Abstract. The Comprehensive Automobile Research System (CARS) is an open-source Python-based automobile emissions inventory model designed to efficiently estimate high-quality emissions from motor vehicle emission sources. It can estimate air pollutant, greenhouse gas, and air toxin criteria at any spatial resolution based on the spatiotemporal resolutions of input datasets. The CARS is designed to utilize local vehicle activity data, such as vehicle travel distance, road-link-level network geographic information system (GIS) information, and vehicle-specific average speed by road type, to generate an automobile emissions inventory for policymakers, stakeholders, and the air quality modeling community. The CARS model adopted the European Environment Agency's on-road automobile emissions calculation methodologies to estimate the hot exhaust, cold start, and evaporative emissions from on-road automobile sources. It can optionally utilize average speed distribution (ASD) of all road types to reflect more realistic vehicle speed variations. In addition, through utilizing high-resolution road GIS data, the CARS can estimate the road-link-level emissions to improve the inventory's spatial resolution. When we compared the official 2015 national mobile emissions from Korea's Clean Air Policy Support System (CAPSS) against the ones estimated by the CARS, there is a significant increase in volatile organic compounds (VOCs) (33 %) and carbon monoxide (CO) (52 %) measured, with a slight increase in fine particulate matter (PM2.5) (15 %) emissions. Nitrogen oxide (NOx) and sulfur oxide (SOx) measurements are reduced by 24 % and 17 %, respectively, in the CARS estimates. The main differences are driven by different vehicle activities and the incorporation of road-specific ASD, which plays a critical role in hot exhaust emission estimates but was not implemented in Korea's CAPSS mobile emissions inventory. While 52 % of vehicles use gasoline fuel and 35 % use diesel, gasoline vehicles only contribute 7.7 % of total NOx emissions, whereas diesel vehicles contribute 85.3 %. However, for VOC emissions, gasoline vehicles contribute 52.1 %, whereas diesel vehicles are limited to 23 %. Diesel buses comprise only 0.3 % of vehicles and have the largest contribution to NOx emissions (8.51 % of NOx total) per vehicle due to having longest daily vehicle kilometer travel (VKT). For VOC emissions, compressed natural gas (CNG) buses are the largest contributor at 19.5 % of total VOC emissions. For primary PM2.5, more than 98.5 % is from diesel vehicles. The CARS model's in-depth analysis feature can assist government policymakers and stakeholders in developing the best emission abatement strategies.
One of the major issues in determining a region’s air quality is the uncertainty of large point sources (LPSs) emissions, which significantly affect the local-regional air quality. In this study, the SO2 and NOx emissions of 5 major LPSs in South Korea were evaluated by comparing the emissions-based concentrations employing a Gaussian dispersion model with aircraft-based measurements from DC-8 “around-the-stack” flights through the National Aeronautics and Space Administration (NASA)/National Institute of Environmental Research (NIER) KORea-U.S. Cooperative Domestic Air Quality (KORUS-AQ) aircraft field campaign. The ratio between modeled and measured concentrations for all 5 LPSs ranged between 0.42 and 1.30 and 0.39 and 1.01 for NOx and SO2, respectively. The results for the Boryeong, Dangjin, and Seocheon power plants (PPs), where the locations and sizes of stacks are easier to specify than industrial complexes (Hyundai Steel and Hankook Glass), yielded better performance, which ranged between 0.82 and 1.30 and 0.79 and 1.01 for NOx and SO2. This level of agreement was very encouraging, considering that the modeled concentrations were based on 30-min averaged emissions compared to less-than-a-minute DC-8 around-the-stack measurements. Based on our analysis, the uncertainty of LPS emissions, at least for NOx and SO2, appears to be small, which implies that the point sources inventory emissions are reasonably accurate. The Dangjin PP’s analysis reveals that the actual measured emissions should be considered in addition to “the official” inventory amounts to reduce emission uncertainty. This detailed comparative analysis verified the method used for this study. The findings of this study are expected to enhance the performance of future LPS emission inventory assessments. In terms of recommendations, the data from the raw emission inventory should include more clear information about the locations of measured stacks to obtain more accurate emission estimates. In addition, the flight measurement duration should be long enough to fly around several times to reduce uncertainties, and the flight positions and altitudes should be varied. By improving LPS inventories through accurate evaluations, more accurate air quality forecasts and better policies could be made. As a result, it is expected that public health can be improved by reducing the time people are exposed to high concentrations of air pollutants.
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