An investigation of PM2.5 concentration changes in Mid-Eastern China before and after COVID-19 outbreak.

With the Chinese government revising ambient air quality standards and strengthening the monitoring and management of pollutants such as PM2.5, the concentrations of air pollutants in China have gradually decreased in recent years. Meanwhile, the strong control measures taken by the Chinese government in the face of COVID-19 in 2020 have an extremely profound impact on the reduction of pollutants in China. Therefore, investigations of pollutant concentration changes in China before and after COVID-19 outbreak are very necessary and concerning, but the number of monitoring stations is very limited, making it difficult to conduct a high spatial density investigation. In this study, we construct a modern deep learning model based on multi-source data, which includes remotely sensed AOD data products, other reanalysis element data, and ground monitoring station data. Combining satellite remote sensing techniques, we finally realize a high spital density PM2.5 concentration change investigation method, and analyze the seasonal and annual, the spatial and temporal characteristics of PM2.5 concentrations in Mid-Eastern China from 2016 to 2021 and the impact of epidemic closure and control measures on regional and provincial PM2.5 concentrations. We find that PM2.5 concentrations in Mid-Eastern China during these years is mainly characterized by "north-south superiority and central inferiority", seasonal differences are evident, with the highest in winter, the second highest in autumn and the lowest in summer, and a gradual decrease in overall concentration during the year. According to our experimental results, the annual average PM2.5 concentration decreases by 3.07 % in 2020, and decreases by 24.53 % during the shutdown period, which is probably caused by China's epidemic control measures. At the same time, some provinces with a large share of secondary industry see PM2.5 concentrations drop by more than 30 %. By 2021, PM2.5 concentrations rebound slightly, rising by 10 % in most provinces.

High-precision estimation of hourly PM2.5 concentration based on a grid scale of satellite-derived products

Continuous and accurate surface pollutant data can provide data support for health effect analysis. Based on the hourly AOD data of the Himawari-8 satellite as the basic data set, this study collected auxiliary parameters including meteorological reanalysis data and geospatial data to estimate the surface PM2.5 hourly concentration. The random forest (RF) and CatBoost models with superior performance were integrated by linear fitting. The experimental results showed that the sample-CV R2 and RMSE of the integrating model were 0.929 and 9.846 μg/m3;time-CV R2 = 0.903, RMSE = 11.521 μg/m3;station-CV R2 = 0.894, RMSE = 12.05 μg/m3, which had the best validation accuracy among all the comparison models and were also better than the estimation results of many previous studies. The spatial and temporal analysis results of PM2.5 showed that the surface PM2.5 concentration was generally high in winter and spring, and low in summer and autumn during the study period. During COVID-2019, PM2.5 concentration on the surface of China showed a significant decreasing trend. The model with the estimation method used in this study can produce reliable surface PM2.5 data products. © 2023 Turkish National Committee for Air Pollution Research and Control

COVID-19 Restriction Movement Control Order (MCO) Impacted Emissions of Peninsular Malaysia Using Sentinel-2a and Sentinel-5p Satellite.

The unprecedented outbreak of Coronavirus Disease 2019 (COVID-19) has impacted the whole world in every aspect including health, social life, economic activity, education, and the environment. The pandemic has led to an improvement in air quality all around the world, including in Malaysia. Lockdowns have resulted in industry shutting down and road travel decreasing which can reduce the emission of Greenhouse Gases (GHG) and air pollution. This research assesses the impact of the COVID-19 lockdown on emissions using the Air Pollution Index (API), aerosols, and GHG which is Nitrogen Dioxide (NO2) in Malaysia. The data used is from Sentinel-5p and Sentinel-2A which monitor the air quality based on Ozone (O3) and NO2 concentration. Using an interpolated API Index Map comparing 2019, before the implementation of a Movement Control Order (MCO), and 2020, after the MCO period we examine the impact on pollution during and after the COVID-19 lockdown. Data used Sentinel-5p, Sentinel-2A, and Air Pollution Index of Malaysia (APIMS) to monitor the air quality that contains NO2 concentration. The result has shown the recovery in air quality during the MCO implementation which indirectly shows anthropogenic activities towards the environmental condition. The study will help to enhance and support the policy and scope for air pollution management strategies as well as raise public awareness of the main causes that contribute to air pollution.

