Assessing the environmental sustainability of the urban ecosystem using Material Flow Analysis: the case of Riyadh housing stock

In 2018, 55% of the global population resided in urban areas, and it is predicted that by 2050 this percentage will reach 68%. The consumption of materials, energy, and water resources is an inextricable consequence of population growth. This is especially apparent in urban areas, where there are significant investments in infrastructure to support urban development. Cities experiencing rapid growth face tremendous challenges, not only in providing the services required, but also to assure that development is sustainable. Urban material flow analysis (MFA) is becoming a popular framework. It links the interaction between urban activities and the environment, by quantifying the material flows that go into the urban system and the impacts of the use of resources, such as air pollution, and solid waste. To date, MFA has primarily focused on accounting for the annual direct mass and energy flows throughout the city. Therefore, it does not form a proper basis for assessing environmental sustainability within an urban system. Contrary to the MFA methodology, the Life Cycle Assessment (LCA) methodology not only examines mass, direct material and energy exchanges with the environment, but also provides a cradle-to-grave assessment of resource use and environmental impacts from a life cycle perspective. With high rate of urban population growth, massive investments in infrastructure are needed and proposed to support urban development. Unfortunately, integrative, systemlevel tools are lacking for urban planners and decision-makers to assess the environmental sustainability of urban development. This thesis proposes an expanded MFA framework synthesizing additional critical urban indicators; including biophysical (i.e., land use) and socio-economic indicators. The aim being to extend MFA beyond an accounting framework, and towards a strategic planning framework. It also outlines the role of LCA in assessing urban flows, and in the evaluation of urban development scenarios, as significant changes currently being made to support 2050 urban infrastructure. The proposed framework involved three consecutive stages. The first stage evaluated the potential use of MFA to assess the sustainability of urban systems. It involved a systemic review to evaluate how MFA can offer system-based perspectives from which to understand the interaction between the urban system and the surrounding environment. After this, MFA was applied to the domestic sector of Riyadh, Saudi Arabia, based on a set of indicators suggested by the literature. The years selected for analysis were 1996, 2006, and 2016 due to data availability. Temporal trends of resource consumption were established and results reported as gross values and on a per capita basis, to create clarity of understanding around the trends. The embodied energy and environmental impact of each MFA component was assessed using the LCA v method. The environmental impacts were classified into five categories: climate change, particulate matter formation, freshwater ecotoxicity, water consumption potential, and fossil fuel potential. The second stage was motivated by studying the long-term impact of the built environment. A dynamic model of the in-use stock based on MFA was developed in this stage, thereby extending the model to include in-use services. The foundation of the analysis was the floor area devoted to housing stock. The input parameters of the model were based on socioeconomic indicators, and intensity factors (e.g., floor area per capita, energy use intensity). Then, LCA was introduced to the model to assess GHG emissions associated with energy (stationary/mobile) and water demands. The rationale was that with a clearer understanding of the impacts associated with resource demand, wellinformed decisions can be made to address any increases in GHG emissions. The third stage extended the scope of the analysis to include assessment of climate change mitigation policies. Initially, it reviewed current initiatives in Saudi Arabia to address the issue of climate change. Then, a set of mitigation policies were identified, and the impact of each policy investigated, and results reported against a base-scenario. The results at each stage helped provide insights into the impact of the built environment in the context of Saudi Arabia. They also demonstrated that the proposed integrative approach can be used to quantify and assess urban flows, while conceptualizing the social and economic characteristics of the city. The findings emphasised that the framework combining stock dynamic and LCA can play a significant role in assisting the formulation of policies related to urban sustainability and climate change mitigation. Although the assessment was limited in its scope, the results afford a strong foundation for future holistic assessmen

Temporal Trends Of Energy Consumption And CO2 Emissions In Riyadh, Saudi Arabia

The consumption of material, energy, and water resources is inextricably linked to population growth with a unique impact on urban areas, especially in light of significant investments in infrastructure to support urban development. Urban metabolism is becoming popular as it provides a framework accounting the mass and energy flows through a city. An urban metabolism study was conducted to estimate the inputs and outputs of energy and pollutants from Riyadh, Saudi Arabia. The objective of this study was to determine the energy consumption of Riyadh using locally generated data from 1986, 1996, 2006, and 2012 and analysing the temporal trends of energy consumption and associated environmental impact. The socioeconomic and biophysical characteristics of Riyadh are well represented in its metabolism indicators. The high growth rate in population along with urban expansion has resulted in an increase in energy consumption. Riyadh has seen an increase in energy consumption at a rate of about 6% per annum. On a per capita basis, preliminary results show that the energy consumption increased by 31% from 1996 to 2012. Also, per capita CO2 emissions have increased by the same percentage. Results also show increasing mobile energy consumption from 20k TJ in 1986 to 157k TJ in 2012, which points to Riyadh’s inefficient urban form. The study findings highlight the importance for developing effective policies for improving the use of resources

Using Material and Energy Flow Analysis to Estimate Future Energy Demand at the City Level

