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109 results for “year 2020”
Yearly survey of barnacle settlement near creekbank plots at GCE LTER study sites in October 2020
To characterize spatial variation in barnacle recruitment at the creekbank, and across a gradient in salinity and distance to ocean, we deployed PVC poles to passive sample barnacle settlement. Eight poles were deployed between 4-5m apart adjacent to the creekbank vegetation monitoring plots at each GCE LTER permanent monitoring site each Fall beginning in 2012. These poles were then collected the following Fall and all barnacle that settled on the poles were identified and counted on 50cm-long sections of the 8, 3/4" diameter PVC poles. Four species settled on poles and were counted and recorded: Chthamalus fragilis, Balanus spp., Geukensia demissa, and Oysters (Crassostrea virginica).
Concentration of dissolved organic carbon in water samples taken from the Upper Clark Fork River (Montana, USA) during water year 2020 (1 Oct 2019 - 30 Sep 2020)
These data were collected to support monitoring of the Upper Clark Fork River restoration, and data collection was funded by the US NSF Long Term Research in Environmental Biology (LTREB) program and the US NSF EPSCoR funded Montana Consortium for Research on Environmental Water Systems. The LTREB monitoring project consists of monthly or bi-weekly water quality monitoring across a 200-km restoration gradient contaminated by historic mining practices to monitor inorganic phosphorus and nitrogen concentrations, biotic standing stocks, and heavy metal contamination. The original analytical intent for these data was to assess the response of river dissolved organic carbon to the floodplain restoration. Data are Aurora Total Organic Carbon combustion analyses of the concentration of organic carbon dissolved in filtered samples of well-mixed river thalweg water. Data are from the 2020 water year (1 Oct 2019 to 30 Sep 2020). Data were collected on the Upper Clark Fork River (USGS HUC 17010201) at project sites distributed along the river from the vicinity of Anaconda to Missoula, Montana, USA.
Concentration of nutrients in water samples collected from the Upper Clark Fork River (Montana, USA) during water year 2020 (1 Oct 2019 - 30 Sept 2020)
The umbrella Upper Clark Fork River (UCFR) Long Term Research in Environmental Biology (LTREB) monitoring project generating these data is conducted separately and complementarily to the $200 million-dollar (USD) superfund project for ecological restoration of the UCFR, associated tributaries, and head water streams including Silver Bow and Warm Springs Creeks. Restoration along the Upper Clark Fork River includes removal of metal-laden floodplain soils, lowering of the floodplain to its original elevation, and re-vegetation of over 70 km of the river's floodplain closest to contaminant sources. The UCFR LTREB project includes bi-weekly water quality monitoring across a 200-km gradient of heavy metal contamination associated with historic mining. Monitoring includes inorganic phosphorus and nitrogen concentrations, biotic standing stocks, and dissolved and whole-water heavy metal concentrations. The UCFR LTREB monitoring project is conducted within the first 200 km of the Upper Clark Fork River and associated tributaries located in western Montana. The current monitoring program began in 2017 and will be completed in the year 2023, with likely funding extension to 2028. Surface water samples represented in this data product are collected from fourteen sites along the mainstem of the UCFR, and three sites representing major tributaries to the UCFR. Water samples are collected at each monitoring site in triplicate and filtered with a 0.7-µm glass fiber filter. Nutrient samples are analyzed using a spectrophotometric flow injection analyzer (AP2) for nitrate (NO3-N), soluble reactive phosphorus (SRP, as representative of PO4-P), and ammonium (NH4-N) concentrations reported in mg/L. The analysis-ready data of this dataset therefore represent Quality Assurance and Quality Control (QAQC) processed NH4-N, SRP, and NO3-N concentrations from fourteen sites along the mainstem of the UCFR and three tributaries, collected in water year 2020 (1 Oct 2019 - 30 Sept 2020).
