scholarly journals Estimation of Satellite-Based SO42− and NH4+ Composition of Ambient Fine Particulate Matter over China Using Chemical Transport Model

2017 ◽  
Vol 9 (8) ◽  
pp. 817 ◽  
Author(s):  
Yidan Si ◽  
Shenshen Li ◽  
Liangfu Chen ◽  
Chao Yu ◽  
Wende Zhu
Keyword(s):  
Particulate Matter ◽  
Transport Model ◽  
Fine Particulate Matter ◽  
Chemical Transport ◽  
Chemical Transport Model ◽  
Fine Particulate

scholarly journals Fine particulate matter source apportionment using a hybrid chemical transport and receptor model approach

2013 ◽  
Vol 13 (10) ◽  
pp. 26657-26698
Author(s):  
Y. Hu ◽  
S. Balachandran ◽  
J. E. Pachon ◽  
J. Baek ◽  
C. Ivey ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Source Apportionment ◽  
Hybrid Method ◽  
Fine Particulate Matter ◽  
Hybrid Approach ◽  
Chemical Transport ◽  
Measurement Information ◽  
Analysis Tool ◽  
Receptor Model ◽  
Fine Particulate

Abstract. A hybrid fine particulate matter (PM2.5) source apportionment approach based on a receptor-model (RM) species balance and species specific source impacts from a chemical transport model (CTM) equipped with a sensitivity analysis tool is developed to provide physically- and chemically-consistent relationships between source emissions and receptor impacts. This hybrid approach enhances RM results by providing initial estimates of source impacts from a much larger number of sources than are typically used in RMs, and provides source-receptor relationships for secondary species. Further, the method addresses issues of source collinearities, and accounts for emissions uncertainties. Hybrid method results also provide information on the resulting source impact uncertainties. We apply this hybrid approach to conduct PM2.5 source apportionment at Chemical Speciation Network (CSN) sites across the US. Ambient PM2.5 concentrations at these receptor sites were apportioned to 33 separate sources. Hybrid method results led to large changes of impacts from CTM estimates for sources such as dust, woodstove, and other biomass burning sources, but limited changes to others. The refinements reduced the differences between CTM-simulated and observed concentrations of individual PM2.5 species by over 98% when using a weighted least squared error minimization. The rankings of source impacts changed from the initial estimates, revealing that CTM-only results should be evaluated with observations. Assessment with RM results at six US locations showed that the hybrid results differ somewhat from commonly resolved sources. The hybrid method also resolved sources that typical RM methods do not capture without extra measurement information on unique tracers. The method can be readily applied to large domains and long (such as multi-annual) time periods to provide source impact estimates for management- and health-related studies.

scholarly journals Chemical transport model simulations of organic aerosol in southern California: model evaluation and gasoline and diesel source contributions

2017 ◽  
Vol 17 (6) ◽  
pp. 4305-4318 ◽  
Author(s):  
Shantanu H. Jathar ◽  
Matthew Woody ◽  
Havala O. T. Pye ◽  
Kirk R. Baker ◽  
Allen L. Robinson
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Southern California ◽  
Transport Model ◽  
Chemical Transport ◽  
Organic Aerosol ◽  
Production Model ◽  
Anthropogenic Sources ◽  
Chemical Transport Model ◽  
Mobile Sources

Abstract. Gasoline- and diesel-fueled engines are ubiquitous sources of air pollution in urban environments. They emit both primary particulate matter and precursor gases that react to form secondary particulate matter in the atmosphere. In this work, we updated the organic aerosol module and organic emissions inventory of a three-dimensional chemical transport model, the Community Multiscale Air Quality Model (CMAQ), using recent, experimentally derived inputs and parameterizations for mobile sources. The updated model included a revised volatile organic compound (VOC) speciation for mobile sources and secondary organic aerosol (SOA) formation from unspeciated intermediate volatility organic compounds (IVOCs). The updated model was used to simulate air quality in southern California during May and June 2010, when the California Research at the Nexus of Air Quality and Climate Change (CalNex) study was conducted. Compared to the Traditional version of CMAQ, which is commonly used for regulatory applications, the updated model did not significantly alter the predicted organic aerosol (OA) mass concentrations but did substantially improve predictions of OA sources and composition (e.g., POA–SOA split), as well as ambient IVOC concentrations. The updated model, despite substantial differences in emissions and chemistry, performed similar to a recently released research version of CMAQ (Woody et al., 2016) that did not include the updated VOC and IVOC emissions and SOA data. Mobile sources were predicted to contribute 30–40 % of the OA in southern California (half of which was SOA), making mobile sources the single largest source contributor to OA in southern California. The remainder of the OA was attributed to non-mobile anthropogenic sources (e.g., cooking, biomass burning) with biogenic sources contributing to less than 5 % to the total OA. Gasoline sources were predicted to contribute about 13 times more OA than diesel sources; this difference was driven by differences in SOA production. Model predictions highlighted the need to better constrain multi-generational oxidation reactions in chemical transport models.

