占用住宅中的VOC运输:通过深度学习进行测量和预测
Rui Zhang1, Xinglei He1, Jialong Liu1
1School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China.
预测室内挥发性有机化合物 (VOC) 是至关重要的. 一个深度学习模型,长期短期记忆 (LSTM),准确地预测了由空气变化率和人类活动影响的VOC水平.
科学领域:
- 环境科学 环境科学
- 室内空气质量 室内空气质量
- 计算化学计算化学
背景情况:
- 室内环境中的挥发性有机化合物 (VOC) 监测对于了解来源和人类暴露至关重要.
- 之前的研究在现实的居住环境中有限地探索了VOC动态.
研究的目的:
- 监测和预测一个占用住宅中十种典型的VOC的时间解析度.
- 确定影响室内VOC水平的关键因素,并评估预测模型的性能.
主要方法:
- 在住宅环境中进行了测量十个VOC的实地活动.
- 使用特征重要性分析来确定影响VOC度的因素,特别是空气变化速率 (ACR).
- 应用并比较了三种深度学习模型 (LSTM,RNN,GRU),使用温度,相对湿度和ACR作为VOC预测的输入特征.
主要成果:
- 空气变化速率 (ACR) 被确定为影响室内VOC度的最重要因素.
- 与RNN和GRU模型相比,长短期记忆 (LSTM) 模型在预测VOC度方面表现优越.
- 观察到人类活动在特定时期显著影响VOC排放水平.
结论:
- 深度学习,特别是LSTM,为在现实的室内环境中估计长期VOC运输和暴露提供了一个有希望的方法.
- 了解环境因素和人类活动的影响是准确室内VOC预测的关键.
- 这项研究为使用预测建模的先进室内空气质量管理策略提供了基础.
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