Vehicular Emission Inventory Research Articles

Improving spatial disaggregation of vehicular emission inventories

ABSTRACT Precise estimates of vehicular emissions at fine spatial scales are essential for effective emission reduction strategies. Achieving high-resolution vehicular emission inventories necessitates detailed data on traffic flow, driving patterns, and vehicle speeds for each road network segment. However, in developing countries, the lack of comprehensive traffic data, limited infrastructure, and insufficient monitoring systems constrains the development of high-resolution inventories. This gap poses significant challenges for accurately quantify emissions in regions that often experience rapid urbanisation and traffic growth. Here, we propose a novel method to enhance the spatial disaggregation of large-scale vehicular emission inventories. By analysing road-level emissions data from 63 Brazilian municipalities, we developed a model that predicts weighting factors to disaggregate vehicular emissions into a gridded format, based on the proportion of primary road lengths. Our findings indicate that the predicted weighting factors significantly improve the spatial disaggregation of vehicular emissions compared to the traditional road density method by reasonably increasing the emissions in high vehicular activity areas. This approach not only provides more accurate representations of vehicular emissions for urban planning in Brazil but also offers a solution that can be adapted to enhance top-down vehicular emissions inventories globally. Our study offers a valuable tool that can be tailored to various regions, enabling more precise urban planning and policy-making for air quality management worldwide.

Investigating the applicability of COPERT 5.5 emission software in Bangladesh and developing countrywide vehicular emission inventories

This study investigated the applicability of the European software COPERT 5.5 (Computer Program to Estimate Emissions from Road Traffic) for Bangladesh, proposed emission factors, and developed countrywide vehicular emission inventories.

Driving factors and clustering analysis of expressway vehicular CO2 emissions in Guizhou Province, China.

Expressways are essential for intercounty trips of passenger travel and freight mobility, which are also an important source of vehicular CO2 emissions in transportation sector. This study takes the expressway system of Guizhou Province as the research objective, and establishes the multi-year expressway vehicular CO2 emission inventories at the county level from 2011 to 2019. We employ the extended STIRPAT model incorporating ridge regression to identify driving factors from six different aspects, and then utilize the affinity propagation cluster method to conduct the differentiation research by dividing Guizhou's counties into four clusters. Based upon clustering analysis, localized and targeted policies are formulated for each cluster to reduce expressway vehicular CO2 emissions. The results indicate that generally: (1) Guizhou's expressway vehicular CO2 emissions manifest a continuously upward trend during 2011-2019. Small-duty passenger vehicle (SDV), light-duty truck (LDT), and heavy-duty truck (HDT) contribute to the largest CO2 emissions in eight vehicle types. (2) GDP and population are the foremost two positive driving factors, followed by urbanization rate and expressway length. The proportion of secondary industry is also a positive driver, but that of tertiary industry exhibits an opposite effect. (3) Regional disparity exists in four county clusters of Guizhou Province. Efficient policies are proposed, such as improving the layout and infrastructure of transportation hubs, promoting multimodal integration, and implementing industrial upgrading as per regional advantages. Sustainable expressway vehicular CO2 emission reduction is realized from both the source of industry and low-carbon modes of transport.

Evolution and Characteristics of Full-process Vehicular VOCs Emissions in Tianjin from 2000 to 2020

