Comparison of Source Apportionment and Sensitivity Analysis in a Particulate Matter Air Quality Model

Koo, Bonyoung; Wilson, Gary; Morris, Ralph; Dunker, Alan M.; Yarwood, Greg

doi:10.1021/es9008129

Cited by 155 publications

(103 citation statements)

References 11 publications

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“…However, PM sensitivity studies show that the main conclusions do not change with other methods such as source apportionment or direct decoupled sensitivity, if the formation of secondary species is not significant (Koo et al, 2007(Koo et al, , 2009Burr and Zhang, 2011).…”

Section: Sensitivity Analysismentioning

confidence: 99%

Source contributions to 2012 summertime aerosols in the Euro-Mediterranean region

et al. 2015

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Abstract. In the Mediterranean area, aerosols may originate from anthropogenic or natural emissions (biogenic, mineral dust, fire and sea salt) before undergoing complex chemistry. In case of a huge pollution event, it is important to know whether European pollution limits are exceeded and, if so, whether the pollution is due to anthropogenic or natural sources. In this study, the relative contribution of emissions to surface PM 10 , surface PM 2.5 and total aerosol optical depth (AOD) is quantified. For Europe and the Mediterranean regions and during the summer of 2012, the WRF and CHIMERE models are used to perform a sensitivity analysis on a 50 km resolution domain (from −10 • W to 40 • E and from 30 • N to 55 • N): one simulation with all sources (reference) and all others with one source removed. The reference simulation is compared to data from the AirBase network and two ChArMEx stations, and from the AERONET network and the MODIS satellite instrument, to quantify the ability of the model to reproduce the observations. It is shown that the correlation ranges from 0.19 to 0.57 for surface particulate matter and from 0.35 to 0.75 for AOD. For the summer of 2012, the model shows that the region is mainly influenced by aerosols due to mineral dust and anthropogenic emissions (62 and 19 %, respectively, of total surface PM 10 and 17 and 52 % of total surface PM 2.5 ). The western part of the Mediterranean is strongly influenced by mineral dust emissions (86 % for surface PM 10 and 44 % for PM 2.5 ), while anthropogenic emissions dominate in the northern Mediterranean basin (up to 75 % for PM 2.5 ). Fire emissions are more sporadic but may represent 20 % of surface PM 2.5 , on average, during the period near local sources. Sea salt mainly contributes for coastal sites (up to 29 %) and biogenic emissions mainly in central Europe (up to 20 %).The same analysis was undertaken for the number of daily exceedances of the European Union limit of 50 µg m −3 for PM 10 (over the stations), and for the number of daily exceedances of the WHO recommendation for PM 2.5 (25 µg m −3 ), over the western part of Europe and the central north. This number is generally overestimated by the model, particularly in the northern part of the domain, but exceedances are captured at the right time. Optimized contributions are computed with the observations, by subtracting the background bias at each station and the specific peak biases from the considered sources. These optimized contributions show that if natural sources such as mineral dust and fire events are particularly difficult to estimate, they were responsible exclusively for 35.9 and 0.7 %, respectively, of the exceedances for PM 10 during the summer of 2012. The PM 25 recommendation of 25 µg m −3 is exceeded in 21.1 % of the cases because of anthropogenic sources exclusively and in 0.02 % because of fires. The other exceedances are induced by a mixed contribution between mainly mineral dust (49.5-67 % for PM 10 exceedance contributions, 4.4-13.8 % for PM 2.5 ), anthropogenic so...

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Section: Sensitivity Analysismentioning

confidence: 99%

Source contributions to 2012 summertime aerosols in the Euro-Mediterranean region

et al. 2015

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“…This approach has been used in the past to isolate the response of complex, nonlinear systems to one particular sector in source apportionment and sensitivity analysis (Cohan et al, 2005). This method has inherent limitations in accurately describing sensitivities but it may be useful to approximate the effect of potential emission reductions in a particular source or origin area as pointed out before in several studies (Koo et al, 2009;Carmichael and Wild, 2010;Leung et al, 2007).…”

Section: Source Apportionment Exercisementioning

confidence: 99%

Emission inventories and modeling requirements for the development of air quality plans. Application to Madrid (Spain)

