Special Issue "Air Quality in New South Wales, Australia"

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Air Quality".

Deadline for manuscript submissions: closed (28 February 2019)

Special Issue Editors

Guest Editor
Prof. Howard A. Bridgman

1. Editor, Air Quality and Climate Change
2. President, Clean Air Society of Australia and New Zealand
3. School of Environmental and Life Sciences, University of Newcastle, 2308 NSW, Australia
Website | E-Mail
Interests: air pollution meteorology; air pollution management; air pollution sources and emissions; air pollution impacts; aerosol and particle pollution
Guest Editor
Dr. Robyn Schofield

Director of the Environmental Science Hub, School of Earth Sciences, University of Melbourne, Australia
Website | E-Mail
Interests: spectroscopic observations of trace gas species; radiative transfer modelling; stratospheric ozone loss kinetics; tropical tropopause layer processes driving stratospheric composition; microphysical modelling; coupled chemistry-climate modelling; urban air quality and health

Special Issue Information

Dear Colleagues,

This proposed Special Issue on “Air Quality in New South Wales” presents the findings of new air quality research in Australia undertaken by (or in association with) the Clean Air and Urban Landscapes hub, which is funded by the National Environmental Science Program on behalf of the Australian Government’s Department of the Environment (see https://www.nespurban.edu.au/ ).

Air quality in Sydney, like most Australian cities, is generally quite good, with typical concentrations of key pollutants at much lower levels than experienced in many other parts of the world. Nevertheless, Australian cities do experience occasional exceedances in ozone and PM2.5, as well as extreme pollution events, often as a result of bushfires or dust storms. Even in the absence of extreme events, natural emissions play a significant role in influencing the Australian urban air-sheds, due to the remoteness from large regional anthropogenic sources. By studying air quality in regions such as New South Wales, we can gain a greater understanding of the underlying atmospheric chemistry in cleaner atmospheric environments. These conditions may be representative of future air quality scenarios for parts of the Northern Hemisphere, as legislation and cleaner technologies reduce man-made air pollution in European, American and Asian cities.

The proposed Special Issue will bring together a comprehensive examination of air quality in Sydney and the greater metropolitan region of New South Wales. It will include a series of papers that describe detailed atmospheric composition and spatial and temporal variability of air quality in the region, using data from the statutory air quality monitoring network and a number of targeted measurement campaigns, including:

  • The Western Air-Shed Particulate Study for Sydney (WASPSS ).
  • Roadside Atmospheric Particulates in Sydney (RAPS)
  • Measurements of Urban Marine and Biogenic Air (MUMBA)
  • The Sydney Particle Study 1 & 2 (SPS1 and SPS2)

This characterization of atmospheric composition in the region is a significant advance on what currently exists in the scientific literature.

The results of the first major intercomparison of air quality models in Australia are presented in a series of papers within the special issue. The modelling intercomparison uses data from 3 measurement campaigns described above (SPS1, SPS2 and MUMBA). 6 models were used including:

  • 2 versions of the Conformal Cubic Atmospheric Model and Chemical Transport Model (CCAM –CTM) – including a benchmarking paper
  • 2 versions of Weather Research and Forecasting model with chemistry (WRF/Chem)
  • 1 version of WRF/Chem with the Regional Ocean Model System (ROMS) (WRF/Chem-ROMS)
  • 1 version of the Community Multiscale Air Quality (CMAQ) model

All the papers examine aspects of air quality within the greater metropolitan region of New South Wales, making the papers a clear coherent set.