Vehicle emissions are an important source of anthropogenic volatile organic compound (VOCs) emissions in urban areas and are commonly quantified using vehicle emission inventories. However, most previous studies on vehicle emission inventories have incomplete emission factors and emission processes or insufficient consideration of meteorological parameters. Based on the localized full-process emission factors attained from tested data and previous studies, a method to develop a monthly vehicular VOC emission inventory of full process for the long-term was established, which covered exhaust and evaporative emissions (including running loss, diurnal breathing loss, hot soak loss, and refueling emission). Then, the method was used to develop a full-process vehicular VOC emission inventory in Tianjin from 2000 to 2020. The results showed that the total vehicular VOC emissions in Tianjin rose slowly and then gradually decreased. In 2020, the total emissions were 21400 tons. The light-duty passenger vehicles were the dominant contributors and covered 75.00% of the total emissions. Unlike the continuous decline in exhaust emissions, evaporative emissions showed an inverted U-shaped trend with an increasing contribution to total emissions yearly, accounting for 31.69% in 2020. Monthly emissions were affected by both vehicle activity and emission factors. VOC emissions were high in autumn and winter and low in spring and summer. During the COVID-19 epidemic in 2020, vehicle activity was limited by closure and control, making VOC emissions significantly lower than those during the same period in previous years. The method and data in this study can provide technical reference and a decision-making basis for air pollution prevention and control.

Vehicular Emission Inventory and Correlation Study for Indian Medium-Sized City

Vehicular Emission Inventory and Correlation Study for Indian Medium-Sized City

High spatiotemporal resolution vehicular emission inventory in Beijing-Tianjin-Hebei and its surrounding areas (BTHSA) during 2000–2020, China

One comprehensive emission inventory of CO, HC, NOX, PM10, PM2.5, BC, CH4, CO2 and N2O with high spatial resolution (0.01° × 0.01°) for 58 cities in Beijing-Tianjin-Hebei and its surrounding areas (BTHSA) during 2000–2020 are developed by using COPERT model and ArcGIS methodology. The results show that vehicular emissions of CO, HC, NOX, PM10, PM2.5, BC and CH4 have begun to decrease or slow their growth rates in recent years due to the implementation of measures to control vehicular emissions. However, vehicular emissions of CO2 increase rapidly due to little fuel economy improvement. Besides, the usage of selective catalytic reduction (SCR) systems by heavy duty truck (HDT) is the main factor impacting the growth trend of vehicular N2O emissions since 2017. By 2020, vehicular emissions of CO, HC, NOX, PM10, PM2.5, BC, CO2, CH4 and N2O are estimated at about 1.65 Mt, 0.35 Mt, 1.39 Mt, 87.44 kt, 55.06 kt, 15.57 kt, 527.71 Mt, 36.20 kt and 8.56 kt, respectively. Therein, China III, IV, IV and IV passenger cars (PCs) are the predominated models for vehicular emissions of CO, HC, CH4 and CO2, accounting for 19.59–28.26 % of the total vehicular emission of corresponding pollutant. Nevertheless, the major contributors of vehicular emissions of NOX, PM10, PM2.5, BC and N2O are China III (29.64 %), III (18.03 %), III (22.81 %), III (42.16 %) and V (22.28 %) HDTs, respectively. The gridded vehicular emissions vary significantly, with emissions of CO, HC, CH4 and CO2 being mainly concentrated in central urban areas of cities (e.g., Beijing, Tangshan, Zhengzhou, Tianjin, Qingdao, Jinan). Nevertheless, the grids with high vehicular emissions of NOX, PM10, PM2.5, BC and N2O are mainly distributed along the expressway and the suburban roads of cities (e.g., Linyi, Tangshan, Jining, Weifang, Shijiazhuang, Tianjin, Baoding). Finally, multi-year uncertainties of vehicular emission inventory are discussed.

Vehicular Ammonia Emissions Significantly Contribute to Urban PM2.5 Pollution in Two Chinese Megacities.

Ammonia (NH3) plays a vital role in the formation of fine particulate matter (PM2.5). Prior studies have primarily focused on the control of agricultural NH3 emissions, the dominant source of anthropogenic NH3 emissions. The air quality impact from vehicular NH3 emissions, which could be particularly important in urban areas, has not been adequately evaluated. We developed high-resolution vehicular NH3 emission inventories for Beijing and Shanghai based on detailed link-level traffic profiles and conducted atmospheric simulations of ambient PM2.5 concentrations contributed by vehicular NH3 emissions. We found that vehicular NH3 emissions shared high proportions among total anthropogenic NH3 emissions in the urban areas of Beijing (86%) and Shanghai (45%), where vehicular NH3 was primarily emitted by gasoline vehicles. Local vehicular NH3 emissions could be responsible for approximately 3% of urban PM2.5 concentrations during wintertime, and the contributions could be much higher during polluted periods (∼3 μg m-3). We also showed that controlling vehicular NH3 emissions will be effective and feasible to alleviate urban PM2.5 pollution for megacities in the near future.

