scholarly journals Performance Evaluation of CCAM-CTM Regional Airshed Modelling for the New South Wales Greater Metropolitan Region

Atmosphere ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 486 ◽  
Author(s):  
Lisa Chang ◽  
Hiep Duc ◽  
Yvonne Scorgie ◽  
Toan Trieu ◽  
Khalia Monk ◽  
...  
Keyword(s):  
Air Quality ◽  
New South ◽  
Sampling Site ◽  
New South Wales ◽  
Diurnal Variations ◽  
Performance Criteria ◽  
Metropolitan Region ◽  
Quality Data ◽  
South Wales ◽  
Modelling System

A comprehensive evaluation of the performance of the coupled Conformal Cubic Atmospheric Model (CCAM) and Chemical Transport Model (CTM) (CCAM-CTM) for the New South Wales Greater Metropolitan Region (NSW GMR) was conducted based on modelling results for two periods coinciding with measurement campaigns undertaken during the Sydney Particle Study (SPS), namely the summer in 2011 (SPS1) and the autumn in 2012 (SPS2). The model performance was evaluated for fine particulate matter (PM2.5), ozone (O3) and nitrogen dioxide (NO2) against air quality data from the NSW Government’s air quality monitoring network, and PM2.5 components were compared with speciated PM measurements from the Sydney Particle Study’s Westmead sampling site. The model tends to overpredict PM2.5 with normalised mean bias (NMB) less than 20%, however, moderate underpredictions of the daily peak are found on high PM2.5 days. The PM2.5 predictions at all sites comply with performance criteria for mean fractional bias (MFB) of ±60%, but only PM2.5 predictions at Earlwood further comply with the performance goal for MFB of ±30% during both periods. The model generally captures the diurnal variations in ozone with a slight underestimation. The model also tends to underpredict daily maximum hourly ozone. Ozone predictions across regions in SPS1, as well as in Sydney East, Sydney Northwest and Illawarra regions in SPS2 comply with the benchmark of MFB of ±15%, however, none of the regions comply with the benchmark for mean fractional error (MFE) of 35%. The model reproduces the diurnal variations and magnitudes of NO2 well, with a slightly underestimating tendency across the regions. The MFE and normalised mean error (NME) for NO2 predictions fall well within the ranges inferred from other studies. Model results are within a factor of two of measured averages for sulphate, nitrate, sodium and organic matter, with elemental carbon, chloride, magnesium and ammonium being underpredicted. The overall performance of CCAM-CTM modelling system for the NSW GMR is comparable to similar model predictions by other regional airshed models documented in the literature. The performance of the modelling system is found to be variable according to benchmark criteria and depend on the location of the sites, as well as the time of the year. The benchmarking of CCAM-CTM modelling system supports the application of this model for air quality impact assessment and policy scenario modelling to inform air quality management in NSW.

scholarly journals Source Contributions to Ozone Formation in the New South Wales Greater Metropolitan Region, Australia

Atmosphere ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 443 ◽  
Author(s):  
Hiep Nguyen Duc ◽  
Lisa Chang ◽  
Toan Trieu ◽  
David Salter ◽  
Yvonne Scorgie
Keyword(s):  
Air Quality ◽  
New South ◽  
New South Wales ◽  
Metropolitan Region ◽  
North West ◽  
Ozone Concentrations ◽  
South Wales ◽  
Source Contributions ◽  
Ozone Levels ◽  
Different Parts

Ozone and fine particles (PM2.5) are the two main air pollutants of concern in the New South Wales Greater Metropolitan Region (NSW GMR) due to their contribution to poor air quality days in the region. This paper focuses on source contributions to ambient ozone concentrations for different parts of the NSW GMR, based on source emissions across the greater Sydney region. The observation-based Integrated Empirical Rate model (IER) was applied to delineate the different regions within the GMR based on the photochemical smog profile of each region. Ozone source contribution was then modelled using the CCAM-CTM (Cubic Conformal Atmospheric model-Chemical Transport model) modelling system and the latest air emission inventory for the greater Sydney region. Source contributions to ozone varied between regions, and also varied depending on the air quality metric applied (e.g., average or maximum ozone). Biogenic volatile organic compound (VOC) emissions were found to contribute significantly to median and maximum ozone concentration in North West Sydney during summer. After commercial and domestic sources, power generation was found to be the next largest anthropogenic source of maximum ozone concentrations in North West Sydney. However, in South West Sydney, beside commercial and domestic sources, on-road vehicles were predicted to be the most significant contributor to maximum ozone levels, followed by biogenic sources and power stations. The results provide information that policy makers can use to devise various options to control ozone levels in different parts of the NSW Greater Metropolitan Region.

scholarly journals Source Contributions to Ozone Formation in the New South Wales Greater Metropolitan Region, Australia

