AI applications in air pollution domain: A systematic review from flue gas treatment to air quality management and carbon capture
空気質分野におけるAI応用：排ガス処理から大気質管理・炭素回収に至る系統的レビュー (AI 翻訳)
SangYoun Kim, T. Woo, Usama Ali, Chanhyeok Jeong, Nayoung Jeon, Mahmoud Amiri, Seungsu Han, ChanYu Park, M. Moosazadeh, Byung-Moon Jun, ChangKyoo Yoo
🤖 gxceed AI 要約
日本語
2016～2025年の906報を系統レビュー。排ガス処理、大気質管理、CO2回収の3領域でAI応用を分類。従来の統計監視からAI駆動の代理モデリングや生成的材料発見へのパラダイムシフトを確認。将来は物理情報ニューラルネットワーク、強化学習、エージェント型AIが重要。
English
This systematic review of 906 publications (2016-2025) categorizes AI applications into flue gas treatment, air quality management, and CO2 capture. It identifies a shift from traditional monitoring to AI-driven surrogate modeling and generative material discovery. Hybrid AI and LLM frameworks improve air quality forecasting (up to 94.46% accuracy), and generative AI accelerates MOF design for carbon capture. Challenges include data scarcity and model interpretability; future directions include Physics-Informed Neural Networks, Reinforcement Learning, and Agentic AI.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
本レビューは、日本のCCUS技術や大気質管理へのAI導入の有効性を評価する基礎資料となる。日本のカーボンニュートラル2050目標とも関連し、AIによる最適化が期待される分野を網羅的に示している。
In the global GX context
This review provides a comprehensive landscape of AI applications in air pollution and carbon capture, relevant to global climate mitigation strategies and environmental disclosure frameworks (e.g., TCFD, ISSB) that require quantification of emission reductions and carbon capture performance.
👥 読者別の含意
🔬研究者:Useful for identifying research gaps and emerging AI methods (PINN, RL, Agentic AI) in air pollution and carbon capture domains.
🏢実務担当者:Industrial environmental managers can leverage insights on AI-based surrogate models for real-time emission control and CO2 capture optimization.
🏛政策担当者:Policymakers can note the potential of AI to enhance air quality management and carbon capture efficiency, supporting climate targets.
📄 Abstract（原文）
This study conducted a systematic bibliometric analysis and comprehensive literature review of 906 publications from 2016 to 2025. The review categorizes AI applications into three core pillars: industrial flue gas treatment, air quality management, and CO2 capture. This analysis identifies a significant paradigm shift from traditional statistical monitoring toward sophisticated AI-driven surrogate modeling, generative material discovery. In industrial flue gas treatment domain, AI-based surrogates have enhanced multi-pollutant prediction accuracy (e.g., R2>0.94). In outdoor and indoor air quality management, hybrid AI and Large Language Model (LLM) frameworks have improved spatiotemporal forecasting and monitoring reliability by up to 94.46% alarm judgement accuracy under data-constrained conditions. Furthermore, in CO2 capture, the integration of generative AI and agentic AI systems has revolutionized adsorbent discovery, enabling rapid inverse design of metal-organic framework (MOF). Despite these advancements, the air pollution domain-AI faces challenges, including data scarcity in transient process, limited model interpretability, and the gap between computational design and experimental validation. Future research should prioritize Physics-Informed Neural Networks for high-fidelity modeling, Reinforcement Learning for adaptive control, and Agentic AI for End-to-End workflow. These emerging AI technologies are essential for developing scalable, high-precision air pollution management systems capable of meeting future environmental challenges.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.4491/eer.2026.089first seen 2026-06-21 05:56:31
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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。