The harm caused by tailpipe emissions from vehicles to air quality and the health of humans outside is increasingly well understood. It is generally accepted that it is a policy priority to remove high-emitting vehicles from the road and swap them for low-emission vehicles, active travel or public transport [1
]. What is less well understood is the exposure of the occupants of various transportation modes to such emissions or other sources of pollution. Aggregate time spent in vehicles is significant and can be measured in hours per day for certain commuters and professional drivers. There is a widespread misconception that people are well protected from pollution when inside vehicles, when in fact their exposure may increase in the cabin due to the ingress of polluted air, originating from proximate external sources (e.g., the vehicle in front), and/or the accumulation of air pollutants as described in the work cited below.
Human exposure to particulate matter is known to be associated with a number of adverse physical health outcomes, including coronary heart disease, stroke, and lung cancer as well as adverse mental health outcomes [2
]. As a result, in 2021, the WHO reduced the guideline exposure limits for particles substantially [5
]. Exposure to volatile organic compounds (VOCs) is similarly linked with adverse human health effects, including asthma, dermatitis and neurologic conditions [6
The academic literature contains a number of studies of air quality exposures on different transport modes, but only a few of them compare different transport modes. Two studies were published on commuter exposures on public transport in Hong Kong, one by Chan et al. in 2002 [8
] in relation to particulate matter and the other by Lau et al. in 2003 [9
] on aromatic VOCs. The first study covered eight different transport modes, including bus, tram, train, taxi, road transport and marine, some with air conditioning. The tram saw the highest exposures, and air-conditioned vehicles were seen as preferable to non-air-conditioned due to their relatively low air exchange rates. The second study tested eight different modes of a similar mix for benzene, toluene, ethylbenzene and m/p/o-xylene. Concentrations were highest on road transport, and lowest on marine. Air-conditioned vehicles typically saw higher levels of VOCs, which was suggested to be as a result of off-gassing from interior materials due to the vehicles being relatively new.
In 2006, Mathur [10
] studied vehicle cabin indoor air quality across cars, buses and trucks in Detroit, Michigan. Nitrogen oxide (NOx
), carbon monoxide (CO) and hydrocarbon (HC) were measured both at the tailpipe and inside the cabin at peak and off-peak times on a range of major roads and highways. Interior gas concentrations were highest on roads with retaining walls and tunnels, as the pollutants were “trapped.” When behind a vehicle pulling away from traffic lights, it was shown that more pollution was sucked into the vehicle’s cabin because of the higher emissions of the preceding vehicle.
Kadiyala et al. [11
] studied the variation in interior pollution between public transport buses on two different alternative fuels in Toledo, Ohio. CO2
, CO, sulphur dioxide (SO2
), nitric oxide (NO) and particulate matter were tested to look at daily, monthly and seasonal patterns across biodiesel and ultra-low-sulphur diesel fuels. CO2
was largely affected by the number of passengers, traffic levels and ventilation settings. Particulate matter was affected by traffic levels, ventilation settings and vehicle speed. Generally, pollutant concentrations were higher in the winter. Ultra-low-sulphur diesel buses typically had higher CO2
concentrations, while CO, NO and particulate matter concentrations were higher on biodiesel buses. This is somewhat paradoxical, and concentrations are explained as being caused by factors other than the vehicle’s own fuel, such as surrounding traffic.
Concentrations of benzene, toluene, ethylbenzene and m/p/o-xylene were further looked at on buses in Changsha, China, in 2011 by Chen et al. [12
]. Levels of these VOCs were seen to increase with in-vehicle temperature and relative humidity but fall with vehicle age and distance travelled. Furthermore, certain plastics, leather trims and air conditioning systems tended to lead to higher concentrations.
More recently, in 2015, Moreno et al. [13
] compared the pollutants inhaled while travelling by bus, tram, subway and on foot in Barcelona, Spain. On particle numbers, subway travel saw the lowest concentrations, while the diesel bus and walking in the city centre—especially at certain peak times—saw the highest concentrations. The greater the number of passengers on public transportation, the higher CO2
tends to be.
