1
|
Sha T, Ma X, Wang J, Tian R, Zhao J, Cao F, Zhang YL. Improvement of inorganic aerosol component in PM 2.5 by constraining aqueous-phase formation of sulfate in cloud with satellite retrievals: WRF-Chem simulations. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 804:150229. [PMID: 34798748 DOI: 10.1016/j.scitotenv.2021.150229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 08/18/2021] [Accepted: 09/05/2021] [Indexed: 06/13/2023]
Abstract
High concentrations of PM2.5 in China have caused severe visibility degradation and health problems. However, it is still challenging to accurately predict PM2.5 and its chemical components in numerical models. In this study, we compared the inorganic aerosol components of PM2.5 (sulfate, nitrate, and ammonium (SNA)) simulated by the Weather Research and Forecasting model fully coupled with chemistry (WRF-Chem) model with in-situ data in a heavy haze-fog event during November 2018 in Nanjing, China. Comparisons show that the model underestimates sulfate concentrations by 81% and fails to reproduce the significant increase of sulfate from early morning to noon, which corresponds to the timing of fog dissipation that suggests the model underestimates the aqueous-phase formation of sulfate in clouds. In addition, the model overestimates both nitrate and ammonium concentrations by 184% and 57%, respectively. These overestimates contribute to the simulated SNA being 77.2% higher than observed. However, cloud water content is also underestimated which is a pathway for important aqueous-phase reactions. Therefore, we constrained the simulated cloud water content based on the Moderate Resolution Imaging Spectroradiometer (MODIS) Liquid Water Path observations. Results show that the simulation with MODIS-corrected cloud water content increases the sulfate by a factor of 3, decreases the Normalized Mean Bias (NMB) by 53.5%, and reproduces its diurnal cycle with the peak concentration occurring at noon. The improved sulfate simulation also improves the simulation of nitrate, which decreases the simulated nitrate bias by 134%. Although the simulated ammonium is still higher than the observations, corrected cloud water content leads to a decrease of the modelled bias in SNA from 77.2% to 14.1%. The strong sensitivity of simulated SNA concentration to the cloud water content provides an explanation for the simulated SNA bias. Hence, uncertainties in cloud water content can contribute to model biases in simulating SNA which are less frequently explored from a process-level perspective and can be reduced by constraining the model with satellite observations.
Collapse
Affiliation(s)
- Tong Sha
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Xiaoyan Ma
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science & Technology, Nanjing 210044, China.
| | - Jun Wang
- Department of Chemical and Biochemical Engineering, Center for Global and Regional Environmental Research, University of Iowa, Iowa City, Iowa 52242, United States
| | - Rong Tian
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Jianqi Zhao
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Fang Cao
- Yale-NUIST Center on Atmospheric Environment, International Joint Laboratory on Climate and Environment Change and Key Laboratory Meteorological Disaster, Ministry of Education & Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disaster, Jiangsu Provincial Key Laboratory of Agricultural Meteorology, College of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Yan-Lin Zhang
- Yale-NUIST Center on Atmospheric Environment, International Joint Laboratory on Climate and Environment Change and Key Laboratory Meteorological Disaster, Ministry of Education & Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disaster, Jiangsu Provincial Key Laboratory of Agricultural Meteorology, College of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China
| |
Collapse
|
2
|
Tian R, Ma X, Sha T, Pan X, Wang Z. Exploring dust heterogeneous chemistry over China: Insights from field observation and GEOS-Chem simulation. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 798:149307. [PMID: 34375256 DOI: 10.1016/j.scitotenv.2021.149307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 06/25/2021] [Accepted: 07/23/2021] [Indexed: 06/13/2023]
Abstract
Dust heterogeneous chemistry plays an important role in tropospheric chemistry, but its parameterization in numerical models is often quite simplified, which hampers accurate prediction of particulate matter and its chemical component. In this study, we investigate the evolution of dust heterogeneous chemical process and its potential impacts on gaseous and aerosol components during a dust pollution episode from March 27 to April 2, 2015 over North China. Based on field measurements, the significant role of relative humidity (RH) in dust heterogeneous chemistry is found and a RH-dependent parameterization for uptake coefficients of HNO3 and SO2 is incorporated in GEOS-Chem to reproduce the dust heterogeneous chemical process. During the study period, observed dust sulfate (DSO4) and dust nitrate (DNIT) exhibit maximum concentrations of 9.1 and 22.8 μg m-3 respectively, accompanied by high RH and gaseous precursor concentrations. DSO4 concentrations are positively related to RH. The observed dust sulfate oxidation ratio (DSOR) is elevated evidently with increased RH, especially when RH is higher than ~40%, implying that enhanced RH could promote heterogeneous oxidation of SO2 to DSO4. Model simulation shows that when incorporating the RH-dependent parameterization, DNIT and DSO4 are generally well captured and the model performance of total sulfate oxidation ratio (TSOR) and total nitrate oxidation ratio (TNOR) are improved. High contribution of DNIT and DSO4 are found to be located over the regions close to source areas (>60%) and downwind regions (>40%), respectively. Sensitivity results show that SO2 and HNO3 reduce by 2-24 μg m-3 and 1-18 μg m-3 when considering dust heterogeneous impacts, thus leading to reduction in non-dust sulfate and non-dust nitrate concentrations. As a result, simulated NH3 increases and ammonium reduces by more than 20%. Our study indicates that the contribution of heterogeneous reactions to sulfate formation is 20-30% over North China.
Collapse
Affiliation(s)
- Rong Tian
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Xiaoyan Ma
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science & Technology, Nanjing 210044, China.
| | - Tong Sha
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Xiaole Pan
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences (CAS), Beijing 100029, China
| | - Zhe Wang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences (CAS), Beijing 100029, China
| |
Collapse
|
3
|
Verma S, Boucher O, Reddy MS, Upadhyaya HC, Le Van P, Binkowski FS, Sharma OP. Modeling and analysis of aerosol processes in an interactive chemistry general circulation model. ACTA ACUST UNITED AC 2007. [DOI: 10.1029/2005jd006077] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
|