Concentration of nutrients in water samples collected from the Upper Clark Fork River (Montana, USA) during water year 2021 (1 Oct 2020 - 30 Sept 2021)
The umbrella Upper Clark Fork River (UCFR) Long Term Research in Environmental Biology (LTREB) monitoring project generating these data is conducted separately and complementarily to the $200 million-dollar (USD) superfund project for ecological restoration of the UCFR, associated tributaries, and head water streams including Silver Bow and Warm Springs Creeks. Restoration along the Upper Clark Fork River includes removal of metal-laden floodplain soils, lowering of the floodplain to its original elevation, and re-vegetation of over 70 km of the river's floodplain closest to contaminant sources. The UCFR LTREB project includes bi-weekly water quality monitoring across a 200-km gradient of heavy metal contamination associated with historic mining. Monitoring includes inorganic phosphorus and nitrogen concentrations, biotic standing stocks, and dissolved and whole-water heavy metal concentrations. The UCFR LTREB monitoring project is conducted within the first 200 km of the Upper Clark Fork River and associated tributaries located in western Montana. The current monitoring program began in 2017 and will be completed in the year 2023, with likely funding extension to 2028. Surface water samples represented in this data product are collected from thirteen sites along the mainstem of the UCFR, and three sites representing major tributaries to the UCFR. Water samples are collected at each monitoring site in triplicate and filtered with a 0.7-µm glass fiber filter. Nutrient samples are analyzed using a spectrophotometric flow injection analyzer (AP2) for nitrate (NO3-N), soluble reactive phosphorus (SRP, as representative of PO4-P), and ammonium (NH4-N) concentrations reported in mg/L. The analysis-ready data of this dataset therefore represent Quality Assurance and Quality Control (QAQC) processed NH4-N, SRP, and NO3-N concentrations from thirteen sites along the mainstem of the UCFR and three tributaries, collected in water year 2021 (1 Oct 2020 - 30 Sept 2021).
Concentration of dissolved organic carbon in water samples taken from the Upper Clark Fork River (Montana, USA) during water year 2021 (1 Oct 2020 - 30 Sep 2021)
The Upper Clark Fork River (UCFR) Long Term Research in Environmental Biology (LTREB) umbrella monitoring project generating these data is conducted separately and complementarily to the 200-million-dollar (USD) superfund project for ecological restoration of the UCFR, associated tributaries, and head water streams including Silver Bow and Warm Springs Creeks. Restoration along the UCFR in western Montana includes removal of metal-laden floodplain soils, lowering of the floodplain to its original elevation, and re-vegetation of over 70 km of the river’s floodplain closest to contaminant sources. The UCFR LTREB project includes bi-weekly water quality monitoring across the first 200 km of the river and its major tributaries along a gradient of heavy metal contamination associated with historic mining. Monitoring includes inorganic phosphorus and nitrogen concentrations, biotic standing stocks, and dissolved and whole-water heavy metal concentrations. The monitoring program began in 2017 with funding extended through 2028. The original analytical intent for these data was to assess the response of river dissolved organic carbon to the floodplain restoration. Data are Aurora Total Organic Carbon combustion analyses of the concentration of organic carbon dissolved in filtered samples of well-mixed river thalweg water. Data are from the 2021 water year (1 Oct 2020 to 30 Sep 2021) from samples collected on the Upper Clark Fork River (USGS HUC 17010201) at project sites distributed along the river from the vicinity of Anaconda to Missoula, Montana, USA.
Real-time ZTD and gradients from 161 EPN stations; year 2020
