Use of a Land Use Regression Model Methodology for the Estimation of Individual Long-Term PM2.5 Exposure Profiles of Urban Residents in Jiujiang City, China
The purpose of this study was to establish a method able to accurately estimate the long-term exposure levels of individuals to fine particulate matter (PM2.5) in Jiujiang City (China) by constructing land use regression (LUR) models. Subsequently, the accuracy of models was further verified. PM2.5 concentrations were continuously collected daily from seven monitoring stations for the construction of daily LUR models from September 1 to 14, 2023. The constructed models used PM2.5 concentrations as the dependent variable, while land use, elevation, population density and road length were used as the predictive variables. Subsequently, twenty volunteers were invited to participate, with their daily PM2.5 exposure estimated based on their work address and home address, allowing their average exposure levels to be calculated. Furthermore, volunteers wore portable PM2.5 detectors continuously for a 14-day period and the average measured PM2.5 level was used as a comparative standard. Results showed that the adjusted R2 values for the 14 daily models ranged from 0.85 to 0.94, with the R2 values generated from leave-one-out-cross-validation tests all greater than 0.61, indicating good prediction accuracy. No significant differences were observed in the measurement accuracy of the LUR modeling method and measurements using a portable PM2.5 detector (p > 0.05). This study aimed to develop a novel method for the accurate and convenient measurement of individual long-term PM2.5 exposure levels for epidemiological studies in urban environments comparable to that of Jiujiang city.
References
[1]
Alan, D., Payam, D., Marta, C., Gustavo, A., Lluis, B., Maria, F. et al. (2024) Development of Land Use Regression, Dispersion, and Hybrid Models for Prediction of Outdoor Air Pollution Exposure in Barcelona. Science of the Total Environment, 954, Article 176632. https://doi.org/10.1016/j.scitotenv.2024.176632
[2]
Azmi, W. N. F. W., Pillai, T. R., Latif, M. T., Koshy, S., & Shaharudin, R. (2023). Application of Land Use Regression Model to Assess Outdoor Air Pollution Exposure: A Review. Environmental Advances, 11, Article 100353. https://doi.org/10.1016/j.envadv.2023.100353
[3]
Bhavsar, N. A., Jowers, K., Yang, L. Z., Guha, S., Lin, X., Peskoe, S. et al. (2024). The Association between Long-Term PM2.5 Exposure and Risk for Pancreatic Cancer: An Application of Social Informatics. American Journal of Epidemiology, 2024, kwae271. https://doi.org/10.1093/aje/kwae271
[4]
Briggs, D. J., Collins, S., Elliott, P., Fischer, P., Kingham, S., Lebret, E. et al. (1997). Mapping Urban Air Pollution Using GIS: A Regression-Based Approach. International Journal of Geographical Information Science, 11, 699-718. https://doi.org/10.1080/136588197242158
[5]
Chen, C., Huang, K., Chen, C., Chang, Y., Li, H., Wang, T. et al. (2024). The Role of PM2.5 Exposure in Lung Cancer: Mechanisms, Genetic Factors, and Clinical Implications. EMBO Molecular Medicine, 17, 31-40. https://doi.org/10.1038/s44321-024-00175-2
[6]
De, H. K., Wang, M., Adam, M., Badaloni, C., Beelen, R., Birk, M. et al. (2013) Development of Land Use Regression Models for Particle Composition in Twenty Study Areas in Europe. Environmental Science & Technology, 47, 5778-5786. https://doi.org/10.1021/es400156t
[7]
Ebrahimi, A. A., Baziar, M., & Zakeri, H. R. (2024). Investigating the Impact of Urban-Environmental Factors on Air Pollutants: A Land Use Regression Model Approach and Health Risk Assessment. Environmental Geochemistry and Health, 46, Article No. 313. https://doi.org/10.1007/s10653-024-02103-2
[8]
Gayraud, L., Mortamais, M., Schweitzer, C., de Hoogh, K., Cougnard-Grégoire, A., Korobelnik, J. et al. (2024). Ambient Air Pollution Exposure and Incidence of Cataract Surgery: The Prospective 3 City-Alienor Study. Acta Ophthalmologica, 2024, 1-8. https://doi.org/10.1111/aos.16790
[9]
Habermann, M., & Gouveia, N. (2013). Aplicação de regressão baseada no uso do solo para predizer a concentração de material particulado inalável no município de São Paulo, Brasil. Engenharia Sanitaria e Ambiental, 17, 155-162. https://doi.org/10.1590/s1413-41522012000200004
[10]
Ma, X., Zou, B., Deng, J., Gao, J., Longley, I., Xiao, S. et al. (2024). A Comprehensive Review of the Development of Land Use Regression Approaches for Modeling Spatiotemporal Variations of Ambient Air Pollution: A Perspective from 2011 to 2023. Environment International, 183, Article 108430. https://doi.org/10.1016/j.envint.2024.108430
[11]
Moon, J., Kim, E., Jang, H., Song, I., Kwon, D., Kang, C. et al. (2024). Long-Term Exposure to PM2.5 and Mortality: A National Health Insurance Cohort Study. International Journal of Epidemiology, 53, dyae140. https://doi.org/10.1093/ije/dyae140
[12]
Nguyen Thi Khanh, H., Rigau-Sabadell, M., Khomenko, S., Pereira Barboza, E., Cirach, M., Duarte-Salles, T. et al. (2025). Ambient Air Pollution, Urban Green Space and Childhood Overweight and Obesity: A Health Impact Assessment for Barcelona, Spain. Environmental Research, 264, Article 120306. https://doi.org/10.1016/j.envres.2024.120306
[13]
Rhee, T., Ji, Y., Yang, S., Lee, H., Park, J., Kim, H. et al. (2024). Combined Effect of Air Pollution and Genetic Risk on Incident Cardiovascular Diseases. Journal of the American Heart Association, 13, e033497. https://doi.org/10.1161/jaha.123.033497
[14]
Rob, B., Gerard, H., Danielle, V., Marloes, E., Konstantina, D., Xanthi, P. et al. (2013) Development of NO2 and NOx Land Use Regression Models for Estimating Air Pollution Exposure in 36 Study Areas in Europe—The ESCAPE Project. Atmospheric Environment, 72, 10-23. https://doi.org/10.1016/j.atmosenv.2013.02.037
[15]
Wang, M., Beelen, R., Basagana, X., Becker, T., Cesaroni, G., de Hoogh, K. et al. (2013). Evaluation of Land Use Regression Models for NO2 and Particulate Matter in 20 European Study Areas: The ESCAPE Project. Environmental Science & Technology, 47, 4357-4364. https://doi.org/10.1021/es305129t
