Project Description:

While air pollution is the most visible environmental impact of transportation systems, water pollution and quality issues are also of great importance in the transportation and environment nexus. Specifically, transportation systems can affect water quality directly in many ways, including stormwater runoff, deicing chemicals, vehicle exhaust, oil spills, and other pollutants. However, the impact of transportation systems on water qualify is not well studied or fully understood. A significant challenge is the slow movement of ground water through aquifers and its long-lasting, detrimental effects on communities, aquatic life, and the overall health of the ecosystem. Addressing this challenge requires continuous and reliable data collection, as well as advanced data analytics techniques. The traditional method of manual data sampling and analysis is not sufficient. In this project, we will design and develop an AI powered remote sensing framework and associated algorithm for roadside water quality monitoring and prediction, as well as algorithms for causality analysis based on long-term historical data. It has been shown that remote sensing systems can be used to monitor water quality issues, and causality analytics is an effective approach to derive environmental impacts in long term. By leveraging the state-of-the-art technologies in distributed sensing, AI and big data analytics, the proposed research provides great potentials for water quality monitoring and causality discovery, leading to a better understanding of the long-term environmental impact of transportation systems.

US DOT Priorities:

This project directly addresses DOT’s research priority of “Preserving the Environment” by developing novel tools and technologies to discover causal relations between the water pollution and transportation systems, leading to effective mitigation strategies to address the water quality challenges. 

Outputs:

The proposed research will produce a robust remote sensing and data analytics framework for monitoring and predicting the water pollution caused by transportation systems. The novelty of the research is that it will incorporate causality analytics to learn the long-term effects of the water pollution. Figure 1 shows the system architecture and the proposed workflow. The outputs include integrated hardware and software framework, as well as causality analysis results on both the collected data and the historic data. The project will also produce publications, presentations, and technical reports.

_{Fig. 1. Causal AI and Remote Sensing for Water Quality Monitoring and Analysis}

Outcomes/Impacts:

The project will produce new knowledge on the environmental impacts of transportation systems, as well as the causal relations. These findings will provide policy makers the rich information they need for making informative decisions on transportation systems design and operations. In addition, by utilizing data from different domains, the project will provide insight on effective and efficient data sharing, which is critical for the community. To further broadening participation, we will involve undergraduate students, and students in underrepresented groups in the research.