Evaluating short-term air pollution-related mental health resilience using a directional network
Abstract Background Recent studies have shown that air pollution is among the most important triggers of mental health risks. However, little is known about the resilience strategies that reduce mental health risks among individuals exposed to pollution. The interconnections of protective and risk f...
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2025-08-01
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| Online Access: | https://doi.org/10.1186/s12889-025-24052-w |
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| author | Mengwei Song Quanjun Liu Qiulin Huang Yuxin Zhang Qing Wang |
| author_facet | Mengwei Song Quanjun Liu Qiulin Huang Yuxin Zhang Qing Wang |
| author_sort | Mengwei Song |
| collection | DOAJ |
| description | Abstract Background Recent studies have shown that air pollution is among the most important triggers of mental health risks. However, little is known about the resilience strategies that reduce mental health risks among individuals exposed to pollution. The interconnections of protective and risk factors further complicate the understanding of mental health resilience. Therefore, this study developed a resilience assessment framework accounting for factor interconnections to evaluate the short-term air pollution-related mental health resilience. Methods Aligned with mental health resilience theory, a Bayesian network model and network analysis were applied to construct a quantitative network of factor interconnections, analyzing 2019–2020 data for 24,261 older adults from the Shandong Aging and Mental Health Survey. On the basis of the network, mental health resilience was assessed via multiple-criteria decision-making method. The most influential pollutants, vulnerable populations and strategies to increase mental health resilience were then proposed. Results Air pollution exposure directly and indirectly affected mental health outcomes, defining a directional network where fine particulate matter emerged as the most influential pollutant. The mean index for short-term air pollution-associated mental health resilience was 0.52 ± 0.18. Resilience was significantly lower among females, adults aged 65–75, and less-educated individuals (mean index: 0.518 (95% CI: 0.515–0.521), 0.517 (95% CI: 0.515–0.520), and 0.516 (95% CI: 0.513–0.519), respectively). Memory lapses for sex/education disparities, irritability and feeling afraid for age disparities demonstrated the steepest disparities across demographic subgroups and bridge nodes—critical junctures that mediate resilience dynamics between populations. In terms of conditional probability, adjusting resilience factors proved more effective than merely reducing exposure. Conclusions This study has illuminated a directional network linking air pollution to mental health, with a specific focus on fine particulate matter. Thus, enhancing mental health resilience against air pollution requires a coordinated yet targeted approach, prioritizing interventions for fine particular matter exposure. Furthermore, policymakers should address resilience disparities by tailoring interventions to mitigate memory lapses (which exhibit sex/education gaps) and irritability and feeling afraid (associated with age-related vulnerabilities). |
| format | Article |
| id | doaj-art-89a59473596e4490bd67a08bddda0599 |
| institution | Kabale University |
| issn | 1471-2458 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Public Health |
| spelling | doaj-art-89a59473596e4490bd67a08bddda05992025-08-20T04:02:44ZengBMCBMC Public Health1471-24582025-08-0125111410.1186/s12889-025-24052-wEvaluating short-term air pollution-related mental health resilience using a directional networkMengwei Song0Quanjun Liu1Qiulin Huang2Yuxin Zhang3Qing Wang4Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong UniversityDepartment of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong UniversityDepartment of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong UniversityDepartment of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong UniversityDepartment of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong UniversityAbstract Background Recent studies have shown that air pollution is among the most important triggers of mental health risks. However, little is known about the resilience strategies that reduce mental health risks among individuals exposed to pollution. The interconnections of protective and risk factors further complicate the understanding of mental health resilience. Therefore, this study developed a resilience assessment framework accounting for factor interconnections to evaluate the short-term air pollution-related mental health resilience. Methods Aligned with mental health resilience theory, a Bayesian network model and network analysis were applied to construct a quantitative network of factor interconnections, analyzing 2019–2020 data for 24,261 older adults from the Shandong Aging and Mental Health Survey. On the basis of the network, mental health resilience was assessed via multiple-criteria decision-making method. The most influential pollutants, vulnerable populations and strategies to increase mental health resilience were then proposed. Results Air pollution exposure directly and indirectly affected mental health outcomes, defining a directional network where fine particulate matter emerged as the most influential pollutant. The mean index for short-term air pollution-associated mental health resilience was 0.52 ± 0.18. Resilience was significantly lower among females, adults aged 65–75, and less-educated individuals (mean index: 0.518 (95% CI: 0.515–0.521), 0.517 (95% CI: 0.515–0.520), and 0.516 (95% CI: 0.513–0.519), respectively). Memory lapses for sex/education disparities, irritability and feeling afraid for age disparities demonstrated the steepest disparities across demographic subgroups and bridge nodes—critical junctures that mediate resilience dynamics between populations. In terms of conditional probability, adjusting resilience factors proved more effective than merely reducing exposure. Conclusions This study has illuminated a directional network linking air pollution to mental health, with a specific focus on fine particulate matter. Thus, enhancing mental health resilience against air pollution requires a coordinated yet targeted approach, prioritizing interventions for fine particular matter exposure. Furthermore, policymakers should address resilience disparities by tailoring interventions to mitigate memory lapses (which exhibit sex/education gaps) and irritability and feeling afraid (associated with age-related vulnerabilities).https://doi.org/10.1186/s12889-025-24052-wAir pollutionMental healthResilienceDirectional networkChina |
| spellingShingle | Mengwei Song Quanjun Liu Qiulin Huang Yuxin Zhang Qing Wang Evaluating short-term air pollution-related mental health resilience using a directional network BMC Public Health Air pollution Mental health Resilience Directional network China |
| title | Evaluating short-term air pollution-related mental health resilience using a directional network |
| title_full | Evaluating short-term air pollution-related mental health resilience using a directional network |
| title_fullStr | Evaluating short-term air pollution-related mental health resilience using a directional network |
| title_full_unstemmed | Evaluating short-term air pollution-related mental health resilience using a directional network |
| title_short | Evaluating short-term air pollution-related mental health resilience using a directional network |
| title_sort | evaluating short term air pollution related mental health resilience using a directional network |
| topic | Air pollution Mental health Resilience Directional network China |
| url | https://doi.org/10.1186/s12889-025-24052-w |
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