodies through out the city;the survey has proven that the lockdown situation that occurred due to the pandemic has resulted in improved water quality which has been determined based on water bodies analysis done for 12 major water bodies, and by the study performed it has been observed that the area of the nonturbid water has increased by 0.148 sq. km after the lockdown situation occurred. The study will be helpful to assess the impacts of lockdown on water bodies to take the sustainable measures which can be taken shortly for the improved regulation of pollutants and other contaminants based on positive effects on the surface water quality. © 2023 Elsevier Inc. All rights reserved.

Investigating the relationship between COVID-19 shutdown and land surface temperature on the Anatolian side of Istanbul using large architectural impermeable surfaces.

Artificial impermeable surfaces are becoming more prevalent, especially in urban areas, as a result of shifting land use and cover, roads, roofs, etc. The modification of land surface temperature (LST) can also be accomplished through artificially impermeable surfaces. Large artificial impermeable surfaces, such as rooftops, parking lots, and other areas of use, can be found in industrial zones, shopping malls, industrial airports, and other locations. For the Anatolian side of Istanbul, 14 Landsat 8 OLI/TIRS imagery images over the years 2016-2022 were investigated. To evaluate how well the study's images could be utilized, correlation and cosine similarity approaches were employed. A total of 12 images may be employed for research LST studies, it was discovered. We looked at closure dates during the COVID-19 epidemic to find out how human migration affected the LST. In addition, the LST value was estimated using the ordinary least squares (OLS) method employing LST and other biophysical indices. A decrease in LST values was seen as a result of the investigation. High levels of similarity and correlation were found between the images used. Results from the Google Mobility Index also provide support to the study. All of these facts provide support to Istanbul's Anatolian side experiencing lower surface temperature values, which consequently affects the city's massive structures.

Did the Covid-19 restrictions influence land surface temperatures in Southeast Asia? A study from Ho Chi Minh City, Vietnam.

There have been a prolonged lockdown period and reduction in human activities in most of the major cities in the world during the Covid-19 pandemic period between the early 2020 and the late 2021. Such a reduction in human activities was believed to have influenced pollution levels and land surface temperatures (LST) in urban areas. This paper describes the variations in LSTs before, during and after the Covid-19 lockdown in Ho Chi Minh City in southern Vietnam, which is the economic hub of the country. For this purpose, Landsat-8 OLI and TIRS images acquired between 2015 and 2022 were used. It is observed that there was a significant reduction of 1 to 1.8 °C in LST in open areas, excepting impervious surfaces and built-up areas, during the strict lockdown period in Ho Chi Minh City, and an increase in LST since then. The observed reduction in LST during the lockdown period in Ho Chi Minh City is in agreement with the reduction in greenhouses gases during the same period in recent studies. Human mobility and industrial activities have been restored in November 2021 in the study area which would explain the regain in LST in the post-lockdown period.

Impact of COVID lockdowns on spatio-temporal variability in land surface temperature and vegetation index.

In urban areas, industrial and human activities are the prime cause that exacerbates the heating effects, also called the urban heat island (UHI) effect. The land surface temperature (LST), normalized difference vegetation index (NDVI), and the proportion of vegetation (Pv) are indicators of measurement of the heating/urbanization effects. In the present work, we investigated the impact of the COVID-19 lockdown, i.e., restricted human activities. We used Landsat-8 OLI/TIRS (level 1) data to investigate spatial and temporal heterogeneity changes in these urbanization indicators during full and partial lockdown periods in 2020 and 2021, with 2019 as the base year. We have selected three cities in India's eastern coal mining belt, Bokaro, Dhanbad, and Ranchi, for the study. Results showed a significant decrease in LST values over all sites, with a maximum reduction over mining sites, i.e., Bokaro and Dhanbad. The LST value decreased by about 13-19% during the lockdown period. Vegetation indices (i.e., NDVI and Pv) showed a substantial increase of about 15% overall sites. With decreased LST values and increased NDVI values, these quantities' correlations became more negative during the lockdown period. More positive changes are noticed over mining sites than non/less mining sites. This indirectly indicates the reduction in the heat-absorbing particles in the environment and surface of these sites, a possible cause for the reduction in LST values substantially. Reduction in coal particles at the land and vegetation surface likely contributed to decreased LST and enhanced vegetation indices. To check the statistical significance of changes in the UHI indicators in the lockdown period, statistical tests (ANOVA and Tukey's test) are performed. Results indicate that most of the case changes have been significant. The study's finding suggests the lockdown's positive impact on the heating/UHI effects. It emphasizes the need for planned lockdowns as city mitigation strategies to overcome pollution and environmental issues.

