Advanced Chemometrics

Unlocking Patterns in Oil Spills
Erscheinungsjahr 2026
ISBN: 978-3-032-10741-1
Verlag: Springer

Buch, Englisch, 55 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 125 g

Reihe: SpringerBriefs in Environmental Science

This book is an extensive work that provides valuable insights into the use of chemometric techniques for the analysis and interpretation of complex data from oil spill disasters. The book's main goal is to give forensic chemists, environmental scientists, and business experts cutting-edge techniques for precisely identifying, categorizing, and tracking the causes of oil spills in order to improve response plans and lessen their negative effects on the environment.

Through the use of chemometric methods to evaluate weathering effects and distinguish between possible origins, the book offers an organized method for conducting forensic examinations of oil spills. In order to support legal processes and environmental assessments, this framework improves the objectivity and effectiveness of forensic analyses. The book contains case studies that show how chemometric approaches can be successfully applied in actual oil spill situations. These real-world examples give readers insight into how the approaches described might be applied in different situations and demonstrate how beneficial they are.

Zielgruppe

Research

Autoren/Hrsg.

Ismail, Azimah

Juahir, Hafizan

Fachgebiete

• Geowissenschaften Umweltwissenschaften Umweltüberwachung, Umweltanalytik, Umweltinformatik
• Geowissenschaften Umweltwissenschaften Umweltverschmutzung, Umweltkriminalität, Umweltrecht
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Umweltfaktoren in der Medizin

Inhaltsverzeichnis

1 Recognition Using Numerical Methods Approach.- 2 Classification of Similar Chemical Properties in Oil Spills.- 3 Dimensionality Reduction of Oil Spill Variables.- 4 Contaminant of Polyaromatic Hydrocarbon in Oil Spills.- 5 Solving Pattern Recognition Challenges Using Artificial Neural Networks (ANNs).- 6 Bridging Machine Learning and Oil Spill Data.

Über Autor(innen)

Azimah Ismail is passionate about sustainability and innovation, dedicating her research to addressing some of today’s most pressing environmental challenges. Her work explores the interactions between activated carbon, oil spill fingerprinting, microplastics, toxins, and marine ecosystems, as well as the development of eco-friendly materials and smart technologies for a greener future. With a background in manufacturing sustainability and environmental science, she combines scientific research, data-driven insights, and IoT applications to create practical solutions that benefit both industry and society. Azimah aims to inspire readers, students, and fellow researchers to see how science and technology can work hand in hand to solve real-world problems and promote sustainable development.

Hafizan Juahir is an expert in environmental modeling and data analytics, with extensive experience in applying multivariate statistical techniques to solve complex environmental problems. His research focuses on water quality assessment, pollution source identification, and the development of predictive models to support evidence-based environmental management. With a strong background in environmental science and data-driven approaches, he integrates advanced computational tools to analyze big data and generate actionable insights for policymakers, researchers, and industry stakeholders. Hafizan is passionate about advancing sustainable solutions and training the next generation of scientists to use data analytics as a powerful tool for protecting natural resources and improving human well-being.