On-road Vehicle Emissions Research Articles

Chemical Profiles of Particulate Matter Emitted from Anthropogenic Sources in Selected Regions of China

Particulate matter (PM) emissions from anthropogenic sources contribute substantially to air pollution. The unequal adverse health effects caused by source-emitted PM emphasize the need to consider the discrepancy of PM-bound chemicals rather than solely focusing on the mass concentration of PM when making air pollution control strategies. Here, we present a dataset about chemical compositions of real-world PM emissions from typical anthropogenic sources in China, including industrial (power, industrial boiler, iron & steel, cement, and other industrial process), residential (coal/biomass burning, and cooking), and transportation sectors (on-road vehicle, ship, and non-exhaust emission). The data was obtained under the same strict quality control condition on field measurements and chemical analysis, minimizing the uncertainty caused by different study approaches. The concentrations of PM-bound chemical components, including toxic elements and PAHs, exhibit substantial discrepancies among different emission sectors. This dataset provides experimental data with informative inputs to emission inventories, air quality simulation models, and health risk estimation. The obtained results can gain insight into understanding on source-specific PMs and tailoring effective control strategies.

Emission inventory of IVOCs from diesel engine emissions in China based on real-world measured data

Intermediate volatility organic compounds (IVOCs), recognized as significant precursors of secondary organic aerosols, play a pivotal role in environmental pollution. This study utilized measured real-world emission factors to construct an inventory of IVOC emissions from diesel engines and to analyze their spatial distribution across China in 2022. Furthermore, the emission inventory derived from this approach was compared with that obtained through the IVOC/primary organic aerosol (POA) ratio method. The following findings were observed: (1) In 2022, the total IVOC emissions from diesel engines in China amounted to approximately 430 Gg, surpassing those emitted by gasoline engines and residential solid fuel combustion. Specifically, the IVOC emissions attributed to on-road diesel vehicles, inland vessels, agricultural machinery, and non-road construction machinery were recorded at 377, 39.3, 10.4 and 3 Gg, respectively. (2) In terms of geographical distribution in 2022, IVOC emissions from diesel engines were predominantly concentrated in Eastern, Southern and Northern China; on-road diesel vehicle emissions were primarily located within logistics hubs or regions characterized by extensive national and arterial road networks; while non-road construction and agricultural machinery emissions were largely confined to Eastern and Central China. Emissions from inland vessels exhibited a more distinct regional pattern concentrated mainly within the Yangtze River Delta, Pearl River Delta, and Bohai Rim areas. (3) A comparative analysis between the inventory established using real-world measured emission factors and that derived via IVOC/POA ratios indicated that the latter methodology may have overestimated emissions associated with inland vessels and non-road construction machinery. Moving forward, reducing IVOC emissions from diesel engines is crucial for enhancing air quality and safeguarding human health-particularly concerning heavy-duty vehicles, light-duty vehicles, and inland vessels.

Impacts of cold starts and hybrid electric vehicles on on-road vehicle emissions

Incorporating cold-start emissions from hybrid electric vehicles (HEVs) into on-road emission modeling is fundamental to accurately assess the environmental impacts aligned with their projected deployment. This study introduces a cold-start emission model, integrating travel time, dynamic emission rates, and cold-start durations by fuel type. This study further explores the anticipated spatiotemporal patterns of annual cold-start emissions. Findings indicate a reliance on gasoline for most on-road vehicles in the next decade, but vehicle electrification could reduce carbon monoxide (CO), hydrocarbon (HC), and nitrogen oxide (NOX) emissions by over 60% by 2035. Cold-start emissions will remain significant (67%-79% of CO, 59%-71% of HC, and 42%-54% of NOX emissions), especially on local roads. Cold-start emissions from HEVs will contribute to 14%, 25%, and 13% of total CO, HC, and NOX emissions in 2035, respectively. This study highlights the importance of accurate modeling in understanding the environmental benefits of vehicle electrification.

Improved Spatial Resolution in Modeling of Nitrogen Oxide Concentrations in the Los Angeles Basin.

