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Describes the atmospheric dispersion of pollutants emitted by a thermal power plant using a three-dimensional modelling system, made by a mass-consistent wind field model and by a Lagrangian particle model. Application of intensive meteorological and air quality field campaigns; Emission rate of sulphur dioxide; Factor that will cause ground-level concentration increases at coastal stations.

The results obtained in convective boundary layer dispersion by two Lagrangian particle models (according to Thomson's 1984 and 1987 papers) are compared. First, the sensitivity of the two models in accounting for the third-order moment of vertical wind speed fluctuations has been assessed, considering homogeneous non-Gaussian turbulence. Then, the ability of the two models to simulate the ground-level concentration field has been tested, by comparing their predictions with tracer data measured in two experiments. The first is one of the well-known water tank experiments performed by Willis and Deardorff in 1978,the second is the Katrex field experiment (Thomas et al., 1983). Theresults obtained in the case of the controlled conditions seem to recommend the use of the model based on Thomson's 1987 paper, whereas the performances of both models are comparable in simulating a real case. [ABSTRACT FROM AUTHOR]

Horizontal non-uniformity has important effects on the structure of the atmospheric boundary layer and, consequently, on pollutant transport and dispersion. These effects can be dominant in coastal sites orin the presence of complex terrain. Advanced 3-D atmospheric dispersion models can be used to calculate short-term pollutant concentrations in such complex situations. The use of new generation models (puff, Eulerian or particle models) in the frame of pollution control networks requires a real-time, fast and reliable evaluation of surface layer similarity scale variables, able to take account of the horizontal non-homogeneity of the computed parameters. A meteorological pre-processor has been built to calculate atmospheric turbulence scale parameters (u*, L, Ho, zi, W*) over a 2-D domain, covering land-use variations, local meteorological data (surface data,SODAR, RASS), and results from diagnostic wind field models. The code implements updated methods to calculate similarity scale parameters, both on shore and over sea (lake) surfaces. The pre-processor is installed in the atmospheric pollution control network of a thermal power plant in Northern Italy, where a system consisting of a mass-consistent wind field model and a Lagrangian particle model gives real-time estimates of pollutant concentrations on an hourly base, using the local air quality control network data. [ABSTRACT FROM AUTHOR]

Objectives: the health status of people living near industrial plants is often exposed to several environmental risk factors, including air pollution. The aim of this study is to assess the relationship between daily PM10 levels and cause-specific mortality in a selection of municipalities near two industrial plants from 2006 to 2015., Design: a time-series design with Poisson regression adjusted for a predefined set of confounders was used to quantify the association between exposure, calculated as daily PM10 levels extrapolated from machine-learning models using satellite data, and cause-specific mortality., Setting and Participants: twenty municipalities near the thermal power plants in Civitavecchia and Brindisi were selected. The municipalities were then divided into three scenarios of chronic exposure derived from SPRAY simulation models of pollutant deposition., Main Outcome Measures: daily cause-specific non-accidental, cardiovascular, and respiratory deaths defined according to the International Classification of Diseases code at the municipality level., Results: a total of 41,942 deaths were observed in the entire area (10,503 in the Civitavecchia area and 31,439 in the Brindisi area), of which approximately 41% were due to cardiovascular causes and 8% due to respiratory causes. The association showed an increase in shortterm effects in municipalities with higher chronic levels of pollution exposure. For example, risk estimates reported as percentage increases per 10-unit increase in PM10 were 6.7% (95% CI 0.9, 12.7%) in scenario 3 (highest exposure) compared to 4.2% (-1.2, 9.9%) and 2.7% (-4.2, 10.2%) in scenarios 2 and 1, respectively, in the area near the Civitavecchia plant. Similar effects were observed for the Brindisi area., Conclusions: despite the well-documented relationship between short-term pollution and mortality, it appears that greater chronic exposure to industrial pollutants leads to increased short-term effects of PM10. The limited number of events suggests that this study could serve as a starting point for a larger investigation.

