Personal exposures to PM during short distance highway travel in India

Traffic is the largest contributor (37%) to urban air pollution in India. During commuting, passengers are significantly exposed to pollutants. We carried out a study on a National Highway (NH) in India to measure personal exposure to Particulate Matter (PM) in five travel modes. PM_2.5 concentrations showed the following trend: Bus > Car FA (fresh air mode of air condition) > Bus AC > Car > Car RC (re-circulation mode of air condition). Highest and lowest concentrations of PM₁₀ were observed in Bus (134 ± 47 µg m⁻³) and Car RC (20 ± 5 µg m⁻³), respectively. The exposures were highest at the rear seats during the Bus AC journeys. In Car FA, the contribution of PM₁ to total concentrations was dominant (61%). Travel modes explained highest variabilities in PM₁₀, PM_2.5 and PM₁ concentrations. In all travel modes, the highest particle counts were observed for PM_0.3–0.5. PM_>0.5–5.0 counts during Bus journeys were comparatively higher than remaining modes. Deposition doses of passengers were as high as 3.22 µg of PM₁₀ (in Bus), 0.66 µg of PM_2.5 (in Bus) and 0.06 µg of PM₁ (in Bus AC) during the ~1 h journey. Our study revealed that Car RC is the safest mode of travel, both in terms of personal exposures and PM depositions in respiratory system. The results from this study can be used to target efforts to reduce personal exposure of highway commuters.     

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.     

Preliminary investigation of PM1, PM2.5, PM10 and its metal elemental composition in tunnels at a subway station in Shanghai,China

In this study, real-time monitoring campaigns were conducted in two tunnels (Line A and Line B) at a subway station in Shanghai, including temperature, relative humidity, PM₁, PM_2.5 and PM₁₀, in order to understand the climate and PM characteristics in the transportation microenvironment. In addition, collected floor dust particles in the tunnel were analyzed by ICP for their metal elemental composition. Strong correlations occurred between all PM levels and meteorological parameters in the tunnel of Line A (with platform screen doors), in comparison with the weak correlations between such parameters in the tunnel of Line B (without platform screen doors). PM_2.5 and PM₁₀ between peak hours and off-peak hours for both lines presented significant differences (p < 0.05), respectively. Nevertheless, PM₁ showed a different pattern, with p > 0.05 for Line A and p < 0.05 for Line B, respectively. In addition, statistical results concluded that PM had an evident weekly variation for both lines. Friday was the highest day of all particulate matters in monitoring periods for both lines. Ratios of PM₁/PM₁₀ and PM_2.5/PM₁₀ were high when trains were out of service and low when trains were in service. Relative abundance of metal elements detected from floor dust particles proved that floor dust particles in tunnels might be a major source of airborne PM in the subway microenvironments, with Fe as the most abundant metal element, followed by Ca, Al, Mg, Mn, Zn, Cu, Cr, Ni, Pb and Hg.     

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.     

Integrated data-driven modeling to estimate PM2.5 pollution from heavy-duty truck transportation activity over metropolitan area

Based on the national emission inventory data from different countries, heavy-duty trucks are the highest on-road PM_2.5 emitters and their representation is estimated disproportionately using current modeling methods. This study expands current understanding of the impact of heavy-duty truck movement on the overall PM_2.5 pollution in urban areas through an integrated data-driven modeling methodology that could more closely represent the truck transportation activities. A detailed integrated modeling methodology is presented in the paper to estimate urban truck related PM_2.5 pollution by using a robust spatial regression-based truck activity model, the mobile source emission and Gaussian dispersion models. In this research, finely resolved spatial–temporal emissions were calculated using bottom-up approach, where hourly truck activity and detailed truck-class specific emissions rates are used as inputs. To validate the proposed methodology, the Cincinnati urban area was selected as a case study site and the proposed truck model was used with U.S. EPA’s MOVES and AERMOD models. The heavy-duty truck released PM_2.5 pollution is estimated using observed concentrations at the urban air quality monitoring stations. The monthly air quality trend estimated using our methodology matches very well with the observed trend at two different continuous monitoring stations with Spearman’s rank correlation coefficient of 0.885. Based on emission model results, it is found that 71 percent of the urban mobile-source PM_2.5 emissions are caused by trucks and also 21 percent of the urban overall ambient PM_2.5 concentrations can be attributed to trucks in Cincinnati urban area.     

Long-term behavior of particulate matters at urban roadside and background locations in Seoul,Korea

The concentrations of particulate matter, PM_2.5, PM₁₀, and TSP at an urban roadside and an urban background station are analyzed. Data collected over a 10 year period are analyzed. The concentrations of the particulates measured at the urban site are systematically larger than at the background station. The mean PM values at the former also exhibit a slight fall over the decade unlike those at the background station. Overall, the particulate matters at both locations are in an intermediate range of global level, e.g., approximately two times lower than those in other Asian regions but higher than in Europe.     

