硫酸盐(SO_(4)^(2-))是我国大气中细颗粒物(PM_(2.5))的主要成分之一,其化学机制十分复杂,空气质量模式对硫酸盐的模拟往往存在一定的低估.为了提高WRF-Chem模式(Weather Research Forecast-Chemistry)对硫酸盐的模拟精度,本文提出了一...硫酸盐(SO_(4)^(2-))是我国大气中细颗粒物(PM_(2.5))的主要成分之一,其化学机制十分复杂,空气质量模式对硫酸盐的模拟往往存在一定的低估.为了提高WRF-Chem模式(Weather Research Forecast-Chemistry)对硫酸盐的模拟精度,本文提出了一种硫酸盐生成复杂机理的替代方法,并开展了针对硫酸盐化学反应速率的同化方法研究.在WRF-Chem模式基础上,新增了一个硫酸盐生成参数化方案,该方案包含两个替代反应和六个待定参数,基于DART(Data Assimilation Research Testbed)系统,同化了近地面SO_(4)^(2-)、SO_(2)、NO_(2)、O_(3)和颗粒物浓度(PM_(2.5)、PM_(10))来调整相关的待定参数.结果表明,经过资料同化调整后,新方案能有效解决模式低估SO_(4)^(2-)、高估SO_(2)的问题,SO_(4)^(2-)的平均偏差由-13.1μg m^(-3)下降为3.5μg m^(-3),SO_(2)由17.0μg m^(-3)下降为6.3μg m^(-3).新方案中反应速率的时空变化特征与基于理论的研究结果符合,表明硫酸盐生成的复杂机理可以用参数化方案代替,且采用资料同化的方法可以实现参数调整.本文发展并改进了WRF-Chem模式中SO_(4)^(2-)生成参数化方案中的反应速率参数,为提高空气质量数值预报的准确性提供了新的思路.展开更多
Sulphate(SO_(4)^(2-))is a main component of PM_(2.5)in China.The chemical formation mechanisms of sulphate are complex,and many air quality models have been used to analyse these mechanisms.To improve the accuracy of ...Sulphate(SO_(4)^(2-))is a main component of PM_(2.5)in China.The chemical formation mechanisms of sulphate are complex,and many air quality models have been used to analyse these mechanisms.To improve the accuracy of Weather Research Forecast-Chemistry(WRF-Chem)on sulphate,an alternative method is proposed in this paper.Moreover,data assimilation is performed to adjust the chemical reaction rates of sulphate.Based on the original reactions,a new sulphate parameterisation scheme,which includes two hypothetical reactions and six undetermined parameters,was added.Based on the WRF-Chem/DART(Data Assistance Research Testbed)system,the near-ground concentrations of SO_(4)^(2-),SO_(2),NO_(2),O_(3)and particulate matter are assimilated to adjust the six parameters.After adjusting the parameters,the new scheme can effectively solve the underestimation of SO_(4)^(2-)and overestimation of SO_(2).The simulation of SO_(4)^(2-)improved as the mean bias changed from-13.1μg m^(-3)to 3.5μg m^(-3)while SO_(2)improved from 17.0μg m^(-3)to 6.3μg m^(-3).The temporal and spatial variation characteristics predicted by the new scheme are consistent with the theoretical research results,indicating that the complex mechanism of sulphate formation could be replaced by the temporal and spatial variation characteristics predicted by the new scheme and that the parameters can be adjusted by data assimilation.Furthermore,the reaction rates of the SO_(4)^(2-)parameterisation scheme of the WRF-Chem model are improved in this study,and a new method for improving the accuracy of the air quality model is provided.展开更多
Nitrous acid(HONO)is a crucial source of OH radicals in the troposphere,significantly enhancing secondary pollutants like secondary organic aerosols(SOA)and peroxyacetyl nitrates(PAN).While prior research has examined...Nitrous acid(HONO)is a crucial source of OH radicals in the troposphere,significantly enhancing secondary pollutants like secondary organic aerosols(SOA)and peroxyacetyl nitrates(PAN).While prior research has examined HONO sources and their total impacts on secondary pollution,the specific enhancement capacity of each individual HONO source remains underexplored.This study uses observational data from 2015 to 2018 for HONO,SOA,and PAN across six sites in China,combined with WRF-Chem model adding six potential HONO sources to evaluate their capacity:traffic emissions(E_traffic),soil emissions(E_soil),indoor-outdoor exchange(E_indoor),nitrate photolysis(P_nit),and NO_(2) heterogeneous reactions on aerosol and ground surfaces(Het_a,Het_g).The simulated HONO contributions near the ground in urban Beijing were:12%from NO+OH(default source),10%-20%from E_traffic,1%-12%from P_nit,2%-10%from Het_a,and 50%-70% from Het_g.For SOA and PAN,we calculated incremental contributions enhanced by each HONO source and derived enhancement ratios(ERs)normalized against HONO’s contribution:~7 for P_nit,~2 for Het_a,~0.9 for Het_g,~0.8 for E_soil,~0.3 for E_traffic,and~0.1 for E_indoor.HONO sources’capacity to enhance secondary pollutants varies,being larger for aerosol-related sources.Vertical analysis on HONO concentration,spatial distribution,RO_(x) radical cycling rates,and OH enhancements revealed that aerosol-related HONO sources,especially P_nit,contribute more to secondary pollution.Future research should focus more on assessing real-world impacts of HONO sources,besides identifying their budgets.Additionally,uptake coefficient(γ)and nitrate photolysis frequency(J_(nitrate))critically affect HONO and secondary pollutant formation,necessitating further investigations.展开更多
