Estimation of local and external contributions of biomass burning to PM2.5 in an industrial zone included in a large urban settlement

A total of 85 PM2.5 samples were collected at a site located in a large industrial zone (Porto Marghera, Venice, Italy) during a 1-year-long sampling campaign. Samples were analyzed to determine water-soluble inorganic ions, elemental and organic carbon, and levoglucosan, and results were processed to investigate the seasonal patterns, the relationship between the analyzed species, and the most probable sources by using a set of tools, including (i) conditional probability function (CPF), (ii) conditional bivariate probability function (CBPF), (iii) concentration weighted trajectory (CWT), and (iv) potential source contribution function (PSCF) analyses. Furthermore, the importance of biomass combustions to PM2.5 was also estimated. Average PM2.5 concentrations ranged between 54 and 16 μg m−3 in the cold and warm period, respectively. The mean value of total ions was 11 μg m−3 (range 1–46 μg m−3): The most abundant ion was nitrate with a share of 44 % followed by sulfate (29 %), ammonium (14 %), potassium (4 %), and chloride (4 %). Levoglucosan accounted for 1.2 % of the PM2.5 mass, and its concentration ranged from few ng m−3 in warm periods to 2.66 μg m−3 during winter. Average concentrations of levoglucosan during the cold period were higher than those found in other European urban sites. This result may indicate a great influence of biomass combustions on particulate matter pollution. Elemental and organic carbon (EC, OC) showed similar behavior, with the highest contributions during cold periods and lower during summer. The ratios between biomass burning indicators (K+, Cl−, NO3−, SO42−, levoglucosan, EC, and OC) were used as proxy for the biomass burning estimation, and the contribution to the OC and PM2.5 was also calculated by using the levoglucosan (LG)/OC and LG/PM2.5 ratios and was estimated to be 29 and 18 %, respectively

Microscopic and mesoscopic traffic models

Besides macroscopic traffic flow models, traffic modelling in freeway systems has also been treated with other general approaches, resulting in microscopic and mesoscopic models. Macroscopic models can surely represent large networks efficiently, since they adopt an aggregate representation of the traffic dynamics, but they generally lack the level of detail needed in modelling the individual drivers\u2019 behaviours and choices. Microscopic models are, instead, conceived to explicitly reproduce the drivers\u2019 responses to traffic patterns, reactions to traffic variations, interactions with other vehicles and route choices, i.e. most of the individual behaviours. Consequently, microscopic models are able to provide a lot of information about the features of traffic flow but they require a high computational effort, especially for large road networks. Mesoscopic models fill the gap between microscopic and macroscopic models, by representing the choices of individual drivers at a probabilistic level, but limiting the level of detail on driving behaviours

Neuroimmune biomarkers in mental illness

Exploration of neuroimmune mechanisms is vital to the understanding of the pathogenesis and pathophysiology of mental disorders. Inflammatory and immune mechanisms are increasingly understood to underpin a number of neuropsychiatric disorders, with an ever-expanding evidence base drawn from basic science to large-scale epidemiological data. Unravelling of these mechanisms should lead to biomarker discovery and potential new avenues for therapeutics that modulate immunological mechanisms. Identification of neuroimmune biomarkers is vital to improving diagnosis, stratification and treatment of mental disorders. There is an urgent clinical need for new therapeutic approaches with poor treatment response and treatment resistance a major problem for many psychiatric disorders including depression and schizophrenia. Neurodegenerative psychiatric disorders such as Alzheimer's also have clear neuroimmune underpinnings and manifest an urgent clinical need for improvements in diagnosis and research towards transformative disease-modifying treatments. This chapter provides some background on the role of the neuroimmune system in mental illness, exploring the role for biomarkers, in addition to reviewing the current state of knowledge in this exciting field. We also reflect on the inherent challenges and methodological pitfalls faced by research in this field, including the complexity of conceptualising multidimensional mental disorders and the dynamic shifting sands of the immune system