Between 11 to 15 February 2019, a dust storm originating from Central Australia with persistent westerly and south westerly winds caused high particles concentration at many sites in the state of New South Wales (NSW), both inland and along the coast. The dust continued on to New Zealand and to Antarctica in the south east. This study uses observed data from air quality monitoring stations in NSW and New Zealand, MODIS 3km AOD (Aerosol Optical Depth) product from Terra/Aqua and lidar aerosol profile from CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) satellite data, and the Weather Research Forecast WRF-Chem model based on GOCART-AFWA (Goddard Chemistry Aerosol Radiation and Transport – Air Force and Weather Agency) dust scheme and GOCART aerosol and gas-phase MOZART (Model for Ozone And Related chemical Tracers) chemistry model to study the long-range transport of aerosols for the period 11 to 15 February 2019 across eastern Australia and onto New Zealand and Antarctica. Wild fires also happened in northern NSW at the same time and their emissions are taken into account in WRF-Chem model by using Fire Inventory from NCAR (FINN) as emission input. Modelling results by the WRF-Chem model show that for the Canterbury region of South Island of New Zealand, peak concentration of PM10 (and PM2.5) as measured on 14 February 2019 at 05:00 UTC at the monitoring stations of Geraldine, Ashburton, Timaru and Woolston (Christchurch), which are more than 100km from each other and at Rangiora, Kaiapoi about 2 hours later, correspond to the prediction of high PM10 due to intrusion of dust to ground level from transported dust layer above. The Aerosol Optical Depth (AOD) observation data from MODIS Terra/Aqua and CALIOP lidar measurements on board CALIPSO satellite also indicate that high altitude of dust, originated from this dust storm event in Australia, was located above Antarctica. This study suggests that at present dust storms in Australia can transport dust from sources in Central Australia to the Tasman sea, New Zealand and Antarctica. This process has been going on for at least the last 170k years as indicated by dust found in ice cores from Antarctica and sediment records in the Tasman Sea.

In early 2020 from April to early June, the metropolitan area of Sydney as well as the rest of New South Wales (NSW, Australia) experienced a period of lockdown to prevent the spread of Covid-19 virus in the community. The effect of reducing anthropogenic activities including transportation had an impact on the urban environment in term of air quality which is shown to have improved for a number of pollutants, such as nitrogen dioxides (NO2) and carbon monoxide (CO), based on monitoring data on ground and from satellite. Besides primary pollutants CO and NOx emitted from mobile sources, PM2.5 (primary and secondary) and secondary ozone (O3) during the lockdown period will also be analysed using both air quality data and modelling method. The results show that NO2, CO and PM2.5 levels decreased during the lockdown, but O3 instead increased. The change in the concentration levels however are small considering the large reduction in traffic volume of ~30%. By estimate the decrease in traffic volume in Sydney region, the corresponding decrease in emission input to the WRF-CMAQ (Weather Research and Forecasting - Community Multiscale Air Quality Modeling System) air quality model is then used to estimate the effect of lockdown on the air quality especially CO, NO2, O3 and PM2.5 in the Greater Metropolitan Region (GMR) of Sydney. COVID-19 lockdown period is an ideal case to study the effect of motor vehicle and mobile source contribution to air pollutants such as those listed above in the GMR.

The Upper Hunter Valley is a major coal mining area in New South Wales (NSW), Australia. Due to the ongoing increase of mining activities, PM10 (air-borne particle with an aerodynamic diameter less than 10 micrometres) pollution has become a major air quality concern in local communities. The early part of this study identified two air quality subregions in the valley. This paper aims to provide a holistic summarisation of the relationships between elevated PM10 pollution in two subregions and the local- and synoptic-scale meteorological conditions for spring and summer, when PM10 pollution is relatively high. A catalogue of twelve synoptic types and a set of six local meteorological patterns have been quantitatively derived and linked to each other using the self-organising map (SOM) technique. The complex meteorology-air pollution relationships have been visualised/interpretated on the SOM planes for two representative locations. It was found that the influence of local meteorological patterns differed significantly for mean PM10 levels vs the occurrence of elevated pollution events and between air quality subregions. In contrast, synoptic types showed generally similar relationships with mean vs elevated PM10 pollution in the valley. Two local meteorological patterns, the hot-dry-northwesterly wind conditions and the hot-dry-calm conditions, were found most PM10 pollution conducive in the valley when combined with a set of synoptic counterparts. These synoptic types are featured with the influence of eastward migrating continental high pressure system and westerly troughs, or a ridge extending northwest towards coastal northern NSW or southern Queensland from the Tasman Sea. The method and results can be used in air quality research for other locations of NSW, or similar regions elsewhere.

The planetary boundary layer height (PBLH) is one of the key factors in influencing the dispersion of the air pollutants in the troposphere and hence the air pollutant concentration on ground level. For this reason, accurate air pollutant concentration depends on the performance of PBLH prediction. Recently, ceilometer, a lidar instrument to measure cloud base height, has been used by atmospheric scientists and air pollution control authorities to determine the mixing level height (MLH) in improving forecasting and understanding the evolution of aerosol layers above ground at a site. In this study, ceilometer data at an urban (Lidcombe) and a rural (Merriwa) location in the New South Wales, Australia was used to validate the PBLH prediction from two air quality models (CCAM-CTM and WRF-CMAQ) as well as to understand the aerosol transport from sources to receptor point at Merriwa for the three case studies where high PM10 concentration was detected in each of the three days. The results show that the PBLH prediction by the two air quality models corresponds reasonably well with observed ceilometer data and the cause and source of high PM10 concentration at Merriwa can be found by using ceilometer MLH data to corroborate with back trajectory analysis of transport of aerosols to the receptor point at Merriwa. Of the three case studies, one had aerosol source from north and north west of Merriwa in remote NSW where windblown dust is the main source, and the other two had sources from south and south east of Merriwa where anthropogenic sources dominate,

The 2019-2020 summer wildfire event on the east coast of Australia was a series of major wildfires occurring from November 2019 to end of January 2020 across the states of Queensland, New South Wales (NSW), Victoria and South Australia. The wildfires were unprecedent in scope and the extensive extend of the wildfires has caused smoke pollutants transported not only to New Zealand but across the Pacific Ocean to South America. At the height of the wildfires, smoke plumes were injected into the stratosphere at height up to 25km and hence transported across the globe. Based on meteorological and air quality simulation using WRF-Chem model, air quality monitoring data collected during the bushfire period and remote sensing data from MODIS and CALIPSO satellites, the extend of the wildfires and the pollutant transport, and their impacts on air quality and health on exposed population in NSW can be analysed. The results showed that WRF-Chem model using Fire Emission Inventory from NCAR (FINN) predicts the dispersion and transport of pollutants and the predicted concentration of PM2.5 and other pollutants from wildfires reasonably well when compared with ground-based and satellite data. The impact on health endpoints such as mortality, respiratory and cardiovascular diseases hospitalisation across the modelling domain is then estimated. The estimated health impact is comparable with previous study based only on observation data, but the results in this study provide much more detailed spatially and temporally with regards to the health impact from the 2019-2020 wildfire.