卫星遥感反演气溶胶光学厚度已被广泛应用于近地面空气污染遥感监测。为揭示福州地区细颗粒物污染的空间分异趋势，利用2014年-2015年的地基监测细颗粒物（PM_2.5）浓度数据、MODIS 3 km气溶胶光学厚度（AOD）卫星数据以及GEOS-FP气象数据，分别构建了估计福州地区近地面PM_2.5浓度的日校正模型和站点-日校正模型，并利用十折交叉验证方法对2个模型进行评价验证。结果表明：（1）日校正模型和站点-日校正模型分别能够解释福州地区PM_2.5浓度76.2%和81.4%的变异，反演的2014年-2015年福州地区近地面PM_2.5浓度和地面实测站点数据之间的相关性R²分别为0.724（RMSE=10.993 μg·m^-3）和0.781（RMSE=9.687 μg·m^-3）；（2）分别针对不同下垫面环境的城市站点和县郊站点数据进行模型拟合验证，两个模型反演的PM_2.5浓度值与地面实测值之间皆具有良好的相关性，R²最高可达0.808；（3）将模型反演的PM_2.5浓度季均值与地面实测季均值进行对比分析，结果也显示二者高度相关，据此反演的2015年福州地区年平均PM_2.5浓度分布图可清晰地揭示福州地区PM_2.5浓度分布的空间变化情况。由此可见，基于MODIS 3 km AOD产品和气象数据建立的近地面PM_2.5浓度遥感估算模型能够很好地反演出福州地区近地面PM_2.5浓度分布情况。

Remote sensing techniques offer a unique opportunity to monitor air quality and are thus crucial for the management and surveillance of the air quality of polluted megacities. MODIS Aerosol Optical Depth (AOD) products with a spatial resolution of 10 km have been widely used to monitor ground-level particulate matters. However, the demands of air quality estimation are difficult to meet in local areas due to the coarse resolution of 10 km AOD. Taking Fuzhou city as an example, this study used the newly released AOD with a spatial resolution of 3 km and meteorological data to map ground-level PM_2.5 concentrations in the city and ultimately reveal the spatial details of the PM_2.5 exposure.Two regression models, namely, the daily calibration model and site daily calibration model, were developed to estimate and map ground-level PM_2.5 concentrations in Fuzhou, China. The MODIS 3 km AOD data for 2014-2015, in situ PM_2.5 concentration data for the same period, and meteorological data of wind speed and relative humidity were used. A simple linear model was also derived and used for comparison with the two calibration models.Results showed that the PM_2.5 concentrations and AOD had an extremely low agreement when a linear fit was applied, with the R² value being 0.117 and the RMSE being 19.510 μg/m^-3. Strong correlations were obtained with the use of the daily calibration model, which yielded an R² of 0.762 and RMSE of 10.146 μg/m^-3. A relatively high degree of agreement was achieved when the site daily calibration model was used; R² was 0.814, and RMSE was 8.965 μg/m^-3. Ten-fold Cross Validation (CV) was conducted to evaluate the performance of the regression models. The CV results showed that the site daily calibration model performed better than the daily calibration model. Correlation coefficients (R²) of the estimated PM_2.5 concentrations with the in situ data were 0.781 (RMSE=9.687 μg·m^-3) and 0.724 (RMSE=10.993 μg·m^-3). In addition, the PM_2.5 concentrations estimated by the site daily calibration model had a better agreement with the observed values for all seasons from 2014 to 2015. The R² of the estimated and observed values of the seasonal average PM_2.5 concentrations for the two models were 0.999 and 0.995, respectively, indicating that both models could reflect daily variations in the relationship among AOD, meteorological data, and PM_2.5 concentrations.In this study, we proposed a daily calibration model and a site daily calibration model using the newly released MODIS 3 km AOD product and meteorological data to estimate ground-level PM_2.5 concentrations in Fuzhou, China. The daily calibration model was used to retrieve the distribution of PM_2.5 concentrations in Fuzhou, as the site effect parameters needed for the site daily calibration model is not available for every 3 km grid. Nevertheless, these two models perform similarly in PM_2.5 estimation. The spatial distribution of PM_2.5 concentrations in Fuzhou derived from the MODIS 3 km AOD exhibits high concentrations over central urban areas and low values over suburban districts. These results clearly reveal the spatial variation of PM_2.5 in the area. This study indicated that the satellite-derived model based on the MODIS 3 km AOD product could work effectively in estimating PM_2.5 concentrations on a local scale.