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.     

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.     

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.     

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.     

Assessment and prediction of the impact of road transport on ambient concentrations of particulate matter PM10

The main challenge facing the air quality management authorities in most cities is meeting the air quality limits and objectives in areas where road traffic is high. The difficulty and uncertainties associated with the estimation and prediction of the road traffic contribution to the overall air quality levels is the major contributing factor. In this paper, particulate matter (PM₁₀) data from 10 monitoring sites in London was investigated with a view to estimating and developing Artificial Neural Network models (ANN) for predicting the impact of the road traffic on the levels of PM₁₀ concentration in London. Twin studies in conjunction with bivariate polar plots were used to identify and estimate the contribution of road traffic and other sources of PM₁₀ at the monitoring sites. The road traffic was found to have contributed between 24% and 62% of the hourly average roadside PM₁₀ concentrations. The ANN models performed well in predicting the road contributions with their R-values ranging between 0.6 and 0.9, FAC2 between 0.6 and 0.95, and the normalised mean bias between 0.01 and 0.11. The hourly emission rates of the vehicles were found to be the most contributing input variables to the outputs of the ANN models followed by background PM₁₀, gaseous pollutants and meteorological variables respectively.     

Atmospheric impact of ship traffic in four Adriatic-Ionian port-cities: Comparison and harmonization of different approaches

Shipping is a growing transport sector representing a relevant share of atmospheric pollutant emissions at global scale. In the Mediterranean Sea, shipping affects air quality of coastal urban areas with potential hazardous effects on both human health and climate. The high number of different approaches for investigating this aspect limits the comparability of results. Furthermore, limited information regarding the inter-annual trends of shipping impacts is available. In this work, an approach integrating emission inventory, numerical modelling (WRF-CAMx modelling system), and experimental measurements at high and low temporal resolution is used to investigate air quality shipping impact in the Adriatic/Ionian area focusing on four port-cities: Brindisi and Venice (Italy), Patras (Greece), and Rijeka (Croatia). Results showed shipping emissions of particulate matter (PM) and NOx comparable to road traffic emissions at all port-cities, with larger contributions to local SO₂ emissions. Contributions to PM_2.5 ranged between 0.5% (Rijeka) and 7.4% (Brindisi), those to PM₁₀ were between 0.3% (Rijeka) and 5.8% (Brindisi). Contributions to particle number concentration (PNC) showed an impact 2–4 times larger with respect to that on mass concentrations. Shipping impact on gaseous pollutants are larger than those to PM. The contribution to total polycyclic aromatic hydrocarbon (PAHs) concentrations was 82% in Venice and 56% in Brindisi, with a different partition gas-particle because of different meteorological conditions. The inter-annual trends analysis showed the primary contribution to PM concentrations decreasing, due to the implementation of the European legislation on the use of low-sulphur content fuels. This effect was not present on other pollutants like PAHs.     

Estimating mortality impacts from vehicle emission reduction efforts: The Tune In and Tune Up program in the San Joaquin Valley

As a response to profoundly poor air quality and associated environmental justice concerns in the San Joaquin Valley region in California, the Tune In & Tune Up (TI&TU) program provides residents with free vehicle emissions testing and vouchers for smog repair. We used data on approximately 19,000 repaired TI&TU vehicles from 2012 to 2018, and several estimation techniques, to quantify a range of nitrogen oxides (NO_X) emissions prevented as a result of the program. We then calculated resulting mortality impacts from reduced exposure to fine particulate matter (PM_2.5) in the form of secondary nitrates. After applying a novel smog repair emissions abatement depreciation function, we find that six years of operation of the TI&TU program has reduced NO_X emissions by approximately 53–302 tons by the end of 2018. Using a concentration response function for ambient PM_2.5, we found that between 0.055 and 0.31 premature deaths have also been avoided. We present multiple methods for assessing public health impacts, which can be used as guidance for evaluating similar transportation-based emission reduction programs.     

