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Busy street canyons can have a large flow of vehicles and reduced air exchange and wind speeds at street level, exposing pedestrians to high pollutant concentrations. The airflow tended to move with vehicles along the canyon and the 1-s concentrations of NO, NO2 and CO were highly skewed close to the road and more normally distributed at sensors some metres above the road. The pollutants were more autocorrelated at these elevated sensors, suggesting a less variable concentration away from traffic in the areas of low turbulence. The kerbside concentrations also showed cyclic changes approximating nearby traffic signal timing. The cross-correlation between the concentration measurements suggested that the variation moved at vehicle speed along the canyon, but slower vertically. The concentrations of NOx and CO were slightly higher at wind speeds of under a metre per second. The local ozone concentrations had little effect on the proportion of NOx present as NO2. Pedestrians on the roadside would be unlikely to exceed the USEPA hourly guideline value for NO2 of 100 ppb. Across the campaign period, 100 individual minutes exceeded the guidelines, though the effect of short-term, high-concentration exposures is not well understood. Tram stops at the carriageway divider are places where longer exposures to higher levels of traffic-associated pollutants are possible. [ABSTRACT FROM AUTHOR]

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread to more than 200 countries with a current case fatality ratio (CFR) of more than 2% globally. The concentration of air pollutants is considered a critical factor responsible for transmitting coronavirus disease among the masses. The photochemical process and coal combustions create respiratory disorders that lead to coronavirus disease. Based on the crucial fact, the study evaluated the impact of nitrous oxide (N2O) emissions, coal combustion, and traffic emissions on COVID-19 cases in a panel of 39 most affected countries of the world. These three air pollution factors are considered to form a lethal smog that negatively affects the patient's respiratory system, leading to increased susceptibility to coronavirus worldwide. The study used the Markov two-step switching regime regression model for obtaining parameter estimates. In contrast, an innovation accounting matrix is used to assess smog factors' intensity on possibly increasing coronavirus cases over time. The results show that N2O emissions, coal combustion, and traffic emissions increase COVID-19 cases in regime-1. On the other hand, N2O emissions significantly increase coronavirus cases in regime-2. The innovation accounting matrix shows that N2O emissions would likely have a more significant share of increasing coronavirus cases with a variance of 33.902%, followed by coal combustion (i.e., 6.643%) and traffic emissions (i.e., 2.008%) over the time horizon. The study concludes that air quality levels should be maintained through stringent environmental policies, such as carbon pricing, sustainable urban planning, green technology advancement, renewable fuels, and pollution less accessible vehicles. All these measures would likely decrease coronavirus cases worldwide. [ABSTRACT FROM AUTHOR]

Vehicle emissions calculation methods mostly use ownership information or annual road monitoring data as the activity level to calculate air pollutant emissions, but it is hard to reflect either the emissions intensity under different conditions or the spatiotemporal characteristics in various sections based on such approaches. This paper presents a method based on the Macroscopic Fundamental Diagram and real-time traffic data to calculate vehicle emissions, which could reflect the operation conditions and emission characteristics of vehicles. Following the ‘Technical Guide for the Compiling of Road Vehicle Air Pollutant Emissions Inventories’, the emissions of three roads with different lane numbers and road grades in Beijing were estimated and verified using this method. Compared with monitoring data, the average deviations of the traffic flow on the Fifth Expressway, Jingfu National Highway, and Jingzhou Provincial Highway were −25.5%, −26.5%, and −13.4%, respectively, and the average deviations of nitrogen oxides emissions were −27.7%, −12.9%, and −12%, respectively. This method showed good application potentials to construct the emissions inventory applied to the block-scale model and analyze the spatiotemporal distribution characteristics of motor vehicle emissions in urban areas.

Within the scope of this study, the emission amounts arising from vehicles cruising on the main highway in the Tekkekoy district center in Turkey, where the General Directorate of Highways counted vehicles in 2017, were calculated. The emission amounts were calculated using the emission factor, the number of vehicles and the length of the highway. The emission factors were selected suitably from the CORINAIR emission factor database in the form of velocity-related equations as vehicle class, type of fuel, emission standard and engine technology. The numbers of vehicles and route lengths were obtained from the data of the State Highways Traffic Volume Map published by the General Directorate of Highways of the Turkish Ministry of Transportation and Infrastructure. Emissions were calculated separately for 5 pollutants (CO, NOx, VOC, PM and SO2) by using the MATLAB programming language. Emission characteristics for each type of pollutant and for each type of vehicle (automobiles, light-duty commercial vehicles, buses and trucks) were identified depending on vehicle speeds. The results showed that the amounts of pollutants released to the atmosphere on the main highway where vehicle counts were made in the Tekkekoy district center in the year 2017 as 138.56 tons for CO, 150.23 tons for NOx, 18.33 tons for VOC, 5,01 tons for PM and 0.20 tons for SO2. Trucks had the highest share for the pollutants NOx and PM respectively as 66.36% and50,3%. Passenger cars had the highest share for the pollutants CO, VOC and SO2 respectively as 72.32%, 64.81% and 50%. The amounts of pollutant emissions were found to be at their minimum values in the speed range of 70–90 km/h.

Adaptive traffic signal control (ATSC) in urban traffic networks poses a challenging task due to the complicated dynamics arising in traffic systems. In recent years, several approaches based on multi-agent deep reinforcement learning (MARL) have been studied experimentally. These approaches propose distributed techniques in which each signalized intersection is seen as an agent in a stochastic game whose purpose is to optimize the flow of vehicles in its vicinity. In this setting, the systems evolves toward an equilibrium among the agents that shows beneficial for the whole traffic network. A recently developed multi-agent variant of the well-established advantage actor-critic (A2C) algorithm, called MA2C (multi-agent A2C) exploits the promising idea of some communication among the agents. In this view, the agents share their strategies with other neighbor agents, thereby stabilizing the learning process even when the agents grow in number and variety. We experimented MA2C in two traffic networks located in Bologna (Italy) and found that its action translates into a significant decrease of the amount of pollutants released into the environment.

