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856 results for “urban studies”
Evaluation of stormwater urban ecological infrastructure in Phoenix, Arizona (USA): a case study of a small-scale bioretention basin system
In 2017, Arizona State University finished construction on a pedestrian mall central to its Tempe campus, which included a small-scale bioretention basin system for stormwater management. This study analyzed the flood control and water quality improvement performance of the small-scale bioretention basin system in the Phoenix Metropolitan Area, AZ USA. Flood control efficacy was quantified by calculating discharge from the basin system using water level loggers and measuring soil moisture levels using soil moisture probes. Stormwater runoff samples were collected for twenty-one storm events and analyzed for nitrogen and phosphorus constituent concentrations. Nutrient concentrations at the system inflow and outflow were used to determine percent change in concentration. Water quality improvement performance was compared to results from previous studies on bioretention basin system performance. These data were used to create relevant graphical figures. Results were obtained by performing statistical analysis calculations on the data measurements. The results indicated that the bioretention basin system performed adequately for flood control and water quality improvement, supporting the use of stormwater urban infrastructure systems in arid and semi-arid climates. Further research can reveal how these systems may perform during more severe storm events and offer improvements for future designs.
The data that support the findings of a review paper "From urban data to city-scale models: A review of traffic simulation case studies"
<p>This dataset contains the data that were used in a review paper "From urban data to city-scale models: A review of traffic simulation case studies". It contains the following files:</p> <ul> <li>keywords with counts.txt - list of keywords and their counts in the considered corpus of traffic simulation case studies. The data were used to produce Figure 2 and Figure 3 in the paper.</li> <li>Papers analysis.xlsx - Excel file containing the data on the reviewed studies. The document has the following sheets: <ul> <li> Appendix A - contains a table short reference, location, simulation period, spatial scale, simulated units and marked categories for a paper;</li> <li>Geography - contains data on geographical distribution of simulated areas between world regions and countries, these data were used to produce Figure 4 in the paper;</li> <li>Software tools - contains data on simulation tools used in the studies. </li> <li>Journals and conferences - contains data on where the reviewed papers were published.</li> </ul> </li> </ul>
Data for manuscript titled 'Impact of urbanization and drought on river water quality, case study of nutrient levels in Cuenca and Giron (Azuay, Ecuador)'
<p>The uploaded zip-file entails the data obtained through four field campaigns performed in the province of Azuay (Ecuador) in the period July 2023 - May 2024, which is used as a basis for the manuscript titled 'Impact of urbanization and drought on river water quality, case study of nutrient levels in Cuenca and Giron (Azuay, Ecuador)' that was submitted to a Special Issue in the journal Water in 2024. The study aimed at illustrating the impact of urbanisation and drought on the abiotic water conditions of the rivers passing through the studied urban areas.</p> <p>The data includes a subfolder with data obtained from an external website (https://generacioncsr.celec.gob.ec/graficasproduccion/) and aligns with the folder structure of the GitHub-repository that contains the analysis scripts (to be added when the manuscript is accepted). The data file only contains the baseline data, while results can be obtained through running the R-scripts in the GitHub-repository. Additional comments on the analyses are also provided in the analysis scripts.</p> <p><strong>DATA COLLECTION</strong></p> <p>Information on the locations was collected prior to the first field campaign (July 2023) and confirmed in the field (and corrected when necessary). The following variables were registered: Date & Time, Coordinates (latitude and longitude, in WGS84 format), Altitude (in meters above mean sea level), Distance (to a fixed location downstream; being the province border), and Category (River or Stream).