Effects of Cold Temperature and Ethanol Content on VOC Emissions from Light-Duty Gasoline Vehicles

2015 ◽  
Vol 49 (21) ◽  
pp. 13067-13074 ◽  
Author(s):  
Ingrid J. George ◽  
Michael D. Hays ◽  
Jason S. Herrington ◽  
William Preston ◽  
Richard Snow ◽  
...  
Keyword(s):  
Cold Temperature ◽  
Voc Emissions ◽  
Light Duty ◽  
Ethanol Content ◽  
Gasoline Vehicles

Evaluation of emission factors for light-duty gasoline vehicles based on chassis dynamometer and tunnel studies in Shanghai, China

2017 ◽  
Vol 169 ◽  
pp. 193-203 ◽  
Author(s):  
Cheng Huang ◽  
Shikang Tao ◽  
Shengrong Lou ◽  
Qingyao Hu ◽  
Hongli Wang ◽  
...  
Keyword(s):  
Emission Factors ◽  
Chassis Dynamometer ◽  
Light Duty ◽  
Gasoline Vehicles

Cold Temperature PM Emissions Measurement:  Method Evaluation and Application to Light Duty Vehicles

2005 ◽  
Vol 39 (24) ◽  
pp. 9424-9430 ◽  
Author(s):  
Jyrki Ristimäki ◽  
Jorma Keskinen ◽  
Annele Virtanen ◽  
Matti Maricq ◽  
Päivi Aakko
Keyword(s):  
Measurement Method ◽  
Cold Temperature ◽  
Method Evaluation ◽  
Light Duty ◽  
Light Duty Vehicles

Characterizing emissions and optical properties of particulate matter from PFI and GDI light-duty gasoline vehicles

2015 ◽  
Vol 90 ◽  
pp. 144-153 ◽  
Author(s):  
R. Bahreini ◽  
J. Xue ◽  
K. Johnson ◽  
T. Durbin ◽  
D. Quiros ◽  
...  
Keyword(s):  
Optical Properties ◽  
Particulate Matter ◽  
Light Duty ◽  
Gasoline Vehicles

scholarly journals Secondary organic aerosol formation exceeds primary particulate matter emissions for light-duty gasoline vehicles

2013 ◽  
Vol 13 (9) ◽  
pp. 23173-23216 ◽  
Author(s):  
T. D. Gordon ◽  
A. A. Presto ◽  
A. A. May ◽  
N. T. Nguyen ◽  
E. M. Lipsky ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Secondary Organic Aerosol ◽  
Cold Start ◽  
Organic Aerosol ◽  
Smog Chamber ◽  
Aerosol Formation ◽  
Gas Emissions ◽  
Light Duty ◽  
Gasoline Vehicles ◽  
Diesel Trucks

Abstract. The effects of photochemical aging on emissions from 15 light-duty gasoline vehicles were investigated using a smog chamber to probe the critical link between the tailpipe and ambient atmosphere. The vehicles were recruited from the California in-use fleet; they represent a wide range of model years (1987 to 2011), vehicle types and emission control technologies. Each vehicle was tested on a chassis dynamometer using the unified cycle. Dilute emissions were sampled into a portable smog chamber and then photochemically aged under urban-like conditions. For every vehicle, substantial secondary organic aerosol (SOA) formation occurred during cold-start tests, with the emissions from some vehicles generating as much as 6 times the amount of SOA as primary particulate matter after three hours of oxidation inside the chamber at typical atmospheric oxidant levels. Therefore, the contribution of light duty gasoline vehicle exhaust to ambient PM levels is likely dominated by secondary PM production (SOA and nitrate). Emissions from hot-start tests formed about a factor of 3–7 less SOA than cold-start tests. Therefore, catalyst warm-up appears to be an important factor in controlling SOA precursor emissions. The mass of SOA generated by photo-oxidizing exhaust from newer (LEV1 and LEV2) vehicles was only modestly lower (38%) than that formed from exhaust emitted by older (pre-LEV) vehicles, despite much larger reductions in non-methane organic gas emissions. These data suggest that a complex and non-linear relationship exists between organic gas emissions and SOA formation, which is not surprising since SOA precursors are only one component of the exhaust. Except for the oldest (pre-LEV) vehicles, the SOA production could not be fully explained by the measured oxidation of speciated (traditional) SOA precursors. Over the time scale of these experiments, the mixture of organic vapors emitted by newer vehicles appear to be more efficient (higher yielding) in producing SOA than the emissions from older vehicles. About 30% of the non-methane organic gas emissions from the newer (LEV1 and LEV2) vehicles could not be speciated, and the majority of the SOA formed from these vehicles appears to be associated with these unspeciated organics. These results for light-duty gasoline vehicles contrast with the results from a companion study of on-road heavy-duty diesel trucks; in that study late model (2007 and later) diesel trucks equipped with catalyzed diesel particulate filters emitted very little primary PM, and the photo-oxidized emissions produced negligible amounts of SOA.

Geospatial Variation of Real-World Tailpipe Emission Rates for Light-Duty Gasoline Vehicles

2020 ◽  
Vol 54 (14) ◽  
pp. 8968-8979
Author(s):  
Tanzila Khan ◽  
H. Christopher Frey ◽  
Nikhil Rastogi ◽  
Tongchuan Wei
Keyword(s):  
Real World ◽  
Emission Rates ◽  
Light Duty ◽  
Gasoline Vehicles

Evaluation of the Precision and Accuracy of Cycle-Average Light Duty Gasoline Vehicles Tailpipe Emission Rates Predicted by Modal Models

2020 ◽  
Vol 2674 (7) ◽  
pp. 566-584
Author(s):  
Tongchuan Wei ◽  
H. Christopher Frey
Keyword(s):  
Goodness Of Fit ◽  
Motor Vehicle ◽  
Vehicle Group ◽  
Specific Power ◽  
Emission Measurement ◽  
Emission Rates ◽  
Light Duty ◽  
Wide Range ◽  
Gasoline Vehicles ◽  
Precision And Accuracy

A vehicle specific power (VSP) modal model and the MOtor Vehicle Emission Simulator (MOVES) Operating Mode (OpMode) model have been used to evaluate and quantify the fuel use and emission rates (FUERs) for on-road vehicles. These models bin second-by-second FUERs based on factors such as VSP, speed, and others. The validity of binning approaches depends on their precision and accuracy in predicting variability in cycle-average emission rates (CAERs). The objective is to quantify the precision and accuracy of the two modeling methods. Since 2008, North Carolina State University has used portable emission measurement systems to measure tailpipe emission rates for 214 light duty gasoline vehicles on 1,677 driving cycles, including 839 outbound cycles and 838 inbound cycles on the same routes. These vehicles represent a wide range of characteristics and emission standards. For each vehicle, the models were calibrated based on outbound cycles and were validated based on inbound cycles. The goodness-of-fit of the calibrated models was assessed using linear least squares regression without intercept between model-predicted versus empirical CAERs for individual vehicles. Based on model calibration and validation, the coefficients of determination ( R2) typically range from 0.60 to 0.97 depending on the vehicle group and pollutant, indicating moderate to high precision, with precision typically higher for higher-emitting vehicle groups. The slopes of parity plots for each vehicle group and all vehicles typically range from 0.90 to 1.10, indicating good accuracy. The two modeling approaches are similar to each other at the microscopic and macroscopic levels.

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