Health impacts of agricultural land use change

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

Agriculture can be a significant source of environmental contamination and thus of human exposure to pollutants. This assessment focused on the potential health effects of inhalation exposure for people in close proximity to agricultural activities.

This assessment attempts to address the question: What are the likely health impacts for the general public of changes in agricultural land use (due to environmental, economic and policy developments) in Greece and Great Britain over the foreseeable future?

Scope

Description

Agricultural emissions from pesticides, aerosols and bioaerosols were considered. These emissions, which depend on land use management, are likely to change as farmers adapt their practices in response to changing climatic conditions, or as a result of policies aimed at adapting to or mitigating climate change. With climate change as the driver, this assessment employed pre-existing land use change scenarios to explore potential health effects. Click here to view the causal diagram.

Scenario(s) and type of assessment

This was a prognostic assessment comparing scenarios of future agricultural land use based on climate change models. It assumed baseline agricultural practices, productivity, and emission rates remained unchanged (i.e. business as usual (BAU)). Change scenarios for Greece included mitigation and derived from the Advanced Terrestrial Ecosystem Analysis and Modelling (ATEAM) project (Schröter et al., 2004), while those for England derived from the Regional Impact Simulator (RegIS) (Holman et al. 2007).

The ATEAM land use change projections in Europe are based on socio-economic and climatic scenarios, and are represented as maps at ca.16x16 km for the decade 1990-2000 (average data) (baseline) and for the years 2020 and 2050. These data were enhanced for Greece using a crop allocation algorithm, which distributes the areas of each crop on a 4x4km grid using supplementary arable land data (ESYE). The scenarios for England did not require enhancement as data for rural land use and cropping were available at a spatial resolution of 5x5km.

In both areas, assessments were done for a baseline year and for two future years (2020 and/or 2050). Business-as-usual scenarios were run to estimate potential impacts under current land use conditions, and change scenarios run using the land use projections provided from these sources. Differences between the two gave an estimate of the impacts attributable to the projected land use changes.

Geographical and temporal scope

The geographical scope included major agricultural areas: Regions of Central Macedonia and Thessaly in Greece, and two regions of England (East Anglia and the northwest). Available data on land use for baseline year (2000 England, 2004 Greece) were employed for enhancing scenario maps. The temporal scope of the scenarios was 2020 and 2050, for which annual average exposure and health impact were computed. The GB assessment made use of the 2020 projections only, as agricultural practice and pesticides were assumed to differ too much from the base year by 2050. This was not an issue for Greece because changes in pesticides and agricultural practices were explicitly taken into account based on CAP policies.

Environmental and health factors

Focusing on inhalation exposures to agricultural pollutants, the following health outcomes were considered: all cause mortality (PM₁₀, PM_2.5); respiratory and cardiovascular morbidity (PM₁₀ and PM_2.5); and adult cancers, and childhood cancers (pesticides).

Health effects due to very acute exposures (e.g. pesticide and ammonia poisoning) were excluded during scoping. Exposure to bioaerosols may be protective in early life (e.g. endotoxin) or increase risks (e.g. pollen). While asthma and allergic disease outcomes were included at scoping, they were subsequently excluded due to lack of exposure response functions and background rates of diseases.

Stakeholders

Stakeholders potentially interested in this assessment include: Farmers, farm-workers and Farmers’ unions; National and European policymakers and authorities (Ministries of Agricultural and Public Health); Crop protection associations; Agrochemical manufacturers, distributors and associated industries; Consumers, NGOs and lobby groups; and Residents in agricultural areas. Several of these stakeholders were contacted to help direct the scoping of this assessment, and to obtain relevant input data.

Assessment method

Exposure assessment

The key sources included pesticides, crops area and livestock counts. Aggregated source data were spatially disaggregated to finer grids, informed by agricultural census data and land cover information, using GIS methods including mask area weighting (for GB) and a stochastic allocation algorithm (for Greece). Change ratios were applied to these baseline source activity grids to generate grids for the scenarios. Emissions factors for individual active ingredients (AI), aerosols and bioaerosols were compiled and applied to transform the source activity grids into grids of emissions (µg/m²/year). Atmospheric dispersion models were used to model regional dispersion profiles, taking account of local meteorology for one year, for one unit emissions of each pollutant type. Distance-weighted kernel functions were then imputed from the dispersion profiles, and applied to the emissions grids to convert emissions into concentrations (µg/m³/year) using GIS-based focal sum model (Vienneau 2010). The focal sum model was compared to a box-model in Greece with good agreement.

