Evi Dons

Development of Land Use Regression Models for PM2.5, PM2.5 Absorbance, PM10 and PMcoarse in 20 European Study Areas; Results of the ESCAPE Project

Land Use Regression (LUR) models have been used increasingly for modeling small-scale spatial variation in air pollution concentrations and estimating individual exposure for participants of cohort studies. Within the ESCAPE project, concentrations of PM(2.5), PM(2.5) absorbance, PM(10), and PM(coarse) were measured in 20 European study areas at 20 sites per area. GIS-derived predictor variables (e.g., traffic intensity, population, and land-use) were evaluated to model spatial variation of annual average concentrations for each study area. The median model explained variance (R(2)) was 71% for PM(2.5) (range across study areas 35-94%). Model R(2) was higher for PM(2.5) absorbance (median 89%, range 56-97%) and lower for PM(coarse) (median 68%, range 32- 81%). Models included between two and five predictor variables, with various traffic indicators as the most common predictors. Lower R(2) was related to small concentration variability or limited availability of predictor variables, especially traffic intensity. Cross validation R(2) results were on average 8-11% lower than model R(2). Careful selection of monitoring sites, examination of influential observations and skewed variable distributions were essential for developing stable LUR models. The final LUR models are used to estimate air pollution concentrations at the home addresses of participants in the health studies involved in ESCAPE.

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.

Development of Land Use Regression Models for Particle Composition in Twenty Study Areas in Europe

Land Use Regression (LUR) models have been used to describe and model spatial variability of annual mean concentrations of traffic related pollutants such as nitrogen dioxide (NO2), nitrogen oxides (NOx) and particulate matter (PM). No models have yet been published of elemental composition. As part of the ESCAPE project, we measured the elemental composition in both the PM10 and PM2.5 fraction sizes at 20 sites in each of 20 study areas across Europe. LUR models for eight a priori selected elements (copper (Cu), iron (Fe), potassium (K), nickel (Ni), sulfur (S), silicon (Si), vanadium (V), and zinc (Zn)) were developed. Good models were developed for Cu, Fe, and Zn in both fractions (PM10 and PM2.5) explaining on average between 67 and 79% of the concentration variance (R(2)) with a large variability between areas. Traffic variables were the dominant predictors, reflecting nontailpipe emissions. Models for V and S in the PM10 and PM2.5 fractions and Si, Ni, and K in the PM10 fraction performed moderately with R(2) ranging from 50 to 61%. Si, NI, and K models for PM2.5 performed poorest with R(2) under 50%. The LUR models are used to estimate exposures to elemental composition in the health studies involved in ESCAPE.

Street characteristics and traffic factors determining road users' exposure to black carbon

Many studies nowadays make the effort of determining personal exposure rather than estimating exposure at the residential address only. While intra-urban air pollution can be modeled quite easily using interpolation methods, estimating exposure in transport is more challenging. The aim of this study is to investigate which factors determine black carbon (BC) concentrations in transport microenvironments. Therefore personal exposure measurements are carried out using portable aethalometers, trip diaries and GPS devices. More than 1500 trips, both by active modes and by motorized transport, are evaluated in Flanders, Belgium. GPS coordinates are assigned to road segments to allow BC concentrations to be linked with trip and road characteristics (trip duration, degree of urbanization, road type, traffic intensity, travel speed and road speed). Average BC concentrations on highways (10.7μg/m(3)) are comparable to concentrations on urban roads (9.6μg/m(3)), but levels are significantly higher than concentrations on rural roads (6.1μg/m(3)). Highways yield higher BC exposures for motorists compared to exposure on major roads and local roads. Overall BC concentrations are elevated at lower speeds (<30km/h) and at speeds above 80km/h, in accordance to vehicle emission functions. Driving on roads with low traffic intensities resulted in lower exposures than driving on roads with higher traffic intensities (from 5.6μg/m(3) for roads with less than 500veh/h, up to 12μg/m(3) for roads with over 2500veh/h). Traffic intensity proved to be the major explanatory variable for in-vehicle BC exposure, together with timing of the trip and urbanization. For cyclists and pedestrians the range in BC exposure is smaller and models are less predictive; for active modes exposure seems to be influenced by timing and degree of urbanization only.

