Climate change and environmental degradation require computational environmental engineering. To simulate and optimize complex environmental processes, advanced computer models, big data analytics, and machine learning are used. A holistic assessment of environmental consequences and the design of interdisciplinary solutions that take into account socioeconomic, environmental, and environmental variables can be possible with the creation of integrated modeling frameworks that reflect the interconnected nature of environmental systems. Increasingly high-resolution remote sensing data and sensor networks enable real-time monitoring and decision-making about the environment. The use of these data streams in conjunction with computational models improves environmental predictions, allowing better management of natural resources and risk reduction. The most challenging aspects are improving model validation and uncertainty quantification, developing robust optimization algorithms, and ensuring accessibility for stakeholders of varying backgrounds. Lastly, computational environmental engineering offers significant promise for addressing future environmental problems by incorporating interdisciplinary approaches, using emerging technologies, and addressing important issues. Previous Chapter. © 2025 selection and editorial matter, Akhilesh Kumar Yadav; individual chapters, the contributors.
