Diurnal variation of small and large ion concentrations in an urban location

A Neutral cluster and Air Ion Spectrometer (NAIS) was used to monitor the concentration of airborne ions on 258 full days between Nov 2011 and Dec 2012 in Brisbane, Australia. The air was sampled from outside a window on the sixth floor of a building close to the city centre, approximately 100 m away from a busy freeway. The NAIS detects all ions and charged particles smaller than 42 nm. It was operated in a 4 min measurement cycle, with ion data recorded at 10 s intervals over 2 min during each cycle. The data were analysed to derive the diurnal variation of small, large and total ion concentrations in the environment. We adapt the definition of Horrak et al (2000) and classify small ions as molecular clusters smaller than 1.6 nm and large ions as charged particles larger than this size...

Metrological performances of a diffusion charger particle counter for personal monitoring

Airborne particles have been shown to be associated with a wide range of adverse health effects, which has led to a recent increase in medical research to gain better insight into their health effects. However, accurate evaluation of the exposure-dose-response relationship is highly dependent on the ability to track actual exposure levels of people to airborne particles. This is quite a complex task, particularly in relation to submicrometer and ultrafine particles, which can vary quite significantly in terms of particle surface area and number concentrations. Therefore, suitable monitors that can be worn for measuring personal exposure to these particles are needed. This paper presents an evaluation of the metrological performance of six diffusion charger sensors, NanoTracer (Philips Aerasense) monitors, when measuring particle number and surface area concentrations, as well as particle number distribution mean when compared to reference instruments. Tests in the laboratory (by generating monodisperse and polydisperse aerosols) and in the field (using natural ambient particles) were designed to evaluate the response of these devices under both steady-state and dynamics conditions. Results showed that the NanoTracers performed well when measuring steady state aerosols, however they strongly underestimated actual concentrations during dynamic response testing. The field experiments also showed that, when the majority of the particles were smaller than 20 nm, which occurs during particle formation events in the atmosphere, the NanoTracer underestimated number concentration quite significantly. Even though the NanoTracer can be used for personal monitoring of exposure to ultrafine particles, it also has limitations which need to be considered in order to provide meaningful results.

Personal exposure to ultrafine particles : the influence of time-activity patterns

Exposure to ultrafine particles (UFPs) is deemed to be a major risk affecting human health. Therefore, airborne particle studies were performed in the recent years to evaluate the most critical micro-environments, as well as identifying the main UFP sources. Nonetheless, in order to properly evaluate the UFP exposure, personal monitoring is required as the only way to relate particle exposure levels to the activities performed and micro-environments visited. To this purpose, in the present work, the results of experimental analysis aimed at showing the effect of the time-activity patterns on UFP personal exposure are reported. In particular, 24 non-smoking couples (12 during winter and summer time, respectively), comprised of a man who worked full-time and a woman who was a homemaker, were analyzed using personal particle counter and GPS monitors. Each couple was investigated for a 48-h period, during which they also filled out a diary reporting the daily activities performed. Time activity patterns, particle number concentration exposure and the related dose received by the participants, in terms of particle alveolar-deposited surface area, were measured. The average exposure to particle number concentration was higher for women during both summer and winter (Summer: women 1.8×104 part. cm-3; men 9.2×103 part. cm-3; Winter: women 2.9×104 part. cm-3; men 1.3×104 part. cm-3), which was likely due to the time spent undertaking cooking activities. Staying indoors after cooking also led to higher alveolar-deposited surface area dose for both women and men during the winter time (9.12×102 and 6.33×102 mm2, respectively), when indoor ventilation was greatly reduced. The effect of cooking activities was also detected in terms of women’s dose intensity (dose per unit time), being 8.6 and 6.6 in winter and summer, respectively. On the contrary, the highest dose intensity activity for men was time spent using transportation (2.8 in both winter and summer).

Robots for environmental monitoring : significant advancements and applications

Robotic systems are increasingly being utilised as fundamental data-gathering tools by scientists, allowing new perspectives and a greater understanding of the planet and its environmental processes. Today's robots are already exploring our deep oceans, tracking harmful algal blooms and pollution spread, monitoring climate variables, and even studying remote volcanoes. This article collates and discusses the significant advancements and applications of marine, terrestrial, and airborne robotic systems developed for environmental monitoring during the last two decades. Emerging research trends for achieving large-scale environmental monitoring are also reviewed, including cooperative robotic teams, robot and wireless sensor network (WSN) interaction, adaptive sampling and model-aided path planning. These trends offer efficient and precise measurement of environmental processes at unprecedented scales that will push the frontiers of robotic and natural sciences.

Spatio-temporal modelling of ultrafine particle number concentration

This thesis developed semi-parametric regression models for estimating the spatio-temporal distribution of outdoor airborne ultrafine particle number concentration (PNC). The models developed incorporate multivariate penalised splines and random walks and autoregressive errors in order to estimate non-linear functions of space, time and other covariates. The models were applied to data from the "Ultrafine Particles from Traffic Emissions and Child" project in Brisbane, Australia, and to longitudinal measurements of air quality in Helsinki, Finland. The spline and random walk aspects of the models reveal how the daily trend in PNC changes over the year in Helsinki and the similarities and differences in the daily and weekly trends across multiple primary schools in Brisbane. Midday peaks in PNC in Brisbane locations are attributed to new particle formation events at the Port of Brisbane and Brisbane Airport.

Optimisation of indoor environmental quality and energy consumption within office buildings

This research investigated airborne particle characteristics and their dynamics inside and around the envelope of mechanically ventilated office buildings, together with building thermal conditions and energy consumption. Based on these, a comprehensive model was developed to facilitate the optimisation of building heating, ventilation and air conditioning systems, in order to protect the health of their occupants and minimise the energy requirements of these buildings.