</p> <p>Information on the physicochemical conditions was collected directly in the field with a <strong>Horiba U-52</strong> multiprobe. The following variables were registered: Temperature, pH, Electrical conductivity (reference at 25 °C), Oxygen level (as concentration), and Turbidity (in NTU).</p> <p>At each site, a bucket was rinsed thrice with prevailing surface water and subsequently filled with a water sample of the top of the water column. The multiprobe was rinsed with this sample water and then submerged in the bucket, followed by continuous stirring (to avoid a decrease of the oxygen levels) until the readings stabilised. After stabilisation, readings were recorded on a separate data sheet prior to being digitalised.</p> <p>Information on the nutrient levels was obtained through the collection of water samples in the field and the subsequent analysis in the laboratory. The following nutrients were selected: ammonium, nitrate, nitrite, and orthophosphate. For the analyses, <strong>Merck test kits</strong> (equivalent to USEPA analyses) were used in combination with a Genesys UV-VIS spectrophotometer (Thermofisher).</p> <p><strong>In the field</strong>, a bucket was rinsed thrice with prevailing surface water and subsequently filled with a water sample of the top of the water column. A polyethylene syringe was rinsed thrice with sample water and subsequently filled prior to being fitted with a 0.45 µm PES filter. About 100 mL of sampled water was filtered and collected in a 250 mL polyethylene bottle that was rinsed with the first 5 mL of filtered water. The bottle was stored in a cooling box and transported to the laboratory.</p> <p><strong>In the laboratory</strong>, the 250 mL bottle was stored at 4 °C until analysis. Within 36 hours, concentrations of ammonium, nitrate, nitrite, and orthophosphate were determined <strong>in triplicate</strong>. More specifically, the following test kits were used to determine said nitrogen and phosphorus concentrations (with quantification range between brackets):</p> <ul> <li>Ammonium: 1.14752.0001 (0.05-3.00 mgN/L)</li> <li>Nitrate: 1.14773.0001 (2-20 mgN/L)</li> <li>Nitrite: 1.14776.0001 (0.02-1.00 mgN/L)</li> <li>Orthophosphate: 1.14848.0001 (0.05-5.00 mgP/L)</li> </ul> <p>Regarding the <strong>spectrophotometric determination</strong>, all analyses were complemented with a blank and a standard with a known concentration of each individual nutrient component. For each nutrient, a specific wavelength was used and the resulting absorbance was converted to the associated nutrient concentration through known factors (similar to the use of calibration curves), after setting the absorbance of the blank as reference absorbance (i.e. a concentration of 0 mg/L). All of the analyses were performed with plastic 1-cm cuvettes during the first campaign, while 5-cm cuvettes were used in the remaining three campaigns due to low nutrient levels (except for nitrate, for which an analysis through 5-cm cuvettes is not supported by the used test kits).</p>
Baltimore Ecosystem Study: Soil moisture and temperature along an urban to rural gradient, 2011 - present
Soil temperature and soil moisture have been measured at multiple locations in and around Baltimore Maryland to provide data on these variables in forests and lawns across an urban to rural gradient. In July 2011, we installed one Decagon Em50 Datalogger with five 5TM VWC/Temperature probes at four established forested, upslope, 20 x 20-m plots, two rural (ORU1, ORU2) and two urban (LEA1, LEA2), at 2 forested riparian sites at two transects along a stream (ORUR, ORLR), and two lawn plots on the campus of the University of Maryland Baltimore County campus (UMBC1, UMBC 2). Probes were buried horizontally at 10cm depth (except UMBC1 and UMBC2 where the five probes are mounted horizontally at a single location at depths of 50, 40, 30, 20 and 10 cm depth). At the upslope forested plots, the five probes are replicates. At the two riparian sites, probes are deployed in either "hummocks (drier, higher)" or in "hollows (lower, wetter)". Soil temperature and soil moisture were measured at hourly intervals on these plots beginning in July 2011. In March 2017, an additional data logger was installed at Hillsdale Park (HD1) in a forested urban area. The five probes at HD1 were buried horizontally at 10cm depth and are replicates. Earlier soil moisture data were collected monthly (1999-2011), and can be found in https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-bes&identifier=417
Challenges of high-fidelity air quality modeling in urban environments - PALM sensitivity study during stable conditions (TURBAN)
