Air quality pollutants and their relationship with meteorological variables in four suburbs of Greater Sydney, Australia
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Air quality pollutants and their relationship with meteorological variables in four suburbs of Greater Sydney, Australia Khaled Haddad 1 & Nicoletta Vizakos 2 Received: 2 April 2020 / Accepted: 17 August 2020 # Springer Nature B.V. 2020
Abstract Meteorological variability plays a pivotal role in ambient air pollution. An in-depth analysis of air pollutants including meteorological variables in four suburbs of greater Sydney, Australia, was carried out for a continuous period of 24 months from January 2016 to 2018. Results revealed significant air quality problems with seasonal trends, for all six pollutants, in all suburbs. Maximum 24-h average PM10 concentrations for the four suburbs were 49.4, 55.3, 74.0 and 102.8 μg/m3 demonstrating severe PM10 air pollution events. NO2 concentrations exceeded national guideline limits and all four suburbs showed higher than recommended concentrations of O3. Generalised additive model analysis displayed varying dependencies between air pollutants and meteorological variables influenced by season and location. Different plots were used to interpret data in terms of meteorological variables. Generally, easterly and southerly winds led to the highest average concentrations of air pollutants for all suburbs. Extremes in air quality pollution concentrations were related to east and west winds and higher wind speeds (4–8 m/s). Wide variations existed in air pollutants between the 10th and 95th percentile values, especially PM10. Minimum and maximum concentration of all analysed pollutants occurred during low temperatures (11.7–18.4 °C) with the exception of ozone favouring higher temperatures (24–38 °C) during hotter months. Results show pollution formation varies in different seasons and suburbs, in relation to meteorological variables. This study can be used to mitigate, improve prediction and forecast accuracy of air pollution. Such studies open the possibilities to explore the effects of air quality and its impact on public health. Keywords Air quality pollutants . Meteorological variables . Generalised additive model . Trend level . Australia
Introduction Highlights • Assess impacts of meteorological variables on air quality pollutants • Assessed temporal variations of air pollutants and meteorological variables • GAMs used to assess dependency of air pollutants on meteorological variables • Extremes of pollutants were assessed in relation to 10th and 95th percentiles • Air pollutant min and max were assessed by trend level and percentile rose plots Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11869-020-00913-8) contains supplementary material, which is available to authorized users. * Khaled Haddad [email protected] 1
Cumberland Council, 16 Memorial Avenue, P.O. Box 42, Merrylands, NSW 2160, Australia
2
Family First Chiropractic, Sydney, NSW, Australia
Air quality data is used worldwide to verify the current standing of air pollution level and associated health risks to the public (Manju et al. 2018). Mult
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