Petroleum hydrocarbons and chlorides, often resulting from spills and other industrial activities, present significant challenges to both ecological systems and human health. Traditional methods for field evaluation of these contaminants can be fraught with challenges, including human error, environmental variability, and calibration issues. Due to this, making reliable field-level decisions can be an uncertain, complex and costly endeavor.

The advent of Artificial Intelligence (AI) in environmental management heralds a new era in soil testing and characterization. This presentation will introduce AI based systems that provide real-time predictions of contamination levels. These can deliver precise data within 20 minutes and achieve over 95% accuracy in identifying contamination levels that exceed regulatory criteria. This dramatically reduces reliance on traditional laboratory analyses, particularly for urgent field-level decisions, thereby accelerating both environmental assessments and remediation efforts.

This presentation will chart the development trajectory of sensor technology, emphasizing the critical integration of AI. We will explore real-world applications in detecting petroleum hydrocarbons and chloride concentrations, presenting case studies that illustrate how artificial intelligence has and will continue to revolutionize site assessment and remediation strategies and tools.