基于自动机器学习集成模型的日NO2模拟 |
摘要点击 1758 全文点击 359 投稿时间:2023-11-09 修订日期:2024-01-19 |
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中文关键词 自动机器学习 NO2 集成模型 遥感 时空分布 |
英文关键词 automatic machine learning NO2 ensemble model remote sensing spatial-temporal distribution |
DOI 10.13227/j.hjkx.20241015 |
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中文摘要 |
为深入了解近地面NO2的空间分布,以长江三角洲地区为研究区,利用NO2站点实测数据和结合对流层观测仪(TROPOMI)的NO2柱浓度数据,充分考虑人口、高程和气象因素对NO2的影响,利用自动机器学习选取了模拟精度较高的5种机器学习算法:ET、RF、XGBoost、LightGBM和Catboost,并将这5种算法利用Stacking模型进行集成后对2020年3月至2021年2月长三角地区逐日NO2浓度进行了模拟. 结果表明,Stacking集成模型的RMSE和MAE值分别为7.078和5.270,其精度优于ET、RF、XGBoost、LightGBM和Catboost这5种单一的算法;长三角三省一市的NO2高浓度值空间分布基本呈现以三省交汇处为中心口朝西南方向的U字形格局,以上海市、杭州市、南京市和合肥市为中心形成的城市群污染尤为显著,超过国家标准日限制的城市共27个,常州市是NO2污染最严重的城市,NO2浓度超标14 d,其次是上海市,超标13 d. NO2浓度季节分布特点为:冬季>秋季>春季>夏季,其中夏季7月9日NO2污染最轻,冬季12月23日NO2污染最严重. |
英文摘要 |
To understand the spatial distribution of NO2 near the surface, we utilized measured data from NO2 monitoring stations and combined it with column concentration data from the Tropospheric Monitoring Instrument (TROPOMI), taking the Yangtze River Delta region as the study area. We considered the impact of factors such as population, elevation, and meteorological conditions on NO2 levels. We used automated machine learning to select five machine-learning algorithms with high simulation accuracy, namely ET, RF, XGBoost, LightGBM, and Catboost, and then integrated these five algorithms using the Stacking model to simulate the daily NO2 concentration in the Yangtze River Delta region from March 2020 to February 2021. The results indicated that the RMAE and MAE values of the Stacking ensemble model were 7.078 and 5.270, respectively, which outperformed the single algorithms of ET, RF, XGBoost, LightGBM, and Catboost. The spatial distribution of high NO2 concentrations in the Yangtze River Delta region, consisting of three provinces and one municipality, exhibited a U-shaped pattern with the convergence point located at the intersection of the three provinces, extending towards the southwest. Notably, urban pollution was particularly significant in the urban agglomerations centered around Shanghai, Hangzhou, Nanjing, and Hefei. There were 27 cities that exceeded the national standard daily limit. Changzhou was the city with the most serious NO2 pollution, with the NO2 concentration exceeding the standard for 14 d, followed by Shanghai, with 13 d. In terms of seasonal variation, the order of severity was as follows: winter, autumn, spring, and summer, with the least NO2 pollution occurring on July 9th during the summer, and the most severe NO2 pollution was observed on December 23rd during the winter. |
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