<p>This dataset contains multi-GNSS real-time products, i.e. ECEF coordinates, zenith total delay (ZTD), and horizontal gradients, together with their uncertainties estimated every 1 minute over the entire year 2020 for 161 EPN (http://epncb.oma.be/) stations.</p> <p>The processing strategy can be found in https://link.springer.com/article/10.1007/s10291-020-01014-w, under to "advanced strategy" configuration, with the exception that only GPS and Galileo observations were considered.</p> <p>For convenience, products are stored in 3 formats (but each contains identical data):</p> <p>1. Standard Matlab MAT files. Each file contains a single table array (Matlab format) with the complete set of estimated parameters for a single station. Table columns are labeled and self-explanatory. A comma-delimited text file can be obtained using the in-build Matlab function "writetable.m".</p> <p>2. Self-explanatory netCDF format (<a href="https://www.unidata.ucar.edu/software/netcdf/docs/file_format_specifications.html">https://www.unidata.ucar.edu/software/netcdf/docs/file_format_specifications.html</a>), which follows the parameter naming convention of the troposphere SINEX v2 format: <a href="https://www.pecny.cz/WWW_FIL/TRO-SINEX/old/sinex_tro_2017-05-28-JD.pdf">https://www.pecny.cz/WWW_FIL/TRO-SINEX/old/sinex_tro_2017-05-28-JD.pdf</a>. Each file contains data for all stations but only for one month.</p> <p>3. Semicolon delimited text files, with a self-explanatory header line. Each file contains daily products for one station.</p> <p> </p> <p>For visualization see:</p> <p>January https://youtu.be/oU_JaSnhoYI</p> <p>February https://youtu.be/O2LnWOjudGA</p> <p>March https://youtu.be/JVvO25oYcV8</p> <p>April https://youtu.be/2wqY7BKnw8g</p> <p>May https://youtu.be/XY62BMSlbT8</p> <p>June https://youtu.be/37cxhQ-0Y7U</p> <p>July https://youtu.be/MSJVtC-DnVM</p> <p>August https://youtu.be/lfG2GnrYiBo</p> <p>September https://youtu.be/CUrdDYr_lOY</p> <p>October https://youtu.be/pKPsTjKmvtE</p> <p>November https://youtu.be/s5gbtEdwiSM</p> <p>December https://youtu.be/rrn1SuzlM2k</p>
Global mangrove soil carbon data set at 30 m resolution for year 2020 (0-100 cm)
<p>Global soil organic carbon stocks in mangrove forests at 30 m resolution, and predicted for 2020 using spatiotemporal ensemble machine learning. Soil organic carbon stock (t/ha) was derived using predictions of soil organic carbon content and bulk density (BD) to 1 m soil depth, which were then aggregated to calculate soil organic carbon stocks.</p> <p>The "mangroves_tiles_SOC_predictions_2020.zip" file contains predictions of SOC content, Bulk Density (BD) and aggregated SOC stocks (t/ha) for 0—100 cm depth interval. Example of a tile:</p> <ul> <li>089E_21N (89E to 90E, 21N to 22N): <ul> <li>sol_db.od_mangroves.typology_m_30m_s0..100cm_2020_global_v0.1.tif = predicted BD aggregated to 0—100 cm;</li> <li>sol_soc.wpct_mangroves.typology_m_30m_s0..0cm_2020_global_v1.1.tif = predicted SOC content (%) at 0 cm depth (surface soil);</li> <li>sol_soc.wpct_mangroves.typology_m_30m_s0..100cm_2020_global_v1.1.tif = predicted SOC content (%) for 0—100 cm;</li> <li>sol_soc.tha_mangroves.typology_m_30m_s0..100cm_2020_global_v0.1.tif = predicted SOC stocks in t/ha (mean value);</li> <li>sol_soc.tha_mangroves.typology_l.std_30m_s0..100cm_2020_global_v0.1.tif = predicted SOC stocks in t/ha lower 95% probability prediction interval;</li> <li>sol_soc.tha_mangroves.typology_u.std_30m_s0..100cm_2020_global_v0.1.tif = predicted SOC stocks in t/ha upper 95% probability prediction interval;</li> </ul> </li> </ul> <p>Example of a tile:</p> <ul> <li>class : RasterLayer</li> <li>dimensions : 4004, 4004, 16032016 (nrow, ncol, ncell)</li> <li>resolution : 0.00025, 0.00025 (x, y)</li> <li>extent : 88.9995, 90.0005, 20.9995, 22.0005 (xmin, xmax, ymin, ymax)</li> <li>crs : +proj=longlat +datum=WGS84 +no_defs</li> <li>source : sol_db.od_mangroves.typology_m_30m_s0..0cm_2002_global_v0.1.tif</li> </ul> <p>To load global mosaics <strong><strong>Soil Carbon t/ha Maps (0—100cm)</strong></strong> as COGs directly into QGIS or similar, best use:</p> <ul> <li> <p><a href="https://s3.eu-central-1.wasabisys.com/openlandmap/mangroves/sol/soc.tha_tnc.mangroves.typology_m_30m_b0..100cm_2019_2020_go_epsg.4326_v1.2.tif">https://s3.eu-central-1.wasabisys.com/openlandmap/mangroves/sol/soc.tha_tnc.mangroves.typology_m_30m_b0..100cm_2019_2020_go_epsg.4326_v1.2.tif</a></p> </li> <li> <p><a href="https://s3.eu-central-1.wasabisys.com/openlandmap/mangroves/sol/soc.tha_tnc.mangroves.typology_l.std_30m_b0..100cm_2019_2020_go_epsg.4326_v1.2.tif">https://s3.eu-central-1.wasabisys.com/openlandmap/mangroves/sol/soc.tha_tnc.mangroves.typology_l.std_30m_b0..100cm_2019_2020_go_epsg.4326_v1.2.tif</a></p> </li> <li> <p><a href="https://s3.eu-central-1.wasabisys.com/openlandmap/mangroves/sol/soc.tha_tnc.mangroves.typology_u.std_30m_b0..100cm_2019_2020_go_epsg.4326_v1.2.tif">https://s3.eu-central-1.wasabisys.com/openlandmap/mangroves/sol/soc.tha_tnc.mangroves.typology_u.std_30m_b0..100cm_2019_2020_go_epsg.4326_v1.2.tif</a></p> </li> </ul>