<h3>Introduction</h3> <p>This dataset contains the PALM model inputs and the source code used to create the simulations for Prague-Legerova scenarios performed in the scope of the <strong>TURBAN</strong> project (<a href="https://www.project-turban.eu/">https://www.project-turban.eu/</a>). Detailed description of the simulations is provided in the referencing scientific paper.</p> <h3>List of simulations</h3> <table> <tbody> <tr> <td><strong>Scenario name</strong></td> <td><strong>Days simulated</strong></td> <td><strong>IBC</strong></td> <td><strong>Configuration changes</strong></td> </tr> <tr> <td>legerovas_s6_sens_base</td> <td>13–15 February 2023</td> <td>ICON</td> <td>-</td> </tr> <tr> <td>legerovas_s6_sens_dtmax</td> <td>13 February 2023</td> <td>ICON</td> <td>dt_max=0.2</td> </tr> <tr> <td>legerovas_s6_sens_heat</td> <td>13 February 2023</td> <td>ICON</td> <td>car anthropogenic heat (custom code)</td> </tr> <tr> <td>legerovas_s6_sens_sgs</td> <td>13 February 2023</td> <td>ICON</td> <td>e_min=0.02</td> </tr> <tr> <td>legerovas_s6_sens_stg</td> <td>13 February 2023</td> <td>ICON</td> <td>STG_PROFILES added</td> </tr> <tr> <td>legerovas_s6_sens_alad</td> <td>13–15 February 2023</td> <td>ALADIN</td> <td>-</td> </tr> <tr> <td>legerovas_s6_sens_alad_heat</td> <td>13 February 2023</td> <td>ALADIN</td> <td>car anthropogenic heat (custom code)</td> </tr> <tr> <td>legerovas_s6_sens_alad_sgs</td> <td>13 February 2023</td> <td>ALADIN</td> <td>e_min=0.02</td> </tr> <tr> <td>legerovas_s6_sens_alad_stg</td> <td>13 February 2023</td> <td>ALADIN</td> <td>STG_PROFILES added</td> </tr> <tr> <td>legerovas_s6_sens_wrf</td> <td>13–15 February 2023</td> <td>WRF</td> <td>-</td> </tr> <tr> <td>legerovas_s6_sens_wrf_heat</td> <td>13 February 2023</td> <td>WRF</td> <td>car anthropogenic heat (custom code)</td> </tr> <tr> <td>legerovas_s6_sens_wrf_sgs</td> <td>13 February 2023</td> <td>WRF</td> <td>e_min=0.02</td> </tr> <tr> <td>legerovas_s6_sens_wrf_stg</td> <td>13 February 2023</td> <td>WRF</td> <td>STG_PROFILES added</td> </tr> </tbody> </table> <h3>Directory structure</h3> <p>The directory inputs contains the model inputs and it is further divided into these subdirectories:</p> <p>- inputs/common: The PALM static driver and the emission drivers for the parent and child domains. These files are common to all simulations</p> <p>- inputs/dynamic/*: These directories contain the dynamic drivers for the parent and child domanis, which contain the initial and boundary conditions (IBC) as well as external radiation data. The three subdirectories aladin, icon and wrf contain IBCs created from the respective mesoscale model outputs. </p> <p>- inputs/legerovas_s6_sens_*: These directories contain the PALM model configuration (p3d) for both domains for each simulation.</p> <p>- inputs/build_config: The included .palm.iofiles configuration file ensures that the files STG_PROFILES are correctly copied from the input directory.</p> <p>The directory palm_sources contains the exact model source used for the simulations. It is derived from the PALM model release 23.04 with additional bugfixes. There are two source archives:</p> <p>- heat.tar.gz: PALM source further modified to include anthropogenic heat from cars, used for the simulations legerovas_s6_sens_*_heat</p> <p>- standard.tar.gz: PALM source used for all other included simulations.</p> <h3>Reproducing the simulations</h3> <p>In order to reproduce the simulations, unpack the respective source code archive and follow the standard installation, configuration and build procedures described in the README.md file within the archive and on the PALM model website http://www.palm-model.org/. Then copy the input files for the respective simulation in the JOBS directory. The common files and the dynamic driver files need to be renamed so that they match the prefix given by the name of the simulation, as is described in the PALM model documentation.</p>
Raw data of the study: Categorizing urban avoiders, utilizers, and dwellers for identifying bird conservation priorities in a northern Andean city