文摘硫酸盐(SO_(4)^(2-))是我国大气中细颗粒物(PM_(2.5))的主要成分之一,其化学机制十分复杂,空气质量模式对硫酸盐的模拟往往存在一定的低估.为了提高WRF-Chem模式(Weather Research Forecast-Chemistry)对硫酸盐的模拟精度,本文提出了一种硫酸盐生成复杂机理的替代方法,并开展了针对硫酸盐化学反应速率的同化方法研究.在WRF-Chem模式基础上,新增了一个硫酸盐生成参数化方案,该方案包含两个替代反应和六个待定参数,基于DART(Data Assimilation Research Testbed)系统,同化了近地面SO_(4)^(2-)、SO_(2)、NO_(2)、O_(3)和颗粒物浓度(PM_(2.5)、PM_(10))来调整相关的待定参数.结果表明,经过资料同化调整后,新方案能有效解决模式低估SO_(4)^(2-)、高估SO_(2)的问题,SO_(4)^(2-)的平均偏差由-13.1μg m^(-3)下降为3.5μg m^(-3),SO_(2)由17.0μg m^(-3)下降为6.3μg m^(-3).新方案中反应速率的时空变化特征与基于理论的研究结果符合,表明硫酸盐生成的复杂机理可以用参数化方案代替,且采用资料同化的方法可以实现参数调整.本文发展并改进了WRF-Chem模式中SO_(4)^(2-)生成参数化方案中的反应速率参数,为提高空气质量数值预报的准确性提供了新的思路.
基金supported by the National Key Research and Development Program of China(Grant Nos.2020YFA0607802&2019YFC0214603)。
文摘Sulphate(SO_(4)^(2-))is a main component of PM_(2.5)in China.The chemical formation mechanisms of sulphate are complex,and many air quality models have been used to analyse these mechanisms.To improve the accuracy of Weather Research Forecast-Chemistry(WRF-Chem)on sulphate,an alternative method is proposed in this paper.Moreover,data assimilation is performed to adjust the chemical reaction rates of sulphate.Based on the original reactions,a new sulphate parameterisation scheme,which includes two hypothetical reactions and six undetermined parameters,was added.Based on the WRF-Chem/DART(Data Assistance Research Testbed)system,the near-ground concentrations of SO_(4)^(2-),SO_(2),NO_(2),O_(3)and particulate matter are assimilated to adjust the six parameters.After adjusting the parameters,the new scheme can effectively solve the underestimation of SO_(4)^(2-)and overestimation of SO_(2).The simulation of SO_(4)^(2-)improved as the mean bias changed from-13.1μg m^(-3)to 3.5μg m^(-3)while SO_(2)improved from 17.0μg m^(-3)to 6.3μg m^(-3).The temporal and spatial variation characteristics predicted by the new scheme are consistent with the theoretical research results,indicating that the complex mechanism of sulphate formation could be replaced by the temporal and spatial variation characteristics predicted by the new scheme and that the parameters can be adjusted by data assimilation.Furthermore,the reaction rates of the SO_(4)^(2-)parameterisation scheme of the WRF-Chem model are improved in this study,and a new method for improving the accuracy of the air quality model is provided.
基金supported by the National Natural Science Foundation of China(Nos.92044302,42075108,42107124,41822703,91544221,91844301,and 22222610)Beijing National Laboratory for Molecular Sciences(No.BNLMS-CXXM-202011)the Natural Science Foundation of Yunnan Province(No.202302AN360006)。
文摘Nitrous acid(HONO)is a crucial source of OH radicals in the troposphere,significantly enhancing secondary pollutants like secondary organic aerosols(SOA)and peroxyacetyl nitrates(PAN).While prior research has examined HONO sources and their total impacts on secondary pollution,the specific enhancement capacity of each individual HONO source remains underexplored.This study uses observational data from 2015 to 2018 for HONO,SOA,and PAN across six sites in China,combined with WRF-Chem model adding six potential HONO sources to evaluate their capacity:traffic emissions(E_traffic),soil emissions(E_soil),indoor-outdoor exchange(E_indoor),nitrate photolysis(P_nit),and NO_(2) heterogeneous reactions on aerosol and ground surfaces(Het_a,Het_g).The simulated HONO contributions near the ground in urban Beijing were:12%from NO+OH(default source),10%-20%from E_traffic,1%-12%from P_nit,2%-10%from Het_a,and 50%-70% from Het_g.For SOA and PAN,we calculated incremental contributions enhanced by each HONO source and derived enhancement ratios(ERs)normalized against HONO’s contribution:~7 for P_nit,~2 for Het_a,~0.9 for Het_g,~0.8 for E_soil,~0.3 for E_traffic,and~0.1 for E_indoor.HONO sources’capacity to enhance secondary pollutants varies,being larger for aerosol-related sources.Vertical analysis on HONO concentration,spatial distribution,RO_(x) radical cycling rates,and OH enhancements revealed that aerosol-related HONO sources,especially P_nit,contribute more to secondary pollution.Future research should focus more on assessing real-world impacts of HONO sources,besides identifying their budgets.Additionally,uptake coefficient(γ)and nitrate photolysis frequency(J_(nitrate))critically affect HONO and secondary pollutant formation,necessitating further investigations.