Traffic emissions are a major source of air pollution and associated damage to human health in India. Many of the Indian metro cities urgently require cleaner transportation technologies to ensure cleaner air. Here, using newly compiled spatially disaggregated, gridded, high-resolution (0.1° × 0.1°) road transport emission inventory for India for 2030 (RTEII) of 74 speciated VOCs, CO, SO2, NOx, NH3, CH4, CO2, BC, OC and PM2.5 from varied fuels and vehicle technologies that are currently in use in India, we investigated changes in emission in response to substitution of the existing vehicular fleet by cleaner alternatives. Three “what-if” intervention scenarios were considered to assess the extent in improvement of air quality due to the reduction in the primary emission of air pollutants. The results show that significant reductions in direct emission of pollutants (Non-Methane VOCs, −91%; CO, −80%; PM2.5, 44%) including toxic VOCs (e.g., isocyanic acid, −76%; BTEX, −93%; as well as individual VOC classes (e.g., sum of OVOCs, −61% and sum of alkenes, −80%) can likely be achieved in 2030 by shifting from highly polluting Internal Combustion Engine (ICE) based 2 and 3-wheeled vehicles to Electric Vehicles (EVs) under scenario 1. The amount of secondary pollutants such as SOA and O3 that can potentially be formed from traffic also showed significant reduction of 94% and 84%, respectively, under scenario 1. Conversion of diesel fuelled vehicles to CNG under scenario 2 can lead to a larger reduction in black carbon emissions (−50%). Scenario 3, in which the benefits of scenarios 1 and 2 are combined, represents the best long-term strategy moving forward, which can result in massive emission reductions of pollutants through existing technologies of greener transport fleets over India. Large scale conversion of the vehicle fleets as explored here can lead to a substantial reduction of air pollution and fewer lives lost.

High resolution exposure surfaces are essential to capture disparities in exposure to traffic-related air pollution in urban areas. In this study, we develop an approach to downscale Chemical Transport Model (CTM) simulations to a hyperlocal level (∼100m) in the Greater Toronto Area (GTA) under three scenarios where emissions from cars, trucks and buses are zeroed out, thus capturing the burden of each transportation mode. This proposed approach statistically fuses CTMs with Land-Use Regression using machine learning techniques. With this proposed downscaling approach, changes in air pollutant concentrations under different scenarios are appropriately captured by downscaling factors that are trained to reflect the spatial distribution of emission reductions. Our validation analysis shows that high-resolution models resulted in better performance than coarse models when compared with observations at reference stations. We used this downscaling approach to assess disparities in exposure to nitrogen dioxide (NO 2 ) for populations composed of renters, low-income households, recent immigrants, and visible minorities. Individuals in all four categories were disproportionately exposed to the burden of cars, trucks, and buses. We conducted this analysis at spatial resolutions of 12, 4, 1 km, and 100 m and observed that disparities were significantly underestimated when using coarse spatial resolutions. This reinforces the need for high-spatial resolution exposure surfaces for environmental justice analyses.

Different types of automated vehicles (AVs) have emerged promptly in recent years, each of which might have different potential impacts on traffic flow and emissions. In this paper, the impacts of autonomous automated vehicles (AAVs) and cooperative automated vehicles (CAVs) on capacity, average traffic speed, average travel time per vehicle, and average delay per vehicle, as well as traffic emissions such as carbon dioxide (CO2), nitrogen oxides (NOx), and particulate matter (PM10) have been investigated through a microsimulation study in VISSIM. Moreover, the moderating effects of different AV market penetration, and different freeway segments on AV’s impacts have been studied. The simulation results show that CAVs have a higher impact on capacity improvement regardless of the type of freeway segment. Compared to other scenarios, CAVs at 100% market penetration in basic freeway segments have a greater capacity improvement than AAVs. Furthermore, merging, diverging, and weaving segments showed a moderating effect on capacity improvements, particularly on CAVs’ impact, with merging and weaving having the highest moderating effect on CAVs’ capacity improvement potential. Taking average delay per vehicle, average traffic speed, and average travel time per vehicle into account, simulation results were diverse across the investigated scenarios. The emission estimation results show that 100% AAV scenarios had the best performance in emission reductions in basic freeway and merging sections, while other scenarios increased emissions in diverging and weaving sections.

Middle East and North African (MENA) countries over the decades are experiencing rapid industrial and infrastructural growth combined with being the global hub of oil and gas industries. These economic transformations are associated with release of air pollutants including urban air toxics (UAT) through industrial, traffic, and constructional activities into ambient urban environments. UAT concentrations levels may exacerbate in most MENA countries considering high number of vehicular traffic populations and petrochemical industries which are one of the main sources of this pollutant. Therefore, the main objective of the study is to review major findings of UAT levels in urban areas across thirteen (13) MENA countries. The study characterizes various measured UAT, assesses their concentrations in ambient environment, and identifies their major sources of emissions by reviewing more than 100 relevant UAT papers across the selected MENA countries. It was found that benzene, heavy metals, formaldehyde, and dioxin-like compounds are the most reported UAT. The study concluded that road traffic, fuel stations, and petrochemical industries were identified as the main sources of ambient UAT levels. It was further reported that most of the studies were based on short-term ambient environment with limited studies in indoor environments. Therefore, it is highly recommended that future research should focus on innovative health impact assessment and epidemiological studies from exposure to UAT levels. Also embarking on sustainable mitigation approaches through urban greenery, eco-industrial estates infrastructural developments, and renewable energy shares will reduce UAT levels and improve human health. [ABSTRACT FROM AUTHOR]

The traffic congestion in the Hsuehshan tunnel and at the Toucheng interchange has led to traffic-related air pollution with increasing concern. To ensure the authenticity of our simulation, the concentration of the last 150 m in Hsuehshan tunnel was simulated using the computational fluid dynamics fluid model. The air quality at the Toucheng interchange along a 2 km length highway was simulated using the California Line Source Dispersion Model. The differences in air quality between rush hours and normal traffic conditions were also investigated. An unmanned aerial vehicle (UAV) with installed PM2.5 sensors was developed to obtain the three-dimensional distribution of pollutants. On different roads, during the weekend, the concentrations of pollutants such as SOx, CO, NO, and PM2.5 were observed to be in the range of 0.003–0.008, 7.5–15, 1.5–2.5 ppm, and 40–80 μg m− 3, respectively. On weekdays, the vehicle speed and the natural wind were 60 km h− 1 and 2.0 m s− 1, respectively. On weekdays, the SOx, CO, NO, and PM2.5 concentrations were found to be in the range of 0.002–0.003, 3–9, 0.7–1.8 ppm, and 35–50 μg m− 3, respectively. The UAV was used to verify that the PM2.5 concentrations of vertical changes at heights of 9.0, 7.0, 5.0, and 3.0 m were 45–48, 30–35, 25–30, and 50–52 μg m− 3, respectively. In addition, the predicted PM2.5 concentrations were 40–45, 25–30, 45–48, and 45–50 μg m− 3 on weekdays. These results provide a reference model for environmental impact assessments of long tunnels and traffic jam-prone areas. These models and data are useful for transportation planners in the context of creating traffic management plans. [ABSTRACT FROM AUTHOR]