Impacts of Winter and Summer COVID-19 Lockdowns on Urban Air Quality in Urumqi, Northwest China

After the COVID-19 pandemic began in 2020, Urumqi, a remote area in northwest China, experienced two lockdowns, in January and July 2020. Based on ground and satellite observations, this study assessed the impacts of these lockdowns on the air quality in Urumqi and the seasonal differences between them. The results showed that, during the wintertime lockdown, PM10, PM2.5, NO2, CO, and SO2 levels decreased by 38, 40, 45, 27, 8%, respectively, whereas O-3 concentrations increased by 113%. During the summer lockdown, PM10, PM2.5, NO2, CO, and SO2 levels decreased by 39, 24, 59, 2, and 13%, respectively, and the O-3 concentrations increased by 21%. During the lockdowns, the NO2 concentrations decreased by 53% in winter and 13% in summer in the urban areas, whereas they increased by 23% in winter and 9% in summer in the suburbs. Moreover, large seasonal differences were observed between winter and summer SO2, CO, and O-3. The lockdown played a vital role in the rapid decline of primary air pollutant concentrations, along with fewer meteorological impacts on air pollution changes in this area. The increase in O-3 concentrations during the COVID-19 lockdowns reflects the complexity of air quality changes during reductions in air pollutant emissions.

Spatiotemporal neural network for estimating surface NO2 concentrations over north China and their human health impact.

Atmospheric nitrogen dioxide (NO2) is an important reactive gas pollutant harmful to human health. The spatiotemporal coverage provided by traditional NO2 monitoring methods is insufficient, especially in the suburban and rural areas of north China, which have a high population density and experience severe air pollution. In this study, we implemented a spatiotemporal neural network (STNN) model to estimate surface NO2 from multiple sources of information, which included satellite and in situ measurements as well as meteorological and geographical data. The STNN predicted NO2 with high accuracy, with a coefficient of determination (R2) of 0.89 and a root mean squared error of 5.8 µg/m3 for sample-based 10-fold cross-validation. Based on the surface NO2 concentration determined by the STNN, we analyzed the spatial distribution and temporal trends of NO2 pollution in north China. We found substantial drops in surface NO2 concentrations ranging between 9.1% and 33.2% for large cities during the 2020 COVID-19 lockdown when compared to those in 2019. Moreover, we estimated the all-cause deaths attributed to NO2 exposure at a high spatial resolution of about 1 km, with totals of 6082, 4200, and 18,210 for Beijing, Tianjin, and Hebei Provinces in 2020, respectively. We observed remarkable regional differences in the health impacts due to NO2 among urban, suburban, and rural areas. Generally, the STNN model could incorporate spatiotemporal neighboring information and infer surface NO2 concentration with full coverage and high accuracy. Compared with machine learning regression techniques, STNN can effectively avoid model overfitting and simultaneously consider both spatial and temporal correlations of input variables using deep convolutional networks with residual blocks. The use of the proposed STNN model, as well as the surface NO2 dataset, can benefit air quality monitoring, forecasting, and health burden assessments.

