57 research outputs found
Light absorption by polar and non-polar aerosol compounds from laboratory biomass combustion
Fresh and atmospherically aged biomass-burning (BB) aerosol mass is mostly comprised of strongly light-absorbing black carbon (BC) and of organic carbon (OC) with its light-absorbing fraction β brown carbon (BrC). There is a lack of data on the physical and chemical properties of atmospheric BB aerosols, leading to high uncertainties in estimates of the BB impact on air quality and climate, especially for BrC. The polarity of chemical compounds influences their fate in the atmosphere including wet/dry deposition and chemical and physical processing. So far, most of the attention has been given to the water-soluble (polar) fraction of BrC, while the non-polar BrC fraction has been largely ignored. In the present study, the light absorption properties of polar and non-polar fractions of fresh and aged BB emissions were examined to estimate the contribution of different-polarity organic compounds to the light absorption properties of BB aerosols.
In our experiments, four globally and regionally important fuels were burned under flaming and smoldering conditions in the Desert Research Institute (DRI) combustion chamber. To mimic atmospheric oxidation processes (5β7 days), BB emissions were aged using an oxidation flow reactor (OFR). Fresh and OFR-aged BB aerosols were collected on filters and extracted with water and hexane to study absorption properties of polar and non-polar organic species. Results of spectrophotometric measurements (absorption weighted by the solar spectrum and normalized to mass of fuel consumed) over the 190 to 900nm wavelength range showed that the non-polar (hexane-soluble) fraction is 2β3 times more absorbing than the polar (water-soluble) fraction. However, for emissions from fuels that undergo flaming combustion, an increased absorbance was observed for the water extracts of oxidized/aged emissions while the absorption of the hexane extracts was lower for the aged emissions for the same type of fuels. Absorption Γ
ngstrΓΆm exponent (AAE) values, computed based on absorbance values from spectrophotometer measurements, were changed with aging and the nature of this change was fuel dependent. The light absorption by humic-like substances (HULIS) was found to be higher in fuels characteristic of the southwestern USA. The absorption of the HULIS fraction was lower for OFR-aged BB emissions. Comparison of the light absorption properties of different-polarity extracts (water, hexane, HULIS) provides insight into the chemical nature of BB BrC and its transformation during oxidation processes
Constraining Aerosol Optical Models Using Ground-Based, Collocated Particle Size and Mass Measurements in Variable Air Mass Regimes During the 7-SEAS/Dongsha Experiment
