Evolutionary relationship between the PM2.5 concentration and urbanization in the Yellow River Ecological and Economic Zone
Tóm tắt
The non-linear influence of urbanization on the PM2.5 concentration and the driving mechanism is an important research topic for ensuring high-quality development of eco-economic zones. Based on the urban panel data for the Yellow River Ecological and Economic Zone from 2010 to 2019, spatial analysis and other methods were used to investigate the temporal and spatial differences, spatial evolution characteristics, and spatial correlation of the PM2.5 concentration in the Yellow River Ecological and Economic Zone. In addition the environmental Kuznets curve (EKC) model was used to test the possible EKC relationships. The results showed that ① the PM2.5 concentration in the Yellow River Ecological and Economic Zone varied widely from 2010 to 2019, and the annual average PM2.5 concentration exhibited an overall fluctuating decreasing trend during the study period; ② the PM2.5 concentration in the Yellow River Ecological and Economic Zone exhibited a spatial distribution that was high in the southeastern region and low in the other regions; ③ a positive spatial correlation existed between the PM2.5 concentration and urbanization in the Yellow River Ecological and Economic Zone from 2010 to 2019; and ④ there was an N-type EKC relationship between the PM2.5 pollution and urbanization in the Yellow River Ecological and Economic Zone. This study provides a new perspective for exploring the relationship between the PM2.5 concentration and urbanization, an important reference for achieving environmental protection and sustainable urban development in the Yellow River Ecological and Economic Zone, and guidance for relevant departments to formulate PM2.5 reduction policies.
Từ khóa
#PM2.5 concentration #Urbanization #Evolution #Yellow river ecological and economic zoneTài liệu tham khảo
Bao and Zhang, 2020 R. Bao A.C. Zhang Does lockdown reduce air pollution? Evidence from 44 cities in northern China Sci. Total Environ. 731 2020 139052 Bao, R., Zhang, A.C.,2020. Does lockdown reduce air pollution? Evidence from 44 cities in northern China. Science of the Total Environment.731,139052.
Chen et al., 2018 M.X. Chen S.S. Guo D.D. Lu Characteristics and patterns of mobile population in Beijing-Tianjin-Hebei urban agglomeration in the context of new urbanization Adv. Geograph. Sci. 37 3 2018 363 372 Chen, M.X., Guo, S.S., Lu, D.D., 2018. Characteristics and patterns of mobile population in Beijing-Tianjin-Hebei urban agglomeration in the context of new urbanization. Advances in Geographical Sciences. 37(3), 363-372.
Cheng et al., 2016 Y. Cheng J.L. Ren Y.B. Chen Dynamic evolution of the spatial pattern of environmental regulation efficiency in China and its driving mechanism Geograph. Res. 35 1 2016 123 136 Cheng, Y., Ren, J.L., Chen, Y.B., et al., 2016. Dynamic evolution of the spatial pattern of environmental regulation efficiency in China and its driving mechanism. Geography Research. 35(1), 123-136.
Ge et al., 2022 Q.X. Ge Y. Liu H. Yang Analysis on spatial-temporal characteristics and driving factors of PM2.5 in Henan Province from 2015 to 2019 [J] Environ. Sci. J. Integr. Environ. Res. 43 4 2022 1697 1705 2022 Ge, Q. X.,Liu, Y.,Yang, H. et al.,2022. Analysis on Spatial-temporal Characteristics and Driving Factors of PM2.5 in Henan Province from 2015 to 2019 [J]. Environmental Science,2022,43(4):1697-1705.
Geng et al., 2013 N. Geng J. Wang Y. Xu PM2. 5 in an industrial district of Zhengzhou, China: chemical composition and source apportionment[J] Particuology 11 1 2013 99 109 Geng, N., Wang, J., Xu, Y., et al, 2013. PM2. 5 in an industrial district of Zhengzhou, China: Chemical composition and source apportionment[J]. Particuology, 11(1): 99-109.
