This readme.txt file was generated on 20210224 by Sarah Hobbie ------------------- GENERAL INFORMATION ------------------- 1. Title of Dataset MSRC2019: Mass, moisture, nitrogen, and phosphorus in street sweepings collected from five cities in the Twin Cities Metropolitan Area, Minnesota 2. Author Information Principal Investigator Contact Information Name: Sarah Hobbie Institution: University of Minnesota Address: Dept of Ecology, Evolution and Behavior Email: shobbie@umn.edu ORCID: https://orcid.org/0000-0001-5159-031X Associate or Co-investigator Contact Information Name: Lawrence Baker Institution: University of Minnesota Address: Dept of Bioproducts and Biosystems Engineering Email: baker127@umn.edu ORCID: N/A Associate or Co-investigator Contact Information Name: Jacques Finlay Institution: University of Minnesota Address: Dept of Ecology, Evolution and Behavior Email: jfinlay@umn.edu ORCID: https://orcid.org/0000-0002-7968-7030 3. Date of data collection: 20100809-20191118 4. Geographic location of data collection (where was data collected?): Cities of Forest Lake, Minneapolis, Prior Lake, Roseville, and Shoreview, MN 5. Information about funding sources that supported the collection of the data: This project was supported by the Minnesota Stormwater Research and Technology Transfer Program administered by the University of Minnesota Water Resources Center through an appropriation from the Clean Water Fund established by Minnesota Clean Water Land and Legacy Amendment and from the Minnesota Stormwater Research Council with financial contributions from: Capitol Region Watershed District, Comfort Lake-Forest Lake Watershed District, Mississippi Watershed Management Organization, Nine Mile Creek Watershed District, Ramsey-Washington Metro Watershed District, South Washington Watershed District, City of Edina, City of Minnetonka, City of Woodbury, Wenck Associates, Minnesota Cities Stormwater Coalition. Additional funding for analysis of Forest Lake sweepings came from the City of Forest Lake and the Comfort Lake-Forest Lake Watershed District, and for analysis of Prior Lake sweepings from the Environmental Protection Agency. -------------------------- SHARING/ACCESS INFORMATION -------------------------- 1. Licenses/restrictions placed on the data: None 2. Links to publications that cite or use the data: Hobbie, S. E., R. A. King, T. Belo, L. A. Baker, and J. C. Finlay. 2020. Developing a Street Sweeping Credit for Stormwater Phosphorus Source Reduction - Final Report. Minnesota Stormwater Research Council Project. University of Minnesota, St. Paul, MN., St. Paul, MN. Kalinosky, P., L. A. Baker, S. E. Hobbie, R. Bintner, and C. Buyarski. 2014. User support manual: Estimating nutrient removal by enhanced street sweeping. University of Minnesota. Kalinosky, P. 2015. Quantifying solids and nutrient recovered through street sweeping in a suburban watershed. Master of Science. University of Minnesota, St. Paul, MN. King, R. A., L. A. Baker, J. C. Finlay, T. Belo, and S. E. Hobbie. 2020. Developing a Street Sweeping Credit for Stormwater Phosphorus Source Reduction - Report to Inform Phosphorus Credit for Street Sweeping. Minnesota Stormwater Research Council Project. University of Minnesota, St. Paul, MN., St. Paul, MN., St. Paul, MN. 3. Links to other publicly accessible locations of the data: None 4. Links/relationships to ancillary data sets: None 5. Was data derived from another source? No 6. Recommended citation for the data: Hobbie, S. E., R. King, T. Belo, L. A. Baker, and J. Finlay, C. 2020. MSRC2019: Mass, moisture, nitrogen, and phosphorus in street sweepings collected from five cities in the Twin Cities Metropolitan Area, Minnesota. Data Repository for U of M, St. Paul, MN. --------------------- DATA & FILE OVERVIEW --------------------- 1. File List A. Filename: MSRC2019_20210224.csv Short description: File containing data on street sweeping routes; canopy cover over routes; sweeping load wet and dry mass; street sweeping total phosphorus, total nitrogen, and total organic carbon concentrations from street sweeping operations in five cities in the Twin Cities Metropolitan Area, Minnesota B. Filename: MSRC2019_Metadata_20220330.csv Short description: Metadata describing variables, units, description of variables, and calculations (if applicable). This file replaces the previous 2021-02-24 version