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Revolutionizing Education: Harnessing Graph Machine Learning for Enhanced Problem-Solving in Environmental Science and Pollution Technology
Amidst the shifting tides of the educational landscape, this research article embarks on a transformative journey delving into the fusion of theoretical principles and pragmatic implementations within the realm of Graph Machine Learning (GML), particularly accentuated within the sphere of nature, environment, and pollution technology. GML emerges as a potent and indispensable tool, adeptly leveraging the intrinsic interconnectedness embedded within environmental datasets. Its application extends far beyond mere analysis towards the profound ability to forecast ecological patterns, prescribe sustainable interventions, and tailor pollution mitigation strategies with precision and efficacy. This article does not merely scratch the surface of GML’s applications but dives deep into its tangible implementations, unraveling its potential to revolutionize environmental science and pollution technology. It endeavors to bridge the gap between theory and practice, weaving together relevant ecological theories and empirical evidence that underpin the theoretical foundations supporting GML’s practical utility in environmental domains. By synthesizing theoretical insights with real-world applications, this research elucidates the profound transformative potential of GML, paving the way for proactive and data-driven approaches toward addressing pressing environmental challenges. In essence, this harmonization of theory and application catalyzes advancing the adoption of GML in environmental science and pollution technology. It not only illuminates the path towards sustainable practices but also lays the groundwork for fostering a holistic understanding of our ecosystem. Through this integration, GML emerges as a beacon guiding us toward a future where environmental stewardship is informed by data-driven insights, leading to more effective and sustainable solutions for the benefit of our planet and future generations.
Journal Article
Data analysis and statistics for geography, environmental science, and engineering
This practical, classroom-tested textbook helps readers learn quantitative methodology, including how to implement advanced analysis methods using an open-source software platform. Based on the author's many years of teaching undergraduate and graduate students in several countries, the book brings together principles of statistics and probability, multivariate analysis, and spatial analysis methods applied to a variety of geographical and environmental models. Theory is accompanied by practical hands-on computer exercises, progressing from easy to difficult. The text also presents a review of mathematical methods, making the book self-contained.
eBook
Citizen Science and STEM Education with R: AI–IoT Forecasting and Reproducible Learning from Open Urban Air Quality Data
Open urban environmental data offer a unique opportunity to connect scientific research, education, and citizen participation. Leveraging IoT-based sensor networks and AI-driven forecasting models, this study integrates open environmental data with reproducible analysis and learning workflows. This study presents a reproducible workflow developed in the Quarto–R environment to analyse and model air-quality dynamics in Madrid between 2020 and 2024. The workflow integrates data acquisition, validation, harmonisation, exploratory analysis, and forecasting using the Prophet model. The analysis focuses on nitrogen dioxide (NO2) and ozone (O3) as representative pollutants of traffic emissions and photochemical processes. Results show a marked decline in NO2 concentrations across traffic stations and a parallel rise in O3 levels in suburban areas, reflecting the combined effects of emission control and regional transport. Beyond its scientific contribution, the Quarto–R workflow functions as a pedagogical tool that embeds transparency, traceability, and active learning throughout the analytical process. By enabling students and researchers to reproduce every step, from raw data to interpreted results, it strengthens data literacy and fosters a deeper understanding of urban sustainability. The framework exemplifies how open data and reproducible computing can be integrated into STEM education and citizen-science initiatives, promoting both environmental awareness and methodological integrity, thus bridging artificial intelligence and experiential learning.
Journal Article
Disrupting buildings : digitalisation and the transformation of deep renovation
\"The worlds extant building stock accounts for a significant portion of worldwide energy consumption and greenhouse gas emissions. In 2020, buildings and construction accounted for 36% of global final energy consumption and 37% of energy related CO2 emissions. The EU estimates that up to 75% of the EUs existing building stock has poor energy performance, 8595% of which will still be in use in 2050. To meet the goals of the Paris Agreement on Climate Change will require a transformation of construction processes and deep renovation of the extant building stock. It is widely recognized that ICTs can play an important role in construction, renovation and maintenance as well as supporting the financing of deep renovation. Technologies such as sensors, big data analytics and machine learning, BIM, digital twinning, simulation, robots, cobots and UAVs, and additive manufacturing are transforming the deep renovation process, improving sustainability performance, and developing new services and markets. This open access book defines a deep renovation digital ecosystem for the 21st century, providing a state-of-the art review of current literature, suggesting avenues for new research, and offering perspectives from business, technology and industry domains.\" -- Provided by publisher.
BOOK
Cohort profile: the Geoscience and Health Cohort Consortium (GECCO) in the Netherlands
PurposeIn the Netherlands, a great variety of objectively measured geo-data is available, but these data are scattered and measured at varying spatial and temporal scales. The centralisation of these geo-data and the linkage of these data to individual-level data from longitudinal cohort studies enable large-scale epidemiological research on the impact of the environment on public health in the Netherlands. In the Geoscience and Health Cohort Consortium (GECCO), six large-scale and ongoing cohort studies have been enriched with a variety of existing geo-data. Here, we introduce GECCO by describing: (1) the phenotypes of the involved cohort studies, (2) the collected geo-data and their sources, (3) the methodology that was used to link the collected geo-data to individual cohort studies, (4) the similarity of commonly used geo-data between our consortium and the nationwide situation in the Netherlands and (5) the distribution of geo-data within our consortium.ParticipantsGECCO includes participants from six prospective cohort studies (eg, 44 657 respondents (18–100 years) in 2006) and it covers all municipalities in the Netherlands. Using postal code information of the participants, geo-data on the address-level, postal code-level as well as neighbourhood-level could be linked to individual-level cohort data.Findings to dateThe geo-data could be successfully linked to almost all respondents of all cohort studies, with successful data-linkage rates ranging from 97.1% to 100.0% between cohort studies. The results show variability in geo-data within and across cohorts. GECCO increases power of analyses, provides opportunities for cross-checking and replication, ensures sufficient geographical variation in environmental determinants and allows for nuanced analyses on specific subgroups.Future plansGECCO offers unique opportunities for (longitudinal) studies on the complex relationships between the environment and health outcomes. For example, GECCO will be used for further research on environmental determinants of physical/psychosocial functioning and lifestyle behaviours.
Journal Article
Artificial Neural Networks in Biological and Environmental Analysis
Based on our knowledge of the functioning human brain, ANNs (artificial neural networks) serve as a modern paradigm for computing. Drawing on the author's substantial experience, this book provides a comprehensive introduction and practical guide to the development, optimization, and application of ANNs in modern environmental and biological analysis. Presenting basic principles together with simulated biological and environmental data sets and real applications in the field, this volume helps scientists use the power of the ANN model to explain physical concepts and to demonstrate complex natural processes.
eBook