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HARP Air Dispersion Modeling and Risk Tool

Description

The HARP Air Dispersion Modeling and Risk Tool, developed by the California Air Resources Board, is Windows-based software that allows users to run air dispersion models and calculate associated health risks from toxic air pollutants. Used primarily in the state’s Hot Spots Analysis & Reporting Program, it integrates modeled concentration outputs with built?in health effect values and demographic data (including a dedicated census database and health database) to support standardized health risk assessments for stationary emission sources in California.

Questions this resource can help answer

What is the maximum individual cancer risk from a specific facility or group of sources?
How many excess cancer cases (cancer burden) are expected in the exposed population under given emissions?
What are the chronic and acute non?cancer hazard indices for nearby residents or sensitive receptors (e.g., schools)?
How do different control options or emission reductions change modeled risks?
Which sources or pollutants contribute most to total risk at key locations, and therefore should be prioritized for mitigation?

How do I use this resource?

To use the HARP Air Dispersion Modeling and Risk Tool, install the Windows software and follow the user guide to set up a project. Import or enter source data (location, stack parameters, emission rates, pollutants), select or link the appropriate meteorological data, and choose your dispersion model options. Run the dispersion modeling to generate concentration fields, then use HARP’s built?in health database and census data modules to convert concentrations into health risk metrics for surrounding populations. Review tabular and graphical outputs (e.g., maximum individual risk, cancer burden, hazard indices) and export results for reports or regulatory submittals.

Pro tips

Start with a simple, single?source test case or an official example before building complex projects.
Double?check units, coordinates, and stack parameters; many errors trace back to input typos.
Use representative, quality?controlled meteorological data and document its source and period.
Keep emission inventories and receptor grids as simple as possible at first, then refine.
Always read model and risk-assessment defaults (exposure durations, age groups, toxicity values) and only override them if you have solid justification.

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