Emission characteristics and light absorption apportionment of carbonaceous aerosols: A tunnel test conducted in an urban with fully enclosed use of E10 petrol

To reduce the heavy dependence on petroleum, bioethanol has been increasingly employed as an alternative and sustainable transportation fuel. However, the characteristics of black carbon (BC) emissions from E10 petrol vehicles (i.e., ethanol-gasoline containing 10% ethanol) are still unclear, especially under real driving conditions. Here, a tunnel test was conducted during a cold winter. This tunnel was characterized by heavy traffic comprising more than 98% E10-fueled gasoline vehicles (GVs). Real-time BC concentrations, traffic parameters and meteorological conditions were recorded during the sampling campaign. The average BC concentration inside the tunnel (10.94 ± 5.02 μg m−3) was almost twice the background concentration. Based on aethalometer AE33 in situ measurements and the minimum R-squared (MRS) method, real-time aerosol light absorption was apportioned. The light absorption proportions of BC, primary brown carbon (BrC1) and secondary brown carbon (BrC2) were 79.86%, 2.78% and 17.36%, respectively, at 370 nm. The BC emission factor (EFBC) of the E10-fueled vehicles was 1.09 ± 0.49 mg km−1·veh−1 and 15.24 ± 6.85 mg·(kg fuel)−1, lower than those of traditional gasoline fueled vehicles in previous studies. This study can support the compilation of vehicular BC emission inventories, provide recommendations for biofuel policies and contribute to comprehensively understanding the climatic impact of E10 petrol.

Tunnel measurements reveal significant reduction in traffic-related light-absorbing aerosol emissions in China

Light-absorbing aerosols (LAA), including black carbon (BC) and brown carbon (BrC), profoundly impact regional and global climate. Vehicle emission is an important source of LAA in urban areas, but real-world emission features of LAA from the urban vehicle fleet are not fully understood. This study evaluates traffic-related BC and BrC emission factors (EFs) and their vehicular emission inventories via road tunnel measurements in Tianjin, China, in 2017 and 2021. The distance-based and fuel-based EFs of BC for the mixed fleet were 1.05 ± 1.28 mg km−1 veh−1 and 0.057 ± 0.057 g (kg-fuel)−1, respectively, in 2021, with a dramatic decrease of 80.6 % compared to those in 2017. The BC EFs for gasoline vehicles (GVs, including traditional gasoline and hybrid vehicles) and diesel vehicles (DVs) were 0.55 ± 0.065 mg km−1 veh−1 and 10.5 ± 2.52 mg km−1 veh−1, respectively, in 2021. Compared to 2017, the BrC EFs also decreased significantly in 2021, by 10.8–53.6 %, with 0.32 ± 0.45 mg km−1 veh−1 and 0.018 ± 0.020 g (kg-fuel)−1 of distance-based and fuel-based EFs for mixed fleet. The BrC EFs for GVs and DVs were 0.091 ± 0.024 mg km−1 veh−1 and 3.06 ± 0.91 mg km−1 veh−1, respectively, in 2021. Based on the BC and BrC EFs for GVs and DVs and annual mileage for each vehicle category, the annual vehicular LAA emission inventories were estimated. From 2017 to 2021, the annual vehicular LAA emissions in Tianjin have been significantly reduced, by 69 % for BC and 10 % for BrC. DVs account for a small amount of the vehicle population (8.4 %), but contribute to most of the BC (83 %) and BrC (86 %). Our study demonstrates the significant reduction of vehicular light-absorbing aerosols emission due to vehicle pollution prevention and control in China.