Datasets and models used in the exposure assessment included

Data / Model	Greece	England
Pesticides (NUTS3)	Sales data collected from local retailers, regional authorities and experts	Pesticide Usage Survey, Central Science Laboratory, DEFRA
Crop area (LAU2)	National Statistical Service of Greece ESYE (2004)	June Agricultural Survey 2000, DEFRA
Livestock (LAU2)	National Statistical Service of Greece (2004)	June Agricultural Survey 2000, DEFRA
Land cover	CORINE 2000, EEA	CORINE 2000, EEA
Admin. areas	Municipalities (LAU2)	2001 Ward Boundaries (LAU2 equivalent)
Population	Data from 2001 census and projections to 2020 and 2050, National Statistical Service of Greece (ESYE)	Postcode headcount 2001 census and subnational population projections to 2031, Office for National Statistics (ONS)
Emissions factors	TNO (pesticide), IER Stuttgart (aerosols), Seedorf et al 2004 (endotoxin), CERTH estimates (pollen)	Same
Dispersion model	CALPUFF	ADMS Urban, CERC
Transformation model	Box-model and Focal sum model	Focal sum model

For England, postcode locations were intersected with pollutant concentration grids to compute population weighted ward exposures. To assess health impact, exposures were combined with age/sex stratified population for 2031, imputed for wards on the basis of county and local authority projections from the ONS.

For Greece, health impact was assessed at 4x4 km grid resolution. Estimates of risk were intersected with the population data in order to investigate changes in exposure (i.e. consequently changes in health impact) between scenarios. While ambient concentration of particulates was directly used to estimate population exposure.

Health effect assessment

A literature search was conducted for available exposure response functions (ERFs) for the inhalation exposures and health effects of interest, with little success. ERFs were thus estimated for pesticides, particulates and endotoxin as follows:

• For pesticide functional groups, an existing systematic review was used as a guide to generate hypothetical RRs for cancers and birth outcomes. These were generated for exposure tertiles: low, medium and high (with the low and high category being 10% lower and higher, respectively, than the medium).
• In GB, a risk analysis using the Rapid Inquire Facility (RIF) developed at Imperial College (Beale 2008) was also conducted to derive England specific RRs for adult cancers. Results from the above systematic review were used to inform choice of cancers to investigate. The RIF analysis was based on ONS Cancer data for years 2001-2005 to get sufficient numbers and account for latency. Exposures in three cateogires (low: bottom 5th percentile, medium: 5th-60th percentile, high: >60th percentile) derived from the 250x250m exposure grids. Significant increased risk, relative to the whole of England, was detected for breast and prostate cancer only.
• In the absence of ERFs for agricultural, existing ERFs for traffic-related PM₁₀ and PM_2.5 documented within the toolkit were used for England (e.g. mortality: 1.06 per 10µg/m³ PM₁₀). ERFs appropriate for local conditions were used for Greece. These ERFs were further refined with local information on hospital admissions.
• For endotoxin: A literature review was carried out and ERFs for asthma, wheeze, hay fever and atopic sensitization were retrieved for adult farmers and school-age children (Braun-Fahrlander et al., 2000).

Due to the lack of adequate ERFs for pesticide functional groups, slope factors and reference doses for individual active substances were collected from toxicological databases (IRIS database, USEPA 2008 and The Reference Dose Tracking Report, Rowland 2006). Slope factors were used to estimate cancer risk based on inhalation intake.

Impact Assessment

The health impact indicators used include: risk, attributable burden of disease and disability-adjusted life years (DALYs).

Risk is an expression of the likelihood (statistical probability) that harm will occur when a receptor (e.g. human or a part of an ecosystem) is exposed to a hazard. An example of a risk indicator is thelikelihood that a certain population (e.g. farmers) will have a certain level of cancer incidence after being exposed to a certain pollutant (e.g. pesticides). The burden of disease provides a comprehensive assessment of the health status of people and gives policy makers the information need to make decisions about health. Where appropriate, DALYs were used to describe and compare the health impact of various environmental exposures. These give an indication of the potential number of healthy life years lost due to premature mortality or morbidity.

