Abstract
As air pollution becomes more serious in China, it is critical to study its influencing factors for targeted environmental governance. Existing researchers have conducted various studies on the factors of PM2.5 (fine particulate matter with a diameter less than or equal to 2.5 microns) pollution. However, these studies mostly conducted analyses focused on macroscopic factors and lacked an impact analysis of specific entity distributions on PM2.5 pollution. Furthermore, most existing studies used ordinary regression models that ignored the spatial heterogeneity of influence of various factors on pollution, leading to biased results. To address these issues, focusing on air quality in heavily polluted city (Weifang City), this study aims to measure the influence of spatial differentiation of the impacts of four roughly classified POIs, namely, industry, restaurants, scenic spots, and parking lots, on PM2.5 pollution quantitatively by using a geographically weighted regression (GWR) model (spatial varying-coefficient regression model). The results indicate that the spatial distribution of effects of industry, restaurant and parking lot POIs on PM2.5 concentrations in Weifang are similar. The impact of all four POIs on PM2.5 is spatially nonstationary and have certain spatial trends, parking lots have the greatest influence on PM2.5 pollution. Based on the findings, pollution prevention and control measures are suggested to be designed based on the actual situation. For instance, some counties in Weifang should be encouraged to develop tertiary industries dominated by tourism. This research investigated the spatial impacts of the specific entity distributions on air pollution and provided targeted advice based on findings, which can contribute to policy-making aimed at air pollution mitigation.
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Data Availability
The datasets generated and/or analyzed during the current study are not publicly available but can be obtained from the corresponding author on reasonable request.
References
Brunsdon, C., Fotheringham, A. S., & Charlton, M. E. (1996). Geographically weighted regression: A method for exploring spatial nonstationarity. Geographical Analysis, 28(4), 281–298.
Brunsdon, C., Fotheringham, A. S., & Charlton, M. E. (1999). Some notes on parametric significance tests for geographically weighted regression. Journal of Regional Science, 39(3), 497–524.
Chen, L., Gao, S., Zhang, H., Sun, Y., Ma, Z., Vedal, S., Mao, J., & Bai, Z. (2018). Spatiotemporal modeling of PM2.5 concentrations at the national scale combining land use regression and Bayesian maximum entropy in China. Environment International, 116, 300–307.
Cheng, Z. H., Li, L. S., & Liu, J. (2017). Identifying the spatial effects and driving factors of urban PM2.5 pollution in China. Ecological Indicators, 82, 61–75.
Fotheringham, A. S., Brunsdon, C., & Charlton, M. E. (2002). Geographically weighted regression. John Wiley & Sons.
He, J. H., Ding, S., & Liu, D. F. (2019). Exploring the spatiotemporal pattern of PM2.5 distribution and its determinants in Chinese cities based on a multilevel analysis approach. Science of the Total Environment, 659, 1513–1525.
He, Q. Q., & Huang, B. (2018). Satellite-based mapping of daily high-resolution ground PM2.5 in China via space-time regression modeling. Remote Sensing of Environment, 206, 72–83.
Jiang, P., Yang, J., Huang, C., & Liu, H. (2018). The contribution of socioeconomic factors to PM2.5 pollution in urban China. Environmental Pollution, 233, 977–985.
Li, C. M., Dai, Z. X., Yang, L. N., & Ma, Z. (2019). Spatiotemporal characteristics of air quality across Weifang from 2014–2018. International Journal of Environmental Research and Public Health, 16(17), 3122.
Li, C. M., Liu, L., Dai, Z. X., & Liu, X. (2020a). Different sourcing point of interest matching method considering multiple constraints. ISPRS International Journal Geo-Information, 9(4), 214.
Li, C. M., Zhang, K., Dai, Z. X., Ma, Z., & Liu, X. (2020). Investigation of the impact of land-use distribution on PM2.5 in Weifang seasonal variations. International Journal Environmental Research Public Health, 17(14), 5135.
Li, G. D., Fang, C. L., Wang, S. J., & Sun, S. (2016). The effect of economic growth, urbanization, and industrialization on fine particulate matter (PM2.5) concentrations in China. Environmental Science Technology, 50(21), 11452–11459.
Li, X., & Zhang, W. (2018). Don’t let the parking lot become a new source of pollution. China Environmental News. http://env.people.com.cn/n1/2018/0326/c1010-29888326.html. Accessed 26 Mar 2018.
Li, Y. X., Dai, Z. X., & Liu, X. L. (2018). Analysis of spatial-temporal characteristics of the PM2.5 concentrations in Weifang City, China. Sustainability, 10, 2906.
Lin, Y. F., Yuan, X. Y., Zhai, T. L., & Wang, J. (2020). Effects of land-use patterns on PM2.5 in China’s developed coastal region: Exploration and solutions. Science Total Environment, 703, 135602.
Liu, X. Q., & Wang, D. (2016). Spatiotemporal evolution of urban air quality and socioeconomic driving forces in China. Journal of Geographical Sciences, 26, 1533–1549.
