Study on the Spatial and Temporal Distribution Characteristics and Influencing Factors of Particulate Matter Pollution in Coal Production Cities in China

In recent years, with the continuous advancement of China’s urbanization process, regional atmospheric environmental problems have become increasingly prominent. We selected 12 cities as study areas to explore the spatial and temporal distribution characteristics of atmospheric particulate matter in the region, and analyzed the impact of socioeconomic and natural factors on local particulate matter levels. In terms of time variation, the particulate matter in the study area showed an annual change trend of first rising and then falling, a monthly change trend of “U” shape, and an hourly change trend of double-peak and double-valley distribution. Spatially, the concentration of particulate matter in the central and southern cities of the study area is higher, while the pollution in the western region is lighter. In terms of social economy, PM2.5 showed an “inverted U-shaped” quadratic polynomial relationship with Second Industry and Population Density, while it showed a U-shaped relationship with Generating Capacity and Coal Output. The results of correlation analysis showed that PM2.5 and PM10 were significantly positively correlated with NO2, SO2, CO and air pressure, and significantly negatively correlated with O3 and air temperature. Wind speed was significantly negatively correlated with PM2.5, and significantly positively correlated with PM10. In terms of pollution transmission, the southwest area of Taiyuan City is a high potential pollution source area of fine particles, and the long-distance transport of PM2.5 in Xinjiang from the northwest also has a certain contribution to the pollution of fine particles. This study is helpful for us to understand the characteristics and influencing factors of particulate matter pollution in coal production cities

Temporal and Spatial Analysis of PM_2.5 and O₃ Pollution Characteristics and Transmission in Central Liaoning Urban Agglomeration from 2015 to 2020

The central Liaoning urban agglomeration is an important heavy industry development base in China, and also an important part of the economy in northeast China. The atmospheric environmental problems caused by the development of heavy industry are particularly prominent. Trajectory clustering, potential source contribution (PSCF), and concentration weighted trajectory (CWT) analysis are used to discuss the temporal and spatial pollution characteristics of PM2.5 and ozone concentrations and reveal the regional atmospheric transmission pattern in central Liaoning urban agglomeration from 2015 to 2020. The results show that: (1) PM2.5 in the central Liaoning urban agglomeration showed a decreasing trend from 2015 to 2020. The concentration of PM2.5 is the lowest in 2018. Except for Benxi (34.7 µg/m3), the concentrations of PM2.5 in other cities do not meet the standard in 2020. The ozone concentration in Anshan, Liaoyang, and Shenyang reached the peaks in 2017, which are 68.76 µg/m3, 66.27 µg/m3, and 63.46 µg/m3 respectively. PM2.5 pollution is the highest in winter and the lowest in summer. The daily variation distribution of PM2.5 concentration showed a bimodal pattern. Ozone pollution is the most serious in summer, with the concentration of ozone reaching 131.14 µg/m3 in Shenyang. Fushun is affected by Shenyang intercity pollution, and the ozone concentration is high. (2) In terms of spatial distribution, the high values of PM2.5 are concentrated in monitoring stations in urban areas. On the contrary, the concentration of ozone in suburban stations is higher. The high concentration of ozone in the northeast of Anshan, Liaoyang, Shenyang to Tieling, and Fushun extended in a band distribution. (3) Through cluster analysis, it is found that PM2.5 and ozone in Shenyang are mainly affected by short-distance transport airflow. In winter, the weighted PSCF high-value area of PM2.5 presents as a potential contribution source zone of the northeast trend with wide coverage, in which the contribution value of the weighted CWT in the middle of Heilongjiang is the highest. The main potential source areas of ozone mass concentration in spring and summer are coastal cities and the Bohai Sea and the Yellow Sea. We conclude that the regional transmission of pollutants is an important factor of pollution, so we should pay attention to the supply of industrial sources and marine sources of marine pollution in the surrounding areas of cities, and strengthen the joint prevention and control of air pollution among regions. The research results of this article provide a useful reference for the central Liaoning urban agglomeration to improve air quality

Analysis of Pollution Characteristics and Emissions Reduction Measures in the Main Cotton Area of Xinjiang

With cotton production in Xinjiang increasing annually, the impact on the environment of agricultural waste produced to improve production has been reflected. This study selected Bozhou of Xinjiang, the main cotton producing region in northern Xinjiang, as the research object, and collected hourly concentration data of six pollutants from 2017 to 2021, and analyzed the spatial and temporal distribution characteristics of each pollutant. At the same time, Morlet wavelet analysis was used to further analyze the variation period of PM2.5 (PM particles with aerodynamic diameters less than 2.5 μm) concentration. The Weather Research and Forecasting model coupled with the Community Multiscale Air Quality (WRF-CMAQ) model was used to evaluate the emissions reduction measures for the most polluted month. The results showed that the concentration of particulate matter (PM particles with aerodynamic diameters less than 2.5 μm and 10 μm) decreased from the southern mountains to the north; moreover, the concentrations of CO (carbon monoxide), NO2 (nitrogen dioxide), and SO2 (sulfur dioxide) in the suburbs were higher than those in the urban center. The concentration of O3 (Ozone) was the highest in summer, while the concentrations of other pollutants were high in autumn and winter. Under the time scale of a = 13, 24, PM2.5 had significant periodic fluctuation. The health risk values of PM2.5 and PM10 in this study were within the scope of the United States Environmental Protection Agency (USEPA) criteria, but it is still necessary to keep a close watch on them. In the context of emissions reduction measures, agricultural sources reduced by 20%, residential sources by 40%, industrial sources by 20%, and transportation sources by 20%; no change in the power source remains. Under these conditions, the daily average value of each pollutant met the first level of the national ambient air quality standard. The research results provide a reference for the local government to formulate heavy pollution emissions reduction policies