<p>This datasheet contains raw data on bird count records made from 2016 and 2019. Data were taken in urban and adjacent non-urban areas of Medellín, Colombia. It was part of a collaborative sampling effort during environmental assessments and personal research, summarizing systematic information on 139 sampling points (124 within the city and 15 in adjacent non-urban areas). All points were sampled under the same protocol in order to facilited data for research; in all cases, sampling was in charge of ornithologist with at least 4 years of previous experience in bird surveys. This protocol consisted in sampling during 10 minutes, four times per point (i.e., repetitions), using a fixed radius of 25 m. </p> <p>Information on bird surveys (Count_Data within the corresponding datasheet tab) contains the ID of each site; whether corresponded to a urban or non-urban site; in what category of urban development the site was located, based on 1000, 500 and 200 m buffers (from the observer during bird counts: moderate, low or high); the taxonomic information of each species (order, family, scientific name); the number of recorded individuals; the repetition or number of the visit (1, 2, 3, or 4); the name of the project; the name of the observer, and the date of sampling. </p> <p>Information on categorization of bird species (Categorization within the corresponding datasheet tab) represents additional information on altitudinal ranges, trophic guilds, distribution, and others. In addition, information on frequency for each bird species is given, according to the location of each sampling site and the way it was grouped. This information was the base for categorizing bird species as urban avoider, utilizer, or dweller, under the calculations and decision rules that are also given within the corresponding cells of the datasheet.</p> <p>Any further information or questions about this data could be ask directly, writing to the e-mails: jgarizabal@unal.edu.co or njmacer@unal.edu.co.</p> <p> </p>
Prevalence of Multimorbidity among Urban–Rural Older Adults in Mongolia: A Cross-Sectional Study
<p>A face-to-face, questionnaire-based cross-sectional study was conducted with 800 valid participants aged ≥60 years in Mongolia from June to September 2023.</p>
Qualitative dataset - Socially just urban food policy implementation: a case study in Groningen (NL)
<p>This qualitative dataset contains the transcripts of 43 interviews that have been conducted with members of social food initiatives (e.g. community gardens and orchards, food assistance, social restaurants, food education projects, social employment trajectories, fair trade campaigns, and so on) in the city of Groningen, as well as the interview guide and the information sheet and consent form that have been used during data collection. In addition, the upload includes the interview guide, posters, assessment table, information sheet and consent form that have been used in a two-part focus group with 3 food policy coordinators of the municipality of Groningen. The data was collected from November 2019 till March 2020. The transcripts have been analysed in NVivo (qualitative data analysis software).The link to and abstract of the paper based on this dataset will be provided when our manuscript gets published.</p> <p><em>This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 765389. </em></p> <p>Project webpage: <a href="https://recoms.eu/">https://recoms.eu/</a></p>
Data from: Assessing the contributions of intraspecific and environmental sources of infection in urban wildlife: Salmonella enterica and white ibis as a case study
Conversion of natural habitats into urban landscapes can expose wildlife to novel pathogens and alter pathogen transmission pathways. Because transmission is difficult to quantify for many wildlife pathogens, mathematical models paired with field observations can help select among competing transmission pathways that might operate in urban landscapes. Here we develop a mathematical model for the enteric bacteria Salmonella enterica in urban-foraging white ibis (Eudocimus albus) in south Florida as a case study to determine (i) the relative importance of contact-based versus environmental transmission among ibis and (ii) whether transmission can be supported by ibis alone or requires external sources of infection. We use biannual field prevalence data to restrict model outputs generated from a Latin hypercube sample of parameter space and select among competing transmission scenarios. We find the most support for transmission from environmental uptake rather than between-host contact and that ibis–ibis transmission alone could maintain low infection prevalence. Our analysis provides the first parameter estimates for Salmonella shedding and uptake in a wild bird and provides a key starting point for predicting how ibis response to urbanization alters their exposure to a multi-host zoonotic enteric pathogen. More broadly, our study provides an analytical roadmap to assess transmission pathways of multi-host wildlife pathogens in the face of scarce infection data.
