The success of industrial operations depends on the effective identification, appraisal, and mitigation of possible hazards and associated environmental concerns. This report provides a complete review of environmental management techniques at the Sukari Gold Mine (SGM), located in the southeastern desert of Egypt. Extensive environmental measurements were taken to assess air and water quality, identify hazards, and analyze risks on the SGM premises. Air quality and noise intensity levels were measured at 39 places around the mine's working region. The findings found noncompliance with the Egyptian Environmental Law's (EEL4/94) noise exposure limitations, with the Power Generator House having the maximum noise levels at 107 dB. Remedial measures such as personal protective equipment (PPE) and exposure limit reduction strategies are being considered to address elevated noise levels. Measurements of particulate matter (PM10) and noxious gases (e.g., CO, SO2, NO2, HCN, and NH3) were conducted in workplace and ambient environments. Elevated PM10 concentrations were particularly concerning in underground regions, forcing the deployment of water depression techniques and improved PPE measures. While gas emissions from most activities remained under regulatory limits, select zones showed hydrogen cyanide (HCN) levels that exceeded permitted thresholds, necessitating specific control actions. Using hazard index (HI) and risk rating assessments, this study found different risk profiles across SGM's workplaces, focusing on high-risk regions for focused intervention. Additionally, a water assessment near a Tailing Storage Facility (TSF) was conducted to monitor the impact of mining activities on groundwater quality. The study revealed that groundwater in the region belongs to the Na–K–Cl–SO₄ and Ca–Mg–Cl–SO₄ water classes, with potential degradation attributed to high mineralization processes induced by aquifer materials and seawater intrusion. The findings underscore the importance of ongoing monitoring, control measures, and implementation of programs to ensure environmental sustainability and minimize risks associated with mining activities in the Sukari Gold Mines. This research highlights the imperative of continuous monitoring, proactive control measures, and the implementation of environmental initiatives to ensure the sustainability of mining operations within the Sukari Gold Mines.

This study presents an integrated computational framework for quantifying industrial impacts on marine ecosystems through the combined assessment of multiple environmental quality indices. The Aquatic Water Quality Index (AWQI) and four diagnostic pollution indices, namely the Heavy Metal Pollution Index (HPI), Metal Index (MI), Degree of Contamination (C_d), and Pollution Index (PI), were applied across 23 offshore sites in Mesaieed Industrial City, Qatar, to establish a high-resolution baseline for evaluating the effects of industrial effluents and brine discharge. Multivariate statistical analyses, including Principal Component Analysis (PCA) and Cluster Analysis (CA), identified Cr, Pb, Mn, Ni, and Zn as the principal drivers of water quality variability, effectively distinguishing anthropogenic influences from natural background conditions. To enable rapid and automated marine environmental assessment, three machine learning models—Artificial Neural Networks (ANN), Random Forest (RF), and Decision Trees (DT)—were developed and evaluated for predicting the investigated indices. Model performance was assessed through rigorous training–testing validation and the Diebold–Mariano test. The results demonstrated that model selection significantly influences predictive accuracy. Among the evaluated algorithms, RF achieved the highest predictive performance for AWQI (R² = 0.88) and C_d (R² = 0.92), whereas ANN performed best for HPI (R² = 0.89), and DT yielded the most accurate predictions for MI (R² = 0.82). Despite the index-specific strengths of individual models, RF emerged as the most robust and generalizable approach, consistently providing superior performance across heterogeneous environmental datasets. The proposed framework advances marine water quality assessment from conventional descriptive monitoring toward a proactive, data-driven paradigm, offering a scalable and cost-effective decision support tool for environmental management, pollution mitigation, and evidence-based coastal governance in industrialized coastal regions.