Air Pollution in Public Transport Microenvironments: A Global Scoping Review of Exposure, Methods, and Gaps

Exposure to traffic pollution has become an increasing concern to public health. A number of studies have demonstrated that the air people breathe in while in transportation is particularly unsafe due to the high concentrations of carbon monoxide (CO), suspended particles (PM10, PM2.5 and PM1) and ultrafine particles (UFPs). Some studies have suggested that peak exposures of approximately one hour- a typical time spent in a transport micro-environment- may have more damaging health effects than the 24- hour sampling times current standards apply to Despite the widespread interest in health effects from exposure to traffic pollutants, there is a distinct lack of research of this kind in New Zealand. The research presented in this thesis was designed to assess the effect of traffic emissions on personal exposure. More specifically, this project intended to examine how exposures differed on different modes of transport and also to investigate the extent to which transport micro-environments such as car parks, bus stops and metro stations contributed to personal exposure levels. This study is the first of its type in New Zealand, which simultaneously monitored CO, PM and UFP concentrations in the transport micro-environment. Vehicular traffic emissions were shown to be a significant source of air pollution in populated urban areas, especially in the transport microenvironment. This results of this study showed that the mode of transport is a significant determinant of personal exposure to pollutants. The information gathered indicated slightly different results for Christchurch and Auckland, possibly due to variations in background levels, traffic counts and meteorological conditions at the time of monitoring. Results from the research also showed that built transport microenvironments could experience extremely high levels of pollutant exposures. Although commuters spend a relatively short time in such environments, such short-term peak exposures could contribute significantly to adverse health effects. The results presented here have relevance for both public health and for policies aimed at reducing human exposures to traffic-related air pollution. It is imperative to incorporate policies which ensure that such built environments are as safe as possible in terms of keeping exposure levels at a minimum.

Exposure to Particulate Pollution in Transportation Microenvironments in Bogotá, Colombia

Relative contributions of ambient air and internal sources to multiple air pollutants in public transportation modes

An approach to predict population exposure to ambient air PM2.5 concentrations and its dependence on population activity for the megacity London

Fine particulate matter and carbon monoxide exposure concentrations in urban street transport microenvironments

Air pollution in cities is associated with adverse health conditions; however, the underlying reasons are poorly understood. Previous particulate matter (PM) imaging studies have mostly used 2D microscopic techniques. In this study, the 3D appearances of roadside and underground railway station airborne PMs and the elemental characteristics of underground station PM were examined. Roadside and underground station PMs were collected using a cyclone particulate matter collection system. The 3D structures of the PMs were examined using an Olympus LEXT OLS4100 confocal microscope. Elemental characteristics of underground railway station PM were evaluated with a Zeiss EVO 50 scanning electron microscope (SEM) in backscattered electron (BSE) mode. Confocal microscopy of both PM sources indicated some particles can have sharp appearing edges. The longest axis length of roadside particles had a median (range) of 4.0 (1.3 to 6.6) μm and height of 1.4 (0.3 to 2.1) μm. SEM imaging of underground railway station PM was consistent in appearance and size with confocal imaging; BSE mode indicated compositions primarily of iron, calcium, and silicon. In conclusion, we observed that some roadside and underground railway station PMs can have sharp appearing surfaces from 3D confocal microscopy. SEM imaging of underground railway station PM was consistent with confocal microscopy and enabled elemental analysis.

Outdoor air pollution and lung cancer: recent epidemiologic evidence.

