Contaminant sources in IEHIAS
- The text on this page is taken from an equivalent page of the IEHIAS-project.
Environmental pollution, like other stressors, originates in sources somewhere in the environment. These sources, no less than the hazards themselves, take many different forms. Many, though not all, relate to human activities and the infrastructure (e.g. roads, industrial plant, houses) used in the process. Some, however, are entirely natural (e.g. radon emissions and the gases and particles released by volcanoes). Moreover many other types of environmental hazard exist, such as earthquakes, floods and storms, which are essentially natural in origin - though some, it has to be admitted, are sometimes triggered or exacerbated by human interference.
Information on the sources of environmental pollution (and other stressors) is clearly vital in integrated impact assessment. It is needed as the starting point for modelling environmental contamination, to estimate how this might change in response to changes in technology or policies affecting socio-economic activities, and ultimately to help identify where preventative action can best be taken. In some cases, also, the exposure-response functions available from epidemiological studies relate not to exposures per se, nor even to environmental concentrations, but to measures of source activity. Examples include the health effects of by-stander exposures to pesticides (which have often been based on pesticide application rates or practices), health risks from landfill sites (which have mainly been based on distance from source), and many estimates of health risks from indoor pollution (which have typically been based on presence/absence of different cooking or heating sources in the home). In these cases, therefore, the assessment has to follow suit, and base its exposure estimates on the same metric.
Fortunately, a great deal of information on sources tends to exist, primarily because of their social and economic importance - which means that they are subject to close monitoring and/or regulation (see links to Characterising sources in the panel to the left). Nevertheless, not all the data we require is usually already available, and for many assessments we need to undertake modelling, either to fill in gaps in the data that do exist, or to estimate how sources may change under the assessment scenario. Links to methods for Modelling source activity are also given to the left.
Depending on the nature of the assessment, a range of information may be required to characterise sources of environmental stressors. In the case of contaminants, this includes information on:
- source location and distribution
- source activity
- source intensity
Source location and distribution
The spatial characteristics of sources are important because they determine to a large extent the distribution both of emissions and environmental concentrations. Spatial location and distribution, however, can be defined in different ways, depending on the type of activity concerned (e.g. whether it is localised or dispersed; a discrete entity such as a factory or a diffuse source such as an agricultural zone), the scale of analysis, and the data sources used to identify the sources. In many inventories, for example, businesses are represented as points, defined by the address or map co-ordinates; in statistical data, however, they are often attributed to administrative areas or zones. These differences can be vital, for they change the apparent relationship with the surrounding population, and thus affect estimates of exposures. Care is also needed, especially with business enterprises, because the stated location is often that of the head office, rather than the premises where the activity is carried out.
Sources are best distinguished in terms of the activity they perform, for this determines to a large extent their potential impacts on the environment. There is, however, no single way of defining 'activities', and thus no single system for classification. Instead, different classification systems have been developed, depending on their purpose, and the type of survey technology being used. In the EU, the NACE system ( Nomenclature générale des Activités économiques dans les Communautés Européennes) is used as the standard for defining economic activities for statistical purposes: this comprises a 4-level hierarchy, containing 21 broad sectors at the highest level and recognising some 500 detailed activity classes at the lowest level. In contrast, land cover maps based on satellite imagery provide discrimination of only a few tens of land cover types (and fewer land use classes). The categories recognised thus depend on the source of the data, and matching between the source categories from different data sets can be problematic.
This refers to the level or magnitude of the activity undertaken at any location. Measures of intensity vary, not only according to the type of activity, but also the way this has been represented spatially and the purpose of the assessment. The intensity of industrial activities, for example, may be defined in terms of the inputs, throughputs or outputs, and in terms of mass, volume or value. Agricultural activities are often measured in terms of area (e.g. under specific crops), livestock numbers, workforce, yield or value of production. In each case, considerable differences (and ambiguities) may arise, depending for example on whether the production measures include or exclude waste materials, the averaging time used (e.g. the definition of a 'day') and whether they represent average or maximum (e.g. total permissible) levels of activity.
