The new 2030 Agenda for Sustainable Development includes water, sanitation, and hygiene (WASH) at its core. A dedicated Sustainable Development Goal (SDG 6) declares a commitment to "ensure availability and sustainable management of water and sanitation for all." Monitoring progress toward this goal will be challenging: direct measures of water and sanitation service quality and use are either expensive or elusive. However, reliance on household surveys poses limitations and likely overstated progress during the Millennium Development Goal period. In Innovations in WASH Impact Measures: Water and Sanitation Measurement Technologies and Practices to Inform the Sustainable Development Goals, we review the landscape of proven and emerging technologies, methods, and approaches that can support and improve on the WASH indicators proposed for SDG target 6.1, "by 2030, achieve universal and equitable access to safe and affordable drinking water for all," and target 6.2, "by 2030, achieve access to adequate and equitable sanitation and hygiene for all and end open defecation, paying special attention to the needs of women and girls and those in vulnerable situations." Although some of these technologies and methods are readily available, other promising approaches require further field evaluation and cost reductions. Emergent technologies, methods, and data-sharing platforms are increasingly aligned with program impact monitoring. Improved monitoring of water and sanitation interventions may allow more cost-effective and measurable results. In many cases, technologies and methods allow more complete and impartial data in time to allow program improvements. Of the myriad monitoring and evaluation methods, each has its own advantages and limitations. Surveys, ethnographies, and direct observation give context to more continuous and objective electronic sensor data. Overall, combined methodologies can provide a more comprehensive and instructive depiction of WASH usage and help the international development community measure our progress toward reaching the SDG WASH goals.

The new 2030 Agenda for Sustainable Development includes water, sanitation, and hygiene (WASH) at its core. A dedicated Sustainable Development Goal (SDG 6) declares a commitment to "ensure availability and sustainable management of water and sanitation for all." Monitoring progress toward this goal will be challenging: direct measures of water and sanitation service quality and use are either expensive or elusive. However, reliance on household surveys poses limitations and likely overstated progress during the Millennium Development Goal period. In Innovations in WASH Impact Measures: Water and Sanitation Measurement Technologies and Practices to Inform the Sustainable Development Goals, we review the landscape of proven and emerging technologies, methods, and approaches that can support and improve on the WASH indicators proposed for SDG target 6.1, "by 2030, achieve universal and equitable access to safe and affordable drinking water for all," and target 6.2, "by 2030, achieve access to adequate and equitable sanitation and hygiene for all and end open defecation, paying special attention to the needs of women and girls and those in vulnerable situations." Although some of these technologies and methods are readily available, other promising approaches require further field evaluation and cost reductions. Emergent technologies, methods, and data-sharing platforms are increasingly aligned with program impact monitoring. Improved monitoring of water and sanitation interventions may allow more cost-effective and measurable results. In many cases, technologies and methods allow more complete and impartial data in time to allow program improvements. Of the myriad monitoring and evaluation methods, each has its own advantages and limitations. Surveys, ethnographies, and direct observation give context to more continuous and objective electronic sensor data. Overall, combined methodologies can provide a more comprehensive and instructive depiction of WASH usage and help the international development community measure our progress toward reaching the SDG WASH goals.

Data Science for Agricultural Innovation and Productivity explores the transformation of agriculture through data-driven practices. This comprehensive book delves into the intersection of data science and farming, offering insights into the potential of big data analytics, machine learning, and IoT integration. Readers will find a wide range of topics covered in 10 chapters, including smart farming, AI applications, hydroponics, and robotics. Expert contributors, including researchers, practitioners, and academics in the fields of data science and agriculture, share their knowledge to provide readers with up-to-date insights and practical applications. The interdisciplinary emphasis of the book gives a well-rounded view of the subject. With real-world examples and case studies, this book demonstrates how data science is being successfully applied in agriculture, inspiring readers to explore new possibilities and contribute to the ongoing transformation of the agricultural sector. Sustainability and future outlook are the key themes, as the book explores how data science can promote environmentally conscious agricultural practices while addressing global food security concerns. Key Features: - Focus on data-driven agricultural practices - Comprehensive coverage of modern farming topics with an interdisciplinary perspective - Expert insights - Sustainability and future outlook -Highlights practical applications Data Science for Agricultural Innovation and Productivity is an essential resource for researchers, data scientists, farmers, agricultural technologists, students, educators, and anyone with an interest in the future of farming through data-driven agriculture.

This book covers how Internet of Things (IoT) has a role in shaping the future of our communities. The author shows how the research and education ecosystem promoting impactful solutions-oriented science can help citizenry, government, industry, and other stakeholders to work collaboratively in order to make informed, socially-responsible, science-based decisions. Accordingly, he shows how communities can address complex, interconnected socio-environmental challenges. This book addresses the key inter-related challenges in areas such as the environment, climate change, mining, energy, agro-economic, water, and forestry that are limiting the development of a sustainable and resilient society -- each of these challenges are tied back to IoT based solutions. Presents research into sustainable IoT with respect to wireless communications, sensing, and systems Provides coverage of IoT technologies in sustainability, health, agriculture, climate change, mining, energy, water management, and forestry Relevant for academics, researchers, policy makers, city planners and managers, technicians, and industry professionals in IoT and sustainability

This book reveals the applications of AI and IoT in smart healthcare and medical systems. It provides core principles, algorithms, protocols, emerging trends, security problems, and the latest e-healthcare services findings. The book also provides case studies and discusses how AI and IoT applications such as wireless devices, sensors, and deep learning could play a major role in assisting patients, doctors, and pharmaceutical staff. It focuses on how to use AI and IoT to keep patients safe and healthy and, at the same time, empower physicians to deliver superlative care. This book is written for researchers and practitioners working in the information technology, computer science, and medical equipment manufacturing industry for products and services having basic- and high-level AI and IoT applications. The book is also a useful guide for academic researchers and students.

Timely data availability is a long-standing challenge in policy-making and analysis for low-income developing countries. This paper explores the use of Google Trends’ data to narrow such information gaps and finds that online search frequencies about a country significantly correlate with macroeconomic variables (e.g., real GDP, inflation, capital flows), conditional on other covariates. The correlation with real GDP is stronger than that of nighttime lights, whereas the opposite is found for emerging market economies. The search frequencies also improve out-of-sample forecasting performance albeit slightly, demonstrating their potential to facilitate timely assessments of economic conditions in low-income developing countries.

This manual provides direction for the preparation of noise and vibration sections of environmental documents for mass transportation projects. The manual has been developed in the interest of promoting quality and uniformity in assessments. It is expected to be used by people associated with or affected by the urban transit industry, including Federal Transit Administration (FTA) staff, grant applicants, consultants and the general public. Each of these groups has an interest in noise/vibration assessment, but not all have the need for all the details of the process. Consequently, this manual has been prepared to serve readers with varying levels of technical background and interests. It sets forth the basic concepts, methods and procedures for documenting the extent and severity of noise impacts from transit projects.