<i>Campus air quality dataset</i>

Chinecherem Umezuruike; Halleluyah Aworinde; Goodness Amodu; Abidemi Adeniyi; Michael Rudolph; Oluwasegun Aroba

doi:10.12688/f1000research.162509.1

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Research Article

Campus air quality dataset

[version 1; peer review: 1 approved with reservations, 1 not approved]

Chinecherem Umezuruike¹, Halleluyah Aworinde^1,2, Goodness Amodu¹, Abidemi Adeniyi^1,3, Michael Rudolph⁴, Oluwasegun Aroba ^4,5

Chinecherem Umezuruike¹, Halleluyah Aworinde^1,2, [...] Goodness Amodu¹, Abidemi Adeniyi^1,3, Michael Rudolph⁴, Oluwasegun Aroba ^4,5

PUBLISHED 03 Apr 2025

Author details Author details

¹ College of Computing and Communication Studies, Bowen University, Iwo, Osun, 4001, Nigeria
² Department of Information Technology, Durban University of Technology, Durban, KwaZulu-Natal, South Africa
³ Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India
⁴ Centre for Ecological Intelligence, University of Johannesburg, Auckland Park, Gauteng, South Africa
⁵ Operations & Quality Management Department, Durban University of Technology, Durban, KwaZulu-Natal, South Africa

Chinecherem Umezuruike
Roles: Data Curation, Investigation

Halleluyah Aworinde
Roles: Conceptualization, Data Curation, Investigation

Goodness Amodu
Roles: Formal Analysis, Methodology, Project Administration

Abidemi Adeniyi
Roles: Resources, Software, Validation

Michael Rudolph
Roles: Supervision, Writing – Review & Editing

Oluwasegun Aroba
Roles: Data Curation, Validation

OPEN PEER REVIEW

REVIEWER STATUS

This article is included in the Data: Use and Reuse collection.

Abstract

Background

Elevated levels of carbon dioxide (CO₂) within academic settings can adversely affect the health and academic efficacy of both students and faculty. High concentrations of CO₂ are correlated with reduced cognitive functioning, compromised decision-making abilities, diminished academic performance, and various health-related issues. The escalating apprehensions regarding the detrimental health consequences of air pollution have precipitated an increase in research focused on air quality assessment and amelioration.

Method

The investigation utilized Internet of Things (IoT) devices that were outfitted with sensors to gather data on various environmental parameters, such as temperature, humidity, CO₂ concentrations, and light intensity. This data underwent analysis through the application of summary statistics to delineate the dataset and to visualize the distribution of variables via scatter matrix plots.

Result

The dataset obtained, which encompasses essential air quality and environmental parameters, is now accessible to the public through the Mendeley repository. The analytical findings illuminated significant characteristics of the data concerning CO₂ levels and their prospective ramifications on the academic milieu.

Conclusion

The amalgamation of IoT technology with summary statistical analysis presents a promising methodology for the real-time surveillance of air quality. This approach yields critical insights into the health and academic ramifications of heightened CO₂ levels within educational environments, underscoring the necessity for ongoing air quality monitoring to enhance campus conditions.

Keywords

Air Quality Monitoring; Artificial Intelligence of Things; Carbon dioxide Level Management; Environmental Monitoring

Corresponding authors: Chinecherem Umezuruike, Abidemi Adeniyi, Oluwasegun Aroba

Competing interests: No competing interests were disclosed.

Grant information: The author(s) declared that no grants were involved in supporting this work.

Copyright: © 2025 Umezuruike C et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The author(s) is/are employees of the US Government and therefore domestic copyright protection in USA does not apply to this work. The work may be protected under the copyright laws of other jurisdictions when used in those jurisdictions.

How to cite: Umezuruike C, Aworinde H, Amodu G et al. Campus air quality dataset [version 1; peer review: 1 approved with reservations, 1 not approved]. F1000Research 2025, 14:388 (https://doi.org/10.12688/f1000research.162509.1) First published: 03 Apr 2025, 14:388 (https://doi.org/10.12688/f1000research.162509.1) Latest published: 03 Apr 2025, 14:388 (https://doi.org/10.12688/f1000research.162509.1)

Value of the data

• To the research community dataset holds considerable importance for scholars owing to its proximity to various pollution sources, potential ramifications for public health, implications for urban development, function as a venue for experimental interventions, ramifications for educational and policy frameworks, and the promotion of interdisciplinary cooperation. The dataset promotes collaborative efforts across disciplines among experts, thereby contributing to a comprehensive approach to addressing air quality challenges and sustainability within the campus environment.
• The dataset benefits various groups and individuals, Campus facilities managers, Environmental researchers, Health and safety officers, Urban planners and architects, Students and educators, and public awareness and advocacy groups
• The dataset can be employed for advanced insights and comprehensive analysis through methodologies such as trend analysis, spatial mapping, source apportionment, health impact assessment, correlation analysis, intervention evaluation, scenario modeling, and comparative studies. These methodologies empower researchers to discern trends, spatial distributions, sources of pollution, health ramifications, correlations with external variables, as well as the efficacy of interventions. Through the implementation of these analytical techniques, scholars can derive significant insights, strengthen evidence-based policymaking, and foster initiatives aimed at enhancing air quality within campus settings.

