scholarly journals Process-based and Observation-constrained SOA Simulations in China: The Role of Semivolatile and Intermediate-Volatility Organic Compounds and OH Levels

2021 ◽  
Author(s):  
Ruqian Miao ◽  
Qi Chen ◽  
Manish Shrivastava ◽  
Youfan Chen ◽  
Lin Zhang ◽  
...  
Keyword(s):  
Organic Compounds ◽  
Northern China ◽  
Organic Aerosol ◽  
Data Sets ◽  
Transport Models ◽  
Residential Sector ◽  
Model Modification ◽  
Chemical Transport Models ◽  
Mass Concentrations

Abstract. Organic aerosol (OA) is a major component of tropospheric submicron aerosol that contributes to air pollution and causes adverse effects on human health. Chemical transport models have difficulties to reproduce the variability of OA concentrations in polluted areas, hindering understanding of the OA budget. Herein, we applied both process-based and observation-constrained schemes to simulate OA in GEOS-Chem. Comprehensive data sets of surface OA, OA components, secondary organic aerosol (SOA) precursors, and oxidants were used for model-observation comparisons. In the revised schemes, updates of the emissions, volatility distributions, and SOA yields of semivolatile and intermediate volatility organic compounds (S/IVOCs) were made. These updates are however insufficient to reproduce the SOA concentrations in observations. The addition of nitrous acid sources is an important model modification, which improves the simulation of surface concentrations of hydroxyl radical (OH) in winter in northern China. The increased surface OH concentrations enhance the SOA formation and lead to greater SOA mass concentrations by over 30 %, highlighting the importance of having good OH simulations in air quality models. There is a greater sensitivity of the SOA formation to the oxidant levels in winter than in summer in China. With all the model improvements, both the process-based and observation-constrained SOA schemes can reproduce the observed mass concentrations of SOA and show spatial and seasonal consistency with each other. Our best model simulations suggest that anthropogenic S/IVOCs are the dominant source of SOA in China with a contribution of over 50 %. The residential sector may be the predominant source of S/IVOCs in winter, despite large uncertainty remains in the emissions of IVOCs from the residential sector in northern China. The industry sector is also an important source of IVOCs, especially in summer. More S/IVOC measurements are needed to constrain their emissions.

scholarly journals Process-based and observation-constrained SOA simulations in China: the role of semivolatile and intermediate-volatility organic compounds and OH levels

2021 ◽  
Vol 21 (21) ◽  
pp. 16183-16201
Author(s):  
Ruqian Miao ◽  
Qi Chen ◽  
Manish Shrivastava ◽  
Youfan Chen ◽  
Lin Zhang ◽  
...  
Keyword(s):  
Organic Compounds ◽  
Nitrous Acid ◽  
Northern China ◽  
Organic Aerosol ◽  
Data Sets ◽  
Transport Models ◽  
Residential Sector ◽  
Chemical Transport Models ◽  
Mass Concentrations

Abstract. Organic aerosol (OA) is a major component of tropospheric submicron aerosol that contributes to air pollution and causes adverse effects on human health. Chemical transport models have difficulties in reproducing the variability in OA concentrations in polluted areas, hindering understanding of the OA budget and sources. Herein, we apply both process-based and observation-constrained schemes to simulate OA in GEOS-Chem. Comprehensive data sets of surface OA, OA components, secondary organic aerosol (SOA) precursors, and oxidants were used for model–observation comparisons. The base models generally underestimate the SOA concentrations in China. In the revised schemes, updates were made on the emissions, volatility distributions, and SOA yields of semivolatile and intermediate-volatility organic compounds (SVOCs and IVOCs) and additional nitrous acid sources. With all the model improvements, both the process-based and observation-constrained SOA schemes can reproduce the observed mass concentrations of SOA and show spatial and seasonal consistency with each other. Our best model simulations suggest that anthropogenic SVOCs and IVOCs are the dominant source of SOA, with a contribution of over 50 % in most of China, which should be considered for pollution mitigation in the future. The residential sector may be the predominant source of SVOCs and IVOCs in winter, despite large uncertainty remaining in the emissions of IVOCs from the residential sector in northern China. The industry sector is also an important source of IVOCs, especially in summer. More SVOC and IVOC measurements are needed to constrain their emissions. Besides, the results highlight the sensitivity of SOA to hydroxyl radical (OH) levels in winter in polluted environments. The addition of nitrous acid sources can lead to over 30 % greater SOA mass concentrations in winter in northern China. It is important to have good OH simulations in air quality models.

scholarly journals Aging of atmospheric aerosols and the role of iron in catalyzing brown carbon formation

2021 ◽  
Author(s):  
Hind A. A. Al-Abadleh
Keyword(s):  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Atmospheric Aerosols ◽  
Secondary Organic Aerosol ◽  
Organic Aerosol ◽  
Atmospheric Oxidation ◽  
Carbon Formation ◽  
Brown Carbon ◽  
Volatile Organic