Land use regression models as a tool for short, medium and long term exposure to traffic related air pollution

BACKGROUND AND AIMS In the HEAPS (Health Effects of Air Pollution in Antwerp Schools) study the importance of traffic-related air pollution on the school and home location on childrens health was assessed. 130 children (aged 6 to 12) from two schools participated in a biomonitoring study measuring oxidative stress, inflammation and cardiovascular markers. METHODS Personal exposure of schoolchildren to black carbon (BC) and nitrogen dioxide (NO2) was assessed using both measured and modeled concentrations. Air quality measurements were done in two seasons at approximately 50 locations, including the schools. The land use regression technique was applied to model concentrations at the childrens home address and at the schools. RESULTS In this paper the results of the exposure analysis are given. Concentrations measured at school 2h before the medical examination were used for assessing health effects of short term exposure. Over two seasons, this short term BC exposure ranged from 514 ng/m(3) to 6285 ng/m(3), and for NO2 from 11 μg/m(3) to 36 μg/m(3). An integrated exposure was determined until 10 days before the childs examination, taking into account exposures at home and at school and the time spent in each of these microenvironments. Land use regression estimates were therefore recalculated into daily concentrations by using the temporal trend observed at a fixed monitor of the official air quality network. Concentrations at the childrens homes were modeled to estimate long term exposure (from 1457 ng/m(3) to 3874 ng/m(3) for BC; and from 19 μg/m(3) to 51 μg/m(3) for NO2). CONCLUSIONS The land use regression technique proved to be a fast and accurate means for estimating long term and daily BC and NO2 exposure for children living in the Antwerp area. The spatial and temporal resolution was tailored to the needs of the epidemiologists involved in this study.

Airway oxidative stress and inflammation markers in exhaled breath from children are linked with exposure to black carbon

BACKGROUND The current study aimed at assessing the associations between black carbon (BC) exposure and markers for airway inflammation and oxidative stress in primary school children in a Western European urban area. METHODS In 130 children aged 6-12 years old, the fraction of exhaled nitric oxide (FeNO), exhaled breath condensate (EBC) pH, 8-isoprostane and interleukin (IL)-1β were measured in two seasons. BC concentrations on the sampling day (2-h average, 8:00-10:00 AM) and on the day before (24-h average) were assessed using measurements at a central monitoring site. Land use regression (LUR) models were applied to estimate weekly average BC exposure integrated for the time spent at home and at school, and seasonal average BC exposure at the home address. Associations between exposure and biomarkers were tested using linear mixed effect regression models. Next to single exposure models, models combining different BC exposure metrics were used. RESULTS In single exposure models, an interquartile range (IQR) increase in 2-h BC (3.10 μg/m(3)) was linked with a 5.9% (95% CI: 0.1 to 12.0%) increase in 8-isoprostane. FeNO increased by 16.7% (95% CI: 2.2 to 33.2%) per IQR increase in 24-h average BC (4.50 μg/m(3)) and by 12.1% (95% CI: 2.5 to 22.8%) per IQR increase in weekly BC (1.73 μg/m(3)). IL-1β was associated with weekly and seasonal (IQR=1.70 μg/m(3)) BC with respective changes of 38.4% (95% CI: 9.0 to 75.4%) and 61.8% (95% CI: 3.5 to 153.9%) per IQR increase in BC. An IQR increase in weekly BC was linked with a lowering in EBC pH of 0.05 (95% CI: -0.10 to -0.01). All associations were observed independent of sex, age, allergy status, parental education level and meteorological conditions on the sampling day. Most of the associations remained when different BC exposure metrics were combined in multiple exposure models, after additional correction for sampling period or after exclusion of children with airway allergies. In additional analyses, FeNO was linked with 24-h PM10 levels, but the effect size was smaller than for BC. 8-Isoprostane was not linked with either 2-h or 24-h concentrations of PM2.5 or PM10. CONCLUSION BC exposure on the morning of sampling was associated with airway oxidative stress while 24-h and weekly exposures were linked with airway inflammation.

Blood Pressure and Same-Day Exposure to Air Pollution at School: Associations with Nano-Sized to Coarse PM in Children

Background Ultrafine particles (UFP) may contribute to the cardiovascular effects of particulate air pollution, partly because of their relatively efficient alveolar deposition. Objective In this study, we assessed associations between blood pressure and short-term exposure to air pollution in a population of schoolchildren. Methods 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. Results 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. Conclusion 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. Citation Pieters N, Koppen G, Van Poppel M, De Prins S, Cox B, Dons E, Nelen V, Int Panis L, Plusquin M, Schoeters G, Nawrot TS. 2015. Blood pressure and same-day exposure to air pollution at school: associations with nano-sized to coarse PM in children. Environ Health Perspect 123:737–742; http://dx.doi.org/10.1289/ehp.1408121

Physical Activity through Sustainable Transport Approaches (PASTA): a study protocol for a multicentre project