Guo et al., 2017 H. Guo T.H. Cheng X.F. Guo Assessment of PM2.5 concentrations and exposure throughout China using ground observations Sci. Total Environ. 601 2017 1024 1030 Guo, H., Cheng, T. H., Guo, X.F., et al., 2017. Assessment of PM2.5 concentrations and exposure throughout China using ground observations. Science of the Total Environment.601,1024-1030
Guo et al., 2021 X.Y. Guo X.Q. Mu Z.S. Ding Nonlinear effects and driving mechanism of multidimensional urbanization on PM2.5 concentrations in the Yangtze River Delta Acta Geograph. Sin. 76 5 2021 1274 1293 Guo, X.Y., Mu, X.Q., Ding, Z.S., et al., 2021. Nonlinear effects and driving mechanism of multidimensional urbanization on PM2.5 concentrations in the Yangtze River Delta. Acta Geograph. Sin.. 76(5), 1274-1293.
He and Lin, 2017 X. He Z.S. Lin Analysis of the influence of the interaction of influencing factors on the change of PM2.5 concentration based on GAM model[J] Environ. Sci. J. Integr. Environ. Res. 38 1 2017 22 32 He, X., Lin, Z.S., 2017. Analysis of the influence of the interaction of influencing factors on the change of PM2.5 concentration based on GAM model[J]. Environmental Science. 38(1), 22-32.
He et al., 2016 X. He Z.S. Lin H.Y. Liu Analysis of influencing factors of PM2.5 concentration in Jiangsu Province based on gray correlation model[J] J. Geogr. 71 7 2016 1119 1129 He, X., Lin, Z.S., Liu, H.Y., et al., 2016. Analysis of influencing factors of PM2.5 concentration in Jiangsu Province based on gray correlation model[J]. Journal of Geography. 71(7), 1119-1129.
Hueglin et al., 2005 C. Hueglin R. Gehrig U. Baltensperger Chemical characterisation of PM2.5, PM10 and coarse particles at urban, nearcity and rural sites in Switzerland[J] Atmos. Environ. 39 4 2005 637 651 Hueglin, C., Gehrig, R., Baltensperger, U., et al., 2005. Chemical characterisation of PM2.5, PM10 and coarse particles at urban, nearcity and rural sites in Switzerland[J]. Atmospheric Environment. 39(4), 637-651.
Jiang, 2019 Y. Jiang Study on the influence of meteorological factors and external transport on atmospheric PM2.5 Hengyang City[D] 2019 South China University Hengyang Jiang, Y., 2019. Study on the influence of meteorological factors and external transport on atmospheric PM2.5 in Hengyang City[D]. Hengyang: South China University.
Landrigan et al., 2018 P.J. Landrigan R. Fuller N.J. Acosta The Lancet Commission on pollution and health[J] Lancet 391 10119 2018 462 512 Landrigan, P. J., Fuller, R., Acosta, N, J., et al., 2018. The Lancet Commission on pollution and health[J]. The lancet.391(10119): 462-512.
Liang et al., 2018 F.C. Liang Q.Y. Xiao Y.J. Wang MAIAC-based long-term spatiotemporal trends of PM2.5 in Beijing, China[J] Sci. Total Environ. 616 2018 1589 1598 Liang, F.C., Xiao, Q.Y, Wang Y.J., et al, 2018. MAIAC-based long-term spatiotemporal trends of PM2.5 in Beijing, China[J]. Science of the Total Environment. 616, 1589-1598.
Lin et al., 2020 Y.F. Lin X.Y. Yuan T.L. Zhai Effects of land-use patterns on PM2. 5 in China's developed coastal region: exploration and solutions[J] Sci. Total Environ. 703 2020 135602 Lin, Y.F., Yuan, X.Y., Zhai, T.L., et al., 2020. Effects of land-use patterns on PM2. 5 in China's developed coastal region: Exploration and solutions[J]. Science of the Total Environment. 703, 135602.