The extent to which emission control technologies and policies have reduced anthropogenic NOx emissions from motor vehicles is large but uncertain. We evaluate a fuel-based emission inventory for southern California during the June 2021 period, coinciding with the Re-Evaluating the Chemistry of Air Pollutants in CAlifornia (RECAP-CA) field campaign. A modified version of the Fuel-based Inventory of Vehicle Emissions (FIVE) is presented, incorporating 1.3 km resolution gridding and a new light-/medium-duty diesel vehicle category. NOx concentrations and weekday-weekend differences were predicted using the WRF-Chem model and evaluated using satellite and aircraft observations. Model performance was similar on weekdays and weekends, indicating appropriate day-of-week scaling of NOx emissions and a reasonable distribution of emissions by sector. Large observed weekend decreases in NOx are mainly due to changes in on-road vehicle emissions. The inventory presented in this study suggests that on-road vehicles were responsible for 55-72% of the NOx emissions in the South Coast Air Basin, compared to the corresponding fraction (43%) in the planning inventory from the South Coast Air Quality Management District. This fuel-based inventory suggests on-road NOx emissions that are 1.5 ± 0.4, 2.8 ± 0.6, and 1.3 ± 0.7 times the reference EMFAC model estimates for on-road gasoline, light- and medium-duty diesel, and heavy-duty diesel, respectively.

Does performance-based environmental regulation improve the quality of innovation responses in the auto industry? The moderating role of corporate governance effectiveness

Innovation is the primary driving force for high-quality development. How can we effectively leverage the role of environmental regulation to force enterprises to innovate and promote high-quality development? This paper breaks through the single dimension of environmental regulation and innovation, and examines the impacts of performance-based environmental regulation stringency (PBR) on high-quality and low-quality innovation responses (HQI and LQI). Analyzing a European country on-road vehicle emissions dataset with Heckman two-stage models, the study demonstrates that performance-based environmental regulation has different impacts on two types of innovation responses. The research results are as follows: (1) Stringent PBR has a significant negative effect on HQI decisions and corresponding magnitude, while PBR promotes the occurrence and magnitude of LQI. (2) The impact of stringent PBR on innovation responses is different among automakers with different corporate governance. Specifically, the inhibitory effect of PBR on HQI and the positive effect on LQI can be alleviated when corporate governance is effective. (3) For transmission mechanisms, stringent PBR promotes HQI through differentiation strategic positioning and leads to LQI through cost leadership strategic positioning. (4) Further discussion found that the shorter implementation timescale of PBR and fewer resources within the automaker can strengthen the adverse impact of PBR on HQI and the promoting effect on LQI. These results provide references for the decision making of scientific environmental policy and the reasonable planning of policy stringency to improve environmental quality and social welfare.

Updating On-Road Vehicle Emissions for China: Spatial Patterns, Temporal Trends, and Mitigation Drivers.

Vehicle emissions in China have been decoupled from rapid motorization owing to comprehensive control strategies. China's increasingly ambitious goals for better air quality are calling for deep emission mitigation, posing a need to develop an up-to-date emission inventory that can reflect the fast-developing policies on vehicle emission control. Herein, large-sample vehicle emission measurements were collected to update the vehicle emission inventory. For instance, ambient temperature correction modules were developed to depict the remarkable regional and seasonal emission variations, showing that the monthly emission disparities for total hydrocarbon (THC) and nitrogen oxide (NOX) in January and July could be up to 1.7 times in northern China. Thus, the emission ratios of THC and NOX can vary dramatically among various seasons and provinces, which have not been considered well by previous simulations regarding the nonlinear atmospheric chemistry of ozone (O3) and fine particulate matter (PM2.5) formation. The new emission results indicate that vehicular carbon monoxide (CO), THC, and PM2.5 emissions decreased by 69, 51, and 61%, respectively, during 2010-2019. However, the controls of NOX and ammonia (NH3) emissions were not as efficient as other pollutants. Under the most likely future scenario (PC [1]), CO, THC, NOX, PM2.5, and NH3 emissions were anticipated to reduce by 35, 36, 35, 45, and 4%, respectively, from 2019 to 2025. These reductions will be expedited with expected decreases of 56, 58, 74, 53, and 51% from 2025 to 2035, which are substantially promoted by the massive deployment of new energy vehicles and more stringent emission standards. The updated vehicle emission inventory can serve as an important tool to develop season- and location-specific mitigation strategies of vehicular emission precursors to alleviate haze and O3 problems.

