Daily travel as an integral part of urban life brings people, the surrounding environment, and transportation systems into interactions. Through daily mobility, people may generate and are exposed to traffic-related pollution, such as air and noise pollution, as well as extreme temperatures. In this editorial, we synthesise the state of the arts in environmental exposure research, discuss the role of transportation in advancing this field toward dynamic exposure (i.e., exposure during travel), and offer directions for future research.
1. The complexity of environmental exposure during travel
Daily travel exposes people to environmental risks such as traffic-related air pollution, noise, and extreme heat, which increase the risks for various physical and mental illnesses (Kraus et al., Citation2015; Li et al., Citation2023; Singh et al., Citation2021). Comparative studies have shown that active travellers are more affected by these risks than car drivers due to lack of shade, proximity to pollution sources, or increased ventilation and pollution inhalation rates during physical activity (Gelb & Apparicio, Citation2021; Karner et al., Citation2015). Nevertheless, urban travel environments can also provide health and well-being benefits for travellers through exposure to green and blue spaces due to cooler air temperature, pollution mitigation, aesthetic quality, and space for physical activity and interactions (WHO, Citation2016). By encouraging the use of active travel modes, street-level green and blue spaces also contribute to traffic pollution reduction (Glazener & Khreis, Citation2019).
Exposure research involves complexity: environmental exposure while on the move is diverse and dynamic in space and time. First, it depends on people’s dynamic spatiotemporal presence and realised travel behaviour, including selected travel mode, route, time, and duration. Considering the dynamics of human presence is crucial for understanding actual exposure as most people rarely stay only at home to undertake their daily activities, therefore relying solely on home-based exposure can bias the results (Kim & Kwan, Citation2021). Second, exposure levels depend on environmental conditions, which constantly change, both spatially and temporally; assuming environmental conditions to stay static across travel modes, routes, and times may lead to erroneous conclusions (Dias & Tchepel, Citation2018). Furthermore, people are simultaneously exposed to multiple environmental influences in diverse compositions, which may result in non-linear health and well-being responses (Fancello et al., Citation2023; Zhang et al., Citation2023). Third, mode of travel, duration, and frequency of exposure should be considered as they greatly vary across groups and significantly affect health and well-being. While effects from short-term exposure may be transient, they are important from an urban liveability perspective and may lead to more permanent health effects in case of regularly repeated exposure situations (Chaix, Citation2018; Zhang et al., Citation2023). Fourth, exposure outcomes also depend on personal sensitivity and vulnerability to environmental risks as people perceive and respond to the same environmental influences differently (European Environment Agency, Citation2018). Coupling data on physical presence, activity, and sensitivity with the data on environmental conditions at a certain time in a certain location is demanding both from data availability and granularity, and computational perspectives. Comparative studies would benefit from shared research protocols and standardised methodological approaches, which also help to decrease uncertainties in data collection, processing, analysis, and interpretation (Kim et al., Citation2023; Poom et al., Citation2021).
Research on the role of daily travel in environmental exposure and related health and well-being outcomes is emerging but still limited to making systematic generalisations across travel modes, types of travel environments, socioeconomics, and geographic contexts (Poom et al., Citation2021). While active travellers – often low-income people – contribute the least to traffic emissions, they become more affected by traffic-related pollution than car users (Gelb & Apparicio, Citation2021; Singh et al., Citation2021). Disparities in environmental conditions exist across residential neighbourhoods, travel networks, and destinations; differences in travel needs, constraints, and realised patterns; and abilities to cope with emerging environmental risks (Brazil, Citation2022; Park & Kwan, Citation2017; Xia & Yeh, Citation2022). Daily travel can offer alleviation from environmental risks and improve beneficial exposure if travel networks and destinations provide better environmental conditions than residential neighbourhoods (Park & Kwan, Citation2017; Wang et al., Citation2021).
