Abstract

Abstract. PMCAMx-2008, a detailed three-dimensional chemical transport model (CTM), was applied to Europe to simulate the mass concentration and chemical composition of particulate matter (PM) during May 2008. The model includes a state-of-the-art organic aerosol module which is based on the volatility basis set framework treating both primary and secondary organic components as semivolatile and photochemically reactive. The model performance is evaluated against high time resolution aerosol mass spectrometer (AMS) ground and airborne measurements. Overall, organic aerosol is predicted to account for 32% of total PM1 at ground level during May 2008, followed by sulfate (30%), crustal material and sea-salt (14%), ammonium (13%), nitrate (7%), and elemental carbon (4%). The model predicts that fresh primary OA (POA) is a small contributor to organic PM concentrations in Europe during late spring, and that oxygenated species (oxidized primary and biogenic secondary) dominate the ambient OA. The Mediterranean region is the only area in Europe where sulfate concentrations are predicted to be much higher than the OA, while organic matter is predicted to be the dominant PM1 species in central and northern Europe. The comparison of the model predictions with the ground measurements in four measurement stations is encouraging. The model reproduces more than 94% of the daily averaged data and more than 87% of the hourly data within a factor of 2 for PM1 OA. The model tends to predict relatively flat diurnal profiles for PM1 OA in many areas, both rural and urban in agreement with the available measurements. The model performance against the high time resolution airborne measurements at multiple altitudes and locations is as good as its performance against the ground level hourly measurements. There is no evidence of missing sources of OA aloft over Europe during this period.

Highlights

  • Fine particulate matter is a prime contributor to various air quality problems such as visibility reduction (Seinfeld and Pandis, 2006), public health (Pope et al, 2002), formation of acid rain (Burtraw et al, 2007) and climate change (Schwartz, 1996)

  • We explore here the role of the assumed aging in biogenic secondary organic aerosol (SOA) and the enthalpies of vaporization ( Hvap) for SOA

  • A detailed three dimensional chemical transport model (CTM), PMCAMx-2008, was applied to the European domain for the first time, in order to simulate the chemical composition and mass of the major PM1 components during the month of May 2008 during which an intensive campaign of measurements was performed in Europe as part of the EUCAARI project

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Summary

Introduction

Fine particulate matter is a prime contributor to various air quality problems such as visibility reduction (Seinfeld and Pandis, 2006), public health (Pope et al, 2002), formation of acid rain (Burtraw et al, 2007) and climate change (Schwartz, 1996). Gelencser et al (2007) studied the sources of organic PM2.5 aerosol in five rural/remote sites in Europe combining radiocarbon measurements with bulk measurements of organic carbon, elemental carbon, and two organic tracers They found that biomass burning and fossil fuel combustion were the main sources in winter, while secondary SOA from non-fossil sources was the dominant contributor (63– 76 % of total carbon in PM2.5) during the summer followed by SOA from fossil fuel combustion. Murphy and Pandis (2009) applied PMCAMx-2008 in the Eastern United States and evaluated the new modeling framework against daily ambient measurements from the EPA STN (Speciated Trends Network) and the IMPROVE (Interagency Monitoring of Protected Visual Environments) data as well as hourly data from PAQS (Pittsburgh Air Quality Study). The model performance is evaluated against hourly averaged AMS ground measurements as well as airborne measurements from an aircraft field campaign over Europe (Morgan et al, 2010b)

PMCAMx-2008 description
Model application
MT: Monoterpene emissions 2 OVOC
Predicted aerosol concentrations
Model evaluation
Sensitivity tests
Findings
Conclusions
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