Particulate matter (PM), such as PM2.5 and PM10, was the major pollutant in a severe air pollution episode in 2013 eastern China. Limited by the coverage of stations, fine-scale monitoring at every corner in the city is difficult, if not impossible. Hyperspectral imagery can capture the ground and air information, from which we can estimate the concentrations of PM. In this study, we develop a multitask learning method to estimate the concentrations of PM based on the 10-m hyperspectral data from the newly-launched Zhuhai-1 satellites. We first convert the raw radiance to top-of-atmosphere (TOA) reflectance using the 1985 Wehrli solar irradiance spectrum. Then, we train a multitask network to simultaneously estimate PM2.5 and PM10 concentrations based on the TOA hyperspectral data. Results show that our method leads to estimations of an R-squared of 0.77 for PM2.5 and an R-squared of 0.42 for PM10.