A Study on Establishing Thermal Output Conditions of Radiant Ceiling Heating Panels for Improving Thermal Comfort of Perimeter Zone in Buildings

Amid concerns over airflow-induced transmission of the COVID-19 virus in buildings frequented by large numbers of people, such as offices, the necessity for radiant ceiling heating panels has increased. This is due to the concern that the airflows emitted from the convection heating systems installed near the ceiling or windows for winter heating may be a major cause of COVID-19 transmission. In this study, we aim to evaluate thermal comfort under various indoor and outdoor environmental conditions of a building and present the thermal output conditions of the radiant ceiling heating panel that can replace the convection heating system while ensuring comfort in the perimeter zone and handling the heating load. As a result, we were able to present, in a chart format, the thermal output conditions that can secure thermal comfort by analyzing the indoor airflow distribution depending on the surface temperature of the radiant ceiling heating panel, the interior surface temperature of the window, and the influence of internal heat generation. Moreover, through derived empirical formulas, we were able to determine the heating conditions of the panel that can secure the necessary heat dissipation while minimizing discomfort, such as downdrafts, even for indoor and outdoor conditions that were not evaluated in this study.

Large-Scale Urban Heating and Pollution Domes over the Indian Subcontinent

The unique geographical diversity and rapid urbanization across the Indian subcontinent give rise to large-scale spatiotemporal variations in urban heating and air emissions. The complex relationship between geophysical parameters and anthropogenic activity is vital in understanding the urban environment. This study analyses the characteristics of heating events using aerosol optical depth (AOD) level variability, across 43 urban agglomerations (UAs) with populations of a million or more, along with 13 industrial districts (IDs), and 14 biosphere reserves (BRs) in the Indian sub-continent. Pre-monsoon average surface heating was highest in the urban areas of the western (42 degrees C), central (41.9 degrees C), and southern parts (40 degrees C) of the Indian subcontinent. High concentration of AOD in the eastern part of the Indo-Gangetic Plain including the megacity: Kolkata (decadal average 0.708) was noted relative to other UAs over time. The statistically significant negative correlation (-0.51) between land surface temperature (LST) and AOD in urban areas during pre-monsoon time illustrates how aerosol loading impacts the surface radiation and has a net effect of reducing surface temperatures. Notable interannual variability was noted with, the pre-monsoon LST dropping in 2020 across most of the selected urban regions (approx. 89% urban clusters) while it was high in 2019 (for approx. 92% urban clusters) in the pre-monsoon season. The results indicate complex variability and correlations between LST and urban aerosol at large scales across the Indian subcontinent. These large-scale observations suggest a need for more in-depth analysis at city scales to understand the interplay and combined variability between physical and anthropogenic atmospheric parameters in mesoscale and microscale climates.

Changes in air pollution, land surface temperature, and urban heat islands during the COVID-19 lockdown in three Chinese urban agglomerations.

COVID-19 has notably impacted the world economy and human activities. However, the strict urban lockdown policies implemented in various countries appear to have positively affected pollution and the thermal environment. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) and aerosol optical depth (AOD) data were selected, combined with Sentinel-5P images and meteorological elements, to analyze the changes and associations among air pollution, LST, and urban heat islands (UHIs) in three urban agglomerations in mainland China during the COVID-19 lockdown. The results showed that during the COVID-19 lockdown period (February 2020), the levels of the AOD and atmospheric pollutants (fine particles (PM2.5), NO2, and CO) significantly decreased. Among them, PM2.5 and NO2 decreased the most in all urban agglomerations, by >14 %. Notably, the continued improvement in air pollution attributed to China's strict control policies could lead to overestimation of the enhanced air quality during the lockdown. The surface temperature in all three urban agglomerations increased by >1 °C during the lockdown, which was mainly due to climate factors, but we also showed that the lockdown constrained positive LST anomalies. The decrease in the nighttime urban heat island intensity (UHIInight) in the three urban agglomerations was greater than that in the daytime quantity by >25 %. The reduction in surface UHIs at night was mainly due to the reduced human activities and air pollutant emissions. Although strict restrictions on human activities positively affected air pollution and UHIs, these changes were quickly reverted when lockdown policies were relaxed. Moreover, small-scale lockdowns contributed little to environmental improvement. Our results have implications for assessing the environmental benefits of city-scale lockdowns.