Lu et al., 2017a D.B. Lu J.H. Xu D.Y. Yang Spatio-temporal variation and inflfluence factors of PM2.5 concentrations in China from 1998 to 2014[J] Atmos. Pollut. Res. 8 2017 1151 1159 Lu, D.B., Xu, J.H., Yang, D.Y., et al., 2017a. Spatio-temporal variation and inflfluence factors of PM2.5 concentrations in China from 1998 to 2014[J]. Atmospheric Pollution Research. 8, 1151-1159.
Lu et al., 2017b D. Lu J. Xu D. Yang Spatio-temporal variation and influence factors of PM2. 5 concentrations in China from 1998 to 2014[J] Atmos. Pollut. Res. 8 6 2017 1151 1159 Lu D, Xu J, Yang D, et al, 2017b . Spatio-temporal variation and influence factors of PM2. 5 concentrations in China from 1998 to 2014[J]. Atmospheric Pollution Research, 8(6): 1151-1159.
Lu et al., 2018 D.B. Lu W.L. Mao D.Y. Yang Effects of land use and landscape pattern on PM2.5 in Yangtze River Delta, China[J] Atmos. Pollut. Res. 9 2018 705 713 Lu, D.B., Mao, W.L., Yang, D.Y., et al., 2018. Effects of land use and landscape pattern on PM2.5 in Yangtze River Delta, China[J]. Atmospheric Pollution Research. 9, 705-713.
Mo et al., 2014 L. Mo X.X. Yu Y. Zhao Correlation analysis between urbanization and particle pollution in Beijing[J] Ecol. Environ. Sci. 23 5 2014 806 811 Mo, L., Yu, X.X., Zhao, Y., et al., 2014. Correlation analysis between urbanization and particle pollution in Beijing[J]. Ecology and Environmental Sciences. 23(5), 806-811.
Mo et al., 2021 Y. Mo D. Booker S. Zhao The application of land use regression model to investigate spatiotemporal variations of PM2.5 in Guangzhou, China: implications for the public health benefits of PM2.5 reduction Sci. Total Environ. 778 2021 146305 Mo, Y., Booker, D., Zhao, S., et al., 2021. The application of land use regression model to investigate spatiotemporal variations of PM2.5 in Guangzhou, China: Implications for the public health benefits of PM2.5 reduction. Science of the Total Environment. 778, 146305.
Qi and Zhang, 2015 Y. Qi Y.G. Zhang A study on the dynamic relationship between the evolution of three industries and PM2.5 emissions in Beijing[J] China Popul.-Resour. Environ. 25 7 2015 15 23 Qi, Y., Zhang, Y.G., 2015. A study on the dynamic relationship between the evolution of three industries and PM2.5 emissions in Beijing[J]. China Population-Resources and Environment. 25(7), 15-23.
Rao et al., 2017 S. Rao Z. Klimont S.J. Smith Future air pollution in the shared socio-economic pathways Global Environ. Change 42 Suppl. C 2017 346 358 Rao, S., Klimont, Z., Smith, S.J., et al., 2017. Future air pollution in the Shared Socio-economic Pathways. Global Environmental Change. 42 (Supplement C), 346-358.
Shafik and Bandyopadhyay, 1992 N. Shafik S. Bandyopadhyay Policy Re⁃search Working Paper Economic Growth and Environmental Quality: Time Series and Cross-Country Evidence[R] vol. 904 1992 Shafik, N., Bandyopadhyay, S., 1992. Economic Growth and Environmental Quality: Time Series and Cross-country Evidence[R]. Policy Re⁃search Working Paper. No.904.
Song et al., 2018 W.Z. Song H.F. Jia Z.L. Li Using geographical semi-variogram method to quantify the difference between NO2 and PM2.5 spatial distribution characteristics in urban areas Sci. Total Environ. 631 2018 688 694 Song, W.Z., Jia, H.F., Li, Z.L., et al., 2018. Using geographical semi-variogram method to quantify the difference between NO2 and PM2.5 spatial distribution characteristics in urban areas. Science of the Total Environment. 631, 688-694.