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.

Changes in Socioeconomic Disparities for Traffic-Related Air Pollution Exposure During Pregnancy Over a 20-Year Period in Texas

Air pollution presents clear environmental justice issues. However, few studies have specifically examined traffic-related air pollution (TRAP), a source driven by historically racist infrastructure policies, among pregnant individuals, a population susceptible to air pollution effects. How these disparities have changed over time is also unclear but has important policy implications. To examine changes in TRAP exposure by sociodemographic characteristics among recorded pregnancies over a 20-year period. This population-based birth cohort study used descriptive analysis among pregnant individuals in Texas from 1996 to 2016. All pregnant individuals with valid residential address, socioeconomic, and demographic data were included. Individual-level race and ethnicity, education, and maternal birthplace data were extracted from birth certificates and neighborhood-level household income and historical neighborhood disinvestment (ie, redlining) data were assessed via residential addresses. Data analysis occurred between June 2022 and June 2023. The main outcome, TRAP exposure at residential addresses, was assessed via traffic levels, represented by total and truck-specific vehicle miles traveled (VMT) within 500 m; nitrogen dioxide (no2) concentrations from a spatial-temporal land use regression model (ie, vehicle tailpipe emissions); and National Air Toxic Agency cancer risk index from on-road vehicle emissions. TRAP exposure differences were assessed by sociodemographic indicators over the 1996 to 2016 period. Among 7 043 598 pregnant people (mean [SD] maternal age, 26.8 [6.1] years) in Texas from 1996 to 2016, 48% identified as Hispanic or Latinx, 4% identified as non-Hispanic Asian or Pacific Islander, 12% identified as non-Hispanic Black, and 36% identified as non-Hispanic White. There were differences in TRAP for pregnant people by all sociodemographic variables examined. The absolute level of these disparities decreased from 1996 to 2016, but the relative level of these disparities increased: for example, in 1996, non-Hispanic Black pregnant individuals were exposed to a mean (SD) 15.3 (4.1) ppb of no2 vs 13.5 (4.4) ppb of no2 for non-Hispanic White pregnant individuals, compared with 2016 levels of 6.7 (2.4) ppb no2 for Black pregnant individuals and 5.2 (2.4) ppb of no2 for White pregnant individuals. Large absolute and relative differences in traffic levels were observed for all sociodemographic characteristics, increasing over time. For example, non-Hispanic Black pregnant individuals were exposed to a mean (SD) of 22 836 (32 844) VMT within 500 m of their homes, compared with 12 478 (22 870) VMT within 500 m of the homes of non-Hispanic White pregnant individuals in 2016, a difference of 83%. This birth cohort study found that while levels of air pollution disparities decreased in absolute terms over the 20 years of the study, relative disparities persisted and large differences in traffic levels remained, requiring renewed policy attention.

Methodology for Mobile Toxics Deterministic Human Health Risk Assessment and Case Study