Previous studies have shown that cooperative adaptive cruise control (CACC) vehicles have capability to improve traffic flow stability and reduce emissions and fuel consumption. However, previous studies did not investigate the relevance between these two aspects. To address this issue, we calculated stability conditions of traffic flow using car-following models. Simulations are also performed to evaluate reductions of emissions and fuel consumption under traffic oscillations. We also compared results of stability and emissions for exploring intrinsic relevance between them. Results show that stability could qualitatively influence reductions of emissions and fuel consumption, which provides potential method to reduce emissions and fuel consumption. [ABSTRACT FROM AUTHOR]

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The large fluctuations in traffic during the COVID-19 pandemic provide an unparalleled opportunity to assess vehicle emission control efficacy. Here we develop a random-forest regression model, based on the large volume of real-time observational data during COVID-19, to predict surface-level NO2, O3, and fine particle concentration in the Los Angeles megacity. Our model exhibits high fidelity in reproducing pollutant concentrations in the Los Angeles Basin and identifies major factors controlling each species. During the strictest lockdown period, traffic reduction led to decreases in NO2 and particulate matter with aerodynamic diameters <2.5 µm by -30.1% and -17.5%, respectively, but a 5.7% increase in O3. Heavy-duty truck emissions contribute primarily to these variations. Future traffic-emission controls are estimated to impose similar effects as observed during the COVID-19 lockdown, but with smaller magnitude. Vehicular electrification will achieve further alleviation of NO2 levels. [ABSTRACT FROM AUTHOR]

Evidence to advocate for cleaner air for people with asthma is not in short supply. We know that air pollution is associated with the development and worsening of the condition and that mitigating interventions can improve respiratory outcomes. We have clear targets, particularly traffic emissions, especially in urban areas, and plenty of potentially effective actions. Road traffic must be reduced, and what remains should be cleaner and greener. Urban green spaces, safe cycle networks and wider pavements will promote active travel and leisure time exercise. Healthcare professionals must ensure people are aware of their air quality, its impact on asthma and the appropriate behaviour to safeguard health. What remains are realistic policies and effective measures, based on the correct scientific evidence, to be taken forth with political courage and investment so that air pollution no longer contributes to the development or worsening of respiratory ill health. [ABSTRACT FROM AUTHOR]

The effects of air pollution on the general public received much attention recently. Personal exposure and deposition fraction of aerosol particles were studied in Vilnius, Lithuania, focusing on individuals working in an office and driving to work. Aerosol monitoring in the urban background was found to give an indication of the minimum concentrations of particulate matter (PM) expected at urban roads, as these correspond to the lowest PM concentrations measured there. In March 2021, PM2.5 concentrations at the urban background monitoring station reached values above the annual limit of 5 μg/m3 the World Health Organization in 50% of cases. Our study shows significant differences in exposure to air pollution in a car cabin and in a modern office. According to the multiple-path particle dosimetry model, the exposure of the person in the office is about 14 times lower than driving a car, where the minute deposition dose for PM1 is 0.072 µg/min for the period when the PM2.5 concentration in the urban background reaches 10 µg/m³. Compared to the PM2.5 mass concentration at the urban background station, the mean PM2.5 concentration in the vehicle reaches values that are 2–3 times higher. During the working day, when driving takes less than 10% of the time considered (commuting plus working), PM exposure during driving accounts for about 80% of the PM exposure caused by PM concentration in the office.

Air pollution impacts human health and the environment, especially in urban cities with substantial industrial activities and vehicular traffic emissions. Despite increasingly strict regulations put in place by regulatory agencies, air pollution is still a significant environmental problem in cities across the world. The objective of this study was to evaluate the environmental pollution from stationary and mobile sources using real-time monitoring and sampling techniques to characterize size-segregated particulate matter (PM), black carbon (BC), and ozone (O3) at the Port of Albany, NY. Air pollution monitoring was carried out for 3 consecutive weeks under a 24-hour cycle in 2018 at the New York State Department of Environmental Conservation (NYSDEC) site within the Port. Sampling was done with an AEROCET 531, optical particle sizer (OPS), ozone monitor, and MicroAeth AE51. Higher mass and number concentrations of size-segregated particles were observed during the daytime. PM2.5 and PM10 concentrations ranged from 1 to 271 micrograms per cubic meter (µg/m3) and 1 to 344 µg/m3, respectively. While these values do not exceed the level of the USEPA 24-hour standards, frequent sharp peaks were observed at higher concentrations. Size-segregated PM at sizes 0.3 µm and 0.374 µm recorded maximum concentrations of 101,631 particle number per cubic centimeter (#/cm3) and 43,432 #/cm3, respectively. Wide variations were observed in the particle number concentrations for 0.3 µm, 0.374 µm, and 0.465 µm sizes, which ranged from 1521 to 101,631 #/cm3; 656 to 43,432 #/cm3; and 311 to 29,271 #/cm3, respectively. BC concentration increased during morning and evening rush hours with the maximum concentration of 11,971 ng/m3 recorded at 8:00 AM. This suggests that mobile sources are the primary contributor to anthropogenic sources of BC within the Port. Episodic elevations in the concentrations of size-segregated PM and BC confirmed the contribution of industrial and vehicular activities around the Port of Albany. This study underscores the importance of measuring particles on a size-segregated basis in order to more fully understand the contributions of the multiple sources present within and surrounding a port environment.

Small data samples are still a critical challenge for spatial predictions. Land use regression (LUR) is a widely used model for spatial predictions with observations at a limited number of locations. Studies have demonstrated that LUR models can overcome the limitation exhibited by other spatial prediction models which usually require greater spatial densities of observations. However, the prediction accuracy and robustness of LUR models still need to be improved due to the linear regression within the LUR model. To improve LUR models, this study develops a land use quantile regression (LUQR) model for more accurate spatial predictions for small data samples. The LUQR is an integration of the LUR and quantile regression, which both have advantages in predictions with a small data set of samples. In this study, the LUQR model is applied in predicting spatial distributions of annual mean PM2.5concentrations across the Greater Sydney Region, New South Wales, Australia, with observations at 19 valid monitoring stations in 2020. Cross validation shows that the goodness-of-fit can be improved by 25.6–32.1% by LUQR models when compared with LUR, and prediction root mean squared error (RMSE) and mean absolute error (MAE) can be reduced by 10.6–13.4% and 19.4–24.7% by LUQR models, respectively. This study also indicates that LUQR is a more robust model for the spatial prediction with small data samples than LUR. Thus, LUQR has great potentials to be widely applied in spatial issues with a limited number of observations.

