NOx Emission Rates Research Articles

Comparison of Inland Ship Emission Results from a Real-World Test and an AIS-Based Model

Inland shipping is pivotal to the comprehensive transport system of China. However, ship emission has become a major air polluter in inland river regions, and relevant emission inventories are urgently needed. Currently, the Automatic identification System based (AIS-based)emission model is widely used in calculating the ocean-going ship emission inventory. However, due to the lack of AIS data in the river area, the inland ship emission inventory mainly uses the fuel consumption method. With the continuous improvement of AIS data quality in the river area, the AIS-based emission model can be adopted in the development of inland ship emission inventory. However, there are few studies on the evaluation of the accuracy of the inland ship emissions using the AIS-based emission model. This study makes a comparison between test data and model-calculated data to evaluate the accuracy of the AIS-based emission models. Inland ship activities are divided into being at berth, maneuvering (port departure and port arrival), and on cruise modes in an AIS-based emission model. The model-calculated CO2, HC, and NOx emission rates can cover those onboard emission test data, but the values from the model are much lower. The total average ratios of test data to model-calculated data for CO2, CO, HC, and NOx are 2.66, 19.12, 2.46, and 3.16 when engine loads are below 60%. In upstream cruise mode, average emission rates of CO2, CO, HC, and NOx from the real-world test are 1.91–6.48, 8.78–27.83, 3.05–8.96, and 4.06–5.96 times higher than those from the AIS-based model, respectively. However, those are only 1.08–1.51, 6.74–9.67, 2.03–3.75, and 1.65–2.75 times higher than those from the AIS-based model in downstream cruise mode.

Artificial Neural Network Modeling and Numerical Simulation of Syngas Fuel and Injection Timing Effects on the Performance and Emissions of a Heavy-Duty Compression Ignition Engine.

The use of syngas as an alternative fuel in compression ignition engines is a potential way to curb the emission of pollutants and optimize the performance of these engines. The present research studied the effect of changing fuel injection timing and adding syngas on the output of a heavy-duty diesel engine. The optimal mode of the turbulence model, fuel spray model, combustion model, and pollutant emission model was used to solve computational fluid dynamics. Changing fuel injection timing from 70 to 10° before top dead center (BTDC) and diesel variants, including conventional diesel, diesel + 20% syngas, and diesel + 40% syngas, is the strategy studied here. It was found that the use of syngas could reduce emissions significantly. The in-cylinder mean effective pressure was the highest for diesel + 20% syngas. On the other hand, increasing the rate of syngas and retarding injection timing reduced the ignition period and in-cylinder temperature. The lowest rate of CO emission was obtained from diesel + 40% syngas at a fuel injection timing of 70° BTDC, whereas the lowest particulate matter emission was related to diesel + 40% syngas at 40° BTDC injection timing. The lowest rate of CO2 emission was related to diesel + 40% syngas at a fuel injection timing of 10° BTDC, while the same timing for conventional diesel exhibited the lowest NOX emission rate. Finally, the performance parameters of the engine including indicated power, indicated fuel consumption, and indicated thermal efficiency were decreased with the increase in syngas fraction, so that their highest values were obtained from the conventional diesel at the injection timing of 40° BTDC. In addition, an artificial neural network (ANN) model based on a standard back-propagation learning algorithm was developed for modeling the performance and emissions of the engine. The results for the optimum ANN model showed that the optimal ANN has two hidden layers with 20–25 neurons and the transfer function of logsig–logsig for hidden layers 1 and 2, respectively, and can predict different parameters of the engine for different modes. The correlation coefficients (R-value) of optimal topology for training, validation, and testing are 0.99992, 0.96612, and 0.93424, respectively. The results for the optimum ANN model showed that the constructed model sufficiently predicts the performance and emissions of the CI diesel engine.