A Google Earth Engine code to analyze residential buildings' real estate values, summer surface thermal anomaly patterns and urban features: a Florence (Italy) case study
<ol> </ol> <p>The layers included in the code were from the study conducted by the research group of CNR-IBE (Institute of BioEconomy of the National Research Council of Italy) and ISPRA (Italian National Institute for Environmental Protection and Research), published by the Sustainability journal (<strong>https://doi.org/10.3390/su14148412</strong>).</p> <p>Link to the <strong>Google Earth Engine (GEE) code</strong> <strong>(link: <a href="https://code.earthengine.google.com/715aa44e13b3640b5f6370165edd3002">https://code.earthengine.google.com/715aa44e13b3640b5f6370165edd3002</a></strong>)</p> <p>You can analyze and visualize the following spatial layers by accessing the GEE link: </p> <ol> <li><strong>Daytime summer land surface temperature</strong> (raster data, horizontal resolution 30 m, from Landsat-8 remote sensing data, years 2015-2019)</li> <li><strong>Surface thermal hot-spot </strong>(raster data, horizontal resolution 30 m) was obtained by using a statistical-spatial method based on the Getis-Ord Gi* approach through the ArcGIS Pro tool.</li> <li><strong>Surface albedo</strong> (raster data, horizontal resolution 10 m, Sentinel-2A remote sensing data, year 2017)</li> <li><strong>Impervious area</strong> (raster data, horizontal resolution 10 m, ISPRA data, year 2017)</li> <li><strong>Tree cover</strong> (raster data, horizontal resolution 10 m, ISPRA data, year 2018)</li> <li><strong>Grassland area</strong> (raster data, horizontal resolution 10 m, ISPRA data, year 2017)</li> <li><strong>Water bodies</strong> (raster data, horizontal resolution 2 m, Geoscopio Platform of Tuscany, year 2016)</li> <li><strong>Sky View Factor</strong> (raster data, horizontal resolution 1 m, lidar data from the OpenData platform of Florence, year 2016)</li> <li><strong>Buildings' units</strong> of Florence (shapefile from the OpenData platform of Florence) include data on the residential real estate value from the Real Estate Market Observatory (OMI) of the National Revenue Agency of Italy (source: https://www1.agenziaentrate.gov.it/servizi/Consultazione/ricerca.htm, accessed on 14 July 2022). Data on the characterization of the buffer area (50 m) surrounding the buildings are included in this shapefile [the names of table attributes are reported in the square brackets]: averaged values of the daytime summer land surface temperature [LST_media], thermal hot-spot pattern [Thermal_cl], mean values of sky view factor [SVF_medio], surface albedo [alb_medio], and average percentage areas of imperviousness [ImperArea%], tree cover [TreeArea%], grassland [GrassArea%] and water bodies [WaterArea%]. </li> </ol> <p>Here attached the .txt file of the <strong>GEE code</strong>. </p> <p> </p> <p><em>E-mail</em></p> <p>Giulia Guerri, CNR-IBE, giulia.guerri@ibe.cnr.it</p> <p>Marco Morabito, CNR-IBE, marco.morabito@cnr.it</p> <p>Alfonso Crisci, CNR-IBE, alfonso.crisci@ibe.cnr.it</p>
Figure 3 in Species identification and seasonal prevalence of house dust mites in Assiut City, Egypt: A descriptive study in an urban area
Figure 3. Dermatophagoides farinae adult female (SEM photo) – a. Dorsal view shows sce (external scapular seta) is much longer than sci (internal scapular seta); b. Ventral view shows the genital system of the female; c. Lateral views shows the finely striated body and prodorsal shield; d. Hysterostoma region and anal opening; e. Epigynium and genital opening; f. Ventral view of the gnathostoma.
Figure 2 in Species identification and seasonal prevalence of house dust mites in Assiut City, Egypt: A descriptive study in an urban area
Figure 2. Dermatophagoides farinae (adult male) – a. Habitus (100×) before being cleared in Hoyer's medium and the enlarged 1st and 3rd pairs of legs are noted; b. Fused apodemes I (arrow) while apodemes II (arrow head) and apodemes III (curved arrow) are not fused (200×); c. Anal plate (arrow), post anal seta 2 (ps2) (curved arrow) (400×).
