Identifying spatiotemporal characteristics of daily fine particulate matter (PM_2.5) concentrations is essential for assessing air quality and understanding the environmental consequences of urbanization. More importantly, exposure analysis can provide basic information for appropriate decision making. This paper demonstrates an integrated method incorporating satellite-based aerosol optical depth, machine learning models and multi-time meteorological parameters to analyze spatiotemporal dynamics and exposure levels of daily PM_2.5 in the economically developed Yangtze River Delta (YRD) from 2016 to 2018. Ten-fold cross validation (CV) was implemented to evaluate the model performance. Compared to the models with daily means of meteorological fields, the models with multi-time meteorological parameters had higher CV R² and lower CV root mean square error (RMSE) values. The model with the best performance achieved sample- (site-) based CV R² values of 0.88 (0.88) and RMSE values of 10.33 (10.35) μg/m³. The YRD region is seriously polluted (exceeding the World Health Organization Interim Targets-1 standard of 35 μg/m³) during our study period, especially in Jiangsu Province, but with an improving trend. The residents in Zhejiang Province suffered the least from exposure, with 39 days (4% of the total days) characterized as over polluted (daily average > 75 μg/m³) in our study period. Air pollution in Shanghai Municipality mitigated the most from 2016 to 2018. With the advantages of high-accuracy and high-resolution (daily and 0.01 ° × 0.01 ° resolutions), the proposed method can guide for environmental policy planning.

确定每日细颗粒物（PM _2.5）浓度的时空特征对于评估空气质量和理解城市化的环境后果至关重要。更重要的是，暴露分析可以为适当的决策提供基本信息。本文演示了一种综合方法，该方法结合了基于卫星的气溶胶光学深度，机器学习模型和多次气象参数，以分析每日PM _2.5的时空动态和暴露水平在2016年至2018年经济发达的长江三角洲（YRD）中进行了评估。对模型的性能进行了十倍交叉验证（CV）。与具有日常气象领域的模型相比，具有多次气象参数的模型具有更高的CV R ²和更低的CV均方根误差（RMSE）值。具有最佳性能的模型实现了基于样本（站点）的CV R ²值为0.88（0.88），RMSE值为10.33（10.35）μg/ m ³。长三角地区受到严重污染（超过了世界卫生组织的“临时目标-1”标准，为35μg/ m ³）在我们的研究期间，尤其是在江苏省，但呈上升趋势。在我们的研究期内，浙江省居民遭受的暴露最少，为39天（占总天数的4％），表现为过度污染（日平均> 75μg/ m ³）。从2016年到2018年，上海市的空气污染减缓幅度最大。该方法具有高精度和高分辨率（每日和0.01°×0.01°分辨率）的优点，可为环境政策规划提供指导。