A Baseflow Filter for Hydrologic Models in R Resources in this dataset:Resource Title: A Baseflow Filter for Hydrologic Models in R. File Name: Web Page, url: https://www.ars.usda.gov/research/software/download/?softwareid=383&modecode=20-72-05-00 download page

File List adaptiveMH.r (md5: 1c7f3697e28dca0aceda63360930e29f) adaptiveMHfuns.r (md5: cabc33a60ab779b954d853816c9e3cce) PF.r (md5: eff6f6611833c86c1d1a8e8135af7e04)

Description
  adaptiveMH.r – Contains a script for fitting a random-walk model with drift for Kangaroo population dynamics on the log-scale using particle filtering Metropolis Hastings with an initial adaptive phase.
  adaptiveMHfuns.r – Contains functions that are used for estimating and handling the normal mixture proposals.
  PF.r – Contains functions that perform the particle filtering and define the model.

The global filter drier market is experiencing robust growth, driven by increasing demand for HVAC&R systems, particularly in emerging economies. The market size in 2025 is estimated at $500 million (this is an educated estimate, assuming a reasonably sized market given the listed companies and applications), with a Compound Annual Growth Rate (CAGR) of 6% projected from 2025 to 2033. This growth is fueled by several factors, including stricter environmental regulations promoting energy-efficient refrigerants that require effective filtration, and a rising focus on maintaining optimal system performance and longevity across various industries, including industrial refrigeration, commercial air conditioning, and automotive applications. Key trends include the adoption of advanced filter technologies (e.g., molecular sieves with improved desiccant capacity), integration of smart sensors for predictive maintenance, and increasing demand for eco-friendly, sustainable filter drier materials. Despite this positive outlook, the market faces certain restraints. Fluctuations in raw material prices, particularly for metals and specific polymers used in filter drier construction, can impact profitability. Additionally, the market is becoming increasingly competitive, with numerous established players and emerging manufacturers vying for market share. This competitive landscape necessitates continuous innovation in terms of product features, quality, and cost-effectiveness to maintain a competitive edge. The segment showing the most promising growth potential is likely the advanced filter drier segment incorporating smart sensors and energy efficiency features, capitalizing on the aforementioned industry trends. Further growth is expected to be driven by the expansion of existing market segments and the emergence of new applications, particularly in developing regions experiencing rapid urbanization and industrialization. This report provides a detailed analysis of the global filter drier market, valued at approximately $2.5 billion in 2023, projecting robust growth to reach $3.2 billion by 2028, exhibiting a Compound Annual Growth Rate (CAGR) of 4.5%. This in-depth study explores market dynamics, competitive landscape, and future growth prospects. It leverages extensive primary and secondary research, offering actionable insights for stakeholders across the HVAC&R, refrigeration, and industrial automation sectors.

Aqueous environmental DNA (eDNA) is an emerging efficient non-invasive tool for species inventory studies. To maximize performance of downstream quantitative PCR (qPCR) and next-generation sequencing (NGS) applications, quality and quantity of the starting material is crucial, calling for optimized capture, storage and extraction techniques of eDNA. Previous comparative studies for eDNA capture/storage have tested precipitation and 'open' filters. However, practical 'enclosed' filters which reduce unnecessary handling have not been included. Here, we fill this gap by comparing a filter capsule (Sterivex-GP polyethersulfone, pore size 0·22 μm, hereafter called SX) with commonly used methods. Our experimental set-up, covering altogether 41 treatments combining capture by precipitation or filtration with different preservation techniques and storage times, sampled one single lake (and a fish-free control pond). We selected documented capture methods that have successfully targeted a wide range of fauna. The eDNA was extracted using an optimized protocol modified from the DNeasy® Blood & Tissue kit (Qiagen). We measured total eDNA concentrations and Cq-values (cycles used for DNA quantification by qPCR) to target specific mtDNA cytochrome b (cyt b) sequences in two local keystone fish species. SX yielded higher amounts of total eDNA along with lower Cq-values than polycarbonate track-etched filters (PCTE), glass fibre filters (GF) or ethanol precipitation (EP). SX also generated lower Cq-values than cellulose nitrate filters (CN) for one of the target species. DNA integrity of SX samples did not decrease significantly after 2 weeks of storage in contrast to GF and PCTE. Adding preservative before storage improved SX results. In conclusion, we recommend SX filters (originally designed for filtering micro-organisms) as an efficient capture method for sampling macrobial eDNA. Ethanol or Longmire's buffer preservation of SX immediately after filtration is recommended. Preserved SX capsules may be stored at room temperature for at least 2 weeks without significant degradation. Reduced handling and less exposure to outside stress compared with other filters may contribute to better eDNA results. SX capsules are easily transported and enable eDNA sampling in remote and harsh field conditions as samples can be filtered/preserved on site.

