Study on the characteristics of PM_2.5 and O₃ compound air pollution and concentration prediction model in the middle reaches of the Yangtze river

PM_2.5 and O₃ were the main factors affecting urban and regional air quality in China, exploring their pollution characteristics and predicting their concentrations were the basic work for the prevention and control of composite atmospheric pollution. The PM_2.5 and O₃ compound air pollution characteristics were analyzed using the concentration data of PM_2.5 and O₃ in two major cities, Yichang and Wuhan, in the middle reaches of the Yangtze River from 2015 to 2023. The machine learning model explainable tool was used to reveal the contribution of meteorological factors such as temperature, humidity, precipitation, sunshine, wind speed and other meteorological factors to the influence of PM_2.5 and O₃ concentrations．Based on nine deep learning methods including GRU, a regression prediction model was constructed for PM_2.5 and O₃ concentrations and the results were as follows.(1)The annual characteristics of O₃ concentrations in Yichang and Wuhan was increased by 3.89µg·m⁻³ and 2.73 µg·m⁻³ on each year from 2015 to 2023, and the increase trend was more greater in summer and autumn; while the PM_2.5 concentrations showed a significant decrease trend, with the trend rates of −3.59 µg·m⁻³·a⁻¹ and −3.36 µg·m⁻³.a-¹ respectively.The decrease was more notable in winter an spring, while indicated that the treatment of pollutant had achieved significant effect in recent years.(2)The monthly changes of PM_2.5 and O₃ concentrations was showed U and M features, and with a weak negative correlation between PM_2.5 and O₃. The compound air pollution days in Yichang and Wuhan were 60d and 39d from 2015 to 2023 respectively, mainly concentrated in February-May and October-December. The days was showed an decreasing trend annually, but higher in 2023 slightly.(3)A total of GRU, BIGRU, Attention-GRU and Attention-BiGRU models were performed better in predicting PM_2.5 and O₃ concentrations in Yichang and Wuhan, which GRU model had the shortest running time. The GRU model would improve PM2.5 and O₃ concentration preciditon effectively.(4)Compared with GRU, the root mean square error for O₃ concentration prediction of the regression prediction model was reduced by 5% and 8% in Yichang and Wuhan, meanwhile PM_2.5 concentration prediction was improved by 20% and 16% respectively. Ts scores of PM_2.5 and O₃ was 60.00% in Yichang and 69.23% in Wuhan. The regression prediction model can provide scientific basis for the prediction of PM_2.5 and O₃ concentration and the control of compound pollution in Yichang and Wuhan cities which were affected by meteorological conditions.