Numerical model of on-road emission rates of diesel buses in Beijing

A total of 14 in-use diesel buses were selected to conduct emission measurement using a portable emissions measurement system （PEMS） in Beijing. Their instantaneous gaseous emission rates, particular matter （PM） emission rates and driving parameters were obtained. The influences of speed, acceleration and vehicle specific power （VSP） on emissions were analyzed. Based on the relationships between these driving parameters and emissions, 24 driving bins defined by speed, ac- celeration and VSP were constructed with cluster analysis to group emission rates for Euro Ⅲ and IV buses, respectively. Then the emissions reductions from Euro Ⅲ to Euro Ⅳ diesel buses were ana- lyzed. Lastly, on-road hot-stabilized emission rate model for diesel buses in Beijing was developed. Through the comparison of the model simulation emission rates with the measured emission rates, the modeled emission results were in good agreement with the measured emission results. In most of the cases, the differences were less than 12 %.

Diagnostic of capacitively coupled radio frequency plasma from electrical discharge characteristics：comparison with optical emission spectroscopy and fluid model simulation

The capacitively coupled radio frequency（CCRF）plasma has been widely used in various fields.In some cases,it requires us to estimate the range of key plasma parameters simpler and quicker in order to understand the behavior in plasma.In this paper,a glass vacuum chamber and a pair of plate electrodes were designed and fabricated,using 13.56 MHz radio frequency（RF）discharge technology to ionize the working gas of Ar.This discharge was mathematically described with equivalent circuit model.The discharge voltage and current of the plasma were measured atdifferent pressures and different powers.Based on the capacitively coupled homogeneous discharge model,the equivalent circuit and the analytical formula were established.The plasma density and temperature were calculated by using the equivalent impedance principle and energy balance equation.The experimental results show that when RF discharge power is 50–300 W and pressure is 25–250 Pa,the average electron temperature is about 1.7–2.1 e V and the average electron density is about 0.5？×10^17–3.6？×10^17m^-3.Agreement was found when the results were compared to those given by optical emission spectroscopy and COMSOL simulation.

Numerical study on discharge characteristics influenced by secondary electron emission in capacitive RF argon glow discharges by fluid modeling

A one-dimensional（1D） fluid model of capacitive RF argon glow discharges between two parallel-plate electrodes at low pressure is employed. The influence of the secondary electron emission on the plasma characteristics in the discharges is investigated numerically by the model. The results show that as the secondary electron emission coefficient increases,the cycle-averaged electric field has almost no change; the cycle-averaged electron temperature in the bulk plasma almost does not change, but it increases in the two sheath regions; the cycle-averaged ionization rate, electron density, electron current density, ion current density, and total current density all increase. Also, the cycle-averaged secondary electron fluxes on the surfaces of the electrodes increase as the secondary electron emission coefficient increases. The evolutions of the electron flux, the secondary electron flux and the ion flux on the powered electrode increase as the secondary electron emission coefficient increases. The cycle-averaged electron pressure heating, electron Ohmic heating, electron heating, and ion heating in the two sheath regions increase as the secondary electron emission coefficient increases. The cycle-averaged electron energy loss increases with increasing secondary electron emission coefficient.

Improving microwave brightness temperature predictions based on Bayesian model averaging ensemble approach

The choices of the parameterizations for each component in a microwave emission model have significant effects on the quality of brightness temperature （Tb） sim- ulation. How to reduce the uncertainty in the Tb simulation is investigated by adopting a statistical post-processing procedure with the Bayesian model averaging （BMA） ensemble approach. The simulations by the community microwave emission model （CMEM） cou- pled with the community land model version 4.5 （CLM4.5） over China's Mainland are con- ducted by the 24 configurations from four vegetation opacity parameterizations （VOPs）, three soil dielectric constant parameterizations （SDCPs）, and two soil roughness param- eterizations （SRPs）. Compared with the simple arithmetical averaging （SAA） method, the BMA reconstructions have a higher spatial correlation coefficient （larger than 0.99） than the C-band satellite observations of the advanced microwave scanning radiometer on the Earth observing system （AMSR-E） at the vertical polarization. Moreover, the BMA product performs the best among the ensemble members for all vegetation classes, with a mean root-mean-square difference （RMSD） of 4 K and a temporal correlation coefficient of 0.64.