During the spring of 2010, NASA Goddard's COMMIT ground-based mobile laboratory was stationed on Dongsha Island off the southwest coast of Taiwan, in preparation for the upcoming 2012 7-SEAS field campaign. The measurement period offered a unique opportunity for conducting detailed investigations of the optical properties of aerosols associated with different air mass regimes including background maritime and those contaminated by anthropogenic air pollution and mineral dust. What appears to be the first time for this region, a shortwave optical closure experiment for both scattering and absorption was attempted over a 12-day period during which aerosols exhibited the most change. Constraints to the optical model included combined SMPS and APS number concentration data for a continuum of fine and coarse-mode particle sizes up to PM2.5. We also take advantage of an IMPROVE chemical sampler to help constrain aerosol composition and mass partitioning of key elemental species including sea-salt, particulate organic matter, soil, non sea-salt sulphate, nitrate, and elemental carbon. Our results demonstrate that the observed aerosol scattering and absorption for these diverse air masses are reasonably captured by the model, where peak aerosol events and transitions between key aerosols types are evident. Signatures of heavy polluted aerosol composed mostly of ammonium and non sea-salt sulphate mixed with some dust with transitions to background sea-salt conditions are apparent in the absorption data, which is particularly reassuring owing to the large variability in the imaginary component of the refractive indices. Extinctive features at significantly smaller time scales than the one-day sample period of IMPROVE are more difficult to reproduce, as this requires further knowledge concerning the source apportionment of major chemical components in the model. Consistency between the measured and modeled optical parameters serves as an important link for advancing remote sensing and climate research studies in dynamic aerosol-rich environments like Dongsha
ΠΠΈΠ°Π»ΡΡΠΎΠ½ΠΎΠ²Π°Ρ ΠΊΠΈΡΠ»ΠΎΡΠ° ΠΊΠ°ΠΊ ΠΏΡΠ΅Π΄ΠΈΠΊΡΠΎΡ ΠΎΠ±ΠΎΡΡΡΠ΅Π½ΠΈΠΉ Ρ ΡΠΎΠ½ΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΎΠ±ΡΡΡΡΠΊΡΠΈΠ²Π½ΠΎΠΉ Π±ΠΎΠ»Π΅Π·Π½ΠΈ Π»Π΅Π³ΠΊΠΈΡ ΠΏΡΠΎΡΠ΅ΡΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠΉ ΡΡΠΈΠΎΠ»ΠΎΠ³ΠΈΠΈ
The frequency of exacerbations of chronic obstructive pulmonary disease (COPD) is one of the main factors determining the outcome. The search for biomarkers which reflect the risk of exacerbations is one of the urgent scientific and practical objectives. Aim. The study aimed to analyze the relationship between the serum concentration of hyaluronic acid (HA) and the frequency of exacerbations of occupational COPD caused by exposure to silica dust and to substantiate the use of HA as a predictor of exacerbations of COPD. Methods. 