改善智利的空气质量预测:集成ARIMA和人工神经网络模型,用于昆特罗和科海克城市
Fidel Vallejo1,2, Diana Yánez3, Patricia Viñán-Guerrero4
1Industrial Engineering, National University of Chimborazo, Riobamba, Ecuador.
PloS one
|January 10, 2025
概括
智利的二氧化硫 (SO2),PM2.5和PM10空气质量模型是使用混合ARIMA-ANN方法开发的. 地理和气候因素对污染水平产生重大影响,需要定制的管理策略.
科学领域:
- 环境科学 环境科学
- 大气化学 大气化学
- 数据科学数据科学数据科学
背景情况:
- 智利在2016-2021年期间经历了重大的空气质量挑战.
- 主要污染物包括二氧化硫 (SO2),细颗粒物 (PM2.5) 和粗颗粒物 (PM10).
- 了解污染物动态对于公共卫生和环境管理至关重要.
研究的目的:
- 在智利开发SO2,PM2.5和PM10的预测模型.
- 研究地理和气候因素对空气质量的影响.
- 评估混合预测模型的有效性.
主要方法:
- 利用智利国家空气质量信息系统 (SINCA) 的数据.
- 采用混合预测策略,集成自回归集成移动平均线 (ARIMA) 和人工神经网络 (ANN).
- 整合了外部共变量,如风速和风向;使用AIC和Ljung-Box测试评估模型.
主要成果:
- 混合ARIMA-ANN模型实现了高预测精度,R2>0.90.
- 地理和气候因素,特别是在Coyhaique,被确定为污染的主要原因.
- 季节性停滞和冬季低温加剧了特定地区的污染.
结论:
- 对智利来说,定制的空气质量管理策略是必不可少的.
- 先进的建模技术,如神经网络,显示了改善空气质量预测的巨大潜力.
- 环境因素在空气污染动态中起着至关重要的作用.
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