Spatial and Statistical Analysis of Commercial Vehicle Activity in Metropolitan Atlanta

Ross, Catherine; Guensler, Randall; Stevens, Paula

doi:10.3141/1625-21

Cited by 5 publications

(7 citation statements)

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“…This decrease in PM 2.5 coincides with enhanced convective mixing [ Seinfeld and Pandis , 1998] due to the strongly sunny and clear conditions during the experiment and as indicated by the increased wind speeds (J. C. St. John et al, unpublished manuscript, 2001). Also, a drop‐off in the mobile source strength with rush hour ending contributes to this trend as is particularly obvious with diel trend with EC and σ ap [ Ross et al , 1998]. The expected evening rush hour peak demonstrates similar trends, but is much less pronounced during the evening rush hour from 1600 to 1900 LT (Figure 3).…”

Section: Resultsmentioning

confidence: 96%

“…The evidence here suggests the important influence of combustion sources associated with morning rush hour traffic. The morning peak in aerosol concentrations coincides with the peak in morning rush hour traffic in Atlanta [ Ross et al , 1998] and also the time period of lowest wind speeds (J. C. St. John et al, unpublished manuscript, 2001) and limited vertical convective activity due to the absence of solar heating. The influence of nearby sources including local industries and a bus depot may have contributed to these trends, though the proximity to the center of the city and the dense mobile source strength in the area is also to some extent responsible [ Edgerton et al , 2000].…”

Section: Resultsmentioning

confidence: 98%

“…[28] The evidence here suggests the important influence of combustion sources associated with morning rush hour traffic. The morning peak in aerosol concentrations coincides with the peak in morning rush hour traffic in Atlanta [Ross et al, 1998] and also the time period of lowest wind speeds (J. C.…”

Section: Teommentioning

confidence: 99%

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Urban aerosol radiative properties: Measurements during the 1999 Atlanta Supersite Experiment

Carrico

Bergin

et al. 2003

J. Geophys. Res.

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[1] As part of the Atlanta Supersite 1999 study, aerosol radiative and related physical and chemical properties are examined on the basis of measurements of PM 2.5 (aerosol particles with aerodynamic diameters, D p , less than 2.5 mm) in urban Atlanta. In addition to potential compliance issues with proposed regulatory standards, PM 2.5 concentrations in Atlanta and the surrounding region are large enough to have an important impact on atmospheric radiative transfer and hence visibility and potentially regional climate. Arithmetic means and standard deviations of the light scattering by PM 2.5 (s sp at 530 nm) and absorption coefficients (s ap at 550 nm) measured at a controlled relative humidity of 49 ± 5% are 121 ± 48 and 16 ± 12 Mm À1 , respectively. Though the mean light extinction coefficient (s ep ) in Atlanta is much larger than background sites, it is comparable to nonurban areas in the interior southeast United States highlighting the contribution of a regional haze here. The single scattering albedo (w o ) in Atlanta is 0.87 ± 0.08 and is $10% lower than reported in nonurban polluted sites, likely a result of the emission of elemental carbon (EC) from mobile sources. A pronounced diel pattern in aerosol properties is observed with clear influences from mobile sources (morning rush hour maxima in concentrations, particularly soot-related indicators) and atmospheric mixing (afternoon minima). A strong linear relationship between s sp and PM 2.5 is observed, and using several techniques, gives a range of mean mass scattering efficiencies (E sp ) from = 3.5 to 4.4 m 2 g À1 . EC and s ap are observed to have a relationship though less strongly correlated than s sp and PM 2.5 . Four methods of determining the mass absorption efficiency of EC give E ap ranging from 5.3 to 18.3 m 2 g À1 . This wide range of values is a result of the variability in aerosol properties, uncertainties in the light absorption method, and in particular, differences in the EC measurement techniques. Best agreement was found using measured EC mass distributions using a multistage impactor in comparison to s ap calculated with a Mie code yielding E ap = 9.5 ± 1.5 m 2 g À1 , while EC mass summed from the impactor stages in comparison to measured s ap gives E ap = 9.3 ± 3.2 m 2 g À1 . Mie light-scattering calculations using inputs of measured mass and EC size distributions give geometric mean light scattering and absorption D p = 0.54 and 0.13 mm, respectively, and show the dominance of the submicrometer diameter particles to light extinction in the urban environment. Based on the measured aerosol optical depth in Atlanta, d a (500 nm) = 0.44 ± 0.22, and other radiative measurements, a best estimate of the average direct aerosol radiative forcing at the top of the atmosphere (a measure of the climate significance) is ÁF = À11 ± 6 W m À2 in Atlanta. This value is an order of magnitude greater than global mean estimates for aerosols underscoring the influence of aerosol particles on radiative transfer in the urban environment.

