Exposure to NO₂ and PM_2.5 While Commuting: Utility of Low-Cost Sensor

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Dear authors, in the next paragraphs, my comments about your manuscript.

 

In discussing a timely and relevant investigation in regard to the scientific area, this research has the potential to be original and to contribute greatly to man's knowledge in the field of interest. The methodology is fascinating and well-grounded with timely references, showing proper grasp of the contemporary state of the art.

The structure of the paper is logical and coherent; thus, permitting the reader to follow the argument smoothly. The introduction does well in placing the problem in context with an explanation of the relevance of the research; however, there is still doubt if the aim is to develop an efficient one for the problem, or to test a new methodology or technology, or even to compare different approaches to solving a problem. If such attempts are really aiming for technological innovation, it is advisable to highlight the expected impact. If other works are compared, a comparative table should be inserted to assist analysis.

The results section is a clear presentation of data for easier understanding of the impact of the study; discussion of the results is well done and provides concrete evidence, thus supporting the conclusion. The statistical treatment and the interpretation of the data were appropriate to test the study's hypotheses.

 

What to further improve upon

 

1.Although the methodology is well elaborated, it is lacking sufficient technical information that may put the replicability of the study in jeopardy. Provide additional information on parameters used in the experiments, including specifications on the conditions under which they were conducted.

 

2.A few sections need to be elaborated since they do not allow for reproducibility by other investigators.

3.Some sentences are so long and convoluted that they hinder the pleasing flow of reading.

4.There is need for sharpening and more precise definitions and descriptions in some areas.

5.Although the presentation of the results is good, the discussion should also delve deeper, particularly in relation to previous studies.

6.There is no critical appraisal of any limitations that may have affected this study and recommendations for future research.

7.Some of the figures/graphics are ill-resolved, making them difficult to read. The line or marker graphs that are hardly visible should increase their contrast for better visibility.

8.In some parts of the article, additional diagrams could supplement explanations for easy comprehension.

9.The term algorithm is not defined and no flowchart or pseudocode is given to clarify the reasoning behind its implementation. Also, while the paper talks about using algorithms and models, it does not specify which ones were used.

10.If they are statistical or machine learning models, then you should specify the parameters, provide justification for their selection, and mention any limitations.

11.Which distribution was used (Normal, Exponential, Poisson, etc.)? Why was this distribution chosen over the others? Did a statistical test validate the distribution choice?

12.The article should report on the sensor: Model and manufacturer; Accuracy and measurement range; Sampling frequency; Calibration; Possible sources of errors.

Author Response

Manuscript ID applsci-3586865 – major revisions

 

Detailed answers to:

Reviewer #1

General Comment:

In discussing a timely and relevant investigation in regard to the scientific area, this research has the potential to be original and to contribute greatly to man's knowledge in the field of interest. The methodology is fascinating and well-grounded with timely references, showing proper grasp of the contemporary state of the art.

The structure of the paper is logical and coherent; thus, permitting the reader to follow the argument smoothly. The introduction does well in placing the problem in context with an explanation of the relevance of the research; however, there is still doubt if the aim is to develop an efficient one for the problem, or to test a new methodology or technology, or even to compare different approaches to solving a problem. If such attempts are really aiming for technological innovation, it is advisable to highlight the expected impact. If other works are compared, a comparative table should be inserted to assist analysis.

The results section is a clear presentation of data for easier understanding of the impact of the study; discussion of the results is well done and provides concrete evidence, thus supporting the conclusion. The statistical treatment and the interpretation of the data were appropriate to test the study's hypotheses.

Answer and Action: Thank you for your thoughtful feedback. We have carefully considered your suggestions and incorporated them into the main text (yellow color). We trust that these revisions will enhance the clarity and presentation of the manuscript, and we hope they will meet your approval.

Answer:

The main objective of this study is to apply and evaluate a novel methodological approach based on the use of low-cost, portable sensors for continuous, individualized assessment of air quality during active commuting. The technological innovation of the research is to demonstrate the feasibility and usability of low-cost sensors to record real-time personal exposure to NO₂ and PM_2.5, thus overcoming the limitations associated with traditional, stationary ambient monitoring stations. Rather than developing a new device, the study emphasizes using the emerging technology in a specific environmental health context, namely assessing the health risks associated with active commuting in polluted areas. By integrating individualized exposure data with comprehensive health risk assessments, the research aims to contribute to advances in personalized environmental health research and support public health interventions. We trust that the following clarifications and additions adequately address the reviewer's concerns.

