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802 results for “urban data”
Data from: Knowledge, attitudes, and practices among Indonesian urban communities regarding HPV infection, cervical cancer, and HPV vaccination
<p><strong>Background</strong>: Few studies explored Indonesian understanding of cervical cancer (CC) and the human papillomavirus (HPV) vaccination. We aimed to investigate the association between knowledge, attitudes, and practices (KAP) and socio-demographical influences <span>related to</span> HPV, CC, and vaccination among Indonesian urban citizens.</p> <p><strong>Methods</strong>: We conducted an online survey during March 2020-August 2021 using the Snowball sampling technique. The socio-demographic characteristic and KAP responses were collected via Google Forms from 400 respondents in Jakarta. The knowledge and attitudes were divided into HPV and CC (aspect 1) and HPV vaccination (aspect 2). Correlation between KAP scores was performed using Spearman's test, and multiple logistic regression analyses were conducted to determine KAP predictors.</p> <p><strong>Results</strong>: Indonesian urban citizens in Jakarta were found to have poor knowledge in individual aspects of the inquiry but moderate knowledge overall, good attitude in inquiry both in each aspect and overall, and unsatisfying practices. Overall, in the general population, men, and women respectively: 50.8%, 32.4%, and 53.6% had good knowledge; 82.0%, 75.2%, and 84.4% expressed positive attitude; and 30.3%, 15.2%, and 35.6% applied favorable practice regarding questions inquired. Knowledge was weakly correlated towards attitude (ρ=0.385) but moderately correlated with practice (ρ=0.485); attitude was moderately correlated with practice (ρ=0.577), all results: p<0.001. Significant odds ratio (OR) for predictors to good knowledge were female sex (OR=2.99), higher education (OR=2.91), and higher mother's education (OR=2.15). Factors related to positive attitudes were higher mother's education (OR=4.13), younger age (OR=1.86), and better results in the knowledge inquiries (OR=2.96). Factors that suggested better practices were female sex (OR=2.33), being employed (OR=1.68), excellent knowledge scores (OR=4.56), and positive attitudes expressions (OR=8.05). Having done one vaccination dose and intention to receive vaccines were significantly influenced by good KAP.</p> <p><strong>Conclusions</strong>: KAP had inter-association to successful CC and HPV prevention programs, and socio-demographical characteristics are critical to influencing better KAP.</p>
Data from: Balanced spatial distribution of green areas creates healthier urban landscapes
<div> <span>The benefits of green infrastructure on human well-being in urban areas are already well established, with strong evidence of the positive effects of the amount and proximity to green areas. However, the understanding of how the spatial distribution and type of green areas affect health is still an open question. <br>Here, we explore how different spatial configurations of green and built-up areas, through a land sharing and sparing framework, and how different types of green areas affect cardiovascular and respiratory hospitalizations in São Paulo city, Brazil. <br>Sharing/sparing indicators were selected as the main explanatory factors in the control of all groups of diseases. Land sharing appeared as a favourable spatial condition to prevent cardiovascular hospitalization, while land sparing and arboreal vegetation were relevant to reduce hospitalization by lower respiratory diseases. <br>For upper respiratory diseases, forests seem to provide a disservice, once they were associated with increased rates of hospitalization by respiratory allergies causes.<br>Considering that hospitalization rates and severity of cardiovascular diseases are substantially higher than those of upper respiratory ones, dense vegetation tends to provide more services than disservices. The land sharing configuration, which is characterized by green areas spread throughout the urban network (in streets, gardens, small squares, or parks), should lead to higher exposure and use of the benefits of green areas, which may then explain the greater prevention of cardiovascular diseases. <br>These novel results indicate that a more balanced distribution of green areas across built-up areas creates healthier urban spaces, and thus can be used as an urban planning strategy to leverage the health benefits provided by green infrastructure. <br>Policy implications: </span><span><span>Aiming to reduce hospitalizations by cardiovascular and pulmonary causes, urban planning should promote the spreading of green areas across the cities, in order to increase daily contact with natural attributes, giving preference to distribution over total quantity of green in urban landscape.</span></span> </div>
