Artificial Intelligence for Air Quality Monitoring and Prediction PDF Download
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Author: Amit Awasthi
Publisher: CRC Press
ISBN: 1040131182
Category : Technology & Engineering
Languages : en
Pages : 303
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Book Description
This book is a comprehensive overview of advancements in artificial intelligence (AI) and how it can be applied in the field of air quality management. It explains the linkage between conventional approaches used in air quality monitoring and AI techniques such as data collection and preprocessing, deep learning, machine vision, natural language processing, and ensemble methods. The integration of climate models and AI enables readers to understand the relationship between air quality and climate change. Different case studies demonstrate the application of various air monitoring and prediction methodologies and their effectiveness in addressing real-world air quality challenges. Features A thorough coverage of air quality monitoring and prediction techniques. In-depth evaluation of cutting-edge AI techniques such as machine learning and deep learning. Diverse global perspectives and approaches in air quality monitoring and prediction. Practical insights and real-world case studies from different monitoring and prediction techniques. Future directions and emerging trends in AI-driven air quality monitoring. This is a great resource for professionals, researchers, and students interested in air quality management and control in the fields of environmental science and engineering, atmospheric science and meteorology, data science, and AI.
Author: Amit Awasthi
Publisher: CRC Press
ISBN: 1040131182
Category : Technology & Engineering
Languages : en
Pages : 303
Get Book Here
Book Description
This book is a comprehensive overview of advancements in artificial intelligence (AI) and how it can be applied in the field of air quality management. It explains the linkage between conventional approaches used in air quality monitoring and AI techniques such as data collection and preprocessing, deep learning, machine vision, natural language processing, and ensemble methods. The integration of climate models and AI enables readers to understand the relationship between air quality and climate change. Different case studies demonstrate the application of various air monitoring and prediction methodologies and their effectiveness in addressing real-world air quality challenges. Features A thorough coverage of air quality monitoring and prediction techniques. In-depth evaluation of cutting-edge AI techniques such as machine learning and deep learning. Diverse global perspectives and approaches in air quality monitoring and prediction. Practical insights and real-world case studies from different monitoring and prediction techniques. Future directions and emerging trends in AI-driven air quality monitoring. This is a great resource for professionals, researchers, and students interested in air quality management and control in the fields of environmental science and engineering, atmospheric science and meteorology, data science, and AI.
Author: Jagriti Saini
Publisher: Springer
ISBN: 9783030964887
Category : Technology & Engineering
Languages : en
Pages : 0
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Book Description
This book presents Internet of Things (IoT) solutions monitoring and assessing a variety of applications areas for indoor air quality (IAQ). This book synthesizes recent developments, presents case studies, and discusses new methods in the area of air quality monitoring, all the while addressing public health concerns. The authors discuss the issues and solutions, including IoT systems that can provide a continuous flow of data retrieved from cost-effective sensors that can be used in multiple applications.The authors present the leading IoT technologies, applications, algorithms, systems, and future scope in this multi-disciplinary domain.
Author: Robert Edward Munn
Publisher: Springer
ISBN: 134905738X
Category : Social Science
Languages : en
Pages : 117
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Book Description
Author: Mukesh Khare
Publisher: BoD – Books on Demand
ISBN: 9535103814
Category : Technology & Engineering
Languages : en
Pages : 268
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Book Description
Air pollution has always been a trans-boundary environmental problem and a matter of global concern for past many years. High concentrations of air pollutants due to numerous anthropogenic activities influence the air quality. There are many books on this subject, but the one in front of you will probably help in filling the gaps existing in the area of air quality monitoring, modelling, exposure, health and control, and can be of great help to graduate students professionals and researchers. The book is divided in two volumes dealing with various monitoring techniques of air pollutants, their predictions and control. It also contains case studies describing the exposure and health implications of air pollutants on living biota in different countries across the globe.
Author: S.P Singal
Publisher: ALPHA SCIENCE INTERNATIONAL LIMITED
ISBN: 1783322438
Category : Technology & Engineering
Languages : en
Pages : 298
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Book Description
AIR QUALITY MONITORING AND CONTROL STRATEGY essentially deals with air quality and underlines a strategy to improve it. To this effect this volume describes briefly the problem of air pollution, impact of various pollutants present in the indoor/outdoor atmosphere on health, the various monitoring techniques/instruments and their practical use, instructions, precautions etc., control instrumentation and environment impact assessment. The answer to questions like the need for air quality monitoring, choice of monitoring location and parameters, averaging time and frequencies etc. has been provided along with the basic statistics required to work out certain statistical figures in air quality. The science of meteorology, an important subject that takes care of dispersion/dilution of air pollutants at a place, has been discussed briefly. A chapter on noise pollution, another vital air toxicant, has also been dealt with to a certain limit. Two case studies have been incorporated to elucidate the importance of EIA and the need to develop a strategy for management of ambient air quality. Revised new standards have also been included.
