BCO-DMO ERDDAP
Accessing BCO-DMO data
log in    
Brought to you by BCO-DMO    

ERDDAP > tabledap > Make A Graph ?

Dataset Title:  Mercury stable isotope values for marine particles from R/V Kilo Moana cruises
KM1418, KM1407 and KM1506 around station ALOHA in 2014 and 2015
Subscribe RSS
Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_788753)
Range: depth = 25.0 to 690.0m
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | Data Access Form | Files
 
Graph Type:  ?
X Axis: 
Y Axis: 
Color: 
-1+1
 
Constraints ? Optional
Constraint #1 ?
Optional
Constraint #2 ?
       
       
       
       
       
 
Server-side Functions ?
 distinct() ?
? ("Hover here to see a list of options. Click on an option to select it.Hover here to see a list of options. Click on an option to select it.Hover here to see a list of options. Click on an option to select it.Hover here to see a list of options. Click on an option to select it.")
 
Graph Settings
Marker Type:   Size: 
Color: 
Color Bar:   Continuity:   Scale: 
   Minimum:   Maximum:   N Sections: 
Y Axis Minimum:   Maximum:   
 
(Please be patient. It may take a while to get the data.)
 
Optional:
Then set the File Type: (File Type information)
and
or view the URL:
(Documentation / Bypass this form ? )
    [The graph you specified. Please be patient.]

 

Things You Can Do With Your Graphs

Well, you can do anything you want with your graphs, of course. But some things you might not have considered are:

The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  Cruise_Number {
    String bcodmo_name "cruise_id";
    String description "Cruise ID number";
    String long_name "Cruise Number";
    String units "unitless";
  }
  Date {
    Int32 _FillValue 2147483647;
    Int32 actual_range 20140219, 20150502;
    String bcodmo_name "date";
    String description "Sampling date (UTC); format: yyyymmdd";
    String long_name "Date";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/ADATAA01/";
    String units "unitless";
  }
  depth {
    String _CoordinateAxisType "Height";
    String _CoordinateZisPositive "down";
    Float64 _FillValue NaN;
    Float64 actual_range 25.0, 690.0;
    String axis "Z";
    String bcodmo_name "depth";
    String description "Mean depth of sample";
    String ioos_category "Location";
    String long_name "Mean Depth";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/DEPH/";
    String positive "down";
    String standard_name "depth";
    String units "m";
  }
  Size_fraction {
    String bcodmo_name "samp_fraction";
    String description "Size fraction";
    String long_name "Size Fraction";
    String units "micrometers (um)";
  }
  d202Hg {
    Float32 _FillValue NaN;
    Float32 actual_range -0.27, 0.17;
    String bcodmo_name "unknown";
    String description "Stable isotope ratio; δ202Hg";
    String long_name "D202 HG";
    String units "per mil (‰)";
  }
  D199Hg {
    Float32 _FillValue NaN;
    Float32 actual_range 0.05, 0.37;
    String bcodmo_name "unknown";
    String description "Stable isotope ratio; Δ199Hg";
    String long_name "D199 HG";
    String units "per mil (‰)";
  }
  D201Hg {
    Float32 _FillValue NaN;
    Float32 actual_range 0.04, 0.32;
    String bcodmo_name "unknown";
    String description "Stable isotope ratio; Δ201Hg";
    String long_name "D201 HG";
    String units "per mil (‰)";
  }
  D200Hg {
    Float32 _FillValue NaN;
    Float32 actual_range -0.07, 0.11;
    String bcodmo_name "unknown";
    String description "Stable isotope ratio; Δ200Hg";
    String long_name "D200 HG";
    String units "per mil (‰)";
  }
  D204Hg {
    Float32 _FillValue NaN;
    Float32 actual_range -0.19, 0.08;
    String bcodmo_name "unknown";
    String description "Stable isotope ratio; D204Hg";
    String long_name "D204 HG";
    String units "per mil (‰)";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv";
    String acquisition_description 
"Small (1-53 \\u00b5m) and large (>53 \\u00b5m) marine particles were sampled on
all three cruises using in situ pumps (WTS-LV, standard, 8 L min\\u207b\\u00b9;
McLane Research Laboratories, East Falmouth); water was passed sequentially
through 53 \\u03bcm pore-size nylon mesh and 1 \\u03bcm pore-size quartz
microfiber (QMA) filters with 142 mm diameter using a mini-MULVFS filter
holder (Bishop et al., 2012). On the spring cruise, one pump was equipped with
a pump head and motor with a maximum flow rate of 30 L min\\u207b\\u00b9, and
high volume samples of particles >53 \\u00b5m were collected. The large
particles collected on the nylon mesh were sonicated and concentrated into a
pre-combusted 47 mm QMA filter.
 
