Inferring Fine-Grained PM2.5 with Bayesian Based Kernel Method for Crowdsourcing System

Air pollution seriously affect people's lives, among which PM
_2.5
is especially harmful for humans health. Although many countries have established fixed air quality monitoring stations (AQMS) to monitor air pollution, the costs of constructing and maintaining for AQMS are extremely expensive and the density of AQMS is very low. To acquire fine-grained concentration of PM
_2.5
, this paper have proposed a novel Bayesian based kernel method. Our model leverage heterogeneous data which jointly using images information, camera lens information, GPS information and magnetic sensor information. To study the relationship between PM
_2.5
concentration and images information, we have established a crowdsourcing system and have collected photos for consecutive 16 months. The performance of the proposed method has been evaluated thoroughly by real dataset we have collected. The results show that, compared with three baselines, our proposed algorithm can reduce up to 35% prediction error in average.