A semi-empirical, receptor-oriented Lagrangian model for simulating fine particulate carbon at rural sites B.A. Schichtel a, * , M.A. Rodriguez b , M.G. Barna a , K.A. Gebhart a , M.L. Pitchford c , W.C. Malm b a National Park Service, CIRA/CSU, 1375 Campus Delivery, Fort Collins, CO 80523, USA b Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO 80523, USA c Division of Atmospheric Sciences, Desert Research Institute, Reno, NV 89512, USA highlights < We present a new semi-empirical Lagrangian particle dispersion model. < The model is used to apportion PM 2.5 carbon at rural locations to major source types. < The results are evaluated against measured data and compared to CMAQ model results. < The model is best used in the analysis of measured carbonaceous aerosols. article info Article history: Received 24 October 2011 Received in revised form 9 April 2012 Accepted 6 July 2012 Keywords: Lagrangian particle dispersion model Carbonaceous aerosols Source apportionment Biomass burning abstract Total fine particulate carbon (TC) is an important contributor to fine particulate matter and is measured in routine national monitoring programs. TC contributes to adverse health effects, regional haze, and climate effects. To resolve these adverse effects, there is a need for tools capable of routine and clima- tological assessments and exploration of the sources contributing to the measured TC. To address this need, a receptor-oriented, Lagrangian particle dispersion model was developed to simulate TC in rural areas, using readily available meteorological and emission inputs. This model was based on the CAPITA (Center for Air Pollution Impact and Trend Analysis) Monte Carlo model (CMC) and simulated the contributions from eight source categories, including biomass burning and secondary organic carbon (SOC) from vegetation. TC removal and formation mechanisms are simulated using a simplified parameterization of atmospheric processes based on pseudo-first-order rate equations. The rate coeffi- cients are empirical functions of meteorological parameters derived from measured, modeled, and literature data. These functions were optimized such that the simulated TC concentrations reproduce the average spatial and seasonal patterns in measured 2008 U.S. TC concentrations, as well as measured SOC fractions at two eastern U.S. sites. The optimized model was used to simulate 2006e2008 rural TC that was evaluated against measured TC. In addition, the model output was compared to TC from a 2006 Eulerian Community Multiscale Air Quality (CMAQ) simulation. It is shown that the CMC model has similar performance metrics as the CMAQ model. Published by Elsevier Ltd. 1. Introduction Carbonaceous aerosols arise from a wide variety of sources, including combustion of fossil fuels, meat cooking, deep frying, and biomass burning (Bond et al., 2004). Secondary organic carbon (SOC) produced from biogenic and combustion volatile organic compounds (VOCs) also contribute to organic aerosols. The diverse carbon sources and atmospheric processing result in a complex mixture of compounds that significantly contribute to fine partic- ulate matter (PM) < 2.5 mm (PM 2.5 )(Hand et al., 2012). High PM 2.5 carbon concentrations can lead to adverse health effects, their efficient scattering and absorption of visible and infrared radiation make them a key factor in the balance of solar radiation, and they contribute to haze in protected national parks and wilderness areas, i.e., class I areas. The Interagency Monitoring of Protected Visual Environments (IMPROVE) and Chemical Speciation Network (CSN) routine monitoring networks collect 24-h, integrated PM 2.5 samples that are analyzed for chemical composition, including organic (OC) and elemental (EC) carbon. The IMPROVE monitoring program is used * Corresponding author. Tel.: þ1 970 491 8581; fax: þ1 970 491 8598. E-mail address: Schichtel@cira.colostate.edu (B.A. Schichtel). Contents lists available at SciVerse ScienceDirect Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv 1352-2310/$ e see front matter Published by Elsevier Ltd. http://dx.doi.org/10.1016/j.atmosenv.2012.07.017 Atmospheric Environment 61 (2012) 361e370