Quantification of particle emission characteristics and development of an emission model for use in transport microenvironments affected by traffic emissions

Vehicle emitted particles are of significant concern based on their potential to influence local air quality and human health. Transport microenvironments usually contain higher vehicle emission concentrations compared to other environments, and people spend a substantial amount of time in these microenvironments when commuting. Currently there is limited scientific knowledge on particle concentration, passenger exposure and the distribution of vehicle emissions in transport microenvironments, partially due to the fact that the instrumentation required to conduct such measurements is not available in many research centres. Information on passenger waiting time and location in such microenvironments has also not been investigated, which makes it difficult to evaluate a passenger’s spatial-temporal exposure to vehicle emissions. Furthermore, current emission models are incapable of rapidly predicting emission distribution, given the complexity of variations in emission rates that result from changes in driving conditions, as well as the time spent in driving condition within the transport microenvironment. In order to address these scientific gaps in knowledge, this work conducted, for the first time, a comprehensive statistical analysis of experimental data, along with multi-parameter assessment, exposure evaluation and comparison, and emission model development and application, in relation to traffic interrupted transport microenvironments. The work aimed to quantify and characterise particle emissions and human exposure in the transport microenvironments, with bus stations and a pedestrian crossing identified as suitable research locations representing a typical transport microenvironment. Firstly, two bus stations in Brisbane, Australia, with different designs, were selected to conduct measurements of particle number size distributions, particle number and PM2.5 concentrations during two different seasons. Simultaneous traffic and meteorological parameters were also monitored, aiming to quantify particle characteristics and investigate the impact of bus flow rate, station design and meteorological conditions on particle characteristics at stations. The results showed higher concentrations of PN20-30 at the station situated in an open area (open station), which is likely to be attributed to the lower average daily temperature compared to the station with a canyon structure (canyon station). During precipitation events, it was found that particle number concentration in the size range 25-250 nm decreased greatly, and that the average daily reduction in PM2.5 concentration on rainy days compared to fine days was 44.2 % and 22.6 % at the open and canyon station, respectively. The effect of ambient wind speeds on particle number concentrations was also examined, and no relationship was found between particle number concentration and wind speed for the entire measurement period. In addition, 33 pairs of average half-hourly PN7-3000 concentrations were calculated and identified at the two stations, during the same time of a day, and with the same ambient wind speeds and precipitation conditions. The results of a paired t-test showed that the average half-hourly PN7-3000 concentrations at the two stations were not significantly different at the 5% confidence level (t = 0.06, p = 0.96), which indicates that the different station designs were not a crucial factor for influencing PN7-3000 concentrations. A further assessment of passenger exposure to bus emissions on a platform was evaluated at another bus station in Brisbane, Australia. The sampling was conducted over seven weekdays to investigate spatial-temporal variations in size-fractionated particle number and PM2.5 concentrations, as well as human exposure on the platform. For the whole day, the average PN13-800 concentration was 1.3 x 104 and 1.0 x 104 particle/cm3 at the centre and end of the platform, respectively, of which PN50-100 accounted for the largest proportion to the total count. Furthermore, the contribution of exposure at the bus station to the overall daily exposure was assessed using two assumed scenarios of a school student and an office worker. It was found that, although the daily time fraction (the percentage of time spend at a location in a whole day) at the station was only 0.8 %, the daily exposure fractions (the percentage of exposures at a location accounting for the daily exposure) at the station were 2.7% and 2.8 % for exposure to PN13-800 and 2.7% and 3.5% for exposure to PM2.5 for the school student and the office worker, respectively. A new parameter, “exposure intensity” (the ratio of daily exposure fraction and the daily time fraction) was also defined and calculated at the station, with values of 3.3 and 3.4 for exposure to PN13-880, and 3.3 and 4.2 for exposure to PM2.5, for the school student and the office worker, respectively. In order to quantify the enhanced emissions at critical locations and define the emission distribution in further dispersion models for traffic interrupted transport microenvironments, a composite line source emission (CLSE) model was developed to specifically quantify exposure levels and describe the spatial variability of vehicle emissions in traffic interrupted microenvironments. This model took into account the complexity of vehicle movements in the queue, as well as different emission rates relevant to various driving conditions (cruise, decelerate, idle and accelerate), and it utilised multi-representative segments to capture the accurate emission distribution for real vehicle flow. This model does not only helped to quantify the enhanced emissions at critical locations, but it also helped to define the emission source distribution of the disrupted steady flow for further dispersion modelling. The model then was applied to estimate particle number emissions at a bidirectional bus station used by diesel and compressed natural gas fuelled buses. It was found that the acceleration distance was of critical importance when estimating particle number emission, since the highest emissions occurred in sections where most of the buses were accelerating and no significant increases were observed at locations where they idled. It was also shown that emissions at the front end of the platform were 43 times greater than at the rear of the platform. The CLSE model was also applied at a signalled pedestrian crossing, in order to assess increased particle number emissions from motor vehicles when forced to stop and accelerate from rest. The CLSE model was used to calculate the total emissions produced by a specific number and mix of light petrol cars and diesel passenger buses including 1 car travelling in 1 direction (/1 direction), 14 cars / 1 direction, 1 bus / 1 direction, 28 cars / 2 directions, 24 cars and 2 buses / 2 directions, and 20 cars and 4 buses / 2 directions. It was found that the total emissions produced during stopping on a red signal were significantly higher than when the traffic moved at a steady speed. Overall, total emissions due to the interruption of the traffic increased by a factor of 13, 11, 45, 11, 41, and 43 for the above 6 cases, respectively. In summary, this PhD thesis presents the results of a comprehensive study on particle number and mass concentration, together with particle size distribution, in a bus station transport microenvironment, influenced by bus flow rates, meteorological conditions and station design. Passenger spatial-temporal exposure to bus emitted particles was also assessed according to waiting time and location along the platform, as well as the contribution of exposure at the bus station to overall daily exposure. Due to the complexity of the interrupted traffic flow within the transport microenvironments, a unique CLSE model was also developed, which is capable of quantifying emission levels at critical locations within the transport microenvironment, for the purpose of evaluating passenger exposure and conducting simulations of vehicle emission dispersion. The application of the CLSE model at a pedestrian crossing also proved its applicability and simplicity for use in a real-world transport microenvironment.

