Emission characteristics of nonmethane hydrocarbons from private cars and taxis at different driving speeds in Hong Kong

Vehicular emissions are the major sources of a number of air pollutants including nonmethane hydrocarbons (NMHCs) in urban area. The emission composition and emission factors of NMHCs from vehicles are currently lacking in Hong Kong. In this study, speciation and emission factors of NMHCs emitted from gasoline-fuelled private cars and liquefied petroleum gas (LPG)-fuelled taxis at different driving speeds were constructed using a chassis dynamometer. Large variations in the contributions of individual NMHC species to total emission were observed for different private cars at different driving speeds. The variations of individual NMHC emissions were relatively smaller for taxis due to their relatively homogeneous year of manufacture and mileages. Incomplete combustion products like ethane, ethene and propene were the major component of both types of vehicles, while unburned fuel component was also abundant in the exhausts of private cars and taxis (i.e. i-pentane and toluene for private car, and propane and butanes for taxi). Emission factors of major NMHCs emitted from private cars and taxis were estimated. High emission factors of ethane, n-butane, i/n-pentanes, methylpentanes, trimethylpentanes, ethene, propene, i-butene, benzene, toluene and xylenes were found for private cars, whereas propane and i/n-butanes had the highest values for taxis. By evaluating the effect of vehicular emissions on the ozone formation potential (OFP), it was found that the contributions of olefinic and aromatic hydrocarbons to OFP were higher than that from paraffinic hydrocarbons for private car, whereas the contributions of propane and i/n-butanes were the highest for taxis. The total OFP value was higher at lower speeds (≤50 km h-1) for private cars while a minimum value at driving speed of 100 km h-1 was found for taxis. At the steady driving speeds, the total contribution of NMHCs emitted from LPG-fuelled taxis to the OFP was much lower than that from gasoline-fuelled private cars. However, at idling state, the contribution of NMHCs from LPG-fuelled vehicles to OFP was comparable to that from gasoline-fuelled vehicles. The findings obtained in this study can be used to mitigate the air pollution caused by vehicles in highly dense urban areas. © 2011 Elsevier Ltd

A Recursive Least M-Estimate Algorithm for Robust Adaptive Filtering in Impulsive Noise: Fast Algorithm and Convergence Performance Analysis

This paper studies the problem of robust adaptive filtering in impulsive noise environment using a recursive least M-estimate algorithm (RLM). The RLM algorithm minimizes a robust M-estimator-based cost function instead of the conventional mean square error function (MSE). Previous work has showed that the RLM algorithm offers improved robustness to impulses over conventional recursive least squares (RLS) algorithm. In this paper, the mean and mean square convergence behaviors of the RLM algorithm under the contaminated Gaussian impulsive noise model is analyzed. A lattice structure-based fast RLM algorithm, called the Huber Prior Error Feedback-Least Squares Lattice (H-PEF-LSL) algorithm1 is derived. It has an order O(N) arithmetic complexity, where N is the length of the adaptive filter, and can be viewed as a fast implementation of the RLM algorithm based on the modified Huber M-estimate function and the conventional PEF-LSL adaptive filtering algorithm. Simulation results show that the transversal RLM and the H-PEF-LSL algorithms have better performance than the conventional RLS and other RLS-like robust adaptive algorithms tested when the desired and input signals are corrupted by impulsive noise. Furthermore, the theoretical and simulation results on the convergence behaviors agree very well with each other.published_or_final_versio

A Huber recursive least squares adaptive lattice filter for impulse noise suppression

This paper proposes a new adaptive filtering algorithm called the Huber Prior Error-Feedback Least Squares Lattice (H-PEF-LSL) algorithm for robust adaptive filtering in impulse noise environment. It minimizes a modified Huber M-estimator based cost function, instead of the least squares cost function. In addition, the simple modified Huber M-estimate cost function also allows us to perform the time and order recursive updates in the conventional PEF-LSL algorithm so that the complexity can be significantly reduced to O(M), where M is the length of the adaptive filter. The new algorithm can also be viewed as an efficient implementation of the recursive least M-estimate (RLM) algorithm recently proposed by the authors [1], which has a complexity of O(M 2). Simulation results show that the proposed H-PEF-LSL algorithm is more robust than the conventional PEF-LSL algorithm in suppressing the adverse influence of the impulses at the input and desired signals with small additional computational cost.published_or_final_versio