78 individuals with a diagnosis of occupational COPD were examined. Respiratory function was assessed based on forced vital capacity of the lungs (FVC, %), the forced expiratory volume in 1 second (FEV1, %) and the calculated ratio of these parameters (FEV1/FVC, %), i.e., modified Tiffno index. The serum concentration of hyaluronic acid (ng/ml) was determined in all individuals using solid-phase enzyme-linked immunosorbent assay (ELISA). The absolute blood level of eosinophils (cell/ΞΌl) was determined by a unified method of morphological study of hemocytes with white blood cell differential count. Results. Serum HA concentration in patients with occupational COPD with frequent exacerbations was 25% higher than in the patients with rare exacerbations (the difference was statistically significant; Ρ = 0,004). The analysis of the obtained data showed that the most significant moderate correlation was found between the level of HA and the frequency of COPD exacerbations (direct relationship, r = 0.32; p < 0.05), and FEV1 and the frequency of COPD exacerbations (feedback, r = -0.32;p < 0.05). A weak relationship was found between the relative number of eosinophils and the frequency of COPD exacerbations (direct relationship, r = 0.2; p < 0.05). Weak correlations were also found between the level of HA and FEV1 (feedback, r = -0.23; p < 0.05), between the level of HA and the relative number of eosinophils (direct relationship, r = 0.18; p < 0.05). Conclusion. Quantitative analysis of serum HA in patients with occupational COPD can be used in clinical practice as a biochemical marker for assessing the risk of exacerbations and progression of bronchopulmonary pathology.Π§Π°ΡΡΠΎΡΠ° ΠΎΠ±ΠΎΡΡΡΠ΅Π½ΠΈΠΉ Ρ
ΡΠΎΠ½ΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΎΠ±ΡΡΡΡΠΊΡΠΈΠ²Π½ΠΎΠΉ Π±ΠΎΠ»Π΅Π·Π½ΠΈ Π»Π΅Π³ΠΊΠΈΡ
(Π₯ΠΠΠ) ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΎΠ΄Π½ΠΈΠΌ ΠΈΠ· Π³Π»Π°Π²Π½ΡΡ
ΡΠ°ΠΊΡΠΎΡΠΎΠ², ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΡΡΠΈΡ
ΠΈΡΡ
ΠΎΠ΄ Π΄Π°Π½Π½ΠΎΠ³ΠΎ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΡ. Π ΡΠ²ΡΠ·ΠΈ Ρ ΡΡΠΈΠΌ ΠΎΡΠΎΠ±ΡΡ Π°ΠΊΡΡΠ°Π»ΡΠ½ΠΎΡΡΡ ΠΏΡΠΈΠΎΠ±ΡΠ΅ΡΠ°Π΅Ρ ΡΠ°ΠΊΠ°Ρ Π½Π°ΡΡΠ½ΠΎ-ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠ°Ρ Π·Π°Π΄Π°ΡΠ°, ΠΊΠ°ΠΊ ΠΏΠΎΠΈΡΠΊ Π±ΠΈΠΎΠΌΠ°ΡΠΊΠ΅ΡΠΎΠ², ΠΎΡΡΠ°ΠΆΠ°ΡΡΠΈΡ
ΡΠΈΡΠΊ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΠΎΠ±ΠΎΡΡΡΠ΅Π½ΠΈΠΉ. Π¦Π΅Π»ΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΡΠ²ΠΈΠ»ΠΎΡΡ ΠΈΠ·ΡΡΠ΅Π½ΠΈΠ΅ Π·Π°Π²ΠΈΡΠΈΠΌΠΎΡΡΠΈ ΠΌΠ΅ΠΆΠ΄Ρ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΠ΅ΠΉ Π³ΠΈΠ°Π»ΡΡΠΎΠ½ΠΎΠ²ΠΎΠΉ ΠΊΠΈΡΠ»ΠΎΡΡ (ΠΠ) Π² ΡΡΠ²ΠΎΡΠΎΡΠΊΠ΅ ΠΊΡΠΎΠ²ΠΈ ΠΈ ΡΠ°ΡΡΠΎΡΠΎΠΉ ΠΎΠ±ΠΎΡΡΡΠ΅Π½ΠΈΠΉ Π₯ΠΠΠ ΠΏΡΠΎΡΠ΅ΡΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠΉ ΡΡΠΈΠΎΠ»ΠΎΠ³ΠΈΠΈ (ΠΠ), ΠΎΠ±ΡΡΠ»ΠΎΠ²Π»Π΅Π½Π½ΠΎΠΉ Π²ΠΎΠ·Π΄Π΅ΠΉΡΡΠ²ΠΈΠ΅ΠΌ ΠΊΡΠ΅ΠΌΠ½Π΅Π·Π΅ΠΌΡΠΎΠ΄Π΅ΡΠΆΠ°ΡΠ΅ΠΉ ΠΏΡΠ»ΠΈ, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΎΠ±ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΠΠ ΠΊΠ°ΠΊ ΠΏΡΠ΅Π΄ΠΈΠΊΡΠΎΡΠ° ΠΎΠ±ΠΎΡΡΡΠ΅Π½ΠΈΠΉ Π₯ΠΠΠ. ΠΠ°ΡΠ΅ΡΠΈΠ°Π»Ρ ΠΈ ΠΌΠ΅ΡΠΎΠ΄Ρ. ΠΠ±ΡΠ»Π΅Π΄ΠΎΠ²Π°Π½Ρ ΠΏΠ°ΡΠΈΠ΅Π½ΡΡ (n = 78) Ρ Π₯ΠΠΠ ΠΠ. Π€ΡΠ½ΠΊΡΠΈΡ Π²Π½Π΅ΡΠ½Π΅Π³ΠΎ Π΄ΡΡ
Π°Π½ΠΈΡ ΠΎΡΠ΅Π½ΠΈΠ²Π°Π»Π°ΡΡ ΠΏΠΎ ΡΠ»Π΅Π΄ΡΡΡΠΈΠΌ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠ°ΠΌ: ΡΠΎΡΡΠΈΡΠΎΠ²Π°Π½Π½Π°Ρ ΠΆΠΈΠ·Π½Π΅Π½Π½Π°Ρ Π΅ΠΌΠΊΠΎΡΡΡ Π»Π΅Π³ΠΊΠΈΡ
(Π€ΠΠΠ, %Π΄ΠΎΠ»ΠΆ.), ΠΎΠ±ΡΠ΅ΠΌ ΡΠΎΡΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ Π²ΡΠ΄ΠΎΡ
Π° Π·Π° 1-Ρ ΡΠ΅ΠΊΡΠ½Π΄Ρ (ΠΠ€Π1, %Π΄ΠΎΠ»ΠΆ.) ΠΈ ΡΠ°ΡΡΠ΅ΡΠ½ΠΎΠ΅ ΡΠΎΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΠ΅ ΡΡΠΈΡ
ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ² (ΠΠ€Π1 / Π€ΠΠΠ, %) β ΠΌΠΎΠ΄ΠΈΡΠΈΡΠΈΡΠΎΠ²Π°Π½Π½ΡΠΉ ΠΈΠ½Π΄Π΅ΠΊΡ Π’ΠΈΡΡΠ½ΠΎ. Π£ Π²ΡΠ΅Ρ
ΠΎΠ±ΡΠ»Π΅Π΄ΡΠ΅ΠΌΡΡ
ΠΏΡΠΈ ΠΏΠΎΠΌΠΎΡΠΈ ΠΌΠ΅ΡΠΎΠ΄Π° ΡΠ²Π΅ΡΠ΄ΠΎΡΠ°Π·Π½ΠΎΠ³ΠΎ ΠΈΠΌΠΌΡΠ½ΠΎΡΠ΅ΡΠΌΠ΅Π½ΡΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° Π² ΡΡΠ²ΠΎΡΠΎΡΠΊΠ΅ ΠΊΡΠΎΠ²ΠΈ ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΠ»Π°ΡΡ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΡ ΠΠ (Π½Π³ / ΠΌΠ»). ΠΠ±ΡΠΎΠ»ΡΡΠ½ΠΎΠ΅ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎ ΡΠΎΠ·ΠΈΠ½ΠΎΡΠΈΠ»ΠΎΠ² Π² ΠΊΡΠΎΠ²ΠΈ (ΠΊΠ»Π΅ΡΠΎΠΊ / ΠΌΠΊΠ») ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΠ»ΠΎΡΡ ΠΏΠΎ ΡΠ½ΠΈΡΠΈΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΌΡ ΠΌΠ΅ΡΠΎΠ΄Ρ ΠΌΠΎΡΡΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΡΠΎΡΠΌΠ΅Π½Π½ΡΡ
ΡΠ»Π΅ΠΌΠ΅Π½ΡΠΎΠ² ΠΊΡΠΎΠ²ΠΈ Ρ Π΄ΠΈΡΡΠ΅ΡΠ΅Π½ΡΠΈΠ°Π»ΡΠ½ΡΠΌ ΠΏΠΎΠ΄ΡΡΠ΅ΡΠΎΠΌ Π»Π΅ΠΉΠΊΠΎΡΠΈΡΠ°ΡΠ½ΠΎΠΉ ΡΠΎΡΠΌΡΠ»Ρ. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ. ΠΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΡ ΠΠ Π² ΡΡΠ²ΠΎΡΠΎΡΠΊΠ΅ ΠΊΡΠΎΠ²ΠΈ Ρ Π±ΠΎΠ»ΡΠ½ΡΡ
Π₯ΠΠΠ ΠΠ Ρ ΡΠ°ΡΡΡΠΌΠΈ ΠΎΠ±ΠΎΡΡΡΠ΅Π½ΠΈΡΠΌΠΈ Π±ΡΠ»Π° ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈ Π·Π½Π°ΡΠΈΠΌΠΎ Π²ΡΡΠ΅ (Π½Π° 25 %) ΡΠ°ΠΊΠΎΠ²ΠΎΠ³ΠΎ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Ρ Ρ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Ρ ΡΠ΅Π΄ΠΊΠΈΠΌΠΈ ΠΎΠ±ΠΎΡΡΡΠ΅Π½ΠΈΡΠΌΠΈ (Ρ = 0,004). ΠΠ°ΠΈΠ±ΠΎΠ»Π΅Π΅ Π·Π½Π°ΡΠΈΠΌΠ°Ρ ΠΊΠΎΡΡΠ΅Π»ΡΡΠΈΠΎΠ½Π½Π°Ρ ΡΠ²ΡΠ·Ρ ΡΡΠ΅Π΄Π½Π΅ΠΉ ΡΠΈΠ»Ρ Π²ΡΡΠ²Π»Π΅Π½Π° ΠΌΠ΅ΠΆΠ΄Ρ ΡΡΠΎΠ²Π½Π΅ΠΌ ΠΠ ΠΈ ΡΠ°ΡΡΠΎΡΠΎΠΉ ΠΎΠ±ΠΎΡΡΡΠ΅Π½ΠΈΠΉ Π₯ΠΠΠ (ΠΏΡΡΠΌΠ°Ρ ΡΠ²ΡΠ·Ρ β ΠΏΡΠΈ r = 0,32; Ρ = < 0,05), Π° ΡΠ°ΠΊΠΆΠ΅ ΠΌΠ΅ΠΆΠ΄Ρ ΠΠ€Π1 ΠΈ ΡΠ°ΡΡΠΎΡΠΎΠΉ ΠΎΠ±ΠΎΡΡΡΠ΅Π½ΠΈΠΉ Π₯ΠΠΠ (ΠΎΠ±ΡΠ°ΡΠ½Π°Ρ ΡΠ²ΡΠ·Ρ β ΠΏΡΠΈ r = β0,32; Ρ < 0,05). ΠΠ±Π½Π°ΡΡΠΆΠ΅Π½Π° ΡΠ»Π°Π±Π°Ρ ΠΊΠΎΡΡΠ΅Π»ΡΡΠΈΡ ΠΌΠ΅ΠΆΠ΄Ρ ΠΎΡΠ½ΠΎΡΠΈΡΠ΅Π»ΡΠ½ΡΠΌ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎΠΌ ΡΠΎΠ·ΠΈΠ½ΠΎΡΠΈΠ»ΠΎΠ² Π² ΠΊΡΠΎΠ²ΠΈ ΠΈ ΡΠ°ΡΡΠΎΡΠΎΠΉ ΠΎΠ±ΠΎΡΡΡΠ΅Π½ΠΈΠΉ Π₯ΠΠΠ (ΠΏΡΡΠΌΠ°Ρ ΡΠ²ΡΠ·Ρ β ΠΏΡΠΈ r = 0,2; Ρ < 0,05). Π’Π°ΠΊΠΆΠ΅ ΡΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½Π° ΡΠ»Π°Π±Π°Ρ ΠΊΠΎΡΡΠ΅Π»ΡΡΠΈΠΎΠ½Π½Π°Ρ ΡΠ²ΡΠ·Ρ ΠΌΠ΅ΠΆΠ΄Ρ ΡΡΠΎΠ²Π½Π΅ΠΌ ΠΠ ΠΈ ΠΠ€Π1 (ΠΎΠ±ΡΠ°ΡΠ½Π°Ρ ΡΠ²ΡΠ·Ρ β ΠΏΡΠΈ r = β0,23; Ρ < 0,05), Π° ΡΠ°ΠΊΠΆΠ΅ ΠΌΠ΅ΠΆΠ΄Ρ ΡΡΠΎΠ²Π½Π΅ΠΌ ΠΠ ΠΈ ΠΎΡΠ½ΠΎΡΠΈΡΠ΅Π»ΡΠ½ΡΠΌ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎΠΌ ΡΠΎΠ·ΠΈΠ½ΠΎΡΠΈΠ»ΠΎΠ² (ΠΏΡΡΠΌΠ°Ρ ΡΠ²ΡΠ·Ρ β ΠΏΡΠΈ r = 0,18; Ρ < 0,05). ΠΠ°ΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅. ΠΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠ΅ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π° ΠΠ Π² ΡΡΠ²ΠΎΡΠΎΡΠΊΠ΅ ΠΊΡΠΎΠ²ΠΈ Ρ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Ρ Π₯ΠΠΠ ΠΠ ΠΌΠΎΠΆΠ΅Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΡΡΡ Π² ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΏΡΠ°ΠΊΡΠΈΠΊΠ΅ Π² ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ Π±ΠΈΠΎΡ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΌΠ°ΡΠΊΠ΅ΡΠ° ΠΎΡΠ΅Π½ΠΊΠΈ ΡΠΈΡΠΊΠ° ΠΎΠ±ΠΎΡΡΡΠ΅Π½ΠΈΠΉ ΠΈ ΠΏΡΠΎΠ³ΡΠ΅ΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π±ΡΠΎΠ½Ρ
ΠΎΠ»Π΅Π³ΠΎΡΠ½ΠΎΠΉ ΠΏΠ°ΡΠΎΠ»ΠΎΠ³ΠΈΠΈ
