让非专家也能轻松分析空气污染排放数据的智能助手
Emission-GPT: A domain-specific language model agent for knowledge retrieval, emission inventory and data analysis
- 基于1万+份专业文档构建领域知识库，支持精准问答
- 仅用自然语言就能查询、可视化排放数据并生成分析报告
- 适合环保研究者、政策制定者快速完成排放清单与情景评估
改善空气质量与应对气候变化依赖于对污染物及温室气体排放的准确理解与分析。然而，排放相关知识分散且高度专业化，现有数据获取与整合方法效率低下，限制了非专家对排放信息的解读能力。为此，我们提出Emission-GPT，一个面向大气排放领域的知识增强型大语言模型代理。该模型基于超过10,000份文献资料（包括标准、报告、指南及同行评审论文）构建的知识库，结合提示工程与问题补全技术，实现领域特定问题的精准回答。Emission-GPT支持用户通过自然语言交互分析排放数据，如查询和可视化排放清单、分析来源贡献，并为自定义场景推荐排放因子。以广东省为例，仅通过简单提示即可从原始数据中提取重点洞察，如点源分布特征与部门趋势。其模块化与可扩展架构，有助于自动化传统人工流程，成为下一代排放清单编制与情景评估的基础工具。
原文摘要 · Abstract (English)
Improving air quality and addressing climate change relies on accurate understanding and analysis of air pollutant and greenhouse gas emissions. However, emission-related knowledge is often fragmented and highly specialized, while existing methods for accessing and compiling emissions data remain inefficient. These issues hinder the ability of non-experts to interpret emissions information, posing challenges to research and management. To address this, we present Emission-GPT, a knowledge-enhanced large language model agent tailored for the atmospheric emissions domain. Built on a curated knowledge base of over 10,000 documents (including standards, reports, guidebooks, and peer-reviewed literature), Emission-GPT integrates prompt engineering and question completion to support accurate domain-specific question answering. Emission-GPT also enables users to interactively analyze emissions data via natural language, such as querying and visualizing inventories, analyzing source contributions, and recommending emission factors for user-defined scenarios. A case study in Guangdong Province demonstrates that Emission-GPT can extract key insights--such as point source distributions and sectoral trends--directly from raw data with simple prompts. Its modular and extensible architecture facilitates automation of traditionally manual workflows, positioning Emission-GPT as a foundational tool for next-generation emission inventory development and scenario-based assessment.
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