Investigation and Optimization of Air Pollution Risk by a Multi-criteria Decision Making Method Using Fuzzy TOPSIS: A Case Study of Construction Workers

Background: Investigating human health during work importantly affect the safety and efficiency of the society. Different types of activity, including industrial, constructional, heavy duty ones, etc., affect the health of workers during their activities. In this study, the Multi-Objective Decision-Making (MODM) method was combined with fuzzy TOPSIS to investigate and optimize the air pollution risk affecting the health and safety of construction workers of Lar City, Fars Province, Iran.Methods: The comparison matrices (binary) and the Phillips–Perron test of different criteria and sub-criteria of health risk, including safety view, safety efficiency, understanding the risk, and risk investigation, were assessed to find the most influential factors for the optimization of the health risk of the workers.Results: Accordingly, the results indicated that “understanding the risk” followed by “safety efficiency” affected the health risk of the construction workers the most. However, among the sub-criteria, the most effective ones were “worker knowledge”, “manager knowledge”, “logistic specialized managers”, “modern facilities”, and “modern technology”. The selection of the linear and non-linear models was conducted according to the F values, and the model parameters were estimated using the Newton-Raphson test. Most coefficients were significant at P= ۰.۹۹. The model also has a high describing value of ۰.۹۷. The final estimated homogeneity coefficient was equal to ۱.۳۱, and it ranged from ۰.۸۶ to ۲.۷۲ for the critical risk investigation. The significance of the present research is to increase the health and safety of workers during work resulting in a more sustainable and healthier environment.Conclusion: Accordingly, the managers could handle the risks, determine the tolerable risks, indicate the risk of each process to control expenses, and take the essential measures for optimization.