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 PM _2.5 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 PM _2.5 levels of LCSDs. The dataset used for the experimentation consists of PM _2.5 values and other parameters (PM ₁₀ , temperature, and humidity) at hourly duration over a period of three months data. We propose new features, by combining PM _2.5 , PM ₁₀ , 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 (R ² ) 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 PM₂.₅ 传感器深度校准

随着时间的推移，空气污染是一个严重的问题。需要一个密集的空气质量监测网络来更新人们对城市空气污染状况的了解。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进 LCSD 与 CAAQMS 的一致性。目前的工作旨在提出一种使用域适应技术的PM _2.5校准方法， 以减少 LCSD 和 CAAQMS 的配置持续时间。在这项工作中提出了一种新的校准方法，用于测量LCSD 的PM _2.5水平。用于实验的数据集由 PM _2.5值和其他参数（PM ₁₀ ，温度和湿度）在三个月的数据中每小时持续一次。我们通过结合 PM _2.5 、PM ₁₀ 、温度和湿度提出了新功能， 显着提高了校准性能。此外，校准模型适用于新LCSD的目标位置，配置时间为两天。 与其他基线模型相比，所提出的模型显示出高相关系数值 (R ² ) 和显着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搭配时间，同时保持高校准性能。