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3,709 results for “urbanization.”
Evaluation of a Partector Pro for atmospheric size distribution and number concentration measurements at an urban background site
<p>Number size distributions, total number concentrations and mean particle sizes have been measured for 70 days at an urban background site in Mülheim-Styrum, Germany with a handheld Partector Pro and a TSI mobility particle size spectrometer (MPSS). The aim of the study was to evaluate the performance of the Partecor Pro against the MPSS. The results show that the size distributions, measured with the Partector Pro agree with the MPSS mostly within ±25% for particle sizes below 113.5 nm, whereas higher, systematic differences were observed for larger particles. The measurement accuracy was shown to be dependent on the geometric mean diameter and the geometric standard deviation of the aerosol. Best results were found for the most abundant size distributions with geometric mean particle diameters ≥30 nm and geometric standard deviations larger than 1.8. The total number concentration, measured by the Partector Pro was found to be in excellent agreement with the MPSS with a slope of the linear fit of 0.9977 and a regression coefficient of R² = 0.9956. The agreement of the geometric mean particle diameter, determined with the Partector Pro and the MPSS was good, but moderately dependent on the particle size distribution. For mean particle sizes between 20 nm and 50 nm, the bias was within ±15%. Higher deviations of up to 30% were observed when the geometric mean particle sizes exceeded 70 nm and when the geometric standard deviations exceeded approximately 2.7.</p>
Urban Heat: Forward-Looking Climate Modeling for Gaza City
<p>We produced actionable data on heat stress in cities to inform analysis and client dialogue on the part of World Bank teams. We applied an urban-scale climate modeling framework to generate datasets describing modeled heat stress exposure for present-day and future conditions under selected climate scenarios. The study domain focuses on Gaza City.</p> <p>More details about the dataset: </p> <ul> <li>The dataset includes calculations for each indicator across three scenarios (<strong>present, SSP1-1.9, SSP3-7.0</strong>) and three twenty-year periods (<strong>2001-2020, 2021-2040, and 2041-2060</strong>). The present period refers to 2001-2020, while the other two periods correspond to the two SSP scenarios.</li> <li>All indicators are available in both <strong>NetCDF</strong> and <strong>GeoTiff</strong> formats.</li> <li>The indicators are calculated at a resolution of <strong>100 m</strong>, consistent with the UrbClim and WBGT simulations. Additionally, downscaled versions of the indicators are provided at a resolution of <strong>30 m</strong>.</li> <li>The UrbClim and WBGT simulations, as well as the postprocessing, are conducted using the regional projection <strong>E</strong><strong>PSG 32636</strong>. The NetCDF and GeoTiff data also adopt this projection. Furthermore, a GeoTiff data file with <strong>EPSG 4326</strong> projection is included.</li> <li>All indicators are calculated as <strong>yearly averages</strong>. Some indicators also have additional calculations for <strong>seasonal averages</strong>, including Spring (MAM), Summer (JJA), Autumn (SON), and Winter (DJF).</li> <li>Ten representative locations within the study domain have been selected to retrieve the WBGT profile on a chosen date (2017-07-11). The results and the locations are stored in wbgt_profile.xlsx.</li> <li>Images for <strong>quick viewing</strong> <strong>in</strong> <strong>png</strong> format visualizing the results for each indicator. Present denotes the period 2001-2020; 2030 denotes the period 2021-2040; & 2050 denotes the period 2041-2060.</li> <li>The NetCDF and GeoTiff data can be found in the data.zip; The png files for quick viewing can be found in quickview.zip; more information about the dataset, including the methodology, all available data list, contact information, etc. can be found in the Technical_Annex_Gaza.docx</li> </ul>
Associated results of Phase 1 of the Urban-PLUMBER model evaluation project
