探索臭氧形成规则和度对前体变化的反应,基于典型工业园区的人工神经网络模拟
Qiaoli Wang1, Dongping Sheng1, Chengzhi Wu2
1College of Environment, Zhejiang University of Technology, Hangzhou, 310032, China.
Heliyon
|October 9, 2023
概括
工业园区面临复杂的臭氧 (O3) 形成. 这项研究使用人工神经网络 (ANN) 发现该区域对氧化 (NOx) 敏感,臭氧水平受到湿度和压力的影响.
科学领域:
- 大气化学 大气化学
- 环境科学 环境科学
- 环境建模中的人工智能
背景情况:
- 工业园区由于密集的前体排放而表现出复杂的臭氧 (O3) 形成.
- 了解臭氧形成对于工业化地区的空气质量管理至关重要.
研究的目的:
- 探索工业园区的臭氧形成规则和对挥发性有机化合物 (VOC) 和氧化 (NOx) 的度反应.
- 开发和应用一个人工神经网络 (ANN) 模型来分析臭氧前体的敏感性.
主要方法:
- 开发一个ANN模型,整合气象因素和污染物度.
- 对不同水平的NOx (氧化和二氧化) 和VOCs的臭氧度反应的分析.
- 对相对湿度 (RH) 和压力 (P) 的臭氧形成敏感性的研究.
主要成果:
- 研究区域被确定为对氧化 (NOx) 敏感的地区.
- 臭氧度受到压力,温度和相对湿度的影响,对氧化 (NO) 和二氧化 (NO2) 的添加有特定的反应.
- 观察到臭氧的局部最大增量反应率 (MIR_L),与VOC线性增加,表明NOx敏感区域的一般MIR可能过度估计.
结论:
- 工业园区的臭氧形成对氧化物敏感,并受到气象条件的调节.
- 这些发现凸显了NOx敏感系统中一般最大增量反应率 (MIR) 的局限性.
- 结果为在工业区制定有针对性的臭氧和前体控制战略提供了关键数据.
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