Predicting fuel energy consumption during earthworks

This research contributes to the assessment of on-site fuel consumption and the resulting carbon dioxide emissions due to earthworks-related processes in residential building projects, prior to the start of the construction phase. Several studies have been carried out on this subject, and have demonstrated the considerable environmental impact of earthworks activities in terms of fuel consumption. However, no methods have been proposed to estimate on-site fuel consumption during the planning stage. This paper presents a quantitative method to predict fuel consumption before the construction phase. The calculations were based on information contained in construction project documents and the definition of equipment load factors. Load factors were characterized for the typical equipment that is used in earthworks in residential building projects (excavators, loaders and compactors), taking into considering the type of soil, the type of surface and the duration of use. We also analyzed transport fuel consumption, because of its high impact in terms of pollution. The proposed method was then applied to a case study that illustrated its practical use and benefits. The predictive method can be used as an assessment tool for residential construction projects, to measure the environmental impact in terms of on-site fuel consumption. Consequently, it provides a significant basis for future methods to compare construction projects.Peer ReviewedPostprint (author's final draft

Machine-Learning Paradigms for Selecting Ecologically Significant Input Variables

Harmful algal blooms, which are considered a serious environmental problem nowadays, occur in coastal waters in many parts of the world. They cause acute ecological damage and ensuing economic losses, due to fish kills and shellfish poisoning as well as public health threats posed by toxic blooms. Recently, data-driven models including machine learning (ML) techniques have been employed to mimic dynamics of algal blooms. One of the most important steps in the application of a ML technique is the selection of significant model input variables. In the present paper, we use two extensively used ML techniques, artificial neural networks (ANN) and genetic programming (GP) for selecting the significant input variables. The efficacy of these techniques is first demonstrated on a test problem with known dependence and then they are applied to a real-world case study of water quality data from Tolo Harbour, Hong Kong. These ML techniques overcome some of the limitations of the currently used techniques for input variable selection, a review of which is also presented. The interpretation of the weights of the trained ANN and the GP evolved equations demonstrate their ability to identify the ecologically significant variables precisely. The significant variables suggested by the ML techniques also indicate chlorophyll-a itself to be the most significant input in predicting the algal blooms, suggesting an auto-regressive nature or persistence in the algal bloom dynamics, which may be related to the long flushing time in the semi-enclosed coastal waters. The study also confirms the previous understanding that the algal blooms in coastal waters of Hong Kong often occur with a life cycle of the order of 1 - 2 weeks

Estudo de caso: minimização e reúso de água em shopping center da região sul do Brasil

