Costs of air pollutants from shipping: a meta-regression analysis

ABSTRACT

This study estimated the external cost of air pollution from shipping by means of a meta-regression analysis, which has not been made before. Three pollutants, which were included in most of the primary studies, were considered: nitrogen oxides (NO_x), sulphur dioxides (SO₂) and particulate matters with a diameter of max 2.5 micrometres (PM_2.5). All primary studies included damages of health and a majority added impacts on agriculture and estimated the cost of air pollutants by transferring cost estimates from studies on costs of air emissions from transports in Europe. Different regression models and estimators were used and robust results were found of statistically significant emission elasticities of below one, i.e. total external costs increase by less than 1% when emissions increase by 1%. There was a small variation between the pollutants, with the highest elasticity for PM_2.5 and lowest for NO_x. Calculations of the marginal external cost of the pollutants showed the same pattern, with this cost being approximately six times higher for PM_2.5 than for the other pollutants. Common to all pollutants was that the marginal external cost decreases when emission increases. Another robust result was a significant increase in the cost of studies published in journals compared with other publication outlets. These findings point out some caution when transferring constant external unit cost of air pollutant from shipping, which is much applied in the literature, and the cost functions estimated in this study could thus provide a complementary transfer mechanism.     

Contrasting the direct use of data from traffic radars and video-cameras with traffic simulation in the estimation of road emissions and PM hotspot analysis

This study investigates the effect of traffic volume and speed data on the simulation of vehicle emissions and hotspot analysis. Data from a microwave radar and video cameras were first used directly for emission modelling. They were then used as input to a traffic simulation model whereby vehicle drive cycles were extracted to estimate emissions. To reach this objective, hourly traffic data were collected from three periods including morning peak (6–9 am), midday (11–2 pm), and afternoon peak (3–6 pm) on a weekday (June 23, 2016) along a high-volume corridor in Toronto, Canada. Traffic volumes were detected by a single radar and two video cameras operated by the Southern Ontario Centre for Atmospheric Aerosol Research. Traffic volume and composition derived from the radar had lower accuracy than the video camera data and the radar performance varied by lane exhibiting poorer performance in the remote lanes. Radar speeds collected at a single point on the corridor had higher variability than simulated traffic speeds, and average speeds were closer after model calibration. Traffic emissions of nitrogen oxides (NOx) and particulate matter (PM₁₀ and PM_2.5) were estimated using radar data as well as using simulated traffic based on various speed aggregation methods. Our results illustrate the range of emission estimates (NO_x: 4.0–27.0 g; PM₁₀: 0.3–4.8 g; PM_2.5: 0.2–1.3 g) for the corridor. The estimates based on radar speeds were at least three times lower than emissions derived from simulated vehicle trajectories. Finally, the PM₁₀ and PM_2.5 near-road concentrations derived from emissions based on simulated speeds were two or three times higher than concentrations based on emissions derived using radar data. Our findings are relevant for project-level emission inventories and PM hot-spot analysis; caution must be exercised when using raw radar data for emission modeling purposes.     

Reducing emissions at land border crossings through queue reduction and expedited security processing

Vehicle border crossings between Mexico and the United States generate significant amounts of air pollution, which can pose health threats to personnel at the ports of entry (POEs) as well as drivers, pedestrians, and local inhabitants. Although these health risks could be substantial, there is little previous work quantifying detailed emission profiles at POEs. Using the Mariposa POE in Nogales, Arizona as a case study, light-duty and heavy-duty vehicle emissions were analyzed with the objective of identifying effective emission reduction strategies such as inspection streamlining, physical infrastructure improvements, and fuel switching. Historical traffic information as well as field data were used to establish a simulation model of vehicle movement in VISSIM. Four simulation scenarios with varied congestion levels were considered to represent real-world seasonal changes in traffic volume. Four additional simulations captured varying levels of expedited processing procedures. The VISSIM output was analyzed using the EPA’s MOVES emission simulation software for conventional air pollutants. For the highest congestion scenario, which includes a 200% increase in vehicle volume, total emissions increase by around 460% for PM_2.5 and NO_x, and 540% for CO, SO₂, GHGs, and NMHC over uncongested conditions for a two-hour period. Expedited processing and queue reduction can reduce emissions in this highest congestion scenario by as much as 16% for PM_2.5, 18% for NO_x, 20% for NMHC, 7% for SO₂ and 15% for GHGs and CO. Other potential mitigation strategies examined include fleet upgrades, fuel switching, and fuel upgrades. Adoption of some or all of these changes would not only reduce emissions at the Mariposa POE, but would have air-quality benefits for nearby populations in both the US and Mexico. Fleet-level changes could have far-reaching improvements in air quality on both sides of the border.     

