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Studies on the association between atherosclerosis and long-term exposure to ambient air pollution suggest that carotid intima-media thickness (CIMT), a marker of subclinical atherosclerosis, is positively associated with particulate matter (PM) exposure. However, there is heterogeneity between the different studies concerning the magnitude of this association. We performed a meta-analysis to determine the strength of the association between CIMT and particulate air pollution. We queried PubMed citation database and Web of Knowledge up to March 2015 in order to identify studies on CIMT and particulate air pollution. Two investigators selected and computerized all relevant information, independently. Eight of the reviewed epidemiological publications provided sufficient details and met our inclusion criteria. Descriptive and quantitative information was extracted from each selected study. The meta-analysis included 18,349 participants from eight cohorts for the cross-sectional association between CIMT and PM and 7,268 participants from three cohorts for the longitudinal analysis on CIMT progression and PM exposure. The average exposure to PM2.5 in the different study populations ranged from 4.1 to 20.8 µg/m3 and CIMT averaged (SD) 0.73 (0.14) mm. We computed a pooled estimate from a random-effects model. In the combined cross-sectional studies, an increase of 5 µg/m3 PM2.5 was associated with a 1.66% (95% CI: 0.86 to 2.46; P<0.0001) thicker CIMT, which corresponds to an average increase of 12.1 µm. None of the studies moved the combined estimate outside the confidence interval of the overall estimate. A funnel plot suggested absence of publication bias. The combined longitudinal estimate showed for each 5 µg/m3 higher PM2.5 exposure, a 1.04 µm per year (95% CI: 0.01 to 2.07; P=0.048) greater CIMT progression. Our meta-analysis supports the evidence of a positive association between CIMT, a marker of subclinical atherosclerosis, and long-term exposure to particulate air pollution.

Background Daily changes in ambient concentrations of particulate matter, nitrogen oxides and ozone are associated with increased cardiopulmonary morbidity and mortality, with the lungs and their function being a vulnerable target. Methods To evaluate the association between daily changes in air pollution and lung function in healthy adults we obtained annual lung function measurements from a routine worker health surveillance program not designed for research purposes. Forced Vital Capacity (FVC), Forced Expiratory Volume in the first second (FEV1), FEV1/FVC and Peak Expiratory flow (PEF) from a cohort of 2449 employees were associated with daily measurements of PM 10 , NO 2 and ozone at a nearby monitoring station in the North of Belgium. Repeated measures were available for the period 2011–2015. Results The mean (SD) PM 10 concentration on the day of the lung function test was 24.9 (15.5) μg/m 3 . A 10 μg PM 10 /m 3 increase on the day of the clinical examination was associated with a 18.9 ml lower FVC (95% CI: -27.5 to −10.3, p  < 0.0001), 12.8 ml lower FEV1 (−19.1 to −6.5; p  < 0.0001), and a 51.4 ml/s lower PEF (−75.0 to −27.0; p  < 0.0001). The FEV1/FVC-ratio showed no associations. An increase of 10 μgNO 2 /m 3 was associated with a reduction in PEF (−66.1 ml/s (−106.6 to −25.6; p  < 0.001)) on the day of the examination. Conclusions We found negative associations between daily variations in ambient air pollution and FVC, FEV1 and PEF in healthy adults.