Background/aim: The relationship between air pollution and respiratory morbidity has been widely addressed in urban and metropolitan areas but little is known about the effects in non-urban settings. Our aim was to assess the short-term effects of PM10 and PM2.5 on respiratory admissions in the whole country of Italy during 2006-2015., Methods: We estimated daily PM concentrations at the municipality level using satellite data and spatiotemporal predictors. We collected daily counts of respiratory hospital admissions for each Italian municipality. We considered five different outcomes: all respiratory diseases, asthma, chronic obstructive pulmonary disease (COPD), lower and upper respiratory tract infections (LRTI and URTI). Meta-analysis of province-specific estimates obtained by time-series models, adjusting for temperature, humidity and other confounders, was applied to extrapolate national estimates for each outcome. At last, we tested for effect modification by sex, age, period, and urbanization score. Analyses for PM 2.5 were restricted to 2013-2015 cause the goodness of fit of exposure estimation., Results: A total of 4,154,887 respiratory admission were registered during 2006-2015, of which 29% for LRTI, 12% for COPD, 6% for URTI, and 3% for asthma. Daily mean PM 10 and PM 2.5 concentrations over the study period were 23.3 and 17 μg/m 3 , respectively. For each 10 μg/m 3 increases in PM 10 and PM 2.5 at lag 0-5 days, we found excess risks of total respiratory diseases equal to 1.20% (95% confidence intervals, 0.92, 1.49) and 1.22% (0.76, 1.68), respectively. The effects for the specific diseases were similar, with the strongest ones for asthma and COPD. Higher effects were found in the elderly and in less urbanized areas., Conclusions: Short-term exposure to PM is harmful for the respiratory system throughout an entire country, especially in elderly patients. Strong effects can be found also in less urbanized areas., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Background: Storage tanks in oil and gas processing facilities contain large volumes of flammable compounds. Once the fuel-air mixture is ignited, it may break out into a large fire or explosion. The growing interest in monitoring air quality and assessing health risks makes the evaluation of the consequences of a fire an important issue. Atmospheric dispersion models, which allow for simulation of the spatial distribution of pollutants, represent an increasingly widespread tool for this type of evaluations., Objectives: The present study discusses the set up and results of a modeling study relevant to a hypothesized fire in an oil refinery., Methods: After choosing the most suitable dispersion models, i.e. the Lagrangian model SPRAY and the puff model CALPUFF, estimation of the required input data is discussed, focusing on the source variables, which represent the most uncertain input data. The results of the simulations were compared to regulatory limits to effectively evaluate the environmental consequences. Finally, a sensitivity analysis was employed to identify the most influential variables., Results: The simulation results revealed that ground concentration values were far below the cited long-term limits. However, the most interesting outcome is that depending on the dispersion model and the source type modeled, different results may be obtained. In addition, the sensitivity study indicates that the source area is the most critical variable, since it determines a significantly different behavior depending on the modeled source types, producing, in some cases, variability in the pollutant ground concentrations on selected receptors up to +/- 60%., Conclusions: Depending on the selected model and the algorithms available to describe the physics of emission, the results showed a different sensitivity to the input variables. Although this can be explained from a mathematical point of view, the problem remains of choosing case by case the option that best approximates the real behavior of the incidental source under investigation., Competing Interests: The authors declare no competing financial interests., (© Pure Earth 2021.)