Saturation Vapor Pressures and Transition Enthalpies of Low-Volatility Organic Molecules of Atmospheric Relevance: From Dicarboxylic Acids to Complex Mixtures
An algorithm for combining electrical mobility and aerodynamic size distributions data when measuring ambient aerosol
Ambient aerosol particles vary in size from a few nanometers to several micrometers. No instrument is currently available to cover such a wide size range, and so a combination of several instruments is usually used. One such combination is that of electrical mobility classifiers and an aerodynamic sizer. Because of the differences in measurement principles between the instruments, difficulties arise in the combination of the measurements into a single size distribution. Here we report a simple algorithm that was developed to combine aerosol size distributions measured with commercially available scanning mobility particle sizers (SNIPS; TSI Inc.) and an aerodynamic particle sizer (APS; TSI Inc.). This algorithm was tested during July 2001 in the Pittsburgh Air Quality Study. The aerosol during the study had both urban and regional origin and is characteristic of urban atmosphere in the Northeastern U.S. The integrated volume concentrations from the SMPS-APS showed a good correlation with PM2.5 mass concentration measurements using a TEOM. The relation of the aerosol mass to its volume is an ``effective{''} density, a ratio of the bulk aerosol density to the shape factor. As a result of the comparison with the TEOM the ambient aerosol in the Pittsburgh area was found to have an effective density of 1.5 +/- 0.3 g cm(-3). Given that the aerosol during the study was found to always contain water, the particles are expected to be spherical and thus the shape factor may be assumed to be 1. This assumption has been supported by a comparison with the MOUDI, using the aerosol density of 1.5 g/cm(3). It should be noted that the estimated aerosol density and the shape factor are applicable to this study only and may be different in other locations
Organic nitrogen in PM<sub>2.5</sub> aerosol at a forest site in the Southeast US
There is growing evidence that organo-nitrogen compounds may constitute a significant fraction of the aerosol nitrogen (N) budget. However, very little is known about the abundance and origin of this aerosol fraction. In this study, the concentration of organic nitrogen (ON) and major inorganic ions in PM2.5 aerosol were measured at the Duke Forest Research Facility near Chapel Hill, NC, during January and June of 2007. A novel on-line instrument was used, which is based on the Steam Jet Aerosol Collector (SJAC) coupled to an on-line total carbon/total nitrogen analyzer and two on-line ion chromatographs. The concentration of ON was determined by tracking the difference in concentrations of total nitrogen and of inorganic nitrogen (determined as the sum of N-ammonium and N-nitrate). The time resolution of the instrument was 30 min with a detection limit for major aerosol components of ~0.1 μg m−3. Nitrogen in organic compounds contributed ~33% on average to the total nitrogen