NCHU Course Outline

Course Name	(中) 環境污染採樣與分析原理(6333)
Course Name	(Eng.) The Principle of Sampling and Analysis of Environmental Pollution
Offering Dept	Department of Soil and Environmental Sciences
Course Type	Elective	Credits	3	Teacher	LIN,YAO-TUNG
Department	Department of Soil and Environmental Sciences/Graduate	Language	English	Semester	2026-FALL
Course Description	This course aims to systematically establish the fundamental principles, method systems, quality assurance, and data-interpretation skills of environmental pollution sampling and analysis. It guides students to understand the complete workflow—sampling design, sample preservation, pretreatment, instrumental analysis, and interpretation of results—across different environmental media including air, noise, surface water, groundwater, soil, and sediment, and to carry out verifiable, traceable, and reproducible environmental monitoring and analysis in accordance with standard and officially published methods. The core spirit of the course lies in “method correctness” and “data credibility.” Students must not only master sampling and analytical techniques for various pollutants, but also understand the quality concepts that run throughout the entire process—measurement uncertainty, quality assurance and quality control (QA/QC), detection limits, and representative sampling—and judge accordingly whether environmental data are sufficient to support subsequent regulatory determination, risk assessment, and environmental decision-making. The course is delivered in person and combines lectures, case-based teaching, problem-based learning (PBL), literature review, peer commentary, and presentation of research results. Each student will, based on their own research topic, collect recent international journal articles, government technical reports, or official analytical methods, analyze their sampling strategies, analytical methods, QA/QC, and research limitations, and present and critique them. The course fully incorporates generative artificial intelligence (AI) as an assistive tool to support literature search, sampling planning, method comparison, data organization, statistical analysis, figure preparation, and report writing. However, AI is positioned to assist learning and improve efficiency, and must not replace professional judgment. All sampling procedures, analytical methods, QA/QC, data interpretation, and research conclusions must be verified against official methods such as Standard Methods, the NIEA methods published by the Ministry of Environment’s National Environmental Research Academy (NERA), U.S. EPA, ISO, and ASTM. The course thereby naturally connects “verification of AI outputs” with the concept of “QA/QC,” emphasizing that checking AI outputs is essentially an extension of quality assurance. Through this course, students will build a professional foundation in environmental sampling and analysis; cultivate competence in method judgment, data processing, quality control, AI application, data verification, and professional communication; and connect these to practical needs in environmental regulation, net-zero monitoring, pollution control, and environmental governance—forming a basis for future work in testing laboratories, government agencies, research institutes, environmental engineering, and sustainability management.
Prerequisites				self-directed learning in the course	Y

Relevance of Course Objectives and Core Learning Outcomes(%)

Teaching and Assessment Methods for Course Objectives

Course Objectives

Competency Indicators

Ratio(%)

Teaching Methods

Assessment Methods

(1) Establish the foundational principles of environmental pollution sampling and analysis, mastering the core stages of sampling design, sample preservation, pretreatment, instrumental analysis, data processing, and interpretation of results.
(2) Correctly handle significant figures, SI units, error propagation, calibration curves, detection limits (MDL/LOD/LOQ), and uncertainty estimation, and judge the credibility and limitations of environmental data.
(3) Understand the principles and elements of QA/QC; become familiar with the method systems of NIEA, Standard Methods, U.S. EPA, ISO, and ASTM; and use officially published methods as the ultimate basis for judging whether an analytical procedure is correct.
(4) For air, noise, surface water, groundwater, soil, and sediment, explain the principles and selection logic of their sampling strategies, preservation conditions, pretreatment, and analytical methods.
(5) Make effective use of AI tools to improve the efficiency of literature search, data organization, statistical analysis, programming, and research integration, and write effective prompts.
(6) Identify errors, outdated content, or fabrications in AI outputs regarding values, method versions, statistical inferences, and citations, and cross-verify them against official methods and primary sources.
(7) Read, synthesize, and critique international journal articles, technical reports, and official analytical methods, and cultivate professional communication and critical thinking through Journal Review, Peer Review, and presentation of results.
(8) Understand the key role of sampling and analytical data in environmental regulatory determination, pollution control, net-zero monitoring, and environmental governance, and consider the professional significance of environmental monitoring from the perspectives of data quality and public decision-making.