Inter-provincial carbon emission intensity factor analysis and carbon intensity projection calculation in China

The extended “STIRPAT” model and the GM(1,1) model are used to predict the factors influencing inter-provincial carbon emission intensity and carbon intensity in China respectively. In this paper, based on the collation of inter-provincial carbon emission data, the extended “STIRPAT” model is formulated for carbon dioxide emissions and carbon intensity emissions, and the Hausman test is used to determine the influence form of the models. The main influencing factors of carbon intensity were identified: economic development level, energy intensity, and energy consumption structure. The paper constructs GM(1,1) model for carbon emission intensity from 2010-2019 using the gray prediction method,and calculates the carbon emission intensity of China’s inter-provincial 2022 by residual test, correlation test, variance, and small error probability test, and then predicts the carbon demand of each province and city in 2022 according to the expected average annual growth rate, and finally concludes that using carbon emission intensity as the carbon emission reduction target of each region, and it cannot fundamentally solve the problem of carbon pollution in China. Compared to the regional carbon emission reduction target, there is a greater degree of regional imbalance in carbon intensity between provinces in China, and the target of reducing carbon emission intensity somehow avoids the fact that the carbon emission reduction intensity target can be achieved without reducing the absolute amount of carbon emissions that continue to increase. The focus of achieving the “double carbon” target lies in the reduction of total carbon emissions, and the target of reducing carbon intensity will eventually be transformed into a binding target of total carbon emissions in the process of implementation, so attention should be shifted from recessiontype carbon reduction and efficiency-type carbon reduction to innovative carbon reduction. It is necessary to increase investment in renewable energy, and gradually expand the scope of application of photovoltaic, and wind power to ensure the reduction of total carbon emissions.

Development of a procedure for estimating the parameters of mechanistic VOC emission source models from chamber testing data

In order to evaluate the impacts of volatile organic compounds(VOCs)emissions from building materials on the indoor air quality beyond the standard chamber test conditions and test period,mechanistic emission source models have been developed in the past.However,very limited data are available for the required model parameters including the initial concentration(C_(m0)),in-material diffusion coefficient(D_(m)),partition coefficient(Kma),and convective mass transfer coefficient(k_(m)).In this study,a procedure was developed for estimating the model parameters by using VOC emission data from standard small chamber tests.In the procedure,initial values of the model parameters were refined by multivariate regression analysis of the measured emission data.To verify the procedure and estimate its uncertainty,simulated chamber test data were generated by adding 10% experimental uncertainties on the theoretical curve from the analytical solution to a mechanistic emission model.Then the procedure was applied to the generated data to estimate the model parameters.Results indicated that estimates converged to the original parameter values used for the data generation and the error of estimated parameters D_(m1)C_(m0) and K_(ma) were within±10%,±23%,and±25%of the true values,respectively.The procedure was further demonstrated by applying it to estimate the model parameters from real chamber test data.Wide application of the procedure would result in a database of mechanistic source model parameters for assessing the impact of VOC emissions on indoor pollution load,which are essential input data for evaluating the effectiveness of various indoor air quality(IAQ)design and control strategies as well as the energy required for meeting given IAQ requirements.

Modeling and predicting low-speed vehicle emissions as a function of driving kinematics

An instantaneous emission model was developed to model and predict the real driving emissions of the low-speed vehicles. The emission database used in the model was measured by using portable emission measurement system （PEMS） under actual traffic conditions in the rural area, and the characteristics of the emission data were determined in relation to the driving kinematics （speed and acceleration） of the low-speed vehicle. The input of the emission model is driving cycle, and the model requires instantaneous vehicle speed and acceleration levels as input variables and uses them to interpolate the pollutant emission rate maps to calculate the transient pollutant emission rates, which will be accumulated to calculate the total emissions released during the whole driving cycle. And the vehicle fuel consumption was determined through the carbon balance method. The model predicted the emissions and fuel consumption of an in-use low-speed vehicle type model, which agreed well with the measured data.

CO_(2) emissions reduction performance of China’s HSR based on substitution effect and demand effect

