Abstract
Conducting high-resolution air quality analysis by applying microscale dispersion models at the regional scale poses a formidable computing challenge, because a huge number of receptors and the extensive network of roadway links (emission sources) must be processed. As a way to minimize computation cost without undermining estimation precision, this study proposes an innovative link screening methodology, using a supervised machine learning random forest (RF) classification algorithm, that eliminates links with zero or negligible concentration contributions from modeled link-receptor combinations. The study uses 79,328 receptor-link pairs randomly selected from the Atlanta Metropolitan area to train and test the model. The final link screening model employs six variables, including link attributes, urban variables, and meteorological conditions. The RF classifier successfully identifies the small portion of links that contribute more than 95% of concentrations that are estimated by the same model using every link-receptor pair. The efficiency and precision of the smaller dispersion model runs developed using the RF classifier (the ‘reduced-link’ model) are compared to the dispersion modeling without the link-screening process (the ‘whole-link’ model) for downtown Atlanta and northwest Atlanta. Results show that AERMOD run-times for reduced-link models are only 0.2%–1.1% of the times required for whole-link models, because far fewer links are handled during the AERMOD simulation (0.1%–0.6% of links in the whole-link model). The correlation between estimates of the two models ranges from 95% to 97%, depending upon the density of the road network, link activity, link emission rates, meteorology, etc.
| Original language | English |
|---|---|
| Article number | 117677 |
| Journal | Atmospheric Environment |
| Volume | 237 |
| DOIs | |
| State | Published - Sep 15 2020 |
| Externally published | Yes |
Funding
This research is funded by the National Center for Sustainable Transportation (NCST) , a University Transportation Center (grant number DOT 69A3551747114 ). This research was supported in part through research cyberinfrastructure resources and services provided by the Partnership for an Advanced Computing Environment (PACE) at the Georgia Institute of Technology , Atlanta, Georgia, USA. The information, data, or work presented herein was funded in part by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. This research is funded by the National Center for Sustainable Transportation (NCST), a University Transportation Center (grant number DOT 69A3551747114). This research was supported in part through research cyberinfrastructure resources and services provided by the Partnership for an Advanced Computing Environment (PACE) at the Georgia Institute of Technology, Atlanta, Georgia, USA. The information, data, or work presented herein was funded in part by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.
| Funders | Funder number |
|---|---|
| National Center for Sustainable Transportation | |
| United States Government | |
| University Transportation Center | |
| Georgia Institute of Technology | |
| University Transportation Center, Missouri University of Science and Technology | DOT 69A3551747114 |
Keywords
- Air quality
- Near-road dispersion modeling
- Supervised link screening (SLS)
- Transportation and air quality conformity