2018 ◽  
Author(s):  
Hiep Nguyen Duc ◽  
Lisa T.-C. Chang ◽  
Toan Trieu ◽  
David Salter ◽  
Yvonne Scorgie
Keyword(s):  
Air Quality ◽  
New South ◽  
New South Wales ◽  
Metropolitan Region ◽  
North West ◽  
Ozone Concentrations ◽  
South Wales ◽  
Source Contributions ◽  
Ozone Levels ◽  
Different Parts

Ozone and fine particles (PM2.5) are the two main air pollutants of concern in the New South Wales Greater Metropolitan Region (NSW GMR) region due to their contribution to poor air quality days in the region. This paper focuses on source contributions to ambient ozone concentrations for different parts of the NSW GMR, based on source emissions across the greater Sydney region. The observation-based Integrated Empirical Rate Model (IER) was applied to delineate the different regions within the GMR based on the photochemical smog profile of each region. Ozone source contribution is then modelled using the CCAM-CTM (Cubic Conformal Atmospheric Model-Chemical Transport Model) modelling system and the latest air emission inventory for the greater Sydney region. Source contributions to ozone varied between regions, and also varied depending on the air quality metric applied (e.g., average or maximum ozone). Biogenic volatile organic compound (VOC) emissions were found to contribute significantly to median and maximum ozone concentration in North West Sydney during summer. After commercial domestic, power station was found to be the next largest anthropogenic source of maximum ozone concentrations in North West Sydney. However, in South West Sydney, beside commercial and domestic sources, on-road vehicles were predicted to be the most significant contributor to maximum ozone levels, followed by biogenic sources and power stations. The results provide information which policy makers can devise various options to control ozone levels in different parts of the NSW Greater Metropolitan Region.

scholarly journals Dust Transport from Inland Australia and Its Impact on Air Quality and Health on the Eastern Coast of Australia during the February 2019 Dust Storm

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 141
Author(s):  
Emilie Aragnou ◽  
Sean Watt ◽  
Hiep Nguyen Duc ◽  
Cassandra Cheeseman ◽  
Matthew Riley ◽  
...  
Keyword(s):  
Air Quality ◽  
Dust Storm ◽  
New South ◽  
New South Wales ◽  
The State ◽  
Dust Storms ◽  
Eastern Coast ◽  
Storm Event ◽  
South Wales ◽  
Dust Storm Event

Dust storms originating from Central Australia and western New South Wales frequently cause high particle concentrations at many sites across New South Wales, both inland and along the coast. This study focussed on a dust storm event in February 2019 which affected air quality across the state as detected at many ambient monitoring stations in the Department of Planning, Industry and Environment (DPIE) air quality monitoring network. The WRF-Chem (Weather Research and Forecast Model—Chemistry) model is used to study the formation, dispersion and transport of dust across the state of New South Wales (NSW, Australia). Wildfires also happened in northern NSW at the same time of the dust storm in February 2019, and their emissions are taken into account in the WRF-Chem model by using Fire Inventory from NCAR (FINN) as emission input. The model performance is evaluated and is shown to predict fairly accurate the PM2.5 and PM10 concentration as compared to observation. The predicted PM2.5 concentration over New South Wales during 5 days from 11 to 15 February 2019 is then used to estimate the impact of the February 2019 dust storm event on three health endpoints, namely mortality, respiratory and cardiac disease hospitalisation rates. The results show that even though as the daily average of PM2.5 over some parts of the state, especially in western and north western NSW near the centre of the dust storm and wild fires, are very high (over 900 µg/m3), the population exposure is low due to the sparse population. Generally, the health impact is similar in order of magnitude to that caused by biomass burning events from wildfires or from hazardous reduction burnings (HRBs) near populous centres such as in Sydney in May 2016. One notable difference is the higher respiratory disease hospitalisation for this dust event (161) compared to the fire event (24).

Air Quality in Association With Rural Coal Mining and Combustion in New South Wales Australia

2019 ◽  
Vol 35 (4) ◽  
pp. 518-527 ◽  
Author(s):  
Michael Hendryx ◽  
Nicholas Higginbotham ◽  
Benjamin Ewald ◽  
Linda H. Connor
Keyword(s):  
Air Quality ◽  
Coal Mining ◽  
New South ◽  
New South Wales ◽  
South Wales

Health Burden of PM2.5 and Socioeconomic Status in the New South Wales Greater Metropolitan Region

2018 ◽  
Vol 2017 (1) ◽  
pp. 645
Author(s):  
Edward Jegasothy ◽  
Richard Broome ◽  
Martin Cope ◽  
Kathryn Emmerson ◽  
Margaret I. Rolfe ◽  
...  
Keyword(s):  
Socioeconomic Status ◽  
New South ◽  
New South Wales ◽  
Metropolitan Region ◽  
Health Burden ◽  
South Wales

