| Content Provider |
World Health Organization (WHO)-Global Index Medicus |
| Author |
Arunachalam, Saravanan Chang, Shih Ying Palma, Ted Breen, Michael Vizuete, William Naess, Brian Isakov, Vlad Valencia, Alejandro |
| Spatial Coverage |
United States |
| Description |
Country affiliation: United States Author Affiliation: Chang SY ( Institute for the Environment, University of North Carolina at Chapel Hill, 100 Europa Drive, Suite 490, Chapel Hill, NC 27517, USA); Vizuete W ( Department of Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.); Valencia A ( Institute for the Environment, University of North Carolina at Chapel Hill, 100 Europa Drive, Suite 490, Chapel Hill, NC 27517, USA.); Naess B ( Institute for the Environment, University of North Carolina at Chapel Hill, 100 Europa Drive, Suite 490, Chapel Hill, NC 27517, USA.); Isakov V ( National Exposure Research Laboratory, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA.); Palma T ( Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA.); Breen M ( National Exposure Research Laboratory, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA.); Arunachalam S ( Institute for the Environment, University of North Carolina at Chapel Hill, 100 Europa Drive, Suite 490, Chapel Hill, NC 27517, USA. Electronic address: sarav@unc.edu.) |
| Abstract |
In this study, we combine information from transportation network, traffic emissions, and dispersion model to develop a framework to inform exposure estimates for traffic-related air pollutants (TRAPs) with a high spatial resolution. A Research LINE source dispersion model (R-LINE) is used to model multiple TRAPs from roadways at Census-block level for two U.S. regions. We used a novel Space/Time Ordinary Kriging (STOK) approach that uses data from monitoring networks to provide urban background concentrations. To reduce the computational burden, we developed and applied the METeorologically-weighted Averaging for Risk and Exposure (METARE) approach with R-LINE, where a set of selected meteorological data and annual average daily traffic (AADT) are used to obtain annual averages. Compared with explicit modeling, using METARE reduces CPU-time by 88-fold (46.8h versus 32min), while still retaining accuracy of exposure estimates. We show two examples in the Piedmont region in North Carolina (~105,000 receptors) and Portland, Maine (~7000 receptors) to characterize near-road air quality. Concentrations for NOx, PM2.5, and benzene in Portland drop by over 40% within 200m away from the roadway. The concentration drop in North Carolina is less than that in Portland, as previously shown in an observation-based study, showing the robustness of our approach. Heavy-duty diesel vehicles (HDDV) contribute over 55% of NOx and PM2.5 near interstate highways, while light-duty gasoline vehicles (LDGV) contribute over 50% of benzene to urban areas where multiple roadways intersect. Normalized mean error (NME) between explicit modeling and METARE in Portland ranges from 12.6 to 14.5% and normalized mean bias (NMB) ranges from -12.9 to -11.2%. When considering a static emission rate (i.e. the emission does not have temporal variability), both NME and NMB improved (10.5% and -9.5%). Modeled concentrations in Detroit, Michigan at an array of near-road monitors are within a factor of 2 of observed values for CO but not NOx. |
| ISSN |
00489697 |
| Volume Number |
538 |
| e-ISSN |
18791026 |
| Journal |
Science of The Total Environment |
| Language |
English |
| Publisher |
Elsevier |
| Publisher Date |
2015-12-15 |
| Publisher Place |
Netherlands |
| Access Restriction |
One Nation One Subscription (ONOS) |
| Subject Keyword |
Air Pollutants Analysis Air Pollution Statistics & Numerical Data Environmental Monitoring Models, Chemical Particulate Matter United States Vehicle Emissions Journal Article Research Support, Non-u.s. Gov't Discipline Environmental Science |
| Content Type |
Text |
| Resource Type |
Article |
| Subject |
Environmental Chemistry Waste Management and Disposal Pollution Environmental Engineering |