A similar study in Guadalajara, Mexico, was conducted in 2021 by Ochoa-Covarrubias et al. [14
]. This research looked at ozone and PM10
(particulate matter less than or equal to 10 μm in diameter) between cycling and buses. It concluded that less than 10% of travellers by bicycle or rapid transit were exposed to the worst air quality levels between the two modes, but that cyclists had the greatest exposures between 18:00 and 21:00 daily because of high levels of traffic.
A case study in London in 2021 by Bos et al. [15
] compared the exposures of taxi drivers to black carbon and nitrogen dioxide (NO2
) between electric and diesel vehicles. Measurements were taken simultaneously inside and outside to calculate the infiltration rate. The average black carbon and NO2
exposures were approximately double in the diesel taxi compared to the electric one, while the driver was working. Airtight vehicle design and the in-built filter were seen as keys for reducing black carbon exposure.
Vehicle interior air quality is largely unregulated in regions around the world, except that it could be argued that work vehicle environments fall within relevant health and safety at work regulations. More widely, the Comité Européen de Normalisation (CEN) Workshop 103 has worked to standardise particle infiltration and CO2
build-up in the cabin of passenger cars and light-duty commercial vehicles, catalysed by the work of Pham et al., in 2019 [16
], which has resulted in a formal methodology, CWA17934. As part of that process, Holland et al., in 2022, [17
] published results assessing the repeatability and reproducibility of the method that was proposed in CEN Workshop 103.
The aim of this research was to develop a better understanding of pollution exposures comparatively across transport modes, to inform policymakers, researchers, operators and the wider public, and with a view to setting priorities under ‘Net Zero’ greenhouse gases. A further objective was to begin to develop an evidence base to inform individual journey mode choice.
The focus of this research was on particulate matter and VOCs. Particles measured included ultrafines (often characterised as PM0.1; particles less than or equal to 0.1 μm in diameter), and VOCs were analysed into their component species using two-dimensional gas chromatography and time-of-flight mass spectrometry equipment capable of detection at the parts-per-trillion level. Therefore, a much wider range of pollutants was tested than in standard air quality monitoring. The risk is that ultrafine particles and certain VOCs are associated with health effects ranging from respiratory disease to cancer, while high CO2 concentrations can impair cognition. Measuring ultrafine particles and speciated VOCs will help characterise pollutants with currently little researched and that are poorly understood. Vehicles can come under health and safety regulations at work, and the use of certain materials is restricted in manufacture under REACH (Registration, Evaluation, Authorisation and Restriction of Chemicals in the European Union), but most of the measured pollutants are currently unregulated, despite their known health risks.
The study was based on a variety of routes, starting at London Paddington and ending in Oxford City Centre. The modes of transport that were studied included diesel and electric/diesel hybrid trains, the London Underground, diesel and electric buses, and old and new cars, including a battery electric vehicle. As a baseline and reference, exposures of pedestrians and cyclists were also measured for relevant journey fragments in Oxford.
2. Materials and Methods
2.1. Routes and Transport
Testing was carried out on several transportation types, as shown in Table 1
. These were selected to cover a range of public, private and active types of transportation that are used in current practise between London and central Oxford, as shown in Figure A1
in Appendix A
. Journeys are typically made up of long-distance and local elements, e.g., a train to Oxford station followed by a local bus. Various combinations of private, public and active travel were chosen to reflect common combinations observed in practice. As the aim of this study was to perform a first-pass test across as many different transport modes as possible, only one test was generally possible per route, except for active travel. Additional repeats would be required to assess the statistical significance of this study’s findings, however, they nonetheless highlight specific transport microenvironments, and where exposure reduction should be prioritised.
The method involved testing each mode sequentially over five days with consistent weather conditions in terms of temperature and precipitation, to allow good comparability. The test route was from London Paddington to Oxford City Centre. The ventilation settings on the cars were standardised, using automatic settings at 21 degrees Celsius, the fresh air setting and mid-fan speeds where applicable. Windows were closed in all cases.