Measurement-based intermediate volatility organic compound emission inventory from on-road vehicle exhaust in China

Intermediate volatility organic compounds (IVOCs) have great potential to form secondary organic aerosols (SOA) in the atmosphere. Thus, a high-resolution IVOC emission inventory is essential for the accurate simulation of SOA formation. This study developed the first nationwide on-road vehicular IVOC emission inventory in China based on localized measurement of the IVOC emission factors and volatility distributions for various vehicle types. The total vehicular IVOC emissions in China in 2019 were estimated to be 241.2 Gg. Heavy-duty trucks, light-duty trucks, and light-duty passenger vehicles contributed the most, accounting for 47.6%, 24.6%, and 16.9% of total vehicular IVOC emissions, respectively. Although much higher in number, gasoline vehicles contributed 15.0%, which was far less than the contribution of diesel vehicles. The two peaks in volatility bins B12–B13 and B16–B17 accounted for 42.2% and 23.7% of the total IVOC emissions, respectively. By gridding the emission inventory into a relatively high resolution of 0.1° × 0.1°, high-emission areas and hotspots were clearly identified. In general, eastern China had substantially higher vehicular IVOC emissions than western China. High-emission areas with emission intensity >10 Mg·grid−1 covered most of the North China Plain, Yangtze River Delta, and Pearl River Delta. The emission intensity over the downtown areas of Beijing and Shanghai exceeded 50 Mg·grid−1. In contrast, IVOC emissions over western China were relatively lower, with a network structure gathering around the traffic arteries serving inter-provincial transportation. This study underscored the importance of having a localized emission factor to better reflect the IVOC emission characteristics from Chinese vehicles and to improve the assessment of their environmental impacts.

Photochemical sensitivity to emissions and local meteorology in Bogotá, Santiago, and São Paulo

This study delves into the photochemical atmospheric changes reported globally during the pandemic by analyzing the change in emissions from mobile sources and the contribution of local meteorology to ozone (O3) and particle formation in Bogotá (Colombia), Santiago (Chile), and São Paulo (Brazil). The impact of mobility reductions (50%–80%) produced by the early coronavirus-imposed lockdown was assessed through high-resolution vehicular emission inventories, surface measurements, aerosol optical depth and size, and satellite observations of tropospheric nitrogen dioxide (NO2) columns. A generalized additive model (GAM) technique was also used to separate the local meteorology and urban patterns from other drivers relevant for O3 and NO2 formation. Volatile organic compounds, nitrogen oxides (NOx), and fine particulate matter (PM2.5) decreased significantly due to motorized trip reductions. In situ nitrogen oxide median surface mixing ratios declined by 70%, 67%, and 67% in Bogotá, Santiago, and São Paulo, respectively. NO2 column medians from satellite observations decreased by 40%, 35%, and 47%, respectively, which was consistent with the changes in mobility and surface mixing ratio reductions of 34%, 25%, and 34%. However, the ambient NO2 to NOx ratio increased, denoting a shift of the O3 formation regime that led to a 51%, 36%, and 30% increase in the median O3 surface mixing ratios in the 3 respective cities. O3 showed high sensitivity to slight temperature changes during the pandemic lockdown period analyzed. However, the GAM results indicate that O3 increases were mainly caused by emission changes. The lockdown led to an increase in the median of the maximum daily 8-h average O3 of between 56% and 90% in these cities.

