Airborne ultrafine particles in Australian cities. There is an acute deficiency of knowledge in Australia on urban airborne ultrafine particles, originating from transport and other anthropogenic sources, which pose significant health and environmental risks. The aim of this project is to address this deficiency by an extensive multi-city, cross-disciplinary study using state of the art instrumentation and data analytic techniques. The outcome will be an in depth, quantitative insight into the c ....Airborne ultrafine particles in Australian cities. There is an acute deficiency of knowledge in Australia on urban airborne ultrafine particles, originating from transport and other anthropogenic sources, which pose significant health and environmental risks. The aim of this project is to address this deficiency by an extensive multi-city, cross-disciplinary study using state of the art instrumentation and data analytic techniques. The outcome will be an in depth, quantitative insight into the characteristics of the particles, their sources and spatial and temporal variation across different urban areas and time scales. Further, the impacts of changing fuels, vehicle technologies, and climate on future trends of the particles will be elucidated.Read moreRead less
Revolutionising water-quality monitoring in the information age. In today’s information age, automated low-cost sensors distributed in the environment have the potential to revolutionise the way we monitor and manage air, water and soil. This project aims to develop novel statistical methods to detect anomalies in the data generated from these in-situ sensors with computationally efficient modelling on river networks through space and time, with the applied goals of automating anomaly detection ....Revolutionising water-quality monitoring in the information age. In today’s information age, automated low-cost sensors distributed in the environment have the potential to revolutionise the way we monitor and manage air, water and soil. This project aims to develop novel statistical methods to detect anomalies in the data generated from these in-situ sensors with computationally efficient modelling on river networks through space and time, with the applied goals of automating anomaly detection in water-quality data and generating predictions of sediment and nutrient concentrations throughout river networks in near-real time. This will represent a fundamental increase in scientific knowledge, which will be immediately useful in the domains of aquatic science, environmental monitoring, and statistics.Read moreRead less