Prof. Howard A. Bridgman


Dr. Robyn Schofield
Guest Editors

Manuscript Submission Information

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Published Papers (13 papers)

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Research

Open AccessArticle
Particle Formation in a Complex Environment
Atmosphere 2019, 10(5), 275; https://doi.org/10.3390/atmos10050275
Received: 29 April 2019 / Revised: 7 May 2019 / Accepted: 9 May 2019 / Published: 14 May 2019
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Abstract
A field aerosol measurement campaign as part of the Measurements of Urban, Marine and Biogenic Air (MUMBA) campaign was conducted between 16 January 2013 and 15 February 2013 in the coastal city of Wollongong, Australia. The objectives of this research were to study [...] Read more.
A field aerosol measurement campaign as part of the Measurements of Urban, Marine and Biogenic Air (MUMBA) campaign was conducted between 16 January 2013 and 15 February 2013 in the coastal city of Wollongong, Australia. The objectives of this research were to study the occurrence frequency, characteristics and factors that influence new particle formation processes. Particle formation and growth events were observed from particle number size distribution data in the range of 14 nm–660 nm measured using a scanning particle mobility sizer (SMPS). Four weak Class I particle formation and growth event days were observed, which is equivalent to 13% of the total observation days. The events occurred during the day, starting after 8:30 Australian Eastern Standard time with an average duration of five hours. The events also appeared to be positively linked to the prevailing easterly to north easterly sea breezes that carry pollutants from sources in and around Sydney. This suggests that photochemical reactions and a combination of oceanic and anthropogenic air masses are among the factors that influenced these events. Full article
(This article belongs to the Special Issue Air Quality in New South Wales, Australia)
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Graphical abstract