The relevant journeys and modes were as follows. The colour-coding of each element corresponds to routes marked on the maps in Figure 1
, Figure 2
and Figure 3
2.1.1. Public Transport #1
Electric/diesel overground train from London Paddington to Oxford (electric from Paddington to Didcot Parkway, and diesel from Didcot Parkway to Oxford)—green—123 km;
Diesel bus from Oxford railway station to Oxford Queens Lane—blue—3 km.
2.1.2. Public Transport #2
London underground service from Paddington to London Waterloo—blue—6 km;
Diesel overground train from Waterloo to Basingstoke—blue—85 km;
Diesel overground train from Basingstoke to Oxford—blue—72 km;
Hybrid bus from Oxford railway station to Oxford Queens Lane—green—3 km.
2.1.3. Internal Combustion Engine Car—10+ Years Old
2.1.4. Diesel Coach Service
2.1.5. Battery Electric Car—Less Than One Year Old
The emissions analyser (known as PIMS, the technical details of which are described in Section 2.2
) was measuring constantly throughout the test days. Upon completion of the testing, the data was downloaded and segmented, based on GPS and time. Desorption tubes were used for each journey. Two tubes were used for each journey, one was used to collect a passive sample, and the other used a pump to collect the sample.
Further details on how each journey was conducted can be found in Appendix A
Testing was carried out between the 17 May and 25 May 2021.
2.2. Exposure Measurement
Exposure over each route was measured using measurement technology and techniques to analyse real cabin air quality, commonly called PIMS (pollution in-cabin measurement system), developed in-house by Emissions Analytics. This is a V2000 unit produced by the National Air Quality Testing Services of the United Kingdom [18
The PIMS analyser is a portable air quality system that measures particle number (PN) with a condensation particle counter (CPC), particle mass (PM) with laser scattering, carbon dioxide (CO2
) with nondispersive infrared (NDIR), and carbon monoxide (CO), nitrogen dioxide (NO2
) and volatile organic compounds (VOCs) all with metal oxide sensors. Ambient temperature, pressure and relative humidity conditions are also recorded, although not reported here. The technical specifications are shown in Figure 4
An accompanying carry case was used for testing whilst walking or cycling, as shown in Figure 5
. The sample was drawn from the top of the case at a flow rate of 100 millilitres per minute and exhausted through the bottom. Thermal desorption tubes were affixed to the outer shell of the case, sampling both actively at this rate and passively onto a separate tube. There was also a GPS unit in the case for measuring speed, location and altitude.
The second dimension of compound separation enabled by two-dimensional gas chromatography (GCxGC) enables the discovery and identification of a wide range of targeted and untargeted compounds, a capability that is rarely available. Only approximately 20% of this group of organic compounds can be easily analysed by one-dimensional gas chromatography (GC), according to Emissions Analytics’ estimates. To be able to separate everything else, the second dimension is required.
The test equipment used is a GCxGC-TOF-MS (2-D GC Time-of-Flight (TOF) Mass Spectrometer (MS)), which was purchased from Markes International [19
] and SepSolve Analytical [20
] of the UK, both of which publish application notes relevant to this type of testing.
This research tested multiple transport modes in real-world conditions, with the primary measurements being particulate number (PN), mass (PM) and VOCs. Secondary measurements for carbon dioxide (CO2) were used as a surrogate for air “freshness”. The VOCs were aggregated by typical functional groups, such as alcohols and alkanes. In addition, VOCs were separately grouped by the likely source. Compounds known to be prevalent in fuels, lubricants and combustion emissions from gasoline and diesels were attributed to transportation sources. Polymers and similar compounds were attributed to plastics, clothing and internal vehicle materials. Fragrances and associated hydrocarbons were attributed to personal care products. This method was relatively simplistic to provide an idea of sources rather than as a result of an exhaustive analysis of potential sources of each compound. It is analogous to studies in air source apportionment and provides a reasonable estimate of the relative contributions of different classes of sources.
