PMID- 36816698
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230224
IS  - 2470-1343 (Electronic)
IS  - 2470-1343 (Linking)
VI  - 8
IP  - 6
DP  - 2023 Feb 14
TI  - Unexpected Performance Improvements of Nitrogen Dioxide and Ozone Sensors by 
      Including Carbon Monoxide Sensor Signal.
PG  - 5917-5924
LID - 10.1021/acsomega.2c07734 [doi]
AB  - Low-cost air quality (LCAQ) sensors are increasingly being used for community air 
      quality monitoring. However, data collected by low-cost sensors contain 
      significant noise, and proper calibration of these sensors remains a widely 
      discussed, but not yet fully addressed, area of concern. In this study, several 
      LCAQ sensors measuring nitrogen dioxide (NO(2)) and ozone (O(3)) were deployed in 
      six cities in the United States (Atlanta, GA; New York City, NY; Sacramento, CA; 
      Riverside, CA; Portland, OR; Phoenix, AZ) to evaluate the impacts of different 
      climatic and geographical conditions on their performance and calibration. Three 
      calibration methods were applied, including regression via linear and polynomial 
      models and random forest methods. When signals from carbon monoxide (CO) sensors 
      were included in the calibration models for NO(2) and O(3) sensors, model 
      performance generally increased, with pronounced improvements in selected cities 
      such as Riverside and New York City. Such improvements may be due to (1) temporal 
      co-variation between concentrations of CO and NO(2) and/or between CO and O(3); 
      (2) different performance levels of low-cost CO, NO(2), and O(3) sensors; and (3) 
      different impacts of environmental conditions on sensor performance. The results 
      showed an innovative approach for improving the calibration of NO(2) and O(3) 
      sensors by including CO sensor signals into the calibration models. Community 
      users of LCAQ sensors may be able to apply these findings further to enhance the 
      data quality of their deployed NO(2) and O(3) monitors.
CI  - (c) 2023 The Authors. Published by American Chemical Society.
FAU - Hasan, Md Hasibul
AU  - Hasan MH
AD  - Department of Civil, Environmental and Construction Engineering, University of 
      Central Florida, Orlando, Florida32816, United States.
FAU - Yu, Haofei
AU  - Yu H
AUID- ORCID: 0000-0002-7930-8934
AD  - Department of Civil, Environmental and Construction Engineering, University of 
      Central Florida, Orlando, Florida32816, United States.
FAU - Ivey, Cesunica
AU  - Ivey C
AUID- ORCID: 0000-0002-4740-2627
AD  - Department of Civil and Environmental Engineering, The University of California, 
      Berkeley, Berkeley, California94720, United States.
FAU - Pillarisetti, Ajay
AU  - Pillarisetti A
AUID- ORCID: 0000-0003-0518-2934
AD  - Environmental Health Sciences, School of Public Health, University of California, 
      Berkeley, California94720, United States.
FAU - Yuan, Ziyang
AU  - Yuan Z
AD  - Sailbri Cooper, Inc., Tigard, Oregon97223, United States.
FAU - Do, Khanh
AU  - Do K
AD  - Department of Chemical and Environmental Engineering, University of California, 
      Riverside, California92521, United States.
FAU - Li, Yi
AU  - Li Y
AUID- ORCID: 0000-0002-1107-7631
AD  - Sailbri Cooper, Inc., Tigard, Oregon97223, United States.
LA  - eng
PT  - Journal Article
DEP - 20230131
PL  - United States
TA  - ACS Omega
JT  - ACS omega
JID - 101691658
PMC - PMC9933490
COIS- The authors declare no competing financial interest.
EDAT- 2023/02/24 06:00
MHDA- 2023/02/24 06:01
PMCR- 2023/01/31
CRDT- 2023/02/23 09:25
PHST- 2022/12/04 00:00 [received]
PHST- 2023/01/16 00:00 [accepted]
PHST- 2023/02/23 09:25 [entrez]
PHST- 2023/02/24 06:00 [pubmed]
PHST- 2023/02/24 06:01 [medline]
PHST- 2023/01/31 00:00 [pmc-release]
AID - 10.1021/acsomega.2c07734 [doi]
PST - epublish
SO  - ACS Omega. 2023 Jan 31;8(6):5917-5924. doi: 10.1021/acsomega.2c07734. eCollection 
      2023 Feb 14.