Development of an Integrated Process, Modeling and Simulation Platform for Performance-Based Design of Low-Energy and High IEQ Buildings

The objective of this study was to develop a Virtual Design Studio (VDS) : a software platform for integrated, coordinated and optimized design of green building systems with low energy consumption, high indoor environmental quality (IEQ), and high level of sustainability. The VDS is intended to assist collaborating architects, engineers and project management team members throughout from the early phases to the detailed building design stages. It can be used to plan design tasks and workflow, and evaluate the potential impacts of various green building strategies on the building performance by using the state of the art simulation tools as well as industrial/professional standards and guidelines for green building system design. Based on the review and analysis of existing professional practices in building system design, particularly those used in U.S., Germany and UK, a generic process for performance-based building design, construction and operation was proposed. It included Assess, Define, Design, Apply, and Monitoring (ADDAM) stages. The current VDS focused on the first three stages. The VDS considers the building design as a multi-dimensional process involving multiple design teams, design factors, and design stages. The intersection among these three dimensions defines a specific design task in terms of who , what and when . It also considers building design as a multi-objective process that aims to enhance the five aspects of performance for green building systems: site sustainability, materials and resource efficiency, water utilization efficiency, energy efficiency and impacts to the atmospheric environment, and IEQ. The current VDS development has been limited to the energy efficiency and IEQ performance with particular focus on thermal, air quality and lighting environmental quality because of their strong interaction with the energy performance of buildings. The VDS software framework contains four major functions: 1) Design coordination: It enables users to define tasks using the Input-Process-Output flow approach, which specifies the anticipated activities (i.e., the process), required input and output information, and anticipated interactions with other tasks. It also allows task scheduling to define the work flow, and sharing of the design data and information via internet. 2) Modeling and simulation: It enables users to perform building simulations to predict the energy consumption and IEQ conditions at any of the design stages by using EnergyPlus and a combined heat, air, moisture and pollutant simulation (CHAMPS) model. A method for co-simulation was developed to allow the use of both models at the same time step for the combined energy and indoor air quality analysis. 3) Results visualization: It enables users to display a 3-D geometric design of the building by reading BIM (building information model) file generated by design software such as SketchUp, and the predicted results of heat, air, moisture, pollutant and light distributions in the building. 4) Performance evaluation: It enables the users to compare the performance of a proposed building design against a reference building that is defined for the same type of buildings under the same climate condition, and predict the percent of improvements over the minimum requirements specified in ASHRAE Standard 55-2010, 62.1-2010 and 90.1-2010. An approach was developed to estimate the potential impact of a design factor on the whole building performance, and hence can assist the user to identify areas that have most pay back for investment. The VDS software was developed by using C++ with the conventional Model, View and Control (MVC) software architecture. The software has been verified by using a simple 3-zone case building. The application of the VDS concepts and framework for building design and performance analysis has been illustrated by using a medium size five story office building that received the LEED Platinum Certification from USGBC

Dense Air Quality Sensor Networks: Validation, Analysis and Benefits

Air pollution is known to be harmful for human health and environments. The official air quality monitoring stations have been established across many smart cities around the world. Unfortunately, these monitoring stations are sparsely located and consequently do not provide high resolution spatio- temporal air quality information. This paper demonstrates how a dense sensor network deployment offers significant advantages in providing better and more detailed air quality information. We use data from a dense sensor network consisting of 126 low- cost sensors (LCSs) deployed in a highly populated district in Nanjing downtown, China. Using data obtained from 13 existing reference stations installed in the same district, we propose three LCSs validation methods to evaluate the performance of LCSs in the network. The methods assess the reliability, accuracy tests, and failure and anomaly detection performance. We also demonstrate how the reliable data generated from the sensor network provides deep insights into air pollution information at a higher spatio-temporal resolution. We further discuss potential improvements and applications derived from dense deployment of LCSs in cities.Peer reviewe

Mining Social Media and Structured Data in Urban Environmental Management to Develop Smart Cities

This research presented the deployment of data mining on social media and structured data in urban studies. We analyzed urban relocation, air quality and traffic parameters on multicity data as early work. We applied the data mining techniques of association rules, clustering and classification on urban legislative history. Results showed that data mining could produce meaningful knowledge to support urban management. We treated ordinances (local laws) and the tweets about them as indicators to assess urban policy and public opinion. Hence, we conducted ordinance and tweet mining including sentiment analysis of tweets. This part of the study focused on NYC with a goal of assessing how well it heads towards a smart city. We built domain-specific knowledge bases according to widely accepted smart city characteristics, incorporating commonsense knowledge sources for ordinance-tweet mapping. We developed decision support tools on multiple platforms using the knowledge discovered to guide urban management. Our research is a concrete step in harnessing the power of data mining in urban studies to enhance smart city development