Cities undergoing rapid growth encounter tremendous challenges, not only in terms of providing services to meet demand, but also in ensuring that development occurs in a sustainable way. This research evaluates the potential contribution of the material and energy flow analysis framework to predicting future energy flows and corresponding CO2 emissions in Riyadh, Saudi Arabia. The research presents a generic material and energy flow analysis model and applies it to the housing stock in Riyadh to estimate future energy demand and to assess associated effects. As the country starts to adopt sustainability measures and plan its transition from a fossil fuel-based energy system towards a renewable-based energy system, an understanding of future energy flows will allow early recognition of potential environmental impacts and provide information to enable accurate predictions of future demand for resources

Comparing Deep Recurrent Networks Based on the MAE Random Sampling, a First Approach

Recurrent neural networks have demonstrated to be good at tackling prediction problems, however due to their high sensitivity to hyper-parameter configuration, finding an appropriate network is a tough task. Automatic hyper-parameter optimization methods have emerged to find the most suitable configuration to a given problem, but these methods are not generally adopted because of their high computational cost. Therefore, in this study we extend the MAE random sampling, a low-cost method to compare single-hidden layer architectures, to multiple-hidden-layer ones. We validate empirically our proposal and show that it is possible to predict and compare the expected performance of an hyper-parameter configuration in a low-cost way.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. This research was partially funded by Ministerio de Economı́a, Industria y Competitividad, Gobierno de España, and European Regional Development Fund grant numbers TIN2016-81766-REDT (http://cirti.es) and TIN2017-88213-R (http://6city.lcc.uma.es)

Hyperparameter Optimization Of Deep Convolutional Neural Networks Architectures For Object Recognition

Recent advances in Convolutional Neural Networks (CNNs) have obtained promising results in difficult deep learning tasks. However, the success of a CNN depends on finding an architecture to fit a given problem. A hand-crafted architecture is a challenging, time-consuming process that requires expert knowledge and effort, due to a large number of architectural design choices. In this dissertation, we present an efficient framework that automatically designs a high-performing CNN architecture for a given problem. In this framework, we introduce a new optimization objective function that combines the error rate and the information learnt by a set of feature maps using deconvolutional networks (deconvnet). The new objective function allows the hyperparameters of the CNN architecture to be optimized in a way that enhances the performance by guiding the CNN through better visualization of learnt features via deconvnet. The actual optimization of the objective function is carried out via the Nelder-Mead Method (NMM). Further, our new objective function results in much faster convergence towards a better architecture. The proposed framework has the ability to explore a CNN architecture’s numerous design choices in an efficient way and also allows effective, distributed execution and synchronization via web services. Empirically, we demonstrate that the CNN architecture designed with our approach outperforms several existing approaches in terms of its error rate. Our results are also competitive with state-of-the-art results on the MNIST dataset and perform reasonably against the state-of-the-art results on CIFAR-10 and CIFAR-100 datasets. Our approach has a significant role in increasing the depth, reducing the size of strides, and constraining some convolutional layers not followed by pooling layers in order to find a CNN architecture that produces a high recognition performance. Moreover, we evaluate the effectiveness of reducing the size of the training set on CNNs using a variety of instance selection methods to speed up the training time. We then study how these methods impact classification accuracy. Many instance selection methods require a long run-time to obtain a subset of the representative dataset, especially if the training set is large and has a high dimensionality. One example of these algorithms is Random Mutation Hill Climbing (RMHC). We improve RMHC so that it performs faster than the original algorithm with the same accuracy

The site of insulin storage inside domestic refrigerators is associated with ketoacidosis and hypoglycemic episodes among patient with diabetes mellitus

BackgroundInsulin storage is vital for use in type 1 diabetes and most patients with type 2 diabetes mellitus. No researchers have investigated the site of insulin storage inside the fridge, the proper rotation of insulin injection sites, and the worsening glucose profile in Tabuk, Saudi Arabia.AimsThe study aimed to assess the association between storing insulin in different sites of the fridge, rotating insulin injection sites, hypoglycemia, and ketoacidosis.Methods This is a cross-sectional study conducted in King Fahd Specialist Hospital, Tabuk, Saudi Arabia during the period from May 2019 to November 2019, 394 participants were approached to collect demographic data, site of insulin storage in domestic refrigerators, number of ketoacidosis and hypoglycaemic attack during the last year, number of insulin injection sites and rotation of injections, and if the patients were regularly checking their glycated haemoglobin.Results Of the 349 patients (98 per cent had type 2 diabetes), 98.3 per cent had a hypoglycaemic episode during the last year, and 11.2 per cent were admitted for ketoacidosis, this is mirrored by not properly storing insulin inside the fridge (39.2 per cent), and inappropriately rotating insulin injection sites (87.4 per cent). Patients who were storing their insulin inappropriately had higher rates of both ketoacidosis and hypoglycaemia than their comparators (0.26±0.65 vs. 0.11±0.39, and 19.51±19.46 vs. 7.07±9.54 respectively, P < 0.05.ConclusionHypoglycaemia and ketoacidosis were common among patients who inappropriately store insulin inside the fridge and wrongly rotate insulin injection sites. Further larger multi-center studies investigating the patient's behaviour to high and low blood sugar and the rates of lipohypertrophy are recommended