시험온도에 따른 LPG 차량의 저온 시동성 및 배출가스 배출특성 연구

- As the interest on the air pollution is gradually rising up at home and abroad, automotive and fuel researchers have been working on the exhaust emission reduction from vehicles through a lot of approaches, which consist of new engine design, innovative after-treatment systems, using clean (eco-friendly alternative) fuels and fuel quality improvement. This research has brought forward various main issues : whether PM emissions should be regulated for diesel and gasoline vehicles and whether gasoline and LPG powered vehi-cles can be further neglected from PM emission inventories. Finally, the greenhouse gas regulation has been discu-ssed including automotive emission regulation. The greenhouse gas and emissions of automotive had many problem that cause of ambient pollution, health effects. Based on various test modes and ambient conditions, this paper discusses the characteristics of LPG on exhaust emissions and greenhouse gases. Also, this paper asse-ssed emission characteristics due to the test temperature. These test temperature were performed by dividing the temperature of the test mode and the lowest local temperature in winter. Through this study, the correlation of vehicle test mode and ambient condition, exhaust emission, greenhouse gas emission was analyzed. Key words : LPLi (Liquid phase LPG injection), Test mode(5-cycle(FTP and SFTP) mode, NEDC, WLTP mode), Greenhouse gas(CO

This study investigates the impact of Road Transport Emission Reduction Policies (RTERPs) on air pollutant and greenhouse gas (GHG) emissions in Vijayawada, a non-attainment city in India. Utilising the Activity-Structure-Emission Factor (ASF) modeling technique, we developed an on-road transportation sector emission inventory for the base year 2021, encompassing both vehicle exhaust and non-exhaust emissions. The study found that vehicle exhaust emissions of PM10, NO2, CO, and HC in 2021 were 4.7 Gg, 5.6 Gg, 17.3 Gg, and 2.4 Gg, respectively.The study evaluated the effectiveness of RTERPs under different scenarios for 2030. Alternative Scenario I (ALT-I-2030), incorporating national-level policies such as vehicle scrappage, cleaner fuels, and electric vehicle promotion, is projected to reduce pollutant emissions by 22-45%. For instance, PM10 emissions are expected to decrease by 22%, while NO2 emissions could see a reduction of up to 45%. ALT-II-2030, due to local-level strategies like low-emission zones in addition to national policies, demonstrates a more significant reduction in vehicle exhaust emissions, ranging from 42% to 68%. Under this scenario, PM10 emissions are projected to decrease by 42%, and NO2 emissions could potentially decline by 68%.While ALT-II-2030 reduces CO2 emissions from vehicle exhaust by 29% (from 550 Gg in 2021 to 390 Gg in 2030), the study highlights the potential for indirect CO2 emissions from coal-based electricity generation to power the growing electric vehicle fleet, potentially offsetting the positive effects of RTERPs.Non-exhaust emissions were also quantified, with resuspended road dust constituting the primary source, contributing approximately 94% of PM emissions (nearly 2.4 Gg) in 2021. Meanwhile, tyre, brake, and road wear contributed to 1%, 3%, and 2% respectively.  The spatial distribution of both vehicle exhaust and non-exhaust emissions exhibits significant heterogeneity, emphasising the need for localised control strategies in urbanising regions. This study underscores the importance of adopting balanced strategies that simultaneously address air quality concerns and promote sustainable transportation systems, aligning with Sustainable Development Goals 11.2 and 11.6.2.Keywords: Road transport emissions, Emission inventory, Urban air quality, Scenario analysis, Exhaust and non-exhaust emissions 

High resolution vehicular PM10 emissions over megacity Delhi: Relative contributions of exhaust and non-exhaust sources