Supplemental data for "Circular utilization of urban tree waste contributes to the mitigation of climate change and eutrophication"
<p>Supplemental data for "Circular utilization of urban tree waste contributes to the mitigation of climate change and eutrophication"</p>
Data on bird abundance in urban woodlands in 32 Swedish cities
<p>The expansion of urban areas is increasingly contributing to biodiversity declines. Several studies have analyzed the effect of increasing urbanization on bird diversity, but few have differentiated effects of urban landscapes (matrix) from changes in focal habitat quality at multiple spatial scales. In this study we analyzed the effect of urbanization on bird communities in individual (local scale) and across multiple (regional scale) cities while controlling for the quality of sampled natural habitats. We conducted bird point counts and habitat quality mapping of trees, dead wood and shrubs in 459 forest remnants along an urbanization gradient in 32 cities in Sweden. We then analyzed how the degree of urbanization affected species richness of woodland-breeding bird and red-listed bird species at a regional and local scale. We also analyzed how urbanization affected beta-diversity and dissimilarity between communities at the different spatial scales, and if dissimilarities in species communities along the urbanization gradient were driven by species nestedness or turnover. We found that urbanization decreased species richness, and the number of red-listed species, at both the regional and local scale. Dissimilarities in woodland-breeding bird communities among urban, semi-urban and peri-urban areas at the local scale were particularly due to turnover, and at the regional scale to nestedness. Since there was no difference in habitat quality among woodlands across the urbanization gradient, we conclude that landscape urbanization systematically causes poorer and more homogeneous bird communities. However, despite that natural habitats in cities contribute less to the regional diversity, they are critical to maintain the local bird communities of individual cities and their surroundings.</p>
Data for: Temperature and not landscape composition shapes wild bee communities in an urban environment
<p>Data for: Temperature and not landscape composition shapes wild bee communities in an urban environment. This dataset was obtained surveying wild bees using pan-traps in the metropolitan city of Rome in 2016.</p>
Data files associated with the U-net prediction of urban canopy flow
<p>Folder "testing_data" includes the input (urban canopy structure), output (LES velocity field), and prediction (velocity field from trained network) for general test cases.</p> <p>Folder "perturbed_data" includes the input (urban canopy structure), output (LES velocity field), and prediction (velocity field from trained network) for perturbed test cases.</p> <p>Input: dataX_xxxxxx</p> <p>Output: dataY_xxxxxx</p> <p>Prediction: pred_xxxxxx</p>
Data set for the manuscript "Uncertainty quantification and physics-informed forecasting for improved urban flood modeling"
<p>This is a data set for the manuscript "Uncertainty quantification and physics-informed forecasting for improved urban flood modeling."</p>
Biogeochemical data from two clear-water and two turbid-water urban ponds in Brussels (Belgium) from June 2021 to December 2023