As an important transportation infrastructure and transportation backbone in China,high-speed rail(HSR)plays a critical role in promoting the development of green and low-carbon transportation.Calculating the CO_(2) emissions reduction performance of HSR will be conduci v e to pr omote the CO_(2) emissions r eduction w ork of the r ail w ay.Based on the Dalkic HSR CO_(2) emissions r eduction performance model,by adjusting the HSR CO_(2) emission factor(CEF HSR),the annual times of departures(T)and other parameters,this study develops a Chinese HSR CO_(2) emissions reduction performance model.Taking the Beijing-Shanghai HSR as the resear c h object,this study conducts a questionnaire survey to explore the substitution effect and demand effect of HSR on different transportation modes;collects data such as passenger v olume,av era ge electricity use and annual times of departures of the Beijing-Shanghai HSR in 2019;and calculates the CO_(2) emissions reduction performance of the Beijing-Shanghai HSR.This study has two main results:(1)It builds a Chinese HSR CO_(2) emissions reduction performance model based on substitution effect and demand effect.(2)In 2019,the CO_(2) emissions of the Beijing-Shanghai HSR are 2898233.62 t,the CO_(2) emissions reduction performance of the Beijing-Shanghai HSR is 17999482.8 t,the annual CO_(2) emissions of the Beijing-Shanghai line in‘No HSR’case are 7.2 times as in the’HSR’case and the PKT of the HSR is 10.2 g/pkm.Based on the research results,this study proposes three CO_(2) emissions reduction policy suggestions.This study would be helpful for further HSR CO_(2) emissions reduction resear c h and departments related to railway transportation management to make CO_(2) emissions reduction policies.

Review of the studies on emission evaluation approaches for operating vehicles

With the increasing international consensus concerning the negative effects of climate change,reducing greenhouse gases has become a higher priority in government policies and research committees.The transportation sector generates approximately 29%of the total greenhouse gas emissions and 25%of the global energy related carbon dioxide(CO2)emissions.Thus,it is essential to understand the influencing factors of vehicle emissions and establish a corresponding evaluation model for the emission estimation of operating vehicles.This paper reviews different methods of measuring vehicle emissions,including laboratory measurements,on-road measurements,and tunnel measurements.Then,we summarize the factors affecting the emission evaluation of operating vehicles based on the vehicle operating features and road environment.Finally,the applications of vehicle emission evaluation models are analyzed,including the emission assessment of the vehicles operating at road segments and intersections.Based on this review,one can conclude that selecting different measurements will significantly impact the assessment of the vehicle emission results and the applicable scope of the measurements.Considering the different influencing factors of the operating vehicle emissions will have an impact on the model application of the vehicle emission evaluation.Moreover,several analysis methods for new technology vehicles can compensate for the potential lack in connectedness with the rapid development of new energy vehicle technology and the improvement of intelligent transportation systems(ITS).

Modeling chemical releases from building materials: The search for extended validity domain and parsimony

This study aims to systematically evaluate and extend the validity domains of multiple mechanistic models of chemical emissions from building materials. We compare the validity domain of the analytical solution with four numerical solutions for a single layer material with one convective surface and a wide range of chemical properties, material thicknesses, and simulation time. We also develop a parsimonious simplified model, ensuring the widest possible validity domain with minimum simulation time. For diffusion coefficients lower than 10−15 m^(2)/s, accuracy of the analytical solution requires at least 5000 positive roots. The numerical method using uneven discretization and finite difference approximation for the boundary conditions is the best numerical solution. The parsimonious combined D- and K-limited model achieves similar accuracy as the best numerical solution except slight overestimates at the interface between the D- and K-limited zones, while having simpler computations and much shorter simulation time. These models show good agreement against experimental data. This study demonstrates that the complex analytical solution can be well approximated by a simpler model with a wide validity domain, enabling the high-throughput screenings of a large number of chemical-product combinations.

Prediction of Spatiotemporal Evolution of Urban Traffic Emissions Based on Taxi Trajectories

With the rapid increase of the amount of vehicles in urban areas,the pollution of vehicle emissions is becoming more and more serious.Precise prediction of the spatiotemporal evolution of urban traffic emissions plays a great role in urban planning and policy making.Most existing methods usually focus on estimating vehicle emissions at historical or current moments which cannot well meet the demands of future planning.Recent work has started to pay attention to the evolution of vehicle emissions at future moments using multiple attributes related to emissions,however,they are not effective and efficient enough in the combination and utilization of different inputs.To address this issue,we propose a joint framework to predict the future evolution of vehicle emissions based on the GPS trajectories of taxis with a multi-channel spatiotemporal network and the motor vehicle emission simulator(MOVES)model.Specifically,we first estimate the spatial distribution matrices with GPS trajectories through map-matching algorithms.These matrices can reflect the attributes related to the traffic status of road networks such as volume,speed and acceleration.Then,our multi-channel spatiotemporal network is used to efficiently combine three key attributes(volume,speed and acceleration)through the feature sharing mechanism and generate a precise prediction of them in the future period.Finally,we adopt an MOVES model to estimate vehicle emissions by integrating several traffic factors including the predicted traffic states,road networks and the statistical information of urban vehicles.We evaluate our model on the Xi′an taxi GPS trajectories dataset.Experiments show that our proposed network can effectively predict the temporal evolution of vehicle emissions.

