Statistical field calibration of a low-cost PM2.5 monitoring network in Baltimore
Statistical field calibration of a low-cost PM2.5 monitoring network in Baltimore
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DOI：
10.1016/j.atmosenv.2020.117761
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发表时间：
2020-12-01
影响因子：
5
通讯作者：
Koehler, Kirsten
中科院分区：
文献类型：
--
作者：
Datta, Abhirup;Saha, Arkajyoti;Koehler, Kirsten
越来越多地部署低成本空气污染监测仪，以丰富高空间和时间分辨率的环境空气污染知识。然而，与监管级（FEM 或 FRM）仪器不同，低成本传感器的通用质量标准尚未建立，且其数据质量差异很大。这要求在负责任地使用此类数据之前进行彻底的评估和校准。这项研究对目前在马里兰州巴尔的摩运行的低成本监测器网络中的 PM2.5 数据进行了评估和现场校准，该市在城市范围内只有一个监管 PM2.5 监测点。对巴尔的摩旧镇监管站点的协同定位分析显示，这些监测器的原始数据存在高度变异性，并且严重高估了 PM2.5 水平。通用实验室校正减少了数据的偏差，但仅部分减轻了高变异性。旧镇八个月的现场共置数据被用来开发增益偏移校准模型，并重新构建为多元线性回归。与原始数据或实验室校正数据相比，统计模型显着提高了预测质量。结果对于用于现场校准的低成本监测器的选择以及训练周期的不同季节选择来说是稳健的。在培训期后的两个月内对原始的、实验室校正的和统计校准的数据进行评估。统计模型与参考数据的一致性最高，产生的 24 小时平均均方根误差 (RMSE) 约为 2 μ g m(-3)。为了评估校准方程到网络中其他监视器的可转移性，在马里兰州埃塞克斯郊区的第二个托管站点进行了跨站点评估。统计校准数据再次产生最低的 RMSE。低成本网络中监测仪的校准 PM2.5 读数提供了对巴尔的摩市内 PM2.5 时空变化的深入了解。
Low-cost air pollution monitors are increasingly being deployed to enrich knowledge about ambient air-pollution at high spatial and temporal resolutions. However, unlike regulatory-grade (FEM or FRM) instruments, universal quality standards for low-cost sensors are yet to be established and their data quality varies widely. This mandates thorough evaluation and calibration before any responsible use of such data. This study presents evaluation and field-calibration of the PM2.5 data from a network of low-cost monitors currently operating in Baltimore, MD, which has only one regulatory PM2.5 monitoring site within city limits. Co-location analysis at this regulatory site in Oldtown, Baltimore revealed high variability and significant overestimation of PM2.5 levels by the raw data from these monitors. Universal laboratory corrections reduced the bias in the data, but only partially mitigated the high variability. Eight months of field co-location data at Oldtown were used to develop a gain-offset calibration model, recast as a multiple linear regression. The statistical model offered substantial improvement in prediction quality over the raw or lab-corrected data. The results were robust to the choice of the low-cost monitor used for field-calibration, as well as to different seasonal choices of training period. The raw, lab-corrected and statistically-calibrated data were evaluated for a period of two months following the training period. The statistical model had the highest agreement with the reference data, producing a 24-h average root-mean-square-error (RMSE) of around 2 mu g m(-3). To assess transferability of the calibration equations to other monitors in the network, a cross-site evaluation was conducted at a second co-location site in suburban Essex, MD. The statistically calibrated data once again produced the lowest RMSE. The calibrated PM2.5 readings from the monitors in the low-cost network provided insights into the intra-urban spatiotemporal variations of PM2.5 in Baltimore.