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The United Nations Sustainable Development Goal (SDG) number four seeks an equitable and widespread education that enables an outcome of sustainable development by 2030. Intersecting the studies of society and earth processes, a geographical education is well placed to make cohesive sense of all the individual knowledge silos that contribute to achieving sustainability. Geography education is compulsory for the first three years of the secondary education curriculum in Australia; however, research has shown that many geography teachers are underprepared and report limitations in their teaching of sustainability. This article engages with this research problem to provide a critical reflection, using experiential knowledge as an analytical lens, on how tertiary level geography training at one Australian regional university can equip undergraduate teacher education students with the values, knowledge, and skills needed to develop their future students’ understanding and appreciation of the principles of sustainability. The authors unpacked a geography minor for a Bachelor of Secondary Education degree at Central Queensland University and, deploying content analysis, explain how three units in that minor can develop these students’ values, knowledge, and skills through fostering initiatives and activities. The analysis was framed by elements of pedagogy that offer learners a context for developing active, global citizenship and participation to understand the interdependencies of ecological, societal, and economic systems including a multisided view of sustainability and sustainable development. The study concluded that the three geography units engage student teachers in sustainable thinking in a variety of ways, which can have a wider application in the geography curricula in other teacher education courses. More importantly, however, the study found that there is a critical need for collaboration between university teachers of sustainability content and university teachers of school-based pedagogy in order to maximise the efficacy of sustainability education in schools.
Michael Danaher; Jiaping Wu; Michael Hewson. Sustainability: A Regional Australian Experience of Educating Secondary Geography Teachers. Education Sciences 2021, 11, 126 .
AMA StyleMichael Danaher, Jiaping Wu, Michael Hewson. Sustainability: A Regional Australian Experience of Educating Secondary Geography Teachers. Education Sciences. 2021; 11 (3):126.
Chicago/Turabian StyleMichael Danaher; Jiaping Wu; Michael Hewson. 2021. "Sustainability: A Regional Australian Experience of Educating Secondary Geography Teachers." Education Sciences 11, no. 3: 126.
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 (NO) concentrations during 1990-2005 at 89 sites around the country. We assessed three methods to back-extrapolate year-2006 NO 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 R and mean-square error R (MSE-R), respectively. We found that changing the year variable led to best performance; predictions captured between 41% (1991; MSE-R = 31%) and 80% (2003; MSE-R = 78%) of spatial variability in NO in a given year, and 76% (MSE-R = 72%) averaged over 1990-2005. We conclude that simple methods for back-extrapolating prior to year-2006 yield valid historical NO 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.
Luke D. Knibbs; Craig.P. Coorey; Matthew J. Bechle; Julian D. Marshall; Michael Hewson; Bin Jalaludin; Geoffrey Morgan; Adrian Barnett. Long-term nitrogen dioxide exposure assessment using back-extrapolation of satellite-based land-use regression models for Australia. Environmental Research 2018, 163, 16 -25.
AMA StyleLuke D. Knibbs, Craig.P. Coorey, Matthew J. Bechle, Julian D. Marshall, Michael Hewson, Bin Jalaludin, Geoffrey Morgan, Adrian Barnett. Long-term nitrogen dioxide exposure assessment using back-extrapolation of satellite-based land-use regression models for Australia. Environmental Research. 2018; 163 ():16-25.
Chicago/Turabian StyleLuke D. Knibbs; Craig.P. Coorey; Matthew J. Bechle; Julian D. Marshall; Michael Hewson; Bin Jalaludin; Geoffrey Morgan; Adrian Barnett. 2018. "Long-term nitrogen dioxide exposure assessment using back-extrapolation of satellite-based land-use regression models for Australia." Environmental Research 163, no. : 16-25.
Bijan Yeganeh; Michael Hewson; Samuel Clifford; Ahmad Tavassoli; Luke Knibbs; Lidia Morawska. Estimating the spatiotemporal variation of NO2 concentration using an adaptive neuro-fuzzy inference system. Environmental Modelling & Software 2018, 100, 222 -235.
AMA StyleBijan Yeganeh, Michael Hewson, Samuel Clifford, Ahmad Tavassoli, Luke Knibbs, Lidia Morawska. Estimating the spatiotemporal variation of NO2 concentration using an adaptive neuro-fuzzy inference system. Environmental Modelling & Software. 2018; 100 ():222-235.