<p>The dataset comprises three files, each containing geo-referenced information with corresponding timestamps. The names of the four ponds are written in French according to the official name defined by Brussels Environment (BE) (i.e. Leybeek, Pêcheries, Tenreuken and Silex).</p> <p>· DATA_Dissolved contains water temperature (°C), Chlorophyll-a concentration (µg L<sup>-1</sup>), oxygen saturation (%O<sub>2</sub>, in %), total suspended matter concentration (TSM, in mg L<sup>-1</sup>), inorganic nutrients (nitrate : NO<sub>3</sub><sup>-</sup>, nitrite : NO<sub>2</sub><sup>-</sup>, ammonium : NH<sub>4</sub><sup>+</sup>, soluble reactive phosphorus : SRP, in µmol L<sup>-1</sup>), partial pressure of CO<sub>2</sub> (pCO<sub>2</sub>, in ppm) , dissolved CH<sub>4</sub> concentration (CH<sub>4</sub>, in nmol L<sup>-1</sup>), N<sub>2</sub>O saturation level (%N<sub>2</sub>O, in %) and <sup>13</sup>C/<sup>12</sup>C ratio of CH<sub>4</sub> (δ<sup>13</sup>C-CH<sub>4</sub>, in ‰) collected from June 2021 to December 2023 in four ponds in Brussels.</p> <p>· DATA_Ebullitive contains wind speed (m s<sup>-1</sup>), water temperature (°C), atmospheric pressure (atm), bubbles flux measured with inverted funnels (mL m<sup>-2</sup> d<sup>-1</sup>), CH<sub>4</sub> content in bubbles (%CH<sub>4</sub>, in %) and <sup>13</sup>C/<sup>12</sup>C ratio of CH<sub>4</sub> (δ<sup>13</sup>C-CH<sub>4</sub>, in ‰) of CH<sub>4</sub> in the bubbles measured with three bubble traps in spring, summer, and fall in 2022 and 2023, totaling 8 days in the Leybeek, Pêcheries, and Tenreuken ponds and 24 days in the Silex pond.</p> <p>· DATA_perturbed_sediments contains <sup>13</sup>C/<sup>12</sup>C ratio of CH<sub>4</sub> (δ<sup>13</sup>C-CH<sub>4</sub>, in ‰) of CH<sub>4</sub> in the bubbles present in the sediment directly sampled with bubble traps by physically perturbing the sediment with a wooden rod the fourth September 2023 in the four ponds.</p> <p><strong>Field sampling and meteorological data</strong></p> <p>Sampling was done from a pontoon, with 60mL polypropylene syringes for gases (CO<sub>2</sub>, CH<sub>4</sub>, N<sub>2</sub>O) and a 2L polyethylene water container for processing at the home laboratory for other variables. Water temperature and %O<sub>2</sub> were measured in-situ with VWR MU 6100H probe. pCO<sub>2</sub> was measured with a Li-Cor Li-840 infrared gas analyser (IRGA) based on the headspace technique with 4 polypropylene syringes (Borges et al., 2019). The Li-Cor 840 IRGA was calibrated before and after each cruise with ultrapure N<sub>2</sub> and a suite of gas standards (Air Liquide Belgium) with CO<sub>2</sub> mixing ratios of 388, 813, 3788 and 8300 ppm. The overall precision of pCO<sub>2</sub> measurements was ±2.0%. Samples for CH<sub>4</sub> and N<sub>2</sub>O were transferred from the syringes with a silicone tube in 60 mL borosilicate serum bottles (Weathon), poisoned with 200 µl of a saturated solution of HgCl<sub>2</sub> and sealed with a butyl stopper and crimped with aluminium cap, without a headspace.</p> <p>Three bubble traps were deployed at 50 cm apart for measuring ebullitive CH<sub>4</sub> flux. The bubble traps consistent in inverted polypropylene funnels (diameter 23.5cm) mounted with 60mL polypropylene syringes and attached with steel rods to a polystyrene float. The volume of gas collected in the funnels was measured every 24 hours with 60mL syringes. The collected gas was stored in pre-evacuated 12 mL vials (Exetainers, Labco, UK) for the analysis of CH<sub>4</sub> concentration and δ<sup>13</sup>C-CH<sub>4</sub>.</p> <p>Wind speed and atmospheric pressure, were retrieved from <a href="https://wow.meteo.be/en" target="_new">https://wow.meteo.be/en</a> for the meteorological station of the Royal Meteorological Institute of St-Lambert (50.8408°N, 4.4234°E) in Brussels, located between 2.5 and 5 kilometers from the surveyed ponds.</p> <p><strong>CH<sub>4</sub> and N<sub>2</sub>O measurements by gas chromatography and </strong><strong>δ</strong><strong><sup>13</sup></strong><strong>C-CH<sub>4</sub> by cavity ring-down spectrometry</strong></p> <p>Measurements of N<sub>2</sub>O and CH<sub>4</sub> concentrations dissolved in water and in the gas samples from bubbles were made with the headspace technique (20mL of ultra-pure N<sub>2</sub>, Air Liquid Belgium, Weiss, 1981) and a gas chromatograph (GC) (SRI 8610C) with a flame ionisation detector for CH<sub>4</sub> (with a methanizer for CO<sub>2</sub>) and electron capture detector for N<sub>2</sub>O calibrated with CO<sub>2</sub>:CH<sub>4</sub>:N<sub>2</sub>O:N<sub>2</sub> gas mixtures (Air Liquide Belgium) with mixing ratios of 1, 10 and 30 ppm for CH<sub>4</sub>, 404, 1018, 3961 ppm for CO<sub>2</sub>, and 0.2, 2.0 and 6.0 ppm for N<sub>2</sub>O. The precision of measurement based on duplicate samples was ±3.9% for CH<sub>4</sub> and ±3.2% for N<sub>2</sub>O.