Michael Hewson

Long-term nitrogen dioxide exposure assessment using back-extrapolation of satellite-based land-use regression models for Australia

&NA; Assessing historical exposure to air pollution in epidemiological studies is often problematic because of limited spatial and temporal measurement coverage. Several methods for modelling historical exposures have been described, including land‐use regression (LUR). Satellite‐based LUR is a recent technique that seeks to improve predictive ability and spatial coverage of traditional LUR models by using satellite observations of pollutants as inputs to LUR. Few studies have explored its validity for assessing historical exposures, reflecting the absence of historical observations from popular satellite platforms like Aura (launched mid‐2004). We investigated whether contemporary satellite‐based LUR models for Australia, developed longitudinally for 2006–2011, could capture nitrogen dioxide (NO2) concentrations during 1990–2005 at 89 sites around the country. We assessed three methods to back‐extrapolate year‐2006 NO2 predictions: (1) ‘do nothing’ (i.e., use the year‐2006 estimates directly, for prior years); (2) change the independent variable ‘year’ in our LUR models to match the years of interest (i.e., assume a linear trend prior to year‐2006, following national average patterns in 2006–2011), and; (3) adjust year‐2006 predictions using selected historical measurements. We evaluated prediction error and bias, and the correlation and absolute agreement of measurements and predictions using R2 and mean‐square error R2 (MSE‐R2), respectively. We found that changing the year variable led to best performance; predictions captured between 41% (1991; MSE‐R2 = 31%) and 80% (2003; MSE‐R2 = 78%) of spatial variability in NO2 in a given year, and 76% (MSE‐R2 = 72%) averaged over 1990–2005. We conclude that simple methods for back‐extrapolating prior to year‐2006 yield valid historical NO2 estimates for Australia during 1990–2005. These results suggest that for the time scales considered here, satellite‐based LUR has a potential role to play in long‐term exposure assessment, even in the absence of historical predictor data. HighlightsWe assessed how well a year‐2006 satellite‐based LUR model captures historical NO2.We used three methods to estimate annual mean NO2 during 1990–2005.We measured their performance using standard LUR validation techniques.Back‐extrapolated 2006 levels captured up to 76% of spatial variability (90–05).

Australian National Electricity Market Model - version 1.10

This working paper provides details of the Australian National Electricity Market (ANEM) model version 1.10 used in the research project titled: An investigation of the impacts of increased power supply to the national grid by wind generators on the Australian electricity industry. The paper provides a comprehensive reference of the ANEM model for the other project publications that use the ANEM model to analysis the sensitivity of four factors to increasing wind power penetration. The four factors include (1) transmission line congestion, (2) wholesale spot prices, (3) carbon dioxide emissions and (4) energy dispatch. The sensitivity of the four factors to wind power penetration is considered in conjunction with sensitivity to weather conditions, electricity demand growth and a major augmentation of the transmission grid of the Australian National Electricity Market (NEM) called NEMLink (AEMO 2010a, 2010b, 2011a, 2011b).The sensitivity analyses use 5 levels of wind power penetration from zero wind power penetration to enough wind power to meet the original 2020 41TWh Large-scale Renewable Energy Target. The sensitivity to weather is developed by using half hourly electricity demand profiles by node from three calendar years 2010, 2011 and 2012. The sensitivity to growth is developed by incrementing the nodal demand profiles over the projection years 2014 to 2025.

The Effect of Increasing the Number of Wind Turbine Generators on Generator Energy in the Australian National Electricity Market from 2014 to 2025

This report investigates the effect of increasing the number of wind turbine generators on energy generation in the Australian National Electricity Market’s (NEM) existing transmission grid from 2014 to 2025. This report answers urgent questions concerning the capability of the existing transmission grid to cope with significant increases in wind power and aid emissions reductions. The report findings will help develop a coherent government policy to phase in renewable energy in a cost effective manner.We use a sensitivity analysis to evaluate the effect of five different levels of wind penetration on energy generation. The five levels of wind penetration span Scenarios A to E where Scenario A represents ‘no wind’ and Scenario E includes all the existing and planned wind power sufficient to meet Australia’s 2020 41TWh Large Renewable Energy Target (LRET). We compare the relative effect of five different levels of wind penetration on energy generation to the effect on emissions. We also use sensitivity analysis to evaluate the effect on energy generation of growth in electricity demand over the projections years 2014 to 2015 and weather over the years 2010 to 2012. The sensitivity analysis uses simulations from the ‘Australian National Electricity Market (ANEM) model version 1.10’ (Wild et al. 2015).

The Effect of Increasing the Number of Wind Turbine Generators on Carbon Dioxide Emissions in the Australian National Electricity Market from 2014 to 2025

This report investigates the effect of increasing the number of wind turbine generators on carbon dioxide emission in the Australian National Electricity Market’s (NEM) existing transmission grid from 2014 to 2025. This report answers urgent questions concerning the capability of the existing transmission grid to cope with significant increases in wind power and aid emissions reductions. The report findings will help develop a coherent government policy to phase in renewable energy in a cost effective manner.We use a sensitivity analysis to evaluate the effect of five different levels of wind penetration on carbon dioxide emissions. The five levels of wind penetration span Scenarios A to E where Scenario A represents ‘no wind’ and Scenario E includes all the existing and planned wind power sufficient to meet Australia’s 2020 41TWh Large Renewable Energy Target (LRET). We also use sensitivity analysis to evaluate the effect on carbon dioxide emissions of growth in electricity demand over the projections years 2014 to 2015 and weather over the years 2010 to 2012. The sensitivity analysis uses simulations from the ‘Australian National Electricity Market (ANEM) model version 1.10’ (Wild et al. 2015).We find increasing wind power penetration decreases carbon dioxide emissions but retail prices fail to reflect the decrease in carbon dioxide emissions. We find Victoria has the largest carbon dioxide emissions and of the states in the NEM Victoria’s emissions respond the least to increasing wind power penetration. Victoria having the largest brown coal generation fleet in the NEM explains this unresponsiveness. Wind power via the merit order effect displaces the more expensive fossil fuel generators first in the order gas, black coal and brown coal. However, brown coal has the highest carbon dioxide emissions per unit of electricity. This is suboptimal for climate change mitigation and the reintroduction of a carbon pricing mechanism would adjust the relative costs of fossil fuels favouring the fuels with the lower emissions per unit of electricity.

Comparing remotely sensed and modelled aerosol properties for a region of low aerosol optical depth

Studies of the second indirect aerosol effect (pollution inhibiting stratiform rainfall) for city size scale during a rain event are rare. However, as urban footprints expand understanding of urbanization effects on meteorology is crucial to mitigate possible adverse impacts such as modification to local cloud cover and precipitation. Here we compare aerosol optical properties from five weather model combinations of chemistry transport schemes with satellite images of aerosol optical properties in clear sky conditions. The WRF-Chem MOSAIC/CBM-Z combination of gas phase chemistry and aerosol transport schemes are shown to correlate well with a MODIS image for the case study presented. The result is important because the study area of Brisbane, Australia is known to have a low aerosol load - creating limitations when correlating model and satellite images of aerosol size distribution. Accordingly, results pave the way for future studies to quantify aerosol impacts on cloud and precipitation in sub-tropical settings.