Simulation Analyses of Two On-Ramp Lane Arrangements

Ramps are vital pieces of infrastructure connecting city traffic networks to freeways.The performance of a ramp is to some extent determined by the on-ramp lane arrangement.In this paper,our primary aim is to evaluate the performance in terms of travel time and vehicle emissions for two on-ramp lane arrangements:added lane and zip merging.We estimate the travel time and CO_(2) emissions on the basis of the speed,and acceleration of vehicles in accordance with the improved comprehensive modal emission model(CMEM),and then analyse the impacts of traffic volume and heavy goods vehicles(HGVs)on travel time and emissions.The impacts of main road traffic flow on travel time and emissions for the two on-ramp lane arrangements are analysed under scenarios with traffic volumes of 800,1000,1200,1400,1600 and 1800 vehs/h/lane.Meanwhile,the relationships between travel time,emissions and various proportions of HGVs(2%,4%,6%,8%and 10%)for both on-ramp lane arrangements are evaluated as well.We eventually present emission contour charts for the two onramp lane arrangements based on the possible combinations of traffic volumes and HGV percentages.

Technical pathways of dual carbon control in China’s buildings sector

Reducing carbon emissions in the buildings sector is of great significance to the realization of China’s carbon peak and neutrality goals.By analyzing factors influencing buildings carbon emissions at the operational stage,this paper applies the China Building Carbon Emission Model(CBCEM)to make medium and long-term forecasts of China’s building operation carbon emissions,discussing the goals and realization paths of China’s dual carbon goals in the buildings sector.The results show that building operation carbon emissions,according to the current development model in the buildings sector,will peak in 2038-2040 with a peak carbon emission of about 3.15 billion tons of CO_(2);however,by 2060,carbon emissions will still be 2.72 billion tons of CO_(2),falling short of China’s dual carbon goals.The carbon saving effects of three scenarios,namely clean grid priority,building photovoltaic priority and energy efficiency enhancement priority,were measured and shown to be significant in all three scenarios,but the building photovoltaic priority and energy efficiency enhancement priority scenarios were superior in comparison.

Development and case study of a science-based software platform to support policy making on air quality

This article describes the development and implementations of a novel software platform that supports real-time, science-based policy making on air quality through a user-friendly interface. The software, RSM-VAT, uses a response surface modeling（RSM） methodology and serves as a visualization and analysis tool（VAT） for three-dimensional air quality data obtained by atmospheric models. The software features a number of powerful and intuitive data visualization functions for illustrating the complex nonlinear relationship between emission reductions and air quality benefits. The case study of contiguous U.S.demonstrates that the enhanced RSM-VAT is capable of reproducing the air quality model results with Normalized Mean Bias 〈 2% and assisting in air quality policy making in near real time.

A hybrid strategy in selecting diverse combinations of innovative sustainable materials for asphalt pavements

This project integrates recent innovations of recycled materials used in designing and building sustainable pavements. An increasing environmental awareness and the demand for improving economic and construction efficiencies, through measures such as con- struction warrantees and goals to reduce air pollution under the Kyoto Protocol, have increased the efforts to implement sustainable materials in roadways. The objective of this research is to develop a systematic approach toward selecting optimum combinations of sustainable materials for the construction of asphalt pavements. The selected materials, warm mix asphalt （WMA）, recycled asphalt shingles {RAS）I and reclaimed asphalt pave- ment {RAP） were incorporated in this study. The results of this research are intended to serve as guidelines in the selection of the mixed sustainable materials for asphalt pave- ments. The approach developed from this project draws upon previous research efforts integrating graphical modeling with optimizing the amount of sustainable materials based on the performance. With regard to moisture susceptibility and rutting potential test re- suits, as well as the MIM analysis based on a 95% confidence interval, the rutting perfor- mance and moisture susceptibility of asphalt mixtures are not significantly different regardless of the percentages of RAS, RAP, or WMA. The optimum mixture choices could be made by the plant emission rankings with consideration of the optimal WMA types, per- centages of RAS/RAP, and WMA production temperatures. The WMA mixtures prepared with 75% RAP and Advera WMA have produced the lowest CO2 emissions among the investigated mixture types.