Olfactory nuisances are an issue of growing concern considering that people's awareness about the effect of pollution on health and environment is increasing and that the perception of odor is related to a possible warning situation. A recent review concluded that odor assessment has to be faced by an integrated multi tool strategy. In this paper we discuss the building of a model by means of a chemometric approach based on artificial neural networks known as Self Organizing Maps. These are applied to data collected by electronic nose continuous monitoring. The Self Organizing Map output was subjected to a second level clusterization by k-means algorithm. The cluster interpretation (i.e. air types classification in terms of "malodor"/"odor free" attributes) is achieved by crosslinking data produced by different instrumental and sensorial approaches, allowing us to establish the Frequency-Intensity-Duration odor characteristics for every identified air type. In order to elucidate our approach we focused our work on a four months survey at a residential site close to an integral cycle steel plant in Trieste (Italy). Odor Control Map proved to be a promising tool to provide valuable visualization support for following the dynamic evolution of the system with time. It allowed us to, for example, identify the relationships among sensor responses in different air types; follow the changes of air types with time. identify possible malodor sources; experimentally evaluate the frequency and duration of air types classified as malodorous. Furthermore, this first application highlighted the possible method improvements that have to be tested in different real environmental scenarios to obtain more robust and refined models. Considering that different annoyances (e.g., odor, noise, presence of specific chemical compounds) can cause possible synergistic health effects, Odor Control Map is a suitable tool to integrate data deriving from different and independent analysis/monitoring to obtain a more comprehensive knowledge on complex environmental phenomena involving dwellings close to industrial plants. (C) 2018 Elsevier B.V. All rights reserved.

The aim of this work is to evaluate the air quality in a mixed urban-industrial environment. Concentration gradients can often be strong, owing to the complex orographic and meteorological context. It can be therefore misleading to rely only on the monitoring network. We used a Eulerian chemistry-transport model (CTM) to simulate the regional background concentrations of PM10, and a Lagrangian model (LM) to simulate the local impact of the emissions from an iron production plant. Firstly, regional scale background concentrations were estimated with a data fusion approach, interpolating the observations of the background stations using concentrations simulated by the CTM as a proxy. Secondly, the residuals over the urban stations were interpolated using concentrations simulated by the LM as a proxy. The performance of this modelling approach was evaluated using independent data and through a leave-one-out cross validation. The effectiveness of different strategies for impact mitigation was also assessed.

This study investigated the risk of lung and bladder cancers in people residing in proximity of a coal-oil-fired thermal power plant in an area of north-eastern Italy, covered by a population-based cancer registry. Incidence rate ratios (IRR) by sex, age, and histology were computed according to tertiles of residential exposure to benzene, nitrogen dioxide (NO2), particular matter, and sulfur dioxide (SO2) among 1076 incident cases of lung and 650 cases of bladder cancers. In men of all ages and in women under 75 years of age, no significant associations were observed. Conversely, in women aged ≥75 years significantly increased risks of lung and bladder cancers were related to high exposure to benzene (IRR for highest vs. lowest tertile: 2.00 for lung cancer and 1.94 for bladder cancer) and NO2 (IRR: 1.72 for lung cancer; and 1.94 for bladder cancer). In these women, a 1.71-fold higher risk of lung cancer was also related to a high exposure to SO2. Acknowledging the limitations of our study, in particular that we did not have information regarding cigarette smoking habits, the findings of this study indicate that air pollution exposure may have had a role with regard to the risk of lung and bladder cancers limited to women aged ≥75 years. Such increased risk warrants further analytical investigations.

In this study, we investigate the dynamics of the bottom layer of the southern Adriatic Sea (eastern Mediterranean basin) by merging experimental measurements and numerical simulations. We hypothesize that the recently observed continuous density decrease over time, which was basically related to a temperature increase, and the following sudden density rise, which was caused by the intrusion of very dense water masses (cold but relatively fresh), constitute one cycle of a general saw-tooth pattern: the alternation of long-lasting and almost linear density decreases (mixing phases) and sudden density increases (dense water intrusion phases). The model results, which provide a basin-scale view of the process, corroborate this theory because they satisfactorily reproduced the observed oceanographic features. We describe the almost linear density decrease in terms of local mixing fostered by the advection of flow instabilities that originate from the large-scale quasi-permanent cyclonic circulation. Conversely, diffusive processes play a minor role in determining the bottom layer thermohaline variability. The interpretation of the experimental findings, supported by the numerical simulations, suggests that similar dynamics might be observed in other basins characterized by similar bathymetric and hydrodynamic features.

