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.

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.

Changes in NO2 and O3 levels due to the pandemic lockdown in the industrial cities of Tehran and Arak, Iran using Sentinel 5P images, Google Earth Engine (GEE) and statistical analysis.

Air pollution has very damaging effects on human health. In recent years the Coronavirus disease (COVID-19) pandemic has created a worldwide economic disaster. Although the consequences of the COVID-19 lockdowns have had severe effects on economic and social conditions, these lockdowns also have also left beneficial effects on improving air quality and the environment. This research investigated the impact of the COVID-19 lockdown on NO2 and O3 pollutants changes in the industrial and polluted cities of Arak and Tehran in Iran. Based on this, the changes in NO2 and O3 levels during the 2020 lockdown and the same period in 2019 were investigated in these two cities. For this purpose, the Sentinel-5P data of these two pollutants were used during the lockdown period from November 19 to December 05, 2020, and at the same time before the pandemic from November 19 to December 05, 2019. For better results, the effect of climatic factors such as rain and wind in reducing pollution was removed. The obtained results indicate a decrease in NO2 and O3 levels by 3.5% and 6.8% respectively in Tehran and 20.97% and 5.67% in Arak during the lockdown of 2020 compared to the same time in 2019. This decrease can be caused by the reduction in transportation and socio-economic and industrial activities following the lockdown measures. This issue can be a solid point to take a step toward controlling and reducing pollution in non-epidemic conditions by implementing similar standards and policies in the future.

Integral Assessment of Atmospheric Air Quality in the Largest Cities of Russia Based on TROPOMI (Sentinel-5P) Data for 2019–2020

This paper considers the level of atmospheric air pollution of the 20 largest cities in Russia in 2019–2020. The data used for the study is initially collected by a TROPOMI instrument (on the Sentinel-5P satellite), including measurements of carbon monoxide, formaldehyde, nitrogen dioxide, sulfur dioxide, and aerosol (aerosol index). The measurements were obtained using the cloud-based platform, Google Earth Engine, which presents L3 level data available for direct analysis. The Tropomi Air Quality Index (TAQI) integrates available TROPOMI measurements into a single indicator. The calculation results showed that most of the cities under consideration (15 out of 20) have a low or higher than usual level of pollution. Formaldehyde (35.7%) and nitrogen dioxide (26.4%) play the main role in the composition of pollution particles. A significant share is occupied by sulfur dioxide (16.4%). The contribution of carbon monoxide and aerosol averages 10.8 and 10.6%, respectively. Air pollution in cities is caused by both natural (wildfires, dust storms) and anthropogenic (seasonal migrations of the population, restrictions due to the COVID-19 pandemic) factors. Estimating atmospheric pollution levels in urban areas using an integral index based on remote data (such as TAQI) can be considered as a valuable information addition to existing ground-based measuring systems within the multisensory paradigm.

Impact of Lockdown on Air Quality During COVID-19 Pandemic: A Case Study of India

It is crucial to study air quality and its impact on human health, as it can leave not only short-term effects but also have long-term effects, especially on people suffering from cardiovascular and lung diseases. During the COVID-19 pandemic, a major lockdown of almost 70 days in four different phases was announced in India. Due to this exercise, many visually observed a drastic change in air quality;however, actual quantifications were limited. Therefore, there is a need to quantify how air quality changed from before to during and post-lockdown scenarios. This study quantifies the COVID-19 India lockdown impact on air quality by analyzing the change in major air pollutants such as SO2, NO2, CO, O3, PM2.5, and PM10. The major objectives of this study are to quantify the change in major air pollutants across India during the lockdown and to identify their trends and respective hotspots. In order to achieve these objectives, air quality estimates are obtained from Sentinel 5P satellite, while PM2.5 and PM10 values are taken from Central Pollution Control Broad's ground monitoring stations. For temporal analysis, different time intervals starting from before the lockdown (i.e., March 1, 2020) till the end of the fourth lockdown (i.e., May 31, 2020) were analyzed across India. Results state that (1) There was a significant decline of − 48.11% and − 11.56% in concentrations of SO2 and NO2, respectively, after averaging values at their respective hotspots (2) A decrease of − 6.78% and − 0.42% was observed in O3 and CO concentration during the lockdown period in the year 2020 compared with the same period in the year 2019. (3) For PM2.5, Kolkata had the maximum drop of − 83.28%, while Bengaluru had the least drop of − 38.86%, whereas, for PM10, Kolkata had the maximum drop again of − 80.53%, while Delhi, on the other hand, had an increment of 13.42% at the end of the fourth lockdown. The results indicate the indirect benefit of the COVID-19 lockdown on air quality. It also provides a better understanding of hotspots and trends that can aid the government and the policy-makers to identify precautionary measures to reduce air pollution and prioritize hotspots. © 2022, Indian Society of Remote Sensing.