Air toxic emissions from on-road mobile sources are significant contributors to the degradation of air quality in urban and dense population centers. Research led by the United States Environmental Protection Agency (EPA) identified more than 1162 hazardous air pollutants (HAPs) in the exhaust and evaporative emissions from on-road mobile sources. However, less than 70 hazardous air pollutants are monitored by regulatory agencies. HAPs emitted from Mobile Sources are known as Mobile Source Air Toxics (MSATs). The EPA estimates that approximately half of the cancer risk and 74% of noncancer health impacts from air toxics is attributed to mobile sources. The quantification of the risk associated with MSATs exposure remains limited to date, and only a few MSATs have ambient air quality standards to protect human health and welfare. This work presents a novel and validated methodology to quantify the myriad health risks associated with exposure to on-road mobile emissions. This methodology is introduced in the form of a pipelined analysis process, which may be employed in existing and new transportation projects. The proposed new methodology integrates results from three different types of models: on-road vehicle emissions inventory models such as MOVES and IVE, air dispersion models such as AERMOD and SCIPUFF, and risk estimate models for human and ecological receptors such as the 2005 Final U.S. EPA Human Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities. The result of this research work is a new methodology that provides regulators and risk analysts with a more detailed awareness of the health impacts of MSATs. A case study of Saint Paul, Minnesota, validated the air dispersion modeled results against monitored data, and the agreement was acceptable (i.e., the estimates were within a factor of two of the observations). Three high-population locations in the Saint Paul area were evaluated for human health risk, with the observation that at two of these locations, the Saint Paul—Ramsey Health Center and Anderson Office Building, the calculated cancer risk is in excess of the target risk level of 1.0E-05 for benzo(a)pyrene. The methodology presented in this paper allows regulators, risk analysts, and air quality engineers to better estimate multi-pathway cancer and noncancer risk associated with acute and chronic exposure to MSATs. Moreover, this work provides a science-based aid to policy decision makers when considering factors that most significantly affect population health and ecology.

A new portable open-path instrument for ambient NH3 and on-road emission measurements

Increased attentions to vehicle emission of NH3 have been paid since it is generally regarded as an important source in urban areas. Here, we developed a movable instrument based on Differential Optical Absorption Spectroscopy (DOAS) principle for detecting on-road NH3, which can avoid the losses in the sampling process attributed to the non-sampling methods. For this mobile DOAS, the temporal resolution, detection limit and relative error for NH3 were 1 min, 2.29 ppbv and 4.57% ± 2.44%, respectively. By employed to the on-road measurements along the arterial highway in Shanghai, the spatial distributions of NH3 and NO were obtained, and their dependence of traffic and road conditions were studied. The slopes of linear regression between NH3 and NO were 0.40, 0.02 and 0.07 on the Middle Ring Road, Outer Ring Road and Chongming Island Ring Road. It indicates that light gasoline vehicles (LGVs) were found to be the main contributor to NH3 emissions, while heavy-duty diesel vehicles (HDVs) mainly emitted NO. Based on the measured NH3 in the tunnel, the mileage-based NH3 emission factor per vehicle was estimated to be 17.9 ± 6.3 mg/km. The reported open-path instrument can be broadly used in on-road pollutant monitoring or vehicle emissions, and the measurements can reveal the real situation of emission characteristics, even find the abnormal operations of vehicle catalyst system.

On-road vehicle emission inventory and its spatial and temporal distribution in the city of Guayaquil, Ecuador

Emissions from mobile sources have become a major concern for health, environmental sustainability and climate change and high-resolution inventories are needed to support the design and assessment of abatement measures in urban areas. This study addresses the development of a traffic emissions inventory for Guayaquil, the second largest city in Ecuador, using the International Vehicle Emissions Model (IVE). Emissions are allocated with a spatial resolution of 1 km × 1 km and a temporal resolution of 1 h using a top-down methodology. This application combines traffic statistics already available in the city with the data from a field campaign to characterize vehicle fleet composition and activity patterns. The estimated annual emissions for the city were 237.1 kt of CO, 46.4 kt of NOx, 28.5 kt of VOC, 7.7 kt of PM10, 0.70 kt of SO2 and 4549.7 kt of CO2. 92.3 % of CO and 85.4 % of VOC were emitted by light gasoline vehicles, including private passenger vehicles and taxis, which represents 68.6 % and 8.8 %, respectively of the total fleet and contributes 52 % and 22 % of the total vehicle kilometer traveled (VKT), respectively. 48.9 % of NOx and 82 % of PM10 were emitted by the bus fleet although buses only represent 7.5 % of the total fleet and contribute 10.6 % of total VKT in the city. 41.1 % and 36.5 % of CO2 were emitted by buses and private vehicles, respectively. Even though, the average age of the fleet is below 10 years, the fleet in Guayaquil presents outdated emission standards and high emission factors. We found the higher emission rates in dense populated areas are associated to secondary roads. There is not much variability of emissions between months, but the typical daily pattern of emissions shows a peak in the morning and another in the afternoon.

