Health effects of air pollution, especially particulate matter (PM), have been widely investigated. However, most of the studies rely on few monitors located in urban areas for short-term ...assessments, or land use/dispersion modelling for long-term evaluations, again mostly in cities. Recently, the availability of finely resolved satellite data provides an opportunity to estimate daily concentrations of air pollutants over wide spatio-temporal domains. Italy lacks a robust and validated high resolution spatio-temporally resolved model of particulate matter. The complex topography and the air mixture from both natural and anthropogenic sources are great challenges difficult to be addressed. We combined finely resolved data on Aerosol Optical Depth (AOD) from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, ground-level PM10 measurements, land-use variables and meteorological parameters into a four-stage mixed model framework to derive estimates of daily PM10 concentrations at 1-km2 grid over Italy, for the years 2006–2012. We checked performance of our models by applying 10-fold cross-validation (CV) for each year. Our models displayed good fitting, with mean CV-R2=0.65 and little bias (average slope of predicted VS observed PM10=0.99). Out-of-sample predictions were more accurate in Northern Italy (Po valley) and large conurbations (e.g. Rome), for background monitoring stations, and in the winter season. Resulting concentration maps showed highest average PM10 levels in specific areas (Po river valley, main industrial and metropolitan areas) with decreasing trends over time. Our daily predictions of PM10 concentrations across the whole Italy will allow, for the first time, estimation of long-term and short-term effects of air pollution nationwide, even in areas lacking monitoring data.
•Estimates of air pollution levels at fine spatiotemporal scale are lacking in Italy•We combined satellite data with land use variables and ground-level PM10 measurements•We estimated daily PM10 concentrations at a 1-km2 grid over Italy, 2006-2012•Our model displayed good cross-validation fitting (R2=0.65) and negligible bias•Spatiotemporal predictions will allow estimation of short and long term health effects of PM10
The association between short-term air pollution exposure and daily mortality has been widely investigated, but little is known about the temporal variability of the effect estimates. We examined the ...temporal relationship between exposure to particulate matter (PM) (PM
, PM
) and gases (NO
, SO
, and CO) with mortality in a large metropolitan area over the last 17 y.
Our analysis included 359,447 nonaccidental deaths among ≥35-y-old individuals in Rome, Italy, over the study period 1998–2014. We related daily concentrations to mortality counts with a time-series Poisson regression analysis adjusted for long-term trends, meteorology, and population dynamics.
Annual average concentrations decreased over the study period for all pollutants (e.g., from 42.9 to 26.6 μg/m
for PM
). Each pollutant was positively associated with mortality, with estimated percentage increases over the entire study period ranging from 0.19% (95% CI: 0.13, 0.26) for a 1-Mg/m
increase in CO (0–1 d lag) to 3.03% (95% CI: 2.44, 3.63) for a 10-μg/m
increase in NO
(0–5 d lag). We did not observe clear temporal patterns in year- or period-specific effect estimates for any pollutant. For example, we estimated that a 10-μg/m
increase in PM
was associated with 1.16% (95% CI: 0.53, 1.79), 0.99% (95% CI: 0.23, 1.77), and 1.87% (95% CI: 1.00, 2.74) increases in mortality for the periods 2001–2005, 2006–2010, and 2011–2014, respectively, and corresponding estimates for a 10-μg/m
increase in NO
were 4.20% (95% CI: 3.15, 5.25), 1.78% (95% CI: 0.73, 2.85), and 3.32% (95% CI: 2.03, 4.63).
Mean concentrations of air pollutants have decreased over the last two decades in Rome, but effect estimates for a fixed increment in each exposure were generally consistent. These findings suggest that there has been little or no change in the overall toxicity of the air pollution mixture over time. https://doi.org/10.1289/EHP19.
The role of chronic exposure to ambient air pollutants in increasing COVID-19 fatality is still unclear.
The study aimed to investigate the association between long-term exposure to air pollutants ...and mortality among 4 million COVID-19 cases in Italy.
We obtained individual records of all COVID-19 cases identified in Italy from February 2020 to June 2021. We assigned 2016-2019 mean concentrations of particulate matter (PM) with aerodynamic diameter
(
), PM with aerodynamic diameter
(
), and nitrogen dioxide (
) to each municipality (
) as estimates of chronic exposures. We applied a principal component analysis (PCA) and a generalized propensity score (GPS) approach to an extensive list of area-level covariates to account for major determinants of the spatial distribution of COVID-19 case-fatality rates. Then, we applied generalized negative binomial models matched on GPS, age, sex, province, and month. As additional analyses, we fit separate models by pandemic periods, age, and sex; we quantified the numbers of COVID-19 deaths attributable to exceedances in annual air pollutant concentrations above predefined thresholds; and we explored associations between air pollution and alternative outcomes of COVID-19 severity, namely hospitalizations or accesses to intensive care units.
