Hiep Duc

The health benefits of reducing air pollution in Sydney, Australia.

Among industrialised countries, fine particle (PM2.5) and ozone levels in the Sydney metropolitan area of Australia are relatively low. Annual mean PM2.5 levels have historically remained below 8 μg/m(3) while warm season (November-March) ozone levels occasionally exceed the Australian guideline value of 0.10 ppm (daily 1 h max). Yet, these levels are still below those seen in the United States and Europe. This analysis focuses on two related questions: (1) what is the public health burden associated with air pollution in Sydney; and (2) to what extent would reducing air pollution reduce the number of hospital admissions, premature deaths and number of years of life lost (YLL)? We addressed these questions by applying a damage function approach to Sydney population, health, PM2.5 and ozone data for 2007 within the BenMAP-CE software tool to estimate health impacts and economic benefits. We found that 430 premature deaths (90% CI: 310-540) and 5800 YLL (95% CI: 3900-7600) are attributable to 2007 levels of PM2.5 (about 2% of total deaths and 1.8% of YLL in 2007). We also estimate about 630 (95% CI: 410-840) respiratory and cardiovascular hospital admissions attributable to 2007 PM2.5 and ozone exposures. Reducing air pollution levels by even a small amount will yield a range of health benefits. Reducing 2007 PM2.5 exposure in Sydney by 10% would, over 10 years, result in about 650 (95% CI: 430-850) fewer premature deaths, a gain of 3500 (95% CI: 2300-4600) life-years and about 700 (95% CI: 450-930) fewer respiratory and cardiovascular hospital visits. These results suggest that substantial health benefits are attainable in Sydney with even modest reductions in air pollution.

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.

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.

Inverse Air-Pollutant Emission and Prediction Using Extended Fractional Kalman Filtering

It is essential to maintain air-quality standards and to take necessary measures when air-pollutant concentrations exceed permissible limits. Pollutants such as ground-level ozone (O3), nitrogen oxides (NOX), and volatile organic compounds (VOCs) emitted from various sources can be estimated at a particular location through integration of observation data obtained from measurement sites and effective air-quality models, using emission inventory data as input. However, there are always uncertainties associated with the emission inventory data as well as uncertainties generated by a meteorological model. This paper addresses the problem of improving the inverse air pollution emission and prediction over the urban and suburban areas using the air-pollution model with chemical transport model (TAPM-CTM) coupled with the extended fractional Kalman filter (EFKF) based on a Matérn covariance function. Here, nitrogen oxide (NO), nitrogen dioxide (NO2), and O3 concentrations are predicted by TAPM-CTM in the airshed of Sydney and surrounding areas. For improvement of the emission inventory, and hence the airquality prediction, the fractional order of the EFKF is tuned using a genetic algorithm (GA). The proposed methodology is verified with measurements at monitoring stations and is then applied to obtain a better spatial distribution of O3 over the region.

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.

Predicting Carbon Monoxide Emissions with Multivariate Adaptive Regression Splines (MARS) and Artificial Neural Networks (ANNs)

Emissions from motor vehicles need to be predicted fairly accurately to ensure an appropriate air quality management plan. This research work explores the use of a nonparametric regression algorithm known as the multivariate adaptive regression splines (MARS) in comparison with the artificial neural networks (ANN) for the purpose of best approximation of the relationship between the input and output from datasets recorded from on-board measurement and dynamometer testings. The performance of the models was evaluated by comparing the MARS and ANN predictions to the measured data using several performance indices. The results are evaluated in terms of accuracy, flexibility and computational efficiency. While MARS are more computationally efficient to reach the final model ANN are slightly more accurate. The proposed techniques may be used to assist in a decision-making policy regarding urban air pollution.

Prediction of NOX vehicular emissions using on-board measurement and chassis dynamometer testing

Motor vehicles’ rate models for predicting emissions of oxides of nitrogen (NOX) are insensitive to their modes of operation such as cruise, acceleration, deceleration and idle, because these models are usually based on the average trip speed. This study demonstrates the feasibility of using other variables such as vehicle speed, acceleration, load, power and ambient temperature to predict NOX emissions. The NOX emissions need to be accurately estimated to ensure that air quality plans are designed and implemented appropriately. For this, we propose to use the non-parametric multivariate adaptive regression splines (MARS) to model NOX emission of vehicle in accordance with on-board measurements and also the chassis dynamometer testing. The MARS methodology is then applied to estimate the NOX emissions. The model approach provides more reliable results of the estimation and offers better predictions of NOX emissions. The results therefore suggest that the MARS methodology is a useful and fairly accurate tool for predicting NOX emission that may be adopted by regulatory agencies in understanding the effect of vehicle operation and NOX emissions.

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.