Background

Research indicates that elevated concentrations of Carbon dioxide (CO₂) within academic environments can exert detrimental effects on the health and academic performance of campus inhabitants. High levels of CO₂ can result in several consequences, including Diminished Cognitive Function, Impaired Decision- Making, Diminished Academic Performance, and Health Concerns.^1,2,3 Moreover, insufficient ventilation, which results in increased CO₂ levels in educational settings, has been documented to obstruct students' performance in cognitive tasks, elevate absenteeism, and diminish test scores.⁴ Therefore the campus air quality dataset aims to provide data on environmental air quality parameters such as CO₂ levels, temperature, humidity, and other relevant environmental factors within the campus facilities. This comprehensive information equips decision-makers at the University to execute targeted strategies for air quality enhancement, thereby ensuring a healthier and more sustainable living milieu for the students. Also, the dataset aids in informed decision-making processes aimed at improving the overall quality of life within the university's accommodation facilities.^5–10

Data description

The AIoT dataset incorporates a heterogeneous assortment of environmental variables, including temperature, humidity, CO₂ concentrations, and light intensity, which were amassed within the university campus setting through the deployment of IoT sensors. This dataset is in a table format and, meticulously structured to facilitate the thorough monitoring and examination of environmental air quality within the hostels, particularly concentrating on the comprehension and regulation of CO₂ concentrations, and parameters captured are:

• Temperature: The dataset comprises temperature measurements expressed in degrees Celsius, thereby providing valuable insights into the thermal conditions prevalent in various locations throughout the campus.
• Humidity: The humidity readings, articulated as a percentage, yield essential information regarding the moisture content present in the indoor atmosphere.
• CO₂ Levels: The concentration of CO₂, quantified in parts per million (ppm), is encapsulated within the dataset, thereby offering crucial data for the assessment of indoor air quality. Light Intensity: The dataset encompasses measurements of light intensity articulated in lux, thereby elucidating the illumination levels throughout the campus.
• Light Intensity: Light intensity is a measure of the amount of light energy per unit area. It quantifies the brightness or luminance of a light source reaching a specific surface or point. Light intensity was measured in lux as the measuring unit.
• Data Granularity and Size: The dataset displays a high degree of granularity, as it records data at short intervals, resulting in a considerable volume of data points. The dataset comprises a substantial number of records, which reflects the varied environmental conditions and activities occurring within the university campus.

Methods

Sensiron SCD30 sensor integrated with a digital light intensity sensor was used to capture all four parameters for air quality as presented in the dataset. The SCD30 and digital light sensors were connected to the grove base using the female-to-female jumper wires, the ESP module received the readings from the sensors, processed the raw data, and stored it in the microSD card for later retrieval.

Analysis and visualization of Dataset: The hostel dataset was analyzed using summary statistics that describe the key characteristics of the data and help in gaining a quick understanding of its distribution, mean and standard deviation were used. Also, the Variable distribution and scatter matrix plots provide a visual representation of the relationships between the different variables in the dataset. Figure 1 shows the schematic diagram showing the connections of sensors.

Figure 1. Schematic diagram showing connections of sensors (Author’s Own Construct).

Result and discussion

5.1. Boys hostel

The male hostel dataset was analyzed using summary statistics that describe the key characteristics of the data and help in gaining a quick understanding of its distribution, central tendency, and variability. The mean and standard deviation were used in this case. Also, the Variable distribution and scatter matrix plots provide a visual representation of the relationships between the different variables in the dataset.

Summary statistics observation

• The mean temperature is approximately 29.59°C with a standard deviation of 12.81°C.
• The mean humidity is around 0.00% with a standard deviation of 123.10%.
• The mean CO₂ concentration is approximately 0.00 ppm with a standard deviation of 62.09 ppm.
• The mean light intensity is around 3764.83 units with a standard deviation of 4508.98 units.

Distribution of variables

• Temperature Distribution: The temperature ranged from 28.99°C to 29.05°C, this indicated that the temperatures within the hostel was consistent and showed little variation. This narrow range suggests a relatively stable environment regarding temperature.
• Humidity Distribution: As shown on Figures 2 to 5, Humidity levels ranges. Humidity levels ranged from 29.59% to 80.79%. This wide range indicates significant variability in humidity, reflecting different environmental conditions in the hostel. A lower humidity level (29.59%) imply drier conditions, while higher readings (up to 80.79%) indicate more humid and potentially uncomfortable environments.
• CO₂ Concentration : CO₂ levels vary from 0.00 ppm to 671.08 ppm. The presence of a zero value denote instances of low ventilation or open spaces, while the maximum concentration suggests potential crowding or poor air quality situations. Elevated CO₂ levels impact the comfort and health of the occupants when the value becomes too high.

Light Intensity: Light intensity ranges between 0 to 12751 units, representing a wide range of lighting conditions within the hostel. The presence of a zero reading indicates very dark or unlit areas, while the maximum level shows that there are areas with bright light. This variation influence the comfort and activity levels of the residents, as different lighting conditions affect mood and productivity.

Figure 2. Boys’ Hostel Variable Distribution Plot (Author’s Own Construct).

Figure 3. Scattered plot for male hostel (Author’s Own Construct).

Figure 4. Girls’ Hostel variable distribution plot (Author’s Own Construct).

Figure 5. Scattered plot for female hostel (Author’s Own Construct).

Scatter plots

• Temperature vs Humidity shows a negative correlation between temperature and humidity. As the temperature increases, the humidity tends to decrease, which aligns with the correlation coefficient of -0.975
• Temperature vs CO₂ shows a weak negative correlation between temperature and CO₂ levels. There is no clear trend indicating that temperature and CO₂ levels are not strongly related.
• Temperature vs Light Intensity shows a moderate positive correlation between temperature and light intensity. Higher temperatures are associated with higher light intensity.
• Humidity vs CO₂ shows a weak positive correlation between humidity and CO₂ levels. There is no clear trend, indicating that humidity and CO₂ levels are not strongly related
• Humidity vs Light Intensity shows a moderate negative correlation between humidity and light intensity. Higher humidity is associated with lower light intensity
• CO₂ vs Light Intensity shows a weak negative correlation between CO₂ levels and light intensity. There is no clear trend, indicating that CO₂ levels and light intensity are not strongly related.

