PM2.5 Concentration Estimation Based on Support Vector Regression: Hybrid Approach Using PM2.5-Sensitive Pixels and Multi-Features

Fine particulate matter (PM2.5) is a hazardous air pollutant that poses serious risks to human health. Long-term exposure to high concentrations of PM2.5 increases the likelihood of developing cardiovascular and respiratory diseases. Therefore, accurately monitoring PM2.5 concentrations are crucial for effective air quality management. However, due to the limited number and uneven distribution of monitoring stations, traditional monitoring methods fail to provide comprehensive data. With advancements in imaging technology and data processing, researchers have focused on estimating PM2.5 concentrations using image-based approaches. We constructed the PM2.5-sensitive pixel (PSP) approach. In addition to the original four image features—Sobel, Dark Channel Prior (DCP), entropy, and contrast—we identified a new image feature and integrate three meteorological variables, relative humidity, temperature, and wind speed, to enhance the estimation of PM2.5 concentrations.