Bridge between Precision PM2.5 Exposure Study and Healthy Life of Citizens: A Mini-review

Yazhu Wang; Chang Gao; Junrui Zhao

doi:10.54097/vpwr8309

Authors

Yazhu Wang
Chang Gao
Junrui Zhao

DOI:

https://doi.org/10.54097/vpwr8309

Keywords:

PM2.5, Precise Exposure, Indoor, Group Differences

Abstract

Air pollution poses a significant health risk to residents. To better address the health and well-being needs of the population, more in-depth research on air pollution is essential. This article focuses on key issues in refined PM2.5 exposure research, including the development of high-precision PM2.5 distribution maps, the estimation of indoor PM2.5 exposure, and the variations in health risks among different populations exposed to PM2.5. Refined PM2.5 exposure research primarily involves three factors: the creation of accurate PM2.5 maps, the assessment of indoor PM2.5 exposure, and the categorization of different population groups. By thoroughly analyzing existing research, this article proposes future research directions and optimization strategies, with the goal of providing valuable insights for furthering refined PM2.5 exposure studies.

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References

[1] P.F. Peng, X.B. Guo, F.M.H. Cheung, et al. The association between PM2.5 exposure and neurological disorders: A systematic review and meta-analysis. Science of the Total Environment, 2019, 655: 1240-1248.

[2] K.J. Maji, A.K. Dikshit, M. Arora, et al. Estimating premature mortality attributable to PM2.5 exposure and benefit of air pollution control policies in China for 2020. Science of the Total Environment, 2018, 612: 683-693.

[3] J. Song, C.L. Li, M. Liu, et al. Spatiotemporal Distribution Patterns and Exposure Risks of PM2.5 Pollution in China. Remote Sensing, 2022, 14(13): 3173.

[4] C.G. Dong, J.Y. Li, Y. Qi. Decomposing PM2.5 air pollution rebounds in Northern China before COVID-19. Environmental Science and Pollution Research International, 2022, 29: 28688 - 28699.

[5] T.T. Li, Y.M. Guo, Y. Liu, et al. Estimating mortality burden attributable to short-term PM2.5 exposure: A national observational study in China. Environment International, 2019, 215: 245 - 251.

[6] K. Hu, K. Keenan, J.M. Hale, et al. A longitudinal analysis of PM2.5 exposure and multimorbidity clusters and accumulation among adults aged 45-85 in China. PLOS Global Public Health, 2022, 2(6): e0000520.

[7] J.F. Lin, H. Zheng, P. Xia, et al. Long-term ambient PM2.5 exposure associated with cardiovascular risk factors in Chinese less educated population. BMC Public Health, 2021, 21(1): 2241.

[8] J. Zhang, X.Y. Wang, M.F. Yan, et al. Sex Differences in Cardiovascular Risk Associated with Long-Term PM2.5 Exposure: A Systematic Review and Meta-Analysis of Cohort Studies. Frontiers in Public Health, 2022, 10: 802167.

[9] M. Mannan, S.G. Al-Ghamdi. Indoor Air Quality in Buildings: A Comprehensive Review on the Factors Influencing Air Pollution in Residential and Commercial Structure. International Journal of Environmental Research and Public Health, 2021, 18(6): 3276.

[10] N. Cressie. The origins of kriging. Mathematical Geology, 1990, 22: 239-252.

[11] G.Y. Lu, D.W. Wong. An adaptive inverse-distance weighting spatial interpolation technique. Computers & Geosciences, 2008, 34(9): 1044-105.

[12] M.A. Oliver, R. Webster. Kriging: a method of interpolation for geographical information systems. International Journal of Geographical Information Systems, 1990, 4(3): 313-332.

[13] H.B. Dai, G.Q. Huang, J.J. Wang, et al. Spatio-Temporal Characteristics of PM2.5 Concentrations in China Based on Multiple Sources of Data and LUR-GBM during 2016-2021. International Journal of Environmental Research and Public Health, 2022, 19(10): 6292.

[14] K.Y. Gu, Y. Zhou, H. Sun, et al. Spatial distribution and determinants of PM2.5 in China’s cities: fresh evidence from IDW and GWR. Environmental Monitoring and Assessment, 2020, 193(1): 15.

[15] J. Ma, Y.X. Ding, V.J.L. Gan, et al. Spatiotemporal Prediction of PM2.5 Concentrations at Different Time Granularities Using IDW-BLSTM. IEEE Access, 2019, 7: 107897-107907.