topic Discussion/Production

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Written Presentation

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Oral Presentation

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Course Content and Homework/Schedule/Tests Schedule

Week	Course Content
Week 1	Course introduction, overview of sampling, AI use policy, and standard-method verification 課程介紹、採樣概論、AI 使用規範與標準方法查核
Week 2	Measurement uncertainty: error, significant figures, SI units, error propagation, calibration, and detection limits (MDL/LOD/LOQ) 量測不確定度：誤差、有效數字、SI、誤差傳遞、校正與偵測極限（MDL／LOD／LOQ）
Week 3	Research-topic planning and literature search 研究主題規劃與文獻搜尋
Week 4	QA/QC principles and the standard-method system QA/QC 原理與標準方法體系
Week 5	Sampling design and the principle of representativeness (Pierre Gy sampling theory; Grid/Composite/Judgmental Sampling) 採樣設計與代表性原理（Pierre Gy 採樣理論；Grid／Composite／Judgmental Sampling）
Week 6	Sample preservation, pretreatment, and matrix effects 樣品保存、前處理與基質效應
Week 7	Air pollution sampling and analysis (PM, gaseous pollutants, VOC adsorption/desorption; GC/GC-MS/ICP-MS) 空氣污染採樣與分析（PM、氣狀污染物、VOC 吸附／脫附；GC／GC-MS／ICP-MS）
Week 8	Midterm report and presentation (PBL interim results) 期中報告與期中簡報（PBL 階段成果）
Week 9	Principles of noise and vibration measurement 噪音與振動之量測原理
Week 10	Surface water sampling and analysis (I): physicochemical parameters (pH, conductivity, DO, turbidity, COD/BOD₅/TOC) 地表水採樣與分析（一）：物化參數（pH、導電度、DO、濁度、COD／BOD₅／TOC）
Week 11	Surface water sampling and analysis (II): heavy metals, organics, and emerging contaminants 地表水採樣與分析（二）：重金屬、有機物與新興污染物
Week 12	Groundwater sampling and the Conceptual Site Model (CSM); low-flow sampling 地下水採樣與場址概念模型（CSM）、低流量採樣
Week 13	Soil and sediment sampling and analysis (sampling design, digestion/extraction, heavy metals/organics) 土壤與底泥採樣與分析（採樣設計、消化萃取、重金屬／有機物）
Week 14	Environmental statistics and AI-assisted data-processing workshop 環境統計與 AI 輔助數據處理工作坊
Week 15	Research presentation (I) and group/individual advising 研究成果發表（一）與小組／個人輔導
Week 16	Research presentation (II) and course wrap-up 研究成果發表（二）與課程總結
self-directed learning	03.Preparing presentations or reports related to industry and academia. In addition to weekly in-person classes, the course includes self-directed learning tasks. Students must complete the following outside class: 1. Review class materials and complete key-point summaries. 2. Use AI tools to assist data collection, method compilation, statistical computation, and literature comparison. 3. Verify AI-output method parameters and values against official methods and primary sources. 4. Collect literature on their research topic, prepare weekly reviews, and develop written suggestions for others’ reviews. 5. Complete data supplementation, data processing, and slide preparation for the midterm and final projects. 6. Write learning reflections. Self-directed learning outcomes are incorporated into the participation grade, reviews, AI use logs, and learning-reflection assessment.

