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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM67183
Volume:
10 |
9 |
Sep 2026
Pages:
1-31
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of Chemistry
Government Degree College Rajendranagar
Department of Physics
Government Degree College Rajendranagar
Department of Computer Science
Government Degree College Rajendranagar
Department of Chemistry
Government Degree College Rajendranagar
Department of Chemistry
Government Degree College Rajendranagar
Department of Chemistry
Government Degree College Rajendranagar
Department of Physics
Government Degree College Rajendranagar
Ventilation and enclosure are primary determinants of indoor environmental heterogeneity in naturally ventilated educational buildings, yet their influence is expressed differently across thermal, hygrometric, barometric and gaseous variables. This study investigated the spatiotemporal variation of BME680-derived environmental parameters using an Internet of Things (IoT)-enabled Real-Time Environmental Intelligence System (REIS) at Government Degree College, Rajendranagar, Telangana, India. A Bosch BME680 sensor integrated with an ESP32 microcontroller was deployed across an Open Corridor, an Open (Ventilated) Room and a Closed Room, representing progressively greater degrees of enclosure and reduced natural air exchange. Temperature, relative humidity, atmospheric pressure and metal-oxide-semiconductor (MOX) gas resistance were monitored at 10:00 AM, 1:00 PM and 4:00 PM during February, March, April and June 2026; pressure-derived altitude was retained as a contextual variable. The four-month dataset revealed parameter-specific spatiotemporal behaviour. Temperature exhibited pronounced seasonal and diurnal variability, with warmer conditions during April and June and frequent midday elevation. Relative humidity and atmospheric pressure showed comparatively limited location-dependent separation. In contrast, gas resistance displayed the clearest and most persistent spatial gradient, following Open Corridor > Open Room > Closed Room across the monitoring period. Under comparable sensor operating conditions, the lower gas resistance observed in the more enclosed environment is consistent with stronger interaction of the MOX sensing layer with reducing gases and VOC-related mixtures under restricted dispersion. This response is interpreted conservatively because the BME680 gas element is non-selective, cross-sensitive to multiple compounds and environmentally dependent; therefore, gas resistance is treated as a semi-quantitative indicator of relative VOC-related gaseous variability rather than as a direct concentration of individual VOCs or total VOCs. Integration of BME680 sensing with ESP32 processing, Wi-Fi communication and cloud visualization enabled spatiotemporal IEQ surveillance and evidence-informed ventilation awareness. The study demonstrates how low-cost multi-parameter IoT sensing can support sustainable-campus environmental management and aligns with SDGs 3, 4, 9, 11, 12 and 13 through healthier learning environments, sustainability education, digital infrastructure, responsible building operation and climate-responsive institutional practice.
KEYWORDS
This study demonstrates that ventilation, enclosure and temporal variation produced distinct BME680 environmental-response patterns at Government Degree College, Rajendranagar. Temperature was influenced mainly by seasonal and diurnal changes, whereas relative humidity and atmospheric pressure showed comparatively limited spatial differentiation. The most consistent location-dependent response was observed for BME680 gas resistance, which followed the order Open Corridor > Open Room > Closed Room. The lower resistance in the more enclosed environment is consistent with a stronger MOX response to reducing gases and VOC-related mixtures under restricted air exchange. Because the BME680 gas element is non-selective and affected by environmental and sensor-operating conditions, gas resistance should be interpreted as a semi-quantitative indicator of relative VOC-related variability rather than as a direct VOC or TVOC concentration. Integration of BME680 sensing with ESP32, Wi-Fi and cloud visualization provides a practical low-cost framework for spatiotemporal indoor environmental surveillance and ventilation awareness. The approach supports sustainable-campus management and provides enabling alignment with SDGs 3, 4, 9, 11, 12 and 13. Future studies should incorporate reference VOC measurements, occupancy information, air-exchange measurements and longer-term monitoring to strengthen quantitative interpretation.
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Publication Date
9/10/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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