Peer Review Report For: Campus air quality dataset [version 1; peer review: 1 approved with reservations, 1 not approved]

Résumé

Background Elevated levels of carbon dioxide (CO 2 ) within academic settings can adversely affect the health and academic efficacy of both students and faculty. High concentrations of CO 2 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, CO 2 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 CO 2 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 CO 2 levels within educational environments, underscoring the necessity for ongoing air quality monitoring to enhance campus conditions.

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