Extensive research has been done on the processes that lead to the formation of secondary organic aerosol (SOA) including atmospheric oxidation of volatile organic compounds (VOCs) from biogenic and anthropogenic...

scholarly journals Simulating secondary organic aerosol in a regional air quality model using the statistical oxidation model – Part 3: Assessing the influence of semi-volatile and intermediate-volatility organic compounds and NO<sub><i>x</i></sub>

2019 ◽  
Vol 19 (7) ◽  
pp. 4561-4594 ◽  
Author(s):  
Ali Akherati ◽  
Christopher D. Cappa ◽  
Michael J. Kleeman ◽  
Kenneth S. Docherty ◽  
Jose L. Jimenez ◽  
...  
Keyword(s):  
Organic Compounds ◽  
Urban Areas ◽  
Southern California ◽  
Secondary Organic Aerosol ◽  
Chemical Transport ◽  
Organic Aerosol ◽  
Wall Loss ◽  
Organic Precursors ◽  
Oxidation Model ◽  
Mass Concentrations

Abstract. Semi-volatile and intermediate-volatility organic compounds (SVOCs and IVOCs) from anthropogenic sources are likely to be important precursors of secondary organic aerosol (SOA) in urban airsheds, yet their treatment in most models is based on limited and obsolete data or completely missing. Additionally, gas-phase oxidation of organic precursors to form SOA is influenced by the presence of nitric oxide (NO), but this influence is poorly constrained in chemical transport models. In this work, we updated the organic aerosol model in the UCD/CIT (University of California at Davis/California Institute of Technology) chemical transport model to include (i) a semi-volatile and reactive treatment of primary organic aerosol (POA), (ii) emissions and SOA formation from IVOCs, (iii) the NOx influence on SOA formation, and (iv) SOA parameterizations for SVOCs and IVOCs that are corrected for vapor wall loss artifacts during chamber experiments. All updates were implemented in the statistical oxidation model (SOM) that simulates the oxidation chemistry, thermodynamics, and gas–particle partitioning of organic aerosol (OA). Model treatment of POA, SVOCs, and IVOCs was based on an interpretation of a comprehensive set of source measurements available up to the year 2016 and resolved broadly by source type. The NOx influence on SOA formation was calculated offline based on measured and modeled VOC:NOx ratios. Finally, the SOA formation from all organic precursors (including SVOCs and IVOCs) was modeled based on recently derived parameterizations that accounted for vapor wall loss artifacts in chamber experiments. The updated model was used to simulate a 2-week summer episode over southern California at a model resolution of 8 km. When combustion-related POA was treated as semi-volatile, modeled POA mass concentrations were reduced by 15 %–40 % in the urban areas in southern California but were still too high when compared against “hydrocarbon-like organic aerosol” factor measurements made at Riverside, CA, during the Study of Organic Aerosols at Riverside (SOAR-1) campaign of 2005. Treating all POA (except that from marine sources) to be semi-volatile, similar to diesel exhaust POA, resulted in a larger reduction in POA mass concentrations and allowed for a better model–measurement comparison at Riverside, but this scenario is unlikely to be realistic since this assumes that POA from sources such as road and construction dust are semi-volatile too. Model predictions suggested that both SVOCs (evaporated POA vapors) and IVOCs did not contribute as much as other anthropogenic precursors (e.g., alkanes, aromatics) to SOA mass concentrations in the urban areas (< 5 % and < 15 % of the total SOA respectively) as the timescales for SOA production appeared to be shorter than the timescales for transport out of the urban airshed. Comparisons of modeled IVOC concentrations with measurements of anthropogenic SOA precursors in southern California seemed to imply that IVOC emissions were underpredicted in our updated model by a factor of 2. Correcting for the vapor wall loss artifact in chamber experiments enhanced SOA mass concentrations although the enhancement was precursor-dependent as well as NOx-dependent. Accounting for the influence of NOx using the VOC:NOx ratios resulted in better predictions of OA mass concentrations in rural/remote environments but still underpredicted OA mass concentrations in urban environments. The updated model's performance against measurements combined with the results from the sensitivity simulations suggests that the OA mass concentrations in southern California are constrained within a factor of 2. Finally, simulations performed for the year 2035 showed that, despite reductions in VOC and NOx emissions in the future, SOA mass concentrations may be higher than in the year 2005, primarily from increased hydroxyl radical (OH) concentrations due to lower ambient NO2 concentrations.

scholarly journals Simulating secondary organic aerosol in a regional air quality model using the statistical oxidation model – Part 3: Assessing the influence of semi-volatile and intermediate volatility organic compounds and NO<sub>X</sub>

2018 ◽  
Author(s):  
Ali Akherati ◽  
Christopher D. Cappa ◽  
Michael J. Kleeman ◽  
Kenneth S. Docherty ◽  
Jose L. Jimenez ◽  
...  
Keyword(s):  
Organic Compounds ◽  
Urban Areas ◽  
Southern California ◽  
Secondary Organic Aerosol ◽  
Chemical Transport ◽  
Organic Aerosol ◽  
Wall Loss ◽  
Organic Precursors ◽  
Oxidation Model ◽  
Mass Concentrations

