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Creative placemaking is a process involving different sectors of society as they collaborate in cultural activities, deepening their relations with each other and the place in which they live and w...

The emission of black carbon (BC) particles, which cause atmospheric warming by affecting radiation budget in the atmosphere, is the result of an incomplete combustion process of organic materials. The recent wildfire event during the summer 2019-2020 in South-Eastern Australia was unprecedented in scale. The wildfires lasted for nearly 3 months over large areas of the two most populated states of New South Wales and Victoria. This study on the emission and dispersion of BC emitted from the biomass burnings of the wildfires using the Weather Research Forecast – Chemistry (WRF-Chem) model is aimed to determine the extent of the BC spatial dispersion and ground concentration distribution and the effect of BC on air quality and radiative transfer at the top of the atmosphere, the atmosphere and on the ground. The predicted aerosol concentration and AOD are compared with the observed data from the New South Wales Department of Planning and Environment (DPE) aethalometer and air quality network and from remote sensing data. The BC concentration as predicted from WRF-Chem model is in general less than the observed data as measured from the aethalometer monitoring network, but the spatial pattern corresponds well, and the correlation is relatively high. The total BC emission into the atmosphere during the event and the effect on radiation budget were also estimated. This study shows that the summer 2019-2020 wildfires affect not only the air quality and health impact on the east coast of Australia but also short-term weather in the region via aerosol interactions with radiation and cloud.

The demand for parenchyma-sparing local therapies for lung cancer is rising owing to an increasing incidence of multifocal lung cancers and patients who are unfit for surgery. With the latest evidence of the efficacy of lung cancer screening, more premalignant or early-stage lung cancers are being discovered and the paradigm has shifted from treatment to prevention. Transbronchial therapy is an important armamentarium in the local treatment of lung cancers, with microwave ablation being the most promising based on early to midterm results. Adjuncts to improve transbronchial ablation efficiency and accuracy include mobile C-arm platforms, software to correct for the CT-to-body divergence, metal-containing nanoparticles, and robotic bronchoscopy. Other forms of energy including steam vapor therapy and pulse electric field are under intensive investigation.

This study investigates the urban-rural and sex differences in the increased risks of the ten most common cancers in China related to high PM2.5 concentration in the southeastern side of Hu line. Pearson correlation coefficient is estimated to reveal how the cancers closely associated with PM2.5 long-term exposure. Then linear regression is conducted to evaluate sex- and area-specific increased risks of those cancers from high level PM2.5 long-term exposure. The major finding is with the increase of every 10 μg/m3 of annual mean PM2.5 concentration, the increase of relative risks for lung cancer incidence and mortality are 15% and 23% for males, and 22% and 24% for females in rural area. For urban area, the increase of relative risk for ovarian cancer incidence is 9% for females, while that for prostatic cancer increases 17% for males. For leukemia, the increase of relative risks for incidence and mortality are 22% and 19% for females in rural area, while in urban area the increase of relative risk for mortality is 9% for males and for incidence is 6% for females. It is also found that with increased PM2.5 exposure, the risks for ovarian and prostatic cancer rise significantly in urban area, while risks for lung cancer and leukemia rise significantly in rural area. The results demonstrate the higher risks for lung cancer and leukemia with increased PM2.5 exposure are more significant for female. This study also suggests that the carcinogenic effects of PM2.5 have obvious sex and urban-rural differences.

