一个综合的方法,结合正矩阵因子化建模,气象学和机器学习来分配表面臭氧前体的来源:在德克萨斯州休斯顿,导致臭氧形成的潜在因素
Delaney Nelson1, Yunsoo Choi1, Bavand Sadeghi2
1Department of Earth and Atmospheric Science, University of Houston, Texas, USA.
Environmental pollution (Barking, Essex : 1987)
|July 22, 2023
概括
休斯顿的高臭氧水平与特定的排放源和天气有关. 一种新的PMF-SHAP方法确定了n-decane和flaring等关键因素,指导空气质量管理策略.
科学领域:
- 环境化学环境化学
- 大气科学 大气科学
- 空气质量管理 管理空气质量
背景情况:
- 休斯顿的臭氧度在美国是最高的.
- 臭氧水平升高对公众健康构成重大风险.
- 了解臭氧形成的驱动因素对于有效的缓解至关重要.
研究的目的:
- 评估排放源和气象因素对休斯顿臭氧形成的影响.
- 区分独特的挥发性有机化合物 (VOC) 来源和影响臭氧生产的局部化学物质.
- 评估正矩阵因子化-夏普利增量解释 (PMF-SHAP) 方法的有效性.
主要方法:
- 利用全面的PMF-SHAP方法分析2017年至2021年的臭氧形成.
- 在城市和工业监测地点确定了不同的VOC来源.
- 应用SHAP分析来量化排放因素和气象变量的影响.
主要成果:
- 短波辐射和湿度是影响臭氧的最重要的气象变量.
- 排放的主要来源包括n-decane,工业排放和石油/天然气燃烧.
- 氧化 (NOx) 的影响是多种多样的:在城市地区 (NOx丰富的制度) 有负面影响,在工业区有积极影响.
结论:
- PMF-SHAP方法是有效的,具有成本效益的工具,用于识别臭氧超值的贡献者.
- 结果为休斯顿开发有针对性的空气质量管理策略提供了洞察力.
- 该方法适用于其他面临类似臭氧挑战的城市地区.
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