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Section: Resultsmentioning

confidence: 96%

Section: Resultsmentioning

confidence: 98%

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Urban aerosol radiative properties: Measurements during the 1999 Atlanta Supersite Experiment

Carrico

Bergin

et al. 2003

J. Geophys. Res.

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“…9 Consequently, systematic differences in operating speed between heavy trucks and passenger vehicles have the potential to adversely affect emissions and the ability to estimate and reduce pollution levels. 10 If speeds inputs are misspecified, there may be severe underestimates or overestimates of emissions. 9 This paper reports results of the research that evaluated whether heavy trucks and passenger vehicles operate differently on-road.…”

Section: Discussionmentioning

confidence: 99%

Evaluating Speed Differences between Passenger Vehicles and Heavy Trucks for Transportation-Related Emissions Modeling

Hallmark

Isebrands

2005

Journal of the Air & Waste Management Association

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Heavy-duty trucks make up only 3% of the on-road vehicle fleet, yet they account for Ͼ7% of vehicle miles traveled in the United States. They also contribute a significant proportion of regulated ambient emissions. Heavy vehicles emit emissions at different rates than passenger vehicles. They may also behave differently on-road, yet may be treated similarly to passenger vehicles in emissions modeling. Input variables to the MOBILE software, such as average vehicle speed, are typically specified the same for heavy trucks as for passenger vehicles. Although not frequently considered in modeling emissions, speed differences between passenger vehicles and heavy trucks may influence emissions, because emission rates are correlated to average speed. Differences were evaluated by collecting average and spot speeds for heavy trucks and passenger vehicles on arterials and spot speeds on freeways in Des Moines, IA, and Minneapolis/St. Paul, MN. Speeds were compared by study site. Space mean speeds for heavy trucks were lower than passenger vehicle speeds for all of the arterials with differences ranging from 0.8 to 19 mph. Spot speeds for heavy trucks were also lower at all of the arterial and freeway locations with differences ranging from 0.8 to 6.1 mph. The impact that differences in on-road speeds had on emissions was also evaluated using MOBILE version 6.2. Misspecification of average truck speed is the most significant at lower and higher speed ranges.

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“…Throughout the relatively young history of research on Spatio-Temporal modeling, a substantial number of models have been presented in the last two decades and many Spatio-Temporal data models and corresponding query languages have been proposed. The main Spatio-Temporal data models generally include the following: Space Time [2][3] [4], Snapshots [5], Simple TimeStamping [6], Base State with amendments [7][8], Spatial Temporal Domain [8], Feature-based spatial-temporal data model [9], Event Based Spatio-Temporal Data Model [10], History Graph Model [11], Spatio-Temporal Entity Relationship (STER) Model [12], Object-Relationship (O-R) Model [13].…”

Section: Introductionmentioning

confidence: 99%

Hair data model: A new data model for Spatio-Temporal data mining

Madraky

Othman

Hamdan

2012

2012 4th Conference on Data Mining and Optimization (DMO)

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Abstract-Spatio-Temporal data is related to many of the issues around us such as satellite images, weather maps, transportation systems and so on. Furthermore, this information is commonly not static and can change over the time. Therefore the nature of this kind of data are huge, analysing data is a complex task. This research aims to propose an intermediate data model that can represented suitable for Spatio-Temporal data and performing data mining task easily while facing problem in frequently changing the data. In order to propose suitable data model, this research also investigate the analytical parameters, the structure and its specifications for Spatio-Temporal data. The concept of proposed data model is inspired from the nature of hair which has specific properties and its growth over the time. In order to have better looking and quality, the data is needed to maintain over the time such as combing, cutting, colouring, covering, cleaning etc. The proposed data model is represented by using mathematical model and later developed the data model tools. The data model is developed based on the existing relational and object-oriented models. This paper deals with the problems of available SpatioTemporal data models for utilizing data mining technology and defines a new model based on analytical attributes and functions.Keywords-hair data model; spatio-temporal data models; data warehouse model.

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Spatial and Statistical Analysis of Commercial Vehicle Activity in Metropolitan Atlanta

Cited by 5 publications

References 1 publication

Urban aerosol radiative properties: Measurements during the 1999 Atlanta Supersite Experiment

Urban aerosol radiative properties: Measurements during the 1999 Atlanta Supersite Experiment

Evaluating Speed Differences between Passenger Vehicles and Heavy Trucks for Transportation-Related Emissions Modeling

Hair data model: A new data model for Spatio-Temporal data mining

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