The aerosol monitoring provides air quality information to the community. Policy and community groups often want to know more than the level of mass concentration, that is, a description of what type of aerosols are being reported. There is a need to more objectively and efficiently, that is, in near real time, classify aerosols. Different instruments are used in different networks, raising the issue of comparability. In New South Wales, Australia, the Air Quality monitoring network uses Tapered Element Oscillating Microbalances (TEOM). The Community DustWatch network uses DustTrak® and subjectively classifies its data into dust, smoke and fog. To understand the instrument measurement differences and the aerosols types in Wagga Wagga (New South Wales, Australia), this paper compares paired continuous PM10 measurements from two instruments, the multi-channel DustTrak DRX model 8532 and the TEOM for the period of August 2011 to December 2014. The Reduced Major Axis (RMA) regression was conducted between TEOM PM10 measurements against subjectively classified DustTrak PM10 for different aerosol types. Two methods namely, a single variable DustTrak PM2.5/PM10 ratio (DTPR) method and the Classification and Regression Trees (CART) method were developed to classify the aerosol type into dust, fog and smoke by their particle size distribution (PSD). Based on the thresholds proposed by a single variable DTPR method, the objective classification was conducted on DustTrak data and the RMA regression was rerun. The major finding is that the multi-channel DustTrak DRX model 8532 can be used to classify aerosol type by their average PSD at Wagga Wagga. The aerosol types were classified by two methods: 1) a single variable DTPR is proposed of ≤ 0.55 as a threshold for dust aerosol; 0.55 0.96 for fog. And 2) the CART analysis that uses two variables PM4–10 and DTPR. The initial cut-off of PM4–10 of The PM10 data measured with TEOM and DustTrak have a poor level of agreement no matter if we use the subjectively or objectively classified data. However, the DustTrak PM10 to the TEOM PM10 ratio improves from 0.38 to 0.76 with objectively classified dust data; the ratio of 0.98 gets closer to 1 with objectively classified smoke data; DustTrak PM10 higher by a factor of 2.4 with objectively classified fog data. We were not expecting a good relationship between the two instruments for fog due to the heated inlet on the TEOM. To improve the characterization of aerosol type in the current DustWatch network (or Rural Air Quality Network since 2018), instruments are suggested to be upgraded to multi-channel instruments.

In this study, the spatial distribution of the Daily Precipitation Concentration Index (DPCI) has been analyzed inside the Greater Sydney Metropolitan Area (GSMA). Accordingly, the rainfall database from the Australian Bureau of Meteorology archive was utilized after comprehensive quality control. The compiled data contains a set of 41 rainfall stations indicating consistent daily precipitation series from 1950 to 2015. In the analysis of the DPCI across GSMA the techniques of Moran’s Spatial Autocorrelation has been applied. In addition, a cross-covariance method was applied to assess the spatial interdependency between vector-based datasets after performing an Ordinary Kriging interpolation. The results identify four well-recognized intense rainfall development zones: the south coast and topographic areas of the Illawarra district characterized by Tasman Sea coastal regions with DPCI values ranging from 0.61 to 0.63, the western highlands of the Blue Mountains, with values between 0.60 and 0.62, the inland regions, with lowest rainfall concentrations between 0.55 and 0.59, and lastly the districts located inside the GSMA with DPCI ranging 0.60 to 0.61. Such spatial distribution has revealed the rainstorm and severe thunderstorm activity in the area. This study applies the present models to identify the nature and mechanisms underlying the distribution of torrential rains over space within the metropolis of Sydney, and to monitor any changes in the spatial pattern under the warming climate.

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.

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.

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 term 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 ground and from satellite. Besides 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 analysed using both air quality data and modelling method. The results show that NO2, CO and PM2.5 levels decreased during the lockdown, but O3 instead increased. The change in the concentration levels however are small considering the large reduction in traffic volume of ~30%. By estimate the decrease in traffic volume in Sydney region, the corresponding decrease in emission input to the WRF-CMAQ (Weather Research and Forecasting - Community Multiscale Air Quality Modeling System) 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. COVID-19 lockdown period is an ideal case to study the effect of motor vehicle and mobile source contribution to air pollutants such as those listed above in the GMR.

