Identifying interactions among air pollutant emissions on diabetes prevalence in Northeast China using a complex network
© 2023. The Author(s) under exclusive licence to International Society of Biometeorology..
BACKGROUND: Low air quality related to ambient air pollution is the largest environmental risk to health worldwide. Interactions between air pollution emissions may affect associations between air pollution exposure and chronic diseases. Therefore, this study aimed to quantify interactions among air pollution emissions and assess their effects on the association between air pollution and diabetes.
METHODS: After constructing long-term emission networks for six air pollutants based on data collected from routine monitoring stations in Northeast China, a mutual information network was used to quantify interactions among air pollution emissions. Multiple linear regression analysis was then used to explore the influence of emission interactions on the association between air pollution exposure and the prevalence of diabetes based on data reported from the Northeast Natural Cohort Study in China.
RESULTS: Complex network analysis detected three major emission sources in Northeast China located in Shenyang and Changchun. The effects of particulate matter (PM2.5 and PM10) and ground-level ozone (O3) emissions were limited to certain communities but could spread to other communities through emissions in Inner Mongolia. Emissions of sulfur dioxide (SO2), nitrogen dioxide (NO2), and carbon monoxide (CO) significantly influenced other communities. These results indicated that air pollutants in different geographic areas can interact directly or indirectly. Adjusting for interactions between emissions changed associations between air pollution emissions and diabetes prevalence, especially for PM2.5, NO2, and CO.
CONCLUSIONS: Complex network analysis is suitable for quantifying interactions among air pollution emissions and suggests that the effects of PM2.5 and NO2 emissions on health outcomes may have been overestimated in previous population studies while those of CO may have been underestimated. Further studies examining associations between air pollution and chronic diseases should consider controlling for the effects of interactions among pollution emissions.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:68 |
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| Enthalten in: |
International journal of biometeorology - 68(2024), 2 vom: 17. Jan., Seite 393-400 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zhang, Hehua [VerfasserIn] |
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| Links: |
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| Themen: |
0UZA3422Q4 |
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| Anmerkungen: |
Date Completed 18.01.2024 Date Revised 18.01.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1007/s00484-023-02597-y |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM366014854 |
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| 245 | 1 | 0 | |a Identifying interactions among air pollutant emissions on diabetes prevalence in Northeast China using a complex network |
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| 520 | |a © 2023. The Author(s) under exclusive licence to International Society of Biometeorology. | ||
| 520 | |a BACKGROUND: Low air quality related to ambient air pollution is the largest environmental risk to health worldwide. Interactions between air pollution emissions may affect associations between air pollution exposure and chronic diseases. Therefore, this study aimed to quantify interactions among air pollution emissions and assess their effects on the association between air pollution and diabetes | ||
| 520 | |a METHODS: After constructing long-term emission networks for six air pollutants based on data collected from routine monitoring stations in Northeast China, a mutual information network was used to quantify interactions among air pollution emissions. Multiple linear regression analysis was then used to explore the influence of emission interactions on the association between air pollution exposure and the prevalence of diabetes based on data reported from the Northeast Natural Cohort Study in China | ||
| 520 | |a RESULTS: Complex network analysis detected three major emission sources in Northeast China located in Shenyang and Changchun. The effects of particulate matter (PM2.5 and PM10) and ground-level ozone (O3) emissions were limited to certain communities but could spread to other communities through emissions in Inner Mongolia. Emissions of sulfur dioxide (SO2), nitrogen dioxide (NO2), and carbon monoxide (CO) significantly influenced other communities. These results indicated that air pollutants in different geographic areas can interact directly or indirectly. Adjusting for interactions between emissions changed associations between air pollution emissions and diabetes prevalence, especially for PM2.5, NO2, and CO | ||
| 520 | |a CONCLUSIONS: Complex network analysis is suitable for quantifying interactions among air pollution emissions and suggests that the effects of PM2.5 and NO2 emissions on health outcomes may have been overestimated in previous population studies while those of CO may have been underestimated. Further studies examining associations between air pollution and chronic diseases should consider controlling for the effects of interactions among pollution emissions | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Air pollution | |
| 650 | 4 | |a Chronic disease | |
| 650 | 4 | |a Complex network analysis | |
| 650 | 4 | |a Diabetes | |
| 650 | 4 | |a Emission range | |
| 650 | 7 | |a Air Pollutants |2 NLM | |
| 650 | 7 | |a Nitrogen Dioxide |2 NLM | |
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| 650 | 7 | |a Ozone |2 NLM | |
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| 650 | 7 | |a Sulfur Dioxide |2 NLM | |
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| 700 | 1 | |a Zhao, Zhiying |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Zhuo |e verfasserin |4 aut | |
| 700 | 1 | |a Xia, Yang |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Yuhong |e verfasserin |4 aut | |
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