Quantifying on-road vehicle emissions during traffic congestion using updated emission factors of light-duty gasoline vehicles and real-world traffic monitoring big data

Light-duty gasoline vehicles (LDGVs) have made up >90 % of vehicle fleets in China since 2019, moreover, with a high annual growth rate (> 10 %) since 2017. Hence, accurate estimates of air pollutant emissions of these fast-changing LDGVs are vital for air quality management, human healthcare, and ecological protection. However, this issue is poorly quantified due to insufficient reserves of timely updated LDGV emission factors, which are dependent on real-world activity levels. Here we constructed a big dataset of explicit emission profiles (e.g., emission factors and accumulated mileages) for 159,051 LDGVs based on an official I/M database by matching real-time traffic dynamics via real-world traffic monitoring (e.g., traffic volumes and speeds). Consequently, we provide robust evidence that the emission factors of these LDGVs follow a clear heavy-tailed distribution. The top 10 % emitters contributed >60 % to the total fleet emissions, while the bottom 50 % contributed <10 %. Such emission factors were effectively reduced by 75.7–86.2 % as official emission standards upgraded gradually (i.e., from China 2 to China 5) within 13 years from 2004 to 2017. Nevertheless, such achievements would be offset once traffic congestion occurred. In the real world, the typical traffic congestions (i.e., vehicle speed <5 km/h) can lead to emissions 5– 9 times higher than those on non-congested roads (i.e., vehicle speed >50 km/h). These empirical analyses enabled us to propose future traffic scenarios that could harmonize emission standards and traffic congestion. Practical approaches on vehicle emission controls under realistic conditions are proposed, which would provide new insights for future urban vehicle emission management.

A human exposure-based traffic assignment model for minimizing fine particulate matter (PM2.5) intake from on-road vehicle emissions

An exposure-based traffic assignment (TA) model and accompanying analysis framework have been developed to quantify primary and secondary fine particulate matter (PM2.5) exposure due to modeled on-road vehicle flow on a regional network at a high spatial resolution. The Chicago Metropolitan Area transportation network is used to demonstrate the model’s decision-informing power. The study compares the spatially distributed exposure impacts due to traffic emissions of two TA optimization scenarios: a baseline user equilibrium with respect to travel time (UET) and a novel system optimal with respect to pollutant intake (SOI). The UET and SOI scenarios are developed through the use of (a) the TA model used for obtaining vehicle flow patterns and characteristics including emissions, (b) a source-receptor matrix for PM2.5 developed through a reduced-complexity air quality model to quantify primary and secondary PM2.5 concentrations across the exposure domain, (c) spatial analysis for assessing exposure profiles at the census tract level, and (d) a health impact model to quantify exposure damages. The SOI scenario yields a 9% – 10% total reduction in exposure damages, with the most impacted census tracts benefiting from up to 20% – 30% of reductions, but leads to a 16% increase in travel time costs. Further reduction to PM2.5 exposure by the SOI is hindered by network constraints, where travel demand in populous areas around the network must still be satisfied. The model can be used to systematically quantify the mitigation potential of different transportation exposure reduction strategies, to assess the exposure impacts of newly developed transportation infrastructure, and to address the equity implications of PM2.5 exposure from traffic, all under realistic system behavior and bounded by actual system constraints.

Well-to-wheel emissions and abatement strategies for passenger vehicles in two Latin American cities.