Influence of test cycle and fuel property on fuel consumption and exhaust emissions of a heavy-duty diesel engine

In this study, dynamometer engine tests were conducted to investigate the impact of test cycle and fuel type on fuel consumption and exhaust emissions of a heavy-duty diesel engine. This study presented novel approaches by utilizing comprehensive statistical analysis to assess these impacts on particulate matter (PM), PM-bound polycyclic aromatic hydrocarbons (PAHs), and multiple gaseous pollutants. Four types of fuels—two conventional diesels (X and Y) and each with 5% blend of biodiesel—were used under European Transient Cycle (ETC) and European Stationary Cycle (ESC). There were statistically significant higher fuel consumption and emission rates (by 9–73%) under ETC than ESC due to more occurrences of lower engine speeds and loads under ESC, and X fuels had higher emission rates of PM and carbon dioxides ([CO2]; 2.1–13%) but lower rates of hydrocarbons ([THC]; 44%) attributed to higher cetane number, sulfur contents and boiling points. Compared with conventional diesel, biodiesel blend had slightly lower emission rates of PM, CO, and NOX (1.7–6.6%) but higher fuel consumption (1%) and CO2 and THC emission rates (0.9–2.1%). The results of this study contributed to the limited datasets on the interactive effects of test cycle with fuel property on diesel vehicle exhaust emissions.

A satellite-data-driven framework to rapidly quantify air-basin-scale NO<sub><i>x</i></sub> emissions and its application to the Po Valley during the COVID-19 pandemic

Abstract. The evolving nature of the COVID-19 pandemic necessitates timely estimates of the resultant perturbations to anthropogenic emissions. Here we present a novel framework based on the relationships between observed column abundance and wind speed to rapidly estimate the air-basin-scale NOx emission rate and apply it at the Po Valley in Italy using OMI and TROPOMI NO2 tropospheric column observations. The NOx chemical lifetime is retrieved together with the emission rate and found to be 15–20 h in winter and 5–6 h in summer. A statistical model is trained using the estimated emission rates before the pandemic to predict the trajectory without COVID-19. Compared with this business-as-usual trajectory, the real emission rates show three distinctive drops in March 2020 (−42 %), November 2020 (−38 %), and March 2021 (−39 %) that correspond to tightened COVID-19 control measures. The temporal variation of pandemic-induced NOx emission changes qualitatively agrees with Google and Apple mobility indicators. The overall net NOx emission reduction in 2020 due to the COVID-19 pandemic is estimated to be 22 %.

Estimation of ship emission rates at a major shipping lane by long-path DOAS measurements

Abstract. Ships are an important source of SO2 and NOx, which are key parameters of air quality. Monitoring of ship emissions is usually carried out using in situ instruments on land, which depend on favourable wind conditions to transport the emitted substances to the measurement site. Remote sensing techniques such as long-path differential optical absorption spectroscopy (LP-DOAS) measurements can supplement those measurements, especially in unfavourable meteorological conditions. In this study 1 year of LP-DOAS measurements made across the river Elbe close to Hamburg (Germany) have been evaluated. Peaks (i.e. elevated concentrations) in the NO2 and SO2 time series were assigned to passing ships, and a method to derive emission rates of SO2, NO2 and NOx from those measurements using a Gaussian plume model is presented. A total of 7402 individual ship passages have been monitored, and their respective NOx, SO2 and NO2 emission rates have been derived. The emission rates, coupled with the knowledge of the ship type, ship size and ship speed, have been analysed. Emission rates are compared to emission factors from previous studies and show good agreement. In contrast to emission factors (in grams per kilogram fuel), the derived emission rates (in grams per second) do not need further knowledge about the fuel consumption of the ship. To our knowledge this is the first time emission rates of air pollutants from individual ships have been derived from LP-DOAS measurements.

First Concurrent Observations of NO2 and CO2 From Power Plant Plumes by Airborne Remote Sensing

AbstractCombined NO2 and CO2 observations have the potential to constrain the identification of the locations and strength of urban CO2 emissions, in particular, point sources such as power plants. We report the first results of airborne spectroscopic NO2 and CO2 observations over an urban area in Japan in February 2018. Inversed emission rates of two stacks of the coal‐fired power plant for CO2 showed relatively good agreement with those estimated by a bottom‐up inventory—the Regional Emission inventory in ASia (REAS) v3.1—within −7% to 40% because the plume shapes were well identified due to constraint by NO2 measurements. The estimated NOx emission rates showed discrepancies more than 80% with those estimated by the REAS v3.1, mainly due to the uncertainties in activity data and emission factors, or in the greatly varying NO/NO2 ratios in fresh plumes, which warrant further investigations when estimating NOx emissions from satellite NO2 observations on km‐scales.