The activity of exploration and production in oil and gas industry is significant greenhouse gas (GHG) emission source. PT. XYZ is one of upstream oil and gas industry in Indonesia and it have large crude oil and gas potential with it reserves that not manage yet. Therefore, GHG emission potential from the activity of exploration and production in PT. XYZ is very large. This study is done for estimate GHG emission reduction potential in PT. XYZ from various activities. Emission inventory is the first step to estimate GHG released to atmosphere. Method of estimation use the method developed by American Petroleum Institute (API). This study considers three types of mitigation measures options, including technical options (scenario 1), behavior option (scenario 2), and policy option (scenario 3). Based on emission inventory, flare and oil storage tank are primary source of GHG emissions in PT. XYZ. Scenario 1 prefers control of GHG emissions in flare and storage tank as primary emission source. While others scenario prefers to control GHG emission from transportation sector. Scenario 1 has potential to reduce emissions by 48.3 %. While scenario 2, and 3 in sequences have potential to reduce emissions by 0.15%, and 0.52%. Emissions flare and oil storage tank can be reduced through the installation of flaring gas recovery unit and vapor recovery unit. Both are effective and efficient in reducing GHG emissions in PT. XYZ. In addition, all mitigation measures of transportation sector provide benefits even though the amount of GHG that can be reduced is not significant.

Greenhouse gas (GHG) emission inventories, which currently inform abatement policy discussions, are developed mostly from national scale data. Nevertheless, although the policy debate tends to take place in global and national arenas, action to abate GHG emissions is inherently within the provenance of local institutions and communities. The purpose of this paper is to examine how much information is lost by not estimating GHG emissions data at scales finer than the whole US. Such information may be critical in bridging global and local policy. Differences in the composition of GHG emission sources based on GHG emission inventories at three nested spatial scales (national, state, local) for four study sites (in Kansas, North Carolina, Ohio and Pennsylvania) are analysed, drawing upon initial results of a large collaborative study known as the ‘Association of American Geographers‐Global Change in Local Places (GCLP)’ project. The concept of spatial sovereignty of emissions is developed to test the cross‐scale reliability of emission inventories. For the test year 1990, close agreement is found in the by‐gas composition of GHG emissions among national, state and local inventories. Spatial sovereignty in this case is maintained. However close agreement is not found in the by‐source composition of GHG emissions among national, state and local inventories. Spatial sovereignty in this case is not maintained. Regular compilation of state and local emissions source inventories may be necessary to track important spatial and temporal deviations from national trends.

<div class="htmlview paragraph">The University of Maryland team converted a model year 2000 Chevrolet Suburban to an ethanol-fueled hybrid-electric vehicle (HEV) and tied for first place overall in the 2000 FutureTruck competition. Competition goals include a two-thirds reduction of greenhouse gas (GHG) emissions, a reduction of exhaust emissions to meet California ultra-low emissions vehicle (ULEV) Tier II standards, and an increase in fuel economy. These goals must be met without compromising the performance, amenities, safety, or ease of manufacture of the stock Suburban. The University of Maryland FutureTruck, <i>Proteus</i>, addresses the competition goals with a powertrain consisting of a General Motors 3.8-L V6 engine, a 75-kW (100 hp) SatCon electric motor, and a 336-V battery pack. Additionally, <i>Proteus</i> incorporates several emissions-reducing and energy-saving modifications; an advanced control strategy that is implemented through use of an on-board computer and an innovative hybrid-electric drive train. Preliminary computer modeling, using ADVISOR, an advanced vehicle simulator, predicted that <i>Proteus</i> would achieve a reduction in exhaust emissions, a fuel efficiency increase of 28%, and a decrease in 0-60 mph time of 0.5-s.<sup><span class="xref">1</span></sup> In the first year of the competition, <i>Proteus</i> achieved a 35% reduction in greenhouse gas emissions, a 20% increase in fuel economy, and reduced regulated tailpipe emissions to less than the stock vehicle in all categories. Continuing development and optimization of <i>Proteus</i>' systems will lead to an even further reduction in emissions and will significantly improve fuel economy.</div>

