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@ARTICLE{Schmitz:902376,
author = {Schmitz, Seán and Towers, Sherry and Villena, Guillermo
and Caseiro, Alexandre and Wegener, Robert and Klemp, Dieter
and Langer, Ines and Meier, Fred and von Schneidemesser,
Erika},
title = {{U}nraveling a black box: {A}n open-source methodology for
the field calibration of small air quality sensors},
reportid = {FZJ-2021-04210},
year = {2021},
abstract = {Abstract. The last two decades have seen substantial
technological advances in the development of low-cost air
pollution instruments using small sensors. While their use
continues to spread across the field of atmospheric
chemistry, the air quality monitoring community, as well as
for commercial and private use, challenges remain in
ensuring data quality and comparability of calibration
methods. This study introduces a seven-step methodology for
the field calibration of low-cost sensors using reference
instrumentation with user-friendly guidelines, open access
code, and a discussion of common barriers to such an
approach. The methodology has been developed and is
applicable for gas-phase pollutants, such as for the
measurement of nitrogen dioxide (NO2) or ozone (O3). A full
example of the application of this methodology to a case
study in an urban environment using both Multiple Linear
Regression (MLR) and the Random Forest (RF) machine-learning
technique is presented with relevant R code provided,
including error estimation. In this case, we have applied it
to the calibration of metal oxide gas-phase sensors (MOS).
Results reiterate previous findings that MLR and RF are
similarly accurate, though with differing limitations. The
methodology presented here goes a step further than most
studies by including explicit, transparent steps for
addressing model selection, validation, and tuning, as well
as addressing the common issues of autocorrelation and
multicollinearity. We also highlight the need for
standardized reporting of methods for data cleaning and
flagging, model selection and tuning, and model metrics. In
the absence of a standardized methodology for the
calibration of low-cost sensors, we suggest a number of best
practices for future studies using low-cost sensors to
ensure greater comparability of research.},
cin = {IEK-8},
cid = {I:(DE-Juel1)IEK-8-20101013},
pnm = {2111 - Air Quality (POF4-211)},
pid = {G:(DE-HGF)POF4-2111},
typ = {PUB:(DE-HGF)25},
doi = {10.5194/amt-2020-489},
url = {https://juser.fz-juelich.de/record/902376},
}