Abstract
PM2.5 is a main air pollutant in China. Considering the unevenly distributed PM2.5 and population in China, an accurate assessment of PM2.5 exposure is needed. In this study, the population-weighted exposure (PWE) is used to measure the overall PM2.5 exposure based on 2766 counties across mainland China. The population exposure risk (PER) is used to better assess the partial PM2.5 exposure risk level for residents at the county level. The PM2.5 PWE and PER are calculated with the latest 2020 census data and the predicted concentrations estimated by spatial models considering both the geographic similarities and aggregation. The PWE differed from the concentrations across China, especially in four heavily polluted regions and three detected high-concentration clusters. In China, the average PM2.5 PWE in 2019 was 39.46 μg/m3, 2.41 μg/m3 higher than the mean concentration (37.05 μg/m3). The exposure in three detected clusters was much higher than in the Sichuan Basin (SCB), the Pearl River Delta (PRD), and the Yangtze River Delta (YRD), suggesting the focus of environmental governance should not only be the traditional heavily polluted areas according to administrative divisions. Regions with high concentrations also differed from regions with high PM2.5 exposure risk. The counties with higher PM2.5 PER were located in east-central and eastern coastal China, different from the distribution of concentrations. This study clarified the necessity of considering spatial aggregation of PM2.5 in LUR models and also emphasized the importance of calculating PM2.5 PWE as exposures in further health effect assessments.
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References
Bai Y, Liu M (2024) Multi-scale spatiotemporal trends and corresponding disparities of PM2.5 exposure in China. Environ Pollut 340:122857. https://doi.org/10.1016/j.envpol.2023.122857
Bao J, Yang X, Zhao Z, Wang Z, Yu C, Li X (2015) The spatial-temporal characteristics of air pollution in China from 2001–2014. Int J Environ Res Public Health 12(12):15875–15887. https://doi.org/10.3390/ijerph121215029
Chen J, Zhou C, Wang S, Hu J (2018) Identifying the socioeconomic determinants of population exposure to particulate matter (PM2.5) in China using geographically weighted regression modeling. Environ Pollut 241:494–503. https://doi.org/10.1016/j.envpol.2018.05.083
Chen Z, Chen D, Xie X, Cai J, Zhuang Y, Cheng N, He B, Gao B (2019) Spatial self-aggregation effects and national division of city-level PM2.5 concentrations in China based on spatio-temporal clustering. J Clean Prod 207:875–881. https://doi.org/10.1016/j.jclepro.2018.10.080
Cordero JM, Narros A, Borge R (2022) True reduction in the air pollution levels in the community of madrid during the COVID-19 lockdown. Front Sustain Cities. https://doi.org/10.3389/frsc.2022.869000
Cui Y, Ji DS, Maenhaut W, Gao WK, Zhang RJ, Wang YS (2020) Levels and sources of hourly PM2.5-related elements during the control period of the COVID-19 pandemic at a rural site between Beijing and Tianjin. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2020.140840
Dastider SR, Chattopadhyay S, Chattopadhyay G (2023) Viewing the interrelationship between some pollutants under the purview of binary and composite fuzzy relation in a post-monsoon and winter scenario over a Metro city of India. Int J Environ Res. https://doi.org/10.1007/s41742-023-00547-5
