Advanced spectroscopic techniques and chemometric analysis for atmospheric organic aerosol characterization and source apportionment

Atmospheric aerosol particles directly impact air quality and participate in controlling the climate system. Organic Aerosol (OA) in general accounts for a large fraction (10–90%) of the global submicron (PM1) particulate mass. Chemometric methods for source identification are used in many disciplines, but methods relying on the analysis of NMR datasets are rarely used in atmospheric sciences. This thesis provides an original application of NMR-based chemometric methods to atmospheric OA source apportionment. The method was tested on chemical composition databases obtained from samples collected at different environments in Europe, hence exploring the impact of a great diversity of natural and anthropogenic sources. We focused on sources of water-soluble OA (WSOA), for which NMR analysis provides substantial advantages compared to alternative methods. Different factor analysis techniques are applied independently to NMR datasets from nine field campaigns of the project EUCAARI and allowed the identification of recurrent source contributions to WSOA in European background troposphere: 1) Marine SOA; 2) Aliphatic amines from ground sources (agricultural activities, etc.); 3) Biomass burning POA; 4) Biogenic SOA from terpene oxidation; 5) “Aged” SOAs, including humic-like substances (HULIS); 6) Other factors possibly including contributions from Primary Biological Aerosol Particles, and products of cooking activities. Biomass burning POA accounted for more than 50% of WSOC in winter months. Aged SOA associated with HULIS was predominant (> 75%) during the spring-summer, suggesting that secondary sources and transboundary transport become more important in spring and summer. Complex aerosol measurements carried out, involving several foreign research groups, provided the opportunity to compare source apportionment results obtained by NMR analysis with those provided by more widespread Aerodyne aerosol mass spectrometers (AMS) techniques that now provided categorization schemes of OA which are becoming a standard for atmospheric chemists. Results emerging from this thesis partly confirm AMS classification and partly challenge it.

Quantifying and modeling ecosystem services provided by urban greening in cities of the Southern Alps, N Italy

Population growth in urban areas is a world-wide phenomenon. According to a recent United Nations report, over half of the world now lives in cities. Numerous health and environmental issues arise from this unprecedented urbanization. Recent studies have demonstrated the effectiveness of urban green spaces and the role they play in improving both the aesthetics and the quality of life of its residents. In particular, urban green spaces provide ecosystem services such as: urban air quality improvement by removing pollutants that can cause serious health problems, carbon storage, carbon sequestration and climate regulation through shading and evapotranspiration. Furthermore, epidemiological studies with controlled age, sex, marital and socio-economic status, have provided evidence of a positive relationship between green space and the life expectancy of senior citizens. However, there is little information on the role of public green spaces in mid-sized cities in northern Italy. To address this need, a study was conducted to assess the ecosystem services of urban green spaces in the city of Bolzano, South Tyrol, Italy. In particular, we quantified the cooling effect of urban trees and the hourly amount of pollution removed by the urban forest. The information was gathered using field data collected through local hourly air pollution readings, tree inventory and simulation models. During the study we quantified pollution removal for ozone, nitrogen dioxide, carbon monoxide and particulate matter (<10 microns). We estimated the above ground carbon stored and annually sequestered by the urban forest. Results have been compared to transportation CO2 emissions to determine the CO2 offset potential of urban streetscapes. Furthermore, we assessed commonly used methods for estimating carbon stored and sequestered by urban trees in the city of Bolzano. We also quantified ecosystem disservices such as hourly urban forest volatile organic compound emissions.

Bayesian space-time data fusion for real-time forecasting and map uncertainty

Environmental computer models are deterministic models devoted to predict several environmental phenomena such as air pollution or meteorological events. Numerical model output is given in terms of averages over grid cells, usually at high spatial and temporal resolution. However, these outputs are often biased with unknown calibration and not equipped with any information about the associated uncertainty. Conversely, data collected at monitoring stations is more accurate since they essentially provide the true levels. Due the leading role played by numerical models, it now important to compare model output with observations. Statistical methods developed to combine numerical model output and station data are usually referred to as data fusion. In this work, we first combine ozone monitoring data with ozone predictions from the Eta-CMAQ air quality model in order to forecast real-time current 8-hour average ozone level defined as the average of the previous four hours, current hour, and predictions for the next three hours. We propose a Bayesian downscaler model based on first differences with a flexible coefficient structure and an efficient computational strategy to fit model parameters. Model validation for the eastern United States shows consequential improvement of our fully inferential approach compared with the current real-time forecasting system. Furthermore, we consider the introduction of temperature data from a weather forecast model into the downscaler, showing improved real-time ozone predictions. Finally, we introduce a hierarchical model to obtain spatially varying uncertainty associated with numerical model output. We show how we can learn about such uncertainty through suitable stochastic data fusion modeling using some external validation data. We illustrate our Bayesian model by providing the uncertainty map associated with a temperature output over the northeastern United States.