Impact of decreased aerosols during the COVID-19 pandemic on winter wheat gross primary productivity and water use efficiency

Rapid and comprehensive lockdowns to contain the coronavirus 2019 (COVID-19) pandemic reduced anthropogenic emissions and, thereby, decreased the aerosol optical depth (AOD) in Xiangyang, Hubei Province. However, their complicated interactions make quantifying the contribution of decreased aerosols to crop growth challenging. Here, we explored the indirect effects of decreased aerosol concentrations on the gross primary productivity (GPP) and water use efficiency (WUE) of winter wheat by quantifying the contributions of key environmental factors. Our results showed high temporal and spatial associations between aerosols (represented by AOD), GPP, and WUE before, during, and after the COVID-19 pandemic. AOD decreased by 23.8% +/- 10.1%, whereas GPP and WUE increased by 16.5% +/- 5.8% and 17.0% +/- 15.3%, respectively. The GeoDetector model revealed that photosynthetically active radiation (PAR) had a major impact on GPP and WUE, followed by precipitation, surface soil moisture, subsurface soil moisture, and surface temperature. Moreover, causality analysis showed a causal relationship between AOD and the dominant factors (PAR and precipitation) during the lockdown, thereby indicating a positive effect of decreased aerosols on GPP and WUE changes of winter wheat. Our findings assist in understanding the mechanisms causing GPP and WUE changes, given the environmental factors that changed significantly during the pandemic. (c) 2022 Society of Photo-Optical Instrumentation Engineers (SPIE)

Analysis and forecasting of air quality index based on satellite data.

OBJECTIVE: The air quality index (AQI) forecasts are one of the most important aspects of improving urban public health and enabling society to remain sustainable despite the effects of air pollution. Pollution control organizations deploy ground stations to collect information about air pollutants. Establishing a ground station all-around is not feasible due to the cost involved. As an alternative, satellite-captured data can be utilized for AQI assessment. This study explores the changes in AQI during various COVID-19 lockdowns in India utilizing satellite data. Furthermore, it addresses the effectiveness of state-of-the-art deep learning and statistical approaches for forecasting short-term AQI. MATERIALS AND METHODS: Google Earth Engine (GEE) has been utilized to capture the data for the study. The satellite data has been authenticated against ground station data utilizing the beta distribution test before being incorporated into the study. The AQI forecasting has been explored using state-of-the-art statistical and deep learning approaches like VAR, Holt-Winter, and LSTM variants (stacked, bi-directional, and vanilla). RESULTS: AQI ranged from 100 to 300, from moderately polluted to very poor during the study period. The maximum reduction was recorded during the complete lockdown period in the year 2020. Short-term AQI forecasting with Holt-Winter was more accurate than other models with the lowest MAPE scores. CONCLUSIONS: Based on our findings, air pollution is clearly a threat in the studied locations, and it is important for all stakeholders to work together to reduce it. The level of air pollutants dropped substantially during the different lockdowns.

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.

Investigation of pre and post environmental impact of the lockdown (COVID-19) on the water quality of the Capibaribe and Tejipió rivers, Recife metropolitan region, Brazil.

The coronavirus pandemic has seriously affected human health, although some improvements on environmental indexes have temporarily occurred, due to changes on socio-cultural and economic standards. The objective of this study was to evaluate the impacts of the coronavirus and the influence of the lockdown associated with rainfall on the water quality of the Capibaribe and Tejipió rivers, Recife, Northeast Brazil, using cloud remote sensing on the Google Earth Engine (GEE) platform. The study was carried out based on eight representative images from Sentinel-2. Among the selected images, two refer to the year 2019 (before the pandemic), three refer to 2020 (during a pandemic), two from the lockdown period (2020), and one for the year 2021. The land use and land cover (LULC) and slope of the study region were determined and classified. Water turbidity data were subjected to descriptive and multivariate statistics. When analyzing the data on LULC for the riparian margin of the Capibaribe and Tejipió rivers, a low permanent preservation area was found, with a predominance of almost 100% of the urban area to which the deposition of soil particles in rivers are minimal. The results indicated that turbidity values in the water bodies varied from 6 mg. L-1 up to 40 mg. L-1. Overall, the reduction in human-based activities generated by the lockdown enabled improvements in water quality of these urban rivers.

