Application of a multi-lag regression approach to determine on-road PM10 and PM2.5 emission rates (original) (raw)
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Effects of improved spatial and temporal modeling of on-road vehicle emissions
Journal of the Air & Waste Management Association, 2012
Numerous emission and air quality modeling studies have suggested the need to accurately characterize the spatial and temporal variations in on-road vehicle emissions. The purpose of this study was to quantify the impact that using detailed traffic activity data has on emission estimates used to model air quality impacts. The on-road vehicle emissions are estimated by multiplying the vehicle miles traveled (VMT) by the fleet-average emission factors determined by road link and hour of day. Changes in the fraction of VMT from heavy-duty diesel vehicles (HDDVs) can have a significant impact on estimated fleet-average emissions because the emission factors for HDDV nitrogen oxides (NO x) and particulate matter (PM) are much higher than those for light-duty gas vehicles (LDGVs). Through detailed road link-level on-road vehicle emission modeling, this work investigated two scenarios for better characterizing mobile source emissions: (1) improved spatial and temporal variation of vehicle type fractions, and (2) use of Motor Vehicle Emission Simulator (MOVES2010) instead of MOBILE6 exhaust emission factors. Emissions were estimated for the Detroit and Atlanta metropolitan areas for summer and winter episodes. The VMT mix scenario demonstrated the importance of better characterizing HDDVactivity by time of day, day of week, and road type. More HDDVactivity occurs on restricted access road types on weekdays and at nonpeak times, compared to light-duty vehicles, resulting in 5-15% higher NO x and PM emission rates during the weekdays and 15-40% lower rates on weekend days. Use of MOVES2010 exhaust emission factors resulted in increases of more than 50% in NO x and PM for both HDDVs and LDGVs, relative to MOBILE6. Because LDGV PM emissions have been shown to increase with lower temperatures, the most dramatic increase from MOBILE6 to MOVES2010 emission rates occurred for PM 2.5 from LDGVs that increased 500% during colder wintertime conditions found in Detroit, the northernmost city modeled. Implications: Air quality model performance relies partly on on-road mobile source emission inventories accurately allocated, both spatially and temporally. This work demonstrates the importance of characterizing the mix of heavy-duty diesel versus lightduty gasoline vehicle activity on an hourly basis on weekdays and weekends by road type. Incorporating detailed activity data increases weekday average NO x and PM emissions 5-15%, with early morning hour emission increases approaching 100%, compared to using one average vehicle activity mix. Application of the methodologies described in this paper will improve the accuracy of on-road emission inventories in the understanding of ozone photochemistry and PM formation.
Quantification of motor vehicle emission factors from on road measurements
2003
Assessment and prediction of the impact of vehicular traffic emissions on air quality and exposure levels requires knowledge of vehicle emission factors. The aim of this study was quantification of emission factors from an on road, over twelve months measurement program conducted at two sites in Brisbane: 1) freeway type (free flowing traffic at about 100 km/h, fleet dominated by small passenger cars-Tora St); and 2) urban busy road with stop/start traffic mode, fleet comprising a significant fraction of heavy duty vehicles-Ipswich Rd. A physical model linking concentrations measured at the road for specific meteorological conditions with motor vehicle emission factors was applied for data analyses. The focus of the study was on submicrometer particles; however the measurements also included supermicrometer particles, PM 2.5 , carbon monoxide, sulfur dioxide, oxides of nitrogen. The results of the study are summarised in this paper. In particular, the emission factors for submicrometer particles were 6.08 x 10 13 and 5.15 x 10 13 particles per vehicle-1 km-1 for Tora St and Ipswich Rd respectively and for supermicrometer particles for Tora St, 1.48 x 10 9 particles per vehicle-1 km-1. Emission factors of diesel vehicles at both sites were about an order of magnitude higher than emissions from gasoline powered vehicles. For submicrometer particles and gasoline vehicles the emission factors were 6.08 x 10 13 and 4.34 x 10 13 particles per vehicle-1 km-1 for Tora St and Ipswich Rd, respectively, and for diesel vehicles were 5.35 x 10 14 and 2.03 x 10 14 particles per vehicle-1 km-1 for Tora St and Ipswich Rd, respectively. For supermicrometer particles at Tora St the emission factors were 2.59 x 10 9 and 1.53 x 10 12 particles per vehicle-1 km-1 , for gasoline and diesel vehicles, respectively.
Modeling the Concentrations of On-Road Air Pollutants in Southern California
High concentrations of air pollutants on roadways, relative to ambient concentrations, contribute significantly to total personal exposure. Estimation of these exposures requires measurements or prediction of roadway concentrations. Our study develops, compares, and evaluates linear regression and nonlinear generalized additive models (GAMs) to estimate on-road concentrations of four key air pollutants, particle-bound polycyclic aromatic hydrocarbons (PB-PAH), particle number count (PNC), nitrogen oxides (NO x ), and particulate matter with diameter <2.5 μm (PM 2.5 ) using traffic, meteorology, and elevation variables. Critical predictors included wind speed and direction for all the pollutants, traffic-related variables for PB-PAH, PNC, and NO x , and air temperatures and relative humidity for PM 2.5 . GAMs explained 50%, 55%, 46%, and 71% of the variance for log or square-root transformed concentrations of PB-PAH, PNC, NO x , and PM 2.5 , respectively, an improvement of 5% to over 15% over the linear models. Accounting for temporal autocorrelation in the GAMs further improved the prediction, explaining 57−89% of the variance. We concluded that traffic and meteorological data are good predictors in estimating on-road traffic-related air pollutant concentrations and GAMs perform better for nonlinear variables, such as meteorological parameters.
