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This study reports a spatiotemporal characterization of formaldehyde and acetaldehyde in the summer and winter of 2017 in the urban area of Shiraz, Iran. Sampling was fulfilled according to EPA Method TO-11 A. The inverse distance weighting (IDW) procedure was used for spatial mapping. Monte Carlo simulations were conducted to evaluate carcinogenic and non-cancer risk owing to formaldehyde and acetaldehyde exposure in 11 age groups. The average concentrations of formal-dehyde/acetaldehyde in the summer and winter were 15.07/8.40 μg m−3 and 8.57/3.52 μg m−3, respectively. The formaldehyde to acetaldehyde ratios in the summer and winter were 1.80 and 2.43, respectively. The main sources of formaldehyde and acetaldehyde were photochemical generation, vehicular traffic, and biogenic emissions (e.g., coniferous and deciduous trees). The mean inhalation lifetime cancer risk (LTCR) values according to the Integrated Risk Information System (IRIS) for formaldehyde and acetaldehyde in summer and winter ranged between 7.55 × 10−6 and 9.25 × 10−5, which exceed the recommended value by US EPA. The average LTCR according to the Office of Environmental Health Hazard Assessment (OEHHA) for formaldehyde and acetaldehyde in summer and winter were between 4.82 × 10−6 and 2.58 × 10−4, which exceeds recommended values for five different age groups (Birth to <1, 1 to <2, 2 to < 3, 3 to <6, and 6 to <11 years). Hazard quotients (HQs) of formaldehyde ranged between 0.04 and 4.18 for both seasons, while the HQs for acetaldehyde were limited between 0.42 and 0.97.

Background Metabolic syndrome (MetS) is a major public health concern due to its high prevalence and association with heart disease and diabetes. Artificial neural networks (ANN) are emerging as a reliable means of modelling relationships towards understanding complex illness situations such as MetS. Using ANN, this research sought to clarify predictors of metabolic syndrome (MetS) in a working age population. Methods Four hundred sixty-eight employees of an oil refinery in Iran consented to providing anthropometric and biochemical measurements, and survey data pertaining to lifestyle, work-related stressors and sleep variables. National Cholesterol Education Programme Adult Treatment Panel ІІI criteria was used for determining MetS status. The Management Standards Indicator Tool and STOP-BANG questionnaire were used to measure work-related stress and obstructive sleep apnoea respectively. With 17 input variables, multilayer perceptron was used to develop ANNs in 16 rounds of learning. ANNs were compared to logistic regression models using the mean squared error criterion for validation. Results Sex, age, exercise habit, smoking, high risk of obstructive sleep apnoea, and work-related stressors, particularly Role, all significantly affected the odds of MetS, but shiftworking did not. Prediction accuracy for an ANN using two hidden layers and all available input variables was 89%, compared to 72% for the logistic regression model. Sensitivity was 82.5% for ANN compared to 67.5% for the logistic regression, while specificities were 92.2 and 74% respectively. Conclusions Our analyses indicate that ANN models which include psychosocial stressors and sleep variables as well as biomedical and clinical variables perform well in predicting MetS. The findings can be helpful in designing preventative strategies to reduce the cost of healthcare associated with MetS in the workplace.

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