[16] J.Y. He, G. Christakos, P. Jankowski. Comparative Performance of the LUR, ANN, and BME Techniques in the Multiscale Spatiotemporal Mapping of PM2.5 Concentrations in North China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(6): 1734-1747.

[17] L. Xiao, G. Christakos, J.Y. He, et al. Space-Time Ground-Level PM2.5 Distribution at the Yangtze River Delta: A Comparison of Kriging, LUR, and Combined BME-LUR Techniques. Journal of Environmental Informatics, 2020, 36(1): 33-42.

[18] W.H. He, H. Meng, J. Han, et al. Spatiotemporal PM2.5 estimations in China from 2015 to 2020 using an improved gradient boosting decision tree. Chemosphere, 2022, 296: 134003.

[19] C.H. Huang, J.L. Hu, T. Xue, et al. High-Resolution Spatiotemporal Modeling for Ambient PM2.5 Exposure Assessment in China from 2013 to 2019. Environmental Science & Technology, 2021, 55(3): 2152-2162.

[20] N. Bruce, R. Perez-Padilla, R. Albalak. Indoor air pollution in developing countries: a major environmental and public health challenge. Bulletin of the World Health Organization, 2020, 78(9): 1078-1092.

[21] V.V. Tran, D. Park, Y.C. Lee. Indoor Air Pollution, Related Human Diseases, and Recent Trends in the Control and Improvement of Indoor Air Quality. International Journal of Environmental Research and Public Health, 2020, 17(8): 2927.

[22] W. Hu, G.S. Downward, B. Reiss, et al. Personal and Indoor PM2.5 Exposure from Burning Solid Fuels in Vented and Unvented Stoves in a Rural Region of China with a High Incidence of Lung Cancer. Environmental Science & Technology, 2014, 48(15): 8456-8464.

[23] Z.J. Shao, X.J. Yin, J. Bi, et al. Spatiotemporal Variations of Indoor PM2.5 Concentrations in Nanjing, China. International Journal of Environmental Research and Public Health, 2019, 16(1): 144.

[24] Z.Q. Wang, W.W. Delp, B.C. Singer. Performance of low-cost indoor air quality monitors for PM2.5 and PM10 from residential sources. Building and Environment, 2020, 171: 106654.

[25] E. Diapouli, A. Chaloulakou, P. Koutrakis. Estimating the concentration of indoor particles of outdoor origin: A review. Journal of the Air & Waste Management Association, 2013, 63(10): 1113-1129.

[26] P. Gemenetzis, P. Moussas, A. Arditsoglou, et al. Mass concentration and elemental composition of indoor PM2.5 and PM10 in University rooms in Thessaloniki, northern Greece. Atmospheric Environment, 2006, 40 (17): 3195-3206.

[27] C. Chen, B. Zhao. Review of relationship between indoor and outdoor particles: I/O ratio, infiltration factor and penetration factor. Atmospheric Environment, 2011, 45(2): 275-288.

[28] S.E. Chatoutsidou, J. Ondracek, O. Tesar, et al. Indoor/outdoor particulate matter number and mass concentration in modern offices. Building and Environment, 2015, 92: 462-474.

[29] L.A. Wallace, T.K. Zhao, N.E. Klepeis. Indoor contribution to PM2.5 exposure using all PurpleAir sites in Washington, Oregon, and California. Indoor Air, 2022, 32(9): e13105.

[30] W.J. Ji, B. Zhao. Contribution of outdoor-originating particles, indoor-emitted particles and indoor secondary organic aerosol (SOA) to residential indoor PM2.5 concentration: A model-based estimation. Building and Environment, 2015, 90: 196-205.

[31] H. Wang, J.W. Li, Z.Q. Gao, et al. High-Spatial-Resolution Population Exposure to PM2.5 Pollution Based on Multi-Satellite Retrievals: A Case Study of Seasonal Variation in the Yangtze River Delta, China in 2013. Remote Sensing, 2019, 11(23): 2724.

[32] Y.M. Song, B. Huang, Q.Q. He, et al. Dynamic assessment of PM2.5 exposure and health risk using remote sensing and geo-spatial big data. Environmental Pollution, 2019, 253: 288-296.

[33] X. Li, T. Yang, Z.T. Zeng, et al. Underestimated or overestimated? Dynamic assessment of hourly PM2.5 exposure in the metropolitan area based on heatmap and micro-air monitoring stations. Science of the Total Environment, 2021, 779: 146283.