Abstract
The prediction of PM2.5 concentrations with high spatiotemporal resolution has been suggested as a potential method for data collection to assess the health effects of exposure. This work predicted the weekly average PM2.5 concentrations in the Yangtze River Delta, China, by using a spatio-temporal model. Integrating land use data, including the areas of cultivated land, construction land, and forest land, and meteorological data, including precipitation, air pressure, relative humidity, temperature, and wind speed, we used the model to estimate the weekly average PM2.5 concentrations. We validated the estimated effects by using the cross-validated R2 and Root mean square error (RMSE); the results showed that the model performed well in capturing the spatiotemporal variability of PM2.5 concentration, with a reasonably large R2 of 0.86 and a small RMSE of 8.15 (μg/m3). In addition, the predicted values covered 94% of the observed data at the 95% confidence interval. This work provided a dataset of PM2.5 concentration predictions with a spatiotemporal resolution of 3 km × week, which would contribute to accurately assessing the potential health effects of air pollution.
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References
Adam-Poupart A, Brand A, Fournier M, Jerrett M, Smargiassi A (2014) Spatiotemporal modeling of ozone levels in Quebec (Canada): a comparison of kriging, land-use regression (LUR), and combined Bayesian maximum entropy-LUR approaches. Environ Health Perspect 122(9):970–976
Beckerman BS, Jerrett M, Serre M, Martin RV, Lee SJ, van Donkelaar A (2013) A hybrid approach to estimating national scale spatiotemporal variability of PM2.5 in the contiguous United States. Environ Sci Technol 47(13):7233–7241
Brokamp C, Jandarov R, Rao MB, LeMasters G, Ryan P (2017) Exposure assessment models for elemental components of particulate matter in an urban environment: a comparison of regression and random forest approaches. Atmos Environ 151:1–11
Cesaroni G, Badaloni C, Gariazzo C, Stafoggia M, Sozzi R, Davoli M (2013) Long-term exposure to urban air pollution and mortality in a cohort of more than a million adults in Rome. Environ Health Perspect 121(3):324–331
Chang HH, Hu X, Liu Y (2014) Calibrating MODIS aerosol optical depth for predicting daily PM2.5 concentrations via statistical downscaling. J Expo Sci Environ Epidemiol 24(4):398–404
Chen Z, Wang JN, Ma GX, Zhang YS (2013) China tackles the health effects of air pollution. Lancet 382(9909):1959–1960
Chen Y, Schleicher N, Fricker M, Cen K, Liu XL, Kaminski U (2016) Long-term variation of black carbon and PM2.5 in Beijing, China with respect to meteorological conditions and governmental measures. Environ Pollut 212:269–278
Choi G, Bell ML, Lee JT (2017) A study on modeling nitrogen dioxide concentrations using land-use regression and conventionally used exposure assessment methods. Environ Res Lett 12(4):044003
Cohen MA, Adar SD, Allen RW, Avol E, Curl CL, Gould T, Hardie D, Ho A, Kinney P, Larson TV et al (2009) Approach to estimating participant pollutant exposures in the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Environ Sci Technol 43(13):4687–4693
Cressie N (2015) Statistics for spatial data. Wiley, New York
Di Q, Kloog I, Koutrakis P, Lyapustin A, Wang Y, Schwartz J (2016) Assessing PM2.5 exposures with high spatiotemporal resolution across the continental United States. Environ Sci Technol 50(9):4712–4721
Fecht D, Beale L, Briggs D (2014) A GIS-based urban simulation model for environmental health analysis. Environ Modell Softw 58:1–11
Fuentes M, Guttorp P, Sampson PD (2006) Using transforms to analyze space-time processes. In: Fínkenstädt B, Held L, Isham V (eds) Statistical methods for spatio-temporal systems. Chapman & Hall/CRC, London