More stringent standards for engines and fuels are progressively implemented as alternatives to reduce on-road vehicle emissions. While electric vehicles appear as a perfect alternative since their engines do not emit pollutants, wear and dust resuspension (W&R) and indirect emissions associated with electricity production remain significant sources of pollution. This work compares well-to-wheel emissions (WTW) and abatement strategies for various types of passenger vehicles in Bogotá and Santiago for different pollutants (CO, PM2.5, SO2, and NOx) and greenhouse gases like CO2 equivalent (CO2-Eq). Results show that WTW baseline emissions are more extensive in Bogotá than in Santiago (i.e., 58 and 30% for PM2.5 and CO2-Eq), mainly due to the higher vehicle activity and older state of Bogotá's fleet. We also evaluated extreme scenarios to assess the potential of a given vehicle technology or energy source to reduce emissions. We assessed, in particular, the replacement of all current vehicles by (1) conventional technologies with stricter emission standards and (2) battery electric vehicles powered with different energy resources. Our results indicate that replacing the current fleet with modern combustion technologies has a lower reduction potential than battery electric vehicles, but these reductions largely depend on the energy mix. Substitution by electric vehicles powered with electricity from renewable energies is the most efficient scenario in both cities. Finally, results also stress the importance of the resuspension of deposited road dust and brake and tire wear emissions in both cities as a crucial source of PM2.5, which must be better controlled.

Effect of Tampering on On-Road and Off-Road Diesel Vehicle Emissions

Illegal manipulation (i.e., tampering) of vehicles is a severe problem because vehicle emissions increase orders of magnitude and significantly impact the environment and human health. This study measured the emissions before and after representative approaches of tampering of two Euro 6 Diesel light-duty passenger cars, two Euro VI Diesel heavy-duty trucks, and a Stage IV Diesel non-road mobile machinery (NRMM) agricultural tractor. With tampering of the selective catalytic reduction (SCR) for NOx, the NOx emissions increased by more than one order of magnitude exceeding 1000 mg/km (or mg/kWh) for all vehicles, reaching older Euro or even pre-Euro levels. The tampering of the NOx sensor resulted in relatively low NOx increases, but significant ammonia (NH3) slip. The particle number emissions increased three to four orders of magnitude, reaching 6–10 × 1012 #/km for the passenger car (one order of magnitude higher than the current regulation limit). The tampered passenger car’s NOx and particle number emissions were one order of magnitude higher even compared to the emissions during a regeneration event. This study confirmed that (i) tampering with the help of an expert technician is still possible, even for vehicles complying with the current Euro standards, although this is not allowed by the regulation; (ii) tampering results in extreme increases in emissions.

Operational Data-Driven Intelligent Modelling and Visualization System for Real-World, On-Road Vehicle Emissions—A Case Study in Hangzhou City, China

On-road vehicle emissions play a crucial role in affecting air quality and human exposure, particularly in megacities. In the absence of comprehensive traffic monitoring networks with the general lack of intelligent transportation systems (ITSs) and big-data-driven, high-performance-computing (HPC) platforms, it remains challenging to constrain on-road vehicle emissions and capture their hotspots. Here, we established an intelligent modelling and visualization system driven by ITS traffic data for real-world, on-road vehicle emissions. Based on the HPC platform (named “City Brain”) and an agile Web Geographic Information System (WebGISs), this system can map real-time (hourly), hyperfine (10~1000 m) vehicle emissions (e.g., PM2.5, NOx, CO, and HC) and associated traffic states (e.g., vehicle-specific categories and traffic fluxes) over the Xiaoshan District in Hangzhou. Our results show sharp variations in on-road vehicle emissions on small scales, which even fluctuated up to 31.2 times within adjacent road links. Frequent and widespread emission hotspots were also exposed. Over custom spatiotemporal scopes, we virtually investigated and visualized the impacts of traffic control policies on the traffic states and on-road vehicle emissions. Such results have important implications for how traffic control policies should be optimized. Integrating this system with chemical transport models and air quality measurements would bridge the technical gap between air pollutant emissions, concentrations, and human exposure.