Probing environmental and tectonic changes underneath Mexico City with the urban seismic field

The sediments underneath Mexico City have unique mechanical properties that give rise to strong site effects. We investigated temporal changes in the seismic velocity at strong-motion and broadband seismic stations throughout Mexico City, including sites with different geologic characteristics ranging from city center locations situated on lacustrine clay to hillside locations on volcanic bedrock. We used autocorrelations of urban seismic noise, enhanced by waveform clustering, to extract subtle seismic velocity changes by coda wave interferometry. We observed and modeled seasonal, co- and post-seismic changes, as well as a long-term linear trend in seismic velocity. Seasonal variations can be explained by self-consistent models of thermoelastic and poroelastic changes in the subsurface shear wave velocity. Overall, sites on lacustrine clay-rich sediments appear to be more sensitive to seasonal surface temperature changes, whereas sites on alluvial and volcaniclastic sediments and on bedrock are sensitive to precipitation. The 2017 Mw 7.1 Puebla and 2020 Mw 7.4 Oaxaca earthquakes both caused a clear drop in seismic velocity, followed by a time-logarithmic recovery that may still be ongoing for the 2017 event at several sites or that may remain incomplete. The slope of the linear trend in seismic velocity is correlated with the downward vertical displacement of the ground measured by interferometric synthetic aperture radar, suggesting a causative relationship and supporting earlier studies on changes in the resonance frequency of sites in the Mexico City basin due to groundwater extraction. Our findings show how sensitively shallow seismic velocity and, in consequence, site effects react to environmental, tectonic and anthropogenic processes. They also demonstrate that urban strong-motion stations provide useful data for coda wave monitoring given sufficiently high-amplitude urban seismic noise.

A COMPARISON OF ESTIMATED LAND SURFACE TEMPERATURE FROM LANDSAT 8 BETWEEN DKI JAKARTA AND PEKAT VILLAGE

Land Surface Temperature (LST) shows the general temperature condition of environment and many factors may affect it such as weather, cloud coverage, and sun exposure time. During the Corona Virus pandemic, Indonesia implemented the Pemberlakuan Pembatasan Kegiatan Masyarakat (PPKM) system that restricted many activities and suspected of influencing LST in one or another way. PPKM system itself does not cover every province in Indonesia;thus, it is suspected that LST between areas either do or do not implement PPKM may differ. This paper aims to map and analyze LST between Jakarta Province and Pekat Village before and during implementation of PPKM using descriptive research method with a quantitative approach. The results show that there is a significant temperature change in Jakarta when the PPKM level changes transpires. Pekat Village also experiences temperature changes although it is not affected by PPKM system. Even though there are some data anomalies, the temperature changes are within expectation. Therefore, this concludes that PPKM brought some slight effects toward LST and its changes. © 2023 ICIC International. All rights reserved.

Effect of vegetation and land surface temperature on NO2 concentration: A Google Earth Engine-based remote sensing approach

With the increasing tension on the global sustainable environment in the urban areas, it is essential to monitor the airborne pollutants and understand the underlying factors that can trigger the situation in a worst-case scenario. Because of its cramped living conditions, excessive coal and fuel usage, and rapid deforestation, the southeast Asian region has historically had worse air quality than the rest of the world. The economic hubs of India and Bangladesh, in particular, have drawn so much attention away from rural regions that unrestrained urbanization is becoming controversial for planners, engineers, and stakeholders in sustainable development. This research combines the two main Asian capital regions, Delhi and Dhaka. It analyzes the change in nitrogen dioxide (NO2) concentration, land surface temperature (LST), and vegetation dynamics across three years (2019-2021) for summer and winter. The NO2 concentration data from Sentinel-5P has been extracted using Google Earth Engine (GEE), and Landsat-8 imagery was utilized for LST, Normalizer Vegetation Index (NDVI), and Enhance Vegetation Index (EVI). The statistical analysis has been carried out by dividing the research regions into one sq. km grid (1512 grids for Delhi and 1485 grids for Dhaka). According to descriptive research, Dhaka's condition is worse than Delhi's, with significant vegetation loss with LST and NO2 concentrations rising. In both research regions, the NO2 concentration is high throughout the winter. The Pearson correlation value demonstrates a negative association between total NO2 concentration and mean NDVI and EVI values and a positive relationship between total NO2 concentration and mean LST. The data have been further assessed using linear regression, which overlaps the correlation result with a maximum R-squared value of 0.2998 for NO2 and EVI in winter 2019.