The Effects of COVID-19 Lockdown on Air Pollutant Concentrations across China: A Google Earth Engine-Based Analysis.

To overcome the spread of the severe COVID-19 outbreak, various lockdown measures have been taken worldwide. China imposed the strictest home-quarantine measures during the COVID-19 outbreak in the year 2020. This provides a valuable opportunity to study the impact of anthropogenic emission reductions on air quality. Based on the GEE platform and satellite imagery, this study analyzed the changes in the concentrations of NO2, O3, CO, and SO2 in the same season (1 February-1 May) before and after the epidemic control (2019-2021) for 16 typical representative cities of China. The results showed that NO2 concentrations significantly decreased by around 20-24% for different types of metropolises, whereas O3 increased for most of the studied metropolises, including approximately 7% in megacities and other major cities. Additionally, the concentrations of CO and SO2 showed no statistically significant changes during the study intervals. The study also indicated strong variations in air pollutants among different geographic regions. In addition to the methods in this study, it is essential to include the differences in meteorological impact factors in the study to identify future references for air pollution reduction measures.

Investigating the effects of drought and lockdowns on smallholder and commercial agricultural production in KwaZulu-Natal using remotely sensed data.

Not many efforts have been made so far to understand the effects of both the 2015-2016 drought and the 2020 lockdown measures on the agricultural production of smallholder vis-a-vis commercial farmers in Kwazulu-Natal. Google Earth Engine, and random forest algorithm, are used to generate a dataset that help to investigate this question. A regression is performed on double differenced data to investigate the effects of interest. A k-mean cluster analysis, is also used to determine whether the distribution patterns of crop production changed with drought and disruption of agricultural production input. Results show that: (1) droughts affected the agricultural production of both areas similarly. Crop cover declined in both areas for one season after droughts were broken. Then recovery was driven by greener, more productive crops rather than the expansion of crop area. (2) The response of both areas to the COVID-19 lockdown was also similar. Both smallholder and commercial areas' Normalised Difference Vegetation Index - a proxy for crop vitality - improved in response to regulations favourable to the sector and improved rainfall. No significant adjustments in crop cover were observed. Production therefore changed primarily at the intensive margin (improved productivity of existing croplands) rather than the extensive (changing the extent of land under cultivation). (3) Cluster analysis allows for a more granular view, showing that the positive impact of lockdowns on agriculture were concentrated in areas with high rainfall and close proximity to metropolitan markets. Both smallholder and commercial farmers therefore are reliant on market access together with favourable environmental conditions for improved production.

AGRINNOVACION 4.0: Methodological classification tool to determine production areas of short-cycle crops

The National Plan for the Improvement of Productivity and Sustainability of the Agricultural Sector aims to be applied in a staggered manner to the entire country, under the name of AGRINNOVACION 4.0 to promote economic recovery and job creation after the COVID-19 pandemic. The objective of this work is to analyze geospatial information of the producers of the AGRINNOVACION 4.0 program using the free Google Earth Engine (GEE) platform, in order to establish the base of the digital agricultural cadastre of the North Zone of Cartago and have a system of geographic information for the application of high-precision technologies, as a basis for the identification model of productive areas with short-cycle crops developed in the North Zone of Cartago. A data acquisition methodology was generated using geographic information systems and machine learning techniques (Random Forest), with good fitting results. For the area under study, it is imperative that the information affected by cloud cover be reduced to make the classification of lands for horticultural use as accurate as possible. The tool is replicable and constitutes a support in the success of the plan for the later stages.

Squatting behavior during the COVID-19 pandemic: The case of the informal settlement "Los Hornos" in Buenos Aires.

