摘要
为探究时空地理加权回归模型(geographical and temporal weighted regression model,GTWR)在反演中国臭氧(O_(3))浓度方面的准确性和适用性。该研究基于O_(3)地面监测站点数据和OMI(ozone monitoring inscument)臭氧柱浓度数据、相对湿度、降水、风速、气温、蒸散发、大气边界层高度、归一化植被指数和人口密度9个辅助变量建立反演O_(3)浓度的GTWR模型分析中国O_(3)浓度的空间分布,并使用地理探测器研究9个驱动因素对O_(3)的影响力、各因子之间的交互作用及作用机制差异。结果表明:1)该研究所选取的9个变量因子之间多重共线性较弱,满足建模条件。2014—2021年各个年份的GTWR模型决定系数(coefficient of determination,R2)均不低于0.81,均方根误差(root mean square error,RMSE)在9.19~10.90μg/m3之间,平均绝对误差(mean absolute error,MAE)介于6.27~7.73μg/m3之间,模型拟合效果较好。2)2014年以来中国O_(3)年均浓度整体呈先升高后降低再缓慢上升的变化趋势并且表现出明显的季节变化特征,季均O_(3)浓度值由高到低依次为夏季、春季、秋季、冬季,暖季浓度较冷季浓度高;在空间分布上存在明显的区域差异,基本形成沿纬度分布的格局,高值区集中在北纬30°~45°之间。3)在地理探测器中,单因子探测中蒸散发、大气边界层高度和气温对于O_(3)的解释力最强分别为0.840、0.797和0.759;当因子间存在交互作用时解释力得到进一步提升,其中蒸散发∩人口密度、相对湿度∩气温交互作用最强,为0.95,表明与单因素作用相比因子间的交互作用对O_(3)浓度影响更为明显;蒸散发、大气边界层高度和气温分别与其他因子的共同作用对O_(3)的空间分布影响差异较大,结合单因子分析结果也说明了三者的重要性强,其他因子相互之间不存在显著性差异。该研究结果可为分析臭氧污染来源和扩散规律提供帮助,进一步给予中国大气环境质量改善决策支持。
The main purpose of this research is to evaluate the effectiveness and practical performance of the spatiotemporal and temporal weighted regression model(GTWR),in order to accurately estimate the ozone(O_(3))concentrations across China.A GTWR model was developed and validated to estimate O_(3)concentrations,according to a comprehensive set of nine auxiliary variables.These variables included the ground-based O_(3)monitoring station,Ozone Monitoring Instrument(OMI)ozone column concentration,relative humidity,precipitation,wind speed,temperature,evapotranspiration,atmospheric boundary layer height,normalized vegetation index(NDVI),and population density.The spatial distribution patterns of O_(3)concentrations were analyzed using geographical detectors.A systematic investigation was then made to explore the influence of these nine driving factors on O_(3)levels,particularly for the impact of these interactions among these factors on the governing mechanisms of O_(3)distribution.The results reveal the following key points:1)Multicollinearity and Model Performance:The nine variables shared a low level of multicollinearity,indicating the reliable performance of the model.GTWR model was achieved in a high level of accuracy over the period from 2014 to 2021,with the coefficient of determination(R2)not less than 0.81.The performance of the model also included a root mean square error(RMSE)ranging from 9.19 to 10.90μg/m3 and a mean absolute error(MAE)between 6.27 and 7.73μg/m3,indicating the robust predictive capabilities.2)Trends in ozone concentrations:the annual average concentration of ozone demonstrated a general upward trend since 2014,characterized by an initial increase,a subsequent decrease,and a gradual rise.There were distinct seasonal variations in the O_(3)levels.The average concentrations were ranked in the descending order of summer,spring,autumn,winter.The higher O_(3)concentrations were observed during warmer seasons,compared with the cooler ones.The spatial distribution of O_(3)concentrations shared a significant regional pattern that aligned closely with the latitude.This distribution of pattern also represented the population density and economic development across China,where the higher O_(3)levels were concentrated in regions between 30°and 45°north latitude.3)Geographical detector analysis:Evapotranspiration,atmospheric boundary layer height,and temperature were the strongest single factors on the O_(3)level,with explanatory powers of 0.840,0.797,and 0.759,respectively.The interactions of most factors shared a dual-factor enhancement,followed by a nonlinear enhancement,indicating joint changes in O_(3).There was no interaction to show the linear or nonlinear weakening.All factors shared an enhancing effect on O_(3)concentration,albeit to varying degrees.Furthermore,the explanatory power was further improved,when the factors interacted.Among them,the strongest interactions were observed between evapotranspiration and population density,as well as the relative humidity and temperature,with explanatory values of 0.95.Therefore,there was a more pronounced impact of factor interactions on O_(3)concentrations,compared with single factors.Ecological detection showed significant differences between evapotranspiration,temperature,and all other factors except atmospheric boundary layer height,and between atmospheric boundary layer height and all other factors except evapotranspiration and temperature.It infers that the combined effects of evapotranspiration,atmospheric boundary layer height,and temperature with other factors posed a greater impact on the spatial distribution of ozone.Single-factor analysis also verified that these three factors shared a stronger effect on ozone.There was no significant difference between the rest factors,indicating their relatively similar mechanisms of impact on ozone concentration.In summary,the GTWR model was a robust tool to analyze the O_(3)concentrations,and effectively capture both spatial and temporal variations.The findings also emphasized the complex interplay between environmental variables and O_(3)levels.The comprehensive models were necessary to consider both the individual and interactive effects of multiple factors.This approach can provide valuable insights into the spatial distribution and temporal dynamics of O_(3).
作者
夏楠
李安澜
全伟琳
唐梦迎
唐玉倩
徐战江
XIA Nan;LI Anlan;QUAN Weilin;TANG Mengying;TANG Yuqian;XU Zhanjiang(College of Geographical and Remote Sensing Sciences,Xinjiang University,Urumqi 830017,China;Xinjiang Key Laboratory of Oasis Ecology,Xinjiang University,Urumqi 830017,China)
出处
《农业工程学报》
CSCD
北大核心
2024年第23期283-293,共11页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家科技部第三次新疆综合科学考察项目吐哈盆地农业水资源利用效率与节水潜力调查评估(2022xjkk1103)。
关键词
臭氧
时空地理加权回归
驱动因子
地理探测器
ozone
geographical and temporal weighted regression model
driving factors
geographical detectors
作者简介
夏楠,博士,副教授,研究方向为干旱区生态环境遥感监测。Email:xn_gis@xju.edu.cn。