Figure 1 in Species identification and seasonal prevalence of house dust mites in Assiut City, Egypt: A descriptive study in an urban area
Figure 1. Dermatophagoides farinae (adult female) – a. Habitus (before being cleared) (10×); b. Habitus (after being cleared in Hoyer's medium) (x100); c. Distal solenidion on tarsus I (arrow head), terminal spinous process (curved arrow) and tarsus II with the two distal solenidia (arrow) (200×); d. Magnified tarsus II with distal solenidia (arrow head) and two small spinous tubercles (long arrow) (400×); e. The low-arched epigynium (arrow) and the faint transverse striations above it (arrow head); f. Bursa copulatrix (arrow), its external opening and sclerotized part (arrow head).
Figure 4 in Species identification and seasonal prevalence of house dust mites in Assiut City, Egypt: A descriptive study in an urban area
Figure 4. Dermatophagoides farinae adult male (SEM photo) – a. Ventral view showing the enlarged first pair of legs. B. The aedeagus; c. The anal plate containing the anal suckers.
GLObal Building heights for Urban Studies (UT-GLOBUS)
<h1><strong>Important note: If you get a message that .zip archive is corrupt, please try updating WinRAR or <em>right-click the folder and</em> <em>select Extract All on Windows or use unzip command on Linux terminal</em>. If the issue persists, email: kamath.harsh@utexas.edu</strong></h1> <p> </p> <p><strong>Abstract</strong> </p> <p>We introduce GLObal Building heights for Urban Studies (UT-GLOBUS), a dataset providing building heights and urban canopy parameters (UCPs) for major cities worldwide. UT-GLOBUS combines open-source spaceborne altimetry (ICESat-2 and GEDI) and coarse resolution urban canopy elevation data with a random forest model to estimate building-level information. Validation using LiDAR data from six U.S. cities showed UT-GLOBUS-derived building heights had an RMSE of 9.1 meters, and mean building height within 1-km² grid cells had an RMSE of 7.8 meters. Testing the UCPs in the urban Weather Research and Forecasting (WRF-Urban) model resulted in a significant improvement (~55% in RMSE) in intra-urban air temperature representation compared to the existing table-based local climate zone approach in Houston, TX. Additionally, we demonstrated the dataset's utility for simulating heat mitigation strategies and building energy consumption using WRF-Urban, with test cases in Chicago, IL, and Austin, TX. Street-scale mean radiant temperature simulations using the SOlar and LongWave Environmental Irradiance Geometry (SOLWEIG) model, incorporating UT-GLOBUS and LiDAR-derived building heights, confirmed the dataset’s effectiveness in modeling human thermal comfort at Baltimore, MD (daytime RMSE = 2.85°C). Thus, UT-GLOBUS can be used for modeling urban hazards with significant socioeconomic and ecological risks, enabling finer scale urban climate simulations and overcoming previous limitations due to the lack of building information.</p> <p><strong>Data</strong></p> <p>We are also supplying a vector file to represent the data coverage, and this file will receive updates as data for new city is added. Building-level data is accessible in vector file format (GeoPackage: .gpkg), which can be converted into raster file format (geoTIFF). These formats are compatible with the SUEWS and SOLWEIG models for the simulation of urban energy balance and thermal comfort. The vector files employ the Universal Transverse Mercator (UTM) projection. Both the vector and raster files are compatible with GIS platforms like QGIS and ArcGIS and can be imported for analysis using programming languages such as Python. We are also providing UCPs required by the BEP-BEM urban model in the urban WRF system in binary file format. Additionally, we provide the urban fractions calculated using ESA world cover dataset (https://esa-worldcover.org/en) for WRF model in binary file format. These files can be directly incorporated into the WRF pre-processing system (WPS). The UT-GLOBUS UCPs are determined using a moving kernel with a size of 1 km2 and spacing of 300 meters in both the X and Y directions</p> <p><strong>Data coverage</strong></p> <p>The 'Coverage_xxxx.gpkg' files provide that geographical extents of cities that are included in our dataset.