Marine particles were collected in two size fractions, small particles in
combusted quartz filters (1-53 \\u03bcm) and large particles in a nylon mesh (>
53 \\u03bcm) using in situ McLane pumps during the three research cruises.
 
The marine particles were analyzed for total Hg concentrations by acid
assisted microwave digestions and aliquots were analyzed by cold vapor atomic
fluorescence spectrophotometry.
 
For THg isotope determination samples were combusted in a two-stage combustion
furnace and Hg(0) g was trapped in a 1% KMnO4 solution. The 1% KMnO4 solution
was analyzed for Hg stable isotope composition using a multiple collector
inductively coupled plasma mass spectrometer.
 
All the methods are detailed in Motta et al., (2019).";
    String awards_0_award_nid "560590";
    String awards_0_award_number "OCE-1433846";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1433846";
    String awards_0_funder_name "NSF Division of Ocean Sciences";
    String awards_0_funding_acronym "NSF OCE";
    String awards_0_funding_source_nid "355";
    String awards_0_program_manager "Donald L. Rice";
    String awards_0_program_manager_nid "51467";
    String cdm_data_type "Other";
    String comment 
"Mercury stable isotope values for marine particles 
  PI: Joel D. Blum (University of Michigan) 
  Co-PIs: Brian N. Popp, Kanesa Seraphin, Jeffrey C. Drazen (University of Hawaii), & Claudia Benitez-Nelson (University of South Carolina) 
  Version date: 21-Feb-2020";
    String Conventions "COARDS, CF-1.6, ACDD-1.3";
    String creator_email "info@bco-dmo.org";
    String creator_name "BCO-DMO";
    String creator_type "institution";
    String creator_url "https://www.bco-dmo.org/";
    String data_source "extract_data_as_tsv version 2.3  19 Dec 2019";
    String date_created "2020-01-28T19:05:57Z";
    String date_modified "2020-02-24T15:51:22Z";
    String defaultDataQuery "&time<now";
    String doi "10.1575/1912/bco-dmo.788753.1";
    Float64 geospatial_vertical_max 690.0;
    Float64 geospatial_vertical_min 25.0;
    String geospatial_vertical_positive "down";
    String geospatial_vertical_units "m";
    String history 
"2020-08-06T13:13:11Z (local files)
2020-08-06T13:13:11Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_788753.das";
    String infoUrl "https://www.bco-dmo.org/dataset/788753";
    String institution "BCO-DMO";
    String instruments_0_acronym "MLVPump";
    String instruments_0_dataset_instrument_description "in situ pumps (WTS-LV, standard, McLane Research Laboratories)";
    String instruments_0_dataset_instrument_nid "788766";
    String instruments_0_description "The Large Volume Pumping System-WTS-LV can be one of several different models of Water Transfer Systems (WTS) Large Volume (LV) pumping systems designed and manufactured by McLane Research Labs (Falmouth, MA, USA). The WTS-LV systems are large volume in-situ filtration systems designed to collect sinking particulates. WTS-LV systems are individual in situ, battery-powered, pumping/filtration units that can be deployed at multiple depths per cast to provide information on how particle flux changes with depth. The McLane WTS-LV series of oceanographic pumps draw ambient water through filters and can pump large volumes of seawater during a single cast. The WTS-LV pumps are designed for use from a hydro-wire and employ advanced control algorithms to dynamically optimize flow rates as material accumulates on a filter.";
    String instruments_0_instrument_name "Large Volume Pumping System-WTS-LV";
    String instruments_0_instrument_nid "512";
    String instruments_0_supplied_name "WTS-LV";
    String instruments_1_acronym "ICP Mass Spec";
    String instruments_1_dataset_instrument_description "multicollector inductively coupled plasma mass spectrometer (MC-ICP-MS; Nu instruments)";
    String instruments_1_dataset_instrument_nid "788763";
    String instruments_1_description "An ICP Mass Spec is an instrument that passes nebulized samples into an inductively-coupled gas plasma (8-10000 K) where they are atomized and ionized. Ions of specific mass-to-charge ratios are quantified in a quadrupole mass spectrometer.";
