Environmental Modelling
Prediction with the assumptions on show
Dispersion, hydrological and noise modelling that quantifies impacts before they exist, with the method and assumptions stated openly.
A model is only as good as what you tell it
Modelling output looks authoritative, contours on a map, concentrations at receptors. But the result depends entirely on the input: emission rates, meteorological data, terrain, and the assumptions chosen where data was missing.
Reviewers know this, which is why they examine the inputs before the conclusions. A model presented without its assumptions is difficult to accept, however sophisticated the software behind it.
What we model
Air dispersion modelling
Prediction of ground-level concentrations from stack and fugitive emissions, using recognised models and site-representative meteorological data.
Odour dispersion
Modelling of odour propagation to receptors, which is usually required where complaints have been raised or are anticipated.
Noise propagation modelling
Prediction of noise levels at receptors from proposed plant, accounting for terrain, barriers and ground effects.
Hydrological modelling
Surface water flow, drainage capacity and flood risk, including the effect of proposed development on existing regimes.
Marine dispersion modelling
Discharge, thermal plume and sediment behaviour in coastal waters, calibrated against measured oceanographic data.
How the work runs
Define
The question and the receptors that matter are established.
Gather
Emission, meteorological and terrain inputs are assembled and checked.
Model
Predictions are run with the method and assumptions documented.
Report
Results are presented with inputs, limitations and confidence stated.
Specialist work is judged on method, not on volume.
Have your conditions reviewedWhat you get out of it
Inputs sourced and stated
Emission rates and their origin are documented, because reviewers examine inputs before conclusions.
Representative meteorology
Met data covers a period long enough to reflect typical conditions, not a convenient window.
Assumptions on show
Where data was missing, the assumption made is declared rather than buried in an appendix.
Limitations stated
Results are presented with their confidence and limits, not as certainties.
Where modelling is required
Impact assessments
Upper-tier EIAs usually require quantitative prediction rather than qualitative description, and modelling is how that is produced.
Discuss your sitePermit applications
Where a facility must demonstrate that emissions will not breach limits at receptors, modelling is the evidence.
Discuss your siteFacility expansions
Additional capacity changes the emission profile, and the cumulative effect has to be assessed rather than the increment alone.
Discuss your siteComplaint investigations
Modelling can establish whether a facility could plausibly be the source of a reported effect, which measurement alone sometimes cannot.
Discuss your siteSite layout decisions
Stack heights, plant positions and barrier design are far cheaper to resolve in a model than after construction.
Discuss your siteCumulative assessments
Where several sources affect the same receptors, only modelling can combine them meaningfully.
Discuss your siteCommon questions
Which dispersion model do you use?
What meteorological data is used?
How accurate is modelling?
Can modelling replace monitoring?
When should modelling be done?
Not sure what your permit requires?
Send us the conditions and we will tell you exactly which parameters, methods and frequencies apply. No charge for the review.