通过基于传感器的测量和易于访问的预测器预测室内每小时的臭氧度
Jiaxin Chen1, Chang Xu1, Su Shi1
1School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China.
Eco-Environment & Health
|August 12, 2025
概括
机器学习模型使用可访问的数据和窗口状态准确预测室内臭氧 (O3) 水平. 整合通风行为显著提高了预测准确性,以便更好地评估暴露.
科学领域:
- 环境科学 环境科学
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
背景情况:
- 室内空气质量,特别是臭氧 (O3) 水平,对人类健康产生重大影响.
- 很少有研究利用机器学习来预测室内O3度.
- 准确的室内O3暴露数据对于流行病学研究至关重要.
研究的目的:
- 开发和验证用于预测每小时室内O3度的机器学习模型.
- 评估易于访问的预测因素,包括通风行为,对室内O3预测准确性的影响.
- 建立一个可扩展的方法,在不同的地理位置进行室内O3暴露评估.
主要方法:
- 在中国18个城市使用低成本传感器每小时收集室内O3数据.
- 使用环境O3度,气象因素和窗户状态 (通风代理) 作为预测因素.
- 开发和评估随机森林模型来预测室内O3水平.
主要成果:
- 将窗口状态作为预测指标的加入提高了模型性能,将R2从0.80增加到0.83,并将RMSE从7.89减少到7.21ppb.
- 这些模型准确地捕捉了室内O3度的每小时变化.
- 室内O3水平始终低于室外度,并且比室外度更稳定.
结论:
- 窗户状态显示的通风行为是改善室内O3预测模型准确性的关键因素.
- 使用可访问变量的机器学习模型为大规模室内O3暴露评估提供了有希望的方法.
- 仅仅依赖环境O3数据可能会导致不准确的个人暴露估计,强调室内监测和预测的重要性.
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