Application of GIS-Based AHP Model for the Impact Assessment of COVID-19 Lockdown on Environment Quality: The Case of Kabul City, Afghanistan

The COVID-19 pandemic has negatively impacted the industrial, financial, and social aspects of our daily life due to the implementation of lockdown to protect against the spread of the virus. In addition, the lockdown deduced by COVID-19 has promising positive impacts on air quality and environmental pollution. This study aims to monitor the effects of lockdown on environmental degradation during the pandemic in Kabul city, the capital of Afghanistan, using geospatial data and a statistical model of the Analytical Hierarchy Process (AHP). To achieve the purpose of the study, the most essential influencing factors on air quality were generated from different sources using Google Earth Engine (GEE) and GIS environment;Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index NDMI) were calculated using Sentinel-2MSI, Carbon Monoxide (CO) was obtained from Sentinel-5P TROPOMI, and land surface temperature was retrieved from MODIS data. The generated thematic layers (before COVID-19, and during a lockdown of COVID-19) were weighted and rated using the AHP analysis. The weighted layers were spatially overlayed to obtain the final output. Consequently, the environmental quality degradation maps before and during COVID-19 were generated to assess the differences over the 22 districts of Kabul city. The findings of the study show that Kabul city is covered by the very low, low, moderate, high, and very high degradation of the environment by 3.17%, 5.33%, 20.54%, 26.63%, 44.32% before COVID-19 in 201,9 respectively, while the percentages are changed to 4.37%, 8.99%. 16.55%, 37.47%, and 32.62% during the lockdown caused by COVID-19 in 2020. The changes in the percentage of environmental degradation in Kabul city particularly in high and very high zones confirm the positive impact of the lockdown of COVID-19.

Effect of green infrastructures supported by adaptative solar shading systems on livability in open spaces

Lack of thermal comfort in the existing building stock in many warm summer climates and the COVID-19 pandemic have increased residents' temporary occupation of urban open spaces. However, climate change and other effects such as urban heat islands are also negatively affecting the livability of these spaces. Therefore, strategies are needed to improve the thermal conditions in these areas. In this context, the research designs, simulates and assesses an urban green infrastructure supported by an adaptative solar shading system. For this purpose, a public square to be renovated in Seville (Spain) is chosen. After an analysis of the current situation, more vegetation is added. However, trees are not planted fully grown, so their cover is not enough in the short term and an artificial system that protects from the sun by casting shade and that adapts to both their growth and the seasons is included. The urban space is characterized by on-site measurements, proposing four (initial, intermediates and final) scenarios using computational fluid dynamics simulations in an holistic microclimate modelling system. In turn, changes in thermal comfort are analyzed using the COMFA model. Results show that the air and surface temperature are decreased, reducing the number of hours in discomfort by 21% thanks to incorporating the green structure and by 30% due to the vegetation. It can be concluded that the use of these temporary urban prostheses enables urban spaces regenerated with vegetation to be enjoyed without waiting 20 or 30 years for the trees to mature, encouraging people to spend more time outdoors from the start of the intervention. © 2023 The Authors

SURFACE URBAN HEAT ISLAND IN MOSCOW DURING THE COVID-19 PANDEMIC LOCKDOWN IN 2020

The influence of the COronaVIrus Disease 2019 (COVID-19) pandemic lockdown (the period of strict quarantine measures) in the spring of 2020 on the ‘Surface Urban Heat Island' (SUHI) geographical phenomenon in Moscow has been studied. For this purpose, we used the measurements of the surface temperature TS made by Moderate Resolution Imaging Spectroradiometer (MODIS) radiometer installed on Terra and Aqua satellites. As a result, TS during the 2020 lockdown, both in the city and surrounding rural zone, was found lower than at the same calendar time in the previous 20 years due to the relatively cold spring. The SUHI intensity as the difference between TS inside Moscow and the surrounding rural zone around it during the lockdown was also lower than usual (on average in the previous 20 years), but this decrease is relatively small and nonsignificant. The Normalized Difference Vegetation Index (NDVI) in Moscow and Moscow region during the lockdown was close to its usual values, but the leaf area index (LAI) was significantly lower than its average values in the previous 20 years. Thus, the weakening of the SUHI during the lockdown in 2020 was caused mostly by lower heat loss due to transpiration in the rural zone. This was associated with the slowdown in vegetation development as a result of the cold spring. Besides, an additional possible reason was the reduction of human activity due to the collapse of many anthropogenic heat sources in the city. According to long-term MODIS data, the SUHI intensity in Moscow and the surface temperature in Moscow region, as well as the NDVI and LAI values, do not demonstrate statistically significant long-term trends in the spring season over the past 21 years, despite climate changes. In spring, during faster snow melting in cities, when it still persists in the rural zone, the SUHI intensity can be record high (up to 8 ºC). © 2022, Russian Geographical Society. All rights reserved.

