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全文摘要次数: 263 全文下载次数: 130
引用本文:

DOI:

10.11834/jrs.20233068

收稿日期:

2023-03-08

修改日期:

2023-10-07

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基于TROPOMI NO2、CO及HCHO重构数据的近地面O3浓度估算研究
陈小娟, 秦凯, Cohen Jason, 何秦
中国矿业大学 环境与测绘学院
摘要:

以TROPOMI大气成分产品为代表的卫星遥感数据在近地面臭氧(O3)浓度估算中表现出很好的潜力。由于云及反演算法局限性等原因,TROPOMI的大气成分产品存在一定的数据缺失,使得基于此的近地面O3浓度估算数据覆盖度较低。为此,本文采用经验正交函数分解插值法(DINEOF)对TROPOMI NO2、CO、HCHO原始数据产品进行缺失像元重构,并基于重构数据产品采用极限梯度提升算法(XGBoost)估算了中国大陆地区2019-2021年高覆盖度的日最大8h平均O3浓度(MDA8 O3)。对比研究了DINEOF方法对TROPOMI NO2、CO、HCHO原始数据产品的缺失像元进行重构后再建模估算O3的方法(方案1),使用TROPOMI NO2、CO、HCHO原始数据产品并对其缺失像元赋空值并输入其他特征变量来建模估算O3的方法(方案2),有TROPOMI NO2、CO、HCHO原始数据产品的建模结果和无TROPOMI NO2、CO、HCHO原始数据产品,只有其他特征变量的建模结果相结合的方法(方案3),均可提升O3模型估算结果的覆盖度。实验表明:方案1的结果最好,其十折交叉验证R2=0.86,RMSE=15.86μg/m3,模型精度和方案2基本一致且高于方案3,在重构区域的模型精度最高(训练集R2=0.82,RMSE=15.57μg/m3),且当重构区域出现O3重污染时(浓度大于160μg/m3),能明显改善模型高值低估现象,结果的空间分布更合理。方案1估算的2019-2021年近地面MDA8 O3的平均覆盖度从33.6%提升到97.2%,使用TROPOMI NO2、CO、HCHO重构数据产品建模估算O3可同时提升模型性能和模型结果的覆盖度。

Estimation of near-surface O3 concentration based on TROPOMI NO2, CO and HCHO reconstruction data
Abstract:

Satellite remote sensing data represented by TROPOMI atmospheric composition products show good potential in the estimation of near-surface O3 concentrations. Due to the limitations of cloud and inversion algorithms, there is a large lack of data for TROPOMI atmospheric composition products, resulting in low coverage of estimation results. Therefore, the DINEOF method was used to reconstruct the missing cells of TROPOMI NO2, CO and HCHO original data products, and estimates the maximum daily 8h average O3 concentration (MDA8 O3) of Chinese mainland high coverage from 2019 to 2021 based on XGBoost. In this paper, three schemes to improve the coverage of O3 model estimation results are compared. Scheme 1 reconstructs the missing cells of TROPOMI NO2, CO and HCHO original data products based on the DINEOF method, and uses the reconstructed data to model and estimate O3. Scheme 2 is based on TROPOMI NO2, CO and HCHO original data products, null values are assigned to their missing cells, and only other characteristic variables are entered to model and estimate O3. Scheme 3 uses a combination of modeling results containing TROPOMI NO2, CO and HCHO original data products and modeling results that do not contain TROPOMI NO2, CO and HCHO original data products, but with other characteristic variables. Experiments show that the results of scheme 1 are the best, its ten-fold cross-validation R2=0.86, RMSE=15.86μg/m3, the model accuracy is basically the same as scheme 2 and higher than that of scheme 3, the model accuracy in the reconstruction region is the highest (training set R2=0.82, RMSE=15.57μg/m3), and when O3 heavy pollution occurs in the reconstruction region (concentration greater than 160μg/m3), the underestimation of the high value of the model can be significantly improved, and the spatial distribution of the results is more reasonable. The average coverage of the near-surface MDA8 O3 estimated in scheme 1 increased from 33.6% to 97.2% from 2019 to 2021. Using TROPOMI NO2, CO, and HCHO refactor data products to model and estimate O3 can improve model performance and coverage of model results.

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