<p><strong>Archive of: https://urban-plumber.github.io/AU-Preston/plots/</strong></p> <p>Files in this folder are associated with the manuscript:</p> <blockquote> <p>“Evaluation of 30 urban land surface models in the Urban-PLUMBER project: Phase 1 results”</p> </blockquote> <p>Files are an archive of the website https://urban-plumber.github.io/AU-Preston/plots/ as of 2nd December 2022.</p> <p>Use of any data must give credit through citation of the above manuscript, the data repository, and other site references as appropriate. </p> <p>Corresponding author: Mathew Lipson (mathew.lipson@bom.gov.au)</p> <p><strong>Usage</strong></p> <p>Load the "index.html" to navigate through plots and results subpages</p> <p><strong>Description</strong></p> <p>These files include results from Phase 1 of the Urban-PLUMBER model evaluation project for urban areas. </p> <p>Data includes:</p> <p> - individual model results (error metrics) and submission metadata <br> - individual model plots (timeseries, subsets, energy closure, distributions)<br> - collective timeseries for every submitted output in the baseline experiment<br> - collective timeseries for every submitted output in the detailed experiment<br> - supplementary material for the manuscript<br> - variable definitions</p> <p><strong>Authors</strong></p> <p>Mathew Lipson, Sue Grimmond, Martin Best, Gab Abramowitz, Andrew Coutts, Nigel Tapper, <br> Jong-Jin Baik, Meiring Beyers, Lewis Blunn, Souhail Boussetta, Elie Bou-Zeid, <br> Martin G. De Kauwe, Cécile de Munck, Matthias Demuzere, Simone Fatichi, <br> Krzysztof Fortuniak, Beom-Soon Han, Maggie Hendry, Yukihiro Kikegawa, Hiroaki Kondo, <br> Doo-Il Lee, Sang-Hyun Lee, Aude Lemonsu, Tiago Machado, Gabriele Manoli, Alberto Martilli, <br> Valéry Masson, Joe McNorton, Naika Meili, David Meyer, Kerry A. Nice, Keith W. Oleson, <br> Seung-Bu Park32, Michael Roth33, Robert Schoetter34, Andres Simon35, Gert-Jan Steeneveld, <br> Ting Sun, Yuya Takane, Marcus Thatcher, Aristofanis Tsiringakis, Mikhail Varentsov, <br> Chenghao Wang, Zhi-Hua Wang</p>
UrbAlytics - Remote Sensing tools for Urban Heat Island Assessment and Climate Change Adaptation through Nature-Based Solutions
<p>Urban Heat Island (UHI) is considered one of the significant problems posed to human beings due to the urbanization and industrialization of human civilization. The leading causes of UHI are the vast amounts of heat urban structures produce as they absorb and re-radiate solar radiation and anthropogenic heat sources. The issue mainly affects cities or metropolises with a vast population and a thriving economy. The problem will worsen significantly in the future due to the predicted three billion people living in urban areas worldwide. Due to the severity of the problem, accessing up-to-date information layers that can support city planners and decision-makers in the context of climate resilience is a demanding problem nowadays.</p> <p><strong>UrbAlytics</strong> is an experimental sub-project of the H2020-funded project <a href="https://ai4copernicus-project.eu/"><strong>AI4Copernicus</strong></a> that aims to bridge Artificial Intelligence with Earth Observations, producing information layers that can support city planners and decision-makers in the context of climate resilience and related challenges in urban areas. This research investigates, thanks to the joint expertise of the partners <a href="https://www.latitudo40.com/"><strong>Latitudo 40</strong></a> and <a href="https://www.landsrl.com/land-research-lab"><strong>LAND Research Lab®</strong></a>, the Urban Heat Island (UHI) effect, evaluating its impacts on cities, assessing Ecosystem Services provided by Blue and Green Infrastructures and proposing a set of Nature-Based Solutions (NBS) for climate adaptation and extreme heat mitigation. </p> <p><strong>The dataset</strong></p> <p>This dataset is the tool's output of a fully automated workflow realized during the project and tested for the cities of <strong>Milan</strong> and <strong>Naples</strong>, pilot users of the experiment. The choice of Milan and Naples allows for different readiness levels, data availability, and urban-climatic conditions.<br> For each city, the dataset contains the following layers for the analysis period 2018-2022.</p> <p><strong> HEATWAVE POTENTIAL RISK (HPR)</strong></p> <p>Risk Assessment mapping concerning extreme heat, considering the severity of the heat island phenomenons, the exposure of sensitive age groups and the vulnerability due to city morphology and surface materials. The risk assessment is the first step in defining a methodology that aims to assess the effectiveness of mitigation and adaptation strategies to climate extremes. It's a value in [0,1], where the higher the value higher the risk.</p> <p><strong> MICROCLIMATIC PERFORMANCE INDEX (MPI)</strong></p> <p>The role of vegetation in the city in abating the Heat Island effect has been widely demonstrated. In this context, deploying Urban Green Infrastructure is recognized as one of the most important strategies to mitigate UHI and promote a resilient city environment. The significance of the mitigation role of the Heat Island phenomenon that vegetation assumes makes it necessary to map Urban Green Infrastructure to estimate a cooling potential. Estimating the microclimatic performance of urban vegetation is crucial to plan adaptation and mitigation actions for the UHI effect. In this work, up-to-date Tree Cover Density and Land Cover maps have been produced using machine learning applied to Sentinel-2 satellite imagery. Those