The COVID-19 outbreak magnified territorial inequalities and increased vulnerability among low-income groups. Inhabitants in informal settlements are structurally disadvantaged in coping with communicative diseases such as the COVID-19 pandemic. Despite that, the pandemic has been accompanied by the proliferation of informal settlements. This study explores how the pandemic caused the squatting on new land with the case of "Los Hornos" in suburban Buenos Aires. We used a random forest algorithm and Google Earth Engine to estimate the rapid growth of a new informal settlement from a series of satellite images from early 2020. We also conducted semi-structured interviews with inhabitants to investigate the link between squatting and COVID-19. The study revealed that squatting on new land during the pandemic was mainly due to economic difficulties, overcrowding in existing informal settlements in the metropolitan center, and speculation in the informal housing market. This case is an example of how the most vulnerable groups bore the brunt of the pandemic, how the households in the existing informal settlement were behaving similar to those in the formal housing market (i.e., away from the urban centers), and how the outbreak had also been an opportunity for collective action of squatting a new land to materialize.

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.

Spatio-Temporal Analysis of Air Pollutants Changes During The COVID-19 Using Sentinel-5P in Google Earth Engine (Case Study: Java Island)

COVID-19 or Coronavirus Disease 2019 has become a global pandemic until several countries have implemented social distancing in restrictions on human activities. The Indonesian Government, in early 2020, set a PSBB (Large-Scale Social Restriction) policy. Still, as the COVID-19 Pandemic progressed, the Indonesian Government finally changed the PSBB and implemented the PPKM (Enforcement of Community Activity Restrictions) policy. COVID-19 has an impact on decreasing human and industrial activities. On the other hand, this will be beneficial due to a decrease in air pollutants. Pollutants come from motor vehicle fumes or other industrial activities. The types of pollutants carried out in this research are Carbon Monoxide (CO), Ozone (O3), Sulfur Dioxide (S02), and Nitrogen Dioxide (NO2). Using Sentinel-5P Imagery, which can record pollutant activity with the daily temporal resolution, the effect of the number of pollutants on the COVID-19 Pandemic can be investigated using the Google Earth Engine and the correlation test method to relate the effect of pollutant concentrations to BMKG meteorological data. The correlation test of pollutant data on sentinel 5P images with BMKG data shows a strong correlation of 0.5045 and 0.795 in Central Java. In addition, changes in the decrease in CO, NO2, and O3 gases occurred in November 2020 - December 2020. Monitoring of pollutants in Java During the COVID-19 Pandemic was packaged in the Website and Google Earth Engine. According to users, the application obtained a usability test result of 89%

Air Quality Analysis During COVID-19 Utilizing Satellite Data

Human health is severely endangered by the novel coronavirus (COVID-19). It is viewed as the worst global health threat humans have faced since the second world war and the WHO recognized it as a pandemic on March 11, 2020. This pandemic led several nations to adopt statewide lockdowns, while the industrial, construction, and transportation activities in several nations were disrupted, which lead to a significant shift in air pollutants. The lockdown, however, significantly impacted the environment and air quality in distinct cities. There are numerous ground stations deployed by pollution control organizations to monitor and collect the air pollutants data, but it is not feasible to set up a ground station in every city. In places where ground stations are not available for data collection, Google Earth Engine (GEE) satellite captured data can be used for data analysis. This study aimed to analyze the changes in air pollutants during the different lockdowns in India, such as nitrogen dioxide(NO2), sulfur dioxide(SO2), and carbon monoxide(CO) that contribute significantly to air pollution. In India, lockdowns were imposed during different periods of 2020, 2021, and 2022, according to COVID-19 waves. The air pollutants data during different waves have been analyzed and compared with the pre-COVID year (2019) data for the same duration. According to the study results, N O2 and S O2 were drastically reduced, but only a minor reduction in CO. Delhi, Jaipur, Ahmedabad, and Mumbai were among the major cities that saw the largest reduction, which was up to 60%. © 2022 IEEE.

IrriMap_CN: Annual irrigation maps across China in 2000–2019 based on satellite observations, environmental variables, and machine learning