Di AJ, Xue Y, Yang XH, Leys J, Guang J, Mei LL, Wang JL, She L, Hu YC, He XW, Che YH, Fan C (2016) Dust aerosol optical depth retrieval and dust storm detection for Xinjiang region using Indian national satellite observations. Remote Sens. https://doi.org/10.3390/rs8090702
Dimakopoulou K, Samoli E, Analitis A, Schwartz J, Beevers S, Kitwiroon N, Beddows A, Barratt B, Rodopoulou S, Zafeiratou S, Gulliver J, Katsouyanni K (2022) Development and evaluation of spatio-temporal air pollution exposure models and their combinations in the Greater London area, UK. Int J Environ Res Public Health. https://doi.org/10.3390/ijerph19095401
Du Y, Sun T, Peng J, Fang K, Liu Y, Yang Y, Wang Y (2018) Direct and spillover effects of urbanization on PM2.5 concentrations in China’s top three urban agglomerations. J Clean Prod 190:72–83. https://doi.org/10.1016/j.jclepro.2018.03.290
Fan ZY, Zhan QM, Yang C, Liu HM, Zhan M (2020) How did distribution patterns of particulate matter air pollution (PM(2.5)and PM10) change in China during the COVID-19 outbreak: a spatiotemporal investigation at Chinese city-level. Int J Environ Res Public Health. https://doi.org/10.3390/ijerph17176274
Fan WF, Xu LY, Zheng HZ (2022) Using multisource data to assess PM2.5 exposure and spatial analysis of lung cancer in Guangzhou, China. Int J Environ Res Public Health. https://doi.org/10.3390/ijerph19052629
Friedrich R, Bickel P (2001) Environmental external costs of transport. Sprnger Verlag, Berlin
Gariazzo C, Carlino G, Silibello C, Renzi M, Finardi S, Pepe N, Radice P, Forastiere F, Michelozzi P, Viegi G, Stafoggia M (2020) A multi-city air pollution population exposure study: combined use of chemical-transport and random-Forest models with dynamic population data. Sci Total Environ 724:138102. https://doi.org/10.1016/j.scitotenv.2020.138102
George PE, Thakkar N, Yasobant S, Saxena D, Shah J (2024) Impact of ambient air pollution and socio-environmental factors on the health of children younger than 5 years in India: a population-based analysis. Lancet Reg Health Southeast Asia 20:100328. https://doi.org/10.1016/j.lansea.2023.100328
Habeebullah TM, Munir S, Zeb J, Morsy EA (2022) Modelling the effect of COVID-19 lockdown on air pollution in Makkah Saudi Arabia with a supervised machine learning approach. Toxics. https://doi.org/10.3390/toxics10050225
He Q, Zhang M, Song Y, Huang B (2020) Spatiotemporal assessment of PM2.5 concentrations and exposure in China from 2013 to 2017 using satellite-derived data. J Clean Prod. https://doi.org/10.1016/j.jclepro.2020.124965. (prepublish)
Huan L, Mingliang F, Xinxin J, Yi S, Drew S, Greg F, Cary S, Kebin H (2016) Health and climate impacts of ocean-going vessels in East Asia. Nat Clim Change. https://doi.org/10.1038/NCLIMATE3083
Huang YC, Karoly ED, Dailey LA, Schmitt MT, Silbajoris R, Graff DW, Devlin RB (2011) Comparison of gene expression profiles induced by coarse, fine, and ultrafine particulate matter. J Toxicol Environ Health A 74(5):296–312. https://doi.org/10.1080/15287394.2010.516238
Huang J, Li X, Zhang Y, Zhai S, Wang W, Zhang T, Yin F, Ma Y (2022) Socio-demographic characteristics and inequality in exposure to PM(2.5): a case study in the Sichuan basin, China. Environ Pollut 316(Pt 2):120630. https://doi.org/10.1016/j.envpol.2022.120630