Characteristics of particulate matter (PM) concentrations influenced by piston wind and train door opening in the Shanghai subway system

More than 9 million passengers take Shanghai’s subway system every work day. The system’s air quality has caused widespread concern because of the potential harm to passengers’ health. We measured the particulate matter (PM) concentrations at three kinds of typical underground platform (side-type, island-type, and stacked-type platforms) and inside the trains in Shanghai’s metro during 7 days of measurements in April and July 2015. Our results demonstrated that the patterns of air quality variation and PM concentrations were similar at the side-type and island-type platforms. We also found that the PM concentrations were higher on the platforms than inside the train and that the PM concentrations in the subway system were positively correlated with those in the ambient air. Piston wind generated by vehicle motion pushes air from the tunnel to the platform, so platform PM concentrations increase when trains approach the platform. However, the piston wind effect varies greatly between locations on the platform. In general, the effect of the piston wind is weaker at the middle of the platform than at both ends. PM concentrations inside the train increase after the doors open, during which time dirty platform air floods into the compartments. PM_1.0 and PM_2.5 were significantly correlated both inside the train and on the platforms. PM_1.0 accounted for 71.9% of PM_2.5 inside the train, which is higher than the corresponding platform values. Based on these results, we propose some practical suggestions to minimize air pollution damage to passengers and staff from the subway system.     

Commuters’ exposure to PM2.5 and CO2 in metro carriages of Shanghai metro system

Air quality inside transportation carriages has become a public concern. A comprehensive measurement campaign was conducted to examine the commuters’ exposure to PM_2.5 (d_p ⩽ 2.5 μm) and CO₂ in Shanghai metro system under different conditions. The PM_2.5 and CO₂ concentrations inside all the measured metro lines were observed at 84 ± 42 μg/m³ and 1253.1 ± 449.1 ppm, respectively. The factors that determine the in-carriage PM_2.5 and CO₂ concentrations were quantitatively investigated. The metro in-carriage PM_2.5 concentrations were significantly affected by the ventilation systems, out-carriage PM_2.5 concentrations and the passenger numbers. The largest in-carriage PM_2.5 and CO₂ concentrations were observed at 132 μg/m³ and 1855.0 ppm inside the carriages equipped with the oldest ventilation systems. The average PM_2.5 and CO₂ concentrations increased by 24.14% and 9.93% as the metro was driven from underground to overground. The average in-carriage PM_2.5 concentrations increased by 17.19% and CO₂ concentration decreased by 16.97% as the metro was driven from urban to the suburban area. It was found that PM_2.5 concentration is proportional to the on-board passenger number at a ratio of 0.4 μg/m³·passenger. A mass-balance model was developed to estimate the in-carriage PM_2.5 concentration under different driving conditions.     

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.     

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.     

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.     

A novel approach for the modelling of air quality dynamics in underground railway stations

Indoor air quality in subterranean train stations is a concern in many places around the globe. However, due to the specificity of each case, numerous parameters of the problem remain unknown, such as the braking discs particle emission rate, the ventilation rate of the station or the complete particle size distribution of the emitted particles. In this study the problem of modelling PM₁₀ concentration evolution in relation with train traffic is hence addressed with a particle-mass conservation model which parameters are fitted using a genetic algorithm. The parameters of the model allow to reproduce the dynamics and amplitude of four field data sets from the French and Swedish underground contexts and comply with realistic bounds in terms of emissions, deposition and ventilation rate.     

Characteristics of air pollutants at near and far field regions of a national highway located at an industrial complex

This paper presents the characterization of air quality monitored at near field region (NFR) and far field region (FFR) of a national highway located at an industrial complex. The pollutants such as PM₁₀, SO₂ and NO₂ were monitored in two campaigns (11th September to 18th October 2012 and 18th January to 17th February 2013). The 24 h average PM₁₀ concentration at NFR and FFR were found to be 86.69 ± 18.56 μg/m³; 73.16 ± 16.21 μg/m³ and 89.44 ± 18.69 μg/m³; 81.91 ± 16.42 μg/m³, respectively during first and second campaign. In both the campaigns PM₁₀, SO₂ and NO₂ concentration at NFR was higher than FFR. The chemical characterization of PM₁₀ at NFR and FFR indicated the abundance of major elements such as Na (NFR = 30% and FFR = 32%), Ca (NFR = 12% and FFR = 14%) and ions namely NO₃⁻ (NFR = 71% and FFR = 68%) and NH₃⁺ (NFR = 15% and FFR = 19%). Further, at FFR, SO₄²⁻ and NO₃⁻ were found to be 18% and 35% higher than NFR indicating the conversions of SO₂ and NO₂ concentration into secondary particles. The measured SO₂ and NO₂ concentrations were 23 and 21% lower at FFR when compared to NFR confirms the secondary formation.The CALPUFF, EPA regulatory model was set up to understand the dynamics of air pollutants at the industrial complex. The predicted PM₁₀, SO₂ and NO₂ concentrations at NFR and FFR were found to be 32.31 ± 1.56 μg/m³ and 31.35 ± 1.27 μg/m³; 0.37 ± 0.21 μg/m³ and 0.06 ± 0.04 μg/m³; 12.83 ± 6.55 μg/m³ and 4.67 ± 2.77 μg/m³, respectively. The model showed moderate predictions for PM₁₀ (R² = 0.44–0.52), SO₂ (R² = 0.41–0.51) and NO₂ (R² = 0.45–0.61) concentrations.     

