Environmental exposure during travel: A research review and suggestions forward

Association between outdoor jogging behavior and PM2.5 exposure: Evidence from massive GPS trajectory data in Beijing

Outdoor jogging is one of the most popular practised exercises worldwide, providing various benefits for health and wellbeing. However, PM2.5 exposure risks of jogging behaviors were rarely explored. This study aims to investigate the association between jogging behavior and PM2.5 exposure with big data. PM2.5 exposure concentration and dose inhalation of individuals were calculated by integrating hourly PM2.5 concentration data and jogging GPS trajectory recorded by a sports app during 2015 in Beijing, after which relationships between jogging behaviors and PM2.5 exposure were unpacked using statistics analysis and structural equation modelling. Experimental results on massive jogging trajectories show that: (1) the average jogging PM2.5 exposure concentration is 60.43 μg/m3, and female joggers inhaled significantly less air pollution dose (19.70 μg) than men (24.91 μg). (2) There exist significant spatiotemporal disparities in jogging exposure to PM2.5. Joggings in the city center, in the morning, on weekdays and in autumn and winter seasons were exposed to higher pollution concentrations. (3) Jogging behavior characteristics, especially distance, activity space size, duration and rotation, were systematically associated with PM2.5 exposure across space and time. (4) The role of gender directly shaped joggers' dose inhalation of PM2.5 pollution and indirectly via duration, timing choice and distance. (5) The effects of weather conditions on joggers' exposure to PM2.5 are mainly via direct effects, whereas the direct impacts of precipitation and wind speed are mitigated by indirect effects stemming from jogging behavior patterns. Our findings provide insights for personal guidance and policy intervention for the sake of promoting physical activity and reducing PM2.5 exposure.

Measuring environmental exposures in people's activity space: The need to account for travel modes and exposure decay

BACKGROUND

Accurately quantifying people's out-of-home environmental exposure is important for identifying disease risk factors. Several activity space-based exposure assessments exist, possibly leading to different exposure estimates, and have neither considered individual travel modes nor exposure-related distance decay effects.

OBJECTIVE

We aimed (1) to develop an activity space-based exposure assessment approach that included travel modes and exposure-related distance decay effects and (2) to compare the size of such spaces and the exposure estimates derived from them across typically used activity space operationalizations.

METHODS

We used 7-day-long global positioning system (GPS)-enabled smartphone-based tracking data of 269 Dutch adults. People's GPS trajectory points were classified into passive and active travel modes. Exposure-related distance decay effects were modeled through linear, exponential, and Gaussian decay functions. We performed cross-comparisons on these three functional decay models and an unweighted model in conjunction with four activity space models (i.e., home-based buffers, minimum convex polygons, two standard deviational ellipses, and time-weighted GPS-based buffers). We applied non-parametric Kruskal-Wallis tests, pair-wise Wilcoxon signed-rank tests, and Spearman correlations to assess mean differences in the extent of the activity spaces and correlations across exposures to particulate matter (PM2.5), noise, green space, and blue space.

RESULTS

Participants spent, on average, 42% of their daily life out-of-home. We observed that including travel modes into activity space delineation resulted in significantly more compact activity spaces. Exposure estimates for PM2.5 and blue space were significantly (p < 0.05) different between exposure estimates that did or did not account for travel modes, unlike noise and green space, for which differences did not reach significance. While the inclusion of distance decay effects significantly affected noise and green space exposure assessments, the decay functions applied appear not to have had any impact on the results. We found that residential exposure estimates appear appropriate for use as proxy values for the overall amount of PM2.5 exposure in people's daily lives, while GPS-based assessments are suitable for noise, green space, and blue space.

SIGNIFICANCE

For some exposures, the tested activity space definitions, although significantly correlated, exhibited differing exposure estimate results based on inclusion or exclusion of travel modes or distance decay effect. Results only supported using home-based buffer values as proxies for individuals' daily short-term PM2.5 exposure.

Cyclists' exposure to air pollution, noise, and greenery: a population-level spatial analysis approach

Urban travel exposes people to a range of environmental qualities with significant health and wellbeing impacts. Nevertheless, the understanding of travel-related environmental exposure has remained limited. Here, we present a novel approach for population-level assessment of multiple environmental exposure for active travel. It enables analyses of (1) urban scale exposure variation, (2) alternative routes' potential to improve exposure levels per exposure type, and (3) by combining multiple exposures. We demonstrate the approach's feasibility by analysing cyclists' air pollution, noise, and greenery exposure in Helsinki, Finland. We apply an in-house developed route-planning and exposure assessment software and integrate to the analysis 3.1 million cycling trips from the local bike-sharing system. We show that especially noise exposure from cycling exceeds healthy thresholds, but that cyclists can influence their exposure by route choice. The proposed approach enables planners and individual citizens to identify (un)healthy travel environments from the exposure perspective, and to compare areas in respect to how well their environmental quality supports active travel. Transferable open tools and data further support the implementation of the approach in other cities.

Bring Your Own Location Data: Use of Google Smartphone Location History Data for Environmental Health Research

BACKGROUND

Environmental exposures are commonly estimated using spatial methods, with most epidemiological studies relying on home addresses. Passively collected smartphone location data, like Google Location History (GLH) data, may present an opportunity to integrate existing long-term time-activity data.