Female hostel

The female hostel dataset was analyzed using summary statistics that describe the key characteristics of the data and help in gaining a quick understanding of its distribution, central tendency, and variability. The mean and standard deviation were used in this case. Also, the Variable distribution and scatter matrix plots provide a visual representation of the relationships between the different variables in the dataset.

Summary statistics observation

• Mean Temperature: The mean temperature of approximately 33.70°C with a standard deviation of 3.21°C indicates that, on average, the temperatures are relatively high. The standard deviation showed that temperatures fluctuate moderately around the mean. This range indicates warm conditions, which may lead to discomfort without proper cooling systems.
• Mean Humidity: The mean humidity level of around 63.53% with a standard deviation of 9.05% indicates a moderately humid environment. The humidity levels showed feeling of dampness, which may lead to uncomfortable living condition.
• Mean CO₂ Concentration: A mean CO₂ concentration of approximately 508.79 ppm with a standard deviation of 62.09 ppm showed that the air quality varies.
• Mean Light Intensity: The mean light intensity of around 6826.76 units with a standard deviation of 4540.86 units suggested that there is significant variability in lighting conditions within the space.

Distribution of variables

• The temperature distribution shows a range from 28.36°C to 38.75°C
• The humidity distribution ranges from 50.13% to 78.29%.
• The CO₂ concentration ranges from 473.38 ppm to 7257.28 ppm, with a notable outlier at the maximum value.
• The light intensity ranges from 0 to 14337 units, indicating a wide range of light conditions.

Scatter plots

• Temperature vs. Humidity: The was a negative correlation between temperature and humidity which suggested an inverse relationship. As temperature increases, humidity decreases.
• Temperature vs. CO₂: The lack of a clear correlation between temperature and CO₂ levels implies that changes in temperature do not regularly affect CO₂ concentrations.
• Temperature vs. Light Intensity: No clear correlation between temperature and light intensity shows that variations in one do not affect the other.
• Humidity vs. CO₂: As displayed in Table 1, the table specification was displayed as the specific area, subject are, data type and location of the data source can be located. No correlation between humidity and CO₂ suggests that changes in moisture levels do not systematically affect carbon dioxide concentrations.
• Humidity vs. Light Intensity: No clear correlation between humidity and light intensity indicates that variations in moisture do not appear to influence light levels.
• CO₂ vs. Light Intensity: No clear correlation between CO₂ levels and light intensity suggests that changes in one do not impact the other.

Table 1. Specifications Table.

Subject	Data Science.
Specific subject area	Data Mining and Statistics.
Type of data	Table, Raw
Data collection	The dataset was collected over 14 days consists of 38,369 columns for the boys’ hostel and 13,469 for the Girls’ Hostel alongside corresponding four attributes i.e. Temperature, Humidity, Carbon Dioxide CO₂, and light intensity for each data collected. Sensiron SCD30 sensor integrated with a digital light intensity sensor was used to capture all four parameters for air quality as presented in the dataset.
Data source location	Institution: Bowen University, Boys and Girls Hostels City/Town/Region: Iwo/Osun Country: Nigeria
Data accessibility	Repository name: Mendeley Data Data identification number: DOI: https://doi.org/10.17632/r95srm9m8m.1 Direct URL to data: https://data.mendeley.com/datasets/r95srm9m8m/1

Conclusion

This dataset proffers valuable insights into the dynamic characteristics of environmental conditions within a campus environment. Researchers and practitioners can leverage this data to analyze trends, formulate predictions, and implement strategies aimed at optimizing indoor air quality, energy consumption, and overall campus sustainability. The extensive dataset possesses considerable significance for researchers and practitioners who are engaged in environmental monitoring, indoor air quality management, and sustainable campus initiatives. The dataset can function as a foundational resource for the formulation, evaluation, and enhancement of data-driven models aimed at CO₂ level forecasting and regulation within university campuses. Moreover, the dataset presents avenues for undertaking comprehensive analyses to extract insights concerning the interplay between environmental parameters and the quality of the indoor atmosphere.

Limitations

Not Applicable.

Ethics and consent

The authors have read and follow the ethical requirements for publication in Data in Brief and confirming that the current work does not involve human subjects, animal experiments, or any data collected from social media platforms.

'Ethical approval and consent were not required.'

Credit author statement

Chinecherem Umezuruike, and Halleluyah Aworinde, took the lead in data collection, Goodness Amodu, Abidemi Adeniyi, Michael Rudolph, Oluwasegun Aroba participated in the preprocessing of the dataset collected, writing and Michael Rudolph proofreading of the manuscript.

All of the authors contributed to the manuscript and gave their approval to the final version after offering constructive criticism and helping to develop the research, analysis, and manuscript.

Data availability

Mendeley: Campus Air Quality Dataset, Doi: https://doi.org/10.17632/r95srm9m8m.1.⁵

This project contains the following underlying data:

• boys hostel data (1).csv
• girls hostel data (1).csv

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

Acknowledgement

We would like to acknowledge the Computing & Analytic Research Group for providing the computing tools required to complete this research. Most especially, we thank Bowen University Iwo for giving us the platform to engage in meaningful research. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