• The effect of lockdown on NO 2 in London, Milan and Paris was assessed. • Data from 52 air quality control units were studied. • Comparison with 2017−2019 highlighted a high decrease in NO 2 due to low traffic. • Despite low traffic, NO 2 concentrations > 40 μg m−3 were registered for several days. • Rethink the vehicles, the city and its vehicular traffic are strongly suggested. Nitrogen dioxide (NO 2) can have harmful effects on human health and can act as a precursor for the formation of other air pollutants in urban environment such as secondary PM 2.5 and ozone. The lockdown measures for CoViD-19 allowed to simulate on a large scale the massive and prolonged reduction of road traffic (the main source for NO 2 in urban environment). This work aims to selectively assess the maximum impact that total traffic blocking measures can have on NO 2. For this reason, three megacities (London, Milan and Paris) were chosen which had similar characteristics in terms of climatic conditions, population, policies of urban traffic management and lockdown measures. 52 air quality control units have been used to compare data measured in lockdown and in the same periods of previous years, highlighting a significant decrease in NO 2 concentration due to traffic (London: 71.1 % - 80.8 %; Milan: 8.6 % - 42.4 %; Paris: 65.7 % - 79.8 %). In 2020 the contribution of traffic in London, Milan and Paris dropped to 3.3 ± 1.3 μg m−3, 6.1 ± 0.8 μg m−3, and 13.4 ± 1.5 μg m−3, respectively. Despite the significant reduction in the NO 2 concentration, in UT stations average NO 2 concentrations higher than 40 μg m−3 were registered for several days. In order to reduce the pollution, the limitation of road traffic could be not enough, but a vision also aimed at rethink the vehicles and their polluting effects should be developed. [ABSTRACT FROM AUTHOR]

Emerging transportation technologies (e.g., electric vehicles) and services (e.g., shared mobility) provide efficient, sustainable and cost-effective alternatives to traditional travel modes. However, whether these innovative technologies bring benefits to different population groups in an equal and reasonable manner is still an open question. The various methods applied to evaluate the equity performance of these systems are also not clearly comparable. In this paper, we comprehensively review methods from the existing literature for assessing the equity of a few important system outcomes: accessibility, traffic emissions, and safety. We also identify the existing challenges of analyzing equity for emerging transportation technologies. We unify the existing methodologies into a three-step framework that includes population measurement, cost/benefit measurement and equity assessment, and we summarize the applicable measurements for each step, in detail. A handful of literature focusing on emerging transportation technologies, such as shared mobility and autonomous vehicles, were also identified and surveyed; the methodologies used were found to fit with the three-step framework. We summarize the major findings and discuss promising directions for developing more sophisticated equity assessment methodologies for emerging transportation technologies. Overall, based on a comprehensive review, this paper contributes a framework for assessing the equity of transportation systems that integrates accessibility, traffic emissions, and safety outcomes. The summarized framework can be an overview resource to assist researchers and transportation planners who require equity analysis methods. The research gaps identified also provide directions for equity research on emerging transportation technologies.

Busy street canyons can have a large flow of vehicles and reduced air exchange and wind speeds at street level, exposing pedestrians to high pollutant concentrations. The airflow tended to move with vehicles along the canyon and the 1-s concentrations of NO, NO2 and CO were highly skewed close to the road and more normally distributed at sensors some metres above the road. The pollutants were more autocorrelated at these elevated sensors, suggesting a less variable concentration away from traffic in the areas of low turbulence. The kerbside concentrations also showed cyclic changes approximating nearby traffic signal timing. The cross-correlation between the concentration measurements suggested that the variation moved at vehicle speed along the canyon, but slower vertically. The concentrations of NOx and CO were slightly higher at wind speeds of under a metre per second. The local ozone concentrations had little effect on the proportion of NOx present as NO2. Pedestrians on the roadside would be unlikely to exceed the USEPA hourly guideline value for NO2 of 100 ppb. Across the campaign period, 100 individual minutes exceeded the guidelines, though the effect of short-term, high-concentration exposures is not well understood. Tram stops at the carriageway divider are places where longer exposures to higher levels of traffic-associated pollutants are possible.

To examine the difference in the major chemical composition of fine particulate matter (PM2.5) between two roadway sites, 24 h integrated PM2.5 samples were simultaneously collected both 15 m (Buk-Ku District Office (BKO) site) and 150 m (Chonnam National University campus (CNU) site) away from busy roads during the summer and winter periods; these samples were taken to determine the concentrations of organic and elemental carbon (OC and EC), water-soluble organic carbon (WSOC), and water-soluble inorganic species. In addition, the real-time aerosol light absorption coefficients (Abs) were measured using a dual-spot seven-wavelength aethalometer at the CNU site to evaluate the influence of traffic and biomass burning (BB) emissions on the concentrations of organic aerosol particles. The hourly NO2 concentration was also observed at an air pollution monitoring network that is about 2 km away from the CNU site. During summer, 24 h PM2.5 concentrations (PM2.5 episode) which exceeded the Korean PM2.5 standard (35 μg/m3) were linked to increases in organic matter (OM) and SO42− concentrations that accounted for on average 35–41% and 26–30%, respectively, of the PM2.5 at the two sites. The increased SO42− concentration was most likely attributable to the inflow of long-range transported aerosols, rather than local production, as demonstrated by both the MODIS (Moderate Resolution Imaging Spectroradiometer) images and transport pathways of air masses reaching the sites. On the other hand, the OM, WSOC, and EC concentrations were directly attributable to traffic emissions at the sampling sites, as supported by the tight correlation between the OC and EC. A small difference between the absorption Ångström exponent (AAE) values calculated at wavelengths of 370–950 nm (AAE370–950nm) and 370–520 nm (AAE370–520nm), and the poor correlation of absorption coefficient by brown carbon (BrC) at 370 nm (AbsBrC370nm) with K+ (R2 = 0.00) also suggest a significant contribution of traffic emissions to OM. However, the wintertime PM2.5 episode was strongly related to the enhanced OM and NO3− concentrations, which contributed 26–28% and 22–23% of the PM2.5 concentration, respectively. It is interesting to note that there were two distinct OC/EC ratios in winter: a lower OC/EC (~3.0), which indicates a significant contribution of traffic emissions to the OC and EC, and a higher OC/EC (~6.5), which suggests an additional influence of BB emissions as well as traffic emissions at the sites. Strong correlations between the OC and EC (R2 = 0.72–0.83) and the enhanced AAE370–520nm values compared to the AAE370–950nm support that BB emissions were also an important contributor to the wintertime OM concentrations as well as traffic emissions at the two sites. A good correlation between the gaseous NO2 and NO3− and meteorological conditions (e.g., low wind speed and high relative humidity) suggest that the heterogeneous oxidation of NO2 on moist particles could be an important contributor to wintertime particulate NO3− formation at the sites. The OC concentrations during summer and winter were higher at the BKO site, with a higher traffic flow and a shorter distance from the roadway than at the CNU site. However, there were slight differences in the concentrations of secondary inorganic species (NO3−, SO42−, and NH4+) between the sites during summer and winter. [ABSTRACT FROM AUTHOR]