of the spreadsheet. C: Filename: ForestLakeRoutes.pdf Short Description: Maps showing locations of street sweeping routes in Forest Lake, MN. D: Filename: MinneapolisRoutes.pdf Short Description: Maps showing locations of street sweeping routes in Minneapolis, MN. E: Filename: PriorLakeRoutes.pdf Short Description: Map showing locations of street sweeping routes in Prior Lake, MN. F: Filename: RosevilleRoutes.pdf Short Description: Map showing locations of street sweeping routes in Roseville, MN. G: Filename: ShoreviewRoutes.pdf Short Description: Maps showing locations of street sweeping routes in Shoreview, MN. H: Filename: MSRC_2010_SweepingRoutes.csv Short Description: File containing the names of each sweeping route included in the study, as designated in the MSRC_2019 database (MSRC2019_20210224.csv) and as designated on the route maps in files B-G. 4. Are there multiple versions of the dataset? no -------------------------- METHODOLOGICAL INFORMATION -------------------------- 1. Description of methods used for collection/generation of data: We collected samples from street sweeping events during spring-summer-fall of 2019, and analyzed swept samples for mass, carbon (C), total nitrogen (TN), and total P (TP) concentrations. These data were combined with similar data collected in a previous study, the Prior Lake Street Sweeping Study (Kalinosky et al. 2014, Kalinosky 2015), to develop this dataset. For Forest Lake, Minneapolis, Roseville, and Shoreview, samples were collected during the warm season in 2019 starting as early as March (Shoreview) and as late as June (Minneapolis) and ending in October (Forest Lake) or mid-November (all other cities). 2019 saw heavy snowfalls in February, which delayed the ability of Minneapolis to collect samples for us. For each city, we selected routes to sample from the cityÕs ongoing street sweeping programs to achieve a range of street sweeping frequencies, canopy cover, and sweeper type. Samples were collected within 24 hours of a route being fully swept and before any precipitation events occurred. The length of time it took to sweep a route varied from one day up to a full week. It was expected that vehicle motion during sweeping operations would result in some amount of settling and compaction of material collected in the hopper. For this reason, sweeper samples were collected after loads were dumped to take advantage of re-mixing. To ensure collection of a representative sample, the pile was visually inspected before sample collection to estimate the portions of sediments and plant debris. Using a small trowel, we combined at least five small amounts of sample into a gallon bag, walking around the pile and scooping from various points. We took care to collect a sample that accurately reflected the composition of the sweeper pile, based on visual inspection. Before sample collection, the outside of the pile was scraped away to avoid sampling material with non-representative moisture content resulting from exposure to sun and wind. Large pieces of trash and woody debris were avoided, but smaller pieces, which were easily picked up, were not separated from the sample. Nitrile gloves were worn to prevent contamination of swept material and to protect the collectorÕs hands. The sampling trowel was cleaned with nanopure water, wiped down with 70% ethanol, and allowed time to air dry fully before being used to collect another sample. A volume of approximately 0.75 to 1 gallon (ca. 4 L) of sweeper waste was collected in 1-gallon sized plastic freezer bags.ÊSamples were stored in a refrigerator until moisture determination. If moisture was not determined within a day, the sample was frozen. The initial processing of all sweeping samples was conducted at the University of Minnesota Department of Ecology, Evolution and Behavior. Frozen sweeper samples were thawed under refrigeration and thawed samples were separated into five fractions during processing: garbage, rocks (inorganics ³ 2mm), coarse organics (organics ³ 2mm), soluble nutrients leached during isolation of the coarse organic fraction (see below), and fine sediments (< 2mm fraction). The wet mass, dry mass, and moisture content (determined by oven drying at 65¡C) of each of the solid fractions were determined for all sweeper samples. We assumed that garbage and rocks did not contribute significantly toÊnutrient