Assessment of different route choice on commuters' exposure to air pollution in Taipei, Taiwan

A review of personal exposure studies in selected Asian countries' public transport microenvironments: lessons learned and future directions

This comprehensive paper conducts an in-depth review of personal exposure and air pollutant levels within the microenvironments of Asian city transportation. Our methodology involved a systematic analysis of an extensive body of literature from diverse sources, encompassing a substantial quantity of studies conducted across multiple Asian cities. The investigation scrutinizes exposure to various pollutants, including particulate matters (PM10, PM2.5, and PM1), carbon dioxide (CO2), formaldehyde (CH2O), and total volatile organic compounds (TVOC), during transportation modes such as car travel, bus commuting, walking, and train rides. Notably, our review reveals a predominant focus on PM2.5, followed by PM10, PM1, CO2, and TVOC, with limited attention given to CH2O exposure. Across the spectrum of Asian cities and transportation modes, exposure concentrations exhibited considerable variability, a phenomenon attributed to a multitude of factors. Primary sources of exposure encompass motor vehicle emissions, traffic dynamics, road dust, and open bus doors. Furthermore, our findings illuminate the influence of external environments, particularly in proximity to train stations, on pollutant levels inside trains. Crucial factors affecting exposure encompass ventilation conditions, travel-specific variables, seat locations, vehicle types, and meteorological influences. The culmination of this rigorous review underscores the need for standardized measurements, enhanced ventilation systems, air filtration mechanisms, the adoption of clean energy sources, and comprehensive public education initiatives aimed at reducing pollutant exposure within city transportation microenvironments. Importantly, our study contributes to the growing body of knowledge surrounding this subject, offering valuable insights for policymakers and researchers dedicated to advancing air quality standards and safeguarding public health.

A pruned feed-forward neural network (pruned-FNN) approach to measure air pollution exposure

Environmental epidemiology studies require accurate estimations of exposure intensities to air pollution. The process from air pollutant emission to individual exposure is however complex and nonlinear, which poses significant modeling challenges. This study aims to develop an exposure assessment model that can strike a balance between accuracy, complexity, and usability. In this regard, neural networks offer one possible approach. This study employed a custom-designed pruned feed-forward neural network (pruned-FNN) approach to calculate the air pollution exposure index based on emission time and rates, terrain factors, meteorological conditions, and proximity measurements. The model's performance was evaluated by cross-validating the estimated exposure indexes with ground-based monitoring records. The pruned FNN can predict pollution exposure indexes (PEIs) that are highly and stably correlated with the monitored air pollutant concentrations (Spearman's rank correlation coefficients for tenfold cross-validation (mean ± standard deviation: 0.906 ± 0.028) and for random cross-validation (0.913 ± 0.024)). The predicted values are also close to the ground truth in most cases (95.5% of the predicted PEIs have relative errors smaller than 10%) when the training datasets are sufficiently large and well-covered. The pruned-FNN method can make accurate exposure estimations using a flexible number of variables and less extensive data in a less money/time-consuming manner. Compared to other exposure assessment models, the pruned FNN is an appropriate and effective approach for exposure assessment that covers a large geographic area over a long period of time.

Comparison of exposure to traffic-related pollutants on different commuting routes to a primary school in Jinan, China

Traffic-related pollutants seriously affect human health, and the commute time to and from school is the time when students are exposed greatest to traffic pollution sources. Field measurements were conducted with hand-held instruments while walking along two selected commuting routes in winter and spring. The measured data were then compared with background monitoring data, and the respiratory deposition dose (RDD) was calculated to assess the exposure risk. Particulate matter intake from 2018 to 2020 was calculated. In winter, the average concentrations of PM_2.5 and PM₁₀ were higher in the afternoon than in the morning. The highest concentration was 2.94 times greater than the background value. The low-concentration distribution area of the low-traffic route that is off the main road (route B) was more significant than that of the high-traffic route that is near the main road (route A). Moreover, the RDD of route B was consistently lower than that of route A, while the average annual amount of PM_2.5 inhalation on route B in 3 years was 16.3% lower than that on route A. Overall, route B is more suitable than route A for students to commute on foot. Based on the findings, a walking route located within a community is a good choice.

