Trace metals in size-fractionated particulate matter in a Portuguese hospital: exposure risks assessment and comparisons with other countries

Hospitals are considered as a special and important type of indoor public place where air quality has significant impacts on potential health outcomes. Information on indoor air quality of these environments, concerning exposures to particulate matter (PM) and related toxicity, is limited though. This work aims to evaluate risks associated with inhalation exposure to ten toxic metals and chlorine (As, Ni, Cr, Cd, Pb, Mn, Se, Ba, Al, Si, and Cl) in coarse (PM2.5–10) and fine (PM2.5) particles in a Portuguese hospital in comparison with studies representative of other countries. Samples were collected during 1 month in one urban hospital; elemental PM characterization was determined by proton-induced X-ray emission. Noncarcinogenic and carcinogenic risks were assessed according to the methodology provided by the United States Environmental Protection Agency (USEPA; Region III Risk-Based Concentration Table) for three different age categories of hospital personnel (adults, >20, and <65 years) and patients (considering nine different age groups, i.e., children of 1–3 years to seniors of >65 years). The estimated noncarcinogenic risks due to occupational inhalation exposure to PM2.5-bound metals ranged from 5.88×10−6 for Se (adults, 55–64 years) to 9.35×10−1 for As (adults, 20–24 years) with total noncarcinogenic risks (sum of all metals) above the safe level for all three age categories. As and Cl (the latter due to its high abundances) were the most important contributors (approximately 90 %) to noncarcinogenic risks. For PM2.5–10, noncarcinogenic risks of all metals were acceptable to all age groups. Concerning carcinogenic risks, for Ni and Pb, they were negligible (<1×10−6) in both PM fractions for all age groups of hospital personnel; potential risks were observed for As and Cr with values in PM2.5 exceeding (up to 62 and 5 times, respectively) USEPA guideline across all age groups; for PM2.5–10, increased excess risks of As and Cr were observed particularly for long-term exposures (adults, 55–64 years). Total carcinogenic risks highly (up to 67 times) exceeded the recommended level for all age groups, thus clearly showing that occupational exposure to metals in fine particles pose significant risks. If the extensive working hours of hospital medical staff were considered, the respective noncarcinogenic and carcinogenic risks were increased, the latter for PM2.5 exceeding the USEPA cumulative guideline of 10−4. For adult patients, the estimated noncarcinogenic and carcinogenic risks were approximately three times higher than for personnel, with particular concerns observed for children and adolescents.

A Clinical Support System Based on Quality of Life Estimation

Quality of life is a concept influenced by social, economic, psychological, spiritual or medical state factors. More specifically, the perceived quality of an individual's daily life is an assessment of their well-being or lack of it. In this context, information technologies may help on the management of services for healthcare of chronic patients such as estimating the patient quality of life and helping the medical staff to take appropriate measures to increase each patient quality of life. This paper describes a Quality of Life estimation system developed using information technologies and the application of data mining algorithms to access the information of clinical data of patients with cancer from Otorhinolaryngology and Head and Neck services of an oncology institution. The system was evaluated with a sample composed of 3013 patients. The results achieved show that there are variables that may be significant predictors for the Quality of Life of the patient: years of smoking (p value 0.049) and size of the tumor (p value < 0.001). In order to assign the variables to the classification of the quality of life the best accuracy was obtained by applying the John Platt's sequential minimal optimization algorithm for training a support vector classifier. In conclusion data mining techniques allow having access to patients additional information helping the physicians to be able to know the quality of life and produce a well-informed clinical decision.