TIME-SERIES ANALYSIS OF AIR QUALITY IN MAJOR CITIES IN THE PHILIPPINES DURING THE COVID-19 PANDEMIC THROUGH SENTINEL-5

Air pollution causes respiratory ailments and drives climate change. Air quality is driven by emissions from various sources, weather patterns, and transport of pollutants. Satellite analysis of pollutants in the atmosphere can provide temporally consistent and spatially wide measurements. In this study, the monthly concentrations of Nitrogen Dioxide (NO2), Sulfur Dioxide (SO2), Carbon Monoxide (CO), and Ozone (O3) from the Sentinel-5 Tropospheric Monitoring Instrument (TROPOMI) were analyzed in four major cities in the Philippines, representing different climate types. Satellite-based measurements of land surface temperature and rainfall were used to investigate meteorological effects to air pollutants. Seasonal patterns were observed in the time series of NO2, O3 and CO alongside rainfall and LST. During the dry season, high LST and low precipitation is observed to be associated with increase in NO2, O3, and CO concentrations. On the other hand, wet seasons show decreases in concentrations of air pollutants, consistent with the washout effect. The NO2 average concentration in NCR is 1.9, 2.1, 2.3 times higher than in Metro Cebu, Davao City, and Legazpi City, respectively. In contrast, SO2 average concentration is highest in Legazpi City due to the nearby active volcano by a maximum factor of 1.8 compared to other cities. In addition, air quality changes brought about by community quarantines were examined since the onset of the COVID-19 crisis. Transition from the pre-quarantine period to the first lockdown shows sudden decrease by 28% in satellite-based retrievals of NO2 in NCR, mainly due to reduced anthropogenic emissions. As tiers of community quarantines were introduced, an increase in pollutant concentrations was observed, returning to pre-pandemic air quality as the guidelines ease physical and economic restrictions. Monitoring and analyzing the patterns in concentration of air pollutants in relation to meteorological and anthropogenic drivers can help in the air quality management in the country. © 43rd Asian Conference on Remote Sensing, ACRS 2022.

Capturing Secchi disk depth by using Sentinel-2 MSI imagery in Jiaozhou Bay, China from 2017 to 2021

Water clarity is a key parameter for assessing changes of aquatic environment. Coastal waters are complex and variable, remote sensing of water clarity for it is often limited by low spatial resolution. The Sentinel-2 Multi-Spectral Instrument (MSI) imagery with a resolution of up to 10 m are employed to solve the problem from 2017 to 2021. Distribution and characteristics of Secchi disk depth (SDD) in Jiaozhou Bay (JZB) are analyzed. Subtle changes in localized small areas are discovered, and main factors affecting the changes are explored. Among natural factors, precipitation and wind play dominant roles in variation in SDD. Human activities have a significant influence on transparency, among which fishery farming has the greatest impact. This is clearly evidenced by the significant improvement of SDD in JZB due to the sharp decrease in human activities caused by coronavirus disease 2019 (COVID-19).Copyright © 2022 The Authors

GIS-based spatial distribution analysis of water quality assessment using water pollution index of Yamuna river at Delhi

Pollution is one of the leading risk factors for the deterioration of the environment, mankind's poor health, and endangerment of the plant kingdom. The exploration of water pollution levels through a new remote sensing model "Water Pollution Index” makes this study unique, which is derived from the weighted overlay technique using land surface temperature, Chlorophyll Index, NCAI, and backscattering values from Sentinel 1, Sentinel 2, and Landsat 8 data sets. This chapter is concerned with the qualitative study of water pollution of the Yamuna river stretch, Delhi. To substantiate the results, sources are taken from different published papers and ground surveys. The objective is to define the pollution level and its contributing factors, algae blooming, sewage debris, coronavirus disease 2019 (COVID-19) shutdown impact, and rain in different seasons for two consecutive years, 2019 and 2020. A noticeable difference is found in the annual result indicating less pollution in 2020 especially in premonsoon data compared to 2019. © 2023 Elsevier Inc. All rights reserved.

Impact of COVID-19 Restrictions on the Urban Thermal Environment of Edmonton, Canada.