This paper presents the results of experiments carried out to evaluate the effect of adding an anticorrosion additive to blended biofuel and lubricating oil on emissions, engine component wear and lubrication characteristics. The blended biofuels consist of 7.5 and 15 per cent palm olein (PO) with ordinary diesel oil (OD). Pure OD was used for comparison purposes. Exhaust emission gases such as NOx, CO and hydrocarbons (HCs) were measured by an exhaust emission analyser for engine operation on 50 per cent throttle at speeds of 800-3600 r/min. To measure engine component wear and lubricating oil characteristics, the engine was operated at 50 per cent throttle at a speed of 2000 r/min for a period of 100 h with each of the fuel samples. The same lubricating oil, conventional SAE 40, was used in all the fuels. A multielement oil analyser (MOA) was used to measure the increase in wear of metals (Fe, Cu, Al, Pb) and the decrease in lubricating oil additives (Zn, Ca) in the lubricating oil used. An ISL automatic Houillon viscometer (ASTM D445) and potentiometric titration (ASTM D2896) were used to measure viscosity and total base number (TBN) respectively. The results show that the addition of anticorrosion additive with biofuel and lubricating oil improves the emission and engine wear characteristics; both the exhaust emission gases (NOx, CO and HCs) and the wear of metals (Fe, Cu, Al and Pd) decrease with the blended fuels in comparison with the base fuel OD. Detailed results, including engine brake power, are discussed.

The application of oxygenated additives seems to be one of more promising modifications of diesel fuels in order to decrease exhaust emissions. The authors have so far tested many oxygenates, from different chemical families, but as sole fuel components. Generally speaking, these oxygenates produced favorable but different changes in exhaust emissions. The objective of this study was to investigate whether the positive effect on emissions could be maximized by the application of packages of multiple oxygenated compounds. Four different oxygenated additive packages were tested. Each package contained a combination of 2 synthetic oxygenates, which represented different chemical groups, namely: glycol ethers, maleates and carbonates. The packages were evaluated as fuel additives at a concentration of 10 % v/v in a Euro 5 diesel fuel. The New European Driving Cycle (NEDC) was selected as a representative test for this study. All the oxygenate packages were additionally tested using the US Federal Test Procedure 75 (FTP-75). This cycle was carried out in order to determine the influence of cycle conditions on oxygenated fuels’ effectiveness as regards reductions in exhaust emissions. The tests were conducted on a Euro 4 passenger car equipped with a direct injection (common rail) turbocharged diesel engine. During the tests, mass emissions of CO, HC, NOx, PM and CO2 were measured. The influence of individual oxygenates on CO, NOx and PM emissions is roughly additive when these oxygenates are applied together (i.e. as a package of additives). There is no such regularity for HC emissions. The research showed that the application of oxygenated additives generally produces a significant reduction in PM emissions and a slight increase in NOx emissions. An increase in CO and HC emissions was observed when maleates and carbonates were used as sole oxygenates. This increase was significantly lower when the oxygenates mentioned above were applied in a package with glycol ethers. The influence of oxygenated additive packages was different over the NEDC and FTP-75 cycles. Generally, the packages produced more favorable changes in exhaust emissions over the FTP-75 cycle, which is more transient and dynamic (stronger accelerations). The reduction in PM emissions was higher over the FTP-75 cycle. In the case of NOx emissions, these were higher by a factor of dozen or so for oxygenated fuels than for neat diesel fuel over the NEDC, whereas over the FTP-75 they was slightly lower for oxygenated fuels than for diesel fuel. In the case of CO and HC emissions, such a clear-cut relationship between the type of driving cycle and emissions changes was not observed. Regardless of test conditions, no significant influence of oxygenated additive packages on CO2 emissions was noted. The application of oxygenated diesel fuels containing packages of oxygenated compounds caused a significant reduction in PM and a small change in NOx emissions, so it produced favorable changes in the PM/NOx emissions trade-off. Favorable changes in PM/NOx emissions produced by the application of oxygenated additive packages were, however, comparable to these achieved with use of the most effective individual oxygenates.KeywordsDiesel fuelOxygenatesFuel additivesExhaust emissionsDiesel vehicle