scholarly journals Fine particulate matter source apportionment using a hybrid chemical transport and receptor model approach

2014 ◽  
Vol 14 (11) ◽  
pp. 5415-5431 ◽  
Author(s):  
Y. Hu ◽  
S. Balachandran ◽  
J. E. Pachon ◽  
J. Baek ◽  
C. Ivey ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Source Apportionment ◽  
Hybrid Method ◽  
Weighted Least Squares ◽  
Fine Particulate Matter ◽  
Hybrid Approach ◽  
Chemical Transport ◽  
Analysis Tool ◽  
Receptor Model ◽  
Fine Particulate

Abstract. A hybrid fine particulate matter (PM2.5) source apportionment approach based on a receptor model (RM) species balance and species specific source impacts from a chemical transport model (CTM) equipped with a sensitivity analysis tool is developed to provide physically and chemically consistent relationships between source emissions and receptor impacts. This hybrid approach enhances RM results by providing initial estimates of source impacts from a much larger number of sources than are typically used in RMs, and provides source–receptor relationships for secondary species. Further, the method addresses issues of source collinearities and accounts for emissions uncertainties. We apply this hybrid approach to conduct PM2.5 source apportionment at Chemical Speciation Network (CSN) sites across the US. Ambient PM2.5 concentrations at these receptor sites were apportioned to 33 separate sources. Hybrid method results led to large changes of impacts from CTM estimates for sources such as dust, woodstoves, and other biomass-burning sources, but limited changes to others. The refinements reduced the differences between CTM-simulated and observed concentrations of individual PM2.5 species by over 98% when using a weighted least-squares error minimization. The rankings of source impacts changed from the initial estimates, further demonstrating that CTM-only results should be evaluated with observations. Assessment with RM results at six US locations showed that the hybrid results differ somewhat from commonly resolved sources. The hybrid method also resolved sources that typical RM methods do not capture without extra measurement information for unique tracers. The method can be readily applied to large domains and long (such as multi-annual) time periods to provide source impact estimates for management- and health-related studies.

scholarly journals Exploring the uncertainty associated with satellite-based estimates of premature mortality due to exposure to fine particulate matter

2015 ◽  
Vol 15 (18) ◽  
pp. 25329-25380 ◽  
Author(s):  
B. Ford ◽  
C. L. Heald
Keyword(s):  
United States ◽  
Particulate Matter ◽  
Transport Model ◽  
Fine Particulate Matter ◽  
Premature Mortality ◽  
The United States ◽  
Satellite Observations ◽  
Population Exposure ◽  
Fine Particulate ◽  
Observational Uncertainty

Abstract. The negative impacts of fine particulate matter (PM2.5) exposure on human health are a primary motivator for air quality research. However, estimates of the air pollution health burden vary considerably and strongly depend on the datasets and methodology. Satellite observations of aerosol optical depth (AOD) have been widely used to overcome limited coverage from surface monitoring and to assess the global population exposure to PM2.5 and the associated premature mortality. Here we quantify the uncertainty in determining the burden of disease using this approach, discuss different methods and datasets, and explain sources of discrepancies among values in the literature. For this purpose we primarily use the MODIS satellite observations in concert with the GEOS-Chem chemical transport model. We contrast results in the United States and China for the years 2004–2011. We estimate that in the United States, exposure to PM2.5 accounts for approximately 4 % of total deaths compared to 22 % in China (using satellite-based exposure), which falls within the range of previous estimates. The difference in estimated mortality burden based solely on a global model vs. that derived from satellite is approximately 9 % for the US and 4 % for China on a nationwide basis, although regionally the differences can be much greater. This difference is overshadowed by the uncertainty in the methodology for deriving PM2.5 burden from satellite observations, which we quantify to be on order of 20 % due to uncertainties in the AOD-to-surface-PM2.5 relationship, 10 % due to the satellite observational uncertainty, and 30 % or greater uncertainty associated with the application of concentration response functions to estimated exposure.