Preparation of Biodiesel from Castor Oil and Performance Evaluation in VCR Engine

Biodiesel can be used as Alternative fuel and acts as Renewable energy source. Rapid growth in industrialization of developing countries is resulting in increasing demand for new and eco-friendly energy sources. In this present research biodiesel was prepared from Castor oil by esterification and Transesterification process. The castor oil biodiesel produced was blended with diesel to obtain B10.Performance evaluation was carried out in VCR engine and emission testing was done by Gas analyzer to know the percentage of CO,HC, NOX and comparison study was done with diesel and biodiesel blend. In this study it was found that NOx emission rate of biodiesel blend increases while percentage of CO, HC increases. Also various performance indicators such as break mean effective pressure, specific fuel consumption; break thermal efficiency was plotted with respect to variation of load by using VCR engine.

On the Ship Particle Number Emission Index: Size‐Resolved Microphysics and Key Controlling Parameters

AbstractShipping particle number emission is important as it can influence cloud condensation nuclei abundance and thus indirectly affect clouds and perturb the Earth's radiation budget. Here, we integrate a size‐resolved Advanced Particle Microphysics module with a photochemical BOX MOdeling eXtension to the Kinetic PreProcessor and employ the resulting model to understand the microphysical and chemical characteristics of ship plumes. Simulated concentrations of key gaseous species and particle numbers are in good agreement compared with measurements from the NOAA Intercontinental Transport and Chemical Transformation (ITCT) 2K2 field study off the California coast. Further analysis reveals that significant new particle formation can occur in the plume and the growth of these secondary particles to 5–20 nm generally dominates the total particle numbers. We show that wind speed, emission rates of SO2 and NOx, solar irradiation, ambient temperature, and background [NH3] have strong nonlinear effects on the ship particle number emission index (EIPN). Depending on the ambient air and meteorological conditions, the model simulations show that EIPN can range from ∼2.5 × 1014 no. kg−1 fuel (dominated by primary particles) to ∼3.0 × 1018 no. kg−1 fuel (dominated by secondary particles). In consideration of the current worldwide expansion of Emission Control Areas, we systematically study how the EIPN decreases with reduction of fuel sulfur content to 0.1%. Our study highlights the necessity of accounting for the nonlinear dependence of secondary particle formation on key controlling parameters in calculating shipping particle number emissions, which is important for determining aerosol indirect climate effects.

Environmental protection and energy efficiency improvement by using natural gas fuel in maritime transportation.

Emissions from vessels are a major environmental concern because of their impacts on the deterioration of the environment, especially global warming of the atmosphere. Therefore, the International Maritime Organization (IMO) concerns significant care to environmental protection through the reduction of exhaust emission and improvement of energy efficiency through technical and operational measures. Among the suggested measures from IMO, the alternative fuel such as natural gas has the priority to be used instead of fossil fuels. The present paper calculates the effect of using natural gas in a dual-fuel engine from environmental and energy efficiency perspectives. As a case study, a container ship has been investigated. The results of the analysis show that the percent of CO2, NOx, and SOx emission reduction corresponding to using a dual-fuel engine operated by natural gas instead of a diesel engine operated by heavy fuel oil is about 30.4%, 85.3%, and 97%, respectively. Moreover, it found that NOx and SOx emission rates of the dual-fuel engine comply with the IMO 2016 and 2020 limits, respectively. Furthermore, the Energy Efficiency Design Index value in the case of using dual-fuel engine is lower than the value by using diesel engine by about 30%, and this value will be 77.18%, 86.84%, and 99.27% of the required value for the first, second, and third phases, respectively, as recommended by IMO.

