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We found an association of the pollution sources with some cancer forms and cardio-respiratory diseases. Although there was a high correlation between the estimated exposures, an indication of specific effects from the different sources emerged.

LUR and dispersion model estimates correlated on average well for NO2 but only moderately for PM10 and PM2.5, with large variability across areas. DM predicted a moderate to large proportion of the measured variation for NO2 but less for PM10 and PM2.5.

Particulate matter mass concentrations measured in the city of Rome (Italy) in the period 2001–2004 have been cross-analysed with concurrent Saharan dust advection events to infer the impact these natural episodes bear on the standard air quality parameter PM₁₀ observed at two city stations and at one regional background station. Natural events such as Saharan dust advections are associated with a definite health risk. At the same time, the Directive 2008/50/EC allows subtraction of PM exceedances caused by natural contributions from statistics used to determine air quality of EU sites. In this respect, it is important to detect and characterise such advections by means of reliable, operational techniques. To assess the PM₁₀ increase we used both the "regional-background method" suggested by EC Guidelines and a "local background" method, demonstrated to be most suited to this central Mediterranean region. In terms of exceedances, the two approaches provided results within ~20% of each other at background sites, and at ~50% of each other in traffic conditions.

The sequence of Saharan advections over the city has been either detected by Polarization Lidar (laser radar) observations or forecast by the operational numerical regional mineral dust model BSC-DREAM8b of the Barcelona Supercomputing Centre. Lidar observations were also employed to retrieve the average physical properties of the dust clouds as a function of height. Over the four-year period, Lidar measurements (703 evenly distributed days) revealed Saharan plumes transits over Rome on 28.6% of the days, with minimum occurrence in wintertime. Dust was observed to reach the ground on 17.5% of the days totalling 88 episodes. Most (90%) of these advections lasted up to 5 days, averaging to ~3 days. Median time lag between advections was 7 days. Typical altitude range of the dust plumes was 0–6 km, with the centre of mass at ~3 km a.g.l. BSC-DREAM8b model simulations (1461 days) predicted Lidar detectable (532 nm extinction coefficient > 0.005 km⁻¹) dust advections on 25.9% of the days, with ground contacts on 13% of the days. As in the Lidar case, the average dust centre of mass was forecast at ~3 km. Along the 703 day Lidar dataset, model forecast and Lidar detection of the presence of dust coincided on 80% of the cases, 92% coincidences are found within a ±1 day window.

Combination of the BSC-DREAM8b and Lidar records leads to about 21% of the days being affected by presence of Saharan dust at the ground. This combined dataset has been used to compute the increase in PM with respect to dust-unaffected previous days. This analysis has shown Saharan dust events to exert a meaningful impact on the PM₁₀ records, causing average increases of the order of 11.9 μg m⁻³. Conversely, PM₁₀ increases computed relying only on the Lidar detections (i.e., presence of dust layers actually observed) were of the order of 15.6 μg m⁻³. Both analyses indicate the annual average contrib...

Background: The evidence on the health effects related to residing close to landfills is controversial. Nine landfills for municipal waste have been operating in the Lazio region (Central Italy) for several decades. We evaluated the potential health effects associated with contamination from landfills using the estimated concentration of hydrogen sulphide (H 2 S) as exposure. Methods : A cohort of residents within 5 km of landfills was enrolled (subjects resident on 1 January 1996 and those who subsequently moved into the areas until 2008) and followed for mortality and hospitalizations until 31 December 2012. Assessment of exposure to the landfill (H 2 S as a tracer) was performed for each subject at enrolment, using a Lagrangian dispersion model. Information on several confounders was available (gender, age, socioeconomic position, outdoor PM 10 concentration, and distance from busy roads and industries). Cox regression analysis was performed [Hazard Ratios (HRs), 95% confidence intervals (CIs)]. Results: The cohort included 242 409 individuals. H 2 S exposure was associated with mortality from lung cancer and respiratory diseases (e.g. HR for increment of 1 ng/m 3 H 2 S: 1.10, 95% CI 1.02–1.19; HR 1.09, 95% CI 1.00–1.19, respectively). There were also associations between H 2 S and hospitalization for respiratory diseases (HR = 1.02, 95% CI 1.00–1.03), especially acute respiratory infections among children (0–14 years) (HR = 1.06, 95% CI 1.02–1.11). Conclusions: Exposure to H 2 S, a tracer of airborne contamination from landfills, was associated with lung cancer mortality as well as with mortality and morbidity for respiratory diseases. The link with respiratory disease is plausible and coherent with previous studies, whereas the association with lung cancer deserves confirmation.

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Background Environmental pollution and weather changes unfavorably impact on cardiovascular disease. However, limited research has focused on ST-elevation myocardial infarction (STEMI), the most severe yet distinctive form of acute coronary syndrome. Methods and results We appraised the impact of environmental and weather changes on the incidence of STEMI, analysing the bivariate and multivariable association between several environmental and atmospheric parameters and the daily incidence of STEMI in two large Italian urban areas. Specifically, we appraised: carbon monoxide (CO), nitrogen dioxide (NO2), nitric oxide (NOX), ozone, particulate matter smaller than 10 μm (PM10) and than 2.5 μm (PM2.5), temperature, atmospheric pressure, humidity and rainfall. A total of 4285 days at risk were appraised, with 3473 cases of STEMI. Specifically, no STEMI occurred in 1920 (44.8%) days, whereas one or more occurred in the remaining 2365 (55.2%) days. Multilevel modelling identified several pollution and weather predictors of STEMI. In particular, concentrations of CO ( p=0.024), NOX ( p=0.039), ozone ( p=0.003), PM10 ( p=0.033) and PM2.5 ( p=0.042) predicted STEMI as early as three days before the event, as well as subsequently, and NO predicted STEMI one day before ( p = 0.010), as well as on the same day. A similar predictive role was evident for temperature and atmospheric pressure (all p < 0.05). Conclusions The risk of STEMI is strongly associated with pollution and weather features. While causation cannot yet be proven, environmental and weather changes could be exploited to predict STEMI risk in the following days.