It is well established that vehicles powered by carbon-based fuels (e.g. gasoline, diesel) have a negative impact on air quality, especially in urban centers. Traditionally, air quality conformity studies analyze the macroenvironmental impact of transportation corridors, as they relate to regional air quality management concerns. Urban residents spend a considerable amount of outdoor time in transportation microenvironments as pedestrians, bicycle commuters, people waiting to use public transport, residents and workers situated along roadways, and commuters within vehicles. An emerging area of research has shown that human health impacts within transport microenvironments can be considerable, but have not been well-characterized. As urban public policies increasingly encourage the use of multi-mode commuting in dense urban centers, decisionmakers need reliable modeling tools to predict and mitigate the health consequences of vehicle-related emissions in transportation microenvironments. This project proposes unique integration of real-time multi-modal personal exposure monitoring with real-time detailed traffic monitoring in order to develop a computer model that links traffic characteristics, emissions, built environment configuration, mode of travel and health impacts. Existing emission and dispersion models do not take direct account of the dynamic nature of traffic and how emissions impact the health of the diversity of urban dwellers, bus commuters, pedestrians, and bicycle riders. Typical approaches use highly aggregated vehicle data and computed link speeds to assign an emission factor to the traffic, an assumed proportion of which is trucks. In addition, existing methods to assess environmental impacts tend to focus on the production source of the pollutant not the impacts, which require information on population activities and its proximity to vehicle flows. Personal exposure studies document significant air quality impacts within the transportation microenvironment but do not integrate dynamic traffic and emission analysis. Previous research argues for a comprehensive approach. From the perspective of mitigating transportation impacts, it is significant to understand: a) exposure to the pollution, which in turn requires assessment of where people are in relation to the source of the pollutant, and b) the relation between pollution and vehicle flows. This research will target highly polluted high exposure hotspots in Portland, e.g. high traffic flows running through a major trip destination area for shopping, entertainment, education, or health care highly dependent on mode of travel, traffic levels and type, and built environment characteristics. Our approach is to conduct field studies to measure personal exposure to particulate matter as a function of transportation mode through pre-determined routes in

In this study, the air quality of underground and overground railway stations was evaluated focusing onthe degree of influence of the outside air quality. The measured components were particulate matter (PM 10 ), carbon dioxide (CO 2 ), carbon monoxide (CO), nitrogen dioxide (NO 2 ), formaldehyde (HCHO), ozone (O 3 ), total airborne bacteria (TAB), total volatile organic carbon (TVOC), and Radon (Rn), which are included in the maintenance standards and recommended standards of the Indoor Air Quality Management Act. Also, the indoor/outdoor concentration ratios of PM10, NO 2 , and O 3 were calculated to estimate the influence of the outdoor air quality. Theconcentrations of PM10 HCHO, TVOC, NO 2 , and Rn in the underground stations were found to be higher than thosein the overground stations. These results indicate that the (present) generation of contaminants are caused by the indoor source of the underground station. The ozone concentration of the overground stations was higher than thatof the underground stations, which indicates that the outdoor ozone concentration influenced that of the overgroundstations directly. Thus, methods of improving the IAQ should take into consideration the types of contamination

Characterization of PM2.5 exposure concentration in transport microenvironments using portable monitors

Numerous studies have identified air pollutant as a primary risk factor for chronic obstructive pulmonary disease (COPD), particularly for fine particulate matter (PM2.5). However, the daily average levels of PM2.5 may not accurately reflect the actual exposure level due to the fluctuating air pollutant levels throughout the day and different human activity patterns. Few studies have comparatively analyzed the association between different exposure metrics of PM2.5 and COPD hospitalization. We aimed to explore the association between PM2.5 and COPD admission in Tianshui city using five different exposure metrics (a) Daily mean concentration (DailyPM2.5mean), (b) Daily hourly peak concentration (DailyPM2.5max), (c) Daily morning concentration (DailyPM2.5mor), (d) Daily evening concentration (DailyPM2.5eve) and (e) Daily excessive concentration hours (PM2.5DECH). The PM2.5DECH was defined as daily total concentration hours > 25 µg/m3. We collected daily records of COPD admission and hourly data on air pollutants and meteorological factors from Tianshui, China, 2018–2019. DailyPM2.5mean, DailyPM2.5max, DailyPM2.5mor, DailyPM2.5eve and PM2.5DECH were used as the exposure variables. A generalized additive model (GAM) based Poisson-distribution combined with a distributed lag non-linear model (DLNM) was applied to quantify the association between five different exposure metrics of PM2.5 and COPD admission. Stratified analyses were conducted to examine the effect modifications of gender, age, and season. The analysis revealed significant associations between five different exposure metrics of PM2.5 and COPD admission. PM2.5-related risks of COPD admission differed by five exposure metrics. An interquartile range (IQR) increment in DailyPM2.5mean at lag06, DailyPM2.5max at lag06, DailyPM2.5mor at lag04, DailyPM2.5eve at lag06 and PM2.5DECH at lag07 was associated with a 21.64%(95%CI: 7.46%, 36.68%), 12.84%(95%CI: 1.47%, 24.58%), 16.47%(95%CI: 0.96%, 33.01%), 31.54%(95%CI: 16.49%, 47.58%), and 30.37%(95%CI: 13.91%, 46.88%) increase in Tianshui’s COPD admissions, respectively. Associations of COPD admission with five different exposure metrics of PM2.5 appeared to be stronger in the cold season and among the females, while we found significantly higher effects in the population < 65 years old. The five metrics of PM2.5 exposure and COPD hospitalization show an exposure-response relationship that resembles a function curve that increases monotonically. This study added evidence for increased risk of admissions associated with exposure to the five metrics of ambient PM2.5 in Tianshui City, China, and DailyPM2.5eve or PM2.5DECH may be a significant risk factor for the increased risk of COPD admission, indicating that we should focus more on the relationship between air pollution and related diseases during the evening peak hours.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-10116-9.