References

1. Brink HW, Loomans MGLC, Mobach MP, et al.: Classrooms' indoor environmental conditions affecting the academic achievement of students and teachers in higher education: A systematic literature review. Wiley Open Access Collection; 2020. Reference Source
2. Pulimeno M, Piscitelli P, Colazzo S, et al.: Indoor air quality at school and students’ performance: Recommendations of the UNESCO Chair on Health Education and Sustainable Development & the Italian Society of Environmental Medicine (SIMA). Health Promot. Perspect. 2020; 10(3): 169–174. PubMed Abstract | Publisher Full Text | Free Full Text
3. Roche IV, Ubalde-Lopez M, Daher C, et al.: The health-related and learning performance effects of air pollution and other urban-related environmental factors on school-age children and adolescents—a scoping review of systematic reviews. Current environmental health reports. 2024; 11(2): 300–316. PubMed Abstract | Publisher Full Text | Free Full Text
4. Umezuruike C, Aworinde H, Aroba OJ: Campus Air Quality Dataset. Mendeley Data. 2024; V1. Publisher Full Text
5. Zhang L, Chen J, Li G: Urban air quality dataset: A comprehensive resource for AI-based pollution prediction and hotspot identification. Environ. Sci. Technol. 2021; 55(15): 10232–10240.
6. Aroba OJ: The Implementation of Augmented Reality in Internet of Things for Virtual Learning in Higher Education. International Journal of Computing Sciences Research. 2024; 8: 2536–2549. Publisher Full Text
7. Deoraj S, Essop MS, Aroba OJ: Investigating a Virtual Queueing System for Durban University of Technology: A Comprehensive Review Approach to Improve Efficiency. 2024 Conference on Information Communications Technology and Society (ICTAS). IEEE; (2024), March; pp. 38–43.
8. Li Y, Zhang H, Wu D: Campus environmental monitoring dataset: A resource for AI-driven optimization of energy consumption and indoor air quality. Sensors. 2022; 22(12): 4789.
9. Aroba OJ, Chisita CT, Buthelezi N, et al.: February. Higher education enterprise resource planning system transformation of supply chain management processes. International Congress on Information and Communication Technology. Singapore: Springer Nature Singapore; 2023; pp. 415–424.
10. Chen X, Wang Y, Liu Z: A novel AI-driven system for real-time air quality forecasting and alerting based on smart city sensor network dataset. J. Environ. Inf. 2023; 45(3): 234–248.

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Version 1

VERSION 1 PUBLISHED 03 Apr 2025

Author details Author details

Chinecherem Umezuruike
Roles: Data Curation, Investigation

Halleluyah Aworinde
Roles: Conceptualization, Data Curation, Investigation

Goodness Amodu
Roles: Formal Analysis, Methodology, Project Administration

Abidemi Adeniyi
Roles: Resources, Software, Validation

Michael Rudolph
Roles: Supervision, Writing – Review & Editing

Oluwasegun Aroba
Roles: Data Curation, Validation

Competing interests

No competing interests were disclosed.

Grant information

The author(s) declared that no grants were involved in supporting this work.

Article Versions (1)

version 1

Published: 03 Apr 2025, 14:388

https://doi.org/10.12688/f1000research.162509.1

© 2025 Umezuruike C et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The author(s) is/are employees of the US Government and therefore domestic copyright protection in USA does not apply to this work. The work may be protected under the copyright laws of other jurisdictions when used in those jurisdictions.

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how to cite this article

Umezuruike C, Aworinde H, Amodu G et al. Campus air quality dataset [version 1; peer review: 1 approved with reservations, 1 not approved]. F1000Research 2025, 14:388 (https://doi.org/10.12688/f1000research.162509.1)

NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.

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Open Peer Review

Version 1

VERSION 1

PUBLISHED 03 Apr 2025

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Reviewer Report 21 Aug 2025

Domenico Suriano, ENEA—Italian National Agency for New Technologies, Brindisi, Italy

Not Approved

https://doi.org/10.5256/f1000research.178719.r393312

This article is focused on a database made with records reporting the concentration of CO2 and other environmental parameters measured in two hostels belonging to a university campus. The text of the article is clear and understandable, the document is well structured, but it has poor originality and its novelty is not clear or ambiguous; moreover, it is featured by serious flaws.
The introduction does not offer an adequate background to the topic. There is a huge scientific literature reporting the issues related to the use of Iot devices for environmental monitoring completely ignored by authors. A lot of similar works related to the issue addressed by this paper are currently present in the scientific panorama, but the authors did not perform an adequate comparison between their work with the similar ones as they should do.
The study design and the methods are poorly described, so that it is almost impossible to replicate the experiment. In particular, the authors did not provide information about how many sensors were used in the experiment and where they were placed, moreover no information about the characteristics of the monitored places were reported. It is not known the volume of the monitored spaces, and also how many occupants were there over the duration of the experiment. These elements have a huge influence on the monitored variables, but no information were reported. Another aspect to consider is that almost no information is available about the device built by the authors and the electronic components composing it. There is just a picture showing an electric scheme, but the electronic component pins are not reported in the scheme, so it is impossible to replicate it. It is also not clear why the authors used a home-made device to perform the measurements while there are a lot of devices available on the market to do it.
The results are poorly presented and commented. The text in the pictures are totally unreadable and no analysis was carried out on the dataset produced; moreover, comparisons with similar datasets or measurements performed in previous works are not present. The reliability and accuracy of the measures are questionable, unconvincing or ambiguous, as matter of facts, it is the first time that a CO2 mean concentration of 0 ppm is measured in an indoor monitored space.
As the methods and the results are unconvincing, the conclusion cannot rely on solid basis.

Is the work clearly and accurately presented and does it cite the current literature?

No
Is the study design appropriate and is the work technically sound?

No
Are sufficient details of methods and analysis provided to allow replication by others?

No
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

No
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: air quality monitoring; Iot devices; gas sensors; gas sensor calibration; air quality monitors design

I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.

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Reviewer Report 28 Apr 2025

Tymoteusz Miller, University of Szczecin, Szczecin, Poland

Approved with Reservations

https://doi.org/10.5256/f1000research.178719.r377521

The article presents an environmental dataset collected via IoT sensors (SCD30 and digital light sensors) deployed in male and female hostels at Bowen University, Nigeria. The dataset records four key variables: temperature, humidity, carbon dioxide (CO₂) concentration, and light intensity. Data were collected over 14 days and analyzed using summary statistics and scatter matrix plots to understand variable distributions and interdependencies. The resulting dataset is publicly available via Mendeley.