The Photo-z InfraRed Telescope (PIRT) is an instrument on the Gamow Explorer, currently proposed for a NASA Astrophysics Medium Explorer. PIRT works in tandem with a companion wide-field instrument, the Lobster Eye X-ray Telescope (LEXT), that will identify x-ray transients likely to be associated with high redshift gamma-ray bursts (GRBs) or electromagnetic counterparts to gravitational wave (GW) events. After receiving an alert trigger from LEXT, the spacecraft will slew to center the PIRT field of view on the transient source. PIRT will then begin accumulating data simultaneously in five bands spanning 0.5 - 2.5 microns over a 10 arc-minute field of view. Each PIRT field will contain many hundreds of sources, only one of which is associated with the LEXT transient. PIRT will gather the necessary data in order to identify GRB sources with redshift $z > 6$, with an expected source localization better than 1 arcsec. A near real-time link to the ground will allow timely follow-up as a target of opportunity for large ground-based telescopes or the James Webb Space Telescope (JWST). PIRT will also allow localization and characterization of GW event counterparts. We discuss the instrument design, the on-board data processing approach, and the expected performance of the system., 10 pages, 5 figures

Biomass burnings either due to Hazards Reduction Burnings (HRBs) in late autumn and early winter or bushfires during summer periods in various part of the world (e.g., CA, USA or New South Wales, Australia) emit large amount of gaseous pollutants and aerosols. The emissions, under favourable meteorological conditions, can cause elevated atmospheric particulate concentrations in metropolitan areas and beyond. One of the pollutants of concern is black carbon (BC), which is a component of aerosol particles. BC is harmful to health and acts as a radiative forcing agent in increasing the global warming due to its light absorption properties. Remote sensing data from satellites have becoming increasingly available for research, and these provide rich datasets available on global and local scale as well as in situ aethalometer measurements allow researchers to study the emission and dispersion pattern of BC from anthropogenic and natural sources. The Department of Planning, Industry and Environment (DPIE) in New South Wales (NSW) has installed recently from 2014 to 2019 a total of nine aethalometers to measure BC in its state-wide air quality network to determine the source contribution of BC and PM2.5 (particulate Matter less than 2.5 &mu, m in diameter) in ambient air from biomass burning and anthropogenic combustion sources. This study analysed the characteristics of spatial and temporal patterns of black carbon (BC) in New South Wales and in the Greater Metropolitan Region (GMR) of Sydney, Australia, by using these data sources as well as the trajectory HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory) modelling tool to determine the source of high BC concentration detected at these sites. The emission characteristics of BC in relation to PM2.5 is dependent on the emission source and is analysed using regression analysis of BC with PM2.5 time series at the receptor site for winter and summer periods. The results show that, during the winter, correlation between BC and PM2.5 is found at nearly all sites while little or no correlation is detected during the summer period. Traffic vehicle emission is the main BC emission source identified in the urban areas but was less so in the regional sites where biomass burnings/wood heating is the dominant source in winter. The BC diurnal patterns at all sites were strongly influenced by meteorology.

The Djumbodis™ dissection system was introduced as an alternative to aortic arch replacement in acute type A aortic dissection involving the arch. In our own experience, some patients with Djumbodis implantation developed distal aortic arch and descending aortic aneurysm during subsequent follow-up and required additional interventions. However, as there is a high incidence of fracture associated with the Djumbodis system, further endovascular interventions are not feasible. We report a case of successful open descending aortic replacement in a patient with a fractured Djumbodis stent system.

This paper introduces a novel all-in-one digital-analog heterogeneous integrated intelligent power module (IPM) which is composed of Si MOSFETs, Gate Driver and Micro Controller Unit (MCU). This novel all-in-one module realized by heterogeneous integration offers a System in Package (SiP) solution without causing serious Electromagnetic Compatibility (EMC), signal integrity and thermal issues. In addition, we designed the three-phase half-bridge inverter system formed by this IPM to compare with the system consisting of the same circuit and same bare dies of MOSFETs (TO-package), Gate Driver and MCU (SO-package). The performance shows that the inverter system which adopted this intelligent power module is capable of reducing system energy consumption, enhancing system efficiency (2-4%), and successfully shrinking system volume(>33%) at the same time. Besides, this SiP IPM can improve system reliability, electromagnetic compatibility (EMC) performance and pass the international standard of CISPR 11. Keywords-SiP, heterogeneous integration, IPM, EMC, power density.

