INDEX: indoor exposure model

The text on this page is taken from an equivalent page of the IEHIAS-project.

INDEX is a simple mass-balance model for estimating exposures to ambient air pollution in the indoor environment. It comprises two components:

• a deterministic model to give point estimates of indoor concentrations for a single room;
• a probabilistic simulator which estimates the statistical distribution of indoor concentrations for <1000 data points (model runs).

Model description

Purpose

The main role of the deterministic model is to give rapid estimates of indoor concentrations and indoor/outdoor concentration ratios for a representative room (e.g. as part of a screening or feasibility study, or to check the plausibility of information derived from other sources).

The main use of the probabilistic simulator is to estimate the potential range of indoor exposures to outdoor-derived air pollution under different air pollution, climate or building design scenarios, as part of an integrated environmental health impact assessment.

Boundaries

Fields:

• Indoor air

Spatial resolution:

• Individual buildings or building types

Temporal resolution:

• 1 minute - 24 hours

Pollutants:

• Designed for particulates - but can also be applied to gaseous pollutants

Source types:

• Ambient (outdoor-derived)

Input

Required:

• Average ambient air pollutant concentration at the building facade (or nearby monitoring site)
• Standard deviation of ambient pollutant concentrations at the building facade, or nearby site (for probabilistic simulation only)
• Wind speed frequency (% of time in each wind speed class) (for probabilistic simulation only)

Optional (defaults provided where input data are not available):

1. Building/room characteristics:
   • Building height
   • Room height
   • Room area
   • Number of external walls
   • External wall area
   • Orientation of external wall
   • Inlet (door+window) area
   • Artificial porosity (proportional to wall area)
   • Natural porisity (via cracks/pores, proportional to wall area)
2. Pollutant characteristics
   • Equivalent diameter (particulates only)
   • Specific density (particulates only)
   • Depositional velocity (non-particulates only)
   • Filtration efficiency (proportion of incoming pollutants filtered during ingress)
3. Meteorological conditions
   • Average windspeed
   • Average wind direction
   • Roughness coefficient (surface roughness of neighbourhood)
   • Vertical windspeed coefficient (change oif windspeed with height)
4. Analytical conditions
   • Time step (default = 1 hour)
   • Monitoring height (above floor level)

Output

1. Deterministic model: point estimates of
   • Air exchange rate
   • Indoor concentration
   • Indoor/outdoor concentration ratio
2. Probabilistic simulator : statistical distributions (N<1000) of
   • Air exchange rate
   • Indoor concentration
   • Indoor/outdoor concentration ratio

Outputs include:

• summary statistics - mean, standard deviation, maximum, minimum;
• tabular data (<1000 data points)
• histogram (indoor concentration only)

Description of processes modelled and of technical details

Software requirements:

Windows Microsoft Excel (2003 or later)

Typical run time:

< 5 minutes

Adaptability:

Program can be modified and adapted by user

Level of expertise required:

Basic

Cost:

Free for use

Developers:

Sirinath Jamieson, David Briggs (Imperial College London)

Owned by:

Imperial College London

Rationale

The model has been developed by adapting and parameterising standard mass-balance equations, based on a detailed review of previous experimental and observational studies. It has been specifically devised to model particulates, but can be adapted for other pollutant species where the relevant pollutant-specific characteristics (e.g. settling velocities) are known.

The model takes no account of room occupancy, so makes no allowance for resuspension caused by human activities inside the room, nor for releases from static indoor sources such as heating or cooking device: these can be substantial. Field validation has also shown that (as with all such models) performance is highly dependent on the acuuracy of estimation of the ambient concentrations and meteorology: estimates are likely to be more reliable where these are based on a nearby monitoring site, or can be modelled for the building facade.

See also

• INDEX Indoor air exposure model_1