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The road transport sector plays a vital role in economic development and vehicle numbers are growing. It provides a set of services to meet the different demands of travel and it is a necessity for human civilization. However, although it is an essential element in regional development schemes, it generates negative externalities, thus constituting one of the most important sources of environmental pollution. This paper aims to develop modelling vehicle emissions, especially, the HC, CO and NOx based on experimental speed profiles, acceleration and technical parameters related to the used vehicle. This helps to determine and study vehicle emissions factor related to different pollutant. Two methods are used to develop two different empirical models: the multiple regression and Artificial Neural Network (ANN). The developed approach was applied to two types of vehicle with different technical characteristics. It was observed that the multiple linear regression method allows to predict vehicle emissions with a coefficient of determination between 0.723 and 0.921 but the ANN model can predict exhaust gases with a correlation coefficient in the range of 0.95–0.99. Simulation results demonstrate the efficiency and superiority of the ANN tool to estimate vehicle emissions compared to multiple linear regression approach.

The use of chlorinated solvents in dry cleaning poses risks to human health. The current study is the first in Tunisia and Sfax city that aims to assess the occupational exposure and health risks of perchloroethylene (PCE) and trichloroethylene (TCE) in the dry cleaning industry. Forty-seven dry cleaning facilities were identified by door-to-door canvassing within a 5 km radius from the city centre, but only 33 accepted to join this study. The exposure assessment was conducted by indoor air measurements for 16 randomly selected facilities. The exposure levels were compared to occupational exposure limit values (OELV). Hazard quotient (HQ) method was used and cancer risks were evaluated. Hazard quotients for both solvents are enormously above 1, with an order of magnitude equal or higher than 103. Corresponding risks are developmental and immune effects for TCE and neurotoxicity for PCE. These results showed that inhalation exposure levels might induce many adverse effects that are probably threatening the occupationally exposed population and even the general one, because of the location of all facilities in residential settings. As for carcinogenic effects, high kidney, hematologic or liver cancer risks were noticed if the lifetime exposure would have these average levels. This study is significant for a better understanding of the dry cleaning industrial sector in Tunisia. Indeed, it responded to many interrogations and suspicions about the qualitative and quantitative exposure conditions, and thus the health status of dry cleaning workers in Sfax.

International audience; Background - Sfax is a very industrialized city located in the southern region of Tunisia where heavy metals (HMs) pollution is now an established matter of fact. The health of its residents mainly those engaged in industrial metals-based activities is under threat. Indeed, such workers are being exposed to a variety of HMs mixtures, and this exposure has cumulative properties. Whereas current HMs exposure assessment is mainly carried out using direct air monitoring approaches, the present study aims to assess health risks associated with chronic occupational exposure to HMs in industry, using a modeling approach that will be validated later on.Methods - To this end, two questionnaires were used. The first was an identification/descriptive questionnaire aimed at identifying, for each company: the specific activities, materials used, manufactured products and number of employees exposed. The second related to the job-task of the exposed persons, workplace characteristics (dimensions, ventilation, etc.), type of metals and emission configuration in space and time. Indoor air HMs concentrations were predicted, based on the mathematical models generally used to estimate occupational exposure to volatile substances (such as solvents). Later on, and in order to validate the adopted model, air monitoring will be carried out, as well as some biological monitoring aimed at assessing HMs excretion in the urine of workers volunteering to participate. Lastly, an interaction-based hazard index HI and a decision support tool will be used to predict the cumulative risk assessment for HMs mixtures.Discussion - One hundred sixty-one persons working in the 5 participating companies have been identified. Of these, 110 are directly engaged with HMs in the course of the manufacturing process. This model-based prediction of occupational exposure represents an alternative tool that is both time-saving and cost-effective in comparison with direct air monitoring approaches. Following validation of the different models according to job processes, via comparison with direct measurements and exploration of correlations with biological monitoring, these estimates will allow a cumulative risk characterization.