Mobile source emission model based on temporal features transfer*

Author(s):  
Zhenyi Xu ◽  
Ruibin Wang ◽  
Renjun Wang ◽  
Xiushan Xia
Author(s):  
Kazunori Shimazaki ◽  
Eiji Miyazaki ◽  
Fumitaka Urayama ◽  
Yugo Kimoto

2009 ◽  
Vol 6 (12) ◽  
pp. 3035-3051 ◽  
Author(s):  
J. van Huissteden ◽  
A. M. R. Petrescu ◽  
D. M. D. Hendriks ◽  
K. T. Rebel

Abstract. Modelling of wetland CH4 fluxes using wetland soil emission models is used to determine the size of this natural source of CH4 emission on local to global scale. Most process models of CH4 formation and soil-atmosphere CH4 transport processes operate on a plot scale. For large scale emission modelling (regional to global scale) upscaling of this type of model requires thorough analysis of the sensitivity of these models to parameter uncertainty. We applied the GLUE (Generalized Likelihood Uncertainty Analysis) methodology to a well-known CH4 emission model, the Walter-Heimann model, as implemented in the PEATLAND-VU model. The model is tested using data from two temperate wetland sites and one arctic site. The tests include experiments with different objective functions, which quantify the fit of the model results to the data. The results indicate that the model 1) in most cases is capable of estimating CH4 fluxes better than an estimate based on the data avarage, but does not clearly outcompete a regression model based on local data; 2) is capable of reproducing larger scale (seasonal) temporal variability in the data, but not the small-scale (daily) temporal variability; 3) is not strongly sensitive to soil parameters, 4) is sensitive to parameters determining CH4 transport and oxidation in vegetation, and the temperature sensitivity of the microbial population. The GLUE method also allowed testing of several smaller modifications of the original model. We conclude that upscaling of this plot-based wetland CH4 emission model is feasible, but considerable improvements of wetland CH4 modelling will result from improvement of wetland vegetation data.


2020 ◽  
Vol 79 (43-44) ◽  
pp. 31913-31930 ◽  
Author(s):  
Ran Ma ◽  
Tong Li ◽  
Dezhi Bo ◽  
Qiang Wu ◽  
Ping An

Author(s):  
Theodore Younglove ◽  
George Scora ◽  
Matthew Barth

Mobile source emission models for years have depended on laboratory-based dynamometer data. Recently, however, portable emission measurement systems (PEMS) have become commercially available and in widespread use, and make on-road real-world measurements possible. As a result, the newest mobile source emission models (e.g., U.S. Environmental Protection Agency's mobile vehicle emission simulator) are becoming increasingly dependent on PEMS data. Although on-road measurements are made under more realistic conditions than laboratory-based dynamometer test cycles, they introduce influencing variables that must be carefully measured for properly developed emission models. Further, test programs that simply measure in-use driving patterns of randomly selected vehicles will result in models that can effectively predict current-year emission inventories for typical driving conditions. However, when predicting more aggressive transportation operations than current typical operations (e.g., higher speeds, accelerations), the model predictions will be less certain. In this paper, various issues associated with on-road emission measurements and modeling are presented. Further, an example on-road emission data set and the reduction in estimation error through the addition of a short aggressive driving test to the in-use data are examined. On the basis of these results, recommendations are made on how to improve the on-road test programs for developing more robust emission models.


2013 ◽  
Vol 80 ◽  
pp. 818-836 ◽  
Author(s):  
Terry L. Friesz ◽  
Ke Han ◽  
Hongcheng Liu ◽  
Tao Yao

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