Gruszecka-Kosowska A (2016) Assessment of the Krakow inhabitants’ health risk caused by the exposure to inhalation of outdoor air contaminants. Stoch Environ Res Risk Assess. https://doi.org/10.1007/s00477-016-1366-8
Guan Q, Cai A, Wang F, Yang L, Xu C, Liu Z (2017) Spatio-temporal variability of particulate matter in the key part of Gansu Province, Western China. Environ Pollut 230:189–198
Gulliver J, de Hoogh K, Fecht D, Vienneau D, Briggs D (2011) Comparative assessment of GIS-based methods and metrics for estimating long-term exposures to air pollution. Atmos Environ 45(39):7072–7080
Guo J, Xia F, Zhang Y, Liu H, Li J, Lou M, He J, Yan Y, Wang F, Min M et al (2017) Impact of diurnal variability and meteorological factors on the PM2.5–AOD relationship: implications for PM2.5 remote sensing. Environ Pollut 221:94–104
He J, Kolovos A (2017) Bayesian maximum entropy approach and its applications: a review. Stoch Environ Res Risk Assess. https://doi.org/10.1007/s00477-017-1419-7
Hu J, Wang Y, Ying Q, Zhang H (2014) Spatial and temporal variability of PM2.5 and PM10 over the North China Plain and the Yangtze River Delta, China. Atmos Environ 95:598–609
Jiang M, Sun W, Yang G, Zhang D (2017) Modelling seasonal GWR of daily PM2.5 with proper auxiliary variables for the Yangtze River Delta. Remote Sens 9(4):346
Kloog I, Koutrakis P, Coull BA, Lee HJ, Schwartz J (2011) Assessing temporally and spatially resolved PM2.5 exposures for epidemiological studies using satellite aerosol optical depth measurements. Atmos Environ 45(35):6267–6275
Krzyzanowski M, Cohen A (2008) Update of WHO air quality guidelines. Air Qual Atmos Health 1(1):7–13
Li L, Wu J, Ghosh JK, Ritz B (2013) Estimating spatiotemporal variability of ambient air pollutant concentrations with a hierarchical model. Atmos Environ 71:54–63
Li X, Song J, Lin T, Dixon J, Zhang G, Ye H (2016) Urbanization and health in China, thinking at the national, local and individual levels. Environ Health 15(1):S32
Lindström J, Szpiro AA, Sampson PD, Oron AP, Richards M, Larson TV, Sheppard L (2014) A flexible spatio-temporal model for air pollution with spatial and spatio-temporal covariates. Environ Ecol Stat 21:411–433
Lu D, Xu J, Yang D, Zhao J (2017) Spatio-temporal variation and influence factors of PM2.5 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 Z, Hu X, Huang L, Bi J, Liu Y (2014) Estimating ground-level PM2.5 in China using satellite remote sensing. Environ Sci Technol 48(13):7436–7444
Ma Z, Hu X, Sayer AM, Levy R, Zhang Q, Xue Y, Tong S, Bi J, Huang L, Liu Y (2016) Satellite-based spatiotemporal trends in PM2.5 concentrations: China, 2004–2013. Environ Health Perspect 124(2):184–192
Mercer LD, Szpiro AA, Sheppard L, Lindstrom J, Adar SD, Allen RW, Avol EL, Oronet AP, Larson T, Liu LJS et al (2011) Comparing universal kriging and land-use regression for predicting concentrations of gaseous oxides of nitrogen (NOx) for the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Atmos Environ 45(26):4412–4420
Morales FEC, Vicini L, Hotta LK, Achcar JA (2017) A nonhomogeneous Poisson process geostatistical model. Stoch Environ Res Risk Assess 31:493–507. https://doi.org/10.1007/s00477-016-1275-x
Onorati R, Sampson P, Guttorp P (2013) A spatio-temporal model based on the SVD to analyze daily average temperature across the Sicily region. J. Environ, Stat, p 5
Sampson PD, Szpiro AA, Sheppard L, Lindström J, Kaufman JD (2011) Pragmatic estimation of a spatio-temporal air quality model with irregular monitoring data. Atmos Environ 45(36):6593–6606
Smith RL, Kolenikov S, Cox LH (2003) Spatiotemporal modeling of PM2.5 data with missing values. J Geophys Res 108(D24):STS 11–1. https://doi.org/10.1029/2002JD002914
Song W, Jia H, Huang J, Zhang Y (2014) A satellite-based geographically weighted regression model for regional PM2.5 estimation over the Pearl River Delta region in China. Remote Sens Environ 154:1–7
