“Transferability of ML-based Global Calibration Models for NO₂ and NO Low-Cost Sensors”

General Comments

The manuscript presents research work looking at the application of machine learning (ML) techniques in developing a transferable calibration methodology for a network of low-cost sensor units (SU) focusing on NO and NO₂ pollutants. This study assessed the performance for collocated and non-collated networks of low-cost sensors in different urban environments in Switzerland and Italy. They incorporated several commonly used variables (raw sensor signals, RH, temperature) in their algorithm but emphasised the key role ozone plays as an input in their model, concluding that the best results were obtained in studies involving co-located networks which have ozone as part of the input variable.

Specific comments

The authors have used low-cost SU that have a pair of electrochemical sensors for the two species of interest (NO, NO2). The reviewer found it odd that the pair of signals were used in the model setup for each species. For instance in modelling the corrected NO₂, both the ‘NO2_A’ and ‘NO2_B’ are used but I would expect these two signals to be very correlated as summarised in Table 2. I would have thought one of the pairs should be sufficient, particularly the one with the best R value in Table 3.

While the reviewer agree with the authors on the inclusion of O₃ for as input variable for the NO₂ calibration (there are ample literature evidence for this), there are very little evidence for the same for NO, thus questioning if this could lead to overfitting/training dependence for NO on O₃ and potentially result in additional error in transferability of this method to regions where O₃ is high but low NO_X.

Technical corrections

Figures 1 & S2, the caption describes the central line of box plots to mean the median but the median are not shown in these figures.

P.11, line 232 & 234, units missing for the RMSE values. Autor need to correct instance of this in the whole manuscript

1. 17, line 311-312: the statement “Moreover, this study advocated enhancing global calibration models by incorporating O₃ measurements from available nearby monitoring stations.” This statement is too generic, it implies that O₃ needs to be considered for all low-cost SU network calibration like CO, PM, CO₂ and NO (see reviewers general comment about this species above) etc.
2. 18 line 333, the statement “The utilization of multiple electrochemical cells within each SU targeting the same pollutant to enhance data reliability” needs to be revised in context of the reviewers general comments above for: 1) NO species and 2) overfitting by incorporating pair sensor of reading for same species.

Transferability of ML-based Global Calibration Models for NO2 and NO Low-Cost Sensors by Abu-Hani et al

The manuscript presents a framework for the global machine learning (ML)-based calibration models for NO2 and NO electrochemical cells using data from low-cost sensor units (SUs) utilized in a previous study by Bigi et al. (2018). This study mainly focuses on calibration transferability among SUs when deployed at the same location (or with the same environmental conditions) and different locations (or with different environmental conditions), given that no explicit overlap exists between the training and testing data distributions. This approach uses a simple standardization to account for sensor-to-sensor variations. In addition, the author claims that a potential improvement in model transferability was achieved by using O3 from nearby regulatory air quality monitoring stations.

Minor comments:

Figures 1 and S2: Where are the central (median) lines in the Box plots? Please include the median line and mean (with a symbol) in the figures.

Figure 4: What does the negative sensor voltage convey? No mention of this in the manuscript.  

Appendix A and Line 222: Although MAE was mentioned as one of the measures for quantifying the deviation between the calibrated values and their corresponding reference values, it was never discussed in the main manuscript. Tables S1-S6 and Figures S3-S6 are not referred to in the main manuscript. 

Line 232: RMSE units are missing.

Figure 9: In the caption, please include how the RMSE relative improvement (%) was estimated.



Altogether, the manuscript is well written and should be considered for publication as this study demonstrates the capability of ML models to generalize calibration models that can be adopted to improve the reliability of low-cost sensors.