</p> <p><strong>How to find your city in the UT-GLOBUS dataset</strong></p> <p>Open the 'coverage' geopackage (.gpkg) files in QGIS or ArcGIS. Click on the city polygons and get the 'Label'/City name. Find a folder with the same 'Label'/City name. All the data for the periticular city will be in the folder.</p> <p><strong>How to run BEP-BEM model in WRF using UT-GLOBUS urban canopy parameters</strong></p> <div>Step 0: Before compiling WRF, go to 'dyn_em' folder and open 'module_initialize_real.F'.</div> <div>Change line 3121 (in version 4.5.2): </div> <div>From </div> <div>grid%HI_URB2D(i,k,j) = grid%URB_PARAM(i,k+117,j) </div> <div>To</div> <div>grid%HI_URB2D(i,k,j) = grid%URB_PARAM(i,k+117,j)*100.</div> <div> </div> <div>1. Change the name of the binary files 'ufrac' and 'urb_param' inside 'urb_fra' and 'GLOBUS_morph' folders, respectively to 00001-tile_x.00001-tile_y.</div> <div>Values for tile_x and tile_y can be found in the index file inside the 'urb_fra' and 'GLOBUS_morph' folders. Make sure to append zeros before tile_x and tile_y values to make 5 digits. </div> <div>Ex: tile_x = 260 and tile_y = 219; Then the binary files should be renamed as 00001-00260.00001-00209 </div> <div> </div> <div>2. Copy the 'urb_fra' and 'GLOBUS_morph' folders to WRF static data directory.</div> <div> </div> <div>3. Change the paths to 'URB_PARAM' and 'FRC_URB2D' variables inside GEOGRID.TBL file as follows:</div> <div> </div> <div>===============================</div> <div>name=URB_PARAM</div> <div> priority=1</div> <div> optional=yes</div> <div> dest_type=continuous</div> <div> fill_missing = 0.</div> <div> z_dim_name=num_urb_params</div> <div> interp_option=default:nearest_neighbor</div> <div> abs_path= Your_WPS_static_data_folder/GLOBUS_morph/</div> <div> flag_in_output=FLAG_URB_PARAM</div> <div>===============================</div> <div>name=FRC_URB2D</div> <div> priority=1</div> <div> optional=yes</div> <div> dest_type=continuous</div> <div> fill_missing = 0.</div> <div> interp_option=default:nearest_neighbor</div> <div> abs_path= Your_WPS_static_data_folder/urb_fra/</div> <div> flag_in_output=FLAG_FRC_URB2D</div> <div>===============================</div> <div> </div> <div>4. Run geogrid.exe. If the domain covers the chosen city:</div> <div> -- 'FRC_URB2D' variable will show the urban fraction.</div> <div> -- 'URB_PARAM[91,:,:]' will show the plan area fraction.</div> <div> -- 'URB_PARAM[94,:,:]' will show the area averaged building heights.</div> <div> -- 'URB_PARAM[95,:,:]' will show the building surface to total area fraction.</div> <div> -- 'URB_PARAM[118-132,:,:]' will show the building height histograms with 5-meter bin size.</div> <div> </div> <div>5. If you see the data in 'FRC_URB2D' and 'URB_PARAM' variables after running the geogrid.exe, GLOBUS data is ingested in WPS and you can continue with ungrib and metgrid as usual.</div> <div> </div> <div>6. For running the model over the domain area which covers more that one city, UT-GLOBUS UCPs can be stitched together. For instance, if two cities are covered in the domain, step number 3 should be modified as follows:</div> <div> </div> <div>===============================</div> <div>name=URB_PARAM</div> <div> priority=1</div> <div> dest_type=continuous</div> <div> fill_missing = 0.</div> <div> z_dim_name=num_urb_params</div> <div> interp_option=default:nearest_neighbor</div> <div> abs_path=Your_WPS_static_data_folder/GLOBUS_morph_for_city-1/ </div> <div>flag_in_output=FLAG_URB_PARAM</div> <div>===============================</div> <div>name=FRC_URB2D</div> <div> priority=1</div> <div> dest_type=continuous</div> <div> fill_missing = 0.