loads, so only the mass of these fractions was tracked, whereas chemical analyses of total phosphorus (TP), total nitrogen (TN), and total organic carbon (TOC) were performed on the fine, coarse organic, and soluble fractions (see below). The moisture content of each sample fraction was determined as the difference between the fresh (wet) weight and the oven dried (65¡C) weight, divided by the dry weight, multiplied by 100. Coarse material retained on the 2mm sieve went through a second fractionation using buoyancy to separate coarse organic material from any adhered sediments. Coarse material was added to 3 liters of nanopure water in a clean 5-liter plastic bucket. Suspended organics were gently agitated for about 1 minute until adhered soil particles appeared to be dislodged. Vegetative material that floated during the process was classified as coarse organic matter (COM). This material was collected by filtering wash water through a 2 mm sieve. To account for nutrients leached during the separationÊprocess, wash water was subsampled for nutrient analysis. Settled particles were collected, oven dried, and sieved to separate additional fines (<2mm) and the remaining rock fraction (>2mm). The total coarse organic matter recovered was then oven dried for nutrient analyses and to determine its dry weight. The wash water was filtered through Whatman 42 filter paper and frozen except for a 20mL aliquot that was acidified and refrigerated for TOC/TDN analysis. Coarse Organic Matter and Sediment C and N. Prior to element analysis, the coarse organic fraction was processed by grinding through a #40 screen on a Wiley Mill (Thomas Scientific no. 3383L40). The ground coarse fraction and fine sediment fraction were pulverized by vigorously shaking within plastic scintillation vials containing 3/8" steel BBs packed into a paint can shaker. Further homogenization was often necessary for the fine sediment fraction since coarse sand was not fully pulverized after this step. This was achieved by grinding samples by hand using a mortar and pestle. TN and TOC content of the coarse and fine sediment fractions were determined through combustion using a Costech ECS 4010 CHNSO Analyzer, using the NIST acetanilide standard. Phosphorus (TP). The total phosphorus (TP) concentration in all fractions was determined by a colorimetric method following digestion. Samples of coarse and fine fractions were ashed prior to digestion in sulfuricÊacid; digests of fine samples were centrifuged at 2500 rpm for 10 min to remove remaining suspended particles that would otherwise interfere with the colorimetric analysis. Persulfate digestion was used for digestion of the soluble constituents in the leachate produced during the float separation step. Absorbance of digests was measured on a Cary 50 Bio UV Visible spectrophotometer at 880 nm in 1 cm cells using molybdate blue/ascorbic acid reagent method. ÒApple NIST 1515Ó reference standards (National Institute of Standards and Technology) were used to calibrate the analyses of coarse organic and fine fractions. NIST phosphorus standard solutions (25 mg P/L) purchased in 10 mL voluette ampules from HACH were used to calibrate analyses for the leachate samples.Ê Leachate DOC/TDN. The concentrations of dissolved organic carbon (DOC) and total dissolved nitrogen (TDN) that leached into the float water were analyzed using a Shimadzu TOC-L analyzer. Samples were acidified with 100 µL of 2 M HCl and stored in muffled 20 mL vials with an airtight seal. Samples were refrigerated and analyzed within a few weeks of filtration. 2. Methods for processing the data: Concentrations of TP, DOC, and TDN in leachate were multiplied by the leachate volume to determine total mass of TP, DOC, and TDN leached out the coarse fraction during the float procedure. This mass was accounted for in reporting the TP, C, and TN concentrations of the coarse fraction by Òadding backÓ these masses to the mass determined during analysis of the coarse fraction element concentrations. We determined total canopy cover for each sweeping route as follows. The city of Forest Lake provided canopy cover information for their routes, but for all other cities, we calculated the percent of each sweeping route covered by tree canopy using ArcGIS Pro (v. 2.4.0, ESRI 2019). Tree canopy information was obtained from the TCMA 1-Meter Urban Tree Canopy