Quantile regression for analysing PM10 concentrations in Petaling Jaya

Abstract

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.