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This paper presents the benefits in terms of air quality from the decommissioning of the coal-fired Liddell Power Station (LPS) in the Greater Metropolitan Region of Sydney (GMR). The impact on air quality (NO, NO2, O3 and PM2.5) are modelled using CCAM-CTM air quality model and the results show that the difference in annual average concentration is small. For NO, the maximum difference is 1 ppb, while for NO2 the value is 0.75 ppb. These are annual average decrease of NO, NO2 concentration over the GMR. The most significant decrease of NO and NO2 occurs in the Upper Hunter near the LPS. For ozone (O3), the reduction of NOx emissions from LPS decommission resulted in a slight increase of O3 over a narrow area near the facility but this increase is gradually decreasing further away from the facility. The increase in O3 is about 0.65 ppb max around LPS. For PM2.5, the annual average decreases in most of the domain. The maximum change is at LPS (about 0.19 µg/m³). [ABSTRACT FROM AUTHOR]

This paper presents two recent simulation studies conducted at NSW Department of Planning and Environment on the impact on air quality across NSW and the Greater Metropolitan Region (GMR) urban areas due to a dust storm in February 2019 and Covid 19 lock down in NSW in 2021. These studies highlight the importance of using suitable air quality models to simulate the emissions and transport of air pollutants over a region to assess the impact of events on air quality and population exposure. The WRF-Chem model is used for the large-scale dust storm event and the WRF-CMAQ is used for assessing emission changes in the GMR due to the Covid-19 lockdown on the urban air quality. Both WRF-Chem and WRF-CMAQ are regional chemical transport models and require high-end Linux-based computing resources. The advantage and disadvantage of applying each of the two models to conduct a particular air quality study will be discussed. [ABSTRACT FROM AUTHOR]

The emission of black carbon (BC) particles, which cause atmospheric warming by affecting radiation budget in the atmosphere, is the result of an incomplete combustion process of organic materials. The recent wildfire event during the summer 2019–2020 in south-eastern Australia was unprecedented in scale. The wildfires lasted for nearly 3 months over large areas of the two most populated states of New South Wales and Victoria. This study on the emission and dispersion of BC emitted from the biomass burnings of the wildfires using the Weather Research Forecast–Chemistry (WRF–Chem) model aims to determine the extent of BC spatial dispersion and ground concentration distribution and the effect of BC on air quality and radiative transfer at the top of the atmosphere, the atmosphere and on the ground. The predicted aerosol concentration and AOD are compared with the observed data using the New South Wales Department of Planning and Environment (DPE) aethalometer and air quality network and remote sensing data. The BC concentration as predicted from the WRF–Chem model, is in general, less than the observed data as measured using the aethalometer monitoring network, but the spatial pattern corresponds well, and the correlation is relatively high. The total BC emission into the atmosphere during the event and the effect on radiation budget were also estimated. This study shows that the summer 2019–2020 wildfires affect not only the air quality and health impact on the east coast of Australia but also short-term weather in the region via aerosol interactions with radiation and clouds. [ABSTRACT FROM AUTHOR]

It has been recognized that the background ozone concentration in urban areas is changing over the years. This article aims to determine the background ozone level (BOL) using ambient air quality data measurements collected at some monitoring stations in the Sydney basin, Australia. A definition of background ozone in the context of the Sydney region is proposed. With this definition, it is possible to estimate the background ozone using ambient measurements of ozone and its precursors. The trend of the BOL is also estimated from the temporal ambient monitoring records as of early 1998-2005. These ozone level changes at different monitoring stations are assessed using the linear regression method. The results are shown to vary between different monitoring sites. This demonstrates that the local conditions at each site are important in determining as to whether an air quality management plan for reducing the ozone level to below the exceedance level is effective and achievable or not. Furthermore, the results obtained are compared with those obtained by the Clapp-Jenkin method, which is based on the relationship between oxidant and nitrogen oxides, assuming a stationary state of photochemical smog function. [ABSTRACT FROM AUTHOR]

This article discusses the use of deep learning technology to enhance air quality forecasting in New South Wales (NSW), Australia. The study analyzes historic air quality monitoring data to fill in gaps and determine background air pollution levels. The researchers applied a hybrid model, combining convolution neural networks (CNN) and long short-term memory (LSTM) neural networks, to forecast hourly ozone and PM2.5 pollution up to 72 hours ahead. The results show that the CNN-LSTM models outperformed benchmark predictions and have the potential to improve air quality forecasting in NSW. [Extracted from the article]