Investigations of the physical and chemical properties of ambient fine particles in urban schools

This thesis reports a comprehensive study on the physical and chemical properties of airborne particles in Brisbane, especially around schools. The sources and potential toxicity of the particles were identified, enabling an assessment of the contributing factors to children's exposure at school. The results from this thesis give a quantitative estimate of the range of airborne particles that children are exposed to at urban schools with different traffic conditions.

HMM relative entropy rate concepts for vision-based aircraft manoeuvre detection

Machine vision is emerging as a viable sensing approach for mid-air collision avoidance (particularly for small to medium aircraft such as unmanned aerial vehicles). In this paper, using relative entropy rate concepts, we propose and investigate a new change detection approach that uses hidden Markov model filters to sequentially detect aircraft manoeuvres from morphologically processed image sequences. Experiments using simulated and airborne image sequences illustrate the performance of our proposed algorithm in comparison to other sequential change detection approaches applied to this application.

Perception Quality Evaluation with Visual and Infrared Cameras in Challenging Environmental Conditions

This work aims to contribute to the reliability and integrity of perceptual systems of unmanned ground vehicles (UGV). A method is proposed to evaluate the quality of sensor data prior to its use in a perception system by utilising a quality metric applied to heterogeneous sensor data such as visual and infrared camera images. The concept is illustrated specifically with sensor data that is evaluated prior to the use of the data in a standard SIFT feature extraction and matching technique. The method is then evaluated using various experimental data sets that were collected from a UGV in challenging environmental conditions, represented by the presence of airborne dust and smoke. In the first series of experiments, a motionless vehicle is observing a ’reference’ scene, then the method is extended to the case of a moving vehicle by compensating for its motion. This paper shows that it is possible to anticipate degradation of a perception algorithm by evaluating the input data prior to any actual execution of the algorithm.

Visual metrics for the evaluation of sensor data quality in outdoor perception

This paper proposes an experimental study of quality metrics that can be applied to visual and infrared images acquired from cameras onboard an unmanned ground vehicle (UGV). The relevance of existing metrics in this context is discussed and a novel metric is introduced. Selected metrics are evaluated on data collected by a UGV in clear and challenging environmental conditions, represented in this paper by the presence of airborne dust or smoke. An example of application is given with monocular SLAM estimating the pose of the UGV while smoke is present in the environment. It is shown that the proposed novel quality metric can be used to anticipate situations where the quality of the pose estimate will be significantly degraded due to the input image data. This leads to decisions of advantageously switching between data sources (e.g. using infrared images instead of visual images).

Visual metrics for the evaluation of sensor data quality in outdoor perception

This paper proposes an experimental study of quality metrics that can be applied to visual and infrared images acquired from cameras onboard an unmanned ground vehicle (UGV). The relevance of existing metrics in this context is discussed and a novel metric is introduced. Selected metrics are evaluated on data collected by a UGV in clear and challenging environmental conditions, represented in this paper by the presence of airborne dust or smoke.

Towards discrimination of challenging conditions for UGVs with visual and infrared sensors

This work aims to contribute to reliability and integrity in perceptual systems of autonomous ground vehicles. Information theoretic based metrics to evaluate the quality of sensor data are proposed and applied to visual and infrared camera images. The contribution of the proposed metrics to the discrimination of challenging conditions is discussed and illustrated with the presence of airborne dust and smoke.

The Marulan data sets: multi-sensor perception in a natural environment with challenging conditions

In this paper we present large, accurately calibrated and time-synchronized data sets, gathered outdoors in controlled and variable environmental conditions, using an unmanned ground vehicle (UGV), equipped with a wide variety of sensors. These include four 2D laser scanners, a radar scanner, a color camera and an infrared camera. It provides a full description of the system used for data collection and the types of environments and conditions in which these data sets have been gathered, which include the presence of airborne dust, smoke and rain.

Laser-camera data discrepancies and reliable perception in outdoor robotics

This work aims to promote integrity in autonomous perceptual systems, with a focus on outdoor unmanned ground vehicles equipped with a camera and a 2D laser range finder. A method to check for inconsistencies between the data provided by these two heterogeneous sensors is proposed and discussed. First, uncertainties in the estimated transformation between the laser and camera frames are evaluated and propagated up to the projection of the laser points onto the image. Then, for each pair of laser scan-camera image acquired, the information at corners of the laser scan is compared with the content of the image, resulting in a likelihood of correspondence. The result of this process is then used to validate segments of the laser scan that are found to be consistent with the image, while inconsistent segments are rejected. Experimental results illustrate how this technique can improve the reliability of perception in challenging environmental conditions, such as in the presence of airborne dust.

A framework for testing robust autonomy of UAS during design and certification

As the number of Uninhabited Airborne Systems (UAS) proliferates in civil applications, industry is increasingly putting pressure on regulation authorities to provide a path for certification and allow UAS integration into regulated airspace. The success of this integration depends on developments in improved UAS reliability and safety, regulations for certification, and technologies for operational performance and safety assessment. This paper focusses on the last topic and describes a framework for quantifying robust autonomy of UAS, which quantifies the system's ability to either continue operating in the presence of faults or safely shut down. Two figures of merit are used to evaluate vehicle performance relative to mission requirements and the consequences of autonomous decision making in motion control and guidance systems. These figures of merit are interpreted within a probabilistic framework, which extends previous work in the literature. The valuation of the figures of merit can be done using stochastic simulation scenarios during both vehicle development and certification stages with different degrees of integration of hardware-in-the-loop simulation technology. The objective of the proposed framework is to aid in decision making about the suitability of a vehicle with respect to safety and reliability relative to mission requirements.