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The world's most severe thunderstorm asthma event occurred in Melbourne, Australia on 21 November 2016, coinciding with the peak of the grass pollen season. The aetiological role of thunderstorms in these events is thought to cause pollen to rupture in high humidity conditions, releasing large numbers of sub-pollen particles (SPPs) with sizes very easily inhaled deep into the lungs. The humidity hypothesis was implemented into a three-dimensional atmospheric model and driven by inputs from three meteorological models. However, the mechanism could not explain how the Melbourne event occurred as relative humidity was very low throughout the atmosphere, and most available grass pollen remained within 40 m of the surface. Our tests showed humidity induced rupturing occurred frequently at other times and would likely lead to recurrent false alarms if used in a predictive capacity. We used the model to investigate a range of other possible pollen rupturing mechanisms which could have produced high concentrations of SPPs in the atmosphere during the storm. The mechanisms studied involve mechanical friction from wind gusts, electrical build up and discharge incurred during conditions of low relative humidity, and lightning strikes. Our results suggest that these mechanisms likely operated in tandem with one another, but the lightning method was the only mechanism to generate a pattern in SPPs following the path of the storm. If humidity induced rupturing cannot explain the 2016 Melbourne event, then new targeted laboratory studies of alternative pollen rupture mechanisms would be of considerable value to help constrain the parameterisation of the pollen rupturing process. [ABSTRACT FROM AUTHOR]

South-east Australia, characterized by arid and semi-arid climate, has experienced large-scale rainfall reductions in recent decades. Larger temporal and spatial drought conditions are predicted in future. The temperate south east coastal zone is characterized by dense forests of Eucalyptus. Drought conditions have implications for the functioning of these indigenous ecosystems, and for emissions of reactive gases that are upwind of major metropolitan regions along the eastern coast. Here, we focus on the impact of drought on the emission of isoprene, a volatile organic compound emitted by a range of trees and shrubs. Previous model calculations grossly overestimate observations of the isoprene mixing ratio, potentially due to overestimated emission factors for native vegetation, but could also be due to drought-induced isoprene emission reductions. We develop the implementation of the Model of Emissions of Gases and Aerosols from Nature (MEGAN) within the CSIRO Chemical Transport Model to include a parameterization of drought using soil moisture. We test this parameterization using two approaches. First, we drive MEGAN using soil moisture fields from two Australian land surface models achieving reductions in isoprene emissions of 24–52% during summer. Second, we use a simple statistical approach to nudge model soil moisture towards satellite observations from the Soil Moisture and Ocean Salinity (SMOS) instrument. This work is the first application of SMOS towards isoprene emission modelling, providing a constraint on surface soil moisture which extends to global scales. Applying these soil moisture approaches, domain average isoprene emissions reduce by 38–58% in summer. Using these results, errors in basal emissions are likely in the region of 40%. Comparison of modelled soil moisture to observations using root density weighted averages across depths of 4 m showed minor differences of up to 0.04 m3 m−3. We find that the choice of land surface model used in the SMOS assimilations has a greater impact on isoprene emissions than adjusting either the nudging strength or the number of soil levels these satellite data may influence. However there are only small differences of 3% when using hourly or 24-hourly soil moisture input data to drive the emission calculations, suggesting that the lower temporal availability of satellite data does not reduce model quality. As the spatial resolution of satellite observations of atmospheric composition and land surface properties begin to approach the resolution of regulatory air quality models, we anticipate that these data will improve model predictive skills further. Image 1 • Decreased isoprene emissions in arid Australia using soil moisture function. • Developed technique to nudge model soil moisture towards satellite SMOS data. • Isoprene emissions decrease by 38–58% and reduce modelled bias. • 3% difference incurred when using daily (rather than hourly) soil moisture input. • Remaining error in basal isoprene emission factors approximately 40%. [ABSTRACT FROM AUTHOR]