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 the criteria air pollutants, greenhouse gases, and air toxics in various temporal resolutions at the national, state, county, and any spatial resolution based on the spatiotemporal resolutions of input datasets. The CARS is designed to utilize the local vehicle activity database, such as vehicle travel distance, road link-level network Geographic Information System (GIS) information, and vehicle-specific average speed by road type, to generate a temporally and spatially enhanced automobile emissions inventory for policymakers, stakeholders, and the air quality modeling community. The CARS model adopted the European Environment Agency's (EEA) onroad automobile emissions calculation methodologies to estimate the hot exhaust, cold start, and evaporative emissions from onroad automobile sources. It can optionally utilize road link-specific average speed distribution (ASD) inputs to reflect more realistic vehicle speed variations by road type than a road-specific single averaged speed approach. Also, utilizing high-resolution road GIS data allows the CARS to 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 moderate increase of VOC (33 %), CO (52 %), and fine particulate matter (PM2.5) (15 %) emissions while NOx and SOx are reduced by 24 % and 17 % in the CARS estimates. The main differences are driven by the usage of different vehicle activities and the incorporation of road-specific ASD, which plays a critical role in hot exhaust emission estimates but wasn’t 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 while diesel vehicles contribute 85.3 %. But for VOC emissions, gasoline vehicles contribute 52.1 % while diesel vehicles are limited to 23 %. While diesel buses are only 0.3 % of vehicles, each vehicle has the largest contribution to NOx emissions (8.51 % of NOx total) due to its longest daily VKT. For VOC, CNG buses are the largest contributor with 19.5 % of total VOC emissions. It indicates that the CNG bus is better for the rural area while the diesel bus is better applicable for the urban area for a better ozone control strategy because the rural area is usually NOx limited for ozone formation and urban area is VOC limited region. 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 develop the best emission abatement strategies.

<strong class="journal-contentHeaderColor">Abstract.</strong> 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 (PM<span class="inline-formula">_2.5</span>) (15 %) emissions. Nitrogen oxide (NO<span class="inline-formula">_<i>x</i></span>) and sulfur oxide (SO<span class="inline-formula">_<i>x</i></span>) 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 NO<span class="inline-formula">_<i>x</i></span> 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 NO<span class="inline-formula">_<i>x</i></span> emissions (8.51 % of NO<span class="inline-formula">_<i>x</i></span> 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 PM<span class="inline-formula">_2.5</span>, 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.

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 the criteria air pollutants, greenhouse gases, and air toxics in various temporal resolutions at the national, state, county, and any spatial resolution based on the spatiotemporal resolutions of input datasets. The CARS is designed to utilize the local vehicle activity database, such as vehicle travel distance, road link-level network Geographic Information System (GIS) information, and vehicle-specific average speed by road type, to generate a temporally and spatially enhanced automobile emissions inventory for policymakers, stakeholders, and the air quality modeling community. The CARS model adopted the European Environment Agency's (EEA) onroad automobile emissions calculation methodologies to estimate the hot exhaust, cold start, and evaporative emissions from onroad automobile sources. It can optionally utilize road link-specific average speed distribution (ASD) inputs to reflect more realistic vehicle speed variations by road type than a road-specific single averaged speed approach. Also, utilizing high-resolution road GIS data allows the CARS to 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 moderate increase of VOC (33 %), CO (52 %), and fine particulate matter (PM2.5) (15 %) emissions while NOx and SOx are reduced by 24 % and 17 % in the CARS estimates. The main differences are driven by the usage of different vehicle activities and the incorporation of road-specific ASD, which plays a critical role in hot exhaust emission estimates but wasn’t 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 while diesel vehicles contribute 85.3 %. But for VOC emissions, gasoline vehicles contribute 52.1 % while diesel vehicles are limited to 23 %. While diesel buses are only 0.3 % of vehicles, each vehicle has the largest contribution to NOx emissions (8.51 % of NOx total) due to its longest daily VKT. For VOC, CNG buses are the largest contributor with 19.5 % of total VOC emissions. It indicates that the CNG bus is better for the rural area while the diesel bus is better applicable for the urban area for a better ozone control strategy because the rural area is usually NOx limited for ozone formation and urban area is VOC limited region. 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 develop the best emission abatement strategies.

