Regression and multivariate models for predicting particulate matter concentration level

Abstract

The devastating health effects of particulate matter (PM) exposure by susceptible populace has made it necessary to evaluate PM pollution. Meteorological parameters and seasonal variation increases PM concentration levels, especially in areas that have multiple anthropogenic activities. Hence, stepwise regression (SR), multiple linear regression (MLR) and principal component regression (PCR) analyses were used to analyse daily average PM concentration levels. The analyses were carried out using daily average PM concentration, temperature, humidity, wind speed and wind direction data from 2006 to 2010. The data was from an industrial air quality monitoring station in Malaysia. The SR analysis established that meteorological parameters had less influence on PM concentration levels having coefficient of determination (R ) result from 23 to 29% based on seasoned and unseasoned analysis. While, the result of the prediction analysis showed that PCR models had a better R result than MLR methods. The results for the analyses based on both seasoned and unseasoned data established that MLR models had R result from 0.50 to 0.60. While, PCR models had R result from 0.66 to 0.89. In addition, the validation analysis using 2016 data also recognised that the PCR model outperformed the MLR model, with the PCR model for the seasoned analysis having the best result. These analyses will aid in achieving sustainable air quality management strategies.

Investigation of PM10 prediction utilizing data mining techniques: Analyze by topic

“…The lagged PM was an important input feature because PM 10 concentrations cannot decrease immediately, except in abnormal events (S. Park et al, 2015). In the feature selection process, the lagged PM was usually selected from raw data (Corani, 2005;Grivas & Chaloulakou, 2006;Konovalov et al, 2009;Nazif et al, 2018). In addition, the model creation process confirmed that models that included the lagged PM improved the model performance and better than the model excluding lagged PM (Cai et al, 2009;Chaloulakou et al, 2003;Schornobay-Lui et al, 2019;Wu et al, 2011).…”

“…The local government agencies had database on only their area such as The Wuhan Environmental Protection Bureau (Wu et al, 2011), The Air Quality Control Company of Tehran, Iran (Nejadkoorki & Baroutian, 2012), The environmental protection agency of Shiraz, Iran (Shekarrizfard et al, 2012), The Helsinki Metropolitan Area Council (Kukkonen, 2003), The Department of Environment, Prefecture of Magnessi (Papanastasiou et al, 2007), The Traffic Noise & Air Quality Unit of Dublin City Council (Alam & McNabola, 2015). The national government agencies had database on many data monitoring stations and data from only the focused stations by researcher were used such as The Department of Environments, Parks and Recreation, Brunei Darussalam (Dotse et al, 2018), The Department of Labor Inspection, Ministry of Labor and Social Insurance, Cyprus (Paschalidou et al, 2011), The Department of Environment, Malaysia (Azid et al, 2013;Nazif et al, 2018;Saufie et al, 2015), The Ministry of Environment and Urban Planning, Turkey (Özdemir & Taner, 2014;Taşpınar, 2015), The Ministry of the Environment and Energy, Greece (Bougoudis et al, 2014;Grivas & Chaloulakou, 2006;Moustris et al, 2013).…”

“…The stepwise regression was a combination of the forward selection and the backward elimination. It was implemented by a number of researchers (Biancofiore et al, 2017;Chaloulakou et al, 2003;Díaz-Robles et al, 2008;Grivas & Chaloulakou, 2006;Huebnerova & Michalek, 2014;Kim Oanh & Leelasakultum, 2011;Nazif et al, 2016Nazif et al, , 2018Russo et al, 2015;Stadlober et al, 2008;Vlachogianni et al, 2011;Vlachogiannis & Sfetsos, 2006).…”

“…Among the various research articles in this study, the MLR is divided into two or more models by periods because there are a highly different factors affect PM 10 concentration, such as natural situation, and anthropogenic activities (Lesar & Filipči c, 2017;Nazif et al, 2018;Vlachogianni et al, 2011).…”

Functional Location-Scale Model to Forecast Bivariate Pollution Episodes

“…Forecasting air quality and concentrations of pollutants in the atmosphere by means of statistical methods is an active area of research given the transcendence of the problem and the difficulty to find optimal solutions using deterministic mathematical models. Among the different methods that can be found in the literature to tackle this problem, models for time series analysis such as the integrated autoregressive moving average-ARIMA [1][2][3], multivariate regression [4][5][6][7], generalized linear or additive models (GAM) [8][9][10][11] and artificial neural networks (ANN) [12][13][14][15][16][17][18][19] are the most extended. Due to the increased access to continuous data over time, functional data analysis [20,21] was also proposed for air quality forecasting and outlier detection [22][23][24].…”