DataSheet1_Development of ground-level NO2 models in Vietnam using machine learning and satellite observations with ancillary data.docx

In this study, the aim was to create daily ground-level NO2 maps for Vietnam spanning from 2019 to 2021. To achieve this, various machine learning models (including the Mixed Effect Model, Neural Network, and LightGBM) were utilized to process satellite NO2 tropospheric columns from Ozone Monitoring Instrument (OMI) and TROPOMI, as well as meteorological and land use maps and ground measurement NO2 data. The LightGBM model was found to be the most effective, producing results with a Pearson r of 0.77, RMSE of 7.93 μg/m³, and Mean Relative Error (MRE) of 42.6% compared to ground truth measurements. The annual average NO2 maps from 2019–2021 obtained by the LightGBM model for Vietnam were compared to a global product and ground stations, and it was found to have superior quality with Pearson r of 0.95, RMSE of 2.27 μg/m³, MRE of 9.79%, based on 81 samples.

本研究旨在生成2019年至2021年越南逐日近地面二氧化氮（NO₂）浓度分布图。为此，本研究采用多种机器学习模型（包括混合效应模型（Mixed Effect Model）、神经网络（Neural Network）以及轻量级梯度提升机（LightGBM）），对臭氧监测仪（Ozone Monitoring Instrument，OMI）和对流层监测仪（TROPOMI）获取的卫星对流层NO₂柱浓度数据、气象与土地利用图及地面实测NO₂数据进行处理。研究表明，轻量级梯度提升机（LightGBM）模型性能最优，其生成的预测结果与地面实测真值相比，皮尔逊相关系数（Pearson r）达0.77，均方根误差（RMSE）为7.93 μg/m³，平均相对误差（MRE）为42.6%。基于81个样本，通过LightGBM模型生成的2019-2021年越南年平均NO₂浓度分布图，与全球同类产品及地面站点实测数据对比后发现，其质量更优，皮尔逊相关系数达0.95，均方根误差为2.27 μg/m³，平均相对误差为9.79%。