气象和交通对空气污染物的影响使用贝叶斯网络和深度学习
Yuan-Chien Lin1, Yu-Ting Lin2, Cai-Rou Chen2
1Department of Civil Engineering, National Central University, No. 300, Zhongda Rd., Zhongli District, Taoyuan 32001, Taiwan, China; Research Center for Hazard Mitigation and Prevention, National Central University, Taoyuan 32001, Taiwan, China; Graduate Institute of Environmental Engineering, National Central University, Taoyuan 32001, Taiwan, China.
Journal of environmental sciences (China)
|December 1, 2024
概括
控制车辆速度超过40公里/小时,交通流量低于1200辆/小时,可以改善城市空气质量. 降雨模式显著影响污染物水平,长期大雨的影响最大.
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:
- 交通排放是城市空气污染的主要来源.
- 预测空气质量需要了解复杂的,非静止的交通影响.
研究的目的:
- 通过大数据分析来调查交通因素对空气质量的因果关系.
- 开发一个准确的空气污染物度预测模型.
主要方法:
- 贝叶斯网络概率模型与降雨事件数据集成.
- 对于非线性关系的通用增量模型 (GAM).
- 长期短期记忆 (LSTM) 网络用于预测.
主要成果:
- 最佳条件:车辆速度>40公里/小时,交通流量<1200辆/小时.
- 确定了四种降雨事件类型,其中I型 (长期大雨) 通过湿沉积对空气质量产生最大影响.
- 在LSTM模型中,CO,NO,NO2,NOx的R2>0.9,O3,PM10,PM2.5.5的R2>0.8得到了实现.
结论:
- 交通管理策略可以显著改善空气质量.
- 降雨特征是空气质量动态的关键因素.
- 拟议的LSTM模型为空气污染物预测提供了高准确度.
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