Development of Multiple Linear Regression for Particulate Matter (PM10) Forecasting during Episodic Transboundary Haze Event in Malaysia

Abdullah, Samsuri; Napi, Nur Nazmi Liyana Mohd; Ahmed, Ali Najah; Mansor, Wan Nurdiyana Wan; Mansor, Amalina Abu; Ismail, Marzuki; Abdullah, Ahmad Makmom; Ramly, Zamzam Tuah Ahmad

doi:10.3390/atmos11030289

Cited by 53 publications

(40 citation statements)

References 39 publications

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“…Relative humidity (RH) and particle deposition are directly proportionate. Particle deposition due to gas-to-particle conversion is favorable at higher RH and low temperature by the mechanisms of evapotranspiration [3,13,19]. This theory proved there is a negative correlation between PM10 and relative humidity in this study.…”

Section: Figure 2 Daily Pm10 Concentrationsupporting

confidence: 70%

“…PM10 concentration for the year 2009-2011 was between 7 µg/m 3 to 488 µg/m 3 . The maximum value of PM10 was exceeded by New Malaysian Ambient Air Quality Standards (NMAAQS) which was caused by transboundary haze, industrial emission, and also motor vehicles [13]. The maximum concentration was caused by haze in October 2010 that affected the southern region of Peninsular Malaysia which the main source caused by biomass burning [14,15].…”

Section: Resultsmentioning

confidence: 99%

“…Figure 2 shows that the higher maximum value for PM10 was in 2009 which is 468 µg/m 3 . The data set shows that averaged 1 year exceeding the NMAAQS which is 40 µg/m 3 and the average of observed years were 47 µg/m 3 (2010) and 51 µg/m 3 for the year 2011 due to local sources such as transportation, industrial activities and open burning effect [11,13]. The mean concentrations for other gaseous pollutants complied with NMAAQS and the temperature (T), relative humidity (RH), and wind speed (WS) was in between the normal mean range in Malaysia which is in between 26.45-27.89℃ for temperature, 79.36%-83.9% for relative humidity and 1.01-1.82 km/hr for wind speed [6].…”

Section: Figure 2 Daily Pm10 Concentrationmentioning

confidence: 99%

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Three-Hour-Ahead of Multiple Linear Regression (MLR) Models for Particulate Matter (PM10) Forecasting

Mansor¹,

Abdullah²,

Dom³

et al. 2021

IJDNE

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The increase of air pollutants emission through anthropogenic activities and natural phenomena in the atmosphere can give an adverse impact on human health especially to some groups of people such as children, the elderly, and people that have cardiovascular problems. Multiple Linear Regression (MLR) model establishments for the particulate matter (PM10) forecasting can be useful, as it provides early warning information to the local authorities and the communities. We aim to develop MLR models for PM10 forecasting in Peninsular Malaysia, specifically in the southern part. In this study, the hourly data of PM10, meteorological factors, and gaseous pollutants from the year 2009-2011 had been used. As a result, the next first hour of the MLR prediction model, PM10,t+1 has been selected as the best-fitted model as compared to the second and third prediction hour models, PM10,t+2, and PM10,t+3, respectively. The PM10,t+1 model was explained 61.4% (R2=0.614) variance in the data which is higher compared to model PM10,t+2 and PM10,t+3 with 42.3% (R2=0.423) and 34.7% (R2=0.347), respectively. Thus, the validation of PM10, t+1 model also has a high accuracy value of R2 (55.1%) as compared to the other two models. We conclude that the development of MLR models is adequate for PM10 forecasting in the industrial area.

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Section: Figure 2 Daily Pm10 Concentrationsupporting

confidence: 70%

Section: Resultsmentioning

confidence: 99%

Section: Figure 2 Daily Pm10 Concentrationmentioning

confidence: 99%

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Three-Hour-Ahead of Multiple Linear Regression (MLR) Models for Particulate Matter (PM10) Forecasting

Mansor¹,

Abdullah²,

Dom³

et al. 2021

IJDNE

Self Cite

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“…Predicting the concentration of PM10 for different hours by using three different step-wise Multiple Linear Regression (MLR) models was proposed in [ 19 ]. In [ 20 ], the authors forecast the concentration of different air pollutants for the current day and the subsequent four days in a highly polluted region utilizing Artificial Neural Networks (ANNs) with Real-Time Correction (RTC).…”