A robust quasi-newton adaptive filtering algorithm for impulse noise suppression

This paper studies the problem of robust adaptive filtering in impulse noise environment using the Quasi-Newton (QN) adaptive filtering algorithm. An M-estimate based cost function is minimized instead of the commonly used mean square error (MSE) to suppress the adverse effect of the impulse noise on the filter coefficients. In particular, a new robust quasi-Newton (R-QN) algorithm using the self-scaling variable metric (SSV) method for unconstrained optimization is studied in details. Simulation results show that the R-QN algorithm is more robust to impulse noise in the desired signal than the RLS algorithm and other QN algorithm considered. Its initial convergence speed and tracking ability to sudden system change are also superior to those of the quasi-Newton algorithm proposed in [1].published_or_final_versio

A robust M-estimate adaptive equaliser for impulse noise suppression

In this paper, a FIR adaptive equaliser for impulse noise suppression is proposed. It is based on the minimization of an M-estimate objective function which has the ability to ignore or down-weight a large error signal when it exceeds certain thresholds. An advantage of the proposed method is that its solution is governed by a system of linear equations, called the M-estimate normal equation. Therefore, traditional fast algorithms like the recursive least squares algorithm can be applied. Using a robust estimation of the thresholds and the recursive least square algorithm, an M-estimate RLS (M-RLS) algorithm is developed. Simulation results show that the proposed algorithm has better convergence performance than the N-RLS and MN-LMS algorithms when the input signal of the equaliser is corrupted by individually or consecutive impulse noises. It also shares the low steady state error of the traditional RLS algorithm.published_or_final_versio

Least mean M -estimate algorithms for robust adaptive filtering in impulse noise

This paper proposes two gradient-based adaptive algorithms, called the least mean M-estimate and the transform domain least mean M -estimate (TLMM) algorithms, for robust adaptive filtering in impulse noise. A robust M -estimator is used as the objective function to suppress the adverse effects of impulse noise on the filter weights. They have a computational complexity of order O(N) and can be viewed, respectively, as the generalization of the least mean square and the transform-domain least mean square algorithms. A robust method for estimating the required thresholds in the M -estimator is also given. Simulation results show that the TLMM algorithm, in particular, is more robust and effective than other commonly used algorithms in suppressing the adverse effects of the impulses. © 2000 IEEE.published_or_final_versio

Transform domain adaptive Volterra filter algorithm based onconstrained optimization

In this paper, a transform domain normalised partially decoupled LMS (TP-LMS) algorithm is proposed based on the partially decoupled transform domain Volterra filter. It is formulated by applying an orthogonal transform to the first order of the partially decoupled Volterra filter, in which the filter weights of a given order are optimised independently of those in the higher order. This approach results in solving the minimum mean square error (MSE) filtering problem as a series of constrained optimisation problems and the modular structure order by order. Simulation of a system identification application indicates TP-LMS algorithms provide a trade-off between convergence speed and computational complexity.published_or_final_versio

A robust statistics based adaptive lattice-ladder filter in impulsive noise

In this paper, a new robust adaptive lattice-ladder filter for impulsive noise suppression is proposed. The filter is obtained by applying the non-linear filtering technique in [l] and the robust statistic approach to the gradient adaptive lattice filter. A systematic method is also developed to determine the corresponding threshold parameters for impulse suppression. Simulation results showed that the performance of the proposed algorithm is better than the conventional RLS, N-RLS, the gradient adaptive lattice normalised-LMS (GAL-NLMS), RMN and ATNA algorithms when the input and desired signals are corrupted by individual and consecutive impulses. The initial convergence, steady-state error, computational complexity and tracking capability of the proposed algorithm are also comparable to the conventional GAL-NLMS algorithm.published_or_final_versio

A robust M-estimate adaptive filter for impulse noise suppression

In this paper, a robust M-estimate adaptive filter for impulse noise suppression is proposed. The objective function used is based on a robust M-estimate. It has the ability to ignore or down weight large signal error when certain thresholds are exceeded. A systematic method for estimating such thresholds is also proposed. An advantage of the proposed method is that its solution is governed by a system of linear equations. Therefore, fast adaptation algorithms for traditional linear adaptive filters can be applied. In particular, a M-estimate recursive least square (M-RLS) adaptive algorithm is studied in detail. Simulation results show that it is more robust against individual and consecutive impulse noise than the MN-LMS and the N-RLS algorithms. It also has fast convergence speed and a low steady state error similar to its RLS counterpart.published_or_final_versio

Recursive Least M-estimate (RLM) adaptive filter for robust filtering in impulse noise

This paper proposes a recursive least M-estimate (RLM) algorithm for robust adaptive filtering in impulse noise. It employs an M-estimate cost function, which is able to suppress the effect of impulses on the filter weights. Simulation results showed that the RLM algorithm performs better than the conventional RLS, NRLS, and OSFKF algorithms when the desired and input signals are corrupted by impulses. Its initial convergence, steady-state error, computational complexity, and robustness to sudden system change are comparable to the conventional RLS algorithm in the presence of Gaussian noise alone.published_or_final_versio