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Modern incinerator plants are equipped with an efficient system for the removal of pollutants and, hence, the gas and particle emissions are generally extremely low. However, it is possible that malfunctions, together with specific meteorological conditions, temporarily cause significant emissions. In this study, the evolution of the inorganic composition of PM10 samples collected in the vicinity of the Turin incinerator before and after its commissioning was assessed. The purpose was to identify the PM sources present in the area, and to evaluate if the operation of the incinerator caused an increase of the concentration of some species. Significant differences were registered among the composition of samples collected in 2012 and 2014, as the latter year was characterized by higher concentrations of Al, As, Ba, Ca, K, Na, Ni, Pb, Sr, and Zn. Considering the position of the incinerator and of the monitoring station, it seems that this increment was not directly caused by the plant emissions. The most probable source of these elements is the highway vehicular traffic, which might have increased due to the travelling of trucks carrying wastes to the incinerator. However, a direct contribution deriving from the incinerator emissions cannot be excluded.

A growing health concern, due to poor air quality, recently led to an increased number of studies regarding air pollution effects on public health. Consequently, close attention is paid to estimation methods of exposure to atmospheric pollutants. This paper aims to meet a specific requirement of epidemiological researchers, that is providing annual air pollution maps at municipality scale for health impact assessment purposes on national basis. Firstly, data fusion through kriging with external drift is implemented, combining pollution data from two different sources, models and measurements, in order to improve the spatial distribution of surface concentrations at grid level. Then, the assimilated data of air pollution are upscaled, so as to obtain concentrations at municipality level. This methodology was applied to Italy and Spain (in Spain, only the second step was carried out since the modeled concentration already included an assimilation procedure). In both countries, for each municipality, an estimate of the concentration value for atmospheric pollutants of major concern for human health (PM10 and NO2) was provided, offering more relevant information from a surveillance point of view.

Graphical abstract, The aim of MED HISS methodology was to test the effectiveness of a low-cost approach to study long-term effects of air pollution, applicable in all European countries. This approach is potentially exportable to other environmental issues where a cohort representative of the country population is needed. The cohort is derived from the National Health Interview Survey, compulsory in European countries, which has information on individual lifestyle factors. In Life Med Hiss approach, subjects recruited have been linked at individual level with health data and have been then followed-up for mortality and hospital admissions outcomes. Exposure values of air pollution (PM2.5 and NO2) have been assigned using national dispersion models, enhanced by the information derived from monitoring station with data fusion techniques, and then upscaled at municipality level (highest level of detail achievable for the Italian Survey). Results for mortality have been used to test the effectiveness of this methodology and are encouraging if compared with European ones. The advantages of this technique are summarized below: • It uses a cohort already available and compulsory in European countries • It uses air quality modelling data, available for most of the countries • It permits to implement versatile environmental surveillance systems

Background: The LIFE MED HISS project aims at setting up a surveillance system on the long term effects of air pollution on health, using data from National Health Interview Surveys and other currently available sources of information in most European countries. Few studies assessed the long term effect of air pollution on hospital admissions in European cohorts. Objective: The objective of this paper is to estimate the long term effect of fine particulate matter (PM2.5) and nitrogen dioxide (NO2) on first-ever (incident) cause-specific hospitalizations in Italy. Methods: We used data from the Italian Longitudinal Study (ILS), a cohort study based on the 1999–2000 National Health Interview Survey (NHIS), followed up for hospitalization (2001–2008) at individual level. The survey contains information on crucial potential confounders: occupational/educational/marital status, body mass index (BMI), smoking habit and physical activity.Annual mean exposure to PM2.5 and NO2 was assigned starting from simulated gridded data at spatial resolution of 4 × 4 km2 firstly integrated with data from monitoring stations and then up-scaled at municipality level.Statistical analyses were conducted using Cox proportional hazard models with robust variance estimator. Results: For each cause of hospitalization we estimated the hazard ratios (HRs) adjusted for confounders with 95% Confidence Interval (CI) related to a 10 μg/m3 increase in pollutants. For PM2.5 and NO2, respectively, we found positive associations for circulatory system diseases [1.05(1.03–1.06); 1.05(1.03–1.07)], myocardial infarction [1.15(1.12–1.18); 1.15(1.12–1.18)], lung cancer [1.18(1.10–1.26); 1.20(1.12–1.28)], kidney cancer [1.24(1.11–1.29); 1.20(1.07–1.33)], all cancers (but lung) [1.06(1.04–1.08); 1.06(1.04–1.08)] and Low Respiratory Tract Infections (LRTI) [1.07 (1.04–1.11); 1.05 (1.02–1.08)]. Discussion: Our results add new evidence on the effects of air pollution on first-ever (incident) hospitalizations, both in urban and rural areas. We demonstrated the feasibility of a low-cost monitoring system based on available data. Keywords: Outdoor air pollution, Nitrogen dioxide, Particulate matter, National Health Interview Survey, Environmental epidemiology, Hospital admissions, Long-term exposure, Coxph analysis

