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Dataset Title:	[Neuse River Estuary WQ] - Biological, chemical, and physical water quality indicators of the Neuse River, North Carolina from 2008 through 2013 (Collaborative Research: Regulation of Phytoplankton Dynamics in Mid- Atlantic Estuaries Subject to Climatic Perturbations)
Institution:	BCO-DMO   (Dataset ID: bcodmo_dataset_767391)
Information:	Summary | License | FGDC | ISO 19115 | Metadata | Background | Subset | Files | Make a graph

 

Variable	Optional Constraint #1		Optional Constraint #2		Minimum    or a List of Values	Maximum
Date (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		"2008-01-15"	"2013-12-09"
Year (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		2008	2013
Season (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		"Fall"	"Winter"
Station (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		0	180
Source (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		"IMS"
depth2 (Depth, meters (m))	!= =~ <= >= = < >		!= =~ <= >= = < >		"1SD"	"S"
YSI_Time (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		"06:51:36"	"17:10:31"
depth (YSI Depth, m)	!= =~ <= >= = < >		!= =~ <= >= = < >		0.1	7.501
YSI_Temp (degrees Celsius)	!= =~ <= >= = < >		!= =~ <= >= = < >		2.04	33.69
YSI_SpecCond (milli Siemens per centimeter)	!= =~ <= >= = < >		!= =~ <= >= = < >		0.081	48.08
YSI_Salinity (parts per thousand)	!= =~ <= >= = < >		!= =~ <= >= = < >		0.04	31.23
YSI_DOsat (unitless (percent))	!= =~ <= >= = < >		!= =~ <= >= = < >		0.2	165.2
YSI_DO (milligrams per liter)	!= =~ <= >= = < >		!= =~ <= >= = < >		0.02	16.11
YSI_pH (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		5.83	9.23
YSI_Turbidity (NTU)	!= =~ <= >= = < >		!= =~ <= >= = < >		0.1	96.2
YSI_Chlraw (relative fluorescence units)	!= =~ <= >= = < >		!= =~ <= >= = < >		0.1	35.8
YSI_Chl (micrograms per liter)	!= =~ <= >= = < >		!= =~ <= >= = < >		0.3	127.3
YSI_BP (millimeters of mercury)	!= =~ <= >= = < >		!= =~ <= >= = < >		753	780
Secchi (meters)	!= =~ <= >= = < >		!= =~ <= >= = < >		0.25	16.16
Kd (per meter)	!= =~ <= >= = < >		!= =~ <= >= = < >		0.476995	5.647
Cdom_Corrected (microgram per liter of quinine sulfate.)	!= =~ <= >= = < >		!= =~ <= >= = < >		21.8067	207.515
POC (micrograms of carbon per liter)	!= =~ <= >= = < >		!= =~ <= >= = < >		18.15	13653.3
PN (micrograms of nitrogen per liter)	!= =~ <= >= = < >		!= =~ <= >= = < >		12.0	2450.65
CtoN (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		0.412105	112.576
DOC (micromolar)	!= =~ <= >= = < >		!= =~ <= >= = < >		232.6	1841.95
DIC (milligrams of carbon per liter)	!= =~ <= >= = < >		!= =~ <= >= = < >		1.976	21.37
NO3_NO2 (micrograms of nitrogen per liter)	!= =~ <= >= = < >		!= =~ <= >= = < >		0.267	941.0
NH4 (micrograms of nitrogen per liter)	!= =~ <= >= = < >		!= =~ <= >= = < >		3.69	1020.0
DIN (micrograms of nitrogen per liter)	!= =~ <= >= = < >		!= =~ <= >= = < >		3.69	1021.06
TDN (micrograms of nitrogen per liter)	!= =~ <= >= = < >		!= =~ <= >= = < >		37.6	1650.0
DON (micrograms of nitrogen per liter)	!= =~ <= >= = < >		!= =~ <= >= = < >		-2222.0	933.26
PO4 (micrograms of phosphorus per liter)	!= =~ <= >= = < >		!= =~ <= >= = < >		1.4	766.0
NtoP (miligrams nitrogen per liter (mg N/L))	!= =~ <= >= = < >		!= =~ <= >= = < >		0.0311301	294.014
SiO2 (micromolar)	!= =~ <= >= = < >		!= =~ <= >= = < >		1.2	155.0
Chla_IWS (micrograms per liter)	!= =~ <= >= = < >		!= =~ <= >= = < >		0.4861	232.71
Correct_Chla_IV (micrograms per liter)	!= =~ <= >= = < >		!= =~ <= >= = < >		0.255	304.36
PPR (milligrams of C per meter cubed per hour)	!= =~ <= >= = < >		!= =~ <= >= = < >		0.922585	329.767
Chlide_a (micrograms per liter)	!= =~ <= >= = < >		!= =~ <= >= = < >		0.0195137	16.9075
Chl_c1c2 (micrograms per liter)	!= =~ <= >= = < >		!= =~ <= >= = < >		0.00175097	17.2613
Perid_corr (micrograms per liter)	!= =~ <= >= = < >		!= =~ <= >= = < >		0.00607597	45.0129
But_fuco (micrograms per liter)	!= =~ <= >= = < >		!= =~ <= >= = < >		0.00419799	0.998646
Phide_a (micrograms per liter)	!= =~ <= >= = < >		!= =~ <= >= = < >		0.0401498	2.52855
Fuco_corr (micrograms per liter)	!= =~ <= >= = < >		!= =~ <= >= = < >		0.0127858	46.5159
Hex_fuco (micrograms per liter)	!= =~ <= >= = < >		!= =~ <= >= = < >		0.00155939	2.40154
Neo (micrograms per liter)	!= =~ <= >= = < >		!= =~ <= >= = < >		0.00487421	0.832047
Pras (micrograms per liter)	!= =~ <= >= = < >		!= =~ <= >= = < >		NaN
Viola (micrograms per liter)	!= =~ <= >= = < >		!= =~ <= >= = < >		0.00171185	27.0399
Diadino (micrograms per liter)	!= =~ <= >= = < >		!= =~ <= >= = < >		0.00466996	20.9497
Anth (micrograms per liter)	!= =~ <= >= = < >		!= =~ <= >= = < >		0.00627465	1.08513
Myxo (micrograms per liter)	!= =~ <= >= = < >		!= =~ <= >= = < >		0.0357131	0.623307
Allo_corr (micrograms per liter)	!= =~ <= >= = < >		!= =~ <= >= = < >		0.00763655	4.34151
Diato (micrograms per liter)	!= =~ <= >= = < >		!= =~ <= >= = < >		0.00156188	3.22123
Monado (micrograms per liter)	!= =~ <= >= = < >		!= =~ <= >= = < >		0.0190179	0.0959401
Lut (micrograms per liter)	!= =~ <= >= = < >		!= =~ <= >= = < >		0.00680866	1.77405
Zea_corr (micrograms per liter)	!= =~ <= >= = < >		!= =~ <= >= = < >		0.00927789	5.3182
Gyro (micrograms per liter)	!= =~ <= >= = < >		!= =~ <= >= = < >		0.00295617	0.665704
Cantha (micrograms per liter)	!= =~ <= >= = < >		!= =~ <= >= = < >		0.00265245	0.257283
Chl_b_corr (micrograms per liter)	!= =~ <= >= = < >		!= =~ <= >= = < >		0.0102913	11.5354
DV_chl_a (micrograms per liter)	!= =~ <= >= = < >		!= =~ <= >= = < >		NaN
Chl_a_corr (micrograms per liter)	!= =~ <= >= = < >		!= =~ <= >= = < >		0.0555769	126.454
Echin (micrograms per liter)	!= =~ <= >= = < >		!= =~ <= >= = < >		0.00127076	0.176636
Phytin_a (micrograms per liter)	!= =~ <= >= = < >		!= =~ <= >= = < >		0.0837401	21.4408
B_car (micrograms per liter)	!= =~ <= >= = < >		!= =~ <= >= = < >		0.00572564	3.47185
TotalChla (micrograms per liter)	!= =~ <= >= = < >		!= =~ <= >= = < >		0.158196	130.861
ISO_DateTime (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		"2008-01-15T11:14:03"	"2013-12-09T12:40:51"
Station_Description (unitless)	!= =~ <= >= = < >		!= =~ <= >= = < >		"DWQ Station J8925000"	"Streets Ferry Brid..."
km0 (kilometers (km))	!= =~ <= >= = < >		!= =~ <= >= = < >		0.0	72.9281
latitude (degrees_north)	!= =~ <= >= = < >		!= =~ <= >= = < >		34.9489	35.2106
longitude (degrees_east)	!= =~ <= >= = < >		!= =~ <= >= = < >		-77.1222	-76.526

 