Open AccessArticle
Roadside Moss Turfs in South East Australia Capture More Particulate Matter Along an Urban Gradient than a Common Native Tree Species
Atmosphere 2019, 10(4), 224; https://doi.org/10.3390/atmos10040224
Received: 28 February 2019 / Revised: 18 April 2019 / Accepted: 19 April 2019 / Published: 24 April 2019
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Abstract
Urbanisation largely consists of removing native vegetation. Plants that remain interact with air quality in complex ways. Pollutants can be detrimental to plant growth; plants sometimes reduce air quality, yet some species also improve it through phytoremediation. A common pollutant of concern to [...] Read more.
Urbanisation largely consists of removing native vegetation. Plants that remain interact with air quality in complex ways. Pollutants can be detrimental to plant growth; plants sometimes reduce air quality, yet some species also improve it through phytoremediation. A common pollutant of concern to human health in urban areas is particulate matter (PM), small particles of solid or liquid. Our study compared roadside moss turfs with leaves of a common Australian tree species, Pittosporum undulatum, in their ability to capture PM along an urban gradient. We sampled nine sites, three in each of three levels of urbanisation: low, medium, and high according to road type (freeway, suburban road, quiet peri-urban road). In addition, we deployed a PM monitor over a two-week period in one site of each urban level to provide concentrations of PM2.5. We used chlorophyll fluorescence (Fv/Fm; maximum quantum yield of photosystem II) as a measure of plant stress. We extracted PM in three size fractions using a filtration and washing technique with water and chloroform. Site averages for moss turfs were between 5.60 and 33.00 mg per g dry weight for total PM compared to between 2.15 and 10.24 mg per g dry weight for the tree leaves. We found that moss was more sensitive to increasing urbanisation, both in terms of trapping proportionately more PM than the leaves, and also in terms of photosynthetic stress, with moss Fv/Fm declining by a site average of 40% from low to high urban “class” (0.76 to 0.45). Our study highlights the stressors potentially limiting moss persistence in cities. It also demonstrates its ability to trap PM, a trait that could be useful in urban applications relating to urban greening or air quality. Full article
(This article belongs to the Special Issue Air Quality in New South Wales, Australia)
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Open AccessArticle
Understanding Spatial Variability of Air Quality in Sydney: Part 2—A Roadside Case Study
Atmosphere 2019, 10(4), 217; https://doi.org/10.3390/atmos10040217
Received: 22 February 2019 / Revised: 5 April 2019 / Accepted: 9 April 2019 / Published: 23 April 2019
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Abstract
Motivated by public interest, the Clean Air and Urban Landscapes (CAUL) hub deployed instrumentation to measure air quality at a roadside location in Sydney. The main aim was to compare concentrations of fine particulate matter (PM2.5) measured along a busy road [...] Read more.
Motivated by public interest, the Clean Air and Urban Landscapes (CAUL) hub deployed instrumentation to measure air quality at a roadside location in Sydney. The main aim was to compare concentrations of fine particulate matter (PM2.5) measured along a busy road section with ambient regional urban background levels, as measured at nearby regulatory air quality stations. The study also explored spatial and temporal variations in the observed PM2.5 concentrations. The chosen area was Randwick in Sydney, because it was also the subject area for an agent-based traffic model. Over a four-day campaign in February 2017, continuous measurements of PM2.5 were made along and around the main road. In addition, a traffic counting application was used to gather data for evaluation of the agent-based traffic model. The average hourly PM2.5 concentration was 13 µg/m3, which is approximately twice the concentrations at the nearby regulatory air quality network sites measured over the same period. Roadside concentrations of PM2.5 were about 50% higher in the morning rush-hour than the afternoon rush hour, and slightly lower (reductions of <30%) 50 m away from the main road, on cross-roads. The traffic model under-estimated vehicle numbers by about 4 fold, and failed to replicate the temporal variations in traffic flow, which we assume was due to an influx of traffic from outside the study region dominating traffic patterns. Our findings suggest that those working for long hours outdoors at busy roadside locations are at greater risk of suffering detrimental health effects associated with higher levels of exposure to PM2.5. Furthermore, the worse air quality in the morning rush hour means that, where possible, joggers and cyclists should avoid busy roads around these times. Full article
(This article belongs to the Special Issue Air Quality in New South Wales, Australia)
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Open AccessArticle
Multiscale Applications of Two Online-Coupled Meteorology-Chemistry Models during Recent Field Campaigns in Australia, Part II: Comparison of WRF/Chem and WRF/Chem-ROMS and Impacts of Air-Sea Interactions and Boundary Conditions
Atmosphere 2019, 10(4), 210; https://doi.org/10.3390/atmos10040210
Received: 2 April 2019 / Revised: 15 April 2019 / Accepted: 18 April 2019 / Published: 20 April 2019