We analyzed 3,995,202 COVID-19 cases, which generated 124,346 deaths. Overall, case-fatality rates increased by 0.7% 95% confidence interval (CI): 0.5%, 0.9%, 0.3% (95% CI: 0.2%, 0.5%), and 0.6% (95% CI: 0.5%, 0.8%) per
increment in
,
, and
, respectively. Associations were higher among elderly subjects and during the first (February 2020-June 2020) and the third (December 2020-June 2021) pandemic waves. We estimated
COVID-19 deaths were attributable to pollutant levels above the World Health Organization 2021 air quality guidelines.
We found suggestive evidence of an association between long-term exposure to ambient air pollutants with mortality among 4 million COVID-19 cases in Italy. https://doi.org/10.1289/EHP11882.
Background: Little is known about the short-term effects of ultrafine particles. Methods: We evaluated the effect of particulate matter with an aerodynamic diameter ≤10 μm (PM10), ≤2.5 μm (PM2.5), ...and ultrafine particles on emergency hospital admissions in Rome 2001–2005. We studied residents aged ≥35 years hospitalized for acute coronary syndrome, heart failure, lower respiratory tract infections, and chronic obstructive pulmonary disease (COPD). Information was available for factors indicating vulnerability, such as age and previous admissions for COPD. Particulate matter data were collected daily at one central fixed monitor. A case-crossover analysis was performed using a time-stratified approach. We estimated percent increases in risk per 14 μg/m³ PM10, per 10 μg/m³ PM2.5, and per 9392 particles/mL. Results: An immediate impact (lag 0) of PM on hospitalizations for acute coronary syndrome (2.3% 95% confidence interval = 0.5% to 4.2%) and heart failure (2.4% 0.3% to 4.5%) was found, whereas the effect on lower respiratory tract infections (2.8% 0.5% to 5.2%) was delayed (lag 2). Particle number concentration showed an association only with admissions for heart failure (lag 0–5; 2.4% 0.2% to 4.7%) and COPD (lag 0; 1.6% 0.0% to 3.2%). The effects were generally stronger in the elderly and during winter. There was no clear effect modification with previous COPD. Conclusions: We found sizeable acute health effects of fine and ultrafine particles. Although differential reliability in exposure assessment, in particular of ultrafine particles, precludes a firm conclusion, the study indicates that particulate matter of different sizes tends to have diverse outcomes, with dissimilar latency between exposure and health response.
ObjectivesFew studies have assessed the effects of policies aimed to reduce traffic-related air pollution. The aims of this study were to evaluate the impact, in terms of air quality and health ...effects, of two low-emission zones established in Rome in the period 2001–2005 and to assess the impact by socioeconomic position (SEP) of the population.MethodsWe evaluated the effects of the intervention on various stages in the full-chain model, that is, pressure (number and age distribution of cars), emissions, PM10 and NO2 concentrations, population exposure and years of life gained (YLG). The impact was evaluated according to a small-area indicator of SEP.ResultsDuring the period 2001–2005, there was a decrease in the total number of cars (−3.8%), NO2 and PM10 emissions and concentrations (from 22.9 to 17.4 μg/m3 for NO2 and from 7.8 to 6.2 μg/m3 for PM10), and in the residents' exposure. In the two low-emission zones, there was an additional decrease in air pollution concentrations (NO2: −4.13 and −2.99 μg/m3; PM10: −0.70 and −0.47 μg/m3). As a result of the policy, 264 522 residents living along busy roads gained 3.4 days per person (921 YLG per 100 000) for NO2 reduction. The gain was larger for people in the highest SEP group (1387 YLG per 100 000) than for residents in the lowest SEP group (340 YLG per 100 000).ConclusionThe traffic policy in Rome was effective in reducing traffic-related air pollution, but most of the health gains were found in well-off residents.