The effects of the COVID-19 pandemic on urban environments are addressed in many recent studies. However, limited research has been conducted to examine the impact of the pandemic on anthropogenic emissions over urban land use types, and their relation to socioeconomic characteristics. Anthropogenic heat, as the main contributor to the urban temperature, is changed by the sudden halt imposed by COVID-19 lockdowns. This study thus focuses on previously under-explored urban thermal environments by quantifying the impact of COVID-19 on urban thermal environments across different land-use types and related socioeconomic drivers in Edmonton, Canada. Using Landsat images, we quantified and mapped the spatial pattern of land surface temperature (LST) for business, industrial, and residential land use areas during both the pandemic lockdown and pre-pandemic periods in the study area. Results show that temperature declined in business and industrial areas and increased in residential areas during the pandemic lockdown. Canadian census and housing price data were then used to identify the potential drivers behind the LST anomaly of residential land use. The most important variables that affected LST during the lockdown were found to be median housing price, visible minority population, postsecondary degree, and median income. This study adds to the expanding body of literature about the impact of the COVID-19 pandemic by providing unique insights into the effect of lockdown on a city's thermal environments across different land use types and highlights critical issues of socioeconomic inequalities, which is useful for future heat mitigating and health equity-informed responses.

Impact of COVID-19 restrictions on air quality and surface urban heat island effect within the main urban area of Urumqi, China.

The outbreak of coronavirus in 2019 (COVID-19) posed a serious global threat. However, the reduction in man-made pollutants during COVID-19 restrictions did improve the ecological environment of cities. Using multi-source remote sensing data, this study explored the spatiotemporal variations in air pollutant concentrations during the epidemic prevention and control period in Urumqi and quantitatively analyzed the impact of different air pollutants on the surface urban heat island intensity (SUHII) within the study area. Urumqi, located in the hinterland of the Eurasian continent, northwest of China, in the central and northern part of Xinjiang was selected as the study area. The results showed that during COVID-19 restrictions, concentrations of air pollutants decreased in the main urban area of Urumqi, and air quality improved. The most evident decrease in NO2 concentration, by 77 ± 1.05% and 15 ± 0.98%, occurred in the middle of the first (January 25 to March 20, 2020) and second (July 21 to September 1, 2020) COVID-19 restriction periods, respectively, compared with the corresponding period in 2019. Air pollutant concentrations and the SUHIIs were significantly and positively correlated, and NO2 exhibited the strongest correlation with the SUHIIs. We revealed that variations in the air quality characteristics and thermal environment were observed in the study area during the COVID-19 restrictions, and their quantitative relationship provides a theoretical basis and reference value for improving the air and ecological environment quality within the study area.

Mapping Changes in Particulate Pollution and Plant Health in the Indian Sub-continent during the COVID-19 Pandemic

Particulate matter (PM) air pollution is ranked the 13th leading cause of mortality across the globe. Lockdowns during the COVID-19 pandemic provided a unique opportunity to assess the potential beneficial impact on air quality and possibly biologic outcomes. The main objectives of this project were to utilize NASA satellite-derived data and: 1) Observe changes in PM2.5 across the four countries before the outbreak of COVID-19 and through the observed case peak months in 2020 and 2021;2) Examine changes in normalized difference vegetation index (NDVI) during, around, and subsequent to COVID-19 peak months;3) Evaluate changes in precipitation and land surface temperature as other potential contributors to changes in plant health. Remote sensing datasets included "Aerosol Optical Thickness” to measure air pollution, and "Normalized Difference Vegetation Index” to examine vegetation health. We found that PM2.5 concentration substantially decreased in some areas of the sub-continent, during the peak months, while NDVI improved. While accompanying precipitation and land surface temperature may account for some of the changes in NDVI, they alone cannot explain the improvement in plant health during shutdowns. Thus, at least some of the decreased plant stress may be attributed to lower emission of atmospheric pollutants, including PM2.5. © 2023 by authors, all rights reserved.

Dynamical characteristics of Amphan and its impact on COVID-19 cases in Bangladesh

In May 2020, a category-5 tropical cyclone (TC) Amphan formed in the Bay of Bengal and struck the coasts of India and Bangladesh. In this study, the relevant dynamic characteristics and aftermaths of Amphan are documented. Through detailed investigation of the reanalysis and observation data, spatiotemporal varying characteristics of the atmospheric and oceanic parameters during the Amphan propagation process were analyzed. Due to a wide range of high sea surface temperature anomaly, Amphan developed rapidly and ultimately led to the local heavy precipitation and strong winds in the coastal areas during its passage. It is also noted that the recorded wave height, wave period, and current speed all amplified when Amphan passed by and the characteristics of wave and current directions are also consistent with the temporal variation of the corresponding wind field. Meanwhile, Amphan occurred in accompany with the ongoing COVID-19 pandemic. In Khulna Division of Bangladesh, the number of newly confirmed COVID-19 cases increased rapidly after Amphan landing, which however was almost nil before the event, indicating there might exist a possible correlation between Amphan and the intensive outbreak of the local COVID-19, and particular attentions should be paid to deal with the multi-type, coexisting disasters if different or even conflicting measures are required.

