Multiple atmospheric pollutants, such as PM2.5, PM10, and NO2, degrades air quality in many parts of the world. Fine-grained air pollution data can help combat the problem, but conventional monitoring stations are too expensive to support high spatial resolution; image-based estimates have the potential to improve spatial coverage. We estimate pollutant concentrations from images using the position-and color-dependent properties of scattering and absorption. We are the first to use images to estimate pollutant concentrations in systems with multiple pollutants. We achieve this by considering the differences in scattering and absorption spectra between different pollutants. Our system improves the accuracy of PM2.5, PM10, and NO2 estimation by 22% for single-scene images in Beijing and Shanghai compared to the best existing image-based techniques.