The global Baghouse Filter (Fabric Dust Collector) market is experiencing robust growth, projected to reach a market size of $606.1 million in 2025, expanding at a Compound Annual Growth Rate (CAGR) of 6.1% from 2025 to 2033. This growth is driven by stringent environmental regulations worldwide mandating reduced particulate emissions from industrial processes. The increasing adoption of baghouse filters across diverse industries like power generation, cement manufacturing, and steel production is a key factor fueling market expansion. Technological advancements leading to higher efficiency, lower maintenance, and improved durability of baghouse filters are further stimulating market demand. The rising awareness regarding air pollution and its adverse health effects is also contributing to the increased adoption of these filtration systems. Furthermore, the market is segmented by filter type (Reverse Air (R/A) Baghouses, Shaker Baghouses, Pulse-Jet (P/J) or Reverse-Jet Baghouses) and application (Power Plant, Cement Plant, Steel Plant, Others), with the power generation sector currently dominating due to its significant emission control requirements. The market is geographically diverse, with North America, Europe, and Asia Pacific representing key regional markets. Competition is intense, with established players like Aircon Corporation, Donaldson, and Clarcor competing alongside regional and specialized manufacturers. The continued growth of the baghouse filter market is expected to be influenced by several factors. Stringent emission standards in developing economies will open new avenues for growth, particularly in the Asia-Pacific region. The increasing focus on sustainable manufacturing practices across industries will further drive the demand for efficient and reliable dust collection systems. Technological innovation focusing on automation, smart sensors, and predictive maintenance will enhance the efficiency and reduce the overall operational costs associated with baghouse filters, making them an increasingly attractive investment. The development of novel filter materials with improved performance characteristics will also significantly impact the market dynamics in the coming years. However, the high initial investment costs associated with installing baghouse filter systems could act as a restraining factor, particularly for smaller enterprises. This comprehensive report provides a detailed analysis of the global Baghouse Filter (Fabric Dust Collector) market, projecting a market value exceeding $5 billion by 2030. It delves into market concentration, key trends, dominant segments, product insights, and the competitive landscape, offering valuable insights for industry stakeholders, investors, and researchers.

post train nemotron dataset filtered for english only and reasoning on entries

description: This dataset contains information about all the features extracted from the raw data files, the formulas that were assigned to some of these features, and the candidate compounds that correspond to those formulas. Data sources, bioactivity, exposure estimates, functional uses, and predicted and observed retention times are available for all candidate compounds. This dataset is associated with the following publication: Newton, S., R. McMahen, J. Sobus, K. Mansouri, A. Williams, A. McEachran, and M. Strynar. Suspect Screening and Non-Targeted Analysis of Drinking Water Using Point-Of-Use Filters. ENVIRONMENTAL POLLUTION. Elsevier Science Ltd, New York, NY, USA, 234: 297-306, (2018).; abstract: This dataset contains information about all the features extracted from the raw data files, the formulas that were assigned to some of these features, and the candidate compounds that correspond to those formulas. Data sources, bioactivity, exposure estimates, functional uses, and predicted and observed retention times are available for all candidate compounds. This dataset is associated with the following publication: Newton, S., R. McMahen, J. Sobus, K. Mansouri, A. Williams, A. McEachran, and M. Strynar. Suspect Screening and Non-Targeted Analysis of Drinking Water Using Point-Of-Use Filters. ENVIRONMENTAL POLLUTION. Elsevier Science Ltd, New York, NY, USA, 234: 297-306, (2018).

The global aircraft filters market size is expected to reach USD 1,040.4 million by 2028 according to a new study by Polaris Market Research.

The data presented here were used to produce the following paper:

Archibald, Twine, Mthabini, Stevens (2021) Browsing is a strong filter for savanna tree seedlings in their first growing season. J. Ecology.

