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. The last 2 decades have seen substantial technological advancesin the development of low-cost air pollution instruments using smallsensors. 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 andcomparability of calibration methods. This study introduces a seven-stepmethodology for the field calibration of low-cost sensor systems usingreference 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 fullexample of the application of this methodology to a case study in an urbanenvironment using both multiple linear regression (MLR) and the randomforest (RF) machine-learning technique is presented with relevant R codeprovided, including error estimation. In this case, we have applied it tothe calibration of metal oxide gas-phase sensors (MOSs). Results reiterateprevious findings that MLR and RF are similarly accurate, though withdiffering limitations. The methodology presented here goes a step furtherthan most studies by including explicit transparent steps for addressingmodel selection, validation, and tuning, as well as addressing the commonissues of autocorrelation and multicollinearity. We also highlight the needfor standardized reporting of methods for data cleaning and flagging, modelselection and tuning, and model metrics. In the absence of a standardizedmethodology for the calibration of low-cost sensor systems, we suggest anumber of best practices for future studies using low-cost sensor systems to ensure greater comparability of research.

Unravelling a black box: an open-source methodology for the field calibration of small air quality sensors