Community Air Sensor Network (CAIRSENSE) project: evaluation of low-cost sensor performance in a suburban environment in the southeastern United States
Community Air Sensor Network (CAIRSENSE) project: evaluation of low-cost sensor performance in a suburban environment in the southeastern United States
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DOI：
10.5194/amt-9-5281-2016
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发表时间：
2016-11-01
影响因子：
3.8
通讯作者：
Buckley, Ken
中科院分区：
文献类型：
--
作者：
Jiao, Wan;Hagler, Gayle;Buckley, Ken
空气污染传感器技术的进步使小型和低成本的系统得以发展，以测量室外空气污染。如果数据质量足够，在一个小的地理区域内部署大量传感器将有潜在的好处，以额外的地理和时间测量分辨率补充传统监测网络。为了了解新兴空气传感器技术的能力，社区空气传感器网络（CAIRSENSE）项目在一个监管空气监测点部署了低成本、连续的、可商用的空气污染传感器，并在美国东南部约2公里的区域内建立了一个本地传感器网络。测量氮氧化物、臭氧、一氧化碳、二氧化硫和颗粒的传感器的搭配显示出高度可变的性能，无论是在与参考监视器的比较方面，还是在多个相同传感器产生相同信号的程度方面。多个臭氧、二氧化氮和一氧化碳传感器显示与参考监测仪的相关性低至非常高，Pearson样本相关系数(r)分别为0.39至0.97、0.25至0.76和0.40至0.82。唯一的二氧化硫传感器测试显示与参考监测仪和错误的高浓度值没有相关性（r < 0.5）。对各种各样的颗粒物（PM）传感器进行了测试，结果各不相同——一些传感器在相同的传感器之间具有非常高的一致性（例如，r = 0.99），但与参考PM2.5监测仪的一致性中等（例如，r = 0.65）。对于与参考监测器（r > 0.5）有中度到强相关性的选定传感器，进行逐步多元线性回归，以确定环境温度、相对湿度（RH）或传感器在采样天数中的年龄是否可以用于校正算法以提高一致性。综合所有因素，NO2传感器的最大改善与参考文献一致(多重相关系数r - adji - origin (2) = 0.57, r - adji -final(2) = 0.81)；然而，其他传感器在一致性方面没有明显改善。一个四节点传感器网络成功地捕获了8个月期间的臭氧（两个节点）和PM（四个节点）数据，并显示了预期的日浓度模式，以及由于附近交通排放而可能发生的臭氧滴定。总体而言，本研究展示了新兴空气质量传感器技术在现实世界中的性能；传感器和参考监测器之间的不一致表明，根据基准监测器对传感器进行现场测试应该是所有实地研究的一个关键方面。
Advances in air pollution sensor technology have enabled the development of small and low-cost systems to measure outdoor air pollution. The deployment of a large number of sensors across a small geographic area would have potential benefits to supplement traditional monitoring networks with additional geographic and temporal measurement resolution, if the data quality were sufficient. To understand the capability of emerging air sensor technology, the Community Air Sensor Network (CAIRSENSE) project deployed low-cost, continuous, and commercially available air pollution sensors at a regulatory air monitoring site and as a local sensor network over a surrounding similar to 2 km area in the southeastern United States. Collocation of sensors measuring oxides of nitrogen, ozone, carbon monoxide, sulfur dioxide, and particles revealed highly variable performance, both in terms of comparison to a reference monitor as well as the degree to which multiple identical sensors produced the same signal. Multiple ozone, nitrogen dioxide, and carbon monoxide sensors revealed low to very high correlation with a reference monitor, with Pearson sample correlation coefficient (r) ranging from 0.39 to 0.97, 0.25 to 0.76, and 0.40 to 0.82, respectively. The only sulfur dioxide sensor tested revealed no correlation (r < 0.5) with a reference monitor and erroneously high concentration values. A wide variety of particulate matter (PM) sensors were tested with variable results - some sensors had very high agreement (e.g., r = 0.99) between identical sensors but moderate agreement with a reference PM2.5 monitor (e.g., r = 0.65). For select sensors that had moderate to strong correlation with reference monitors (r > 0.5), step-wise multiple linear regression was performed to determine if ambient temperature, relative humidity (RH), or age of the sensor in number of sampling days could be used in a correction algorithm to improve the agreement. Maximum improvement in agreement with a reference, incorporating all factors, was observed for an NO2 sensor (multiple correlation coefficient R-adj-orig(2) = 0.57, R-adj-final(2) = 0.81); however, other sensors showed no apparent improvement in agreement. A four-node sensor network was successfully able to capture ozone (two nodes) and PM (four nodes) data for an 8-month period of time and show expected diurnal concentration patterns, as well as potential ozone titration due to nearby traffic emissions. Overall, this study demonstrates the performance of emerging air quality sensor technologies in a real-world setting; the variable agreement between sensors and reference monitors indicates that in situ testing of sensors against benchmark monitors should be a critical aspect of all field studies.