Most studies about particulate matter (PM) estimation have been done based on satellite-derived optical depth aerosol (AOD) products. But, the use of AOD products having coarse resolution is not possible for PM map generation in small spatial coverage such as local cities. To solve this issue, a PM estimation framework is proposed in this work which accepts the original calibrated radiance of MODIS-Level 1 images as input. There are no intermediate computations for atmospheric reflectance or aerosol thickness calculation. A deep neural network consisting of recurrent layers is proposed to extract the relationship between the grey level values of the satellite image bands and the PM measurements in different days and locations. Two individual networks are trained for PM_2.5 and PM₁₀ concentrations. The PM_2.5 map and PM₁₀ map of Tehran city are generated. The performance of the proposed method is compared with several recently published air pollution studies. The results show that the proposed method is a simple, low cost and efficient approach for PM generation of small-scaled coverage using free available Moderate Resolution Imaging Spectroradiometer (MODIS) images.

大多数关于颗粒物（PM）估计的研究都是基于卫星衍生的光学深度气溶胶（AOD）产品进行的。但是，在诸如当地城市之类的小空间覆盖范围内，无法使用分辨率较粗的AOD产品生成PM地图。为了解决这个问题，在这项工作中提出了一个PM估计框架，该框架接受MODIS-Level 1图像的原始校准辐射度作为输入。没有用于大气反射率或气溶胶厚度计算的中间计算。提出了一个由递归层组成的深度神经网络，以提取卫星图像波段的灰度值与不同日期和位置的PM测量值之间的关系。对两个单独的网络进行了PM _2.5和PM ₁₀培训浓度。生成了德黑兰市的PM _2.5地图和PM ₁₀地图。将该方法的性能与最近发表的几项空气污染研究进行了比较。结果表明，所提出的方法是一种免费，可用的中分辨率成像光谱仪（MODIS）图像，用于小规模覆盖的PM生成的简单，低成本，高效方法。