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orderBy orderByDescending orderByClosest orderByCount orderByLimit orderByMax orderByMin orderByMinMax orderByMean orderBySum (" Date Year Season Station Source depth2 YSI_Time depth YSI_Temp YSI_SpecCond YSI_Salinity YSI_DOsat YSI_DO YSI_pH YSI_Turbidity YSI_Chlraw YSI_Chl YSI_BP Secchi Kd Cdom_Corrected POC PN CtoN DOC DIC NO3_NO2 NH4 DIN TDN DON PO4 NtoP SiO2 Chla_IWS Correct_Chla_IV PPR Chlide_a Chl_c1c2 Perid_corr But_fuco Phide_a Fuco_corr Hex_fuco Neo Pras Viola Diadino Anth Myxo Allo_corr Diato Monado Lut Zea_corr Gyro Cantha Chl_b_corr DV_chl_a Chl_a_corr Echin Phytin_a B_car TotalChla ISO_DateTime Station_Description km0 latitude longitude Date Year Season Station Source depth2 YSI_Time depth YSI_Temp YSI_SpecCond YSI_Salinity YSI_DOsat YSI_DO YSI_pH YSI_Turbidity YSI_Chlraw YSI_Chl YSI_BP Secchi Kd Cdom_Corrected POC PN CtoN DOC DIC NO3_NO2 NH4 DIN TDN DON PO4 NtoP SiO2 Chla_IWS Correct_Chla_IV PPR Chlide_a Chl_c1c2 Perid_corr But_fuco Phide_a Fuco_corr Hex_fuco Neo Pras Viola Diadino Anth Myxo Allo_corr Diato Monado Lut Zea_corr Gyro Cantha Chl_b_corr DV_chl_a Chl_a_corr Echin Phytin_a B_car TotalChla ISO_DateTime Station_Description km0 latitude longitude Date Year Season Station Source depth2 YSI_Time depth YSI_Temp YSI_SpecCond YSI_Salinity YSI_DOsat YSI_DO YSI_pH YSI_Turbidity YSI_Chlraw YSI_Chl YSI_BP Secchi Kd Cdom_Corrected POC PN CtoN DOC DIC NO3_NO2 NH4 DIN TDN DON PO4 NtoP SiO2 Chla_IWS Correct_Chla_IV PPR Chlide_a Chl_c1c2 Perid_corr But_fuco Phide_a Fuco_corr Hex_fuco Neo Pras Viola Diadino Anth Myxo Allo_corr Diato Monado Lut Zea_corr Gyro Cantha Chl_b_corr DV_chl_a Chl_a_corr Echin Phytin_a B_car TotalChla ISO_DateTime Station_Description km0 latitude longitude Date Year Season Station Source depth2 YSI_Time depth YSI_Temp YSI_SpecCond YSI_Salinity YSI_DOsat YSI_DO YSI_pH YSI_Turbidity YSI_Chlraw YSI_Chl YSI_BP Secchi Kd Cdom_Corrected POC PN CtoN DOC DIC NO3_NO2 NH4 DIN TDN DON PO4 NtoP SiO2 Chla_IWS Correct_Chla_IV PPR Chlide_a Chl_c1c2 Perid_corr But_fuco Phide_a Fuco_corr Hex_fuco Neo Pras Viola Diadino Anth Myxo Allo_corr Diato Monado Lut Zea_corr Gyro Cantha Chl_b_corr DV_chl_a Chl_a_corr Echin Phytin_a B_car TotalChla ISO_DateTime Station_Description km0 latitude longitude Date Year Season Station Source depth2 YSI_Time depth YSI_Temp YSI_SpecCond YSI_Salinity YSI_DOsat YSI_DO YSI_pH YSI_Turbidity YSI_Chlraw YSI_Chl YSI_BP Secchi Kd Cdom_Corrected POC PN CtoN DOC DIC NO3_NO2 NH4 DIN TDN DON PO4 NtoP SiO2 Chla_IWS Correct_Chla_IV PPR Chlide_a Chl_c1c2 Perid_corr But_fuco Phide_a Fuco_corr Hex_fuco Neo Pras Viola Diadino Anth Myxo Allo_corr Diato Monado Lut Zea_corr Gyro Cantha Chl_b_corr DV_chl_a Chl_a_corr Echin Phytin_a B_car TotalChla ISO_DateTime Station_Description km0 latitude longitude ")