scholarly journals Modelling of the public health costs of fine particulate matter and results for Finland in 2015

2019 ◽  
Author(s):  
Jaakko Kukkonen ◽  
Mikko Savolahti ◽  
Yuliia Palamarchuk ◽  
Timo Lanki ◽  
Väinö Nurmi ◽  
...  
Keyword(s):  
Public Health ◽  
Particulate Matter ◽  
Health Outcomes ◽  
Urban Areas ◽  
Transport Model ◽  
Fine Particulate Matter ◽  
Ambient Air ◽  
Health Costs ◽  
The Public ◽  
Fine Particulate

Abstract. We have developed an integrated tool of assessment that can be used for evaluating the public health costs caused by the concentrations of fine particulate matter (PM2.5) in ambient air. The model can be used in assessing the impacts of various alternative air quality abatement measures, policies and strategies. The model has been applied for the evaluation of the costs of the domestic emissions that influence the concentrations of PM2.5 in Finland in 2015. The model includes the impacts on human health; however, it does not address the impacts on climate change or the state of the environment. First, the national Finnish emissions were evaluated using the Finnish Regional Emission Scenarios model (FRES) on a resolution of 250 × 250 m2 for the whole of Finland. Second, the atmospheric dispersion was analyzed by using the chemical transport model SILAM and the source-receptor matrices contained in the FRES model. Third, the health impacts were assessed by combining the spatially resolved concentration and population datasets, and by analyzing the impacts for various health outcomes. Fourth, the economic impacts for the health outcomes were evaluated. The model can be used to evaluate the costs of the health damages for various emission source categories, for a unit of emissions of PM2.5. It was found that economically the most effective measures would be the reduction of the emissions in urban areas of (i) road transport, (ii) non-road vehicles and machinery, and (iii) residential wood combustion. The reduction of the precursor emissions of PM2.5 was clearly less effective, compared with reducing directly the emissions of PM2.5. We have also designed a user-friendly web-based tool of assessment that is available open access.

scholarly journals Source-Resolved Variability of Fine Particulate Matter and Human Exposure in an Urban Area

2021 ◽  
Author(s):  
Pablo Garcia Rivera ◽  
Brian T. Dinkelacker ◽  
Ioannis Kioutsioukis ◽  
Peter J. Adams ◽  
Spyros N. Pandis
Keyword(s):  
Urban Areas ◽  
Power Plants ◽  
Transport Model ◽  
Fine Particulate Matter ◽  
Grid Cell ◽  
Chemical Transport ◽  
Population Exposure ◽  
Chemical Transport Model ◽  
Atmospheric Pollutant ◽  
The Impact

Abstract. Increasing the resolution of chemical transport model (CTM) predictions in urban areas is important to capture sharp spatial gradients in atmospheric pollutant concentrations and better inform air quality and emissions controls policies that protect public health. The chemical transport model PMCAMx was used to assess the impact of increasing model resolution on the ability to predict the source-resolved variability and population exposure to PM2.5 at 36 x 36, 12 x 12, 4 x 4, and 1 x 1 km resolutions over the city of Pittsburgh during typical winter and summer periods (February and July 2017). At the coarse resolution, county-level differences can be observed, while increasing the resolution to 12 x 12 km resolves the urban-rural gradient. Increasing resolution to 4 x 4 km resolves large stationary sources such as power plants and the 1 x 1 km resolution reveals intra-urban variations and individual roadways within the simulation domain. Regional pollutants that exhibit low spatial variability such as PM2.5 nitrate show modest changes when increasing the resolution beyond 12 x 12 km. Predominantly local pollutants such as elemental carbon and organic aerosol have gradients that can only be resolved at the 1 x 1 km scale. Contributions from some local sources are enhanced by weighting the average contribution from each source by the population in each grid cell. The average population weighted PM2.5 concentration does not change significantly with resolution, suggesting that extremely high resolution PM2.5 predictions may not be necessary for effective urban epidemiological analysis.

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