</p> <p>The δ<sup>13</sup>C-CH<sub>4</sub> was measured in gas of the headspace (20mL of synthetic air, Air Liquid Belgium) equilibrated with the water sample (total volume 60mL) for water samples and directly on gas stored in Exetainers for gas samples from the bubble traps. The gas samples were diluted to obtain a final partial pressure of CH<sub>4</sub> in the cavity below 10 ppm to fall within the recommended operational concentration range of the instrument, prior to injection into a cavity ring-down spectrometer (G2201-I, Isotopic Analyzer, Picarro) with a Small Sample Introduction Module 2 (SSIM, Picarro). Data were corrected with curves of δ<sup>13</sup>C-CH<sub>4</sub> as a function of concentration based on two gas standards from Airgas Specialty Gases with certified δ<sup>13</sup>C-CH<sub>4</sub> values of -23.9±0.3 ‰ and -69.0±0.3 ‰.</p> <p><strong>Chlorophyll-a, total suspended matter, and dissolved inorganic nutrients</strong></p> <p>Water was filtered through Whatman GF/F glass microfiber filters (porosity 0.7 µm) with a diameter of 47 mm for total suspended matter (TSM) and Chlorophyll-<em>a</em> (Chl-<em>a</em>). Filters for TSM were dried in the oven at 50C° and filters for Chl-<em>a</em> were kept frozen (-20°C). The weight of each filter was determined before and after filtration of a known volume of water using an Explorer™ Pro EP214C analytical microbalance (accuracy: ±0.1mg) for determination of TSM. Filtered water was stored in 50 mL plastic bottles and frozen (-20°C) for analysis of dissolved nutrients. Chl-<em>a</em> was measured on extracts with 90% acetone by fluorimetry (Kontron model SFM 25) (Yentsch and Menzel, 1963) with a limit of detection of 0.01 µg L<sup>-1</sup>. Ammonium (NH<sub>4</sub><sup>+</sup>) was determined by the nitroprusside-hypochlorite-phenol staining method (Grasshoff and Johannsen, 1972), with a limit of detection of 0.05 µmol L<sup>-1</sup>. Nitrite (NO<sub>2</sub><sup>-</sup>) and nitrate (NO<sub>3</sub><sup>-</sup>) were determined before and after reduction of NO<sub>3</sub><sup>-</sup> to NO<sub>2</sub><sup>-</sup> by a cadmium-copper column, using the Griess acid reagent staining method (Grasshoff and Kremling, 2009), with a detection limit of 0.01 and 0.1 µmol L<sup>-1</sup>, respectively. Soluble reactive phosphorus (SRP) was determined by the ammonium molybdate, ascorbic acid and potassium antimony tartrate staining method (Koroleff, 1983), with a limit of detection of 0.1 µmol L<sup>-1</sup>. Concentration of dissolved inorganic nitrogen (DIN) was calculated as the sum NH<sub>4</sub><sup>+</sup>, NO<sub>2</sub><sup>-</sup> and NO<sub>3</sub><sup>-</sup> concentrations.</p> <p> </p> <p>Borges, A.V., Darchambeau, F., Lambert, T., Morana, C., Allen, G.H., Tambwe, E., and Bouillon, S.: Variations in dissolved greenhouse gases (CO2, CH4, N2O) in the Congo River network overwhelmingly driven by fluvial-wetland connectivity. Biogeosciences 16 (19), 3801–3834. <a href="https://doi.org/10.5194/bg-16-3801-2019">https://doi.org/10.5194/bg-16-3801-2019</a>, 2019.</p> <p>Grasshoff, K., and Johannsen, H.: A new sensitive and direct method for the automatic determination of ammonia in sea water. ICES J. Mar. Sci. 34 (3), 516–521. <a href="https://doi.org/10.1093/icesjms/34.3.516">https://doi.org/10.1093/icesjms/34.3.516</a>, 1972.</p> <p>Grasshoff, K., Kremling, K., and Ehrhardt, M.: Methods of Seawater Analysis: Determination of Nitrite. John Wiley & Sons, 2009.</p> <p>Koroleff, J.: Determination of total phosphorus by alkaline persulphate oxidation. Methods of Seawater Analysis. Verlag Chemie, Wienheim, pp. 136–138, 1983.</p> <p>Weiss, R. F.: Determinations of carbon dioxide and methane by dual catalyst flame ionization chromatography and nitrous oxide by electron capture chromatography. J. Chromatogr. Sci. 19, 611–616. <a href="https://doi.org/10.1093/chromsci/19.12.611">https://doi.org/10.1093/chromsci/19.12.611</a>, 1981.</p> <p>Yentsch, C.S., and Menzel, D.W.: A method for the determination of phytoplankton chlorophyll and phaeophytin by fluorescence. In: Deep Sea Research and Oceanographic Abstracts, 10. Elsevier, pp. 221–231. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/0011-7471(63)90358-9" target="_blank" rel="noopener">https://doi.org/10.1016/0011-7471(63)90358-9</a>, 1963. </p>
Data from: A country-wide examination of effects of urbanization on common birds
Open the record for dataset details and reuse information.
