Martine Van Poppel

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.

The Aeroflex: A Bicycle for Mobile Air Quality Measurements

Fixed air quality stations have limitations when used to assess peoples real life exposure to air pollutants. Their spatial coverage is too limited to capture the spatial variability in, e.g., an urban or industrial environment. Complementary mobile air quality measurements can be used as an additional tool to fill this void. In this publication we present the Aeroflex, a bicycle for mobile air quality monitoring. The Aeroflex is equipped with compact air quality measurement devices to monitor ultrafine particle number counts, particulate mass and black carbon concentrations at a high resolution (up to 1 second). Each measurement is automatically linked to its geographical location and time of acquisition using GPS and Internet time. Furthermore, the Aeroflex is equipped with automated data transmission, data pre-processing and data visualization. The Aeroflex is designed with adaptability, reliability and user friendliness in mind. Over the past years, the Aeroflex has been successfully used for high resolution air quality mapping, exposure assessment and hot spot identification.

Methodology for setup and data processing of mobile air quality measurements to assess the spatial variability of concentrations in urban environments.

A case study is presented to illustrate a methodology for mobile monitoring in urban environments. A dataset of UFP, PM2.5 and BC concentrations was collected. We showed that repeated mobile measurements could give insight in spatial variability of pollutants at different micro-environments in a city. Streets of contrasting traffic intensity showed increased concentrations by a factor 2-3 for UFP and BC and by <10% for PM2.5. The first quartile (P25) of the mobile measurements at an urban background zone seems to be good estimate of the urban background concentration. The local component of the pollutant concentrations was determined by background correction. The use of background correction reduced the number of runs needed to obtain representative results. The results presented, are a first attempt to establish a methodology for setup and data processing of mobile air quality measurements to assess the spatial variability of concentrations in urban environments.

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.

Wintertime spatio-temporal variation of ultrafine particles in a Belgian city

Simultaneous measurements of ultrafine particles (UFPs) were carried out at four sampling locations situated within a 1 km(2) grid area in a Belgian city, Borgerhout (Antwerp). All sampling sites had different orientation and height of buildings and dissimilar levels of anthropogenic activities (mainly traffic volume). The aims were to investigate: (i) the spatio-temporal variation of UFP within the area, (ii) the effect of wind direction with respect to the volume of traffic on UFP levels, and (iii) the spatial representativeness of the official monitoring station situated in the study area. All sampling sites followed similar diurnal patterns of UFP variation, but effects of local traffic emissions were evident. Wind direction also had a profound influence on UFP concentrations at certain sites. The results indicated a clear influence of local weather conditions and the more dominant effect of traffic volumes. Our analysis indicated that the regional air quality monitoring station represented the other sampling sites in the study area reasonably well; temporal patterns were found to be comparable though the absolute average concentrations showed differences of up to 35%.

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

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.

Changed gene expression in brains of mice exposed to traffic in a highway tunnel.

Context: Air pollution has been suggested to have an impact on the brain. Objective: The objective was to assess the expression of inflammation-related genes in the brains of mice that had been exposed for 5 days to a well-characterized traffic-polluted environment, i.e. a highway tunnel. Materials and methods: Twenty C57BL6 mice were randomly allocated to four groups of five animals. Two groups were placed in the tunnel for 5 days (mean PM 2.5, 55.1 μg/m3, mean elemental carbon, EC 13.9 μg/m3) in cages with or without filter, two control groups were housed outside the tunnel. Animals were assessed within 24 hours after the last exposure day. Lung injury and inflammation were assessed by bronchoalveolar lavage (BAL) and histology. Blood leukocytosis and coagulation parameters were determined in peripheral blood. The olfactory bulb and hippocampus were analyzed for changes in expression of inflammatory genes and brain-derived neurotrophic factor (BDNF). Results and discussion: Although carbon particles were abundant in alveolar macrophages of exposed mice and absent in non-exposed mice, there was no evidence of pulmonary or systemic inflammation. There was an increased expression of genes involved in inflammatory response (COX2, NOS2, NOS3, and NFE2L2) in the hippocampus of the exposed mice. In the olfactory bulb, a downregulation was found for IL1α, COX2, NFE2L2, IL6, and BDNF. Conclusion: Although this short-term exposure to traffic-related pollution did not induce pulmonary or systemic inflammation, the expression of inflammatory genes was affected in different brain areas. The decreased BDNF expression in the olfactory bulb suggests lower brain neurotrophic support in response to traffic-related air pollution.

Prediction of ultrafine particle number concentrations in urban environments by means of Gaussian process regression based on measurements of oxides of nitrogen

Gaussian process regression is used to predict ultrafine particle (UFP) number concentrations. We infer their number concentrations based on the concentrations of NO, NO2, CO and O3 at half hour and 5?min resolution. Because UFP number concentrations follow from a dynamic process, we have used a non-stationary kernel based on the addition of a linear and a rational quadratic kernel. Simultaneous measurements of UFP and gaseous pollutants were carried out during one month at three sampling locations situated within a 1?km2 area in a Belgian city, Antwerp. The method proposed provides accurate predictions when using NO and NO2 as covariates and less accurate predictions when using CO and O3. We have also evaluated the models for different training periods and we have found that a training period of at least seven days is suitable to let the models learn the UFP number concentration dynamics in different typologies of traffic. Prediction of UFP number concentrations using Gaussian process regression.Simultaneous measurement at three urban sites of NO/NO2 and UFP.NO and NO2 are the inputs of the model; UFP is the target variable.Similar model performance at three urban sites at 5 and 30?min resolution.