Multiple linear regression and regression with time series error models in forecasting PM10 concentrations in Peninsular Malaysia

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15_Multiple_linear_regression_and_regression_with_time_series_error_models_in_forecasting_PM10_concentrations_in_Peninsular_Malaysia.pdf (1.63 MB)

Date

2018

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Publisher

Elsevier

Abstract

Frequent haze occurrences in Malaysia have made the management of PM10 (particulate matter with aerodynamic less than 10 μm) pollution a critical task. This requires knowledge on factors associating with PM10 variation and good forecast of PM10 concentrations. Hence, this paper demonstrates the prediction of 1-day-ahead daily average PM10 concentrations based on predictor variables including meteorological parameters and gaseous pollutants.

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Keywords

Multiple linear regression, Regression with time series error, PM10, Forecast

Citation

Ng, K. Y., & Awang, N. (2018). Multiple linear regression and regression with time series error models in forecasting PM 10 concentrations in Peninsular Malaysia. Environmental monitoring and assessment, 190, 1-11.

URI

https://repoemc.ukm.my/handle/123456789/557

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