Integrated data-driven cross-disciplinary framework to prevent chemical water pollution

Area of Science:

• Environmental Science
• Chemical Engineering
• Data Science

Background:

• Chemical innovation and water treatment experts often work in isolation, hindering effective management of emerging contaminants.
• A fragmented, reactive approach to chemical pollution results in significant societal costs.
• Current methods lack connectivity between chemical life cycle stages and water remediation technologies.

Purpose of the Study:

• To propose an integrated, data-driven framework for proactive management of chemical water pollution.
• To enhance expert capabilities and foster novel, co-beneficial approaches across scientific domains.
• To bridge the gap between upstream chemical innovation and downstream water treatment solutions.

Main Methods:

• Developing a data-driven framework integrating chemical life cycle and water treatment domains.
• Promoting open and FAIR (findable, accessible, interoperable, reusable) data practices.
• Establishing common knowledge bases and platforms for interdisciplinary collaboration.

Main Results:

• The framework enhances expert capabilities and facilitates novel approaches with cross-domain co-benefits.
• It fosters proactive strategies for addressing chemical water pollution.
• Implementation requires concerted efforts in data sharing and platform development.

Conclusions:

• An integrated, data-driven framework is essential for effectively managing chemical water pollution.
• Adopting open data practices and common knowledge platforms are key to operationalizing the framework.
• Proactive, collaborative approaches can mitigate societal costs and improve ecosystem health.