Temporal and Spatial Analysis of PM2.5 and O3 Pollution Characteristics and Transmission in Central Liaoning Urban Agglomeration from 2015 to 2020

The central Liaoning urban agglomeration is an important heavy industry development base in China, and also an important part of the economy in northeast China. The atmospheric environmental problems caused by the development of heavy industry are particularly prominent. Trajectory clustering, potential source contribution (PSCF), and concentration weighted trajectory (CWT) analysis are used to discuss the temporal and spatial pollution characteristics of PM2.5 and ozone concentrations and reveal the regional atmospheric transmission pattern in central Liaoning urban agglomeration from 2015 to 2020. The results show that: (1) PM2.5 in the central Liaoning urban agglomeration showed a decreasing trend from 2015 to 2020. The concentration of PM2.5 is the lowest in 2018. Except for Benxi (34.7 µg/m3), the concentrations of PM2.5 in other cities do not meet the standard in 2020. The ozone concentration in Anshan, Liaoyang, and Shenyang reached the peaks in 2017, which are 68.76 µg/m3, 66.27 µg/m3, and 63.46 µg/m3 respectively. PM2.5 pollution is the highest in winter and the lowest in summer. The daily variation distribution of PM2.5 concentration showed a bimodal pattern. Ozone pollution is the most serious in summer, with the concentration of ozone reaching 131.14 µg/m3 in Shenyang. Fushun is affected by Shenyang intercity pollution, and the ozone concentration is high. (2) In terms of spatial distribution, the high values of PM2.5 are concentrated in monitoring stations in urban areas. On the contrary, the concentration of ozone in suburban stations is higher. The high concentration of ozone in the northeast of Anshan, Liaoyang, Shenyang to Tieling, and Fushun extended in a band distribution. (3) Through cluster analysis, it is found that PM2.5 and ozone in Shenyang are mainly affected by short-distance transport airflow. In winter, the weighted PSCF high-value area of PM2.5 presents as a potential contribution source zone of the northeast trend with wide coverage, in which the contribution value of the weighted CWT in the middle of Heilongjiang is the highest. The main potential source areas of ozone mass concentration in spring and summer are coastal cities and the Bohai Sea and the Yellow Sea. We conclude that the regional transmission of pollutants is an important factor of pollution, so we should pay attention to the supply of industrial sources and marine sources of marine pollution in the surrounding areas of cities, and strengthen the joint prevention and control of air pollution among regions. The research results of this article provide a useful reference for the central Liaoning urban agglomeration to improve air quality

Analysis of Emission Reduction Measures and Simulation of PM_2.5 Concentrations in the Main Cotton Production Areas of Xinjiang in 2025

Cotton production in Xinjiang is increasing year by year, and the improved crop yields have had an impact on the environment. This study investigated the changes in six significant pollutants (PM2.5, PM10, SO2, NO2, O3, and CO) from 2017 to 2022 in Xinjiang. We compiled a biomass burning emission inventory to make the MEIC emission inventory more complete. The Weather Research and Forecasting Community Multiscale Air Quality (WRF–CMAQ) model was employed to simulate air quality in different reduction scenarios in 2025, and it explored ways to alleviate air pollution in the main cotton areas of Xinjiang. The result shows that the main pollutant in Xinjiang is particulate matter (PM particles with aerodynamic diameters less than 2.5 µm and 10 µm), and the concentration of particulate matter decreased from the northern mountains toward the south. The concentrations of O3 (ozone) were highest in summer, while the concentrations of other pollutants were high in autumn and winter. If the pollution is not strictly controlled in terms of emission reduction, it is impossible to achieve the target of a 35 μg/m3 PM2.5 concentration in the planting area. In the scenario of enhanced emission reduction measures and the scenario of higher intensity emission reduction measures, there was a failure to reach the target, despite the reduction in the PM2.5 concentration. In the best emission reduction scenario, PM2.5 in Xinjiang is expected to drop to 22.5 μg/m3 in November and 34 μg/m3 in March, respectively. Therefore, in the optimal emission reduction scenario, the target of 35 μg/m3 will be reached. This study emphasized the importance of future air pollution mitigation and identified a feasible pathway to achieve the target of 35 μg/m3 PM2.5 concentration by 2025. The research findings provide useful insights for the local government which can be used to develop strategies aimed at mitigating substantial pollution emissions

Mapping CO₂ spatiotemporal transfers embodied in China's trade using a global dynamic network model endogenizing fixed capital

A systematic approach that accurately assesses carbon emissions is essential to design climate policies. To fully consider the impacts of the intertemporal dynamics of using the past-formed capital for future production, endogenizing capital as an input into carbon accounting system has been proposed, and lead to the reallocation of emissions. However, little is known about how this reallocation occur, i.e., how carbon flows from the past-formed capital to the products it is used to produce and to the consumers who purchase these products. Here, enabled by a global CO2 transfer network model with capital endogenization, we take China as an example to trace the full process of CO2 spatiotemporal transfer and re-assess CO2 footprints. China contributed more than 40% of the global capital-related CO2 emissions with only 14% of global capital consumption. China drove domestic and foreign CO2 emissions mainly through purchasing service products, while foreign regions outsourced an order of magnitude higher emissions to China by importing products such as electricity, machinery and equipment. Along temporal horizons, CO2 emitted in historical years contributed 87% of the total emissions embodied in China's capital consumption, while new-formed capital contained 5.50 Gt CO2 emissions, which will be attributed to the future. Based on this, China's dynamic CO2 footprint was re-assessed as 4.65 Gt, an increase of more than 1/4 over the traditional results. This increase comes mainly from service products directly consuming building structure, especially real estate and public administration services. This study provided new understanding of CO2 accounting and identified new hotspots for differentiated climate policies.</p