2. Environmental exposure during travel: a transdisciplinary research agenda
Environmental exposure during travel is at the intersection of multiple disciplines due to its cross-boundary subject matter (). It deals with the locations of both environmental risks and people who are exposed to the risks – a focus area of environmental science and geography. Such exposure and emissions often occur during daily travel; thus it naturally links to transportation. The impacts of exposure on health and well-being, as well as alleviation strategies, are matters of interest for public health and urban planning researchers.
Figure 1. Transdisciplinary framework for studying environmental exposure.
Traditionally, environmental exposure studies focused on static exposure at home locations and not during travel. Adding the dynamics to exposure research requires a deep understanding of the transportation systems and daily human activities. Transportation can and should be an integral part of this research area due to its uniqueness in data, methods, and substantial knowledge of human travel behaviour and traffic-related pollution. and the following subsections will elaborate on the potential use of transportation studies to advance environmental exposure research.
Table 1. Mapping the role of transportation in exposure research.
2.1. Mobility-driven environmental data
Transportation researchers can stimulate the supply of environmental data with higher resolution, duration, reliability, quality of data collection, data accessibility and methodology. As air quality and noise prediction models often rely on oversimplified transportation assumptions, transportation researchers can provide more realistic inputs, such as travel behavioural patterns and traffic assignment.
Travel behaviour theory and methods can be leveraged to incorporate human behaviour into environmental monitoring and assessing dynamic exposure. For example, researchers can measure traveller’s behaviour and perceptions of exposure as part of travel surveys and deploy mobile environmental sensors along with GPS tracking (Glasgow et al., Citation2019). Additionally, environmental sensors can be carried by participants or mounted on cars, public transit, and bicycles to enhance sensing coverage.
Spatial components in transportation datasets, such as GPS timestamps and locations, are often un(der)utilised. Taking advantage of GPS data and other high spatio-temporal data sources such as smart-card data, cellphone data, and on-road sensors could improve our understanding of the spatio-temporal patterns of activities and dynamic environmental exposure. This could also help us test planning scenarios for policy and practice (see Sections 2.2 and 2.3).
2.2. Actual and potential exposure
To date, most studies are disaggregated with small sample sizes and focuses on the actual exposure (i.e., exposure that accounts for travellers’ mode and route choices). Actual exposure can be measured in the form of activity space, which refers to the spatial area that contains a person’s frequently visited location (Horton & Reynolds, Citation1971). While actual exposure studies can be accurate in their estimation of exposure and associated health impacts for the studied population, they have limited generalizability and are therefore difficult to translate to policymaking and urban interventions.
The limitation of sample size and the reliance on hectic measurement of activity space signify the need for large-scale studies on potential exposure and population health outcomes. One way of measuring potential exposure is using space-time prisms. Grounded in time geography, space-time prisms represent the area that a person can visit within a time budget (Hägerstrand, Citation1970) and therefore is potentially exposed to its surrounding environment (i.e., potential exposure) (Wang et al., Citation2018). To some extent, some measures of activity space can be considered a subset of space-time prism, and therefore actual exposure is a subset of potential exposure in those cases. Another way to measure potential exposure is using scenario simulations where researchers design potential interventions such as identifying green travel routes away from traffic pollution to estimate travellers’ exposure level and health outcomes (Hankey et al., Citation2017; Willberg et al., Citation2023). We contend that potential exposure complements, not replaces, the use of actual exposure, and both have great value in providing a holistic understanding of the dynamic exposure and health impacts of environmental exposure.
2.3. From individual to population level
Most existing exposure research deals with individual exposure, which limits its ability to understand the aggregate health impacts of transportation and guide health-promoting planning policy. Scaling up from individual-level data to the population level is largely missing, yet it is a critical step towards addressing population health impacts of environmental exposure during daily travel. Transportation researchers could contribute to this area of research by integrating actual and potential exposure, as well as leveraging large-scale simulations such as agent-based or activity-based travel demand models to enable the studies of system-wide impacts of individual choices and behaviours (Chapizanis et al., Citation2021; Gurram et al., Citation2019; Shin & Bithell, Citation2023).
