Infrastructure Engineering - Research Publications
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ItemThe carbon footprint of treating patients with septic shock in the intensive care unit(College of Intensive Care Medicine of Australia and New Zealand, 2018-12-01)OBJECTIVE: To use life cycle assessment to determine the environmental footprint of the care of patients with septic shock in the intensive care unit (ICU). DESIGN, SETTING AND PARTICIPANTS: Prospective, observational life cycle assessment examining the use of energy for heating, ventilation and air conditioning; lighting; machines; and all consumables and waste associated with treating ten patients with septic shock in the ICU at BarnesJewish Hospital, St. Louis, MO, United States (US-ICU) and ten patients at Footscray Hospital, Melbourne, Vic, Australia (Aus-ICU). MAIN OUTCOME MEASURES: Environmental footprint, particularly greenhouse gas emissions. RESULTS: Energy use per patient averaged 272 kWh/day for the US-ICU and 143 kWh/day for the Aus-ICU. The average daily amount of single-use materials per patient was 3.4 kg (range, 1.0-6.3 kg) for the US-ICU and 3.4 kg (range, 1.2-8.7 kg) for the Aus-ICU. The average daily particularly greenhouse gas emissions arising from treating patients in the US-ICU was 178 kg carbon dioxide equivalent (CO2-e) emissions (range, 165-228 kg CO2-e), while for the Aus-ICU the carbon footprint was 88 kg CO2-e (range, 77-107 kg CO2-e). Energy accounted for 155 kg CO2-e in the US-ICU (87%) and 67 kg CO2-e in the Aus-ICU (76%). The daily treatment of one patient with septic shock in the US-ICU was equivalent to the total daily carbon footprint of 3.5 Americans' CO2-e emissions, and for the Aus-ICU, it was equivalent to the emissions of 1.5 Australians. CONCLUSION: The carbon footprints of the ICUs were dominated by the energy use for heating, ventilation and air conditioning; consumables were relatively less important, with limited effect of intensity of patient care. There is large opportunity for reducing the ICUs' carbon footprint by improving the energy efficiency of buildings and increasing the use of renewable energy sources.
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ItemNo Preview AvailableEditorial(Informa UK Limited, 2015-06-01)
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ItemA Novel Approach for 3D Modeling and Geovisualization of Easement Rights in Apartments(CMV Verlag, 2018-12-01)
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ItemA novel approach for 3D neighbourhood analysis(Copernicus GmbH, 2017-09-12)Abstract. Population growth and lack of land in urban areas have caused massive developments such as high rises and underground infrastructures. Land authorities in the international context recognizes 3D cadastres as a solution to efficiently manage these developments in complex cities. Although a 2D cadastre does not efficiently register these developments, it is currently being used in many jurisdictions for registering land and property information. Limitations in analysis and presentation are considered as examples of such limitations. 3D neighbourhood analysis by automatically finding 3D spaces has become an issue of major interest in recent years. Whereas the neighbourhood analysis has been in the focus of research, the idea of 3D neighbourhood analysis has rarely been addressed in 3 dimensional information systems (3D GIS) analysis. In this paper, a novel approach for 3D neighbourhood analysis has been proposed by recording spatial and descriptive information of the apartment units and easements. This approach uses the coordinates of the subject apartment unit to find the neighbour spaces. By considering a buffer around the edges of the unit, neighbour spaces are accurately detected. This method was implemented in ESRI ArcScene and three case studies were defined to test the efficiency of this approach. The results show that spaces are accurately detected in various complex scenarios. This approach can also be applied for other applications such as property management and disaster management in order to find the affected apartments around a defined space.
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ItemMulti-scale analysis of bias correction of soil moisture( 2014-07-29)Abstract. Remote sensing, in situ networks and models are now providing unprecedented information for environmental monitoring. To conjunctively use multi-source data nominally representing an identical variable, one must resolve biases existing between these disparate sources, and the characteristics of the biases can be non-trivial due to spatiotemporal variability of the target variable, inter-sensor differences with variable measurement supports. One such example is of soil moisture (SM) monitoring. Triple collocation (TC) based bias correction is a powerful statistical method that increasingly being used to address this issue but is only applicable to the linear regime, whereas nonlinear method of statistical moment matching is susceptible to unintended biases originating from measurement error. Since different physical processes that influence SM dynamics may be distinguishable by their characteristic spatiotemporal scales, we propose a multi-time-scale linear bias model in the framework of a wavelet-based multi-resolution analysis (MRA). The joint MRA-TC analysis was applied to demonstrate scale-dependent biases between in situ, remotely-sensed and modelled SM, the influence of various prospective bias correction schemes on these biases, and lastly to enable multi-scale bias correction and data adaptive, nonlinear de-noising via wavelet thresholding.