Data for: Sun, Ogushi, Tseng -Lepidoptera species richness and community composition in urban street trees
<p>The triple threats of climate change, habitat loss, and environmental pollution have stimulated discussion on how urban areas can be modified to both mitigate heat increases and provide habitat for wildlife such as insects. The strategy of using trees to reduce temperatures has been adopted by numerous cities. However, the majority of street trees planted around the world are non-native. Studies conducted in non-urban areas have demonstrated in comparison to native plants, non-native plants are less likely to support native insect diversity. Here we use a database approach to quantify the number of native Lepidoptera species associated with 76 of the most common street tree species planted in Vancouver, Canada. We tested the prediction that compared to non-native trees, native street trees will support a higher diversity and unique community of native Lepidoptera. As predicted, native street trees were associated with five times as many native Lepidoptera species, and the Lepidoptera communities supported by native vs. non-native street trees were distinct. There was no difference in native Lepidoptera associations between broadleaf vs. coniferous street trees. These results are consistent with studies that have used active sampling techniques to investigate insect richness on a smaller subset of native and non-native tree species. Collectively, these data provide good evidence that the planting native instead of non-native trees will help stem the loss of insect diversity in urban areas.</p>
Data from: Impacts of organic matter amendments on urban soil carbon and soil quality: A meta-analysis
<p>Organic matter amendment application is an important avenue of beneficial waste diversion and is used to improve soil quality in agricultural and urban settings. In urban regions, amendments are used to support local food production, maintain vegetation for landscaping and recreational use, and reclaim disturbed soils. Urban regions generate large quantities of wasted organic resources for potential application aiding in creating a circular nutrient economy. There is a growing interest in understanding the effects of amendments such as compost, biosolids, and biochar on soil properties in agricultural settings. Gaps remain, however, in assessing their effects in urban land uses. We conducted a literature review to assess the effects of compost, biochar, and biosolids on soil carbon and soil quality of urban soils managed for gardening, landscaping, recreation, and reclamation. Application of organic matter amendments led to an average increase of 3.6 units of soil organic matter% (SOM%). Compost and biochar improved SOM% the most, by 3.1 and 6.5 units of SOM%, respectively. Biosolids resulted in the smallest increase in SOM% but had greater nutrient benefits than other amendments. Parameters related to chemical and physical soil quality improved with the application of amendments. Gaps in the literature remain, such as assessing urban gardens, soil to depths greater than 30 cm, and the persistence of SOM in amended soils. This meta-analysis proposes that organic matter amendments are a powerful means to improve soil quality in urban regions, provide vital cobenefits to surrounding communities, and increase soil carbon storage.</p>
Data for "Elevated urban energy risks due to climate-driven biophysical feedbacks"
<p>This dataset contains the global multi-model urban climate and energy projections from Li et al. (2024), "Elevated urban energy risks due to climate-driven biophysical feedbacks", published in <em>Nature Climate Change</em>. It contains global monthly mean projections of urban 2-meter air temperature, and urban cooling and heating energy fluxes derived from 25 Earth system models (ESMs) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Details about how this dataset was generated are described in the article. This dataset may be useful for multiple communities interested in future energy risks, climate change impacts and vulnerability, and climate-sensitive adaptation and energy planning.</p> <p>For more details, please refer to the README.md file included in the dataset.</p>
Plane-wave propagation path data from wideband MIMO channel sounding in an urban microcellular scenario
<p>We provide plane-wave propagation path data from a wideband MIMO radio channel sounding measurement in an urban microcell scenario. The binary Matlab file includes: direction of departure (DOD: variables "par.PhiTx" and "par.ThetaTx" in [rad]), direction of arrival (DOA: "par.PhiRx" and "par.ThetaRx" in [rad]), delay ("par.Tau" to be multiplied with 8.3ns, the tab length), and complex polarimetric path gain ("par.Alpha" is a 2x2 matrix, where element [1,1]=TXtheta ->RXtheta, [1,2]=TXphi ->RXtheta, [2,1]=TXtheta->RXphi, and [2,2]=TXphi->RXphi), for the 30 strongest signal paths (from TX to RX) at each of the 4574 RX locations along the route described below. In the element names above, the term "theta" refers to the vertically polarised component, and accordingly the term "phi" referes to the horizontally polarised component.<br> Note #1: The exact RX location for each individual measured radio channel was NOT recorded (see route description below). <br> Note #2: The complex path gain (par.Alpha) is NOT calibrated, but depends on the initially fixed AGC level in the receiver, which was chosen to provide the best dynamic range for the given mobile (RX) route.<br> Both these limitations are seen reasonable since this dataset is meant for the realistic *statistical comparison* of the performance of different RX antennas in a microcell environment (and not to determine the actual received power at each exact location of the measured route).<br> Background information: The provided dataset is processed and is based on a radio channel sounder measurement at 5.3 GHz, carried out in downtown Helsinki, Finland, in April 2004. The uniform rectangular transmit (TX) array was placed at 10 m height in Aleksanterinkatu-street (an approx. 15-m wide street canyon), in front of the Nordea building, broadside pointing westwards (towards Stockmann building). The semishperical receive (RX) array was moved at 1.6-m height and for about 50 m along Aleksanterinkatu-street in line-of-sight (LOS), i.e. from in front of Kluuvi shopping centre westwards just across the crossing of Kluuvikatu-street. The TX and RX arrays cover the relevant azimuth and elevation ranges, so that this plane wave propagation path data can directly be combined with the polarimetric directional radiation pattern(s) of an antenna (array).</p>
kongdd/ChinaHW_data: Data of "Contributions of anthropogenic warming and urbanization to intensification of heatwaves over China"
<p>No description provided.</p>
Anonymised human location data for urban mobility research
Open the record for dataset details and reuse information.
