Fine-Scale Mapping of Particulate Matter Using Landsat Imagery and Low-Cost Sensor Data from Purpleair: A Case Study of Los Angeles

Fine-scale monitoring of particulate matter (PM) is essential to assess the patterns and sources of air pollution. However, to this date, its usage is still limited. In this study, we explore the usage of Landsat imagery for PM mapping at 30 m spatial resolution, where air pollutants at the street level can be distinguished. With in-situ data from the low-cost sensors of the PurpleAir network, we developed a multitask neural network that can simultaneously estimate all three metrics of PM, i.e., PM1, PM2.5, and PM10. With 936 monitoring stations from 25 dates, a total of 15,250 valid observations were used to construct the model. With external validation, results show that the model can achieve R
²
of 0.767, 0.803, and 0.799 in estimating PM1, PM2.5, and PM10, respectively, at the street level. Using one multitask neural network to predict PM1, PM2.5, and PM10 improves the efficiency of real-life air pollutant monitoring.