Motor Vehicle Emission Simulator Research Articles

Sensitivity of air quality to vehicle ammonia emissions in the United States

The US Environmental Protection Agency (EPA) estimates on-road vehicles emissions using the Motor Vehicle Emission Simulator (MOVES). We developed updated ammonia emission rates for MOVES based on road-side exhaust emission measurements of light-duty gasoline and heavy-duty diesel vehicles. The resulting nationwide on-road vehicle ammonia emissions are 1.8, 2.1, 1.8, and 1.6 times higher than the MOVES3 estimates for calendar years 2010, 2017, 2024, and 2035, respectively, primarily due to an increase in light-duty gasoline vehicle NH3 emission rates. We conducted an air quality simulation using the Community Multi-Scale Air Quality (CMAQv5.3.2) model to evaluate the sensitivity of modeled ammonia and fine particulate matter (PM2.5) concentrations in calendar year 2017 using the updated on-road vehicle ammonia emissions. The average monthly urban ammonia ambient concentrations increased by up to 2.3 ppbv in January and 3.0 ppbv in July. The updated on-road NH3 emission rates resulted in better agreement of modeled ammonia concentrations with 2017 annual average ambient ammonia measurements, reducing model bias by 5.8 % in the Northeast region. Modeled average winter PM2.5 concentrations increased in urban areas, including enhancements of up to 0.5 μg/m3 in the northeast United States. The updated ammonia emission rates have been incorporated in MOVES4 and will be used in future versions of the NEI and EPA's modeling platforms.

Integrated MOVES model and machine learning method for prediction of CO2 and NO from light-duty gasoline vehicle

With rapid urbanization and industrialization, the number of light-duty gasoline vehicles (LDGVs) in China has continued to grow rapidly, leading to a significant increase in traffic pollution. Therefore, it is essential to accurately calculate the emission of LDGVs for air quality monitoring and management. Fortunately, Motor Vehicle Emission Simulator (MOVES) is a sophisticated model for estimating mobile source emissions with good prediction accuracy. However, the parameters of MOVES are based on the field tests in the US, which is worth exploring whether MOVES can be applied to other countries. Hence, in this paper, we used the portable emission measurement system (PEMS) to conduct real driving emission (RDE) tests of LDGVs, aiming to explore the possibility of the MOVES application in China. Based on the field tests, we modified basic parameters in the MOVES model, but unsatisfactory prediction performance was obtained. Existing research on improving MOVES performance mainly involved new binning of operating modes, but these methods had limited improvements. Though studies have also used machine learning methods for predicting LDGV emissions, they lacked comparison and integration with the MOVES model. To further improve the prediction accuracy, we proposed a novel road vehicle emission model that integrated the machine learning method and the MOVES to predict the road-level emission rates of NO and CO2 emissions of LDGVs. In addition, we employed the Boruta algorithm to capture the key influencing factors and promote prediction performance. The enhanced model outperformed MOVES and achieved higher R2 values. On average, the improvement for CO2 was 0.132, and for NO, it was 0.261. This work will provide references for MOVES improvements in practical scenarios and better predict pollutant emissions for LDGVs using limited resources of field tests in cities outside the US.

Dynamic Meteorology-induced Emissions Coupler (MetEmis) development in the Community Multiscale Air Quality (CMAQ): CMAQ-MetEmis