Kong LQ, Tian GJ (2020) Assessment of the spatio-temporal pattern of PM25 and its driving factors using a land use regression model in Beijing, China. Environ Monit Assess. https://doi.org/10.1007/s10661-019-7943-9
Lange CL, Smith VA, Kahler DM (2022) Pittsburgh air pollution changes during the COVID-19 lockdown. Environ Adv 7:100149–100149. https://doi.org/10.1016/j.envadv.2021.100149
Lee HJ (2019) Benefits of high resolution PM2.5 prediction using satellite MAIAC AOD and land use regression for exposure assessment: California examples. Environ Sci Technol 53(21):12774–12783. https://doi.org/10.1021/acs.est.9b03799
Leonardi GS, Houthuijs D, Steerenberg PA, Fletcher T, Armstrong B, Antova T, Lochman I, Lochmanova A, Rudnai P, Erdei E, Musial J, Jazwiec-Kanyion B, Niciu EM, Durbaca S, Fabianova E, Koppova K, Lebret E, Brunekreef B, van Loveren H (2000) Immune biomarkers in relation to exposure to particulate matter: a cross-sectional survey in 17 cities of Central Europe. Inhal Toxicol 12(4):1–14. https://doi.org/10.1080/08958370050164833
Li Z, Che W, Frey HC, Lau AKH, Lin C (2017) Characterization of PM(2.5) exposure concentration in transport microenvironments using portable monitors. Environ Pollut 228:433–442. https://doi.org/10.1016/j.envpol.2017.05.039
Lim CC, Kim H, Vilcassim MJR, Thurston GD, Gordon T, Chen L-C, Lee K, Heimbinder M, Kim S-Y (2019) Mapping urban air quality using mobile sampling with low-cost sensors and machine learning in Seoul, South Korea. Environ Int 131:105022. https://doi.org/10.1016/j.envint.2019.105022
Liu N, Zou B, Feng H, Wang W, Tang Y, Liang Y (2019) Evaluation and comparison of multiangle implementation of the atmospheric correction algorithm, Dark Target, and Deep Blue aerosol products over China. Atmos Chem Phys 19(12):8243–8268. https://doi.org/10.5194/acp-19-8243-2019
Liu JL, Cai PL, Dong J, Wang JS, Li RK, Song XF (2021) Assessment of the dynamic exposure to PM2.5 based on hourly cell phone location and land use regression model in Beijing. Int J Environ Res Public Health. https://doi.org/10.3390/ijerph18115884
Liu Y, Tong D, Cheng J, Davis SJ, Yu S, Yarlagadda B, Clarke LE, Brauer M, Cohen AJ, Kan H, Xue T, Zhang Q (2022) Role of climate goals and clean-air policies on reducing future air pollution deaths in China: a modelling study. Lancet Planet Health 6(2):e92–e99. https://doi.org/10.1016/S2542-5196(21)00326-0
Lu D, Xu J, Yang D, Zhao J (2017) Spatio-temporal variation and influence factors of PM 2.5 concentrations in China from 1998 to 2014. Atmos Pollut Res 8(6):1151–1159. https://doi.org/10.1016/j.apr.2017.05.005
Ma Y, Yin F, Zhang T, Zhou XA, Li X (2016) Selection of the maximum spatial cluster size of the spatial scan statistic by using the maximum clustering set-proportion statistic. PLoS ONE 11(1):e0147918. https://doi.org/10.1371/journal.pone.0147918
Marks T, Begum T, McDermott K, Khwaja HA (2023) Real-time size distributions of air pollutants from a wastewater treatment plant in the New York State Capital District. Int J Environ Res. https://doi.org/10.1007/s41742-023-00545-7
Ming L, Jin L, Li J, Fu P, Yang W, Liu D, Zhang G, Wang Z, Li X (2017) PM2.5 in the Yangtze River Delta, China: chemical compositions, seasonal variations, and regional pollution events. Environ Pollut 223:200–212. https://doi.org/10.1016/j.envpol.2017.01.013
