scholarly journals Seasonal Spatial and Temporal Distribution of Chlorophyll-a Concentration over Kuwait and the Arabian Gulf using Satellite and In-Situ Data

2021 ◽  
Author(s):  
Jasem A. Albanai
Keyword(s):  
Spatial Resolution ◽  
Arabian Gulf ◽  
Temporal Distribution ◽  
Spatial And Temporal Distribution ◽  
Important Indicator ◽  
Near Surface ◽  
In Situ Data ◽  
Level 3 ◽  
Level 2

The concentration of chlorophyll-a (chlor-a) is an important indicator of marine water quality, as it is considered an indicator of the phytoplankton density in a specific area. Remote sensing techniques have been developed to measure the near-surface concentration of chlor-a in water across the correlation between spectral bands and in situ data. This algorithm applies to sensors of varying spatial, temporal and spectral resolutions. However, in this study, chlor-a level 2 and 3 products of SNPP – VIIRS spectrometer (Equation OC3) of NASA OceanColor suite was relied upon to study the spatial and temporal distribution of chlor-a concentration in the Arabian Gulf (also known as the Persian Gulf) and the State of Kuwait’s water (located to the north-eastern part of the Arabian Gulf) from 2012 to 2019. Ground truthing points (n = 192) matched to the level 2 products have been used to build an empirical model and cross-validate it. The correlation was positive where was 0.79 and the validation RMSE was = ± 0.64 mg/m-3. The derived algorithm was then applied to chlor-a level 3 seasonal products. Additionally, the chlor-a concentration values of Kuwaiti waters have been enhanced using the IDW algorithm to increase the spatial resolution, as it is considered as a small area compared to the spatial resolution of level 3 chlor-a products. The model derived from IDW was tested using the Mann Whitney test (Sig = 0.948 p > 0.01). However, the result showed that the chlor-a concentration is higher in Kuwait Bay compared to Kuwaiti water, and it is higher in Kuwaiti water compared to the Arabian Gulf. The coasts have higher concentrations too, when compared to the open water. Generally, the chlor-a increases in winter and makes a semi-regular cycle during the years of study; this cycle is more regular in the Gulf’s waters than in Kuwait’s.

scholarly journals Trend and dynamic of chlorophyll-a concentration over the Arabian Gulf A long-term study using MODIS data (2004 – 2019)

2021 ◽  
Author(s):  
Jasem A Albanai ◽  
Keyword(s):  
Chlorophyll A ◽  
Arabian Gulf ◽  
Important Indicator ◽  
Near Surface ◽  
Annual Cycles ◽  
Chlorophyll A Concentration ◽  
Long Term Study ◽  
Level 3 ◽  
The State Of Kuwait

Chlorophyll-a concentration in water is an indicator of phytoplankton density, which in turn is crucial, as it represents the base of the ecological life in seas and oceans. Any increase or decrease in the number of phytoplankton may contribute to disruption to the ecological cycle in the seawater. The density of phytoplankton is also an important indicator of water quality. Traditionally, water samples are collected in the field and analysed in the laboratory to find the density of phytoplankton in a specified amount of water. Recently, remote sensing has led to the development of advanced and remote methods to detect phytoplankton density, chiefly by extracting near-surface chlorophyll-a concentrations. In this study, MODIS (Aqua) Level 3 data (64 images) were used to extract average chlorophyll-a concentration at time points from 2004 to 2019 (over 16 years) for the Arabian Gulf, where the Level 2 (11 images) data were used to determine the accuracy of the estimated values via MODIS (Aqua) using field data taken from the waters of the State of Kuwait (25 points). The results showed good accuracy for MODIS (RMSE = ± 1.066), and it also shows that the temporal seasonal averages change in a annual-cycles, and that the trend decreased from 2004 to 2019, by about 0.7 mg/m−3

scholarly journals Uncertainty Characterization of HOAPS-3.3 Latent Heat Flux Related Parameters

2017 ◽  
Author(s):  
Julian Kinzel ◽  
Marc Schröder ◽  
Karsten Fennig ◽  
Axel Andersson ◽  
Rainer Hollmann
Keyword(s):  
Latent Heat ◽  
Heat Fluxes ◽  
Specific Humidity ◽  
Boreal Winter ◽  
Western Boundary ◽  
Near Surface ◽  
In Situ Data ◽  
Uncertainty Measures ◽  
Global Mean