Data from Demonstration of quantum network protocols over a 14-km urban fiber link
<p>Datasets used to create all plots and results found in the paper "Data from Demonstration of quantum network protocols over a 14-km urban fiber link" from AG Eschner, Saarland University</p>
Data Used in Manuscript Entitled "Measurement Report: Urban Ammonia and Amines in Houston, Texas"
<p>Data for manuscript: L. Tiszenkel, J. Flynn, S. H. Lee, Measurement Report: Urban Ammonia and Amines in Houston, Texas.</p> <p>Mansucript has been accepted for publication in Atmospheric Chemistry and Physics</p> <p>Preprint and discussion are available at https://doi.org/10.5194/egusphere-2024-1230</p>
Montreal climate data for building simulations with urban heat island effects and nature-based solutions
<p>As cities face rising temperatures, increased frequency of extreme weather events, and altered precipitation patterns, buildings are subjected to increasing energy demand, heat stress, thermal comfort issues, and decreased service life. Therefore, evaluating building performance under changing climate conditions is essential for building sustainable and resilient communities. Unique climate characteristics of cities, such as the urban heat island effect, are not well simulated by global or regional climate models, and is therefore often not included in typical building analyses. Consequently, a computationally efficient approach is used to generate “urbanized” climate data, derived from regional climate models, to prepare building simulation climate data that incorporate urban effects. We demonstrate this process using existing climate data for Montreal airport’s weather station and extend it to prepare projections for scenarios where nature-based solutions, such as increased greenery and albedo, were implemented. We find significant improvements in the representation of the urban heat island and subsequent cooling effects of nature-based solutions in the urbanized climate data. This dataset allows building practitioners to evaluate building performance under historical and potential future changes in climate, considering the complex interactions within the urban canopy and the implementation of mitigation efforts such as nature-based solutions.</p> <p>This dataset contains hourly historical and future weather files for use in building simulations for the city of Montreal, Canada. While similar weather files are usually based on measurements taken at a city's nearby airport, the current dataset utilizes a novel statistical-dynamical downscaling technique which involves the use of the dynamical Weather Research and Forecasting (WRF) model combined with a statistical approach and climate projections from an ensemble of 15 Canadian Regional Climate Model 4 (CanRCM4) to generate urban climate data which includes the effects of the urban heat island and different nature-based solutions (NBS) as mitigation strategies (such as increasing surface albedo and greenery). Additionally, different levels of implementation of these mitigation strategies were produced, for example, when the albedo is increased to 0.40 (ALBD40) and 0.80 (ALBD80), and similarly for the green and combined scenarios, GRN40, GRN80, COMB40, and COMB80. The URBAN scenario is considered the control case where the urban heat island effects are accounted for in the data, but the NBS scenarios are not yet implemtned. </p> <p>The data are stored in large CSV files, where the rows consists of all 15 realizations of the CanRCM4 ensemble and the variables make up the columns. For example, each 31-year period is repeated 15 times, once for each of the RCM realizations. Therefore, there are 4,073,400 (15x31x8760) rows in each file. We recommend viewing the data using packages from Python or R. </p> <p> </p> <p>The historical and future global warming thresholds and their corresponding time periods are as follows:</p> <table> <tbody> <tr> <td> <p><strong>Global Warming Scenario</strong></p> </td> <td> <p><strong>Time Period</strong></p> </td> </tr> <tr> <td> <p><strong>Historical</strong></p> </td> <td> <p>1991-2021</p> </td> </tr> <tr> <td> <p><strong>Global Warming 0.5ºC</strong></p> </td> <td> <p>2003-2033</p> </td> </tr> <tr> <td> <p><strong>Global Warming 1.0ºC</strong></p> </td> <td> <p>2014-2044</p> </td> </tr> <tr> <td> <p><strong>Global Warming 1.5ºC</strong></p> </td> <td> <p>2024-2054</p> </td> </tr> <tr> <td> <p><strong>Global Warming 2.0ºC</strong></p> </td> <td> <p>2034-2064</p> </td> </tr> <tr> <td> <p><strong>Global Warming 