A Tool For Advancing AERMOD Project-Level Near-Road Air Quality Analysis Based on Satellite Image Maps
Focus Area
Air Quality
Subcommittee
Air Quality
Status
Archived
Cost
Under $99k
Timeframe
Under 1 year
Research Idea Scope
As the U.S. Environmental Protection Agency (EPA)’s preferred air dispersion model for refined transportation project analyses, AERMOD is used to model roadway “link” source using AREA, VOLUME, LINE, RLINE, or RLINEXT. The importance for appropriate description of road source geometry and placement of receptors in AERMOD have been highlighted by the US EPA. Spatially coding the road geometry and distributing receptors, on the other hand, are two of the most difficult challenges in the highway modeling process, as they require intensive manual efforts to code the coordinates of roadways and near-road receptors in AERMOD format, and sometimes can have a high potential for analysis error – this is especially true when using non-commercial AERMOD software to model roadways with complex geometric designs (e.g., horizontal curves with various radius).
By taking advantage of aerial/satellite images from US Geological Survey, Google Earth, or other public geographical resources that contain roadway horizontal profiles, the proposed research will develop a Python-based tool that enables the users to realize the following six functions:
1) Upload satellite image as base map and draw the road horizontal polylines on the interactive graph panel;
2) Convert the roadway links into road geometry in AERMOD format, depending on source type (i.e., LINE, RLINE, RLINEXT, AREA, or VOLUME) that road links are modeled as;
3) Based on the road geometric characteristics, generate multi-layers of near-road receptors around roadways, and gridded receptors far from roadways, with spacing intervals specified by users;
4) Take MOVES link emissions output and convert into emission rates in AERMOD required units, depending on the designated AERMOD source type;
5) Take all the information from the above modules, plus required meteorology files as input, and compile to an “AERMOD.inp” that is ready for AERMOD run;
6) Run AERMOD, organize AERMOD concentrations output file as a CSV table, and visualize concentration profile.
The following research tasks are anticipated:
Task 1: Algorithm design. The research team will design algorithms that convert drawn roadway polylines to LINE, RLINE, RLINEXT, AREA and VOLUME sources based on the polyline node coordinates and the width of roadways, convert MOVES link-level emission rates (grams/mile or grams/hour) to the source-specific emission rate for AERMOD (e.g., grams/second/m2 for AREA, grams/second for VOLUME, etc.), and generate near-road receptors via buffer function.
Task 2: Tool development. The research team will develop a Python-based tool that realize the designed algorithms in Task 1. A geo-processing module “GeoPandas” and a drawing panel “Canvas” in Python will be used to extract nodes information from polyline, and convert to AERMOD sources. The tool will be designed to take polylines, roadway width, designated AERMOD source type, pre-determined location of origin point, and MOVES link-level emissions results as the input to generate “AERMOD.inp”. The team will also design and develop the result compilation and visualization function.
Task 3: Tool verification. Verify the tool results. Develop a website to distribute the tool.
Urgency and Payoff
The tool will be very useful to relieve users from heavily loaded tasks of spatially coding geometry in AERMOD. It can also significantly speed up the AERMOD input preparation process, and allows convenient tests of multiple configurations from all these source types for comparison analysis. The use of the tool can also help minimize processing errors associated with the geometric coding task in AERMOD near-road dispersion modeling. This study will deliver the following research products
– The team will prepare technical report.
– The team will deliver an open-source python-based tool. The team will also prepare a tool user guideline document.
– The team will prepare a presentation as required by the sponsor.
Submitted
06/18/2021