Quantifying Anthropogenic Dust Emissions. Issue 2 (21st February 2018)

• Record Type: Journal Article
• Authors: Webb, Nicholas P.; Pierre, Caroline
• Is Part Of: Earth's future. Volume 6:Issue 2(2018)
• Page Start: 286
• Page End: 295
• Publication Date: 2018-02-21
• Subjects: wind erosion -- dust -- land use -- land cover change -- agriculture -- drylands; Environmental sciences -- Periodicals; Environmental sciences; Periodicals; 550
• DOI: 10.1002/2017EF000766
• Languages: English
• ISSNs: 2328-4277

Abstract

Abstract: Anthropogenic land use and land cover change, including local environmental disturbances, moderate rates of wind‐driven soil erosion and dust emission. These human‐dust cycle interactions impact ecosystems and agricultural production, air quality, human health, biogeochemical cycles, and climate. While the impacts of land use activities and land management on aeolian processes can be profound, the interactions are often complex and assessments of anthropogenic dust loads at all scales remain highly uncertain. Here, we critically review the drivers of anthropogenic dust emission and current evaluation approaches. We then identify and describe opportunities to: (1) develop new conceptual frameworks and interdisciplinary approaches that draw on ecological state‐and‐transition models to improve the accuracy and relevance of assessments of anthropogenic dust emissions; (2) improve model fidelity and capacity for change detection to quantify anthropogenic impacts on aeolian processes; and (3) enhance field research and monitoring networks to support dust model applications to evaluate the impacts of disturbance processes on local to global‐scale wind erosion and dust emissions. Key Points: Land use and land cover change impact dust emissions, but uncertainty in estimates remains large State‐and‐transition models linking ecological and aeolian processes provide a framework for accurate and management‐relevant assessments Interdisciplinary approaches that couple dust models with land surface and agricultural models show potential for resolving the drivers