Quantification of the contributions from traffic source to arable roadside soil heavy metal loadings is a challenge. The contribution depends on: traffic intensity, road type and distance from the road. At a field scale (3.9 ha), 720 topsoil samples were taken. The aim of the study was to monitor and assess the impact of regional/local roads with various conditions of traffic and period of use on the distribution of Cd, Zn, Pb and Cu in the arable roadside topsoil in their vicinity. PCA indicated the occurrence of two main gradients of 11 environmental elements influencing the distribution of heavy metals in the soils of the neighbouring land. The first gradient was associated mainly with the distance from the edge of the road. The second gradient was associated with the degree of contamination of the soils and with the road type, defined by the traffic volume and period of being use. Anova reviled lack of influence of the factors for Cu contents. Unlike Cu, for Cd, Pb and Zn, the significant impact was observed for both factors and interactions between them. The concentrations of Cd, Pb and Zn, regardless of the distance from the road were 0.21–0.58 mg Cd kg−1 d.m., 13.60–41.96 mg Pb kg−1 d.m. and 40.31–63.97 mg Zn kg−1 d.m. In case of increasing distance from the road, generally the contents of Pb, Zn and Cd contents were decreasing. However, only in the case the oldest and carrying the highest traffic road was a clear, statistically significant differences noted for following distances from the road on the content of Cd, Pb and Zn. Analysis of spread gave trend curves, for Pb, Cd and Zn they were parabolas. The curves let reduce sapling distances to 65 m, 45 and 47 m for Cd, Pb and Zn, respectively. [ABSTRACT FROM AUTHOR]

Bad air quality due to free pollutants such as particulate matter (PM), carbon dioxide (CO 2 ), nitrogen oxides (NO x ) and volatile organic components (VOC) increases the risk of long-term health diseases. The impact of traffic-calming measures on air quality has been studied using specialized equipment at control sites or mounted on cars to monitor pollutants levels. However, this approach suffers from a large number of variables on the experiments such as vehicles types, number of monitored vehicles, driver's behavior, traffic density, time of the day, elapsed monitoring time, road conditions and weather. In this work, we use a cellular automata and an instantaneous traffic emissions model to capture the effect of speed humps on traffic flow and on the generation of CO 2 , NO x , VOC and PM pollutants. This approach allows us to study and characterize the effect of many speed humps on a single lane. We found that speed humps significantly promote the generation of pollutants when the number of vehicles on a lane is low. Our results may provide insight into urban planning strategies to reduce the generation of traffic emissions and lower the risk of long-term health diseases. [ABSTRACT FROM AUTHOR]

Air pollution is a major environmental health risk, particularly in low- and middle-income countries. In Brazil, the government has implemented progressive stages of traffic emission controls since 1986 through a program called "Proconve". In this study, we evaluated the effectiveness of two of these stages, Proconve stages L-5 and P-7, in reducing air pollution in the 10 most populated cities in Brazil. We used Bayesian structural time-series models to estimate the causal effect of the vehicle emission control measures on the air pollution time series for each city and air pollutant (PM 2.5 , O 3 , NO 2 , and CO), accounting for potential confounding factors such as temperature and humidity. While previous studies have shown positive effects of control measures on air quality, our study suggests that the effects may not always be statistically significant, which has implications for the design and interpretation of future studies. Our study provides valuable information for policymakers in Brazil and adds to the international literature on the effectiveness of air pollution control measures. Despite the lack of statistically significant results, our study highlights the importance of considering statistical power and unmeasured confounding factors when evaluating the effectiveness of air pollution control measures. • This is the first study evaluating effectiveness of emission control on air quality in Brazil. • We suggest that the current control may not be as effective as anticipated in reducing air pollution. • We highlight the importance of considering statistical power and unmeasured confounding factors. [ABSTRACT FROM AUTHOR]

Ammonia (NH 3) is an important air pollutant with crucial impacts on air quality, ecosystems and climate change. However, NH 3 is not included in routine air quality monitoring and the availability of long-term NH 3 measurements is still limited, resulting in large uncertainties in our knowledge of the spatial distribution and sources of NH 3. Here we performed 1 year (March 2021–February 2022) of atmospheric NH 3 measurements at a regional background station, the Station for Observing Regional Processes of the Earth System (SORPES) in the western Yangtze River Delta of China. We found that the annual mean NH 3 concentration was 12.2 ± 4.6 ppb and exhibited apparent seasonal variations, with a maximum in June and a minimum in February, influenced by agricultural activities, air temperature, gas-particle partitioning and precipitation. Moreover, air temperature and absolute humidity correlated well with NH 3 , indicating that they are important factors in influencing NH 3 levels. The diurnal variation of NH 3 showed a single peak in the morning and higher concentrations during the day. In spring and autumn, the NH 3 morning peak can be attributed to dew evaporation. The air mass backward trajectory, local wind direction and velocity analysis suggested that NH 3 was influenced by both local emissions and regional transport from nearby cities. During the COVID-19 lockdown, a strong reduction of NO x (−77%) and a weak reduction of NH 3 (−14%) were observed compared to the pre-lockdown, highlighting that traffic emissions have a minor impact on NH 3 at the SORPES station. Our results provide more insights into the characteristics and sources of atmospheric NH 3 in background regions influenced by mixed air pollution plumes. [Display omitted] • Long-term online measurements of NH 3 are conducted in Nanjing, eastern China. • Air temperature and absolute humidity have important impacts on NH 3 variation, and dew evaporation is a significant source of NH 3 morning peaks. • NH 3 and NO x decreased by 14% and 77% during the COVID-19 lockdown and traffic emissions have minor effects on NH 3 levels. [ABSTRACT FROM AUTHOR]

Urban air quality in cities is strongly influenced by road traffic emissions. Micro-scale models have often been used to evaluate the pollutant concentrations at the scale of the order of meters for estimating citizen exposure. Nonetheless, retrieving emissions information with the required spatial and temporal details is still not an easy task. In this work, we use our modelling system PMSS (Parallel Micro Swift Spray) with an emission dataset based on Floating Car Data (FCD), containing hourly data for a large number of road links within a 1 × 1 km2 domain in the city of Rome for the month of May 2013. The procedures to obtain both the emission database and the PMSS simulations are hosted on CRESCO (Computational Centre for Research on Complex Systems)/ENEAGRID HPC facilities managed by ENEA. The possibility of using such detailed emissions, coupled with HPC performance, represents a desirable goal for microscale modeling that can allow such modeling systems to be employed in quasi-real time and nowcasting applications. We compute NOx concentrations obtained by: (i) emissions coming from prescribed hourly modulations of three types of roads, based on vehicle flux data in the FCD dataset, and (ii) emissions from the FCD dataset integrated into our modelling chain. The results of the simulations are then compared to concentrations measured at an urban traffic station.