Abstract. Semi-volatile and intermediate-volatility organic compounds (SVOCs and IVOCs) from anthropogenic sources are likely to be important precursors of secondary organic aerosol (SOA) in urban airsheds yet their treatment in most models is based on limited and obsolete data, or completely missing. Additionally, gas-phase oxidation of organic precursors to form SOA is influenced by the presence of nitric oxide (NO), but this influence is poorly constrained in chemical transport models. In this work, we updated the organic aerosol model in the UCD/CIT chemical transport model to include (i) a semi-volatile and reactive treatment of primary organic aerosol (POA), (ii) emissions and SOA formation from IVOCs, (iii) the NOX influence on SOA formation, and (iv) SOA parameterizations for SVOCs and IVOCs that are corrected for vapor wall loss artifacts during chamber experiments. All updates were implemented in the statistical oxidation model (SOM) that simulates the chemistry, thermodynamic properties, and gas/particle partitioning of organic aerosol (OA). Model treatment of POA, SVOCs, and IVOCs was based on an interpretation of a comprehensive set of source measurements and resolved broadly by source type. The NOX influence on SOA formation was calculated offline based on measured and modeled VOC:NOX ratios. And finally, the SOA formation from all organic precursors (including SVOCs and IVOCs) was modeled based on recently derived parameterizations that accounted for vapor wall loss artifacts in chamber experiments. The updated model was used to simulate a two week summer episode over southern California at a model resolution of 8 km. When combustion-related POA was treated as semi-volatile, modeled POA mass concentrations were reduced by 30–50 % in the urban areas in southern California but were still too high when compared against measurements made at Riverside, CA during the Study of Organic Aerosols at Riverside (SOAR-1) campaign of 2005. Treating all POA (except that from marine sources) to be semi-volatile resulted in a larger reduction in POA mass concentrations and allowed for a better model-measurement comparison at Riverside. Model predictions suggested that both SVOCs (evaporated POA vapors) and IVOCs did not contribute significantly to SOA mass concentrations in the urban areas (<5 % and <15 % of the total SOA respectively) as the timescales for SOA production appeared to be shorter than the timescales for transport out of the urban airshed. Comparisons of modeled IVOC concentrations with measurements of anthropogenic SOA precursors in southern California seemed to imply that IVOC emissions were underpredicted in our updated model by a factor of 2. We suspect that these missing IVOCs might arise from the use of volatile chemical products such as pesticides, coatings, cleaning agents, and personal care products. Correcting for the vapor wall loss artifact in chamber experiments enhanced SOA mass concentrations although the enhancement was precursor- as well as NOX-dependent. Accounting for the influence of NOX using the VOC:NOX ratios resulted in better predictions of OA mass concentrations in rural/remote environments but still underpredicted OA mass concentrations in urban environments, potentially due to the missing urban emissions/chemical source of OA. Finally, simulations performed for the year 2035 showed that despite reductions in VOC and NOX emissions in the future, SOA mass concentrations may be higher than in the year 2005, primarily from increased hydroxyl radical (OH) concentrations due to lower ambient NO2 concentrations.

Participant recruitment and data collection through Facebook: the role of personality factors

2017 ◽  
Author(s):  
Sean Chandler Rife ◽  
Kelly L. Cate ◽  
Michal Kosinski ◽  
David Stillwell
Keyword(s):  
Data Collection ◽  
Social Research ◽  
Personality Factors ◽  
The Internet ◽  
Data Sets ◽  
Social Networking Service ◽  
Participant Recruitment ◽  
Conducting Research ◽  
Promising Avenue

As participant recruitment and data collection over the Internet have become more common, numerous observers have expressed concern regarding the validity of research conducted in this fashion. One growing method of conducting research over the Internet involves recruiting participants and administering questionnaires over Facebook, the world’s largest social networking service. If Facebook is to be considered a viable platform for social research, it is necessary to demonstrate that Facebook users are sufficiently heterogeneous and that research conducted through Facebook is likely to produce results that can be generalized to a larger population. The present study examines these questions by comparing demographic and personality data collected over Facebook with data collected through a standalone website, and data collected from college undergraduates at two universities. Results indicate that statistically significant differences exist between Facebook data and the comparison data-sets, but since 80% of analyses exhibited partial η2 &lt; .05, such differences are small or practically nonsignificant in magnitude. We conclude that Facebook is a viable research platform, and that recruiting Facebook users for research purposes is a promising avenue that offers numerous advantages over traditional samples.

Air quality forecasting system based on chemical transport models

2019 ◽  
Vol 4 ◽  
pp. 203-218
Author(s):  
I.N. Kusnetsova ◽  
◽  
I.U. Shalygina ◽  
M.I. Nahaev ◽  
U.V. Tkacheva ◽  
...  
Keyword(s):  
Air Quality ◽  
Chemical Transport ◽  
Transport Models ◽  
Chemical Transport Models ◽  
Forecasting System ◽  
Air Quality Forecasting
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