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The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  Date {
    String bcodmo_name "date";
    String description "Date of water sample collection ; filtration ; and in situ measurements.";
    String long_name "Date";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/ADATAA01/";
    String time_precision "1970-01-01";
    String units "unitless";
  }
  Year {
    Int16 _FillValue 32767;
    Int16 actual_range 2008, 2013;
    String bcodmo_name "year";
    String description "Year of sampling";
    String long_name "Year";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/YEARXXXX/";
    String units "unitless";
  }
  Season {
    String bcodmo_name "season";
    String description "The season when the water sample was collected and filtered and when the in situ measurements were performed in the field.";
    String long_name "Season";
    String units "unitless";
  }
  Station {
    Int16 _FillValue 32767;
    Int16 actual_range 0, 180;
    String bcodmo_name "station";
    String description "The name of the fixed sampling station.";
    String long_name "Station";
    String units "unitless";
  }
  Source {
    String bcodmo_name "unknown";
    String description "The organization that conducted the sampling.";
    String long_name "Source";
    String units "unitless";
  }
  depth2 {
    String bcodmo_name "depth";
    String description "Depth level from which the water sample was collected and where the in situ measurements were made (S=surface ; B=bottom   Surface (S) refers to a surface water sample or in situ measurement taken at a depth of approximately 0.2 meters.  Bottom (B) refers to a bottom water sample or in situ measurement taken at a depth of approximately 0.5 meters above the sediment layer.  Surface water samples were collected by submerging 10 liter high-density polyethylene containers just below the water surface or by filling the containers with surface water collected from bucket casts.  Bottom water samples were collected with a horizontal plastic Van Dorn sampler. Starting December 2007 ; all samples collected with diaphragm pump and a weighted ; marked hose. All containers were kept in dark coolers at ambient temperature during transport to the laboratory.  All filtration was done within a few hours of collection and when conditions permitted ; on board the research vessel.";
    String long_name "Depth";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/DEPH/";
    String standard_name "depth";
    String units "meters (m)";
  }
  YSI_Time {
    String bcodmo_name "time";
    String description "Exact time (hours:minutes:seconds) when the in situ measurements were made.  This time is an approximate water sampling time.";
    String long_name "YSI Time";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/AHMSAA01/";
    String units "unitless";
  }
  depth {
    String _CoordinateAxisType "Height";
    String _CoordinateZisPositive "down";
    Float64 _FillValue NaN;
    Float64 actual_range 0.1, 7.501;
    String axis "Z";
    String bcodmo_name "depth";
    String description "Exact depth (meters) where the in situ measurements were made.";
    String ioos_category "Location";
    String long_name "YSI Depth";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/DEPH/";
    String positive "down";
    String standard_name "depth";
    String units "m";
  }
  YSI_Temp {
    Float32 _FillValue NaN;
    Float32 actual_range 2.04, 33.69;
    String bcodmo_name "temperature";
    String description "In situ water temperature";
    String long_name "YSI Temp";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/TEMPP901/";
    String units "degrees Celsius";
  }
  YSI_SpecCond {
    Float32 _FillValue NaN;
    Float32 actual_range 0.081, 48.08;
    String bcodmo_name "conductivity";
    String description "In situ specific conductivity";
    String long_name "YSI Spec Cond";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P02/current/CNDC/";
    String units "milli Siemens per centimeter";
  }
  YSI_Salinity {
    Float32 _FillValue NaN;
    Float32 actual_range 0.04, 31.23;
    String bcodmo_name "sal";
    Float64 colorBarMaximum 37.0;
    Float64 colorBarMinimum 32.0;
    String description "In situ salinity";
    String long_name "Sea Water Practical Salinity";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/PSALST01/";
    String units "parts per thousand";
  }
  YSI_DOsat {
    Float32 _FillValue NaN;
    Float32 actual_range 0.2, 165.2;
    String bcodmo_name "O2_sat_pcnt";
    String description "In situ dissolved oxygen saturation";
    String long_name "YSI DOsat";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/OXYSZZ01/";
    String units "unitless  (percent)";
  }
  YSI_DO {
    Float32 _FillValue NaN;
    Float32 actual_range 0.02, 16.11;
    String bcodmo_name "dissolved Oxygen";
    String description "In situ dissolved oxygen concentration";
    String long_name "YSI DO";
    String units "milligrams per liter";
  }
  YSI_pH {
    Float32 _FillValue NaN;
    Float32 actual_range 5.83, 9.23;
    String bcodmo_name "pH";
    String description "In situ pH.";
    String long_name "YSI P H";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/PHXXZZXX/";
    String units "unitless";
  }
  YSI_Turbidity {
    Float32 _FillValue NaN;
    Float32 actual_range 0.1, 96.2;
    String bcodmo_name "turbidity";
    String description "In situ turbidity";
    String long_name "YSI Turbidity";
    String units "NTU";
  }
  YSI_Chlraw {
    Float32 _FillValue NaN;
    Float32 actual_range 0.1, 35.8;
    String bcodmo_name "chl_raw";
    String description "In situ chlorophyll fluorescence";
    String long_name "YSI Chlraw";
    String units "relative fluorescence units";
  }
  YSI_Chl {
    Float32 _FillValue NaN;
    Float32 actual_range 0.3, 127.3;
    String bcodmo_name "fluorescence";
    String description "In situ chlorophyll concentration from fluorescence";
    String long_name "YSI Chl";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/CPHLPM01/";
    String units "micrograms per liter";
  }
  YSI_BP {
    Int16 _FillValue 32767;
    Int16 actual_range 753, 780;
    String bcodmo_name "press_bar";
    String description "Surface barometric pressure";
    String long_name "YSI BP";
    String units "millimeters of mercury";
  }
  Secchi {
    Float32 _FillValue NaN;
    Float32 actual_range 0.25, 16.16;
    String bcodmo_name "depth_secchi";
    String description "Depth at which the secchi disk is no longer visible";
    String long_name "Secchi";
    String units "meters";
  }
  Kd {
    Float32 _FillValue NaN;
    Float32 actual_range 0.476995, 5.647;
    String bcodmo_name "beam_cp";
    String description "Diffuse light attenuation coefficient";
    String long_name "KD";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/ATTNZZ01/";
    String units "per meter";
  }
  Cdom_Corrected {
    Float32 _FillValue NaN;
    Float32 actual_range 21.8067, 207.515;
    String bcodmo_name "CDOM";
    String description "Colored or chromophoric dissolved organic (matter humic substances) concentration as microgram per liter of quinine sulfate.";
    String long_name "Cdom Corrected";
    String units "microgram per liter of quinine sulfate.";
  }
  POC {
    Float32 _FillValue NaN;
    Float32 actual_range 18.15, 13653.3;
    String bcodmo_name "POC";
    String description "Particulate organic carbon concentration";
    String long_name "Particulate Organic Carbon";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/CORGCAP1/";
    String units "micrograms of carbon per liter";
  }
  PN {
    Float32 _FillValue NaN;
    Float32 actual_range 12.0, 2450.65;
    String bcodmo_name "N";
    String description "Particulate nitrogen concentration";
    String long_name "PN";
    String units "micrograms of nitrogen per liter";
  }
  CtoN {
    Float32 _FillValue NaN;
    Float32 actual_range 0.412105, 112.576;
    String bcodmo_name "C_to_N";
    String description "Calculated molar ratio of particulate organic carbon";
    String long_name "Cto N";
    String units "unitless";
  }
  DOC {
    Float32 _FillValue NaN;
    Float32 actual_range 232.6, 1841.95;
    String bcodmo_name "DOC";
    String description "Dissolved organic carbon concentration";
    String long_name "DOC";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/CORGZZZX/";
    String units "micromolar";
  }
  DIC {
    Float32 _FillValue NaN;
    Float32 actual_range 1.976, 21.37;
    String bcodmo_name "DIC";
    String description "Dissolved inorganic carbon concentration";
    String long_name "DIC";
    String units "milligrams of carbon per liter";
  }
  NO3_NO2 {
    Float32 _FillValue NaN;
    Float32 actual_range 0.267, 941.0;
    String bcodmo_name "NO3_NO2";
    Float64 colorBarMaximum 50.0;
    Float64 colorBarMinimum 0.0;
    String description "Nitrate plus nitrite concentration";
    String long_name "Mole Concentration Of Nitrate In Sea Water";
    String units "micrograms of nitrogen per liter";
  }
  NH4 {
    Float32 _FillValue NaN;
    Float32 actual_range 3.69, 1020.0;
    String bcodmo_name "Ammonium";
    Float64 colorBarMaximum 5.0;
    Float64 colorBarMinimum 0.0;
    String description "Ammonium concentration";
    String long_name "Mole Concentration Of Ammonium In Sea Water";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/AMONAAZX/";
    String units "micrograms of nitrogen per liter";
  }
  DIN {
    Float32 _FillValue NaN;
    Float32 actual_range 3.69, 1021.06;
    String bcodmo_name "Dissolved Inorganic Nitrogen";
    String description "Calculated dissolved inorganic nitrogen concentration";
    String long_name "DIN";
    String units "micrograms of nitrogen per liter";
  }
  TDN {
    Float32 _FillValue NaN;
    Float32 actual_range 37.6, 1650.0;
    String bcodmo_name "Total Dissolved Nitrogren";
    String description "Total dissolved nitrogen concentration organic plus inorganic species";
    String long_name "TDN";
    String units "micrograms of nitrogen per liter";
  }
  DON {
    Float32 _FillValue NaN;
    Float32 actual_range -2222.0, 933.26;
    String bcodmo_name "Dissolved Organic Nitrogen";
    String description "Calculated dissolved organic nitrogen concentration";
    String long_name "DON";
    String units "micrograms of nitrogen per liter";
  }
  PO4 {
    Float32 _FillValue NaN;
    Float32 actual_range 1.4, 766.0;
    String bcodmo_name "PO4";
    String description "Orthophosphate concentration";
    String long_name "Mass Concentration Of Phosphate In Sea Water";
    String units "micrograms of phosphorus per liter";
  }
  NtoP {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0311301, 294.014;
    String bcodmo_name "unknown";
    String description "The calculated molar ratio of nitrogen (N) to phosphorus (P)";
    String long_name "Nto P";
    String units "miligrams nitrogen per liter (mg N/L)";
  }
  SiO2 {
    Float32 _FillValue NaN;
    Float32 actual_range 1.2, 155.0;
    String bcodmo_name "silica";
    String description "Silica concentration";
    String long_name "Si O2";
    String units "micromolar";
  }
  Chla_IWS {
    Float32 _FillValue NaN;
    Float32 actual_range 0.4861, 232.71;
    String bcodmo_name "chlorophyll a";
    Float64 colorBarMaximum 30.0;
    Float64 colorBarMinimum 0.03;
    String colorBarScale "Log";
    String description "Chlorophyll a concentration measured by in vitro fluorometry (micrograms per liter) integrated throughout the water column to 2x the secchi depth.  Water samples for this measurement were collected using the integrated water sampler IWS) which collects vertically integrated water samples.";
    String long_name "Concentration Of Chlorophyll In Sea Water";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/CPHLHPP1/";
    String units "micrograms per liter";
  }
  Correct_Chla_IV {
    Float32 _FillValue NaN;
    Float32 actual_range 0.255, 304.36;
    String bcodmo_name "chlorophyll a";
    Float64 colorBarMaximum 30.0;
    Float64 colorBarMinimum 0.03;
    String colorBarScale "Log";
    String description "Chlorophyll a concentration measured by in vitro fluorometry";
    String long_name "Concentration Of Chlorophyll In Sea Water";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/CPHLHPP1/";
    String units "micrograms per liter";
  }
  PPR {
    Float32 _FillValue NaN;
    Float32 actual_range 0.922585, 329.767;
    String bcodmo_name "Primary Production";
    String description "Primary productivity by light/dark 14C bicarbonate incorporation";
    String long_name "PPR";
    String units "milligrams of C per meter cubed per hour";
  }
  Chlide_a {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0195137, 16.9075;
    String bcodmo_name "chlide_a";
    String description "Chlorophyllide a concentration by HPLC analysis";
    String long_name "Chlide A";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/CIDAHPP1/";
    String units "micrograms per liter";
  }
  Chl_c1c2 {
    Float32 _FillValue NaN;
    Float32 actual_range 0.00175097, 17.2613;
    String bcodmo_name "chl_a_tot";
    String description "Chlorophyll c1 and c2 concentration by HPLC analysis";
    String long_name "CHL C1C2";
    String units "micrograms per liter";
  }
  Perid_corr {
    Float32 _FillValue NaN;
    Float32 actual_range 0.00607597, 45.0129;
    String bcodmo_name "peridinin";
    String description "Peridinin concentration by HPLC analysis";
    String long_name "Perid Corr";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/PERIHPP1/";
    String units "micrograms per liter";
  }
  But_fuco {
    Float32 _FillValue NaN;
    Float32 actual_range 0.00419799, 0.998646;
    String bcodmo_name "fucox_but";
    String description "19'-Butanoyloxyfucoxanthin concentration by HPLC analysis";
    String long_name "But Fuco";
    String units "micrograms per liter";
  }
  Phide_a {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0401498, 2.52855;
    String bcodmo_name "pheophorbide a";
    String description "Pheophorbide-a concentration by HPLC analysis";
    String long_name "Phide A";
    String units "micrograms per liter";
  }
  Fuco_corr {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0127858, 46.5159;
    String bcodmo_name "fucox";
    String description "Fucoxanthin concentration by HPLC analysis";
    String long_name "Fuco Corr";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/FUCXHPP1/";
    String units "micrograms per liter";
  }
  Hex_fuco {
    Float32 _FillValue NaN;
    Float32 actual_range 0.00155939, 2.40154;
    String bcodmo_name "fucox_hex";
    String description "19'-Hexanoyloxyfucoxanthin concentration by HPLC analysis";
    String long_name "Hex Fuco";
    String units "micrograms per liter";
  }
  Neo {
    Float32 _FillValue NaN;
    Float32 actual_range 0.00487421, 0.832047;
    String bcodmo_name "neox";
    String description "9'-cis Neoxanthin concentration by HPLC analysis";
    String long_name "Neo";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/NEOXHPP1/";
    String units "micrograms per liter";
  }
  Pras {
    Float64 _FillValue NaN;
    String bcodmo_name "prasinox";
    String description "Prasinoxanthin concentration by HPLC analysis";
    String long_name "Pras";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/PRSXHPP1/";
    String units "micrograms per liter";
  }
  Viola {
    Float32 _FillValue NaN;
    Float32 actual_range 0.00171185, 27.0399;
    String bcodmo_name "violax";
    String description "Violaxanthin concentration by HPLC analysis";
    String long_name "Viola";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/VILXHPP1/";
    String units "micrograms per liter";
  }
  Diadino {
    Float32 _FillValue NaN;
    Float32 actual_range 0.00466996, 20.9497;
    String bcodmo_name "diadinox";
    String description "Diadinoxanthin concentration by HPLC analysis";
    String long_name "Diadino";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/DIADHPP1/";
    String units "micrograms per liter";
  }
  Anth {
    Float32 _FillValue NaN;
    Float32 actual_range 0.00627465, 1.08513;
    String bcodmo_name "antherax";
    String description "Antheraxanthin concentration by HPLC analysis";
    String long_name "Anth";
    String units "micrograms per liter";
  }
  Myxo {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0357131, 0.623307;
    String bcodmo_name "unknown";
    String description "Myxoxanthophyll concentration by HPLC analysis";
    String long_name "Myxo";
    String units "micrograms per liter";
  }
  Allo_corr {
    Float32 _FillValue NaN;
    Float32 actual_range 0.00763655, 4.34151;
    String bcodmo_name "allox";
    String description "Alloxanthin concentration by HPLC analysis";
    String long_name "Allo Corr";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/ALLOHPP1/";
    String units "micrograms per liter";
  }
  Diato {
    Float32 _FillValue NaN;
    Float32 actual_range 0.00156188, 3.22123;
    String bcodmo_name "diatox";
    String description "Diatoxanthin concentration by HPLC analysis";
    String long_name "Diato";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/DIATHPP1/";
    String units "micrograms per liter";
  }
  Monado {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0190179, 0.0959401;
    String bcodmo_name "monadoxanthin";
    String description "Monadoxanthin concentration by HPLC analysis";
    String long_name "Monado";
    String units "micrograms per liter";
  }
  Lut {
    Float32 _FillValue NaN;
    Float32 actual_range 0.00680866, 1.77405;
    String bcodmo_name "lutein";
    String description "Lutein concentration by HPLC analysis";
    String long_name "Lut";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/LUTNHPP1/";
    String units "micrograms per liter";
  }
  Zea_corr {
    Float32 _FillValue NaN;
    Float32 actual_range 0.00927789, 5.3182;
    String bcodmo_name "zeax";
    String description "Zeaxanthin concentration by HPLC analysis";
    String long_name "Zea Corr";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/ZEOXHPP1/";
    String units "micrograms per liter";
  }
  Gyro {
    Float32 _FillValue NaN;
    Float32 actual_range 0.00295617, 0.665704;
    String bcodmo_name "Gyroxanthin-Diester";
    String description "Gyroxanthin concentration by HPLC analysis";
    String long_name "Gyro";
    String units "micrograms per liter";
  }
  Cantha {
    Float32 _FillValue NaN;
    Float32 actual_range 0.00265245, 0.257283;
    String bcodmo_name "unknown";
    String description "Canthaxanthin concentration by HPLC analysis";
    String long_name "Cantha";
    String units "micrograms per liter";
  }
  Chl_b_corr {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0102913, 11.5354;
    String bcodmo_name "chl_b";
    String description "Chlorophyll b concentration by HPLC analysis";
    String long_name "Chl B Corr";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/CHLBHPP1/";
    String units "micrograms per liter";
  }
  DV_chl_a {
    Float64 _FillValue NaN;
    String bcodmo_name "chl_a2";
    Float64 colorBarMaximum 30.0;
    Float64 colorBarMinimum 0.03;
    String colorBarScale "Log";
    String description "Divinyl chlorophyll a concentration by HPLC analysis";
    String long_name "Concentration Of Chlorophyll In Sea Water";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/DVCAHPP1/";
    String units "micrograms per liter";
  }
  Chl_a_corr {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0555769, 126.454;
    String bcodmo_name "chlorophyll a";
    Float64 colorBarMaximum 30.0;
    Float64 colorBarMinimum 0.03;
    String colorBarScale "Log";
    String description "Chlorophyll a concentration by HPLC analysis";
    String long_name "Concentration Of Chlorophyll In Sea Water";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/CPHLHPP1/";
    String units "micrograms per liter";
  }
  Echin {
    Float32 _FillValue NaN;
    Float32 actual_range 0.00127076, 0.176636;
    String bcodmo_name "unknown";
    String description "Echinenone concentration by HPLC analysis";
    String long_name "Echin";
    String units "micrograms per liter";
  }
  Phytin_a {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0837401, 21.4408;
    String bcodmo_name "pheophytin a";
    String description "Pheophytin a concentration by HPLC analysis";
    String long_name "Phytin A";
    String units "micrograms per liter";
  }
  B_car {
    Float32 _FillValue NaN;
    Float32 actual_range 0.00572564, 3.47185;
    String bcodmo_name "carotene_b";
    String description "?-Carotene concentration by HPLC analysis";
    String long_name "B Car";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/BCARHPP1/";
    String units "micrograms per liter";
  }
  TotalChla {
    Float32 _FillValue NaN;
    Float32 actual_range 0.158196, 130.861;
    String bcodmo_name "chl_a_tot";
    String description "Sum of chlorophyll a and chlorophyllide a concentrations by HPLC analysis . Concentrations below detection assumed to by zero for this calculation.";
    String long_name "Total Chla";
    String units "micrograms per liter";
  }
  ISO_DateTime {
    String bcodmo_name "DateTime";
    String description "Date and YSI_Time columns combined into ISO 8601 date format";
    String long_name "ISO Date Time";
    String source_name "ISO_DateTime";
    String time_precision "1970-01-01T00:00:00Z";
    String units "unitless";
  }
  Station_Description {
    String bcodmo_name "site_descrip";
    String description "The physical location of the sampling station ; such as at or near a particular river marker ; buoy ; road or bridge.  Lists other names that may also be used to refer to this station.";
    String long_name "Station Description";
    String units "unitless";
  }
  km0 {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 72.9281;
    String bcodmo_name "length";
    String description "The distance  of the sampling station from station 0.";
    String long_name "KM0";
    String units "kilometers (km)";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 _FillValue NaN;
    Float64 actual_range 34.9489, 35.2106;
    String axis "Y";
    String bcodmo_name "latitude";
    Float64 colorBarMaximum 90.0;
    Float64 colorBarMinimum -90.0;
    String description "North latitude of station in decimal degrees";
    String ioos_category "Location";
    String long_name "Latitude";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/LATX/";
    String standard_name "latitude";
    String units "degrees_north";
  }
  longitude {
    String _CoordinateAxisType "Lon";
    Float64 _FillValue NaN;
    Float64 actual_range -77.1222, -76.526;
    String axis "X";
    String bcodmo_name "longitude";
    Float64 colorBarMaximum 180.0;
    Float64 colorBarMinimum -180.0;
    String description "West longitude of station in decimal degrees";
    String ioos_category "Location";
    String long_name "Longitude";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/LONX/";
    String standard_name "longitude";
    String units "degrees_east";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson";
    String acquisition_description 
"Bi-weekly water sampling and in situ measurements were performed at fixed
sampling stations.\\u00a0 Water samples and in situ measurements were collected
at the surface (approximately 0.2 meters) and at the bottom of the water
column (approximately 0.5 meters from the sediment layer).\\u00a0 These data
are included in the worksheet titled \\\"NRE Dataset.\\\"\\u00a0 In situ
measurements were also performed throughout the water column in 0.5 meter
depth increments.\\u00a0 These data are included in the worksheet titled \\\"NRE
YSI Profiles.\\\"\\u00a0 Parameters measured include: temperature, salinity,
specific conductivity, dissolved oxygen (DO), pH, chlorophyll fluorescence,
photosynthetically active radiation (PAR), turbidity, barometric pressure,
secchi depth, colored dissolved organic matter (CDOM), particulate organic
carbon (POC) and nitrogen (PN), dissolved organic and inorganic carbon,
dissolved inorganic nutrient concentrations (nitrate/nitrite, ammonium, total
dissolved nitrogen, phosphate and silicic acid), chlorophyll a, primary
productivity and diagnostic phytoplankton pigment concentrations (chlorophylls
and carotenoids).\\u00a0 Calculated parameters include:\\u00a0 diffuse light
attenuation coefficient (Kd), carbon to nitrogen molar ratio (C:N), dissolved
inorganic nitrogen (DIN; nitrate/nitrite plus ammonium), dissolved organic
nitrogen (DON; total dissolved nitrogen minus dissolved inroganic nitrogen)
and the nitrogen to phosporus molar ratio (N:P).\\u00a0\\u00a0
 
