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Hypertensive disorders are common complications during pregnancy (HDP) with substantial public health impact. Acute and chronic particulate matter (PM) exposure during pregnancy increases the risk of HDP, although the underlying molecular mechanisms remain unclear. Extracellular vesicles (EVs) may be the ideal candidates for mediating the effects of PM exposure in pregnancy as they are released in response to environmental stimuli. The INSIDE project aims to investigate this mechanism in pregnancy outcomes. The study population is enrolled at the Fetal Medicine Unit of Fondazione IRCCS Ca'Granda-Ospedale Maggiore Policlinico at 10-14 weeks of gestation. Exposure to PM 10 and PM 2.5 is assessed using the flexible air quality regional model (FARM) and Bayesian geostatistical models. Each woman provides a blood sample for EV analysis and circulating biomarker assessment. Moreover, a subgroup of recruited women (n = 85) is asked to participate in a cardiovascular screening program including a standard clinical evaluation, a non-invasive assessment of right ventricular function, and pulmonary circulation at rest and during exercise. These subjects are also asked to wear a personal particulate sampler, to measure PM 10 , PM 2.5 , and PM 1 . The INSIDE study is expected to identify the health impacts of PM exposure on pregnancy outcomes.

Background: Exposure to air pollution is associated with a short-term increase in mortality, and this field has begun to focus on health impact assessment., Objectives: Our aim was to estimate the impact of PM10 on mortality within 2 days from the exposure in the Italian region of Lombardy for the year 2007, at the municipality level, examining exposure entailed by daily intermunicipality commuting and accounting for uncertainty propagation., Methods: We combined data from different sources to derive probabilistic distributions for all input quantities used to calculate attributable deaths (mortality rates, PM10 concentrations, estimated PM10 effects, and commuting flows) and applied a Monte Carlo procedure to propagate uncertainty and sample the distribution of attributable deaths for each municipality., Results: We estimated that annual average PM10 concentrations above the World Health Organization-recommended threshold of 20 μg/m3 were responsible for 865 short-term deaths (80% credibility interval: 475, 1,401), 26% of which were attributable to PM10 above the European Union limit of 40 μg/m3. Reducing annual average PM10 concentrations > 20 μg/m3 by 20% would have reduced the number of attributable deaths by 36%. The largest estimated impacts were along the basin of the Po River and in the largest cities. Commuting contributed to the spatial distribution of the estimated impact., Conclusions: Our estimates, which incorporated uncertainty quantification, indicate that the short-term impact of PM10 on mortality in Lombardy in 2007 was notable, and that reduction in air pollution would have had a substantial beneficial effect on population health. Using commuting data helped to identify critical areas for prioritizing intervention.

A core challenge in epidemiological analysis of the impact of exposure to air pollution on health is assessment of the individual exposure for subjects at risk. Geographical information systems (GIS)-based pollution mapping, such as kriging, has become one of the main tools for evaluating individual exposure to ambient pollutants. We applied universal Bayesian kriging to estimate the residential exposure to gaseous air pollutants for children living in a high-risk area (Milazzo- Valle del Mela in Sicily, Italy). Ad hoc air quality monitoring campaigns were carried out: 12 weekly measurements for sulphur dioxide (SO2) and nitrogen dioxide (NO2) were obtained from 21 passive dosimeters located at each school yard of the study area from November 2007 to April 2008. Universal Bayesian kriging was performed to predict individual exposure levels at each residential address for all 6- to 12-years-old children attending primary school at various locations in the study area. Land use, altitude, distance to main roads and population density were included as covariates in the models. A large geographical heterogeneity in air quality was recorded suggesting complex exposure patterns. We obtained a predicted mean level of 25.78 (± 10.61) µg/m(3) of NO2 and 4.10 (± 2.71) µg/m(3) of SO2 at 1,682 children's residential addresses, with a normalised root mean squared error of 28% and 25%, respectively. We conclude that universal Bayesian kriging approach is a useful tool for the assessment of realistic exposure estimates with regard to ambient pollutants at home addresses. Its prediction uncertainty is highly informative and can be used for both designing subsequent campaigns and for improved modelling of epidemiological associations.