基于TROPOMI的近地面臭氧浓度估算研究  

Estimation of Near-Surface Ozone Concentration Based on TROPOMI

作　　者：张少磊 杜宁[1] 王莉[1] 吴宇宏 吴迪 周彬 

机构地区：[1]贵州大学矿业学院,贵州 贵阳

出　　处：《应用数学进展》2023年第1期361-366,共6页Advances in Applied Mathematics

摘　　要：在PM2.5浓度逐年下降的背景下,臭氧浓度不降反升,目前臭氧已经成为国内大气的主要污染物之一。本文利用当今大气监测中时空分辨率最高的TROPOMI传感器,并基于大数据关联思想,构建GWR-RF臭氧浓度估算模型,融合臭氧浓度地面监测数据、欧洲中期天气预报中心的ERA5数据集、高分辨率遥感影像(TROPOMI_NO2)数据,构建训练整体数据集,用以估算2019年四川省每日最大8 h平均臭氧浓度(O3_8h)。研究结R2为0.94,RMSE为10.5 μg•m−3,MAE为7.49 μg•m−3,表明GWR-RF模型对O3_8有较好的估算性能。In the background of decreasing PM2.5 concentration year by year, ozone concentration does not fall on the contrary, ozone has become one of the main pollutants in the domestic atmosphere. In this paper, the TROPOMI sensor, which has the highest spatial and temporal resolution in current atmospheric monitoring, and based on the idea of big data association, is used to construct the GCR-RF ozone concentration estimation model, which integrates the ground monitoring data of ozone concentration, the ERA5 dataset of the European Center for Medium Range Weather Predic-tion (ECMWF) and the high resolution remote sensing image (TROPOMI_NO2) data. A training da-taset was constructed to estimate the daily maximum 8h mean ozone concentration (O3_8h) in Chengdu in 2019. The results showed that R2, RMSE and MAE were 0.94, 10.5 μg•m−3 and 7.49 μg•m−3, indicating that the GWR-RF model had good performance in estimating O3_8h.

关 键 词：近地面臭氧 对流层观测仪(TROPOMI_NO2) ERA5 GWR-RF 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]