The need for fast responses options to manage the potential risks of exposure of population to pollutants from planned or unplanned activities is becoming critical and require a constant improvement in its management. The Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT) developed by the US National Oceanic and Atmospheric Administration (NOAA), is a complete system for computing both simple air parcel trajectories and complex dispersion and deposition simulations. These simulations include tracking and forecasting the smoke from prescribed burns and wildfires, dust, volcanic ash, the release of radioactive material, and pollutants from industrial incidents. The model can also be aggregated as a decisions support tool in the different phases of emissions events management. It has been tailored to run in New South Wales with finer spatial resolution meteorological data from Bureau of Meteorology to increase the accuracy of the trajectory and dispersion modelling. This system, called 'HYSPLIT in NSW', has been successfully configured to produce operational atmospheric trajectory and dispersion forecasts twice daily since July 2019. The system provides greater certainty in predicting the transportation of smoke from wildfires and hazard reduction burns, and is able to identify areas that are likely to be affected by regional dust storms and industrial incidents. 'HYSPLIT in NSW' also supports both research applications and emergency response events that require modelling the transport and dispersion of harmful pollutants released to the atmosphere (e.g., Port Macquarie peat fire in August 2019). It is an efficient modelling tool as it does not require huge amount of computing resources. The model is often applied to support the needs from air quality regulators or local emergency managers for air parcel trajectory and dispersion forecast. [ABSTRACT FROM AUTHOR]

A crucial aspect of regional air quality modelling is the accurate characterisation of local meteorology, as this plays a significant role in the transport and chemical transformation of pollution across a region. This study explores the potential of The Weather Research and Forecasting (WRF) model as an alternative meteorological model to support near-time air quality predictions for the NSW Greater Metropolitan Region (NSW GMR). We develop an optimal WRF configuration for the NSW GMR by downscaling the latest Bureau of Meteorology (BOM) Australian Community Climate and Earth-System Simulator (ACCESS) Numerical Weather Prediction (NWP) data using a three nested domain setup with resolutions of 12km, 4km and 1 km by varying physical parameterisations including two cumulus schemes, two planetary boundary layer schemes, two microphysical schemes, two compatible shortwave and longwave radiation schemes and a standard land surface model. Thus, a set of 16 experiments were executed and evaluated on different days involving high ozone days, bushfire smoke, dust storms and heatwaves. Each simulation was initialised at 12UTC with a forecast horizon of four days, including three hourly updates of boundary conditions. All simulations were evaluated with metrics including Mean Absolute Errors (MAE) and Index of Agreement (IOA) calculated over selected stations operated by NSW Department of Planning, Industry and Environment (NSW DPIE) and BOM for surface and vertical variables (temperature (T), relative humidity (RH), wind speed (WS)) including precipitation (PRECIP) and planetary boundary layer heights (PBLH). Overall, results showed that the best configuration produced acceptable agreements at the surface for T (IOA > 0.8), RH (IOA > 0.7) and WSP (IOA > 0.6). Errors in PBLH were elevated in the mid-boundary layer but was within 100m where vertical profiles of temperature and winds were well simulated. Similarly, experiments with double moment microphysical schemes were better in simulating precipitation. Other performance metrics for the experiments conducted under different case days will also be discussed. [ABSTRACT FROM AUTHOR]

China has implemented two national clean air actions in 2013–2017 and 2018–2020, respectively, with the aim of reducing primary emissions and hence improving air quality at a national level. It is important to examine the effectiveness of such emission reductions and assess the resulting changes in air quality. However, such evaluation is difficult as meteorological factors can amplify, or obscure the changes of air pollutants, in addition to the emission reduction. In this study, we applied the random forest machine learning technique to decouple meteorological influences from emissions changes, and examined the deweathered trends of air pollutants in 12 Chinese mega-cities during 2013–2020. The observed concentrations of all criteria pollutants except O 3 showed significant declines from 2013 to 2020, with PM 2.5 annual decline rates of 6–9% in most cities. In contrast, O 3 concentrations increased with annual growth rates of 1–9%. Compared with the observed results, all the pollutants showed smoothed but similar variation in trend and annual rate-of-change after weather normalization. The response of O 3 to NO 2 concentrations indicated significant regional differences in photochemical regimes, and the differences between observed and deweathered results provided implications for volatile organic compound emission reductions in O 3 pollution mitigation. We further evaluated the effectiveness of first and second clean air actions by removing the meteorological influence. We found that the meteorology can make negative or positive contribution in reducing pollutant concentrations from emission reduction, depending on type of pollutants, locations, and time period. Among the 12 mega-cities, only Beijing showed a positive meteorological contribution in amplifying reductions in main pollutants except O 3 during both clean air action periods. Considering the large and variable impact of meteorological effects in changing air quality, we suggest that similar deweathered analysis is needed as a routine policy evaluation tool on a regional basis. [Display omitted] • We used a random forest method to remove meteorological effect for air quality data. • The deweathered results show smoothed but similar variation in annual trends. • The meteorology can play positive or negative role depending on specific conditions. • Beijing shows positive meteorology in reducing pollutants for two clean air actions. [ABSTRACT FROM AUTHOR]

