Grain-size analysis data from sediment samples in support of oceanographic and water-quality measurements at Thompsons Beach and Stone Harbor, New Jersey, collected in September 2018 and March 2022

Two sediment samples were collected at Stone Harbor, NJ on September 18, 2018 and Thompsons Beach, NJ on September 19, 2018 using a small grab sampler.

Two sediment samples were collected on March 22, 2022; one at Stone Harbor, NJ using a small metal scoop while on foot, and one at Thompsons Beach, NJ using a small (ponar type) grab sampler from a boat.

The samples were analyzed in the Sediment Laboratory at the USGS Woods Hole Coastal and Marine Science Center using the HORIBA LA-960 laser diffraction unit and sieving of the ≥ -2 phi fraction. Samples submitted to the Sediment Laboratory for grain-size analysis were assigned unique analysis identifiers (ANALYSIS_ID) and divided into batches of no more than 30 samples. Each batch is entered into a Microsoft Excel data entry spreadsheet (LD Worksheet Template_SS02.xlsx and LD Worksheet Template_RB01.xlsx, where SS02 and RB01 are the identifiers assigned to the sample submission) to record the initial and dried sample weights, as well as the sieved coarse fraction weights. Each batch was also entered into macro-enabled Microsoft Excel data entry spreadsheets (GrainSizeWorksheet_LD1-30_SS02(batch_1).xlsm or GrainSizeWorksheet_LD31-60_SS02(batch_1).xlsm, and GrainSizeWorksheet_LD1-30_RB01(batch_1).xlsm or GrainSizeWorksheet_LD31-60_RB01(batch_1).xlsm, where "LD1-30” and “LD31-60” refer to the pre-labeled and weighed glass laser diffraction vials in which the samples will be run, and “batch_1” refers to the sample batch) to record the measurement data coming from the laser diffraction unit and incorporate the initial, dried, and sieved weights. The sample submission identifier for these samples is "SS02" or "RB01".
About 10-15 grams of wet sediment were placed in a pre-weighed beaker and the gross weight was recorded. The sample was wet sieved through a 4 mm (No. 5) sieve. If there was any coarse fraction remaining in the sieve, the coarse material was oven dried at 100°C in a pre-weighed beaker and weighed again when dry. This coarse fraction was dry sieved to determine the individual weights of the -2 to -5 phi fractions, and the weights were recorded in the data entry spreadsheet LD Worksheet Template_SS02.xlsx and LD Worksheet Template_RB01.xlsx. The fine fraction in water was collected in a pre-labeled and weighed glass laser diffraction vial. If there was any coarse fraction remaining in the sieve from wet sieving, this vial was also oven dried at 100°C and weighed when dry. If there was no coarse fraction remaining from wet sieving, the sample proceeded directly to processing for analyses by the HORIBA LA-960 laser diffraction unit.
Fine fractions ready for analysis by the HORIBA laser diffraction unit were rehydrated with distilled water if they had been dry. Fifteen (15) ml of pre-mixed 40 g/l sodium hexametaphosphate [(NaPO3)6] was added to each sample. If the height of the fluid in the laser diffraction vial was less than 5 cm, more distilled water was added to raise the level to no more than 8 cm in the vial. The samples were gently stirred, covered, and allowed to soak for at least 1 hour (for samples that were not dried) or up to 24 hours (for samples that were dried). Soaked vials were placed into an ultrasonic bath and run for 10 minutes at a frequency of 37 Hz with a power level of 100. If the samples appeared to be fully disaggregated, they were placed into pre-determined autosampler locations and were run using the HORIBA LA-960 for Windows software to get the fine fraction grain-size distributions. The fine fraction distribution data was added to the appropriate data entry spreadsheets (GrainSizeWorksheet_LD1-30_SS02(batch_1).xlsm, GrainSizeWorksheet_LD31-60_SS02(batch_1).xlsm, GrainSizeWorksheet_LD1-30_RB01(batch_1).xlsm, or GrainSizeWorksheet_LD31-60_RB01(batch_1).xlsm). The spreadsheet was used to calculate a continuous phi class distribution from the original fractions. All samples were run in duplicate. This step and the following step were completed by Brian Buczkowski.

A continuous phi class distribution from the original fractions was transposed to the "results" tab in the macro-enabled Microsoft Excel data entry workbook (GrainSizeWorksheet_LD1-30_SS02(batch_1).xlsm, GrainSizeWorksheet_LD31-60_SS02(batch_1).xlsm, GrainSizeWorksheet_LD1-30_RB01(batch_1).xlsm, or GrainSizeWorksheet_LD31-60_RB01(batch_1).xlsm where SS02 and RB01 are the identifiers assigned to the sample submission, “LD1-30” and “LD31-60” refer to the pre-labeled and weighed glass laser diffraction vials in which the samples were run, and “batch_1” refers to the sample batch). Macros in the workbook (“GS_MoM_Arithmatic,” "GS_statistics," and "sedimentname") were run to calculate grain-size classification and statistical analyses and finish processing the data. Sample, navigation, and field identifiers, along with continuous phi class distribution data, grain-size classification, and statistical analysis results were copied and pasted into the final Microsoft Excel spreadsheet (SS02_GS-LD_results.xlsx and RB01_GS-LD_results.xlsx). The processed data were quality control checked and assigned a quality grade based on the examination of the analytical data. Processed data were released to the submitter and incorporated into the laboratory's database. All raw analytical data generated by the samples were archived in the Sediment Laboratory. Initial processing 20220511. These data were reprocessed using Microsoft Excel data processing workbook (version 2.1) after an issue was identified in the original Microsoft Excel data processing workbook (version 1.0) used to calculate grain size statistics and phi class distributions from raw data obtained from the Sediment Laboratory's Horiba LA-960 laser diffraction unit.

The results from the Microsoft Excel spreadsheet were compiled into NFWF_GrainSize_Compiled_v2.xlsx and imported into MATLAB R2023A where the column headings were given unique names, and the significant figures were adjusted appropriately. The final data were then exported to a CSV file. A second CSV file was made to create a shapefile where negative phi values appearing in the field names were replaced with an 'M' for minus, since the shapefile cannot accommodate negative values in the field names (e.g. PHI_-1 is written as PHI_M1). This CSV file was imported into ArcGIS Pro 3.1.0 and a shapefile (.shp) and associated files were saved.