Variation of spatio-temporal distribution of on-road vehicle emissions based on real-time RFID data

High-resolution vehicular emissions inventories are important for managing vehicular pollution and improving urban air quality. This study developed a vehicular emission inventory with high spatio-temporal resolution in the main urban area of Chongqing, based on real-time traffic data from 820 RFID detectors covering 454 roads, and the differences in spatio-temporal emission characteristics between inner and outer districts were analysed. The result showed that the daily vehicular emission intensities of CO, hydrocarbons, PM2.5, PM10, and NOx were 30.24, 3.83, 0.18, 0.20, and 8.65 kg/km per day, respectively, in the study area during 2018. The pollutants emission intensities in inner district were higher than those in outer district. Light passenger cars (LPCs) were the main contributors of all-day CO emissions in the inner and outer districts, from which the contributors of NOx emissions were different. Diesel and natural gas buses were major contributors of daytime NOx emissions in inner districts, accounting for 40.40%, but buses and heavy duty trucks (HDTs) were major contributors in outer districts. At nighttime, due to the lifting of truck restrictions and suspension of buses, HDTs become the main NOx contributor in both inner and outer districts, and its three NOx emission peak hours were found, which are different to the peak hours of total NOx emission by all vehicles. Unlike most other cities, bridges and connecting channels are always emission hotspots due to long-time traffic congestion. This knowledge will help fully understand vehicular emissions characteristics and is useful for policymakers to design precise prevention and control measures.

Analysis of vehicular CO2 emission in the Central Plains of China and its driving forces

The Central Plains of China, represented by Henan province, faces a dramatic rise in vehicular stock and CO2 emissions. The refined-resolution(1 km × 1 km) vehicular CO2 emission inventory for Henan province was developed to identify emission patterns. Results show that CO2 emissions in Henan province reached 77.04 Mt in 2019, and LDGV and HDDT were the major sources that emitted 42.34% and 35.96% of CO2 emissions, respectively. Based on gridded emission, Moran's Index was used to identify spatial distribution patterns of vehicular CO2. The higher CO2 emission intensity areas were concentrated in the central and northern of the province and urban areas in each city, especially in Zhengzhou and its surrounding cities. Moreover, the analysis of the driving forces behind the differences in emissions among cities using the multi-regional (M-R) spatial decomposition model revealed that income and population-scale are significant impacts. In cities such as Zhengzhou, emissions may be dramatically increase owing to high economic growth expectations. ‘Polarization phenomenon’ of CO2 emission distribution should be vigilant. Findings provided insights for refined policy-making in Henan province to limit CO2 emission: (1) Take cities as transportation hubs, e.g., Zhengzhou and Shangqiu, and that in the traffic radiation circle, e.g., Jiaozuo and Zhoukou, as the critical areas for CO2 emission reduction; (2) Promote electric vehicles as replacement for traditional fuel vehicles; especially for cities with large passenger car emissions, such as Zhengzhou, and cities with large truck emissions, such as Shangqiu and Zhoukou; actively guide new consumer groups to choose EVs, especially in cities with high growth expectations such as Zhengzhou; (3) Rely on the advantages of transportation network to promote the ‘road to railway’ of bulk cargo transportation and mainly focus on highways with higher CO2 density, such as Beijing-Hong Kong&Macao Expressway, Shanghai-Xi'an Expressway, Da Guang Expressway, and Lian Huo Expressway.

Investigating the Applicability of Copert 5.5 Emission Software in Bangladesh, Thus Estimating Countrywide Vehicular Emission Inventories, Lockdown Impacts, and Future Projections

Investigating the Applicability of Copert 5.5 Emission Software in Bangladesh, Thus Estimating Countrywide Vehicular Emission Inventories, Lockdown Impacts, and Future Projections

Brazilian vehicular emission inventory software – BRAVES

• We present the BRAzilian Vehicular Emissions inventory Software – BRAVES. • The new method combines a top-down approach with county vehicular information. • The model calculates emissions by fleet category and emission process at county scale. • Local emissions patterns are captured by our new model. • Our model is easily adapted to other developing countries and data scarce regions. In this work, we present the BRAzilian Vehicular Emissions inventory Software - BRAVES which combines a bottom-up method with fleet information of each county. The new model uses a probabilistic approach that accounts for the fleet characteristics, fuel consumption, vehicle deterioration, and intensity of use, to calculate the vehicular emissions from the exhaust, tires, roads, brakes wear, soil resuspension, refueling, and evaporative emissions. Brazil is the fourth largest vehicle producer in the world. However, there is a lack of detailed information on vehicular emissions, especially in middle and small counties. BRAVES can estimate emissions of a larger variety of pollutants at the county scale and segregate the emissions by formation process and fleet category, a major advantage over national and state inventories. BRAVES is a compromise of more sophisticated models that provide detailed information in data-scarce areas, like small counties and could be easily adapted to other developing countries.