Reduction of sedentary time and an increase in physical activity offer potential to improve public health. However, quantifying physical activity behaviour under real world conditions is a major challenge and no standard of good practice is available. Our aim was to compare the results of physical activity and sedentary behaviour obtained with a self-reported instrument (Global Physical Activity Questionnaire (GPAQ)) and a wearable sensor (SenseWear) in a repeated measures study design. Healthy adults (41 in Antwerp, 41 in Barcelona and 40 in London) wore the SenseWear armband for seven consecutive days and completed the GPAQ on the final day. This was repeated three times. We used the Wilcoxon signed rank sum test, Spearman correlation coefficients, mixed effects regression models and Bland-Altman plots to study agreement between both methods. Mixed models were used to assess the effect of personal characteristics on the absolute and relative difference between estimates obtained with the GPAQ and SenseWear. Moderate to vigorous energy expenditure and duration derived from the GPAQ were significantly lower (p<0.05) compared to the SenseWear, yet these variables showed significant correlations ranging from 0.45 to 0.64. Estimates of vigorous-intensity physical activity in particular showed high similarity (r>0.59). Results for sedentary behaviour did not differ, yet were poorly correlated (r<0.25). The differences between all variables were reproducible across repeated measurements. In addition, we observed a relationship between these differences and BMI, body fat and physical activity domain. Due to the lack of a standardized protocol, results from different studies measuring physical activity and sedentary behaviour are difficult to compare. Therefore, we suggested an easy-to-implement approach for future studies adding the GPAQ to the wearable of choice as a basis for comparisons.

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. 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. 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. 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. 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.

This explorative study examines personal and neighbourhood characteristics associated with short-distance trips made by car, bicycle or walking in order to identify target groups for future interventions. Data were derived from 'Mobility Research Netherlands (2004-2009; MON)', a dataset including information regarding trips made by household members (n = ±53,000 respondents annually). Using postal codes of household addresses, MON data were enriched with data on neighbourhood typologies. Multilevel logistic modelling was used to calculate odds ratio (OR) of active transport versus car use associated with four different trip purposes (shopping (reference), commuting, taking or bringing persons or sports). A total of 277,292 short distance trips made by 102,885 persons were included in analyses. Compared to women shopping, women less often take active transport to sports clubs (OR = 0.88) and men less often take active transport for shopping (OR = 0.92), or for bringing or taking persons (OR = 0.76). Those aged 25-34 years (OR = 0.83) and 35-44 years (OR = 0.96) were more likely to use active transport for taking or bringing persons than persons belonging to the other age groups (relative to trips made for shopping by those 65 years or over). A higher use of active transport modes by persons with an university or college degree was found and particularly persons living in urban-centre neighbourhoods were likely to use active transport modes. IN DEVELOPING POLICIES PROMOTING A MODE SHIFT SPECIAL ATTENTION SHOULD BE GIVEN TO THE FOLLOWING GROUPS: a) men making short distance trips for taking or bringing persons, b) women making short distance trips to sport facilities, c) persons belonging to the age groups of 25-44 years of age, d) Persons with a primary school or lower general secondary education degree and persons with a high school or secondary school degree and e) persons living in rural or urban-green neighbourhoods.

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 ± SD of 9003 ± 4864 ng/m3. The comparison between the two methods showed good agreement (Pearson’s r = 0.74, Lin’s 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.

Several studies have shown that a significant amount of daily air pollution exposure is inhaled during trips. In this study, car drivers assessed their own black carbon exposure under real-life conditions (223 h of data from 2013). The spatiotemporal exposure of the car drivers is modeled using a data science approach, referred to as “microscopic land-use regression” (µLUR). In-vehicle exposure is highly dynamical and is strongly related to the local traffic dynamics. An extensive set of potential covariates was used to model the in-vehicle black carbon exposure in a temporal resolution of 10 s. Traffic was retrieved directly from traffic databases and indirectly by attributing the trips through a noise map as an alternative traffic source. Modeling by generalized additive models (GAM) shows non-linear effects for meteorology and diurnal traffic patterns. A fitted diurnal pattern explains indirectly the complex diurnal variability of the exposure due to the non-linear interaction between traffic density and distance to the preceding vehicles. Comparing the strength of direct traffic attribution and indirect noise map-based traffic attribution reveals the potential of noise maps as a proxy for traffic-related air pollution exposure. An external validation, based on a dataset gathered in 2010–2011, quantifies the exposure reduction inside the vehicles at 33% (mean) and 50% (median). The EU PM Euro 5 PM emission standard (in force since 2009) explains the largest part of the discrepancy between the measurement campaign in 2013 and the validation dataset. The µLUR methodology provides a high resolution, route-sensitive, seasonal and meteorology-sensitive personal exposure estimate for epidemiologists and policy makers.

