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Volatile organic compounds (VOCs) are important precursors to the formation of ozone and fine particulate matter, the two pollutants of most concern in Sydney, Australia. Despite this importance, there are very few published measurements of ambient VOC concentrations in Australia. In this paper, we present mole fractions of several important VOCs measured during the campaign known as MUMBA (Measurements of Urban, Marine and Biogenic Air) in the Australian city of Wollongong (34°S). We particularly focus on measurements made during periods when clean marine air impacted the measurement site and on VOCs of biogenic origin. Typical unpolluted marine air mole fractions during austral summer 2012-2013 at latitude 34°S were established for CO2 (391.0 ± 0.6 ppm), CH4 (1760.1 ± 0.4 ppb), N2O (325.04 ± 0.08 ppb), CO (52.4 ± 1.7 ppb), O3 (20.5 ± 1.1 ppb), acetaldehyde (190 ± 40 ppt), acetone (260 ± 30 ppt), dimethyl sulphide (50 ± 10 ppt), benzene (20 ± 10 ppt), toluene (30 ± 20 ppt), C8H10 aromatics (23 ± 6 ppt) and C9H12 aromatics (36 ± 7 ppt). The MUMBA site was frequently influenced by VOCs of biogenic origin from a nearby strip of forested parkland to the east due to the dominant north-easterly afternoon sea breeze. VOCs from the more distant densely forested escarpment to the west also impacted the site, especially during two days of extreme heat and strong westerly winds. The relative amounts of different biogenic VOCs observed for these two biomes differed, with much larger increases of isoprene than of monoterpenes or methanol during the hot westerly winds from the escarpment than with cooler winds from the east. However, whether this was due to different vegetation types or was solely the result of the extreme temperatures is not entirely clear. We conclude that the clean marine air and biogenic signatures measured during the MUMBA campaign provide useful information about the typical abundance of several key VOCs and can be used to constrain chemical transport model simulations of the atmosphere in this poorly sampled region of the world.

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.

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.