Spatial and temporal variations in PM2.5 and associated health risk assessment in Saudi Arabia using remote sensing

Classification of aerosols using particle linear depolarization ratio (PLDR) over seven urban locations of Asia

Active lidar remote sensing has been used to obtain detailed and quantitative information about the properties of aerosols. We have analyzed the spatio-temporal classification of aerosols using the parameters of particle linear depolarization ratio and single scattering albedo from Aerosol Robotic Network (AERONET) over seven megacities of Asia namely; Lahore, Karachi, Kanpur, Pune, Beijing, Osaka, and Bandung. We find that pollution aerosols dominate during the winter season in all the megacities. The concentrations, however, vary concerning the locations, i.e., 70-80% pollution aerosols are present over Lahore, 40-50% over Karachi, 90-95% over Kanpur and Pune, 60-70% and over Beijing and Osaka. Pure Dust (PD), Pollution Dominated Mixture (PDM), and Dust Dominated Mixture (DDM) are found to be dominant during spring and summer seasons.This proposes that dust over Asia normally exists as a mixture with pollution aerosols instead of pure form. We also find that black carbon (BC) dominated pollution aerosols.

A study on identifying synergistic prevention and control regions for PM2.5 and O3 and exploring their spatiotemporal dynamic in China

Air pollutants, notably ozone (O3) and fine particulate matter (PM2.5) give rise to evident adverse impacts on public health and the ecotope, prompting extensive global apprehension. Though PM2.5 has been effectively mitigated in China, O3 has been emerging as a primary pollutant, especially in summer. Currently, alleviating PM2.5 and O3 synergistically faces huge challenges. The synergistic prevention and control (SPC) regions of PM2.5 and O3 and their spatiotemporal patterns were still unclear. To address the above issues, this study utilized ground monitoring station data, meteorological data, and auxiliary data to predict the China High-Resolution O3 Dataset (CHROD) via a two-stage model. Furthermore, SPC regions were identified based on a spatial overlay analysis using a Geographic Information System (GIS). The standard deviation ellipse was employed to investigate the spatiotemporal dynamic characteristics of SPC regions. Some outcomes were obtained. The two-stage model significantly improved the accuracy of O3 concentration prediction with acceptable R2 (0.86), and our CHROD presented higher spatiotemporal resolution compared with existing products. SPC regions exhibited significant spatiotemporal variations during the Blue Sky Protection Campaign (BSPC) in China. SPC regions were dominant in spring and autumn, and O3-controlled and PM2.5-dominated zones were detected in summer and winter, respectively. SPC regions were primarily located in the northwest, north, east, and central regions of China, specifically in the Beijing-Tianjin-Hebei urban agglomeration (BTH), Shanxi, Shaanxi, Shandong, Henan, Jiangsu, Xinjiang, and Anhui provinces. The gravity center of SPC regions was distributed in the BTH in winter, and in Xinjiang during spring, summer, and autumn. This study can supply scientific references for the collaborative management of PM2.5 and O3.

Defining the role of renewable energy, economic growth, globalization, energy consumption, and population growth on PM2.5 concentration: evidence from South Asian countries

Rapid industrialization and economic development in South Asia (SA) caused serious air pollution-related issues. Air pollutants, particularly fine particulate matter (PM_2.5), have negative effects on health, instigating widespread concern. The current study is an attempt to analyze the impact of non-renewable energy (NRE), globalization (GLO), GDP, renewable energy (RE), and population (POP) on PM_2.5 concentration in SA from 1998 to 2020. In doing so, this study incorporated advanced and robust econometric techniques, i.e., Pesaran (Economet Rev 34(6-10), 1089-1117, 2015), to check the cross-sectional dependency, and the unit root presence checked through Cross-sectional Im, Pesaran, and Shin (CIPS) and Cross-sectionally Augmented Dickey-Fuller (CADF) unit root tests. Moreover, the long and short-run association among the selected variables was analyzed through Westerlund and Edgerton (Econ Lett 97(3), 185-190, 2007), cointegration test, and cross-sectional augmented ARDL (CS-ARDL). The empirical results indicate that the panel was cross-sectionally correlated, stationary at the first difference, and co-integrated in the long run. Moreover, the CS-ARDL model indicates a positive association between GDP and PM_2.5 concentration. Similarly, NRE and POP contribute significantly to increasing the PM_2.5 concentration in SA. However, RE and GLO play an important role to decrease the PM_2.5 concentration in SA.

Understanding Anthropogenic PM2.5 Concentrations and Their Drivers in China during 1998-2016

Air pollution poses serious challenges for human health and wellbeing. It also affects atmospheric visibility and contributes to climate change. As social and economic processes have increased, anthropogenic PM_2.5 pollution caused by intensive human activities has led to extremely severe air pollution. Spatiotemporal patterns and drivers of anthropogenic PM_2.5 concentrations have received increasing attention from the scientific community. Nonetheless, spatiotemporal patterns and drivers of anthropogenic PM_2.5 concentrations are still inadequately understood. Based on a time series of remotely sensed anthropogenic PM_2.5 concentrations, this study analyzed the spatiotemporal patterns of this crucial pollutant in China from 1998 to 2016 using Sen's slope estimator and the Mann-Kendall trend model. This, in combination with grey correlation analysis (GCA), was used to reveal the socioeconomic factors influencing anthropogenic PM_2.5 concentrations in eastern, central, and western China from 1998 to 2016. The results were as follows: (1) the average annual anthropogenic concentration of PM_2.5 in China increased quickly and reached its peak value in 2007, then remained stable in the following years; (2) only 63.30 to 55.09% of the land area reached the threshold value of 15 μg/m³ from 1998 to 2016; (3) regarding the polarization phenomenon of anthropogenic PM_2.5 concentrations existing in eastern and central China, the proportion of gradient 1 (≤15 μg/m³) gradually decreased and gradient 3 (≥35 μg/m³) gradually increased; and (4) the urbanization level (UR), population density (PD), and proportion of secondary industry to gross domestic product (SI) were the dominant socioeconomic factors affecting the formation of anthropogenic PM_2.5 concentrations in eastern, central, and western China, independently. The improvements in energy consumption per gross domestic product (EI) have a greater potential for mitigating anthropogenic PM_2.5 emissions in central and western China. These findings allow an interpretation of the spatial distribution of anthropogenic PM_2.5 concentrations and the mechanisms influencing anthropogenic PM_2.5 concentrations, which can help the Chinese government develop effective abatement strategies.