Estimating transboundary economic damages from climate change and air pollution for subnational incentives for green on-road freight

Subnational incentives to adopt zero emission vehicles (ZEVs) are critical for reducing the external economic damages posed by transportation to air quality and the climate. Few studies estimate these damages for on-road freight, especially at scales relevant for subnational policies requiring cross-border cooperation. Here, we assess the damages to US receptors from emissions of air pollutants (PM_2.5, NO_x, SO₂, NH₃), and greenhouse gases (CO₂, CH₄, N₂O) from medium and heavy duty freight trucking, and the benefits of ZEV adoption by census division in the Province of Ontario. We develop an integrated modelling framework connecting a travel demand model, a mobile emissions simulator, and a regression based marginal damages model of air pollutants and climate change. We estimate $1.9 billion (2010 USD) in annual cross-border damages, or $0.16/VKT, resulting from scaled up atmospheric emissions from a ‘typical day’ of medium and heavy duty truck traffic volume for Ontario in 2012. This implies approximately $8000 per truck per year in damages, which could inform an economic incentive for emission reduction. The provincial goal of 5% ZEV adoption would reduce GHG emissions in 2012 by 800 ktCO₂e, yielding $89 Million (2010 USD) in cross-border benefits annually, with air quality co-benefits of $83/tCO₂e. This result varies between −19% and 22% based on sensitivity analysis for travel and emissions models, though economic damages are likely the largest uncertainty source. Such advances in subnational scale integrated modeling of the environmental impacts of freight can offer insights into the sustainable design of clean freight policy and programs.     

Identifying contributions of on-road motor vehicles to urban air pollution using travel demand model data

Ambient concentrations of pollutants are correlated with emissions, but the contribution to ambient air quality of on-road mobile sources is not necessarily equal to their contribution to regional emissions. This is true for several reasons such as the distribution of other pollution sources and regional topology, as well as meteorology. In this paper, using a dataset from a travel demand model for the Sacramento metropolitan area for 2005, regional vehicle emissions are disaggregated into hourly, gridded emission inventories, and transportation-related concentrations are estimated using an atmospheric dispersion model. Contributions of on-road motor vehicles to urban air pollution are then identified at a regional scale. The contributions to ambient concentrations are slightly higher than emission fractions that transportation accounts for in the region, reflecting that relative to other major pollution sources, mobile sources tend to have a close proximity to air quality monitors in urban areas. The contribution results indicate that the impact of mobile sources on PM₁₀ is not negligible, and mobile sources have a significant influence on both NO_x and VOC pollution that subsequently results in secondary particulate matter and ozone formation.     

Preferences for autonomous and alternative fuel-powered heavy-duty trucks in Germany

Germany is by far the largest contributor of greenhouse gas emissions in the European Union but adopted its own climate action plan to achieve greenhouse gas neutrality by 2050. The country’s third-largest emitter of greenhouse gas emissions is the transportation sector. As of January 2019, 99.7% of heavy-duty trucks registered in Germany run on diesel while the share of alternative fuel-powered passenger cars increases steadily. Apart from rising emissions, the industry faces a growing shortage of qualified truck drivers. A solution to increasing emissions and the shortage of drivers are autonomous and alternative fuel-powered heavy-duty trucks. We employed a choice-based conjoint analysis with employees from freight companies in Germany to find out how they assess the main attributes of innovative trucks. Our results reveal that the maximum driving range is the most important attribute followed by the refueling/recharging time. Tank-to-wheel emissions, on the other hand, was ranked as the least relevant attribute. Moreover, we present customers’ preference shares for future heavy-duty trucks until 2035. According to our results, freight companies are generally open to switching from conventional to low emission and (conditionally-) automated heavy-duty trucks, however, a close collaboration between truck manufacturers, customers, infrastructure companies, and policymakers is essential to spur the penetration of autonomous and alternative fuel-powered heavy-duty trucks.     