Borge

Lumbreras

Pérez

et al. 2014

Science of The Total Environment

126

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HIGHLIGHTS• A comprehensive and flexible urban emission inventory was developed for Madrid.• Options for multi-scale consistency are discussed (from European to street level).• Urban background N0 2 concentration levels well described by CMAQ (MB -2.2 ug/m 3 ).• N0 2 concentration levels in Madrid are dominated by local traffic (up to 90%).• A 31% reduction of NO x emissions may allow Madrid meeting the N0 2 European standards. ABSTRACTModeling is an essential tool for the development of atmospheric emission abatement measures and air quality plans. Most often these plans are related to urban environments with high emission density and population exposure. However, air quality modeling in urban areas is a rather challenging task. As environmental standards become more stringent (e.g. European Directive 2008/50/EC), more reliable and sophisticated modeling tools are needed to simulate measures and plans that may effectively tackle air quality exceedances, common in large urban areas across Europe, particularly for N0 2 . This also implies that emission inventories must satisfy a number of conditions such as consistency across the spatial scales involved in the analysis, consistency with the emission inventories used for regulatory purposes and versatility to match the requirements of different air quality and emission projection models. This study reports the modeling activities carried out in Madrid (Spain) highlighting the atmospheric emission inventory development and preparation as an illustrative example of the combination of models and data needed to develop a consistent air quality plan at urban level. These included a series of source apportionment studies to define contributions from the international, national, regional and local sources in order to understand to what extent local authorities can enforce meaningful abatement measures. Moreover, source apportionment studies were conducted in order to define contributions from different sectors and to understand the maximum feasible air quality improvement that can be achieved by reducing emissions from those sectors, thus targeting emission reduction policies to the most relevant activities. Finally, an emission scenario reflecting the effect of such policies was developed and the associated air quality was modeled.

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“…It can conduct source apportionment for particulate matter (PM) species, including sulfate, particulate nitrate, ammonium, particulate mercury, secondary organic aerosol, and six categories of primary PM (e.g., elemental carbon, primary organic aerosol, crustal fine, other fine, crustal coarse, and other coarse). The source apportionment can be calculated in parallel to the main program in CAMx for calculating pollutant concentrations, which means that selecting PSAT does not affect the CAMx simulation results (Koo et al, 2009;Huang et al, 2010).…”

Section: Camx Model With Psatmentioning

confidence: 99%

Application of Trajectory Clustering and Source Apportionment Methods for Investigating Trans-Boundary Atmospheric PM10 Pollution

Cheng

Wang

et al. 2013

Aerosol Air Qual. Res.

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A modeling framework was proposed to investigate the impact of trans-boundary air pollutant transport on regional air quality. This was based on a combination of the HYSPLIT trajectory model, the CAMx air quality model, and the MM5 meteorological model. The examination of atmospheric PM 10 pollution in Guangzhou within the Pearl River Delta (PRD) region of southern China was used as a case study. The HYSPLIT and MM5 models were used to qualitatively identify the dominant PM 10 pollutant transport pathways that led to PM 10 pollution events in Guangzhou, with five clusters of air mass trajectories being examined. The emission source contribution through each transport pathway to Guangzhou's PM 10 concentration was then quantified using a MM5-CAMx modeling system. The results illustrated that the trans-boundary PM 10 transport played a critical role in the formation of PM 10 pollution events in Guangzhou, with a mean contribution ratio of nearly 49%. In particular, two air mass trajectory clusters that originated from Guangzhou's surrounding regions were found to be the main pollutant transport pathways, and three surrounding cities (Foshan, Dongguan and Huizhou) had a total emission contribution of nearly 30% to Guangzhou's PM 10 concentration through these two pathways. The emissions from these three cities also accounted for 70 to 94% of the total trans-boundary contributions from Guangzhou's nine surrounding cities through the five transport pathways. As a result, in order to improve Guangzhou's air quality, coordinated effort is required to reduce emissions in both Guangzhou itself and its three surrounding cities. It is expected that the presented modeling approach can be applied to air quality studies in many other regions.

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Comparison of Source Apportionment and Sensitivity Analysis in a Particulate Matter Air Quality Model

Cited by 155 publications

References 11 publications

Source contributions to 2012 summertime aerosols in the Euro-Mediterranean region

Source contributions to 2012 summertime aerosols in the Euro-Mediterranean region

Emission inventories and modeling requirements for the development of air quality plans. Application to Madrid (Spain)

Application of Trajectory Clustering and Source Apportionment Methods for Investigating Trans-Boundary Atmospheric PM10 Pollution

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