</div> <div> interp_option=default:nearest_neighbor</div> <div> abs_path= Your_WPS_static_data_folder/urb_fra_for_city-1/</div> <div> flag_in_output=FLAG_FRC_URB2D</div> <div>===============================</div> <div>name=URB_PARAM</div> <div> priority=2</div> <div> dest_type=continuous</div> <div> fill_missing = 0.</div> <div> z_dim_name=num_urb_params</div> <div> interp_option=default:nearest_neighbor</div> <div> abs_path= Your_WPS_static_data_folder/GLOBUS_morph_for_city-2/</div> <div>===============================</div> <div>name=FRC_URB2D</div> <div> priority=2</div> <div> dest_type=continuous</div> <div> fill_missing = 0.</div> <div> interp_option=default:nearest_neighbor</div> <div> abs_path= Your_WPS_static_data_folder/urb_fra_for_city-2/</div> <div>===============================</div> <div> </div> <div><strong>References</strong></div> <div> <ol> <li>Skamarock, W., Klemp, J., Dudhia, J., Gill, D., Liu, Z., Berner, J., Wang, W., Powers, J., Duda, M., Barker, D., Huang, X., 2021. A Description of the advanced research WRF model.</li> <li>Martilli, A., Clappier, A., Rotach, M.W., 2002. An urban surface exchange parameterisation for mesoscale models. Boundary Layer Meteorol 104, 261–304. https://doi.org/10.1023/A:1016099921195</li> <li>Sun, T., Grimmond, S., 2019. A Python-enhanced urban land surface model SuPy (SUEWS in Python, v2019.2): Development, deployment and demonstration. Geosci Model Dev 12, 2781–2795. https://doi.org/10.5194/gmd-12-2781-2019</li> <li>Lindberg, F., Holmer, B., Thorsson, S., 2008. SOLWEIG 1.0 - Modelling spatial variations of 3D radiant fluxes and mean radiant temperature in complex urban settings. Int J Biometeorol 52, 697–713. https://doi.org/10.1007/s00484-008-0162-7</li> <li>Software: QGIS (https://www.qgis.org/en/site/)</li> </ol> </div>
Figure 1. Study area. A. South America and Brazil. B in Far from urban areas: plastic uptake in fish populations of subtropical headwater streams
Figure 1. Study area. A. South America and Brazil. B. Brazil and the state of Rio Grande do Sul. C. Rio Grande do Sul and the Sinos River Basin. D. The numbers from 1 to 7 in the white dots show the sampling sites in the upper section of the Sinos River basin. The colour gradient represents the terrain elevation (light green elevations of 30m altitude and dark brown elevations of 980m). The red polygons are the urban areas.
Figure. CCA ordination of bird species. The eigenvalue of the first axis is 0.164 and of the second axis is 0.127. Species abbreviations are the first 3 letters of their genus followed by the first 3 letters of the species epithet. in Birds and small urban parks: a study in a high plateau city
Figure. CCA ordination of bird species. The eigenvalue of the first axis is 0.164 and of the second axis is 0.127. Species abbreviations are the first 3 letters of their genus followed by the first 3 letters of the species epithet.
Figure 2 in Year-round monitoring of bat records in an urban area: Kharkiv (NE Ukraine), 2013, as a case study
Figure 2. Daily average temperature in Kharkiv during 2013. Division of the year by phenological periods (blue – winter, green – spring, yellow – summer, orange – autumn), and periods of the bat life cycle; total number of bat records in a day (red columns). I-XII - months of the year.
Figure 12 in Year-round monitoring of bat records in an urban area: Kharkiv (NE Ukraine), 2013, as a case study
Figure 12. The body mass dynamics by month for all E. serotinus during 2013 in Kharkiv. F – ♀♀, M – ♂♂ (red dot – mean value, line – median value, whiskers – min and max values, open dot – outlier).
Figure 8 in Year-round monitoring of bat records in an urban area: Kharkiv (NE Ukraine), 2013, as a case study
Figure 8. Relationship between the number of recorded bats and the percentage of those that were dead or significantly injured, as shown using k-mean clustering.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.