Methods  
 Water sampling was conducted bi-weekly. When collection was split over two
days, a single date was used based on the upstream or majority stations.
 
Stations were selected to cover the entire length of the Neuse River Estuary
from Streets Ferry Bridge (Station 0) to the mouth of the estuary where it
flows into Pamlico Sound.\\u00a0 When possible, efforts were made to select
locations with key stationary features (channel markers, buoys and land
markers) to allow easy station identification in the field.
 
Surface water samples were collected by submerging 10 liter high-density
polyethylene containers just below the water surface or by filling the
containers with surface water collected from bucket casts.\\u00a0 Bottom water
samples were collected with a horizontal plastic Van Dorn sampler. Starting
December 2007, all samples collected with diaphragm pump and a weighted,
marked hose. All containers were kept in dark coolers at ambient temperature
during transport to the laboratory.\\u00a0 All filtration was done within a few
hours of collection and when conditions permitted, on board the research
vessel.
 
Prior to the 09/13/2000 sampling date, in situ measurements were performed at
discrete depths using a Hydrolab Data Sonde 3 equipped with a multiprobe and
SVR3 display logger.\\u00a0 Beginning on the 09/13/2000 sampling date, in situ
measurements were performed at discrete depths on the sunlit side of the
research vessel using a Yellow Springs Instruments (YSI Incoporated, Ohio)
multiparameter sonde (Model 6600 or 6600 EDS-S Extended Deployment System)
equipped with a YSI conductivity/temperature probe (Model 6560), a YSI
chlorophyll probe (Model 6025), a YSI pH probe (Model 6561 or 6566), a YSI
pulsed dissolved oxygen probe (Model 6562), a self cleaning YSI turbidity
probe (Model 6026 or 6136), and beginning on the 07/30/2003 sampling date, a
flat Li-Cor sensor (UWQ-PAR 6067).\\u00a0 The YSI sonde was coupled to a either
a YSI 610 DM datalogger or a YSI 650 MDS Multi-parameter Display System
datalogger.\\u00a0 In situ measurements were performed at the surface
(approximately 0.2 meters) and at the bottom of the water column
(approximately 0.5 meters from the sediment layer).\\u00a0 These data are
included in the worksheet titled \\\"NRE Dataset.\\\"\\u00a0 In situ measurements
were also performed throughout the water column in 0.5 meter depth
increments.\\u00a0 These data are included in the worksheet titled \\\"NRE YSI
Profiles.\\\"\\u00a0 The data were stored on the datalogger and downloaded to
Ecowin software upon return to the laboratory.
 
The secchi disk was deployed off of the sunlit side of the research
vessel.\\u00a0 The depth (in meters) at which the secchi disk was no longer
visible by the naked eye was recorded as the secchi depth.
 
The diffuse light attenuation coefficient, Kd, was calculated from depth
profiles of photosynthetically active radiation (PAR, 400-700 nm).\\u00a0 Prior
to the 07/30/2003 sampling date, PAR measurements were performed with a
spherical underwater quantum sensor (LI-COR LI-193SA) coupled to a LI-COR
LI-1000 datalogger.\\u00a0 Beginning on the 07/30/2003 sampling date, a flat
underwater quantum sensor (LI-COR LI-193SA) attached to a Yellow Springs
Instruments YSI 6600 or YSI 6600 EDS-S sonde was used to measure PAR.\\u00a0
Measurements of PAR were performed on the sunlit side of the research vessel
in 0.5 meter depth increments, beginning just below the water surface.\\u00a0
The diffuse attenuation coefficient is the slope of the linear regression
between natural log transformed PAR data and depth.\\u00a0
 
Colored dissolved organic matter (CDOM) was measured using a Turner Designs
TD-700 fluorometer configured with a near-UV mercury vapour lamp, a 350 nm
excitation filter, and a 410\\u2013600 nm emission filter. The fluorometer was
calibrated to quinine sulfate (QS) solutions made up in 2 N sulfuric acid.
Water samples were vacuum filtered (less than 25 kilopascal) using pre-
combusted Whatman glass microfibre filters (GF/F) and the filtrate was stored
in scintillation vials in the dark at 4 degrees Celsius until fluorometric
analysis.\\u00a0 The official decision (3/2/2017) is that cdom results from
12/1/2003 through 4/25/2011 would be multiplied by a corrective factor of
2.0.\\u00a0 Results for sample date of 5/9/2011 and after do not need
correcting.\\u00a0 It is believed the stock solution was made wrong, making a
1L recipe for 600 ug/L in a 500 ml flask equals 1200 ug/L stock
solution.\\u00a0 Standards were still calibrated according to recipe, but were
actually 2x as strong.\\u00a0
 
\\u00a0The official decision (3/2/2017) is that cdom results from 12/1/2003
through 4/25/2011 would be multiplied by a corrective factor of 2.0.\\u00a0
Results for sample date of 5/9/2011 and after do not need correcting.\\u00a0 It
is believed the stock solution was made wrong, making a 1L recipe for 600 ug/L
in a 500 ml flask equals 1200 ug/L stock solution.\\u00a0 Standards were still
calibrated according to recipe, but were actually 2x as strong.\\u00a0
 
Particulate organic carbon (POC) concentrations were determined by elemental
analysis of material collected on pre-combusted Whatman GF/F glass fiber
filters.\\u00a0 Carbonates were removed from the filters by vapor phase
acidification using concentrated hydrochloric acid (HCl).\\u00a0 After drying
at 60 0C, the filters were rolled in tin disks and injected into a PE 2400
Series II CHNS/O Analyzer calibrated with acetanilide ending in June
2014.\\u00a0 Starting on the Neuse River sample date of June 2, 2014, a Costech
Analytical Technologies, Inc. Elemental Combustion System CHNS-O ECS 4010 was
used for elemental analysis by \\\"flash combustion/chromatographic separation
and multi-detector techniques\\\".\\u00a0 The Costech Instrument utilizes EAS
Clarity Software.\\u00a0 Atropine standards are used to develop a calibration
curve (C 70.56%, N 4.84%, and carbon response ratio of 0.025 +/-0.003).\\u00a0
NIST Buffalo River Sediment Reference Material 8704 (C 3.351% +/-0.017, N
0.20% +/-0.04) and/or Acetanilide Bypass (C 71.09%, N 10.36%, carbon response
ratio of 0.055 +/- 0.003) may used for calibration or a check standard.
 
The molar ratio of particulate organic carbon (POC) to particulate nitrogen
(PN), or C:N, was calculated by dividing POC by PN.\\u00a0 (Carbon ug/L
/12.011)/(Nitrogen ug/L/14.007).
 