Spatial effect of anti-COVID measures on land surface temperature (LST) in urban areas: A case study of a medium-sized city

This case study investigates the magnitude and nature of the spatial effect generated by the anti-COVID measures on land surface temperature (LST) in the city of Targu Mures (Marosvasarhely), Romania. The measures were taken by the Romanian government during the state of emergency (March 16 - May 14, 2020) due to the SARS-CoV-2 coronavirus pandemic. The study shows that - contrary to previous studies carried out on cities in China and India in most of the urban areas of Marosvasarhely LST has increased in the period of health emergency in 2020 concerning the large average of the years 2000-2019. Remote sensing data from the MODIS and the Landsat satellites show. that MODIS data, having a moderate spatial (approximately 1 km) but good temporal resolution (daily measurements), show a temperature increase of +0.78 degrees C, while Landsat data, having better spatial (30 m) but lower temporal resolution, show an even greater increase, +2.36 degrees C in the built-up areas. The difference in temperature increase is mainly due to the spatial resolution difference between the two TIR band sensors. The LST anomaly analysis performed with MODIS data also shows a positive anomaly increase of 1 degrees C. However, despite this increase, with the help of the hotspot-coldspot analysis of the Getis-Ord Gi* statistic we were able to identify 46 significant coldspots that showed a 1- 2 degrees C decrease of LST in April 2020 compared to the average of the previous years in April. Most of these coldspots correspond to factory areas, public transport epicenters, shopping centers, industrial polygons. and non-residential areas. This shows that anti-COVID measures in the medium-sized city of Marosvasarhely had many effects on LST in particular areas that have links to the local economy, trade. and transport. Paired sample t-test for areas identified with LST decrease shows that there is a statistically significant difference in the average LST observed before and after anti-COVID measures were applied. MODIS-based LST is satisfactory for recognizing patterns and trends at large or moderate geographical scales. However, for a hotspot-coldspot analysis of the urban heat islands, it is more suitable to use Landsat data.

Investigating The Effect of Covid Induced Lockdown On Land Surface Temperature Over Ahmedabad City

The present study focuses over Ahmedabad City of Gujarat State, India for the time period 1st March to 30th June comprising of the Pre-Lockdown Phase (PLP), the National Lockdown Phase - 1 (NLP1) and the Unlock Phase - 1 (ULP1). We have considered this time period over the years 2019, 2020 and 2021 to explore the effect of COVID induced lockdown on LST and understanding its variation. Satellite data acquired from AQUA - MODIS with a spatial and temporal resolution of 1 Km and 1-2 days respectively was used for the analysis of the LST. The average LST over Ahmedabad was 314.18 K, 311.79 K and 315.67 K for PLP over the years 2019, 2020 and 2021. For NLP1 the average LST over those years were 321.68 K, 318.73 K and 319.39 K respectively. And for the ULP1 the average LST over those years were 319.87 K, 314.07 K and 312.19 K respectively. We observe a 2.38 %, 2.22 % and 1.17 % increase in LST from the PLP to NLP1 during the years 2019, 2020 and 2021. The increase of LST during the NLP1 in 2020 showed that as the pollution decreased, the active elements that were present in the atmosphere which caused disturbance to the sensor on the satellite while calculating LST were reduced and we got a brighter top of surface. The decrease in LST from 2019 levels for the ULP1 is also observed indicating the effects of lockdown and onset of monsoon in 2020 and 2021. © 2021 IEEE.