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Abstract
Air-sea interactions play an important role in atmospheric circulation and boundary layer conditions through changing convection processes and surface heat fluxes, particularly in coastal areas. These changes can affect the concentrations, distributions, and lifetimes of atmospheric pollutants. In this Part II paper, the [...] Read more.
Air-sea interactions play an important role in atmospheric circulation and boundary layer conditions through changing convection processes and surface heat fluxes, particularly in coastal areas. These changes can affect the concentrations, distributions, and lifetimes of atmospheric pollutants. In this Part II paper, the performance of the Weather Research and Forecasting model with chemistry (WRF/Chem) and the coupled WRF/Chem with the Regional Ocean Model System (ROMS) (WRF/Chem-ROMS) are intercompared for their applications over quadruple-nested domains in Australia during the three following field campaigns: The Sydney Particle Study Stages 1 and 2 (SPS1 and SPS2) and the Measurements of Urban, Marine, and Biogenic Air (MUMBA). The results are used to evaluate the impact of air-sea interaction representation in WRF/Chem-ROMS on model predictions. At 3, 9, and 27 km resolutions, compared to WRF/Chem, the explicit air-sea interactions in WRF/Chem-ROMS lead to substantial improvements in simulated sea-surface temperature (SST), latent heat fluxes (LHF), and sensible heat fluxes (SHF) over the ocean, in terms of statistics and spatial distributions, during all three field campaigns. The use of finer grid resolutions (3 or 9 km) effectively reduces the biases in these variables during SPS1 and SPS2 by WRF/Chem-ROMS, whereas it further increases these biases for WRF/Chem during all field campaigns. The large differences in SST, LHF, and SHF between the two models lead to different radiative, cloud, meteorological, and chemical predictions. WRF/Chem-ROMS generally performs better in terms of statistics and temporal variations for temperature and relative humidity at 2 m, wind speed and direction at 10 m, and precipitation. The percentage differences in simulated surface concentrations between the two models are mostly in the range of ±10% for CO, OH, and O3, ±25% for HCHO, ±30% for NO2, ±35% for H2O2, ±50% for SO2, ±60% for isoprene and terpenes, ±15% for PM2.5, and ±12% for PM10. WRF/Chem-ROMS at 3 km resolution slightly improves the statistical performance of many surface and column concentrations. WRF/Chem simulations with satellite-constrained boundary conditions (BCONs) improve the spatial distributions and magnitudes of column CO for all field campaigns and slightly improve those of the column NO2 for SPS1 and SPS2, column HCHO for SPS1 and MUMBA, and column O3 for SPS2 at 3 km over the Greater Sydney area. The satellite-constrained chemical BCONs reduce the model biases of surface CO, NO, and O3 predictions at 3 km for all field campaigns, surface PM2.5 predictions at 3 km for SPS1 and MUMBA, and surface PM10 predictions at all grid resolutions for all field campaigns. A more important role of chemical BCONs in the Southern Hemisphere, compared to that in the Northern Hemisphere reported in this work, indicates a crucial need in developing more realistic chemical BCONs for O3 in the relatively clean SH. Full article
(This article belongs to the Special Issue Air Quality in New South Wales, Australia)
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Open AccessArticle
Vehicle Ammonia Emissions Measured in An Urban Environment in Sydney, Australia, Using Open Path Fourier Transform Infra-Red Spectroscopy
Atmosphere 2019, 10(4), 208; https://doi.org/10.3390/atmos10040208
Received: 25 February 2019 / Revised: 12 April 2019 / Accepted: 13 April 2019 / Published: 19 April 2019
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Abstract
Airborne particulate matter (PM) is a major health risk in urban settings. Ammonia (NH3) from vehicle exhaust is an under-recognised ingredient in the formation of inorganic PM and there remains a shortage of data to properly quantify the role of NH [...] Read more.
Airborne particulate matter (PM) is a major health risk in urban settings. Ammonia (NH3) from vehicle exhaust is an under-recognised ingredient in the formation of inorganic PM and there remains a shortage of data to properly quantify the role of NH3 from vehicles in PM formation. An Open-path Fourier transform infra-red (OP-FTIR) spectrometer measured atmospheric NH3, carbon monoxide (CO) and carbon dioxide (CO2) at high temporal resolution (5 min) in Western Sydney over 11 months. The oxides of nitrogen (NO2 and NO; NOx) and sulphur dioxide (SO2) were measured at an adjacent air quality monitoring station. NH3 levels were maxima in the morning and evening coincident with peak traffic. During peak traffic NH3:CO ratio ranged from 0.018 to 0.022 ppbv:ppbv. Results were compared with the Greater Metropolitan Region 2008 (GMR2008) emissions inventory. Measured NH3:CO was higher during peak traffic times than the GMR2008 emissions estimates, indicating an underestimation of vehicle NH3 emissions in the inventory. Measurements also indicated the urban atmosphere was NH3 rich for the formation of ammonium sulphate ((NH4)2SO4) particulate was SO2 limited while the formation of ammonium nitrate (NH4NO3) was NH3 limited. Any reduction in NOx emissions with improved catalytic converter efficiency will be accompanied by an increase in NH3 production and potentially with an increase in NH4NO3 particulate. Full article
