manual model to predict highway related carbon monoxide concentrations
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manual model to predict highway related carbon monoxide concentrations by David A. Doctor

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Published by Southeast Michigan Council of Governments in Detroit .
Written in


  • Automobiles -- Motors -- Exhaust gas -- Mathematical models.,
  • Air -- Pollution -- Mathematical models.,
  • Carbon monoxide -- Mathematical models.

Book details:

Edition Notes

Bibliography: leaves 36-38.

Statementby David A. Doctor ; prepared in cooperation with the Michigan Department of State Highways & Transportation, with the assistance of the U.S. Department of Transportation, Federal Highway Administration, and the Urban Mass Transportation Administration ; prepared by Southeast Michigan Council of Governments.
SeriesTransportation working paper ;, 15
ContributionsSoutheast Michigan Council of Governments.
LC ClassificationsTD886.5 .D62
The Physical Object
Paginationv, 38 leaves :
Number of Pages38
ID Numbers
Open LibraryOL4487841M
LC Control Number79317042

Download manual model to predict highway related carbon monoxide concentrations


The Intersection Midblock Model (IMM) (NYSDOT, ), a computerized model originally part of the Carbon Monoxide Hot Spot Guidelines, is based on the same principles as the verification procedure, but it allows added flexibility in performing an analysis because it is totally computer based. This paper presents a hybrid model based on data mining and GIS models designed to predict vehicular Carbon Monoxide (CO) emitted from traffic on the New Klang Valley Expressway, Malaysia. CAL3QHC dispersion model was used to predict the present and future carbon monoxide (CO) levels at a busy signalized intersection. This study attempted to identify CO “hot-spots” at nearby areas of the intersection during typical A.M. and P.M. peak hours. The CO concentration “hot-spots” had been identified at Commercial Park and. Prediction of carbon monoxide concentration and optimization of the smoke exhaust system in a busbar corridor Article (PDF Available) in Building Simulation 7(6) December with Reads.

Carbon Monoxide Analysis for Highway Projects: Phase II 6. Performing Organization Code 7. Author(s) 8. (COSIM) to estimate worst-case carbon monoxide (CO) concentrations that could result after the whereas a refined model gives a more accurate prediction of a project™s impact and. Carbon monoxide concentrations tend to be highly varia- ble over short periods of time, especially at sampling sites near roads where traffic flows are constantly changing. of Carbon Monoxide. The range is 0 to 2, parts per million by volume (ppmv). The CO analyzer features two adjustable alarm contacts. • Analog or digital (optional) signals with adjustable zero and span values and in addition to these features, a “One Touch Cal” auto-calibration function. 2. INSTALLATION. The robustness of the proposed methodology was checked to predict the next day carbon monoxide (CO) concentrations in Tehran metropolitan. Thereafter, a comparison was carried out between the results of the present study and another research on uncertainty determination of adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN).Cited by:

CALINE3 model predicts the dispersion of pollutants released from roadways in the receptor places at a certain radius from the source. This model was used to evaluate the dispersion of particulate matter CO) emitted from Yadegar-e-Emam Expressway (YEE) as one of the most congested highways in by: 8. Street canyon module and gaussian line source module of a regional-scale dispersion model Indic Airviro were used to simulate ambient carbon monoxide (CO) concentrations due to traffic flow at two. Model validation is accomplished by comparing measured carbon monoxide concentrations with concentrations predicted by the models. The sensitivity analysis indicates that the EPA HIWAY model predicts higher pollutant concentrations than the two California models for oblique and crosswind Cited by: road, The first method was based on a hybrid model which was a combination of ANN (a neural model based on radial basis iimctions – RBF) and the Pasquille model. In the other method the multilayer perception – MLP only, was applied to predict the level of carbon monoxide .