Natural variability in exposure to fine particles and their trace elements during typical workdays in an urban area

Studies on the natural human exposures to fine particulate matter (PM_2.5) and their elements composition are practically non-existent in South America. In order to understand the natural exposure of the typical Brazilian population to PM_2.5 and their trace element composition, we measured PM_2.5 concentrations and collected mass on filters for nine continuous hours during a typical workday of volunteers. In addition, bus routes were performed at peak and non-peak periods, mimicking the routine activity of the population. Mean concentrations of PM_2.5 in the bus and car groups were similar while the fraction of BCe was higher for the bus group. For all routes, mean PM_2.5 concentrations were higher during peak than non-peak hours, with an average of 43.5 ± 33.1 μg m⁻³ and 14.3 ± 10.2 μg m⁻³, respectively. The trace elements S, K and Na originated mainly from vehicle emissions; Na was associated with the presence of biofuel in diesel. Toxic elements (Pb, Cr, Cu, Ni, Zn, Mn) were found at low levels as evident by the total hazard index that ranged from 2.15 × 10⁻⁰³ to 1.38 for volunteers. For all routes, the hazard index ranged from 2.25 × 10⁻⁰³ to 5.03. Average PM_2.5 respiratory deposition dose was estimated to be 0.60 μg/kg-hour for peak hours. Potential health damages to people during their movements and at workplaces close to the traffic were identified. Improvements in the design of the building to reduce the entrance of air pollutants as well as the use of filters in the buses could help to limit population exposure.     

Environmental assessment of electrification of road transport in Norway: Scenarios and impacts

The study develops scenarios regarding the introduction of electric vehicles to the passenger vehicle fleet of Norway to reach the 2020 Norwegian greenhouse gas reduction target and a more extreme target to limit global temperature increase to two degrees. A process-based life cycle assessment approach is integrated with a temporally variable inventory model to evaluate the environmental impacts of these scenarios. We find that greenhouse gases in the reference scenario increase by 10% in 2020 in comparison to 2012; while for the more intensive improvements in conventional vehicles, this increase is reduced to 2%. For electric vehicles deployment scenarios, although the fleet share will reduce the tailpipe greenhouse gas emissions by 8–26%, with the upper end representing the two-degree reduction target, emissions reductions over the entire life cycle are only 3–15%. Electric vehicles also reduce emissions of NO_x, SO₂ and particulates reducing acidification, smog formation and particulate formation impacts, however, with addition of large numbers of electric vehicles significant trade-offs in toxicity impacts are found.     

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.     

Atmospheric dispersion modeling near a roadway under calm meteorological conditions

Atmospheric pollutant dispersion near sources is typically simulated by Gaussian models because of their efficient compromise between reasonable accuracy and manageable computational time. However, the standard Gaussian dispersion formula applies downwind of a source under advective conditions with a well-defined wind direction and cannot calculate air pollutant concentrations under calm conditions with fluctuating wind direction and/or upwind of the emission source. Attempts have been made to address atmospheric dispersion under such conditions. This work evaluates the performance of standard and modified Gaussian plume models using measurements of NO₂, PM₁₀, PM_2.5, five inorganic ions and seven metals conducted near a freeway in Grenoble, France, during 11–27 September 2011. The formulation for calm conditions significantly improves model performance. However, it appears that atmospheric dispersion due to vehicle-induced turbulence is still underestimated. Furthermore, model performance is poor for particulate species unless road dust resuspension by traffic is explicitly taken into account.     

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.     

Eco-driver training within the City of Calgary’s municipal fleet: Monitoring the impact

This article highlights eco-driving as an available policy option to reduce climate altering GHG emissions. Recognizing the need to reduce the environmental impact of its fleet operations, the City of Calgary is a leader in developing programs and policies that aim to reduce GHG emissions and associated pollutants resulting from the use of fossil fuels. Among local action taken against climate change, the City sought to quantify CO₂ emissions reductions from their municipal fleet as a result of eco-driver training, with a specific focus on engine idling. Fifteen drivers from the Development & Building Approvals Business Unit had in-vehicle monitoring technology (CarChips®) installed into their vehicles as part of a three-phase research process. The results show that gasoline and hybrid vehicles decreased average idling between 4% and 10% per vehicle per day, leading to an average emissions decrease of 1.7 kg of CO₂ per vehicle per day.     