We present the first representation of grass pollen in a 3-D dispersion model in Australia, tested using observations from eight counting sites in Victoria. The region's population has high rates of allergic rhinitis and asthma, and this has been linked to the high incidence of grass pollen allergy. Despite this, grass pollen dispersion in the Australian atmosphere has not been studied previously, and its source strength is untested. We describe 10 pollen emission source methodologies examining the strengths of different immediate and seasonal timing functions, and the spatial distribution of the sources. The timing function assumes a smooth seasonal term, modulated by an hourly meteorological function. A simple Gaussian representation of the pollen season worked well (average r=0.54), but lacked the spatial and temporal variation that the satellite-derived enhanced vegetation index (EVI) can provide. However, poor results were obtained using the EVI gradient (average r=0.35), which provides the timing when grass turns from maximum greenness to a drying and flowering period; this is due to noise in the spatial and temporal variability from this combined spatial and seasonal term. Better results were obtained using statistical methods that combine elements of the EVI dataset, a smooth seasonal term and instantaneous variation based on historical grass pollen observations (average r=0.69). The seasonal magnitude is inferred from the maximum winter-time EVI, whereas the timing of the season peak is based on the day of the year when the EVI falls to 0.05 below its winter maximum. Measurements are vital to monitor changes in the pollen season, and the new pollen measurement sites in the Victorian network should be maintained. [ABSTRACT FROM AUTHOR]

We present the first representation of grass pollen in a 3D dispersion model anywhere in Australia, tested using observations from eight counting sites in Victoria. The region's population has high rates of allergic rhinitis and asthma, and this has been linked to the high incidence of grass pollen allergy. Despite this, grass pollen dispersion in the Australian atmosphere has not been studied previously, and its source strength is untested. We describe ten pollen emission source methodologies examining the strengths of different immediate and seasonal timing functions, and spatial distribution of the sources. The timing function assumes a smooth seasonal term, modulated by an hourly meteorological function. A simple Gaussian representation of the pollen season worked well (average r=0.54), but lacks the spatial and temporal variation that the satellite-derived Enhanced Vegetation Index (EVI) data can provide. However poor results were obtained using the EVI gradient (average r=0.35), which gives the timing when grass turns from maximum greenness to a drying and flowering period; this is due to the greater spatial and temporal variability from this combined spatial and seasonal term. Better results were obtained using statistical methods that combine elements of the EVI dataset, a smooth seasonal term and instantaneous variation based on historical grass pollen observations (average r=0.69). The seasonal magnitude is inferred from the maximum winter-time EVI, while the timing of the peak of the season was based on the day of the year when the EVI falls to 0.05 below its winter maximum. Measurements are vital to monitor changes in the pollen season, and the new pollen measurement sites in the Victorian network should be maintained. [ABSTRACT FROM AUTHOR]

The 2014 fire in the Hazelwood open-cut mine of brown coal, located in the State of Victoria (Australia), burned for 45 days. The fire sent dense smoke over the nearby town of Morwell and beyond, resulting in one of the worst air quality incidents in Victoria. Precision air monitoring of PM 2.5 (particulate matter 2.5 μm or less in diameter) and carbon monoxide (CO), which has been reported previously, started a few days after the fire at two locations in Morwell and measured PM 2.5 levels up to 19 times higher than the 24-h Australian air quality standard. Because of the sparseness of the monitors and the fact that the fire was most intense prior to the start of the air monitoring, it is likely that the smoke concentrations in Morwell were even greater than measured. Thus, the concentration measurements are insufficient in time and space for a comprehensive study of exposure and health impacts due to the smoke. We reconstruct the hourly spatial distributions of smoke (as represented by PM 2.5 and CO) in and around Morwell by first developing a rigorous methodology for estimating the fire emissions, and then using them in a high-resolution prognostic meteorological and dispersion model with local wind data assimilation and an appropriate plume rise mechanism. Larger-scale modelling is also conducted to estimate the background concentrations without the mine fire. The model simulates the number of observed exceedances and the observed maximum concentrations exceeding the air quality standards for both PM 2.5 and CO within a factor of 2. At the monitor south of Morwell near the fire, the model predicts hourly PM 2.5 and CO concentrations as high as 3730 μg m−3 and 58.6 ppm, respectively, in the early phase of the fire; these levels are much higher than those recorded by the subsequent air monitoring. The modelled PM 2.5 fields are being used by other researchers to estimate the impact of smoke exposure on health outcomes in the local community. • Smoke from an open-cut coal mine fire burning for 45 days seriously impacted a nearby town population. • A modelling methodology is formulated and tested to reconstruct the fire emissions, and the resulting PM 2.5 and CO fields. • The results indicate very high smoke levels and several exceedances of the national air quality standards for PM 2.5 and CO. • The modelled smoke fields are being used by other researchers in epidemiological studies. [ABSTRACT FROM AUTHOR]