Stone, 2008 B. Stone Urban sprawl and air quality in large US cities[J] J. Environ. Manag. 86 4 2008 688 698 Stone, B., 2008. Urban sprawl and air quality in large US cities[J]. Journal of Environmental Management. 86(4), 688-698.
Wang and Fang, 2016 Z.B. Wang C.L. Fang Spatial- temporal characteristics and determinants of PM2.5 in the Bohai Rim urban agglomeration[J] Chemosphere 148 2016 148 162 Wang, Z.B., Fang, C.L., 2016. Spatial- temporal characteristics and determinants of PM2.5 in the Bohai Rim Urban Agglomeration[J]. Chemosphere. 148, 148-162.
Wang et al., 2014 H.C. Wang L.M. Zhang X.W. Jiang Spatial correlation between industrial development and environmental pollution in the Pan-Yangtze River Delta[J] China Popul.-Resour. Environ. 24 S1 2014 55 59 Wang, H.C., Zhang, L.M., Jiang, X.W., 2014. Spatial correlation between industrial development and environmental pollution in the Pan-Yangtze River Delta[J]. China Population-Resources and Environment. 24(S1), 55-59.
Wang et al., 2018 X.M. Wang G.H. Tian D.Y. Yang Responses of PM2.5 pollution to urbanization in China[J] Energy Pol. 123 2018 602 610 Wang, X.M., Tian, G.H., Yang, D.Y., et al., 2018. Responses of PM2.5 pollution to urbanization in China[J]. Energy Policy. 123, 602-610.
Wang et al., 2019 Z.B. Wang L.W. Liang X.J. Wang Spatial and temporal evolution patterns of PM2.5 in urban agglomerations in China and its influencing factors[J] J. Geogr. 74 12 2019 2614 2630 Wang, Z.B., Liang, L.W., Wang X.J., 2019. Spatial and temporal evolution patterns of PM2.5 in urban agglomerations in China and its influencing factors[J]. Journal of Geography. 74(12), 2614-2630.
Wang et al., 2020 J.J. Wang X.S. Xia X.F. Cheng Spatial and temporal distribution characteristics of PM_(2.5) concentration in Hefei and analysis of influencing factors[J] Yangtze River Basin Resources and Environment 29 6 2020 1413 1421 Wang, J.J., Xia, X.S., Cheng, X.F., et al., 2020. Spatial and temporal distribution characteristics of PM_(2.5) concentration in Hefei and analysis of influencing factors[J]. Yangtze River Basin Resources and Environment. 29(6), 1413-1421.
Wu et al., 2017 J.S. Wu X. Wang J.C. Li Comparison of spatially divergent PM2.5 concentration simulation models: taking Beijing-Tianjin-Hebei region as an example[J] Environ. Sci. J. Integr. Environ. Res. 38 6 2017 2191 2201 Wu, J.S., Wang, X., Li, J.C., et al., 2017. Comparison of spatially divergent PM2.5 concentration simulation models: Taking Beijing-Tianjin-Hebei region as an example[J]. Environmental Science. 38(6), 2191-2201.
Xu et al., 2019 S. Xu B. Zou J.X. Gong Spatial and temporal correlation characteristics of urbanization and PM2.5 concentration in China from 2001-2015[J] China Environ. Sci. 39 2 2019 469 477 Xu, S., Zou, B., Gong, J.X., 2019. Spatial and temporal correlation characteristics of urbanization and PM2.5 concentration in China from 2001-2015[J]. China Environmental Science. 39(2), 469-477.
Yan and Qi, 2017 Y.X. Yan S.Z. Qi An examination of the spatial and temporal effects of foreign direct investment on urban haze (PM2.5) pollution in China[J] China Popul.-Resour. Environ. 27 4 2017 68 77 Yan, Y.X., Qi, S.Z. 2017. An examination of the spatial and temporal effects of foreign direct investment on urban haze (PM2.5) pollution in China[J]. China Population-Resources and Environment. 27(4), 68-77.