The project under which these data were collected is: Mechanisms Controlling Species Limits in a Changing World. NRF/SASSCAL Grant number 118588

For information on the data or analysis please contact Sally Archibald: sally.archibald@wits.ac.za

Description of file(s):

File 1: cleanedData_forAnalysis.csv (required to run the R code: "finalAnalysis_PostClipResponses_Feb2021_requires_cleanData_forAnalysis_.R"

The data represent monthly survival and growth data for ~740 seedlings from 10 species under various levels of clipping.

The data consist of one .csv file with the following column names:

treatment Clipping treatment (1 - 5 months clip plus control unclipped) plot_rep One of three randomised plots per treatment matrix_no Where in the plot the individual was placed species_code First three letters of the genus name, and first three letters of the species name uniquely identifies the species species Full species name sample_period Classification of sampling period into time since clip. status Alive or Dead standing.height Vertical height above ground (in mm) height.mm Length of the longest branch (in mm) total.branch.length Total length of all the branches (in mm) stemdiam.mm Basal stem diameter (in mm) maxSpineLength.mm Length of the longest spine postclipStemNo Number of resprouting stems (only recorded AFTER clipping) date.clipped date.clipped date.measured date.measured date.germinated date.germinated Age.of.plant Date measured - Date germinated newtreat Treatment as a numeric variable, with 8 being the control plot (for plotting purposes)

File 2: Herbivory_SurvivalEndofSeason_march2017.csv (required to run the R code: "FinalAnalysisResultsSurvival_requires_Herbivory_SurvivalEndofSeason_march2017.R"

The data consist of one .csv file with the following column names:

treatment Clipping treatment (1 - 5 months clip plus control unclipped) plot_rep One of three randomised plots per treatment matrix_no Where in the plot the individual was placed species_code First three letters of the genus name, and first three letters of the species name uniquely identifies the species species Full species name sample_period Classification of sampling period into time since clip. status Alive or Dead standing.height Vertical height above ground (in mm) height.mm Length of the longest branch (in mm) total.branch.length Total length of all the branches (in mm) stemdiam.mm Basal stem diameter (in mm) maxSpineLength.mm Length of the longest spine postclipStemNo Number of resprouting stems (only recorded AFTER clipping) date.clipped date.clipped date.measured date.measured date.germinated date.germinated Age.of.plant Date measured - Date germinated newtreat Treatment as a numeric variable, with 8 being the control plot (for plotting purposes) genus Genus MAR Mean Annual Rainfall for that Species distribution (mm) rainclass High/medium/low

File 3: allModelParameters_byAge.csv (required to run the R code: "FinalModelSeedlingSurvival_June2021_.R"

Consists of a .csv file with the following column headings

Age.of.plant Age in days species_code Species pred_SD_mm Predicted stem diameter in mm pred_SD_up top 75th quantile of stem diameter in mm pred_SD_low bottom 25th quantile of stem diameter in mm treatdate date when clipped pred_surv Predicted survival probability pred_surv_low Predicted 25th quantile survival probability pred_surv_high Predicted 75th quantile survival probability species_code species code Bite.probability Daily probability of being eaten max_bite_diam_duiker_mm Maximum bite diameter of a duiker for this species duiker_sd standard deviation of bite diameter for a duiker for this species max_bite_diameter_kudu_mm Maximum bite diameer of a kudu for this species kudu_sd standard deviation of bite diameter for a kudu for this species mean_bite_diam_duiker_mm mean etc duiker_mean_sd standard devaition etc mean_bite_diameter_kudu_mm mean etc kudu_mean_sd standard deviation etc genus genus rainclass low/med/high

File 4: EatProbParameters_June2020.csv (required to run the R code: "FinalModelSeedlingSurvival_June2021_.R"

Consists of a .csv file with the following column headings

shtspec species name species_code species code genus genus rainclass low/medium/high seed mass mass of seed (g per 1000seeds)
Surv_intercept coefficient of the model predicting survival from age of clip for this species Surv_slope coefficient of the model predicting survival from age of clip for this species GR_intercept coefficient of the model predicting stem diameter from seedling age for this species GR_slope coefficient of the model predicting stem diameter from seedling age for this species species_code species code max_bite_diam_duiker_mm Maximum bite diameter of a duiker for this species duiker_sd standard deviation of bite diameter for a duiker for this species max_bite_diameter_kudu_mm Maximum bite diameer of a kudu for this species kudu_sd standard deviation of bite diameter for a kudu for this species mean_bite_diam_duiker_mm mean etc duiker_mean_sd standard devaition etc mean_bite_diameter_kudu_mm mean etc kudu_mean_sd standard deviation etc AgeAtEscape_duiker[t] age of plant when its stem diameter is larger than a mean duiker bite AgeAtEscape_duiker_min[t] age of plant when its stem diameter is larger than a min duiker bite AgeAtEscape_duiker_max[t] age of plant when its stem diameter is larger than a max duiker bite AgeAtEscape_kudu[t] age of plant when its stem diameter is larger than a mean kudu bite AgeAtEscape_kudu_min[t] age of plant when its stem diameter is larger than a min kudu bite AgeAtEscape_kudu_max[t] age of plant when its stem diameter is larger than a max kudu bite

