Merched Azzi

Modeling Anthropogenic Trends in Air Quality Data

Abstract Rao and Zurbenko suggest an effective method for detecting changes in air quality due to changes in emissions in the presence of meteorological fluctuations.1 On the other hand, it is well known that air quality time series display long-range dependence (LRD),2 and various methods have been suggested for modeling this component. This paper attempts to show that the LRD component can be used to model the anthropogenic changes in air quality data. We estimate the anthropogenic trend component using the Rao-Zurbenko method and the LRD component using the Haslett-Raftery algorithm in an ozone time series and an NO 2 time series. These two methods produce nearly identical results.

Neural network-based meta-modelling approach for estimating spatial distribution of air pollutant levels

Continuous measurements of the air pollutant concentrations at monitoring stations serve as a reliable basis for air quality regulations. Their availability is however limited only at locations of interest. In most situations, the spatial distribution beyond these locations still remains uncertain as it is highly influenced by other factors such as emission sources, meteorological effects, dispersion and topographical conditions. To overcome this issue, a larger number of monitoring stations could be installed, but it would involve a high investment cost. An alternative solution is via the use of a deterministic air quality model (DAQM), which is mostly adopted by regulatory authorities for prediction in the temporal and spatial domain as well as for policy scenario development. Nevertheless, the results obtained from a model are subject to some uncertainties and it requires, in general, a significant computation time. In this work, a meta-modelling approach based on neural network evaluation is proposed to improve the estimated spatial distribution of the pollutant concentrations. From a dispersion model, it is suggested that the spatially-distributed pollutant levels (i.e. ozone, in this study) across a region under consideration is a function of the grid coordinates, topographical information, solar radiation and the pollutants precursor emission. Initially, for training the model, the input-output relationship is extracted from a photochemical dispersion model called The Air Pollution Model and Chemical Transport Model (TAPM-CTM), and some of those input-output data are correlated with the ambient measurements collected at monitoring stations. Here, improved radial basis function networks, incorporating a proposed technique for selection of the network centres, will be developed and trained by using the data obtained and the forward selection approach. The methodology is then applied to estimate the ozone concentrations in the Sydney basin, Australia. Once executed, apart from the advantage of inexpensive computation, it provides more reliable results of the estimation and offers better predictions of ozone concentrations than those obtained by using the TAPM-CTM model only, when compared to the measurement data collected at monitoring stations.

Spatial distribution characteristics of some air pollutants in Sydney

Spatial distribution characteristics may be used to help in siting of air monitoring stations. This technique is also helpful in predicting variations in the concentrations of air pollutants due to changes in meteorology.

Enhanced radial basis function neural networks for ozone level estimation

Assessment of air pollutant profiles by using measurements involves some limitations in the implementation. For this, deterministic air quality models are often used. However, its simulation usually needs high computational requirements due to complex chemical reactions involved. In this paper, a neural network-based metamodel approach is used in conjunction with a deterministic model and some measured data to approximate the non-linear ozone concentration relationship. For this, algorithms for performance enhancement of a radial basis function neural network (RBFNN) are developed. The proposed method is then applied to estimate the spatial distribution of ozone concentrations in the Sydney basin. The experimental comparison between the proposed RBFNN algorithm and the conventional RBFNN algorithm demonstrates the effectiveness and efficiency in estimating the spatial distribution of ozone level.

A reactive state-space model for prediction of urban air pollution

The generic reaction set (GRS) model offers a convenient framework for studying photochemical smog production. Its highly condensed seven equations are deduced from the principal reactions that produce photochemical smog (such as photolysis of reactive organic species, oxidation of NO to NO2, photolysis of NO2, etc.), and have been validated with the CSIRO outdoor smog chamber data. The performance of the model has been found comparable to more detailed photochemical mechanisms such as the CBM-IV. This paper expands the GRS model to include spatial advection and diffusion in the airshed. Via an appropriate numerical scheme, this extended dynamic model is transformed into the state space form, from which interpolation and prediction can be performed using the Kalman algorithm. The model is implemented on a simple grid of seven stations in the Sydney monitoring network. One-step ahead forecasts are derived for observed as well as unobserved locations. Comparison with observed data indicate that the model performs quite well, in particular, it traces the ozone episodes accurately.