Miri M, Ghassoun Y, Dovlatabadi A, Ebrahimnejad A, Löwner MO (2019) Estimate annual and seasonal PM1, PM2.5 and PM10 concentrations using land use regression model. Ecotoxicol Environ Saf 174:137–145. https://doi.org/10.1016/j.ecoenv.2019.02.070
Mohajeri N, Hsu S-C, Milner J, Taylor J, Kiesewetter G, Gudmundsson A, Kennard H, Hamilton I, Davies M (2023) Urban–rural disparity in global estimation of PM2·5 household air pollution and its attributable health burden. Lancet Planet Health 7(8):e660–e672. https://doi.org/10.1016/S2542-5196(23)00133-X
Muñoz E, Martín ML, Turias IJ, Jimenez-Come MJ, Trujillo FJ (2014) Prediction of PM10 and SO2 exceedances to control air pollution in the Bay of Algeciras, Spain. Stoch Environ Res Risk Assess 28(6):1409–1420. https://doi.org/10.1007/s00477-013-0827-6
Nori-Sarma A, Thimmulappa RK, Venkataramana GV, Fauzie AK, Dey SK, Venkareddy LK, Berman JD, Lane KJ, Fong KC, Warren JL, Bell ML (2020) Low-cost NO2 monitoring and predictions of urban exposure using universal kriging and land-use regression modelling in Mysore, India. Atmos Environ. https://doi.org/10.1016/j.atmosenv.2020.117395
Nyhan M, Grauwin S, Britter R, Misstear B, McNabola A, Laden F, Barrett SRH, Ratti C (2016) “Exposure Track” the impact of mobile-device-based mobility patterns on quantifying population exposure to air pollution. Environ Sci Technol 50(17):9671–9681. https://doi.org/10.1021/acs.est.6b02385
Rybarczyk Y, Zalakeviciute R (2021) Assessing the COVID-19 impact on air quality: a machine learning approach. Geophys Res Lett. https://doi.org/10.1029/2020gl091202
Saucy A, Röösli M, Künzli N, Tsai M-Y, Sieber C, Olaniyan T, Baatjies R, Jeebhay M, Davey M, Flückiger B, Naidoo R, Dalvie M, Badpa M, de Hoogh K (2018) Land use regression modelling of outdoor NO2 and PM2.5 concentrations in three low income areas in the Western Cape Province, South Africa. Int J Environ Res Public Health 15(7):1452. https://doi.org/10.3390/ijerph15071452
Schikowski T, Adam M, Marcon A, Cai Y, Vierkotter A, Carsin AE, Jacquemin B, Al Kanani Z, Beelen R, Birk M, Bridevaux PO, Brunekeef B, Burney P, Cirach M, Cyrys J, de Hoogh K, de Marco R, de Nazelle A, Declercq C, Forsberg B, Hardy R, Heinrich J, Hoek G, Jarvis D, Keidel D, Kuh D, Kuhlbusch T, Migliore E, Mosler G, Nieuwenhuijsen MJ, Phuleria H, Rochat T, Schindler C, Villani S, Tsai MY, Zemp E, Hansell A, Kauffmann F, Sunyer J, Probst-Hensch N, Kramer U, Kunzli N (2014) Association of ambient air pollution with the prevalence and incidence of COPD. Eur Respir J 44(3):614–626. https://doi.org/10.1183/09031936.00132213
Song J, Li C, Liu M, Hu Y, Wu W (2022) Spatiotemporal distribution patterns and exposure risks of PM2.5 pollution in China. Remote Sens. https://doi.org/10.3390/rs14133173
Su X, Wang L, Gui X, Yang L, Li L, Zhang M, Qin W, Tao M, Wang S, Wang L (2022) Retrieval of total and fine mode aerosol optical depth by an improved MODIS Dark Target algorithm. Environ Int. https://doi.org/10.1016/j.envint.2022.107343
Tian X, Zeng J, Li X, Li S, Zhang T, Deng Y, Yin F, Ma Y (2024) Assessing the short-term effects of PM2.5 and O3 on cardiovascular mortality using high-resolution exposure: a time-stratified case cross-over study in Southwestern China. Environ Sci Pollut Res 31(3):3775–3785. https://doi.org/10.1007/s11356-023-31276-z