I can’t believe your attitude: a joint estimation of best worst attitudes and electric vehicle choice

The number of conventionally fuelled motor vehicles in use is increasing worldwide despite warnings about finite fossil fuel and the detrimental impacts of burning such fuels. While electric vehicles, the subject of much research, generate far less emissions and offer the potential for power from renewable sources, they are yet to significantly penetrate the market. Tangible barriers such as price and vehicle range still exist, but consumer attitudes also drive behaviour. This paper examines attributes in a framework relatively new to transportation and energy policy; best–worst scaling. This method is widely considered an improvement over traditional methods of eliciting attitudes and beliefs, where respondents select attitudes they find best or worst from a set of attitudinal statements. To avoid potential endogeneity bias, we jointly model attitudes and choice for the first time with best–worst data. It is found that energy crisis, air quality and climate change concerns influence behaviour with respect to vehicle range and that travel behaviour change and forms of government incentives are needed influences on behaviour with respect to vehicle emissions. It is argued that correctly modelling attitudes reduces the error term of the vehicle choice model and provides policy makers with an improved lens for assessing behaviour. Additionally, the methods described within can easily be adapted to other policy scenarios.     

Impacts of fuel quality on indoor environment onboard a ship: From policy to practice

Environmental considerations, concerning the negative impacts of ship exhaust gases and particles on ambient air quality, are behind the requirements of cleaner marine fuels currently applied in designated emission control areas (ECAs). We investigated the impact of a ship operating on two types of fuel on the indoor air quality onboard. Gaseous and particulate air pollutants were measured in the engine room and the accommodation sections on-board an icebreaker operating first on Heavy Fuel Oil (HFO, 1%-S), and later Marine Diesel Oil (MDO, 0.1%-S). Statistically significant decrease of SO₂, NOx, PM_2.5 and particle number concentration were observed when the ship was operating on MDO. Due to the higher content of alkylated PAHs in MDO compared to HFO, the concentration of PAHs increased during operation on MDO. The particulate PAHs classified as carcinogens, were similar to or lower in the MDO campaign. Chemical analysis of PM_2.5 revealed that the particles consisted mainly of organic carbon and sulfate, although the fraction of metals was quite large in particles from the engine room. Principal Component Analysis of all measured parameters showed a clear difference between HFO and MDO fuel on the indoor environmental quality on-board the ship. This empirical study poses a first example on how environmental policy-making impacts not only the primary target at a global level, but also brings unexpected localized benefits at workplace level. The study emphasizes the need of further investigations on the impact of new marine fuels and technologies on the indoor air environments on board.     

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.     

Is in-cabin exposure to carbon monoxide and fine particulate matter amplified by the vehicle’s self-pollution potential? Quantifying the rate of exhaust intrusion

In-cabin exposure has increased in recent years due to longer commute and/or prolonged times in cars. The intrusion of the vehicle’s own exhaust into the passenger’s compartment has been recognized as a process that amplifies in-cabin passenger exposure. Quantifying its contribution is hampered by uncertainties associated with its measurement method such as trace tests and the lack of data regarding certain critical physical parameters, particularly those pertaining to air exchange rate (AER) and particulate matter deposition rate (DR). In this study, we present a hybrid methodology combining field measurements with a single-zone mass balance to estimate these parameters as well as the source term that represents vehicle self-pollution. In- and out-vehicle carbon monoxide (CO) and fine particulate matter (PM_2.5) were monitored concurrently in test vehicles under idle and moving conditions using several common ventilation modes. In addition to defining a hybrid methodology to characterize the underlying physical parameters, this study found that vehicle self-pollution can account for approximately 15 and 30% of CO and PM_2.5 exposure experienced by vehicle occupants respectively. Vehicle self-exhaust intrusion may constitute a significant PM exposure route for vehicle-based occupations or commuters with prolonged time in vehicles.     

