Evaluation of Air Quality Index by Spatial Analysis Depending on Vehicle Traffic during the COVID-19 Outbreak in Turkey

Effect of lockdown and associated mobility changes amid COVID-19 on air quality in the Kathmandu Valley, Nepal

The COVID-19 pandemic caused a setback for Nepal, leading to nationwide lockdowns. The study analyzed the impact of lockdown on air quality during the first and second waves of the COVID-19 pandemic in the Kathmandu Valley. We analyzed 5 years of ground-based air quality monitoring data (2017-2021) from March to July and April to June for the first and second wave lockdowns, respectively. A significant decrease in PM2.5 (particulate matter with an aerodynamic diameter ≤ 2.5 μm) concentrations was observed during the lockdowns. The highest rate of decline in PM2.5 levels was observed during May and July compared to the pre-pandemic year. The PM2.5 concentration during the lockdown period remained within the WHO guideline limit and NAAQS for the maximum number of days compared to the lockdown window in the pre-pandemic years (2017-2019). Likewise, lower PM2.5 levels were observed during the second wave lockdown, which was characterized by a targeted lockdown approach (smart lockdown). We found a significant correlation of PM2.5 concentration with community mobility changes (i.e., walking, driving, and using public transport) from the Spearman correlation analysis. Lockdown measures restricted human mobility that led to a lowering of PM2.5 concentrations. Our findings can be helpful in developing urban air quality control measures and management strategies, especially during high pollution episodes.

A new approach to COVID-19 data mining: A deep spatial-temporal prediction model based on tree structure for traffic revitalization index

The outbreak of the COVID-19 epidemic has had a huge impact on a global scale and its impact has covered almost all human industries. The Chinese government enacted a series of policies to restrict the transportation industry in order to slow the spread of the COVID-19 virus in early 2020. With the gradual control of the COVID-19 epidemic and the reduction of confirmed cases, the Chinese transportation industry has gradually recovered. The traffic revitalization index is the main indicator for evaluating the degree of recovery of the urban transportation industry after being affected by the COVID-19 epidemic. The prediction research of traffic revitalization index can help the relevant government departments to know the state of urban traffic from the macro level and formulate relevant policies. Therefore, this study proposes a deep spatial-temporal prediction model based on tree structure for the traffic revitalization index. The model mainly includes spatial convolution module, temporal convolution module and matrix data fusion module. The spatial convolution module builds a tree convolution process based on the tree structure that can contain directional features and hierarchical features of urban nodes. The temporal convolution module constructs a deep network for capturing temporal dependent features of the data in the multi-layer residual structure. The matrix data fusion module can perform multi-scale fusion of COVID-19 epidemic data and traffic revitalization index data to further improve the prediction effect of the model. In this study, experimental comparisons between our model and multiple baseline models are conducted on real datasets. The experimental results show that our model has an average improvement of 21%, 18%, and 23% in MAE, RMSE and MAPE indicators, respectively.

The relationship between PM10 and meteorological variables in the mega city Istanbul

PM₁₀, one of the air pollutants, occurs regularly in İstanbul during the winter months, namely in December, January, and February. PM₁₀ pollutant is affected by numerous factors. Among these factors are various meteorological variables and climatological factors. This article aims to determine the relationship between PM₁₀ and meteorological variables (wind speed, wind direction, temperature, and relative humidity) and to interpret these results. PM₁₀ and meteorological data were examined between 2011 and 2018. To determine the relationship, multiple linear regression, Pearson's correlation coefficient (PCC), Spearman's rank correlation, Kendall Tau correlation, autocorrelation function (ACF), cross-correlation function (CCF), and visuals were determined using the R program (open-air) packages. In the study, the relationship between wind, temperature, and relative humidity with PM₁₀ was determined, and it was observed that the PM₁₀ concentration was maximum between January and February. PM₁₀ concentrations have a positive relationship with relative humidity and wind direction, while a negative relationship with wind speed and temperature was observed. The correlation values for relative humidity and temperature were found to be 0.01 and - 0.15, respectively. Furthermore, the relationship between wind speed and PM₁₀ was calculated from multiple linear regression model, and the estimated value was - 0.12 while looking at the wind direction value, it was approximately 0.03.

COVID-19 Lockdown and the Impact on Mobility, Air Quality, and Utility Consumption: A Case Study from Sharjah, United Arab Emirates

This study presents an analysis of the impact of COVID-19 lockdown on people’s mobility trends, air quality, and utility consumption in Sharjah, United Arab Emirates (UAE). Records of lockdown and subsequent easing measures, infection and vaccination rates, community mobility reports, remotely sensed and ground-based air quality data, and utility (electricity, water, and gas) consumption data were collected and analyzed in the study. The mobility trends reflected the stringency of the lockdown measures, increasing in the residential sector but decreasing in all other sectors. The data showed significant improvement in air quality corresponding to the lockdown measures in 2020 followed by gradual deterioration as the lockdown measures were eased. Electricity and water consumption increased in the residential sector during the lockdown; however, overall utility consumption did not show significant changes. The changes in mobility were correlated with the relevant air quality parameters, such as NO2, which in turn was highly correlated to O3. The study provides data and analysis to support future planning and response efforts in Sharjah. Furthermore, the methodology used in the study can be applied to assess the impacts of COVID-19 or similar events on people’s mobility, air quality and utility consumption at other geographical locations.