Environmental Time Series Analysis and Estimation with Extended Kalman Filtering

This paper addresses the problem of air pollutant profile estimation by using measurements collected from different weather stations. An algorithm is developed, based on an Extended Kalman Filter to handle missing temporal data and using the statistical Kriging method to interpolate spatial data. Combination of extended Kalman filtering with Matérn covariance function has proven to be useful in exploiting meteorological information to build reliable air quality models. We have applied the developed algorithm to estimate air pollutant profiles in the Sydney basin, which is subject to a variety of pollutant sources, including fossil-fueled electric power generation plants, high motor vehicle usage, aviation and shipping traffic. The results have shown that the proposed approach can improve accuracy of the estimation profiles.

Smog chamber study of the role of NH3 in new particle formation fromphoto-oxidation of aromatic hydrocarbons

Ammonia (NH3) is a major contributor to secondary aerosol in the atmosphere and can alter the kinetics of their formation. However, systematic studies related to the role of NH3 in aerosol nucleation processes and further particle size growth under complex scenarios are lacking. In this study, we conducted 16 experiments in the CSIRO smog chamber under dry conditions using aromatic hydrocarbons (toluene, o-/m-/p-xylene) and different concentrations of NH3. The presence of NH3 did not change the gas-phase chemistry or nucleation onset time, but slowed the nucleation rate (5%-94%) once it began. From the response of nitrogen oxides (NOx) measurement and mechanism modeling results, we hypothesised that the surface reaction between NH3 and nitric acid played a central role in aerosol nucleation and further growth. After nucleation, the subsequently formed ammonium nitrate and organic condensation vapours may partition together into the initial growth process of new particles, thus increasing the aerosol initial growth rate (8%-90%) and size growth potentials (7%-108%), and leading to high aerosol mass formation. Further investigation implied that the initial growth and further growth rate determine the aerosol mass concentration, rather than the nucleation rate. We conclude that both the initial NOx concentration and volatile organic compound (VOC)/NOx ratio are crucial for the initial and further growth, and aerosol mass of new particles, when NH3 levels are high. Our results provide crucial insights into the complex chemistry of VOC/NOx/NH3 in the atmosphere, and highlight the importance of NH3 reduction for particulate matter control.

Modelling of Photochemical Smog

This chapter is concerned with air chemistry and its applications in air-quality modelling. Photochemical smog, i.e., the formation of high ground-level ozone concentrations, has been one of the main topics of air quality research in the last three decades. The chemistry of ground-level ozone is different from that of stratospheric ozone. Photochemical smog is a anthropogenic air pollution problem in many urban areas around the world, which is related to population increases and the reliance on motor vehicles for transport.

Development of a new smog chamber for studying the impact of different UV lamps on SAPRC chemical mechanism predictions and aerosol formation

Environmental context Chemical mechanisms are an important component of predictive air quality models that are developed using smog chambers. In smog chamber experiments, UV lamps are often used to simulate sunlight, and the choice of lamp can influence the obtained data, leading to differences in model predictions. We investigate the effect of various UV lamps on the prediction accuracy of a key mechanism in atmospheric chemistry. Abstract A new smog chamber was constructed at CSIRO following the decommissioning of the previous facility. The new chamber has updated instrumentation, is 35 % larger, and has been designed for chemical mechanism and aerosol formation studies. To validate its performance, characterisation experiments were conducted to determine wall loss and radical formation under irradiation by UV lamps. Two different types of blacklights commonly used in indoor chambers are used as light sources, and the results using these different lamps are investigated. Gas-phase results were compared against predictions from the latest version of the SAPRC chemical mechanism. The SAPRC mechanism gave accurate results for hydrocarbon reaction and oxidation formation for propene and o-xylene experiments, regardless of the light source used, with variations in ozone concentrations between experiment and modelled results typically less than 10 % over 6-h irradiation. The SAPRC predictions for p-xylene photooxidation showed overprediction in the rate of oxidation, although no major variations were determined in mechanism results for different blacklight sources. Additionally, no significant differences in the yields of aerosol arising from new particle formation were discernible regardless of the light source used under these conditions.

Options for Sustainable Energy Planning for Australia

Access to sustainable, affordable and secure energy is one of the major Australian strategic priorities to maintain and improve the health of Australians, sustain economic growth, and to mitigate climate change. Australia is investing in clean, efficient, reliable energy systems to secure a prosperous and, environmentally sustainable future. In addition, exploring the options to ensure energy security by diversification of energy sources is an important aspect for securing stability in meeting and delivering the future energy requirements of different industry sectors. This paper discusses options to manage the production of electricity in Australia using available Australian resources while maintaining international competitiveness.