Identifying low-PM2.5 exposure commuting routes for cyclists through modeling with the random forest algorithm based on low-cost sensor measurements in three Asian cities

Cyclists can be easily exposed to traffic-related pollutants due to riding on or close to the road during commuting in cities. PM_2.5 has been identified as one of the major pollutants emitted by vehicles and associated with cardiopulmonary and respiratory diseases. As routing has been suggested to reduce the exposures for cyclists, in this study, PM_2.5 was monitored with low-cost sensors during commuting periods to develop models for identifying low exposure routes in three Asian cities: Taipei, Osaka, and Seoul. The models for mapping the PM_2.5 in the cities were developed by employing the random forest algorithm in a two-stage modeling approach. The land use features to explain spatial variation of PM_2.5 were obtained from the open-source land use database, OpenStreetMap. The total length of the monitoring routes ranged from 101.36 to 148.22 km and the average PM_2.5 ranged from 13.51 to 15.40 μg/m³ among the cities. The two-stage models had the standard k-fold cross-validation (CV) R² of 0.93, 0.74, and 0.84 in Taipei, Osaka, and Seoul, respectively. To address spatial autocorrelation, a spatial cross-validation approach applying a distance restriction of 100 m between the model training and testing data was employed. The over-optimistic estimates on the predictions were thus prevented, showing model CV-R² of 0.91, 0.67, and 0.78 respectively in Taipei, Osaka, and Seoul. The comparisons between the shortest-distance and lowest-exposure routes showed that the largest percentage of reduced averaged PM_2.5 exposure could reach 32.1% with the distance increases by 37.8%. Given the findings in this study, routing behavior should be encouraged. With the daily commuting trips expanded, the cumulative effect may become significant on the chronic exposures over time. Therefore, a route planning tool for reducing the exposures shall be developed and promoted to the public.

Environmental exposure during travel: A research review and suggestions forward

Daily travel through the urban fabric exposes urban dwellers to a range of environmental conditions that may have an impact on their health and wellbeing. Knowledge about exposures during travel, their associations with travel behavior, and their social and health outcomes are still limited. In our review, we aim to explain how the current environmental exposure research addresses the interactions between human and environmental systems during travel through their spatial, temporal and contextual dimensions. Based on the 104 selected studies, we identify significant recent advances in addressing the spatiotemporal dynamics of exposure during travel. However, the conceptual and methodological framework for understanding the role of multiple environmental exposures in travel environments is still in an early phase, and the health and wellbeing impacts at individual or population level are not well known. Further research with greater geographical balance is needed to fill the gaps in the empirical evidence, and linking environmental exposures during travel with the causal health and wellbeing outcomes. These advancements can enable evidence-based urban and transport planning to take the next step in advancing urban livability.

Identification of High Personal PM2.5 Exposure during Real Time Commuting in the Taipei Metropolitan Area

There has been an increase in the network of mass rapid transit (MRT) and the number of automobiles over the past decades in the Taipei metropolitan area, Taiwan. The effects of these changes on PM2.5 exposure for the residents using different modes of transportation are unclear. Volunteers measured PM2.5 concentrations while commuting in different modes of transportation using a portable PM2.5 detector. Exposure to PM2.5 (median (range)) was higher when walking along the streets (40 (10–275) µg/m3) compared to riding the buses (35 (13–65) µg/m3) and the cars (15 (8–80) µg/m3). PM2.5 concentrations were higher in underground MRT stations (80 (30–210) µg/m3) and inside MRT cars running in underground sections (80 (55–185) µg/m3) than those in elevated MRT stations (33 (15–35) µg/m3) and inside MRT cars running in elevated sections (28 (13–68) µg/m3) (p < 0.0001). Riding motorcycle also was associated with high PM2.5 exposure (75 (60–105 µg/m3), p < 0.0001 vs. walking). High PM2.5 concentrations were noted near the temples (588 ± 271 µg/m3) and in the underground food court of a night market (405 ± 238 µg/m3) where the eatery stalls stir-fried and grilled food (p < 0.0001 vs. walking). We conclude that residents in the Taipei metropolitan area may still be exposed to high PM2.5 during some forms of commuting, including riding underground MRT.

Air pollution, physical activity and health: A mapping review of the evidence

BACKGROUND

Exposure to air pollution and physical inactivity are both significant risk factors for non-communicable diseases (NCDs). These risk factors are also linked so that the change in exposure in one will impact risks and benefits of the other. These links are well captured in the active transport (walking, cycling) health impact models, in which the increases in active transport leading to increased inhaled dose of air pollution. However, these links are more complex and go beyond the active transport research field. Hence, in this study, we aimed to summarize the empirical evidence on the links between air pollution and physical activity, and their combined effect on individual and population health.

OBJECTIVES AND METHODS

We conducted a non-systematic mapping review of empirical and modelling evidence of the possible links between exposure to air pollution and physical activity published until Autumn 2019. We reviewed empirical evidence for the (i) impact of exposure to air pollution on physical activity behaviour, (ii) exposure to air pollution while engaged in physical activity and (iii) the short-term and (iv) long-term health effects of air pollution exposure on people engaged in physical activity. In addition, we reviewed (v) public health modelling studies that have quantified the combined effect of air pollution and physical activity. These broad research areas were identified through expert discussions, including two public events performed in health-related conferences.

