Regional Reference Center on Environment and Health

Modena, Italy

Regional Reference Center on Environment and Health

Modena, Italy
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Candela S.,Epidemiology Unit | Bonvicini L.,Epidemiology Unit | Ranzi A.,Regional Reference Center on Environment and Health | Baldacchini F.,Epidemiology Unit | And 10 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.


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.


PubMed | Public Health Service, Regional Reference Center on Environment and Health and Epidemiology Unit
Type: | Journal: Environment international | Year: 2015

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 15-49years residing near seven incinerators of the Emilia-Romagna Region (Northern Italy) in the period 2002-2006.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.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).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.


PubMed | Karolinska Institutet, Danish Cancer Society, IUTA Institute fur Energie und Umwelttechnik e.V., University of Augsburg and 24 more.
Type: | Journal: 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.


Beelen R.,University Utrecht | Hoek G.,University Utrecht | Vienneau D.,Imperial College London | Eeftens M.,University Utrecht | And 56 more authors.
Atmospheric Environment | Year: 2013

Estimating within-city variability in air pollution concentrations is important. Land use regression (LUR) models are able to explain such small-scale within-city variations. Transparency in LUR model development methods is important to facilitate comparison of methods between different studies. We therefore developed LUR models in a standardized way in 36 study areas in Europe for the ESCAPE (European Study of Cohorts for Air Pollution Effects) project.Nitrogen dioxide (NO2) and nitrogen oxides (NOx) were measured with Ogawa passive samplers at 40 or 80 sites in each of the 36 study areas. The spatial variation in each area was explained by LUR modelling. Centrally and locally available Geographic Information System (GIS) variables were used as potential predictors. A leave-one out cross-validation procedure was used to evaluate the model performance.There was substantial contrast in annual average NO2 and NOx concentrations within the study areas. The model explained variances (R2) of the LUR models ranged from 55% to 92% (median 82%) for NO2 and from 49% to 91% (median 78%) for NOx. For most areas the cross-validation R2 was less than 10% lower than the model R2. Small-scale traffic and population/household density were the most common predictors. The magnitude of the explained variance depended on the contrast in measured concentrations as well as availability of GIS predictors, especially traffic intensity data were important. In an additional evaluation, models in which local traffic intensity was not offered had 10% lower R2 compared to models in the same areas in which these variables were offered.Within the ESCAPE project it was possible to develop LUR models that explained a large fraction of the spatial variance in measured annual average NO2 and NOx concentrations. These LUR models are being used to estimate outdoor concentrations at the home addresses of participants in over 30 cohort studies. © 2013 Elsevier Ltd.


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.


De Hoogh K.,Imperial College London | Wang M.,University Utrecht | Adam M.,Swiss Tropical and Public Health Institute | Adam M.,University of Basel | And 59 more authors.
Environmental Science and Technology | Year: 2013

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 PM 2.5) explaining on average between 67 and 79% of the concentration variance (R2) 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 R2 ranging from 50 to 61%. Si, NI, and K models for PM2.5 performed poorest with R2 under 50%. The LUR models are used to estimate exposures to elemental composition in the health studies involved in ESCAPE. © 2013 American Chemical Society.


Cyrys J.,Helmholtz Center for Environmental Research | Cyrys J.,University of Augsburg | Eeftens M.,University Utrecht | Heinrich J.,Helmholtz Center for Environmental Research | And 59 more authors.
Atmospheric Environment | Year: 2012

The ESCAPE study (European Study of Cohorts for Air Pollution Effects) investigates long-term effects of exposure to air pollution on human health in Europe. This paper documents the spatial variation of measured NO2 and NOx concentrations between and within 36 ESCAPE study areas across Europe.In all study areas NO2 and NOx were measured using standardized methods between October 2008 and April 2011. On average, 41 sites were selected per study area, including regional and urban background as well as street sites. The measurements were conducted in three different seasons, using Ogawa badges. Average concentrations for each site were calculated after adjustment for temporal variation using data obtained from a routine monitor background site.Substantial spatial variability was found in NO2 and NOx concentrations between and within study areas; 40% of the overall NO2 variance was attributable to the variability between study areas and 60% to variability within study areas. The corresponding values for NOx were 30% and 70%. The within-area spatial variability was mostly determined by differences between street and urban background concentrations. The street/urban background concentration ratio for NO2 varied between 1.09 and 3.16 across areas. The highest median concentrations were observed in Southern Europe, the lowest in Northern Europe.In conclusion, we found significant contrasts in annual average NO2 and NOx concentrations between and especially within 36 study areas across Europe. Epidemiological long-term studies should therefore consider different approaches for better characterization of the intra-urban contrasts, either by increasing of the number of monitors or by modelling. © 2012 Elsevier Ltd.


Eeftens M.,University Utrecht | Tsai M.-Y.,Swiss Tropical and Public Health Institute | Tsai M.-Y.,University of Basel | Tsai M.-Y.,University of Washington | And 54 more authors.
Atmospheric Environment | Year: 2012

The ESCAPE study (European Study of Cohorts for Air Pollution Effects) investigates relationships between long-term exposure to outdoor air pollution and health using cohort studies across Europe. This paper analyses the spatial variation of PM2.5, PM2.5 absorbance, PM10 and PMcoarse concentrations between and within 20 study areas across Europe.We measured NO2, NOx, PM2.5, PM2.5 absorbance and PM10 between October 2008 and April 2011 using standardized methods. PMcoarse was determined as the difference between PM10 and PM2.5. In each of the twenty study areas, we selected twenty PM monitoring sites to represent the variability in important air quality predictors, including population density, traffic intensity and altitude. Each site was monitored over three 14-day periods spread over a year, using Harvard impactors. Results for each site were averaged after correcting for temporal variation using data obtained from a reference site, which was operated year-round.Substantial concentration differences were observed between and within study areas. Concentrations for all components were higher in Southern Europe than in Western and Northern Europe, but the pattern differed per component with the highest average PM2.5 concentrations found in Turin and the highest PMcoarse in Heraklion. Street/urban background concentration ratios for PMcoarse (mean ratio 1.42) were as large as for PM2.5 absorbance (mean ratio 1.38) and higher than those for PM2.5 (1.14) and PM10 (1.23), documenting the importance of non-tailpipe emissions. Correlations between components varied between areas, but were generally high between NO2 and PM2.5 absorbance (average R2 = 0.80). Correlations between PM2.5 and PMcoarse were lower (average R2 = 0.39). Despite high correlations, concentration ratios between components varied, e.g. the NO2/PM2.5 ratio varied between 0.67 and 3.06.In conclusion, substantial variability was found in spatial patterns of PM2.5, PM2.5 absorbance, PM10 and PMcoarse. The highly standardized measurement of particle concentrations across Europe will contribute to a consistent assessment of health effects across Europe. © 2012 Elsevier Ltd.


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.

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