Blockchain-based isotopic big data-driven tracing of global PM sources and interventions

Area of Science:

• Environmental Science
• Atmospheric Chemistry
• Data Science

Background:

• Controlling atmospheric particulate matter (PM) pollution requires effective source tracing and intervention assessment.
• Isotopic techniques offer precise source tracing but lack global, long-term multi-compound data.
• Existing data limitations hinder comprehensive analysis of PM pollution dynamics.

Purpose of the Study:

• To establish a comprehensive, global-scale isotopic database for particulate matter (PM) and its emissions.
• To enable retrospective and predictive analysis of PM pollution sources and their contributions.
• To inform and guide future air pollution intervention strategies using isotopic big data.

Main Methods:

• Compilation of 34,815 isotopic fingerprints from 1,890 pollution events across 66 countries.
• Development of a blockchain-based database for long-term, secure data storage and accessibility.
• Estimation of source contributions to PM2.5 and its compounds using isotopic data.

Main Results:

• PM sources are identified as distinct, dynamically changing, and often asynchronous with implemented interventions.
• Biomass burning is highlighted as a significant and increasing contributor to PM pollution.
• Projections indicate potential PM level reductions by 2100 under climate mitigation, but risks remain without natural emission controls.

Conclusions:

• The blockchain-based isotopic database provides unprecedented insights into global PM pollution.
• Understanding dynamic source contributions is vital for designing effective and timely air pollution control measures.
• Isotopic big data is essential for guiding future interventions and achieving air quality targets aligned with WHO guidelines.