Spatiotemporal analysis of PM2.5 in large coastal domains by combining Land Use Regression and Bayesian Maximum Entropy
JIANG Qu-tu1, HE Jun-yu1, WANG Zhan-shan2, YE Guan-qiong1, CHEN Qian3, XIAO Lu1
1. Institute of Island & Coastal Ecosystems, Zhejiang University, Zhoushan 316021, China; 2. Beijing Municipal Environmental Monitoring Center, Beijing 100048, China; 3. School of Geographic and Environment, Jiangxi Normal University, Nanchang 330022, China
Abstract：By combining Land Use Regression (LUR) and Bayesian Maximum Entropy (BME), this study constructed a LUR model based on the parameters of elevation, distance to sea, length of roads and Normalized Difference Vegetation Index (NDVI) to generate a global map of PM2.5 distribution in a large costal area in 2015, china. The Bayesian Maximum Entropy was further introduced in the interpolation of LUR space-time residuals. Because of the introduction of BME, the cross-validation results showed that the R2 increased from 0.36 to 0.85, and the root-mean-square error (RMSE) decreased from 23.53μg/m3 to 11.08μg/m3. The average concentration of PM2.5 in the northern coastal areas was higher than that of the southern areas, and the highest concentration of PM2.5 appeared in the inland area of Beijing, Tianjin, Hebei and Shandong provinces during winter times. The annual spatial distribution of PM2.5 was further integrated with population density in Shandong province for risk exposure analysis. The outcome showed that the outdoor population exposure of PM2.5 decreased from inland to sea, and the highest Per capita outdoor exposure value occurred in the central city, Jinan (85.5μg/m3), while the lowest value occurred in coastal areas of Yantai and Weihai.
江曲图, 何俊昱, 王占山, 叶观琼, 陈倩, 肖璐. 基于LUR/BME的海岸带地区PM2.5时空特性研究[J]. 中国环境科学, 2017, 37(2): 424-431.
JIANG Qu-tu, HE Jun-yu, WANG Zhan-shan, YE Guan-qiong, CHEN Qian, XIAO Lu. Spatiotemporal analysis of PM2.5 in large coastal domains by combining Land Use Regression and Bayesian Maximum Entropy. CHINA ENVIRONMENTAL SCIENCECE, 2017, 37(2): 424-431.
Yang Y, Christakos G, Huang W, et al. Uncertainty assessment of PM2.5 contamination mapping using spatiotemporal sequential indicator simulations and multi-temporal monitoring data[J]. Science Reports, 2016,6:24-35.
[2]
Zou B, Luo Y Q, Wan N, et al. Performance comparison of LUR and OK in PM2.5 concentration mapping:a multidimensional perspective[J]. Science Reports, 2015,5:86-98.
[3]
Lv B, Hu Y, Chang H H, et al. Improving the Accuracy of Daily PM2.5 Distributions Derived from the Fusion of Ground-Level Measurements with Aerosol Optical Depth Observations, a Case Study in North China[J]. Environmental Science & Technology, 2016,50(9):4752-4759.
[4]
Zhang W, Capps S L, Hu Y, et al. Development of the high-order decoupled direct method in three dimensions for particulate matter:enabling advanced sensitivity analysis in air quality models[J]. Geoscientific Model Development Discussions, 2011,4(4):355-368.
Wu J S, Li J C, Peng J, et al. Applying land use regression model to estimate spatial variation of PM2.5 in Beijing, China[J]. Environmental Science Pollution Research, 2015,22(9):7045-7061.
[7]
Briggs D J, Collins S, Elliott P, et al. Mapping urban air pollution using GIS:a regression-based approach[J]. International Journal of Geographical Information Science, 1997,11(7):699-718.
[8]
Henderson S B, Beckerman B, Jerret M, et al. Application of land use regression to estimate long-term concentrations of traffic-related nitrogen oxides and fine particulate matter[J]. Environmental Science & Technology, 2007,41(7):2422-2428.
