FutureLearn

Chemometrics in Air Pollution (FutureLearn)

Offered by University of Malaya,
Chemometrics in Air Pollution (FutureLearn)

This course briefly introduces the causes and effects of air pollution in Asian, chemometric models and chemometric application. This course briefly introduces the causes and effects of air pollution. Air pollution is a growing concern the we experience in our daily life. But not everyone has a clear understanding of what the sources of air pollution are. Here, you will not only learn how to identify them, but also understand the potential impact air pollution has in our present and future.

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What are Chemometric models?
You will be able to learn the Chemometrics models, a new discipline in analytical science and how do Chemometrics models apply in mitigating the air pollution issue.
You will also learn how the initial analysis or preprocessing of the data is a significantly important step in the use of Chemometric models.

Chemometric applications in air pollution
In this course you’ll be able to see real examples that demonstrate and apply the Chemometric model. Making it a proficient and ideal tool mitigating air pollution.
To register in this course, you are required to have basic knowledge in Chemistry, Physics and Statistics.

What topics will you cover?

  • Fundamental of air pollution
  • Composition of air pollutants
  • Basic statistics in chemometric modeling
  • Initial statistical analysis in chemometric
  • Fundamental of chemometric receptor models
  • Data reduction and pattern recognition

What will you achieve?
By the end of the course, you‘ll be able to...

  • Interpret air pollution
  • Classify composition of air pollutants; sources and effects
  • Identify the initial knowledge on the data using basic statistics
  • Explore the versatile directions of application for chemometric receptor models
  • Describe the ways of data reduction using principal component analysis -multiple linear regression (PCA-MLR) and principal component analysis -absolute principal component score (PCA-APCS)
  • Evaluate the quantitative non-negative data distribution by Positive matrix factorization (PMF) model
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