Metia, S, Ha, QP, Duc, HN & Scorgie, Y 2020, 'Urban air pollution estimation using unscented Kalman filtered inverse modeling with scaled monitoring data', Sustainable Cities and Society, vol. 54.View/Download from: UTS OPUS or Publisher's site
© 2019 The increasing rate of urbanization requires effective and reliable techniques for air quality monitoring and control. For this, the Air Pollution Model and Chemical Transport Model (TAPM-CTM) has been developed and used in Australia with emissions inventory data, synoptic data and terrain data used as its input parameters. Since large uncertainties exist in the emissions inventory (EI), further refinements and improvements are required for accurate air quality prediction. This study evaluates the performance of urban air quality forecasting, using TAPM-CTM, and improves accuracy of air pollution estimation by using a two-stage optimization technique to upgrade EI with validation from monitoring data. The first stage is based on statistical analysis for EI correction and the second stage is based on the unscented Kalman filter (UKF) to take into account the spatio-temporal distributions of air pollutant levels utilizing a Matérn covariance function. The predicted nitrogen monoxide (NO) and nitrogen dioxide (NO2) concentrations with a priori emissions are first compared with observations at monitoring stations in the New South Wales (NSW). Ozone (O3) is also considered since at the ground level it represents a major air pollutant affecting human health and the environment. In the second stage, with the improved EI, TAPM-CTM model errors are reduced further by using the UKF to calibrate EI. Results obtained show effectiveness of the proposed technique, which is promising for air quality inverse modeling, an important aspect of air pollution control in smart cities to achieve environmental sustainability.
Metia, S, Ha, QP, Hiep, ND & Azzi, M 2018, 'Estimation of Power Plant Emissions With Unscented Kalman Filter', IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, vol. 11, no. 8, pp. 2763-2772.View/Download from: UTS OPUS or Publisher's site
Metia, S, Oduro, SD, Hiep, ND & Ha, Q 2016, 'Inverse Air-Pollutant Emission and Prediction Using Extended Fractional Kalman Filtering', IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, vol. 9, no. 5, pp. 2051-2063.View/Download from: UTS OPUS or Publisher's site
Oduro, SD, Metia, S, Duc, H, Hong, G & Ha, QP 2015, 'Multivariate adaptive regression splines models for vehicular emission prediction', Visualization in Engineering, vol. 3, no. 13, pp. 1-12.View/Download from: UTS OPUS or Publisher's site
Rate models for predicting vehicular emissions of nitrogen oxides (NO X ) are insensitive to the vehicle 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 (NO X ) emissions to ensure that the emission inventory is accurate and hence the air quality modelling and management plans are designed and implemented appropriately.
We propose to use the non-parametric Boosting-Multivariate Adaptive Regression Splines (B-MARS) algorithm to improve the accuracy of the Multivariate Adaptive Regression Splines (MARS) modelling to effectively predict NO X emissions of vehicles in accordance with on-board measurements and the chassis dynamometer testing. The B-MARS methodology is then applied to the NO X emission estimation.
The model approach provides more reliable results of the estimation and offers better predictions of NO X emissions.
The results therefore suggest that the B-MARS methodology is a useful and fairly accurate tool for predicting NO X emissions and it may be adopted by regulatory agencies.
© 2020, Springer Nature Singapore Pte Ltd. In this paper, we develop an estimation model for carbon monoxide (CO) air pollution concentrations. CO is an important pollutant which is used to calculate an air quality index (AQI). AQI becomes less reliable as the proportion of data missing due to equipment failure and periods of calibration increases. This paper presents the Unscented Kalman filter (UKF) to predict missing data of atmospheric carbon monoxide concentrations using the time series data of monitoring stations.
Oduro, SD, Metia, S, Duc, H & Ha, QP 2015, 'Predicting carbon monoxide emissions with multivariate adaptive regression splines (MARS) and artificial neural networks (ANNs)', 32nd International Symposium on Automation and Robotics in Construction and Mining: Connected to the Future, Proceedings, International Symposium on Automation and Robotics in Construction, IAARC, Oulu, Finland, pp. 1-9.View/Download from: UTS OPUS or Publisher's site
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.