2.4. Health and well-being
While many studies have considered the disparities in health impacts of traffic-related exposure among travellers of different travel modes and socio-economic groups, most studies have neglected the spatial distribution of risks and treated the health impacts as homogenous within a city (Braun et al., Citation2023; Mueller et al., Citation2015; Tainio et al., Citation2021). Future research should consider the spatial patterns of environmental risks and benefits when studying the health impact of daily travel to inform equitable planning practice. This is especially important when cities are segregated and/or the investments are disproportionately in favour of advantageous neighbourhoods, leaving more risks in the disadvantaged communities (Anguelovski et al., Citation2022; Braun et al., Citation2021).
Daily mobility enhances travellers’ well-being through (1) the act of travelling itself [also known as the positive utility of travel, see Mokhtarian and Salomon (Citation2001)], which may provide a sense of novelty, freedom, happiness, and satisfaction, (2) the pleasures derived from activities during travel such as talking to a companion or listening to music, and (3) through the fulfilment of activities at destinations (De Vos et al., Citation2020; Le et al., Citation2020; Mokhtarian, Citation2005). While travel-based well-being has been well studied in recent years, it is unknown how actual and perceived exposure to the surrounding environment affects well-being, and how they might enhance or offset some of the benefits of daily travel mentioned above.
The cumulative effects of environmental exposure during daily travel can manifest as short-term or long-term health and well-being outcomes. For example, fine particulate matter (PM2.5) is linked to increased risks of neurological and mental illness (Cohen et al., Citation2017). Noise, traffic congestion, and heat stress have been found to increase stress, anxiety, and depressive symptoms (Lan et al., Citation2022; Roberts & Helbich, Citation2021). Conversely, exposure to beneficial factors such as green and blue space (e.g., lakes, rivers) reduces stress and anxiety (Lan et al., Citation2022). Life-course exposure (i.e., exposome) to traffic-related pollution has also received some initial attention, however, there is much room for development in future research to investigate the cumulative effects of dynamic exposure on health and well-being, especially within the exposome framework.
3. Emerging opportunities and challenges in dynamic exposure research from a transportation perspective
Several opportunities arise for dynamic exposure research, such as emerging technologies to measure and model exposure and health and well-being outcomes. For example, wearable sensors can be paired with GPS devices and/or smartphones to understand the context of exposure as well as health outcomes (Birenboim et al., Citation2021; Chaix, Citation2018; Kim et al., Citation2023). Computational methods are also improved substantially, with the use of machine learning and artificial intelligence to generate predictions of pollution with high accuracy (even in real-time), simulate scenarios for urban interventions (e.g., healthy, green active travel routes), or provide personalised mobility suggestions (Sanchez et al., Citation2023; Son et al., Citation2023).
However, challenges remain, most notably in the comparibility and generalizability of the results. Most studies are concentrated in a few cities in China, Europe, and the US (Gelb & Apparicio, Citation2021; Poom et al., Citation2021; Yoo & Roberts, Citation2022), usually for a single city and using different instruments, making it difficult to compare the results. Meanwhile, the spatial patterns of human activities and environmental risks and benefits are highly variable, leading to the lack of generalizability of the results.
