基于WRF-CMAQ模型的辽宁中部城市群PM_(2.5)化学组分特征

Numerical Simulation Study on Chemical Composition of PM_(2.5) in Urban Agglomeration of Central Liaoning Province According to WRF-CMAQ Model

为了探究北方寒冷地区城市PM_(2.5)化学组分特征,采用WRF-CMAQ模型对辽宁中部城市群2019年1月、4月、7月、10月及一次重污染过程(2019年1月11—14日)的PM_(2.5)化学组分展开模拟分析.结果表明:WRF-CMAQ模型分析下SO_(2)、NO_(2)、PM_(10)、PM_(2.5)浓度模拟值与监测值的相关系数(R)在0.63~0.82之间,PM_(2.5)组分中SO_(4)^(2-)、NO_(3)^(-)、NH_(4)^(+)、EC、OC浓度的相关系数(R)在0.59~0.88之间,WRF-CMAQ模型对大气污染物及PM_(2.5)主要化学组分的模拟效果较好,可以反映PM_(2.5)及其组分的时空变化特征.通过对模拟结果的进一步分析发现,辽宁中部城市群PM_(2.5)中SNA(SO_(4)^(2-)、NO_(3)^(-)、NH_(4)^(+)三者的合称)的占比为37%,与成渝城市群、长三角地区、京津冀地区城市相比,PM_(2.5)二次污染程度较低,一次污染仍是PM_(2.5)的主要来源.1月、4月、7月、10月PM_(2.5)中[NO_(3)^(-)][SO_(4)^(2-)](质量浓度比)分别为0.62、0.44、0.15、0.50,表明该区域的燃煤污染对PM_(2.5)的贡献大于机动车尾气的贡献,该现象在秋冬季尤为明显;硫氧化率(SOR)普遍处于较高水平,分别为0.34、0.54、0.61、0.58,表明该区域燃煤排放的SO_(2)更易对PM_(2.5)产生贡献.同时,全年OC/EC(质量浓度比)的平均值为3.6,说明碳气溶胶的贡献主要来自机动车尾气的排放与化石燃料燃烧.通过分析2019年1月11—14日重污染过程PM_(2.5)组分浓度的逐小时变化发现,该时段中SOR与NOR分别是1月平均值的1.2与2.0倍,NOR的提升导致PM_(2.5)中NO_(3)^(-)浓度占比上升了8%,超过SO_(4)^(2-)的占比,这表明该重污染过程中机动车尾气对PM_(2.5)的贡献超过平常时段.研究显示,辽宁中部城市群的大气污染呈燃煤与机动车尾气为主的复合型污染特征,尤其在重污染天气下,实施工业限产的同时,加强机动车限行尤为重要.

In order to study the chemical composition characteristics of PM_(2.5) in cold northern cities,the research uses WRF(Weather Research and Forecasting Model)in combination with CMAQ(Congestion Mitigation and Air Quality Model)to simulate and analyze the chemical components of PM_(2.5) in the urban agglomerations of central Liaoning Province in January,April,July and October 2019 and in the heavily polluted weather from January 11 th to 14 th,2019.Bases on the WRF-CMAQ analysis,the correlation R between the simulation and monitor values of SO_(2),NO_(2),PM_(10),PM_(2.5) is between 0.63 and 0.82,while the correlation R of PM_(2.5) components,including SO_(4)^(2-),NO_(3)^(-),NH_(4)^(+),EC,and OC,is between 0.59 and 0.88.Therefore,the WRF-CMAQ model satisfactorily simulates air pollutants and the major chemical components of PM_(2.5),reflecting the spatiotemporal variation characteristics of PM_(2.5) and its components.Through further analysis of the simulation results,the SNA(SO_(4)^(2-),NO_(3)^(-),NH_(4)^(+))of PM_(2.5) in the urban agglomeration of Liaoning Province is 37%.Compared with Chengdu-Chongqing urban agglomeration,the Yangtze River Delta and the Beijing-Tianjin-Hebei cities,the secondary pollution level of PM_(2.5) is relative low,and primary pollution is still the main source of PM_(2.5).In January,April,July,and October,the[NO_(3)^(-)][SO_(4)^(2-)] is 0.62,0.44,0.15 and 0.50,respectively,indicating that the contribution of coal-burning pollution to PM_(2.5) in this area is greater than that of motor vehicle exhaust,especially in Autumn and Winter.SOR is generally at a high level,which is 0.34,0.54,0.61 and 0.58 respectively,indicating SO_(2) emissions from coal burning in this area are more likely to contribute to PM_(2.5).Meanwhile,the annual average value of OC/EC is 3.6,indicating that the contribution of carbon aerosols comes mainly from vehicle exhaust emissions and fossil fuel combustion.The study analyzes the hourly variance of PM_(2.5) components during the heavy pollution period from January 11 th to 14 th,2019.SOR and NOR are 1.2 and 2.0 times respectively of the January average values.The increase of NOR caused the NO_(3) in PM_(2.5) to increase by 8%,exceeding the proportion of SO_(4)^(2-),which indicates that motor vehicle exhaust contribution to PM_(2.5) during heavy pollution period is more critical than that in the normal period,and represents the compound pollution characteristics.The study shows that the air pollution in the central urban agglomeration of Liaoning Province presents the characteristics of compound pollution mainly caused by coal burning and vehicle exhaust.Especially in the heavy pollution weather,it is particularly important to strengthen the motor vehicle restriction while implementing the industrial production restriction.