Chicago/Turabian StyleBijan Yeganeh; Michael Hewson; Samuel Clifford; Ahmad Tavassoli; Luke Knibbs; Lidia Morawska. 2018. "Estimating the spatiotemporal variation of NO2 concentration using an adaptive neuro-fuzzy inference system." Environmental Modelling & Software 100, no. : 222-235.
William Paul Bell; Phillip Wild; John Foster; Michael Hewson. Revitalising the wind power induced merit order effect to reduce wholesale and retail electricity prices in Australia. Energy Economics 2017, 67, 224 -241.
AMA StyleWilliam Paul Bell, Phillip Wild, John Foster, Michael Hewson. Revitalising the wind power induced merit order effect to reduce wholesale and retail electricity prices in Australia. Energy Economics. 2017; 67 ():224-241.
Chicago/Turabian StyleWilliam Paul Bell; Phillip Wild; John Foster; Michael Hewson. 2017. "Revitalising the wind power induced merit order effect to reduce wholesale and retail electricity prices in Australia." Energy Economics 67, no. : 224-241.
We applied three soft computing methods including adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and back-propagation artificial neural network (BPANN) algorithms for estimating the ground-level PM2.5 concentration. These models were trained by comprehensive satellite-based, meteorological, and geographical data. A 10-fold cross-validation (CV) technique was used to identify the optimal predictive model. Results showed that ANFIS was the best-performing model for predicting the variations in PM2.5 concentration. Our findings demonstrated that the CV-R2 of the ANFIS (0.81) is greater than that of the SVM (0.67) and BPANN (0.54) model. The results suggested that soft computing methods like ANFIS, in combination with spatiotemporal data from satellites, meteorological data and geographical information improve the estimate of PM2.5 concentration in sparsely populated areas. We used comprehensive dataset to develop a satellite-based model for estimating the PM2.5 concentration.Representative animations are created to visualize the spatiotemporal variation of the predictors.We applied ANFIS for the first time as a core model to estimate the spatiotemporal variation of PM2.5 concentration.We compared ANFIS with support vector machine and back-propagation artificial neural network.Adaptive model identification technique has been used to identify the optimal predictive model.
Bijan Yeganeh; Michael G. Hewson; Samuel Clifford; Luke Knibbs; Lidia Morawska. A satellite-based model for estimating PM2.5 concentration in a sparsely populated environment using soft computing techniques. Environmental Modelling & Software 2017, 88, 84 -92.
AMA StyleBijan Yeganeh, Michael G. Hewson, Samuel Clifford, Luke Knibbs, Lidia Morawska. A satellite-based model for estimating PM2.5 concentration in a sparsely populated environment using soft computing techniques. Environmental Modelling & Software. 2017; 88 ():84-92.
Chicago/Turabian StyleBijan Yeganeh; Michael G. Hewson; Samuel Clifford; Luke Knibbs; Lidia Morawska. 2017. "A satellite-based model for estimating PM2.5 concentration in a sparsely populated environment using soft computing techniques." Environmental Modelling & Software 88, no. : 84-92.
Including satellite observations of nitrogen dioxide (NO2) in land-use regression (LUR) models can improve their predictive ability, but requires rigorous evaluation. We used 123 passive NO2 samplers sited to capture within-city and near-road variability in two Australian cities (Sydney and Perth) to assess the validity of annual mean NO2 estimates from existing national satellite-based LUR models (developed with 68 regulatory monitors). The samplers spanned roadside, urban near traffic (≤100 m to a major road), and urban background (>100 m to a major road) locations. We evaluated model performance using R2 (predicted NO2 regressed on independent measurements of NO2), mean-square-error R2 (MSE-R2), RMSE, and bias. Our models captured up to 69% of spatial variability in NO2 at urban near-traffic and urban background locations, and up to 58% of variability at all validation sites, including roadside locations. The absolute agreement of measurements and predictions (measured by MSE-R2) was similar to their correlation (measured by R2). Few previous studies have performed independent evaluations of national satellite-based LUR models, and there is little information on the performance of models developed with a small number of NO2 monitors. We have demonstrated that such models are a valid approach for estimating NO2 exposures in Australian cities.
Luke D. Knibbs; Craig P. Coorey; Matthew J. Bechle; Christine T. Cowie; Mila Dirgawati; Jane S. Heyworth; Guy B. Marks; Julian D. Marshall; Lidia Morawska; Gavin Pereira; Michael G. Hewson. Independent Validation of National Satellite-Based Land-Use Regression Models for Nitrogen Dioxide Using Passive Samplers. Environmental Science & Technology 2016, 50, 12331 -12338.
