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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},
}