Abstract. Latent heat fluxes (LHF) are one of the main contributors to the global energy budget. As the density of LHF measurements over the global oceans is generally poor, the potential of remotely sensed LHF for meteorological applications is enormous. However, to date none of the available satellite products include estimates of systematic, random retrieval, and sampling uncertainties, all of which are essential for assessing their quality. Here, this challenge is taken on by applying regionally independent multi-dimensional bias analyses to LHF-related parameters (wind speed U, near-surface specific humidity qa, and sea surface saturation specific humidity qs) of the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite (HOAPS) climatology. In connection with multiple triple collocation analyses, it is demonstrated how both instantaneous (gridded) uncertainty measures may be assigned to each pixel (grid box). A high-quality in situ data archive including buoys and selected ships serves as the ground reference. Results show that systematic LHF uncertainties range between 15–50 W m-2 with a global mean of 25 W m-2. Local maxima are mainly found over the subtropical ocean basins as well as along the western boundary currents. Investigations indicate that contributions by qa (U) to the overall LHF uncertainty are in the order of 60 % (25 %). From an instantaneous point of view, random retrieval uncertainties are specifically large over the subtropics with a global average of 37 W m-2. In a climatological sense, their magnitudes become negligible, as do respective sampling uncertainties. Time series analyses show footprints of climate events, such as the strong El Niño during 1997/98. Regional and seasonal analyses suggest that largest total (i.e., systematic + instantaneous random) LHF uncertainties are seen over the Gulf Stream and the Indian monsoon region during boreal winter. In light of the uncertainty measures, the observed continuous global mean LHF increase up to 2009 needs to be treated with caution. First intercomparisons to other LHF climatologies (in situ, satellite) reveal overall resemblance with few, yet distinct exceptions.

The limitations of in situ data for validating satellite-derived spatio-temporal ocean products.

2021 ◽  
Author(s):  
Rory Scarrott ◽  
Fiona Cawkwell ◽  
Mark Jessopp ◽  
Caroline Cusack
Keyword(s):  
Surface Temperature ◽  
Surface Heterogeneity ◽  
Ocean Surface ◽  
Bias Reduction ◽  
Data Validation ◽  
Near Surface ◽  
In Situ Data ◽  
Spatio Temporal ◽  
Image Pixels

<p>The Ocean-surface Heterogeneity MApping (OHMA) algorithm is an objective, replicable approach to map the spatio-temporal heterogeneity of ocean surface waters. It is used to processes hypertemporal, satellite-derived data and produces a single-image surface heterogeneity (SH) dataset for the selected parameter of interest. The product separates regions of dissimilar temporal characteristics. Data validation is challenging because it requires In-situ observations at spatial and temporal resolutions comparable to the hyper-temporal inputs. Validating this spatio-temporal product highlighted the need to optimise existing vessel-based data collection efforts, to maximise exploitation of the rapidly-growing hyper-temporal data resource.</p><p>For this study, the SH was created using hyper-temporal 1km resolution satellite derived Sea Surface Temperature (SST) data acquired in 2011. Underway ship observations of near surface temperature collected on multiple scientific surveys off the Irish coast in 2011 were used to validate the results. The most suitable underway ship SST data were identified in ocean areas sampled multiple times and with representative measurements across all seasons.</p><p>A 3-stage bias reduction approach was applied to identify suitable ocean areas. The first bias reduction addressed temporal bias, i.e., the temporal spread of available In-situ ship transect data across each satellite image pixel. Only pixels for which In-situ data were obtained at least once in each season were selected; resulting in 14 SH image pixels deemed suitable out of a total of 3,677 SH image pixels with available In-situ data. The second bias reduction addressed spatial bias, to reduce the influence of over-sampled areas in an image pixel with a sub-pixel approach. Statistical dispersion measures and statistical shape measures were calculated for each of the sets of sub-pixel values. This gave heterogeneity estimates for each cruise transit of a pixel area. The third bias reduction addressed bias of temporally oversampled seasons. For each transit-derived heterogeneity measure, the values within each season were averaged, before the annual average value was derived across all four seasons in 2011.</p><p>Significant associations were identified between satellite SST-derived SH values, and In-situ heterogeneity measures related to shape; absolute skewness (Spearman’s Rank, n=14, ρ[12]= +0.5755, P<0.05), and kurtosis (Spearman’s Rank, n=14, ρ[12] = 0.5446, P < 0.05). These are a consequence of (i) locally-extreme measurements, and/or (ii) increased presence of sharp transitions detected spatially by In-situ data. However, the number and location of suitable In-situ validation sites precluded a robust validation of the SH dataset (14 pixels located in Irish waters, for a dataset spanning the North Atlantic). This requires more targeted data. The approach would have benefited from more samples over the winter season, which would have enabled more offshore validation sites to be incorporated into the analysis. This is a challenge that faces satellite product developers, who want to deliver spatio-temporal information to new markets. There is a significant opportunity for dedicated, transit-measured (e.g. Ferry box data), validation sites to be established. These could potentially synergise with key nodes in global shipping routes to maximise data collected by vessels of opportunity, and ensure consistent data are collected over the same area at regular intervals.</p>