AMA StyleLuke D. Knibbs, Craig P. Coorey, Matthew J. Bechle, Christine T. Cowie, Mila Dirgawati, Jane S. Heyworth, Guy B. Marks, Julian D. Marshall, Lidia Morawska, Gavin Pereira, Michael G. Hewson. Independent Validation of National Satellite-Based Land-Use Regression Models for Nitrogen Dioxide Using Passive Samplers. Environmental Science & Technology. 2016; 50 (22):12331-12338.
Chicago/Turabian StyleLuke D. Knibbs; Craig P. Coorey; Matthew J. Bechle; Christine T. Cowie; Mila Dirgawati; Jane S. Heyworth; Guy B. Marks; Julian D. Marshall; Lidia Morawska; Gavin Pereira; Michael G. Hewson. 2016. "Independent Validation of National Satellite-Based Land-Use Regression Models for Nitrogen Dioxide Using Passive Samplers." Environmental Science & Technology 50, no. 22: 12331-12338.
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.
Phillip Wild; William Paul Bell; John Foster; Michael Hewson. Australian National Electricity Market Model - Version 1.10. SSRN Electronic Journal 2016, 1 .
AMA StylePhillip Wild, William Paul Bell, John Foster, Michael Hewson. Australian National Electricity Market Model - Version 1.10. SSRN Electronic Journal. 2016; ():1.
Chicago/Turabian StylePhillip Wild; William Paul Bell; John Foster; Michael Hewson. 2016. "Australian National Electricity Market Model - Version 1.10." SSRN Electronic Journal , no. : 1.
This report investigates the effect of increasing the number of wind turbine generators on wholesale spot prices in the Australian National Electricity Market’s (NEM) existing transmission grid from 2014 to 2025. This reports answers urgent questions concerning the capability of the existing transmission grid to cope with significant increases in wind power. 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 wholesale spot prices. 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 wholesale spot prices 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 divergence in the prices between states and similar prices for nodes within states. This pattern reflects the findings in our transmission congestion report (Bell et al. 2015a). Only 14 of the 68 transmission lines in the ANEM Model (Wild et al. 2015) are congested but these 14 congested transmission lines include six of the NEM’s interstate interconnectors and eight of the intrastate transmission lines although only three of the intrastate transmission lines exhibited any significant degree of congestion. This supports Garnaut’s (2011, p. 38) assessment on gold plating intrastate transmission and under investing in interstate transmission.
William Paul Bell; Phillip Wild; John Foster; Michael Hewson. The Effect of Increasing the Number of Wind Turbine Generators on Wholesale Spot Prices in the Australian National Electricity Market from 2014 to 2025. SSRN Electronic Journal 2016, 1 .
AMA StyleWilliam Paul Bell, Phillip Wild, John Foster, Michael Hewson. The Effect of Increasing the Number of Wind Turbine Generators on Wholesale Spot Prices in the Australian National Electricity Market from 2014 to 2025. SSRN Electronic Journal. 2016; ():1.
Chicago/Turabian StyleWilliam Paul Bell; Phillip Wild; John Foster; Michael Hewson. 2016. "The Effect of Increasing the Number of Wind Turbine Generators on Wholesale Spot Prices in the Australian National Electricity Market from 2014 to 2025." SSRN Electronic Journal , no. : 1.
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).
William Paul Bell; Phillip Wild; John Foster; Michael Hewson. The Effect of Increasing the Number of Wind Turbine Generators on Generator Energy in the Australian National Electricity Market from 2014 to 2025. SSRN Electronic Journal 2016, 1 .
AMA StyleWilliam Paul Bell, Phillip Wild, John Foster, Michael Hewson. The Effect of Increasing the Number of Wind Turbine Generators on Generator Energy in the Australian National Electricity Market from 2014 to 2025. SSRN Electronic Journal. 2016; ():1.
Chicago/Turabian StyleWilliam Paul Bell; Phillip Wild; John Foster; Michael Hewson. 2016. "The Effect of Increasing the Number of Wind Turbine Generators on Generator Energy in the Australian National Electricity Market from 2014 to 2025." SSRN Electronic Journal , no. : 1.
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.
William Paul Bell; Phillip Wild; John Foster; Michael Hewson. The Effect of Increasing the Number of Wind Turbine Generators on Carbon Dioxide Emissions in the Australian National Electricity Market from 2014 to 2025. SSRN Electronic Journal 2016, 1 .