Author: Pius Lee
Publisher: MDPI
ISBN: 3038428396
Category : Science
Languages : en
Pages : 211
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Book Description
This book is a printed edition of the Special Issue "Air Quality Monitoring and Forecasting" that was published in Atmosphere
Author: Sue Ellen Haupt
Publisher: Springer Science & Business Media
ISBN: 1402091192
Category : Science
Languages : en
Pages : 418
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Book Description
How can environmental scientists and engineers use the increasing amount of available data to enhance our understanding of planet Earth, its systems and processes? This book describes various potential approaches based on artificial intelligence (AI) techniques, including neural networks, decision trees, genetic algorithms and fuzzy logic. Part I contains a series of tutorials describing the methods and the important considerations in applying them. In Part II, many practical examples illustrate the power of these techniques on actual environmental problems. International experts bring to life ways to apply AI to problems in the environmental sciences. While one culture entwines ideas with a thread, another links them with a red line. Thus, a “red thread“ ties the book together, weaving a tapestry that pictures the ‘natural’ data-driven AI methods in the light of the more traditional modeling techniques, and demonstrating the power of these data-based methods.
Author: David Bradford Ramsay
Publisher:
ISBN:
Category :
Languages : en
Pages : 215
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Book Description
Air pollution is responsible for :1/8 of deaths around the world. While the importance of air quality has led to a boom in inexpensive air sensors, studies have shown that the status quo of sparse, fixed sensors cannot accurately capture personal exposure levels of nearby populations. Especially in urban landscapes, pollutant concentrations can vary over just a few seconds or a few meters. Unfortunately, the portable monitors that are capable of accurately measuring these pollutants cost thousands of dollars. That hasn't stopped a deluge of cheap, portable consumer devices from entering the market. These solutions frequently claim better accuracy, but universally fail under real-world validation. Instead of competing to build a more accurate sensor, we take the approach of trying to predict when we can trust the cheap sensor we have, based on ambient conditions and measurements. Well-designed, sub-$100 sensors have recently started to perform with high precision and accuracy. While their fundamental operation is sound, these affordable sensors cannot incorporate costly, industry standard techniques for mitigating issues like cross-sensitivity, dynamic airflow, or high humidity. Fortunately, if the core principles of the device are robust, machine learning techniques should be able to predict systematic measurement failure based on a handful of related indicators. In this thesis, we test and demonstrate the potential for logistic regression machine learning techniques to predict and classify sensor measurements as 'correct' or 'incorrect' with high reliability. These techniques are also useful for quantifying sensor precision as well as cross-seasonal prediction strength. After demonstrating the value of this approach, we implement a scalable database solution using a semantic web technology know as ChainAPI. The tools developed for this framework allow automatic learning algorithms to crawl through the database, access the most recent data, update their training model, and populate the database with the processed data for other crawling scripts to interact with. This backend has implications for air quality data storage, interaction, and exchange. Finally, we build a portable, Bluetooth enabled air quality device that connects to ChainAPI through a mobile phone app, and takes advantage of the machine learning algorithms running in its backend. This device improves the reliability of sensor data compared with similar-cost systems. The learnAir device empowers individuals to trust their personal air quality data, and provokes a dialog about sensor reliability in the citizen sensing community. Its novel database architecture promotes new ways of interacting with large, dynamic datasets, and new tools to characterize affordable sensors and devices. Finally, applied logistic regression algorithms assure the accuracy of cheap, distributed sensor data- creating a trusted way for researchers to collaborate with citizen scientists from around the world.
Author: Suneeta Satpathy
Publisher: CRC Press
ISBN: 1040088864
Category : Science
Languages : en
Pages : 180
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Book Description
This book discusses the adverse effects of climatic changes on our planet. It examines AI-based tools and technologies and how they can assist in identifying energy emission reductions, CO2 removal, and support the development of greener transportation networks, monitoring deforestation, and forecasting extreme weather events. AI for Climate Change and Environmental Sustainability identifies and discusses in detail the importance of environmental sustainability based on accomplishment of the UN's 17 Sustainable Developmental Goals (SDGs). It presents the various AI-based possibilities for accelerating international efforts to safeguard the environment and conserve natural resources. The authors offer a comprehensive analysis of the emerging field of climate change in relation to Internet of Things, artificial intelligence, machine learning, and deep learning. The book discusses AI developments, applications, and best practices that will help us transition to a low-carbon future on both a regional and global scale. It provides case studies with analytical results pertinent to climate change and weather prediction and includes chapters with a research-oriented approach, which can encourage new developments in the field of sustainable climate and green environment. The book can be used as a primary textbook for graduate and postgraduate students in technology and science, as well as a reference for researchers, academics, and IT professionals working on climate change and sustainability initiatives.
Author: Saverio De Vito
Publisher: Springer Nature
ISBN: 3031084764
Category : Science
Languages : en
Pages : 183
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Book Description
This volume offers expert contributions proposing new and recently set scientific standards for smart air quality (AQ) networks data processing, along with results obtained during field deployments of pervasive and mobile systems. The book is divided into 5 main sections; 1) future air quality networks, 2) general data processing techniques, 3) field deployments performances, 4) special applications, and 5) cooperative and regulatory efforts. The authors offer different sources of data for the production of trustworthy insights, including spatio-temporal predictive AQ maps meant to boost citizen awareness, and informed participation in remediation and prevention policies. Readers will learn about the best and most up-to-date practices for measuring and assessing air quality, while also learning about current regulatory statuses regarding air quality technology design and implementation. The book will be of interest to air quality regulatory agencies, citizen science groups, city authorities, and researchers and students working with air quality sensors and geostatistics.