1. Is the work clearly and accurately presented and does it cite the current literature?
Answer: Partly
Commentary: The overall structure and clarity of the manuscript are fair, but some key issues need improvement:

The introduction provides a reasonable justification for studying indoor air quality but lacks a sharper focus on the novelty or gap this dataset fills.
Figures (scatter plots, distributions) are referenced but not described in sufficient detail; some captions are vague (“Author’s Own Construct”).
The literature cited is reasonably current, but the connection between references and specific claims (e.g., effects on cognition, ventilation effects) needs to be made more explicit in-text.
Writing quality is inconsistent: some sections (like scatter plot interpretations) are redundant or overgeneralized.

Suggestions for improvement:

Clarify which specific studies your work complements or expands upon.
Tighten descriptions of figures and plots; interpret key findings beyond mere correlation.
Improve sentence structure and clarity throughout the Results and Discussion sections.

2. Is the study design appropriate and is the work technically sound?
Answer: Partly
Commentary:

The use of IoT-based sensors is appropriate and aligns with recent environmental monitoring practices.
However, the article lacks depth in explaining why certain sensors were chosen, and how data quality was ensured (e.g., sensor calibration, error margins).
The methods used (summary statistics and scatter plots) are very basic and don't leverage the potential analytical depth (e.g., temporal trends, anomaly detection, or time-of-day analysis).
There is no mention of data preprocessing steps (e.g., handling of missing values or outliers), which are essential for replicability.

Suggestions:

Justify the sensor selection and discuss technical reliability.
Enhance the methodological rigor by applying more advanced analytical techniques.
Include a brief section on sensor validation and data cleaning.

3. Are sufficient details of methods and analysis provided to allow replication by others?
Answer: Partly
Commentary:

Hardware connections and data collection procedures are described, but important implementation details are missing.
The manuscript does not explain how often the data were collected (sampling rate), what format/time zones were used, or if time-of-day effects were considered.
Scatter matrix visualizations are mentioned, but no code or tools used (e.g., Python, R, Excel) are documented.

Suggestions:

Provide clearer procedural details (e.g., sampling interval, data logging frequency).
Describe the analysis pipeline, ideally sharing scripts or code.
Add sensor specifications in a supplementary file.

4. If applicable, is the statistical analysis and its interpretation appropriate?
Answer: Partly
Commentary:

The analysis relies solely on means, standard deviations, and scatterplots with informal correlation interpretations.
In several instances, contradictory or unrealistic values appear (e.g., CO₂ mean = 0.00 ppm in boys’ hostel, humidity std dev = 123.10%), which suggest possible data recording or processing errors.
No inferential statistics are provided (e.g., ANOVA, hypothesis testing), which limits the interpretability of differences between male and female hostel environments.

Suggestions:

Address anomalies and double-check for data errors or incorrect summary statistics.
Add a statistical comparison between datasets (e.g., t-test for temperature differences).
Discuss the practical relevance of observed values (e.g., CO₂ thresholds related to health standards).

5. Are all the source data underlying the results available to ensure full reproducibility?
Answer: Yes
Commentary:

The datasets are openly accessible on Mendeley under a valid DOI.
Both male and female hostel datasets are available in CSV format, which supports transparency and reproducibility.

6. Are the conclusions drawn adequately supported by the results?
Answer: Partly
Commentary:

The conclusion that IoT-based monitoring is feasible is supported, but the claim that the dataset "yields critical insights" is overstated based on the basic analysis.
Statements on policy or decision-making applications are speculative and not grounded in the presented results.

Suggestions:

Temper the conclusion to reflect the exploratory and descriptive nature of the analysis.
Recommend future work directions (e.g., longer-term data collection, predictive modeling).

Critical Points to Address Before Acceptance

Clarify anomalies in reported statistics (e.g., CO₂ and humidity values).
Expand the methods section with sufficient detail to ensure reproducibility (sampling rate, software used, code availability).
Strengthen the analysis – even basic regression or time series plots would add significant value.
Improve language and figure descriptions for clarity and readability.
Add interpretative context to results (e.g., are CO₂ levels within acceptable indoor air quality standards?).

Revision Required
The dataset has potential value for future environmental or AI-related work, but the article in its current form needs revision to meet scientific standards for clarity, rigor, and replicability.

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Partly
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Environmental InformaticsArtificial Intelligence and Machine LearningInternet of Things (IoT) and Sensor NetworksEnvironmental Data ScienceSmart Campus and Urban SustainabilityStatistical Data Analysis and Visualization

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

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Author Response 04 May 2025

Oluwasegun Aroba, Centre for Ecological Intelligence, University of Johannesburg, Auckland Park, South Africa

04 May 2025

Author Response
F1000Research Response
Response 1
We thank the reviewer for this observation. We clarified that, while existing studies aim generally at indoor conditions, there exists little publicly accessible data specifically tracking ... Continue reading
F1000Research Response
Response 1
We thank the reviewer for this observation. We clarified that, while existing studies aim generally at indoor conditions, there exists little publicly accessible data specifically tracking campus residential indoor air quality, particularly distinguishing by gender-separated male and female residences. Our data addresses this very specific gap, enabling targeted analysis by gender-differentiated residential environments on campus and thus providing novel grounds for follow-up research as well as for planning interventions.
Response 2
The figure gave the descriptive captions of the content, axes labels, measurement units, and key insights. The scatter plot illustrates the relationship between CO2 concentrations and humidity levels in female hostel rooms ensuring clarity and immediate interpretability.
Response 3
The study reviewed the existing studies on CO₂ levels and cognitive performance. This was discussed in the study to establish relationships with existing studies. Similarly, references on other state-of-the-art studies on air quality are directly linked to our recorded dataset results, contributing to overall clarity and evidence.
Response 4
The manuscript has been subjected to thorough proofread to eliminate some grammar error.

Responses to Critical Points

The reported 0.00ppm for CO2 and humidity stated clearly the starting point of the reading values.

