Particle number size distributions are among the most important parameters in trying to understand the characteristics of particle population. Atmospheric particles were measured in an interaction of ...mixed environments in the Southeastern coastal city of Wollongong, Australia, during a comprehensive field campaign known as Measurements of Urban, Marine and Biogenic Air (MUMBA). MUMBA ran in summer season between 21st December 2012 and 15th February 2013. Particle number concentrations measured during this campaign were indicative of the interplay between marine environments and urban air which met the objective of this campaign. Particle number size distributions ranging from 14 nm to 660 nm in diameter, as measured by Scanning Mobility Particle Sizer (SMPS) in this study, were grouped using Principal Component Analysis. Based on strong component loadings (value ≥ 0.75), three different factors were identified (i) Small Factor (NS): 15 nm < Dp < 50 nm, (ii) Medium Factor (NM): 60 nm < Dp < 150 nm and (iii) Large Factor (NL): 210 nm < Dp < 450 nm. The three factors describe 89% of the dataset cumulative variance. Particles in this region are dependent upon the interaction between the sources, and cannot be viewed as a simple mixture of biogenic and anthropogenic sources associated with various mechanical processes. The particles observed in the morning were found to be influenced by combustion emissions, presumably primarily from traffic, which is most obvious in NL. The particle population during the day was found to be influenced by a mixture of marine sources and secondary aerosols production initiated by photochemical oxidation. The local steel works and the urban environment were the major contributors of particles at night. Secondary organic aerosols were identified in this study by the mass ratio of organic carbon to elemental carbon (OC/EC). Biogenic sources influenced secondary organic aerosols formation as a moderate correlation (R2 = 0.6) was observed between secondary organic aerosols mass and biogenic isoprene. The processes described in this paper are likely repeated at other coastal urban environments worldwide.
•Particle number size distributions were observed in a mixed natural and urban-coastal city environments.•Particle number size distributions were grouped using Principal Component Analysis.•The particle population was influenced by the transport of air pollutants from the north, primarily from Sydney.•Morning rush hours coincide with enhanced level of particle number size distribution ranging from 14 nm to 660 nm in diameter.•Secondary organic aerosols were observed.
Volatile organic compounds (VOCs) play a key role in the formation of ozone and secondary organic aerosol, the two most important air pollutants in Sydney, Australia. Despite their importance, there ...are few available VOC measurements in the area. In this paper, we discuss continuous GC-MS measurements of 10 selected VOCs between February (summer in the southern hemisphere) and June (winter in the southern hemisphere) of 2019 in a semi-urban area between natural eucalypt forest and the Sydney metropolitan fringe. Combined, isoprene, methacrolein, methyl-vinyl-ketone, α-pinene, p-cymene, eucalyptol, benzene, toluene xylene and tri-methylbenzene provide a reasonable representation of variability in the total biogenic VOC (BVOC) and anthropogenic VOC (AVOC) loading in the area. Seasonal changes in environmental conditions were reflected in observed BVOC concentrations, with a summer peak of 8 ppb, dropping to approximately 0.1 ppb in winter. Isoprene, and its immediate oxidation products methacrolein (MACR) and methyl-vinyl-ketone (MVK), dominated BVOC concentrations during summer and early autumn, while monoterpenes comprised the larger fraction during winter. Temperature and solar radiation drive most of the seasonal variation observed in BVOCs. Observed levels of isoprene, MACR and MVK in the atmosphere are closely related with variations in temperature and photosynthetically active radiation (PAR), but chemistry and meteorology may play a more important role for the monoterpenes. Using a nonlinear model, temperature explains 51% and PAR 38% of the isoprene, MACR and MVK variation. Eucalyptol dominated the observed monoterpene fraction (contributing ~75%), with p-cymene (20%) and α-pinene (5%) also present. AVOCs maintain an average concentration of ~0.4 ppb, with a slight decrease during autumn–winter. The low AVOC concentrations observed indicate a relatively small anthropogenic influence, generally occurring when (rare) northerly winds transport Sydney emissions to the measurement site. The site is influenced by domestic, commercial and vehicle AVOC emissions. Our observed AVOC concentrations can be explained by the seasonal changes in meteorology and the emissions in the area as listed in the NSW emissions inventory and thereby act as an independent validation of this inventory. We conclude that the variations in atmospheric composition observed during the seasons are an important variable to consider when formulating air pollution control policies over Sydney given the influence of biogenic sources during summer, autumn and winter.