In November 2016, an unprecedented epidemic thunderstorm asthma event in Victoria, Australia, resulted in many thousands of people developing breathing difficulties in a very short period of time, including 10 deaths, and created extreme demand across the Victorian health services. To better prepare for future events, a pilot forecasting system for epidemic thunderstorm asthma (ETSA) risk has been developed for Victoria. The system uses a categorical risk-based approach, combining operational forecasting of gusty winds in severe thunderstorms with statistical forecasts of high ambient grass pollen concentrations, which together generate the risk of epidemic thunderstorm asthma. This pilot system provides the first routine daily epidemic thunderstorm asthma risk forecasting service in the world that covers a wide area, and integrates into the health, ambulance, and emergency management sector. Epidemic thunderstorm asthma events have historically occurred infrequently, and no event of similar magnitude has impacted the Victorian health system since. However, during the first three years of the pilot, 2017--19, two high asthma presentation events and four moderate asthma presentation events were identified from public hospital emergency department records. The ETSA risk forecasts showed skill in discriminating between days with and without health impacts. However, even with hindsight of the actual weather and airborne grass pollen conditions, some high asthma presentation events occurred in districts that were assessed as low risk for ETSA, reflecting the challenge of predicting this unusual phenomenon. [ABSTRACT FROM AUTHOR]

• Soil emissions were a continuous source of NO to the atmosphere at both sites. • Meteorological conditions affect soil NO emission. • Diel variations of solar radiation and VPD partly explain soil NO diel patterns. • Diel patterns indicate both anthropogenic and biogenic sources of NO. • Soils contributed a small but significant fraction of atmospheric NO x at both sites. Nitrogen oxides, particularly NO and NO 2 (NO x), are primary air pollutants that also play an essential role in the atmospheric oxidation of volatile organic compounds, resulting in ozone and secondary organic aerosol formation. It is therefore critical to fully characterise NO x sources and sinks to understand tropospheric photochemistry and hence local- to regional-scale air quality. Human activities such as transport and power plants are well-known NO x emission sources in urban areas, whereas natural sources such as soils have been considered to contribute more substantially in rural and remote areas. However, soil NO emissions are poorly characterised and therefore underrepresented in models. To improve our understanding of soil as a source of NO, we measured diurnal patterns in soil NO concentrations at a suburban site in the UK and a remote field site in Australia to determine whether soils contribute to local atmospheric NO, and to identify the potential drivers of soil NO emissions. Mean soil NO concentrations in both UK campaigns 1.76 ± 0.92 ppb in summer and 0.91 ± 0.37 ppb in winter) were higher than those measured in Australia (0.73 ± 0.73 ppb). The diel patterns of NO concentrations (both sites) and emissions rates (Australia) showed a clear peak corresponding to local emission sources, but variation in NO was also related to either vapour pressure deficit (R 2 = 0.88 in UK summer, R 2 = 0.51 in Australia, both p < 0.05) or solar radiation (R 2 = 0.06 with p > 0.4 in UK summer, R 2 = 0.71 with p < 0.05 in Australia) during the daylight hours, indicating biogenic origin of soil NO. Our work demonstrates that biogenic soil emissions of NO are non-negligible, estimated at around 1.32 % of total NO emissions at the remote site, and 0.22 % at the urban site, and must be accounted for in global and regional atmospheric chemistry-climate modelling and NO x reduction strategies. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper documents the tropospheric chemical mechanism scheme used in the TOMCAT 3-D chemical transport model. The current scheme includes a more detailed representation of hydrocarbon chemistry than previously included in the model, with the inclusion of the emission and oxidation of ethene, propene, butane, toluene and monoterpenes. The model is evaluated against a range of surface, balloon, aircraft and satellite measurements. The model is generally able to capture the main spatial and seasonal features of high and low concentrations of carbon monoxide (CO), ozone (O3), volatile organic compounds (VOCs) and reactive nitrogen. However, model biases are found in some species, some of which are common to chemistry models and some that are specific to TOMCAT and warrant further investigation. The most notable of these biases are (1) a negative bias in Northern Hemisphere (NH) winter and spring CO and a positive bias in Southern Hemisphere (SH) CO throughout the year, (2) a positive bias in NH O3 in summer and a negative bias at high latitudes during SH winter and (3) a negative bias in NH winter C2 and C3 alkanes and alkenes. TOMCAT global mean tropospheric hydroxyl radical (OH) concentrations are higher than estimates inferred from observations of methyl chloroform but similar to, or lower than, multi-model mean concentrations reported in recent model intercomparison studies. TOMCAT shows peak OH concentrations in the tropical lower troposphere, unlike other models which show peak concentrations in the tropical upper troposphere. This is likely to affect the lifetime and transport of important trace gases and warrants further investigation. [ABSTRACT FROM AUTHOR]