Additionally, as new transportation technologies emerge, the choice set is ever-expanding, thus the modelling of traffic and human activities becomes more complicated. Recent technologies such as electric vehicles (EV), ride hailing, micromobility, and information and communication technologies (ICT; e.g., teleworking, online shopping, telehealth) have been found to alter travel behaviour – not always in a sustainable direction (Caldarola & Sorrell, Citation2022; Hamamoto, Citation2019; Langbroek et al., Citation2017; Le et al., Citation2022; Shah et al., Citation2022). For example, while it was thought that EV and ICT could reduce pollution, some initial evidence suggests that they might introduce rebound effects where travellers drive more due to the saving time and monetary costs of not paying for gas (in the case of EV), or not making a commute or shopping trip (in the case of ICT) (Hostettler Macias et al., Citation2022; Le et al., Citation2022). EVs also do not eliminate harmful traffic-related pollution: the friction from road dust, brakes, and tire wear could generate PM2.5 and 6PPD, and could be even more than ICE vehicles as EVs tend to be heavier (Harrison et al., Citation2021; Woo et al., Citation2022). Modelling traffic-related pollution and estimating exposure during travel thus become more complicated.
4. Concluding remarks
Transportation research could play a significant role in advancing knowledge in exposure and health, however, this role has not been fully taken advantage of. The transportation landscape is rapidly changing with emerging technologies and new travel options, which arguably alter the way people travel and the level of exposure. At the same time, it is facing external challenges including climate change, emerging pollutants, and growing inequality. With this combination of opportunities and challenges, transportation research becomes more important than ever in illuminating the health and equity impacts of daily travel.
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References
- Anguelovski, I., Connolly, J. J. T., Cole, H., Garcia-Lamarca, M., Triguero-Mas, M., Baró, F., … Minaya, J. M. (2022). Green gentrification in European and North American cities. Nature Communications, 13, 3816. https://doi.org/10.1038/s41467-022-31572-1
- Birenboim, A., Helbich, M., & Kwan, M.-P. (2021). Advances in portable sensing for urban environments: Understanding cities from a mobility perspective. Computers, Environment and Urban Systems, 88, 101650. https://doi.org/10.1016/j.compenvurbsys.2021.101650
- Braun, L. M., Le, H. T. K., Voulgaris, C. T., & Nethery, R. C. (2021). Healthy for whom? Equity in the spatial distribution of cycling risks in Los Angeles. Journal of Transport & Health, 23, 101227. https://doi.org/10.1016/j.jth.2021.101227
- Braun, L. M., Le, H. T. K., Turley Voulgaris, C., & Nethery, R. C. (2023). Who benefits from shifting metal-to-pedal? Equity in the health tradeoffs of cycling. Transportation Research Part D: Transport and Environment, 115, 103540. https://doi.org/10.1016/j.trd.2022.103540
- Brazil, N. (2022). Environmental inequality in the neighborhood networks of urban mobility in US cities. Proceedings of the National Academy of Sciences, 119(17), e2117776119. https://doi.org/10.1073/pnas.2117776119
- Caldarola, B., & Sorrell, S. (2022). Do teleworkers travel less? Evidence from the English national travel survey. Transportation Research Part A: Policy and Practice, 159, 282–303. https://doi.org/10.1016/j.tra.2022.03.026
- Chaix, B. (2018). Mobile sensing in environmental health and neighborhood research. Annual Review of Public Health, 39, 367–384. https://doi.org/10.1146/annurev-publhealth-040617-013731