maps have been interpolated and combined, creating 20 Blue and Green Infrastructures classes. Each category's microclimatic performance score was attributed based on evapotranspiration potential, shading and albedo. The output is a map with integer values in [1, 20], where the lower the value higher the microclimatic performance. </p> <p><strong> PARK COOL ISLANDS (PCI)</strong></p> <p>Park Cool Islands layer identifies the most performing areas during extreme summer heatwaves, according to their size and relevant characteristics, providing reliable information to citizens and urban planners about the safest and coolest areas during extreme heatwaves. Since the green areas' type and composition can influence their cooling effects, we considered both the size and composition of urban parks to identify the most performing green areas in terms of the Park Cool Island effect. <strong> </strong>The layer distinguishes between major and minor Park Cool Islands. <em>Major PCI</em> includes areas covered by at least 50% of tree canopy coverage and bigger than 2 hectares with an estimated cooling distance of 300 m buffer<strong>. </strong><em>Minor PCI</em> includes green areas whose surface is between 1 and 2 hectares as well as those green areas bigger than 2 hectares but covered by less than 50% of tree canopy coverage, with an estimated cooling distance of 100 m buffer.</p> <p> </p> <p><strong>Contact Information</strong></p> <p>If you would like further information about the dataset or if you experience any issues downloading files, please contact us <a href="mailto:giovanni.giacco@latitudo40.com">giovanni.giacco@latitudo40.com</a>, <a href="mailto:giulia.castellazzi@landsrl.com">giulia.castellazzi@landsrl.com</a></p>
Dataset for "Improving data quality of low-cost light-scattering PM sensors: Towards automatic air quality monitoring in urban environments"
<p>The dataset contains the data used in the article "Improving data quality of low-cost light-scattering PM sensors: Towards automatic air quality monitoring in urban environments".</p> <p>A low-cost monitoring system composed of 14 monitoring stations was positioned at the official monitoring station of Torino Rubino in the city of Turin (Italy). The official station is managed by the environmental agency ARPA Piemonte.</p> <p>Each low-cost station contains four low-cost light-scattering PM sensors (Honeywell HPMA115S0-XXX), one temperature and relative humidity sensor (DHT22), and one atmospheric pressure sensor (BME/BMP280).<br>The sampling time of the PM sensors was set to one second, while the other sensors generated measurements every 3-4 seconds.</p> <p>The official monitoring station uses both a gravimetric and a beta attenuation instrument for measuring PM.</p> <p>The data contained in this dataset was collected from October 2020 to November 2021. It contains the PM2.5, relative humidity, and temperature measurements of the low-cost monitoring system and the official measurements of the beta attenuation device.</p> <p>Measurements of low-cost sensors are expressed in UTC, while official measurements are expressed in UTC+1.</p> <p>Official PM measurements can be also found at https://aria.ambiente.piemonte.it/qualita-aria/dati.</p>
Ecosystem services, nature preferences, and valuation of urban green and blue spaces - Pre-publication data
<p>Data used in the analysis for the manuscript: "Ecosystem services, nature preferences, and valuation of urban green and blue spaces".</p> <p>The data was collected in a web-based survey of Tel Aviv-Yafo, Israel residents between March 21, 2021 and May 5, 2021. </p>
Prioritizing equity in urban heat reduction: Beyond cooling as a metric
<p>Archived simulations for five heat wave events happened over Houston from 2017 - 2019 with different urban overheating mitigation strategies. </p>
Infrastructure and the ethnographic-cartographic production of urban bird species richness
<p>The qualitative bio-geographies of human geographers and the quantitative mappings of biogeographers share a goal: how to understand living with non-human life. Yet they rarely bridge the conceptual and methodological gap between them. This paper theorizes how the concept of infrastructure can bridge this ethnographic-cartographic divide. Infrastructure is not just an inanimate shell. It is also a system of relation, a dynamic patterning of socionatural form emerging out of the experiences and affective moments of its constituents. As a proof of concept, we quantified and compared urban bird species richness and frequency for Tallahassee, Florida over a 17-year period (2000–2017) for two co-occurring observational infrastructures, eBird and a wildlife rehabilitation center that serves the city. Species common to both infrastructures comprised 94% of all eBird observations and 99% of all rehab records. Their differences reflected contrasts in how