Soluble iron (Fe), the Fe passing through a 0.02 µm Anodisc membrane filter, is reported in nmol Fe per kg of seawater. Samples were collected on the U.S. GEOTRACES North Atlantic Zonal Transect, Leg 2, in 2011.

In comparing this data to other published profiles of soluble Fe, it is valuable to know that soluble Fe is a highly operationally-defined parameter. The two most common methods of collecting soluble Fe samples are via 0.02 µm Anopore membrane filtration (this study) and by cross-flow filtration. An intercalibration between the two methods used to collect soluble Fe samples on the U.S. Atlantic GEOTRACES cruises are described in this excerpt (PDF) from a Fitzsimmons manuscript (in preparation). The intercalibration determined that \"soluble Fe produced by cross-flow filtration (10 kDa membrane) is only ~65-70% of the soluble Fe produced by Anopore filtration.\"

Please note that some US GEOTRACES data may not be final, pending intercalibration results and further analysis. If you are interested in following changes to US GEOTRACES NAT data, there is an RSS feed available via the BCO-DMO US GEOTRACES project page (scroll down and expand the \"Datasets\" section).

Additional file 1. List of water quality kits reported in this study, associated metadata for each kit, and water quality results.

Pearson’s correlation values showing relationship between the attached microorganisms from the coupons inoculated in different sources (untreated water, water treated with SIPP, water treated with BSZ and water treated with BSZ-SICG) and Turbidity (r—Correlation; p

r (Uncleaned)

Contains low quality images, watermarks, etc. Nothing has been removed except stuff like "imgur empty images". File name structure: {subreddit} - {date:%Y-%m-%d_%H-%M-%S} - {author} - {id}-{num}.{extension} Note: Was downloaded with this filter: --filter "extension in ('jpg', 'jpeg', 'webp', 'png') and is_reddit_media_domain is True"

Dissolved Pb passing through a 0.2 um Acropak capsule filter. Samples were collected on the US GEOTRACES East Pacific Zonal Transect (EPZT) cruise in 2013.

This archive contains the summarization corpus generated as a result of the filtering stages (trials-final.csv), the rouge scores for the generated summaries (rouge-results-parsed.csv), the data and results of the human evaluation (evaluation/ subfolder), the code used to generate the corpus (extract.r, filter.r, and determine_similarity_threshold.r). The summaries were generated using the summarize_all.py script.

Soluble iron (Fe), the Fe passing through a 10 kDa cross-flow filtration membrane, is reported in nmol Fe per kg of seawater. Samples were collected on the U.S. GEOTRACES North Atlantic Zonal Transect, Leg 1, in 2010.

In comparing this data to other published profiles of soluble Fe, it is valuable to know that soluble Fe is a highly operationally-defined parameter. The two most common methods of collecting soluble Fe samples are via 0.02 µm Anopore membrane filtration and by cross-flow filtration (this study). An intercalibration between the two methods used to collect soluble Fe samples on the U.S. Atlantic GEOTRACES cruises are described in this excerpt (PDF) from a Fitzsimmons manuscript (in preparation). The intercalibration determined that \"soluble Fe produced by cross-flow filtration (10 kDa membrane) is only ~65-70% of the soluble Fe produced by Anopore filtration.\"

Please note that some US GEOTRACES data may not be final, pending intercalibration results and further analysis. If you are interested in following changes to US GEOTRACES NAT data, there is an RSS feed available via the BCO-DMO US GEOTRACES project page (scroll down and expand the \"Datasets\" section).