The concept model of sustainable buildings refurbishment

Sustainable development principles reaching many spheres of human activities, public buildings refurbishment is not an exemption in this case. Buildings refurbishment supports excellent opportunities to reduce energy consumption in buildings as well as encourages other sustainable refurbishment principles implementation ‐ citizens’ healthcare, environment protection, rational resources use, information about sustainable refurbishment dissemination and stakeholders groups’ awareness. During the pilot refurbishment FP‐6 project Brita in PuBs, authors of this article have developed conceptual sustainable public buildings refurbishment model. Model was created basing on sustainable development principles, their consideration in decision making process and model efficiency influencing factors. In order to demonstrate models’ application possibilities following the healthcare principle, practical case study of Vilnius Gediminas Technical University main building pollution mapping is given at the end of this article. Santrauka Darnios plėtros principai skverbiasi į daugelį veiklos krypčių, neaplenkdami ir visuomeninių pastatų atnaujinimo proceso. Pastatų atnaujinimas – tai puiki galimybė ne tik sumažinti suvartojamos pastate energijos apimtis, bet ir užtikrinti kitus darnios renovacijos principus – rūpinimąsi gyventojų sveikata, aplinkos tausojimą, racionalų išteklių naudojimą, taip pat ir informacijos apie darnią pastatų renovaciją prieinamumą. Vykdant demonstracinį FP-6 projektą Brita in PuBs, straipsnio autoriai sukūrė koncepcinį darnios visuomeninių pastatų renovacijos modelį, kuriame atsižvelgiama į darnios plėtros principus, jų taikymą priimant sprendimus ir modelio efektyvumą veikiančius veiksnius. Siekiant pademonstruoti modelio realizavimo galimybes, paskutiniame straipsnio skyriuje rūpinimosi sveikata principas iliustruojamas renovuojamo VGTU centrinio pastato užterštumo žemėlapio sudarymu. First published online: 18 Oct 201

Air pollution concentration fuzzy evaluation based on evidence theory and the K-nearest neighbor algorithm

Background: Air pollution, characterized by complex spatiotemporal dynamics and inherent uncertainty, poses significant challenges in accurate air quality prediction, and current methodologies often fail to adequately address these complexities.Objective: This study presents a novel fuzzy modeling approach for estimating air pollution concentrations.Methods: This fuzzy evaluation method integrates an improved evidence theory with comprehensive weighting and the K-nearest neighbor (KNN) interval distance within the framework of the matter-element extension model. This involves generating the basic probability assignment (BPA) based on interval similarity, performing sequential fusion using the Dempster–Shafer evidence theory, enhancing the fusion results via comprehensive weighting, and conducting fuzzy evaluation of air pollution concentrations using the matter-element extension KNN interval distance.Results: Our method achieved significant improvements in monitoring air pollution concentrations, incorporating spatiotemporal factors and pollutant concentrations more effectively than existing methods. Implementing sequential fusion and subjective–objective weighting reduced the error rate by 38% relative to alternative methods.Discussion: Fusion of multi-source air pollution data via this method effectively mitigates inherent uncertainty and enhances the accuracy of the KNN method. It produces more comprehensive air pollution concentration fusion results, improving accuracy by considering spatiotemporal correlation, toxicity, and pollution levels. Compared to traditional air-quality indices, our approach achieves greater accuracy and better interpretability, making it possible to develop more effective air quality management strategies. Future research should focus on expanding the dataset to include more diverse geographical and meteorological conditions, further refining the model to integrate external factors like meteorological data and regional industrial activity, and improving computational efficiency for real-time applications