Exit humidity of wet scrubbers for underground coal mines

A wet scrubber is a device used in underground coal mines for the exhaust treatment system of various internal combustion engines (generally diesel) primarily as a spark arrestor with a secondary function to remove pollutants from the exhaust gas. A pool of scrubbing liquid (generally water based) is used in conjunction with a Diesel Particulate Filter (DPF). Scrubbers are widely used in underground applications of diesel engines as their exhaust contains high concentration of harmful diesel particulate matter (DPM) and other pollutant gases. Currently the DPFs have to be replaced frequently because moisture output from the wet scrubber blocks the filter media and causes reduced capacity. This paper presents experimental and theoretical studies on the heat and mass transfer mechanisms of the exhaust flow both under and above the water surface, aiming at finding the cause and effects of the moisture reaching the filters and employing a solution to reduce the humidity and DPM output, and to prolong the change-out period of the DPF. By assuming a steady flow condition, heat transfer from the inlet exhaust gas balances energy required for the water evaporation. Hence the exit humidity will decrease with the increase of exit temperature. Experiments on a real scrubber are underway.

Study of particulate matter removal mechanism by using non-thermal plasma technology

Numbers of diesel engines in both stationary and mobile applications are increasing nowadays. Diesel engines emit lower Hydrocarbon (HC) and Carbon monoxide (CO) than gasoline engines. However, they can produce more nitrogen oxides (NOx) and have higher particulate matter (PM). On the other hand, emissions standards are getting stringent day by day due to considerable concerns about unregulated pollutants and particularly ultrafine particles deleterious effect on human health. Non-thermal plasma (NTP) treatment of exhaust gas is known as a promising technology for both NOx and PM reduction by introducing plasma inside the exhaust gas. Vehicle exhaust gases undergo chemical changes when exposed to plasma. In this study, the PM removal mechanism using NTP by applying high voltage pulses of up to 20 kVpp with a repetition rate of 10 kHz are investigated. It is found that, voltage increase not necessarily has a positive effect on PM removal in diesel engine emissions.

