Isoprene and monoterpene emissions in south-east Australia: comparison of a multi-layer canopy model with MEGAN  and with atmospheric observations

Emmerson, Kathryn M.; Cope, Martin; Galbally, Ian E.; Lee, Sunhee; Nelson, Peter F.

doi:10.5194/acp-18-7539-2018

Cited by 39 publications

(33 citation statements)

References 48 publications

(66 reference statements)

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“…CCAM uses the Australian land surface scheme, CABLE [64]. Biogenic emissions are provided by the Australian Biogenic Canopy and Grass Emissions Model (ABCGEM; [65]). Gas-phase chemistry is modelled using an extended version of the CB05 mechanism with updated toluene chemistry.…”

Section: Summary Of Chemical Transport Modelsmentioning

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: Summary Of Chemical Transport Modelsmentioning

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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“…The C-CTM is a modelling framework which simulates the emissions, transport and wet and dry deposition of chemical species in the atmosphere. Previously, the C-CTM has successfully been used in several air quality applications, including investigating smoke from biomass burning [33], shipping emissions [34] and biogenic emissions [35,36].…”

Section: Regional Air Quality Modelling Using C-ctmmentioning

confidence: 99%

“…The inventory specifies on-and off-road mobile, aircraft, shipping, commercial, domestic and industrial point sources at a resolution of 1 km. Natural biogenic emissions of isoprene and monoterpenes come from the Australian Biogenic Canopy and Grass Emissions Model (ABCGEM, [35]). Monoterpenes are treated as a lumped species.…”

Section: Regional Air Quality Modelling Using C-ctmmentioning

confidence: 99%

Urban Air Quality in a Coastal City: Wollongong during the MUMBA Campaign

et al. 2018

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We present findings from the Measurements of Urban, Marine and Biogenic Air (MUMBA) campaign, which took place in the coastal city of Wollongong in New South Wales, Australia. We focus on a few key air quality indicators, along with a comparison to regional scale chemical transport model predictions at a spatial resolution of 1 km by 1 km. We find that the CSIRO chemical transport model provides accurate simulations of ozone concentrations at most times, but underestimates the ozone enhancements that occur during extreme temperature events. The model also meets previously published performance standards for fine particulate matter less than 2.5 microns in diameter (PM2.5), and the larger aerosol fraction (PM10). We explore the observed composition of the atmosphere within this urban air-shed during the MUMBA campaign and discuss the different influences on air quality in the city. Our findings suggest that further improvements to our ability to simulate air quality in this coastal city can be made through more accurate anthropogenic and biogenic emissions inventories and better understanding of the impact of extreme temperatures on air quality. The challenges in modelling air quality within the urban air-shed of Wollongong, including difficulties in accurate simulation of the local meteorology, are likely to be replicated in many other coastal cities in the Southern Hemisphere.

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“…As biogenic emission is a major contribution to ozone formation, it is important for the model to account for it as accurately as possible. Emmerson et al [30] found that the Australian Biogenic Canopy and Grass Emission Model (ABCGEM), as implemented in the CCAM-CTM model used in this study, accounts for biogenic VOC emission (especially monoterpenes) from Australian eucalyptus vegetation better than the model of Emissions of Gases and Aerosols from Nature (MEGAN) when these modelled emissions are compared with observations.…”

Section: Discussionmentioning

confidence: 89%

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

Chang

Trieu

et al. 2018

Atmosphere

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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.

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Isoprene and monoterpene emissions in south-east Australia: comparison of a multi-layer canopy model with MEGAN and with atmospheric observations

Cited by 39 publications

References 48 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

Urban Air Quality in a Coastal City: Wollongong during the MUMBA Campaign

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

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