Action: We have changed the aim of the study to provide a clearer description of the subject and objectives of the research

from:

This study includes the first results on active commuting in the most polluted region of Poland, which will enable a quantitative assessment of whether the health benefits of using a bicycle instead of a private car/bus or walking for short distances outweigh the health risks. The principal aim is to address the data gaps in NO₂ and PM_2.5 exposure to facilitate a comprehensive health risk assessment (HRA), including the deposition of related doses of PM_2.5 in the human respiratory tract system.

into:

This study presents the first research findings on active commuting in the most polluted region of Poland, with a specific focus on continuous, individualized air quality monitoring to enhance the estimation of personal exposure levels. The principal aim is to address critical data gaps concerning exposure to NO₂ and PM_2.5 through the application of a novel methodological approach, based on the deployment of low-cost, portable sensors. The incorporation of low-cost sensor technology constitutes a significant technological innovation, enabling the collection of high-resolution, real-time exposure data that reflects the dynamic nature of individual activity patterns and varying microenvironments. This approach represents a substantial advancement over traditional reliance on fixed ambient monitoring stations, which are limited in their ability to capture the true variability of personal exposure during active commuting. The study seeks to provide a robust basis for comprehensive health risk assessments (HRA), including the estimation of inhaled dose of NO₂ and PM_2.5 . By delivering individualized exposure data, the research aims to more accurately evaluate whether the health benefits of active commuting, such as cycling or walking, outweigh the associated risks of air pollution exposure. Ultimately, by demonstrating the utility of low-cost sensor technology in environmental health studies, this research aspires to contribute to the broader adoption of cost-effective monitoring solutions, thereby facilitating improved population health outcomes in urban areas.

 

Comment 1: Although the methodology is well elaborated, it is lacking sufficient technical information that may put the replicability of the study in jeopardy. Provide additional information on parameters used in the experiments, including specifications on the conditions under which they were conducted.

Answer:

The measurements were systematically conducted on each regular working day during the study period (from January to November 2022), ensuring comprehensive coverage of typical daily conditions. The participants were not instructed or constrained in their choice of transportation mode; instead, they independently selected the means of transport based on their personal needs, preferences, and circumstances and  weather conditions on a given day. This methodological decision was deliberate, aiming to capture natural variations in exposure levels under real-world conditions, thereby enhancing the ecological validity and practical applicability of the findings.

It should be emphasized that the study was designed to reflect everyday commuting scenarios without artificial interference from the research team. Consequently, measurements encompassed a variety of environmental and operational conditions, such as different weather situations, traffic intensities, and urban or suburban settings. These factors were inherent to the data collection process, ensuring that the results are representative of actual exposure patterns typically encountered by the general population during daily commutes.

Nonetheless, detailed records of the type of transportation, date, and approximate duration of each journey were maintained, allowing for contextual interpretation of the obtained results.

 

Action: The following fragment has been added to 2.2. Study design:

The measurements were systematically conducted on each regular working day during the study period (from January to November 2022), ensuring comprehensive coverage of typical daily conditions. The participants were not instructed or constrained in their choice of transportation mode; instead, they independently selected the means of transport based on their personal needs, preferences, and circumstances, and weather conditions on a given day. This methodological decision was deliberate, aiming to capture natural variations in exposure levels under real-world conditions, thereby enhancing the ecological validity and practical applicability of the findings.

 

Comment 2: A few sections need to be elaborated since they do not allow for reproducibility by other investigators.

Answer: we kindly request a more detailed specification regarding which particular parts should be expanded or clarified. Such guidance will allow us to address the concerns more precisely and enhance the overall transparency and replicability of the study.

 

Comment 3: Some sentences are so long and convoluted that they hinder the pleasing flow of reading.

Answer: The revision of manuscript has been proofread using the Grammarly software to ensure grammatical and stylistic correctness, identifying and revising overly long or complex sentences and to improve the overall flow and readability.

 

Comment 4: There is need for sharpening and more precise definitions and descriptions in some areas.

Answer: we kindly request more detailed indications regarding the specific areas or definitions that, in the Reviewer's opinion, require sharpening or further clarification. Such guidance will allow us to make the necessary adjustments in a manner fully aligned with the Reviewer's expectations.

 

Comment 5: Although the presentation of the results is good, the discussion should also delve deeper, particularly in relation to previous studies.

Answer: We have revised the Discussion section by providing a deeper analysis of the results in the context of previous studies. To enhance clarity and structure, the Discussion has also been divided into subsections, and two figures were added, allowing for a more systematic comparison with existing literature and a more comprehensive interpretation of our findings. Additionally, we have used the ranking of concentration with increasing order compared to other studies

 

Comment 6: There is no critical appraisal of any limitations that may have affected this study and recommendations for future research.