- Chapizanis, D., Karakitsios, S., Gotti, A., & Sarigiannis, D. A. (2021). Assessing personal exposure using Agent Based Modelling informed by sensors technology. Environmental Research, 192, 110141. https://doi.org/10.1016/j.envres.2020.110141
- Cohen, A. J., Brauer, M., Burnett, R., Anderson, H. R., Frostad, J., Estep, K., Balakrishnan, K., Brunekreef, B., Dandona, L., Dandona, R., Feigin, V., Freedman, G., Hubbell, B., Jobling, A., Kan, H., Knibbs, L., Liu, Y., Martin, R., Morawska, L., … Forouzanfar, M. H. (2017). Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: An analysis of data from the Global Burden of Diseases Study 2015. The Lancet, 389(10082), 1907–1918. https://doi.org/10.1016/S0140-6736(17)30505-6
- De Vos, J., Singleton, P. A., & Dill, J. (2020). Travel, health and well-being: A focus on past studies, a special issue, and future research. Journal of Transport & Health, 19, 100973. https://doi.org/10.1016/j.jth.2020.100973
- Dias, D., & Tchepel, O. (2018). Spatial and temporal dynamics in air pollution exposure assessment. International Journal of Environmental Research and Public Health, 15(3), 558. https://doi.org/10.3390/ijerph15030558
- European Environment Agency. (2018). Unequal exposure and unequal impacts: Social vulnerability to air pollution, noise and extreme temperatures in Europe. Publications Office of the European Union. https://data.europa.eu/doi/10.2800324183
- Fancello, G., Vallée, J., Sueur, C., van Lenthe, F. J., Kestens, Y., Montanari, A., & Chaix, B. (2023). Micro urban spaces and mental well-being: Measuring the exposure to urban landscapes along daily mobility paths and their effects on momentary depressive symptomatology among older population. Environment International, 178, 108095. https://doi.org/10.1016/j.envint.2023.108095
- Gelb, J., & Apparicio, P. (2021). Cyclists’ exposure to atmospheric and noise pollution: A systematic literature review. Transport Reviews, 41(6), 742–765. https://doi.org/10.1080/01441647.2021.1895361
- Glasgow, T. E., Le, H. T. K., Scott Geller, E., Fan, Y., & Hankey, S. (2019). How transport modes, the built and natural environments, and activities influence mood: A GPS smartphone app study. Journal of Environmental Psychology, 66, 101345. https://doi.org/10.1016/j.jenvp.2019.101345
- Glazener, A., & Khreis, H. (2019). Transforming our cities: Best practices towards clean air and active transportation. Current Environmental Health Reports, 6(1), 22–37. https://doi.org/10.1007/s40572-019-0228-1
- Gurram, S., Stuart, A. L., & Pinjari, A. R. (2019). Agent-based modeling to estimate exposures to urban air pollution from transportation: Exposure disparities and impacts of high-resolution data. Computers, Environment and Urban Systems, 75, 22–34. https://doi.org/10.1016/j.compenvurbsys.2019.01.002
- Hägerstrand, T. (1970). What about people in Regional Science? Papers of the Regional Science Association, 24(1), 6–21. https://doi.org/10.1007/BF01936872
- Hamamoto, M. (2019). An empirical study on the behavior of hybrid-electric vehicle purchasers. Energy Policy, 125, 286–292. https://doi.org/10.1016/j.enpol.2018.10.042
- Hankey, S., Lindsey, G., & Marshall, J. D. (2017). Population-level exposure to particulate air pollution during active travel: Planning for low-exposure, health-promoting cities. Environmental Health Perspectives, 125(4), 527–534. https://doi.org/10.1289/EHP442
- Harrison, R. M., Allan, J., Carruthers, D., Heal, M. R., Lewis, A. C., Marner, B., Murrells, T., & Williams, A. (2021). Non-exhaust vehicle emissions of particulate matter and VOC from road traffic: A review. Atmospheric Environment, 262, 118592. https://doi.org/10.1016/j.atmosenv.2021.118592
- Horton, F. E., & Reynolds, D. R. (1971). Effects of urban spatial structure on individual behavior. Economic Geography, 47(1), 36–48. https://doi.org/10.2307/143224