A valuation of the environmental performance of vehicles: an analysis and comparison of two methodologies

The European Clean Vehicle Directive was introduced in 2009 to create an obligation on public authorities to take into account the impact of energy consumption, carbon dioxide (CO₂) emissions and pollutant emissions into their purchasing decisions for road transport vehicles. This should stimulate the market for clean and energy-efficient vehicles and improve transport's impact on environment, climate change and energy use. Therefore the so-called ‘Operational Lifetime Cost’ of a vehicle is calculated, divided into the cost for energy consumption, CO₂ and pollutant (nitrous oxide, particulate matter, non-methane hydrocarbons) emissions. In Belgium, a different methodology has been developed to calculate the environmental impact of a vehicle, called ‘Ecoscore’, based on a well-to-wheel approach. More pollutants are included compared to the Clean Vehicle methodology, but also indirect emissions are taken into account. In this paper, both methodologies are compared and used to analyze the environmental performance of passenger cars with different fuel types and from different vehicle segments. Similar rankings between both methodologies are obtained; however, the large impact of energy use (and CO₂ emissions) in the Clean Vehicle methodology disadvantages compressed natural gas cars, as well as diesel cars equipped with particulate filters, compared to the Ecoscore methodology.     

Developments of CO2-emissions and costs for small electric and combustion engine vehicles in Germany

Electric vehicles have the potential to lower emissions in the mobility sector, but especially high costs might hinder their market development. This paper aims to access environmental and economic impacts and potentials by comparing CO₂-emissions and costs of small vehicles. Considering actual data it is analysed, if and under which conditions electric vehicles are financially competitive for private consumers and under which conditions emissions can be saved. For this, a multiple-stage approach is focusing on (1) emissions during production and operation, (2) private costs and (3) external costs of emissions. A model of total cost of ownership is applied for the analysis of private and external costs.Results show that emissions of electric vehicles exceed emissions of combustion engine vehicles in the production phase, but electric vehicles cause fewer emissions during operation. Total emissions can be saved by electric vehicles even with low annual driving distances (2500–5500 km/a today). Results highly depend on the form of electricity production.Today, private costs of electric vehicles exceed the costs of combustion engine vehicles. Due to cost decreases electric vehicles can gain financial advantages in the future. External costs are high, especially for combustion engine vehicles (up to 15% of private costs), but in none of the considered cases high enough to give electric vehicles a financial advantage today. This picture will change in the future.     

Indoor air quality and passenger thermal comfort in Beijing metro transfer stations

In this study, particulate matter was investigated as the primary pollutant in the air quality of Beijing Metro transfer stations, and passenger thermal comfort during the transfer process was evaluated by using the relative warmth index (RWI). Passenger thermal comfort level is not ideal in 87% of the measured space and is slightly hot overall, with an RWI range of 0.20–0.43. Although 20% of the measured space has lower values than ASHRAE’s cooling comfort class, the thermal comfort level of most measured space is good in winter morning rush hours, with an RWI range from −0.18 to 0.28. The particulate matter (PM) concentration is related not only to the season and spatial depth but also to the transfer design of the metro station. During the morning rush period, the concentration ranges difference of PM₁₀ and PM_2.5 in winter are 262.9 μg/m³ and 125.5 μg/m³, respectively, which are 1.43 and 1.46 times higher than those of in summer. There are significant differences in the PM concentration and RWI values between the island and lateral platforms of Beijing Metro transfer stations, and the design of the lateral platform is superior to that of the island platform. Another exploratory experiment is conducted to determine if the PM concentration has a potential effect on human metabolic rate. The data in this paper provide a valuable reference for further comfort research and environmental control in metro station, and the conclusions may guide the further underground space design of metro transfer stations.