The environmental readings were recorded every 5 minutes over 14 consecutive days with SCD30 sensors calibrated to do so. Python (version 3.11) using pandas and matplotlib libraries was used to preprocess data and log. Scripts for full preprocessing and analysis, as well as the raw dataset, have been uploaded in a public repository

The scatter plots in the manuscript show the relationship between temperate and humidity and between CO2 and occupancy patterns.

All figure captions were made to be descriptive, with descriptions of what appears in each figure, including units and context. The language throughout the manuscript were thoroughly proof read for clarity, concision, and technical correctness, eliminating redundancy and enhancing reader comprehension.

The study shows data interpretation of values observed in the context of common indoor air quality standards. The study findings have been clearly demarcated to maximise the relevance and utility of the results.
F1000Research Response
Response 1
We thank the reviewer for this observation. We clarified that, while existing studies aim generally at indoor conditions, there exists little publicly accessible data specifically tracking campus residential indoor air quality, particularly distinguishing by gender-separated male and female residences. Our data addresses this very specific gap, enabling targeted analysis by gender-differentiated residential environments on campus and thus providing novel grounds for follow-up research as well as for planning interventions.
Response 2
The figure gave the descriptive captions of the content, axes labels, measurement units, and key insights. The scatter plot illustrates the relationship between CO2 concentrations and humidity levels in female hostel rooms ensuring clarity and immediate interpretability.
Response 3
The study reviewed the existing studies on CO₂ levels and cognitive performance. This was discussed in the study to establish relationships with existing studies. Similarly, references on other state-of-the-art studies on air quality are directly linked to our recorded dataset results, contributing to overall clarity and evidence.
Response 4
The manuscript has been subjected to thorough proofread to eliminate some grammar error.

Responses to Critical Points

The reported 0.00ppm for CO2 and humidity stated clearly the starting point of the reading values.

The environmental readings were recorded every 5 minutes over 14 consecutive days with SCD30 sensors calibrated to do so. Python (version 3.11) using pandas and matplotlib libraries was used to preprocess data and log. Scripts for full preprocessing and analysis, as well as the raw dataset, have been uploaded in a public repository

The scatter plots in the manuscript show the relationship between temperate and humidity and between CO2 and occupancy patterns.

All figure captions were made to be descriptive, with descriptions of what appears in each figure, including units and context. The language throughout the manuscript were thoroughly proof read for clarity, concision, and technical correctness, eliminating redundancy and enhancing reader comprehension.

The study shows data interpretation of values observed in the context of common indoor air quality standards. The study findings have been clearly demarcated to maximise the relevance and utility of the results.
Competing Interests: No competing interests were disclosed. Close
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COMMENTS ON THIS REPORT

Author Response 04 May 2025

Oluwasegun Aroba, Centre for Ecological Intelligence, University of Johannesburg, Auckland Park, South Africa

04 May 2025

Author Response
F1000Research Response
Response 1
We thank the reviewer for this observation. We clarified that, while existing studies aim generally at indoor conditions, there exists little publicly accessible data specifically tracking ... Continue reading
F1000Research Response
Response 1
We thank the reviewer for this observation. We clarified that, while existing studies aim generally at indoor conditions, there exists little publicly accessible data specifically tracking campus residential indoor air quality, particularly distinguishing by gender-separated male and female residences. Our data addresses this very specific gap, enabling targeted analysis by gender-differentiated residential environments on campus and thus providing novel grounds for follow-up research as well as for planning interventions.
Response 2
The figure gave the descriptive captions of the content, axes labels, measurement units, and key insights. The scatter plot illustrates the relationship between CO2 concentrations and humidity levels in female hostel rooms ensuring clarity and immediate interpretability.
Response 3
The study reviewed the existing studies on CO₂ levels and cognitive performance. This was discussed in the study to establish relationships with existing studies. Similarly, references on other state-of-the-art studies on air quality are directly linked to our recorded dataset results, contributing to overall clarity and evidence.
Response 4
The manuscript has been subjected to thorough proofread to eliminate some grammar error.

Responses to Critical Points

The reported 0.00ppm for CO2 and humidity stated clearly the starting point of the reading values.

The environmental readings were recorded every 5 minutes over 14 consecutive days with SCD30 sensors calibrated to do so. Python (version 3.11) using pandas and matplotlib libraries was used to preprocess data and log. Scripts for full preprocessing and analysis, as well as the raw dataset, have been uploaded in a public repository

The scatter plots in the manuscript show the relationship between temperate and humidity and between CO2 and occupancy patterns.

All figure captions were made to be descriptive, with descriptions of what appears in each figure, including units and context. The language throughout the manuscript were thoroughly proof read for clarity, concision, and technical correctness, eliminating redundancy and enhancing reader comprehension.

The study shows data interpretation of values observed in the context of common indoor air quality standards. The study findings have been clearly demarcated to maximise the relevance and utility of the results.
F1000Research Response
Response 1
We thank the reviewer for this observation. We clarified that, while existing studies aim generally at indoor conditions, there exists little publicly accessible data specifically tracking campus residential indoor air quality, particularly distinguishing by gender-separated male and female residences. Our data addresses this very specific gap, enabling targeted analysis by gender-differentiated residential environments on campus and thus providing novel grounds for follow-up research as well as for planning interventions.
Response 2
The figure gave the descriptive captions of the content, axes labels, measurement units, and key insights. The scatter plot illustrates the relationship between CO2 concentrations and humidity levels in female hostel rooms ensuring clarity and immediate interpretability.
Response 3
The study reviewed the existing studies on CO₂ levels and cognitive performance. This was discussed in the study to establish relationships with existing studies. Similarly, references on other state-of-the-art studies on air quality are directly linked to our recorded dataset results, contributing to overall clarity and evidence.
Response 4
The manuscript has been subjected to thorough proofread to eliminate some grammar error.