Based on the development of the concept of a resource-saving and environmentally friendly society, needing to develop low-carbon and sustainable urban transportation. Most of the pollutants come from the emissions of motor vehicle exhaust. Therefore, this paper analyzes the relationship between driving behavior and traffic emissions, to constrain driver behavior to reduce pollutant emissions. The GPS data are preprocessed by using Navicat for data integration, data screening, data sorting, etc., and then, the speed data are cleaned by using a combination of box-and-line plots and linear interpolation in SPSS. Second, this paper uses principal component analysis (PCA) to downsize 12 indicators such as average speed, average acceleration, and maximum speed and then adopts K-MEANS and K-MEDOIDS methods to cluster the driver's behavioral indicators, selects the aggregation method based on the clustering indexes optimally, and analyzes the driver's driving state by using the symbolic approximation aggregation method; finally, according to the above research results and combined with the MOVES traffic emission model to analyze the relationship between the driver's driving mode, driving state, and traffic emissions, the decision tree can be used to predict the unknown driving mode of the driver to estimate the degree of its emissions., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Du, Kang, Gao and Wang.)

In this study, we test the performance of a compact gas chromatograph with photoionization detector (GC-PID) and optimize the configuration to detect ambient (sub-ppb) levels of benzene, toluene, ethylbenzene, and xylene isomers (BTEX). The GC-PID system was designed to serve as a relatively inexpensive (~10 k USD) and field-deployable air toxic screening tool alternative to conventional benchtop GCs. The instrument uses ambient air as a carrier gas and consists of a Tenax-GR sorbent-based preconcentrator, a gas sample valve, two capillary columns, and a photoionization detector (PID) with a small footprint and low power requirement. The performance of the GC-PID has been evaluated in terms of system linearity and sensitivity in field conditions. The BTEX-GC system demonstrated the capacity to detect BTEX at levels as high as 500 ppb with a linear calibration range of 0–100 ppb. A detection limit lower than 1 ppb was found for all BTEX compounds with a sampling volume of 1 L. No significant drift in the instrument was observed. A time-varying calibration technique was established that requires minimal equipment for field operations and optimizes the sampling procedure for field measurements. With an analysis time of less than 15 min, the compact GC-PID is ideal for field deployment of background and polluted atmospheres for near-real time measurements of BTEX. The results highlight the application of the compact and easily deployable GC-PID for community monitoring and screening of air toxics.

Sustainability is a key point in the design and management of mobility and traffic systems, which affects also the development of traffic control strategies for freeway networks. According to sustainability-related concepts, freeway traffic controllers should be devised not only for maximally exploiting the road capacity and decreasing vehicles travel delays, but also for reducing pollutant emissions, fuel consumptions, accidents, noise, and so on. This paper analyses the state of the art of freeway traffic control strategies characterised by sustainability-related objectives, in particular referred to traffic emissions and traffic safety, by providing a classification of the research papers in this area. The final part of the paper highlights the main research challenges for modelling and control techniques brought by the introduction of emerging information and communication technologies, that are becoming more and more widespread and are transforming the concept of vehicles in intelligent and connected agents, able to measure the traffic state and to implement specific traffic control policies. [ABSTRACT FROM AUTHOR]

Abstract Background Previous work reported increased rates of acute cardiovascular hospitalizations associated with increased PM 2.5 concentrations in the previous few days across urban centers in New York State from 2005 to 2016. These relative rates were higher after air quality policies and economic changes resulted in decreased PM 2.5 concentrations and changes in PM composition (e.g. increased secondary organic carbon), compared to before and during these changes. Changes in PM composition and sources may explain this difference. Objectives To estimate the rate of acute cardiovascular hospitalizations associated with increases in source specific PM 2.5 concentrations. Methods Using source apportioned PM 2.5 concentrations at the same NYS urban sites, a time-stratified case-crossover design, and conditional logistic regression models adjusting for ambient temperature and relative humidity, we estimated the rate of these acute cardiovascular hospitalizations associated with increases in mean source specific PM 2.5 concentrations in the previous 1, 4, and 7 days. Results Interquartile range (IQR) increases in spark-ignition emissions (GAS) concentrations were associated with increased excess rates of cardiac arrhythmia hospitalizations (2.3%; 95% CI = 0.4%, 4.2%; IQR = 2.56 μg/m3) and ischemic stroke hospitalizations (3.7%; 95% CI = 1.1%, 6.4%; 2. 73 μg/m3) over the next day. IQR increases in diesel (DIE) concentrations were associated with increased rates of congestive heart failure hospitalizations (0.7%; 95% CI = 0.2% 1.3%; 0.51 μg/m3) and ischemic heart disease hospitalizations (0.8%; 95% CI = 0.3%, 1.3%; 0.60 μg/m3) over the next day, as hypothesized. However, secondary sulfate PM 2.5 (SS) was not. Increased acute cardiovascular hospitalization rates were also associated with IQR increases in concentrations of road dust (RD), residual oil (RO), and secondary nitrate (SN) over the previous 1, 4, and 7 days, but not other sources. Conclusions These findings suggest a role of several sources of PM 2.5 in New York State (i.e. traffic emissions, non-traffic emissions such as brake and tire wear, residual oil, and nitrate that may also reflect traffic emissions) in the triggering of acute cardiovascular events. Highlights • Spark ignition & diesel PM associated with cardiovascular (CV) hospitalizations • Secondary nitrate, residual oil, road dust PM associated with CV hospitalizations • Associations observed over previous 1, 4, or 7 days • Suggest role of traffic & non-traffic emissions in triggering CV hospitalizations [ABSTRACT FROM AUTHOR]

