Relationship between PM 10  and PM 2.5  levels in high-traffic area determined using path analysis and linear regression

Sahanavin, Narut; Prueksasit, Tassanee; Tantrakarnapa, Kraichat

doi:10.1016/j.jes.2017.01.017

Cited by 31 publications

(23 citation statements)

References 26 publications

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“…Several methodologies have been developed and applied to forecast air quality across the world, including deterministic, statistical, and machine learning methods [ 22 , 23 , 24 , 25 , 26 ]. Some studies showed that statistical models are more accurate and efficient compared to deterministic models, particularly in regions with complexed terrain [ 27 , 28 , 29 , 30 ] Moreover, prediction of NO 2 , PM 10 , PM 2.5 , and O 3 MAX concentrations based on MLR and CART models have been successfully implemented in Macao, Bangkok, Changsha City, Beijing, Bilbao, and Pakistan [ 26 , 31 , 32 , 33 , 34 , 35 ].…”

Section: Introductionmentioning

confidence: 99%

Statistical Forecast of Pollution Episodes in Macao during National Holiday and COVID-19

Lei

Monjardino

Mendes

et al. 2020

IJERPH

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Statistical methods such as multiple linear regression (MLR) and classification and regression tree (CART) analysis were used to build prediction models for the levels of pollutant concentrations in Macao using meteorological and air quality historical data to three periods: (i) from 2013 to 2016, (ii) from 2015 to 2018, and (iii) from 2013 to 2018. The variables retained by the models were identical for nitrogen dioxide (NO2), particulate matter (PM10), PM2.5, but not for ozone (O3) Air pollution data from 2019 was used for validation purposes. The model for the 2013 to 2018 period was the one that performed best in prediction of the next-day concentrations levels in 2019, with high coefficient of determination (R2), between predicted and observed daily average concentrations (between 0.78 and 0.89 for all pollutants), and low root mean square error (RMSE), mean absolute error (MAE), and biases (BIAS). To understand if the prediction model was robust to extreme variations in pollutants concentration, a test was performed under the circumstances of a high pollution episode for PM2.5 and O3 during 2019, and the low pollution episode during the period of implementation of the preventive measures for COVID-19 pandemic. Regarding the high pollution episode, the period of the Chinese National Holiday of 2019 was selected, in which high concentration levels were identified for PM2.5 and O3, with peaks of daily concentration exceeding 55 μg/m3 and 400 μg/m3, respectively. The 2013 to 2018 model successfully predicted this high pollution episode with high coefficients of determination (of 0.92 for PM2.5 and 0.82 for O3). The low pollution episode for PM2.5 and O3 was identified during the 2020 COVID-19 pandemic period, with a low record of daily concentration for PM2.5 levels at 2 μg/m3 and O3 levels at 50 μg/m3, respectively. The 2013 to 2018 model successfully predicted the low pollution episode for PM2.5 and O3 with a high coefficient of determination (0.86 and 0.84, respectively). Overall, the results demonstrate that the statistical forecast model is robust and able to correctly reproduce extreme air pollution events of both high and low concentration levels.

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Section: Introductionmentioning

confidence: 99%

Statistical Forecast of Pollution Episodes in Macao during National Holiday and COVID-19

Lei

Monjardino

Mendes

et al. 2020

IJERPH

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“…In recent research, PM 2.5 and PM 10 have been the focus of air pollution control in China [65][66][67][68]. According to studies at home and abroad, there exist certain correlations between PM 10 and PM 2.5 [69][70][71][72]. In order to fully understand the relationship between PM 2.5 and other major pollutants in Shanghai, we have calculated the ratio of PM 2.5 /PM 10 , PM 2.5 /SO 2 , PM 2.5 /NO 2 , PM 2.5 /CO, and PM 2.5 /O 3 , according to the 2017 Shanghai Environmental Bulletin [26] and the Shanghai Air Quality Monthly Report from January-October 2018 [73].…”

Section: Discussionmentioning

confidence: 99%

Primary Pollutants and Air Quality Analysis for Urban Air in China: Evidence from Shanghai

Yan

Sun

et al. 2019

Sustainability

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In recent years, China's urban air pollution has caused widespread concern in the academic world. As one of China's economic and financial centers and one of the most densely populated cities, Shanghai ranks among the top in China in terms of per capita energy consumption per unit area. Based on the Shanghai Energy Statistical Yearbook and Shanghai Air Pollution Statistics, we have systematically analyzed Shanghai’s atmospheric pollutants from three aspects: Primary pollutants, pollutants changing trends, and fine particulate matter. The comprehensive pollution index analysis method, the grey correlation analysis method, and the Euclid approach degree method are used to evaluate and analyze the air quality in Shanghai. The results have shown that Shanghai's primary pollutants are PM2.5 and O3, and the most serious air pollution happens during the first half of the year, particularly in the winter. This is because it is the peak period of industrial energy use, and residential heating will also lead to an increase in energy consumption. Furthermore, by studying the particulate pollutants of PM2.5 and PM10, we clearly disclosed the linear correlation between PM2.5 and PM10 concentrations in Shanghai which varies seasonally.

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“…There are quite a number of statistical analyses which involve PM10 in Malaysia. Some of the statistical analyses that were of interest of past researchers are the regression, used in the studies by Abdullah et al (2017), Juneng et al (2011), Mert Cubukcu & Sinem Ozcan (2015 and Ul-saufie et al (2012), correlation analysis in Biancofiore et al (2017), How & Ling (2016) and Wie & Moon (2017), path analysis in Sahanavin et al (2018) and Markov Chain Model in a study by Mohamad et al (2017).…”

Section: Introductionmentioning

confidence: 99%

Classification of High and Low Level of Pm10 Concentrations in Klang and Shah Alam, Malaysia

Ahmat¹,

Musa²,

Nazamid³

et al. 2019

MJoC

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Particulate matter (PM) comprises of a complex mixture of small solid or liquid particles of organic and inorganic elements that floats freely in air. PM10 is defined as a particulate matter with an aerodynamic diameter of 10 m or less. The main objective of this paper is to classify the level of PM10 in selected locations in Peninsular Malaysia using discriminant analysis. Two important components considered in this study, namely; the meteorological factors and pollutant factors. The meteorological factors comprise of wind speed, wind direction, humidity and temperature while pollutant factors consist of Carbon Monoxide (CO), Sulphur Dioxide (SO2), Nitrogen Dioxide (NO2) and Ozone (O3). The classification of high or low level of PM10 concentrations was based on the Malaysia Ambient Air Quality Guideline (MAAQG). The findings indicated that the classification equation differs from location to location due to different levels of PM10 concentrations, location of monitoring stations and factors affecting air pollution in that location. The simulation data also verified that the classification of PM10 concentration was almost similar to the real condition that occurred in Klang in October 2015.

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Relationship between PM 10 and PM 2.5 levels in high-traffic area determined using path analysis and linear regression

Cited by 31 publications

References 26 publications

Statistical Forecast of Pollution Episodes in Macao during National Holiday and COVID-19

Statistical Forecast of Pollution Episodes in Macao during National Holiday and COVID-19

Primary Pollutants and Air Quality Analysis for Urban Air in China: Evidence from Shanghai

Classification of High and Low Level of Pm10 Concentrations in Klang and Shah Alam, Malaysia

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