scholarly journals Evaluation of Sentinel-3A OLCI Products Derived Using the Case-2 Regional CoastColour Processor over the Baltic Sea

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3609 ◽  
Author(s):  
Kyryliuk ◽  
Kratzer
Keyword(s):  
Remote Sensing ◽  
Baltic Sea ◽  
Attenuation Coefficient ◽  
Secchi Depth ◽  
Local Algorithm ◽  
The Baltic Sea ◽  
In Situ Data ◽  
The Baltic ◽  
Level 2

In this study, the Level-2 products of the Ocean and Land Colour Instrument (OLCI) data on Sentinel-3A are derived using the Case-2 Regional CoastColour (C2RCC) processor for the SentiNel Application Platform (SNAP) whilst adjusting the specific scatter of Total Suspended Matter (TSM) for the Baltic Sea in order to improve TSM retrieval. The remote sensing product “kd_z90max” (i.e., the depth of the water column from which 90% of the water-leaving irradiance are derived) from C2RCC-SNAP showed a good correlation with in situ Secchi depth (SD). Additionally, a regional in-water algorithm was applied to derive SD from the attenuation coefficient Kd(489) using a local algorithm. Furthermore, a regional in-water relationship between particle scatter and bench turbidity was applied to generate turbidity from the remote sensing product “iop_bpart” (i.e., the scattering coefficient of marine particles at 443 nm). The spectral shape of the remote sensing reflectance (Rrs) data extracted from match-up stations was evaluated against reflectance data measured in situ by a tethered Attenuation Coefficient Sensor (TACCS) radiometer. The L2 products were evaluated against in situ data from several dedicated validation campaigns (2016–2018) in the NW Baltic proper. All derived L2 in-water products were statistically compared to in situ data and the results were also compared to results for MERIS validation from the literature and the current S3 Level-2 Water (L2W) standard processor from EUMETSAT. The Chl-a product showed a substantial improvement (MNB 21%, RMSE 88%, APD 96%, n = 27) compared to concentrations derived from the Medium Resolution Imaging Spectrometer (MERIS), with a strong underestimation of higher values. TSM performed within an error comparable to MERIS data with a mean normalized bias (MNB) 25%, root-mean square error (RMSE) 73%, average absolute percentage difference (APD) 63% n = 23). Coloured Dissolved Organic Matter (CDOM) absorption retrieval has also improved substantially when using the product “iop_adg” (i.e., the sum of organic detritus and Gelbstoff absorption at 443 nm) as a proxy (MNB 8%, RMSE 56%, APD 54%, n = 18). The local SD (MNB 6%, RMSE 62%, APD 60%, n = 35) and turbidity (MNB 3%, RMSE 35%, APD 34%, n = 29) algorithms showed very good agreement with in situ data. We recommend the use of the SNAP C2RCC with regionally adjusted TSM-specific scatter for water product retrieval as well as the regional turbidity algorithm for Baltic Sea monitoring. Besides documenting the evaluation of the C2RCC processor, this paper may also act as a handbook on the validation of Ocean Colour data.

