Alessia Di Gilio

Carbonaceous PM2.5 and secondary organic aerosol across the Veneto region (NE Italy)

Organic and elemental carbon (OC-EC) were measured in 360 PM2.5 samples collected from April 2012 to February 2013 at six provinces in the Veneto region, to determine the factors affecting the carbonaceous aerosol variations. The 60 daily samples have been collected simultaneously in all sites during 10 consecutive days for 6 months (April, June, August, October, December and February). OC ranged from 0.98 to 22.34 μg/m(3), while the mean value was 5.5 μg/m(3), contributing 79% of total carbon. EC concentrations fluctuated from 0.19 to 11.90 μg/m(3) with an annual mean value of 1.31 μg/m(3) (19% of the total carbon). The monthly OC concentration gradually increased from April to December. The EC did not vary in accordance with OC. However the highest values for both parameters were recorded in the cold period. The mean OC/EC ratio is 4.54, which is higher than the values observed in most of the other European cities. The secondary organic carbon (SOC) contributed for 69% of the total OC and this was confirmed by both the approaches OC/EC minimum ratio and regression. The results show that OC, EC and SOC exhibited higher concentration during winter months in all measurement sites, suggesting that the stable atmosphere and lower mixing play important role for the accumulation of air pollutant and hasten the condensation or adsorption of volatile organic compounds over the Veneto region. Significant meteorological factors controlling OC and EC were investigated by fitting linear models and using a robust procedure based on weighted likelihood, suggesting that low wind speed and temperature favour accumulation of emissions from local sources. Conditional probability function and conditional bivariate probability function plots indicate that both biomass burning and vehicular traffic are probably the main local sources for carbonaceous particulate matter emissions in two selected cities.

Neural network model for the prediction of PM10 daily concentrations in two sites in the Western Mediterranean

An artificial neural network (ANN) was developed and tested to forecast PM10 daily concentration in two contrasted environments in NE Spain, a regional background site (Montseny), and an urban background site (Barcelona-CSIC), which was highly influenced by vehicular emissions. In order to predict 24-h average PM10 concentrations, the artificial neural network previously developed by Caselli et al. (2009) was improved by using hourly PM concentrations and deterministic factors such as a Saharan dust alert. In particular, the model input data for prediction were the hourly PM10 concentrations 1-day in advance, local meteorological data and information about air masses origin. The forecasted performance indexes for both sites were calculated and they showed better results for the regional background site in Montseny (R(2)=0.86, SI=0.75) than for urban site in Barcelona (R(2)=0.73, SI=0.58), influenced by local and sometimes unexpected sources. Moreover, a sensitivity analysis conducted to understand the importance of the different variables included among the input data, showed that local meteorology and air masses origin are key factors in the model forecasts. This result explains the reason for the improvement of ANNs forecasting performance at the Montseny site with respect to the Barcelona site. Moreover, the artificial neural network developed in this work could prove useful to predict PM10 concentrations, especially, at regional background sites such as those on the Mediterranean Basin which are primarily affected by long-range transports. Hence, the artificial neural network presented here could be a powerful tool for obtaining real time information on air quality status and could aid stakeholders in their development of cost-effective control strategies.

Discontinuous and Continuous Indoor Air Quality Monitoring in Homes with Fireplaces or Wood Stoves as Heating System

Around 50% of the world’s population, particularly in developing countries, uses biomass as one of the most common fuels. Biomass combustion releases a considerable amount of various incomplete combustion products, including particulate matter (PM) and polycyclic aromatic hydrocarbons (PAHs). The paper presents the results of Indoor Air Quality (IAQ) measurements in six houses equipped with wood burning stoves or fireplaces as heating systems. The houses were monitored for 48-h periods in order to collect PM10 samples and measure PAH concentrations. The average, the maximum and the lowest values of the 12-h PM10 concentration were 68.6 μg/m3, 350.7 μg/m3 and 16.8 μg/m3 respectively. The average benzo[a]pyrene 12-h concentration was 9.4 ng/m3, while the maximum and the minimum values were 24.0 ng/m3 and 1.5 ng/m3, respectively. Continuous monitoring of PM10, PAHs, Ultra Fine Particle (UFP) and Total Volatile Organic Compounds (TVOC) was performed in order to study the progress of pollution phenomena due to biomass burning, their trends and contributions to IAQ. The results show a great heterogeneity of impacts on IAQ in terms of magnitude and behavior of the considered pollutants’ concentrations. This variability is determined by not only different combustion technologies or biomass quality, but overall by different ignition mode, feeding and flame management, which can also be different for the same house. Moreover, room dimensions and ventilation were significant factors for pollution dispersion. The increase of PM10, UFP and PAH concentrations, during lighting, was always detected and relevant. Continuous monitoring allowed singling out contributions of other domestic sources of considered pollutants such as cooking and cigarettes. Cooking contribution produced an impact on IAQ in same cases higher than that of the biomass heating system.

