Reconstructing global PM2.5monitoring dataset from OpenAQ using a two-step spatio-temporal model based on SES-IDW and LSTM

Abstract

Fine particulate matter (PM2.5) is widely concerned for its harmful impacts on global environment and human health, making air pollution monitoring so crucial and indispensable. As the world's first open, real-time, and historical air quality platform, OpenAQ collects and provides government measurement and research-level data from various channels. However, despite OpenAQ's innovation in providing us with ground-measured PM2.5 worldwide, we find significant data gaps in time series for most of the sites. The incompleteness of the data directly affects the public perception of PM2.5 concentration levels and hinders the progress of research related to air pollution. To address these issues, a two-step hybrid model named ST-SILM, i.e. spatio-temporal model with single exponential smoothing-inverse distance weighted (SES-IDW) and long short-term memory (LSTM), is proposed to repair the missing data from PM2.5 sites worldwide collected from OpenAQ from 2017 to 2019. Both spatio-temporal correlation and neighborhood fields are considered and established in the model. To be specific, SES-IDW were firstly used to repair missing values, and secondly, the LSTM network was employed to reconstruct the time series of continuous missing data. After the global ground-measured PM2.5 was reconstructed, the light gradient boosting machine model was applied to remote sensing estimation of the original ground-measured PM2.5 and of the reconstructed ground-measured PM2.5 to further verify the performance of ST-SILM. Experiment results show that the estimation accuracy of the reconstructed dataset is better (R 2 from 2017 to 2019 increased by 0.02, 0.02, and 0.01 compared with the original dataset). Therefore, it is concluded that the proposed model can effectively reconstruct data from PM2.5 sites worldwide.