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Eeftens M.,University Utrecht | Beelen R.,University Utrecht | De Hoogh K.,Imperial College London | Bellander T.,Karolinska Institutet | And 53 more authors.
Environmental Science and Technology | Year: 2012

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 PM2.5, PM2.5 absorbance, PM10, and PMcoarse 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 (R2) was 71% for PM2.5 (range across study areas 35-94%). Model R2 was higher for PM2.5 absorbance (median 89%, range 56-97%) and lower for PMcoarse (median 68%, range 32- 81%). Models included between two and five predictor variables, with various traffic indicators as the most common predictors. Lower R2 was related to small concentration variability or limited availability of predictor variables, especially traffic intensity. Cross validation R2 results were on average 8-11% lower than model R2. 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. © 2012 American Chemical Society. Source

Candela S.,Epidemiology Unit | Bonvicini L.,Epidemiology Unit | Ranzi A.,Regional Reference Center on Environment and Health | Baldacchini F.,Epidemiology Unit | And 9 more authors.
Environment International | Year: 2015

Background: Miscarriages are an important indicator of reproductive health but only few studies have analyzed their association with exposure to emissions from municipal solid waste incinerators.This study analyzed the occurrence of miscarriages in women aged 15-49. years residing near seven incinerators of the Emilia-Romagna Region (Northern Italy) in the period 2002-2006. Methods: We considered all pregnancies occurring in women residing during the first trimester of pregnancy within a 4km radius of each incinerator. Addresses were geocoded and exposures were characterized by a dispersion model (ADMS Urban model) producing pollution maps for incinerators based on PM10 stack measurements and for other pollution sources based on NOx ground measurements. Information on pregnancies and their outcomes was obtained from the Hospital Discharge Database. Simplified True Abortion Risks (STAR)×100 estimated pregnancies were calculated. We ran logistic regressions adjusting for maternal characteristics, exposure to other sources of pollution, and sites, considering the whole population and stratifying by miscarriage history. Results: The study analyzed 11,875 pregnancies with 1375 miscarriages. After adjusting for confounders, an increase of PM10 due to incinerator emissions was associated with an increased risk of miscarriage (test for trend, p=0.042). The odds ratio for the highest quartile of exposed versus not exposed women was 1.29, 95% CI 0.97-1.72. The effect was present only for women without previous miscarriages (highest quartile of exposed versus not exposed women 1.44, 95% CI 1.06-1.96; test for trend, p=0.009). Conclusion: Exposure to incinerator emissions is associated with an increased risk of miscarriage. This result should be interpreted with those of a previous study on reproductive health conducted in the same area that observed an association between incinerator exposure and preterm births. © 2015 Elsevier Ltd. Source

Candela S.,Epidemiology Unit | Ranzi A.,Regional Reference Center on Environment and Health | Bonvicini L.,Epidemiology Unit | Baldacchini F.,Epidemiology Unit | And 8 more authors.
Epidemiology | Year: 2013

BACKGROUND: The few studies that have investigated the relationship between emissions from municipal solid-waste incinerators and adverse pregnancy outcomes have had conflicting results. We conducted a study to assess the effects of air emissions from the eight incinerators currently in operation in the Emilia-Romagna Region of Italy on reproductive outcomes (sex ratio, multiple births, preterm births, and small for gestational age [SGA] births). METHODS: We considered all births (n = 21,517) to women residing within a 4-km radius of an incinerator at the time of delivery during the period 2003-2010 who were successfully linked to the Delivery Certificate database. This source also provided information on maternal characteristics and deliveries. Each newborn was georeferenced and characterized by a specific level of exposure to incinerator emissions, categorized in quintiles of PM10, and other sources of pollution (NOx quartiles), evaluated by means of ADMS-Urban system dispersion models. We ran logistic regression models for each outcome, adjusting for exposure to other pollution sources and maternal covariates. RESULTS: Incinerator pollution was not associated with sex ratio, multiple births, or frequency of SGA. Preterm delivery increased with increasing exposure (test for trend, P < 0.001); for the highest versus the lowest quintile exposure, the odds ratio was 1.30 (95% confidence interval = 1.08-1.57). A similar trend was observed for very preterm babies. Several sensitivity analyses did not alter these results. CONCLUSIONS: Maternal exposure to incinerator emissions, even at very low levels, was associated with preterm delivery. Copyright © 2013 by Lippincott Williams & Wilkins. Source