Water year 2020 monitoring of the inorganic carbon system (pH and total alkalinity) in the Upper Clark Fork River (Montana, USA)
These data were collected by the University of Montana and Montana State University to support the Upper Clark Fork River restoration monitoring project supported by the US NSF Long Term Research in Environmental Biology (LTREB) program. The original analytical intent for these data was to assess the response of river inorganic carbon system to the floodplain restoration. Data are lab analyses of pH and total alkalinity in samples of well-mixed river thalweg water. Data are from the 2020 water year (1 Oct 2019 to 30 Sep 2020). Data were collected on the Upper Clark Fork River (USGS HUC 17010201) at project sites distributed along the river from the vicinity of Anaconda to Missoula, Montana, USA. Lab analyses include high-precision colorimetric pH analysis and gran titration of alkalinity.
Year 2020, PIE LTER wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA
Wind sensor measurements (wind speed and wind direction) for 2020 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute average measurements.
Copernicus EMS fire activations delimitations (2012 - 2020) rasterised at 30m and aggregated per year and season
<p>This dataset was created as part of the <a href="https://opendatascience.eu/">Geo-harmonizer project</a>, with the scope of making open data easier to access. It contains all the fire activations (forest fire, wild fire, wildfire) mapped by the<a href="https://emergency.copernicus.eu/mapping/list-of-activations-rapid"> Copernicus Emergency Rapid Mapping Service</a> between 2012 and 2020. To obtain these GeoTIFFs, the vector data packages from CEMS were individually downloaded, rasterized and mosaicked per year and season, resampled at 30-m and reprojected to <a href="https://epsg.io/3035">EPSG 3035: ETRS89-extended / LAEA Europe</a>. If no CEMS fire activation was identified in a specific year and season, the raster was not created. The rasters are provided as COG files, type=16Int, nodata value is 255.</p> <p>To allow an easier and faster search through all 2012 - 2020 CEMS fire activations, we have prepared a point vector layer (geojson) containing one point for each fire activation area of interest with the following attributes attached: CEMS identification number <ems_id>, area of interest defined by CEMS <ems_aoi>, URL link to the CEMS activation <ems_link>, year of the event <year_start>, <year_end> , <season> and the name of the <geo_harmonizer_raster> where the 30m rasterised delimitations of the burned areas of the corresponding fire activation can be found. </p> <p>For any additional questions regarding the data please contact the author at codrina.ilie[at]terrasigna.com.</p> <p>The Copernicus Emergency Rapid Mapping Service data access policy is available <a href="https://emergency.copernicus.eu/mapping/sites/default/files/files/CopernicusEMS-Data_and_Dissemination_Policy.pdf">here</a>.</p>
The SPIN covid19 RMRIO dataset: Global trade network data for the years 2016-2026 reflecting macroeconomic effects of the covid19 pandemic - B. Data for 2020 - 2026 - Covid scenario
