Detailed knowledge of the spatial distribution of sources is a crucial prerequisite for the application of pollen dispersion models such as, for example, COSMO-ART (COnsortium for Small-scale MOdeling-Aerosols and Reactive Trace gases). However, this input is not available for the allergy-relevant species such as hazel, alder, birch, grass or ragweed. Hence, plant distribution datasets need to be derived from suitable sources. We present an approach to produce such a dataset from existing sources using birch as an example. The basic idea is to construct a birch dataset using a region with good data coverage for calibration and then to extrapolate this relationship to a larger area by using land use classes. We use the Swiss forest inventory (1 km resolution) in combination with a 74-category land use dataset that covers the non-forested areas of Switzerland as well (resolution 100 m). Then we assign birch density categories of 0%, 0.1%, 0.5% and 2.5% to each of the 74 land use categories. The combination of this derived dataset with the birch distribution from the forest inventory yields a fairly accurate birch distribution encompassing entire Switzerland. The land use categories of the Global Land Cover 2000 (GLC2000; Global Land Cover 2000 database, 2003, European Commission, Joint Research Centre; resolution 1 km) are then calibrated with the Swiss dataset in order to derive a Europe-wide birch distribution dataset and aggregated onto the 7 km COSMO-ART grid. This procedure thus assumes that a certain GLC2000 land use category has the same birch density wherever it may occur in Europe. In order to reduce the strict application of this crucial assumption, the birch density distribution as obtained from the previous steps is weighted using the mean Seasonal Pollen Index (SPI; yearly sums of daily pollen concentrations). For future improvement, region-specific birch densities for the GLC2000 categories could be integrated into the mapping procedure.
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http://dx.doi.org/10.1007/s00484-011-0505-7 | DOI Listing |
Ying Yong Sheng Tai Xue Bao
October 2024
College of Forestry, Agricultural University of Hebei, Baoding 071000, Hebei, China.
We elucidated the changes of soil microbial biomass and community structure in soil profiles under four typical land use types (farmland, grassland, secondary forest and plantation)and across five soil layers (0-10, 10-20, 20-30, 30-40, 40-50 cm) in the northern mountainous region of Hebei Province. We measured soil microbial biomass by phospholipid fatty acid (PLFA) method, and investigated the effects of land use and soil depth on soil microbial biomass and community structure by variance analysis, correlation analysis and redundancy analysis. The results showed that soil water content, bulk density, and organic carbon content of farmland differed significantly from other land use types.
View Article and Find Full Text PDFMaterials (Basel)
November 2024
Institute of Wood Sciences and Furniture, Warsaw University of Life Sciences-SGGW, Nowoursynowska St. 159, 02-776 Warsaw, Poland.
This study investigates the effects of suberic acid residue (SAR) additions on structural single-layer particleboard (like the P5 type, according to EN 312) properties, specifically the water absorption (WA), thickness swelling (TS), modulus of rupture (MOR), modulus of elasticity (MOE), screw withdrawal resistance (SWR), and internal bond (IB) strength. The results indicate that finer SAR fractions (1/0.25 and 2/1) reduce the WA after 2 h of soaking, while larger fractions increase the WA after 24 h, with only the smallest fraction meeting the TS standards.
View Article and Find Full Text PDFJ Environ Manage
January 2025
Department of Ecology and Biogeography, Nicolaus Copernicus University in Toruń, Poland; Centre for Climate Change Research, Nicolaus Copernicus University in Toruń, Poland.
Sensors (Basel)
November 2024
Department of Electrical and Computer Engineering, Mississippi State University, 406 Hardy Road 216 Simrall Hall Mississippi State, Starkville, MS 39762, USA.
Perception systems for assisted driving and autonomy enable the identification and classification of objects through a concentration of sensors installed in vehicles, including Radio Detection and Ranging (RADAR), camera, Light Detection and Ranging (LIDAR), ultrasound, and HD maps. These sensors ensure a reliable and robust navigation system. Radar, in particular, operates with electromagnetic waves and remains effective under a variety of weather conditions.
View Article and Find Full Text PDFJ Environ Manage
December 2024
School of Forestry, Northeast Forestry University, Harbin, 150040, China; Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, Northeast Forestry University, Harbin, 150040, China. Electronic address:
Despite decades of recovery, soil carbon in heavily burned areas has failed to reach pre-fire levels. It is unclear whether stand management practices can promote soil organic carbon accumulation at such sites. This study evaluated the changes in soil labile organic carbon (LOC) fractions (including dissolved organic carbon (DOC), microbial biomass carbon (MBC), and easily oxidizable organic carbon (EOC)) and the carbon pool management index (CPMI) after the thinning of a heavily burned area in the Daxing'an Mountains and selected sample plots.
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