摘要
本文以2017-2021年汾渭平原典型城市西安大气PM2.5和O3浓度数据为基础,运用机器学习方法分析了 PM2.5和O3的变化特征和趋势,讨论了污染气体(NO2、SO2、CO和HCHO)与气象因素(温度、相对湿度、风速、大气压力、边界层高度和太阳辐射)对PM2.5和O3浓度变化的交互影响.Theil-Sen趋势分析发现2017-2021年西安市PM2.5和O3分别以每年6.03％和2.06％的速度下降.单因素广义加性模型(GAM)中,NO2、SO2和CO对PM2.5浓度变化影响的模型解释率较高,温度、太阳辐射和大气压对O3浓度变化影响的模型解释率较高.多因素GAM模型中,PM2.5和O3均呈现非线性变化,模型的解释方差分别为84.9％和75.0％,拟合程度较高.通过等高线图分析了多个气象因素和多种污染气体两两交互作用分别对PM2.5和O3浓度的影响,其中温度和污染气体(NO2、SO2、CO和O3)对PM2.5浓度的影响更大;温度和太阳辐射对O3的影响更大.NO2和CO分别与气象条件两两相互作用时,PM2.5浓度随NO2和CO的增高而增高,O3浓度则与NO2和CO的变化趋势相反.结合本地污染物源清单,建议加强控制工业源和移动源排放,有助于降低PM2.5和O3的浓度.
Abstract
Based on the concentrations of PM2.5 and O3 in Xi'an,Fenwei Plain from 2017 to 2021,this study analyzed the change characteristics and trend of PM2.5 and O3,and discussed the interaction effects of pollutant gases(NO2,SO2,CO and HCHO)and meteorological factors(temperature,RH,wind speed,atmospheric pressure,boundary layer height and solar radiation)on PM2.5 and O3 by using machine learning method.Theil-Sen trend analysis found that PM2.5 and O3 decreased by 6.03％and 2.06％per year from 2017 to 2021,respectively.For the single influencing factor GAM models,the model explanation rate of the effects of NO2,SO2 and CO on PM2.5 is higher,temperature,solar radiation and pressure on O3 is higher.In the multiple influencing factors GAM model,all factors exhibited a non-linear relationship with PM2.5 and O3,and the contributions to the change of PM2.5 and O3 are 84.9％and 75.0％with significant impact,also suggesting a good model fit.Contour map were used to analyze the pairwise interaction of several meteorological factors and polluting gases on the concentration of PM2.5 and O3,respectively,which found that temperature and pollution gases(NO2,SO2,CO and O3)have considerable impact on PM2.5 concentration.For O3,the influence of temperature and solar radiation is greater.NO2 and CO interact with meteorological conditions,PM2.5 increased with the increase of NO2 and CO,however O3 showed opposite trends.According to the local pollutant source inventory,it is suggested to strengthen the emission of pollutants from industrial sources and mobile sources,which helps to reduce the concentration of PM2.5 and O3.
基金项目
"西部之光"人才培养计划(XAB2021YN05)
黄土与第四纪地质国家重点实验室开放基金(SKLLQG1944)
NETL
NSTL
万方数据