ABSTRACT
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.
ABSTRACT
Referring to a total lockdown due to COVID-19 in Metropolitan France, this study investigates the geospatial correlation between nighttime light emission and the relative change of NO2 air pollution (dNO(2) %). To address the research problem, near-surface NO2 data and nighttime light data were implemented. Stable night lights were obtained for a long period on average (2014-2019) using Day-Night Band (DNB) data from the Visible Infrared Imaging Radiometer Suite (VIIRS). The relative change in tropospheric NO2 was calculated using Sentinel-5P satellite data from the Tropospheric Monitoring Instrument (TROPOMI). The dNO(2) calculation was performed considering an equivalent reference period (April 2019) to the major lockdown period in France (April 2020). The correlation between the variables DNB nighttime lights and dNO(2) was tested with a statistical T-test. The findings revealed an intense phenomenon of decreasing NO2 air pollution in France (decreases by -25% to -50%). Decreases < -50% were mainly recorded in the greater Paris metropolitan area, in Alsace, and other locations. The results showed a strong and statistically significant inverse geospatial correlation between the two variables under anti-COVID-19 control measures. The higher was the emission of nighttime lights, the higher was the degree of tropospheric NO2 decrease in the regions of France (R-2 =0.72). It is concluded that employing remote sensing techniques, DNB nighttime light is a reliable indicator to estimate the degree of air decontamination. DNB as an independent variable is recommended for future research on changes in the concentration of other pollutant gases.