AMA StyleWilliam Paul Bell, Phillip Wild, John Foster, Michael Hewson. The Effect of Increasing the Number of Wind Turbine Generators on Carbon Dioxide Emissions in the Australian National Electricity Market from 2014 to 2025. SSRN Electronic Journal. 2016; ():1.
Chicago/Turabian StyleWilliam Paul Bell; Phillip Wild; John Foster; Michael Hewson. 2016. "The Effect of Increasing the Number of Wind Turbine Generators on Carbon Dioxide Emissions in the Australian National Electricity Market from 2014 to 2025." SSRN Electronic Journal , no. : 1.
This report compares the effect of increasing the number of wind turbine generators on transmission line congestion in the Australian National Electricity Market’s (NEM) under the existing transmission grid and an augmented version of the transmission grid called NEMLink (AEMO 2010a, 2010b, 2011a, 2011b). The comparison is made from 2014 to 2025. We use a sensitivity analysis to compare the effect of five different levels of wind penetration on transmission congestion in the original NEM grid and NEMLink augmented grid. 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 original 2020 41TWh Large Renewable Energy Target (LRET). We also use sensitivity analysis to evaluate the effect on transmission congestion of growth in electricity demand over the projections years 2014 to 2025 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).
William Paul Bell; John Foster; Michael Hewson. NEMLink: Augmenting the Australian National Electricity Market Transmission Grid to Facilitate Increased Wind Turbine Generation and Its Effect on Transmission Congestion. SSRN Electronic Journal 2016, 1 .
AMA StyleWilliam Paul Bell, John Foster, Michael Hewson. NEMLink: Augmenting the Australian National Electricity Market Transmission Grid to Facilitate Increased Wind Turbine Generation and Its Effect on Transmission Congestion. SSRN Electronic Journal. 2016; ():1.
Chicago/Turabian StyleWilliam Paul Bell; John Foster; Michael Hewson. 2016. "NEMLink: Augmenting the Australian National Electricity Market Transmission Grid to Facilitate Increased Wind Turbine Generation and Its Effect on Transmission Congestion." SSRN Electronic Journal , no. : 1.
This paper analyses wind speed and electricity demand correlation to determine the ability of wind turbine generators to meet electricity demand in the Australian National Electricity Market (NEM) without the aid of energy storage. With the proposed increases in the number of windfarms to meet the Large-scale Renewable Energy Target (LRET), this correlation study is formative to identifying price and power stability issues and determining what transmission structure is required to best facilitate the absorption of wind power. We calculate correlations between wind speed and electricity demand data for the years 2010 to 2012 using Weather Research & Forecasting Model (WRF 2015) wind speed data and Australian Energy Market Operator (AEMO) electricity demand data. We calculate state level correlations to identify potential bottlenecks in the interconnectors that link each state’s transmission network. The transmission lines within each state tend to be less of a constraint. We find a small temporal increase in correlation between electricity demand and wind speed. This we attribute to an unwitting renewable energy portfolio effect with the increase in solar PV and solar water heating. Strengthening this portfolio effect is the decline in manufacturing that makes household domestic demand relatively larger. Comparing our study with an earlier correlation analysis by Bannister and Wallace (2011) tends to confirm our initial findings. We find the most advantage from the lack of correlation between wind speed between the NEM’s peripheral states including Queensland, South Australia and Tasmania. Additionally, the correlation between electricity demand and wind speed is strongest between these states. Similarly, we find the most advantage from the lack of correlation between electricity demand in each of these states. The self-interest groups within Victoria and New South Wales and the transmission companies geographically contained within each state hinders the development of optimal interconnector capacity to maximise the benefit of wind power in the peripheral states and the NEM generally.
William Paul Bell; Phillip Wild; John Foster; Michael Hewson. Wind speed and electricity demand correlation analysis in the Australian National Electricity Market: Determining wind turbine generators’ ability to meet electricity demand without energy storage. Economic Analysis and Policy 2015, 48, 182 -191.
AMA StyleWilliam Paul Bell, Phillip Wild, John Foster, Michael Hewson. Wind speed and electricity demand correlation analysis in the Australian National Electricity Market: Determining wind turbine generators’ ability to meet electricity demand without energy storage. Economic Analysis and Policy. 2015; 48 ():182-191.
