Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, USA.
Department of Civil & Environmental Engineering, University of Washington, Seattle, WA 18195, USA.
Sensors (Basel). 2021 Jun 19;21(12):4214. doi: 10.3390/s21124214.
We designed and built a network of monitors for ambient air pollution equipped with low-cost gas sensors to be used to supplement regulatory agency monitoring for exposure assessment within a large epidemiological study. This paper describes the development of a series of hourly and daily field calibration models for Alphasense sensors for carbon monoxide (CO; CO-B4), nitric oxide (NO; NO-B4), nitrogen dioxide (NO; NO2-B43F), and oxidizing gases (OX-B431)-which refers to ozone (O) and NO. The monitor network was deployed in the Puget Sound region of Washington, USA, from May 2017 to March 2019. Monitors were rotated throughout the region, including at two Puget Sound Clean Air Agency monitoring sites for calibration purposes, and over 100 residences, including the homes of epidemiological study participants, with the goal of improving long-term pollutant exposure predictions at participant locations. Calibration models improved when accounting for individual sensor performance, ambient temperature and humidity, and concentrations of co-pollutants as measured by other low-cost sensors in the monitors. Predictions from the final daily models for CO and NO performed the best considering agreement with regulatory monitors in cross-validated root-mean-square error (RMSE) and R measures (CO: RMSE = 18 ppb, R = 0.97; NO: RMSE = 2 ppb, R = 0.97). Performance measures for NO and O were somewhat lower (NO: RMSE = 3 ppb, R = 0.79; O: RMSE = 4 ppb, R = 0.81). These high levels of calibration performance add confidence that low-cost sensor measurements collected at the homes of epidemiological study participants can be integrated into spatiotemporal models of pollutant concentrations, improving exposure assessment for epidemiological inference.
我们设计并构建了一个环境空气污染监测网络,该网络配备了低成本气体传感器,用于补充监管机构监测,以进行大型流行病学研究中的暴露评估。本文描述了为 AlphaSense 一氧化碳 (CO; CO-B4)、一氧化氮 (NO; NO-B4)、二氧化氮 (NO2-B43F) 和氧化气体 (OX-B431)——指臭氧 (O) 和 NO 的传感器开发一系列小时和日常现场校准模型。监测网络于 2017 年 5 月至 2019 年 3 月在美国华盛顿州普吉特海湾地区部署。监测器在整个地区进行轮换,包括在两个普吉特海湾清洁空气机构监测站点进行校准,以及在 100 多处住宅(包括流行病学研究参与者的住所)进行监测,目的是改善参与者所在地的长期污染物暴露预测。当考虑到个别传感器性能、环境温度和湿度以及监测器中其他低成本传感器测量的共污染物浓度时,校准模型得到了改善。考虑到与交叉验证均方根误差 (RMSE) 和 R 度量的监管监测器的一致性,最终的每日 CO 和 NO 模型的预测表现最佳(CO:RMSE = 18 ppb,R = 0.97;NO:RMSE = 2 ppb,R = 0.97)。NO 和 O 的性能指标略低(NO:RMSE = 3 ppb,R = 0.79;O:RMSE = 4 ppb,R = 0.81)。这些高水平的校准性能增加了信心,即可以将在流行病学研究参与者家中收集的低成本传感器测量值整合到污染物浓度的时空模型中,从而改善流行病学推断的暴露评估。