Answer: Appropriate subsections addressing the limitations of the study and recommendations for future research have been added at the end of the Discussion section:

4.4. Limitations of the presented approach

Despite careful planning and execution, this study has several important limitations that must be considered when interpreting the results. The analysis was based on data collected from two individuals, which limits the generalizability of the results to a broader population. Individual differences in routes, pace of movement and physiology of the participants may have influenced the results. Due to different starting locations and different urban conditions (city center vs. suburban areas), exposure to pollutants may have differed significantly, complicating direct comparability between participants.

The study adopted standard values of minute ventilation rates (VR) corresponding to different modes of transportation, without direct measurement of individual exercise, which may have affected estimates of inhaled pollutant doses. The study was conducted over a period of 11 months, so the results reflect conditions specific to the year 2022 in Gliwice, which may vary at other times due to changing emission trends and meteorological conditions. In addition, Gliwice, as a city with high levels of air pollution, may not be representative of localities with better air quality or other urban characteristics, thus limiting the ability to extrapolate conclusions to other locations.

4.5. Future research needs

In view of these limitations, further research is advisable to deepen and consolidate the results obtained. In particular, it is recommended to increase the number of participants. Including a larger and more diverse group of people - representing different age groups, genders, physical activity levels and varied travel routes - would increase the generalizability of the results. It would also be important to include measurements of actual ventilation rates (e.g., using portable spirometers), which would allow more precise estimates of inhaled pollutant doses.

Conducting similar studies in cities with different pollution levels and urban structures would allow comparison and evaluation of the impact of local conditions on exposure. It would also be beneficial to carry out measurements over a number of years to allow analysis of time trends and assessment of the impact of environmental policies on reducing exposure to air pollution.

In addition, future studies could examine the effects of protective measures such as the use of face masks, travel route modifications (e.g., avoiding highly congested streets) or the use of vehicles equipped with advanced air filtration systems on actual exposure levels. Nevertheless, according to the authors, the most recommended approach would be to verify the results using more precise instruments (e.g., reference-grade equipment) under real-world conditions to assess the accuracy of low-cost sensors in individual exposure studies.

 

Comment 7: Some of the figures/graphics are ill-resolved, making them difficult to read. The line or marker graphs that are hardly visible should increase their contrast for better visibility.

Answer: We have adjusted the figures to improve their readability. Specifically, we brightened one of the colors significantly for better contrast, and increased the size of the markers to ensure they are more clearly visible.

 

Comment 8: In some parts of the article, additional diagrams could supplement explanations for easy comprehension.

Answer: We thank the Reviewer for the insightful comment. We have carefully considered the suggestion to include additional diagrams. We have added two figures presenting order of exposure concentrations and doses of our study in compared with other studies. Nevertheless, we remain open to specific recommendations should the Reviewer identify particular areas where a diagram would be especially beneficial.

 

Figure 6. Comparison of PM2.5 exposure concentrations (μg/m³) and inhaled doses (μg per km or per trip) by different modes of transport, based on location-specific data from this and other studies.

 

Figure 7. Comparison of NO₂ exposure concentrations (μg/m³) and inhaled doses (μg per km or per trip) by different modes of transport, based on location-specific data from this and other studies.

Comment 9: The term algorithm is not defined and no flowchart or pseudocode is given to clarify the reasoning behind its implementation. Also, while the paper talks about using algorithms and models, it does not specify which ones were used.

Answer: The algorithm mentioned in section 2.3 are verbally described and justified. Those are simple conditional statements for separating one trip from another for data consistency. We do not believe that adding a flowchart or providing pseudocode would contribute to the paper’s clarity. Instead, we have removed the word “algorithm”, so that the reader is not confused as to the main aim of this paper, and we have rewritten section 2.3 slightly.

 

Comment 10: If they are statistical or machine learning models, then you should specify the parameters, provide justification for their selection, and mention any limitations.

Answer: This paper used no machine learning models, only simple data validation scripts written in Python for data consistency.

 

Comment 11: Which distribution was used (Normal, Exponential, Poisson, etc.)? Why was this distribution chosen over the others? Did a statistical test validate the distribution choice?

Answer: We have changed the lines: 217-31

The Shapiro-Wilk test was used to check the normality of the distribution of the variables (PM_2.5 and NO₂), during both heating and non-heating season. The results of the Shapiro-Wilk test showed significant deviations from the normal distribution (all p < 0.0001), so the distribution was not normal, which precluded the use of parametric tests. For this reason, we used non-parametric Mann-Whitney and Kruskal-Wallis tests. It was not indicated that the data had a Poisson, exponential or other specific distribution - it was only stated that it was not normal, which justified the choice of nonparametric tests.

 

Comment 12: The article should report on the sensor: Model and manufacturer; Accuracy and measurement range; Sampling frequency; Calibration; Possible sources of errors.

