Summarising climate and air quality (ozone) data on self-organising maps: a Sydney case study

Environ Monit Assess. 2016 Feb;188(2):103. doi: 10.1007/s10661-016-5113-x. Epub 2016 Jan 20.

Abstract

This paper explores the classification and visualisation utility of the self-organising map (SOM) method in the context of New South Wales (NSW), Australia, using gridded NCEP/NCAR geopotential height reanalysis for east Australia, together with multi-site meteorological and air quality data for Sydney from the NSW Office of Environment and Heritage Air Quality Monitoring Network. A twice-daily synoptic classification has been derived for east Australia for the period of 1958-2012. The classification has not only reproduced the typical synoptic patterns previously identified in the literature but also provided an opportunity to visualise the subtle, non-linear change in the eastward-migrating synoptic systems influencing NSW (including Sydney). The summarisation of long-term, multi-site air quality/meteorological data from the Sydney basin on the SOM plane has identified a set of typical air pollution/meteorological spatial patterns in the region. Importantly, the examination of these patterns in relation to synoptic weather types has provided important visual insights into how local and synoptic meteorological conditions interact with each other and affect the variability of air quality in tandem. The study illustrates that while synoptic circulation types are influential, the within-type variability in mesoscale flows plays a critical role in determining local ozone levels in Sydney. These results indicate that the SOM can be a useful tool for assessing the impact of weather and climatic conditions on air quality in the regional airshed. This study further promotes the use of the SOM method in environmental research.

Keywords: Air quality; Climate; Data visualisation; Environmental monitoring data; Self-organising map; Synoptic type.

MeSH terms

  • Air Pollutants / analysis*
  • Air Pollution / analysis
  • Air Pollution / statistics & numerical data
  • Climate*
  • Environmental Monitoring / methods*
  • New South Wales
  • Ozone / analysis*
  • Weather

Substances

  • Air Pollutants
  • Ozone