Michael Hewson

TL;DR: A generalised estimating equation model was used to predict annual and monthly average ambient NO2 concentrations measured by a national monitoring network from 2006 through 2011 and found that a simple approach using tropospheric NO2 column data yielded models with slightly better predictive ability than those produced using a more involved approach that required simulation of surface-to-column ratios.

TL;DR: In this article, the authors developed a model to predict nitrogen dioxide (NO2) concentrations with national coverage of Australia by using satellite observations of tropospheric NO2 columns combined with other predictor variables.

TL;DR: In this article, the effect of increasing the number of wind turbine generators on wholesale spot prices in the Australian National Electricity Market's (NEM), given the existing transmission grid, from 2014 to 2025, was investigated.

TL;DR: In this paper, the adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and back-propagation artificial neural network (BPANN) algorithms were used to estimate the ground-level PM2.5 concentration.

TL;DR: A soft computing algorithm called adaptive neuro-fuzzy inference system (ANFIS) is employed to estimate the NO2 variations for the first time in application to spatiotemporal air pollution modelling and is found to have better performance and higher accuracy than the multiple regression model.