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.
Firefighters in the line of duty are exposed to many hazardous air toxics released from burning vegetation and other materials that may cause severe health risks. Current literature does not consider ...complex mixtures and cumulative impacts of these air toxics when assessing firefighter exposure risk. This study aims to determine if cumulative exposure to mixtures of air toxics will put firefighters at risk in the context of current firefighter exposure regulations. This was tested using CO exposure and air toxics data collected during prescribed burns. Personal CO monitors were deployed to 122 firefighters at 31 prescribed burns across Australian temperate forests. CO concentrations were used to determine equivalent exposures for air toxics using their emission ratio, which were then used to calculate cumulative exposure. Average personal monitor CO exposure was 9.2 ppm across all burns, with 1-min maximum ranging between 0.3 and 703 ppm. Work safe Australia exposure standards were exceeded for 6 firefighters out of 122. Five individual firefighters had average cumulative exposures exceeding 100% exposure limit. Six toxicological classes were identified to be the most hazardous, causing eye disorders, upper and lower respiratory disorders, skin disorders and cancer. Across these classes, three toxins contributed significantly to the cumulative exposure; respirable particles (< 3 μm), formaldehyde and acrolein. Using the cumulative exposure methodology as described here and a reduction in the CO exposure standards to account for the numerous air toxics emitted during a fire would supply decision makers with a tool to better identify exposure risks.
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.
The Sydney Particle Study involved the comprehensive measurement of
meteorology, particles and gases at a location in western Sydney during
February–March 2011 and April–May 2012. The aim of this ...study was to
increase scientific understanding of particle formation and transformations
in the Sydney airshed. In this paper we describe the methods used to collect
and analyse particle and gaseous samples, as well as the methods employed
for the continuous measurement of particle concentrations, particle
microphysical properties, and gaseous concentrations. This paper also
provides a description of the data collected and is a metadata record for
the data sets published in Keywood et al. (2016a,
https://doi.org/10.4225/08/57903B83D6A5D) and Keywood et al. (2016b,
https://doi.org/10.4225/08/5791B5528BD63).
Environmental cycling of the toxic metal mercury (Hg) is ubiquitous, and still not completely understood. Volatilisation and emission of mercury from vegetation, litter and soil during burning ...represents a significant return pathway for previously-deposited atmospheric mercury. Rates of such emission vary widely across ecosystems as they are dependent on species-specific uptake of atmospheric mercury as well as fire return frequencies. Wildfire burning in Australia is currently thought to contribute between 1 and 5% of the global total of mercury emissions, yet no modelling efforts to date have utilised local mercury emission factors (mass of emitted mercury per mass of dry fuel) or local mercury emission ratios (ratio of emitted mercury to another emitted species, typically carbon monoxide). Here we present laboratory and field investigations into mercury emission from burning of surface fuels in dry sclerophyll forests, native to the temperate south-eastern region of Australia. From laboratory data we found that fire behaviour — in particular combustion phase — has a large influence on mercury emission and hence emission ratios. Further, emission of mercury was predominantly in gaseous form with particulate-bound mercury representing <1% of total mercury emission. Importantly, emission factors and emission ratios with respect to carbon monoxide and carbon dioxide, from both laboratory and field data all show that gaseous mercury emission from biomass burning in Australian dry sclerophyll forests is currently overestimated by around 60%. Based on these results, we recommend a mercury emission factor of 28.7 ± 8.1 μg Hg kg−1 dry fuel, and emission ratio of gaseous elemental mercury relative to carbon monoxide of 0.58 ± 0.01 × 10−7, for estimation of mercury release from the combustion of Australian dry sclerophyll litter.
•Wildfire mercury release from eucalypt forests is currently overestimated by 60%.•Under natural wildfire fuel moistures, 99% of release is elemental mercury.•Fire progression impacts heavily; most mercury is released during flaming stage.•Recommended emission ratio for GEM/CO is 0.58 ± 0.01 × 10−7.
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.
PDF
