Evaluation of Calibration Approaches for Indoor Deployments of PurpleAir Monitors.
Evaluation of Calibration Approaches for Indoor Deployments of PurpleAir Monitors.
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PurpleAir 监视器室内部署的校准方法评估。
DOI：
10.1016/j.atmosenv.2023.119944
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
McCormack,Meredith
中科院分区：
文献类型：
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作者：
Koehler,Kirsten;Wilks,Megan;Green,Tim;Rule,AnaM;Zamora,MistiL;Buehler,Colby;Datta,Abhirup;Gentner,DrewR;Putcha,Nirupama;Hansel,NadiaN;Kirk,GregoryD;Raju,Sarath;McCormack,Meredith
低成本的空气质量监测器在研究人员和社区成员中越来越受欢迎，以了解污染物浓度的变化。几项研究已经产生了这些传感器的环境空气校准方法。这些校准已被证明主要取决于相对湿度，颗粒大小分布和颗粒组成，这可能是不同的室内环境。然而，尽管大多数人大部分时间都在室内，但人们对室内常用设备的准确性知之甚少。这是因为在室内环境中工作的传感器的校准数据很少。在本研究中，我们试图评估PurpleAir细颗粒物监测仪原始数据的准确性，以及已发布的复杂性不同的校准方法的准确性，范围从简单地应用线性校正到需要将过滤器样本与重量浓度放在一起进行校正的方法。基线访视期间。我们的数据包括PurpleAir设备，这些设备在每个家庭中与重量分析样品一起放置1周（来自151个家庭的265个样品）。每周平均重量浓度范围在检测限（3微克/立方米）和330微克/立方米之间。使用制造商提供的内部校准，我们发现PurpleAir监测器和重量浓度（R> 0.91）之间存在很强的相关性。然而，与重量浓度相比，PurpleAir数据大大高估了室内浓度（使用制造商提供的内部校准，平均偏倚误差≥ 23.6 μg/m3）。基于环境空气数据的校准保持了较高的相关性（R≥ 0.92），并大大降低了偏倚（例如，使用美国范围内的校准方法，平均偏倚误差= 10.1 μg/m3）。使用重力样品从基线访问，以校准数据为以后的访问导致内部校准的改进，但表现不如更简单的校准方法的基础上，环境空气污染数据。此外，当周平均浓度不超过30 μg/m3时，基于环境空气污染数据的校准效果最好，这可能是因为用于训练这些模型的大多数数据都低于该浓度。
Low-cost air quality monitors are growing in popularity among both researchers and community members to understand variability in pollutant concentrations. Several studies have produced calibration approaches for these sensors for ambient air. These calibrations have been shown to depend primarily on relative humidity, particle size distribution, and particle composition, which may be different in indoor environments. However, despite the fact that most people spend the majority of their time indoors, little is known about the accuracy of commonly used devices indoors. This stems from the fact that calibration data for sensors operating in indoor environments are rare. In this study, we sought to evaluate the accuracy of the raw data from PurpleAir fine particulate matter monitors and for published calibration approaches that vary in complexity, ranging from simply applying linear corrections to those requiring co-locating a filter sample for correction with a gravimetric concentration during a baseline visit. Our data includes PurpleAir devices that were co-located in each home with a gravimetric sample for 1-week periods (265 samples from 151 homes). Weekly-averaged gravimetric concentrations ranged between the limit of detection (3 μg/m 3) and 330 μg/m 3. We found a strong correlation between the PurpleAir monitor and the gravimetric concentration (R> 0.91) using internal calibrations provided by the manufacturer. However, the PurpleAir data substantially overestimated indoor concentrations compared to the gravimetric concentration (mean bias error≥ 23.6 μg/m 3 using internal calibrations provided by the manufacturer). Calibrations based on ambient air data maintained high correlations (R≥ 0.92) and substantially reduced bias (eg mean bias error= 10.1 μg/m 3 using a US-wide calibration approach). Using a gravimetric sample from a baseline visit to calibrate data for later visits led to an improvement over the internal calibrations, but performed worse than the simpler calibration approaches based on ambient air pollution data. Furthermore, calibrations based on ambient air pollution data performed best when weekly-averaged concentrations did not exceed 30 μg/m 3, likely because the majority of the data used to train these models were below this concentration.
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影响因子：
7.4
作者：
Seongjun Park;Shinhye Lee;M. Yeo;Donghyun Rim
通讯作者：
Seongjun Park;Shinhye Lee;M. Yeo;Donghyun Rim
光散射传感器颗粒物监测占空比的研究
DOI：
10.23919/splitech52315.2021.9566363
发表时间：
2021
期刊：
2021 6th International Conference on Smart and Sustainable Technologies (SpliTech)
影响因子：
--
作者：
P. Chiavassa;F. Gandino;E. Giusto
通讯作者：
E. Giusto
“在大气条件下使用 PurpleAir 测量 PM2.5”的补充材料
影响因子：
5.2
作者：
K. Ardon;Yuval Dryer;Jake N. Williams;Nastaran Moghimi
通讯作者：
Nastaran Moghimi
影响因子：
5.2
作者：
Magi, Brian I.;Cupini, Calvin;Hauser, Cindy
通讯作者：
Hauser, Cindy
影响因子：
4.5
作者：
Giordano, Michael R.;Malings, Carl;Subramanian, R.
通讯作者：
Subramanian, R.