Chemistry of aerosol particles and fog droplets during fall-winter season in the Po Valley

Air quality represents a key issue in the so-called pollution “hot spots”: environments in which anthropogenic sources are concentrated and dispersion of pollutants is limited. One of these environments, the Po Valley, normally experiences exceedances of PM10 and PM2.5 concentration limits, especially in winter when the ventilation of the lower layers of the atmosphere is reduced. This thesis provides a highlight of the chemical properties of particulate matter and fog droplets in the Po Valley during the cold season, when fog occurrence is very frequent. Fog-particles interactions were investigated with the aim to determine their impact on the regional air quality. Size-segregated aerosol samples were collected in Bologna, urban site, and San Pietro Capofiume (SPC), rural site, during two campaigns (November 2011; February 2013) in the frame of Supersito project. The comparison between particles size-distribution and chemical composition in both sites showed the relevant contribution of the regional background and secondary processes in determining the Po Valley aerosol concentration. Occurrence of fog in November 2011 campaign in SPC allowed to investigate the role of fog formation and fog chemistry in the formation, processing and deposition of PM10. Nucleation scavenging was investigated with relation to the size and the chemical composition of particles. We found that PM1 concentration is reduced up to 60% because of fog scavenging. Furthermore, aqueous-phase secondary aerosol formation mechanisms were investigated through time-resolved measurements. In SPC fog samples have been systematically collected and analysed since the nineties; a 20 years long database has been assembled. This thesis reports for the first time the results of this long time series of measurements, showing a decrease of sulphate and nitrate concentration and an increase of pH that reached values close to neutrality. A detailed discussion about the occurred changes in fog water composition over two decades is presented.

Sviluppo di tecniche per la progettazione delle reti di monitoraggio della qualita' dell'aria

L’obiettivo del lavoro consiste nell’implementare una metodologia operativa volta alla progettazione di reti di monitoraggio e di campagne di misura della qualità dell’aria con l’utilizzo del laboratorio mobile, ottimizzando le posizioni dei dispositivi di campionamento rispetto a differenti obiettivi e criteri di scelta. La revisione e l’analisi degli approcci e delle indicazioni fornite dalla normativa di riferimento e dai diversi autori di lavori scientifici ha permesso di proporre un approccio metodologico costituito da due fasi operative principali, che è stato applicato ad un caso studio rappresentato dal territorio della provincia di Ravenna. La metodologia implementata prevede l’integrazione di numerosi strumenti di supporto alla valutazione dello stato di qualità dell’aria e degli effetti che gli inquinanti atmosferici possono generare su specifici recettori sensibili (popolazione residente, vegetazione, beni materiali). In particolare, la metodologia integra approcci di disaggregazione degli inventari delle emissioni attraverso l’utilizzo di variabili proxy, strumenti modellistici per la simulazione della dispersione degli inquinanti in atmosfera ed algoritmi di allocazione degli strumenti di monitoraggio attraverso la massimizzazione (o minimizzazione) di specifiche funzioni obiettivo. La procedura di allocazione sviluppata è stata automatizzata attraverso lo sviluppo di un software che, mediante un’interfaccia grafica di interrogazione, consente di identificare delle aree ottimali per realizzare le diverse campagne di monitoraggio

Novel investigation methods in Computational Social Dynamics and Complex Systems

In this thesis the evolution of the techno-social systems analysis methods will be reported, through the explanation of the various research experience directly faced. The first case presented is a research based on data mining of a dataset of words association named Human Brain Cloud: validation will be faced and, also through a non-trivial modeling, a better understanding of language properties will be presented. Then, a real complex system experiment will be introduced: the WideNoise experiment in the context of the EveryAware european project. The project and the experiment course will be illustrated and data analysis will be displayed. Then the Experimental Tribe platform for social computation will be introduced . It has been conceived to help researchers in the implementation of web experiments, and aims also to catalyze the cumulative growth of experimental methodologies and the standardization of tools cited above. In the last part, three other research experience which already took place on the Experimental Tribe platform will be discussed in detail, from the design of the experiment to the analysis of the results and, eventually, to the modeling of the systems involved. The experiments are: CityRace, about the measurement of human traffic-facing strategies; laPENSOcosì, aiming to unveil the political opinion structure; AirProbe, implemented again in the EveryAware project framework, which consisted in monitoring air quality opinion shift of a community informed about local air pollution. At the end, the evolution of the technosocial systems investigation methods shall emerge together with the opportunities and the threats offered by this new scientific path.