Abstract In this study, weekly samples of ambient PM 0.25 (particulate matter with an aerodynamic diameter <0.25 μm) were collected in three contrasting locations, including central Los Angeles (USC), north Long Beach (NLB), and the Port of Long Beach (PRT), during June and July of 2017 to evaluate the chemical composition of ambient PM 0.25 and identify the sources that contribute to the oxidative potential of ambient PM 0.25 in these locations. Special focus was given in exploring the impact of emissions from the Ports of Los Angeles and Long Beach on the oxidative potential of ambient PM 0.25 measured across these sites. The oxidative potential of the collected samples was quantified by means of an in vitro cell-based alveolar macrophage (AM) assay. We used multiple linear regression (MLR) analysis to link individual measured species, used as source markers, to the oxidative potential of the ambient PM 0.25 across the monitoring sites. Results from the MLR analysis indicated that vehicular emissions and secondary organic aerosols (SOA) were the major contributors to the oxidative potential of ambient PM 0.25 across the three sites, with corresponding contributions of 40 ± 2% and 39 ± 3%, respectively. Emissions of PM 0.25 related to port activities, including emissions from ships, locomotives, and heavy-duty vehicles (HDVs) operating at the port, accounted for 16 ± 3% of the overall oxidative potential of the ambient PM 0.25 samples. The concentrations of the marker species at the three different sites suggested that the contributions of port-related emissions to the oxidative potential of PM 0.25 decreased from the port area to central Los Angeles, underscoring the greater impact of these emissions on the PM 0.25 toxicity in the communities near the Ports of Los Angeles and Long Beach, whereas we observed larger impact of SOA formation and vehicular emissions on the oxidative potential of ambient PM 0.25 in the receptor sites located further inland. Graphical abstract Unlabelled Image Highlights • PM 0.25 oxidative potential (OP) source contributions were evaluated. • MLR approach was used for source apportionment. • Major contributors to OP were traffic and SOA across all sites. • Contributions of port emissions were higher in the areas adjacent to the POLB. • Results indicated a 35% decrease in PM 0.25 mass concentrations from 2007 to 2017. [ABSTRACT FROM AUTHOR]

Abstract The main purpose of this study is to investigate NO 2 temporal distribution in two urban areas in Romania based on data measured by stationary monitoring stations during 2016 and 2017. Analysis of the measurement data show that NO 2 emissions from traffic type sources has a higher contribution to the urban pollution than industry type source. The analysis of emission trend in the two cities (for 2 years), shows an effective reduction in emissions from industrial type source but an increase in emissions from traffic type source with 12.88 to 28.45 %. [ABSTRACT FROM AUTHOR]

Abstract This paper studied the effects of different route guidance strategies on traffic emissions in intelligent transportation systems. Six route guidance strategies were applied in three different two-route scenarios based on the Nagel–Schreckenberg cellular automaton model. The real data of traffic emissions under different vehicle operational modes were used. The simulations of the traffic emissions, the emissions per vehicle, the emissions per unit flux, average travel time per vehicle and the relationship between the emissions per unit flux and the ratio of dynamic vehicles by using six route guidance strategies were obtained. Results showed that mean velocity route guidance strategy (MVS) and congestion coefficient route guidance strategy (CCS) were the best ones. They can effectively reduce the traffic emissions and improve the efficiency of traffic network. In terms of the total emissions, the value of emissions on the symmetrical two-route scenario with two exits was much lower than that on the one with one exit. Finally, we drew two conclusions based on the simulation results: (i) The route guidance strategy based on traffic data of the whole road is beneficial to reduce traffic emissions. (ii) The simpler and smoother road can lead to more reduction of traffic emissions and higher efficiency of traffic network. Highlights • This paper studies the effects of route guidance strategies on traffic emissions. • Based on the CA model, we compared the emissions of different route guidance strategies. • The route guidance strategy based on traffic data of the whole road is beneficial to reduce traffic emissions. • The simpler and smoother road can lead to more reduction of traffic emissions and higher efficiency of traffic network. [ABSTRACT FROM AUTHOR]

Highlights • Smart personal air quality monitoring system (SPAMS). • Calibration of sensors in the laboratory and at field. • Personal exposure measurement at urban background site, urban road and beach road using sensors. • Spatial variation of air pollutants for personal exposure measurement. Abstract This paper deals with the development of a smart personal air quality mon- itoring system (SPAMS) for real time air quality monitoring. The commer- cially available low-cost CO, NO 2 , O 3 , PM, temperature (T) and humidity (RH) sensors along with the microcontroller and GPRS were integrated in SPAMS. The developed SPAMS was calibrated in the laboratory and vali- dated through field measurements. Field measurements were performed by walking on footpaths and travelling in the bus during various times of a day and different days in a week at selected locations in Chennai city, India. Re- sults indicated that the PM2.5 concentration was highest in busy traffic site (mean = 22.7 ± 8.45 μg/m3) and lowest in urban background (mean = 9.3 ± 5.75 μg/m3). Whereas, the CO concentration was lowest at beach road (mean = 0.26 ± 0.01 ppm). Not much variation was observed in the average concentrations of NO 2 and O 3. On the other hand, while travelling in buses, higher concentration of CO and NO 2 were observed during the morning and evening periods due to the peaking traffic flow. On contrary O 3 concentration was found to be highest during afternoon due to photochemical reactions. [ABSTRACT FROM AUTHOR]

Abstract Urban development, growing industrialization, and increasing demand for mobility have led to elevated levels of air pollution in many large cities in Latin America, where air quality standards and WHO guidelines are frequently exceeded. The conurbation of the metropolitan area of La Paz/El Alto is one of the fastest growing urban settlements in South America with the particularity of being located in a very complex terrain at a high altitude. As many large cities or metropolitan areas, the metropolitan area of La Paz/El Alto and the Altiplano region are facing air quality deterioration. Long-term measurement data of the equivalent black carbon (eBC) mass concentrations and particle number size distributions (PNSD) from the Global Atmosphere Watch Observatory Chacaltaya (CHC; 5240 m a.s.l., above sea level) indicated a systematic transport of particle matter from the metropolitan area of La Paz/El Alto to this high altitude station and subsequently to the lower free troposphere. To better understand the sources and the transport mechanisms, we conducted eBC and PNSDs measurements during an intensive campaign at two locations in the urban area of La Paz/El Alto from September to November 2012. While the airport of El Alto site (4040 m a.s.l.) can be seen as representative of the urban and Altiplano background, the road site located in Central La Paz (3590 m a.s.l.) is representative for heavy traffic-dominated conditions. Peaks of eBC mass concentrations up to 5 μg m−3 were observed at the El Alto background site in the early morning and evening, while minimum values were detected in the early afternoon, mainly due to thermal convection and change of the planetary boundary layer height. The traffic-related eBC mass concentrations at the road site reached maximum values of 10–20 μg m−3. A complete traffic ban on the specific Bolivian Day of Census (November 21, 2012) led to a decrease of eBC below 1 μg m−3 at the road site for the entire day. Compared to the day before and after, particle number concentrations decreased by a factor between 5 and 25 over the particle size range from 10 to 800 nm, while the submicrometer particle mass concentration dropped by approximately 80%. These results indicate that traffic is the dominating source of BC and particulate air pollution in the metropolitan area of La Paz/El Alto. In general, the diurnal cycle of eBC mass concentration at the Chacaltaya observatory is anti-correlated to the observations at the El Alto background site. This pattern indicates that the traffic-related particulate matter, including BC, is transported to higher altitudes with the developing of the boundary layer during daytime. The metropolitan area of La Paz/El Alto seems to be a significant source for BC of the regional lower free troposphere. From there, BC can be transported over long distances and exert impact on climate and composition of remote southern hemisphere. Highlights • The nation-wide traffic ban during the national Day of Census in Bolivia. • Traffic counts for 80% of the total particle mass concentration. • Diurnal cycle of BC at the Alti Plano with noon minimum due to thermal convection. • BC at Chacaltaya shows the opposite behavior with a maximum at noon. • Indication of transport of traffic-related BC into the lower free troposphere. [ABSTRACT FROM AUTHOR]

