Jones, N. A., H. Ross, T. Lynam, P. Perez, and A. Leitch. 2011. Mental models: an interdisciplinary synthesis of theory and methods. Ecology and Society 16(1): 46. https://doi.org/10.5751/ES-03802-160146

Street canyons are generally highly polluted urban environments due to high traffic emissions and impeded dispersion. Green infrastructure (GI) is one potential passive control system for air pollution in street canyons, yet optimum GI design is currently unclear. This review consolidates findings from previous research on GI in street canyons and assesses the suitability of different GI forms in terms of local air quality improvement. Studies on the effects of various GI options (trees, hedges, green walls, green screens and green roofs) are critically evaluated, findings are synthesised, and possible recommendations are summarised. In addition, various measurement methods used for quantifying the effectiveness of street greening for air pollution reduction are analysed. Finally, we explore the findings of studies that have compared plant species for pollution mitigation. We conclude that the influences of different GI options on air quality in street canyons depend on street canyon geometry, meteorological conditions and vegetation characteristics. Green walls, green screens and green roofs are potentially viable GI options in existing street canyons, where there is typically a lack of available planting space. Particle deposition to leaves is usually quantified by leaf washing experiments or by microscopy imaging techniques, the latter of which indicates size distribution and is more accurate. The pollutant reduction capacity of a plant species largely depends on its macromorphology in relation to the physical environment. Certain micromorphological leaf traits also positively correlate with deposition, including grooves, ridges, trichomes, stomatal density and epicuticular wax amount. The complexity of street canyon environments and the limited number of previous studies on novel forms of GI in street canyons mean that offering specific recommendations is currently unfeasible. This review highlights a need for further research, particularly on green walls and green screens, to substantiate their efficacy and investigate technical considerations.

The increasing development of urban centers brings serious challenges for traffic management. In this paper, we introduce a smart visual sensor, developed for a pilot project taking place in the Australian city of Liverpool (NSW). The project’s aim was to design and evaluate an edge-computing device using computer vision and deep neural networks to track in real-time multi-modal transportation while ensuring citizens’ privacy. The performance of the sensor was evaluated on a town center dataset. We also introduce the interoperable Agnosticity framework designed to collect, store and access data from multiple sensors, with results from two real-world experiments.

Floods are amongst the most common and devastating of all natural hazards. The alarming number of flood-related deaths and financial losses suffered annually across the world call for improved response to flood risks. Interestingly, the last decade has presented great opportunities with a series of scholarly activities exploring how camera images and wireless sensor data from Internet-of-Things (IoT) networks can improve flood management. This paper presents a systematic review of the literature regarding IoT-based sensors and computer vision applications in flood monitoring and mapping. The paper contributes by highlighting the main computer vision techniques and IoT sensor approaches utilised in the literature for real-time flood monitoring, flood modelling, mapping and early warning systems including the estimation of water level. The paper further contributes by providing recommendations for future research. In particular, the study recommends ways in which computer vision and IoT sensor techniques can be harnessed to better monitor and manage coastal lagoons—an aspect that is under-explored in the literature.