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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},
}