concentration in PM2.5, illustrating the importance of this aerosol component. Absolute concentrations of ON, however, were relatively low (<1.0 μg m−3) with an average of 0.16 μg m−3. The absolute and relative contribution of ON to the total aerosol nitrogen budget was practically the same in January and June. In January, the concentration of ON tended to be higher during the night and early morning, while in June it tended to be higher during the late afternoon and evening. Back-trajectories and correlation with wind direction indicate that higher concentrations of ON occur in air masses originating over the continental US, while marine air masses are characterized by lower ON concentrations. The data presented in this study suggests that ON has a variety of sources, which are very difficult to quantify without information on chemical composition of this important aerosol fraction
Activation properties of ambient aerosol in the Netherlands
A cloud chamber has been used to study the cloud activation of ambient aerosol in The Netherlands. The large dimensions and throughput of the chamber allowed unperturbed collection of aerosol and droplets with cascade impactors and on-line measurements with cloud monitors (FSSP) inside the facility. The study provided maxima for the number of man-made aerosol acting as cloud nuclei in marine clouds in The Netherlands. Emphasis was given to the investigation of cloud formation in marine air, since sensitivity studies had shown that such clouds are most effectively influenced by the (extra) anthropogenic aerosol particles. For this reason the supersaturations in the study were low (on average 0.12\%), similar to those in actual marine stratus. The effect of the anthropogenic aerosols on cloud formation was determined by comparing the number of droplets formed in `'clean'' arctic marine air to the number of droplets formed in `'polluted'' marine air (air which had travelled over the U.K.). Air masses with the total aerosol number concentration of the order of 100 cm(-3) were considered as `'clean'' marine air. Air masses with higher aerosol concentrations were divided into `'moderately'' and `'heavily'' polluted with total aerosol concentrations of the order of 1000 and 10,000 cm(-3), respectively. In the clean marine air all potential cloud nuclei (particles lar er than the threshold size of the smallest reference particles that were activated al given supersaturation) were activated and the number of cloud droplets formed was on average 45 cm(-3). In the moderately polluted air 72\% of potential cloud nuclei were activated and the average droplet number was 190 cm(-3). The difference in the actual cloud droplet number and the number of potential cloud nuclei could be explained by the presence of water-insoluble particles which do not activate. In the heavily polluted air the average droplet concentration was around 320 cm(-3) which is, on average, 24\% of the number of potential cloud nuclei. Copyright (C) 1996 Elsevier Science Lt
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