Development of an emissions inventory model for mobile sources

Traffic represents one of the largest sources of primary air pollutants in urban areas. As a consequence, numerous abatement strategies are being pursued to decrease the ambient concentrations of a wide range of pollutants. A mutual characteristic of most of these strategies is a requirement for accurate data on both the quantity and spatial distribution of emissions to air in the form of an atmospheric emissions inventory database. In the case of traffic pollution, such an inventory must be compiled using activity statistics and emission factors for a wide range of vehicle types. The majority of inventories are compiled using ‘passive’ data from either surveys or transportation models and by their very nature tend to be out-of-date by the time they are compiled. Current trends are towards integrating urban traffic control systems and assessments of the environmental effects of motor vehicles. In this paper, a methodology for estimating emissions from mobile sources using real-time data is described. This methodology is used to calculate emissions of sulphur dioxide (SO₂), oxides of nitrogen (NO_x), carbon monoxide (CO), volatile organic compounds (VOC), particulate matter less than 10 μm aerodynamic diameter (PM₁₀), 1,3-butadiene (C₄H₆) and benzene (C₆H₆) at a test junction in Dublin. Traffic data, which are required on a street-by-street basis, is obtained from induction loops and closed circuit televisions (CCTV) as well as statistical data. The observed traffic data are compared to simulated data from a travel demand model. As a test case, an emissions inventory is compiled for a heavily trafficked signalized junction in an urban environment using the measured data. In order that the model may be validated, the predicted emissions are employed in a dispersion model along with local meteorological conditions and site geometry. The resultant pollutant concentrations are compared to average ambient kerbside conditions measured simultaneously with on-line air quality monitoring equipment.     

Estimating the environmental costs of port related emissions: The case of Kaohsiung

This study estimates the emission costs of ships and trucks in the Port of Kaohsiung, Taiwan, focusing mainly on particular matter and volatile organic compounds. By calculating annual ship and truck emissions we find that the major contributors are tankers, container ships and bulk ships and trucks. Using a bottom-up methodology, the combined environmental costs of ships and trucks are estimated to be over $123 million per year.     

Instantaneous emissions models set in GIS for the TRANSIMS outputs

A novel methodology that provides more detailed estimates of vehicular polluting emissions is offered, in order to contribute to the improvement and the precision of emission inventories of vehicle sources through the consideration of instantaneous speed changes or acceleration instead of average vehicular speeds. This paper presents the construction and application of an instantaneous emissions model designated hereunder as “Transims’s Snapshots-Based Emissions”, which is set on a Geographic Information System that incorporates instantaneous fuel consumption factors and fuel-based emission factors to attain highest resolution of both, spatial and temporal distribution of vehicular polluting emissions based on traffic simulation through cellular automata with TRANSIMS. This work was applied to the road network of the Mexico City Metropolitan Area as case study. The development of this powerful tool led to obtaining 86,400 maps of the spatial and temporal distribution of vehicular emissions per vehicle circulating on the road network, including the following pollutants: carbon monoxide and carbon dioxide, nitrogen oxides, total hydrocarbons, sulfur oxides, polycyclic aromatic hydrocarbons, black carbon, particles PM₁₀ and PM_2.5. The said maps allowed identification with highest level of detail, of the emissions and Hot-spots of fuel consumption. Also, the model permitted to obtain the emissions’ longitudinal profiles of a given vehicle along its route. This study shows that the integration method of the polynomial regression models represents an opportunity for each city to develop more easily and openly its own regional emissions models without requiring deeper programming knowledge.     

Long distance truck tracking from advanced point detectors using a selective weighted Bayesian model

Truck flow patterns are known to vary by season and time-of-day, and to have important implications for freight modeling, highway infrastructure design and operation, and energy and environmental impacts. However, such variations cannot be captured by current truck data sources such as surveys or point detectors. To facilitate development of detailed truck flow pattern data, this paper describes a new truck tracking algorithm that was developed to estimate path flows of trucks by adopting a linear data fusion method utilizing weigh-in-motion (WIM) and inductive loop point detectors. A Selective Weighted Bayesian Model (SWBM) was developed to match individual vehicles between two detector locations using truck physical attributes and inductive waveform signatures. Key feature variables were identified and weighted via Bayesian modeling to improve vehicle matching performance. Data for model development were collected from two WIM sites spanning 26 miles in California where only 11 percent of trucks observed at the downstream site traversed the whole corridor. The tracking model showed 81 percent of correct matching rate to the trucks declared as through trucks from the algorithm. This high accuracy showed that the tracking model is capable of not only correctly matching through vehicles but also successfully filtering out non-through vehicles on this relatively long distance corridor. In addition, the results showed that a Bayesian approach with full integration of two complementary detector data types could successfully track trucks over long distances by minimizing the impacts of measurement variations or errors from the detection systems employed in the tracking process. In a separate case study, the algorithm was implemented over an even longer 65-mile freeway section and demonstrated that the proposed algorithm is capable of providing valuable insights into truck travel patterns and industrial affiliation to yield a comprehensive truck activity data source.     