Responses to Critical Points

The reported 0.00ppm for CO2 and humidity stated clearly the starting point of the reading values.

The environmental readings were recorded every 5 minutes over 14 consecutive days with SCD30 sensors calibrated to do so. Python (version 3.11) using pandas and matplotlib libraries was used to preprocess data and log. Scripts for full preprocessing and analysis, as well as the raw dataset, have been uploaded in a public repository

The scatter plots in the manuscript show the relationship between temperate and humidity and between CO2 and occupancy patterns.

All figure captions were made to be descriptive, with descriptions of what appears in each figure, including units and context. The language throughout the manuscript were thoroughly proof read for clarity, concision, and technical correctness, eliminating redundancy and enhancing reader comprehension.

The study shows data interpretation of values observed in the context of common indoor air quality standards. The study findings have been clearly demarcated to maximise the relevance and utility of the results.
Competing Interests: No competing interests were disclosed. Close
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Version 1

VERSION 1 PUBLISHED 03 Apr 2025

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	1	2
Version 1 03 Apr 25	read	read

Tymoteusz Miller, University of Szczecin, Szczecin, Poland
Domenico Suriano, ENEA—Italian National Agency for New Technologies, Brindisi, Italy

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Reviewer Report

2 Views

21 Aug 2025 | for Version 1

Domenico Suriano, ENEA—Italian National Agency for New Technologies, Brindisi, Italy

2 Views Cite this report Responses(0)

Not Approved

Is the work clearly and accurately presented and does it cite the current literature?

No
Is the study design appropriate and is the work technically sound?

No
Are sufficient details of methods and analysis provided to allow replication by others?

No
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

No
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

air quality monitoring; Iot devices; gas sensors; gas sensor calibration; air quality monitors design

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Reviewer Report

13 Views

28 Apr 2025 | for Version 1

Tymoteusz Miller, University of Szczecin, Szczecin, Poland

13 Views Cite this report Responses(1)

Approved With Reservations

The introduction provides a reasonable justification for studying indoor air quality but lacks a sharper focus on the novelty or gap this dataset fills.
Figures (scatter plots, distributions) are referenced but not described in sufficient detail; some captions are vague (“Author’s Own Construct”).
The literature cited is reasonably current, but the connection between references and specific claims (e.g., effects on cognition, ventilation effects) needs to be made more explicit in-text.
Writing quality is inconsistent: some sections (like scatter plot interpretations) are redundant or overgeneralized.

Suggestions for improvement:

Clarify which specific studies your work complements or expands upon.
Tighten descriptions of figures and plots; interpret key findings beyond mere correlation.
Improve sentence structure and clarity throughout the Results and Discussion sections.

2. Is the study design appropriate and is the work technically sound?
Answer: Partly
Commentary:

The use of IoT-based sensors is appropriate and aligns with recent environmental monitoring practices.
However, the article lacks depth in explaining why certain sensors were chosen, and how data quality was ensured (e.g., sensor calibration, error margins).
The methods used (summary statistics and scatter plots) are very basic and don't leverage the potential analytical depth (e.g., temporal trends, anomaly detection, or time-of-day analysis).
There is no mention of data preprocessing steps (e.g., handling of missing values or outliers), which are essential for replicability.

Suggestions:

Justify the sensor selection and discuss technical reliability.
Enhance the methodological rigor by applying more advanced analytical techniques.
Include a brief section on sensor validation and data cleaning.

3. Are sufficient details of methods and analysis provided to allow replication by others?
Answer: Partly
Commentary:

Hardware connections and data collection procedures are described, but important implementation details are missing.
The manuscript does not explain how often the data were collected (sampling rate), what format/time zones were used, or if time-of-day effects were considered.
Scatter matrix visualizations are mentioned, but no code or tools used (e.g., Python, R, Excel) are documented.

Suggestions:

Provide clearer procedural details (e.g., sampling interval, data logging frequency).
Describe the analysis pipeline, ideally sharing scripts or code.
Add sensor specifications in a supplementary file.

4. If applicable, is the statistical analysis and its interpretation appropriate?
Answer: Partly
Commentary:

The analysis relies solely on means, standard deviations, and scatterplots with informal correlation interpretations.
In several instances, contradictory or unrealistic values appear (e.g., CO₂ mean = 0.00 ppm in boys’ hostel, humidity std dev = 123.10%), which suggest possible data recording or processing errors.
No inferential statistics are provided (e.g., ANOVA, hypothesis testing), which limits the interpretability of differences between male and female hostel environments.

Suggestions:

Address anomalies and double-check for data errors or incorrect summary statistics.
Add a statistical comparison between datasets (e.g., t-test for temperature differences).
Discuss the practical relevance of observed values (e.g., CO₂ thresholds related to health standards).

5. Are all the source data underlying the results available to ensure full reproducibility?
Answer: Yes
Commentary:

The datasets are openly accessible on Mendeley under a valid DOI.
Both male and female hostel datasets are available in CSV format, which supports transparency and reproducibility.

6. Are the conclusions drawn adequately supported by the results?
Answer: Partly
Commentary:

The conclusion that IoT-based monitoring is feasible is supported, but the claim that the dataset "yields critical insights" is overstated based on the basic analysis.
Statements on policy or decision-making applications are speculative and not grounded in the presented results.

Suggestions:

Temper the conclusion to reflect the exploratory and descriptive nature of the analysis.
Recommend future work directions (e.g., longer-term data collection, predictive modeling).

Critical Points to Address Before Acceptance

Clarify anomalies in reported statistics (e.g., CO₂ and humidity values).
Expand the methods section with sufficient detail to ensure reproducibility (sampling rate, software used, code availability).
Strengthen the analysis – even basic regression or time series plots would add significant value.
Improve language and figure descriptions for clarity and readability.
Add interpretative context to results (e.g., are CO₂ levels within acceptable indoor air quality standards?).

