Air pollution is a severe problem growing over time. A dense air-quality monitoring network is needed to update the people regarding the air pollution status in cities. A low-cost sensor device (LCSD) based dense air-quality monitoring network is more viable than continuous ambient air quality monitoring stations (CAAQMS). An in-field calibration approach is needed to improve agreements of the LCSDs to CAAQMS. The present work aims to propose a calibration method for PM2.5 using domain adaptation technique to reduce the collocation duration of LCSDs and CAAQMS. A novel calibration approach is proposed in this work for the measured PM2.5 levels of LCSDs. The dataset used for the experimentation consists of PM2.5 values and other parameters (PM10, temperature, and humidity) at hourly duration over a period of three months data. We propose new features, by combining PM2.5, PM10, temperature, and humidity, that significantly improved the performance of calibration. Further, the calibration model is adapted to the target location for a new LCSD with a collocation time of two days. The proposed model shows high correlation coefficient values (R2) and significantly low mean absolute percentage error (MAPE) than that of other baseline models. Thus, the proposed model helps in reducing the collocation time while maintaining high calibration performance.

中文翻译：

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基于域适应的低成本 PM钪.钪 传感器深度校准


空气污染是一个随着时间的推移而日益严重的问题。需要一个密集的空气质量监测网络来让人们了解城市的空气污染状况。基于低成本传感器设备（LCSD）的密集空气质量监测网络比连续环境空气质量监测站（CAAQMS）更可行。需要采用现场校准方法来改善 LCSD 与 CAAQMS 的一致性。本工作旨在提出一种使用域自适应技术的 PM2.5 校准方法，以减少 LCSD 和 CAAQMS 的搭配持续时间。这项工作提出了一种新颖的校准方法，用于测量 LCSD 的 PM2.5 水平。用于实验的数据集包含三个月数据期间每小时的 PM2.5 值和其他参数（PM10、温度和湿度）。我们提出了新功能，通过结合 PM2.5、PM10、温度和湿度，显着提高了校准性能。此外，校准模型适用于新康乐及文化事务署的目标位置，搭配时间为两天。与其他基线模型相比，所提出的模型显示出较高的相关系数值（R2）和显着较低的平均绝对百分比误差（MAPE）。因此，所提出的模型有助于减少配置时间，同时保持高校准性能。