The diffusion of cleaner vehicles in CO2 emission trading designs

The accelerated diffusion of cleaner vehicles to reduce CO₂ emissions in transport can be explicitly integrated in emission trading designs by making use of cross-sectoral energy efficiency investment opportunities that are found in data on CO₂ emissions during the production and the use of cars and trucks. We therefore elaborate the introduction of tradable certificates that are allocated or grandfathered to manufacturers that provide vehicles (and other durable goods) that enable their customers to reduce their own CO₂ emissions. This certificate is an allowance for each tonne CO₂ avoided. Manufacturers can then sell these certificates on the emission market and use the revenues to lower the price of their cleanest vehicles. This mechanism should partially overcome the price difference with less efficient cars. In a simulation, we found that the introduction of the certificate in tradable permit systems can lead to very significant reductions of CO₂ emissions. The simulations indicate that CO₂ emissions resulting from the car fleet can be reduced by 25 to 38% over a period of 15 years (starting in 1999). For the truck fleet, the reduction potential is more limited but still very interesting.     

Health externalities of ship air pollution at port – Piraeus port case study

This paper is employing the well-known methodology of impact pathway approach to assess the external costs in human health from ship air pollution at port areas. The passenger port of Piraeus, Greece is the scenery of the study. Piraeus port is in the vicinity of the greater Athens metropolitan area where almost half of the country’s population lives. Hence, this port is the central hub of the Greek coastal passenger ship system which connects the islands of the Aegean Sea with land and is characterized by heavy ship traffic. The case study presented in this paper assesses the annual external cost in human health from air emissions produced by all passenger ships and cruise ships calling the port of Piraeus. Health cost from ships at port has been estimated at both local (Athens metropolitan area) and regional level (entire territory of Greece). Results show that higher costs occur at the local level. The dominant pollutants creating this cost are particulate matter (PM_2.5, and PM₁₀). Overall, the results indicate that the health impact of Piraeus’s passenger port emissions is not negligible; however the cost of PM₁₀ is considerably lower than the corresponded cost deriving from the land based industries of the Athens regional area for which comparison has been available.     

The role of light duty vehicles in future air pollution: a case study of Sacramento

Abstract

On-road light-duty vehicles (LDVs) play an important role in contributing to urban air pollution. Although vehicles are getting cleaner, regional growth in vehicle population and vehicle miles traveled would somewhat offset California's efforts in transportation pollution reduction. To better understand the role of LDVs in future air pollution, we conduct a case study for Sacramento, California, and investigate future trends in urban air pollution attributable to the light-duty fleet. Results indicate that ambient concentrations of CO, NO _x , and total organic gases (TOGs) caused by future light-duty fleets would dramatically decrease over coming years. The resulting concentrations in 2030 might be as low as approximately 20% of the 2005 concentrations. These reflect the improvements in vehicle/fuel technologies and standards in California. However, the future particulate matter (PM₁₀) pollution could be slightly worse than that caused by the 2005 fleet. This is a result of the growing fleet-average emission factors of particulates from 2005 to 2030. For purposes of future particulate control, more attention needs to be paid to LDVs, besides heavy-duty vehicles.     

Estimation of road transport related air pollution in Saint Petersburg using European and Russian calculation models

The paper analyzes Russian and European emission and dispersion models aimed at the estimation of road transport related air pollution on street and regional scale as exemplified with St. Petersburg, Russia. It demonstrates the results of model calculations of peak concentrations of main harmful substances (NО_X, CO and PM₁₀) along the St. Petersburg Ring Road at high traffic volume and adverse meteorological conditions (calm, temperature inversion) executed by means of a Russian street pollution model, and it evaluates the computed results against the measurements from monitoring stations. The paper also examines the ways of adaptation of the COPERT IV model – a software tool for calculation of air pollutant and greenhouse gas emissions from road transport on regional or country scale – to the inventory conditions of the Russian Federation, compares the COPERT IV numerical estimates with the national inventory data. It also reveals the obstacles and possibilities in the harmonization of the Russian and European approaches.     

