基于智能城市的人工智能为PM10和PM2.5低成本传感器进行校准
Ricardo Gómez1, José Rodríguez2, Roberto Ferro2
1Dirección de Ingeniería Electrónica, Facultad de Ingeniería, Universidad ECCI, Bogotá 111311, Colombia.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
低成本传感器 (LCS) 为空气质量监测提供了可行的解决方案,但需要校准. 这项研究表明,将气象数据和特定的预处理方法结合起来,可以显著提高液压控制系统对颗粒物 (PM) 监测的准确性.
科学领域:
- 环境科学 环境科学
- 传感器技术 传感器技术
- 数据科学数据科学数据科学
背景情况:
- 颗粒物 (PM) 暴露会导致全球严重的健康问题,包括呼吸道和心血管疾病.
- 传统的空气质量监测网络 (AQMN) 在空间覆盖方面存在局限性和高成本.
- 低成本传感器 (LCS) 为空气质量监测提供了一种经济有效的替代方案,但需要校准以解决精度问题.
研究的目的:
- 评估各种低成本传感器 (LCS) 校准模型在监测PM2.5和PM10度方面的有效性.
- 评估预处理技术,包括快速动态时变形 (FastDTW),对LCS数据准确性的影响.
- 为了确定气象因素如相对湿度 (RH),温度和吸收流对传感器性能的影响.
主要方法:
- 同时监测PM2.5和PM10使用LCS节点和T640X参考传感器.
- 使用Automet站收集气象数据 (RH,温度,吸收流量).
- 使用过,细分和FastDTW进行数据预处理,然后使用统计,机器学习 (ML) 和深度学习 (DL) 模型进行校准.
主要成果:
- 快速DTW预处理对于提高LCS数据质量至关重要.
- 结合RH,温度和吸收流量可以显著提高PM监测的准确性.
- 随机森林和XGBoost模型在LCS校准中表现出最高的性能.
结论:
- 校准的LCS网络为连续的,街道规模的空气质量监测提供了具有成本效益和实用的解决方案.
- 这种方法通过提供详细的微尺度数据来补充现有的卫星和MAX-DOAS方法.
- 开发的校准策略对于使用LCS技术进行可靠和准确的空气质量评估至关重要.
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