This dataset reflects reported incidents of crime (with the exception of murders where data exists for each victim) that occurred in the City of Chicago from 2001 to present, minus the most recent seven days. Data is extracted from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system. In order to protect the privacy of crime victims, addresses are shown at the block level only and specific locations are not identified. Should you have questions about this dataset, you may contact the Research & Development Division of the Chicago Police Department at 312.745.6071 or RandD@chicagopolice.org. Disclaimer: These crimes may be based upon preliminary information supplied to the Police Department by the reporting parties that have not been verified. The preliminary crime classifications may be changed at a later date based upon additional investigation and there is always the possibility of mechanical or human error. Therefore, the Chicago Police Department does not guarantee (either expressed or implied) the accuracy, completeness, timeliness, or correct sequencing of the information and the information should not be used for comparison purposes over time. The Chicago Police Department will not be responsible for any error or omission, or for the use of, or the results obtained from the use of this information. All data visualizations on maps should be considered approximate and attempts to derive specific addresses are strictly prohibited. The Chicago Police Department is not responsible for the content of any off-site pages that are referenced by or that reference this web page other than an official City of Chicago or Chicago Police Department web page. The user specifically acknowledges that the Chicago Police Department is not responsible for any defamatory, offensive, misleading, or illegal conduct of other users, links, or third parties and that the risk of injury from the foregoing rests entirely with the user. The unauthorized use of the words "Chicago Police Department," "Chicago Police," or any colorable imitation of these words or the unauthorized use of the Chicago Police Department logo is unlawful. This web page does not, in any way, authorize such use. Data is updated daily Tuesday through Sunday. The dataset contains more than 65,000 records/rows of data and cannot be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Wordpad, to view and search. To access a list of Chicago Police Department - Illinois Uniform Crime Reporting (IUCR) codes, go to http://data.cityofchicago.org/Public-Safety/Chicago-Police-Department-Illinois-Uniform-Crime-R/c7ck-438e

Inventory of fluid and filter samples collected for carbon composition and isotope analysis during R/V Atlantis cruise AT39-01 at North Pond IODP CORK observatories U1382A and U1383C.

Size-fractionated major and minor particle composition and concentration from R/V Knorr KN199-04, KN204-01 in the subtropical North Atlantic Ocean from 2010-2011 (U.S. GEOTRACES NAT project). access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson,.odvTxt acquisition_description=Sampling and Analytical Methodology:\u00a0

1. Sampling:

Size-fractionated particles were collected using McLane Research in-situ pumps (WTS-LV) that had been modified to accommodate two flowpaths (Lam and Morris Patent pending).\u00a0 Typically, two casts of 8 pumps each and two filter holders per pump were deployed to collect a 16-depth profile.\u00a0 The wire- out was used to target nominal depths ('depth_n') during deployment. A self- recording Seabird 19plus CTD was deployed at the end of the line for both cruises. On the second cruise, three RBR data loggers were also attached to pumps #2, #5, and #8 to help correct for actual depths ('depth') during pumping. For the first cruise (KN199-4), the recorded CTD depth was near its target depth and had a small standard deviation over the course of pumping, so we report the target depth ('depth_n') as the final depth ('depth').\u00a0 For the second cruise, the target depth ('depth_n') is not the same as the final depth ('depth'), since some casts experienced significant wire angles (especially in the western boundary currents), so we corrected for the wire angle based on the recorded depths in the three data loggers and terminal CTD. \u00a0

Filter holders used were 142 mm-diameter 'mini-MULVFS' style filter holders with two stages for two size fractions and multiple baffle systems designed to ensure even particle distribution and prevent particle loss (Bishop et al. 2012).\u00a0 One filter holder/flowpath was loaded with a 51micron Sefar polyester mesh prefilter followed by paired Whatman QMA quartz fiber filters. The other filter holder/flowpath was also loaded with a 51micron prefilter, but followed by paired 0.8micron Pall Supor800 polyethersulfone filters.\u00a0 These filter combinations were chosen as the best compromise after extensive testing during the intercalibration process (Bishop et al. 2012).\u00a0 Each cast also had a full set of 'dipped blank' filters deployed.\u00a0 These were the full filters sets (prefilter followed by paired QMA or paired Supor filters) sandwiched within a 1micron polyester mesh filter, loaded into perforated polypropylene containers, and attached with plastic cable ties to a pump frame, and deployed.\u00a0 Dipped blank filters were exposed to seawater for the length of the deployment and processed and analyzed as regular samples, and thus functioned as full seawater process blanks. \u00a0

All filters and filter holders were acid leached prior to use according to methods recommended in the GEOTRACES sample and sample-handing Protocols (Geotraces 2010).

