Hybrid Measurement of Air Quality as a Mobile Service: An Image Based Approach

Abstract

Air pollution is becoming a serious issue that threatens everyone's daily life. Accordingly how to measure the local air quality easily and quickly becomes an urgent problem. Sensor-based methods are relatively expensive, and image based air quality measurement is a promising direction as it is often less cost. Meanwhile web service of precise air quality measurement is of great importance as it allows to timely monitor the air pollution and can provide recommendations for decision makers. This paper devises an effective web service to address this challenging problem. Specifically we offer a service letting mobile device users to upload photos taken outdoor with meta information. Once the background web service computation is finished, we return the air quality level at their location to the users. In our service system, it includes three basic modules: 1) using GPS location information to get the basic air quality value from the nearest official air quality station, 2) using photo by our air quality assessment based on dictionary learning for image representation to compute air quality. In this method we add ℓ21 norm to the target function to promote the nonzero coefficients of the words aggregate within similar quality level, 3) using photo uploaded by user by our CNN based photo air pollution estimation method to obtain the air quality estimation. Finally we combine the above three estimations to reach the final estimation to the end users. Empirical experiments are conducted on the real-world dataset that collaborates the efficacy of our method.