Estimation of Pollutant Emissions in Real Driving Conditions Based on Data from OBD and Machine Learning.

This article proposes a methodology for the estimation of emissions in real driving conditions, based on board diagnostics data and machine learning, since it has been detected that there are no models for estimating pollutants without large measurement campaigns. For this purpose, driving data are obtained by means of a data logger and emissions through a portable emissions measurement system in a real driving emissions test. The data obtained are used to train artificial neural networks that estimate emissions, having previously estimated the relative importance of variables through random forest techniques. Then, by the application of the K-means algorithm, labels are obtained to implement a classification tree and thereby determine the selected gear by the driver. These models were loaded with a data set generated covering 1218.19 km of driving. The results generated were compared to the ones obtained by applying the international vehicle emissions model and with the results of the real driving emissions test, showing evidence of similar results. The main contribution of this article is that the generated model is stronger in different traffic conditions and presents good results at the speed interval with small differences at low average driving speeds because more than half of the vehicle’s trip occurs in urban areas, in completely random driving conditions. These results can be useful for the estimation of emission factors with potential application in vehicular homologation processes and the estimation of vehicular emission inventories.

Identification of on-road vehicle CO2 emission pattern in China: A study based on a high-resolution emission inventory

As the country with the largest anthropogenic CO2 emission, China is greatly influential on fighting with climate change. However, CO2 emitted from on-road vehicles in China is a barrier to its carbon neutral due to the inevitable increase trend of vehicle quantity. Thus, to meet the requirement of refined policy-making for vehicular CO2 abatement, we calculated the CO2 emissions from 37 vehicular types and 3 fuel categories across 339 cities in China through the bottom-up method, and developed a national vehicular CO2 emission inventory in a high spatial resolution (1 km × 1 km). Additionally, machine learnings were conducted to the inventory for the emission pattern identification. It was found that the total vehicular CO2 emission in 2019 was 1090 million tons, and 77.1% of CO2 was emitted by vehicles of light-duty gasoline vehicle (LDGV) and heavy-duty diesel truck (HDDT). In addition, for the grids accompanying CO2 emissions, 75% of vehicular CO2 emissions were contributed by 15% of grids (hot grids). Furthermore, results of machine learning showed that LDGVs mainly distributed in economically advanced regions of which vehicular structures were relatively simple, while HDDTs were widely applied on the national scale. Based on the results above, two measures were proposed: (1) Electric cars have to be strongly promoted in hot grids for the LDGV replacement. (2) Long-distance freight tool replacements are urgently required in the national wide. Our study provided a studying base for further investigations on decarbonization and a new insight of China's vehicular CO2 emission controls.

Real-world vehicular emissions in the Indian megacity: Carbonaceous, metal and morphological characterization, and the emission factors

This study develops real-world vehicular fleet emissions factors (EFs) through twin-site measurements for the first time in India. Real-time PM2.5, black carbon (BC), CO, and CO2, and gravimetric PM2.5 and PM10 were simultaneously measured at the kerbside and a background location in Mumbai. Particulate matter was further characterized for metals, carbonaceous constituents and morphology. The measured fleet average (±SD) EFs of PM2.5, PM2.5–10, and PM10 were 435 (±312), 774 (±259), and 1028 (±403), while for BC and CO it was 184 (±48) and 15,000 (±2000) mg (kg of fuel)−1, respectively. The EFOC and EFEC were 182 (±116) and 137 (±54) mg (kg of fuel)−1, while the EFmetals in PM2.5 and PM10, were 57 and 96 mg (kg of fuel)−1, respectively. The EFcoarse PM was ~1.8 times higher than EFPM2.5, suggesting the dominance of non-exhaust vehicular sources. The estimated uncertainties for the measured vehicular EF of CO, PM, and its chemical constituents ranged 44–128%. Traffic composition, temperature and humidity significantly affect the measured EFs which likely contributed to the uncertainty. These EFs would be useful for building a more accurate vehicular emission inventory and thus helping in urban air quality management, and also as inputs to climate models and policymaking.