- Hostettler Macias, L., Ravalet, E., & Rérat, P. (2022). Potential rebound effects of teleworking on residential and daily mobility. Geography Compass, 16(9), e12657. https://doi.org/10.1111/gec3.12657
- Karner, A., Hondula, D. M., & Vanos, J. K. (2015). Heat exposure during non-motorized travel: Implications for transportation policy under climate change. Journal of Transport & Health, 2(4), 451–459. https://doi.org/10.1016/j.jth.2015.10.001
- Kim, E.-K., Conrow, L., Röcke, C., Chaix, B., Weibel, R., & Perchoux, C. (2023). Advances and challenges in sensor-based research in mobility, health, and place. Health & Place, 79, 102972. https://doi.org/10.1016/j.healthplace.2023.102972
- Kim, J., & Kwan, M.-P. (2021). How neighborhood effect averaging might affect assessment of individual exposures to air pollution: A study of ozone exposures in Los Angeles. Annals of the American Association of Geographers, 111(1), 121–140. https://doi.org/10.1080/24694452.2020.1756208
- Kraus, U., Breitner, S., Hampel, R., Wolf, K., Cyrys, J., Geruschkat, U., Gu, J., Radon, K., Peters, A., & Schneider, A. (2015). Individual daytime noise exposure in different microenvironments. Environmental Research, 140, 479–487. https://doi.org/10.1016/j.envres.2015.05.006
- Lan, Y., Roberts, H., Kwan, M.-P., & Helbich, M. (2022). Daily space-time activities, multiple environmental exposures, and anxiety symptoms: A cross-sectional mobile phone-based sensing study. Science of The Total Environment, 834, 155276. https://doi.org/10.1016/j.scitotenv.2022.155276
- Langbroek, J. H. M., Franklin, J. P., & Susilo, Y. O. (2017). Electric vehicle users and their travel patterns in Greater Stockholm. Transportation Research Part D: Transport and Environment, 52, 98–111. https://doi.org/10.1016/j.trd.2017.02.015
- Le, H. T. K., Buehler, R., Fan, Y., & Hankey, S. (2020). Expanding the positive utility of travel through weeklong tracking: Within-person and multi-environment variability of ideal travel time. Journal of Transport Geography, 84, 102679. https://doi.org/10.1016/j.jtrangeo.2020.102679
- Le, H. T. K., Carrel, A. L., & Shah, H. (2022). Impacts of online shopping on travel demand: A systematic review. Transport Reviews, 42(3), 273–295. https://doi.org/10.1080/01441647.2021.1961917
- Li, R., Chester, M. V., Middel, A., Vanos, J. K., Hernandez-Cortes, D., Buo, I., & Hondula, D. M. (2023). Effectiveness of travel behavior and infrastructure change to mitigate heat exposure. Frontiers in Sustainable Cities, 5, 112938. https://doi.org/10.3389/frsc.2023.1129388
- Mokhtarian, P. L. (2005). Travel as a desired end, not just a means. Transportation Research Part A: Policy and Practice, 39(2–3), 93–96. https://doi.org/10.1016/j.tra.2004.09.005
- Mokhtarian, P. L., & Salomon, I. (2001). How derived is the demand for travel? Some conceptual and measurement considerations. Transportation Research Part A: Policy and Practice, 35(8), 695–719. https://doi.org/10.1016/S0965-8564(00)00013-6
- Mueller, N., Rojas-Rueda, D., Cole-Hunter, T., de Nazelle, A., Dons, E., Gerike, R., … Nieuwenhuijsen, M. (2015). Health impact assessment of active transportation: A systematic review. Preventive Medicine, 76, 103–114. https://doi.org/10.1016/j.ypmed.2015.04.010
- Park, Y. M., & Kwan, M.-P. (2017). Multi-contextual segregation and environmental justice research: Toward fine-scale spatiotemporal approaches. International Journal of Environmental Research and Public Health, 14(10), 1205. https://doi.org/10.3390/ijerph14101205
- Poom, A., Willberg, E., & Toivonen, T. (2021). Environmental exposure during travel: A research review and suggestions forward. Health & Place, 70, 102584. https://doi.org/10.1016/j.healthplace.2021.102584
