Independent Researcher, 428 Woodley Way, Santa Rosa, CA 95409, USA.
Department of Civil and Environmental Engineering, Stanford University, 1008 Cardiff Lane, Redwood City, CA 94061, USA.
Sensors (Basel). 2023 Jan 19;23(3):1160. doi: 10.3390/s23031160.
Low-cost monitors make it possible now for the first time to collect long-term (months to years) measurements of potential indoor exposure to fine particles. Indoor exposure is due to two sources: particles infiltrating from outdoors and those generated by indoor activities. Calculating the relative contribution of each source requires identifying an infiltration factor. We develop a method of identifying periods when the infiltration factor is not constant and searching for periods when it is relatively constant. From an initial regression of indoor on outdoor particle concentrations, a Forbidden Zone can be defined with an upper boundary below which no observations should appear. If many observations appear in the Forbidden Zone, they falsify the assumption of a single constant infiltration factor. This is a useful quality assurance feature, since investigators may then search for subsets of the data in which few observations appear in the Forbidden Zone. The usefulness of this approach is illustrated using examples drawn from the PurpleAir network of optical particle monitors. An improved algorithm is applied with reduced bias, improved precision, and a lower limit of detection than either of the two proprietary algorithms offered by the manufacturer of the sensors used in PurpleAir monitors.
低成本的监测器使得首次有可能对潜在的室内细颗粒物进行长期(数月至数年)测量。室内暴露有两个来源:从室外渗透进来的颗粒物和室内活动产生的颗粒物。要计算每个来源的相对贡献,需要确定一个渗透因子。我们开发了一种识别渗透因子不恒定的时间段并寻找相对恒定时间段的方法。从室内外颗粒物浓度的初始回归中,可以定义一个禁止区域,其上限低于该上限不应出现任何观测值。如果许多观测值出现在禁止区域内,那么它们就会否定单一恒定渗透因子的假设。这是一个有用的质量保证特性,因为调查人员可以在数据中搜索出现很少观测值的禁止区域的子集。使用来自 PurpleAir 光学粒子监测器网络的示例来说明了这种方法的有用性。与 PurpleAir 监测器中使用的传感器的制造商提供的两种专有算法相比,该方法应用了改进的算法,具有更低的偏差、更高的精度和更低的检测限。