Biodiesel with controlled physicochemical properties, a means to further reduce diesel engine particle emissions

The issue of particle emissions from diesel engines is still a matter of concern due its deleterious effects both on human health and environment(Ristovski et al., 2012). Recently, International Agency for Research on Cancer (IARC) inclusion of diesel engine exhaust particles as carcinogenic to human health added a new margin on it. Apart from the use of after treatment technology, biodiesel is also considered as potential way to reduce particle emission alongside with other emissions(Xue, Grift, & Hansen, 2011). Global biodiesel production is still reasonably small compared to its counterpart fossil diesel, but even this small amount comes from a wide variety of feed stocks. Contrary to fossil diesel, the important physicochemical properties of biodiesel vary among different feed stocks(Hoekman, Broch, Robbins, Ceniceros, & Natarajan, 2012).

Volatile properties of CNG and Diesel bus emissions produced during steady state and transient driving modes

An analysis of the emissions from 14 CNG and 5 Diesel buses was conducted during April & May, 2006. Studies were conducted at both steady state and transient driving modes on a vehicle dynamometer utilising a CVS dilution system. This article will focus on the volatile properties of particles from 4 CNG and 4 Diesel vehicles from within this group with a priority given to the previously un-investigated CNG emissions produced at transient loads. Particle number concentration data was collected by three CPC’s (TSI 3022, 3010 & 3782WCPC) having D50 cut-offs set to 5nm, 10nm & 20nm respectively. Size distribution data was collected using a TSI 3080 SMPS with a 3025 CPC during the steady state driving modes. During transient cycles mono-disperse “slices” of between 5nm & 25nm were measured. The volatility of these particles was determined by placing a thermodenuder before the 3022 and the SMPS and measuring the reduction in particle number concentration as the temperature in the thermodenuder was increased. This was then normalised against the total particle count given by the 3010 CPC to provide high resolution information on the reduction in particle concentration with respect to temperature.

Influences of biodiesel chemical compositions and physical properties on engine exhaust particle emissions

This thesis presents a comprehensive study on the influences of biodiesel chemical composition and physical properties on diesel engine exhaust particle emissions. It examines biodiesels from several feedstocks having wide variations in their chemical composition (carbon chain length, unsaturation and oxygen content) and physical properties (density, viscosity, surface tension, boiling point etc.), and evaluates their influence on exhaust particle emissions. The outcome of this thesis is significant since it reveals the importance of regulating biodiesels chemical composition in order to ensure lowest possible emissions with better overall performance.

Solar powered high voltage energization for vehicular exhaust cleaning: A step towards possible retrofitting in vehicles

This paper proposes a novel way of generating high voltage for electric discharge plasma in controlling NOx emission in diesel engine exhaust. A solar powered high frequency electric discharge topology has been suggested that will improve the size and specific energy density required when compared to the traditional repetitive pulse or 50 Hz AC energization. This methodology has been designed, fabricated and experimentally verified by conducting studies on real diesel engine exhaust.

Influence of biodiesel fuel composition on the morphology and microstructure of particles emitted from diesel engines

This study investigates the morphology, microstructure and surface composition of Diesel engine exhaust particles. The state of agglomeration, the primary particle size and the fractal dimension of exhaust particles from petroleum Diesel (petrodiesel) and biodiesel blends from microalgae, cotton seed and waste cooking oil were investigated by means of high resolution transmission electron microscopy. With primary particle diameters between 12-19 nm, biodiesel blend primary particles are found to be smaller than petrodiesel ones (21±2 nm). Also it was found that soot agglomerates from biodiesels are more compact and spherical, as their fractal dimensions are higher, e.g. 2.2±0.1 for 50% algae biodiesel compared to 1.7±0.1 for petrodiesel. In addition, analysis of the chemical composition by means of x-ray photoelectron spectroscopy revealed an up to a factor of two increased oxygen content on the primary particle surface for biodiesel. The length, curvature and distance of graphene layers were measured showing a greater structural disorder for biodiesel with shorter fringes of higher tortuosity. This change in carbon chemistry may reflect the higher oxygen content of biofuels. Overall, it seems that the oxygen content in the fuels is the underlying reason for the observed morphological change in the resulting soot particles.