The main issues related to water conservation in urban centers are the increase in water supply cost, demand growth, pollution and differences in the distribution of water resources. Water conservation, the controlled and efficient use of water, includes both measures as reasonable means of water reuse. Thus, conservation practices are an effective way to meet demand and supply water to new activities and users without jeopardizing the supplying water bodies and preserving the natural environment. This study aims to examine the water management of a shopping mall and the use of rainwater harvesting combined with greywater reuse. For buildings in general, water loss is common due to leaks in the hydraulic and restroom equipment. These losses, which are caused by a high volume of water used and wasted in the system, are often the result of design errors, incorrect maintenance procedures and users' bad habits In southern Brazil, where there is rainfall almost all year long, water shortages occasionally occur, particularly in some winter mouths. One difficulty that appears on rainwater studies is the proper determination of rainwater volume that can be used to address water supply systems. In this work, the simulation method was used to determine this volume. Thus, simulations with the following variables: rainfall, catchment area and water consumption were performed. For mall's hydraulic systems, segmented alternatives are adopted. That is, focusing on the use of rainwater or greywater reuse. Other alternatives of effluent reuse have been slightly discussed due to sanitary issues, those are effluents from toilets and kitchen sinks. The adoption of greywater may be feasible if there is a significant flow of greywater to comply water demand for toilet flushing. The inspections made in this study found that the quantity of sinks was insufficient to supply an adequate amount of water to toilets and urinals. The greywater reuse system was found to be infeasible in terms of demand and supply of water. Conversely, the rainwater harvesting system was entirely feasible and easily supplied water to all restrooms and contributed to the cooling of the air conditioning system with a short payback period. One of the challenges of this work was the need to compare the actual water consumption with a water consumption parameter used in buildings. Thus, a method that addresses the generation of specific consumption indexes for specific activity (like a mall) was used. The water consumption indices showed that this mall has a satisfactory water management program.Fundação Araucária; CAPESAs principais questões relacionadas com a conservação da água nos centros urbanos são: o aumento do custo de abastecimento de água, o crescimento da demanda, a poluição e as diferenças na distribuição dos recursos hídricos. A conservação da água está associada ao uso controlado e eficiente da água, e contempla tanto medidas de uso racional quanto de reúso de água. Assim, as práticas conservacionistas são uma maneira inteligente de otimizar e regular a demanda e oferta de água para novas atividades e usuários, sem, contudo, comprometer o suprimento dos corpos hídricos e a preservação do ambiente natural. Este estudo tem por objetivo analisar a gestão da água de um shopping center (SC) e o aproveitamento de águas pluviais (APs) combinado com reúso de água cinza (AC). Nas edificações, de um modo geral, são frequentes os desperdícios de água provocados por vazamentos nos sistemas hidráulicos e nas peças sanitárias. A causa desses elevados volumes de água utilizada no sistema, muitas vezes, é decorrente de concepções inadequadas de projeto, de procedimentos incorretos de manutenção e maus hábitos dos usuários. No sul do Brasil, onde existe a ocorrência de chuvas durante quase todo o ano a escassez de água ocorre principalmente em alguns meses de inverno. Uma das dificuldades que aparecem nos estudos de AP é a correta determinação do volume de AP que poderá ser utilizada nos sistemas de abastecimento de água. Assim, neste trabalho, foi utilizado o método da simulação para determinar este volume. Foram realizadas simulações com as seguintes variáveis: precipitação, área de captação e consumo de água. Para os sistemas hidráulicos do SC, foram adotadas alternativas segmentadas. Ou seja, com ênfase no uso da AP e reutilização de AC. Outras alternativas de reúso de efluentes foram pouco abordadas devido a questões sanitárias, sendo essas, o efluente de vasos sanitários e pias de cozinha. A adoção das AC pode ser viável, se houver um fluxo significativo de AC, que supra a demanda de água requerida para o funcionamento adequado dos vasos sanitários e mictórios. As inspeções conduzidas neste estudo constataram, que o número de pias eram insuficientes para suprir água aos vasos sanitários e mictórios. Portanto, o sistema de reúso de AC foi considerado inviável em termos de demanda e oferta de água. Por outro lado, constatou-se a viabilidade do sistema de aproveitamento de APs, que pode facilmente fornecer água para todos os sanitários, além de contribuir para o arrefecimento do sistema de ar condicionado, tendo ainda um curto período de retorno. Um dos desafios deste trabalho foi a necessidade de comparar o consumo real de água com parâmetros de consumo de água utilizados em edifícios. Assim, foi utilizado um método que aborda a geração de índices de consumo de água específico para a actividade específica (SC). Os índices de consumo de água demonstraram que o SC estudado possui um programa de gestão de água adequado

Development of Artificial Intelligence Approach to Nowcasting and Forecasting Oyster Norovirus Outbreaks along the U.S. Gulf Coast

