Improvement of PM2.5 and O3 forecasting by integration of 3D numerical simulation with deep learning techniques

Abstract

Air pollution is a major impediment to the sustainable development of cities and society. Governed by emission characteristics and meteorological conditions, the formation and destruction of fine particulate matter (PM2.5) and ozone (O3) are complicated, and accurate predictions of the concentrations of these two major secondary atmospheric pollutants remain challenging. In this study, by combining meteorological and air pollutant data from ground observations and the Weather Research and Forecasting (WRF)-Community Multiscale Air Quality (CMAQ) model simulations, a deep learning model structure based on long short-term memory layers (LSTM) was developed and applied to predict the PM2.5 and O3 concentrations in the future 48 h period. The forecasting improvement was extended to the whole Greater Bay Area by introducing a spatial correction (SC) method to the CMAQ simulation results. Compared with the original CMAQ forecast, the new method gained a 26% reduction in mean absolute error (MAE) and a 33% reduction in root mean square error (RMSE), respectively, in terms of PM2.5; it also achieved a 40% reduction in MAE and a 34% reduction in RMSE in terms of O3. SC method, applied to the whole GBA region, also reduced the overall MAE and RMSE by 10% and 17% in terms of PM2.5 and by 31% and 25% in terms of O3, respectively. Using an AI approach, our study provides new perspectives for further improving air quality forecasting from both temporal and spatial perspectives, thus increasing the smartness and resilience of the cities and promoting environmentally sustainable development in the area.