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.     

The impact of Low Emission Zones on particulate matter concentration and public health

A common policy for reducing particulate matter concentrations in the European Union is the introduction of Low Emission Zones (LEZs), which may only be entered by vehicles meeting predefined emission standards. This paper examines the effectiveness of LEZs for reducing PM₁₀ levels in urban areas in Germany and quantifies the associated health impacts from reduced air pollution within the zones. We employ a fixed effects panel data model for daily observations of PM₁₀ concentrations from 2000 to 2009 and control, inter alia, for local meteorological conditions and traffic volume. We apply the regression outputs to a concentration response function derived from the epidemiological literature to calculate associated health impacts of the introduction of LEZs in 25 German cities with 3.96 million inhabitants. Associated uncertainties are accounted for in Monte-Carlo simulations. It is found that the introduction of LEZs has significantly reduced inner city PM₁₀ levels. We estimate the total mean health impact from reduced air pollution in 2010 due to the introduction of stage 1 zones to be ∼760 million EUR in the 25 LEZ cities in the sample, whereas total mean health benefits are ∼2.4 billion EUR for the more stringent stage 2 zones when applied in the same cities.     

In-train air quality assessment of the railway transit system in Beijing: A note

This study examines the concentrations of air pollutants in passenger carriages on a number of lines of the Beijing railway transit system differentiating between services with and without air conditioning. In-train air quality monitoring found PM₁₀ concentrations are extremely high compared with other cities. Integrated factor assessment results show that the in-train air quality in the ground railway transit system where there is air conditioning is more acceptable than in the underground system.     

Effects of the 80 km/h and variable speed limits on air pollution in the metropolitan area of barcelona

In 2008 the regional government of Catalonia (Spain) reduced the maximum speed limit on several stretches of congested urban motorway in the Barcelona metropolitan area to 80 km/h, while in 2009 it introduced a variable speed system on other stretches of its metropolitan motorways. We use the differences-in-differences method, which enables a policy impact to be measured under specific conditions, to assess the impact of these policies on emissions of NO_x and PM₁₀. Empirical estimation indicate that reducing the speed limit to 80 km/h causes a 1.7–3.2% increase in NO_x and 5.3–5.9% in PM₁₀. By contrast, the variable speed policy reduced NO_x and PM₁₀ pollution by 7.7–17.1% and 14.5–17.3%. As such, a variable speed policy appears to be a more effective environmental policy than reducing the speed limit to a maximum of 80 km/h.     

A system based approach to develop hybrid model predicting extreme urban NOx and PM2.5 concentrations

One of the major drawbacks of conventional air quality models is their inability in accurately predicting extreme air pollutant concentrations. Hybrid modelling is one of the techniques that estimates/predicts the ‘entire range’ of the distribution of pollutant concentrations by combining the deterministic based models (capable in predicting average range) with suitable statistical (probability) distribution models (capable in predicting extreme range). This research paper describes system based approach in developing hybrid model to predict hourly averages as well as extreme percentile ranges of NO_x and PM_2.5 concentrations at two urban locations having complex traffic heterogeneity, highly variable tropical meteorology and different geographical characteristics. At one of the selected locations i.e. Delhi megacity, during winters, hybridization of AERMOD and Lognormal predicts NO_x and PM_2.5 concentrations satisfactorily with index of agreement ‘d’ values of 0.98–0.99, respectively; however, during summers, AERMOD-Log-logistic and AERMOD-Lognormal are best predicting NO_x and PM_2.5 concentrations with d values of 0.98–0.96, respectively. In another, i.e., Chennai, a coastal megacity, AERMOD-Lognormal predicts PM_2.5 concentrations satisfactorily with d values of 0.98 and 0.99 during winter and summer seasons, respectively. Further, hybrid model has also been used to evaluate regulatory compliance.     

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.     

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.