In Situ Isotopic Analysis of Uraninite Microstructures from the Oklo-Okélobondo Natural Fission Reactors, Gabon

2002 ◽  
Vol 713 ◽  
Author(s):  
Mostafa Fayek ◽  
Keld A. Jensen ◽  
Rodney C. Ewing ◽  
Lee R. Riciputi
Keyword(s):  
Spatial Resolution ◽  
Spent Nuclear Fuel ◽  
Spatial Scales ◽  
Ionization Mass ◽  
Near Surface ◽  
Chain Reactions ◽  
Isotopic Analyses ◽  
Fission Reactors

ABSTRACTUranium deposits can provide important information on the long-term performance of radioactive waste forms because uraninite (UO2+X) is similar to the UO2 in spent nuclear fuel. The Oklo-Okélobondo U-deposits, Gabon, serve as natural laboratory where the long-term (hundreds to billions of years) migration of uranium and other radionuclides can be studied over large spatial scales (nm to km). The natural fission reactors associated with the Oklo- Okélobondo U-deposits occur over a range of depths (100 to 400 m) and provide a unique opportunity to study the behavior of uraninite in near surface oxidizing environments versus more reducing conditions at depth. Previously, it has been difficult to constrain the timing of interaction between U-rich minerals and post-depositional fluids. These problems are magnified because uraninite is susceptible to alteration, it continuously self-anneals radiation damage, and because these processes are manifested at the nm to μm scale. Uranium, lead and oxygen isotopes can be used to study fluid-uraninite interaction, provided that the analyses are obtained on the micro-scale. Secondary ionization mass spectrometry (SIMS) permits in situ measurement of isotopic ratios with a spatial resolution on the scale of a few μm. Preliminary U-Pb results show that uraninite from all reactor zones are highly discordant with ages aaproaching the timing of fission chain reactions (1945±50 Ma) and resetting events at 1180±47 Ma and 898±46 Ma. Oxygen isotopic analyses show that uraninite from reactors that occur in near surface environments (δ18O= −14.4‰ to −8.5‰) have reacted more extensively with groundwater of meteoric origin relative to reactors located at greater depths (μ18O= −10.2‰ to −7.3‰). This study emphasizes the importance of using in situ high spatial resolution analysis techniques for natural analogue studies.

scholarly journals Estimate of bias in Aura TES HDO/H<sub>2</sub>O profiles from comparison of TES and in situ HDO/H<sub>2</sub>O measurements at the Mauna Loa observatory

2011 ◽  
Vol 11 (9) ◽  
pp. 4491-4503 ◽  
Author(s):  
J. Worden ◽  
D. Noone ◽  
J. Galewsky ◽  
A. Bailey ◽  
K. Bowman ◽  
...  
Keyword(s):  
Bias Correction ◽  
Trace Gases ◽  
Lower Troposphere ◽  
Vertical Profiles ◽  
Thermal Contrast ◽  
Near Surface ◽  
Mauna Loa ◽  
In Situ Data ◽  
Infrared Radiances

Abstract. The Aura satellite Tropospheric Emission Spectrometer (TES) instrument is capable of measuring the HDO/H2O ratio in the lower troposphere using thermal infrared radiances between 1200 and 1350 cm−1. However, direct validation of these measurements is challenging due to a lack of in situ measured vertical profiles of the HDO/H2O ratio that are spatially and temporally co-located with the TES observations. From 11 October through 5 November 2008, we undertook a campaign to measure HDO and H2O at the Mauna Loa observatory in Hawaii for comparison with TES observations. The Mauna Loa observatory is situated at 3.1 km above sea level or approximately 680 hPa, which is approximately the altitude where the TES HDO/H2O observations show the most sensitivity. Another advantage of comparing in situ data from this site to estimates derived from thermal IR radiances is that the volcanic rock is heated by sunlight during the day, thus providing significant thermal contrast between the surface and atmosphere; this thermal contrast increases the sensitivity to near surface estimates of tropospheric trace gases. The objective of this inter-comparison is to better characterize a bias in the TES HDO data, which had been previously estimated to be approximately 5 % too high for a column integrated value between 850 hPa and 500 hPa. We estimate that the TES HDO profiles should be corrected downwards by approximately 4.8 % and 6.3 % for Versions 3 and 4 of the data respectively. These corrections must account for the vertical sensitivity of the TES HDO estimates. We estimate that the precision of this bias correction is approximately 1.9 %. The accuracy is driven by the corrections applied to the in situ HDO and H2O measurements using flask data taken during the inter-comparison campaign and is estimated to be less than 1 %. Future comparisons of TES data to accurate vertical profiles of in situ measurements are needed to refine this bias estimate.