2.5ºC</strong></p> </td> <td> <p>2042-2072</p> </td> </tr> <tr> <td> <p><strong>Global Warming 3.0ºC</strong></p> </td> <td> <p>2051-2081</p> </td> </tr> <tr> <td> <p><strong>Global Warming 3.5ºC</strong></p> </td> <td> <p>2064-2094</p> </td> </tr> </tbody> </table> <p> </p> <p>The following variables are included in the files:</p> <table> <tbody> <tr> <td><strong>Variable</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td><strong>RUN</strong></td> <td>Run number (R1-R15) of Canadian Regional Climate Model, CanRCM4 large ensemble associated with the selected reference year data</td> </tr> <tr> <td><strong>YEAR</strong></td> <td>Year associated with the record</td> </tr> <tr> <td><strong>MONTH</strong></td> <td>Month associated with the record</td> </tr> <tr> <td><strong>DAY</strong></td> <td>Day of the month associated with the record</td> </tr> <tr> <td><strong>HOUR</strong></td> <td>Hour associated with the record</td> </tr> <tr> <td><strong>YDAY</strong></td> <td>Day of the year associated with the record</td> </tr> <tr> <td><strong>DRI_kJPerM2</strong></td> <td>Direct horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td><strong>DHI_kJperM2</strong></td> <td>Diffused horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td><strong>DNI_kJperM2</strong></td> <td>Direct normal irradiance in kJ/m2 (total from previous HOUR to the <em>HOUR</em> indicated)</td> </tr> <tr> <td><strong>GHI_kJperM2</strong></td> <td>Global horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td><strong>TCC_Percent</strong></td> <td>Instantaneous total cloud cover at the HOUR in % (range: 0-100)</td> </tr> <tr> <td><strong>RAIN_Mm</strong></td> <td>Total rainfall in mm (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td><strong>WDIR_ClockwiseDegFromNorth</strong></td> <td>Instantaneous wind direction at the HOUR in degrees (measured clockwise from the North)</td> </tr> <tr> <td><strong>WSP_MPerSec</strong></td> <td>Instantaneous wind speed at the HOUR in meters/sec</td> </tr> <tr> <td><strong>RHUM_Percent</strong></td> <td>Instantaneous relative humidity at the HOUR in %</td> </tr> <tr> <td><strong>TEMP_K</strong></td> <td>Instantaneous temperature at the HOUR in Kelvin</td> </tr> <tr> <td><strong>ATMPR_Pa</strong></td> <td>Instantaneous atmospheric pressure at the HOUR in Pascal</td> </tr> <tr> <td><strong>SnowC_Yes1No0 </strong></td> <td>Instantaneous snow-cover at the HOUR (1 - snow; 0 - no snow)</td> </tr> <tr> <td><strong>SNWD_Cm</strong></td> <td>Instantaneous snow depth at the HOUR in cm</td> </tr> </tbody> </table>
Toronto climate data for building simulations with urban heat island effects and nature-based solutions
<p>As cities face rising temperatures, increased frequency of extreme weather events, and altered precipitation patterns, buildings are subjected to increasing energy demand, heat stress, thermal comfort issues, and decreased service life. Therefore, evaluating building performance under changing climate conditions is essential for building sustainable and resilient communities. Unique climate characteristics of cities, such as the urban heat island effect, are not well simulated by global or regional climate models, and is therefore often not included in typical building analyses. Consequently, a computationally efficient approach is used to generate “urbanized” climate data, derived from regional climate models, to prepare building simulation climate data that incorporate urban effects. We demonstrate this process using existing climate data for Toronto airport’s weather station and extend it to prepare projections for scenarios where nature-based solutions, such as increased greenery and albedo, were implemented. We find significant improvements in the representation of the urban heat island and subsequent cooling effects of nature-based solutions in the urbanized climate data. This dataset allows building practitioners to evaluate building performance under historical and potential future changes in climate, considering the complex interactions within the urban canopy and the implementation of mitigation efforts such as nature-based solutions.