Abstract. There have been consistent efforts to improve the spatiotemporal representations of biogenic/anthropogenic emission sources for photochemical transport modeling for better accuracy of local/regional air quality forecasts. While biogenic emissions, bi-directional NH3 from fertilizer applications, and point source plume rise are dynamically coupled in the Community Multiscale Air Quality (CMAQ) “inline”, there are still known meteorology-induced emissions sectors (e.g., on-road mobile sources, residential heating, and livestock waste), with little or no accounting for the meteorological impacts in the currently operational chemical and aerosol forecasts, but they are represented with static, not weather-aware annual or monthly county total emissions and standard monthly, weekly, or daily temporal allocation profiles to disaggregate them on finer timescales for the hourly air quality forecasts. It often results in poor forecasting performance due to the poor spatiotemporal representations of precursor pollutants during high ozone and PM2.5 episodes. The main focus of this study is to develop a dynamic inline coupler within the CMAQ system for the on-road mobile emission sector that requires significant computational resources in the current modeling application. To improve their accuracy and spatiotemporal representations, we developed the inline coupler module called CMAQ-MetEmis (for meteorology-induced emission sources within CMAQ version 5.3.2 modeling system). It can dynamically estimate meteorology-induced hourly gridded on-road mobile emissions within the CMAQ, using simulated meteorology without any computational burden to the CMAQ modeling system. To understand the impacts of meteorology-driven on-road mobile emissions on local air quality, the CMAQ is applied over the continental U.S. for 2 months (January and July 2019) for two emissions scenarios, namely (a) “static” on-road vehicle emissions based on static temporal profiles and (b) inline CMAQ-MetEmis on-road vehicle emissions. Overall, the CMAQ-MetEmis coupler allows us to dynamically simulate on-road vehicle emissions from the MOtor Vehicle Emission Simulator (MOVES) on-road emission model for CMAQ, with a better spatiotemporal representation based on the simulated meteorology inputs when compared to the static scenario. The domain total of daily volatile organic compound (VOC) emissions from the inline scenario shows that the largest impacts are from the local meteorology, which is approximately 10 % lower than the ones from the static scenario. In particular, the major difference in the VOC estimates was shown over the California region. These local meteorology impacts on the on-road vehicle emissions via CMAQ-MetEmis revealed an improvement in the hourly NO2, daily maximum ozone, and daily average PM2.5 patterns, with a higher agreement and correlation with daily ground observations.

Airborne Air Pollutant Emission Characteristics of Mobile Vehicles in Taiwan

This study examines the air pollutant emission characteristics, activity intensity, and trends of mobile sources from 2013 to 2021. The target pollutants include criteria pollutants (fine particulate matters, nitrogen oxides, and hydrocarbons) and hazardous air pollutants (benzene, formaldehyde, and BaP). The results indicated that the activity intensity levels of road mobile sources in Taiwan were148 × 109, 156 × 109, 159 × 109, and 155 × 109 km/year in 2013, 2016, 2019, and 2021, respectively, with the largest proportion attributed to gasoline passenger cars (42.6%), followed by four-stroke motorcycles (32.6%). An emission factor of PM2.5 was estimated by EPA’s MOVES (Motor Vehicle Emission Simulator) model, and the results showed that the emission sequence was diesel > gasoline > motorcycle; the NOx emission factor was estimated using the MOBILE6.2 model, and the results showed that the order was diesel > gasoline > motorcycle; the HC emission factor was compiled with the use of gasoline vehicle dynamometer data, and the results showed that motorcycle > gasoline vehicles. Further results showed that the emission sequence for benzene was motorcycle > gasoline ≥ diesel; the formaldehyde emission sequence was diesel > motorcycle ≥ gasoline. The BaP emission factors of different vehicle types were estimated using MOVES, and the emission factors of old heavy-duty diesel vehicles were the highest.

Impact of oxygenated fuels on atmospheric emissions in major Colombian cities

The use of oxygenated gasolines has increased in Latin America over the past several years with the goal to reduce oil dependence, improve gasoline combustion and emit fewer pollutants to the atmosphere. Countries such as Brazil, Colombia, Mexico, Argentina, and Peru blend their gasoline with ethanol in different proportions. In Colombia, the regulation establishes a 10% vol of ethanol in gasoline (E10). Ethers are also used to oxygenate gasolines. MTBE (methyl tert-butyl ether) is blended in gasoline primarily in Mexico, Venezuela, and Chile, while ETBE (ethyl tertiary-butyl ether) is used in Argentina. The U.S. EPA Motor Vehicle Emissions Simulator (MOVES) model was adapted to Colombia to evaluate gasoline blends with two renewable oxygenates (ethanol and ETBE) on the emission of criteria pollutants (PM2.5, CO, NOx, SO2), Total Gaseous Hydrocarbons (TGH), Volatile Organic Compounds (VOCs), and toxic compounds. We followed the same approach used to build MOVES-Mexico and assess fuel specifications changes on vehicle emissions.The MOVES-Colombia model finds that E10 increases fuel volatility, VOCs, NOx, PM2.5, and SO2 emissions, and decreases CO relative to the unoxygenated base gasoline (E0). The impact of E10 consumption on air quality is due not only to primary pollutants, but also to the emission of ozone and PM2.5 precursors. Compared to E10 and E0, the ETBE blends reduce fuel volatility and emissions of VOCs, TGH, CO, NOx, PM2.5, and SO2. The introduction of ETBE could help major urban areas to meet air quality standards even with their current vehicle fleets. This work highlights that the effect of new fuel specifications needs to be properly assessed in terms of air quality before they are put in place. The development of modeling tools, such as MOVES-Colombia, facilitates the study of fuel quality and vehicle technology on pollutant emissions.