The coupled Conformal Cubic Atmospheric Model (CCAM) and Chemical Transport Model (CTM) (CCAM-CTM) was undertaken with eleven emission scenarios segregated from the 2008 New South Wales Greater Metropolitan Region (NSW GMR) Air Emission Inventory to predict major source contributions to ambient PM2.5 and exposure in the NSW GMR. Model results illustrate that populated areas in the NSW GMR are characterised with annual average PM2.5 of 6&ndash, 7 &micro, g/m3, while natural sources including biogenic emissions, sea salt and wind-blown dust contribute 2&ndash, 4 &micro, g/m3 to it. Summer and winter regional average PM2.5 ranges from 5.2&ndash, 6.1 &micro, g/m3 and 3.7&ndash, 7.7 &micro, g/m3 across Sydney East, Sydney Northwest, Sydney Southwest, Illawarra and Newcastle regions. Secondary inorganic aerosols (particulate nitrate, sulphate and ammonium) and sodium account for up to 23% and 18% of total PM2.5 mass in both summer and winter. The increase in elemental carbon (EC) mass from summer to winter is found across all regions but particularly remarkable in the Sydney East region. Among human-made sources, &ldquo, wood heaters&rdquo, is the first or second major source contributing to total PM2.5 and EC mass across Sydney in winter. &ldquo, On-road mobile vehicles&rdquo, is the top contributor to EC mass across regions, and it also has significant contributions to total PM2.5 mass, particulate nitrate and sulphate mass in the Sydney East region. &ldquo, Power stations&rdquo, is identified to be the third major contributor to the summer total PM2.5 mass across regions, and the first or second contributor to sulphate and ammonium mass in both summer and winter. &ldquo, Non-road diesel and marine&rdquo, plays a relatively important role in EC mass across regions except Illawarra. &ldquo, Industry&rdquo, is identified to be the first or second major contributor to sulphate and ammonium mass, and the second or third major contributor to total PM2.5 mass across regions. By multiplying modelled predictions with Australian Bureau of Statistics 1-km resolution gridded population data, the natural and human-made sources are found to contribute 60% (3.55 &micro, g/m3) and 40% (2.41 &micro, g/m3) to the population-weighted annual average PM2.5 (5.96 &micro, g/m3). Major source groups &ldquo, &ldquo, industry&rdquo, on-road motor vehicles&rdquo, power stations&rdquo, and &ldquo, non-road diesel and marine&rdquo, accounts for 31%, 26%, 19%, 17% and 6% of the total human-made sources contribution, respectively. The results in this study enhance the quantitative understanding of major source contributions to ambient PM2.5 and its major chemical components. A greater understanding of the contribution of the major sources to PM2.5 exposures is the basis for air quality management interventions aiming to deliver improved public health outcomes.