Motor Vehicle Contributions to Ambient PM10 and PM2.5 at Selected Urban Areas in the USA
Environmental Monitoring and Assessment, 2006
A source apportionment study was carried out to estimate the contribution of motor vehicles to ambient particulate matter (PM) in selected urban areas in the USA. Measurements were performed at seven locations during the period September 7, 2000 through March 9, 2001. Measurements included integrated PM 2.5 and PM 10 concentrations and polycyclic aromatic hydrocarbons (PAHs). Ambient PM 2.5 and PM 10 were apportioned to their local sources using the chemical mass balance (CMB) receptor model and compared with results obtained using scanning electron microscopy (SEM). Results indicate that PM 2.5 components were mainly from combustion sources, including motor vehicles, and secondary species (nitrates and sulfates). PM 10 consisted mainly of geological material, in addition to emissions from combustion sources. The fractional contributions of motor vehicles to ambient PM were estimated to be in the range from 20 to 76% and from 35 to 92% for PM 2.5 and PM 10 , respectively.
Journal of the Air and Waste Management Association, 2011
Fuel-based emission factors for 143 light-duty gasoline vehicles (LDGVs) and 93 heavy-duty diesel trucks (HDDTs) were measured in Wilmington, CA using a zero-emission mobile measurement platform (MMP). The frequency distributions of emission factors of carbon monoxide (CO), nitrogen oxides (NO x), and particle mass with aerodynamic diameter below 2.5 mm (PM 2.5) varied widely, whereas the average of the individual vehicle emission factors were comparable to those reported in previous tunnel and remote sensing studies as well as the predictions by Emission Factors (EMFAC) 2007 mobile source emission model for Los Angeles County. Variation in emissions due to different driving modes (idle, low-and high-speed acceleration, low-and high-speed cruise) was found to be relatively small in comparison to intervehicle variability and did not appear to interfere with the identification of high emitters, defined as the vehicles whose emissions were more than 5 times the fleet-average values. Using this definition, approximately 5% of the LDGVs and HDDTs measured were high emitters. Among the 143 LDGVs, the average emission factors of NO x , black carbon (BC), PM 2.5 , and ultrafine particle (UFP) would be reduced by 34%, 39%, 44%, and 31%, respectively, by removing the highest 5% of emitting vehicles, whereas CO emission factor would be reduced by 50%. The emission distributions of the 93 HDDTs measured were even more skewed: approximately half of the NO x and CO fleet-average emission factors and more than 60% of PM 2.5 , UFP, and BC fleet-average emission factors would be reduced by eliminating the highest-emitting 5% HDDTs. Furthermore, high emissions of BC, PM 2.5 , and NO x tended to cluster among the same vehicles.
During the spring and summer of 2000, 2001, and 2002, gaseous and particulate matter (PM) fuel-based emission factors for approximately 150,000 low-tailpipe, individual vehicles in the Las Vegas, NV, area were measured via on-road remote sensing. For the gaseous pollutants (carbon monoxide, hydrocarbons, and nitrogen oxide), a commercial vehicle emissions remote sensing system (VERSS) was used. The PM emissions were determined using a Lidar-based VERSS. Emission distributions and their shapes were analyzed and compared with previous studies. The large skewness of the distributions is evident for both gaseous pollutants and PM and has important implications for emission reduction policies, because the majority of emissions are attributed to a small fraction of vehicles. Results of this Las Vegas study and studies at other geographical locations were compared. The gaseous pollutants were found to be close to those measured by VERSS in other U.S. cities. The PM emission factors for spark ignition and diesel vehicles are in the range of previous tunnel and dynamometer studies.
Road vehicle emission factors development: A review
Atmospheric Environment, 2013
h i g h l i g h t s < The accuracy of road emission models is directly linked to the quality of their emission factors. < Road vehicles have a large natural variability in their emission profiles. < Emission factors may have different resolution according to their intended use. < Emission modellers should combine laboratory data with real-world measurements.
Journal of the Air & Waste Management Association, 2004
During the spring and summer of 2000, 2001, and 2002, gaseous and particulate matter (PM) fuel-based emission factors for ϳ150,000 low-tailpipe, individual vehicles in the Las Vegas, NV, area were measured via on-road remote sensing. For the gaseous pollutants (carbon monoxide, hydrocarbons, and nitrogen oxide), a commercial vehicle emissions remote sensing system (VERSS) was used. The PM emissions were determined using a Lidar-based VERSS. Emission distributions and their shapes were analyzed and compared with previous studies. The large skewness of the distributions is evident for both gaseous pollutants and PM and has important implications for emission reduction policies, because the majority of emissions are attributed to a small fraction of vehicles. Results of this Las Vegas study and studies at other geographical locations were compared. The gaseous pollutants were found to be close to those measured by VERSS in other U.S. cities. The PM emission factors for spark ignition and diesel vehicles are in the range of previous tunnel and dynamometer studies.