Metia, S, Oduro, SD, Ha, QP & Duc, H 2014, 'Air Pollution Prediction Using Matern FunctionBased Extended Fractional Kalman Filtering', The 13th International Conference on Control, Automation, Robotics and Vision, International Conference on Control, Automation, Robotics and Vision, IEEE, Singapore, pp. 758-763.View/Download from: UTS OPUS or Publisher's site
It is essential to maintain air quality standards
and inform people when air pollutant concentrations exceed
permissible limits. For example, ground-level ozone, a harmful
gas formed by NOX and VOCs emitted from various sources, can
be estimated through integration of observation data obtained
from measurement sites and effective air-quality models. This
paper addresses the problem of predicting air pollution emissions
over 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 Mat´ern
covariance function. Here, the ozone concentration is predicted in
the airshed of Sydney and surrounding areas, where the length
scale parameter l is calculated using station coordinates. For
improvement of the air quality prediction, the fractional order
of the EFKF is tuned by using a Genetic Algorithm (GA). The
proposed methodology is validated at monitoring stations and
applied to obtain a spatial distribution of ozone over the region.
Oduro, SD, Metia, S, Duc, H, Hong, G & Ha, QP 2014, 'Prediction of NOX Vehicular Emissions using On-Board Measurement and Chassis Dynamometer Testing', Proceedings of The 31st International Symposium on Automation and Robotics in Construction and Mining, International Symposium on Automation and Robotics in Construction, University of Technology, Sydney, Sydney Australia, pp. 584-591.View/Download from: UTS OPUS or Publisher's site
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.
Metia, S, Oduro, SD, Ha, QP, Duc, H & Azzi, M 2013, 'Environmental Time Series Analysis and Estimation with Extended Kalman Filtering', Proceedings of the 2013 First International Conference on Artificial Intelligence, Modelling & Simulation, International Conference on Artificial Intelligence, Modelling and Simulation, IEEE, Kota Kinabalu, Sabah, Malaysia, pp. 202-207.View/Download from: UTS OPUS or Publisher's site
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 Mat´ern 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.
Oduro, SD, Metia, S, Duc, H & Ha, QP 2013, 'CO2 Vehicular Emission Statistical Analysis with Instantaneous Speed and Acceleration as Predictor Variables', IEEE 2013 International Conference on Control, Automation and Information Sciences, International Conference on Control, Automation and Information Sciences, IEEE, Nha Trang, Vietnam, pp. 152-157.View/Download from: UTS OPUS or Publisher's site
Models for predicting vehicular emissions of carbon dioxide (CO2) are usually insensitive to vehicle modes of operation (such as cruise, acceleration, deceleration, and idling) as they are based on the average speed of motor vehicles. In the present study, real world on-road second-by-second data are used to improve the accuracy of air quality models by considering modal emissions of CO2 in terms of vehicles instantaneous speed and acceleration. A regression analysis approach is used with speed and acceleration as the predictor variables while CO2 emission factor as the outcome variable for vehicles manufactured in 2002 and 2008. The results show that there is significantly a linear relationship between CO2, speed and acceleration/deceleration in which speed, as compared to acceleration, has a stronger correlation with respect to the CO2 emission factor. Also, for 2002 and 2008 vehicles, every 1m/s increase in speed will emit respectively 0.041g/s and 0.034g/s CO2, whereas an increase in acceleration by 1m/s2 will produce 0.025g/s and 0.008g/s of CO2 emission in the case of constant predictors. While speed and acceleration are all significant predictors of CO2 emission, it is concluded from the magnitude of the t-statistics that speed has a greater impact than acceleration in predicting CO2 emission.
Mukherjee, BK & Metia, S 2009, 'Exploiting Fractional Order PID Controller Methods in Improving the Performance of Integer Order PID Controllers: A GA Based Approach', IAENG TRANSACTIONS ON ENGINEERING TECHNOLOGIES, VOL 3, International Multi-Conference of Engineers and Computer Scientists, AMER INST PHYSICS, Kowloon, PEOPLES R CHINA, pp. 143-+.
Mukherjee, BK & Metia, S 2009, 'Fractional Order Modeling and GA Based Tuning for Analog Realization with Lossy Capacitors of a PID Controller', IMECS 2009: INTERNATIONAL MULTI-CONFERENCE OF ENGINEERS AND COMPUTER SCIENTISTS, VOLS I AND II, International Multi-Conference of Engineers and Computer Scientists, INT ASSOC ENGINEERS-IAENG, Kowloon, PEOPLES R CHINA, pp. 1197-+.