Effect of Seasonal Land Surface Temperature Variation on COVID-19 Infection Rate: A Google Earth Engine-Based Remote Sensing Approach.

This study aims to identify the effect of seasonal land surface temperature variation on the COVID-19 infection rate. The study area of this research is Bangladesh and its 8 divisions. The Google Earth Engine (GEE) platform has been used to extract the land surface temperature (LST) values from MODIS satellite imagery from May 2020 to July 2021. The per-day new COVID-19 cases data has also been collected for the same date range. Descriptive and statistical results show that after experiencing a high LST season, the new COVID-19 cases rise. On the other hand, the COVID-19 infection rate decreases when the LST falls in the winter. Also, rapid ups and downs in LST cause a high number of new cases. Mobility, social interaction, and unexpected weather change may be the main factors behind this relationship between LST and COVID-19 infection rates.

Urban Heat Island Mitigation Strategy based on Local Climate Zone Classification using Landsat 8 satellite imagery

Appropriate strategies on urban climate mitigation should be formulated by considering the physical morphology of the urban landscape. This study aimed to investigate, analyze, and promote possible strategies to mitigate Jakarta’s urban heat island (UHI) phenomena. Jakarta’s local climate zone (LCZ) was classified into 17 classes using Landsat 8 data and the random forest method. Land surface temperature (LST) characteristic in each LCZ class was analyzed from 2018, 2019 and 2020. The result revealed that most of the local climate zone in Jakarta is dominated by LCZ 6 (open low-rise) and LCZ 3 (compact low-rise), which is the typical residential area in Jakarta. However, the mean LST in 2018, 2019 and 2020 showed that LCZ 3 (compact low-rise) and LCZ 7 (lightweight low-rise) are the areas that were most likely causing high surface temperature with the highest UHI intensity. During the COVID-19 pandemic in 2020, LST in Jakarta decreased drastically in some parts of the area, especially in public facility such as airport. However, the LST value in low-rise areas (LCZ 3 and LCZ 7) remains higher than the other LCZ classes. Materials of the building and land cover play a significant role in raising the land surface temperature. Therefore, mitigation strategies for urban heat islands in Jakarta should be focused on such particular areas mentioned.

Investigating the impacts of COVID-19 lockdown on air quality, surface Urban Heat Island, air temperature and lighting energy consumption in City of Melbourne

The COVID-19 pandemic has threatened city economies and residents' public health and quality of life. Similar to most cities, Melbourne imposed extreme preventive lockdown measures to address this situation. It would be reasonable to assume that during the two phases of lockdowns, in autumn (March) and winter (June to August) 2020, air quality parameters, air temperature, Surface Urban Heat Island (SUHI), and lighting energy consumption most likely increased. As such, to test this assumption, Sentinel 5, ERA-5 LAND, Sentinel 1 and 2, NASA SRTM, MODIS Aqua and Terra, and VIIRS satellite imageries are utilized to investigate the alterations of NO₂, SO₂, CO, UV Aerosol Index (UAI), air temperature, SUHI, and lighting energy consumption factors in the City of Melbourne. Furthermore, satellite imageries of SentiThe results indicate that the change rates of NO₂ (1.17 mol/m2) and CO (1.64 mol/m2) factors were positive. Further, the nighttime SUHI values increased by approximately 0.417 °C during the winter phase of the lockdown, while during the summer phase of the lockdown, the largest negative change rate was in NO₂ (−100.40 mol/m2). By contrast, the largest positive change rate was in SO₂ and SUHI at night. The SO₂ values increased from very low to 330 μm mol/m2, and the SUHI nighttime values increased by approximately 4.8 °C. From the spatial point of view, this study also shows how the effects on such parameters shifted based on the urban form and land types across the City of Melbourne by using satellite data as a significant resource to analyze the spatial coverage of these factors. The findings of this study demonstrate how air quality factors, SUHI, air temperature, and lighting energy consumption changed from pre-lockdown (2019) to lockdown (2020), offering valuable insights regarding practices for managing SUHI, lighting energy consumption, and air pollution.

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.