Is a land use regression model capable of predicting the cleanest route to school?

Land Use Regression (LUR) modeling is a widely used technique to model the spatial variability of air pollutants in epidemiology. In this study, we explore whether a LUR model can predict home-to-school commuting exposure to black carbon (BC). During January and February 2019, 43 children walking to school were involved in a personal monitoring campaign measuring exposure to BC and tracking their home-to-school routes. At the same time, a previously developed LUR model for the study area was applied to estimate BC exposure on points along the route. Personal BC exposure varied widely with mean \ub1 SD of 9003 \ub1 4864 ng/m3. The comparison between the two methods showed good agreement (Pearson\u2019s r = 0.74, Lin\u2019s Concordance Correlation Coefficient = 0.6), suggesting that LUR estimates are capable of catching differences among routes and predicting the cleanest route. However, the model tends to underestimate absolute concentrations by 29% on average. A LUR model can be useful in predicting personal exposure and can help urban planners in Milan to build a healthier city for schoolchildren by promoting less polluted home-to-school routes

Physical activity through sustainable transport approaches (PASTA): protocol for a multi-centre, longitudinal study

BACKGROUND: Physical inactivity is one of the leading risk factors for non-communicable diseases, yet many are not sufficiently active. The Physical Activity through Sustainable Transport Approaches (PASTA) study aims to better understand active mobility (walking and cycling for transport solely or in combination with public transport) as an innovative approach to integrate physical activity into individuals' everyday lives. The PASTA study will collect data of multiple cities in a longitudinal cohort design to study correlates of active mobility, its effect on overall physical activity, crash risk and exposure to traffic-related air pollution. METHODS/DESIGN: A set of online questionnaires incorporating gold standard approaches from the physical activity and transport fields have been developed, piloted and are now being deployed in a longitudinal study in seven European cities (Antwerp, Barcelona, London, Oerebro, Rome, Vienna, Zurich). In total, 14000 adults are being recruited (2000 in each city). A first questionnaire collects baseline information; follow-up questionnaires sent every 13 days collect prospective data on travel behaviour, levels of physical activity and traffic safety incidents. Self-reported data will be validated with objective data in subsamples using conventional and novel methods. Accelerometers, GPS and tracking apps record routes and activity. Air pollution and physical activity are measured to study their combined effects on health biomarkers. Exposure-adjusted crash risks will be calculated for active modes, and crash location audits are performed to study the role of the built environment. Ethics committees in all seven cities have given independent approval for the study. DISCUSSION: The PASTA study collects a wealth of subjective and objective data on active mobility and physical activity. This will allow the investigation of numerous correlates of active mobility and physical activity using a data set that advances previous efforts in its richness, geographical coverage and comprehensiveness. Results will inform new health impact assessment models and support efforts to promote and facilitate active mobility in cities

Transport mode choice and body mass index: Cross-sectional and longitudinal evidence from a European-wide study.

BACKGROUND: In the fight against rising overweight and obesity levels, and unhealthy urban environments, the renaissance of active mobility (cycling and walking as a transport mode) is encouraging. Transport mode has been shown to be associated to body mass index (BMI), yet there is limited longitudinal evidence demonstrating causality. We aimed to associate transport mode and BMI cross-sectionally, but also prospectively in the first ever European-wide longitudinal study on transport and health. METHODS: Data were from the PASTA project that recruited adults in seven European cities (Antwerp, Barcelona, London, Oerebro, Rome, Vienna, Zurich) to complete a series of questionnaires on travel behavior, physical activity levels, and BMI. To assess the association between transport mode and BMI as well as change in BMI we performed crude and adjusted linear mixed-effects modeling for cross-sectional (n = 7380) and longitudinal (n = 2316) data, respectively. RESULTS: Cross-sectionally, BMI was 0.027 kg/m2 (95%CI 0.015 to 0.040) higher per additional day of car use per month. Inversely, BMI was -0.010 kg/m2 (95%CI -0.020 to -0.0002) lower per additional day of cycling per month. Changes in BMI were smaller in the longitudinal within-person assessment, however still statistically significant. BMI decreased in occasional (less than once per week) and non-cyclists who increased cycling (-0.303 kg/m2, 95%CI -0.530 to -0.077), while frequent (at least once per week) cyclists who stopped cycling increased their BMI (0.417 kg/m2, 95%CI 0.033 to 0.802). CONCLUSIONS: Our analyses showed that people lower their BMI when starting or increasing cycling, demonstrating the health benefits of active mobility