</p> <p>This dataset contains hourly historical and future weather files for use in building simulations for the city of Toronto, Canada. While similar weather files are usually based on measurements taken at a city's nearby airport, the current dataset utilizes a novel statistical-dynamical downscaling technique which involves the use of the dynamical Weather Research and Forecasting (WRF) model combined with a statistical approach and climate projections from an ensemble of 15 Canadian Regional Climate Model 4 (CanRCM4) to generate urban climate data which includes the effects of the urban heat island and different nature-based solutions (NBS) as mitigation strategies (such as increasing surface albedo and greenery). Additionally, different levels of implementation of these mitigation strategies were produced, for example, when the albedo is increased to 0.40 (ALBD40) and 0.80 (ALBD80), and similarly for the green and combined scenarios, GRN40, GRN80, COMB40, and COMB80. The URBAN scenario is considered the control case where the urban heat island effects are accounted for in the data, but the NBS scenarios are not yet implemtned. </p> <p>The data are stored in large CSV files, where the rows consists of all 15 realizations of the CanRCM4 ensemble and the variables make up the columns. For example, each 31-year period is repeated 15 times, once for each of the RCM realizations. Therefore, there are 4,073,400 (15x31x8760) rows in each file. We recommend viewing the data using packages from Python or R. </p> <p> </p> <p>The historical and future global warming thresholds and their corresponding time periods are as follows:</p> <table> <tbody> <tr> <td> <p><strong>Global Warming Scenario</strong></p> </td> <td> <p><strong>Time Period</strong></p> </td> </tr> <tr> <td> <p><strong>Historical</strong></p> </td> <td> <p>1991-2021</p> </td> </tr> <tr> <td> <p><strong>Global Warming 0.5ºC</strong></p> </td> <td> <p>2003-2033</p> </td> </tr> <tr> <td> <p><strong>Global Warming 1.0ºC</strong></p> </td> <td> <p>2014-2044</p> </td> </tr> <tr> <td> <p><strong>Global Warming 1.5ºC</strong></p> </td> <td> <p>2024-2054</p> </td> </tr> <tr> <td> <p><strong>Global Warming 2.0ºC</strong></p> </td> <td> <p>2034-2064</p> </td> </tr> <tr> <td> <p><strong>Global Warming 2.5ºC</strong></p> </td> <td> <p>2042-2072</p> </td> </tr> <tr> <td> <p><strong>Global Warming 3.0ºC</strong></p> </td> <td> <p>2051-2081</p> </td> </tr> <tr> <td> <p><strong>Global Warming 3.5ºC</strong></p> </td> <td> <p>2064-2094</p> </td> </tr> </tbody> </table> <p> </p> <p>The following variables are included in the files:</p> <table> <tbody> <tr> <td><strong>Variable</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td><strong>RUN</strong></td> <td>Run number (R1-R15) of Canadian Regional Climate Model, CanRCM4 large ensemble associated with the selected reference year data</td> </tr> <tr> <td><strong>YEAR</strong></td> <td>Year associated with the record</td> </tr> <tr> <td><strong>MONTH</strong></td> <td>Month associated with the record</td> </tr> <tr> <td><strong>DAY</strong></td> <td>Day of the month associated with the record</td> </tr> <tr> <td><strong>HOUR</strong></td> <td>Hour associated with the record</td> </tr> <tr> <td><strong>YDAY</strong></td> <td>Day of the year associated with the record</td> </tr> <tr> <td><strong>DRI_kJPerM2</strong></td> <td>Direct horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td><strong>DHI_kJperM2</strong></td> <td>Diffused horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td><strong>DNI_kJperM2</strong></td> <td>Direct normal irradiance in kJ/m2 (total from previous HOUR to the <em>HOUR</em> indicated)</td> </tr> <tr> <td><strong>GHI_kJperM2</strong></td> <td>Global horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td><strong>TCC_Percent</strong></td> <td>Instantaneous total cloud cover at the HOUR in % (range: 0-100)</td> </tr> <tr> <td><strong>RAIN_Mm</strong></td> <td>Total rainfall in mm (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td><strong>WDIR_ClockwiseDegFromNorth</strong></td> <td>Instantaneous wind direction at the HOUR in degrees (measured clockwise from the North)</td> </tr> <tr> <td><strong>WSP_MPerSec</strong></td> <td>Instantaneous wind speed at the HOUR in meters/sec</td> </tr> <tr> <td><strong>RHUM_Percent</strong></td> <td>Instantaneous relative humidity at the HOUR in %</td> </tr> <tr> <td><strong>TEMP_K</strong></td> <td>Instantaneous temperature at the HOUR in Kelvin</td> </tr> <tr> <td><strong>ATMPR_Pa</strong></td> <td>Instantaneous atmospheric pressure at the HOUR in Pascal</td> </tr> <tr> <td><strong>SnowC_Yes1No0 </strong></td> <td>Instantaneous snow-cover at the HOUR (1 - snow; 0 - no snow)</td> </tr> <tr> <td><strong>SNWD_Cm</strong></td> <td>Instantaneous snow depth at the HOUR in cm</td> </tr> </tbody> </table>
R Data for CPBS Report 23SDSU01 - Urban Demographic Shift of Pedestrian and Bicyclist Collisions, Equity, and Police Enforcement
<p>Data for the statistical program R.</p>
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