期刊
ENVIRONMENT INTERNATIONAL
卷 82, 期 -, 页码 10-20出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.envint.2015.05.002
关键词
Particulate matter; Life cycle impact assessment modeling; Physical particle properties; Chemical particle properties; Ambient air pollution
资金
- Federal Office for the Environment within the Wood Action Plan
Particulate matter (PM) causes severe damage to human health globally. Airborne PM is a mixture of solid and liquid droplets suspended in air. It consists of organic and inorganic components, and the particles of concern range in size from a few nanometers to approximately 10 mu m. The complexity of PM is considered to be the reason for the poor understanding of PM and may also be the reason why PM in environmental impact assessment is poorly defined. Currently, life cycle impact assessment is unable to differentiate highly toxic soot particles from relatively harmless sea salt. The aim of this article is to present a new impact assessment for PM where the impact of PM is modeled based on particle physico-chemical properties. With the new method, 2781 characterization factors that account for particle mass, particle number concentration, particle size, chemical composition and solubility were calculated. Because particle sizes vary over four orders of magnitudes, a sound assessment of PM requires that the exposure model includes deposition of particles in the lungs and that the fate model includes coagulation as a removal mechanism. for ultrafine particles. The effects model combines effects from particle size, solubility and chemical composition. The first results from case studies suggest that PM that stems from emissions generally assumed to be highly toxic (e.g. biomass combustion and fossil fuel combustion) might lead to results that are similar compared with an assessment of PM using established methods. However, if harmless PM emissions are emitted, established methods enormously overestimate the damage. The new impact assessment allows a high resolution of the damage allocatable to different size fractions or chemical components. This feature supports a more efficient optimization of processes and products when combating air pollution. (C) 2015 Elsevier Ltd. All rights reserved.
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