Gas turbine diagnostics using a soft computing approach

A fuzzy system is developed using a linearized performance model of the gas turbine engine for performing gas turbine fault isolation from noisy measurements. By using a priori information about measurement uncertainties and through design variable linking, the design of the fuzzy system is posed as an optimization problem with low number of design variables which can be solved using the genetic algorithm in considerably low amount of computer time. The faults modeled are module faults in five modules: fan, low pressure compressor, high pressure compressor, high pressure turbine and low pressure turbine. The measurements used are deviations in exhaust gas temperature, low rotor speed, high rotor speed and fuel flow from a base line 'good engine'. The genetic fuzzy system (GFS) allows rapid development of the rule base if the fault signatures and measurement uncertainties change which happens for different engines and airlines. In addition, the genetic fuzzy system reduces the human effort needed in the trial and error process used to design the fuzzy system and makes the development of such a system easier and faster. A radial basis function neural network (RBFNN) is also used to preprocess the measurements before fault isolation. The RBFNN shows significant noise reduction and when combined with the GFS leads to a diagnostic system that is highly robust to the presence of noise in data. Showing the advantage of using a soft computing approach for gas turbine diagnostics.

Three- and Seven-Point Optimally Weighted Recursive Median Filters for Gas Turbine Diagnostics

Measured health signals incorporate significant details about any malfunction in a gas turbine. The attenuation of noise and removal of outliers from these health signals while preserving important features is an important problem in gas turbine diagnostics. The measured health signals are a time series of sensor measurements such as the low rotor speed, high rotor speed, fuel flow, and exhaust gas temperature in a gas turbine. In this article, a comparative study is done by varying the window length of acausal and unsymmetrical weighted recursive median filters and numerical results for error minimization are obtained. It is found that optimal filters exist, which can be used for engines where data are available slowly (three-point filter) and rapidly (seven-point filter). These smoothing filters are proposed as preprocessors of measurement delta signals before subjecting them to fault detection and isolation algorithms.

Fuzzy logic intelligent system for gas turbine module and system fault isolation

A fuzzy logic intelligent system is developed for gas-turbine fault isolation. The gas path measurements used for fault isolation are exhaust gas temperature, low and high rotor speed, and fuel flow. These four measurements are also called the cockpit parameters and are typically found in almost all older and newer jet engines. The fuzzy logic system uses rules developed from a model of performance influence coefficients to isolate engine faults while accounting for uncertainty in gas path measurements. It automates the reasoning process of an experienced powerplant engineer. Tests with simulated data show that the fuzzy system isolates faults with an accuracy of 89% with only the four cockpit measurements. However, if additional pressure and temperature probes between the compressors and before the burner, which are often found in newer jet engines, are considered, the fault isolation accuracy rises to as high as 98%. In addition, the additional sensors are useful in keeping the fault isolation system robust as quality of the measured data deteriorates.

A cascaded discharge plasma-adsorbent technique for engine exhaust treatment

A cascaded system of electrical discharges (non-thermal plasma) and adsorption process was investigated for the removal of oxides of Nitrogen (NOx) and total hydrocarbons (THC) from an actual diesel engine exhaust. The non-thermal plasma and adsorption processes were separately studied first and then the cascaded process was studied. In this study, different types of adsorbents were used. The NOx removal efficiency was higher with plasma-associated adsorption (cascaded) process compared to the individual processes and the removal efficiency was found almost invariant in time. When associated by plasma, among the adsorbents studied, activated charcoal and MS-13X were more effective for NOx and THC removal respectively. The experiments were conducted at no load and at 50% load conditions. The plasma reactor was kept at room temperature throughout the experiment, while the temperature of the adsorbent reactor was varied. A relative comparison of adsorbents was discussed at the end.

Enhanced Performance of Discharge Plasma in Raw Engine Exhaust Treatment-Operation under Different Temperatures and Loads

This paper reports improved performance of discharge plasma in raw engine exhaust treatment. For the purpose of investigation, both filtered and raw diesel engine exhaust were separately treated by the discharge plasma. In raw exhaust environment, the discharge plasma exhibits a superior performance with regard to NOx removal, energy consumption and formation of by-products. In this study, experiments were conducted at conditions of different temperatures and loads.