John L Pearcea, Jason Beringera, Neville Nichollsa, Rob J Hyndmanb and Nigel J Tappera
a School of Geography and Environmental Science, Monash University, Melbourne, Australia
b Department of Econometrics and Business Statistics, Monash University, Melbourne, Australia
Atmospheric Environment (2011), 45(6), 1328-1336.
Quantifying the observed relationships between local meteorology and air pollution provides air quality managers with a knowledge base that may prove useful in understanding how climate change may potentially impact air quality. This paper presents the estimated response of ozone (O3), particulate matter ≤ 10 μm (PM10), and nitrogen dioxide (NO2) to individual local meteorological variables in Melbourne, Australia over the period of 1999 to 2006. The relationships have been quantified after controlling for long-term trends, seasonality, weekly emissions, spatial variation, and temporal persistence using the framework of a generalized additive modelling (GAM). The nature of the response of each pollutant to individual meteorological variables is presented using partial residual plots described on a percentage scale as marginal effects. The aggregate impact of local meteorology in the models was found to explain 26.3% of the variance in O3, 21.1% in PM10, and 26.7% in NO2. High temperatures resulted in strongest positive response for all pollutants with a 150% increase above the mean for O3 and PM10 and a 120% for NO2. Other variables, such as boundary layer height, winds, water vapour pressure, radiation, precipitation and mean sea-level pressure, display some importance for one or more of the pollutants, but their impact in the models was less pronounced. Overall, this analysis presents a solid foundation for understanding the importance of local meteorology as a driver of regional air pollution in Melbourne in a framework that can be applied in other regions. Additionally, these results can be used to corroborate findings from studies using numerical air quality models.
Keywords: air pollution, climate change, generalized additive models, and meteorology.