Lorimer, G. S. (1986). The kernel method for air quality modelling. Mathematical foundation. Atmospheric Environment, 20(7), 1447–1452.
Łowicki, D. (2019). Landscape pattern as an indicator of urban air pollution of particulate matter in Poland. Ecological Indicators, 97, 17–24.
Lu, D. B., Xu, J. H., Yang, D. Y., & Zhao, J. (2017). Spatio-temporal variation and influence factors of PM2.5 concentrations in China from 1998 to 2014. Atmospheric Pollution Research, 8(6), 1151–1159.
Lu, D. B., Xu, J. H., Yue, W. Z., Mao, W., Yang, D., & Wang, J. (2020). Response of PM2.5 pollution to land use in China. Journal of Cleaner Production, 244, 118741.
Lyu, W., Li, Y., Guan, D., Zhao, H., Zhang, Q., & Liu, Z. (2016). Driving forces of Chinese primary air pollution emissions: an index decomposition analysis. Journal of Cleaner Production, 133, 136–44.
Qin, Z. M., Tang, W. Y., Yin, Y. C., Mao, M., & Wang, B. (2017). Spatial distribution of PM2.5 emission from cooking sources in Chengdu based on internet big data method. Acta Scientiae Circumstantiae, 37(12), 66–73.
Torkmahalleh, M. A., Gorjinezhad, S., Keles, M., Ozturk, F., & Hopke, P. K. (2017). Size segregated PM and its chemical composition emitted from heated corn oil. Environmental Research, 154, 101.
Wang, S. J., Hua, G. H., & Li, C. F. (2019). Urbanization, air quality, and the panel threshold effect in China based on kernel density estimation. Emerging Markets Finance and Trade, 55(15), 3575–3590.
Wang, S. J., Zhou, C. S., Wang, Z. B., Feng, K., & Hubacek, K. (2017). The characteristics and drivers of fine particulate matter (PM2.5) distribution in China. Journal of Cleaner Production, 142(4), 1800–1809.
Wang, Z. B., & Fang, C. L. (2016). Spatial-temporal characteristics and determinants of PM2.5 in the Bohai Rim Urban Agglomeration. Chemosphere, 148, 148–162.
Wu, C. D., Chen, Y. C., Pan, W. C., Zeng, Y. T., Chen, M. J., Guo, Y. L., & Lung, S. C. (2017). Land-use regression with long-term satellite-based greenness index and culture-specific sources to model PM2.5 spatial-temporal variability. Environmental Pollution, 224, 148–57.
Yan, D., Lei, Y. L., Shi, Y. K., Zhu, Q., Li, L., & Zhang, Z. (2018). Evolution of the spatiotemporal pattern of PM2.5 concentrations in China—a case study from the Beijing-Tianjin-Hebei region. Atmospheric Environment, 183, 225–233.
Yang, D., Wang, X., Xu, J., Xu, C., Lu, D., Ye, C., Wang, Z., & Bai, L. (2018). Quantifying the influence of natural and socioeconomic factors and their interactive impact on PM2.5 pollution in China. Environmental Pollution, 241, 475–83.
Yang, H., Chen, W., & Liang, Z. (2017). Impact of land use on PM2.5 pollution in a representative city of middle China. International Journal of Environmental Research and Public Health, 14(5), 462.
Yang, Y., Lan, H. F., & Li, J. (2020). Spatial econometric analysis of the impact of socioeconomic factors on PM2.5 concentration in China’s Inland Cities: A case study from Chengdu plain economic zone. International Journal of Environmental Research and Public Health, 17(1), 74.
Zhan, D., Kwan, M. P., Zhang, W., Yu, X., Meng, B., & Liu, Q. (2018). The driving factors of air quality index in China. Journal of Cleaner Production, 197, 1342–51.
Zhang, H. L., Wang, Y. G., Hu, J. L., Ying, Q., & Hu, X. M. (2015). Relationships between meteorological parameters and criteria air pollutants in three megacities in China. Environmental Research, 140, 242–254.
Zhao, D. T., Chen, H., Li, X. D., & Ma, X. (2018). Air pollution and its influential factors in China’s hot spots. Journal of Cleaner Production, 185, 619–627.
Zhao, Z. W. (2019). Research on emission characteristics of particulate components from cooking fume [D]. Dalian Polytechnic University.
Funding
This research was funded by the National Natural Science Foundation of China under grant number 41907389 and 41871375, National Key Research and Development Program of China (2018YFB2100700).
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CL conceived the original idea for the study, and all coauthors conceived and designed the methodology. ZD, YZ, ZW, and JY conducted the processing and analysis of the data. CL, ZD, YZ, and ZM drafted the manuscript. All authors read and approved the final manuscript.
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Li, C., Zou, Y., Dai, Z. et al. The Impacts of POI Data on PM2.5: A Case Study of Weifang City in China. Appl. Spatial Analysis 15, 421–440 (2022). https://doi.org/10.1007/s12061-021-09408-0
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DOI: https://doi.org/10.1007/s12061-021-09408-0