pyaermod 1.8.0

Python wrapper for EPA's AERMOD air dispersion model

PyAERMOD

Python wrapper for EPA's AERMOD atmospheric dispersion model.

PyAERMOD automates input file generation, model execution, output parsing, and result visualization — replacing manual text-file editing with a type-safe Python API.

Installation

pip install pyaermod             # core (input generation + output parsing)
pip install pyaermod[viz]        # + matplotlib/folium visualization
pip install pyaermod[geo]        # + geospatial export (GeoTIFF, Shapefile)
pip install pyaermod[gui]        # + Streamlit interactive GUI
pip install pyaermod[all]        # everything

For development:

git clone https://github.com/atmmod/pyaermod.git
cd pyaermod
pip install -e ".[dev,all]"

Quick Start

Generate AERMOD Input

from pyaermod.input_generator import (
    AERMODProject, ControlPathway, SourcePathway, ReceptorPathway,
    MeteorologyPathway, OutputPathway, PointSource, CartesianGrid,
    PollutantType, TerrainType,
)

control = ControlPathway(
    title_one="My Facility",
    pollutant_id=PollutantType.PM25,
    averaging_periods=["ANNUAL", "24"],
    terrain_type=TerrainType.FLAT,
)

sources = SourcePathway()
sources.add_source(PointSource(
    source_id="STACK1", x_coord=500.0, y_coord=500.0,
    base_elevation=10.0, stack_height=50.0, stack_temp=400.0,
    exit_velocity=15.0, stack_diameter=2.0, emission_rate=1.5,
))

receptors = ReceptorPathway()
receptors.add_cartesian_grid(CartesianGrid.from_bounds(
    x_min=0, x_max=2000, y_min=0, y_max=2000, spacing=100,
))

meteorology = MeteorologyPathway(
    surface_file="met_data.sfc", profile_file="met_data.pfl",
)
output = OutputPathway(receptor_table=True, max_table=True)

project = AERMODProject(control, sources, receptors, meteorology, output)
project.write("facility.inp")

Run AERMOD & Parse Results

from pyaermod.runner import run_aermod
from pyaermod.output_parser import parse_aermod_output

result = run_aermod("facility.inp")
results = parse_aermod_output(result.output_file)

df = results.get_concentrations("ANNUAL")
print(results.summary())

Parse POSTFILE Output

from pyaermod.postfile import read_postfile

# Auto-detects text vs binary format
post = read_postfile("postfile.out")
df = post.to_dataframe()

# Binary postfile with deposition data
dep = read_postfile("depo_post.out", has_deposition=True)
print(dep.to_dataframe()[["concentration", "dry_depo", "wet_depo"]])

Features

Source Types (10)

POINT, AREA, AREACIRC, AREAPOLY, VOLUME, LINE, RLINE, RLINEXT, BUOYLINE, OPENPIT

Advanced Modeling

• Background concentrations — uniform, period-specific, or sector-dependent
• Deposition — dry, wet, or combined for gas and particle emissions
• NO2/SO2 chemistry — OLM, PVMRM, ARM2, GRSM with ozone data
• Source groups — custom groupings with per-group PLOTFILE output
• EVENT processing — date/receptor-specific analysis

Preprocessors

• AERMET — meteorological data preprocessing (Stages 1-3)
• AERMAP — terrain elevation extraction with DEM download pipeline

Analysis & Visualization

• Output parsing to pandas DataFrames
• POSTFILE parser for timestep-level results (text and binary formats)
• Contour plots, interactive Folium maps, 3D surfaces, wind roses
• Geospatial export: GeoTIFF, GeoPackage, Shapefile, GeoJSON

Validation & Automation

• Input validation across all AERMOD pathways
• Building downwash / BPIP integration (point, area, and volume sources)
• Batch processing with parallel execution
• Interactive Streamlit GUI (pyaermod-gui)

Project Structure

src/pyaermod/
    __init__.py          # Public API
    input_generator.py   # AERMOD input file generation (all source types)
    validator.py         # Configuration validation
    runner.py            # AERMOD subprocess execution
    output_parser.py     # Output file parsing
    postfile.py          # POSTFILE output parser
    visualization.py     # Matplotlib/Folium plots
    advanced_viz.py      # 3D surfaces, wind roses, animations
    aermet.py            # AERMET preprocessor wrapper
    aermap.py            # AERMAP input generation
    terrain.py           # DEM download + AERMAP pipeline
    geospatial.py        # Coordinate transforms, GIS export
    bpip.py              # Building downwash calculations
    gui.py               # Streamlit web GUI
tests/                   # 1383 tests, 98% coverage
examples/                # Example scripts and Jupyter notebooks
docs/                    # Architecture and quickstart guides

Requirements

• Python >= 3.11
• numpy, pandas (core)
• AERMOD executable (free from EPA SCRAM)

Documentation

• Quick Start Guide
• GUI User Guide
• Architecture
• API Reference
• Examples

License

MIT

Disclaimer

PyAERMOD is a wrapper around AERMOD, not a reimplementation. It uses official EPA binaries for all calculations and maintains regulatory acceptance. Always validate results against EPA test cases for your specific use case.