OBJECTIVES

We aimed to evaluate the potential use of GLH data for capturing long-term retrospective time-activity data for environmental health research.

METHODS

We included 378 individuals who participated in previous Global Positioning System (GPS) studies within the Washington State Twin Registry. GLH data consists of location information that has been routinely collected since 2010 when location sharing was enabled within android operating systems or Google apps. We created instructions for participants to download their GLH data and provide it through secure data transfer. We summarized the GLH data provided, compared it to available GPS data, and conducted an exposure assessment for nitrogen dioxide (NO2) air pollution.

RESULTS

Of 378 individuals contacted, we received GLH data from 61 individuals (16.1%) and 53 (14.0%) indicated interest but did not have historical GLH data available. The provided GLH data spanned 2010-2021 and included 34 million locations, capturing 66,677 participant days. The median number of days with GLH data per participant was 752, capturing 442 unique locations. When we compared GLH data to 2-wk GPS data (∼1.8 million points), 95% of GPS time-activity points were within 100m of GLH locations. We observed important differences between NO2 exposures assigned at home locations compared with GLH locations, highlighting the importance of GLH data to environmental exposure assessment.

DISCUSSION

We believe collecting GLH data is a feasible and cost-effective method for capturing retrospective time-activity patterns for large populations that presents new opportunities for environmental epidemiology. Cohort studies should consider adding GLH data collection to capture historical time-activity patterns of participants, employing a "bring-your-own-location-data" citizen science approach. Privacy remains a concern that needs to be carefully managed when using GLH data. https://doi.org/10.1289/EHP10829.

Air (ine)quality in the European Union

Purpose of Review

This paper presents an analytical review of recent research on social inequality caused or compounded by ambient air pollution in the European Union.

Recent Findings

While empirical studies have developed significantly both in the academic and institutional arena, they have largely focused on only one aspect: the exposure and sensitivity of individuals and groups to air pollution according to various criteria, documenting substantial and overlapping inequality.

Summary

While EU policy should better address this proven impact inequality, research is also needed on new fronts of air (ine)quality (namely mental health impact and indoor air quality) as well as other types of ambient air inequality (such as inequality in responsibility and impact of air pollution mitigation policy).

Supplementary Information

The online version contains supplementary material available at 10.1007/s40572-022-00348-6.

Field Evaluation and Calibration of Low-Cost Air Pollution Sensors for Environmental Exposure Research

This paper seeks to evaluate and calibrate data collected by low-cost particulate matter (PM) sensors in different environments and using different aggregated temporal units (i.e., 5-s, 1-min, 10-min, 30 min intervals). We first collected PM concentrations (i.e., PM₁, PM_2.5, and PM₁₀) data in five different environments (i.e., indoor and outdoor of an office building, a train platform and lobby of a subway station, and a seaside location) in Hong Kong, using five AirBeam2 sensors as the low-cost sensors and a TSI DustTrak DRX Aerosol Monitor 8533 as the reference sensor. By comparing the collected PM concentrations, we found high linearity and correlation between the data reported by the AirBeam2 sensors in different environments. Furthermore, the results suggest that the accuracy and bias of the PM data reported by the AirBeam2 sensors are affected by rainy weather and environments with high humidity and a high level of hygroscopic salts (i.e., a seaside location). In addition, increasing the aggregation level of the temporal units (i.e., from 5-s to 30 min intervals) increases the correlation between the PM concentrations obtained by the AirBeam2 sensors, while it does not significantly improve the accuracy and bias of the data. Lastly, our results indicate that using a machine learning model (i.e., random forest) for the calibration of PM concentrations collected on sunny days generates better results than those obtained with multiple linear models. These findings have important implications for researchers when designing environmental exposure studies based on low-cost PM sensors.

Risk Preference, Health Risk Perception, and Environmental Exposure Nexus: Evidence from Rural Women as Pig Breeders, China

Rural women are an integral part of the agricultural economy. Still, their exposure to environmental pollution, especially in the context of risk preference and health risk perception, has not gained much attention in the existing literature. So to explore this notion, a survey and experimental data of 714 rural Chinese women as pig breeders are taken, we innovatively evaluate the degree of environmental exposure from the pre-exposure, in-exposure, post-exposure intervention of women breeders, and two-stage least squares (2SLS) method is employed to address the endogeneity issue between health risk perception and environmental exposure. The results show that rural women breeders suffer from severe environmental exposure, and the degree of environmental exposure is up to 72.102(Min = 0, Max = 100). Risk preference also emerges as a crucial determinant behind their environmental exposure, but health risk perception significantly deters the degree of environmental exposure. The health risk perception can offset risk preference effects on women breeders' environmental exposure by 15.15%. Moreover, considering the heterogeneity of the breeding scale, it is found that the impact of risk preference and health risk perception on women breeders' environmental exposure is an inverted U-shaped relationship, i.e., the results are at the turning stage when the breeding scale is 31-40 heads. Based on the empirical findings, the study offers guidelines for policymakers to enhance awareness amongst women breeders regarding health and pollution and encourage them to opt for environment-friendly breeding. Moreover, this research also has substantial guiding significance for related research on environmental exposure of rural women in other developing countries.