<p>The SPIN covid19 RMRIO dataset is a time series of MRIO tables covering years from 2016-2026 on a yearly basis. The dataset covers 163 sectors in 155 countries.</p> <p>This repository includes data for years from 2020 to 2026 (<em>covid</em> scenario).<br> Code, method material and data for years 2016-2019 are stored in the following repository: <a href="http://doi.org/10.5281/zenodo.5713811">10.5281/zenodo.5713811</a><br> Data for the <em>counterfactual</em> scenario are stored in the following repository: <a href="https://doi.org/10.5281/zenodo.5713839">10.5281/zenodo.5713839</a></p> <p>Tables are generated using the <a href="https://github.com/TBeaufils/SPIN">SPIN method</a>, based on the <a href="https://doi.org/10.5281/ZENODO.3993659">RMRIO tables</a> for the year 2015, GDP, imports and exports data from the <a href="https://data.imf.org/?sk=4c514d48-b6ba-49ed-8ab9-52b0c1a0179b">International Financial Statistics</a> (IFS) and the World Economic Outlooks (WEO) of <a href="https://www.imf.org/en/Publications/WEO/weo-database/2019/October">October 2019</a> and <a href="https://www.imf.org/en/Publications/WEO/weo-database/2021/April">April 2021</a>.</p> <p>The <em>covid</em> scenario is in line with April 2021 WEO's data and includes the macroeconomic effects of Covid 19.</p> <p>All tables are labelled in 2015 US$ and valued in basic prices.</p>
The SPIN covid19 RMRIO dataset: Global trade network data for the years 2016-2026 reflecting macroeconomic effects of the covid19 pandemic - C. Data for 2020 - 2026 - Counterfactual scenario
<p>The SPIN covid19 RMRIO dataset is a time series of MRIO tables covering years from 2016-2026 on a yearly basis. The dataset covers 163 sectors in 155 countries.</p> <p>This repository includes data for years from 2020 to 2026 (<em>counterfactual</em> scenario).<br> Code, method material and data for years 2016-2019 are stored in the following repository: <a href="http://doi.org/10.5281/zenodo.5713811">10.5281/zenodo.5713811</a><br> Data for the <em>covid</em> scenario are stored in the following repository: <a href="https://doi.org/10.5281/zenodo.5713825">10.5281/zenodo.5713825</a></p> <p>Tables are generated using the <a href="https://github.com/TBeaufils/SPIN">SPIN method</a>, based on the <a href="https://doi.org/10.5281/ZENODO.3993659">RMRIO tables</a> for the year 2015, GDP, imports and exports data from the <a href="https://data.imf.org/?sk=4c514d48-b6ba-49ed-8ab9-52b0c1a0179b">International Financial Statistics</a> (IFS) and the World Economic Outlooks (WEO) of <a href="https://www.imf.org/en/Publications/WEO/weo-database/2019/October">October 2019</a> and <a href="https://www.imf.org/en/Publications/WEO/weo-database/2021/April">April 2021</a>.</p> <p>The<em> counterfactual</em> scenario is in line with October 2019 WEO's data and simulates the global economy without Covid 19.</p> <p>All tables are labelled in 2015 US$ and valued in basic prices.</p>
PIE LTER, Hach, OTT RLS measurements of water column depth at 15 minute intervals in the lower Plum Island Sound off the Ipswich Bay Yacht Club pier, Ipswich, MA, year 2020.
Measurements of water column depth at 15 minute intervals in Plum Island Sound at the Ipswich Bay Yacht Club, for year 2020. OTT radar level sensor (RLS) installed September 20, 2011 out of the water under the concrete pad on the Ipswich Bay Yacht Club pier, Ipswich, MA. RLS was mounted so that continuous year round measurements can be conducted without the concern of ice flows damaging the sensor.
PIE LTER year 2020, meteorological data, 15 minute intervals, from the PIE LTER Marshview Farm weather station located in Newbury, MA
Year 2020 meteorological measurements at MBL Marshview Farm of air temperature, humidity, precipitation, solar radiation, photosynthetically active radiation (PAR), wind speed and direction and barometric pressure. Sensors conduct measurements every 5 secs and measurements are reported as averages or totals for 15 minute intervals. 15 minute averages are reported for air temperature, humidity, solar radiation, PAR, wind speed and direction and barometric pressure. 15 minute totals are reported for precipitation.
PIE LTER, Year 2020, 15 minute interval, water quality measurements of water column temperature, salinity, oxygen, and depth near the mouth of Plum Island Sound, Massachusetts
Year 2020, 15 minute measurements of water column temperature, salinity, oxygen and depth in Plum Island Sound at the Ipswich Bay Yacht Club, Ipswich, MA. There were sifgnificant datalogger power, radio telecommunication and sonde cabling corrosion issues during the Covid-19 Spring - Fall of 2020. There are many missing data time periods due to issues and some limited field work capabilities due to Covid working restrictions.