Evaluating the application of multipollutant exposure metrics in air pollution health studies

Population-based short-term air pollution health studies often have limited spatiotemporally representative exposure data, leading to concerns of exposure measurement error. To compare the use of monitoring and modeled exposure metrics in time-series analyses of air pollution and cardiorespiratory emergency department (ED) visits. We obtained daily counts of ED visits for Atlanta, GA during 2009-2013. We leveraged daily ZIP code level concentration estimates for eight pollutants from nine exposure metrics. Metrics included central monitor (CM), monitor-based (inverse distance weighting, kriging), model-based [community multiscale air quality (CMAQ), land use regression (LUR)], and satellite-based measures. We used Poisson models to estimate air pollution health associations using the different exposure metrics. The approach involved: (1) assessing CM-based associations, (2) determining if non-CM metrics can reproduce CM-based associations, and (3) identifying potential value added of incorporating full spatiotemporal information provided by non-CM metrics. Using CM exposures, we observed associations between cardiovascular ED visits and carbon monoxide, nitrogen dioxide, fine particulate matter, elemental and organic carbon, and between respiratory ED visits and ozone. Non-CM metrics were largely able to reproduce CM-based associations, although some unexpected results using CMAQ- and LUR-based metrics reduced confidence in these data for some spatiotemporally-variable pollutants. Associations with nitrogen dioxide and sulfur dioxide were only detected, or were stronger, when using metrics that incorporate all available monitoring data (i.e., inverse distance weighting and kriging). The use of routinely-collected ambient monitoring data for exposure assignment in time-series studies of large metropolitan areas is a sound approach, particularly when data from multiple monitors are available. More sophisticated approaches derived from CMAQ, LUR, or satellites may add value when monitoring data are inadequate and if paired with thorough data characterization. These results are useful for interpretation of existing literature and for improving exposure assessment in future studies. This study compared and interpreted the use of monitoring and modeled exposure metrics in a daily time-series analysis of air pollution and cardiorespiratory emergency department visits. The results suggest that the use of routinely-collected ambient monitoring data in population-based short-term air pollution and health studies is a sound approach for exposure assignment in large metropolitan regions. CMAQ-, LUR-, and satellite-based metrics may allow for health effects estimation when monitoring data are sparse, if paired with thorough data characterization. These results are useful for interpretation of existing health effects literature and for improving exposure assessment in future air pollution epidemiology studies.

Particle-bound polycyclic aromatic hydrocarbon concentrations in transportation microenvironments

Numerous observational studies have assessed the association between ambient air pollution and chronic disease incidence, but there is no uniform approach to create an exposure metric that captures the variability in air pollution through time and determines the most relevant exposure window. The purpose of the present study was to assess ways of modeling exposure to air pollution in relation to incident hypertension. We simulated data on incident hypertension to assess the performance of six air pollution exposure metrics, using characteristics from the Black Women’s Health Study. Each metric made different assumptions about how to incorporate time trends in pollutant data, and the most relevant window of exposure. We use observed values for particulate matter ≤2.5 microns (PM2.5) for this cohort to create the six exposure metrics and fit Cox proportional hazards models to the simulated data using the six metrics. The optimal exposure metric depends on the underlying association between PM2.5 and disease, which is unknown. Metrics that incorporate exposure information from multiple years tend to be more robust and suffer from less bias. This study provides insight into factors that influence the metric used to quantifying exposure to PM2.5 and suggests the need for careful sensitivity analyses.