The spatial distribution of potentially toxic elements in land near a heavily loaded highway in the West Attica region (Greece) is discussed. This study aimed to investigate the extent of soil contamination with trace elements on land in the margin of highways. The concentration of thirty-five elements in topsoil, car ash, and road sediment bulk samples was determined. Statistical and spatial analysis was applied for evaluating the geochemical dispersion of the examined elements. Geo-accumulation index was estimated. Elements content were compared to criteria and screening values established by the literature. The concentration of As, Cu, Mn, Ni, P, Pb, and Zn in topsoil appeared to be influenced by traffic emissions. It is found that engine exhaust, tyre wear, and brake lining contribute to the As, Ba, Cu, Mn, Ni, P, Pb, and Zn content recorded in topsoil in the margin of highways. The bulk sample of road sediment is moderately contaminated to extremely contaminated with Cu, Mn, Ni, P, Pb, and Zn. Comparison of elements content in topsoil with screening values revealed that Al, As, B, Ba, Cd, Co, Cr, Cu, Fe, Mn, Ni, Sb, V, and Zn pose a potential risk for plants.

Air pollution caused by PM 2.5 is a significant public health concern, with vehicle emissions being a major contributor. Previous research focused on assessing personal exposure in local traffic microenvironments, but this approach is resource-intensive and challenging to implement city-wide. Traditional methods of assessing population exposure have limitations in reflecting population mobility and spatiotemporal heterogeneity. This study presents a method for obtaining high-resolution spatiotemporal distributions of the population and their exposure to traffic-related PM 2.5 in Guangzhou using multisource data. The results indicate that the simulated population distribution was consistent with census data and allow identification of population density hotspots. Meanwhile, this study also examines 24-hour population changes for dynamic high-resolution assessment of population exposure. The findings show that low-income individuals and rural-urban migrants residing in urban villages have a mean population exposure exceeding 5.18 μg/min, surpassing the PM 2.5 exposure of commuters in enclosed cabins. This emphasizes the urgent need to mitigate the negative impacts of traffic-related PM 2.5 on public health. [ABSTRACT FROM AUTHOR]

Transportation emissions significantly affect human health, air quality, and climate in urban areas. This study conducted experiments in an urban tunnel in Taipei, Taiwan, to characterize vehicle emissions under real driving conditions, providing emission factors of PM 2.5 , eBC, CO, and CO 2. By applying multiple linear regression, it derives individual emission factors for heavy-duty vehicles (HDVs), light-duty vehicles (LDVs), and motorcycles (MCs). Additionally, the oxidative potential using dithiothreitol assay (OP DTT) was established to understand PM 2.5 toxicity. Results showed HDVs dominated PM 2.5 and eBC concentrations, while LDVs and MCs influenced CO and CO 2 levels. The CO emission factor for transportation inside the tunnel was found to be higher than those in previous studies, likely owing to the increased fraction of MCs, which generally emit higher CO levels. Among the three vehicle types, HDVs exhibited the highest PM 2.5 and eBC emission factors, while CO and CO 2 levels were relatively higher for LDVs and MCs. The OP DTTm demonstrated that fresh traffic emissions were less toxic than aged aerosols, but higher OP DTTv indicated the impact on human health cannot be ignored. This study updates emission factors for various vehicle types, aiding in accurate assessment of transportation emissions' effects on air quality and human health, and providing a guideline for formulating mitigation strategies. [Display omitted] • Evaluated emission factors for heavy-duty, light-duty vehicles and motorcycles. • Quantify potential toxicity of fleet emissions using oxidative potential. • Motorcycles could contribute significantly to elevated CO emissions in urban areas. • Fresh traffic emissions show less toxicity than aged aerosols but still pose risks. • Updated emission factors guide mitigation strategies for air quality and health. [ABSTRACT FROM AUTHOR]

[Display omitted] • PM 2.5 concentrations of heavy-duty diesel trucks were simulated at high resolution. • The health and economic benefits of the emission control scenarios were analyzed. • Agglomeration effects and emission hotspots of PM 2.5 concentrations were mined. • Updating the emission standard to China 6 would avoid 611 premature deaths. PM 2.5 emissions from heavy-duty diesel trucks (HDDTs) have a significant impact on air quality, human health, and climate change, and seriously threaten the UN Sustainable Development Goals. Globally, a series of emission control measures have been implemented to reduce pollution emissions from HDDTs. Current studies assessing the impact of these measures on air quality and human health have mainly used coarse-grained emission data as input to dispersion model, resulting in the inability to capture the spatiotemporal variability of pollutant concentrations and tending to increase the uncertainty of health impact assessment results. In this study, we quantified the impact of pollution control policies for HDDTs in Beijing on PM 2.5 concentrations, human health, and economic losses by integrating policy scenario analysis, pollution dispersion simulation, public health impact and economic benefit assessment models, supported by high spatiotemporal resolution emission data from HDDTs. The results show that PM 2.5 concentrations from HDDTs exhibit significant spatial aggregation characteristics, with the intensity of aggregation at night being about twice as high as that during the day. The emission hotspots are mainly concentrated in the sixth, fifth and fourth rings and major highways. Compared to the "business as usual" scenario in 2018, the current policy of updating the fuel standard to China VI and the emission standard to China 6 can reduce PM 2.5 concentrations by 96.72%, thereby avoiding 612 premature deaths, which is equivalent to obtaining economic benefits of 1.65 billion CNY. This study further emphasizes the importance of high spatiotemporal resolution emission data during traffic dispersion modeling. The results can help improve the understanding of the effectiveness of emission reduction measures for HDDTs from a health benefit perspective. [ABSTRACT FROM AUTHOR]