Impact of Restricted Crossing U-Turns on Vehicular Emissions

In recent years, several innovative intersections have been designed and implemented as an alternative to typical two-way intersections. The Restricted Crossing U-Turn (RCUT) is a type of innovative intersection design that limits left-turns from the minor road by directing cross-street traffic to one of two auxiliary U-turns. This design seeks to improve intersection safety by reducing conflict points, as well as increase the capacity and decrease travel time on certain movements, such as the mainline through traffic. However, the change in the cross-street vehicle paths through the intersection can affect acceleration profiles and emissions. This research develops a methodology to test emission differences through simulation. Models of an RCUT and a traditional intersection were constructed and tested under varying turn volumes and mainline speeds conditions. CO2 and NOx emissions were calculated using Vissim simulation vehicle trajectories and the Motor Vehicle Emissions Simulator (MOVES) model. The modeled emissions values were used to compare the overall emissions performance of the RCUT versus a traditional intersection layout. The RCUT intersection layout was associated with an increase in average cross-street through and left-turn movement emissions because of the longer travel distances. However, a reduction in emissions was seen on the main line, primarily as a result of the additional green-time afforded by the RCUT design. Where the mainline volume to side-street ratio becomes sufficiently large, the RCUT performed better in overall intersection emissions levels.

Carbon Emission Pattern of Driving Car on Vertical Curves of Highway

Low carbon road design has always been a research hotspot for scholars. One of the primary road variables affecting the carbon emission of automobiles is vertical curve, which serves as the primary geometric alignment of the longitudinal portion of the road. The instantaneous speed and acceleration data of cars on various vertical curve sections are gathered for this study by combining simulation and real vehicle experiment methods. This study then built a database of carbon emissions for cars on various vertical curve sections based on the Motor Vehicle Emission Simulator (MOVES) model, used Statistical Product and Service Solutions (SPSS) to analyze the data, and developed a carbon emission model for cars on various vertical curve sections. Finally, it was found that the average error of the carbon emission model for different vertical curve sections was 8.37%, and the accuracy of carbon emission modelling could meet the requirements. The results show that the main influencing factor of carbon emission of cars on vertical curve sections is the radius of vertical curve, and the carbon emission of cars is inversely proportional to the radius of vertical curve. The smaller the value of vertical curve radius is, the more the engine output increases and the greater the carbon emission level will be. This research will provide reference and theoretical support for low-carbon design of highway longitudinal slope sections.

Emissions of air pollutants from non-road construction machinery in Beijing from 2015 to 2019

Construction machinery (CM) is considered to be a significant source of air pollution. The estimation of emissions from CM is essential for policy-makers to control air pollution and reduce carbon emissions. In this study, air pollutant emissions from CM with four emissions standards (Beijing I-IV) in Beijing from 2015 to 2019 were estimated by combining data from the Non-road Mobile Source Emissions Inventory Compiled Technical Guidelines, Motor Vehicle Emission Simulator (MOVES) and actual measurements. Taking an example for 2019, emissions of hydrocarbons (HCs), carbon monoxide (CO), nitrogen oxides (NOx), particulate matter (PM) and carbon dioxide (CO2) were estimated to be 4.80 Gg, 16.51 Gg, 27.77 Gg, 1.35 Gg and 5.09 Tg, respectively, representing annual mean decreases of 13.2%, 13.1%, 10.8%, 15.2% and 3.5%, respectively, over the five-year period. Tongzhou, Shunyi and Changping Districts contributed 53–67% of total CM emissions in 2019. Among the ten types of CM considered, loaders were the largest contributors to total emissions, accounting for 41–54% of total CM emissions in Beijing from 2015 to 2019. Machinery with a mean power above 75 kW accounted for the largest share (67–78%) of total CM emissions in Beijing from 2015 to 2019. Our results contribute to the limited data of estimated CM emissions and can help develop control strategies to improve air quality and alleviate climate change.