Turap Y, Talifu D, Wang X, Abulizi A, Maihemuti M, Tursun Y, Ding X, Aierken T, Rekefu S (2019a) Temporal distribution and source apportionment of PM2.5 chemical composition in Xinjiang, NW-China. Atmos Res 218:257–268. https://doi.org/10.1016/j.atmosres.2018.12.010
Wang G, Fakhar K (2015) Lie symmetry analysis, nonlinear self-adjointness and conservation laws to an extended (2+1)-dimensional Zakharov–Kuznetsov–Burgers equation. Comput Fluids 119:143–148. https://doi.org/10.1016/j.compfluid.2015.06.033
Wang H, Ma J, He Q (2015) Mass concentration and composition of TSP in Kashgar Xinjiang, China. Arab J Geosci 8(9):6723–6730. https://doi.org/10.1007/s12517-014-1745-8
Wang Y, Wen Y, Wang Y, Zhang S, Zhang KM, Zheng H, Xing J, Wu Y, Hao J (2020) Four-month changes in air quality during and after the COVID-19 lockdown in six megacities in China. Environ Sci Technol Lett 7(11):802–808. https://doi.org/10.1021/acs.estlett.0c00605
Wang B, Yuan Q, Yang Q, Zhu L, Li T, Zhang L (2021) Estimate hourly PM2.5 concentrations fromHimawari-8TOA reflectance directly using geo-intelligent long short-term memory network. Environ Pollut. https://doi.org/10.1016/j.envpol.2020.116327
Wang W, Xiao X, Qian J, Chen S, Liao F, Yin F, Zhang T, Li X, Ma Y (2022) Reclaiming independence in spatial-clustering datasets: a series of data-driven spatial weights matrices. Stat Med 41(15):2939–2956. https://doi.org/10.1002/sim.9395
Wong YJ, Shiu HY, Chang JHH, Ooi MCG, Li HH, Homma R, Shimizu Y, Pei-Te C, Maneechot L, Sulaiman NMN (2022) Spatiotemporal impact of COVID-19 on Taiwan air quality in the absence of a lockdown: influence of urban public transportation use and meteorological conditions. J Clean Prod. https://doi.org/10.1016/j.jclepro.2022.132893
Xu Y, Xue W, Lei Y, Zhao Y, Cheng S, Ren Z, Huang Q (2018) Impact of meteorological conditions on PM2.5 pollution in China during Winter. Atmosphere 9(11):429. https://doi.org/10.3390/atmos9110429
Xu H, Bechle MJ, Wang M, Szpiro AA, Vedal S, Bai Y, Marshall JD (2019) National PM2.5 and NO2 exposure models for China based on land use regression, satellite measurements, and universal kriging. Sci Total Environ 655:423–433. https://doi.org/10.1016/j.scitotenv.2018.11.125
Yan D, Lei Y, Shi Y, Zhu Q, Li L, Zhang Z (2018) Evolution of the spatiotemporal pattern of PM25 concentrations in China—a case study from the Beijing-Tianjin-Hebei region. Atmos Environ 183:225–233. https://doi.org/10.1016/j.atmosenv.2018.03.041
Yang Y, Christakos G (2015) Spatiotemporal characterization of ambient PM2.5 concentrations in Shandong Province (China). Environ Sci Technol 49(22):13431–13438. https://doi.org/10.1021/acs.est.5b03614
Yang H, Guo Z, Leng Q (2021) High-resolution population exposure to PM2.5 in Nanchang Urban Region using multi-source data. Pol J Environ Stud 30(5):4801–4814. https://doi.org/10.15244/pjoes/134297
Ye W-F, Ma Z-Y, Ha X-Z (2018a) Spatial-temporal patterns of PM2.5 concentrations for 338 Chinese cities. Sci Total Environ 631–632:524–533. https://doi.org/10.1016/j.scitotenv.2018.03.057
Ying L, Jianghao W, Kang W, Junjie Z (2018) Population exposure to ambient PM2.5 at the subdistrict level in China. Int J Environ Res Public Health. https://doi.org/10.3390/ijerph15122683
Yousefi R, Shaheen A, Wang F, Ge Q, Wu R, Lelieveld J, Wang J, Su X (2023) Fine particulate matter (PM2.5) trends from land surface changes and air pollution policies in China during 1980–2020. J Environ Manag 326:116847. https://doi.org/10.1016/j.jenvman.2022.116847