Answer:  Model and manufacturer is Flow 2 personal sensor (produced by Plume Labs, France). The following text has been added in section 2.2:

Self-calibrates with cutting-edge machine-learning algorithms contained in Flow’s pro-prietary firmware [28]. According to the manufacturer, the correlation coefficient for NO₂ with the reference device APNA370 NO_x monitor (produced by HORIBA Corp., Japan), is in the range from 84 to 99% (average 96%), while for PM2.5 the reference device was an AeroTrak 9306 Handheld Particle Counter (produced by TSI Incorporated, Shoreview,MN, USA) with correlation in the range from 87.3% to 97.0% (average 90.9%) [29]. It should be noted that manufacturers often achieve high correlations with reference-grade sensors under strictly controlled conditions, while the details of the calibration procedures are typically protected as trade secrets. However, under real-world conditions, the accuracy of many low-cost sensors generally falls within a ±10% range for most air pollutants. Furthermore, regardless of the precision of the calibration process, low-cost sensors are highly sensitive to meteorological factors and require an acclimatization period when the monitoring environment changes [30,31]. Among the studies published to date, Crnosija et al. [32] proved in their study that Flow air quality sensor (the same model as used in our study) can be a helpful instrument for monitoring air quality…

 

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

With regard to the limitation of spatial specificity, what strategies do the authors intend to implement?  It is possible that comparable research could be undertaken in other cities in order to validate the findings on a wider scale.

 Would the authors be able to provide further information regarding the sample size and demographics, as well as address the ways in which these factors might affect the variability in inhaled dose?  Did the calculation of the dose take into account the physiological differences that exist between individuals, such as the rate of breathing and fitness?

 How did the authors account for changes in the routes taken or the amount of time spent commuting when walking, cycling, and driving were all considered?  Was there any consideration given to the filtration or ventilation status of the vehicles during the analysis?

 Were changes based on the day of the week or the time of day taken into consideration throughout the analysis?  If this is not the case, how may these temporal considerations have an effect on the outcomes?

 The fact that PM2.5 concentrations were greater during the season that did not involve heating may appear to be surprising given the contributions that are anticipated to come from emissions connected to heating during the winter.  Is it possible for the authors to provide an explanation for this finding?  Could it be because of enhanced atmospheric dispersion throughout the winter months or because of local sources during the season when there is no heating?

The introduction lacks motivation and the clear need of the study. Thefore the authors needs to add recent articles such as:

1. Mukundan, Arvind, Chia-Cheng Huang, Ting-Chun Men, Fen-Chi Lin, and Hsiang-Chen Wang. "Air pollution detection using a novel snap-shot hyperspectral imaging technique." Sensors 22, no. 16 (2022): 6231.
2. Chen, Chi-Wen, Yu-Sheng Tseng, Arvind Mukundan, and Hsiang-Chen Wang. "Air pollution: Sensitive detection of PM2. 5 and PM10 concentration using hyperspectral imaging." Applied Sciences 11, no. 10 (2021): 4543.

Author Response

Manuscript ID applsci-3586865 – major revisions

 

Detailed answers to:

Reviewer #2

We have carefully considered your suggestions and incorporated them into the main text (blue color). We trust that these revisions will enhance the clarity and presentation of the manuscript, and we hope they will meet your approval.

Comment 1: With regard to the limitation of spatial specificity, what strategies do the authors intend to implement? It is possible that comparable research could be undertaken in other cities in order to validate the findings on a wider scale.

Answer: We would like to thank the Reviewer for this valuable comment, which is consistent with the comment made by Reviewer #1. We fully acknowledge the limitation related to the spatial specificity of our study. At this stage, our primary objective was to perform a detailed evaluation within a specific urban environment. Nevertheless, we recognize the importance of validating the findings across different locations. In future research, we intend to extend our study to additional cities with varying climatic, urban, and pollution characteristics in order to assess the broader applicability and robustness of the results. We have added a statement regarding this intention in the revised manuscript in the following sections:

4.4. Limitations of the presented approach

Despite careful planning and execution, this study has several important limitations that must be considered when interpreting the results. The analysis was based on data collected from two individuals, which limits the generalizability of the results to a broader population. Individual differences in routes, pace of movement and physiology of the participants may have influenced the results. Due to different starting locations and different urban conditions (city center vs. suburban areas), exposure to pollutants may have differed significantly, complicating direct comparability between participants.

The study adopted standard values of minute ventilation rates (VR) corresponding to different modes of transportation, without direct measurement of individual exercise, which may have affected estimates of inhaled pollutant doses. The study was conducted over a period of 11 months, so the results reflect conditions specific to the year 2022 in Gliwice, which may vary at other times due to changing emission trends and meteorological conditions. In addition, Gliwice, as a city with high levels of air pollution, may not be representative of localities with better air quality or other urban characteristics, thus limiting the ability to extrapolate conclusions to other locations.

