Blockchain-based isotopic big data-driven tracing of global PM sources and interventions
Yuming Huang, Xiangyu Li, Yuehan Wu, Chaoyang Xue, Jiashuo Li, Yongfeng Lin, Wei Nie, Xian Liu, Qian Liu, Greg Michalski, Jingwei Zhang, Zheng Zong, Dawei Lü, Guibin Jiang
Abstract
Abstract Tracing sources and assessing intervention effectiveness are crucial for controlling atmospheric particulate matter (PM) pollution. Isotopic techniques enable precise top-down tracing, but the absence of long-term, global-scale multi-compound isotopic data limits comprehensive analysis. Here, we establish a blockchain-based isotopic database, compiling 34,815 isotopic fingerprints of global PM and its emissions from 1,890 pollution events across 66 countries. This allows retrospective analysis and predictions, revealing that PM sources are distinct, dynamically changing over time, and often asynchronous with interventions. Additionally, we estimate source contributions to PM 2.5 and its compounds, highlighting the increasing impact of biomass burning. Furthermore, projections indicate that by 2100, PM levels may decline to 5.38 ± 0.16 μg/m³ in the Americas and 13.9 ± 1.82 μg/m³ in Asia under climate mitigation scenarios but will still exceed WHO guidelines without further controls on natural emissions. Guiding future interventions with isotopic big data is essential for addressing air pollution challenges.