PN was measured using a condensation particle counter (CPC) with a lower size cutoff of 15 nm. VOCs were captured on thermal desorption tubes and then measured on the GCxGC-TOF-MS instrument in order to perform a nontargeted analysis in the C2 to C44 range. The principal compounds identified were quantified using external standards. An internal standard of toluene-D8 was used across all the tests to ensure comparability.
The estimated inhalation rates of air are based on a range of sources [21
2.3. Compound Separation, Indentification and Source Appointment
Compounds with similar chemical and physical properties elute in clusters in a GCxGC analysis. This means that identifying one component in the cluster can provide clues as to the identity of neighbouring peaks. Complex samples contain thousands of individual analytes; by using GCxGC, the number of identifiable peaks compared to a one-dimensional GC analysis increases exponentially. Detecting and identifying more peaks in a sample can provide valuable information about the individual components in a mixture that would otherwise be impossible with a single peak alone and can increase certainty.
Time-of-flight (TOF) is a mass analyser that uses an electric field to accelerate generated ions through the same electrical potential and then measures the time each ion takes to reach the detector. If the ions all have the same charge, their kinetic energies will be identical and, therefore, each ion’s velocity will depend only on its mass. This means that lighter ions reach the detector first, while heavier ions take longer.
Thermal desorption (TD) is a readily automated gas extraction technology based on standard gas chromatography and provides an efficient, high-sensitivity alternative to conventional solvent extraction. The process of thermal desorption involves the extraction of volatile or semi-volatile organic compounds from a sorbent or adsorbent material by heating the sample in a flow of inert gas. Pumped and diffusive monitoring are versatile sampling options for packed tubes, being compatible with both single- and multibed sorbents.
For each compound identified, desk research was used to determine the most likely source and potential health effects. Multiple sources were used for this, including, for example, PubMed, a free search engine accessing primarily the MEDLINE database of references and abstracts on life sciences and biomedical topics, maintained by the United States National Library of Medicine at the National Institutes of Health. These classifications are not definitive and, within the scope of the project, they could not be validated. Nevertheless, by comparing multiple sources, and cross-referencing against the known compounds in products such as deodorants and in other sources such as combustion emissions, it is possible to make these preliminary associations. ‘Personal care’ products are a collective term for primary odours originating from deodorants, shampoos and perfumes. The diesel components may arise from fuel evaporating or being emitted from exhausts as unburnt fuel. Lubricant and additive components may arise in a similar way. Compounds from plastics are most likely to arise from clothing, luggage and containers, together with the internal furnishings of the vehicle such as seats, carpets and plastic surfaces.
The health risks were also compared against the product manufacturer hazard codes as disclosed on the European Chemicals Agency’s online database.
2.4. Quality Control
Zero calibration of the V2000 monitors was undertaken on high-efficiency particulate-absorbing filtered air. The particle number CPC is inherently linear, and the spans of the remaining sensors were checked by the equipment manufacturer. The consistency of measurements on the GCxGC-TOF-MS was ensured by the use of a deuterated toluene internal standard.
Personal exposure to a wide range of air pollutants was tested in real-world conditions across nine different transport modes on journeys from London Paddington to Oxford City Centre. Exposures to particles and VOCs varied significantly between the modes. Walking and cycling between Oxford Station and Oxford City Centre were included as a reference, and these modes saw the lowest average VOC concentration exposures, low particle mass exposures and low ultrafine particle exposures per unit of time.
A material fraction ranging from 18% to 29% for particles of the exposures during train travel appears to arise while accessing the platform, waiting for the train, boarding and disembarking, as shown in Figure 8
and Figure 9
. The electric/diesel train from Paddington to Oxford saw the highest exposures to polycyclic aromatic hydrocarbons and other nitrogen-containing compounds, which are often carcinogens, as shown in Table 5
. On average, the electric/diesel train had good filtration of particles in the carriage, but relatively poor air freshness in terms of concentration of CO2
. In contrast, diesel trains had poor filtration but good air quality.
Both the London Underground and diesel trains from Waterloo saw the highest concentrations of PM2.5
particle mass, as shown in Figure 9
. This would be accounted for by larger particles, which in the case of the Underground may be due to metallic particles from rail abrasion, and possibly some fluff and higher concentrations of human debris than on other modes of transport.