This review introduces an emerging research field that is focused on studying the effect of exposure to air pollution during exercise on cognition, with specific attention to the impact on concentrations of brain-derived neurotrophic factor (BDNF) and inflammatory markers. It has been repeatedly demonstrated that regular physical activity enhances cognition, and evidence suggests that BDNF, a neurotrophin, plays a key role in the mechanism. Today, however, air pollution is an environmental problem worldwide and the high traffic density, especially in urban environments and cities, is a major cause of this problem. During exercise, the intake of air pollution increases considerably due to an increased ventilation rate and particle deposition fraction. Recently, air pollution exposure has been linked to adverse effects on the brain such as cognitive decline and neuropathology. Inflammation and oxidative stress seem to play an important role in inducing these health effects. We believe that there is a need to investigate whether the well-known benefits of regular physical activity on the brain also apply when physical activity is performed in polluted air. We also report our findings about exercising in an environment with ambient levels of air pollutants. Based on the latter results, we hypothesize that traffic-related air pollution exposure during exercise may inhibit the positive effect of exercise on cognition.

Ultrafine particles (UFP) may contribute to the cardiovascular effects of particulate air pollution, partly because of their relatively efficient alveolar deposition. In this study, we assessed associations between blood pressure and short-term exposure to air pollution in a population of schoolchildren. In 130 children (6-12 years of age), blood pressure was determined during two periods (spring and fall 2011). We used mixed models to study the association between blood pressure and ambient concentrations of particulate matter and ultrafine particles measured in the schools' playground. Independent of sex, age, height, and weight of the child, parental education, neighborhood socioeconomic status, fish consumption, heart rate, school, day of the week, season, wind speed, relative humidity, and temperature on the morning of examination, an interquartile range (860 particles/cm3) increase in nano-sized UFP fraction (20-30 nm) was associated with a 6.35 mmHg (95% CI: 1.56, 11.14; p = 0.01) increase in systolic blood pressure. For the total UFP fraction, systolic blood pressure was 0.79 mmHg (95% CI: 0.07, 1.51; p = 0.03) higher, but no effects on systolic blood pressure were found for the nano-sized fractions with a diameter > 100 nm, nor PM2.5, PMcoarse, and PM10. Diastolic blood pressure was not associated with any of the studied particulate mass fractions. Children attending school on days with higher UFP concentrations (diameter < 100 nm) had higher systolic blood pressure. The association was dependent on UFP size, and there was no association with the PM2.5 mass concentration.

Microcirculation plays an important role in the physiology of cardiovascular health. Air pollution is an independent risk factor for the development and progression of cardiovascular diseases, but the number of studies on the relation between air pollution and the microcirculation is limited. We examined the relationship between short-term changes in air pollution and microvascular changes. We measured retinal microvasculature using fundus image analysis in a panel of 84 healthy adults (52% female), 22-63 years of age, during January-May 2012. Blood vessels were measured as central retinal arteriolar/venular equivalent (CRAE/CRVE), with a median of 2 measurements (range, 1-3). We used monitoring data on particulate air pollution (PM10) and black carbon (BC). Mixed-effect models were used to estimate associations between CRAE/CRVE and exposure to PM10 and BC using various exposure windows. CRAE and CRVE were associated with PM10 and BC concentrations, averaged over the 24 hr before the retinal examinations. Each 10-µg/m3 increase in PM10 was associated with a 0.93-µm decrease (95% CI: -1.42, -0.45; p = 0.0003) in CRAE and a 0.86-µm decrease (95% CI: -1.42, -0.30; p = 0.004) in CRVE after adjusting for individual characteristics and time varying conditions such as ambient temperature. Each 1-µg/m3 increase in BC was associated with a 1.84-µm decrease (95% CI: -3.18, -0.51; p < 0.001) in CRAE. Our findings suggest that the retinal microvasculature responds to short-term changes in air pollution levels. These results support a mechanistic pathway through which air pollution can act as a trigger of cardiovascular events at least in part through effects on the microvasculature.