Exploring the use of machine-learning techniques to integrate ground- and remote sensing-based observations for efficient near-surface soil moisture mapping

2020 ◽  
Author(s):  
Sarah Schönbrodt-Stitt ◽  
Paolo Nasta ◽  
Nima Ahmadian ◽  
Markus Kurtenbach ◽  
Christopher Conrad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Spatial Resolution ◽  
Surface Soil ◽  
Bare Soil ◽  
Small Scale ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
Campania Region

&lt;p&gt;Mapping near-surface soil moisture (&lt;em&gt;&amp;#952;&lt;/em&gt;) is of tremendous relevance for a broad range of environment-related disciplines and meteorological, ecological, hydrological and agricultural applications. Globally available products offer the opportunity to address &lt;em&gt;&amp;#952;&lt;/em&gt; in large-scale modelling with coarse spatial resolution such as at the landscape level. However, &lt;em&gt;&amp;#952;&lt;/em&gt; estimation at higher spatial resolution is of vital importance for many small-scale applications. Therefore, we focus our study on a small-scale catchment (MFC2) belonging to the &amp;#8220;Alento&amp;#8221; hydrological observatory, located in southern Italy (Campania Region). The goal of this study is to develop new machine-learning approaches to estimate high grid-resolution (about 17 m cell size) &lt;em&gt;&amp;#952;&lt;/em&gt; maps from mainly backscatter measurements retrieved from C-band Synthetic Aperture Radar (SAR) based on Sentinel-1 (S1) images and from gridded terrain attributes. Thus, a workflow comprising a total of 48 SAR-based &lt;em&gt;&amp;#952;&lt;/em&gt; patterns estimated for 24 satellite overpass dates (revisit time of 6 days) each with ascendant and descendent orbits will be presented. To enable for the mapping, SAR-based &lt;em&gt;&amp;#952;&lt;/em&gt; data was calibrated with in-situ measurements carried out with a portable device during eight measurement campaigns at time of satellite overpasses (four overpass days in total with each ascendant and descendent satellite overpasses per day in November 2018). After the calibration procedure, data validation was executed from November 10, 2018 till March 28, 2019 by using two stationary sensors monitoring &lt;em&gt;&amp;#952;&lt;/em&gt; at high-temporal (1-min recording time). The specific sensor locations reflected two contrasting field conditions, one bare soil plot (frequently kept clear, without disturbance of vegetation cover) and one non-bare soil plot (real-world condition). Point-scale ground observations of &lt;em&gt;&amp;#952;&lt;/em&gt; were compared to pixel-scale (17 m &amp;#215; 17 m), SAR-based &lt;em&gt;&amp;#952;&lt;/em&gt; estimated for those pixels corresponding to the specific positions of the stationary sensors. Mapping performance was estimated through the root mean squared error (RMSE). For a short-term time series of &lt;em&gt;&amp;#952;&lt;/em&gt; (Nov 2018) integrating 136 in situ, sensor-based &lt;em&gt;&amp;#952;&lt;/em&gt; (&lt;em&gt;&amp;#952;&lt;/em&gt;&lt;sub&gt;insitu&lt;/sub&gt;) and 74 gravimetric-based &lt;em&gt;&amp;#952;&lt;/em&gt; (&lt;em&gt;&amp;#952;&lt;/em&gt;&lt;sub&gt;gravimetric&lt;/sub&gt;) measurements during a total of eight S1 overpasses, mapping performance already proved to be satisfactory with RMSE=0.039 m&amp;#179;m&lt;sup&gt;-&lt;/sup&gt;&amp;#179; and R&amp;#178;=0.92, respectively with RMSE=0.041 m&amp;#179;m&lt;sup&gt;-&lt;/sup&gt;&amp;#179; and R&amp;#178;=0.91. First results further reveal that estimated satellite-based &lt;em&gt;&amp;#952;&lt;/em&gt; patterns respond to the evolution of rainfall. With our workflow developed and results, we intend to contribute to improved environmental risk assessment by assimilating the results into hydrological models (e.g., HydroGeoSphere), and to support future studies on combined ground-based and SAR-based &lt;em&gt;&amp;#952;&lt;/em&gt; retrieval for forested land (future missions operating at larger wavelengths e.g. NISARL-band, Biomass P-band sensors).&lt;/p&gt;

scholarly journals MODIS STANDARD (OC3) CHLOROPHYLL-A ALGORITHM EVALUATION IN INDONESIAN SEAS

