以高程、距海距离、道路、归一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PM2.5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PM2.5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 PM2.5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.%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.
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