Abstract
Abstract. Source apportionment modeling provides valuable information on the contributions of different source sectors and/or source regions to ozone (O3) or fine particulate matter (PM2.5) concentrations. This information can be useful in designing air quality management strategies and in understanding the potential benefits of reducing emissions from a particular source category. The Comprehensive Air quality Model with Extensions (CAMx) offers unique source attribution tools, called the Ozone and Particulate Source Apportionment Technology (OSAT/PSAT), which track source contributions. We present results from a CAMx source attribution modeling study for a summer month and a winter month using a recently evaluated European CAMx modeling database developed for Phase 3 of the Air Quality Model Evaluation International Initiative (AQMEII). The contributions of several source sectors (including model boundary conditions of chemical species representing transport of emissions from outside the modeling domain as well as initial conditions of these species) to O3 or PM2.5 concentrations in Europe were calculated using OSAT and PSAT, respectively. A 1-week spin-up period was used to reduce the influence of initial conditions. Evaluation focused on 16 major cities and on identifying source sectors that contributed above 5 %. Boundary conditions have a large impact on summer and winter ozone in Europe and on summer PM2.5, but they are only a minor contributor to winter PM2.5. Biogenic emissions are important for summer ozone and PM2.5. The important anthropogenic sectors for summer ozone are transportation (both on-road and non-road), energy production and conversion, and industry. In two of the 16 cities, solvent and product also contributed above 5 % to summertime ozone. For summertime PM2.5, the important anthropogenic source sectors are energy, transportation, industry, and agriculture. Residential wood combustion is an important anthropogenic sector in winter for PM2.5 over most of Europe, with larger contributions in central and eastern Europe and the Nordic cities. Other anthropogenic sectors with large contributions to wintertime PM2.5 include energy, transportation, and agriculture.
Highlights
Photochemical grid models (PGMs), such as the Comprehensive Air quality Model with Extensions (CAMx; Ramboll Environ, 2014) and the Community Multiscale Air Quality (CMAQ) model (Byun and Schere, 2006), are widely used in air quality management to assess the effectiveness of potential control strategies for secondary pollutants such as O3 and fine particulate matter (PM2.5)
For the road transport sector (SNAP 7), it should be noted that the provided emission inventory does not include information on the composition of the vehicle fleet in different cities in Europe because the emission inventory was made available to the AQME participants with source contributions grouped according the Standard Nomenclature for Air Pollution (SNAP) classification but without any additional information about the car fleet or other proxies introduced in emission computation
The contributions of the various source sectors to ozone and PM2.5 concentrations were calculated for these cities and are discussed
Summary
Photochemical grid models (PGMs), such as the Comprehensive Air quality Model with Extensions (CAMx; Ramboll Environ, 2014) and the Community Multiscale Air Quality (CMAQ) model (Byun and Schere, 2006), are widely used in air quality management to assess the effectiveness of potential control strategies for secondary pollutants such as O3 and fine particulate matter (PM2.5). It suffers from the limitation that the sum of zero-out impacts over all sources will not equal the total concentration (Koo et al, 2009) Tagged species methods, such as the Ozone and Particulate Source Apportionment Technology (OSAT/PSAT) in CAMx (Dunker et al, 2002; Yarwood et al, 2007), can effi-. Karamchandani et al.: Source-sector contributions to European ozone ciently track contributions from many source sectors and/or regions and provide source contributions that sum to the total concentration These methods are increasingly being used to help understand complex air quality issues (e.g., Wagstrom et al, 2008; Burr and Zhang, 2011; Baker and Kelly, 2014; Collet et al, 2014; Wang et al, 2009; Li et al, 2012; Skyllakou et al, 2014). As part of Phase 3, a CAMx modeling database has been developed and evaluated for Europe (Solazzo et al, 2017), and this database was used in the study described in this paper
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