Chicago/Turabian StyleWilliam Paul Bell; Phillip Wild; John Foster; Michael Hewson. 2015. "Wind speed and electricity demand correlation analysis in the Australian National Electricity Market: Determining wind turbine generators’ ability to meet electricity demand without energy storage." Economic Analysis and Policy 48, no. : 182-191.
Land-use regression (LUR) is a technique that can improve the accuracy of air pollution exposure assessment in epidemiological studies. Most LUR models are developed for single cities, which places limitations on their applicability to other locations. We sought to develop 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. We used a generalised estimating equation (GEE) model to predict annual and monthly average ambient NO2 concentrations measured by a national monitoring network from 2006 through 2011. The best annual model explained 81% of spatial variation in NO2 (absolute RMS error=1.4 ppb), while the best monthly model explained 76% (absolute RMS error=1.9 ppb). We applied our models to predict NO2 concentrations at the ~350,000 census mesh blocks across the country (a mesh block is the smallest spatial unit in the Australian census). National population-weighted average concentrations ranged from 7.3 ppb (2006) to 6.3 ppb (2011). We 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. The models were capable of capturing within-urban variability in NO2, and offer the ability to estimate ambient NO2 concentrations at monthly and annual time scales across Australia from 2006–2011. We are making our model predictions freely available for research
Luke D. Knibbs; Michael G. Hewson; Matthew J. Bechle; Julian D. Marshall; Adrian Barnett. A national satellite-based land-use regression model for air pollution exposure assessment in Australia. Environmental Research 2014, 135, 204 -211.
AMA StyleLuke D. Knibbs, Michael G. Hewson, Matthew J. Bechle, Julian D. Marshall, Adrian Barnett. A national satellite-based land-use regression model for air pollution exposure assessment in Australia. Environmental Research. 2014; 135 ():204-211.
Chicago/Turabian StyleLuke D. Knibbs; Michael G. Hewson; Matthew J. Bechle; Julian D. Marshall; Adrian Barnett. 2014. "A national satellite-based land-use regression model for air pollution exposure assessment in Australia." Environmental Research 135, no. : 204-211.
The majority of studies assessing aerosol effects on rainfall use coarse spatial scale (1° latitude/longitude or more) and multi-seasonal or decadal data sets. Here, we present results from a spatial correlation of aerosol size distribution and rain rate for selected stratiform and cumuliform precipitation events. The chemistry transport version of the Weather Research and Forecasting model was used to estimate aerosol parameters during rain events Aerosol maps were then compared with observations of rainfall using geostatistics for the first time. The cross-variogram analysis showed that anthropogenic aerosol was associated with areas of less intense rain within the stratiform system studied. For cumuliform systems, cross-variogram analysis found that anthropogenic emissions may be associated with enhanced rain downwind of aerosol emissions. We conclude that geostatistics provides a promising new technique to investigate relationships between aerosols and rainfall at spatial scales of 1 km which complements more commonly used methods to study aerosol effects on rainfall.
Michael Hewson; Hamish McGowan; Stuart Phinn; Steven Peckham; Georg Grell. Exploring Aerosol Effects on Rainfall for Brisbane, Australia. Climate 2013, 1, 120 -147.
AMA StyleMichael Hewson, Hamish McGowan, Stuart Phinn, Steven Peckham, Georg Grell. Exploring Aerosol Effects on Rainfall for Brisbane, Australia. Climate. 2013; 1 (3):120-147.
Chicago/Turabian StyleMichael Hewson; Hamish McGowan; Stuart Phinn; Steven Peckham; Georg Grell. 2013. "Exploring Aerosol Effects on Rainfall for Brisbane, Australia." Climate 1, no. 3: 120-147.
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.
Michael Hewson; Hamish McGowan; Stuart Phinn. Comparing remotely sensed and modelled aerosol properties for a region of low aerosol optical depth. 2012 IEEE International Geoscience and Remote Sensing Symposium 2012, 2512 -2515.
AMA StyleMichael Hewson, Hamish McGowan, Stuart Phinn. Comparing remotely sensed and modelled aerosol properties for a region of low aerosol optical depth. 2012 IEEE International Geoscience and Remote Sensing Symposium. 2012; ():2512-2515.
Chicago/Turabian StyleMichael Hewson; Hamish McGowan; Stuart Phinn. 2012. "Comparing remotely sensed and modelled aerosol properties for a region of low aerosol optical depth." 2012 IEEE International Geoscience and Remote Sensing Symposium , no. : 2512-2515.