estimation of aerosol water and chemical composition from aeronet sun–sky radiometer measurements at cabauw, the netherlands

Clicks: 91
ID: 177139
2014
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #683 of 850 articles by views in Journal of agricultural and food chemistry

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 850 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Abstract
Remote sensing of aerosols provides important information on atmospheric aerosol abundance. However, due to the hygroscopic nature of aerosol particles observed aerosol optical properties are influenced by atmospheric humidity, and the measurements do not unambiguously characterize the aerosol dry mass and composition, which complicates the comparison with aerosol models. In this study we derive aerosol water and chemical composition by a modeling approach that combines individual measurements of remotely sensed aerosol properties (e.g., optical thickness, single-scattering albedo, refractive index and size distribution) from an AERONET (Aerosol Robotic Network) Sun–sky radiometer with radiosonde measurements of relative humidity. The model simulates water uptake by aerosols based on the chemical composition (e.g., sulfates, ammonium, nitrate, organic matter and black carbon) and size distribution. A minimization method is used to calculate aerosol composition and concentration, which are then compared to in situ measurements from the Intensive Measurement Campaign At the Cabauw Tower (IMPACT, May 2008, the Netherlands). Computed concentrations show good agreement with campaign-average (i.e., 1–14 May) surface observations (mean bias is 3% for PM10 and 4–25% for the individual compounds). They follow the day-to-day (synoptic) variability in the observations and are in reasonable agreement for daily average concentrations (i.e., mean bias is 5% for PM10 and black carbon, 10% for the inorganic salts and 18% for organic matter; root-mean-squared deviations are 26% for PM10 and 35–45% for the individual compounds). The modeled water volume fraction is highly variable and strongly dependent on composition. During this campaign we find that it is >0.5 at approximately 80% relative humidity (RH) when the aerosol composition is dominated by hygroscopic inorganic salts, and <0.1 when RH is below 40%, especially when the composition is dominated by less hygroscopic compounds such as organic matter. The scattering enhancement factor (f(RH), the ratio of the scattering coefficient at 85% RH and its dry value at 676 nm) during 1–14 May is 2.6 ± 0.5. The uncertainty in AERONET (real) refractive index (0.025–0.05) is the largest source of uncertainty in the modeled aerosol composition and leads to an uncertainty of 0.1–0.25 (50–100%) in aerosol water volume fraction. Our methodology performs relatively well at Cabauw, but a better performance may be expected for regions with higher aerosol loading where the uncertainties in the AERONET inversions are smaller.
Reference Key
beelen2014atmosphericestimation Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;A. J. van Beelen;G. J. H. Roelofs;O. P. Hasekamp;J. S. Henzing;T. Röckmann
Journal Journal of agricultural and food chemistry
Year 2014
DOI
10.5194/acp-14-5969-2014
URL
Keywords

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

No comments yet. Be the first to comment on this article.