4.5. Future research needs

In view of these limitations, further research is advisable to deepen and consolidate the results obtained. In particular, it is recommended to increase the number of participants. Including a larger and more diverse group of people - representing different age groups, genders, physical activity levels and varied travel routes - would increase the generalizability of the results. It would also be important to include measurements of actual ventilation rates (e.g., using portable spirometers), which would allow more precise estimates of inhaled pollutant doses.

Conducting similar studies in cities with different pollution levels and urban structures would allow comparison and evaluation of the impact of local conditions on exposure. It would also be beneficial to carry out measurements over a number of years to allow analysis of time trends and assessment of the impact of environmental policies on reducing exposure to air pollution.

In addition, future studies could examine the effects of protective measures such as the use of face masks, travel route modifications (e.g., avoiding highly congested streets) or the use of vehicles equipped with advanced air filtration systems on actual exposure levels. Nevertheless, according to the authors, the most recommended approach would be to verify the results using more precise instruments (e.g., reference-grade equipment) under real-world conditions to assess the accuracy of low-cost sensors in individual exposure studies.

 

Comment 2: Would the authors be able to provide further information regarding the sample size and demographics, as well as address the ways in which these factors might affect the variability in inhaled dose? Did the calculation of the dose take into account the physiological differences that exist between individuals, such as the rate of breathing and fitness?

Answer: In our study, we considered one male and one female individual, and the ventilation rates were adjusted based on their age, sex, and mode of transport. These parameters were incorporated into the dose calculation to partially account for physiological differences influencing inhaled dose variability. This information has been clarified in the revised manuscript below eq. 2

…The study included a male and a female participant, so the VE was selected based on the gender and age of the study participants following [36]. For a male, the VE was 0.0573, 0.0144 and 0.0316 m³/min for bicycle, vehicle, and walking, while for female 0.0457, 0.0106 and 0.0229 m³/min, respectively. In addition, inhalation doses were calculated without gender division. The following simplification was used, VE were assumed as 0.6 m³/h (0.028 m³/min) for passive modes of transport (vehicle) and 1.7 m³/h (1 m³/min) for active modes of transport (bicycle, walking).

 

Comment 3: How did the authors account for changes in the routes taken or the amount of time spent commuting when walking, cycling, and driving were all considered? Was there any consideration given to the filtration or ventilation status of the vehicles during the analysis?

Answer: We thank the reviewer for raising this important methodological point. In the present study, both the starting and ending points of the commuting routes were fixed and identical for all three modes of transport—walking, cycling, and vehicle use—ensuring consistency in the general direction and purpose of travel. However, the exact routes taken may have varied slightly between participants and transport modes due to individual preferences and infrastructure constraints (e.g., choice of quieter streets, availability of cycling lanes or pedestrian pathways).

Differences in commuting duration were taken into account in the analysis through the calculation of both the inhaled dose and the dose normalized per kilometer traveled. This approach allows for a more accurate comparison of exposure by accounting for the combined effects of concentration, time, and distance.

Regarding the vehicle environment, we acknowledge that the status of the ventilation or filtration system (e.g., use of cabin air filters or open vs. closed air circulation) was not specifically recorded or included in the analysis. We recognize this as a potential source of variability in personal exposure and have noted it as a limitation of the study in the revised manuscript.

 

Comment 4: Were changes based on the day of the week or the time of day taken into consideration throughout the analysis?  If this is not the case, how may these temporal considerations have an effect on the outcomes?

Answer:

All measurements in the present study were conducted exclusively on weekdays, specifically during regular commuting periods to and from the university. As such, potential differences related to weekend patterns were intentionally excluded. The study design aimed to capture exposure under typical daily conditions associated with academic or professional routines, which are generally characterized by higher traffic density and activity levels. We acknowledge that time-of-day variations may still influence pollutant concentrations; however, the measurements were consistently performed within comparable time windows to minimize diurnal variability.

Action:

The following text has been added in section 2.2 study design:

The measurements were systematically conducted on each regular working day during the study period (from January to November 2022), ensuring comprehensive coverage of typical daily conditions. The participants were not instructed or constrained in their choice of transportation mode; instead, they independently selected the means of transport based on their personal needs, preferences, and circumstances, and weather conditions on a given day. This methodological decision was deliberate, aiming to capture natural variations in exposure levels under real-world conditions, thereby enhancing the ecological validity and practical applicability of the findings.

 

Comment 5: The fact that PM2.5 concentrations were greater during the season that did not involve heating may appear to be surprising given the contributions that are anticipated to come from emissions connected to heating during the winter. Is it possible for the authors to provide an explanation for this finding? Could it be because of enhanced atmospheric dispersion throughout the winter months or because of local sources during the season when there is no heating?