Introduction Only one-third of the European population meets the minimum recommended levels of physical activity (PA). Physical inactivity is a major risk factor for non-communicable diseases. Walking and cycling for transport (active mobility, AM) are well suited to provide regular PA. The European research project Physical Activity through Sustainable Transport Approaches (PASTA) pursues the following aims: (1) to investigate correlates and interrelations of AM, PA, air pollution and crash risk; (2) to evaluate the effectiveness of selected interventions to promote AM; (3) to improve health impact assessment (HIA) of AM; (4) to foster the exchange between the disciplines of public health and transport planning, and between research and practice. Methods and analysis PASTA pursues a mixed-method and multilevel approach that is consistently applied in seven case study cities. Determinants of AM and the evaluation of measures to increase AM are investigated through a large scale longitudinal survey, with overall 14 000 respondents participating in Antwerp, Barcelona, London, Örebro, Rome, Vienna and Zurich. Contextual factors are systematically gathered in each city. PASTA generates empirical findings to improve HIA for AM, for example, with estimates of crash risks, factors on AM-PA substitution and carbon emissions savings from mode shifts. Findings from PASTA will inform WHOs online Health Economic Assessment Tool on the health benefits from cycling and/or walking. The studys wide scope, the combination of qualitative and quantitative methods and health and transport methods, the innovative survey design, the general and city-specific analyses, and the transdisciplinary composition of the consortium and the wider network of partners promise highly relevant insights for research and practice. Ethics and dissemination Ethics approval has been obtained by the local ethics committees in the countries where the work is being conducted, and sent to the European Commission before the start of the survey. The PASTA website (http://www.pastaproject.eu) is at the core of all communication and dissemination activities.

Implementation and validation of a modeling framework to assess personal exposure to black carbon

Because people tend to move from one place to another during the day, their exposure to air pollution will be determined by the concentration at each location combined with the exposure encountered in transport. In order to estimate the exposure of individuals in a population more accurately, the activity-based modeling framework for Black Carbon exposure assessment, AB(2)C, was developed. An activity-based traffic model was applied to model the whereabouts of individual agents. Exposure to black carbon (BC) in different microenvironments is assessed with a land use regression model, combined with a fixed indoor/outdoor factor for exposure in indoor environments. To estimate exposure in transport, a separate model was used taking into account transport mode, timing of the trip and degree of urbanization. The modeling framework is validated using weeklong time-activity diaries and BC exposure as revealed from a personal monitoring campaign with 62 participants. For each participant in the monitoring campaign, a synthetic population of 100 model-agents per day was made up with all agents meeting similar preconditions as each real-life agent. When these model-agents pass through every stage of the modeling framework, it results in a distribution of potential exposures for each individual. The AB(2)C model estimates average personal exposure slightly more accurately compared to ambient concentrations as predicted for the home subzone; however the added value of a dynamic model lies in the potential for detecting short term peak exposures rather than modeling average exposures. The latter may bring new opportunities to epidemiologists: studying the effect of frequently repeated but short exposure peaks on long term exposure and health.

Spatial Variation and Land Use Regression Modeling of the Oxidative Potential of Fine Particles.

Background Oxidative potential (OP) has been suggested to be a more health-relevant metric than particulate matter (PM) mass. Land use regression (LUR) models can estimate long-term exposure to air pollution in epidemiological studies, but few have been developed for OP. Objectives We aimed to characterize the spatial contrasts of two OP methods and to develop and evaluate LUR models to assess long-term exposure to the OP of PM2.5. Methods Three 2-week PM2.5 samples were collected at 10 regional background, 12 urban background, and 18 street sites spread over the Netherlands/Belgium in 1 year and analyzed for OP using electron spin resonance (OPESR) and dithiothreitol (OPDTT). LUR models were developed using temporally adjusted annual averages and a range of land-use and traffic-related GIS variables. Results Street/urban background site ratio was 1.2 for OPDTT and 1.4 for OPESR, whereas regional/urban background ratio was 0.8 for both. OPESR correlated moderately with OPDTT (R2 = 0.35). The LUR models included estimated regional background OP, local traffic, and large-scale urbanity with explained variance (R2) of 0.60 for OPDTT and 0.67 for OPESR. OPDTT and OPESR model predictions were moderately correlated (R2 = 0.44). OP model predictions were moderately to highly correlated with predictions from a previously published PM2.5 model (R2 = 0.37–0.52), and highly correlated with predictions from previously published models of traffic components (R2 > 0.50). Conclusion LUR models explained a large fraction of the spatial variation of the two OP metrics. The moderate correlations among the predictions of OPDTT, OPESR, and PM2.5 models offer the potential to investigate which metric is the strongest predictor of health effects. Citation Yang A, Wang M, Eeftens M, Beelen R, Dons E, Leseman DL, Brunekreef B, Cassee FR, Janssen NA, Hoek G. 2015. Spatial variation and land use regression modeling of the oxidative potential of fine particles. Environ Health Perspect 123:1187–1192; http://dx.doi.org/10.1289/ehp.1408916