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, ceilometers, a lidar instrument to measure cloud base height, have 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 New South Wales, Australia, were used to investigate the relationship of air pollutant surface concentrations and surface meteorological variables with MLH, 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 the receptor point at Merriwa for the three case studies where high PM10 concentration was detected in each of the three days. The results showed that surface ozone and temperature had a positive correlation with MLH, while relative humidity had negative correlation. For other pollutants (PM10, PM2.5, NO2), no clear results were obtained, and the correlation depended on the site and regional emission characteristics. The results also showed that the PBLH prediction by the two air quality models corresponded reasonably well with the observed ceilometer data and the cause and source of high PM10 concentration at Merriwa can be found by using ceilometer MLH data to corroborate back trajectory analysis of the transport of aerosols to the receptor point at Merriwa. Of the three case studies, one had aerosol sources from the north and north west of Merriwa in remote NSW, where windblown dust is the main source, and the other two had sources from the south and south east of Merriwa, where anthropogenic sources dominate. [ABSTRACT FROM AUTHOR]

Dust storms originating from Central Australia and western New South Wales frequently cause high particle concentrations at many sites across New South Wales, both inland and along the coast. This study focussed on a dust storm event in February 2019 which affected air quality across the state as detected at many ambient monitoring stations in the Department of Planning, Industry and Environment (DPIE) air quality monitoring network. The WRF-Chem (Weather Research and Forecast Model—Chemistry) model is used to study the formation, dispersion and transport of dust across the state of New South Wales (NSW, Australia). Wildfires also happened in northern NSW at the same time of the dust storm in February 2019, and their emissions are taken into account in the WRF-Chem model by using Fire Inventory from NCAR (FINN) as emission input. The model performance is evaluated and is shown to predict fairly accurate the PM2.5 and PM10 concentration as compared to observation. The predicted PM2.5 concentration over New South Wales during 5 days from 11 to 15 February 2019 is then used to estimate the impact of the February 2019 dust storm event on three health endpoints, namely mortality, respiratory and cardiac disease hospitalisation rates. The results show that even though as the daily average of PM2.5 over some parts of the state, especially in western and north western NSW near the centre of the dust storm and wild fires, are very high (over 900 µg/m3), the population exposure is low due to the sparse population. Generally, the health impact is similar in order of magnitude to that caused by biomass burning events from wildfires or from hazardous reduction burnings (HRBs) near populous centres such as in Sydney in May 2016. One notable difference is the higher respiratory disease hospitalisation for this dust event (161) compared to the fire event (24). [ABSTRACT FROM AUTHOR]

As atmospheric fresh soot particles age, they become coated with other chemical species. This transforms their physicochemical properties and affects their optical characteristics, which is of great importance to air quality, the environment and climate change. One of the predominantly occurring states of soot particles in the ambient environment is the core-shell mixing state. In this study, we used the core-shell model to calculate the optical absorption, scattering and extinction efficiency, absorption proportion and absorption exponent of coated soot particles. We then investigated the effects of different core sizes (D0), incident wavelengths (λ), coating materials and coating thicknesses on these optical characteristics. Absorption efficiency and absorption proportion of soot particles decreased as the coating became thicker, at core sizes of D0 = 20, 50 and 100 nm and λ = 405, 532 and 781 nm, regardless of the type of coating material. As the coating thickness increased, the absorption exponent (β) of inorganic-coated soot particles tended to rise and then fall, while the β value of organic-coated soot particles kept increasing. Our results advance our scientific understanding of the interaction of optical properties with chemical composition, mixing state, and aging processes of soot particles in the atmosphere. [ABSTRACT FROM AUTHOR]