Development of vehicle emission rates based on vehicle-specific power and velocity

The US Environmental Protection Agency developed the MOtor Vehicle Emission Simulator (MOVES) operating modes, which defines modal emission rates according to vehicle speed and vehicle-specific power using binning method. However, as MOVES was based on emissions data for vehicle fleets in the US, it is used primarily to estimate US emissions. To adopt this approach in other regions, here, we take into account regional conditions, such as vehicle fleet composition, emissions regulations, and driving environments. Real-world emissions test data for 17 light-duty gasoline and diesel vehicles mainly sold in Korea were used to develop CO2, NOx, and CO emission rates. Typically, the vehicle experiment and data acquisition are costly and time consuming, the amount of data needed to develop robust emission rates were considered. In addition, we studied how a re-binning of vehicle-specific power and velocity could lead to better emission rates estimates from on-road vehicles. To compare the estimates by different binning methods and real-world emissions, root mean square error (RMSE) and R-squared (R2) values were adopted. The comparison result shows that the re-binning method-based emission predictions were more accurate than MOVES prediction results under the real-world condition. The R2 of CO2 and NOx predictions were increased from 075 to 0.78 and from 0.17 to 0.2, respectively. The CO prediction accuracy was slightly increased. These findings provide the re-binning method is advantageous for developing modal-based emission rates using real-world emissions test data.

Activity-based integrated modelling for assessing COVID-19 impacts on transport operations and emissions

ABSTRACT This study combines an integrated transport, land-use, and energy (iTLE) modeling system with traffic microsimulation model and emission simulator for a holistic analysis of COVID-19 pandemic related changes in traffic flows and emissions. An activity-based travel demand model within iTLE informs pandemic traffic operation scenarios for traffic microsimulation modeling. Link-based simulation outputs inform a finer-grained emission estimation process within a MOtor Vehicle Emission Simulator. Results suggest that the overall network performance improves during lockdown as average delays and queue time decrease by 42.04% and 5.9% respectively compared to pre-COVID condition. Emission results reveal that GHG emissions significantly decrease (64%) in lockdown while it starts increasing gradually in post-pandemic period. Link-based emission analysis indicates that major arterial streets achieve a significant reduction in air pollutant emission. The findings of this study will help transportation planners, engineers, and policymakers to devise effective policies for the improvement of transport operations and emissions.

Combined Effect of Changes in Transit Service and Changes in Occupancy on Per-Passenger Energy Consumption

Many transit providers changed their schedules and route configurations during the COVID-19 pandemic, providing more frequent bus service on major routes and curtailing other routes, to reduce the risk of COVID-19 exposure. This research first assessed the changes in Metropolitan Atlanta Rapid Transit Authority (MARTA) service configurations by reviewing the pre-pandemic versus during-pandemic General Transit Feed Specification (GTFS) files. Energy use per route for a typical week was calculated for pre-pandemic, during-closure, and post-closure periods by integrating GTFS data with MOVES-Matrix transit energy and emission rates (MOVES signifying MOtor Vehicle Emission Simulator). MARTA automated passenger counter data were appended to the routes, and energy use per passenger-mile was compared across routes for the three periods. The results showed that the coupled effect of transit frequency shift and ridership decrease from 2019 to 2020 increased route-level energy use for over 87% of the routes and per-passenger-mile energy use for over 98% of the routes. In 2021, although MARTA service had largely returned to pre-pandemic conditions, ridership remained in an early stage of recovery. Total energy use decreased to about pre-pandemic levels, but per-passenger energy use remained higher for more than 91% of routes. The results confirm that while total energy use is more closely associated with trip schedules and routes, per-passenger energy use depends on both trip service and ridership. The results also indicate a need for data-based transit planning, to help avoid inefficiency associated with over-provision of service or inadequate social distancing protection caused by under-provision of service.

