scholarly journals Review and uncertainty assessment of size-resolved scavenging coefficient formulations for below-cloud snow scavenging of atmospheric aerosols

2013 ◽  
Vol 13 (19) ◽  
pp. 10005-10025 ◽  
Author(s):  
L. Zhang ◽  
X. Wang ◽  
M. D. Moran ◽  
J. Feng

Abstract. Theoretical parameterizations for the size-resolved scavenging coefficient for atmospheric aerosol particles scavenged by snow (Λsnow) need assumptions regarding (i) snow particle–aerosol particle collection efficiency E, (ii) snow-particle size distribution N(Dp), (iii) snow-particle terminal velocity VD, and (iv) snow-particle cross-sectional area A. Existing formulas for these parameters are reviewed in the present study, and uncertainties in Λsnow caused by various combinations of these parameters are assessed. Different formulations of E can cause uncertainties in Λsnow of more than one order of magnitude for all aerosol sizes for typical snowfall intensities. E is the largest source of uncertainty among all the input parameters, similar to rain scavenging of atmospheric aerosols (Λrain) as was found in a previous study by Wang et al. (2010). However, other parameters can also cause significant uncertainties in Λsnow, and the uncertainties from these parameters are much larger than for Λrain. Specifically, different N(Dp) formulations can cause one-order-of-magnitude uncertainties in Λsnow for all aerosol sizes, as is also the case for a combination of uncertainties from both VD and A. Assumptions about dominant snow-particle shape (and thus different VD and A) will cause an uncertainty of up to one order of magnitude in the calculated scavenging coefficient. In comparison, uncertainties in Λrain from N(Dp) are smaller than a factor of 5, and those from VD are smaller than a factor of 2. As expected, Λsnow estimated from empirical formulas generated from field measurements falls in the upper range of, or is higher than, the theoretically estimated values, which can be explained by additional processes/mechanisms that influence field-derived Λsnow but that are not considered in the theoretical Λsnow formulas. Predicted aerosol concentrations obtained by using upper range vs. lower range of Λsnow values (a difference of around two orders of magnitude in Λsnow) can differ by a factor of 2 for just a one-centimetre snowfall (liquid water equivalent of approximately 1 mm). Based on the median and upper range of theoretically generated Λsnow and Λsnow values, it is likely that, for typical rain and snow events, the removal of atmospheric aerosol particles by snow is more effective than removal by rain for equivalent precipitation amounts, although a firm conclusion requires much more evidence.

2013 ◽  
Vol 13 (6) ◽  
pp. 14823-14869 ◽  
Author(s):  
L. Zhang ◽  
X. Wang ◽  
M. D. Moran ◽  
J. Feng

Abstract. Theoretical parameterizations for the size-resolved scavenging coefficient for atmospheric aerosol particles scavenged by snow (Λsnow) need assumptions regarding (i) snow particle–aerosol particle collection efficiency E, (ii) snow particle size distribution N(Dp), (iii) snow particle terminal velocity VD, and (iv) snow particle cross-sectional area A. Existing formulas for these parameters are reviewed in the present study and uncertainties in Λsnow caused by various combinations of these parameters are assessed. Different formulations of E can cause uncertainties in Λsnow of more than one order of magnitude for all aerosol sizes for typical snowfall intensities. E is the largest source of uncertainty among all the input parameters, similar to rain scavenging of atmospheric aerosols (Λrain) as was found in a previous study by Wang et al. (2010). However, other parameters can also cause significant uncertainties in Λsnow, and the uncertainties from these parameters are much larger than for Λrain. Specifically, different N(Dp) formulations can cause one-order-of-magnitude uncertainties in Λsnow for all aerosol sizes, as is also the case for a combination of uncertainties from both VD and A. In comparison, uncertainties in Λrain from N(Dp) are smaller than a factor of 5 and those from VD are smaller than a factor of 2. Λsnow estimated from one empirical formula generated from field measurements falls in the upper range of, or is slightly higher than, theoretically estimated values. The predicted aerosol concentrations obtained using different Λsnow formulas can differ by a factor of two for just a one-centimeter snowfall (liquid water equivalent of approximately 1 mm). It is likely that, for typical rain and snow event the removal of atmospheric aerosol particles by snow is more effective than removal by rain for equivalent precipitation amounts, although a firm conclusion requires much more evidence.


