John L Pearcea, Jason Beringera, Neville Nichollsa, Rob J Hyndmanb, Petteri Uotilaa, 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(1), 128-136.

The influence of synoptic-scale circulations on air quality is an area of increasing interest to air quality management in regards to future climate change. This study presents an analysis where the dominant synoptic ‘types’ over the region of Melbourne, Australia are determined and linked to regional air quality. First, a self-organising map (SOM) is used to generate a time series of synoptic charts that classify the annual daily circulation affecting Melbourne into 20 different synoptic types. SOM results are then employed within the framework of a generalized additive model (GAM) to identify links between synoptic-scale circulations and observed changes air pollutant concentrations. The GAMs estimate shifts in pollutant concentrations under each synoptic type after controlling for long-term trends, seasonality, weekly emissions, spatial variation, and temporal persistence. Results showed the aggregate impact of synoptic circulations in the models to be quite modest as only 5.1% of the daily variance in O3, 4.7% in PM10, and 7.1% in NO2 were explained by shifts in synoptic circulations. Further analysis of the partial residual plots identified that despite a modest response at the aggregate level, individual synoptic categories had differential effects on air pollutants. In particular, increases of up to 40% in NO2 and PM10 and 30% in O3 occur when a synoptic conditions result in a north-easterly gradient wind over the Melbourne area. Additionally, NO2 and PM10 levels also showed increases of up to 40% when a strong high pressure system was centered directly over the Melbourne area. In sum, the unified approach of SOM and GAM proved to be a complementary suite of tools capable of identifying the entire range synoptic circulation patterns over a particular region and quantifying how they influence local air quality.

Keywords: air pollution, generalized additive models, self-organizing maps, and synoptic meteorology.

Working paper


1 2 3 5
March 7th, 2017

Coherent Probabilistic Forecasts for Hierarchical Time Series

February 28th, 2017

Forecasting with temporal hierarchies

February 14th, 2017

The Australian Macro Database: An online resource for macroeconomic research in Australia

February 14th, 2017

Macroeconomic forecasting for Australia using a large number of predictors

January 13th, 2017

Visualising forecasting algorithm performance using time series instance spaces

January 1st, 2017

Associations between outdoor fungal spores and childhood and adolescent asthma hospitalisations

January 1st, 2017

Grouped functional time series forecasting: an application to age-specific mortality rates

December 7th, 2016

Exploring the influence of short-term temperature patterns on temperature-related mortality: a case-study of Melbourne, Australia

October 13th, 2016

Reconciling forecasts: the hts and thief packages

September 20th, 2016

smoothAPC package for R

September 20th, 2016

stR package for R

September 15th, 2016

Forecasting large collections of related time series

August 22nd, 2016

thief package for R

June 21st, 2016

Exploring time series collections used for forecast evaluation

May 25th, 2016

ISCRR time series workshop

May 6th, 2016

Automatic foRecasting using R

February 29th, 2016

On sampling methods for costly multi-objective black-box optimization

February 19th, 2016

Dynamic Algorithm Selection for Pareto Optimal Set Approximation

February 4th, 2016

Forecasting uncertainty in electricity smart meter data by boosting additive quantile regression

January 30th, 2016

Bayesian rank selection in multivariate regressions

January 25th, 2016

Probabilistic Energy Forecasting: Global Energy Forecasting Competition 2014 and Beyond

January 24th, 2016

Long-term forecasts of age-specific participation rates with functional data models

January 1st, 2016

Bagging exponential smoothing methods using STL decomposition and Box-Cox transformation

January 1st, 2016

Fast computation of reconciled forecasts for hierarchical and grouped time series

December 31st, 2015

Measuring forecast accuracy

November 26th, 2015

Forecasting hierarchical and grouped time series through trace minimization

November 2nd, 2015

Forecasting big time series data using R

October 7th, 2015

Optimal forecast reconciliation for big time series data

October 5th, 2015

Google workshop: Forecasting and visualizing big time series data

September 16th, 2015


August 25th, 2015

New IJF editors

August 17th, 2015

Machine learning bootcamp

August 7th, 2015

Statistical issues with using herbarium data for the estimation of invasion lag-phases

June 30th, 2015

Exploring the feature space of large collections of time series

June 26th, 2015

Exploring the boundaries of predictability: what can we forecast, and when should we give up?

June 25th, 2015

Automatic algorithms for time series forecasting

June 23rd, 2015

MEFM: An R package for long-term probabilistic forecasting of electricity demand

June 19th, 2015

Probabilistic forecasting of peak electricity demand

June 10th, 2015

Do human rhinovirus infections and food allergy modify grass pollen–induced asthma hospital admissions in children?

June 8th, 2015

STR: A Seasonal-Trend Decomposition Procedure Based on Regression

June 4th, 2015

Probabilistic time series forecasting with boosted additive models: an application to smart meter data

June 1st, 2015

Large-scale unusual time series detection

May 26th, 2015

Visualization of big time series data

May 22nd, 2015

Probabilistic forecasting of long-term peak electricity demand

April 20th, 2015

A note on the validity of cross-validation for evaluating time series prediction

April 4th, 2015

Discussion of “High-dimensional autocovariance matrices and optimal linear prediction”

April 1st, 2015

Change to the IJF editors

February 23rd, 2015

Visualization and forecasting of big time series data

January 12th, 2015

Visualizing and forecasting big time series data

December 24th, 2014

Bivariate data with ridges: two-dimensional smoothing of mortality rates