Agricultural irrigation, as an important practice to protect crops from drought and promote grain yield, has a long history in China. A timely and precise dataset about the extent and dynamics of irrigated areas is necessary for water allocation and agricultural management but is scarce in China. Here we developed annual irrigated cropland maps across China (IrriMap_CN) at 500-m resolution from 2000 to 2019, using MODIS data, machine-learning method, and Google Earth Engine platform. First, we generated annual nationwide training samples by strictly screening the existing irrigation maps downscaled from the statistical data. Second, we implemented locally adaptive random forest classifiers in 511 nominal 1° × 1° grid cells across China with MODIS vegetation indices, climatic factors, and topography variables. Third, we conducted nationwide pixel-wise validation of the IrriMap_CN using independent samples. The validation results based on more than 3000 ground truth points revealed that IrriMap_CN had high accuracies ranging from 77.2% to 85.9%. The time series of IrriMap_CN detected substantial expansion of irrigated areas in Xinjiang and Heilongjiang (more than 50% in total) and pronounced decreases in Sichuan, Jiangsu, and Hebei. The analyses of irrigation frequency, start time, and end time implied that North China Plain was the most intensive irrigated area;but the irrigation area showed a decreasing trend since 2000, consistent with the reduced agricultural water consumption. The annual irrigation datasets allow us to understand the spatiotemporal dynamics of irrigated croplands in China and are expected to contribute to the improvement of earth system models and facilitate sustainable agricultural water management. • Annual irrigation maps (IrriMap_CN) were generated for China in 2000–2019. • Nationwide training samples were extracted from existing irrigation maps. • IrriMap_CN highlights the declining irrigation area in North China Plain. • Cropland reclamation/occupation and water supply are key to irrigation area changes. [ FROM AUTHOR] Copyright of Remote Sensing of Environment is the property of Elsevier B.V. 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.)

Quantifying Nighttime Light Changes in KL, Putrajaya, and Selangor from Human Activities Impact of Pandemic COVID-19

The Malaysian government has implemented extensive physical distancing measures to prevent and control virus transmission in response to the pandemic COVID-19. Particularly in the Kuala Lumpur, Putrajaya, and Selangor regions, quantitative, spatially disaggregated information about the population-scale shifts in an activity caused by these measures is extremely rare. A next-generation space-borne low-light imager called the Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS-DNB) can monitor changes in human activities. However, a cross-country examination of COVID-19 replies has not yet utilized the potential. To understand how communities have complied with COVID-19 measures in the two years since the pandemic. This study aims to quantify nighttime light (NTL) before and during COVID-19 using multi-year (2019-2021) monthly time series data derived from VIIRS nighttime light (NTL) products covering urban areas in Selangor, Putrajaya, and Kuala Lumpur. The NTL was processed in the Google Earth Engine (GEE) platform. NTL data has documented the link between curfew orders, nationwide closures, and the uneven response to control measures between and within the areas. Our findings demonstrate satellite images from VIIRS DNB can examine public opinion regarding national curfews and lockdowns, laws, and the sociocultural elements that influence their effectiveness, particularly in unstable and sparsely populated areas. Statistical T-test analysis revealed that the p-value for Kuala Lumpur was 0.01687, and less than 0.05 meant a significant difference between NTL reduction before and during COVID-19. Petaling showed a p-value of 0.0034 and less than 0.05, indicating a significant difference between NTL reduction before and during COVID-19. However, for area Putrajaya, the p-value is 0.0957, and more than 0.05 means there is no significant difference between the reduction of NTL before and during COVID-19. © 2022 IEEE.

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.

Changes in temporal pattern and spatial distribution of environmental pollutants in 8 Asian countries owing to COVID-19 pandemic.

This study investigated the changes in air pollutant's concentration, spatio-temporal distribution and sensitivity of changes in air pollutant's concentration during pre and post COVID-19 outbreak. We employed Google Earth Engine Platform to access remote sensing datasets of air pollutants across Asian continent. Air pollution and cumulative confirmed-COVID cases data of Asian countries (Afghanistan, Bangladesh, China, India, Iran, Iraq, Pakistan, and Saudi Arabia) have been collected and analyzed for 2019 and 2020. The results indicate that aerosol index (AI) and nitrogen dioxide (NO2) is significantly reduced during COVID outbreak i.e. in year 2020. In addition, we found significantly positive (P < 0.05, 95% confidence interval, two-tailed) correlation between changes in AI and NO2 concentration for net active-COVID case increment in almost each country. For other atmospheric gases i.e. carbon monoxide (CO), formaldehyde (HCHO), ozone (O3), and Sulfur dioxide (SO2), insignificant and/or significant negative correlation is also observed. These results suggest that the atmospheric concentration of AI and NO2 are good indicators of human activities. Furthermore, the changes in O3 shows significantly negative correlation for net active-COVID case increment. In conclusion, we observed significant positive environmental impact of COVID-19 restrictions in Asia. This study would help and assist environmentalist and policy makers in restraining air pollution by implementing efficient restrictions on human activities with minimal economic loss.