    String instruments_1_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB15/";
    String instruments_1_instrument_name "Inductively Coupled Plasma Mass Spectrometer";
    String instruments_1_instrument_nid "530";
    String instruments_1_supplied_name "MC-ICP-MS";
    String instruments_2_acronym "CVAFS";
    String instruments_2_dataset_instrument_nid "788764";
    String instruments_2_description "A Cold Vapor Atomic Fluorescent Spectrophotometer (CVAFS) is an instrument used for quantitative determination of volatile heavy metals, such as mercury. CVAFS make use of the characteristic of mercury that allows vapor measurement at room temperature. Mercury atoms in an inert carrier gas are excited by a collimated UV light source at a particular wavelength. As the atoms return to their non-excited state they re-radiate their absorbed energy at the same wavelength. The fluorescence may be detected using a photomultiplier tube or UV photodiode.";
    String instruments_2_instrument_name "Cold Vapor Atomic Fluorescence Spectrophotometer";
    String instruments_2_instrument_nid "692";
    String instruments_2_supplied_name "cold vapor atomic fluorescence spectrophotometry";
    String keywords "bco, bco-dmo, biological, chemical, cruise, Cruise_Number, d199, D199Hg, d200, D200Hg, d201, D201Hg, d202, d202Hg, d204, D204Hg, data, dataset, date, depth, dmo, erddap, fraction, management, mean, Mean_Depth, number, oceanography, office, preliminary, size, Size_fraction";
    String license "https://www.bco-dmo.org/dataset/788753/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/788753";
    String param_mapping "{'788753': {'Mean_Depth': 'master - depth'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/788753/parameters";
    String people_0_affiliation "University of Michigan";
    String people_0_person_name "Joel D. Blum";
    String people_0_person_nid "560587";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "University of South Carolina";
    String people_1_person_name "Claudia R. Benitez-Nelson";
    String people_1_person_nid "51092";
    String people_1_role "Co-Principal Investigator";
    String people_1_role_type "originator";
    String people_2_affiliation "University of Hawaii at Manoa";
    String people_2_affiliation_acronym "SOEST";
    String people_2_person_name "Jeffrey C. Drazen";
    String people_2_person_nid "491313";
    String people_2_role "Co-Principal Investigator";
    String people_2_role_type "originator";
    String people_3_affiliation "University of Hawaii at Manoa";
    String people_3_affiliation_acronym "SOEST";
    String people_3_person_name "Brian N. Popp";
    String people_3_person_nid "51093";
    String people_3_role "Co-Principal Investigator";
    String people_3_role_type "originator";
    String people_4_affiliation "University of Hawaii";
    String people_4_person_name "Kanesa Seraphin";
    String people_4_person_nid "537131";
    String people_4_role "Co-Principal Investigator";
    String people_4_role_type "originator";
    String people_5_affiliation "Woods Hole Oceanographic Institution";
    String people_5_affiliation_acronym "WHOI BCO-DMO";
    String people_5_person_name "Shannon Rauch";
    String people_5_person_nid "51498";
    String people_5_role "BCO-DMO Data Manager";
    String people_5_role_type "related";
    String project "Hg_Biogeochemistry";
    String projects_0_acronym "Hg_Biogeochemistry";
    String projects_0_description 
"NSF award abstract:
Mercury is a pervasive trace element that exists in several states in the marine environment, including monomethylmercury (MMHg), a neurotoxin that bioaccumulates in marine organisms and poses a human health threat. Understanding the fate of mercury in the ocean and resulting impacts on ocean food webs requires understanding the mechanisms controlling the depths at which mercury chemical transformations occur. Preliminary mercury analyses on nine species of marine fish from the North Pacific Ocean indicated that intermediate waters are an important entry point for MMHg into open ocean food webs. To elucidate the process controlling this, researchers will examine mercury dynamics in regions with differing vertical dissolved oxygen profiles, which should influence depths of mercury transformation. Results of the study will aid in a better understanding of the pathways by which mercury enters the marine food chain and can ultimately impact humans. This project will provide training for graduate and undergraduate students, and spread awareness on oceanic mercury through public outreach and informal science programs.