Background: Air pollution exposure has been associated with an increase in mortality rates, but few studies have focused on life expectancy, and most studies had restricted spatial coverage. A limited body of evidence is also suggestive for a beneficial association between residential exposure to greenness and mortality, but the evidence for such an association with life expectancy is still very scarce. Objective: To investigate the association of exposure to air pollution and greenness with mortality and life expectancy in Spain. Methods: Mortality data from 2148 small areas (average population of 20,750 inhabitants, and median population of 7672 inhabitants) covering Spain for years 2009–2013 were obtained. Average annual levels of PM10, PM2.5, NO2 and O3 were derived from an air quality forecasting system at 4 × 4 km resolution. The normalized difference vegetation index (NDVI) was used to assess greenness in each small area. Air pollution and greenness were linked to standardized mortality rates (SMRs) using Poisson regression and to life expectancy using linear regression. The models were adjusted for socioeconomic status and lung cancer mortality rates (as a proxy for smoking), and accounted for spatial autocorrelation. Results: The increase of 5 μg/m3 in PM10, NO2 and O3 or of 2 μg/m3 in PM2.5 concentration resulted in a loss of life in years of 0.90 (95% credibility interval CI: 0.83, 0.98), 0.13 (95% CI: 0.09, 0.17), 0.20 years (95% CI: 0.16, 0.24) and 0.64 (0.59, 0.70), respectively. Similar associations were found in the SMR analysis, with stronger associations for PM2.5 and PM10, which were associated with an increased mortality risk of 3.7% (95% CI: 3.5%, 4.0%) and 5.7% (95% CI: 5.4%, 6.1%). For greenness, a protective effect on mortality and longer life expectancy was only found in areas with lower socioeconomic status. Conclusions: Air pollution concentrations were associated to important reductions in life expectancy. The reduction of air pollution should be a priority for public health. Keywords: Air pollution, Particulate matter, Ozone, NO2, Greenness, Mortality

Introduction There are few studies assessing the role played by susceptibility factors on mortality related to long term exposure to air pollution. The objective of this study is to estimate the ri...

In the last two decades, increasing attention has been given to air pollution around the world, mainly because of its impact on human health and on the environment. In the Po valley (northern Italy), one of the most troublesome pollutant is PM10 (particulate matter with an aerodynamic diameter of less than 10 micron). In order to assess PM10 concentration over an entire region, environmental agencies need models to predict PM10 at unmonitored sites. To choose among possible predictive models and then meet the agencies’ request, we focus on the class of Bayesian hierarchical models as they provide a flexible framework for incorporating relevant covariates as well as spatio-temporal interactions. We consider six alter- native models for PM10 concentration in Piemonte region (north-western Po Valley), during the winter season October 2005–March 2006. Our aim is to choose a model that is satisfactory in terms of goodness of fit, interpretability, parsimony, prediction capability and computational costs. In order to support this choice, we propose a comparison approach based on a set of criteria summarized in a table that can be easily communicated to non-statisticians. The comparison findings allow to provide Piemonte environmental agencies with an effective statistical model for building reliable PM10 concentration maps, equipped with the corresponding uncertainty measure.

Environmental local agencies have to enforce European directives that impose a land classification, according to air quality status, to distinguish zones needing further actions from those needing only maintenance. This paper presents a land classification in zones featured by different criticality levels of atmospheric pollution, considering pollutant time series as functional data: we call this proposal “Functional Zoning”. Our proposal is articulated in order to also meet two specific requirements: upscaling pollutant concentration data to the municipality scale, since municipalities are the reference territorial administrative units for undertaking actions; aggregating different pollutants in order to provide a multi-pollutant zoning outcome reflecting the air quality status. Specifically, we present three different alternatives to upscale data from a regular grid to the municipality scale. Then, to aggregate by pollutant, we evaluate two strategies summarizing time series: the assessment of an air quality index and the use of the Multivariate Functional Principal Component Analysis (MFPCA). The partition of municipalities is obtained by clustering air quality time series and MFPCA scores. In particular, the proposed functional zoning is carried out for Piemonte (Northern Italy), considering the hourly concentration fields of the main pollutants. We obtain six classifications of the same land and we propose a comparison study of the different strategies’ results, by mapping and analyzing the differences between clusters’ labels. By taking into account the comparison study’s findings, we finally suggest an analysis strategy to environmental agencies and policy makers to obtain an easily interpretable outcome at a very reasonable computational cost.

The Planetary Boundary Layer (PBL) affects the pollutant dispersion process. Within the EpiAir Project (Air pollution and health: epidemiological surveillance and prevention) we performed a preliminary analysis to identify adverse (or opportune) situation for pollutant dispersion. Knowledge of the meteo-diffusive peculiarity of a city is an efficient support in identifying and planning preventive actions.