Heterogeneity Analysis of Operating Mode Distribution for Modeling Energy Consumption of Light-Duty Vehicles

The use of vehicle operating mode (OpMode) distribution is widely accepted for estimating energy consumption and emissions in the Motor Vehicle Emission Simulator (MOVES) model. However, the heterogeneity of driving behavior may lead to errors when using the default OpMode distribution. To improve the accuracy of energy consumption estimations, it is necessary to recognize the heterogeneity in OpMode distribution among different driving behaviors. With this aim, this paper designs a speed-specific indicator of energy efficiency reflecting driving behavior based on the speed-specific vehicle-specific power (VSP) distribution. The paper uses field data from 26,082 drivers recorded second by second during workdays. It also discusses the intra-heterogeneity and inter-heterogeneity of driving behavior based on unsupervised algorithm clustering. The findings of this paper are as follows. (1) The speed-specific VSP distribution clearly reflects the differences in energy efficiency of individuals’ driving behavior. (2) The energy efficiency indicator reflects the multidimensional inter-heterogeneity and intra-heterogeneity of driving behavior. (3) Drivers’ varied driving behavior causes heterogeneity in energy efficiency at different speeds, possibly causing an error of 6.34% in the emissions estimations. (4) Drivers of electric vehicles (EVs) and hybrid electric vehicles (HEVs) show more aggressive driving behaviors than drivers of conventional vehicles (CVs), which may cause an energy estimation error of over 6% for EVs and HEVs. Thus, the OpMode distribution of EVs, HEVs, and CVs should be modeled separately for on-road energy estimations.

High-Resolution Fuel Consumption Model for Better Characterizations of High-Speed Scenarios

A detailed and accurate fuel model fuel consumption model that reflects real-world fuel consumption is required as input for devising and executing a model policy for prospective regulatory tools. The fuel consumption model based on the vehicle-specific power (VSP) has rapidly become the primary development direction since the release of the Motor Vehicle Emissions Simulator (MOVES) model. However, fuel consumption cannot be accurately characterized under high-speed scenarios. This work develops two fuel consumption models for the light-duty (gasoline) vehicles that can better characterize fuel consumption for light-duty vehicles under high-speed scenarios. For model 1, the VSP of −5kW/ton is a crucial turning point. When VSP∈ [−30, −5] kW/ton, the fuel rate is only determined by speed. When VSP∈(−5, 30], the fuel rate will gradually increase with VSP, and the growth characteristics will vary with speed. Model 2 develops the new interpretations for VSP and forms the one-to-one correspondence between the fuel rate and the new VSP. The two models can separately improve the accuracy by 12.2% and 13.8% compared with the conventional model. The fuel factor differences become significant when speed is higher than 65 km/h, which are separately 30.66% and 28.13% higher than the conventional VSP model when the speed is 100 km/h. Further, the fuel factors of the two models for freeways are, respectively, 6.33% and 7.56% higher than the conventional VSP model, and the distinction for arterial, collector, and local street roads is not notable.

Developing and analyzing eco-driving strategies for on-road emission reduction in urban transport systems - A VR-enabled digital-twin approach

A vast number of pollutants are generated from on-road commuting vehicles, and there is an increasing need to explore vehicle emission monitoring and mitigation strategies. Traditionally, vehicle emissions can be monitored and measured directly from on-vehicle devices such as a sensor at the tailpipe, or based on expensive data collection tools such as roadside units; while another mainstream research estimates vehicle emissions by relying on the connection to the vehicle motions, which can approximately calculate vehicle emissions under certain traffic conditions. This paper proposes a virtual reality (VR) enabled digital twin platform for on-road emission monitoring, and it develops and evaluates eco-driving strategies within a specific area. The proposed approach, integrating a VR-based digital environment, a micro-simulation model for background traffic, and a Motor Vehicle Emission Simulator for emission estimation, offers an alternative to collect and examine vehicle emissions such as NOx under various traffic conditions. A case study on a central business area in Melbourne is conducted and eco-driving strategies are tested in two scenarios. The first scenario concerns the impact of hybrid electric vehicles and connected autonomous vehicles, which points to the long-term benefit of having controllable and cleaner modes of transportation as a strategy. Results showed that manipulating the penetration rate of emission-friendly engines or motions could reduce vehicle emissions effectively. The second scenario concerns the real-time eco-routing based on emission-optimum, which points to the short-term benefit of emission control strategies. Human-in-the-loop experiments were conducted to test drivers’ responses toward routing options. Results showed that over 90% of participants would follow the eco-routing recommendations completely. The presented study offers an alternative to data-generating, analyzing, and managing approaches for on-road emissions in urban transportation systems.

