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Regression and multivariate models for predicting particulate matter concentration level.

Amina Nazif1, Nurul Izma Mohammed2, Amirhossein Malakahmad2

  • 1Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, 32610 Bandar, Seri Iskandar, Perak Darul Ridzuan, Malaysia. aminanazif@yahoo.co.uk.

Environmental Science and Pollution Research International
|October 17, 2017
PubMed
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Particulate matter (PM10) pollution is influenced by weather and seasons. Principal Component Regression (PCR) models better predict PM10 levels than Multiple Linear Regression (MLR), aiding air quality management.

Area of Science:

  • Environmental Science
  • Air Quality Monitoring
  • Atmospheric Chemistry

Background:

  • Particulate matter (PM10) poses significant health risks, necessitating pollution evaluation.
  • Meteorological factors and seasonal changes exacerbate PM10 concentrations, particularly near industrial zones.

Purpose of the Study:

  • To analyze daily average PM10 concentration levels using various statistical models.
  • To assess the influence of meteorological parameters on PM10 levels.
  • To compare the predictive performance of different regression models for air quality management.

Main Methods:

  • Utilized stepwise regression (SR), multiple linear regression (MLR), and principal component regression (PCR).
  • Analyzed daily average PM10 concentration, temperature, humidity, wind speed, and direction data from 2006-2010.
Keywords:
Air pollutionParticulate matterPredictionRegression analysis

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  • Validated models using 2016 data.

    • Main Results:

    • SR showed limited influence of meteorological parameters (R² 23-29%).
    • PCR models demonstrated superior predictive accuracy (R² 0.66-0.89) compared to MLR models (R² 0.50-0.60).
    • PCR, especially for seasoned data, outperformed MLR in validation.

    Conclusions:

    • Principal Component Regression is a more effective tool for PM10 prediction than Multiple Linear Regression.
    • Findings support the development of sustainable air quality management strategies.
    • Understanding PM10 drivers is crucial for public health protection.