Sustainable grazing systems for the Central Tablelands of New South Wales. 4. Soil water dynamics and runoff events for differently-managed pasture types

2006 ◽  
Vol 46 (4) ◽  
pp. 483 ◽  
Author(s):  
J. D. Hughes ◽  
I. J. Packer ◽  
D. L. Michalk ◽  
P. M. Dowling ◽  
W. McG. King ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Soil Water ◽  
New South ◽  
New South Wales ◽  
Ground Cover ◽  
Environmental Data ◽  
Quality Data ◽  
Water Runoff ◽  
South Wales ◽  
Management Factors

Soil water, runoff amount and quality, pasture production and environmental data were measured for a pastoral prime lamb enterprise in the Central Tablelands of New South Wales from 1998 to 2002. There were 4 pasture treatments: fertilised and sown chicory (CH), fertilised and sown introduced pastures (SP), fertilised naturalised pastures (FN) and unfertilised naturalised pastures (UN). Two grazing management regimes, tactically grazed (TG) and continuously grazed (CG) were imposed on the SP, FN and UN treatments. The CH treatment was rotationally grazed. To compare pasture and grazing system water use, maximum soil water deficit values (SWDMax) were calculated from neutron moisture meter data. SWDMax was influenced by both environmental and management factors. Management factors that influenced SWDMax were herbage mass of perennials, degree of perenniality, and the perennial species present. Environmental factors accounted for >50% of the variation in SWDMax. Inclusion of management factors (perennial herbage mass of C3 and C4 species and percentage perennial herbage mass), accounted for an additional 16% of variation. While the influence of pasture management appears to be relatively small, importantly, management is the only avenue available to land managers for influencing SWDMax. The UNTG and all sown treatments, with greater perennial herbage mass or greater C4 herbage mass consistently produced the highest SWDMax. Runoff amount and quality data are presented for ground cover percentages which generally exceeded 80% for the experimental period. Runoff as a proportion of rain received during the experiment was <3%. Environmental factors explained 47% of variation in runoff, while pasture herbage mass and ground cover percentage explained an additional 2% of variation. Water quality was monitored on 3 treatments (SPTG, FNTG and UNCG) for total nitrogen (N), total phosphorus (P) and total suspended solids (TST) over a 6-month period. The mean values for total N and P were below the acceptable contaminant concentration for agricultural irrigation water. An important outcome of this research is the concept of a practical Targeted Water Management Plan (TWMP) which devises a framework for optimum water usage and productivity at a landscape scale.

scholarly journals The Summer 2019–2020 Wildfires in East Coast Australia and Their Impacts on Air Quality and Health in New South Wales, Australia

2021 ◽  
Vol 18 (7) ◽  
pp. 3538
Author(s):  
Hiep Duc Nguyen ◽  
Merched Azzi ◽  
Stephen White ◽  
David Salter ◽  
Toan Trieu ◽  
...  
Keyword(s):  
Air Quality ◽  
Cardiovascular Diseases ◽  
East Coast ◽  
New South ◽  
New South Wales ◽  
Health Impact ◽  
Observation Data ◽  
South Wales ◽  
Exposed Population ◽  
The Impact

The 2019–2020 summer wildfire event on the east coast of Australia was a series of major wildfires occurring from November 2019 to end of January 2020 across the states of Queensland, New South Wales (NSW), Victoria and South Australia. The wildfires were unprecedent in scope and the extensive character of the wildfires caused smoke pollutants to be transported not only to New Zealand, but also across the Pacific Ocean to South America. At the peak of the wildfires, smoke plumes were injected into the stratosphere at a height of up to 25 km and hence transported across the globe. The meteorological and air quality Weather Research and Forecasting with Chemistry (WRF-Chem) model is used together with the air quality monitoring data collected during the bushfire period and remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellites to determine the extent of the wildfires, the pollutant transport and their impacts on air quality and health of the exposed population in NSW. The results showed that the WRF-Chem model using Fire Emission Inventory (FINN) from National Center for Atmospheric Research (NCAR) to simulate the dispersion and transport of pollutants from wildfires predicted the daily concentration of PM2.5 having the correlation (R2) and index of agreement (IOA) from 0.6 to 0.75 and 0.61 to 0.86, respectively, when compared with the ground-based data. The impact on health endpoints such as mortality and respiratory and cardiovascular diseases hospitalizations across the modelling domain was then estimated. The estimated health impact on each of the Australian Bureau of Statistics (ABS) census districts (SA4) of New South Wales was calculated based on epidemiological assumptions of the impact function and incidence rate data from the 2016 ABS and NSW Department of Health statistical health records. Summing up all SA4 census district results over NSW, we estimated that there were 247 (CI: 89, 409) premature deaths, 437 (CI: 81, 984) cardiovascular diseases hospitalizations and 1535 (CI: 493, 2087) respiratory diseases hospitalizations in NSW over the period from 1 November 2019 to 8 January 2020. The results are comparable with a previous study based only on observation data, but the results in this study provide much more spatially and temporally detailed data with regard to the health impact from the summer 2019–2020 wildfires.