Section: Related Workmentioning

confidence: 99%

Multi-Horizon Air Pollution Forecasting with Deep Neural Networks

Arsov

Zdravevski

Lameski

et al. 2021

Sensors

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Air pollution is a global problem, especially in urban areas where the population density is very high due to the diverse pollutant sources such as vehicles, industrial plants, buildings, and waste. North Macedonia, as a developing country, has a serious problem with air pollution. The problem is highly present in its capital city, Skopje, where air pollution places it consistently within the top 10 cities in the world during the winter months. In this work, we propose using Recurrent Neural Network (RNN) models with long short-term memory units to predict the level of PM10 particles at 6, 12, and 24 h in the future. We employ historical air quality measurement data from sensors placed at multiple locations in Skopje and meteorological conditions such as temperature and humidity. We compare different deep learning models’ performance to an Auto-regressive Integrated Moving Average (ARIMA) model. The obtained results show that the proposed models consistently outperform the baseline model and can be successfully employed for air pollution prediction. Ultimately, we demonstrate that these models can help decision-makers and local authorities better manage the air pollution consequences by taking proactive measures.

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“…The exponential growth in the pandemic has seen quarantines and lockdowns enforced wide and large, leading to significant global and regional economic disruptions. In line with the inverse relationship between economic activity and air quality (Marquez and Smith, 1999), reported improvements in air quality across multiple urban centers where quarantines and lockdowns have been imposed (Abdullah et al, 2020;Cadotte, 2020;Sharma et al, 2020). The lockdowns are the "largest scale experiment ever" into global air quality, and lessons for future policy responses which must be explored (Watts and Kommenda, 2020).…”

Section: Accepted Manuscript Introductionmentioning

confidence: 95%

Investigating the Implications of COVID-19 on PM2.5 in Pakistan

Sipra

Aslam

Syed

et al. 2021

Aerosol Air Qual. Res.

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There are profound impacts of Coronavirus disease-19 (COVID-19) globally, nationally and locally. To assess the impact of COVID-19 on the hourly concentrations of particular matter < 2.5 microns (PM2.5) in Lahore and Karachi, Pakistan, this study employs multifractal analysis. Comparative analysis of high frequency (hourly) PM2.5 data for both cities from February to April for 2019 and 2020 revealed inner dynamics of time series through seasonal and trend decomposition (STL) first, then multifractal detrended fluctuation analysis (MFDFA). The empirical findings confirmed existence of multifractality in hourly PM2.5. Based on multifractal properties, efficiency of Lahore declines during COVID-19. Furthermore, a varying impact of COVID-19 is found on the strength of multifractality of PM2.5 under consideration. Drastic, significant change is found on the efficiency of air quality in Lahore before and during COVID-19 periods. Before COVID-19, PM2.5 remains relatively efficient, while during COVID-19 period it shows high multifractality, the opposite of which is true for Karachi. However, all series exhibit anti-persistent (negatively correlated) behavior before and during COVID-19, with respect to the remainder component of PM2.5 concentration. This means that when PM2.5 concertation is high at a given time, in the next period, the concentration will be low. Intuitively, this is accurate, given that hourly PM2.5 concentrations change with economic activity, which oscillates in daily cycles of high and low PM2.5 concentrations. In Lahore significantly lower efficiency is observed during COVID-19; no conclusion on lockdown can be made. In Karachi, higher efficiency was achieved COVID-19, which was well correlated with the before COVID-19 period, implying effective lockdown policy. The confirmation and examination of multifractality in PM2.5 concentrations of Lahore and Karachi presents researchers and policymakers with a distinct signature for the impact of COVID-19. It provides validation for the development of such policy evaluation tools, with reference to air quality in Pakistan.

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Development of Multiple Linear Regression for Particulate Matter (PM10) Forecasting during Episodic Transboundary Haze Event in Malaysia

Cited by 53 publications

References 39 publications

Three-Hour-Ahead of Multiple Linear Regression (MLR) Models for Particulate Matter (PM10) Forecasting

Three-Hour-Ahead of Multiple Linear Regression (MLR) Models for Particulate Matter (PM10) Forecasting

Multi-Horizon Air Pollution Forecasting with Deep Neural Networks

Investigating the Implications of COVID-19 on PM2.5 in Pakistan

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