Poor safety conditions and performance are consequences of individual factors as well as organizational and group factors. However, little attention has been afforded to the sequential impact of these factors on safety-related behaviors (compliance and participation) in the Saudi Arabian electrical construction industry. This study examines the causal effects of leadership and attitudes on safety compliance and participation mediated by motivation and knowledge. The research collected 636 surveys in electrical construction projects for nine large contractors between November 2018 and July 2019 in Saudi Arabia. Structural equation modeling (SEM) was used to determine the mechanism by which leadership and attitudes affected safety compliance and participation through motivation and knowledge. The results indicate that safety leadership and attitude factors as well as their interactions predicted safety motivation and knowledge. Additionally, these factors affected safety participation and compliance via workers’ motivation and knowledge. Safety motivation and safety knowledge positively affected workers’ participation and compliance. Management should encourage and regularly assess effective leadership and attitudes and developing motivation and knowledge among employees can improve organizations’ safety-related behavior performance.

Although marine fishing is one of the most hazardous occupations, research on the occupational safety and health (OSH) conditions aboard marine fishing vessels is scarce. For instance, little is known about the working conditions of vulnerable groups such as young and aging fishermen. The objective of the current paper is to study the OSH conditions of young and aging fishermen compared to middle-aged fishermen in the small- and medium-size (SM) marine fishing sector. A cross-sectional study was designed, and 686 fishermen working aboard SM fishing vessels were interviewed to collect information about their safety and health. The associations of physical and psychosocial work conditions with safety and health outcomes, e.g., injuries, illnesses and job satisfaction, are presented. The results of the current study can be utilized in the design of effective accident prevention and OSH training programs for the three age groups and in the regulation of working conditions aboard fishing vessels.

Air quality monitoring and assessment are essential issues for sustainable environmental protection. The monitoring process is composed of data collection, evaluation, and decision-making. Several important pollutants, such as SO₂, CO, PM₁₀, O₃, NOx, H₂S, location, and many others, have important effects on air quality. Air quality should be recorded and measured based on the total effect of pollutants that are collectively prescribed by a numerical value. In Canada, the Air Quality Health Index (AQHI) is used which is one numerical value based on the total effect of some concentrations. Therefore, evolution is required to consider the complex, ill-defined air pollutants, hence several naive and noble approaches are used to study AQHI. In this study, three approaches such as hybrid data-driven ANN, nonlinear autoregressive with external (exogenous) input (NARX) with a neural network, and adaptive neuro-fuzzy inference (ANFIS) approaches are used for estimating the air quality in an urban area (Jeddah city—industrial zone) for public health concerns. Over three years, 1771 data were collected for pollutants from 1 June 2016 until 30 September 2019. In this study, the Levenberg-Marquardt (LM) approach was employed as an optimization method for ANNs to solve the nonlinear least-squares problems. The NARX employed has a two-layer feed-forward ANN. On the other hand, the back-propagation multi-layer perceptron (BPMLP) algorithm was used with the steepest descent approach to reduce the root mean square error (RMSE). The RMSEs were 4.42, 0.0578, and 5.64 for ANN, NARX, and ANFIS, respectively. Essentially, all RMSEs are very small. The outcomes of approaches were evaluated by fuzzy quality charts and compared statistically with the US-EPA air quality standards. Due to the effectiveness and robustness of artificial intelligent techniques, the public’s early warning will be possible for avoiding the harmful effects of pollution inside the urban areas, which may reduce respiratory and cardiovascular mortalities. Consequently, the stability of air quality models was correlated with the absolute air quality index. The findings showed notable performance of NARX with a neural network, ANN, and ANFIS-based AQHI model for high dimensional data assessment.

Many accidents occur due to various factors when undertaking construction projects. One of the most important achievements when undertaking a construction project is to accomplish the project without major accidents. This paper studies the effect of stress, fatigue and workload on construction contract worker in term of their safety performance results using the Situation Awareness Rating Technique (SART), with reference to how it relates to the root causes of accidents. Investigating the factors associated with accidents helps to identify and minimize the associated mistakes or causes and reduce or avoid accidents and improve safety performance in construction projects. Data were collected using a SART survey of employee of 18 contractors working on construction of electrical substations in the western area of Saudi Arabia. It was found that most accidents occurred at the worker level because of their lack of awareness and training. It is apparent that SART affects the safety performance of contractors and it varies with the position, age and experience of workers. To implement a safe working environment free of accidents, it is necessary to improve all job levels according to their insufficient area and activate a full safety management system with periodic reviews to improve it and make it more effective.