Cover Classification (Knight 2016) and using the Metro Regional Centerlines Collaborative (MRCC) Local Centerlines shapefile (MRCC Collaborative 2018), both obtained from the Minnesota Geospatial Database (MnGeo: https://gisdata.mn.gov/). The baselayer used was an aerial photograph from the MnGeo Web Mapping Service (Metropolitan Council 2016). Knight, Joe. TCMA 1-Meter Urban Tree Canopy Classification. St. Paul, MN: University of Minnesota, Nov 10, 2016. Minnesota Geospatial Commons. March 13, 2020. https://gisdata.mn.gov/dataset/base-treecanopy-twincities Law, N.L., et al. 2008. Deriving Reliable Pollutant Removal Rates for Municipal Street Sweeping and Storm Drain Cleanout Programs in the Chesapeake Bay Basin. Report prepared by the Center for Watershed Protection as fulfillment of the U.S. EPA Chesapeake Bay Program grant CB-973222-01 MRCC Collaborative. Metro Regional Centerlines Collaborative Local Centerlines. St. Paul, MN: Metropolitan Council, Feb 15, 2018. Minnesota Geospatial Commons. March 13, 2020. https://www.metrogis.org/projects/centerlines-initiative.aspx Metropolitan Council. Digital Orthoimagery, Twin Cities, Minnesota, Spring 2016, 1-ft Resolution. St Paul, MN: Minnesota Geospatial Information Center (MnGeo). April 4, 2016. Minnesota Geospatial Commons. March 13, 2020. https://www.mngeo.state.mn.us/chouse/wms/wms_image_server_layers.html To calculate canopy cover, we made new layers containing the centerlines for each route from the larger MRCC Local Centerlines shapefile. Street area was approximated by creating a 15-ft buffer on both sides of the centerline, as 30 ft is the approximate street width for most streets on the sweeping routes in this study. We checked the fit of the street area against the aerial photograph and manually corrected the area polygon in cases where it deviated significantly from the aerial photograph of the actual street location. The street area polygon was then used to clip the tree canopy cover raster, and then the number of 1m x 1m pixels in each canopy cover class were summed and used to calculate the percent canopy cover with the following equation: Tree canopy cover (%) = total number of tree pixels/total number of pixels x 100% For all cities except Shoreview, wet mass of the sweeping load was reported to us by the sweeper driver. To calculate the total dry mass of the sweeping loads from their fresh weights, we determined a weighted average moisture content for the sweeping load based on the proportion of the load dry mass in coarse and fine sediment fractions and their respective moisture contents. The weighted average moisture content was used to convert the total sweeping load wet mass to sweeping load dry mass (i.e., total dry solids of sweeping load). To calculate sweeping load dry mass, sweeping load wet mass was multiplied by the ratio of dry/wet mass of the subsample used for moisture determination. Sweeping load dry mass = sweeping load wet mass x dry mass of subsample/wet mass of subsample To obtain total nutrient concentration of sweepings and total nutrient recovered during sweeping, we calculated weighted averages of the nutrient concentrations from each fraction Ð the coarse fraction, fine sediments fraction, and the soluble fraction, leached during washing of the coarse fraction. First, we calculated the mass (in mg) of each nutrient (TN or TP) in each fraction, and these masses were used to calculate the total mass of the nutrients in the sample. For the coarse and fine sediment fractions, nutrient mass was calculated using the equation below. Nutrient mass of fraction (mg) = [Nutrient (mg/kg)] x dry mass of fraction (kg) For the soluble (leachate) fraction, the nutrient (nut.) mass was calculated by multiplying by the volume of the leachate water used in L rather than the fraction dry mass. Following these calculations, the masses of the nutrient in three different fractions were summed to obtain the total nutrient mass: coarse (> 2mm), fine (< 2mm), and leachate (leached while cleaning litter). This total nutrient mass was divided by the total mass of the sample to obtain the nutrient concentration of sweepings ([Nut.sweepings]). [Nut.sweepings] (mg/kg) = [Coarse Nut.Mass (mg) + Fine Nut.Mass (mg) + Leachage Nut.Mass (mg)]/Total Sample Dry Mass (kg) We then determined the total mass of nutrients recovered in the whole sweeper load as follows: Nutrient recovered in sweepings (kg) = [Nut.sweepings] (mg/kg) x Sweeping Load Dry Mass (kg) 3. Instrument- or software-specific information needed to interpret the data: None. 4. Standards and calibration information, if appropriate: See above. 