This is a fine piece of measurement calibration paper. My understanding on measurement techniques are very thin, so I will only comment on data processing:

(1) From my point of view, the authors applied different algorithms to fit the low cost sensor data and compared the performance, but the authors do not discuss how the results can be used to predict the PM and other pollutants. Specifically, is it used to predict unobserved locations or forecast short-term future? 

(2) Also, let's say, RF seems to outperform other methods, is RF calibrated output considered to be the final product? or low cost sensor raw data still should be the reference? Do the authors consider the better performance from RF that can be overfitting, and other methods with a lower predictive performance can be more explainable?

(3) The formula in Equation (1) does not seem valid for MLR, because all those terms are correlated. Additional treatments or justifications are needed. 

Minor comments:

p1,l10: a typo "algorithms"

p2,l5: additional parentheses

Table 4: a typo II, III for O3 

This study presents a low-cost multi-parameter air quality monitoring system (LCS) that incorporates diverse machine learning algorithms. While the utilization of GA-BP techniques is emphasized, the paper falls short in clearly elucidating its novelty, thereby preventing a comprehensive understanding of its unique contribution. Additionally, the presence of several structural weaknesses within the paper necessitates significant revisions to enhance its coherence and overall quality. 

Specific Comments:

Introduction

The introduction would benefit from a more comprehensive review of recent literature. Additionally, the presence of unclear terms, such as "multi-dimensional multi-response" on line 16, requires clarification to ensure a precise and unambiguous understanding.In addition, the rationale behind the selection of the five specific algorithms used in the study remains unclear. Providing a clear justification for the choice of these algorithms would enhance the understanding of the research methodology and its relevance to the study's objectives.

Measurement setup

This section would greatly benefit from expansion to ensure a comprehensive understanding of the study. Specifically, there is a need for more clarity regarding the data collection process, including details on the quantity of data collected for each pollutant and any procedures employed for outlier removal. Additionally, in Section 2.1, the inclusion of a map illustrating the data collection site would provide crucial contextual information. In Section 2.2, it is essential to specify the precise names of the sensors used or provide access to datasheets, particularly for the Alphasense sensors. Clarifying whether the PM sensor used is named PM300S, for instance, would enhance the transparency of the study. Moreover, the paragraph discussing laboratory tests requires expansion. Given the apparent linearity of the sensor response to concentrations, it is necessary to explicate the rationale behind testing non-linear methods. Exploring concentration curves at various temperatures and humidity levels would contribute to a more thorough analysis. Lastly, directly citing the manufacturer of the reference monitors mentioned on line 23 of page 5, as well as providing information on the methodology employed for the weekly calibrations, would significantly strengthen the study's transparency.

Calibration method 

The equation (2) seems unclear; there might be a typographical error with X' instead of X. Furthermore, in section 3.2, it would be beneficial to include additional statistics such as Mean Squared Error (MSE) or Mean Absolute Error (MAE) to provide a more comprehensive evaluation of the model's performance.

Results and discussion

Paragraph 4.1 presents intriguing insights; however, it could benefit from a clearer presentation. For instance, the method of determining the number of trees in the random forest is not explicitly elucidated. Additionally, while it is evident that a sub-period was chosen for testing, the rationale behind this selection remains unexplained. Clarifying these aspects would enhance the overall coherence and understanding of the paragraph.

In paragraph 4.2, including the size of each segment, as well as the reference temperature, humidity, and concentration range, would enhance the comprehensiveness of the experimental setup and contribute to a more detailed understanding of the study. In Figure 9 (b), including a normalized version of the Root Mean Square Error (RMSE) would be beneficial to enable an accurate comparison among the three periods. The same principle applies to Table 3; including a normalized version of the Root Mean Square Error (RMSE) would facilitate an accurate comparison among the different parameters. 

Furthermore, the text mentions a division into train and test sets. It would be valuable to clarify whether a cross-validation was also conducted to ensure the robustness of the model evaluation. These consideration are also valid for the results of gas mesurements. Moreover tt would be insightful to include a discussion of the varying results obtained for each segment. For instance, a better detailed analysis of why the performance of SO2 is consistently good for period II but considerably poorer for the other periods would enrich the understanding of the data and provide valuable insights into the underlying factors influencing the results.

Finally, there is a typo on line 6 of page 14 (dada instead of data).

Conclusion 

The conclusion paragraph would benefit from a more explicit discussion on the presence of a recommended algorithm for calibration and a thorough examination of its potential limitations. By addressing the challenges associated with generalizing black box models, notably random forests, the conclusion could provide a more nuanced understanding of the practical implications and constraints that may arise from the study's findings.