scholarly journals Seasonal and Long-Term Trend of on-Road Gasoline and Diesel Vehicle Emission Factors Measured in Traffic Tunnels

2020 ◽  
Vol 10 (7) ◽  
pp. 2458 ◽  
Author(s):  
Xiang Li ◽  
Timothy R. Dallmann ◽  
Andrew A. May ◽  
Albert A. Presto

Emissions of gaseous and particulate pollutants from on-road gasoline and diesel vehicles were measured in a traffic tunnel under real-world driving conditions. Emission factors were attributed to gasoline and diesel vehicles using linear regression against the fraction of fuel consumed by diesel vehicles (% fuelD). We measured 67% higher NOx emissions from gasoline vehicles in winter than in spring (2 versus 1.2 g NO2 kg fuel−1). Emissions of CO, NOx, and particulate matter from diesel vehicles all showed impacts of recent policy changes to reduce emissions from this source. Comparison of our measurements to those of a previous study ~10 years prior in a nearby traffic tunnel on the same highway showed that emission factors for both gasoline and diesel vehicles have fallen by 50–70%. To further confirm this long-term trend, we summarized emission factors measured in previous tunnel studies in the U.S. since the 1990s. More restrictive emission standards are effective at reducing emissions from both diesel and gasoline vehicles, and decreases in observed emissions can be mapped to specific vehicle control policies. The trend of diesel-to-gasoline emission factor ratios revealed changes in the relative importance of vehicle types, though fuel-specific emission factors of NOx and elemental carbon (EC) are still substantially larger (~5–10 times) for diesel vehicles than gasoline vehicles.

2020 ◽  
Vol 5 ◽  
pp. 100055 ◽  
Author(s):  
Yuji Fujitani ◽  
Katsuyuki Takahashi ◽  
Akihiro Fushimi ◽  
Shuichi Hasegawa ◽  
Yoshinori Kondo ◽  
...  

Author(s):  
Albert E. Beaton ◽  
James R. Chromy
Keyword(s):  

2021 ◽  
Vol 38 (10) ◽  
pp. 1791-1802
Author(s):  
Peiyan Chen ◽  
Hui Yu ◽  
Kevin K. W. Cheung ◽  
Jiajie Xin ◽  
Yi Lu

AbstractA dataset entitled “A potential risk index dataset for landfalling tropical cyclones over the Chinese mainland” (PRITC dataset V1.0) is described in this paper, as are some basic statistical analyses. Estimating the severity of the impacts of tropical cyclones (TCs) that make landfall on the Chinese mainland based on observations from 1401 meteorological stations was proposed in a previous study, including an index combining TC-induced precipitation and wind (IPWT) and further information, such as the corresponding category level (CAT_IPWT), an index of TC-induced wind (IWT), and an index of TC-induced precipitation (IPT). The current version of the dataset includes TCs that made landfall from 1949–2018; the dataset will be extended each year. Long-term trend analyses demonstrate that the severity of the TC impacts on the Chinese mainland have increased, as embodied by the annual mean IPWT values, and increases in TCinduced precipitation are the main contributor to this increase. TC Winnie (1997) and TC Bilis (2006) were the two TCs with the highest IPWT and IPT values, respectively. The PRITC V1.0 dataset was developed based on the China Meteorological Administration’s tropical cyclone database and can serve as a bridge between TC hazards and their social and economic impacts.


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