Yang, 2019 D.Y. Yang Research on PM2.5 Concentration Estimation in the Yangtze River Delta Based on Hierarchical Spatial and Temporal modeling[D] 2019 East China Normal University Shanghai Yang, D.Y., 2019. Research on PM2.5 concentration estimation in the Yangtze River Delta based on hierarchical spatial and temporal modeling[D]. Shanghai: East China Normal University.
Yang and Wang, 2017 C. Yang Y. Wang Study on the spatial and temporal characteristics and influencing factors of PM2.5 in Yangtze River Economic Zone[J] China Popul.-Resour. Environ. 27 1 2017 91 100 Yang, C., Wang, Y., 2017. Study on the spatial and temporal characteristics and influencing factors of PM2.5 in Yangtze River Economic Zone[J]. China Population-Resources and Environment. 27(1), 91-100.
Yang et al., 2018a D.Y. Yang X.M. Wang J.H. Xu Quantifying the inflfluence of natural and socioeconomic factors and their interactive impact on PM2.5 pollution in China[J] Environ. Pollut. 241 2018 475 483 Yang, D.Y., Wang, X.M., Xu, J.H., et al., 2018a. Quantifying the inflfluence of natural and socioeconomic factors and their interactive impact on PM2.5 pollution in China[J]. Environmental Pollution. 241, 475-483.
Yang et al., 2018b D.Y. Yang C. Ye X.M. Wang Global distribution and evolvement of urbanization and PM2.5 (1998–2015) [J] Atmos. Environ. 182 2018 171 178 Yang, D.Y., Ye, C., Wang, X.M., et al., 2018b. Global distribution and evolvement of urbanization and PM2.5 (1998-2015) [J]. Atmospheric Environment. 182, 171-178.
Ye et al., 2017 C. Ye M. Chen J. Duan Uneven development, urbanization and production of space in the middle-scale region based on the case of Jiangsu province, China Habitat Int. 66 2017 106 116 Ye, C., Chen, M., Duan, J., et al., 2017. Uneven development, urbanization and production of space in the middle-scale region based on the case of Jiangsu province, China. Habitat International. 66, 106-116.
Yin et al., 2021 Z.H. Yin J.H. Cheng W.H. Chen Testing and policy implications of the Kuznets inverted U-shaped correlation between carbon intensity and electrification environment in China's low-carbon transition process[J] China Environ. Manag. 13 3 2021 40 47 Yin, Z.H., Cheng, J.H., Chen, W.H., et al., 2021. Testing and policy implications of the Kuznets inverted U-shaped correlation between carbon intensity and electrification environment in China's low-carbon transition process[J]. China Environmental Management. 13(3), 40-47.
Zhang et al., 2020 C.C. Zhang H.G. Zhang X.J. Hu Spatial variation characteristics analysis of PM2.5 influencing factors by regions in China[J] Practice and understanding of mathematics 50 3 2020 187 193 Zhang, C.C., Zhang, H.G., Hu, X.J., 2020. Spatial variation characteristics analysis of PM2.5 influencing factors by regions in China[J]. Practice and understanding of mathematics. 50(3), 187-193.
Zheng et al., 2020 H. Zheng S. Kong N. Chen Significant changes in the chemical compositions and sources of PM2. 5 in Wuhan since the city lockdown as COVID-19 Sci. Total Environ. 739 2020 140000 Zheng, H., Kong, S., Chen, N., et al,2020. Significant changes in the chemical compositions and sources of PM2. 5 in Wuhan since the city lockdown as COVID-19. Science of the total environment, 739: 140000.
Zhou et al., 2019 L. Zhou C.H. Zhou F. Yang Spatio-temporal evolution and the influencing factors of PM2.5 in China between 2000 and 2015[J] J. Geogr. Sci. 29 2 2019 253 270 Zhou, L., Zhou, C.H., Yang, F., 2019. Spatio-temporal evolution and the influencing factors of PM2.5 in China between 2000 and 2015[J]. Journal of Geographical Sciences. 29(2), 253-270.