Szpiro AA, Sampson PD, Sheppard L, Lumley T, Adar SD, Kaufman J (2010) Predicting intra-urban variation in air pollution concentrations with complex spatio-temporal dependencies. Environmetrics 21:606–631
Van DA, Martin RV, Brauer M, Hsu NC, Kahn RA, Levy RC, Lyapustin A, Sayer AM, Winker DM (2016) Global estimates of fine particulate matter using a combined geophysical-statistical method with information from satellites, models, and monitors. Environ Sci Technol 50(7):3762
Wang M, Beelen R, Bellander T, Birk M, Cesaroni G, Cirach M, Cyrys J, Hoogh K, Declercq C, Dimakopoulou K et al (2014) Performance of multi-city land use regression models for nitrogen dioxide and fine particles. Environ Health Perspect 122(8):843–849
Wang Z, Lu F, He HD, Lu QC, Wang D, Peng ZR (2015) Fine-scale estimation of carbon monoxide and fine particulate matter concentrations in proximity to a road intersection by using wavelet neural network with genetic algorithm. Atmos Environ 104:264–272
Wang G, Zhang R, Gomez ME, Yang L, Zamora ML, Hu M, Lin Y, Peng J, Guo S, Meng J et al (2016) Persistent sulfate formation from London Fog to Chinese haze. Proc Natl Acad Sci 113(48):13630–13635
Wang Z, Lu QC, He HD, Wang D, Gao Y, Peng ZR (2017) Investigation of the spatiotemporal variation and influencing factors on fine particulate matter and carbon monoxide concentrations near a road intersection. Front Earth Sci PRC 11(1):63–75
Wilton D, Szpiro A, Gould T, Larson T (2010) Improving spatial concentration estimates for nitrogen oxides using a hybrid meteorological dispersion/land use regression model in Los Angeles, CA and Seattle, WA. Sci Total Environ 408(5):1120–1130
Xiao Q, Wang Y, Chang HH, Meng X, Geng G, Lyapustin A, Liu Y (2017) Full-coverage high-resolution daily PM2.5 estimation using MAIAC AOD in the Yangtze River Delta of China. Remote Sens Environ 199:437–446
Xie Y, Wang Y, Zhang K, Dong W, Lv B, Bai Y (2015) Daily estimation of ground-level PM2.5 concentrations over Beijing using 3 km resolution MODIS AOD. Environ Sci Technol 49(20):12280–12288
Yang D, Xu C, Wang J, Zhao Y (2017) Spatiotemporal epidemic characteristics and risk factor analysis of malaria in Yunnan Province, China. BMC Public Health 17:66
You W, Zang Z, Zhang L, Li Y, Pan X, Wang W (2016) National-scale estimates of ground-level PM2.5 concentration in China using geographically weighted regression based on 3 km resolution MODIS AOD. Remote Sens 8(3):184
Zang Z, Wang W, You W, Li Y, Ye F, Wang C (2017) Estimating ground-level PM2.5 concentrations in Beijing, China using aerosol optical depth and parameters of the temperature inversion layer. Sci Total Environ 575:1219–1227
Zheng Y, Zhang Q, Liu Y, Geng G, He K (2016) Estimating ground-level PM2.5 concentrations over three megalopolises in China using satellite-derived aerosol optical depth measurements. Atmos Environ 124:232–242
Zou B, Xu S, Sternberg T, Fang X (2016) Effect of land use and cover change on air quality in urban sprawl. Sustainability 8(7):677
Acknowledgements
The authors are grateful to the Resource and Environmental Science Data Center (http://www.resdc.cn) of the Chinese Academy of Sciences and the China Meteorological Data Sharing Service System (http://cdc.cma.gov.cn/) for providing data. The authors appreciate the insightful comments of anonymous reviewers.
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Yang, D., Lu, D., Xu, J. et al. Predicting spatio-temporal concentrations of PM2.5 using land use and meteorological data in Yangtze River Delta, China. Stoch Environ Res Risk Assess 32, 2445–2456 (2018). https://doi.org/10.1007/s00477-017-1497-6
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DOI: https://doi.org/10.1007/s00477-017-1497-6