Data for the MLCS 2020 paper "A Year of Automated Anomaly Detection in a Datacenter"
<p>This contains the data used for the paper by Ahmed et. al in the MLCS 2020 paper "A Year of Automated Anomaly Detection in a Datacenter". Each of the four CSV files corresponds to one of the quarters discussed in the paper, and each has a metadata file containing information about the query that produced them. The CSV files contain the 'raw' log messages, and an eventID that identifies which pattern the log entry matched; the eventID is used to group together log messages of the same type. These logfiles were collected on the CloudLab facility (https://cloudlab.us/) from Jan 1 - Dec 30, 2019.</p> <p>The violated_unviolated_sessions_*.txt files each contain 20 randomly-selected sessions: half of the sessions were labeled by the invariant miner as being 'normal', and the other half 'anomalous'. CloudLab developers and system administrators were asked to label these sessions manually (and were not given the invariant miner's labels). The corresponding *_manual_labels.txt contain the labels that the administrators assigned, and in some cases additional correspondence with the administrators and information about which manual labels matched the invariant miner and which did not.</p>
Hydrogeological Survey in the Mincio River and Goito aquifer for the hydrological year 2020-2021
<p>Data collected from 2020 to 2021 in surface- and groundwater in the Goito aquifer and Mincio River (Po Plain, northen Italy). These data were published in <a href="https://doi.org/10.3390/hydrology9030044">https://doi.org/10.3390/hydrology9030044</a>.</p>
Fig. 2. SNSB-BSPG 2020 XCIII 18 containing a in A new glimpse on trophic interactions of 100-million-year old lacewing larvae
Fig. 2. SNSB-BSPG 2020 XCIII 18 containing a neuropteran larva with attached mite from Hukawng Valley, Kachin State, Myanmar; Turonian– Cenomanian, Cretaceous, 90–100 mya; in dorsal (A1) and ventral (A2) views.
Changes in sugar-sweetened beverage consumption in the first two years (2018 – 2020) of San Francisco's tax: A prospective longitudinal study
<p><strong>Background:</strong> Sugar-sweetened beverage (SSB) taxes are a promising strategy to decrease SSB consumption, and their inequitable health impacts, while raising revenue to meet social objectives. In 2016, San Francisco passed a one cent per ounce tax on SSBs. This study compared SSB consumption in San Francisco to that in San José, before and after tax implementation in 2018.</p> <p><strong>Methods & findings</strong>: A longitudinal panel of adults (n = 1,443) was surveyed from zip codes in San Francisco and San José, CA with higher densities of Black and Latino residents, racial/ethnic groups with higher SSB consumption in California. SSB consumption was measured at baseline (11/17–1/18), one (11/18–1/19), and two years (11/19-1/20) after the SSB tax was implemented in January 2018. Average daily SSB consumption (in ounces) was ascertained using the BevQ-15 instrument and modeled as both continuous and binary (high consumption: ≥6 oz (178 ml) versus low consumption: <6 oz) daily beverage intake measures. Weighted generalized linear models (GLMs) estimated difference-in-differences of SSB consumption between cities by including variables for year, city, and their interaction, adjusting for demographics and sampling source. In San Francisco, average SSB consumption in the sample declined by 34.1% (-3.68 oz, p = 0.004) from baseline to 2 years post-tax, versus San José which declined 16.5% by 2 years post-tax (-1.29 oz, p = 0.157), a non-significant difference-in-differences (-17.6%, adjusted AMR = 0.79, p = 0.224). The probability of high SSB intake in San Francisco declined significantly more than in San José from baseline to 2-years post-tax (AOR[interaction] = 0.49, p = 0.031). The difference-in-differences of odds of high consumption, examining the interaction between cities, time and poverty, was far greater (AOR[city*year 2*federal poverty level] = 0.12, p = 0.010) among those living below 200% of the federal poverty level 2-years post-tax.</p> <p><strong> Conclusions:</strong> Average SSB intake declined significantly in San Francisco post-tax, but the difference in differences between cities over time did not vary significantly. Likelihood of high SSB intake declined significantly more in San Francisco by year 2 and more so among low-income respondents.</p>
Changes in sugar-sweetened beverage consumption in the first two years (2018 – 2020) of San Francisco’s tax: A prospective longitudinal study
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