Understanding the influence of COVID-19 induced lockdown on urban thermal environment of Ranchi city, India

The objective of the study is to understand the pattern of land surface temperature (LST) and normalized difference vegetation index (NDVI) developed in Ranchi city during Covid-19-induced lockdown (2020) and its comparison with previous years. The study incorporated Landsat 8 (Operational land imager) data from United States Geological Survey and air temperature and relative humidity data from power.larc.nasa.gov for the years 2017, 2019 and 2020. The results exposed a drastic change in the LST and NDVI pattern of the city. The mean LST of the city during April has declined from 39.80°C in 2017 to 32.38°C in 2020. Similarly, the mean LST of May also declined from 38.41°C in 2017 to 34.84°C in 2020. On the contrary, the city experienced an ascending growth of NDVI from 0.24 to 0.26 in April and May 2017 to 0.349 and 0.37 in 2020, respectively. Additionally, the city portrays declining air temperature with enhanced relative humidity. Ranchi city also exhibited relatively maximum area under ecologically excellent category in the year 2020 and reduced area under ecologically the worst category based on urban thermal field variance index. Thus, reduced temperature with augmented humidity and NDVI developed a healthy urban environment. [ FROM AUTHOR] Copyright of Geografisk Tidsskrift is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Assessing the Effect of COVID-19 Lockdown on Surface Urban Heat Island for Different Land Use /Cover Types Using Remote Sensing

Rapid urbanization significantly affects the near-surface temperature for major cities worldwide. A greater understanding of spatial-temporal changes of the near-surface temperature has given better insights into global issues such as climate change, thermal discomfort, and energy consumption. This remote sensing-based study aims to investigate the spatial-temporal variations of land surface temperature before and after the lockdown, implemented in response to the spread of COVID-19, for three major Indian cities located in different climate zones viz., i) New Delhi (inland), ii) Hyderabad (inland), and iii) Mumbai (coastal). The land surface temperature variation is assessed for 2015-2020 on different land cover types, i.e., buildings, barren land, roads, vegetation, and water. Landsat 8 OLI/TIRS images are employed to classify land use/cover types using Normalized Difference Vegetation Index, Normalized Difference Built-up Index, and Land Surface Temperature maps. Results show that for New Delhi, Hyderabad, and Mumbai, the mean land surface temperature decreases by 5°C, 1.9°C, and 0.26°C, respectively, in April 2020 (after lockdown) in comparison with April 2019. Results for Mumbai do not vary significantly compared to the other two cities. Overall, the performed analysis presents evidence of the impact produced by the COVID-19 lockdown on the surface urban heat island for different land use/cover types.

Improvement in air quality and its impact on land surface temperature in major urban areas across India during the first lockdown of the pandemic.

The SARS CoV-2 (COVID-19) pandemic and the enforced lockdown have reduced the use of surface and air transportation. This study investigates the impact of the lockdown restrictions in India on atmospheric composition, using Sentinel-5Ps retrievals of tropospheric NO2 concentration and ground-station measurements of NO2 and PM2.5 between March-May in 2019 and 2020. Detailed analysis of the changes to atmospheric composition are carried out over six major urban areas (i.e. Delhi, Mumbai, Kolkata, Chennai, Bangalore, and Hyderabad) by comparing Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth (AOD) and land surface temperature (LST) measurements in the lockdown year 2020 and pre-lockdown (2015-2019). Satellite-based data showed that NO2 concentration reduced by 18% (Kolkata), 29% (Hyderabad), 32-34% (Chennai, Mumbai, and Bangalore), and 43% (Delhi). Surface-based concentrations of NO2, PM2.5, and AOD also substantially dropped by 32-74%, 10-42%, and 8-34%, respectively over these major cities during the lockdown period and co-located with the intensity of anthropogenic activity. Only a smaller fraction of the reduction of pollutants was associated with meteorological variability. A substantial negative anomaly was found for LST both in the day (-0.16 °C to -1 °C) and night (-0.63 °C to -2.1 °C) across select all cities, which was also consistent with air temperature measurements. The decreases in LST could be associated with a reduction in pollutants, greenhouse gases and water vapor content. Improvement in air quality with lower urban temperatures due to lockdown may be a temporary effect, but it provides a crucial connection among human activities, air pollution, aerosols, radiative flux, and temperature. The lockdown for a shorter-period showed a significant improvement in environmental quality and provides a strong evidence base for larger scale policy implementation to improve air quality.