A systematic comparison of machine learning methods for modeling of dynamic processes applied to combustion emission rate modeling

Ten established, data-driven dynamic algorithms are surveyed and a practical guide for understanding these methods generated. Existing Python programming packages for implementing each algorithm are acknowledged, and the model equations necessary for prediction are presented. A case study on a coal-fired power plant’s NOx emission rates is performed, directly comparing each modeling method’s performance on a mutual system. Each model is evaluated by its root mean squared error (RMSE) on out-of-sample future horizon predictions. Optimal hyperparameters are identified using either an exhaustive search or genetic algorithm. The top five model structures of each method are used to recursively predict future NOx emission rates over a 60-step time horizon. The RMSE at each future timestep is determined, and the recursive output prediction trends compared against measurements in time. The GRU neural network is identified as the best candidate for representing the system, demonstrating accurate and stable predictions across the future horizon by all considered models, while satisfactory performance was observed in several of the ARX/NARX formulations. These efforts have contributed 1) a concise resource of multiple proven dynamic machine learning methods, 2) a practical guide explaining the use of these methods, effectively lowering the “barrier-to-entry” of deploying such models in control systems, 3) a comparison study evaluating each method’s performance on a mutual system, 4) demonstration of accurate multi-timestep emissions modeling suitable for systems-level control, and 5) generalizable results demonstrating the suitability of each method for prediction over a multi-step future horizon to other complex dynamic systems.

A Vehicle Activity-based Windowing approach to evaluate real-world NOx emissions from Modern Heavy-duty Diesel Trucks

The Not-to-Exceed (NTE) protocol developed in 2004 is currently used for verification of emissions compliance during in-fleet operation. However, the NTE protocol has come under scrutiny for assessment of test activity only under specific modes of in-use operation (i.e., favorable to NOx reduction aftertreatment system), therefore resulting in a limited amount of in-use activity being utilized for evaluation of the engine's compliance. This paper presents the application of an alternative concept, i.e., Vehicle Activity-based Windowing (ABW) approach to assess in-use NOx emissions rates while segregating engine and aftertreatment dependent modes of operating conditions. To this aim, in-use gaseous emissions were measured using a portable emissions measurement system (PEMS) from four modern heavy-duty (HD) trucks that were operated along four distinctive routes in Southern California. All the four trucks were equipped with diesel-fueled engines certified to the United States Environmental Protection Agency (U.S. EPA) 2010 emissions standards and were equipped with a diesel oxidation catalyst (DOC), diesel particulate filter (DPF), and selective catalytic reduction (SCR) aftertreatment configuration. Results of the study indicate that the ABW approach provides a unique opportunity of utilizing more than 90% of test activity acquired from valid ABW trips while demonstrating a characteristic comparison of NOx emissions levels from low versus medium/high power engine operation that are within as well as outside the bounds of efficient SCR operating conditions.

Effects of passenger load, road grade, and congestion level on real-world fuel consumption and emissions from compressed natural gas and diesel urban buses

This study investigated the effects of passenger load, road grade, and congestion level on the fuel consumption and emissions from a Euro VI compressed natural gas (CNG) urban bus and a Euro V diesel urban bus. Testing was performed under real-traffic conditions in Madrid, Spain, using a portable emission measurement system (PEMS). The PEMS data also were combined with the vehicle specific power (VSP) methodology to analyse the differences between the performance of the two types of buses and develop an energy-based emission model. Between the empty and 4000 kg passenger load cases, the fuel consumption and CO2 emissions for the diesel bus showed a significant increase by approximately 25%. With an increase in the road grade, and congestion level, the fuel consumption and CO2 emissions of both types of buses increased, by 6–55%. Unlike in the case of the diesel bus, the NOx emissions of the CNG bus decreased by 40–50% as the level of road grade and congestion increased. At intervals of VSP ≥ 2 kW/t, NOx emission rates for the CNG bus were approximately 60% lower than those of the diesel bus. Finally, the proposed VSP-based model estimated the fuel consumption and the CO2 and NOx emission factors with relative total errors of less than 13%.