Data on source activities is available in many different forms, and via different providers and technologies.
Many of the source activities that have the greatest potential for impacts on the environment are now subject to registration and regular inspection and/or reporting, for the purpose of regulation and control. As a result, a growing number of inventories are available, listing and describing specific source activities. In Europe, a prime example is the European Pollutant Release and Transfer Register (E-PRTR) which lists and maps all major emission sources to land, air and water. European (and global) databases are also available for several large commercial operations, such as airports and power stations. At national level, similar databases and registers are variously maintained for a range of other activities, including: waste treatment and disposal (e.g. landill sites, incinerators); radio, TV and mobile phone transmitters; powerlines.
Many industrial and other economic activities are subject to regular auditing and reporting regimes, both for the purposes of financial management and various forms of government regulation and policy (e.g. taxation, employment, environmental regulation). These systems produce a large volume of information on the business activities of the economic sectors concerned, and much of this is ultimately published by the government agencies concerned in the form of economic or business statistics (increasingly available on-line). In the EU, aggregated data derived from these reporting systems is also held and disseminated by Eurostat.
Satellite and land cover data
Remote sensing (especially from space) now provides an invaluable source of data on the Earth surface, and in particular of information on land cover. These can, in turn, be used to deduce information about land use, and thus about source activities. The maps are most reliable in discriminating between different vegetation cover, and are therefore especially useful for detecting agricultural land uses. Urban areas present far greater difficulties, because relationships between building characteristics and activity are far more ambiguous (many buildings, for example, have multiple uses). In Europe, the CORINE Land Cover 2000 (available from the European Environment Agency) provides coverage of all member states (with some change data for 2006). More detailed land cover maps also exist at national level for many EU countries.
Other data sources
A range of other data sources also provide socio-economic and related information, which can be used to characterise human activities (and thus give indications about emission sources). National population censuses, for example, often include questions about the home environment (e.g. cooking/heating practices), occupation and travel behaviours. Marketing organisations collect a huge volume of detailed data on consumption and service usage, much of it at a very fine spatial resolution. Because of their commercial value, the data may be costly to acquire, but do offer a level of detail achievable with few other data sources.
The spatial distribution of sources of stressors is one of the most important factors influencing environmental impacts on health. Although pollutants (and many other hazards) are mobile, their intensity typically declines with distance away from their source, so that patterns of risk to human health broadly reflect the source distribution. Partly for this reason, place of residence relative to the source has often been used as a proxy for exposure (or risk) in epidemiological studies - for example, of landfill sites, road traffic, nuclear power stations - with the consequence that the exposure-response functions available for use in the assessment relate to source distribution. Information on source distribution is therefore vital in most assessments, either as direct input into the exposure metric or (more generally) as a basis for modelling the distribution and environmental fate of the hazard.
Sources clearly vary in the character of their distribution, and in the structure of each specific source. Some are localised, others more widely dispersed; some exist as clearly definable entities (e.g. a building); others are more diffuse and relate to a broad, and possibly poorly defined area. The spatial distribution of sources can thus be represented in different ways:
- as points - e.g. a chimney stack
- as lines - e.g. a road
- as areas - e.g. an industrial zone
Which is most relevant for the assessment depends on the spatial scale of the analysis, the specific hazards and environmental pathways, and target populations of interest.
Detailed information on source location and distribution can be obtained from a number of different data providers, and in different forms including:
- land cover maps (and the satellite data on which they are based) - e.g. the Corine Land Cover 2000
- Infrastructure maps - e.g. road maps
- national or regional topographic maps
- emission inventories - e.g. the European Pollutant Release and Transfer Register (E-PRTR)
Sources of pollution vary not only in their location and type but also in their intensity: the scale of the operations being carried out, the amounts of materials being consumed or produced, and thus the total quantities of waste materials being generated. Data on source intensity are therefore valuable in impact assessments, both as inputs into emission models and, where these are not available, as proxies for emissions in their own right.