School Air Quality: Pollutants, Monitoring and Toxicity

In recent years the use of synthetic materials in building and furnishing, the adoption of new lifestyles, the extensive use of products for environmental cleaning and personal hygiene have contributed to the deterioration of the indoor air quality (IAQ) and introduced new sources of risk to humans. Indoor environments include home work places such as offices, public buildings such as hospitals, schools, kindergartens, sports halls, libraries, restaurants and bars, theatres and cinemas and finally cabins of vehicles. Indoor environments in schools have been of particular public concern. According to recent studies, children aged between 3 and 14 spend 90 % of the day indoors both in winter and summer. Adverse environmental effects on the learning and performance of students in schools could have both immediate and lifelong consequences, for the students and for society. In fact, children have greater susceptibility to some environmental pollutants than adults, because they breathe higher volumes of air relative to their body weights and their tissues and organs are actively growing. This review describes methods for the assessment of indoor air quality in schools. To this aim, monitoring strategies for sampling and measurement of indoor air pollutants will be discussed. The paper’s goal involves four major points: (1) characteristics of indoor environments, chemical pollutants and their sources within school; (2) monitoring strategies; (3) sampling and analysis techniques; (4) an overview of findings from scientific literature. Finally, we summarize available knowledge about IAQ in schools highlighting key gaps and suggesting priority topics and strategies for research. Moreover, it provides useful tools to support the stakeholder for development of strategies of prevention and mitigation in school environments in order to improve the indoor air quality.

Role of the Ionic Component and Carbon Fractions in the Fine and Coarse Fractions of Particulate Matter for the Identification of Pollution Sources: Application of Receptor Models

Particulate matter (PM) is a very complex mixture of many inorganic and organic compounds of primary and secondary origin and this is the main reason why the desired reduction of its concentration and the identification of its many sources constitute a very difficult task. It is widely recognised that atmospheric particles are responsible for adverse effects on the ecosystem, the climate and the health of human beings (Pope & Dockery, 2006). Epidemiological studies have shown a consistent association of the mass concentration of urban air thoracic particles (PM10 particles with an aerodynamic diameter smaller than 10 μm), and its sub-fraction fine particles (PM2.5 particles with an aerodynamic diameter smaller than 2.5 μm), with mortality and morbidity among cardio-respiratory patients (WHO, 2005). Recent studies indicate that PM10 is associated to respiratory responses while PM2.5 may contribute to cardiovascular diseases (Wyzga, 2002). The chemical characteristics of the particulate fractions and biological mechanisms responsible for these adverse health effects are still unknown as well as the aerosol parameters (mass, particle size, surface area, etc) involved in the health impacts (Hauck et al., 2004). In addition, there is an indication that the increase in the atmospheric aerosol burden delays the global warming attributed to the increase in greenhouse gasses (GHG: CO2, CH4, N2O, halocarbons). Whether the increase in GHGs since preindustrial times is producing a warming of 2.3 Wm,anthropogenic contributions to aerosols (primarily sulphate, organic carbon, black carbon, nitrate and dust) together produce a cooling effect, with a total direct radiative forcing of -0.5 Wm2 and an indirect cloud albedo forcing of -0.7 Wm (IPCC, 2007). In recent years many studies have been carried out to determine the chemical composition of atmospheric particulate matter (Vecchi et al., 2007). Most of these studies were devoted to the identification of the main particle sources, with the purpose to identify viable strategies for their reduction. In this chapter we focus the attention mostly on the ionic component of