Dissolved organic carbon (DOC) concentration was measured using a Shimadzu
TOC-5000A Analyzer:\\u00a0 Water samples were vacuum filtered (less than 25
kilopascal) using pre-combusted Whatman glass microfibre filters (GF/F).\\u00a0
The filtrate was stored in pre-combusted glass scintillation vials with Teflon
closures and frozen at -20 degrees Celsius until analysis.\\u00a0 The Shimadzu
TOC-5000A Analyzer uses high temperature catalytic oxidation followed by non-
dispersive infrared analysis of the CO2 produced.\\u00a0 Samples were acidified
to a pH less than 2 and sparged with air before they were analyzed for non-
volatile organic carbon.\\u00a0 DOC values in 1996 were run from previously run
nutrient samples. Starting February 2018, all stations were collected.\\u00a0
Prior to Feb. 2018 only NR 0, 30, 70, 100, 120, and 160 surface and bottom
stations were measured.
 
Nitrate/nitrite (NO3- / NO2-) concentration was determined using a
Lachat/Zellweger Analytics QuikChem 8000 flow injection autoanalyzer
(Milwaukee, WI, USA) using method FIA 31-107-04-1-C:\\u00a0 Water samples were
vacuum filtered (less than 25 kiloPascals) using pre-combusted Whatman glass
microfibre filters (GF/F).\\u00a0 The filtrate was stored in high-density
polyethylene bottles and frozen at -20 degrees Celsius until analysis.\\u00a0
Two replicates were run from the same bottle.\\u00a0 Method detection limits
(MDL, \\u00b5g L-1) were: before 4Nov02 = 1.06; beginning 4Nov02 = 3.68;
beginning 11Jul06 = 0.6; beginning 1Dec09 = 0.27; beginning 13Feb12 = 0.36;
beginning 18Feb15 = 0.71.\\u00a0 MDL was changed to 0.88 on a sample date of
8/21/2017.
 
Ammonium (NH4+) concentration was determined using a Lachat/Zellweger
Analytics QuikChem 8000 flow injection autoanalyzer (Milwaukee, WI) using
method FIA 31-107-06-1-A/B:\\u00a0 Water samples were vacuum filtered (less
than 25 kiloPascals) using pre-combusted Whatman glass microfibre filters
(GF/F).\\u00a0 The filtrate was stored in high-density polyethylene bottles and
frozen (-20 degrees Celsius) until analysis.\\u00a0 Two replicates were run
from the same bottle.\\u00a0 \\u00a0Method detection limits (MDL, \\u00b5g L-1)
were: before 4Nov02 = 4.69; beginning 4Nov02 = 4.31; beginning 11Jul06 = 2.55;
beginning 1Dec09 = 3.98; beginning 13Feb12 = 2.87; beginning 18Feb15 =
3.34.\\u00a0 MDL was changed to 1.05 on sample date 8/21/2017.
 
Dissolved inorganic nitrogen (DIN) concentration was calculated by summing
nitrate/nitrite (NO3- / NO2-) and ammonium (NH4+).\\u00a0 If either NO3- / NO2-
or NH4+ were below the detection limit (-9999), they were taken to be zero for
this calculation.
 
Total dissolved nitrogen (TDN) was measured by in-line digestion using the
Lachat/Zellweger Analytics QuikChem 8000 flow injection autoanalyzer
(Milwaukee, WI, USA) using method FIA 31-107-04-3-B for low total nitrogen for
brackish/fresh waters (detection level: 0.1 - 5.0 milligrams nitrogen per
liter):\\u00a0 Water samples were vacuum filtered (less than 25 kiloPascals)
using pre-combusted Whatman glass microfibre filters (GF/F).\\u00a0 The
filtrate was stored in high-density polyethylene bottles and frozen at -20
degrees Celsius until analysis.\\u00a0 Two replicates were run from the same
bottle.\\u00a0 Total dissolved nitrogen by in-line digestion works by oxidizing
all the nitrogen compounds to nitrate by heating to 100 degrees Celsius and
adding energy via UV light.\\u00a0 The pH is dropped from 9.1 to 3 during the
decomposition.\\u00a0 The entire digestion occurs prior to the injection
valve.\\u00a0 The nitrate/nitrite concentration is then determined using
standard colorimetric techniques similar to the strict nitrate/nitrite
manifold. Method detection limits (MDL, \\u00b5g L-1) were: beginning 1Nov04 =
78; beginning 11Jul06 = 35.4 beginning 1Dec09 = 25.6; beginning 13Feb12 =
36.9; beginning 14Jan13 = 19.6; beginning 18Feb15 = 10.5.\\u00a0 MDL changed to
7.30 on sample date of 8/21/2017\\u00a0
 
Dissolved organic nitrogen (DON) was calculated by subtracting dissolved
inorganic nitrogen (DIN) from total dissolved nitrogen (TDN).\\u00a0 If the DIN
value used in the calculation was below the detection limit, it was taken to
be zero for this calculation.\\u00a0 At one point DON was determined by high
temperature oxidation using the Antek 7000N or Antek 7000V analyzer.
 
Orthophosphate (PO43-) was determined using a Lachat/Zellweger Analytics
QuikChem 8000 flow injection autoanalyzer (Milwaukee, WI) using method FIA
31-115-01-1-F/G:\\u00a0 Water samples were vacuum filtered (less than 25
kiloPascals) using pre-combusted Whatman glass microfibre filters
(GF/F).\\u00a0 The filtrate was stored in high-density polyethylene bottles and
frozen at -20 degrees Celsius until analysis.\\u00a0 Two replicates were run
from the same bottle.\\u00a0 Method detection limits (MDL, \\u00b5g L-1) were:
before 4Nov02 = 0.35; beginning 4Nov02 = 0.74; beginning 1Nov04 = 1.68;
beginning 11Jul06 = 1.84; beginning 1Dec09 = 0.62; beginning 13Feb12 = 0.69;
beginning 18Feb15 = 0.61.\\u00a0 MDL was changed to 1.80 on the sample date of
8/21/2017.
 
The molar ratio of nitrogen (N) to phosphorus (P), or N:P, was calculated by
dividing dissolved inorganic nitrogen (DIN) by orthophosphate (PO43-)
concentrations.
 
Silicic acid (SiO2) was measured after vacuum filtration (< 25 kPA) of the
collected water samples through pre-combusted (3-4 hours at 450 0C) Whatman
GF/F glass fiber filters.\\u00a0 The filtrate was stored in high-density
polyethylene bottles and frozen (-20 0C) until analysis.\\u00a0 Two replicates
were run from the same sample bottle.\\u00a0 Nitrate plus nitrite
concentrations were determined using a Lachat QuikChem 8000 flow injection
autoanalyzer (Milwaukee, WI, USA).\\u00a0 Method detection limits (MDL,
\\u00b5M) were: before 4Nov02 = 0.18; beginning 4Nov02 =1.24; beginning 1Nov04
= 1.86; beginning 11Jul06 = 0.75; beginning 1Dec09 = 0.75; beginning 13Feb12 =
0.09; beginning 18Feb15 = 0.08.\\u00a0 MDL was changed to 0.03 on sample date
of 8/21/2017.
 
Chlorophyll a (Chl a) measurements prior to the 08/17/1999 sampling date were
measured on a Shimadzu UV-160U spectrophotometer using the trichromatic
equation following sonication (45-60 s) and overnight extraction of glass
fiber filters in 90 % acetone.\\u00a0 Beginning on the 08/17/1999 sampling
date, Chl a concentration was measured using the modified in vitro
fluorescence technique in EPA Method 445.0 (Welshmeyer 1994, Arar et al.\\u00a0
1997): Fifty milliliters of each water sample was vacuum filtered (less than
25 kilopascals) in duplicate at low ambient light conditions using 25 mm
Whatman glass microfibre filters (GF/F).\\u00a0 The filters were blotted dry,
wrapped in foil and frozen immediately at -20 degrees Celsius until
analysis.\\u00a0 Chlorophyll a was extracted from the filter using a tissue
grinder and 10 mL of 90 percent reagent grade aqueous acetone (v/v with
deionized water, Fisher Scientific NF/FCC Grade). The samples remained in the
acetone overnight at -20 degrees Celsius.\\u00a0 The extracts were filter-
clarified using a centrifuge and analyzed on a Turner Designs TD-700
fluorometer that was configured for the non-acidification method of
Welschmeyer (1994).\\u00a0 The value reported is the average chlorophyll a
concentration measured from the two filters.\\u00a0 The fluorometer was
calibrated with a known concentration of pure Chl a that was determined using
a Shimadzu UV-160U spectrophotometer and the extinction coefficients of
Jeffrey and Humphrey (1975).\\u00a0 The calibration was checked daily against a
solid secondary standard (Turner Designs, proprietary formula).\\u00a0 As of
August 2010, fluorescence was also measured on a TurnerDesigns Trilogy
fluorometer.\\u00a0 References: 1.\\u00a0 Welschmeyer, N.A. 1994. Fluorometric
analysis of chlorophyll a in the presence of chlorophyll b and pheopigments.
Limnol. Oceanogr. 39:1985-1992.\\u00a0 2.\\u00a0 Arar, E.J., W.L. Budde, and
T.D. Behymer.\\u00a0 1997.\\u00a0 Methods for the determination of chemical
substances in marine and environmental matrices.\\u00a0 EPA/600/R-97/072.\\u00a0
National Exposure Research Laboratory, U.S. Environmental Protection Agency,
Cincinnati, Ohio.\\u00a0 3. Jeffrey, S.W., R.F.C. Mantoura, and S.W.
Wright.\\u00a0 1997.\\u00a0 Phytoplankton pigments in oceanography:\\u00a0
Guidelines to modern methods.\\u00a0 UNESCO Publishing, Paris, France.
 
Spec was used to determine chla up until AUGUST 1999.\\u00a0 The spec results
before Aug 1999 are corrected to correspond to the change in analysis with the
Turner Designs fluorometer.\\u00a0 Figure 1 presents raw and log transformed
regressions between the HPLC and SPEC determinations of chl a in the Neuse
during calendar year 1998.\\u00a0 It appears that the SPEC method produces chl
a values that are roughly 15 per cent higher than the HPLC method.\\u00a0
Figure 2 presents similar regressions between HPLC and FLUO determinations of
chl a in the Neuse from August \\u2013 December of 1999.\\u00a0 It appears that
the FLUO method produces chl a values that are roughly 67 per cent higher than
the HPLC method.\\u00a0 These figures suggest two important problems for
utilizing existing chl a data in water quality modeling in the Neuse; (i) a
decision must be made which analysis technique will be accepted as the
standard for determining chl a, and (ii) a correction must be applied to
equilibrate IMS chl a values determined by the SPEC and FLUO methods.
 