the motivations for experiencing birds intersected with bird habitat preferences, behavior, and contingencies of urban history and development. eBird observations had high species richness (295 spp) and reflected the growing popularity among birds and a small number of active birders for visiting stormwater retention lakes recently modified to improve bird habitat. Rehabilitation records had a lower richness (194 spp) and exhibited a much more even distribution of bird encounters among individual residents as well as community institutions like schools, universities, law enforcement, and other government organizations. Infrastructural perspectives convey how affective and individualistic encounters with non-humans can link to emergent biogeographic mappings and how urban biodiversity is relationally and heterogeneously produced rather than simply contained in cities. </p>
Data from: Environmental filtering of life-history trait diversity in urban populations of Arabidopsis thaliana
<p>The challenges to which plants are exposed in urban environments represent, in miniature, the challenges plants face as a result of global environmental change. Hence, urban habitats provide a unique opportunity to assess whether processes of local adaptation are taking place despite the short temporal and geographical scales that characterize the Anthropocene. We quantified the ecological diversity of urban habitats hosting A. thaliana populations. Using plant community indicators, we show that these patches differ in their levels of soil nutrient content and disturbance. Accordingly, plants in each patch displayed a range of flowering time, size and fitness. Using a deep sampling approach coupled with reduced genome-sequencing, we demonstrate that most individuals can be assigned to a limited set of clonal lineages; the genetic diversity of these lineages is a sample of the diversity observed in western European populations of the species, indicating that established urban populations originate from a broad regional pool of lineages. We assessed the genetic and phenotypic diversity of these lineages in a set of common garden experiments. We report marked genetic differences in life-history traits, including time of primary and secondary dormancy as well as of flowering. These genetic differences in life-history traits are not randomly distributed but sorted out by ecological differences among sites of origin.</p> <p>Synthesis: Our study shows that the genetically diverse phenology of a regional A. thaliana gene pool is not randomly distributed but filtered by heterogeneity in the urban environment. To out knowledge, this report is the first to show a pattern indicative of environmental filtering enhancing local genetic adaptation within urban environments. We conclude that environmental filtering helps maintain functional diversity within species.</p>
Enhancing Safe Sleep Practices of Urban Low-Income Mothers
ClinicalTrials.gov study NCT03070639. IPD Sharing: NO. Countries: 1. Publications: 2.
Impact Evaluation of Urban Water Supply Improvements on Cholera and Other Diarrhoeal Diseases in Uvira, Democratic Republic of Congo
ClinicalTrials.gov study NCT02928341. IPD Sharing: NO. Countries: 1. Publications: 3.
A Prospective Cohort Pilot Study of the Clinical and Molecular Epidemiology of Staphylococcus Aureus in Pregnant Women at the Time of Group B Streptococcal Screening in a Large Urban Medical Center in
ClinicalTrials.gov study NCT00532324. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Preventing Cigarette Use Among Urban Youth Via an M-Health Primary Care Preventive Intervention
ClinicalTrials.gov study NCT03855410. IPD Sharing: NO. Countries: 1. Publications: 4.
Automatic Chlorination and Child Health in Urban Bangladesh
ClinicalTrials.gov study NCT02606981. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Urban Walking Circuits
ClinicalTrials.gov study NCT01854008. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effectiveness of Smoking-cessation Interventions for Urban Hospital Patients
ClinicalTrials.gov study NCT01363245. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Alive & Thrive Nigeria Breastfeeding Promotion in Urban Private Facilities Study
ClinicalTrials.gov study NCT04835051. IPD Sharing: YES. Countries: 1. Publications: 1.
Cockroach Allergen Reduction by Extermination Alone in Low-Income, Urban Homes: A Randomized Control Trial
ClinicalTrials.gov study NCT00341445. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Global Mental Health of Urban Mothers
ClinicalTrials.gov study NCT04363177. IPD Sharing: NO. Countries: 2. Publications: 8.
UrbanHEAT: Health Behaviors, Outcomes, and Disparities in Individually Experienced Temperature Across an Urban Community
ClinicalTrials.gov study NCT06778252. IPD Sharing: NO. Countries: 1. Publications: 9.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.