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 the criteria air pollutants, greenhouse gases, and air toxics in various temporal resolutions at the national, state, county, and any spatial resolution based on the spatiotemporal resolutions of input datasets. The CARS is designed to utilize the local vehicle activity database, such as vehicle travel distance, road link-level network Geographic Information System (GIS) information, and vehicle-specific average speed by road type, to generate a temporally and spatially enhanced automobile emissions inventory for policymakers, stakeholders, and the air quality modeling community. The CARS model adopted the European Environment Agency's (EEA) onroad automobile emissions calculation methodologies to estimate the hot exhaust, cold start, and evaporative emissions from onroad automobile sources. It can optionally utilize road link-specific average speed distribution (ASD) inputs to reflect more realistic vehicle speed variations by road type than a road-specific single averaged speed approach. Also, utilizing high-resolution road GIS data allows the CARS to 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 moderate increase of VOC (33 %), CO (52 %), and fine particulate matter (PM2.5) (15 %) emissions while NOx and SOx are reduced by 24 % and 17 % in the CARS estimates. The main differences are driven by the usage of different vehicle activities and the incorporation of road-specific ASD, which plays a critical role in hot exhaust emission estimates but wasn’t 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 while diesel vehicles contribute 85.3 %. But for VOC emissions, gasoline vehicles contribute 52.1 % while diesel vehicles are limited to 23 %. While diesel buses are only 0.3 % of vehicles, each vehicle has the largest contribution to NOx emissions (8.51 % of NOx total) due to its longest daily VKT. For VOC, CNG buses are the largest contributor with 19.5 % of total VOC emissions. It indicates that the CNG bus is better for the rural area while the diesel bus is better applicable for the urban area for a better ozone control strategy because the rural area is usually NOx limited for ozone formation and urban area is VOC limited region. 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 develop the best emission abatement strategies.

<strong class="journal-contentHeaderColor">Abstract.</strong> 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 (PM<span class="inline-formula">_2.5</span>) (15 %) emissions. Nitrogen oxide (NO<span class="inline-formula">_<i>x</i></span>) and sulfur oxide (SO<span class="inline-formula">_<i>x</i></span>) 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 NO<span class="inline-formula">_<i>x</i></span> 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 NO<span class="inline-formula">_<i>x</i></span> emissions (8.51 % of NO<span class="inline-formula">_<i>x</i></span> 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 PM<span class="inline-formula">_2.5</span>, 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.

<strong class="journal-contentHeaderColor">Abstract.</strong> 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 the criteria air pollutants, greenhouse gases, and air toxics in various temporal resolutions at the national, state, county, and any spatial resolution based on the spatiotemporal resolutions of input datasets. The CARS is designed to utilize the local vehicle activity database, such as vehicle travel distance, road link-level network Geographic Information System (GIS) information, and vehicle-specific average speed by road type, to generate a temporally and spatially enhanced automobile emissions inventory for policymakers, stakeholders, and the air quality modeling community. The CARS model adopted the European Environment Agency's (EEA) onroad automobile emissions calculation methodologies to estimate the hot exhaust, cold start, and evaporative emissions from onroad automobile sources. It can optionally utilize road link-specific average speed distribution (ASD) inputs to reflect more realistic vehicle speed variations by road type than a road-specific single averaged speed approach. Also, utilizing high-resolution road GIS data allows the CARS to 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 moderate increase of VOC (33&thinsp;%), CO (52&thinsp;%), and fine particulate matter (PM_2.5) (15&thinsp;%) emissions while NO_x and SO_x are reduced by 24&thinsp;% and 17&thinsp;% in the CARS estimates. The main differences are driven by the usage of different vehicle activities and the incorporation of road-specific ASD, which plays a critical role in hot exhaust emission estimates but wasn&rsquo;t implemented in Korea&rsquo;s CAPSS mobile emissions inventory. While 52% of vehicles use gasoline fuel and 35&thinsp;% use diesel, gasoline vehicles only contribute 7.7&thinsp;% of total NO_x emissions while diesel vehicles contribute 85.3&thinsp;%. But for VOC emissions, gasoline vehicles contribute 52.1&thinsp;% while diesel vehicles are limited to 23&thinsp;%. While diesel buses are only 0.3&thinsp;% of vehicles, each vehicle has the largest contribution to NO_x emissions (8.51&thinsp;% of NO_x total) due to its longest daily VKT. For VOC, CNG buses are the largest contributor with 19.5&thinsp;% of total VOC emissions. It indicates that the CNG bus is better for the rural area while the diesel bus is better applicable for the urban area for a better ozone control strategy because the rural area is usually NO_x limited for ozone formation and urban area is VOC limited region. For primary PM_2.5, more than 98.5&thinsp;% is from diesel vehicles. The CARS model's in-depth analysis feature can assist government policymakers and stakeholders develop the best emission abatement strategies.

Major US electric utility climate pledges have the potential to collectively reduce power sector emissions by one-third