{"slug":"reading-aermod-output-contour-maps","title":"How to Read AERMOD Output: Concentration Contours and Receptor Grids","excerpt":"An AERMOD run produces tables of concentrations and contour maps. Here is how to interpret them and check the result before it reaches a regulator.","date":"2026-05-29","author":"DSTechnoverse","image":"https://images.unsplash.com/photo-1460925895917-afdab827c52f?auto=format&fit=crop&w=1200&q=80","category":"Air Dispersion Modelling","tags":["AERMOD","output","contour maps","GLC","interpretation"],"content":"An AERMOD study ends with numbers and maps, but those outputs only mean something if you can read them. Whether you run the model yourself or review a subcontractor's work, here is how to interpret AERMOD output and sanity-check it.\n\n## The core output: ground-level concentration\n\nAERMOD predicts **ground-level concentration (GLC)** of each pollutant at every receptor, for each averaging period. The headline result is usually the **maximum predicted GLC** — the highest concentration anywhere on the grid — compared against the relevant standard.\n\n## Receptor grids\n\nReceptors are the points where AERMOD calculates concentration. A typical study uses:\n\n- A **Cartesian grid** across the study area (coarser far out, finer near the source)\n- **Discrete receptors** at specific sensitive locations\n- Optional **boundary receptors** along the site fence line\n\nThe finer the grid near the source, the more confident you can be that you have captured the true maximum.\n\n## Reading a contour map\n\nA concentration contour map draws lines of equal GLC, like elevation lines on a topographic map. When you look at one, check:\n\n- **The peak sits downwind of the source**, not on top of it — plumes need distance to reach the ground\n- **The shape follows the wind rose** — the plume stretches toward prevailing downwind directions\n- **Gradients are smooth** — sudden jumps often signal a grid or input problem\n\n## The compliance check\n\nFor each pollutant and averaging period, the study compares:\n\n> Maximum predicted GLC (+ background) vs the ambient standard\n\nIf the total is below the standard everywhere, the source passes. If not, the study explores mitigation — taller stacks, lower emissions, or changed operations.\n\n## Quick sanity checks before submission\n\n1. Does the maximum occur at a physically sensible location?\n2. Are background concentrations added where required?\n3. Are the correct averaging periods and standards applied?\n4. Do discrete sensitive receptors show acceptable values?\n5. Is every number traceable back to the model inputs?\n\nReading AERMOD output is part arithmetic and part physical intuition. If a result looks wrong, it usually is — and catching it before submission is what separates a clean study from a rejected one.\n\n## Which number in the output actually matters\n\nAERMOD produces a great deal of output, and the first task is knowing which value the standard refers to. Comparing the wrong statistic against a limit is one of the most common errors in submitted reports.\n\n| Standard type | The statistic to extract |\n|---|---|\n| Annual average | Mean of all hours across the modelling year |\n| 24-hour, with 2% exceedance allowance | 98th percentile of daily averages, not the maximum |\n| 1-hour NO2 | 98th percentile of hourly values |\n| 8-hour CO | Highest running 8-hour mean |\n| Screening or worst case | Highest predicted value at any receptor |\n\nIndian NAAQS for 24-hour and shorter periods permits exceedance in up to 2% of the time in a year, provided no two consecutive readings exceed. This means the value to compare against the standard is generally the 98th percentile, not the single highest hour. Presenting the absolute maximum against a 24-hour standard is conservative but not what the standard asks for, and reviewers will note the mismatch either way.\n\nAERMOD is configured to produce these directly. The output table should state the statistic, the averaging period, the receptor at which it occurs and its coordinates.\n\n## Reading the contour map\n\nA contour plot answers four questions, and each has a diagnostic value beyond the headline number.\n\n| What to look at | What it tells you |\n|---|---|\n| Where the maximum sits | Typically 300 m to 2 km from a tall stack; much closer suggests downwash |\n| The shape of the plume | Should reflect the wind rose — elongated along prevailing directions |\n| Distance to the standard contour | How far the compliance boundary extends |\n| Behaviour at sensitive receptors | The number the public hearing will ask about |\n| Position relative to the grid edge | If the maximum touches the edge, the grid is too small |\n| Terrain interaction | Elevated ground intercepting the plume shows as local peaks |\n\nThe plume shape is a genuine sanity check. A contour pattern that does not resemble the wind rose indicates something is wrong — usually the wrong met file, or coordinates in the wrong projection. A perfectly circular pattern around a stack in an area with a strong prevailing wind is not a plausible result.\n\nThe location of the maximum is the other quick check. For a tall stack in flat terrain with no nearby buildings, the maximum ground-level concentration should occur some hundreds of metres to a couple of kilometres downwind. If it appears immediately adjacent to the stack, either building downwash is operating or the stack parameters are wrong.\n\n## From predicted increment to compliance\n\nThis is the step most often done incorrectly, and it is arithmetic rather than modelling.