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    Ai Technologies For Pollution Detection and Remediation Efforts
    (Book Rivers, 2025) Aqeel Ahmad Khan, Uzma
    The escalating challenges of environmental pollution demand innovative and sustainable solutions beyond traditional monitoring and remediation methods. Artificial Intelligence (AI) technologies have emerged as transformative tools in detecting, predicting, and mitigating pollution across air, water, and soil ecosystems. By leveraging advanced algorithms, machine learning models, and sensor-based data integration, AI enables real time monitoring, precise source identification, and predictive analytics for environmental risks. Moreover, AI-powered robotics and autonomous systems are increasingly being deployed in pollution remediation efforts, such as waste segregation, water purification, and soil restoration. This paper critically examines the role of AI in environmental protection, highlighting its applications in pollution detection and remediation. It also explores case studies, opportunities, and ethical challenges associated with deploying AI- driven solutions. The findings suggest that while AI offers significant potential to revolutionize environmental management, its effective implementation requires robust infrastructure, interdisciplinary collaboration, and responsible governance frameworks.
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    Mixed Traffic Modelling: An Overview of Car Following and Lane Change Models
    (IGI Global, 2024) Mohd Sadat; Syed Aqeel Ahmad,Mehmet Ali Silgu
    Traffic modelling has gained importance due to the adoption of intelligent transportation systems and software based on traffic models providing a platform to test and improve such systems. Modelling mixed traffic has proved to be a challenging task due to variations in vehicle dimensions and composi- tion along with non-lane-based driving. Most of the simulation software is based on the car following models and lane change models which were originally developed for lane-based traffic. Several attempts have been made to adapt these models for mixed traffic by extending them to include new parameters.This study summarizes lane change models used along with car following for mixed traffic. It can be concluded from past studies that lateral manoeuvre varies with the longitudinal speed in a non-linear manner. Sub-models or specific parameters are needed to model the lateral behaviour of each class of vehicle. Trajectory data analysis and subsequent models have also pointed towards the need for vehicle pair-dependent parameters.