Answer: We appreciate the reviewer’s insightful comment regarding the unexpectedly higher average concentrations of PM2.5 during the non-heating season. As noted in the manuscript, lines 311-318:

For PM_2.5 concentrations, seemingly contradictory results are observed between the measures of central tendency. During the heating season, the mean concentration is 8.1 µg/m³ (median: 3.2 µg/m³), with values ranging from 2.0 to 45.5 µg/m³. During the non-heating season, the mean concentration is 9.9 µg/m³ (median: 2.6 µg/m³) and values range from 2.0 to 117.8 µg/m³ indicating higher mean concentration compared to heating season, but lower median concentration. The discrepancy between the mean and median values in the non-heating season suggests greater variability and the presence of outliers, which inflated the mean value.

A probable explanation for this observation may relate to short-term local sources (e.g., construction activity, resuspended road dust, or increased vehicular emissions during dry and warmer periods) that are more prevalent in the non-heating season. Furthermore, specific meteorological conditions—such as lower relative humidity and less atmospheric stability in warmer months—may contribute to elevated peak concentrations under certain circumstances. Conversely, while the heating season is typically associated with increased emissions from residential heating, atmospheric conditions such as temperature inversions and reduced vertical mixing can suppress dispersion and lead to more consistently elevated background levels, which may explain the higher median concentrations observed during that period.

 

Comment 6: The introduction lacks motivation and the clear need of the study. Thefore the authors needs to add recent articles such as:

Mukundan, Arvind, Chia-Cheng Huang, Ting-Chun Men, Fen-Chi Lin, and Hsiang-Chen Wang. "Air pollution detection using a novel snap-shot hyperspectral imaging technique." Sensors 22, no. 16 (2022): 6231.

Chen, Chi-Wen, Yu-Sheng Tseng, Arvind Mukundan, and Hsiang-Chen Wang. "Air pollution: Sensitive detection of PM2. 5 and PM10 concentration using hyperspectral imaging." Applied Sciences 11, no. 10 (2021): 4543.

Answer:

Our low-cost sensor is equipped with a light-scattering laser counter for measuring particulate matter concentrations and a metal oxide sensor for detecting gas concentrations. These components are consistent with the study’s aim of assessing personal exposure using portable and affordable monitoring tools under real-life conditions. We respectfully maintain that including references to hyperspectral imaging (HSI)—a technique that is significantly more complex, costly, and not commonly employed in mobile or low-cost exposure assessment—would not substantially enhance the motivation or clarify the need for the present study. HSI, while valuable in other contexts (e.g., remote sensing, materials analysis), falls outside the technological and methodological scope of this research. Nonetheless, we defer to the Editor's judgment and will comply with any request to include such references should it be deemed appropriate.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

This study examines personal exposure to PM_2.5 and NO₂ across different commuting modes and seasons in an industrial European city. Data was collected and compared from low-cost sensors (personal exposure) and from stationary monitoring stations, offering valuable insights into the intra-urban variability of air pollution exposure. The campaign was well designed and the methods were clearly described.

Seasonal trends were observed from stationary monitoring data, although such trends were not clear in the mobile sensor measurements. The findings suggest that walking leads to higher inhaled doses of both pollutants compares to cycling and driving, which is consistent with existing literature. The significant gender differences in inhaled doses are interesting but require further investigation, although most of the authors findings were well explained.

The topic is of clear importance for both urban air quality management and public health policy. Therefore, I recommend the paper for publication after addressing some minor shortcomings.

 

General comments

1. The are several grammar mistakes throughout the manuscript that affect the paper’s overall quality. A thorough proofreading is recommended
2. I suggest replacing “woman” and “man” with “female participants” and “male participants” for consistency and clarity.

Specific comments

1. Line 20-21 (and in a few other lines) “in compare to” – please rephrase
2. Line 26 (and in a few other lines): “walk” – it should be “walking”
3. Line 62 “however Poles..” – please rephrase in a clearer way
4. Lines 130-132 – December seems to be excluded from both categories, though its typically a month that falls under the heating season in areas with similar climate. It may be helpful if the authors clarify the reason of exclusion.
5. Lines 175-177 - Speed ranges are based on literature or are they empirically derived from real world data?
6. Lines 177-184 – How missing data points where handled?
7. Descriptive Statistics - I would like to ask the authors if the changes in temperature, and relative humidity could influence the respiratory rates and/ or participants activity levels and if it was taken into consideration.
8. Line 259 – “in compare” “show” – both need correction
9. Lines 272-276 - These lines are a bit confusing and potentially contradictory, as the authors mention “higher concentrations” in the heating season (higher median value) whereas the average value is lower than that of the non-heating season. From the authors results PM2.5 values showed larger variations which explains the higher average values and lower median. I suggest this sentence should be rephrased.