The TOMCAT 3-D chemical transport model has been updated with the emissions and chemical degradation of ethene, propene, toluene, butane and monoterpenes. The full tropospheric chemical mechanism is described and the model is evaluated against a range of surface, balloon, aircraft and satellite measurements. The model is generally able to capture the main spatial and seasonal features of high and low concentrations of carbon monoxide (CO), ozone (O3), volatile organic compounds (VOCs) and reactive nitrogen. However, model biases are found, some of which are common to chemistry models and some that are specific to TOMCAT and warrant further investigation. Simulated O3 is found to generally lie within the range of ozonesonde observations and shows good agreement with surface sites. The most notable exceptions to this are during winter at high latitudes, when O3 is underestimated, and during summer over North America, when O3 is overestimated. Global Ozone Monitoring Experiment-2 (GOME-2) comparisons suggest that TOMCAT sub-column tropospheric O3 in DJF may also be underestimated outside of the Arctic, particularly near tropical regions. TOMCAT CO is negatively biased during winter and spring in the Northern Hemisphere (NH) when compared to ground-based observations and MOPITT (Measurements Of Pollution In The Troposphere) satellite data. In contrast, CO is positively biased throughout the year in the Southern Hemisphere (SH). The negative bias in the NH is a common feature in chemistry models and TOMCAT lies well within the range of biases found in other models, while the TOMCAT SH positive bias is at the upper range of positive biases reported in other models. Using two simulations with different boundary conditions highlighted the sensitivity of model performance to the chosen emission dataset when simulating VOCs, nitrogen oxides (NOx) and peroxyacetyl nitrate (PAN). VOC measurements show winter/spring negative biases in C2-C3 alkanes and alkenes, which is likely driven by underestimated anthropogenic emissions. TOMCAT is able to capture the seasonal minima and maxima of PAN and HNO3. However, comparisons to an aircraft climatology show that PAN may be overestimated in winter and HNO3 may be overestimated in winter and spring in regions over North America. The model showed different biases in NOx, depending on location, with evidence of underestimated Asian emissions contributing to negative model biases over China and underestimated fire emissions contributing to negative biases in the SH. TOMCAT global mean tropospheric hydroxyl radical (OH) concentrations are higher than estimates inferred from observations of methyl chloroform, but similar to, or lower than, multi-model mean concentrations reported in recent model intercomparison studies. TOMCAT shows peak OH concentrations in the tropical lower troposphere, unlike other models, which show peak concentrations in the tropical upper troposphere. This is likely to affect the lifetime and transport of important trace gases and warrants further investigation. [ABSTRACT FROM AUTHOR]

Abstract: Alkyl nitrates (C1–C5) were measured at two sites (near urban and rural) in southeast England during the Tropospheric Organic Chemistry Experiment (TORCH). Methyl nitrate was the dominant species during both campaigns accounting for on average about one third of the total measured alkyl nitrates. High mixing ratios (>50 pptv) and variability of methyl nitrate were observed at the near urban site (TORCH1) that were not seen at the rural site (TORCH2) and which could not be explained by local photochemical production or direct emissions. The diurnal variation of methyl nitrate during TORCH1 showed a morning maximum that would be consistent with nighttime chemistry followed by transport to the surface by boundary layer dynamics. Similarly, elevated morning mixing ratios were also observed during TORCH2 although the magnitudes were much smaller. As a result, methyl nitrate could represent a tracer for nighttime chemistry seen at the ground the following day. At both campaigns, the dominant source of short chain alkyl nitrates and carbonyl precursor radicals (≤C4) were from decomposition of larger compounds. The magnitude of the source increased with decreasing carbon number consistent with increasing total precursor abundance. Non-photochemical emissions of acetaldehyde and acetone could not be accounted for by automobile exhaust emissions alone and indicated that other direct sources are likely important in this environment. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]