[9]
Miyawaki J, Kamei S, Sakayama K, et al. Modeling the Intraurban Variability of Ambient Traffic Pollution in Toronto, Canada[J]. Journal of Toxicology & Environmental Health Part A, 2007,70(3/4):200-212.
[10]
Beckerman B S, Jerrett M, Martin R V, et al. Application of the deletion/substitution/addition algorithm to selecting land use regression models for interpolating air pollution measurements in California[J]. Atmospheric Environment, 2013,77(68):172-177.
[11]
Hassan A, Seyed Mahmood T S, Henderson S B, et al. Land use regression models to estimate the annual and seasonal spatial variability of sulfur dioxide and particulate matter in Tehran, Iran.[J]. Science of the Total Environment, 2014,489(488-489C):343-353.
Li C, Du S Y, Bai Z P, et al. Application of land use regression for estimating concentrations of major outdoor air pollutants in Jinan, China[J]. Journal of Zhejiang University-SCIENCE A, 2010, 11(11):857-867.
[14]
Liu W, Li X, Chen Z, et al. Land use regression models coupled with meteorology to model spatial and temporal variability of NO2, and PM10, in Changsha, China[J]. Atmospheric Environment, 2015,116:272-280.
[15]
Liu C, Henderson B H, Wang D, et al. A land use regression application into assessing spatial variation of intra-urban fine particulate matter PM2.5 and nitrogen dioxide NO2 concentrations in City of Shanghai, China.[J]. Science of the Total Environment, 2016,565:607-615.
[16]
Akita Y, Baldasano J M, Beelen R, et al. Large scale air pollution estimation method combining land use regression and chemical transport modeling in a geostatistical framework.[J]. Environmental Science & Technology, 2014,48(8):4452-4459.
[17]
Yu H L, Wang C H, Liu M C, et al. Estimation of Fine Particulate Matter in Taipei Using Landuse Regression and Bayesian Maximum Entropy Methods[J]. International Journal of Environmental Research & Public Health, 2011,8(6):2153-2169.
[18]
Reyes J, Serre M L. An LUR/BMEFramework to Estimate PM2.5 Explainedby on Road Mobile and StationarySources[J]. Environmental Science & Technology, 2014,48(3):1736-1744.
[19]
Matheron G. The Theory of Regionalized Variables and Its Application[J]. 1971,5:218.
[20]
Beckerman B S, Jerrett M, Serre M, et al. A Hybrid Approach to Estimating National Scale Spatiotemporal Variability of PM2.5 in the Contiguous United States[J]. Environmental Science & Technology, 2013,47(13):7233-7241.
[21]
Christakos G. A Bayesian maximum-entropy view to the spatial estimation problem[J]. Mathematical Geosciences, 1990,22(7):763-777.
[22]
Christakos G, Serre M L. BME analysis of spatiotemporal particulate matter distributions in North Carolina[J]. Atmospheric Environment, 2000,34(20):3393-3406.
Henderson S B, Beckerman B, Jerrett M, Brauer M. Application of Land Use Regression to Estimate Long-Term Concentrations of Traffic-Related Nitrogen Oxides and Fine Particulate Matter[J]. Environmental Science & Technology, 2007,41(7):2422-2428.
[25]
Hart J E, Yanosky J D, Puett R C, et al. Spatial Modeling of PM10 and NO2 in the Continental United States, 1985~2000[J]. Environmental Health Perspectives, 2009,117(11):1690-1696.
[26]
Wang M, Sampson P D, Hu J, et al. Combining Land-Use Regression and Chemical Transport Modeling in a Spatiotemporal Geostatistical Model for Ozone and PM2.5.[J]. Environmental Science & Technology, 2016,50(10):5111-5118.
[27]
Bechle M J, Millet D B, Marshall J D. National Spatiotemporal Exposure Surface for NO2:Monthly Scaling of a SatelliteDerived Land-Use Regression, 2000~2010.[J]. Environmental Science & Technology, 2015,49(20):12297-12305.
Yang Y, Christakos G. Spatiotemporal Characterization of Ambient PM2.5 Concentrations in Shandong Province (China).[J]. Environmental Science & Technology, 2015,49(22):13431-13438.