RESULTS AND DISCUSSION

The current literature suggests that air pollution may decrease physical activity levels during high air pollution episodes or may prevent people from engaging in physical activity overall in highly polluted environments. Several studies have estimated fine particulate matter (PM2.5) exposure in active transport environment in Europe and North-America, but the concentration in other regions, places for physical activity and for other air pollutants are poorly understood. Observational epidemiological studies provide some evidence for a possible interaction between air pollution and physical activity for acute health outcomes, while results for long-term effects are mixed with several studies suggesting small diminishing health gains from physical activity due to exposure to air pollution for long-term outcomes. Public health modelling studies have estimated that in most situations benefits of physical activity outweigh the risks of air pollution, at least in the active transport environment. However, overall evidence on all examined links is weak for low- and middle-income countries, for sensitive subpopulations (children, elderly, pregnant women, people with pre-existing conditions), and for indoor air pollution.

CONCLUSIONS

Physical activity and air pollution are linked through multiple mechanisms, and these relations could have important implications for public health, especially in locations with high air pollution concentrations. Overall, this review calls for international collaboration between air pollution and physical activity research fields to strengthen the evidence base on the links between both and on how policy options could potentially reduce risks and maximise health benefits.

Peak Inhalation Exposure Metrics Used in Occupational Epidemiologic and Exposure Studies

Peak exposures are of concern because they can potentially overwhelm normal defense mechanisms and induce adverse health effects. Metrics of peak exposure have been used in epidemiologic and exposure studies, but consensus is lacking on its definition. The relevant characteristics of peak exposure are dependent upon exposure patterns, biokinetics of exposure, and disease mechanisms. The objective of this review was to summarize the use of peak metrics in epidemiologic and exposure studies. A comprehensive search of Medline, Embase, Web of Science, and NIOSHTIC-2 databases was conducted using keywords related to peak exposures. The retrieved references were reviewed and selected for indexing if they included a peak metric and met additional criteria. Information on health outcomes and peak exposure metrics was extracted from each reference. A total of 1,215 epidemiologic or exposure references were identified, of which 182 were indexed and summarized. For the 72 epidemiologic studies, the health outcomes most frequently evaluated were: chronic respiratory effects, cancer and acute respiratory symptoms. Exposures were frequently assessed using task-based and full-shift time-integrated methods, qualitative methods, and real-time instruments. Peak exposure summary metrics included the presence or absence of a peak event, highest exposure intensity and frequency greater than a target. Peak metrics in the 110 exposure studies most frequently included highest exposure intensity, average short-duration intensity, and graphical presentation of the real-time data (plots). This review provides a framework for considering biologically relevant peak exposure metrics for epidemiologic and exposure studies to help inform risk assessment and exposure mitigation.

Exposure to ultrafine particles in different transport modes in the city of Rome

There is evidence of adverse health impacts from human exposure to particulate air pollution, including increased rates of respiratory and cardiovascular illness, hospitalizations, and pre-mature mortality. Most recent hypotheses assign an important role to ultrafine particles (UFP) (<0.1 μm) and to associated transition metals (in particular Fe). In a large city like Rome, where many active people spend more than one hour per day in private or public transportation, it may be important to evaluate the level of exposure to harmful pollutants which occurs during urban travelling. In this context, the aim of this work was to examine the relative contribution of different transport modes to total daily exposure. We performed experimental measurements during both morning and evening traffic peak hours throughout the winter season (December 2013-March 2014), for a total of 98 trips. Our results suggest that the lowest UFP exposures are experienced by underground train commuters, with an average number concentration of 14 134 cm^-3, and are largely a reflection of the routes being at greater distance from vehicular traffic. Motorcyclists experienced significantly higher average concentrations (73 168 cm^-3) than all other exposure classes, and this is most likely a result of the presence of high-concentration and short-duration peaks which do not occur when the same routes are traveled by car. UFP concentrations in subway train environments were found to be comparable to urban background levels. Still, in underground trains we found the highest values of PM₁₀ mass concentration with a maximum value of 422 μg/m³. PM₁₀ concentration in trains was found to be four and two times higher than what was measured in car and motorbike trips, respectively. Transport mode contribution to total integrated UFP daily exposure was found to be 16.3%-20.9% while travelling by car, 28.7% for motorbike trips, and 8.7% for subway trips. Due to lower exposure times, commuting by car and motorbike is comparable to other daily activities in terms of exposure. Our data can provide relevant information for transport decision-making and increase environmental awareness in the hope that the information about inhaled pollutants can translate into a more rational approach to urban travelling.