The coach had the weakest performance overall, with the highest exposure to alkanes and aromatics, the second highest levels of ultrafine particles and poor air freshness. Furthermore, the journey duration was the longest, and so the total human impact was magnified.
The nine-year-old diesel internal combustion engine car saw low VOC and particle mass exposures, which are consistent with good filtration on this premium-segment vehicle; the filtration may be less effective on nonpremium cars. Ultrafine particulate exposures were higher. The battery electric vehicle, which was new but drawn from a lower vehicle segment, saw similar particle exposures but VOC exposures were significantly higher, suggesting fresh air was prioritised over cabin filtration of pollutants.
Across all the modes, the single biggest source of VOC exposure appears to be personal care products. The second most prevalent source is vehicle fuel and lubricants, which lead to the inhalation of hydrocarbon vapours, which have potentially serious health effects. VOCs from plastics, clothes and interior materials were prevalent, particularly on the electric/diesel train and coach journeys. For fine particles (PM2.5), there was also little correlation between particle mass and particle number, suggesting that size distributions of particles varied widely between journeys.
As a general observation, it should be noted that otherwise ‘low pollution’ journeys can be affected by short exposures to high concentrations, which was seen on some of the journey segments on foot, for example when passing a restaurant emitting cooking smells or a cigarette smoker, when changing trains, and when stationary in roadside ‘hotspots’.
The primary limitation of this study is that resources were deliberately spread across as many transport modes as possible to gain a first-pass understanding of the relative exposures. Nevertheless, there are more combinations of transport modes, as well as additional routes between London and Oxford, that could be tested. Whether the chosen modes and routes are representative of actual behaviour should be verified. Further, the results from the selected modes and routes would benefit from being reproduced. Additional work could also look at the variation in exposures with respect to time of day, day of the week and season, as well as quantify the health, safety and comfort effects of exposure to these pollutants while travelling.
Human exposures to pollution while travelling are often instinctively associated with emissions from road vehicles. An associational line of thought is that public transportation is automatically better for human health than private transportation. This initial piece of research comparing pollution exposures on different types of transportation—public, private and active—indicates a much more complex picture, in which exposures are governed less directly by the type of vehicle propulsion. Key findings include:
Exposure during train journeys is dominated by time spent waiting on the platform, boarding and alighting from the train, when the doors open at stops, and from emissions from other passengers (e.g., associated with personal care products);
The coach journey tested saw some of the highest exposures due to the relatively long length of the journey and the number of passengers in a relatively confined space; exposures we high during transfer to the coach, and emissions were drawn in from other vehicles on the road, probably due to a poor ventilation system;
Private cars typically afford a high level of protection to the driver and passengers, through a combination of better filtration on the ventilation system together will less time spent in public spaces (although filtration efficacy will vary between makes and models of vehicles);
Active travel, whether cycling or walking, saw generally low exposures, even in city centre areas. While the traveller may be exposed to occasional large pollution spikes from hotspots, these contrast with the relatively clean air they are exposed to for the majority of their journey.
The impact of this study should be to motivate and inform further research to develop an understanding of the casual links. From there, more effective mitigations can be considered in policy development and consumer behaviour. For example, it may be more about the design of the ventilation systems in vehicles and train stations that most affect in-cabin and in-station exposures, than the vehicles or fuel types themselves. This contrasts, of course, with the effect of the vehicle on the surrounding environment, which is greater the more the vehicle emits. A high-emitting vehicle with clean internal air is, of course, a possibility. However, at the system level, the cleaner the vehicles are, the lower the emissions of pollutants associated with our travel, and the less pollution there is available to enter inside the vehicles. With a deeper understanding, it may be possible to improve health outcomes amongst travellers more efficiently and cost-effectively using targeted interventions. Further, it should provide momentum to standardisation activities—especially at the United Nations Economic Commission for Europe (UNECE) and Comité Européen de Normalisation (CEN)—on measuring and comparing in-vehicle pollution levels.