5. Environmental/experimental conditions: See above. 6. Describe any quality-assurance procedures performed on the data: Ê We examined all data collected for outliers or potential analysis errors using a combination of statistical tests for outliers and graphical analysis. We used the scores function (outliers package v. 0.14) in R to check all numeric variables for extreme values (> 99th percentile of the distribution). For any variable with potential outliers identified in this way, we made Cleveland dot plots to see if data points showed distinct separation from the rest of the data. Points that were not reasonably close together were excluded from analysis from the data set. We also checked the nutrient data by making bivariate plots of C and N concentrations to see if any samples had unusual stoichiometry. Based on those plots a few additional samples were excluded from analysis. Below we list the samples that were excluded from analysis as outliers and the reasoning: P110: negative %moisture and no litter fresh weight P359: negative %moisture, float soil wet weight is more than dirty litter fresh weight P302: missing most data P305: missing most data P17.5: missing about half the data P16: exclude from analyses with P load or concentration (no soil P concentration data) P148: exclude from analyses with P load or concentration (no soil P concentration data) P71: float water concentration outlier P193: very high N float water concentration (relative to C and P concentration) P10: low P concentration for litter relative to C concentration Ê The following samples had data that was modified from the initial entry. Forest Lake sample taken on 10/10/2019 did not have a route, but sweeping logs showed this was from route 5 (FL5) so the entry was updated with the correct route. FL samples 41 and 54 had initial values of 0 ppm for soil P concentration, but these samples were not run so the data was updated to "NA" for these samples. There is also one forest lake sample (F99) with a very high dry mass for the sweeping load weight, but this was confirmed to be a very large sweeping event that had 5 different sweeper loads so that value remained in the data set. 7. People involved with sample collection, processing, analysis and/or submission: Rachel King, PhD student Tessa Belo, Technician ----------------------------------------- DATA-SPECIFIC INFORMATION FOR: MSRC2019_20210224.csv ----------------------------------------- 1. Number of variables: 83 2. Number of cases/rows: 575 3. Missing data codes: Code/symbol NA Definition Not available 4. Variable List Metadata, including the Variable name, Units or other information, Description, Calculation (if applicable) are included in the file MSRC2019_Metadata_20220330.csv unique_id sample_id city route_id collection_date last_sweep_day sweeping_dates sweeper_type sweeping_pile_weight_kg sample_volume_m3 sweeping_pile_weight_lbs route_distance_mi time_since_last_sweep_d efficiency_lbs_mi efficiency_lbP_mi total_P_load_lbP_dry total_P_per_unit litter_P soil_P perc_water_by_wt perc_litter_by_wt perc_soil_by_wt perc_rock_trash_by_wt continuity_check sample_fresh_g garbage_fresh_g sample_min_garbage_fresh_g lab_sample_fresh_wt_g initial_soil_fresh_g initial_soil_dry_g prop_H2O_soil float_soil_dry_g calc_float_soil_wet_wt_g total_soil_wet_wt_g total_soil_dry_wt_g dirty_litter_fresh_g fresh_litter_g litter_dry_g fresh_soil_to_litter dry_soil_to_litter rock_dry_g total_dry_g total_moist_perc litter_moist_perc soil_moist_perc H2O_used_float_ml float_water_P_ppm float_water_C_ppm float_water_N_ppm litter_P_perc litter_C_perc litter_N_perc soil_P_ppm new_soil_P_perc old_soil_P_ppm soil_C_perc soil_N_perc soil_OM_perc litter_OM_perc month year canopy_cover_deciduous canopy_cover_coniferous canopy_cover_total curb_meters curb_miles total_P_conc_ppm total_C_conc_ppm total_N_conc_ppm total_moist_perc_drywt litter_moist_perc_drywt soil_moist_perc_drywt perc_litter_by_drywt perc_soil_by_drywt total_OM_perc total_dry_solids_lbs total_dry_lbs_per_mi total_N_load_lbN sweep_freq_avg_d canopy_cat sweep_freq_cat cmP_persweep cmP_totals 1