Evaluation

Class participation (incl. placement assessment) 5%
Assignments and AI method-verification tasks (uncertainty, QA/QC, Sampling Design) 10%
Journal Review 15%
Peer Review (incl. weekly written peer-review forms) 10%
Midterm report and presentation (incl. peer assessment and group contribution) 15%
Research report (Term Paper) 25%
Oral presentation of research results 10%
Learning Portfolio (incl. AI use log and official-method verification) 10%

Textbook & other References

(1) Textbooks
1. APHA, AWWA, & WEF. Standard Methods for the Examination of Water and Wastewater. 24th ed. Washington, DC: APHA Press, 2023. (A continuously updated Standard Methods Online is also available.)
2. Skoog, D. A., West, D. M., Holler, F. J., & Crouch, S. R. Fundamentals of Analytical Chemistry. 10th ed. Boston, MA: Cengage Learning, 2022.
3. Skoog, D. A., Holler, F. J., & Crouch, S. R. Principles of Instrumental Analysis. 7th ed. Boston, MA: Cengage Learning, 2018.
4. Miller, J. N., & Miller, J. C. Statistics and Chemometrics for Analytical Chemistry. 7th ed. Harlow: Pearson, 2018.
5. Keith, L. H. Environmental Sampling and Analysis: A Practical Guide. Boca Raton, FL: Lewis Publishers/CRC Press, 1991. (A practical classic.)
6. Helsel, D. R., Hirsch, R. M., Ryberg, K. R., Archfield, S. A., & Gilroy, E. J. Statistical Methods in Water Resources. USGS Techniques and Methods, Book 4, Chapter A3, 2020. (Freely available.)
7. Lodge, J. P. Jr. (Ed.). Methods of Air Sampling and Analysis. 3rd ed. Chelsea, MI: Lewis Publishers, 1988. (Used as a basic reference for air-sampling theory; for real-time air-pollution sampling and analysis, follow the latest U.S. EPA/NIOSH published methods.)
(2) Official Methods
All sampling design, analytical procedures, QA/QC, and interpretation of results in this course use the latest official published methods and international standards as the ultimate basis for verification.
1. Ministry of Environment, National Environmental Research Academy (NERA): NIEA published methods and the “Method Query System.” (Provides standard methods, technical documents, and quality-control requirements for various environmental pollutants.)
2. U.S. EPA Test Methods / SW-846: Test Methods for Evaluating Solid Waste, Physical/Chemical Methods.
3. U.S. EPA Compendium Methods for the Determination of Toxic Organic Compounds in Ambient Air, and Compendium Methods for the Determination of Inorganic Compounds in Ambient Air.
4. ISO 5667, Water Quality — Sampling (ISO 5667-1:2023 provides general principles for the design of sampling programmes and sampling techniques for water).
5. ISO 18400, Soil Quality — Sampling (ISO 18400-104:2018 provides guidance on site-investigation and soil-sampling strategies).
6. ASTM International Standards, including standards for environmental-media sampling, pollution investigation, site characterization, and analytical QA.
7. NIOSH Manual of Analytical Methods (NMAM), 5th Edition: sampling and analytical methods for occupational-exposure monitoring, covering air, surfaces, and biological samples.
8. Bureau of Standards, Metrology and Inspection (MOEA): relevant Chinese National Standards (CNS) for sampling and testing.
(3) Guidelines
1. U.S. EPA. Guidance on Environmental Data Verification and Data Validation (EPA QA/G-8).
2. U.S. EPA. Guidance for Quality Assurance Project Plans (EPA QA/G-5).
3. U.S. EPA. Guidance on Systematic Planning Using the Data Quality Objectives Process (EPA QA/G-4).
4. NERA/NIEA environmental-testing QC guidance, general rules for environmental testing methods, and category-specific technical documents.
5. Technical reports on environmental monitoring, pollution investigation, sampling design, QA/QC, and risk assessment from USGS, U.S. EPA, NIOSH, WHO, and other agencies.

Teaching Aids & Teacher's Website

https://ecampus2.nchu.edu.tw/eCampus3P/Learn/Default.aspx

Office Hours

Monday 11:00 - 18:00 or by appointment

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Update Date, year/month/day：2026/06/30 14:31:33	Printed Date, year/month/day：2026 / 7 / 09
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