Primary Productivity rate was measured using an adaptation of Steeman
Nielsen's (1952) 14C bicarbonate method (Paerl et al. 1998).\\u00a0 This method
of measuring primary productivity allows direct measurement of carbon uptake
and measures only net photosynthesis:\\u00a0 Water samples were stored in 10
Liter high density polyethylene containers overnight in the research pond, a
flow through system that receives water from the adjacent Bogue Sound, thereby
simulating ambient water temperatures.\\u00a0 The following morning the water
samples were removed from the pond and transported to the laboratory for
analysis.\\u00a0 Water samples (76 milliliters) were added to three clear
plastic square bottles to determine light uptake of carbon in triplicate and
to 1 dark bottle to determine dark uptake of carbon.\\u00a0 A solution of
radioactive carbonate (300 microliters) was added to each bottle.\\u00a0 The
bottles were incubated for 4 hours in the pond.\\u00a0 The light bottles were
incubated underneath a field light simulator, while the dark bottles were
incubated in a covered perforated bucket that was submerged in the pond.\\u00a0
The FLS was used to simulate the ambient light conditions that phytoplankton
are exposed to in the estuary (mixing conditions).\\u00a0 The FLS is comprised
of a rotating wheel with varying levels of screening.\\u00a0 During the
incubation period, photosynthetically active radiation (PAR) measurements were
performed using a 2 pi Li-Cor LI-192SA spherical quantum sensor attached to a
Li-Cor data logger.\\u00a0 After the incubation period, the samples were
returned to the laboratory, shaken and the entire contents were gently vacuum
filtered (less than 25 kilopascals) using 25 mm Whatman glass microfibre
filters (GF/F).\\u00a0 The filters were placed in wooden drying trays and
treated with concentrated hydrochloric acid fumes for 40 minutes to an hour to
remove inorganic 14C.\\u00a0 The filters were folded in half and placed in 7
milliliter plastic scintillation vials.\\u00a0 Five milliliters of liquid
scintillation cocktail (ecolume or cytoscint) was added to the vials.\\u00a0
The vials were capped, shaken, stored in the dark for 3-24 hours and then
assayed for radioactivity using a Beckman liquid scintillation counter.\\u00a0
In addition to the samples, triplicate voucher samples were used to quantify
the radioactivity of the 14C added.\\u00a0 Voucher samples consisted of 100
microliter of 14C and 100 microliters of phenylethylamine.\\u00a0 These vials
also received 5 milliliters of liquid scintillation cocktail.\\u00a0 A
background vial and two 14C background standards were used.\\u00a0 \\u00a0The
quantity of carbon fixed is proportional to the fraction of radioactive carbon
assimilated.\\u00a0 (Paerl, H.W., J.L. Pinckney, J.M. Fear, and B.L. Peierls
1998. Ecosystem responses to internal and watershed organic matter loading:
consequences for hypoxia in the eutrophying Neuse River Estuary, North
Carolina, USA. Marine Ecology Progress Series 166: 17-25; Steemann Nielsen, E.
1952. The use of radio-active carbon (C14) for measuring organic production in
the sea. Journal du Conseil permanent international pour L'Exploration de la
Mer 18: 117-140)
 
Diagnostic phytoplankton photopigments were identified, separated and
quantified by high performance liquid chromatography coupled to an in-line
photodiode array spectrophotometer (Jeffrey et al.\\u00a0 1997):\\u00a0 Known
volumes of water sample (500-1000 milliliters, enough to obtain color on the
filter) were vacuum filtered (less than 25 kiloPascals) through 25 or 47
millimeter Whatman glass microfibre filters (GF/F) under reduced light
conditions.\\u00a0 The filters were blotted dry, folded in half, wrapped in
foil and then immediately frozen at -20 degrees Celsius until analysis.\\u00a0
The filters were placed in 15 milliliter centrifuge tubes containing 1.5-3.0
milliliters of 100% acetone (HPLC Grade), sonicated for 30-60 seconds using a
Fisher Sonic Dismembrator 300 with microtip and extracted at -20 degrees
Celsius for 12-24 hours.\\u00a0 After extraction the samples were centrifuged
at 4500 rpm and the supernatant (i.e.- the combined extracted pigments)
collected & filtered into amber glass autosampler vials using Millipex
Millipore 0.45 micometer PTFE.\\u00a0 Two hundred microliters of extractant
from each vial was injected into the HPLC system using a Spectra Physics (now
Thermo Separations Products) AS3000 autosampler and SP8800 pump, running a
non-linear, 55 minute, 2-solvent gradient adapted from Van Heukelem et.al.
1994 or 1995?.\\u00a0 The nonlinear, variable flow, binary gradient consisted
of solvent A [80% methanol : 20% ammonium acetate (0.5 M adjusted to pH 7.2)]
and B (80% methanol : 20% acetone).\\u00a0 The extractant was separated into
individual pigments using a series of C18 reverse-phase columns to optimize
photopigment separations:\\u00a0 The column order was a Rainin Microsorb guard
column (0.46 x 1.5 centimeters, 3 micrometer packing) followed by a single
monomeric reverse-phase C18 column (Rainin Microsorb-MV, 0.46 x 10 cm, 3
\\u00b5m packing) followed by two polymeric reverse-phase C18 columns (Vydac
201TP5, 0.46 x 25 cm, 5 \\u00b5m packing).\\u00a0 The columns were kept at a
constant 52 degrees Celsius in an Alltech 330 column heater.\\u00a0 The
separated pigments were then passed through an in line Shimadzu SPD-M10AV
photodiode array detector which measured the absorbance of the
sample/extractant, scanning the range of 350-800 nanometers every 2
seconds.\\u00a0 The data was collected and analyzed using Shimadzu's EZChrom
software.\\u00a0 Individual pigments are identified using a combination of peak
retention time and absorbance spectrum shape.\\u00a0 Retention times and
absorbance spectra are identified for each pigment by analyzing known pigments
(either as pure standards or pigments or isolated from algal cultures).\\u00a0
Pigments are quantified from their peak areas, calculated at 440nm. A
calibration curve is generated by injecting various volumes of a mixed
standard composed of known quantities of seven pure pigment standards
(fucoxanthin, zeaxanthin, bacteriochlorophyll a, canthaxathin, chlorophyll b,
chlorophyll a, echinenone and \\u00df-carotene) and calculating the peak areas
of those pigments\\u00a0 \\u00a0The peak areas are regressed against the known
quantities of each pigment to calculate the slope (Response Factor) for that
pigment.\\u00a0 Response factors for pigments we do not have reference
standards for are calculated using the ratio of absorbance coefficients of
each pigment to its closest structurally related reference pigment,
multiplying the known pigment's response factor by that ratio. Pigments
extracted from the samples are then quantified by multiplying the peak areas
of a chromatogram at 440nm by the response factors. Pigment values listed as
below detection were below the software threshold for peak detection or had
spectra below a similarity of 0.9 compared to library spectra. Technician
expert judgement was used in difficult cases.
 
The HPLC derived diagnostic photopigment concentrations were analyzed using
the ChemTax matrix factorization program (Mackey 1996).\\u00a0 This program
uses the steepest decent algorithm to determine the best fit based on an
initial estimate of pigment ratios for algal classes.\\u00a0 The initial
pigment ratio matrix used in the Chemtax analysis was derived from:\\u00a0
Mackey M.D., Mackey D.J., Higgins H.W., & Wright S.W.\\u00a0 1996.\\u00a0
CHEMTAX- a program for estimating class abundances from chemical markers:
application to HPLC measurements of phytoplankton.\\u00a0 Marine Ecology
Progress Series 144: 265-283, and consisted of nine photopigments
(alloxanthin, antheraxanthin, chlorophyll b, total chlorophyll a (chlorophyll
a + chlorophyllide a), fucoxanthin, lutein, peridinin, violaxanthin, and
zeaxanthin) for five algal groups that constitute the bulk of the
phytoplankton community in the Neuse River and Estuary (chlorophytes,
cryptophytes, cyanobacteria, diatoms, and dinoflagellates).\\u00a0 In order to
reduce the variation of pigment ratios due to large changes in phytoplankton
species composition with depth, season, and salinity regime, homogenous data
groupings of the HPLC pigment data were performed prior to running on
Chemtax:\\u00a0 HPLC pigment data was grouped by Depth Level (surface or
bottom) then by Season (winter, spring, summer and fall) then by Salinity
regime (oligohaline: <5.0 ppt, mesohaline: 5.01 - 18.0 ppt, polyhaline: >18.01
ppt).\\u00a0 When there were less than 10 samples in a given homogenous
grouping (Chemtax requires at least 10 samples per run), the data was grouped
by oligohaline + mesohaline or mesohaline + polyhaline (This is indicated in
the comments section).
 