ObjectiveEnvironmental air pollution has been associated with disruption of the immune system at a molecular level. The primary aim of the present study was to describe the association between ...long-term exposure to air pollution and risk of developing immune-mediated conditions.MethodsWe conducted a retrospective observational study on a nationwide dataset of women and men. Diagnoses of various immune-mediated diseases (IMIDs) were retrieved. Data on the monitoring of particulate matter (PM)10 and PM2.5 concentrations were retrieved from the Italian Institute of Environmental Protection and Research. Generalised linear models were employed to determine the relationship between autoimmune diseases prevalence and PM.Results81 363 subjects were included in the study. We found a positive association between PM10 and the risk of autoimmune diseases (ρ+0.007, p 0.014). Every 10 µg/m3 increase in PM10 concentration was associated with an incremental 7% risk of having autoimmune disease. Exposure to PM10 above 30 µg/m3 and PM2.5 above 20 µg/m3 was associated with a 12% and 13% higher risk of autoimmune disease, respectively (adjusted OR (aOR) 1.12, 95% CI 1.05 to 1.20, and aOR 1.13, 95% CI 1.06 to 1.20). Exposure to PM10 was associated with an increased risk of rheumatoid arthritis; exposure to PM2.5 was associated with an increased risk of rheumatoid arthritis, connective tissue diseases (CTDs) and inflammatory bowel diseases (IBD).ConclusionLong-term exposure to air pollution was associated with higher risk of developing autoimmune diseases, in particular rheumatoid arthritis, CTDs and IBD. Chronic exposure to levels above the threshold for human protection was associated with a 10% higher risk of developing IMIDs.
Wildfires are relevant sources of PM emissions and can have an important impact on air pollution and human health. In this study, we examine the impact of wildfire PM emissions on the Piemonte ...(Italy) air quality regional monitoring network using a Generalized Additive Mixed Model. The model is implemented with daily PM10 and PM2.5 concentrations sampled for 8 consecutive years at each monitoring site as the response variable. Meteorological data retrieved from the ERA5 dataset and the observed burned area data stored in the Carabinieri Forest Service national database are used in the model as explanatory variables. Spline functions for predictive variables and smooths for multiple meteorological variables’ interactions improved the model performance and reduced uncertainty levels. The model estimates are in good agreement with the observed PM data: adjusted R2 range was 0.63–0.80. GAMMs showed rather satisfactory results in order to capture the wildfires contribution: some severe PM pollution episodes in the study area due to wildfire air emissions caused peak daily levels up to 87.3 µg/m3 at the Vercelli PM10 site (IT1533A) and up to 67.7 µg/m3 at the Settimo Torinese PM2.5 site (IT1130A).
Minimetrò (MM) is a ropeway public mobility system that has been in operation in the city of Perugia for about ten years to integrate with urban mobility and lighten vehicular traffic in the historic ...city center. The purpose of this work was to evaluate the impact of MM as a source of pollutants in the urban context, and the exposure of people in the cabins and the platforms along the MM line. These topics have been investigated by means of intensive measurement and sampling campaigns performed in February and June 2015 on three specific sites of the MM line representative of different sources and levels of urban pollution. Stationary and dynamic measurements of particle size distribution, nanoparticle and black carbon aerosol number and mass concentrations measurements were performed by means of different bench and portable instruments. Aerosol sampling was carried out using low volume and high-volume aerosol samplers, and the samples nalysed by off-line methods. Results show that MM is a considerable source of atmospheric particulate matter having characteristics very similar to those of the common urban road dust in Perugia. In the lack of clear indications on road dust effect, the contribution of MM to the aerosol in Perugia cannot be neglected.
This paper illustrates the main results of a spatio-temporal interpolation process of PM10 concentrations at daily resolution using a set of 410 monitoring sites, distributed throughout the Italian ...territory, for the year 2015. The interpolation process is based on a Bayesian hierarchical model where the spatial-component is represented through the Stochastic Partial Differential Equation (SPDE) approach with a lag-1 temporal autoregressive component (AR1). Inference is performed through the Integrated Nested Laplace Approximation (INLA). Our model includes 11 spatial and spatio-temporal predictors, including meteorological variables and Aerosol Optical Depth. As the predictors’ impact varies across months, the regression is based on 12 monthly models with the same set of covariates. The predictive model performance has been analyzed using a cross-validation study. Our results show that the predicted and the observed values are well in accordance (correlation range: 0.79–0.91; bias: 0.22–1.07μg/m3; RMSE: 4.9–13.9μg/m3). The model final output is a set of 365 gridded (1 km × 1 km) daily PM10 maps over Italy equipped with an uncertainty measure. The spatial prediction performance shows that the interpolation procedure is able to reproduce the large scale data features without unrealistic artifacts in the generated PM10 surfaces. The paper presents also two illustrative examples of practical applications of our model, exceedance probability and population exposure maps.
•Bayesian spatio-temporal hierarchical model for PM10 concentrations.•Model estimation and spatial prediction implemented using the INLA-SPDE approach.•Daily high-resolution PM10 concentrations map for a large domain like Italy.•Gaussian Markov random fields allow fast PM10 estimate on large spacetime domains.•Probability of exceedance and population exposure map as secondary model outcomes.
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