Simulation of O3 and NOx in São Paulo street urban canyons with VEIN (v0.2.2) and MUNICH (v1.0).

We evaluate the performance of the Model of Urban Network of Intersecting Canyons and Highways (MUNICH) in simulating ozone (O3) and nitrogen oxides (NOx) concentrations within the urban street canyons in the São Paulo metropolitan area (SPMA). The MUNICH simulations are performed inside the Pinheiros neighborhood (a residential area) and Paulista Avenue (an economic hub), which are representative urban canyons in the SPMA. Both zones have air quality stations maintained by the São Paulo Environmental Agency (CETESB), providing data (both pollutant concentrations and meteorological) for model evaluation. Meteorological inputs for MUNICH are produced by a simulation with the Weather Research and Forecasting model (WRF) over triple-nested domains with the innermost domain centered over the SPMA at a spatial grid resolution of 1 km. Street coordinates and emission flux rates are retrieved from the Vehicular Emission Inventory (VEIN) emission model, representing the real fleet of the region. The VEIN model has an advantage to spatially represent emissions and present compatibility with MUNICH. Building height is estimated from the World Urban Database and Access Portal Tools (WUDAPT) local climate zone map for SPMA. Background concentrations are obtained from the Ibirapuera air quality station located in an urban park. Finally, volatile organic compound (VOC) speciation is approximated using information from the São Paulo air quality forecast emission file and non-methane hydrocarbon concentration measurements. Results show an overprediction of O3 concentrations in both study cases. NOx concentrations are underpredicted in Pinheiros but are better simulated in Paulista Avenue. Compared to O3, NO2 is better simulated in both urban zones. The O3 prediction is highly dependent on the background concentration, which is the main cause for the model O3 overprediction. The MUNICH simulations satisfy the performance criteria when emissions are calibrated. The results show the great potential of MUNICH to represent the concentrations of pollutants emitted by the fleet close to the streets. The street-scale air pollutant predictions make it possible in the future to evaluate the impacts on public health due to human exposure to primary exhaust gas pollutants emitted by the vehicles.

Scenario analysis of vehicular emission abatement procedures in Xi'an, China.

Vehicular emissions contribute significantly to air pollution, and the number of vehicles in use is continuing to rise. Policymakers thus need to formulate vehicular emission reduction policies to improve urban air-quality. This study used different vehicle control scenarios to predict the associated potential of mitigating carbon monoxide (CO), volatile organic compounds (VOCs), nitrogen oxide (NOx), particulate matter with an aerodynamic diameter less than 2.5μm (PM2.5), and particulate matter with an aerodynamic diameter less than 10μm (PM10) in Xi'an China, in 2020 and 2025. One business-as-usual scenario and six control scenarios were established, and vehicular emission inventory was developed according to each scenario. The results revealed that eliminating high-emission vehicles and optimizing after-treatment devices would effectively reduce vehicular emissions. In addition, increasing the number of alternative fuel vehicles, restraining vehicle use, and restraining the growth of the vehicle population would all have certain effects on CO and VOCs emissions, but the effects would not be significant for NOx, PM2.5, and PM10. The results also indicated that if all control measures were stringently applied together, emissions of CO, VOCs, NOx, PM2.5, and PM10 would be reduced by 51.66%, 51.58%, 30.19%,71.12%, and 71.81% in 2020, and 53.55%, 51.44%, 19.09%, 54.88%, and 55.51%, in 2025, respectively.