Distance (in river kilometers) was calculated using ESRI ArcGIS
software.\\u00a0 Distances were calculated using projected station locations
(North Carolina State Plane 1983 meters projection).\\u00a0 Distances from
station 0 through 30 (upper river stations) were measured along the main
channel of the river. Distances from stations 30 to 180 were measured as
straight lines between stations";
    String awards_0_award_nid "762165";
    String awards_0_award_number "OCE-0825466";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=0825466";
    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 "David L. Garrison";
    String awards_0_program_manager_nid "50534";
    String cdm_data_type "Other";
    String comment 
"Biological, chemical, and physical water quality indicators of the Neuse River. 
  PI: Hans Paerl 
  Version: 2019-05-13";
    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 "2019-05-13T13:27:33Z";
    String date_modified "2019-05-15T16:27:40Z";
    String defaultDataQuery "&amp;time&lt;now";
    String doi "10.1575/1912/bco-dmo.767391.1";
    Float64 Easternmost_Easting -76.526;
    Float64 geospatial_lat_max 35.2106;
    Float64 geospatial_lat_min 34.9489;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -76.526;
    Float64 geospatial_lon_min -77.1222;
    String geospatial_lon_units "degrees_east";
    Float64 geospatial_vertical_max 7.501;
    Float64 geospatial_vertical_min 0.1;
    String geospatial_vertical_positive "down";
    String geospatial_vertical_units "m";
    String history 
"2024-12-20T07:26:14Z (local files)
2024-12-20T07:26:14Z https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_767391.html";
    String infoUrl "https://www.bco-dmo.org/dataset/767391";
    String institution "BCO-DMO";
    String instruments_0_acronym "LI-COR LI-193 PAR";
    String instruments_0_dataset_instrument_description "Prior to the 07/30/2003 sampling date, PAR measurements were performed with a spherical underwater quantum sensor (LI-COR LI-193SA) coupled to a LI-COR LI-1000 datalogger.  Beginning on the 07/30/2003 sampling date, a flat underwater quantum sensor (LI-COR LI-193SA) attached to a Yellow Springs Instruments YSI 6600 or YSI 6600 EDS-S sonde was used to measure PAR.";
    String instruments_0_dataset_instrument_nid "767456";
    String instruments_0_description "The LI-193 Underwater Spherical Quantum Sensor uses a Silicon Photodiode and glass filters encased in a waterproof housing to measure PAR (in the 400 to 700 nm waveband) in aquatic environments. Typical output is in micromol s-1 m-2.  The LI-193 Sensor gives an added dimension to underwater PAR measurements as it measures photon flux from all directions. This measurement is referred to as Photosynthetic Photon Flux Fluence Rate (PPFFR) or Quantum Scalar Irradiance. This is important, for example, when studying phytoplankton, which utilize radiation from all directions for photosynthesis. LI-COR began producing Spherical Quantum Sensors in 1979; serial numbers for the LI-193 begin with SPQA-XXXXX (licor.com).";
    String instruments_0_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L22/current/TOOL0458/";
    String instruments_0_instrument_name "LI-COR LI-193 PAR Sensor";
    String instruments_0_instrument_nid "432";
    String instruments_0_supplied_name "spherical underwater quantum sensor (LI-COR LI-193SA)";
    String instruments_10_dataset_instrument_description "Bottom water samples were collected with a horizontal plastic Van Dorn sampler.";
    String instruments_10_dataset_instrument_nid "767452";
    String instruments_10_description "A free-flushing water sample bottle comprising a cylinder (polycarbonate, acrylic or PVC) with a stopper at each end. The bottle is closed by means of a messenger from the surface releasing the tension on a latex band and thus pulling the two stoppers firmly into place. A thermometer can be mounted inside the bottle. One or more bottles can be lowered on a line to allow sampling at a single or multiple depth levels. Van Dorn samplers are suitable for for physical (temperature), chemical and biological sampling in shallow to very deep water. Bottles are typically lowered vertically through the water column although a horizontal version is available for sampling near the seabed or at thermoclines or chemoclines. Because of the lack of metal parts the bottles are suitable for trace metal sampling, although the blue polyurethane seal used in the Alpha version may leach mercury. The Beta version uses white ASA plastic seals that do not leach mercury but are less durable.";
    String instruments_10_instrument_name "Van Dorn water sampler";
    String instruments_10_instrument_nid "755357";
    String instruments_10_supplied_name "plastic Van Dorn sampler";
    String instruments_1_acronym "HPLC";
    String instruments_1_dataset_instrument_description "Two hundred microliters of extractant from each vial was injected into the HPLC system using a Spectra Physics (now Thermo Separations Products) AS3000 autosampler and SP8800 pump, running a non-linear, 55 minute, 2-solvent gradient adapted from Van Heukelem et.al. 1994 or 1995?.";
    String instruments_1_dataset_instrument_nid "767462";
    String instruments_1_description "A High-performance liquid chromatograph (HPLC) is a type of liquid chromatography used to separate compounds that are dissolved in solution. HPLC instruments consist of a reservoir of the mobile phase, a pump, an injector, a separation column, and a detector. Compounds are separated by high pressure pumping of the sample mixture onto a column packed with microspheres coated with the stationary phase. The different components in the mixture pass through the column at different rates due to differences in their partitioning behavior between the mobile liquid phase and the stationary phase.";
    String instruments_1_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB11/";
    String instruments_1_instrument_name "High Performance Liquid Chromatograph";
    String instruments_1_instrument_nid "506";
    String instruments_1_supplied_name "HPLC system";
    String instruments_2_acronym "Nutrient Autoanalyzer";
    String instruments_2_dataset_instrument_description 
"Nitrate/nitrite (NO3- / NO2-) concentration was determined using a Lachat/Zellweger Analytics QuikChem 8000 flow injection autoanalyzer (Milwaukee, WI, USA) using method FIA 31-107-04-1-C.
Ammonium (NH4+) concentration was determined using a Lachat/Zellweger Analytics QuikChem 8000 flow injection autoanalyzer (Milwaukee, WI) using method FIA 31-107-06-1-A/B.
Total dissolved nitrogen (TDN) was measured by in-line digestion using the Lachat/Zellweger Analytics QuikChem 8000 flow injection autoanalyzer (Milwaukee, WI, USA) using method FIA 31-107-04-3-B for low total nitrogen for brackish/fresh waters (detection level: 0.1 - 5.0 milligrams nitrogen per liter).
Orthophosphate (PO43-) was determined using a Lachat/Zellweger Analytics QuikChem 8000 flow injection autoanalyzer (Milwaukee, WI) using method FIA 31-115-01-1-F/G.";
    String instruments_2_dataset_instrument_nid "767460";
    String instruments_2_description "Nutrient Autoanalyzer is a generic term used when specific type, make and model were not specified.  In general, a Nutrient Autoanalyzer is an automated flow-thru system for doing nutrient analysis (nitrate, ammonium, orthophosphate, and silicate) on seawater samples.";
    String instruments_2_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB04/";
    String instruments_2_instrument_name "Nutrient Autoanalyzer";
    String instruments_2_instrument_nid "558";
    String instruments_2_supplied_name "Lachat/Zellweger Analytics QuikChem 8000 flow injection autoanalyzer";
    String instruments_3_acronym "UV Spectrophotometer-Shimadzu";
    String instruments_3_dataset_instrument_description "Dissolved organic carbon (DOC) concentration was measured using a Shimadzu TOC-5000A Analyzer:  Water samples were vacuum filtered (less than 25 kilopascal) using pre-combusted Whatman glass microfibre filters (GF/F).  The filtrate was stored in pre-combusted glass scintillation vials with Teflon closures and frozen at -20 degrees Celsius until analysis.  The Shimadzu TOC-5000A Analyzer uses high temperature catalytic oxidation followed by non-dispersive infrared analysis of the CO2 produced.  Samples were acidified to a pH less than 2 and sparged with air before they were analyzed for non-volatile organic carbon.  DOC values in 1996 were run from previously run nutrient samples. Starting February 2018, all stations were collected.  Prior to Feb. 2018 only NR 0, 30, 70, 100, 120, and 160 surface and bottom stations were measured.";
    String instruments_3_dataset_instrument_nid "767459";
    String instruments_3_description "The Shimadzu UV Spectrophotometer is manufactured by Shimadzu Scientific Instruments (ssi.shimadzu.com). Shimadzu manufacturers several models of spectrophotometer; refer to dataset for make/model information.";
    String instruments_3_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB20/";
    String instruments_3_instrument_name "UV Spectrophotometer-Shimadzu";
    String instruments_3_instrument_nid "595";
    String instruments_3_supplied_name "Shimadzu TOC-5000A Analyzer";
    String instruments_4_acronym "Secchi Disc";
    String instruments_4_dataset_instrument_description "The secchi disk was deployed off of the sunlit side of the research vessel.  The depth (in meters) at which the secchi disk was no longer visible by the naked eye was recorded as the secchi depth.";
    String instruments_4_dataset_instrument_nid "767455";
    String instruments_4_description "Typically, a 16 inch diameter white/black quadrant disc used to measure water optical clarity";
    String instruments_4_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L22/current/TOOL0430/";
    String instruments_4_instrument_name "Secchi Disc";
    String instruments_4_instrument_nid "609";
    String instruments_4_supplied_name "secchi disk";
    String instruments_5_acronym "CHN_EA";
    String instruments_5_dataset_instrument_description "After drying at 60 0C, the filters were rolled in tin disks and injected into a PE 2400 Series II CHNS/O Analyzer calibrated with acetanilide ending in June 2014.  Starting on the Neuse River sample date of June 2, 2014, a Costech Analytical Technologies, Inc. Elemental Combustion System CHNS-O ECS 4010 was used for elemental analysis by \"flash combustion/chromatographic separation and multi-detector techniques\".  The Costech Instrument utilizes EAS Clarity Software.  Atropine standards are used to develop a calibration curve (C 70.56%, N 4.84%, and carbon response ratio of 0.025 +/-0.003).  NIST Buffalo River Sediment Reference Material 8704 (C 3.351% +/-0.017, N 0.20% +/-0.04) and/or Acetanilide Bypass (C 71.09%, N 10.36%, carbon response ratio of 0.055 +/- 0.003) may used for calibration or a check standard.";
    String instruments_5_dataset_instrument_nid "767458";
    String instruments_5_description "A CHN Elemental Analyzer is used for the determination of carbon, hydrogen, and  nitrogen content in organic and other types of materials, including  solids, liquids, volatile, and viscous samples.";
    String instruments_5_instrument_name "CHN Elemental Analyzer";
    String instruments_5_instrument_nid "625";
    String instruments_5_supplied_name "PE 2400 Series II CHNS/O Analyzer";
    String instruments_6_acronym "HydroLab DS4";
    String instruments_6_dataset_instrument_description "Prior to the 09/13/2000 sampling date, in situ measurements were performed at discrete depths using a Hydrolab Data Sonde 3 equipped with a multiprobe and SVR3 display logger.";
    String instruments_6_dataset_instrument_nid "767453";
    String instruments_6_description "Sensors for temperature, conductivity, salinity, specific conductance, TDS, pH, ORP, dissolved oxygen, turbidity, chlorophyll a, blue-green algae, Rhodamine WT, ammonium, nitrate, chloride, ambient light (PAR), and total dissolved gas.";
    String instruments_6_instrument_name "HydroLab Datasonde 4 Multiprobe";
    String instruments_6_instrument_nid "642";
    String instruments_6_supplied_name "Hydrolab Data Sonde 3";
    String instruments_7_acronym "YSI Sonde 6-Series";
    String instruments_7_dataset_instrument_description "Beginning on the 09/13/2000 sampling date, in situ measurements were performed at discrete depths on the sunlit side of the research vessel using a Yellow Springs Instruments (YSI Incoporated, Ohio) multiparameter sonde (Model 6600 or 6600 EDS-S Extended Deployment System) equipped with a YSI conductivity/temperature probe (Model 6560), a YSI chlorophyll probe (Model 6025), a YSI pH probe (Model 6561 or 6566), a YSI pulsed dissolved oxygen probe (Model 6562), a self cleaning YSI turbidity probe (Model 6026 or 6136), and beginning on the 07/30/2003 sampling date, a flat Li-Cor sensor (UWQ-PAR 6067).";