Investigation into the Impact of Weight on Diesel Trucks’ NOx Emissions: An Overweight Case Study

To build an understanding of the emission contribution, especially nitrogen oxides (NOx) emissions, of many overweight/oversized trucks operating in the Dallas-Fort Worth area, U.S., the North Central Texas Council of Governments commissioned the Texas A&M Transportation Institute to conduct an emission testing study. The study team collected and analyzed data on these vehicles’ activities and emissions characteristics. Vehicle activity data collection was performed using portable activity measurement systems, and exhaust emissions data was measured using two portable emission measurement systems. Using methodologies developed by the Environmental Protection Agency for the MOtor Vehicle Emissions Simulator (MOVES) model, both datasets were processed and expressed as emission rates for three loading scenarios. Researchers observed that, because of the sensitivity of the performance of selective catalytic reduction (SCR) to exhaust temperature, NOx data observed during a test cycle may not result in emission rates that are representative of the real-world operations. The team developed a methodology to adjust the resulting NOx rates for SCR-equipped trucks. The findings of the analysis suggest that the current MOVES opMode equations do not appear to adequately capture the impact of weight on the emission rates of heavy-duty diesel vehicles. The team also found that SCR-equipped trucks can have lower emission rates under heavier loads, especially in higher-speed and higher-power opMode bins, which typically have higher exhaust temperatures. Under normal loads (approximately 55,000 lb), SCR does not seem to provide a NOx emission reduction benefit at speeds under 50 mph compared with exhaust gas recirculation-equipped trucks.

Emissions analysis of the Port Drayage Truck Replacement Program and local air quality: The case of the Port of New York and New Jersey

This paper quantifies the expected amount of emissions reduction from the Port Drayage Truck Replacement Program (TRP) at the Port of New York and New Jersey (PANYNJ). The TRP is a voluntary subsidy program to replace old heavy-duty diesel trucks with newer diesel engine-year models that have better fuel economy and lower emissions. Using the Port Authority’s data from their 2019 Port Emission Inventory and monthly Port Truck Pass reports in 2020, this research calculates the on-terminal idling emissions based on the U.S. Environmental Protection Agency (US EPA) Motor Vehicle Emission Simulator (MOVES) model. If all eligible trucks are replaced, annual NOx emissions would be reduced by 12.8 percent (98.5 tons), and PM2.5 by 1.6 percent (5.2 tons). This result underscores the important impact of TRP implementation to local air quality, especially for the near-port residents. It is inferred that the near-port populations include lower income populations with higher asthma rates where nitrogen oxides (NOx) could be reduced below the hazardous level after TRP implementation. Overall, considering the marginal benefits, costs, and time, TRP remains potentially the most affordable and practical interim policy to immediately reduce the local emissions among other alternative fuel options.

Developing a heavy-duty vehicle activity database to estimate start and idle emissions

Heavy-duty vehicle start and idling activities were characterized from two datasets to improve emission estimates in the MOtor Vehicle Emission Simulator (MOVES): 1. Fleet DNA from the National Renewable Energy Laboratory (NREL) and 2. A dataset collected by the University of California, Riverside for the California Air Resources Board. The combined dataset includes 564 commercial vehicles, over 23,000 vehicle days of operation and covers seven of the nine heavy-duty source types defined by MOVES. The start and idle activities are characterized and illustrated across MOVES source types, vocations, fleets, days, and hours. This study provides the most comprehensive analysis yet made publicly available to characterize start and idle activity for heavy-duty vehicles within the United States. The results also show there is significant uncertainty in the average heavy-duty idle and start activity due to the large variation in activity across fleets and vocations, and sparsity of nation-wide vehicle population data by vocation.