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Partly
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Environmental InformaticsArtificial Intelligence and Machine LearningInternet of Things (IoT) and Sensor NetworksEnvironmental Data ScienceSmart Campus and Urban SustainabilityStatistical Data Analysis and Visualization

Respond to this report

Responses (1)

Author Response

04 May 2025

Oluwasegun Aroba, Centre for Ecological Intelligence, University of Johannesburg, Auckland Park, South Africa

F1000Research Response
Response 1
We thank the reviewer for this observation. We clarified that, while existing studies aim generally at indoor conditions, there exists little publicly accessible data specifically tracking campus residential indoor air quality, particularly distinguishing by gender-separated male and female residences. Our data addresses this very specific gap, enabling targeted analysis by gender-differentiated residential environments on campus and thus providing novel grounds for follow-up research as well as for planning interventions.
Response 2
The figure gave the descriptive captions of the content, axes labels, measurement units, and key insights. The scatter plot illustrates the relationship between CO2 concentrations and humidity levels in female hostel rooms ensuring clarity and immediate interpretability.
Response 3
The study reviewed the existing studies on CO₂ levels and cognitive performance. This was discussed in the study to establish relationships with existing studies. Similarly, references on other state-of-the-art studies on air quality are directly linked to our recorded dataset results, contributing to overall clarity and evidence.
Response 4
The manuscript has been subjected to thorough proofread to eliminate some grammar error.

Responses to Critical Points

The reported 0.00ppm for CO2 and humidity stated clearly the starting point of the reading values.
The environmental readings were recorded every 5 minutes over 14 consecutive days with SCD30 sensors calibrated to do so. Python (version 3.11) using pandas and matplotlib libraries was used to preprocess data and log. Scripts for full preprocessing and analysis, as well as the raw dataset, have been uploaded in a public repository
The scatter plots in the manuscript show the relationship between temperate and humidity and between CO2 and occupancy patterns.
All figure captions were made to be descriptive, with descriptions of what appears in each figure, including units and context. The language throughout the manuscript were thoroughly proof read for clarity, concision, and technical correctness, eliminating redundancy and enhancing reader comprehension.
The study shows data interpretation of values observed in the context of common indoor air quality standards. The study findings have been clearly demarcated to maximise the relevance and utility of the results.

View more View less

Competing Interests

No competing interests were disclosed.

Alongside their report, reviewers assign a status to the article:

Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested

Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.

Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions

[1] 1. Brink HW, Loomans MGLC, Mobach MP, et al.: Classrooms' indoor environmental conditions affecting the academic achievement of students and teachers in higher education: A systematic literature review. Wiley Open Access Collection; 2020. Reference Source

[2] 2. Pulimeno M, Piscitelli P, Colazzo S, et al.: Indoor air quality at school and students’ performance: Recommendations of the UNESCO Chair on Health Education and Sustainable Development & the Italian Society of Environmental Medicine (SIMA). Health Promot. Perspect. 2020; 10(3): 169–174. PubMed Abstract | Publisher Full Text | Free Full Text

[3] 3. Roche IV, Ubalde-Lopez M, Daher C, et al.: The health-related and learning performance effects of air pollution and other urban-related environmental factors on school-age children and adolescents—a scoping review of systematic reviews. Current environmental health reports. 2024; 11(2): 300–316. PubMed Abstract | Publisher Full Text | Free Full Text

[4] 4. Umezuruike C, Aworinde H, Aroba OJ: Campus Air Quality Dataset. Mendeley Data. 2024; V1. Publisher Full Text

[5] 5. Zhang L, Chen J, Li G: Urban air quality dataset: A comprehensive resource for AI-based pollution prediction and hotspot identification. Environ. Sci. Technol. 2021; 55(15): 10232–10240.

[6] 6. Aroba OJ: The Implementation of Augmented Reality in Internet of Things for Virtual Learning in Higher Education. International Journal of Computing Sciences Research. 2024; 8: 2536–2549. Publisher Full Text

[7] 7. Deoraj S, Essop MS, Aroba OJ: Investigating a Virtual Queueing System for Durban University of Technology: A Comprehensive Review Approach to Improve Efficiency. 2024 Conference on Information Communications Technology and Society (ICTAS). IEEE; (2024), March; pp. 38–43.

[8] 8. Li Y, Zhang H, Wu D: Campus environmental monitoring dataset: A resource for AI-driven optimization of energy consumption and indoor air quality. Sensors. 2022; 22(12): 4789.

[9] 9. Aroba OJ, Chisita CT, Buthelezi N, et al.: February. Higher education enterprise resource planning system transformation of supply chain management processes. International Congress on Information and Communication Technology. Singapore: Springer Nature Singapore; 2023; pp. 415–424.

[10] 10. Chen X, Wang Y, Liu Z: A novel AI-driven system for real-time air quality forecasting and alerting based on smart city sensor network dataset. J. Environ. Inf. 2023; 45(3): 234–248.

Campus air quality dataset

Abstract

Background

Method

Result

Conclusion

Keywords

Value of the data

Background

Data description

Methods

Figure 1. Schematic diagram showing connections of sensors (Author’s Own Construct).

Result and discussion

5.1. Boys hostel

Summary statistics observation

Distribution of variables

Figure 2. Boys’ Hostel Variable Distribution Plot (Author’s Own Construct).

Figure 3. Scattered plot for male hostel (Author’s Own Construct).

Figure 4. Girls’ Hostel variable distribution plot (Author’s Own Construct).

Figure 5. Scattered plot for female hostel (Author’s Own Construct).

Scatter plots

Female hostel

Summary statistics observation

Distribution of variables

Scatter plots

Table 1. Specifications Table.

Conclusion

Limitations

Data availability

Acknowledgement

References

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