4.7 Article

Variability of black carbon mass concentrations, sub-micrometer particle number concentrations and size distributions: results of the German Ultrafine Aerosol Network ranging from city street to High Alpine locations

Journal

ATMOSPHERIC ENVIRONMENT
Volume 202, Issue -, Pages 256-268

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.atmosenv.2018.12.029

Keywords

Particle number size distribution; Black carbon; Long-term measurement; Spatial variability; Spatial clustering

Funding

  1. German Federal Environment Ministry (BMU) [FE 370343200, FE 371143232]
  2. North Rhine-Westphalia Agency for Nature, Environment and Consumer Protection (LANUV)
  3. EU-Ziel3 project UltraSchwarz (German title: Ultrafeinstaub und Gesundheit im Erzgebirgskreis und Region Usti) [100083657]
  4. European Regional Development Fund Financing Programme Central Europe [3CE288P]
  5. European Union-Research Infrastructure Action in the frame of the H2020 program for Integrating and opening existing national and regional research infrastructures of European interest [N654109]
  6. German Environmental Agency (Umweltbundesamt)

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This work reports the first statistical analysis of multi-annual data on tropospheric aerosols from the German Ultrafine Aerosol Network (GUAN). Compared to other networks worldwide, GUAN with 17 measurement locations has the most sites equipped with particle number size distribution (PNSD) and equivalent black carbon (eBC) instruments and the most site categories in Germany ranging from city street/roadside to High Alpine. As we know, the variations of eBC and particle number concentration (PNC) are influenced by several factors such as source, transformation, transport and deposition. The dominant controlling factor for different pollutant parameters might be varied, leading to the different spatio-temporal variations among the measured parameters. Currently, a study of spatio-temporal variations of PNSD and eBC considering the influences of both site categories and spatial scale is still missing. Based on the multi-site dataset of GUAN, the goal of this study is to investigate how pollutant parameters may interfere with spatial characteristics and site categories.

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