Oyster norovirus outbreaks pose increasing risks to human health and seafood industry worldwide. This study presents an Artificial Intelligence (AI)-based approach to identifying the primary cause of oyster norovirus outbreaks, nowcasting and forecasting the growing risk of oyster norovirus outbreaks in coastal waters. AI models were developed using Artificial Neural Networks (ANNs) and Genetic Programming (GP) methods and time series of epidemiological and environmental data. Input variable selection techniques, including Random Forests (RF) and Forwards Binary Logistic Regression (FBLR), were used to identify the significant model input variables among six independent environmental predictors including water temperature, solar radiation, gage height, salinity, wind, and rainfall and various combinations of the variables with different time lags. In terms of nowcasting, a risk-based GP model was developed to nowcast daily risks of oyster norovirus outbreaks along the Northern Gulf of Mexico coast, showing the true positive and negative rates of 78.53% and 88.82%, respectively. In terms of forecasting, an ANN model, called ANN-2Day, was presented. The forecasting model was capable of reproducing all historical oyster norovirus outbreaks with the true positive and negative rates of 100.00% and 99.84%, respectively. The sensitivity analysis results of the ANN-2Day model further indicated that oyster norovirus outbreaks were generally linked to the extreme combination of antecedent environmental conditions characterized by low water temperature, low solar radiation, low gage height, low salinity, strong wind, and heavy precipitation. In addition to the GP and ANN-2Day models, a remote sensing–based model was constructed using MODIS Aqua level 2 products. The remote sensing-based model enabled oyster management authorities to expand the prediction of norovirus outbreak risks from areas where monitoring data were accessible to other oyster harvest areas where monitoring stations are not available. In conclusion, the developed AI models enables public health agencies and oyster harvesters to better plan for management interventions and thus makes it possible to achieve a paradigm shift of their daily management and operation from primarily reacting to epidemic incidents of norovirus infection after they have occurred to eliminating (or at least reducing) the risk of costly incidents

Optimizing Information Gathering for Environmental Monitoring Applications

The goal of environmental monitoring is to collect information from the environment and to generate an accurate model for a specific phenomena of interest. We can distinguish environmental monitoring applications into two macro areas that have different strategies for acquiring data from the environment. On one hand the use of fixed sensors deployed in the environment allows a constant monitoring and a steady flow of information coming from a predetermined set of locations in space. On the other hand the use of mobile platforms allows to adaptively and rapidly choose the sensing locations based on needs. For some applications (e.g. water monitoring) this can significantly reduce costs associated with monitoring compared with classical analysis made by human operators. However, both cases share a common problem to be solved. The data collection process must consider limited resources and the key problem is to choose where to perform observations (measurements) in order to most effectively acquire information from the environment and decrease the uncertainty about the analyzed phenomena. We can generalize this concept under the name of information gathering. In general, maximizing the information that we can obtain from the environment is an NP-hard problem. Hence, optimizing the selection of the sampling locations is crucial in this context. For example, in case of mobile sensors the problem of reducing uncertainty about a physical process requires to compute sensing trajectories constrained by the limited resources available, such as, the battery lifetime of the platform or the computation power available on board. This problem is usually referred to as Informative Path Planning (IPP). In the other case, observation with a network of fixed sensors requires to decide beforehand the specific locations where the sensors has to be deployed. Usually the process of selecting a limited set of informative locations is performed by solving a combinatorial optimization problem that model the information gathering process. This thesis focuses on the above mentioned scenario. Specifically, we investigate diverse problems and propose innovative algorithms and heuristics related to the optimization of information gathering techniques for environmental monitoring applications, both in case of deployment of mobile and fixed sensors. Moreover, we also investigate the possibility of using a quantum computation approach in the context of information gathering optimization

Optimal Design of a Rain Gauge Network to Improve Streamflow Forecasting

Enhanced streamflow forecasting has always been an important task for researchers and water resources managers. However, streamflow forecasting is often challenging owing to the complexity of hydrologic systems. The accuracy of streamflow forecasting mainly depends on the input data, especially rainfall as it constitutes the key input in transforming rainfall into runoff. This emphasizes the need for incorporating accurate rainfall input in streamflow forecasting models in order to achieve enhanced streamflow forecasting. Based on past research, it is well-known that an optimal rain gauge network is necessary to provide high quality rainfall estimates. Therefore, this study focused on the optimal design of a rain gauge network and integration of the optimal network-based rainfall input in artificial neural network (ANN) models to enhance the accuracy of streamflow forecasting. The Middle Yarra River catchment in Victoria, Australia was selected as the case study catchment, since the management of water resources in the catchment is of great importance to the majority of Victorians