Luisella Ciancarella

Association between PM10, PM2.5, NO2, O3 and self-reported diabetes in Italy: A cross-sectional, ecological study

Introduction Air pollution represents a serious threat to health on a global scale, being responsible for a large portion of the global burden of disease from environmental factors. Current evidence about the association between air pollution exposure and Diabetes Mellitus (DM) is still controversial. We aimed to evaluate the association between area-level ambient air pollution and self-reported DM in a large population sample in Italy. Materials and methods We extracted information about self-reported and physician diagnosed DM, risk factors and socio-economic status from 12 surveys conducted nationwide between 1999 and 2013. We obtained annual averaged air pollution levels for the years 2003, 2005, 2007 and 2010 from the AMS-MINNI national integrated model, which simulates the dispersion and transformation of pollutants. The original maps, with a resolution of 4 x 4 km2, were normalized and aggregated at the municipality class of each Italian region, in order to match the survey data. We fit logistic regression models with a hierarchical structure to estimate the relationship between PM10, PM2.5, NO2 and O3 four-years mean levels and the risk of being affected by DM. Results We included 376,157 individuals aged more than 45 years. There were 39,969 cases of DM, with an average regional prevalence of 9.8% and a positive geographical North-to-South gradient, opposite to that of pollutants’ concentrations. For each 10 μg/m3 increase, the resulting ORs were 1.04 (95% CI 1.01–1.07) for PM10, 1.04 (95% CI 1.02–1.07) for PM2.5, 1.03 (95% CI 1.01–1.05) for NO2 and 1.06 (95% CI 1.01–1.11) for O3, after accounting for relevant individual risk factors. The associations were robust to adjustment for other pollutants in two-pollutant models tested (ozone plus each other pollutant). Conclusions We observed a significant positive association between each examined pollutant and prevalent DM. Risk estimates were consistent with current evidence, and robust to sensitivity analysis. Our study adds evidence about the effects of air pollution on diabetes and suggests a possible role of ozone as an independent factor associated with the development of DM. Such relationship is of great interest for public health and deserves further investigation.

Study of the Impact of Low vs. High Resolution Meteorology on Air Quality Simulations Using the MINNI Model Over Italy

Modelling air quality requires the description of a large number of processes interacting each other. In order to properly model concentrations of atmospheric pollutants it is crucial to have a realistic reproduction of meteorological parameters, which can be critical in areas presenting a complex orography like the Italian peninsula. This work shows an analysis of the results obtained with the national model MINNI at two different horizontal resolutions (20 and 4 km), for a whole year over Italy. Comparisons between modelled and observed temperature and pollutants concentrations are carried out. The prediction of temperature is improved with the increase of model spatial resolution, as it is for pollutants like NO2 and CO, while the improvement is not always evident for O3 concentrations. Results are discussed providing an interpretation of the observed features.

Mapping air pollutants at municipality level in Italy and Spain in support to health impact evaluations

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.

Application of a Land Cover Indicator to Characterize Spatial Representativeness of Air Quality Monitoring Stations Over Italy

In order to achieve a cost-effective control of air quality in one region and to evaluate effects on population of long term exposure to air pollution, the assessment of spatial representativeness of air quality monitoring stations is of fundamental relevance. In this work, the area of representativeness has been assessed by means of a synthetic indicator describing the dependency of concentration on land cover distribution. The rationale is that, the more variable is the indicator in the surroundings of the station, the less representative are the concentrations measured at the air quality station in the surroundings. Pollutants under investigation were PM2.5 and O3 and the CORINE land cover map of 2006 was used with ad hoc modifications. The variability of the indicator was explored within circular buffers around the sites, with increasing radii resulting below the established threshold of 20 % for almost all cases. Results showed that the methodology allows an useful and quick assessment of spatial representativeness of a monitoring site, without the need of dedicated measurement campaigns.

Air quality modeling and inhalation health risk assessment for a new generation coal-fired power plant in Central Italy

An assessment of potential carcinogenic and toxic health outcomes related to atmospheric emissions from the new-generation coal fired power plant of Torrevaldaliga Nord, in Central Italy, has been conducted. A chemical-transport model was applied on the reference year 2010 in the area of the plant, in order to calculate airborne concentrations of a set of 17 emitted pollutants of health concern. Inhalation cancer risks and hazard quotients, for each pollutant and for each target organ impacted via the inhalation pathway, were calculated and mapped on the study domain for the overall ambient concentrations and for the sole contribution of the plant to airborne concentrations, allowing to assess the relative contribution of the power plant to the risk from all sources. Cancer risks, cumulated on all pollutants, resulted around 5 × 10-5 for the concentrations from all sources and below 3 × 10-7 for the plant contribution, mainly targeting the respiratory system. On each part of the study domain, the plant contributed for less than 6% to the overall cancer risk. Hazard quotients from all sources, cumulated on all pollutants, reached values of 2.5 for the respiratory and 1.5 for the cardiovascular systems. Hazard quotients of non-carcinogenic risks from the plant, cumulated on all pollutants, resulted below 0.03 for the respiratory system and 0.02 for the cardiovascular system. On each part of the study domain, the plant contributed for less than 5% to the respiratory and cardiovascular risks. Both cancer risks and hazard quotients related to the plant are far below international thresholds for human health protection, while the values from all sources require consideration. The proposed method provides an instrument for prospective health risk assessment of large industrial sources, with some limitations presented and discussed.

M-TraCE: a new tool for high-resolution computation and statistical elaboration of backward trajectories on the Italian domain

Air backward trajectory calculations are commonly used in a variety of atmospheric analyses, in particular for source attribution evaluation. The accuracy of backward trajectory analysis is mainly determined by the quality and the spatial and temporal resolution of the underlying meteorological data set, especially in the cases of complex terrain. This work describes a new tool for the calculation and the statistical elaboration of backward trajectories. To take advantage of the high-resolution meteorological database of the Italian national air quality model MINNI, a dedicated set of procedures was implemented under the name of M-TraCE (MINNI module for Trajectories Calculation and statistical Elaboration) to calculate and process the backward trajectories of air masses reaching a site of interest. Some outcomes from the application of the developed methodology to the Italian Network of Special Purpose Monitoring Stations are shown to assess its strengths for the meteorological characterization of air quality monitoring stations. M-TraCE has demonstrated its capabilities to provide a detailed statistical assessment of transport patterns and region of influence of the site under investigation, which is fundamental for correctly interpreting pollutants measurements and ascertaining the official classification of the monitoring site based on meta-data information. Moreover, M-TraCE has shown its usefulness in supporting other assessments, i.e., spatial representativeness of a monitoring site, focussing specifically on the analysis of the effects due to meteorological variables.