Transformation of the Earth's social and ecological systems is occurring at a rate and magnitude unparalleled in human experience. Data science is a revolutionary new way to understand human-environment relationships at the heart of pressing challenges like climate change and sustainable development. However, data science faces serious shortcomings when it comes to human-environment research. There are challenges with social and environmental data, the methods that manipulate and analyze the information, and the theory underlying the data science itself; as well as significant legal, ethical and policy concerns. This timely book offers a comprehensive, balanced, and accessible account of the promise and problems of this work in terms of data, methods, theory, and policy. It demonstrates the need for data scientists to work with human-environment scholars to tackle pressing real-world problems, making it ideal for researchers and graduate students in Earth and environmental science, data science and the environmental social sciences.

Environmental Systems Science: Theory and Practical Applications looks at pollution and environmental quality from a systems perspective. Credible human and ecological risk estimation and prediction methods are described, including life cycle assessment, feasibility studies, pollution control decision tools, and approaches to determine adverse outcome pathways, fate and transport, sampling and analysis, and cost-effectiveness. The book brings translational science to environmental quality, applying groundbreaking methodologies like informatics, data mining, and applications of secondary data systems. Multiple human and ecological variables are introduced and integrated to support calculations that aid environmental and public health decision making. The book bridges the perspectives of scientists, engineers, and other professionals working in numerous environmental and public health fields addressing problems like toxic substances, deforestation, climate change, and loss of biological diversity, recommending sustainable solutions to these and other seemingly intractable environmental problems. The causal agents discussed include physical, chemical, and biological agents, such as per- and polyfluoroalkyl substances (PFAS), SARS-CoV-2 (the COVID-19 virus), and other emerging contaminants. - Provides an optimistic and interdisciplinary approach, underpinned by scientific first principles and theory to evaluate pollutant sources and sinks, applying biochemodynamic methods, measurements and models - Deconstructs prior initiatives in environmental assessment and management using an interdisciplinary approach to evaluate what has worked and why - Lays out a holistic understanding of the real impact of human activities on the current state of pollution, linking the physical sciences and engineering with socioeconomic, cultural perspectives, and environmental justice - Takes a life cycle view of human and ecological systems, from the molecular to the planetary scale, integrating theories and tools from various disciplines to assess the current and projected states of environmental quality - Explains the elements of risk, reliability and resilience of built and natural systems, including discussions of toxicology, sustainability, and human-pollutant interactions based on spatial, biological, and human activity information, i.e. the exposome

Environmental Social Science offers a new synthesis of environmental studies, defining the nature of human-environment interactions and providing the foundation for a new cross-disciplinary enterprise that will make critical theories and research methods accessible across the natural and social sciences. Makes key theories and methods of the social sciences available to biologists and other environmental scientists Explains biological theories and concepts for the social sciences community working on the environment Helps bridge one of the difficult divides in collaborative work in human-environment research Includes much-needed descriptions of how to carry out research that is multinational, multiscale, multitemporal, and multidisciplinary within a complex systems theory context

Technological Trajectories and the Human Environment provides a surprising projection of a much greener planet, based on long-range analysis of trends in the efficient use of energy, materials, and land. The authors argue that we will decarbonize the global energy system and drastically reduce greenhouse gas emissions. We will dematerialize the economy by leaner manufacturing, better product design, and smart use of materials. We will significantly increase land areas reserved for nature by conducting highly productive and environmentally friendly agriculture on less land than is used today, even as global population doubles. The book concludes that the technological opportunities before us offer the possibility of a vastly superior industrial ecology. Rich in both data and theory, the book offers fresh analyses essential for everyone in the environmental arena concerned with global change, sustainable development, and profitable investments in technology.

With the dramatic development of air-space-ground-sea environmental monitoring networks and large-scale high-resolution Earth simulators, Environmental science is facing opportunities and challenges of big data. Environmental Data Analysis focuses on state-of-the-art models and methods for big environmental data and demonstrates their applications through various case studies in the real world. It covers the comprehensive range of topics in data analysis in space, time and spectral domains, including linear and nonlinear environmental systems, feature extraction models, data envelopment analysis, risk assessments, and life cycle assessments. The 2nd Edition adds emerging network models, including neural networks, complex networks, downscaling analysis and streaming data on network. This book is a concise and self-contained work with enormous amount of information. It is a must-read for environmental scientists who struggle to conduct big data mining and data scientists who try to find the way into environmental science.

We are living at the dawn of what has been termed ‘the fourth paradigm of science,’ a scientific revolution that is marked by both the emergence of big data science and analytics, and by the increasing adoption of the underlying technologies in scientific and scholarly research practices. Everything about science development or knowledge production is fundamentally changing thanks to the ever-increasing deluge of data. This is the primary fuel of the new age, which powerful computational processes or analytics algorithms are using to generate valuable knowledge for enhanced decision-making, and deep insights pertaining to a wide variety of practical uses and applications. This book addresses the complex interplay of the scientific, technological, and social dimensions of the city, and what it entails in terms of the systemic implications for smart sustainable urbanism. In concrete terms, it explores the interdisciplinary and transdisciplinary field of smart sustainable urbanism and the unprecedented paradigmatic shifts and practical advances it is undergoing in light of big data science and analytics. This new era of science and technology embodies an unprecedentedly transformative and constitutive power—manifested not only in the form of revolutionizing science and transforming knowledge, but also in advancing social practices, producing new discourses, catalyzing major shifts, and fostering societal transitions. Of particular relevance, it is instigating a massive change in the way both smart cities and sustainable cities are studied and understood, and in how they are planned, designed, operated, managed, and governed in the face of urbanization. This relates to what has been dubbed data-driven smart sustainable urbanism, an emerging approach based on a computational understanding of city systems and processes that reduces urban life to logical and algorithmic rules and procedures, while also harnessing urban big data to provide a more holistic and integrated view or synoptic intelligence of the city. This is increasingly being directed towards improving, advancing, and maintaining the contribution of both sustainable cities and smart cities to the goals of sustainable development. This timely and multifaceted book is aimed at a broad readership. As such, it will appeal to urban scientists, data scientists, urbanists, planners, engineers, designers, policymakers, philosophers of science, and futurists, as well as all readers interested in an overview of the pivotal role of big data science and analytics in advancing every academic discipline and social practice concerned with data–intensive science and its application, particularly in relation to sustainability.

Climate change is occurring, is caused largely by human activities, and poses significant risks for--and in many cases is already affecting--a broad range of human and natural systems. The compelling case for these conclusions is provided in Advancing the Science of Climate Change, part of a congressionally requested suite of studies known as America's Climate Choices. While noting that there is always more to learn and that the scientific process is never closed, the book shows that hypotheses about climate change are supported by multiple lines of evidence and have stood firm in the face of serious debate and careful evaluation of alternative explanations. As decision makers respond to these risks, the nation's scientific enterprise can contribute through research that improves understanding of the causes and consequences of climate change and also is useful to decision makers at the local, regional, national, and international levels. The book identifies decisions being made in 12 sectors, ranging from agriculture to transportation, to identify decisions being made in response to climate change. Advancing the Science of Climate Change calls for a single federal entity or program to coordinate a national, multidisciplinary research effort aimed at improving both understanding and responses to climate change. Seven cross-cutting research themes are identified to support this scientific enterprise. In addition, leaders of federal climate research should redouble efforts to deploy a comprehensive climate observing system, improve climate models and other analytical tools, invest in human capital, and improve linkages between research and decisions by forming partnerships with action-oriented programs.