LAUSR

Air pollution has become a worldwide concerned issue and automatical estimation of air quality can provide a positive guidance to both individual and industrial behaviors. Given that the traditional instrument-based method requires high economic, labor costs on instrument purchase and maintenance, this paper proposes an effective, efficient, and cheap photo-based method for the air quality estimation in the case of particulate matter (PM2.5). The success of the proposed method lies in extracting two categories of features (including the gradient similarity and distribution shape of pixel values in the saturation map) by observing the photo appearances captured under different PM2.5 concentrations. Specifically, the gradient similarity is extracted to measure the structural information loss with the consideration that PM2.5 attenuates the light rays emitted from the objects and accordingly distorts the structures of the formed photo. Meanwhile, the saturation map is fit by the Weibull distribution to quantify the color information loss. By combining two features, a primary PM2.5 concentration estimator is obtained. Next, a nonlinear function is adopted to map the primary one to the real PM2.5 concentration. Sufficient experiments on real data captured by professional PM2.5 instrument demonstrate the effectiveness and efficiency of the proposed method. Specifically, it is highly consistent with real sensor’s measures and requires low implementation time.