Assessing air quality and health benefits of the Clean Truck Program in the Alameda corridor,CA

In this paper, vehicle microscopic simulation and emission models were combined with an air pollutant dispersion model and a health assessment tool to quantify some social costs resulting from urban freight transportation in the Alameda corridor that links the Ports of Los Angeles and Long Beach to downtown Los Angeles. Traffic on two busy freeways, the I-710 and the I-110, and some heavily trafficked arterial roads was analyzed to estimate the health impacts caused by drayage truck emissions of particulate matter (PM) for four different years: 2005, which serves as a baseline for various pollution inventories, as well as 2008, 2010 and 2012. These years correspond to deadlines for the Clean Truck Program (CTP), which was put in place to improve air quality in the Alameda corridor. Results show that the health costs from particulate matter (PM) emitted by drayage trucks exceeded 440 million dollars in 2005. However, these costs decreased by 36%, 90%, and 96% after accounting for the requirements of the 2008, 2010, and 2012 CTP deadlines. These results quantify the magnitude of the social costs generated by drayage trucks in the Alameda corridor, suggest that these costs justified replacing drayage trucks operating there, and indicate that the Clean Truck Program likely exceeded its target.     

Air quality impacts of electric vehicle adoption in Texas

Widespread adoption of plug-in electric vehicles (PEVs) may substantially reduce emissions of greenhouse gases while improving regional air quality and increasing energy security. However, outcomes depend heavily on the electricity generation process, power plant locations, and vehicle use decisions. This paper provides a clear methodology for predicting PEV emissions impacts by anticipating battery-charging decisions and power plant energy sources across Texas. Life-cycle impacts of vehicle production and use and Texans’ exposure to emissions are also computed and monetized. This study reveals to what extent PEVs are more environmentally friendly, for most pollutant species, than conventional passenger cars in Texas, after recognizing the emissions and energy impacts of battery provision and other manufacturing processes. Results indicate that PEVs on today’s grid can reduce GHGs, NO_x, PM₁₀, and CO in urban areas, but generate significantly higher emissions of SO₂ than existing light-duty vehicles. Use of coal for electricity production is a primary concern for PEV growth, but the energy security benefits of electrified vehicle-miles endure. As conventional vehicle emissions rates improve, it appears that power grids must follow suit (by improving emissions technologies and/or shifting toward cleaner generation sources) to compete on an emissions-monetized basis with conventional vehicles in many locations. Moreover, while PEV pollution impacts may shift to more remote (power plant) locations, dense urban populations remain most strongly affected by local power plant emissions in many Texas locations.     

Analyzing spatiotemporal traffic line source emissions based on massive didi online car-hailing service data

Nowadays, the massive car-hailing data has become a popular source for analyzing traffic operation and road congestion status, which unfortunately has seldom been extended to capture detailed on-road traffic emissions. This study aims to investigate the relationship between road traffic emissions and the related built environment factors, as well as land uses. The Computer Program to Calculate Emissions from Road Transport (COPERT) model from European Environment Agency (EEA) was introduced to estimate the 24-h NOx emission pattern of road segments with the parameters extracted from Didi massive trajectory data. Then, the temporal Fuzzy C-Means (FCM) Clustering was used to classify road segments based on the 24-h emission rates, while Geographical Detector and MORAN’s I were introduced to verify the impact of built environment on line source emissions and the similarity of emissions generated from the nearby road segments. As a result, the spatial autoregressive moving average (SARMA) regression model was incorporated to assess the impact of selected built environment factors on the road segment emission rate based on the probabilistic results from FCM. It was found that short road length, being close to city center, high density of bus stations, more ramps nearby and high proportion of residential or commercial land would substantially increase the emission rate. Finally, the 24-h atmospheric NO₂ concentrations were obtained from the environmental monitor stations, to calculate the time variational trend by comparing with the line source traffic emissions, which to some extent explains the contribution of on-road traffic to the overall atmospheric pollution. Result of this study could guide urban planning, so as to avoid transportation related built environment attributes which may contribute to serious atmospheric environment pollutions.     