Results

Great Britain

Differences in attributable burden between the BAU and change scenarios (L2020 and H2020) gave an estimate of the impacts attributable to the projected land use changes. Health risks due to pesticides were assessed at the ward level while risks due to particulates were assessed at the county level. Little difference between L2020 and H2020 was detected, thus results for H2020 are reported.

Only a slight increase in mortality due to particulates was detected (0.5 per year for PM10) due to the land use change across both study areas, i.e. East Anglia and the Northwest combined.

In terms of pesticides, the toxicological risk analysis for six carcinogenic herbicides estimated two attributable cases per year across both study areas. The results from the hypothetical RR were dismissed due to sensitivity of the attributable burden calculation to definition of the exposure tertiles.

Of the seven adult cancers explored in the RIF risk analysis, risk was only detected for breast and prostate cancer in areas with total pesticide concentrations exceeding 3.6 and 0.04 ng/m³, respectively. In terms of attributable burden due to land use change, an estimated 5 and 9 cases of breast and prostate cancer, respectively were estimated in the Northwest, while the estimate in East Anglia was 2 breast cancer and 3 prostate cancer cases per year. The marginally larger increase in the Northwest results from a greater proportion of the population shifting between exposure categories e.g., wards shift from lower categories in the BAU to higher categories in the change scenarios.

Greece

Comparisons in attributable burden between the BAU and mitigation scenarios show very small differences both for pesticides and particulates. Health risks from pesticides were carried out only for farmers who are exposed to pesticides due to their proximity to the emitting sources, whereas exposure estimates to particulates is based on the general population. It should be noted that aggregated risk estimates from pesticides, for each scenario, were found to be below the 10^-6 value, commonly considered to be an indicator of significant risk. For the pesticides, the difference in the number of cases attributed to cancer is estimated to be 2E-5 and 1E-5, for the years 2020 and 2050, respectively, based on analysis involving 20 carcinogenic active substances. These small differences are in accord with the estimated small concentration in air (maximum at 2.2 ng/m³) and the concomitant very low risk (approx. 3E-8).

Moreover, the results show that the effect of increased cultivation of energy crops (in the context of mitigation scenarios) on health impact is almost negligible, since the total quantity of pesticides used in the energy crops considered is small, in comparison to that used in edible crops. For the particulates (PM₁₀), the changes in the number of cases between the BAU and the mitigation scenarios with respect to various health effects are also very small. For example, the difference in the number of cases for the cardiovascular diseases is 4.6E-2 (for year 2020) and 8E-2 (for year 2050). Similarly, the difference in cases attributed to respiratory health effects is 1.8E-2 (for year 2020) and 3E-2 (for year 2050).

See also

• WP3.3 Full Assessment Report+Annex Final_0
• Beale, L., Abellan, JJ., Hodgson, S. and Jarup, L. 2008. Methodological issues and approaches to spatial epidemiology. Environmental Health Perspectives 116(8): 1105-1110.
• Braun-Fahrlander C., Riedler J., Herz U., Eder W., Waser M., Grize L., Maisch S., Carr D., Florian G., Bufe A., Lauener R.P., Schierl R., Renz H., Nowak D. and von Mutius E. 2002 Environmental exposure to endotoxin and its relation to asthma in school-age children, The New England Journal of Medicine. 347(12), pp 869-877.
• Holman IP, Berry PM, Mokrech M, Richards JA, Audsley E,Harrison PA, Rounsevell MDA, Nicholls RJ, Shackley S,Henriques C (2007). Simulating the effects of future climate and socio-economic change in East Anglia and North West England: the RegIS2 project. Summary Report. UKCIP, Oxford 2007.
• Rowland, J. 2006 Chemicals Evaluated for Carcinogenic Potential, Office of Pesticide Programs, US EPA.
• Schröter D. et al. 2004 “Advanced Terrestrial Ecosystem Analysis and Modelling (ATEAM)”, Final report 2004, Section 5 and 6 and Annex 1 to 6, Reporting period: 01.01.2001 30.06.2004, Contract n°EVK2-2000-00075, Potsdam Institute for Climate Impact Research (PIK), Potsdam, Germany.
• Seedorf, J. 2004 An emission inventory of livestock-related bioaerosols for Lower Saxony, Germany. Atmospheric Environment 38, 6565–6581.
• USEPA. 2008b Integrated Risk Information System (IRIS) (2008)
• Vienneau D, de Hoogh K, Briggs D. A GIS-based method for modelling air pollution exposures across Europe. Sci Total Environ, 2009; 408:255-266.