Lamont Doherty Earth Observatory of Columbia University, 16 Rt. 9W, Palisades, NY 10964, USA; Key Laboratory of Surficial Geochemistry, Ministry of Education, Nanjing University, 163 Xianlin Ave, Qixia, Nanjing 210023, China.
Lamont Doherty Earth Observatory of Columbia University, 16 Rt. 9W, Palisades, NY 10964, USA.
Environ Res. 2018 Jul;164:39-44. doi: 10.1016/j.envres.2018.01.045. Epub 2018 Feb 22.
Fine particulate matter (PM) is associated with various adverse health outcomes. The MicroPEM (RTI, NC), a miniaturized real-time portable particulate sensor with an integrated filter for collecting particles, has been widely used for personal PM exposure assessment. Five-day deployments were targeted on a total of 142 deployments (personal or residential) to obtain real-time PM levels from children living in New York City and Baltimore. Among these 142 deployments, 79 applied high-efficiency particulate air (HEPA) filters in the field at the beginning and end of each deployment to adjust the zero level of the nephelometer. However, unacceptable baseline drift was observed in a large fraction (> 40%) of acquisitions in this study even after HEPA correction. This drift issue has been observed in several other studies as well. The purpose of the present study is to develop an algorithm to correct the baseline drift in MicroPEM based on central site ambient data during inactive time periods.
A running baseline & gravimetric correction (RBGC) method was developed based on the comparison of MicroPEM readings during inactive periods to ambient PM levels provided by fixed monitoring sites and the gravimetric weight of PM collected on the MicroPEM filters. The results after RBGC correction were compared with those using HEPA approach and gravimetric correction alone. Seven pairs of duplicate acquisitions were used to validate the RBGC method.
The percentages of acquisitions with baseline drift problems were 42%, 53% and 10% for raw, HEPA corrected, and RBGC corrected data, respectively. Pearson correlation analysis of duplicates showed an increase in the coefficient of determination from 0.75 for raw data to 0.97 after RBGC correction. In addition, the slope of the regression line increased from 0.60 for raw data to 1.00 after RBGC correction.
The RBGC approach corrected the baseline drift issue associated with MicroPEM data. The algorithm developed has the potential for use with data generated from other types of PM sensors that contain a filter for weighing as well. In addition, this approach can be applied in many other regions, given widely available ambient PM data from monitoring networks, especially in urban areas.
细颗粒物（PM）与各种不良健康后果有关。微型 PM 环境监测仪（RTI，NC）是一种小型化的实时便携式颗粒物传感器，具有集成的过滤器，用于收集颗粒物，已广泛用于个人 PM 暴露评估。在总共 142 次个人或住宅部署中，目标是进行为期 5 天的部署，以从居住在纽约市和巴尔的摩的儿童身上获得实时 PM 水平。在这 142 次部署中，79 次在每次部署开始和结束时在现场使用高效空气过滤器（HEPA）过滤器，以调整浊度计的零点。然而，即使在经过 HEPA 校正后，本研究中仍有很大一部分（>40%）采集数据出现不可接受的基线漂移。在其他几项研究中也观察到了这种漂移问题。本研究的目的是开发一种算法，根据非活动期间中心站环境数据来校正 MicroPEM 中的基线漂移。
基于 MicroPEM 在非活动期间的读数与固定监测点提供的环境 PM 水平以及 MicroPEM 过滤器上收集的 PM 的重量进行比较，开发了一种运行基线和重量校正（RBGC）方法。比较了 RBGC 校正后与使用 HEPA 方法和单独重量校正的结果。使用 7 对重复采集来验证 RBGC 方法。
原始、HEPA 校正和 RBGC 校正数据的基线漂移问题采集比例分别为 42%、53%和 10%。重复采集的 Pearson 相关性分析显示，从原始数据的决定系数 0.75增加到 RBGC 校正后的 0.97。此外，从原始数据的回归线斜率 0.60增加到 RBGC 校正后的 1.00。
RBGC 方法校正了与 MicroPEM 数据相关的基线漂移问题。所开发的算法有可能用于其他类型的包含称重过滤器的 PM 传感器生成的数据。此外，鉴于监测网络提供的广泛可用的环境 PM 数据，特别是在城市地区，这种方法可以在许多其他地区应用。