Yu Z, Yuzhou L, Xunfei D, Huajin C, Michael LG, Xueyou S, Lizhong Z, Minghua Z (2017) Spatiotemporal prediction of continuous daily PM2.5 concentrations across China using a spatially explicit machine learning algorithm. Atmos Environ. https://doi.org/10.1016/j.atmosenv.2017.02.023
Yuan-An J, Ying C, Yi-Zhou Z, Peng-Xiang C, Xing-Jie Y, Jing F, Su-Qin B (2013) Analysis on changes of basic climatic elements and extreme events in Xinjiang, China during 1961–2010. Adv Clim Change Res. https://doi.org/10.3724/SP.J.1248.2013.020
Yueting D, Ming Z, Sai C, Wenwen W, Rui N (2019) The environmental Kuznets curve for PM 2.5 pollution in Beijing-Tianjin-Hebei region of China: a spatial panel data approach. J Clean Prod. https://doi.org/10.1016/j.jclepro.2019.02.229
Zhang Y, Ding Z, Xiang Q, Wang W, Huang L, Mao F (2020) Short-term effects of ambient PM1 and PM2.5 air pollution on hospital admission for respiratory diseases: Case-crossover evidence from Shenzhen, China. Int J Hyg Environ Health 224:113418. https://doi.org/10.1016/j.ijheh.2019.11.001
Zhang LN, Yang CY, Xiao QY, Geng GN, Cai J, Chen RJ, Meng X, Kan HD (2021) A satellite-based land use regression model of ambient NO2 with high spatial resolution in a Chinese City. Remote Sens. https://doi.org/10.3390/rs13030397
Zhao CC, Pan JH, Zhang LL (2021) Spatio-temporal patterns of global population exposure risk of PM2.5 from 2000–2016. Sustainability. https://doi.org/10.3390/su13137427
Ziyue C, Danlu C, Chuanfeng Z, Mei-po K, Jun C, Yan Z, Bo Z, Xiaoyan W, Bin C, Jing Y, Ruiyuan L, Bin H, Bingbo G, Kaicun W, Bing X (2020) Influence of meteorological conditions on PM 2.5 concentrations across China: a review of methodology and mechanism. Environ Int. https://doi.org/10.1016/j.envint.2020.105558
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This work was supported by the National Natural Science Foundation of China (Grant Numbers 81803332 and 82373689) Yue Ma; the National Natural Science Foundation of China (Grant Numbers 81872713) Fei Yin; and the Sichuan Science & Technology Program (Grant Number 2021YFS0181) Yue Ma.
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Xuelin Li: Conceptualization, Methodology, Software, Formal analysis, Data curation, Validation, Writing—original draft, Writing—review & editing. Jingfei Huang: Formal analysis, Data curation, Validation, Writing—original draft, Writing—review & editing. Yi Zhang: Writing—review & editing. Siwei Zhai: Writing—review & editing. Xinyue Tian: Software, Writing—review & editing. Sheng Li: Software, review & editing. Wei Wang: Software, Writing—review & editing. Tao Zhang: Writing—review & editing. Fei Yin: Funding acquisition, Writing—review & editing. Yue Ma: Conceptualization, Methodology, Supervision, Funding acquisition, Writing—original draft, Writing—review & editing.
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Li, X., Huang, J., Zhang, Y. et al. Comprehensively Assessing PM2.5 Exposure Across Mainland China with Estimated Concentrations Considering Spatial Aggregation. Int J Environ Res 18, 45 (2024). https://doi.org/10.1007/s41742-024-00603-8
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DOI: https://doi.org/10.1007/s41742-024-00603-8