Automated Estimation of Construction Equipment Emission Using Inertial Sensors and Machine Learning Models

The construction industry is one of the main producers of greenhouse gasses (GHG). With the looming consequences of climate change, sustainability measures including quantifying the amount of air pollution during a construction project have become an important project objective in the construction industry. A major contributor to air pollution during construction projects is the use of heavy equipment. Therefore, efficient operation and management can substantially reduce a project’s carbon footprint and other environmental harms. Using unintrusive and indirect methods to predict on-road vehicle emissions has been a widely researched topic. Nevertheless, the same is not true in the case of construction equipment. This paper describes the development and deployment of a framework that uses machine learning (ML) methods to predict the level of emissions from heavy construction equipment. Data is collected via an Internet of Things (IoT) approach with accelerometer and gyroscope sensors as data collection nodes. The developed framework was validated using an excavator performing real-world construction work. A portable emission measurement system (PEMS) was used along with the inertial sensors to record the amount of CO, NOX, CO2, SO2, and CH4 pollution emitted by the equipment. Different ML algorithms were developed and compared to identify the best model to predict emission levels from inertial sensors data. The results show that Random Forest with the coefficient of determination (R2) of 0.94, 0.91, and 0.94, and normalized root-mean-square error (NRMSE) of 4.25, 6.42, and 5.17 for CO, NOX, and CO2, respectively, was the best algorithm among different models evaluated in this study.

High-resolution mapping of regional traffic emissions using land-use machine learning models

Abstract. On-road vehicle emissions are a major contributor to significant atmospheric pollution in populous metropolitan areas. We developed an hourly link-level emissions inventory of vehicular pollutants using two land-use machine learning methods based on road traffic monitoring datasets in the Beijing–Tianjin–Hebei (BTH) region. The results indicate that a land-use random forest (LURF) model is more capable of predicting traffic profiles than other machine learning models on most occasions in this study. The inventories under three different traffic scenarios depict a significant temporal and spatial variability in vehicle emissions. NOx, fine particulate matter (PM2.5), and black carbon (BC) emissions from heavy-duty trucks (HDTs) generally have a higher emission intensity on the highways connecting to regional ports. The model found a general reduction in light-duty passenger vehicles when traffic restrictions were implemented but a much more spatially heterogeneous impact on HDTs, with some road links experiencing up to 40 % increases in the HDT traffic volume. This study demonstrates the power of machine learning approaches to generate data-driven and high-resolution emission inventories, thereby providing a platform to realize the near-real-time process of establishing high-resolution vehicle emission inventories for policy makers to engage in sophisticated traffic management.

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.

Rapid assessments of light-duty gasoline vehicle emissions using on-road remote sensing and machine learning

In-time and accurate assessments of on-road vehicle emissions play a central role in urban air quality and health policymaking. However, official insight is hampered by the Inspection/Maintenance (I/M) procedure conducted in the laboratory annually. It not only has a large gap to real-world situations (e.g., meteorological conditions) but also is incapable of regular supervision. Here we build a unique dataset including 103,831 light-duty gasoline vehicles, in which on-road remote sensing (ORRS) measurements are linked to the I/M records based on the vehicle identification numbers and license plates. On this basis, we develop an ensemble model framework that integrates three machining learning algorithms, including neural network (NN), extreme gradient boosting (XGBoost), and random forest (RF). We demonstrate that this ensemble model could rapidly assess the vehicle-specific emissions (i.e., CO, HC, and NO). In particular, the model performs quite well for the passing vehicles under normal conditions (i.e., lower VSP (<18 kw/t), temperature (6–32 °C), relative humidity (<80%), and wind speed (<5 m/s)). Together with the current emission standard, we identify a large number of the ‘dirty’ (2.33%) or ‘clean’ (74.92%) vehicles in the real world. Our results show that the ORRS measurements, assisted by the machine-learning-based ensemble model developed here, can realize day-to-day supervision of on-road vehicle-specific emissions. This approach framework provides a valuable opportunity to reform the I/M procedures globally and mitigate urban air pollution deeply.