全球定位系统2024，Vol.49Issue(2)：69-75,7.DOI:10.12265/j.gnss.2023151
融合GNSS、ERA5、大气污染物的PM2.5浓度预测研究
Study on PM2.5 concentration prediction by integrating GNSS,ERA5 PWV,and atmospheric pollutants
摘要
Abstract
The prediction of air quality during the winter and spring seasons can be used for the public to make reasonable arrangements for travel and traffic management by relevant government departments.The main influencing factors of PM2.5 concentration include atmospheric pollutants,precipitable water vapor(PWV),etc.To improve the accuracy of PM2.5 concentration prediction,taking Beijing-Tianjin-Hebei region as an example,it was combined fast Fourier transform(FFT)and LSTM neural network methods,considered observation elements such as GNSS,ERA5 PWV,and atmospheric pollutants,and constructed the PM2.5 concentration prediction model to predict the concentration of PM2.5 in the next 24 hours.It was used GNSS PWV to correct the ERA5 PWV in the region and evaluated the accuracy.The public change period of air pollutants,ERA5 PWV and other observation elements are extracted by FFT,and the optimal public period is 78 hours;Select various factors with the best common cycle length as the model input,and the PM2.5 concentration of the 24 hour sequence as the model output.Evaluate model accuracy through RMSE evaluation indicators.The research results are indicated that the accuracy of ERA5 PWV correction model based on GNSS is better than 2 mm in autumn and winter seasons.The prediction accuracy of the FFT-LSTM model is 10.22 μg/m3 in plain,8.56 μg/m3 in mountainous,and 9.02 μg/m3 in plateau regions,while the predicted time limit of 24 hours.It can effectively predict the PM2.5 concentration in the next 24 hours.This model can provide reference for relevant departments in air pollution control.关键词
细颗粒物(PM2.5)/大气污染物/GNSS水汽/ERA5 水汽/快速傅里叶变换(FFT)/长短期记忆(LSTM)Key words
PM2.5/atmospheric pollutants/GNSS PWV/ERA5 PWV/FFT/LSTM分类
天文与地球科学引用本文复制引用
刘严萍,司甜,毕慧丽,张曼琪,王勇,许祖豪..融合GNSS、ERA5、大气污染物的PM2.5浓度预测研究[J].全球定位系统,2024,49(2):69-75,7.基金项目
天津市教委科研计划项目(2021ZD001) （2021ZD001）
国家级大学生创新创业训练计划项目(202310792010) （202310792010）