Effects of the COVID-19 Pandemic on the Air Quality of the Metropolitan Region of São Paulo: Analysis Based on Satellite Data, Monitoring Stations and Records of Annual Average Daily Traffic Volumes on the Main Access Roads to the City

This paper presents an analysis of the effects of the COVID-19 pandemic on the air quality of the Metropolitan Region of São Paulo (MRSP). The effects of social distancing are still recent in the society; however, it was possible to observe patterns of environmental changes in places that had adhered transportation measures to combat the spread of the coronavirus. Thus, from the analysis of the traffic volumes made on some of the main access highways to the MRSP, as well as the monitoring of the levels of fine particulate matter (PM2.5), carbon monoxide (CO) and nitrogen dioxide (NO2), directly linked to atmospheric emissions from motor vehicles–which make up about 95% of air polluting agents in the region in different locations–we showed relationships between the improvement in air quality and the decrease in vehicles that access the MRSP. To improve the data analysis, therefore, the isolation index parameter was evaluated to provide daily information on the percentage of citizens in each municipality of the state that was effectively practicing social distancing. The intersection of these groups of data determined that the COVID-19 pandemic reduced the volume of vehicles on the highways by up to 50% of what it was in 2019, with the subsequent recovery of the traffic volume, even surpassing the values from the baseline year. Thus, the isolation index showed a decline of up to 20% between its implementation in March 2020 and December 2020. These data and the way they varied during 2020 allowed to observe an improvement of up to 50% in analyzed periods of the pollutants PM2.5, CO and NO2 in the MRSP. The main contribution of this study, alongside the synergistic use of data from different sources, was to perform traffic flow analysis separately for light and heavy duty vehicles (LDVs and HDVs). The relationships between traffic volume patterns and COVID-19 pollution were analyzed based on time series.

Air Quality Evaluation and Improvement of China’s Three Major Urban Agglomerations Based on the Modified MetaFrontier Dynamic Slack-Based Measures Model

Urban agglomeration has become a unique form of cities during the rapid development of emerging economies. With the increasing attention on global energy and environmental efficiency, air quality evaluation and pollution control have become important standards to measure the health and orderly development of such agglomerations. Based on panel data of 60 cities in the three major urban agglomerations of Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD), this study uses the Modified MetaFrontier Dynamic SBM model to evaluate their air quality over the 5-year period of 2013–2017. The results present that the development level of air pollution prevention and control in China’s three major urban agglomerations is relatively low, and YRD as the most developed area has the worst effect of air pollution prevention and control. The MetaFrontier and Group Frontier Efficiency analysis confirms the conclusion of the cluster analysis that a significant two-level differentiation exists in China’s three urban agglomerations. Moreover, China’s three major urban agglomerations are still in the stage of high energy consumption and high development. Lastly, we point out different recommendations for industrial structure and governance foci of the three major urban agglomerations. Dust prevention technology should be improved to reduce PM2.5 in BTH, desulfurization technology should be enhanced to cut industrial SO2 emissions in YRD, and better emission reduction targets and other targeted measures should be formulated in PRD.

Accessibility of Vaccination Centers in COVID-19 Outbreak Control: A GIS-Based Multi-Criteria Decision Making Approach

The most important protective measure in the pandemic process is a vaccine. The logistics and administration of the vaccine are as important as its production. The increasing diffusion of electronic devices containing geo-referenced information generates a large production of spatial data that are essential for risk management and impact mitigation, especially in the case of disasters and pandemics. Given that vaccines will be administered to the majority of people, it is inevitable to establish vaccination centres outside hospitals. Site selection of vaccination centres is a major challenge for the health sector in metropolitan cities due to the dense population and high number of daily cases. A poor site selection process can cause many problems for the health sector, workforce, health workers, and patients. To overcome this, a three-step solution approach is proposed: (i) determining eight criteria using from the experience of the advisory committee, (ii) calculating criterion weights using Analytic Hierarchy Process (AHP), and performing spatial analysis of criteria using Geographic Information System (GIS), (iii) assigning potential vaccination centres by obtaining a suitability map and determining service areas. A case study is performed for Bağcılar, Istanbul district, using the proposed methodology. The results show that the suitable areas are grouped in three different areas of the district. The proposed methodology provides an opportunity to execute a scientific and strategic vaccination programme and to create a map of suitable vaccination centres for the countries.