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We describe the process by which the Long-Term Ecological Research (LTER) Network standardised their metadata through the adoption of the Ecological Metadata Language (EML). We describe the strategies developed to improve motivation and to complement the information teclmology resources available at the LTER sites. EML implementation is presented as a mapping process that was accomplished per site in stages, with metadata quality ranging from 'discovery level' to rich-content level over time. As of publication, over 6000 rich-content standardised records have been published using EML, potentially enabling the goal of machine-mediated, metadata-driven data synthesis. Brunt, J. (2009) 'The Long-Term Ecological Research community metadata standardisation project: a progress report', Int. J. Metadata Semantics and Ontologies, Vol. Biographical notes: I. San Gil received his PhD in Mechanical Engineering from Yale University in 2001. He is currently the metadata project coordinator and senior systems analyst for the National Biological Information Infrastructure and Long-Term Ecological Network. His current research interests include metadata management systems, bioinformatics, and metadata-driven systems. Karen Baker holds an MS from the University of California at Los Angeles, and she is currently the information manager at Scripps Institution of Oceanography for ClCOFI, Palmer LTER and California Current Ecosystem LTERs. John Campbell holds a PhD from the State University of New York and he is currently the information manager at Hubbard Brook LTER. Ellen G. Denny holds a MFS from the Yale School of Forestry and Environmental Studies, and is part of the information management team for the Hubbard Brook LTER site. Kristin Vanderbilt received her PhD (Biology) at the U. of New Mexico, where she is currently an Associate Research Professor and the Sevilleta LTER Information Manager. Brian Riordan received an MS from the University of Alaska -Fairbanks, he currently works at the private sector on GIS. Rebecca Koskela is a Bioinformatics Specialist at the Arctic Region Supercomputing Center at the University of Alaska Fairbanks campus, Rebecca was a member of the senior management team at the Aventis Cambridge Genome Center. Jason Downing is the current Information Manager at the Bonanza Creek LTER. Sabine Grabner received her MS (Meteorology) from the James Brunt is an Associate Director for Information Management of the LTER Network Office, he leads and supervises a staff of six who provide operations and maintenance of LTER cyberinfrastructure, design and develop the L TER Network Information System, and provide stewardship of LTER Network databases and websites. He pursued a unique MS mixing Ecology, Computer Science, and Experimental Statistics at NMSU.

Poor air quality directly affects human health and has become an increasingly important environmental issue in Tianjin, China. The suspension of particulate matter (PM) in the atmosphere is not only from industrial pollutants, but also soil wind erosion; however, the contributions from farmland, woodland, and grassland have rarely been considered in this region. We conducted an assessment of PM sources through wind erosion, dust emission, and dust transportation from urban and rural areas to the central district in Tianjin, and our results demonstrated that the spatial variability of wind erosion and dust emission strongly depends on land use, particle size distribution and meteorological conditions. The equations in this study were empirical, and soil properties such as aggregation and crusting, as well as surface characteristics such as canopy height and residue cover, were not considered. The dust emission capacity of woodland and grassland was the lowest because of vegetation coverage. The values obtained in this study may overestimate emissions, because soil aggregation was not considered. The yearly dust amounts of PM [15][16][17][18][19][20] (particles with aerodynamic diameter from 15 µm to 20 µm), PM 10-15 (particles with aerodynamic diameter from 10 µm to 15 µm), and PM 10 (particles with aerodynamic diameter less than 10 µm) from wind erosion in 2009 from the urban area in Tianjin were estimated as 5,400 t, 5700 t and 17,300 t, respectively, while those from the rural area were 14,000 t, 15,300 t and 40,700 t, respectively. The dust emission contributed from farmland accounted for 99.5%, and that from woodland and grassland only accounted for 0.5%. The PM 10 transported to the central district and PM 10 concentrations in the days with the 20% highest PM 10 concentrations in the central district in 2009 were compared. The R 2 was 0.74, which meant the two variables were highly correlated.

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Wind erosion of soil is a major concern of the agricultural community, as it removes the most fertile part of the soil and thus degrades soil productivity. Furthermore, dust emissions due to wind erosion degrade air quality, reduce visibility, and cause perturbations to regional radiation budgets. PM 10 emitted from the soil surface can travel hundreds of kilometers downwind before being deposited back to the surface. Thus, it is necessary to address agricultural air pollutant sources within a regional air quality modeling system in order to forecast regional dust storms and to understand the impact of agricultural activities and land-management practices on air quality in a changing climate. The Wind Erosion Prediction System (WEPS) is a new tool in regional air quality modeling for simulating erosion from agricultural fields. WEPS represents a significant improvement, in comparison to existing empirical windblown dust modeling algorithms used for air quality simulations, by using a more process-based modeling approach. This is in contrast with the empirical approaches used in previous models, which could only be used reliably when soil, surface, and ambient conditions are similar to those from which the parameterizations were derived. WEPS was originally intended for soil conservation applications and designed to simulate conditions of a single field over multiple years. In this work, we used the EROSION submodel from WEPS as a PM 10 emission module for regional modeling by extending it to cover a large region divided into Euclidean grid cells. The new PM 10 emission module was then employed within a regional weather and chemical transport modeling framework commonly used for comprehensive simulations of a wide range of pollutants to evaluate overall air quality conditions. This framework employs the Weather Research and Forecasting (WRF) weather model along with the Community Multi-scale Air Quality (CMAQ) model to treat ozone, particulate matter, and other air pollutants. To demonstrate the capabilities of the WRF/EROSION/CMAQ dust modeling framework, we present here results from simulations of dust storms that occurred in central and eastern Washington during 4 October 2009 and 26 August 2010. Comparison of model results with observations indicates that the modeling framework performs well in predicting the onset and timing of the dust storms and the spatial extent of their dust plumes. The regional dust modeling framework is able to predict elevated PM 10 concentrations hundreds of kilometers downwind of erosion source regions associated with the windblown dust, although the magnitude of the PM 10 concentrations are extremely sensitive to the assumption of surface soil moisture and model wind speeds. Future work will include incorporating the full WEPS model into the regional modeling framework and targeting field measurements to evaluate the modeling framework more extensively.