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基于XGBoost-LME模型的京津冀地区近地面臭氧浓度估算
摘要点击 552  全文点击 135  投稿时间:2023-07-12  修订日期:2023-09-22
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中文关键词  近地面臭氧  TROPOMI数据  京津冀地区  XGBoost-LME模型  时空分布
英文关键词  near-surface ozone  TROPOMI data  Beijing-Tianjin-Hebei Region  XGBoost-LME model  spatio-temporal distribution
作者单位E-mail
龚德才 贵州大学矿业学院, 贵阳 550025 2643821034@qq.com 
杜宁 贵州大学矿业学院, 贵阳 550025 ndu1@gzu.edu.cn 
王莉 贵州大学矿业学院, 贵阳 550025  
张显云 贵州大学矿业学院, 贵阳 550025  
李隆 贵州大学矿业学院, 贵阳 550025  
张洪飞 贵州大学矿业学院, 贵阳 550025  
中文摘要
      高时空分辨率的近地面臭氧浓度分布数据对监测和防控大气臭氧污染,提高人居环境具有重要意义. 使用TROPOMI-L3 NO2、 HCHO产品和ERA5-land高分辨率数据作为估算变量,构建XGBoost-LME模型估算京津冀地区近地面臭氧浓度. 结果表明:①在估算变量中地表2 m温度(T2M)、2 m露点温度(D2M)、地表太阳向下辐射(SSRD)、对流层甲醛(HCHO)和对流层二氧化氮(NO2)是影响京津冀地区近地面臭氧浓度的重要因素,其中T2M、SSRD和D2M相关系数分别达到0.82、0.75和0.71. ②XGBoost-LME模型相较其它模型,其各项指标均为最优,十折交叉验证R2、MAE和RMSE分别为0.951、 9.27 μg·m-3和13.49 μg·m-3,同时,该模型在不同时间尺度均表现良好. ③在时间上,2019年京津冀地区近地面臭氧浓度存在显著的季节性差异,四季浓度变化为:夏季>春季>秋季>冬季,2019年该地区近地面臭氧月均浓度总体呈现出先上升后下降的倒“V”趋势,其中9月呈现小幅上升趋势,全年最大值出现在7月,最小值在12月;在空间分布上,2月和3月京津冀全域近地面臭氧浓度分布基本为同一水平,1、11和12月呈现出不显著的北高南低的空间分布趋势,其余月份该地区近地面臭氧浓度空间分布均呈现出南高北低的分布特征,高值区主要在南部海拔较低、人口密集和工业排放量较大的平原地区,低值区则主要在北部海拔较高、人口稀疏、植被覆盖率高和工业排放量低的山地地区.
英文摘要
      High spatiotemporal resolution data on near-surface ozone concentration distribution is of great significance for monitoring and controlling atmospheric ozone pollution and improving the living environment. Using TROPOMI-L3 NO2, HCHO products, and ERA5-land high-resolution data as estimation variables, an XGBoost-LME model was constructed to estimate the near-surface ozone concentration in the Beijing-Tianjin-Hebei Region. The results showed that: ① Through correlation analysis, surface 2 m temperature (T2M), 2 m dewpoint temperature (D2M), surface solar radiation downwards (SSRD), tropospheric formaldehyde (HCHO), and tropospheric nitrogen dioxide (NO2) were important factors affecting the near-surface ozone concentration in the Beijing-Tianjin-Hebei Region. Among them, T2M, SSRD, and D2M had strong correlations, with correlation coefficients of 0.82, 0.75, and 0.71, respectively. ② Compared with that of other models, the XGBoost-LME model had the best performance in terms of various indicators. The ten-fold cross-validation evaluation indicators R2, MAE, and RMSE were 0.951, 9.27 μg·m-3, and 13.49 μg·m-3, respectively. At the same time, the model performed well at different time scales. ③ In terms of time, there was a significant seasonal difference in near-surface ozone concentration in the Beijing-Tianjin-Hebei Region in 2019, with the concentration changing in the order of summer > spring > autumn > winter. The monthly average ozone concentration in the region showed an inverted “V” trend, with a slight increase in September. The highest value occurred in July, whereas the lowest value occurred in December. In terms of spatial distribution, the near-surface ozone concentrations in the Beijing-Tianjin-Hebei Region during the months of February and March were generally at the same levels. In January, November, and December, there was a relatively insignificant trend of higher concentrations in the north and lower concentrations in the south. For the remaining months, the spatial distribution of near-surface ozone concentrations in this area predominantly exhibited a pattern of higher concentrations in the south and lower concentrations in the north. High-value areas were predominantly found in the plain regions of the southern part with lower altitudes, dense population, and higher industrial emissions; low-value areas, on the other hand, were primarily located in mountainous areas of the northern part with higher altitudes, sparse population, higher vegetation coverage, and lower industrial emissions.

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