基于LightGBM的广州市O3和PM2.5浓度预报订正OA

In order to improve the accuracy of air quality models for predicting O3 and PM2.5 concentrations in Guangzhou,a LightGBM algorithm was employed to establish correction models for O3 and PM2.5 concentration forecasts in three time periods of 0-24 h,24-48 h,and 48-72 h,using the observations from Guangzhou national control stations and hourly forecast data.The SHAP method was then applied to interpret and analyze the correction models.The results show that the LightGBM correction model can significantly improve the O3 and PM2.5 concentration forecasts for each station and forecast time period.The error of each station significantly reduced and its distribution is more consistent after correction.As the forecast time period increases,the effect of the correction models on forecast improvement slightly decreases.Overall correction effects across all time period showed the root mean square error(RMSE),average absolute deviation(AAD),and correlation coefficient of O3 concentration from the model and observation changed from 39.5 μg·m-3,30.3 μg·m-3,and 0.61 to 23.3 μg·m-3,16.9 μg·m-3,and 0.86,respectively.For PM₂.₅ concentrations,the RMSE,AAD,and correlation coefficient changed from 18.3 μg·m-3,12.7 μg·m-3,and 0.26 to 9.7 μg·m-3,6.9 μg·m-3,and 0.76,respectively.Key features among different correction models showed little variation.For the O3 concentration correction model,the most important features mainly include O3 concentration,temperature,relative humidity,NO2,shortwave radiation,wind direction and speed.For the PM2.5 concentration correction model,the most important features mainly include air pressure,air quality index,temperature,relative humidity,air pressure,PM10,O3,wind direction and speed.The influence of each feature on the correction models conforms to the generation and accumulation mechanism of O3 and PM2.5.The case study further proves the effectiveness and practicality of the correction model.