    String instruments_7_dataset_instrument_nid "767454";
    String instruments_7_description "YSI 6-Series water quality sondes and sensors are instruments for environmental monitoring and long-term deployments. YSI datasondes accept multiple water quality sensors (i.e., they are multiparameter sondes). Sondes can measure temperature, conductivity, dissolved oxygen, depth, turbidity, and other water quality parameters. The 6-Series includes several models. More from YSI.";
    String instruments_7_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L22/current/TOOL0737/";
    String instruments_7_instrument_name "YSI Sonde 6-Series";
    String instruments_7_instrument_nid "663";
    String instruments_7_supplied_name "Yellow Springs Instruments (YSI Incoporated, Ohio) multiparameter sonde (Model 6600 or 6600 EDS-S Extended Deployment System)";
    String instruments_8_acronym "TD-700";
    String instruments_8_dataset_instrument_description "Colored dissolved organic matter (CDOM) was measured using a Turner Designs TD-700 fluorometer configured with a near-UV mercury vapour lamp, a 350 nm excitation filter, and a 410–600 nm emission filter.";
    String instruments_8_dataset_instrument_nid "767457";
    String instruments_8_description "The TD-700 Laboratory Fluorometer is a benchtop fluorometer designed to detect fluorescence over the UV to red range. The instrument can measure concentrations of a variety of compounds, including chlorophyll-a and fluorescent dyes, and is thus suitable for a range of applications, including chlorophyll, water quality monitoring and fluorescent tracer studies. Data can be output as concentrations or raw fluorescence measurements.";
    String instruments_8_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L22/current/TOOL0510/";
    String instruments_8_instrument_name "Turner Designs 700 Laboratory Fluorometer";
    String instruments_8_instrument_nid "694";
    String instruments_8_supplied_name "Turner Designs TD-700 fluorometer";
    String instruments_9_acronym "Spectrophotometer";
    String instruments_9_dataset_instrument_description "Diagnostic phytoplankton photopigments were identified, separated and quantified by high performance liquid chromatography coupled to an in-line photodiode array spectrophotometer (Jeffrey et al.  1997)";
    String instruments_9_dataset_instrument_nid "767461";
    String instruments_9_description "An instrument used to measure the relative absorption of electromagnetic radiation of different wavelengths in the near infra-red, visible and ultraviolet wavebands by samples.";
    String instruments_9_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB20/";
    String instruments_9_instrument_name "Spectrophotometer";
    String instruments_9_instrument_nid "707";
    String instruments_9_supplied_name "in-line photodiode array spectrophotometer";
    String keywords "allo, Allo_corr, ammonia, ammonium, anth, B_car, bco, bco-dmo, biological, but, But_fuco, c1c2, cantha, car, carbon, cdom, Cdom_Corrected, chemical, chemistry, chl, Chl_a_corr, Chl_b_corr, Chl_c1c2, chla, Chla_IWS, chlide, Chlide_a, chlorophyll, chlorophyll-a, chlraw, colored, commerce, concentration, concentration_of_chlorophyll_in_sea_water, cond, corr, Correct_Chla_IV, corrected, cto, CtoN, data, dataset, date, density, department, depth, depth2, description, diadino, diato, dic, din, dissolved, dmo, doc, don, dosat, DV_chl_a, earth, Earth Science > Oceans > Ocean Chemistry > Ammonia, Earth Science > Oceans > Ocean Chemistry > Chlorophyll, Earth Science > Oceans > Ocean Chemistry > Nitrate, Earth Science > Oceans > Ocean Chemistry > Phosphate, Earth Science > Oceans > Salinity/Density > Salinity, echin, erddap, fuco, Fuco_corr, gyro, hex, Hex_fuco, iso, km0, latitude, longitude, lut, management, mass, mass_concentration_of_phosphate_in_sea_water, matter, mole, mole_concentration_of_ammonium_in_sea_water, mole_concentration_of_nitrate_in_sea_water, monado, myxo, n02, neo, nh4, nitrate, no3, NO3_NO2, nto, NtoP, O2, ocean, oceanography, oceans, office, organic, oxygen, particulate, perid, Perid_corr, phide, Phide_a, phosphate, phytin, Phytin_a, po4, POC, ppr, practical, pras, preliminary, salinity, science, sea, sea_water_practical_salinity, season, seawater, secchi, SiO2, source, spec, station, Station_Description, tdn, temperature, time, total, TotalChla, turbidity, viola, water, year, ysi, YSI_BP, YSI_Chl, YSI_Chlraw, YSI_Depth, YSI_DO, YSI_DOsat, YSI_pH, YSI_Salinity, YSI_SpecCond, YSI_Temp, YSI_Time, YSI_Turbidity, zea, Zea_corr";
    String keywords_vocabulary "GCMD Science Keywords";
    String license "https://www.bco-dmo.org/dataset/767391/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/767391";
    Float64 Northernmost_Northing 35.2106;
    String param_mapping "{'767391': {'Lat': 'flag - latitude', 'YSI_Depth': 'flag - depth', 'Lon': 'flag - longitude'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/767391/parameters";
    String people_0_affiliation "University of North Carolina at Chapel Hill";
    String people_0_affiliation_acronym "UNC-Chapel Hill";
    String people_0_person_name "Hans Paerl";
    String people_0_person_nid "734605";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "Woods Hole Oceanographic Institution";
    String people_1_affiliation_acronym "WHOI BCO-DMO";
    String people_1_person_name "Mathew Biddle";
    String people_1_person_nid "708682";
    String people_1_role "BCO-DMO Data Manager";
    String people_1_role_type "related";
    String project "climate_phyto_estuaries";
    String projects_0_acronym "climate_phyto_estuaries";
    String projects_0_description 
"NSF Award Abstract:
Climatic perturbations by drought-flood cycles, tropical storms, and hurricanes are increasingly important in Mid-Atlantic estuaries, leading to ecosystem-scale responses of the plankton system with significant trophic implications. Recent observations support an emerging paradigm that climate dominates nutrient enrichment in these ecosystems, explaining seasonal and interannual variability of phytoplankton floral composition, biomass (chl-a), and primary production (PP). This project will evaluate this paradigm in the two largest estuaries in the United States, Chesapeake Bay (CB) and Albemarle-Pamlico Sound-Neuse River Estuary (APS-NRE) by quantifying responses to climatic perturbations. This project will: (1) resolve long-term trends of plankton biomass/production from high variability driven by climatic forcing, such as drought-flood cycles that generate significant departures from the norm; (2) quantify the role of episodic wind and precipitation events, such as those associated with frontal passages, tropical storms, and hurricanes, that evoke consequential spikes of biomass/production outside the resolution of traditional methods. The field program will focus on event-scale forcing of phytoplankton dynamics by collecting shipboard, aircraft remote sensing, and satellite (SeaWiFS, MODIS-A) data, analyzing extensive monitoring data for CB and APS-NRE to develop context, and quantifying effects of climatic perturbations on phytoplankton dynamics as departures from long-term averages. The rapid-response sampling will be paired with numerical simulations using coupled hydrodynamic biogeochemical models based on the Regional Ocean Modeling System (ROMS). This combination of observations and modeling will be used to explore mechanistic links and test empirical relationships obtained from field data.
Intellectual Merit. Drought-flood cycles, tropical storms, and hurricanes are occurring at increasing severity and frequency, exerting significant pressures on land margin ecosystems. Research and monitoring in these ecosystems has focused singularly on eutrophication for nearly five decades. Recognition of climatic perturbations as the underlying cause of phytoplankton variability represents a significant departure from this singular focus. This project will combine observations and modeling to significantly extend our knowledge of how climate regulates phytoplankton dynamics in estuaries. Progress in calibrating and validating hydrodynamic biogeochemical models with data collected in CB and APS-NRE by this project will lead to predictive capabilities thus far unattained, allowing us to evaluate the paradigm that climatic perturbations regulate phytoplankton dynamics in estuaries.
Broader Impacts: Addressing the effects of climatic perturbations on phytoplankton dynamics in estuaries with a combination of data collection, analysis, and mechanistic modeling has societal benefits for scientists and resource managers. Applications in addition to ?basic? science include the consideration of climatic forcing in designing effective nutrient management strategies. Specific impacts include: (1) quantifying the effects of climatic perturbations on planktonic processes for important estuarine-coastal ecosystems; (2) extending empirically-based water quality criteria forward by enabling predictions of floral composition, chl-a, and PP in changing climate conditions; (3) combining observations and mechanistic models to support scenario analysis, allowing us to distinguish long-term trends from variability imposed by climate. This project will offer a graduate course in physical transport processes and plankton productivity that will benefit from this research, support two Ph.D. students, and train undergraduates in NSF REU and minority outreach programs at HPL-UMCES and IMS-UNC. The main products will be peer-reviewed publications and presentations at scientific meetings. The three PIs maintain active web sites that will be used to distribute results and data.
NOTE:
Dr. Harding was the original Lead PI. Dr. Michael R. Roman was named as substitute PI when Dr. Harding served as a Program Director in the NSF Biological Oceanography Program for two years, and through his move to UCLA thereafter. Dr. Harding is responsible for the data holdings on this project and for coordinating their submittal to BCO-DMO.";
    String projects_0_end_date "2013-09";
    String projects_0_geolocation "The two largest estuaries in the United States, Chesapeake Bay (CB) and Albemarle-Pamlico Sound- Neuse River Estuary (APS-NRE).";
    String projects_0_name "Collaborative Research: Regulation of Phytoplankton Dynamics in Mid-Atlantic Estuaries Subject to Climatic Perturbations";
    String projects_0_project_nid "491333";
    String projects_0_project_website "http://paerllab.web.unc.edu/projects/modmon/";
    String projects_0_start_date "2008-10";
    String publisher_name "Biological and Chemical Oceanographic Data Management Office (BCO-DMO)";
    String publisher_type "institution";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 34.9489;
    String standard_name_vocabulary "CF Standard Name Table v55";
    String subsetVariables "Source,Pras,DV_chl_a";
    String summary "The Neuse River Estuary Water Quality Dataset is a compilation of the biological, chemical and physical water quality data that was collected along the length of the Neuse River Estuary, NC from March 14, 1985 to February 15, 1989 and from January 24, 1994 to the present.  The primary purpose of this dataset was to provide long-term environmental information to supplement experimental, process-based research, including the Atlantic Coast Environmental Indicators Consortium (ACE-INC) project as well as other laboratory studies.";
    String title "[Neuse River Estuary WQ] - Biological, chemical, and physical water quality indicators of the Neuse River, North Carolina from 2008 through 2013 (Collaborative Research: Regulation of Phytoplankton Dynamics in Mid-Atlantic Estuaries Subject to Climatic Perturbations)";
    String version "1";
    Float64 Westernmost_Easting -77.1222;
    String xml_source "osprey2erddap.update_xml() v1.3";
  }
}

 

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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 Data Access Protocol (DAP) and its selection constraints.

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.

• (easy) You can get data by using the dataset's Data Access Form or Subset form. They make the URL for you.
• (easy) You can make a graph or map by using the dataset's Make A Graph form. It makes the URL for you.
• (not hard) You can bypass the forms and get the data or make a graph or map by generating the URL by hand or with a computer program or script.

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.

 

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ERDDAP, Version 2.22
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