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"en": "Assessing pollutant ventilation in a city-boulevard using large-eddy simulation\n\nThis dataset contains input and output information for a computational fluid dynamics study in an urban environment. The simulations were conducted using the large-eddy simulation (LES) model PALM.\n\nIn short:\nModel: PALM modelling system (https://palm.muk.uni-hannover.de)\nRevision: 1904\nThe modified code parts of PALM revision 1904 used in this study are available online at https://www.mdpi.com/2073-4433/9/2/65/s1.\nMore details about the study can be found in Kurppa et al. (2018): Ventilation and Air Quality in City Blocks Using Large-Eddy Simulation—Urban Planning Perspective, Atmosphere 9(2), 27.\n\nDescription:\nThis study utilizes LES (Large-Eddy Simulation) modeling to examine how the structural layout of different urban perimeter block arrangements influences the air quality within street canyons. The objective is to reveal whether changes in the perimeter block orientation and shape can alter the wind conditions enough to result in meaningful differences in the local pollutant dispersion and ventilation characteristics. \n\nThis numerical investigation considers four alternative city boulevard designs for the Hämeenlinnanväylä area that forms part of the new Helsinki City Plan. Each design facilitates an equal amount of gross floor area, but features a distinctly different city block plan layout. The efficiency with which each boulevard design is capable of dispersing traffic emissions from its street canyons is assessed by performing high-resolution LES analysis with a model that features a detailed representation of the urban landscape including trees planted on the boulevard and surrounding forests. The LES simulations yield detailed descriptions of the atmospheric turbulence and how it interacts with the\nurban structures in each boulevard alternative. The solution also incorporates a simultaneously evolution of air parcels, representing elements of traffic emissions, which are continuously discharged from the streets. Air parcels are modelled using a Lagrangian particle model within PALM. The ventilation and dispersion performance of each design package is evaluated and compared by analyzing the distribution of these air parcel concentrations and their rate of vertical transport and dilution. Two contrasting meteorological scenarios are considered in the simulations: south-westerly wind with a neutral stratification and easterly wind with a stable stratification.\n\n\nThe data has been partitioned as follows:\n\ninput_data --> wd{wind direction in degrees} --> {atmospheric stability} --> V{number of city-plan version}:\n\n • CANOPY_HEIGHT{_number of the sub-domain}: raster map on the height of vegetation\n • PARIN{_number of the sub-domain}_batch{batch number}: parameter file to run the simulation\n • PARTICLE_SOURCE_02: source map of air parcels for the sub-domain 2\n • TOPOGRAPHY_DATA{_number of the sub-domain}: land-surface elevation and buildings\n\n\nraw_output_data --> wd{wind direction in degrees} --> {atmospheric stability} --> V{number of city-plan version}:\n\n • V{number of city-plan version}_M01_orig.nc: 40-minute time series with a time step of 5 seconds. Includes the three wind components (u, v and w) and the concentration of air parcels (pr) over nearly the entire sub-domain 2.\n • V{number of city-plan version}_M02_orig.nc: 5-minute time series with a time step of 0.07/0.15 seconds. Includes the three wind components (u, v and w) and the concentration of air parcels (pr) over a small area in the street canyon.\n • V{number of city-plan version}_M03_orig.nc: 5-minute time series with a time step of 5 seconds. Includes the three wind components (u, v and w) and the concentration of air parcels (pr) over nearly the entire sub-domain 2.\n\n\ncalculated_output_data (for the definitions of all variables, see Kurppa et al. (2018)):\n\n • dilution: dilution of particles away from the street canyons\n ◦ per_column: column-averaged dilution rate\n ▪ D_col_wd{wind direction}_{atmospheric stability}_V{number of city-plan version}\n ◦ total_volume: volume-averaged dilution rate over the whole domain\n ▪ D_vol_wd{wind direction}_{atmospheric stability}_V{number of city-plan version}\n • flux_high_frequency: flux on air parcels calculated at frequency every 0.07 (neutral)/0.15 (stable) seconds.\n ◦ flux_HF_wd{wind direction}_{atmospheric stability}_V{number of city-plan version}\n • flux_low_frequency: flux on air parcels calculated at frequency every five seconds.\n ◦ flux_LF_wd{wind direction}_{atmospheric stability}_V{number of city-plan version}\n • surface_following: surface following datasets calculated from the raw data. Similar folder partitioning and naming.\n\n\nscripts: Python-scripts to analyse the data. \n\nNaming conversion for the number of city-plan version between Kurppa et al. (2018) and the dataset provided here:\nV_par = V1\nV_per = V2\nV_perHV = V3\nV_J-J = V4",
