Longitudinal indoor air quality dataset (CO2, PM, Temp, RH) in South African classrooms across six infrastructure types (2023-2025)

Research Hypothesis & Context:
In the resource-constrained South African education sector, temporary infrastructure (shipping containers and prefabricated units) is frequently used as a permanent solution for classroom overcrowding. This study hypothesises that these temporary structures offer significantly poorer Indoor Air Quality (IAQ) and thermal comfort compared to permanent brick infrastructure, potentially impacting learner cognition and health.
This dataset provides a longitudinal comparative analysis of these building types for occupied and unoccupied dates. It captures the "real-world" learning environment, accounting for external factors unique to the region, such as rolling national power outages, dictating mechanical ventilation availability.

Methodology & Data Collection:
Data was collected via a custom LoRaWAN IoT network deployed in primary schools in Stellenbosch, South Africa.
Timeframe: Feb 2023 - Jan 2025 (24 mos.), capturing multiple seasonal cycles.
Sampling: Intervals from 11 min (early deployment) to 3 min (primary deployment) to capture high-resolution data.
Sensors: DHT22 (T/RH), COZIR-LP-5000 (CO2), and PMS5003 (PM 1.0, 2.5, 10).

Data Interpretation:
The dataset comprises ~2.8 mil data points across 6 infrastructure types: Container (with and without a wood-panel wall retrofit), Mobile/Prefab, and Brick (First/Second Floor/Single Storey).

Files and Structure:
rawmeasurements.csv: This file exceeds 100MB and contains ~2.8 million rows. May require statistical software (Python, R, Stata) to process. Contains the primary time-series vectors for CO2, T, RH, and PM.
roomdetails.csv: Static metadata linking roomcode to physical attributes (dimensions, window size, building materials, and orientation).
weatherinfo.csv: Daily ambient conditions including Temperature, Wind Speed/Direction, and Solar Irradiance (GHI, DNI, DHI) for energy modelling, as well as ASHRAE 55-2023 thermal comfort metrics Tpma, and 80% thermal acceptability limits. (Full weather data not published due to copyrights, may be requested)
occupancyschedules.csv: High-resolution binary arrays (0/1) estimating room occupancy based on school timetables.
powerofftimes.json: Logs of power outage events, allowing researchers to correlate spikes in CO2 or Temperature with forced HVAC outages.
CodeBook.xlsx: The master dictionary for all variable codes and units.

Notable Findings & Usage:
The data reveals distinct thermal profiles where uninsulated containers exhibit extreme temperature fluctuations compared to brick structures. High CO2 accumulation rates observed during winter months highlight ventilation deficits when windows are closed to conserve heat.

Research Potential:
Public Health: Using CO2 as a proxy for viral transmission risk in crowded spaces.
Building Physics: Energy modelling using the provided Solar Irradiance and power outage data.
Policy: Providing evidence-based recommendations for school infrastructure procurement in developing economies.