Prediction of pm10 using multiple linear regression and nonlinear regression Model in industrial areas
| dc.contributor.author | Mohamad Eizlan Haqimi Bin Mat Zain | |
| dc.date.accessioned | 2021-01-25T02:18:09Z | |
| dc.date.available | 2021-01-25T02:18:09Z | |
| dc.date.issued | 2019-07 | |
| dc.description.abstract | Air pollution is a major problem that occurs in Malaysia. Particulate Matter is one of the pollutants that contributed to air pollution that can cause adverse effect to living organisms. Particulate Matter (PM) is the general term used for a mixture of solid particles and liquid droplets found in the air. PM10 refer to particles of aerodynamic diameter less than 10 µm. The objective is to predict particulate matter concentration (PM10) by using multiple linear regression and nonlinear regression in industrial areas. There are three industrial monitoring stations that are Pasir Gudang, Nilai and Perai and one background station which is Jerantut. The parameters were divided into two which are meteorological parameters and gaseous parameters. Meteorological parameters consist of temperature (ºC), wind speed (m/s) and relative humidity (%) while for gaseous parameters are Ozone (O3), Carbon Monoxide (CO), Sulphur Dioxide (SO2) and Nitrogen Dioxide (NO2). The daily mean data was used and divided into training data (70%) and validation data (30%) from 2015 until 2017. Highest maximum value of PM10 concentration was recorded at Perai in 2015 377.56 µg/m3) due to massive land and forest fire in Sumatra and Kalimantan, Indonesia. The result shows that Nonlinear Regression is the best model to predict the PM10 concentration for next day compared to Multiple Linear Regression. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/11000 | |
| dc.language.iso | en | en_US |
| dc.title | Prediction of pm10 using multiple linear regression and nonlinear regression Model in industrial areas | en_US |
| dc.type | Other | en_US |
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