Speaker
Description
Air pollution remains a serious environmental and public health challenge, creating a strong need for accurate, affordable, and real-time monitoring systems, especially in developing contexts such as South Africa. Recent studies show that smart air quality monitoring can be significantly improved through the integration of low-cost sensor networks, machine learning, and environmental data sources, with models such as Random Forest, XGBoost, LSTM, and hybrid approaches proving effective for prediction, calibration, and trend analysis. At the same time, dashboard-based and GIS-supported studies demonstrate the value of combining multiple environmental data sources to assess spatiotemporal variations in pollutants such as PM1.0,PM2.5, PM4.0, PM10.0, CO2, including Temperature(◦C) and Humidity, especially where dense ground based monitoring is limited. This research therefore proposes a smart indoor air quality monitoring framework for South Africa that uses intelligent indoor sensing systems to collect real-time environmental data, analyze pollutant behavior, and support early warning and decision-making in indoor spaces such as homes, schools, offices, and laboratories. By combining continuous sensing, data-driven analysis, and predictive modelling, the study aims to contribute to healthier indoor environments, improved public awareness, and more effective air quality management strategies in South Africa.
| Apply for student award at which level: | MSc |
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| Consent on use of personal information: Abstract Submission | Yes, I ACCEPT |