Air pollution has significant impacts on human health. Temperature inversions, especially near surface temperature inversions, can amplify air pollution by preventing convective movements and trapping pollutants close to the ground, thus decreasing air quality and increasing health issues. This effect of temperature inversions implies that trends in their frequency, strength and duration can have important implications for air quality. In this study, we evaluate the ability of three reanalysis-driven high-resolution regional climate model (RCM) simulations to represent near surface inversions at 9 sounding sites in southeast Australia. Then we use outputs of 12 historical and future RCM simulations (each with three time periods: 1990?2009, 2020?2039, and 2060?2079) from the NSW/ACT (New South Wales/Australian Capital Territory) Regional Climate Modelling (NARCliM) project to investigate changes in near surface temperature inversions. The results show that there is a substantial increase in the strength of near surface temperature inversions over southeast Australia which suggests that future inversions may intensify poor air quality events. Near surface inversions and their future changes have clear seasonal and diurnal variations. The largest differences between simulations are associated with the driving GCMs, suggesting that the large-scale circulation plays a dominant role in near surface inversion strengths. Fil: Ji, Fei. New South Wales. Office of Environment and Heritage; Australia Fil: Evans, Jason Peter. University of New South Wales; Australia Fil: Di Luca, Alejandro. University of New South Wales; Australia Fil: Jiang, Ningbo. New South Wales. Office of Environment and Heritage; Australia Fil: Olson, Roman. Yonsei University; Corea del Sur Fil: Fita Borrell, Lluís. Centre National de la Recherche Scientifique; Francia. Universite Pierre et Marie Curie; Francia. Laboratoire de Meteorologie Dynamique; Francia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina Fil: Argüeso, Daniel. Universidad de Las Islas Baleares. Departamento de Fisica; España Fil: Chang, Lisa T. C.. New South Wales. Office of Environment and Heritage; Australia Fil: Scorgie, Yvonne. New South Wales. Office of Environment and Heritage; Australia Fil: Riley, Matt. New South Wales. Office of Environment and Heritage; Australia

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&rsquo, s air quality monitoring network, and PM2.5 components were compared with speciated PM measurements from the Sydney Particle Study&rsquo, 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 &plusmn, 60%, but only PM2.5 predictions at Earlwood further comply with the performance goal for MFB of &plusmn, 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 &plusmn, 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.

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&mdash, 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 &micro, g/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).

This letter investigates the effect of repeated uniaxial mechanical stress on bias-induced degradation behavior in polycrystalline thin-film transistors (TFTs). After 100 000 iterations of channel-width-direction mechanical compression, serious threshold voltage degradation and an abnormal hump are observed. Simulation indicates that the strongest mechanical stress occurs at both sides of the channel edge, between the polycrystalline silicon and gate insulator. Since these stress points produce oxide traps in the gate insulator, the degradation of threshold voltage shift and parasitic current path can be attributed to electron trapping at these intense mechanical stress points. In addition, the degradation becomes serious with diminishing TFT size.

Accurate air quality modelling is an essential tool, both for strategic assessment (regulation development for emission controls) and for short-term forecasting (enabling warnings to be issued to protect vulnerable members of society when the pollution levels are predicted to be high). Model intercomparison studies are a valuable support to this work, being useful for identifying any issues with air quality models, and benchmarking their performance against international standards, thereby increasing confidence in their predictions. This paper presents the results of a comparison study of six chemical transport models which have been used to simulate short-term hourly to 24 hourly concentrations of fine particulate matter less than and equal to 2.5 &micro, m in diameter (PM2.5) and ozone (O3) for Sydney, Australia. Model performance was evaluated by comparison to air quality measurements made at 16 locations for O3 and 5 locations for PM2.5, during three time periods that coincided with major atmospheric composition measurement campaigns in the region. These major campaigns included daytime measurements of PM2.5 composition, and so model performance for particulate sulfate (SO42&minus, ), nitrate (NO3&minus, ), ammonium (NH4+) and elemental carbon (EC) was evaluated at one site per modelling period. Domain-wide performance of the models for hourly O3 was good, with models meeting benchmark criteria and reproducing the observed O3 production regime (based on the O3/NOx indicator) at 80% or more of the sites. Nevertheless, model performance was worse at high (and low) O3 percentiles. Domain-wide model performance for 24 h average PM2.5 was more variable, with a general tendency for the models to under-predict PM2.5 concentrations during the summer and over-predict PM2.5 concentrations in the autumn. The modelling intercomparison exercise has led to improvements in the implementation of these models for Sydney and has increased confidence in their skill at reproducing observed atmospheric composition.