Literature DB >> 34205429

Deployment, Calibration, and Cross-Validation of Low-Cost Electrochemical Sensors for Carbon Monoxide, Nitrogen Oxides, and Ozone for an Epidemiological Study.

Christopher Zuidema1, Cooper S Schumacher1, Elena Austin1, Graeme Carvlin1, Timothy V Larson1,2, Elizabeth W Spalt1, Marina Zusman1, Amanda J Gassett1, Edmund Seto1, Joel D Kaufman1,3,4, Lianne Sheppard1,5.   

Abstract

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 (NO2; NO2-B43F), and oxidizing gases (OX-B431)-which refers to ozone (O3) and NO2. 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 R2 measures (CO: RMSE = 18 ppb, R2 = 0.97; NO: RMSE = 2 ppb, R2 = 0.97). Performance measures for NO2 and O3 were somewhat lower (NO2: RMSE = 3 ppb, R2 = 0.79; O3: RMSE = 4 ppb, R2 = 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.

Entities:  

Keywords:  air pollution; environmental epidemiology; exposure assessment; hazardous gases; low-cost sensors; sensor network

Year:  2021        PMID: 34205429     DOI: 10.3390/s21124214

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  5 in total

1.  Evaluating the Performance of Using Low-Cost Sensors to Calibrate for Cross-Sensitivities in a Multipollutant Network.

Authors:  Misti Levy Zamora; Colby Buehler; Hao Lei; Abhirup Datta; Fulizi Xiong; Drew R Gentner; Kirsten Koehler
Journal:  ACS ES T Eng       Date:  2022-04-11

Review 2.  Evolution and Applications of Recent Sensing Technology for Occupational Risk Assessment: A Rapid Review of the Literature.

Authors:  Giacomo Fanti; Andrea Spinazzè; Francesca Borghi; Sabrina Rovelli; Davide Campagnolo; Marta Keller; Andrea Borghi; Andrea Cattaneo; Emanuele Cauda; Domenico Maria Cavallo
Journal:  Sensors (Basel)       Date:  2022-06-27       Impact factor: 3.847

3.  Dysprosium Doped Zinc Oxide for NO2 Gas Sensing.

Authors:  Ghada El Fidha; Nabila Bitri; Sarra Mahjoubi; Fatma Chaabouni; Eduard Llobet; Juan Casanova-Chafer
Journal:  Sensors (Basel)       Date:  2022-07-10       Impact factor: 3.847

4.  Within-City Variation in Ambient Carbon Monoxide Concentrations: Leveraging Low-Cost Monitors in a Spatiotemporal Modeling Framework.

Authors:  Jianzhao Bi; Christopher Zuidema; David Clausen; Kipruto Kirwa; Michael T Young; Amanda J Gassett; Edmund Y W Seto; Paul D Sampson; Timothy V Larson; Adam A Szpiro; Lianne Sheppard; Joel D Kaufman
Journal:  Environ Health Perspect       Date:  2022-09-28       Impact factor: 11.035

5.  Calibration of SO2 and NO2 Electrochemical Sensors via a Training and Testing Method in an Industrial Coastal Environment.

Authors:  Sofía Ahumada; Matias Tagle; Yeanice Vasquez; Rodrigo Donoso; Jenny Lindén; Fredrik Hallgren; Marta Segura; Pedro Oyola
Journal:  Sensors (Basel)       Date:  2022-09-26       Impact factor: 3.847

  5 in total

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