Won Wan-Sik, Noh Jinhong, Oh Rosy, Lee Woojoo, Lee Jong-Won, Su Pei-Chen, Yoon Yong-Jin
School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore.
Department of Aerospace Industrial and Systems Engineering, Hanseo University, Taean, Chungcheongnam-do, 32158, Republic of Korea.
Sci Rep. 2023 Aug 12;13(1):13150. doi: 10.1038/s41598-023-40468-z.
Low-cost particulate matter (PM) sensors have been widely used following recent sensor-technology advancements; however, inherent limitations of low-cost monitors (LCMs), which operate based on light scattering without an air-conditioning function, still restrict their applicability. We propose a regional calibration of LCMs using a multivariate Tobit model with historical weather and air quality data to improve the accuracy of ambient air monitoring, which is highly dependent on meteorological conditions, local climate, and regional PM properties. Weather observations and PM (fine inhalable particles with diameters ≤ 2.5 μm) concentrations from two regions in Korea, Incheon and Jeju, and one in Singapore were used as training data to build a visibility-based calibration model. To validate the model, field measurements were conducted by an LCM in Jeju and Singapore, where R and the error after applying the model in Jeju improved (from 0.85 to 0.88) and reduced by 44% (from 8.4 to 4.7 μg m), respectively. The results demonstrated that regional calibration involving air temperature, relative humidity, and other local climate parameters can efficiently correct the bias of the sensor. Our findings suggest that the proposed post-processing using the Tobit model with regional weather and air quality data enhances the applicability of LCMs.
随着近期传感器技术的进步，低成本颗粒物（PM）传感器已得到广泛应用；然而，基于光散射且无空调功能的低成本监测器（LCM）的固有局限性仍然限制了它们的适用性。我们提出使用具有历史天气和空气质量数据的多元托比特模型对LCM进行区域校准，以提高对气象条件、当地气候和区域PM特性高度依赖的环境空气监测的准确性。来自韩国仁川和济州岛以及新加坡的两个地区的气象观测数据和PM（直径≤2.5μm的细可吸入颗粒物）浓度用作训练数据，以建立基于能见度的校准模型。为了验证该模型，在济州岛和新加坡通过LCM进行了现场测量，在济州岛应用该模型后，R值提高了（从0.85提高到0.88），误差降低了44%（从8.4μg/m降低到4.7μg/m）。结果表明，涉及气温、相对湿度和其他当地气候参数的区域校准可以有效地校正传感器的偏差。我们的研究结果表明，使用托比特模型结合区域天气和空气质量数据进行的后处理提高了LCM的适用性。