Mercury isotopic variations can provide insight into a wide variety of environmental processes. Isotopic compositions of mercury display mass-dependent fractionation (MDF) during most biotic and abiotic chemical reactions and mass-independent fractionation (MIF) during photochemical radical pair reactions. The unusual combination of MDF and MIF can provide information on reaction pathways and the biogeochemical history of mercury. Results from preliminary research provide strong evidence that net MMHg formation occurred below the surface mixed layer in the pycnocline and suggested that MMHg in low oxygen intermediate waters is an important entry point for mercury into open ocean food webs. These findings highlight the critical need to understand how MMHg levels in marine biota will respond to changes in atmospheric mercury emissions, deposition of inorganic mercury to the surface ocean, and hypothesized future expansion of oxygen minimum zones. Using field collections across ecosystems with contrasting biogeochemistry and mercury isotope fractionation experiments researchers will fill key knowledge gaps in mercury biogeochemistry. Results of the proposed research will enable scientists to assess the biogeochemical controls on where in the water column mercury methylation and demethylation likely occur.
Related background publication with supplemental data section:
Joel D. Blum, Brian N. Popp, Jeffrey C. Drazen, C. Anela Choy & Marcus W. Johnson. 2013. Methylmercury production below the mixed layer in the North Pacific Ocean. Nature Geoscience 6, 879–884. doi:10.1038/ngeo1918";
    String projects_0_end_date "2017-07";
    String projects_0_geolocation "Pacific Subtropical Gyre, Station ALOHA 22.75N 158W; equatorial Pacific (10N 155W, 5N 155W)";
    String projects_0_name "Collaborative Research: Isotopic insights to mercury in marine food webs and how it varies with ocean biogeochemistry";
    String projects_0_project_nid "560580";
    String projects_0_start_date "2014-08";
    String publisher_name "Biological and Chemical Oceanographic Data Management Office (BCO-DMO)";
    String publisher_type "institution";
    String sourceUrl "(local files)";
    String standard_name_vocabulary "CF Standard Name Table v55";
    String summary "This dataset contains the mercury stable isotope ratios collected in marine particles during R/V Kilo Moana cruises around Station ALOHA. These data were published in Motta et al., (2019) with supporting information.";
    String title "Mercury stable isotope values for marine particles from R/V Kilo Moana cruises KM1418, KM1407 and KM1506 around station ALOHA in 2014 and 2015";
    String version "1";
    String xml_source "osprey2erddap.update_xml() v1.3";
  }
}

 

Using tabledap to Request Data and Graphs from Tabular Datasets

tabledap lets you request a data subset, a graph, or a map from a tabular dataset (for example, buoy data), via a specially formed URL. tabledap uses the OPeNDAP (external link) Data Access Protocol (DAP) (external link) and its selection constraints (external link).

The URL specifies what you want: the dataset, a description of the graph or the subset of the data, and the file type for the response.

Tabledap request URLs must be in the form
https://coastwatch.pfeg.noaa.gov/erddap/tabledap/datasetID.fileType{?query}
For example,
https://coastwatch.pfeg.noaa.gov/erddap/tabledap/pmelTaoDySst.htmlTable?longitude,latitude,time,station,wmo_platform_code,T_25&time>=2015-05-23T12:00:00Z&time<=2015-05-31T12:00:00Z
Thus, the query is often a comma-separated list of desired variable names, followed by a collection of constraints (e.g., variable<value), each preceded by '&' (which is interpreted as "AND").

For details, see the tabledap Documentation.


 
ERDDAP, Version 2.02
Disclaimers | Privacy Policy | Contact