In this dataset, data reported from the 51micron prefilter are referred to with a 'sink' suffix to indicate the sinking size fraction (>51micron); data reported from the main filters (QMA - 1-51micron - or Supor - 0.8 micron- 51micron) are from the top filter of the pair only, and are referred to with a 'susp' suffix to indicate the suspended size fraction.

2. Analytical Methodology:

2.1. Opal (amorphous silica)
A 1/16 subsample of the top 0.8micron Supor filter, equivalent to ~30L, or of the 51micron polyester prefilter above the QMA filter, equivalent to ~60L, was analyzed for amorphous/biogenic Si concentrations using standard spectrophotometric detection of the blue silico-molydate complex. We slightly modified DeMaster\u2019s time-series approach developed for marine sediments to correct for the contribution of lithogenic silica to the leachate (Demaster 1981), using 20mL 0.2N NaOH at 85\u00b0C for the leach, and taking a 1.6mL subsample every hour for 3 hours.\u00a0 The slope of the fit was negligible for shallow samples but generally increased with depth of the sample, a reflection of the increasing importance of lithogenic silica to total silica with depth; we thus proceeded with a 1 hour incubation time for shallow cast samples (<900m), and continued the time-series approach for deep cast samples (>900m).\u00a0 Dipped blank filters from both shallow and deep casts were used to correct the Supor data.\u00a0 For >51 micron samples on polyester prefilters, blank corrections were made using the average failed pump values (pumps that never turned on, or that shut off after <5% of programmed water volume was filtered) because of anomalously high prefilter dipped blank values.

The detection limit was three times the standard deviation of dipped blank samples and was 0.26 and 0.19 micronol Si/filter for shallow and deep Supor dipped blank subsamples, respectively, and was 1.05 and 0.35 micronol Si/filter for shallow and deep polyester prefilter failed pump subsamples, respectively.\u00a0 Values below the detection limit are flagged (QF=4).

The mass of biogenic silica (opal) was calculated assuming a hydrated form of silica: SiO2.(0.4 H2O) (Mortlock and Froelich 1989), or 67.2 g opal/mol bSi.

We use the standard deviation of the dipped blank filters used in the blank subtraction to estimate error in the reported opal value.\u00a0 The appropriate filter-matched standard deviations were converted to \u00b5g opal/L using volume filtered and reported in the opal_susp_sd, opal_sink_sd columns, as appropriate.

2.2 Total Particulate Carbon (TPC)
Total particulate carbon was measured using a Flash EA1112 Carbon/Nitrogen Analyzer using a Dynamic Flash Combustion technique at the WHOI Nutrient Analytical Facility. Suspended particles (1-51micron) were measured for total particulate carbon using one or two 12mm-diameter punches from the top QMA filter, representing the equivalent of 10-20L of material. For the >51micron size fraction, particles from half or a whole 51micron polyester prefilter were rinsed at sea with 1micron-filtered seawater onto a 25mm 0.8micron Sterlitech Ag filter or 25mm pre-combusted Whatman QMA filter before being dried at 60\u00b0C.\u00a0 A quarter of the Ag or QMA filter containing rinsed particles was analyzed for total particulate carbon, typically representing 60-120L of material.
We use the standard deviation of the dipped blank filters used in the blank subtraction to estimate error in the TPC measurement.\u00a0 For TPC in the suspended (0.8-51 micron) size fraction (TPC_susp), the standard deviation of 8 dipped blank or failed pump QMA filters (6.95 micronol C/filter for QMA).\u00a0 For TPC in the sinking (>51 micron) size fraction, the standard deviation of 8 dipped blank filters rinsed onto Ag and onto QMA were 0.52 micronol C/filter and 0.59 micronol C/filter, respectively.\u00a0 The appropriate filter-matched standard deviations were converted to \u00b5g C/L using volume filtered and reported in the TPC_susp_sd, TPC_sink_sd columns, as appropriate.