Environmental assessment of a hybrid system composed of solid oxide fuel cell, gas turbine and multiple effect evaporation desalination system

This study deals with a solid oxide fuel cell- gas turbine (SOFC-GT) hybrid system coupled with a multi-effect evaporation desalination plant with steam condensation. The environmental evaluation is also done due to the importance of waste energy recovery especially waste heat in power generation systems. The evaporation desalination plant is studied for using the excess heat to produce freshwater. The thermodynamic relationships governing different components of the system are first provided, including fuel cells, heat exchangers, gas turbine, and desalination plant. Next, given the absence of previous research on the environmental effects of cogeneration systems, despite its necessity, the study system is analyzed from an environmental point of view. Accordingly, the impacts of the system performance parameters, including the fuel consumption coefficients, compressor pressure ratio, fuel pre-reforming percentage, and the steam to carbon ratio are investigated on the CO2, CO, and NOx emission rates. Based on the findings, it is concluded that of different species, the impacts of CO, CO2, and NOx emission rates are significant on the environment. Thus, the impacts of pressure ratio and pre-reforming percentage on their emission rates have been studied. The results revealed with increasing the compressor pressure ratio, increasing the fuel consumption coefficients, and decreasing the fuel cell's exhaust temperature, the CO and NOx emission rates and corresponding social costs diminished. On the other hand, with elevation of the ratio of steam to carbon, the recovery rate, the fuel cell's exhaust temperature, the concerned gas emission rates, and corresponding social costs increased.

Real-world activity, fuel use, and emissions of heavy-duty compressed natural gas refuse trucks

Over 50% of new refuse truck sales have been compressed natural gas (CNG). Compared to diesel, CNG is less expensive on diesel gallon equivalent (dge) basis. This study quantifies the real-world fuel use and tailpipe exhaust emissions from three front- and three side-loader refuse trucks, each with a spark ignition CNG engine, three-way catalyst, and similar gross weight. Measurements were made at 1 Hz using a portable emissions measurement system (PEMS). Inter-cycle and inter-vehicle variability is quantified. Effect of vehicle weight was analyzed and comparisons were made with MOVES predicted cycle average emission rates. In total, about 220,000 s of data covering 490 miles of operation were recorded. The average fuel economy was 1.9 miles per dge. On average the trucks spent 53% of time in idle, which includes trash collection activity. The average speeds were 10 mph and 5 mph, for front- and side-loader trucks, respectively. Overall, compared to side-loader trucks, front-loader trucks had 55% better fuel economy and 60% lower emission rates. Compared to diesel trucks, CNG truck cycle average NOx and PM emission rates, at 1.2 g/mile and 0.006 g/mile respectively, were substantially lower while CO and HC rates, at 29 g/mile and 6 g/mile respectively, were considerably higher. Fuel use and CO2 emissions rates increased by 10% due to increase in truck weight during trash collection, while CO emissions rates increased by up to 30%. Compared to measured values, MOVES estimated cycle average fuel use and CO2 emissions were 25% lower, CO emissions are 70% lower, and NOx emissions were 200% higher. Results from this study can be used to improve solid waste life cycle and tailpipe emission factor models and, when combined with previous studies on diesel refuse trucks, evaluate the effect on fuel use and emissions from adoption of CNG refuse trucks.

The satellite data revolution: How new satellite instruments can provide better estimates of NOx pollution