- Roberts, H., & Helbich, M. (2021). Multiple environmental exposures along daily mobility paths and depressive symptoms: A smartphone-based tracking study. Environment International, 156, 106635. https://doi.org/10.1016/j.envint.2021.106635
- Sanchez, T. W., Shumway, H., Gordner, T., & Lim, T. (2023). The prospects of artificial intelligence in urban planning. International Journal of Urban Sciences, 27(2), 179–194. https://doi.org/10.1080/12265934.2022.2102538
- Shah, H., Carrel, A. L., & Le, H. T. (2022). Impacts of teleworking and online shopping on travel: A tour-based analysis. Transportation, 1–29. https://doi.org/10.1007/s11116-022-10321-9
- Shin, H., & Bithell, M. (2023). TRAPSim: An agent-based model to estimate personal exposure to non-exhaust road emissions in central Seoul. Computers, Environment and Urban Systems, 99, 101894. https://doi.org/10.1016/j.compenvurbsys.2022.101894
- Singh, V., Meena, K. K., & Agarwal, A. (2021). Travellers’ exposure to air pollution: A systematic review and future directions. Urban Climate, 38, 100901. https://doi.org/10.1016/j.uclim.2021.100901
- Son, T. H., Weedon, Z., Yigitcanlar, T., Sanchez, T., Corchado, J. M., & Mehmood, R. (2023). Algorithmic urban planning for smart and sustainable development: Systematic review of the literature. Sustainable Cities and Society, 94, 104562. https://doi.org/10.1016/j.scs.2023.104562
- Tainio, M., Jovanovic Andersen, Z., Nieuwenhuijsen, M. J., Hu, L., de Nazelle, A., An, R., Garcia, L. M. T., Goenka, S., Zapata-Diomedi, B., Bull, F., & Sá, T. H. de. (2021). Air pollution, physical activity and health: A mapping review of the evidence. Environment International, 147, 105954. https://doi.org/10.1016/j.envint.2020.105954
- Wang, B., Xu, T., Gao, H., Ta, N., Chai, Y., & Wu, J. (2021). Can daily mobility alleviate green inequality from living and working environments? Landscape and Urban Planning, 214, 104179. https://doi.org/10.1016/j.landurbplan.2021.104179
- Wang, J., Kwan, M.-P., & Chai, Y. (2018). An innovative context-based crystal-growth activity space method for environmental exposure assessment: A study using GIS and GPS trajectory data collected in Chicago. International Journal of Environmental Research and Public Health, 15(4), 703. https://doi.org/10.3390/ijerph15040703
- WHO. (2016). Urban green spaces and health. Copenhagen: WHO Regional Office for Europe. https://www.who.int/europe/publications/m/item/urban-green-space-and-health--intervention-impacts-and-effectiveness.
- Willberg, E., Poom, A., Helle, J., & Toivonen, T. (2023). Cyclists’ exposure to air pollution, noise, and greenery: A population-level spatial analysis approach. International Journal of Health Geographics, 22(1), 5. https://doi.org/10.1186/s12942-023-00326-7
- Woo, S.-H., Jang, H., Lee, S.-B., & Lee, S. (2022). Comparison of total PM emissions emitted from electric and internal combustion engine vehicles: An experimental analysis. Science of The Total Environment, 842, 156961. https://doi.org/10.1016/j.scitotenv.2022.156961
- Xia, C., & Yeh, A. G. O. (2022). Mobility as a response to environmental hazards in the urban context: A new perspective on mobility and inequality. Travel Behaviour and Society, 27, 192–203. https://doi.org/10.1016/j.tbs.2022.01.008
- Yoo, E., & Roberts, J. E. (2022). Static home-based versus dynamic mobility-based assessments of exposure to urban green space. Urban Forestry & Urban Greening, 70, 127528. https://doi.org/10.1016/j.ufug.2022.127528
- Zhang, L., Zhou, S., & Kwan, M.-P. (2023). The temporality of geographic contexts: Individual environmental exposure has time-related effects on mood. Health & Place, 79, 102953. https://doi.org/10.1016/j.healthplace.2022.102953
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