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For catalytic converter-equipped light-duty gasoline vehicles (LDGV), the hot-stabilized tailpipe emissions for pollutants such as carbon monoxide (CO), hydrocarbons (HC), and nitrogen oxides (NOx) are well controlled. However, there are few reported real-world measurements of cold starts. Idling cold start and hot-stabilized trip exhaust emissions were measured for 37 LDGVs using a portable emissions measurement system (PEMS). Five vehicles were also measured for transient driving cold starts. On average, it took approximately 400, 150, 330, and 120 s to accumulate 90 percent of the idle cold start increments for fuel use, CO, HC, and NOx, respectively. Driving cold start increments were substantially higher than idling cold start increments, whereas cold start duration was typically shorter. For example, driving cold start contributed approximately 64%, 68%, 58%, and 4.5% of the trip total CO, HC, NOx, and carbon dioxide (CO2), respectively. This study is unique in quantifying the cold start contribution on a trip basis with real-world data. Although the cold start increment is sensitive to driving compared to idling, in either case, cold starts contribute substantially to total exhaust mass emissions. Furthermore, driver decisions regarding driving versus idle can substantially affect the contribution of cold starts, especially for CO and NOx. [ABSTRACT FROM AUTHOR]

Real-world vehicle fuel use and emission rates are sensitive to road grade. There is a need for a practical method for measuring road grade in combination with on-board measurement of vehicle activity, energy use, and emissions using portable emissions measurement systems (PEMS). This paper focuses on quantification of the accuracy and precision of a low-cost method using a stand-alone global positioning system (GPS) receiver with an in-built barometric altimeter. Approximately 100 one-way runs were made on each of several study routes. The sensitivity of average grade estimates to the averaging distance over which grade is estimated is quantified. The repeatability of vehicle location and distance traveled is quantified. The run-to-run variability and confidence intervals for average estimates of grade are quantified. The accuracy of the grade estimates is evaluated in comparison to LIDAR-based estimates. The low-cost method is shown to be accurate, but imprecision in the measurements leads to a need for typically at least 10 or more repeated runs, depending on the desired precision of the average estimate of grade. [ABSTRACT FROM AUTHOR]

Abstract: In the studies of fate and transport of air emissions from animal feeding operations, Gaussian based dispersion models have been commonly used to predict downwind pollutant concentrations through forward modeling approach, or to derive emission rates and emission factors through inverse dispersion modeling approach. In the Gaussian dispersion modeling process, downwind sampling location and sampling height could generate significant impact on accuracy of the model validation, or inverse modeling results based upon field measurements. This study theoretically analyzed the impact of downwind locations and sampling height on Gaussian dispersion modeling. It was discovered that the field sampling needs to be conducted at the locations beyond the plume touching-ground distance, at a downwind distance as short as 5 m for the case scenario with zero rise of emission plume under the atmospheric stability class C, or as long as 297 m for the case scenario with 15 m rise of emission plume under the atmospheric stability class F. In order to measure the PM concentrations of the dispersion plume, the minimum sampling height at the locations within the plume touching-ground distance varied from ground level to as high as almost 14 m, whereas for the locations beyond the plume touching-ground distance, a sampling height of ground level would be acceptable. [ABSTRACT FROM AUTHOR]