Code
ENST2006
Credits
25
Graduate Attributes
Introduction
This unit provides students with foundational knowledge and practical skills in environmental sampling and analytical techniques for assessing air, water, and soil contamination. It covers the entire process of environmental monitoring, from field sampling planning to laboratory analysis and data interpretation. Emphasis is placed on practical approaches to environmental investigations, including selection of appropriate instrumentation, adherence to quality control procedures, and management of sample collection, storage, calibration, and records. Students will gain experience evaluating environmental data using core statistical techniques, including hypothesis testing, factorial experimental design, and multiple linear regression. These tools enable students to analyze the variability of environmental parameters, draw meaningful conclusions, and support evidence-based decision making. In addition to traditional statistical methods, students will learn modern data analysis and basic machine learning techniques. For data analysis, students use MATLAB to perform tasks such as data visualization, trend analysis, and pattern recognition. In addition, students will complete the self-paced course "Introduction to Machine Learning" offered by MathWorks. This course introduces key concepts and workflows in machine learning and teaches students how to apply new computational tools to environmental data sets. At the end of this unit, students will have a comprehensive understanding of traditional and modern methods for collecting, processing, and analyzing environmental data. This will prepare them for research, consultancy or supervisory roles in environmental engineering and science.
Lecture
12 x 2 Hours Semester
Science Laboratory
12 x 1 Hours Semester
Workshop
12 x 2 Hours Semester
Unit Learning Outcomes
- 1 apply knowledge of contaminants, their modes of action, their persistence and their transports in the ecosystem to investigate the risks of pollution and effects of contaminants on ecosystems, GC1, GC2, GC3, GC6
- 2 apply basic procedures and techniques to monitor the environmental condition and system functioning in terrestrial, aquatic and soil ecosystems following national standards, GC1, GC3, GC4, GC6
- 3 quantify and analyse solutions for environmental contamination and comment critically on the outcome, GC1, GC2, GC3, GC6
- 4 explain the principles of data analytics and machine learning in environmental engineering, GC1, GC3
- 5 work on a design project within a team, and monitor and reflect on the project's progress and member's responsibilities, GC1, GC5, GC6
Course Learning Outcomes
- 1 Demonstrate a conceptual understanding of fundamental science, mathematics, data analytics, information science, and computing underpinning the broad field of engineering
- 2 Solve complex environmental engineering problems of industrial and societal significance through the application of discipline-specific and integrated bodies of knowledge, design and sustainability principles
- 6 Demonstrate lifelong learning habits, teamwork and leadership abilities, project management skills, and the ability to identify opportunities for career-wide professional growth, necessary for advancing a career in engineering and beyond
Assessment Breakdown
Recent Unit Changes & Response to Student Feedback
Students are encouraged to provide feedback through student surveys (such as Insight and the annual Student Experience Survey) and interactions with teaching staff. Listed below are some recent changes to the unit as a result of student feedback. Students are encouraged to provide feedback through student surveys (such as Insight and the annual Student Experience Survey) and interactions with teaching staff. There have been no recent changes to the unit as a result of student feedback