Quantile regression for analysing PM10 concentrations in Petaling Jaya

Kar Yong Ng, Norhashidah Awang


Particulate matter with diameter less than 10µm (PM10) data usually exhibit different variations as they include normal days and pollution days. This paper applied quantile regression (QR) technique to inspect the changing relationship between predictor variables and PM10 concentrations at Petaling Jaya monitoring station in the year 2014 over different PM10 distributions. For comparative purpose, multiple linear regression (MLR) using ordinary least squares (OLS) estimation approach was also performed. The QR analysis results showed that the interrelationship between predictor variables and PM10 was not consistent across the PM10 quantile distributions and hence, proved discordancy with MLR estimates. The lagged PM10 concentration was the only important factor throughout the quantile distributions of PM10. It was found that the effects of lagged PM10, temperature, carbon monoxide (CO) increased from low to high quantile distributions, while the effects of lagged humidity, east-west wind component, wind speed and nitrogen monoxide (NO) showed the otherwise patterns. The lagged NO associated significantly with PM10 at low quantiles, whereas the lagged temperature and CO associated significantly at high quantiles only. Lagged humidity, east-west wind component and wind speed correlated significantly and negatively with PM10 at low and middle quantiles. Ozone (O3), however, had effect of changing nature from positive association at low PM10 distributions to negative association at high levels. Thus, QR is helpful to provide a more complete description of predictor variable effects on PM10 at different distributions, and may assist in PM10 management especially during haze periods.


Ordinary least squares; quantile regression; PM10

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Anderson, J. O., Thundiyil, J. G., Stolbach, A. 2012. Clearing the air: A review of the effects of particulate matter air pollution on human health. Journal of Medical Toxicology 8, 166-175.

Baur, D., Saisana, M., Schulze, N. 2004. Modelling the effects of meteorological variables on ozone concentration - A quantile regression approach. Atmospheric Environment 38, 4689-4699.

Bhattacharjee, H., Drescher, M., Good, T., Hartley, Z., Leza, J-D., Lin, B., Moss, J., Massey, R., Nishino, T., Ryder, S., Sachs, N., Tozan, Y., Taylor, C., Wu, D. 1999. Particulate Matter in New Jersey, Introduction, Section 1. Princeton University.

DOE (Department of Environment). 2000. A guide to Air Pollution Index (API) in Malaysia. Kuala Lumpur: Ministry of Science, Technology and the Environment.

DOE (Department of the Environment). 2015. Malaysia Environmental Quality Report 2014. Kuala Lumpur: Ministry of Natural Resources and Environment Malaysia.

Dominick, D., Latif, M. T., Juahir, H., Aris, A. Z., Zain, S. M. 2012. An assessment of influence of meteorological factors on PM10 and NO2 at selected stations in Malaysia. Sustainable Environment Research 22, 305-315.

Koenker, R. 2005. Quantile Regression. New York: Cambridge University Press.

Koenker, R., Bassett, G. 1978. Regression quantiles. Econometrica 46, 33-50.

Koenker, R., Hallock, K. F. 2001. Quantile regression. Journal of Economic Perspectives 15(4), 143-156.

Koenker, R., Machado, J. A. F. 1999. Goodness of fit and related inference processes for quantile regression. Journal of the American Statistical Association 94, 1296-1310.

Liew, J., Latif, M. T., Tangang, F. T. 2011. Factors influencing the variations of PM10 aerosol dust in Klang Valley, Malaysia during the summer. Atmospheric Environment 45, 4370-4378.

Munir, S. 2016. Modelling the non-linear association of particulate matter (PM10) with meteorological parameters and other air pollutants—a case study in Makkah. Arabian Journal of Geosciences 9: 64.

Peng, R. D., Chang, H. H., Bell, M. L., McDermott, A., Zeger, S. L., Samet, J. M., Dominici, F. 2008. Coarse particulate matter air pollution and hospital admissions for cardiovascular and respiratory diseases among medicare patients. JAMA 299, 2172-2179.

Rahman, S. R. A., Ismail, S. N. S., Ramli, M. F., Latif, M. T., Abidin, E. Z., Praveena, S. M. 2015. The assessment of ambient air pollution trend in Klang Valley, Malaysia. World Environment 5, 1-11.

Shaadan, N., Jemain, A. A., Latif, M. T., Mohd Deni, S. 2015. Anomaly detection and assessment of PM10 functional data at several locations in the Klang Valley, Malaysia. Atmospheric Pollution Research 6, 365-375.

Shaharuddin, M., Zaharim, A., Mohd. Nor, M. J., Karim, O. A., Sopian, K. 2008. Application of wavelet transform on airborne suspended particulate matter and meteorological temporal variations. WSEAS Transactions on Environment and Development 4, 89-98.

Siwek, K., Osowski, S. 2012. Improving the accuracy of prediction of PM10 pollution by the wavelet transformation and an ensemble of neural predictors. Engineering Applications of Artificial Intelligence 25, 1246-1258.

Ul-Saufie, A. Z., Yahaya, A. S., Ramli, N. A., Abdul Hamid, H. 2012. Future PM10 concentration prediction using quantile regression models. The proceedings of 2012 2nd International Conference on Environmental and Agriculture Engineering IPCBEE, IACSIT Press, Singapore 37, 15-19.

Ul-Saufie, A. Z., Yahaya, A. S., Ramli, N. A., Rosaida, N., Abdul Hamid, H. 2013. Future daily PM10 concentrations prediction by combining regression models and feedforward backpropagation models with principle component analysis (PCA). Atmospheric Environment 77, 621-630.

Vinceti, M., Malagoli, C., Malavolti, M., Cherubini, A., Maffeis, G., Rodolfi, R., Heck, J. E., Astolfi, G., Calzolari, E., Nicolini, F. 2016. Does maternal exposure to benzene and PM10 during pregnancy increase the risk of congenital anomalies? A population-based case-control study. Science of the Total Environment 541, 444-450.

Yu, K., Lu, Z., Stander, J. 2003. Quantile regression: applications and current research areas. The Statistician 52(3), 331-350.

Zhao, W., Fan, S., Guo, H., Gao, B., Sun, J., Chen, L. 2016. Assessing the impact of local meteorological variables on surface ozone in Hong Kong during 2000-2015 using quantile and multiple line regression models. Atmospheric Environment 144, 182-193.

DOI: http://dx.doi.org/10.11113/mjfas.v13n2.530


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