Comments on the Quality of English Language

Also mentioned in the comments. I recommend a thorough proofreading as there are several grammar mistakes

Author Response

Manuscript ID applsci-3586865 – major revisions

Detailed answers to:

Reviewer #3

This study examines personal exposure to PM_2.5 and NO₂ across different commuting modes and seasons in an industrial European city. Data was collected and compared from low-cost sensors (personal exposure) and from stationary monitoring stations, offering valuable insights into the intra-urban variability of air pollution exposure. The campaign was well designed and the methods were clearly described.

Seasonal trends were observed from stationary monitoring data, although such trends were not clear in the mobile sensor measurements. The findings suggest that walking leads to higher inhaled doses of both pollutants compares to cycling and driving, which is consistent with existing literature. The significant gender differences in inhaled doses are interesting but require further investigation, although most of the authors findings were well explained.

The topic is of clear importance for both urban air quality management and public health policy. Therefore, I recommend the paper for publication after addressing some minor shortcomings.

 

Answer and Action: Thank you for your thoughtful feedback. We have carefully considered your suggestions and incorporated them into the main text (green color). We trust that these revisions will enhance the clarity and presentation of the manuscript, and we hope they will meet your approval.

 

General comments

Comment 1: The are several grammar mistakes throughout the manuscript that affect the paper’s overall quality. A thorough proofreading is recommended

Answer: Thank you very much for your valuable comment. The revision of manuscript has been proofread using the Grammarly software to ensure grammatical and stylistic correctness.

 

Comment 2: I suggest replacing “woman” and “man” with “female participants” and “male participants” for consistency and clarity.

Answer: It have been corrected throughout the text.

 

Specific comments 

Comment 1: Line 20-21 (and in a few other lines) “in compare to” – please rephrase

Answer: It have been corrected throughout the text.

 

Comment 2: Line 26 (and in a few other lines): “walk” – it should be “walking”

Answer: Thank you for pointing this out. We have corrected the terminology by replacing “walk” with “walking” throughout the manuscript and in all related figures to ensure consistency and clarity.

 

Comment 3: Line 62 “however Poles.” – please rephrase in a clearer way

Answer: the sentence has been divided and changed from:

“In Poland 83% of population know how to ride a bike, which is more than the 63% global average, 69% own one and 44% use it at least once a week, however Poles treat cycling as a sport activity rather than primary mode of transport for 2 km distance (18%) [7].”

into:

In Poland, 83% of the population knows how to ride a bicycle, which is higher than the global average of 63%. Additionally, 69% of Poles own a bicycle, and 44% use it at least once a week. However, cycling is perceived more as a sport activity rather than a primary mode of transportation for short distances, with only 18% using it for trips of approximately 2 km [7].

 

Comment 4: Lines 130-132 – December seems to be excluded from both categories, though its typically a month that falls under the heating season in areas with similar climate. It may be helpful if the authors clarify the reason of exclusion.

Answer: We appreciate the reviewer’s observation. December was not included in the analysis because we had to return sensor to DTU.

 

Comment 5: Lines 175-177 - Speed ranges are based on literature or are they empirically derived from real world data?

Answer: Speed ranges were empirically derived based on the usual speeds achieved by a cyclist.

 

Comment 6: Lines 177-184 – How missing data points where handled?

Answer: If data points were missing (they were zeros in the database), the estimation of zeros was performed during data recording. The first and last measurements for a given path are not taken into consideration, because the average of the previous and next measurements from the estimated point is performed during data estimation. Because later analysis is based on calculating mean values etc., such an approach seems the most appropriate, since adding approximated values might represent the actual measurements.

 

Comment 7: Descriptive Statistics - I would like to ask the authors if the changes in temperature, and relative humidity could influence the respiratory rates and/ or participants activity levels and if it was taken into consideration.

Answer:

Yes, changes in temperature and relative humidity can influence both respiratory rates and participants’ activity levels. These phenomena are well-documented in the scientific literature  .

Respiratory Rate: High temperature combined with high humidity increases thermal stress on the human body. To dissipate excess heat, the respiratory system often responds by accelerating the breathing rate (tachypnea) [1, 2].

Low temperatures, particularly when accompanied by low humidity, may induce bronchoconstriction in sensitive individuals (e.g., asthmatics), which can also lead to an elevated respiratory rate [3–5].

Activity Levels: Under conditions of high temperature and humidity, individuals often demonstrate a decrease in physical activity levels due to the quicker onset of fatigue and the risk of hyperthermia [1, 6, 7].

