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@ARTICLE{Schmitz:903110,
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}nravelling a black box: an open-source methodology for
the field calibration of small air quality sensors},
journal = {Atmospheric measurement techniques},
volume = {14},
number = {11},
issn = {1867-1381},
address = {Katlenburg-Lindau},
publisher = {Copernicus},
reportid = {FZJ-2021-04834},
pages = {7221 - 7241},
year = {2021},
abstract = {The last 2 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, and 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 sensor systems 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 (MOSs). 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 sensor systems, we suggest a number
of best practices for future studies using low-cost sensor
systems to ensure greater comparability of research.},
cin = {IEK-8},
ddc = {550},
cid = {I:(DE-Juel1)IEK-8-20101013},
pnm = {2111 - Air Quality (POF4-211)},
pid = {G:(DE-HGF)POF4-2111},
typ = {PUB:(DE-HGF)16},
UT = {WOS:000720375400001},
doi = {10.5194/amt-14-7221-2021},
url = {https://juser.fz-juelich.de/record/903110},
}