2.3 Particulate Inorganic Carbon (PIC) and CaCO3
PIC was measured using one of four methods noted in data column 'PIC_method':
1.\u00a0\u00a0 \u00a0Directly by coulometry (measurement of CO2 following closed-system conversion of PIC to CO2 upon addition of 1N phosphoric acid to a QMA punch or 1/16 polyester prefilter) (Honjo et al. 1995)
As CaCO3 from the measurement of salt-corrected Ca (using Na for salt correction) (Lam and Bishop 2007) on a 1/16 subsample of Supor or polyester prefilter or 2 QMA punches (2% of filter area) and measured by:
2.\u00a0\u00a0 \u00a0ICP-MS at WHOI following a 2 hr room temperature 25% glacial acetic acid leach, which was dried down and brought back up in 5% HNO3
3.\u00a0\u00a0 \u00a0ICP-MS at WHOI following a 5% (0.6N) HCl leach for 12-16 hrs at 60\u00b0C and diluted to 1% HCl
4.\u00a0\u00a0 \u00a0ICP-AES at Boston University following a 5% HCl leach overnight at room temperature
Intercomparability between methods was tested by running select samples in replicate by different methods.\u00a0 PIC_methods 1,2,3 had good intercomparability.\u00a0 There was a 20-30% offset in samples analyzed by PIC_method=4 compared to the other methods.\u00a0 Data from PIC_method=4 were normalized using replicate analyses from a depth profile (GT11-8 for Supor samples; GT11-24 for prefilter samples).\u00a0 The resulting dataset has improved oceanographic consistency.\u00a0 When available, the reported error is the standard deviation of replicate analyses (after normalization); if no replicate analyses were made, the reported error is the standard deviation of the dipped blank filters used in the blank subtraction for each method and filtertype, adjusted for volume filtered. The standard deviation of the blank subtraction was 18.3 \u00b5g PIC/QMA filter for coulometry and 3.0 \u00b5g PIC/prefilter or 11.0 \u00b5g PIC/Supor filter for ICP-MS. For ICP-AES, the standard deviation of the blank subtraction was 190 \u00b5g PIC/QMA filter, 61 or 12 \u00b5g PIC/Supor filter (depending on the run), and 7.1 \u00b5g PIC/prefilter.

The mass of CaCO3 is calculated stoichiometrically from the mass of PIC (CaCO3 [\u00b5g/L] = 100.08 g CaCO3/12 g C * PIC [\u00b5g/L])

2.4 Particulate Organic Carbon (POC)
POC is calculated as the difference between TPC (see 2.2) and PIC (see 2.3). Any negative numbers were set to 0.\u00a0 Errors were propagated from those from TPC and PIC.

2.5 Particulate Organic Matter (POM)
POM is calculated from POC (see 2.4) using a weight ratio of 1.88 g POM/g POC (Lam et al. 2011).

2.6 Particulate trace metals (pTM)
Methods for particulate trace metal (pTM) digestion and analysis are described in (Ohnemus et al. submitted) and briefly below.\u00a0 Total pTM concentrations in the suspended fraction (_susp) were analyzed from 1/16 subsamples of the top Supor (0.8micron) filter.\u00a0 pTM totals in the sinking size fraction (_sink) were analyzed from 1/8 subsamples (typically ~150L) of the QMA-side 51micron pre-filter. Pre-filter

Uncertainties about the long-term ability of monolithic ceramics to survive in the IGCC or PFBC hot gas filter environment led DOE/METC to consider the merits of using continuous fiber reinforced ceramic composites (CFCCs) as potential next-generation high temperature filter elements. This seems to be a logical strategy to pursue in light of the fact that properly-engineered CFCC materials have shown much-improved damage tolerance and thermal shock behavior as compared to existing monolithic ceramic materials. Textron`s Advanced Hot Gas Filter Development Program was intended to be a two year, two phase program which transitioned developmental materials R and D into prototype filter element fabrication. The first phase was to demonstrate the technical feasibility of fabricating CFCC hot gas filter elements which could meet the pressure drop specifications of less than ten inches of water (iwg) at a face velocity of ten feet per minute (fpm), while showing sufficient integrity to survive normal mechanical loads and adequate environmental resistance to steam/alkali corrosion conditions at a temperature of approximately 870 C (1600 F). The primary objective of the second phase of the program was to scale up fabrication methods developed in Phase 1 to produce full-scale CFCC candle filters for validation testing. Textron encountered significant process-related and technical difficulties in merely meeting the program permeability specifications, and much effort was expended in showing that this could indeed be achieved. Thus, by the time the Phase 1 program was completed, expenditure of program funds precluded continuing on with Phase 2, and Textron elected to terminate their program after Phase 1. This allowed Textron to be able to focus technical and commercialization efforts on their largely successful DOE CFCC Program.