Emissions of nitrogen dioxide in the northeast U.S. during the 2020 COVID-19 lockdown

We have quantified the emissions of Nitrogen dioxide (NO2) in the Northeast megalopolis of the United States during the COVID-19 lockdown. The measurement of NO2 emission serves as the indicator for the emission of the group of nitrogen oxides (NOx). Approximately 56% of NO2 emissions in the US are from mobile sources, and the remainder is from stationary sources. Since 2002, clean air regulations have resulted in approximately 5% compound annual reduction of NOx emissions in the US (8.2% in the study area). Therefore, when studying the impact of sporadic events like an epidemic on emissions, it is necessary to account for the persistent reduction of emissions due to policy driven emission reduction measures. Using spaceborne sensors, ground monitors, National Emission Inventory data, and the US Motor Vehicle Emission Simulator, we quantified the reduction of total NOx emissions, distinguishing stationary sources from on-road mobile sources (trucks and automobiles). When considering total NOx emissions (stationary and mobile combined), we find that the pandemic restrictions resulted in 3.4% reduction of total NOx emissions in the study area in 2020. This is compared to (and in addition to) the expected 8.2% policy driven reduction of NOx emissions in 2020. This somewhat low reduction of emissions is because most stationary sources (factories, power plants, etc.) were operational during the pandemic. Truck traffic, a significant source of mobile emissions, also did not decline significantly (average 4.8% monthly truck traffic reduction in the study area between March and August 2020), as they were delivering goods during the lockdown. On the other hand, automobile traffic, responsible for 24% of total NOx emissions, dropped significantly, 52% in April, returning to near normal after 5 months. While the reduction of automobile traffic was significant, especially in the early months of the pandemic, its effect on emissions was relatively insignificant.

Evaluation of the Operational Effects of Autonomous and Connected Vehicles through Microsimulation

Proper evaluation of traffic operations integrating connected and autonomous vehicles (CAVs) requires accurate representation of these emerging technologies in microscopic simulation. This paper evaluates the ability of microscopic simulator PTV-VISSIM (Version 10.0) to simulate CAVs, and presents a comprehensive CAV model extension. In addition, emissions modeling is integrated with VISSIM to calculate real-time energy and emission estimates. The evaluation of VISSIM revealed that its internal CAV modeling has several limitations, such as modeling connectivity and complex vehicle behavior. For external modeling, there are two available VISSIM interfaces. The Component Object Model (COM) Application Programming Interface (API) is the superior approach for fetching data and modeling connectivity, whereas the External Driver Model (EDM) is a better tool for lateral and longitudinal control. The simulation extension developed leveraged both interfaces. An isolated signalized intersection was simulated to demonstrate the impact of connected vehicle (CV), autonomous vehicle (AV), and CAV traffic on speed, delay, and travel time. In addition, trajectory data, combined with the Motor Vehicle Emission Simulator (MOVES) method, were utilized to obtain energy, fuel consumption, and greenhouse gas emissions. The results show that CAVs result in net improvement in travel time and speed. However, emissions did not follow the same trend. While increasing AV penetration rates resulted in emissions reductions, increasing CV and CAV penetration rates resulted in higher emissions. While the CV logic chosen for testing seeks to maximize the likelihood of vehicle arrival-on-green, the algorithm likely results in increased variations in second-by-second accelerations, leading to overall higher emissions.

Valuing traffic emissions' effect on historic buildings: environmental assessment to promote historic buildings’ sustainability

PurposeWith increased populations and movement of people worldwide, traffic emissions will lead to an increase in carbon dioxide emissions, which is one of the greenhouse gases. This will increase outdoor air pollution and other environmental risks that will impact people's health and livelihood, crops, as well as the built environment such as architecture, buildings and other structures. Few studies have been devoted to addressing the effect of air pollution on historic buildings. However, there is no specific study on the impact of traffic-related emissions.Design/methodology/approachA thorough analysis has been conducted in selecting eight historic districts (HDs) among all of 31 located within the city of San Antonio. Motor Vehicle Emission Simulator (MOVES) software has been utilized to process the data collected from the average annual daily traffic (AADT) and vehicle operational speeds and evaluate the amounts of emissions for each HD.FindingsMOVES outcomes showed that HDs are under the threat of deterioration caused by traffic emissions. Therefore, transportation and environmental planners need to implement sustainable solutions to reduce the impact of CO2 emissions on HDs and, ultimately, historic buildings.Originality/valueThis study will help policymakers plan a better course of action for the future conditions associated with the increased traffic volumes. It is also beneficial for the developers, preservationists, architects and all other stakeholders willing to preserve the history of a country.