【摘要】 以高程、距海距离、道路、归_化植被指数构建大区域土地利用回归模型LUR,并结合贝叶斯最大熵BME对LUR模型的残差进行时空分析,得到2015年中国沿海部分省市PM_2.5的时空分布.交叉验证结果表明在引入BME模型后R²由0.36提高至0.85,均方根误差RMSE由23.53μg/m³降低至11.08μg/m³;整体海岸带地区以长江三角洲为界PM_2.5浓度呈现南低北高,且以京津冀及山东内陆区域秋冬季污染最为严重,同时以山东省为例进行各市室外人口空气污染暴露分析,表明沿内陆至近海,人均PM_2.5暴露浓度逐步递减,以济南85.5μg/m³最高,沿海区域烟台,威海等地较低.更多还原

【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 PM_2.5 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 R 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 PM_2.5 in the northern coastal areas was higher than that of the southern areas,and the highest concentration of PM_2.5 appeared in the inland area of Beijing,Tianjin,Hebei and Shandong provinces during winter times.The annual spatial distribution of PM_2.5 was further integrated with population density in Shandong province for risk exposure analysis.The outcome showed that the outdoor population exposure of PM_2.5 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.更多还原