(This article belongs to the Special Issue Air Quality in New South Wales, Australia)
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Open AccessArticle
Multiscale Applications of Two Online-Coupled Meteorology-Chemistry Models during Recent Field Campaigns in Australia, Part I: Model Description and WRF/Chem-ROMS Evaluation Using Surface and Satellite Data and Sensitivity to Spatial Grid Resolutions
Atmosphere 2019, 10(4), 189; https://doi.org/10.3390/atmos10040189
Received: 26 February 2019 / Revised: 1 April 2019 / Accepted: 4 April 2019 / Published: 8 April 2019
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Abstract
Air pollution and associated human exposure are important research areas in Greater Sydney, Australia. Several field campaigns were conducted to characterize the pollution sources and their impacts on ambient air quality including the Sydney Particle Study Stages 1 and 2 (SPS1 and SPS2), [...] Read more.
Air pollution and associated human exposure are important research areas in Greater Sydney, Australia. Several field campaigns were conducted to characterize the pollution sources and their impacts on ambient air quality including the Sydney Particle Study Stages 1 and 2 (SPS1 and SPS2), and the Measurements of Urban, Marine, and Biogenic Air (MUMBA). In this work, the Weather Research and Forecasting model with chemistry (WRF/Chem) and the coupled WRF/Chem with the Regional Ocean Model System (ROMS) (WRF/Chem-ROMS) are applied during these field campaigns to assess the models’ capability in reproducing atmospheric observations. The model simulations are performed over quadruple-nested domains at grid resolutions of 81-, 27-, 9-, and 3-km over Australia, an area in southeastern Australia, an area in New South Wales, and the Greater Sydney area, respectively. A comprehensive model evaluation is conducted using surface observations from these field campaigns, satellite retrievals, and other data. This paper evaluates the performance of WRF/Chem-ROMS and its sensitivity to spatial grid resolutions. The model generally performs well at 3-, 9-, and 27-km resolutions for sea-surface temperature and boundary layer meteorology in terms of performance statistics, seasonality, and daily variation. Moderate biases occur for temperature at 2-m and wind speed at 10-m in the mornings and evenings due to the inaccurate representation of the nocturnal boundary layer and surface heat fluxes. Larger underpredictions occur for total precipitation due to the limitations of the cloud microphysics scheme or cumulus parameterization. The model performs well at 3-, 9-, and 27-km resolutions for surface O3 in terms of statistics, spatial distributions, and diurnal and daily variations. The model underpredicts PM2.5 and PM10 during SPS1 and MUMBA but overpredicts PM2.5 and underpredicts PM10 during SPS2. These biases are attributed to inaccurate meteorology, precursor emissions, insufficient SO2 conversion to sulfate, inadequate dispersion at finer grid resolutions, and underprediction in secondary organic aerosol. The model gives moderate biases for net shortwave radiation and cloud condensation nuclei but large biases for other radiative and cloud variables. The performance of aerosol optical depth and latent/sensible heat flux varies for different simulation periods. Among all variables evaluated, wind speed at 10-m, precipitation, surface concentrations of CO, NO, NO2, SO2, O3, PM2.5, and PM10, aerosol optical depth, cloud optical thickness, cloud condensation nuclei, and column NO2 show moderate-to-strong sensitivity to spatial grid resolutions. The use of finer grid resolutions (3- or 9-km) can generally improve the performance for those variables. While the performance for most of these variables is consistent with that over the U.S. and East Asia, several differences along with future work are identified to pinpoint reasons for such differences. Full article
(This article belongs to the Special Issue Air Quality in New South Wales, Australia)
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Open AccessArticle
Understanding Spatial Variability of Air Quality in Sydney: Part 1—A Suburban Balcony Case Study
Atmosphere 2019, 10(4), 181; https://doi.org/10.3390/atmos10040181
Received: 25 February 2019 / Revised: 20 March 2019 / Accepted: 2 April 2019 / Published: 4 April 2019
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Abstract
There is increasing awareness in Australia of the health impacts of poor air quality. A common public concern raised at a number of “roadshow” events as part of the federally funded Clean Air and Urban Landscapes Hub (CAUL) project was whether or not [...] Read more.
There is increasing awareness in Australia of the health impacts of poor air quality. A common public concern raised at a number of “roadshow” events as part of the federally funded Clean Air and Urban Landscapes Hub (CAUL) project was whether or not the air quality monitoring network around Sydney was sampling air representative of typical suburban settings. In order to investigate this concern, ambient air quality measurements were made on the roof of a two-storey building in the Sydney suburb of Auburn, to simulate a typical suburban balcony site. Measurements were also taken at a busy roadside and these are discussed in a companion paper (Part 2). Measurements made at the balcony site were compared to data from three proximate regulatory air quality monitoring stations: Chullora, Liverpool and Prospect. During the 16-month measurement campaign, observations of carbon monoxide, oxides of nitrogen, ozone and particulate matter less than 2.5-µm diameter at the simulated urban balcony site were comparable to those at the closest permanent air quality stations. Despite the Auburn site experiencing 10% higher average carbon monoxide amounts than any of the permanent air quality monitoring sites, the oxides of nitrogen were within the range of the permanent sites and the pollutants of greatest concern within Sydney (PM2.5 and ozone) were both lowest at Auburn. Similar diurnal and seasonal cycles were observed between all sites, suggesting common pollutant sources and mechanisms. Therefore, it is concluded that the existing air quality network provides a good representation of typical pollution levels at the Auburn “balcony” site. Full article