"fi": "LES-virtausmallitarkastelu kaupunkibulevardilla\n\nTämä aineisto sisältää lähtö- ja tulosteaineistot kaupunkiympäristössä toteutetulle virtausmallitarkastelulle. Tutkimuksessa käytettiin suurten pyörteiden simulaatio (large-eddy simulation, LES) -mallia PALM.\n\n\nLyhyesti:\n\nMalli: PALM modelling system (https://palm.muk.uni-hannover.de) Malliversio: 1904; Lähdekoodiin tehdyt muokkaukset ladattavissa https://www.mdpi.com/2073-4433/9/2/65/s1; Lisätietoa tutkimuksesta Kurppa et al. (2018): Ventilation and Air Quality in City Blocks Using Large-Eddy Simulation—Urban Planning Perspective, Atmosphere 9(2), 27.\n\n\nKuvaus:\n\nTutkimuksessa tarkasteltiin kaupunkikortteleiden tuulettumista eri kaupunkikortteliratkaisuissa. Tavoitteena oli selvittää, onko kortteleiden sijoittelulla, massoittelulla ja korkeusvaihteluilla merkitystä ilman epäpuhtauksien kulkeutumiseen ja ilmanlaatuun katukuiluissa ja sisäpihoilla, ja asettaa tämän mukaan eri korttelimallit paremmuusjärjestykseen. Tutkimuskohteena oli neljä Helsingin Hämeenlinnanväylän alueelle laadittua teoreettista bulevardikaupunkirakennetta. Tutkimuksessa ilman virtausta ja virtauksen mukana liikkuvia, ilman epäpuhtauksia kuvaavia ilmapaketteja mallinnettiin LES-virtausmallilla ja Lagrangelaisella partikkelimallilla. Tarkastelu tehtiin kahdessa eri meteorologisessa tilanteessa: lounaistuulella (225 astetta) ja neutraalilla kerrostuneisuudella sekä itätuulella (90 astetta) ja stabiililla kerrostuneisuudella. Projekti toteutettiin yhdessä Ilmatieteen laitoksen, Helsingin seudun ympäristöpalveluiden ja Helsingin kaupungin kanssa.\n\n\nAineisto on jaoteltu seuraavasti:\n\ninput_data --> wd{tuulensuunta asteina} --> {ilmakehän kerrostuneisuus} --> V{kortteliversionumero}:\n\n • CANOPY_HEIGHT{_alialuenumero}: kasvillisuuden korkeuskartta\n • PARIN{_alialuenumero}_batch{eräajonumero}: parametritiedosto mallin ajoon\n • PARTICLE_SOURCE_02: alialueen 2 lähde ilmansaasteita kuvaaville ilmapaketeille\n • TOPOGRAPHY_DATA{_alialuenumero}: maanpinnan muodot ja rakennukset\n\n\nraw_output_data --> wd{tuulensuunta asteina} --> {ilmakehän kerrostuneisuus} --> V{kortteliversionumero}:\n\n • V{kortteliversionumero}_M01_orig.nc: 40 minuutin aikasarja viiden sekunnin aika-askeleella. Sisältää tuulen kolme komponenttia (u, v ja w) sekä ilmapakettien pitoisuuden (pr) lähes koko alialueelta 2.\n • V{kortteliversionumero}_M02_orig.nc: 5 minuutin aikasarja 0.07 sekunnin aika-askeleella. Sisältää tuulen kolme komponenttia (u, v ja w) sekä ilmapakettien pitoisuuden (pr) pieneltä alueelta katukuilusta.\n • V{kortteliversionumero}_M03_orig.nc: 5 minuutin aikasarja viiden sekunnin aika-askeleella. Sisältää tuulen kolme komponenttia (u, v ja w) sekä ilmapakettien pitoisuuden (pr) lähes koko alialueelta 2.\n\n\ncalculated_output_data (muuttujien määritelmät löytyvät julkaisusta Kurppa et al. (2018)):\n\n • dilution: ilmapakettien poistuminen katukuilusta (eli ilmapakettipitoisuuksien laimeneminen)\n ◦ per_column: keskiarvo pystysuunnassa per hilapiste\n ▪ D_col_wd{wind direction}_{atmospheric stability}_V{number of city-plan version}\n ◦ total_volume: keskiarvo koko alueen tilavuuden yli\n ▪ D_vol_wd{wind direction}_{atmospheric stability}_V{number of city-plan version}\n • flux_high_frequency: ilmapakettien pystysuuntainen vuo laskettuna korkean aikaresoluution aineistosta (V{kortteliversionumero}_M02_orig.nc)\n ◦ flux_HF_wd{wind direction}_{atmospheric stability}_V{number of city-plan version}\n • flux_low_frequency: ilmapakettien pystysuuntainen vuo laskettuna matalamman aikaresoluution mutta pitkästä aikasarjasta (V{kortteliversionumero}_M01_orig.nc)\n ◦ flux_LF_wd{wind direction}_{atmospheric stability}_V{number of city-plan version}\n • surface_following: maanpinnan muotoja seuraava ainesto. Laskettu raaka-aineistosta (raw_data_output). Sama kansiorakenne ja tiedostojen nimeäminen kuin raaka-aineistossa.\n\nscripts: Python-skriptejä aineiston työstämiseen.\n\n\nKortteliversioiden nimeämiserot Kurppa et al. (2018) -julkaisun ja tämän aineiston välillä:\n\nV_par = V1\nV_per = V2\nV_perHV = V3\nV_J-J = V4"
