title: Biomagnetic monitoring of industry-derived particulate pollution.
abstract: Clear association exists between ambient PM₁₀ concentrations and adverse health outcomes. However, determination of the strength of associations between exposure and illness is limited by low spatial-resolution of particulate concentration measurements. Conventional fixed monitoring stations provide high temporal-resolution data, but cannot capture fine-scale spatial variations. Here we examine the utility of biomagnetic monitoring for spatial mapping of PM₁₀ concentrations around a major industrial site. We combine leaf magnetic measurements with co-located PM₁₀ measurements to achieve inter-calibration. Comparison of the leaf-calculated and measured PM₁₀ concentrations with PM₁₀ predictions from a widely-used atmospheric dispersion model indicates that modelling of stack emissions alone substantially under-predicts ambient PM₁₀ concentrations in parts of the study area. Some of this discrepancy might be attributable to fugitive emissions from the industrial site. The composition of the magnetic particulates from vehicle and industry-derived sources differ, indicating the potential of magnetic techniques for source attribution.
authors: Centre for Environmental Magnetism and Palaeomagnetism, Lancaster Environment Centre, University of Lancaster, Lancaster, UK. r.hansard@lancaster.ac.uk
authors: R
authors: R
authors: Hansard
authors: B A
authors: BA
authors: Maher
authors: R
authors: R
authors: Kinnersley
mesh_terms: D000393:Air Pollutants / Q000032:analysis / Q000737:chemistry / Q000378:metabolism*; D000397:Air Pollution / Q000706:statistics & numerical data; D004784:Environmental Monitoring / Q000379:methods*; D008280:Magnetics; D008956:Models, Chemical; D010316:Particle Size; D052638:Particulate Matter / Q000032:analysis / Q000737:chemistry / Q000378:metabolism*; D018515:Plant Leaves / Q000737:chemistry / Q000378:metabolism*; D014197:Trees / Q000378:metabolism
chemical_list: D000393:Air Pollutants; D052638:Particulate Matter