Rapid assessment of As and other elements in naturally-contaminated calcareous soil through hyperspectral VIS-NIR analysis.

Although arsenic (As) toxicity in soil vary depending on its chemical forms and oxidation states, regulatory limits for this compartment rely on total As content. Conventional methods of total As determination are expensive and time-consuming. The development of predictive techniques might enable a speditive assessment of As contamination in those scenarios, such as thermal spring sites, where exposure to the metalloid poses a threat to human health. The objective of this study was to assess the suitability of Visible Near Infrared spectrophotometry for predicting the total As content in highly calcareous thermal spring soils and the same aim was pursued for those elements (i.e. Al, Fe and Mn) the chemistry of which is tightly connected with that of As. A Partial Least Square approach, including cross-validation and external independent test, was used to relate the concentrations of the target elements to spectral data. The most accurate prediction was found for As with Pearson's coefficient, RMSE, RPD and SEP being equal to 0.94, 69.65, 2.9 and 66.99, respectively. Less accurate predictions were found for Al (r = 0.88; RMSE = 11014; RPD = 1.96; SEP = 11014), Fe (r = 0.93; RMSE = 6921.1; RPD = 2.45; SEP = 6462.4), and Mn (r = 0.92; RMSE = 542.01; RPD = 2.43; SEP = 529.79).

Pilot study on the short-term prediction of symptoms in children with hay fever monitored with e-Health technology.

Forecasting symptoms of pollen-related allergic rhinoconjunctivitis at the level of individual patients would be useful to improve disease control and plan pharmacological intervention. Information Technology nowadays facilitates a more efficient and easier monitoring of patients with chronic diseases. We aimed this study at testing the efficiency of a model to short-term forecast symptoms of pollen-AR at the "individual" patient level. We analysed the data prospectively acquired from a group of 21 Italian children affected by pollen-related allergic rhinoconjunctivitis and recorded their symptoms and medication "Average Combined Score" (ACS) on a daily basis during April-June 2010-2011 through an informatics platform (Allergymonitor™). The dataset used for prediction included 15 variables in four categories: (A) date, (B) meteo-climatic, (C) atmospheric concentration of 5 pollen taxa, and (D) intensity of the patient's IgE sensitization. A Partial Least Squares Discriminant Analysis approach was used in order to predict ACS values above a fixed threshold value (0.5). The best performing predicting model correctly classified 77.8% ± 10.3% and 75.5% ± 13.2% of the recorded days in the model and test years, respectively. In this model, 9/21 patients showed ≥ 80% correct classification of the recorded days in both years. A better performance was associated with a higher degree of patient's atopic sensitization and a time lag > 1. Symptom forecasts of seasonal allergic rhinitis are possible in highly polysensitised patients in areas with complex pollen exposure. However, only predictive models tailored to the individual patient's allergic susceptibility are accurate enough. Multicenter studies in large population samples adopting the same acquisition data system on smart phones are now needed to confirm this encouraging outcome.