\n\n| Component | Source |\n|---|---|\n| Predicted increment | AERMOD output, correct statistic |\n| Existing background | Ambient monitoring at representative locations |\n| Other committed sources | Cumulative assessment, if required |\n| **Total** | Sum of the above |\n| Compared against | The applicable NAAQS limit |\n\nA worked example makes the difference visible. Predicted PM10 increment of 21 ug/m3 looks comfortable against a 100 ug/m3 24-hour standard. Add a monitored background of 78 ug/m3 and the total is 99 ug/m3 — technically compliant, with essentially no margin, and a very different conclusion for the project. Any additional source in the area pushes it over.\n\nBackground monitoring should be recent, representative of the receptor area, and from stations not dominated by the project's own existing emissions. The monitoring period, station locations and the statistic used should all appear in the report.\n\n## Receptor tables, and what they must show\n\nContour maps communicate; tables demonstrate. Every report needs both.\n\n| Column | Content |\n|---|---|\n| Receptor name | Village, school, hospital, or grid ID |\n| Coordinates | UTM or lat-long, stated projection |\n| Elevation | As used in the model |\n| Predicted increment | By pollutant and averaging period |\n| Background | The value added |\n| Total | Increment plus background |\n| Standard | The applicable limit |\n| Percentage of standard | Total as a share of the limit |\n\nThe final column is what a reviewer reads first. It converts a table of numbers into an immediate judgement about how much headroom exists.\n\nSensitive receptors should be listed individually by name, at their real coordinates. Reporting \"the nearest village\" without naming it, or approximating its position to a grid node, invites objection at the public hearing.\n\n## Sanity checks before the results leave your desk\n\n| Check | Expectation |\n|---|---|\n| Maximum located inside the grid, not on its edge | Otherwise extend the domain |\n| Plume orientation matches the wind rose | Otherwise check met file and projection |\n| Maximum distance plausible for the stack height | Hundreds of metres to a few km |\n| Annual average well below the short-term maximum | Typically by a factor of 5 to 20 |\n| Concentrations fall with distance | Except where terrain intercepts |\n| Order of magnitude sensible against emission rate | A rough hand calculation catches unit errors |\n| Sensitive receptor values consistent with nearby grid points | Otherwise a coordinate error |\n\nThe order-of-magnitude check is worth doing every time. A quick screening estimate, or an AERSCREEN run, should land within a factor of a few of the refined result. A refined result an order of magnitude away from a screening estimate almost always means a unit error somewhere in the inputs — see [the AERMOD input data checklist](/blog/aermod-input-data-checklist).\n\nFor the review process around these checks see [how to QA/QC an air dispersion modelling report](/blog/qa-qc-air-dispersion-report), and for the errors that cause rejection, [10 common AERMOD modelling mistakes](/blog/common-aermod-mistakes). Our [AERMOD air dispersion modelling service](/services/aermod-air-dispersion-modelling) delivers contour maps, receptor tables and compliance assessment. Current limits are published by the [Central Pollution Control Board](https://cpcb.nic.in/air-quality-standard/).\n\n## Frequently Asked Questions\n\n### What does AERMOD output show?\n\nGround-level concentrations of each modelled pollutant at every receptor, for each averaging period requested. The key outputs are the maximum and percentile values by averaging period, the receptor at which they occur, and the spatial pattern shown as contours.\n\n### How do I read an AERMOD contour map?\n\nLocate the maximum and check it lies within the grid rather than on its edge; confirm the plume orientation matches the prevailing wind directions in the wind rose; check the distance of the maximum is plausible for the stack height; and read off values at named sensitive receptors.\n\n### Should I compare AERMOD results directly to NAAQS?\n\nNo. AERMOD predicts only your source's increment. Compliance is assessed on the total — predicted increment plus monitored ambient background, and other committed sources where a cumulative assessment is required.\n\n### Which statistic should I use for a 24-hour standard?\n\nGenerally the 98th percentile of daily averages, since Indian NAAQS permits exceedance in up to 2% of the time. Presenting the single highest 24-hour value is more conservative but does not match how the standard is defined.\n\n### Where should the maximum concentration occur?\n\nFor a tall stack in flat terrain, typically several hundred metres to a couple of kilometres downwind. A maximum immediately beside the stack usually indicates building downwash is operating, or that stack parameters were entered incorrectly.\n\n### What if the maximum falls at the edge of the receptor grid?\n\nThe grid is too small. Extend the domain and re-run until the maximum is comfortably inside it, otherwise the true peak concentration lies outside the area you assessed.\n\n\n---\n\n**Need a modelling or EIA data desk without the overhead?** DSTechnoverse works as a confidential, white-label subcontractor for air and water dispersion modelling and EIA data analysis. [Get in touch](/contact) to scope your next project.","related":[{"slug":"common-aermod-mistakes","title":"10 Common AERMOD Modelling Mistakes That Fail Regulatory Review"},{"slug":"aermet-meteorological-data-preprocessing","title":"AERMET Explained: How Meteorological Data Is Prepared for AERMOD"},{"slug":"aermod-building-downwash-bpip","title":"AERMOD Building Downwash (BPIP-PRIME): A Practical Overview"},{"slug":"aermod-modelling-consultant-india","title":"AERMOD Modelling Consultant in India: What They Do and How to Hire One"}]}