2017 ◽  
Vol 11 (1) ◽  
pp. 11
Author(s):  
Gathot Winarso ◽  
Yennie Marini
Keyword(s):  
Pacific Ocean ◽  
Research And Development ◽  
Chlorophyll A ◽  
A Algorithm ◽  
Concentration Data ◽  
Indonesian Seas ◽  
In Situ Data ◽  
The North ◽  
Level 2

The MODIS-estimated chlorophyll-a information was widely used in some operational application in Indonesia. However, there is no information about the performance of MODIS chlorophyll-a in Indonesian seas and there is no data used in development of algorithm was taken in Indonesian seas. Even the algorithm was validated in other area, it is important to know the performance of the algorithm work in Indonesian seas. Performance of MODIS Standard (OC3) algorithm at Indonesian seas was analyzed in this paper. The in-situ chlorophyll-a concentration data was collected during MOMSEI (Monsoon Offset Monitoring and Its Social and Ecosystem Impact) 2012 Cruise 25th April – 12th   May 2012 and also from archived data of the Research and Development Center for Marine Coastal Resources, Agency of Marine and Fisheries Research and Development, Indonesian Ministry of  Marine Affairs and Fisheries. The in-situ data used in this research is located in Indian Ocean the west of Sumatera part and Pacific Ocean the north of Papua Province part. Satellite data which is used is Ocean Color MODIS Level-2 Product that downloaded from NASA and MODIS L-0 from LAPAN Ground Station. MODIS Level 0 from LAPAN then processed to Level-2  using latest SeaDAS Software. The match-up resulted the MNB(%) is -4.8% that means satellite-estimated was underestimate in 4.8 % and RMSE is 0.058. When the data was separated following to the data source, the correlation and trend line equation became better. From MOMSEI Cruise data, the MNB(%) was -18.8% and RMSE 0.05. From Pacific Ocean Data, MNB (%) was -27 % and RMSE 0.049. From SONNE Cruise 2005, MNB (%) was -27 % and RMSE 0.049. MODIS standard algorithm is work well in Indonesia case-1 seawaters, which contain chlorophyll-a only, and derived that influence to the electromagnetic wave.

scholarly journals Localization of thrombospondin in clots formed in situ

Blood ◽  
1985 ◽  
Vol 66 (5) ◽  
pp. 1098-1104
Author(s):  
JE Murphy-Ullrich ◽  
DF Mosher
Keyword(s):  
Monoclonal Antibodies ◽  
Polymorphonuclear Leukocytes ◽  
Polyclonal Antibodies ◽  
Temporal Distribution ◽  
Spatial And Temporal Distribution ◽  
Time Points ◽  
Von Willebrand ◽  
Willebrand Factor ◽  
Time Point

Thrombospondin is a principal glycoprotein secreted by thrombin- stimulated platelets and has known affinities for fibrinogen and fibrin. We studied the distribution of thrombospondin in clots formed in situ on Formvar-coated coverslips at 37 degrees C for intervals up to 17 hours. The distributions of three other major platelet granular proteins--fibrinogen, fibronectin, and von Willebrand factor (vWF)-- were also determined. The portions of the clots adhering to the coverslips after stripping, washing, and fixation with formaldehyde were stained for the four proteins by the peroxidase-antiperoxidase technique. Monoclonal antibodies were used to localize thrombospondin, fibronectin, and vWF; affinity-purified polyclonal antibodies were used to localize fibrinogen. Platelets stained positively for all four proteins. Thrombospondin was maximally present in the fibrin meshwork from 1 1/2 to 2 hours, after which the intensity of staining decreased until only trace amounts of thrombospondin were detectable between four and 17 hours. Antifibrinogen and, to a lesser extent, antifibronectin stained the fibrin meshwork at all time points. The vWF was not detectable in the fibrin meshwork at any time point. Staining of polymorphonuclear leukocytes (PMNLs) in a fine granular pattern was found with antithrombospondin. The fraction of PMNLs staining positively was 6% to 14% at 1/2 to 4 hours and increased at eight hours to 27%. At 17 hours, 52% of the PMNLs stained for thrombospondin. More than 48% of the PMNLs stained with antifibrinogen at all time points. PMNLs did not stain for either fibronectin or vWF. These studies indicate that thrombospondin is a transient component of the temporary fibrin meshwork and has a unique spatial and temporal distribution in the hemostatic plug.

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