Modelos de avaliação integrada para melhorar a qualidade do ar urbano

Currently, air pollution represents one of the main environmental causes of mortality. It is also responsible by cutting lives short, reducing productivity through working days lost across the economy, increasing medical costs, and by considerable economic impacts. Europe's most serious air pollutants in terms of harm to human health are particulate matter, nitrogen dioxide and ground-level ozone. The principal objective of this thesis is to explore the capabilities of Integrated Assessment Modelling tools to cost-efficiently evaluate measures to improve the air quality, and furthermore to develop an urban Integrated Assessment Model (IAM). For this purpose a review of current integrated assessment methodologies to improve air quality, from simple (e.g. scenario approach) to more comprehensive ones (e.g. optimization approach) was done and some application tests were performed. Based on identified advantages of the revised approaches the Integrated Urban Air Pollution Assessment Model (IUAPAM) was designed and evaluated through its application to a selected urban case study (Porto Urban Area) considering different emission scenarios. The developed model is able to reproduce rapidly emission reduction scenarios and to estimate health impacts, making use of Artificial Neural Networks. Moreover, the use of Multi-Criteria Decision Analysis (MCDA) allows including social aspects and ranking air quality measures/scenarios. This research work contributes to a better understanding of the utility of IAM tools that are available to support the air quality decision-making process. IUPAM revealed to be useful to quickly evaluate the effect of local and regional policies focused on air pollution improvementAtualmente a poluição atmosférica representa uma das principais causas ambientais de mortalidade. Ela é ainda responsável pela redução da esperança média de vida, redução da produtividade devido à redução de dias de trabalho, aumento de custos hospitalares, e por impactos económicos consideráveis. Os poluentes mais relevantes em termos de efeitos na saúde humana são o material particulado, o dióxido de azoto e o ozono troposférico. O objetivo principal da presente tese é o desenvolvimento e teste de um Modelo de Avaliação Integrada (MAI) que permita apoiar a seleção custo-eficiente de medidas de melhoria de qualidade do ar em cidades. Com essa finalidade foi efetuada uma revisão das atuais metodologias de avaliação integrada da qualidade do ar, das mais simples (análise de cenário) às mais complexas (abordagem de otimização), e foram efetuados alguns testes de aplicação que permitiram identificar as principais vantagens e limitações de cada abordagem. Foi desenvolvido um Modelo de Avaliação Integrada à Escala Urbana (MAIEU) que ultrapassa algumas das dificuldades das ferramentas existentes e aproveita as suas vantagens. O modelo foi avaliado através da sua aplicação a um caso de estudo urbano (Grande Porto) e a diferentes cenários de emissões. É capaz de reproduzir rapidamente cenários de redução de emissões, e de estimar os seus impactes na saúde, recorrendo a Redes Neuronais Artificiais. Para além disso, o uso de Análise Multicritério permitiu incluir aspetos sociais e criar uma classificação de medidas/cenários de qualidade do ar. Este trabalho contribui para uma melhor compreensão da utilidade dos MAI, disponíveis para apoiar o processo de tomada de decisão. O MAIEU, revelou ser útil para avaliar rapidamente o efeito de políticas regionais e locais focadas na melhoria da poluição atmosférica à escala urbanaPrograma Doutoral em Ciências e Engenharia do Ambient

Integrated human exposure to air pollution

The book “Integrated human exposure to air pollution” aimed to increase knowledge about human exposure in different micro-environments, or when citizens are performing specific tasks, to demonstrate methodologies for the understanding of pollution sources and their impact on indoor and ambient air quality, and, ultimately, to identify the most effective mitigation measures to decrease human exposure and protect public health. Taking advantage of the latest available tools, such as internet of things (IoT), low-cost sensors and a wide access to online platforms and apps by the citizens, new methodologies and approaches can be implemented to understand which factors can influence human exposure to air pollution. This knowledge, when made available to the citizens, along with the awareness of the impact of air pollution on human life and earth systems, can empower them to act, individually or collectively, to promote behavioral changes aiming to reduce pollutants’ emissions. Overall, this book gathers fourteen innovative studies that provide new insights regarding these important topics within the scope of human exposure to air pollution. A total of five main areas were discussed and explored within this book and, hopefully, can contribute to the advance of knowledge in this field

Air Pollution Control and Sustainable Development

This book brings together the latest research findings on the state of air pollution control and its impact on economic growth in different countries. The book has substantial content and rich discussion. It is suitable for students and researchers at different levels to learn the status of air pollution, governance policies and their effects, and the relationship between pollution control and economic growth in countries around the world

Principles and Applications of Data Science

Data science is an emerging multidisciplinary field which lies at the intersection of computer science, statistics, and mathematics, with different applications and related to data mining, deep learning, and big data. This Special Issue on “Principles and Applications of Data Science” focuses on the latest developments in the theories, techniques, and applications of data science. The topics include data cleansing, data mining, machine learning, deep learning, and the applications of medical and healthcare, as well as social media