在中国六个城市使用AXA (ACSM,Xact,Aethalometer) 仪器设置评估实时源分配方法
Manousos I Manousakas1,2, Tianqu Cui3, Qiyuan Wang4,5
1PSI Center for Energy and Environmental Sciences, 5232, Villigen, Switzerland. m.manousakas@ipta.demokritos.gr.
Scientific reports
|February 19, 2026
概括
本研究引入了一个近实时源分配 (NRT) 模型,用于快速分析空气质量. 该NRT模型在几分钟内准确识别了中国城市的主要颗粒物来源,有助于污染管理.
科学领域:
- 环境科学 环境科学
- 大气化学 大气化学
- 空气质量监测 空气质量监测
背景情况:
- 颗粒物污染对城市空气质量和公共健康构成重大风险.
- 准确及时识别颗粒物来源对于有效的空气质量管理战略至关重要.
研究的目的:
- 评估一种创新的近实时源分配 (NRT) 模型,用于识别主要的PM源.
- 评估模型的性能和可靠性,使用来自中国六个城市的高时间分辨率数据.
主要方法:
- 这项研究使用了AXA仪器设置 (ACSM,Xact,Aethalometer) 来获取数据.
- 开发了一个近实时源分配 (NRT) 模型,并应用于2020-22年PM2.5数据.
- 模型性能与优化源分配分析以及使用减少数据集的稳定性测试进行了验证.
主要成果:
- 在所有研究的城市中,次要的PM成分是主要的来源,占PM2.5质量的66%,占PM2.5质量的66%.
- 还确定了主要来源,包括固体燃料燃烧 (10-30%) 和偶尔发生的尘埃事件.
- 该NRT模型显示出高可靠性和准确性,与优化方法有很强的相关性 (R2>0.82).
结论:
- NRT模型是实时空气质量管理的宝贵工具,可以快速识别源.
- 该系统的操作可靠性和适应性支持及时的缓解策略,以改善城市空气质量.
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