Impact of Temperature on Tuberculosis estimated from Satellite Remote Sensing before and during COVID-19 Pandemic

Several epidemiological studies have examined the effect of temperature on health, such as tuberculosis (TB). Previous researches have used temperature data from local station sites but the temperature in an area is spatially variable. For example temperature in the urban area is normally higher than in the rural area because of the urban heat island. Satellite remote sensing data can provide area information and is, therefore useful to quantify the effect of environmental hazards on health in a wider region. To study the impact of temperature on TB, this study estimates Land Surface Temperature (LST) using Landsat 8 data in Yogyakarta City, Indonesia from 2016 to 2020 and analyzes the relationship between temperature and TB cases. The LST estimates were first verified by the temperature data obtained from the Meteorological, Climatological, and Geophysical Agency. Tuberculosis cases from 2016 until 2020 were collected from Public Health Office. The correlation patterns have also been examined before and after the COVID-19 pandemic. The result shows that the satellite-derived LST reasonably matches the ground measurement, and a negative correlation between TB cases and LST can be recognized: the cases number is higher in low LST while the cases number is lower when LST is higher. This can be explained that the increase of TB case number in the lower temperature is because ultraviolet radiation kills bacterium, which impedes the spread of TB in dwellings. However, this correlation cannot be observed after COVID-19 outbreak. The number of TB cases in 2020 (during COVID-19 pandemic) is generally lower than the previous year (2016-2019, before COVID-19 pandemic) in the study area. This study suggests that social restriction policy may potentially affect the spread of TB and thus shows the irrelevant relationship between LST and TB cases during COVID-19 pandemic. © ACRS 2021.All right reserved.

Estimating city NOX emissions from TROPOMI high spatial resolution observations – A case study on Yangtze River Delta, China

In this study, TROPOspheric Monitoring Instrument (TROPOMI) observations were resampled to obtain 0.01° × 0.01° NO2 VCD (vertical column density) over Yangtze River Delta (YRD), China. Based on this high spatial resolution satellite observations, NO2 VCDs in megacities cluster of YRD region were examined with a reduction of ~35% during COVID-19 lockdown. The adjusted Exponentially-Modified Gaussian (EMG) model was used to estimate the NOX emission in typical cities under regionally polluted YRD region. Taking 100 km of mass integration interval as an example, during 2018–2019, the averaged NOX emission of Shanghai, Hangzhou, Nanjing, and Ningbo is 139.65 mol/s, 84.49 mol/s, 79.87 mol/s and 88.73 mol/s, respectively. This estimation has a good correlation with Multi-resolution Emission Inventory for China (MEIC) emission with R more than 0.9 but lower results mainly due to the underestimation of NO2 VCD by TROPOMI in polluted areas. It was also found that the NOX emissions of Ningbo are higher than expected, which is closely related to massive ship emissions. This study indicates that this approach based on adjusted EMG model can enhance the ability to quantify NOX emissions at city level by utilizing the high spatial resolution observations of TROPOMI.

An Impact Analysis of Artificial Light at Night (ALAN) on Bats. A Case Study of the Historic Monument and Natura 2000 Wisloujscie Fortress in Gdansk, Poland.

The artificial light at night (ALAN) present in many cities and towns has a negative impact on numerous organisms that live alongside humans, including bats. Therefore, we investigated if the artificial illumination of the historic Wisloujscie Fortress in Gdansk, Poland (part of the Natura 2000 network), during nighttime events, which included an outdoor electronic dance music (EDM) festival, might be responsible for increased light pollution and the decline in recent years of the pond bat (Myotis dasycneme). An assessment of light pollution levels was made using the methods of geographical information system (GIS) and free-of-charge satellite remote sensing (SRS) technology. Moreover, this paper reviewed the most important approaches for environmental protection of bats in the context of ecological light pollution, including International, European, and Polish regulatory frameworks. The analysis of this interdisciplinary study confirmed the complexity of the problem and highlighted, too, the need for better control of artificial illumination in such sensitive areas. It also revealed that SRS was not the best light pollution assessment method for this particular case study due to several reasons listed in this paper. As a result, the authors' proposal for improvements also involved practical recommendations for devising suitable strategies for lighting research and practice in the Natura 2000 Wisloujscie Fortress site located adjacent to urban areas to reduce the potential negative impact of ALAN on bats and their natural habitats.