The ability of meteorological models to accurately characterise regional meteorology plays a crucial role in the performance of photochemical simulations of air pollution. As part of the research funded by the Australian government's Department of the Environment Clean Air and Urban Landscape hub, this study set out to complete an intercomparison of air quality models over the Sydney region. This intercomparison would test existing modelling capabilities, identify any problems and provide the necessary validation of models in the region. The first component of the intercomparison study was to assess the ability of the models to reproduce meteorological observations, since it is a significant driver of air quality. To evaluate the meteorological component of these air quality modelling systems, seven different simulations based on varying configurations of inputs, integrations and physical parameterizations of two meteorological models (the Weather Research and Forecasting (WRF) and Conformal Cubic Atmospheric Model (CCAM)) were examined. The modelling was conducted for three periods coinciding with comprehensive air quality measurement campaigns (the Sydney Particle Studies (SPS) 1 and 2 and the Measurement of Urban, Marine and Biogenic Air (MUMBA)). The analysis focuses on meteorological variables (temperature, mixing ratio of water, wind (via wind speed and zonal wind components), precipitation and planetary boundary layer height), that are relevant to air quality. The surface meteorology simulations were evaluated against observations from seven Bureau of Meteorology (BoM) Automatic Weather Stations through composite diurnal plots, Taylor plots and paired mean bias plots. Simulated vertical profiles of temperature, mixing ratio of water and wind (via wind speed and zonal wind components) were assessed through comparison with radiosonde data from the Sydney Airport BoM site. The statistical comparisons with observations identified systematic overestimations of wind speeds that were more pronounced overnight. The temperature was well simulated, with biases generally between ±2 °C and the largest biases seen overnight (up to 4 °C). The models tend to have a drier lower atmosphere than observed, implying that better representations of soil moisture and surface moisture fluxes would improve the subsequent air quality simulations. On average the models captured local-scale meteorological features, like the sea breeze, which is a critical feature driving ozone formation in the Sydney Basin. The overall performance and model biases were generally within the recommended benchmark values (e.g., ±1 °C mean bias in temperature, ±1 g/kg mean bias of water vapour mixing ratio and ±1.5 m s−1 mean bias of wind speed) except at either end of the scale, where the bias tends to be larger. The model biases reported here are similar to those seen in other model intercomparisons. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Poor air quality is often associated with hot weather, but the quantitative attribution of high temperatures on air quality remains unclear. In this study, the effect of elevated temperatures on air quality is investigated in Greater Sydney using January 2013, a period of extreme heat during which temperatures at times exceeded 40 °C, as a case study. Using observations from 17 measurement sites and the Weather Research and Forecasting Chemistry (WRF-Chem) model, we analyse the effect of elevated temperatures on ozone in Sydney by running a number of sensitivity studies in which: (1) the model is run with biogenic emissions generated by MEGAN and separately run with monthly average Model of Emissions of Gases and Aerosols from Nature (MEGAN) biogenic emissions (for January 2013); (2) the model results from the standard run are compared with those in which average temperatures (for January 2013) are only applied to the chemistry; (3) the model is run using both averaged biogenic emissions and temperatures; and (4 and 5) the model is run with half and zero biogenic emissions. The results show that the impact on simulated ozone through the effect of temperature on reaction rates is similar to the impact via the effect of temperature on biogenic emissions and the relative impacts are largely additive when compared to the run in which both are averaged. When averaged across 17 sites in Greater Sydney, the differences between ozone simulated under standard and averaged model conditions are as high as 16 ppbv. Removing biogenic emissions in the model has the effect of removing all simulated ozone episodes during extreme heat periods, highlighting the important role of biogenic emissions in Australia, where Eucalypts are a key biogenic source. [ABSTRACT FROM AUTHOR]