Modelling waste transport networks
- The text on this page is taken from an equivalent page of the IEHIAS-project.
Many of the source activities that pose threats to human health are associated with networks, such as roads, airline routes, rivers or transmission lines. While statistical data on the activities occurring on these networks are often available, these give only a very generalised picture of the real source distribution, and are likely to lead to substantial errors in assessments of health risk. In particular, they are likely to mask (and in most cases under-estimate) the highest risks that tend to occur on, or close to, the network, or at particular nodes in the system (e.g. road intersections) where activity tends to converge.
Network models provide a means of addressing this deficiency. A wide range of network modelling techniques exist. Some of the most useful in relation to integrated impact assessments are those available in GIS. Amongst other things, these provide the capability to identify lowest cost (e.g. shortest, quickest) routes between any two localities, or to search the network to find all locations accessible within a specified transport cost (e.g. in terms of distance or travel time). They also enable estimates of network activity to be linked to spatial variations in population density, thereby providing a means of exposure assessment. Use of these network methods is illustrated here in relation to modelling of road transport of domestic waste.
Waste transport is potentially an important contributor to the overall human exposure to air pollutants arising from waste management activities. As part of a case study to explore the use of integrated environnmental health impact assessment (see link to Waste report under See also, below), a method was developed to estimate truck flows associated with waste collection (e.g. from households to waste management sites) and waste transport (e.g. in between waste management sites) using information on waste generation capacity, road network and types of road, and the capacity of storage bins and collection vehicles.
The approach is summarised in Figure 1.
The model uses household density as a proxy for rates of municipal waste generation. It then makes use of geographical information system (GIS) functionality to construct a shortest cost path between residential areas and waste treatment sites. Costs are assigned to different routes on the basis of their road type - higher costs being gven to minor roads and lower costs to major roads (step 1). Waste is assumed to go to the treatment site which offers the lowest cost path (cost * length). Using approximations of the volume of an individual truck, estimates are thus made of the total waste volume transported along each route, and collected at each treatment site (step 2). This information is then used to estimate atmospheric emissions both during transport along each route and from each waste treatment site.