Long-term exposure to air pollution has been related to mortality in several epidemiological studies. The investigations have assessed exposure using various methods achieving different accuracy in predicting air pollutants concentrations. The comparison of the health effects estimates are therefore challenging. This paper aims to compare the effect estimates of the long-term effects of air pollutants (particulate matter with aerodynamic diameter less than 10 μm, PM 10 , and nitrogen dioxide, NO 2 ) on cause-specific mortality in the Rome Longitudinal Study, using exposure estimates obtained with different models and spatial resolutions. Annual averages of NO 2 and PM 10 were estimated for the year 2015 in a large portion of the Rome urban area (12 × 12 km 2 ) applying three modelling techniques available at increasing spatial resolution: 1) a chemical transport model (CTM) at 1km resolution; 2) a land-use random forest (LURF) approach at 200m resolution; 3) a micro-scale Lagrangian particle dispersion model (PMSS) taking into account the effect of buildings structure at 4 m resolution with results post processed at different buffer sizes (12, 24, 52, 100 and 200 m). All the exposures were assigned at the residential addresses of 482,259 citizens of Rome 30+ years of age who were enrolled on 2001 and followed-up till 2015. The association between annual exposures and natural-cause, cardiovascular (CVD) and respiratory (RESP) mortality were estimated using Cox proportional hazards models adjusted for individual and area-level confounders. We found different distributions of both NO 2 and PM 10 concentrations, across models and spatial resolutions. Natural cause and CVD mortality outcomes were all positively associated with NO 2 and PM 10 regardless of the model and spatial resolution when using a relative scale of the exposure such as the interquartile range (IQR): adjusted Hazard Ratios (HR), and 95% confidence intervals (CI), of natural cause mortality, per IQR increments in the two pollutants, ranged between 1.012 (1.004, 1.021) and 1.018 (1.007, 1.028) for the different NO 2 estimates, and between 1.010 (1.000, 1.020) and 1.020 (1.008, 1.031) for PM 10 , with a tendency of larger effect for lower resolution exposures. The latter was even stronger when a fixed value of 10 μg/m 3 is used to calculate HRs. Long-term effects of air pollution on mortality in Rome were consistent across different models for exposure assessment, and different spatial resolutions., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Developmental dyslexia is one of the most common neurobehavioral disorders affecting children, but prevalence data on this condition are poor. The objective of the present study is to determine the prevalence of dyslexia in Italy in an unselected school population, using clearly defined diagnostic criteria and methods., Methods: Cross-sectional study carried out in nine Italian Regions: two located in Northern Italy (Friuli Venezia Giulia and Veneto), three in Central Italy (Marche, Lazio and Umbria) and four in Southern Italy (Abruzzo, Molise, Puglia and Sardegna). Three consecutive levels of screening were carried out: the first two at school, to screen the population and identify children with suspect dyslexia; the last in centers with multi-professional staff specialized in learning disabilities to confirm the diagnosis. The key outcome measure is the prevalence of dyslexia, defined as the ratio between the number of children confirmed positive at the third level of screening and the total number of children enrolled in the study., Results: We finally recruited 11094 children aged 8-10 years, of which 9964 constituted the final working sample after applying exclusion criteria and including only children who received parents' consent to participate. The prevalence of dyslexia in the whole sample was 3.5% (95% CI 3.2-3.9%), with little differences between Northern, Central and Southern Italy (respectively 3.6%, 3.2% and 3.7%). In almost two out of three children with dyslexia the disorder had not been previously diagnosed., Conclusions: This study confirms that in primary school children at the age of 8-10 years in Italy dyslexia is widely underestimated. Reliable data on dyslexia prevalence are needed to allocate necessary human and financial resources both to Health Services and Schools, ensuring timely support to children and families., Competing Interests: The authors have declared that no competing interests exist.

The composition in Volatile Organic Compounds (VOC) of the biogas produced by seven landfills of Giugliano (Naples, Campania, Italy) was determined and VOC emission rates assessed to verify if these compounds represent a potential threat to the population living nearby. VOC composition in the biogas could not be predicted, as heterogeneous waste was dumped from the late 1980s to the early 2000s and then underwent biological degradation. No data are available on the amount and composition of VOC in the biogas before the landfills closure as no operational biogas collection system was present. In this study, VOC composition was determined by gas chromatography-mass spectrometry (GC-MS), after collecting samples from collection pipes and from soil fractures in cover soil or capping. Individual VOC were quantified and data compared with those collected at two landfills in Latium, when they were still in operation. Relevant differences were observed, mainly due to waste aging, but no specific VOC revealing toxic waste dumping was found, although the concurrent presence of certain compounds suggested that dumping of industrial wastes might have occurred. The average VOC emission was assessed and a dispersion model was run to find out if the emitted plume could affect the health of population. The results suggested that fugitive emissions did not represent a serious danger, since the concentrations simulated at the neighboring cities were below the threshold limits for acute and chronic diseases. However, VOC plume could cause annoyance at night when the steady state conditions of the atmosphere enhance pollutants accumulation in the lower layers. In addition, some of the emitted VOC, such as alkylbenzenes and monoterpenes, can contribute to tropospheric ozone formation., (Copyright © 2018 Elsevier B.V. All rights reserved.)