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"en": "This dataset contains data on the distribution and abundance of the Glanville fritillary butterfly (Melitaea cinxia), its host plants Plantago lanceolata and Veronica spicata, and its parasitoid Cotesia melitaearum, in the Åland archipelago in Finland. \n\nThe study species M. cinxia has been surveyed since 1991 in a metapopulation network of circa 4000 dry meadow habitat patches in a study area of circa 50 by 70 km. The presence of one of its specialist parasitoids C. melitaearum was included in 1993. This dataset covers the years 1993-2017. \nData are recorded bi-annually by field assistants. First over a two-week period from late August to early September the patches are visited to search for the presence and abundance of the M. cinxia larval over-wintering nests. Second in the following spring, the occupied patches are revisited to record data on overwinter survival of the larval families and on the potential presence of the parasitoid . Before 2010, data were recorded by hand on paper maps and forms and then entered manually to spreadsheets, and later to a database. The locations and shapes of habitat patches were recorded with GPS devices. From 2010 onwards, the data have been systematically collected and managed with the EarthCape database management system (earthcape.com) Earthcape is used in the field on tablet / laptop computers to find the patches and record data with both GPS and topographic maps. The resulting updated database is then checked for errors, cleaned up and merged with the central database, also maintained with Earthcape. The database is relational in structure in order to handle the different levels of data collection. For more details on how the survey is conducted and data recorded, see Ojanen et al. 2013\n\nThis dataset contains data on three nested levels: Patch, Locality visit and Nest. The patch-level file has one row per patch, and the variables describe location and geometry for the (habitat) patch. The Locality visit -file has one row per location*survey combination, and the variables describe patch level habitat quality, host plant abundance and quality, total numbers of M. cinxia nests in the patch classified in several different ways, and total numbers of C. melitaearum in the patch for the particular survey. The nest-level file has one row per M. cinxia over-wintering nest, and the variables describe the location*survey when it was recorded, detailed location information, and host plant and dead/alive status of each nest.\n\nOver the 25 years of surveying, the exact variables recorded have varied somewhat according to researcher’s needs and the development of theory around the survey (Ovaskainen & Saastamoinen 2018). Certain core variables have been recorded every year and those have been included in this dataset, but we also include some variables that have not been recorded every year and therefore have missing data for some years.\n\nThe network of habitat patches is temporally dynamic and thus has also changed shape over the years: new habitat patches of M. cinxia host plants can appear and existing ones can disappear. Patches can also change shape as the host plants spread to new areas or when e.g. construction has removed host plants from other areas. The patch-level geometry variable always gives the most recently recorded geometry of the patch, but when the patch shape has changed, the new geometry has been recorded on the Locality visit level for the year in question. Sometimes separate patches merge into a single patch. In this dataset, patch merging has been performed retroactively to facilitate using the data as a long-term repeated survey. When two patches have merged, the data from those two patches has been retroactively merged in all the previous years, as if they had always been a single patch.\n\nReferences:\nOjanen, S., Nieminen, M., Meyke, E., Pöyry, J. & Hanski, I. 2013. Long‐term metapopulation study of the Glanville fritillary butterfly (Melitaea cinxia): survey methods, data management, and long‐term population trends. Ecology and Evolution 3: 3713-3737.\nOvaskainen, O. & Saastamoinen, M. 2018. Frontiers in Metapopulation Biology: The Legacy of Ilkka Hanski. Annual Review of Ecology, Evolution, and Systematics 49:231-252."
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