2010 ◽  
Vol 3 (2) ◽  
pp. 1361-1398 ◽  
Author(s):  
T. Hohaus ◽  
D. Trimborn ◽  
A. Kiendler-Scharr ◽  
I. Gensch ◽  
W. Laumer ◽  
...  

Abstract. In many environments organic matter significantly contributes to the composition of atmospheric aerosol particles influencing its properties. Detailed chemical characterization of ambient aerosols is critical in order to understand the formation process, composition, and properties of aerosols in the atmosphere. However, current analytical methods are far from full speciation of organic aerosols and often require long sampling times. Offline methods are also subjected to artifacts during aerosol collection and storage. In the present work a new technique for quasi-online compound specific measurements of organic aerosol particles was developed. The Aerosol Collection Module (ACM) is designed to sample, collect and transfer gasified atmospheric aerosol particles. The system focuses particles into a beam which is directed to a cooled sampling surface. The sampling takes places in a high vacuum environment where the gas phase from the sample volume is removed. After collection the particle sample is evaporated from the collection surface through heating and transferred to a detector. For laboratory characterization the ACM was interfaced with a Gas Chromatograph Mass Spectrometer system (GC-MS). The particle collection efficiency, gas phase transfer efficiency, and linearity of the ACM-GC-MS were determined using laboratory generated octadecane aerosols. The ACM-GC-MS is linear over the investigated mass range of 10 to 100 ng and a recovery rate of 100% was found for octadecane particles. The ACM-GC-MS was applied to investigate secondary organic aerosol (SOA) formed from β-pinene oxidation. Nopinone, myrtanal, myrtenol, 1-hydroxynopinone, 3-oxonopinone, 3,7-dihydroxynopinone, and bicyclo[3,1,1]hept-3-ene-2-one were found as products in the SOA. The ACM results are compared to quartz filter samples taken in parallel to the ACM measurements. First measurements of ambient atmospheric aerosols are presented.


2006 ◽  
Vol 6 (12) ◽  
pp. 4519-4527 ◽  
Author(s):  
H. Wex ◽  
A. Kiselev ◽  
M. Ziese ◽  
F. Stratmann

Abstract. A calibration for LACIS (Leipzig Aerosol Cloud Interaction Simulator) for its use as a CCN (cloud condensation nuclei) detector has been developed. For this purpose, sodium chloride and ammonium sulfate particles of known sizes were generated and their grown sizes were detected at the LACIS outlet. From these signals, the effective critical super-saturation was derived as a function of the LACIS wall temperature. With this, LACIS is calibrated for its use as a CCN detector. The applicability of LACIS for measurements of the droplet activation, and also of the hygroscopic growth of atmospheric aerosol particles was tested. The activation of the urban aerosol particles used in the measurements was found to occur at a critical super-saturation of 0.46% for particles with a dry diameter of 75 nm, and at 0.42% for 85 nm, respectively. Hygroscopic growth was measured for atmospheric aerosol particles with dry diameters of 150, 300 and 350 nm at relative humidities of 98 and 99%, and it was found that the larger dry particles contained a larger soluble volume fraction of about 0.85, compared to about 0.6 for the 150 nm particles.


2003 ◽  
Vol 34 (2) ◽  
pp. 225-242 ◽  
Author(s):  
Berko Sierau ◽  
Frank Stratmann ◽  
Matthias Pelzing ◽  
Christian Neusüß ◽  
Diana Hofmann ◽  
...  

2012 ◽  
Vol 7 (9) ◽  
pp. 1651-1667 ◽  
Author(s):  
Markku Kulmala ◽  
Tuukka Petäjä ◽  
Tuomo Nieminen ◽  
Mikko Sipilä ◽  
Hanna E Manninen ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document