Measurements from satellite instruments orbiting Earth can be useful in quantifying the abundance of air pollution in locations with few or no monitors. Even in the United States, a country with 327 million people, there are fewer than 500 active NO2 air quality monitors, representing ~1 monitor per 1 million residents. The typical downside of satellite instruments is that they acquire measurements of gaseous pollutants at spatial extents analogous to entire city sizes (>20 x 20 km2). However more recently, an instrument launched by the European Space Agency (TROPOMI), is able to quantify NO2 air pollution at sub-city spatial resolution (3.5 x 5.5 km2) and with enhanced sensitivity. In this work, we use high-resolution data from TROPOMI to generate 2019 annual human exposure estimates to NO2. This is accomplished by merging the satellite data with other variables, which are highly correlated with the spatial heterogeneities of NO2, such as roadway density, population density, and atmospheric boundary layer depth. This new exposure estimate, at 1 x 1 km2 spatial resolution, is developed by training to the existing network of ground measurements, where the quantities of NO2 are well-known, and then is applied to the United States as a test-bed (R2 > 0.6), with further goals to apply globally. In an additional step, we also quantify NOx emission rates from power plants and large cities, by identifying the source of the pollution using the satellite spatial maps and then tracking the plume decay over time. Using these two complementary methods, we better understand the sources of NO2 pollution and the human exposure to it. These new NO2 exposure estimates can be used in epidemiological studies to explore the relationship between NO2 and health outcomes, and in health impact assessments to estimate the health benefits of emission regulations over time.

Numerical Modeling of Volatile Organic Compounds (VOC) Emissions during Preheating of Magnesia-Carbon Bricks in a Basic Oxygen Furnace

The refractory preheating process in oxygen furnaces is a dynamic input of energy in a chemically complex system requiring special attention to chemical emissions relative to permissible release limits. This particular industrial and regulatory interest is the emission of volatile organic compounds (VOC), given their detrimental impacts on human health. In the present work, a mathematical model was developed to predict the emission rates of volatile organics during the preheating of a 260-ton basic oxygen furnace. A numerical heat transfer model was developed using finite difference techniques to obtain the thermal profile and then integrated with chemical thermodynamics using FactSage 7.0 (CRCT, Polytechnique Montreal Quebec Canada, H3C 3A7). The parameters that affected VOC emissions were preheating process times, burner gas composition, heating rate, and burner geometry. Two different preheating procedures were compared, and emission rates were predicted with extended use of a top burner providing the greatest degree of emissions control. The mathematical model was validated against plant data with respect to average emission rates of CO, CO2, SOX, and NOX.

Retrieving tropospheric NO<sub>2</sub> vertical column densities around the city of Beijing and estimating NO<sub><i>x</i></sub> emissions based on car MAX-DOAS measurements

Abstract. We carried out 19 city-circle-around car multi-axis differential optical absorption spectroscopy (MAX-DOAS) experiments on the 6th Ring Road of Beijing in January, September, and October 2014. The tropospheric vertical column densities (VCDs) of NO2 were retrieved from measured spectra by the MAX-DOAS technique and used to estimate the emissions of NOx (≡NO+NO2) from urban Beijing during the experimental periods. The offline LAPS-WRF-CMAQ model system was used to simulate the wind fields by assimilation of observational data and calculate the NO2-to-NOx concentration ratios, both of which are also needed for the estimation of NOx emissions. The NOx emissions in urban Beijing for the different months derived from the car MAX-DOAS measurements in this study were compared to the multi-resolution emission inventory in China for 2012 (MEIC 2012). Our car MAX-DOAS measurements showed higher NO2 VCD in January than in the other two months. The wind field had obvious impacts on the spatial distribution of NO2 VCD, with the mean NO2 VCD along the 6th Ring Road typically being higher under the southerly wind than under the northerly wind. In addition to the seasonal difference, the journey-to-journey variations of estimated NOx emission rates (ENOx) were large even within the same month, mainly due to uncertainties in the calculations of wind speed, the ratio of NO2 and NOx concentration, and the decay rate of NOx from the emission sources to the measured positions under different meteorological conditions. The ranges of ENOx during the heating and non-heating periods were 22.6×1025 to 31.3×1025 and 9.6×1025 to 12.0×1025 molec. s−1, respectively. The average ENOx values in the heating and non-heating periods were 26.9±6.1×1025 molec. s−1 and 11.0±1.2×1025 molec. s−1, respectively. The uncertainty range of ENOx was 20 %–52 %. The monthly emission rates from MEIC 2012 are found to be lower than the estimated ENOx, particularly in January. Our results provide important information and datasets for the validation of satellite products and also show how car MAX-DOAS measurements can be used effectively for dynamic monitoring and updating of the NOx emissions from megacities such as Beijing.

