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Empirical ozone isopleths at urban and suburban sites through evolutionary procedure-based models
Journal of Hazardous Materials ( IF 13.6 ) Pub Date : 2021-06-17 , DOI: 10.1016/j.jhazmat.2021.126386
Francisca M Santos 1 , Álvaro Gómez-Losada 2 , José C M Pires 1
Affiliation  

Ozone (O3) is a reactive oxidant that causes chronic effects on human health, vegetation, ecosystems and materials. This study aims to create O3 isopleths in urban and suburban environments, based on machine learning with air quality data collected from 2001 to 2017 at urban (EA) and suburban (CC) monitoring stations from Madrid (Spain). Artificial neural network (ANN) models have powerful fitting performance, describing correctly several complex and nonlinear relationships such as O3 and his precursors (VOC and NOx). Also, ANN learns from the experience provided by data, contrary to mechanistic models based on the fundamental laws of natural sciences. The determined isopleths showed a different behaviour of the VOC-NOx-O3 system compared to the one achieved with a mechanistic model (EKMA curve): e.g. for constant NOx concentrations, O3 concentrations decreased with VOC concentrations in the ANN model. Considering the difficulty to model all the phenomena (and acquired all the required data) that influences O3 concentrations, the statistical models may be a solution to describe this system correctly. The applied methodology is a valuable tool for defining mitigation strategies (control of precursors’ emissions) to reduce O3 concentrations. However, as these models are obtained by air quality data, they are not geographical transferable.



中文翻译:

通过基于进化程序的模型在城市和郊区的经验臭氧等值线

臭氧 (O 3 ) 是一种活性氧化剂,会对人类健康、植被、生态系统和材料造成慢性影响。本研究旨在基于机器学习,利用 2001 年至 2017 年在马德里(西班牙)城市 (EA) 和郊区 (CC) 监测站收集的空气质量数据,在城市和郊区环境中创建 O 3等值线。人工神经网络 (ANN) 模型具有强大的拟合性能,可以正确描述几种复杂的非线性关系,例如 O 3及其前体(VOC 和 NO x)。此外,ANN 从数据提供的经验中学习,这与基于自然科学基本定律的机械模型相反。确定的等值线显示出 VOC-NO x的不同行为-O 3系统与使用机械模型(EKMA 曲线)实现的系统相比:例如,对于恒定的 NO x浓度,在 ANN 模型中,O 3浓度随 VOC 浓度而降低。考虑到难以对影响 O 3浓度的所有现象(并获取所有所需数据)进行建模,统计模型可能是正确描述该系统的解决方案。所应用的方法是定义缓解策略(控制前体排放)以降低 O 3浓度的宝贵工具。然而,由于这些模型是通过空气质量数据获得的,因此它们不能在地理上转移。

更新日期:2021-06-22
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