(This article belongs to the Special Issue Air Quality in New South Wales, Australia)
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Open AccessArticle
Major Source Contributions to Ambient PM2.5 and Exposures within the New South Wales Greater Metropolitan Region
Atmosphere 2019, 10(3), 138; https://doi.org/10.3390/atmos10030138
Received: 27 February 2019 / Revised: 8 March 2019 / Accepted: 8 March 2019 / Published: 13 March 2019
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Abstract
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 [...] Read more.
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 6–7 µg/m3, while natural sources including biogenic emissions, sea salt and wind-blown dust contribute 2–4 µg/m3 to it. Summer and winter regional average PM2.5 ranges from 5.2–6.1 µg/m3 and 3.7–7.7 µg/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, “wood heaters” is the first or second major source contributing to total PM2.5 and EC mass across Sydney in winter. “On-road mobile vehicles” 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. “Power stations” 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. “Non-road diesel and marine” plays a relatively important role in EC mass across regions except Illawarra. “Industry” 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 µg/m3) and 40% (2.41 µg/m3) to the population-weighted annual average PM2.5 (5.96 µg/m3). Major source groups “wood heaters”, “industry”, “on-road motor vehicles”, “power stations” and “non-road diesel and marine” 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. Full article
(This article belongs to the Special Issue Air Quality in New South Wales, Australia)
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Open AccessArticle
Skill-Testing Chemical Transport Models across Contrasting Atmospheric Mixing States Using Radon-222
Atmosphere 2019, 10(1), 25; https://doi.org/10.3390/atmos10010025
Received: 27 November 2018 / Revised: 2 January 2019 / Accepted: 3 January 2019 / Published: 11 January 2019
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Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Air Quality in New South Wales, Australia)
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Open AccessArticle
Urban Air Quality in a Coastal City: Wollongong during the MUMBA Campaign
Atmosphere 2018, 9(12), 500; https://doi.org/10.3390/atmos9120500
Received: 26 November 2018 / Revised: 11 December 2018 / Accepted: 12 December 2018 / Published: 17 December 2018
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Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Air Quality in New South Wales, Australia)
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Open AccessArticle
Performance Evaluation of CCAM-CTM Regional Airshed Modelling for the New South Wales Greater Metropolitan Region
Atmosphere 2018, 9(12), 486; https://doi.org/10.3390/atmos9120486
Received: 6 November 2018 / Revised: 1 December 2018 / Accepted: 5 December 2018 / Published: 8 December 2018
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Abstract
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 [...] Read more.
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’s air quality monitoring network, and PM2.5 components were compared with speciated PM measurements from the Sydney Particle Study’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 ±60%, but only PM2.5 predictions at Earlwood further comply with the performance goal for MFB of ±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 ±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. Full article
(This article belongs to the Special Issue Air Quality in New South Wales, Australia)
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Open AccessArticle
Hot Summers: Effect of Extreme Temperatures on Ozone in Sydney, Australia
Atmosphere 2018, 9(12), 466; https://doi.org/10.3390/atmos9120466
Received: 29 September 2018 / Revised: 14 November 2018 / Accepted: 21 November 2018 / Published: 27 November 2018
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Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Air Quality in New South Wales, Australia)
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Open AccessArticle
Source Contributions to Ozone Formation in the New South Wales Greater Metropolitan Region, Australia
Atmosphere 2018, 9(11), 443; https://doi.org/10.3390/atmos9110443
Received: 27 September 2018 / Revised: 9 November 2018 / Accepted: 11 November 2018 / Published: 13 November 2018
Cited by 3 | PDF Full-text (11321 KB) | HTML Full-text | XML Full-text
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Air Quality in New South Wales, Australia)
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