scholarly journals The Effect of Lockdown Period during the COVID-19 Pandemic on Air Quality in Sydney Region, Australia

2021 ◽  
Vol 18 (7) ◽  
pp. 3528
Author(s):  
Hiep Duc ◽  
David Salter ◽  
Merched Azzi ◽  
Ningbo Jiang ◽  
Loredana Warren ◽  
...  
Keyword(s):  
Air Quality ◽  
Anthropogenic Activities ◽  
Meteorological Data ◽  
Traffic Volume ◽  
Metropolitan Region ◽  
Quality Data ◽  
Air Quality Model ◽  
Quality Model ◽  
Mobile Sources ◽  
South Wales

In early 2020 from April to early June, the metropolitan area of Sydney as well as the rest of New South Wales (NSW, Australia) experienced a period of lockdown to prevent the spread of COVID-19 virus in the community. The effect of reducing anthropogenic activities including transportation had an impact on the urban environment in terms of air quality which is shown to have improved for a number of pollutants, such as Nitrogen Dioxides (NO2) and Carbon Monoxide (CO), based on monitoring data on the ground and from a satellite. In addition to primary pollutants CO and NOx emitted from mobile sources, PM2.5 (primary and secondary) and secondary Ozone (O3) during the lockdown period will also be analyzed using both statistical methods on air quality data and the modelling method with emission and meteorological data input to an air quality model. By estimating the decrease in traffic volume in the Sydney region, the corresponding decrease in emission input to the Weather Research and Forecasting—Community Multiscale Air Quality Modelling System (WRF-CMAQ) air quality model is then used to estimate the effect of lockdown on the air quality especially CO, NO2, O3, and PM2.5 in the Greater Metropolitan Region (GMR) of Sydney. The results from both statistical and modelling methods show that NO2, CO, and PM2.5 levels decreased during the lockdown, but O3 instead increased. However, the change in the concentration levels are small considering the large reduction of ~30% in traffic volume.

scholarly journals Dust Transport from Inland Australia and its Impact on Air Quality and Health on the Eastern Coast of Australia during the February 2019 Dust Storm.

2020 ◽  
Author(s):  
Emilie Aragnou ◽  
Sean Watt ◽  
Hiep Nguyen Duc ◽  
Cassandra Cheeseman ◽  
Matt Riley ◽  
...  
Keyword(s):  
Air Quality ◽  
Dust Storm ◽  
Respiratory Diseases ◽  
New South ◽  
New South Wales ◽  
The State ◽  
Storm Event ◽  
Cardiac Diseases ◽  
South Wales ◽  
Dust Storm Event

Dust storms originating from Central Australia and western New South Wales frequently cause high particles concentration at many sites across New South Wales, both inland and along the coast. This study focussed on a dust storm event in February 2019 which affect air quality across the state as detected at many ambient monitoring stations in the Department of Planning, Industry and Environment (DPIE) air quality monitoring network. The WRF-Chem (Weather Research and Forecast Model &ndash; Chemistry) model is used to study the formation, dispersion and transport of dust across the state of New South Wales (NSW, Australia). Wildfires also happened in northern NSW at the same time of the dust storm in February 2019, and their emissions are taken into account in WRF-Chem model by using Fire Inventory from NCAR (FINN) as emission input. The model performance is evaluated and is shown to predict fairly accurate the PM2.5 and PM10 concentration as compared to observation. The predicted PM2.5 concentration over New South Wales during 5 days from 11 to 15 February 2019 is then used to estimate the impact of the February 2019 dust storm event on three health endpoints namely mortality, respiratory and cardiac diseases hospitalisation rates. The results show that even though as the daily average of PM2.5 over some parts of the state, especially in western and north western NSW near the centre of the dust storm and wild fires, are very high (over 900 &micro;g/m3), the population exposure is low due to the sparse population. The top five Statistical Area Level 4 regions with the most impact in term of mortality, respiratory diseases hospitalisation and cardiac disease hospitalisation are Far West and Orana, Newcastle and Lake Macquarie, New England and North West, Sydney &ndash; Inner South West and either Central Coast (mortality) or Sydney &ndash; Parramatta (respiratory diseases hospitalisation) or Sydney &ndash; Inner West (cardiac diseases hospitalisation). Generally, the health impact is similar in order of magnitude to that caused by biomass burnings events from wildfires or from hazardous reduction burnings (HRBs) near populous centres such as in Sydney in May 2016. One notable difference is the higher respiratory diseases hospitalisation for this dust event (161) compared to fire event (24).

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