Measures of source intensity
Source intensity can be described in many different ways, and which is most appropriate or informative is likely to depend on circumstances, including the type of source activity being considered, the specific pollutants or other agents of interest, and the environmental pathways and health impacts of concern. Typically, sources can be characterised in terms of four main attributes:
- the geographic volume or extent of the operation (e.g. total operational area; road length);
- operational numbers (e.g. numbers of workers employed; numbers of road users; livestock numbers; numbers of households or residents);
- quantities of inputs consumed (e.g. energy consumption; total volume or mass of raw materials);
- quantities of throughputs or outputs produced (e.g. volume or mass of materials produced; value of total sales; traffic volume).
No one of these tells the complete picture about the magnitude of source activities, and clearly each is likely to give only an approximate measure of the potential impacts on the environment. In terms of emissions, measures from further down the causal chain (i.e. more closely related to output) are likely to provide better indicators. Nevertheless, this is is not always true, for rates of release are influenced also by where within the process waste materials are produced and the effectiveness of emission controls. In the case of animal farming, for example, livestock numbers may be a better predictor of emission potential than meat or milk production because even non-productive animals generate wastes; in the case of road transport, emissions are highly dependent on traffic speed, so may vary with road type (e.g. between motorways and urban streets), independently of traffic flow.
Data types and sources
A wide range of data on source activities is generally collected, as part of business surveys, taxation returns and reporting under environmental regulations. For some major industrial sources, these may be available at a site-specific level (e.g. as part of industrial registers); likewise, detailed data on traffic flows and composition may be available for individual network links where monitoring or modelling is carried out for the purpose of management. In many cases, however, detailed data such as these are not readily available for reasons of commercial confidentiality, public privacy or security. In these cases, aggregated data have to be used (e.g. statistical data on employment, consumption or production; population census data). For some widely distributed emission sources, this aggregation may not greatly degrade the quality of the data as a basis for analysis; for localised sources, or where the releases are likely to be locally confined (e.g. due to release into soils or confined water bodies), aggregation can greatly distort the apparent distribution of source activities. In these cases, therefore, some form of modelling may be necessary to try to enhance the spatial resolution of the data (see link to Modelling source activity, in panel to left).
Modelling source activity
Although a wealth of information on human activities exists, assessments still have to rely to a large extent on methods for modelling sources of environmental contaminants. Even where data on emission sources are available, they are often too highly aggregated to show the more local effects of individual sources, so the data have to be disaggregated for the purpose of asssessments. More generally, the scenarios that underpin many assessments involve some sort of change in emission sources. Sometimes these changes are explicit: for example, the scenario may specify that the use of certain pesticides will be banned. In these cases the need to model how these changes take place (e.g. what alternative forms of pest control will be used) and their consequences is inherent in the assessment. Often, however, they are implicit: an emission reduction scenario may specify the amount by which emissions will decline but not give any indication of how this will be achieved. Even in this case, however, the implied changes in source activity need to be analysed, for otherwise any collateral effects (e.g. associated with the new technologies that have to be introduced) would be ignored, and the assessment would be biased.
Two main approaches to modelling can therefore be identified, in relation to source activities:
- Spatial modelling is required to estimate the geographic distribution of source activities. One of their main applications is to disaggregate broad scale socio-economic data to a more local level, in order to allow a more detailed analysis of impacts. National or regional data on energy consumption or traffic flows, for example, may be disaggregated to a local level, in order to simulate the spatial distribution of emission sources, and thus to model local variations in exposures and health impacts.
- Change models are required to simulate the way in which policy or other (e.g. technological) developments feed through the socio-economic system (e.g. via prices) to affect specific socio-economic decisions and practices (e.g. production, consumption). A range of different modelling approaches may be used for this purpose, including statistical, econometric, engineering or biophysical models, or (where quantitative modelling is inappropriate) the use of expert elicitation methods.
These approaches are not necessarily alternatives; often a combination of methods will be needed - for example, to simulate macro-economic changes and then downscale these to the local level, or to model changes in the spatial distribution of sources (e.g. urban areas) in response to policy or other drivers.