Health impact assessment of active transportation: A systematic review

Objective Walking and cycling for transportation (i.e. active transportation, AT), provide substantial health benefits from increased physical activity (PA). However, risks of injury from exposure to motorized traffic and their emissions (i.e. air pollution) exist. The objective was to systematically review studies conducting health impact assessment (HIA) of a mode shift to AT on grounds of associated health benefits and risks. Methods Systematic database searches of MEDLINE, Web of Science and Transportation Research International Documentation were performed by two independent researchers, augmented by bibliographic review, internet searches and expert consultation to identify peer-reviewed studies from inception to December 2014. Results Thirty studies were included, originating predominantly from Europe, but also the United States, Australia and New Zealand. They compromised of mostly HIA approaches of comparative risk assessment and cost–benefit analysis. Estimated health benefit–risk or benefit–cost ratios of a mode shift to AT ranged between − 2 and 360 (median = 9). Effects of increased PA contributed the most to estimated health benefits, which strongly outweighed detrimental effects of traffic incidents and air pollution exposure on health. Conclusion Despite different HIA methodologies being applied with distinctive assumptions on key parameters, AT can provide substantial net health benefits, irrespective of geographical context

The impact of black carbon (BC) on mode-specific galvanic skin response (GSR) as a measure of stress in urban environments

Previous research has shown that walking and cycling could help alleviate stress in cities, however there is poor knowledge on how specific microenvironmental conditions encountered during daily journeys may lead to varying degrees of stress experienced at that moment. We use objectively measured data and a robust causal inference framework to address this gap. Using a Bayesian Doubly Robust (BDR) approach, we find that black carbon exposure statistically significantly increases stress, as measured by Galvanic Skin Response (GSR), while cycling and while walking. Augmented Outcome Regression (AOR) models indicate that greenspace exposure and the presence of walking or cycling infrastructure could reduce stress. None of these effects are statistically significant for people in motorized transport. These findings add to a growing evidence-base on health benefits of policies aimed at decreasing air pollution, improving active travel infrastructure and increasing greenspace in cities

Wearable sensors for personal monitoring and estimation of inhaled traffic-related air pollution: evaluation of methods

Physical activity and ventilation rates have an effect on an individual’s dose and may be important to consider in exposure–response relationships; however, these factors are often ignored in environmental epidemiology studies. The aim of this study was to evaluate methods of estimating the inhaled dose of air pollution and understand variability in the absence of a true gold standard metric. Five types of methods were identified: (1) methods using (physical) activity types, (2) methods based on energy expenditure, METs (metabolic equivalents of task), and oxygen consumption, (3) methods based on heart rate or (4) breathing rate, and (5) methods that combine heart and breathing rate. Methods were compared using a real-life data set of 122 adults who wore devices to track movement, black carbon air pollution, and physiological health markers for 3 weeks in three European cities. Different methods for estimating minute ventilation performed well in relative terms with high correlations among different methods, but in absolute terms, ignoring increased ventilation during day-to-day activities could lead to an underestimation of the daily dose by a factor of 0.08–1.78. There is no single best method, and a multitude of methods are currently being used to approximate the dose. The choice of a suitable method for determining the dose in future studies will depend on both the size and the objectives of the study

Day-to-day intrapersonal variability in mobility patterns and association with perceived stress: A cross-sectional study using GPS from 122 individuals in three European cities

Many aspects of our life are related to our mobility patterns and individuals can exhibit strong tendencies towards routine in their daily lives. Intrapersonal day-to-day variability in mobility patterns has been associated with mental health outcomes. The study aims were: (a) calculate intrapersonal day-to-day variability in mobility metrics for three cities; (b) explore interpersonal variability in mobility metrics by sex, season and city, and (c) describe intrapersonal variability in mobility and their association with perceived stress. Data came from the Physical Activity through Sustainable Transport Approaches (PASTA) project, 122 eligible adults wore location measurement devices over 7-consecutive days, on three occasions during 2015 (Antwerp: 41, Barcelona: 41, London: 40). Participants completed the Short Form Perceived Stress Scale (PSS-4). Day-to-day variability in mobility was explored via six mobility metrics using distance of GPS point from home (meters:m), distance travelled between consecutive GPS points (m) and energy expenditure (metabolic equivalents:METs) of each GPS point collected (n = 3,372,919). A Kruskal-Wallis H test determined whether the median daily mobility metrics differed by city, sex and season. Variance in correlation quantified day-to-day intrapersonal variability in mobility. Levene's tests or Kruskal-Wallis tests were applied to assess intrapersonal variability in mobility and perceived stress. There were differences in daily distance travelled, maximum distance from home and METS between individuals by sex, season and, for proportion of time at home also, by city. Intrapersonal variability across all mobility metrics were highly correlated; individuals had daily routines and largely stuck to them. We did not observe any association between stress and mobility. Individuals are habitual in their daily mobility patterns. This is useful for estimating environmental exposures and in fuelling simulation studies