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ItemImproving operational flood ensemble prediction by the assimilation of satellite soil moisture: comparison between lumped and semi-distributed schemes( 2014-09-23)Abstract. Assimilation of remotely sensed soil moisture data (SM–DA) to correct soil water stores of rainfall-runoff models has shown skill in improving streamflow prediction. In the case of large and sparsely monitored catchments, SM–DA is a particularly attractive tool. Within this context, we assimilate active and passive satellite soil moisture (SSM) retrievals using an ensemble Kalman filter to improve operational flood prediction within a large semi-arid catchment in Australia (>40 000 km2). We assess the importance of accounting for channel routing and the spatial distribution of forcing data by applying SM–DA to a lumped and a semi-distributed scheme of the probability distributed model (PDM). Our scheme also accounts for model error representation and seasonal biases and errors in the satellite data. Before assimilation, the semi-distributed model provided more accurate streamflow prediction (Nash–Sutcliffe efficiency, NS = 0.77) than the lumped model (NS = 0.67) at the catchment outlet. However, this did not ensure good performance at the "ungauged" inner catchments. After SM–DA, the streamflow ensemble prediction at the outlet was improved in both the lumped and the semi-distributed schemes: the root mean square error of the ensemble was reduced by 27 and 31%, respectively; the NS of the ensemble mean increased by 7 and 38%, respectively; the false alarm ratio was reduced by 15 and 25%, respectively; and the ensemble prediction spread was reduced while its reliability was maintained. Our findings imply that even when rainfall is the main driver of flooding in semi-arid catchments, adequately processed SSM can be used to reduce errors in the model soil moisture, which in turn provides better streamflow ensemble prediction. We demonstrate that SM–DA efficacy is enhanced when the spatial distribution in forcing data and routing processes are accounted for. At ungauged locations, SM–DA is effective at improving streamflow ensemble prediction, however, the updated prediction is still poor since SM–DA does not address systematic errors in the model.
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ItemEstimating Annual Water Storage Variations Using Microwave-based Soil Moisture Retrievals( 2016-11-21)Abstract. Due to their shallow vertical support, remotely-sensed surface soil moisture retrievals are commonly regarded as being of limited value for water budget applications requiring the characterization of temporal variations in total terrestrial water storage (S). However, advances in our ability to estimate evapotranspiration remotely now allow for the direct evaluation of approaches for quantifying annual variations in S via water budget closure considerations. By applying an annual water budget analysis within a series of medium-scale (2,000–10,000 km2) basins within the United States, we demonstrate that, despite their clear theoretical limitations, surface soil moisture retrievals derived from passive microwave remote sensing contain significant information concerning relative inter-annual variations in S. This suggests the possibility of using (relatively) higher-resolution microwave remote sensing to enhance the spatial resolution of S estimates acquired from gravity remote sensing. However, challenging calibration issues regarding the relationship between S and surface soil moisture must be resolved before the approach can be used for absolute water budget closure.
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ItemNo Preview AvailableA predictive model for spatio-temporal variability in stream water quality( 2019-07-23)Abstract. Degraded water quality in rivers and streams can have large economic, societal and ecological impacts. Stream water quality can be highly variable both over space and time. To develop effective management strategies for riverine water quality, it is critical to be able to predict these spatio-temporal variabilities. However, our current capacity to model stream water quality is limited, particularly at large spatial scales across multiple catchments. This is due to a lack of understanding of the key controls that drive spatio-temporal variabilities of stream water quality. To address this, we developed a Bayesian hierarchical statistical model to analyse the spatio-temporal variability in stream water quality across the state of Victoria, Australia. The model was developed based on monthly water quality monitoring data collected at 102 sites over 21 years. The modelling focused on six key water quality constituents: total suspended solids (TSS), total phosphorus (TP), filterable reactive phosphorus (FRP), total Kjeldahl nitrogen (TKN), nitrate-nitrite (NOx), and electrical conductivity (EC). Among the six constituents, the models explained varying proportions of variation in water quality. EC was the most predictable constituent (88.6 % variability explained) and FRP had the lowest predictive performance (19.9 % variability explained). The models were validated for multiple sets of calibration/validation sites and showed robust performance. Temporal validation revealed a systematic change in the TSS model performance across most catchments since an extended drought period in the study region, highlighting potential shifts in TSS dynamics over the drought. Further improvements in model performance need to focus on: (1) alternative statistical model structures to improve fitting for the low concentration data, especially records below the detection limit; and (2) better representation of non-conservative constituents by accounting for important biogeochemical processes. We also recommend future improvements in water quality monitoring programs which can potentially enhance the model capacity, via: (1) improving the monitoring and assimilation of high-frequency water quality data; and (2) improving the availability of data to capture land use and management changes over time.
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ItemLoss of Sirt1 function improves intestinal anti-bacterial defense and protects from colitis-induced colorectal cancer.(Public Library of Science (PLoS), 2014)Dysfunction of Paneth and goblet cells in the intestine contributes to inflammatory bowel disease (IBD) and colitis-associated colorectal cancer (CAC). Here, we report a role for the NAD+-dependent histone deacetylase SIRT1 in the control of anti-bacterial defense. Mice with an intestinal specific Sirt1 deficiency (Sirt1int-/-) have more Paneth and goblet cells with a consequent rearrangement of the gut microbiota. From a mechanistic point of view, the effects on mouse intestinal cell maturation are mediated by SIRT1-dependent changes in the acetylation status of SPDEF, a master regulator of Paneth and goblet cells. Our results suggest that targeting SIRT1 may be of interest in the management of IBD and CAC.
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ItemAntibiotic use and abuse: a threat to mitochondria and chloroplasts with impact on research, health, and environment.(Wiley, 2015-10)Recently, several studies have demonstrated that tetracyclines, the antibiotics most intensively used in livestock and that are also widely applied in biomedical research, interrupt mitochondrial proteostasis and physiology in animals ranging from round worms, fruit flies, and mice to human cell lines. Importantly, plant chloroplasts, like their mitochondria, are also under certain conditions vulnerable to these and other antibiotics that are leached into our environment. Together these endosymbiotic organelles are not only essential for cellular and organismal homeostasis stricto sensu, but also have an important role to play in the sustainability of our ecosystem as they maintain the delicate balance between autotrophs and heterotrophs, which fix and utilize energy, respectively. Therefore, stricter policies on antibiotic usage are absolutely required as their use in research confounds experimental outcomes, and their uncontrolled applications in medicine and agriculture pose a significant threat to a balanced ecosystem and the well-being of these endosymbionts that are essential to sustain health.