Tsai M.-Y.,Swiss Tropical and Public Health Institute | Tsai M.-Y.,University of Basel | Tsai M.-Y.,University of Washington | Hoek G.,University Utrecht | And 62 more authors.
Environment International | Year: 2015

An increasing number of epidemiological studies suggest that adverse health effects of air pollution may be related to particulate matter (PM) composition, particularly trace metals. However, we lack comprehensive data on the spatial distribution of these elements.We measured PM2.5 and PM10 in twenty study areas across Europe in three seasonal two-week periods over a year using Harvard impactors and standardized protocols. In each area, we selected street (ST), urban (UB) and regional background (RB) sites (totaling 20) to characterize local spatial variability. Elemental composition was determined by energy-dispersive X-ray fluorescence analysis of all PM2.5 and PM10 filters. We selected a priori eight (Cu, Fe, K, Ni, S, Si, V, Zn) well-detected elements of health interest, which also roughly represented different sources including traffic, industry, ports, and wood burning.PM elemental composition varied greatly across Europe, indicating different regional influences. Average street to urban background ratios ranged from 0.90 (V) to 1.60 (Cu) for PM2.5 and from 0.93 (V) to 2.28 (Cu) for PM10.Our selected PM elements were variably correlated with the main pollutants (PM2.5, PM10, PM2.5 absorbance, NO2 and NOx) across Europe: in general, Cu and Fe in all size fractions were highly correlated (Pearson correlations above 0.75); Si and Zn in the coarse fractions were modestly correlated (between 0.5 and 0.75); and the remaining elements in the various size fractions had lower correlations (around 0.5 or below). This variability in correlation demonstrated the distinctly different spatial distributions of most of the elements. Variability of PM10_Cu and Fe was mostly due to within-study area differences (67% and 64% of overall variance, respectively) versus between-study area and exceeded that of most other traffic-related pollutants, including NO2 and soot, signaling the importance of non-tailpipe (e.g., brake wear) emissions in PM. © 2015 Elsevier Ltd. Source

Wang M.,University Utrecht | Beelen R.,University Utrecht | Basagana X.,Center for Research in Environmental Epidemiology | Basagana X.,IMIM Hospital del Mar Research Institute | And 44 more authors.
Environmental Science and Technology | Year: 2013

Land use regression models (LUR) frequently use leave-one-out-cross- validation (LOOCV) to assess model fit, but recent studies suggested that this may overestimate predictive ability in independent data sets. Our aim was to evaluate LUR models for nitrogen dioxide (NO2) and particulate matter (PM) components exploiting the high correlation between concentrations of PM metrics and NO2. LUR models have been developed for NO2, PM2.5 absorbance, and copper (Cu) in PM10 based on 20 sites in each of the 20 study areas of the ESCAPE project. Models were evaluated with LOOCV and "hold-out evaluation (HEV)" using the correlation of predicted NO2 or PM concentrations with measured NO2 concentrations at the 20 additional NO2 sites in each area. For NO2, PM2.5 absorbance and PM10 Cu, the median LOOCV R2s were 0.83, 0.81, and 0.76 whereas the median HEV R 2 were 0.52, 0.44, and 0.40. There was a positive association between the LOOCV R2 and HEV R2 for PM2.5 absorbance and PM10 Cu. Our results confirm that the predictive ability of LUR models based on relatively small training sets is overestimated by the LOOCV R2s. Nevertheless, in most areas LUR models still explained a substantial fraction of the variation of concentrations measured at independent sites. © 2013 American Chemical Society. Source

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