Transport most likely to cause air pollution peak exposures in everyday life: Evidence from over 2000 days of personal monitoring

Background Air quality standards are typically based on long term averages – whereas a person may encounter exposure peaks throughout the day. Exposure peaks may contribute meaningfully to health impacts beyond their contribution to long term averages, and therefore should be considered alongside longer-term exposures. We aim to define and explain peak exposure to black carbon air pollution and look at the relationship between short peak exposures and longer term personal exposure. Methods A peak detection algorithm was applied to pooled data from two independent studies. High-resolution personal black carbon monitoring was performed in 175 healthy adult volunteers for a minimum of two 24-h periods per person. At the same time, we retrieved information on the time-activity pattern. Data covered Belgium, Spain, and the United Kingdom. In total, 2053 monitoring days were included. Results Exposure profiles revealed 2.8 ± 1.6 (avg ± SD) peaks per person per day. The average black carbon concentration during a peak was 4206 ng/m³. On 5.5% of the time participants were exposed to peak concentrations, but this contributed to 21.0% of their total exposure. The short time in transport (8%), was responsible for 32.7% of the peaks. 24.1% of the measurements in transport were categorized as peak exposure; while sleeping this was only 0.9%. When considering transport modes, participants were most likely to encounter peaks while cycling (34.0%). Most peaks were encountered at rush hour, from Monday through Friday, and in the cold season. Gender and age had no impact on the presence of peaks. Daily average black carbon exposure showed only a moderate correlation with peak frequency (r = 0.44). This correlation coefficient increased when considering longer term exposure to r > 0.60 from 10 days onward. Conclusions The occurrence of peaks varied substantially over time, across microenvironments and transport modes. Daily average exposure was moderately correlated with peak frequency. Real-time air pollution alerting systems may use the peak detection algorithm to support citizens in self-management of air pollution health effects

Evaluation of Different Recruitment Methods: Longitudinal, Web-Based, Pan-European Physical Activity Through Sustainable Transport Approaches (PASTA) Project

BACKGROUND: Sufficient sample size and minimal sample bias are core requirements for empirical data analyses. Combining opportunistic recruitment with a Web-based survey and data-collection platform yields new benefits over traditional recruitment approaches. OBJECTIVE: This paper aims to report the success of different recruitment methods and obtain data on participants' characteristics, participation behavior, recruitment rates, and representativeness of the sample. METHODS: A longitudinal, Web-based survey was implemented as part of the European PASTA (Physical Activity through Sustainable Transport Approaches) project, between November 2014 and December 2016. During this period, participants were recruited from 7 European cities on a rolling basis. A standardized guide on recruitment strategy was developed for all cities, to reach a sufficient number of adult participants. To make use of the strengths and minimize weakness, a combination of different opportunistic recruitment methods was applied. In addition, the random sampling approach was applied in the city of Örebro. To reduce the attrition rate and improve real-time monitoring, the Web-based platform featured a participant's and a researchers' user interface and dashboard. RESULTS: Overall, 10,691 participants were recruited; most people found out about the survey through their workplace or employer (2300/10691, 21.51%), outreach promotion (2219/10691, 20.76%), and social media (1859/10691, 17.39%). The average number of questionnaires filled in per participant varied significantly between the cities (P<.001), with the highest number in Zurich (11.0, SE 0.33) and the lowest in Örebro (4.8, SE 0.17). Collaboration with local organizations, the use of Facebook and mailing lists, and direct street recruitment were the most effective approaches in reaching a high share of participants (P<.001). Considering the invested working hours, Facebook was one of the most time-efficient methods. Compared with the cities' census data, the composition of study participants was broadly representative in terms of gender distribution; however, the study included younger and better-educated participants. CONCLUSIONS: We observed that offering a mixed recruitment approach was highly effective in achieving a high participation rate. The highest attrition rate and the lowest average number of questionnaires filled in per participant were observed in Örebro, which also recruited participants through random sampling. These findings suggest that people who are more interested in the topic are more willing to participate and stay in a survey than those who are selected randomly and may not have a strong connection to the research topic. Although direct face-to-face contacts were very effective with respect to the number of recruited participants, recruiting people through social media was not only effective but also very time efficient. The collected data are based on one of the largest recruited longitudinal samples with a common recruitment strategy in different European cities