Identification and Time Series Analysis of PM<sub>2.5</sub> and O<sub>3</sub> Associated Health Risk Prevention and Control Areas
Air pollution of PM<sub>2.5</sub> and O<sub>3</sub> is a global health concern. Traditional approaches for identifying air pollution control areas mainly relied on pollutant concentrations, neglecting population distribution and exposure. This study proposes a method to divid...
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MDPI AG
2025-04-01
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| Series: | Toxics |
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| Online Access: | https://www.mdpi.com/2305-6304/13/5/356 |
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| author | Xinyu Huang Bin Zou Shenxin Li |
| author_facet | Xinyu Huang Bin Zou Shenxin Li |
| author_sort | Xinyu Huang |
| collection | DOAJ |
| description | Air pollution of PM<sub>2.5</sub> and O<sub>3</sub> is a global health concern. Traditional approaches for identifying air pollution control areas mainly relied on pollutant concentrations, neglecting population distribution and exposure. This study proposes a method to divide these areas from a health risk perspective, comparing their objectivity and rationality with the government-defined key regions. The results show that for PM<sub>2.5</sub>, the health risk population and average risk rates in the prevention and control areas were 0.993 million (0.1286%), 1.030 million (0.1283%), and 1.023 million (0.1202%) in 2010, 2015, and 2020, significantly higher than in the key areas: 0.778 million (0.1252%), 0.834 million (0.1278%), and 0.825 million (0.1212%). Similarly, for O<sub>3</sub>, the figures in the prevention and control areas were 0.096 million (0.01228%), 0.095 million (0.01243%), and 0.110 million (0.01316%), also higher than in the key areas: 0.0757 million (0.01218%), 0.078 million (0.01189%), and 0.090 million (0.01315%). Additionally, the Gini coefficients for PM<sub>2.5</sub>, O<sub>3</sub>, and overall health risks in the prevention and control areas were lower (0.182, 0.203, 0.284) compared to those in the key areas (0.207, 0.216, 0.292). This study provides a method for defining air pollution control regions based on health risks, offering significant insights for pollution zoning and prevention strategies |
| format | Article |
| id | doaj-art-ad292563608f47069f5e72bc0baf73cb |
| institution | OA Journals |
| issn | 2305-6304 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| series | Toxics |
| spelling | doaj-art-ad292563608f47069f5e72bc0baf73cb2025-08-20T02:34:02ZengMDPI AGToxics2305-63042025-04-0113535610.3390/toxics13050356Identification and Time Series Analysis of PM<sub>2.5</sub> and O<sub>3</sub> Associated Health Risk Prevention and Control AreasXinyu Huang0Bin Zou1Shenxin Li2School of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaSchool of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaSchool of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaAir pollution of PM<sub>2.5</sub> and O<sub>3</sub> is a global health concern. Traditional approaches for identifying air pollution control areas mainly relied on pollutant concentrations, neglecting population distribution and exposure. This study proposes a method to divide these areas from a health risk perspective, comparing their objectivity and rationality with the government-defined key regions. The results show that for PM<sub>2.5</sub>, the health risk population and average risk rates in the prevention and control areas were 0.993 million (0.1286%), 1.030 million (0.1283%), and 1.023 million (0.1202%) in 2010, 2015, and 2020, significantly higher than in the key areas: 0.778 million (0.1252%), 0.834 million (0.1278%), and 0.825 million (0.1212%). Similarly, for O<sub>3</sub>, the figures in the prevention and control areas were 0.096 million (0.01228%), 0.095 million (0.01243%), and 0.110 million (0.01316%), also higher than in the key areas: 0.0757 million (0.01218%), 0.078 million (0.01189%), and 0.090 million (0.01315%). Additionally, the Gini coefficients for PM<sub>2.5</sub>, O<sub>3</sub>, and overall health risks in the prevention and control areas were lower (0.182, 0.203, 0.284) compared to those in the key areas (0.207, 0.216, 0.292). This study provides a method for defining air pollution control regions based on health risks, offering significant insights for pollution zoning and prevention strategieshttps://www.mdpi.com/2305-6304/13/5/356air pollutionatmospheric remote sensinghealth risk assessmenthealth equityhealth GIS |
| spellingShingle | Xinyu Huang Bin Zou Shenxin Li Identification and Time Series Analysis of PM<sub>2.5</sub> and O<sub>3</sub> Associated Health Risk Prevention and Control Areas Toxics air pollution atmospheric remote sensing health risk assessment health equity health GIS |
| title | Identification and Time Series Analysis of PM<sub>2.5</sub> and O<sub>3</sub> Associated Health Risk Prevention and Control Areas |
| title_full | Identification and Time Series Analysis of PM<sub>2.5</sub> and O<sub>3</sub> Associated Health Risk Prevention and Control Areas |
| title_fullStr | Identification and Time Series Analysis of PM<sub>2.5</sub> and O<sub>3</sub> Associated Health Risk Prevention and Control Areas |
| title_full_unstemmed | Identification and Time Series Analysis of PM<sub>2.5</sub> and O<sub>3</sub> Associated Health Risk Prevention and Control Areas |
| title_short | Identification and Time Series Analysis of PM<sub>2.5</sub> and O<sub>3</sub> Associated Health Risk Prevention and Control Areas |
| title_sort | identification and time series analysis of pm sub 2 5 sub and o sub 3 sub associated health risk prevention and control areas |
| topic | air pollution atmospheric remote sensing health risk assessment health equity health GIS |
| url | https://www.mdpi.com/2305-6304/13/5/356 |
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