The economy of alternative fuels when including the cost of air pollution

In this study, the costs involved in the use of petrol, diesel, natural gas, biogas, and methanol (produced from natural gas and biomass) in cars and heavy trucks are compared. The cost includes fuel cost, extra capital cost for vehicles using alternative fuels, and the environmental cost of VOC, NO_x, particulate and CO₂ emission based on actual 1996 and estimated 2015 emission factors. The costs have been calculated separately for rural, urban and city-centre traffic. A complete macroeconomic assessment of the effect of introducing alternative fuels is not, however, included in the study. The study shows that no alternative fuel can compete with petrol and diesel in rural traffic when the economic valuation of CO₂ emission is taken as current Swedish CO₂ taxes ($200/tonne C). In cities with a natural gas network, natural gas is the fuel with the lowest cost for both cars and heavy trucks, based on 1996 emission factors. Methanol from natural gas and biogas from waste products can also compete with diesel in urban traffic. With predicted improvements in technology and subsequent emission reductions, no alternative fuel can compete with petrol in any of the traffic situations studied by 2015, and only in city-centre traffic will alternative fuels be less costly than diesel in heavy vehicles. Of the biomass-based fuels studied, low-cost biogas from waste products is the most competitive one and is, already at current CO₂ taxes, the fuel with lowest cost for heavy trucks in urban traffic in areas where natural gas networks do not exist. To enable the more widespread use of biomass-based fuels, i.e. using feedstocks such as energy crops or logging residues that are available in larger amounts, the economic valuation of CO₂ emission has to be 2–2.5 times higher than current Swedish CO₂ tax level.     

Particulate matter concentrations and heavy metal contamination levels in the railway transport system of Sydney,Australia

Sampling campaign was conducted over six weeks to determine particulate matter (PM) concentrations from Sydney Trains airport line (T2) at both underground and ground levels using DustTrak. Dust samples were collected and analysed for 12 metals (Fe, Ca, Mn, Cr, Zn, Cu, Pb, Al, Co, Ni, Ba and Na) by atomic emission spectroscopy. Average underground PM₁₀ and PM_2.5 concentrations from inside the trains were 2.8 and 2.5 times greater than at ground level. Similarly, PM₁₀ and PM_2.5 concentrations on underground platforms were 2.7 and 2.5 times greater than ground level platforms. Average underground PM concentrations exceeded the national air quality standards for both PM₁₀ (50 µg/m³) and PM_2.5 (25 µg/m³). Correlation analysis showed a strong to moderate association between PM concentrations at ground level and background PM concentrations (r² from 0.952 to 0.500). The findings suggested that underground PM concentrations were less influenced by the ambient background than at ground level. The metal concentrations decreased in the order of Fe, Cr, Ca, Al, Na, Ba, Mn, Zn, Cu, Ni, Co and Pb. The pollution index (PI) and enrichment factor (EF) values were calculated to identify the levels and sources of contamination in the underground railway microenvironments. PM was remarkably rich in Fe with a mean concentration of 73.51 mg/g and EF of 61.31, followed by Ni and Cr. These results noticeably indicated a high level of metal contamination in the underground environments, with the principal contribution from track abrasion and wear processes.     

Sources and exposure risk of trace elements for traffic policemen in roadside locations

Samples of PM_2.5 and PM₁₀ at four types of roadside location (major road, secondary road, branch road, and expressway) in Tianjin were collected and analyzed in 2015. The average annual roadside PM_2.5 and PM₁₀ concentrations were higher than the national ambient air quality standard (NAAQS: GB3095-2012). The chromium (Cr), manganese (Mn), nickel (Ni), zinc (Zn), arsenic (As), and cadmium (Cd) concentrations in both PM_2.5 and PM₁₀ over four seasons displayed significant differences (p < 0.05). An enrichment factor (EF) analysis revealed that Cd, copper (Cu), Zn, As, Ni, and Pb in PM_2.5 and PM₁₀ mainly originated from anthropogenic sources. A factor analysis (FA) and correlation analysis (CA) revealed that vehicle emissions (exhaust and non-exhaust), soil dust, coal combustion, and industrial emissions were the main sources of roadside PM_2.5 and PM₁₀ in Tianjin. Both the total hazard quotients (total HQ) and the total carcinogenic risk (total CR) for selected elements in PM_2.5 and PM₁₀ were within acceptable limits. The HQ of Pb was higher than for other metals, and it should therefore be given special attention. The CR for traffic policemen was highest for Cr exposure (1.01 × 10⁻⁵ for PM_2.5 and 1.52 × 10⁻⁵ for PM₁₀), followed by As and Ni. A sensitivity analysis showed that the total contributions of the metal concentrations, exposure time (ET), and exposure frequency (EF) accounted for over 50% of the risk for Cr, As, and Ni, suggesting that these metals had the greatest impact on the uncertainty of health risk assessments.