On-Road NOx and Smoke Emissions of Diesel Light Commercial Vehicles-Combining Remote Sensing Measurements from across Europe.

Light commercial vehicles (LCVs) account for about 10-15% of road traffic in Europe. There have only been few investigations on their on-road emission performance. Here, on-road remote sensing vehicle emission measurements from 18 locations across four European countries are combined for a comprehensive analysis of NOx and smoke emission rates from diesel LCV in the past two decades. This allows differentiating the performance by emission standards, model years, curb weights, engine loads, manufacturers, vehicle age, and temperature, as well as by measurement devices. We find a general consistency between devices and countries. On-road NOx emission rates have been much higher than type approval limit values for all manufacturers, but some perform systematically better than others. Emission rates have gone down only with the introduction of Euro 6a-b emission standards since the year 2015. Smoke emission rates are considered a proxy for particulate emissions. Their emissions have decrease substantially from the year 2010 onward for all countries and size classes measured. This is consistent with the substantial tightening of the particulate matter emission limit value that typically forced the introduction of a diesel particulate filter. The average NOx emission rate increases with engine load and decreasing ambient temperatures, particularly for Euro 4 and 5 emission classes. This explains to a large extent the differences in the absolute level between the measurement sites together with differences in fleet composition. These dependencies have already been observed earlier with diesel passenger cars; they are considered part of an abnormal emission control strategy. Some limited increase of the NOx emission rate is observed for Euro 3 vehicles older than 10 years. The strong increase for the youngest Euro 6 LCVs might rather reflect technology advances with successively younger models than genuine deterioration. However, the durability of emission controls for Euro 6 vehicles should be better monitored closely. Smoke emission rates continuously increase with vehicle age, suggesting a deterioration of the after-treatment system with use.

Analysis of vehicle CO and NOx road emissions test based on PEMS

ABSTRACT To provide a corresponding preparation and foundation for the implementation and testing of China 6, as well as provide a strong reference for the formulation of China automotive test cycle (CATC) in the future, portable emission measurement system (PEMS) was applied to carry out CO and NOx emissions test on conventional roads for a light-duty gasoline vehicle (Toyota Levin) and a heavy-duty diesel vehicle (KING LONG bus) in Nanjing. The results showed that the CO emission rate of the Toyota car was mainly determined by the speed. As the vehicle speed increased, the CO emission rate increased rapidly while it was below 0.035 g/s. The CO emission rate of KING LONG bus accelerated with the increase of the vehicle speed at the lower speed and began to decrease when the speed reached 60 km/h. In terms of NOx emission rate, as the vehicle speed increased, the NOx emission rates of both vehicles increased. The CO and NOx emission factors of the two models showed similar patterns, both of which decreased significantly as the vehicle speed increased. Compared with the corresponding emission limits, the CO emission of both vehicles was higher, especially for light vehicle, and it was more than 3 times the limit. The NOx emission of both vehicles met the corresponding emission standards. The CO and NOx emission rates of light vehicles were positively correlated with specific power of vehicle (VSP). When VSP was less than 16, the two pollutants emission rate variation with VSP of heavy vehicle showed the same trend, while it was different when VSP was more than 16.

On-line classification of coal combustion quality using nonlinear SVM for improved neural network NOx emission rate prediction

A nonlinear support vector machine (SVM) uses engineered features to classify the quality of currently combusting coal as it is fired in an operating electric utility generator. The SVM classification result selects a unique neural network regression model to predict NOx emission rate. A two-part exhaustive grid-search and 5-fold cross-validation routine identifies the radial basis kernel as optimal for the SVM, achieving a classification accuracy of greater than 66%. The accuracy of the modified neural network structure improves on the original structure by 40%. This work contributes 1) evidence of feature engineering to enhance raw features in a complex industrial process and to provide otherwise unavailable data, 2) the formulation of a novel hybrid machine learning approach combining SVMs and neural networks with differing objectives harmoniously, and 3) a demonstrated improvement in neural network NOx emission rate prediction accuracy at a live operating electric utility generator due to SVM classification.