DOI: 10.6084/m9.figshare.13518593.v1
title: Data for Messager et al. 2020 STOTEN - Data for Low-cost biomonitoring and high-resolution, scalable models of urban metal pollution

description: Published in Science of the Total Environment - https://doi.org/10.1016/j.scitotenv.2020.144280<br><br>Last updated: 2021/01/05<br>Please notify the author of any anomaly in the dataset or accompanying metadata and scripts<br>Purpose: In this study, we demonstrate novel scalable methods to monitor and predict urban metal pollution at high resolution (&lt; 5 meterm) across large areas (10,000‐–100,000 km2) to guide pollution reduction and stormwater management. We showcase and calibrate predictive models of Zn, Cu, and a synthetic index of pollution for the Puget Sound region of Washington State, U.S. We also exemplify their transferability across the entire United States.We leveraged widely and freely available datasets of car traffic characteristics and land use as predictor variables and trained the models with biological monitoring data of metal concentrations in epiphytic moss from &gt; 100 trees based on new rapid and low-cost protocols introduced in this study. <br>Reference compendium DOI: 10.6084/m9.figshare.13518593Github repositories:- https://github.com/messamat/traffic (R - Downloading and formatting Bing Traffic REST API)- https://github.com/messamat/stormwater_samplingPy (Python - Geographic data pre- and post-processing)- https://github.com/messamat/stormwater_samplingR (R - data analysis)<br>--------------------------------------------------------------------------------<br>COMPENDIUM CONTENT--------------------------------------------------------------------------------1. Messageretal_2020_STOTEN_SupplementaryInformation.pdf Copy of supplementary information available online with the article. <br>2. README_technicaldoc_20210105.docx A technical document explaining the workflow from start to finish. See Table S1 in 1. Messageretal_2020_STOTEN_SupplementaryInformation.pdf for additional information and URL links to data sources. It is recommended to reproduce the same directory structure explained in this document for re-using the scripts.<br>3. /src directory Contains all scripts used in this study. See 2. README_technicaldoc_20210105.docx for a walkthrough of the data analysis workflow.<br>4. /data directory Non-reproduceable or downloadable data necessary for the analysis - /field_data: metadata for each site (note that corrected location of all used sites adjusted with high-resolution satellite imagery is in results directory) - /XRF20190501: XRF data for various sampling rounds, used in data analysis.<br>5. /results directory Intermediate and final results from the data analysis - vectordat_formatted.gdb/XRFsites_aeasel (n=107, datum: WGS84, projection: Albers_Conical_Equal_Area) Location and metadata for sites used in analysis<br> - airsites_znpred_pls_mapformat.shp (n=195, datum: WGS84, projection: Lambert_Conformal_Conic) Air quality monitoring stations as used in Fig. 3 of manuscript. <br> - vectordat_formatted.gdb/hpmstiger (n=19,073,588, datum: WGS84, projection: Albers_Conical_Equal_Area) Formatted road network. - Mercator_1SP.prj Reference projection file for Web Mercator used by Bing<br> - congestionPS.tif (94001x84002 pixels, 1 band, 4.7773143 m resolution, signed integer 32 Bit pixel depth, NoData Value 65536, compression LZW, projection Mercator_1SP, linear unit 1 meter) Formatted raster of Bing congestion index for the Puget Sound (PS) watershed based on Bing™ Maps REST Services Application Programming Interface (API) The congestion conditions across the study area were downloaded Bing Traffic hourly from from November 28th 2018 at 08:00 am to December 5th 2018 at 08:00, depicted based by color and translated as follows - blank:0, green:1, yellow:2, orange:3, red:4 All hourly snapshots were then averaged, multiplied by 1000, and converted to integer<br> - transitPS.tif (48066x46464 pixels, 1 band. 4.7773143 m resolution, unsigned integer 32 Bit pixel depth, NoData Value 2147483647, compression LZW, datum WGS 84, projection Albers Conical Equal Area) Formatted raster depicting ten times (10*)the weekly number of overground transit vehicles passing by each pixel. - pred_zn.tif (63400x59094 pixels, 1 band, 4.7773143 m resolution, floating point 32 Bit pixel depth, NoData Value -3.40282306074e+38, compression LZW, datum WGS 84, projection Albers Conical Equal Area) Raster of predicted relative Zinc (Zn) concentration (min-max standardized from 0 to 100)<br> - pred_cu.tif (63400x59094 pixels, 1 band, 4.7773143 m resolution, floating point 32 Bit pixel depth, NoData Value -3.40282306074e+38, compression LZW, datum WGS 84, projection Albers Conical Equal Area) Raster of predicted relative Copper (Cu) concentration (min-max standardized from 0 to 100)<br> - pred_pi.tif (63400x59094 pixels, 1 band, 4.7773143 m resolution, floating point 32 Bit pixel depth, NoData Value -3.40282306074e+38, compression LZW, datum WGS 84, projection Albers Conical Equal Area) Raster of predicted 100*pollution index<br>