The problem of pollutant dispersion is a crucial issue for life quality in urban areas.

A significant percentage of atmospheric threats for people and property are associated to severe weather events, e.g., to phenomena that occur at small scales (say a few kilometres) and in short amounts of time (say from a few minutes up to a few hours). For this reason adaptation policies would achieve relevant benefits from a circumstantial estimate of how frequency and intensity of these severe weather events might change in the future due to the global climate change. Even if state-of-the-art climate models, both global and regional, can supply precise information on how much and how fast global temperature and rain pattern might change due to climate variations, the same numerical models can not take into account directly small scale events. Nevertheless, the challenge of global change impacts on severe weather is not a lost battle, or at least a battle that is not worth to be fight, this because there are a few possible and complementary approaches, both based on climate models, that can be taken into account. Unfortunately, even the previous mentioned approaches would be correct, state-of-the-art climate models might still be not ready for this task because of their troubles in reproducing correctly the large scale quantities (the ingredients) necessary to infer the needed information on the future frequency and intensity of local severe weather events. Climate models, however, already have a lot of room to be improved with parametrizations (the so called “physics” of the models) that might better reproduce a wide variety of atmospheric behaviours. A lot of work has to be done, but the road is not yet closed, and does not seem, so far, that there are insurmountable walls at the horizon.

The measurement of the physical characteristics of hailstones reaching the ground is usually carried out by means of hailpads, on which the impact of hailstones leaves dents. Hailstone dents provide information about parameters, such as the number N of hailstones, their size M, and their kinetic energy E. In the case of intense hailfalls, however, the dents often overlap and the final measurement may not be totally reliable. This paper presents a computerized simulation with the aim of assessing measurement errors caused by dent overlap. The simulated dents represent several random hailfalls with both exponential size distributions and mono-dispersed size distributions. The simulated hailpads were measured following the procedure employed in the case of hailpads exposed to authentic hailfalls, and it was thus possible to assess the error due to dent overlap. The results show that dent overlap makes it impossible to measure all the dents, which means that in a real hailfall the number of hailstones registered will often be lower than the number of hailstones that actually hit the ground (up to 25% may go undetected). Consequently, the energy and mass of the hailstones are also underestimated (they may be up to 50% higher than the values registered on a hailpad). The maximum size registered, however, does not depend on the degree of overlapping and neither does the slope parameter A of the exponential distribution, except when lambda takes higher values. Finally, the authors suggest a heuristic correction of the data obtained by real hailpads based on the results of the simulations. An example is provided that applies these corrections to the 228 hailfalls registered by the Italian hailpad network over a period of 10 yr. The results show that, on average, the correction applied because of overlapping increases the number of hailstones in 3.2%, the mass in 1.9%, and the energy in 5.4%. However, there are cases in which these corrections reached much higher values of up to 6.9% in N and M, and up to 25.2% in E. It is therefore advisable to correct dent overlap before carrying out a regional climatic study of hail, since this study would certainly be affected by the errors accumulated by all the hailpads.

In this work, eight years of cloud-to-ground lightning data are used as a proxy observable to describe the hourly frequency of deep moist convection occurrence over an area characterized by complex geography in the southern side of the Alps (Friuli Venezia Giulia). The study area is divided in eight sub-zones, defined according to the climatic differences of the southern side of the Alps, in particular those related to the precipitation regime. In the eight sub-zones, the hourly frequency of cloud-to-ground lightning shows two different behaviours: bimodal and single-mode. Single-mode hourly frequencies, in turn, can be divided into two more classes: afternoon maximum and evening maximum. A conceptual model for the explanation of the observed features is proposed. This conceptual model takes into account the atmospheric instability, which has a maximum during afternoon, and the convection forcing represented by the switch in the breezes regime, which is stronger in the late morning and evening. The proposed conceptual model is coherent with the complementary meteorological parameters taken into account (hourly rain, temperature and wind distribution and speed) and it is capable to describe, at least qualitatively, the observed behaviours in cloud-to-ground lightning hourly distribution.

This paper is an overview of hailpad research from its origin until today using as a search criterion the bibliographic references on the subject in the Web of Science database (ISI). The search was carried out on 1st September 2008. Among the more than 3·107 scientific documents included in the Science Citation Index Expanded (SCI-EXPANDED) from 1945 to the present, the search engine identified 41 containing the word “hailpad⁎” (the asterisk is a wildcard for any letter or group of letters).