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

H., Duc; Chang, Lisa T.-C.; Trieu, Toan; Salter, David; Scorgie, Yvonne

doi:10.3390/atmos9110443

Cited by 9 publications

(4 citation statements)

References 31 publications

(22 reference statements)

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“…This will result in less than optimum comparisons between observed and simulated meteorological quantities. Detailed evaluations at more appropriate sites are the subject of separate investigations (e.g., [25,26,[29][30][31]).…”

Section: Introductionmentioning

confidence: 99%

Skill-Testing Chemical Transport Models across Contrasting Atmospheric Mixing States Using Radon-222

Chambers

Guérette

Monk³

et al. 2019

Atmosphere

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We propose a new technique to prepare statistically-robust benchmarking data for evaluating chemical transport model meteorology and air quality parameters within the urban boundary layer. The approach employs atmospheric class-typing, using nocturnal radon measurements to assign atmospheric mixing classes, and can be applied temporally (across the diurnal cycle), or spatially (to create angular distributions of pollutants as a top-down constraint on emissions inventories). In this study only a short (<1-month) campaign is used, but grouping of the relative mixing classes based on nocturnal mean radon concentrations can be adjusted according to dataset length (i.e., number of days per category), or desired range of within-class variability. Calculating hourly distributions of observed and simulated values across diurnal composites of each class-type helps to: (i) bridge the gap between scales of simulation and observation, (ii) represent the variability associated with spatial and temporal heterogeneity of sources and meteorology without being confused by it, and (iii) provide an objective way to group results over whole diurnal cycles that separates ‘natural complicating factors’ (synoptic non-stationarity, rainfall, mesoscale motions, extreme stability, etc.) from problems related to parameterizations, or between-model differences. We demonstrate the utility of this technique using output from a suite of seven contemporary regional forecast and chemical transport models. Meteorological model skill varied across the diurnal cycle for all models, with an additional dependence on the atmospheric mixing class that varied between models. From an air quality perspective, model skill regarding the duration and magnitude of morning and evening “rush hour” pollution events varied strongly as a function of mixing class. Model skill was typically the lowest when public exposure would have been the highest, which has important implications for assessing potential health risks in new and rapidly evolving urban regions, and also for prioritizing the areas of model improvement for future applications.

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Section: Introductionmentioning

confidence: 99%

Skill-Testing Chemical Transport Models across Contrasting Atmospheric Mixing States Using Radon-222

Chambers

Guérette

Monk³

et al. 2019

Atmosphere

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“…These papers describe the campaign in detail. WASPSS also included a roadside air quality study [37] and finalization of observations of previous campaigns [38][39][40][41][42] for use in a collaborative air quality modelling study (and intercomparison) of the greater metropolitan region of New South Wales [43][44][45][46][47][48][49].…”

Section: Introductionmentioning

confidence: 99%

Air Quality Impacts of Smoke from Hazard Reduction Burns and Domestic Wood Heating in Western Sydney

et al. 2019

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Air quality was measured in Auburn, a western suburb of Sydney, Australia, for approximately eighteen months during 2016 and 2017. A long open-path infrared spectrometer sampled path-averaged concentrations of several gaseous species, while other pollutants such as PM 2.5 and PM 10 were sampled by a mobile air quality station. The measurement site was impacted by a number of indoor wood-heating smoke events during cold winter nights as well as some major smoke events from hazard reduction burning in the spring of 2017. In this paper we compare the atmospheric composition during these different smoke pollution events and assess the relative overall impact on air quality from domestic wood-heaters and prescribed forest fires during the campaign. No significant differences in the composition of smoke from these two sources were identified in this study. Despite the hazard reduction burning events causing worse peak pollution levels, we find that the overall exposure to air toxins was greater from domestic wood-heaters due to their higher frequency and total duration. Our results suggest that policy-makers should place a greater focus on reducing wood-smoke pollution in Sydney and on communicating the issue to the public.

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“…The model simulations used for the intercomparison exercise were subsequently used in a number of additional studies, including benchmarking the performance of the DPIE's operational model [30] and using it to identify the major sources of O 3 [31] and PM 2.5 [32] in the greater Sydney region. Other studies explored the role of extreme temperature days in driving O 3 pollution events [33] and the relative performance of the WRF-Chem model with and without coupling to the Regional Ocean Model System [34,35].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Regional Air Quality Models over Sydney, Australia: Part 2, Comparison of PM2.5 and Ozone

et al. 2020

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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 µm 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−), nitrate (NO3−), 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.

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

Cited by 9 publications

References 31 publications

Skill-Testing Chemical Transport Models across Contrasting Atmospheric Mixing States Using Radon-222

Skill-Testing Chemical Transport Models across Contrasting Atmospheric Mixing States Using Radon-222

Air Quality Impacts of Smoke from Hazard Reduction Burns and Domestic Wood Heating in Western Sydney

Evaluation of Regional Air Quality Models over Sydney, Australia: Part 2, Comparison of PM2.5 and Ozone

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