Effects of Anthropogenic Emissions from Different Sectors on PM2.5 Concentrations in Chinese Cities

Yang, Jie; Liu, Pengfei; Song, Hongquan; Chen, Miao; Feng, Weiyue; Yu, Xiangyao; Wang, Wenjie; Liu, Xinyu; Zhao, Mengxin

doi:10.3390/ijerph182010869

Cited by 4 publications

(2 citation statements)

References 70 publications

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“…In addition, if the present model is used, the concept of spatial dispersion can be used to observe the interactions between independent variables. In the former case, after analyzing the influence of emission factors of various industries on urban PM 2.5 pollution concentrations, buildings and traffic were identified as the main influencing factors [47]. In addition, the results of a latter study, which used this model to analyze the influencing factors of lead (Pb) in particulate matter in residential areas, showed that automobile exhaust, human daily life activities, and industrial emissions interacted to produce the effects [48].…”

Section: Geodetectormentioning

confidence: 99%

Spatial-Temporal Characteristics and Influencing Factors of Particulate Matter: Geodetector Approach

Mun

Jung

2022

Land

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In 2019, South Korea’s Framework Act on The Management of Disasters and Safety was revised to include respirable particulate matter as a social disaster. Urban air pollution, especially particulate matter pollution, has been a serious threat to socioeconomic development and public health. In order to address this problem, strong climate crisis response strategies and policies to improve urban air quality are necessary. Therefore, it is of great importance to assess the frequency of urban air pollution occurrence and its influencing factors. The objective of this study is to develop consistent methodologies for the construction of an index system and for assessing the influencing factors of urban particulate matter pollution based on population, social welfare, land use, environmental, transportation, and economic governance considerations. We applied the local indicators of spatial association and geographical detector methods, and 35 influencing factors were selected to assess their influence on urban air pollution occurrence in 229 cities and counties in South Korea. The results indicated the spatial pattern of the particulate matter concentration in these locations showed strong spatial correlation, and it was confirmed that there was a difference in distribution according to the season. As a result of the analysis of influencing factors, it was found that environment and land use characteristics were the main influencing factors for PM10 and PM2.5. The explanatory power between the two influencing factors of particulate matter was greater than that of a single influencing factor. In addition, most influencing factors resulted in both positive and negative effects on urban fine particulate matter pollution. The interaction relationship of all factors showed a strong action effect in the case of both PM10 and PM2.5, so it was confirmed that all influencing factors were interdependent. In particular, the findings proved that combining the two factors would have a more pronounced effect on particulate matter than when they were independent. We confirmed the significant results for the factors affecting particulate matter. This study offers suggestions on reducing urban air pollution occurrence that can be used to provide a basis and reference for the government to form policies on urban air pollution control in cities and counties.

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Section: Geodetectormentioning

confidence: 99%

Spatial-Temporal Characteristics and Influencing Factors of Particulate Matter: Geodetector Approach

Mun

Jung

2022

Land

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“…In addition, anthropogenic emissions (AE S ) from industrial, transportation, agricultural, and residential sectors also have a significant influence on PM2.5 concentrations. Anthropogenic precursors, such as sulfur dioxide, nitrogen oxide, volatile organic compounds, and ammonia, aggravate PM2.5 pollution by generating secondary aerosols through a series of photochemical reactions [ 12 , 16 , 24 , 25 ]. Numerous studies have also analyzed the impact of PM2.5 concentrations in terms of urbanization [ 26 , 27 , 28 ], urban form [ 29 , 30 , 31 , 32 ], and industrial structure [ 33 , 34 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Effects of Meteorological Conditions and Anthropogenic Emissions on PM2.5 Concentrations over Major Cities of the Yellow River Basin

Yao

Liu

Song

et al. 2022

IJERPH

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The mechanism behind PM2.5 pollution is complex, and its performance at multi-scales is still unclear. Based on PM2.5 monitoring data collected from 2015 to 2021, we used the GeoDetector model to assess the multi-scale effects of meteorological conditions and anthropogenic emissions, as well as their interactions with PM2.5 concentrations in major cities in the Yellow River Basin (YRB). Our study confirms that PM2.5 concentrations in the YRB from 2015 to 2021 show an inter-annual and inter-season decreasing trend and that PM2.5 concentrations varied more significantly in winter. The inter-month variation of PM2.5 concentrations shows a sinusoidal pattern from 2015 to 2021, with the highest concentrations in January and December and the lowest from June to August. The PM2.5 concentrations for major cities in the middle and downstream regions of the YRB are higher than in the upper areas, with high spatial distribution in the east and low spatial distribution in the west. Anthropogenic emissions and meteorological conditions have similar inter-annual effects, while air pressure and temperature are the two main drivers across the whole basin. At the sub-basin scale, meteorological conditions have stronger inter-annual effects on PM2.5 concentrations, of which temperature is the dominant impact factor. Wind speed has a significant effect on PM2.5 concentrations across the four seasons in the downstream region and has the strongest effect in winter. Primary PM2.5 and ammonia are the two main emission factors. Interactions between the factors significantly enhanced the PM2.5 concentrations. The interaction between ammonia and other emissions plays a dominant role at the whole and sub-basin scales in summer, while the interaction between meteorological factors plays a dominant role at the whole-basin scale in winter. Our study not only provides cases and references for the development of PM2.5 pollution prevention and control policies in YRB but can also shed light on similar regions in China as well as in other regions of the world.

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Inequalities in PM2.5 and SO2 Exposure Health Risks in Terms of Emissions in China, 2013–2017

Cui

Wang

et al. 2022

Atmosphere

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Exploring the health risks of diseases attributed to PM2.5 and SO2 exposure and analyzing the differences in their distribution over emissions can provide useful insights for decision-makers to reduce premature mortality due to PM2.5 and SO2 exposure. This study used exposure-response functions, health risk inequality curve (HRICU, based on Lorenz curve), and the health risk inequality coefficient (HRICO, based on Gini coefficient) to estimate population health risks of PM2.5 and SO2 exposure in China from 2013 to 2017 based on a full-coverage, high-precision PM2.5 and SO2 concentration and emission dataset. The inequality in the distribution of premature mortality was explored in terms of pollutant emissions. The results showed that (1) premature mortalities from cardiovascular disease (CVD) and respiratory disease (RD) due to PM2.5 and SO2 exposure decreased by 21% and 54%, respectively, from 2013 to 2017. (2) At a national scale, the HRICO value for the distribution of PM2.5 and SO2 health risks on emissions were lower than 0.10 and 0.20, respectively. (3) More than 20% of provinces had HRICO values above 0.1 for PM2.5 or SO2. The provinces near the national borders generally had higher HRICO for PM2.5, while the province with the most severe inequity in the distribution of SO2 health risks on emissions appeared in Xinjiang Uygur Autonomous Region, Ningxia Hui Autonomous Region, and Hainan Province.

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Effects of Anthropogenic Emissions from Different Sectors on PM2.5 Concentrations in Chinese Cities

Cited by 4 publications

References 70 publications

Spatial-Temporal Characteristics and Influencing Factors of Particulate Matter: Geodetector Approach

Spatial-Temporal Characteristics and Influencing Factors of Particulate Matter: Geodetector Approach

Multi-Scale Effects of Meteorological Conditions and Anthropogenic Emissions on PM2.5 Concentrations over Major Cities of the Yellow River Basin

Inequalities in PM2.5 and SO2 Exposure Health Risks in Terms of Emissions in China, 2013–2017

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