Conversely, under moderate thermal and humidity conditions, physical activity levels are generally higher, as the overall physiological comfort favors more prolonged and intense exertion.

It should be emphasized that these physiological responses are also dependent on individual participant characteristics (such as age, health condition, and climatic acclimatization). Therefore, it is advisable to control for or consider these factors during experimental design and data analysis.

 

In our study, however, the potential impact of temperature and humidity was not specifically accounted for. The participants themselves decided on their mode of commuting, choosing either an active method (bicycle or walking) or a passive method (car or public transportation) depending on weather conditions.

 

1. White, M.D.: Components and mechanisms of thermal hyperpnea. J Appl Physiol. 101, 655–663 (2006). https://doi.org/10.1152/JAPPLPHYSIOL.00210.2006/ASSET/IMAGES/ZDG0080667560005.JPEG
2. Mekhuri, S., Quach, S., Barakat, C., Sun, W., Nonoyama, M.L.: A cross-sectional survey on the effects of ambient temperature and humidity on health outcomes in individuals with chronic respiratory disease. Can J Respir Ther. 59, 256–269 (2023). https://doi.org/10.29390/001c.90653
3. Castellani, J.W., Tipton, M.J.: Cold Stress Effects on Exposure Tolerance and Exercise Performance. Compr Physiol. 6, 443–469 (2016). https://doi.org/10.1002/CPHY.C140081
4. Koskela, H.O., Koskela, A.K., Tukiainen, H.O.: Bronchoconstriction due to cold weather in COPD: The roles of direct airway effects and cutaneous reflex mechanisms. Chest. 110, 632–636 (1996). https://doi.org/10.1378/chest.110.3.632
5. Koskela, H., Tukiainen, H.: Facial cooling, but not nasal breathing of cold air, induces bronchoconstriction: A study in asthmatic and healthy subjects. European Respiratory Journal. 8, 2088–2093 (1995). https://doi.org/10.1183/09031936.95.08122088
6. Deming, N.J., Carr, K.W., Anna, J.L., Dupre, B.R., Smith, M.E., Dinenno, F.A., Richards, J.C.: Self-selected fluid volume and flavor strength does not alter fluid intake, body mass loss, or physiological strain during moderate-intensity exercise in the heat. J Therm Biol. 89, 102575 (2020). https://doi.org/10.1016/J.JTHERBIO.2020.102575
7. Lim, C.L.: Fundamental Concepts of Human Thermoregulation and Adaptation to Heat: A Review in the Context of Global Warming. Int J Environ Res Public Health. 17, 7795 (2020). https://doi.org/10.3390/IJERPH17217795

 

Comment 8: Line 259 – “in compare” “show” – both need correction

Answer: The fragment has been corrected into: Compared to the non-heating season, the heating season exhibited higher atmospheric pressures (995–998 hPa), which are typical for colder weather conditions.

 

Comment 9: Lines 272-276 - These lines are a bit confusing and potentially contradictory, as the authors mention “higher concentrations” in the heating season (higher median value) whereas the average value is lower than that of the non-heating season. From the authors results PM2.5 values showed larger variations which explains the higher average values and lower median. I suggest this sentence should be rephrased.

Answer: We are aware that the median, as a more robust measure of central tendency resistant to extreme values, more accurately reflects the typical concentration levels and should, therefore, be considered the more representative measure in this case. However, averages are more commonly used in air quality studies, and we chose to report them in order to facilitate comparisons with the results of other researchers.

Action: We corrected the fragment from:

Similarly, for PM2.5 during the heating season, both the average was 8.07 µg/m³ (median 3.16 µg/m³) and the distribution of values from 2.00 to 45.46 µg/m³ indicate higher concentrations compared to the non-heating season, where the average is 9.95 µg/m³ (median 2.62 µg/m³) and range from 2.00 to 117.78 µg/m³.

into:

For PM_2.5 concentrations, seemingly contradictory results were observed between the measures of central tendency. During the heating season, the average concentration was 8.1 µg/m³ (median: 3.2 µg/m³), with values ranging from 2.0 to 45.5µg/m³, indicating higher concentrations compared to the non-heating season, where the average concentration was 9.9 µg/m³ (median: 2.6 µg/m³) and values ranged from 2.0 to 117. 8 µg/m³. The discrepancy between the average and median values in the non-heating season suggests greater variability and the presence of outliers, which inflated the average.

 

Comments on the Quality of English Language

Also mentioned in the comments. I recommend a thorough proofreading as there are several grammar mistakes

Answer:

The revision of manuscript has been proofread using the Grammarly software to ensure grammatical and stylistic correctness.

 

 

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The authors considere suggestions and incorporated them into the manuscript.

Reviewer 2 Report

Comments and Suggestions for Authors

The paper can be accepted without any further changes.