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Air pollution, atmospheric and local meteorological data for Graz, Austria from 2014 to end of 2021
<p>The data covers a timeframe from January 2014 to November 2021 in a daily frequency, and covers two sources:</p> <ul> <li>The environmental and pollutant data was provided by the Austrian government under the following license: CC-BY-4.0: Land Steiermark - <a href="http://data.steiermark.gv.at">data.steiermark.gv.at</a> <ul> <li>Air quality (<em>Lovric_et_al_air_pollutants.csv</em>) by means of NO<sub>2</sub>, NO, NO<sub>x</sub>, PM<sub>10</sub> and O<sub>3</sub> was measured at five sites in Graz, Austria (Süd (<em>eng. South</em>) - S, Nord (<em>eng. North</em>) - N, West (<em>eng. West</em>) - W, Don Bosco – D, Ost (<em>eng. East</em>) – O). In addition weather conditions like temperature, percipitation, relative humidity, pressure, wind speed and direction are added (<em>Lovric_et_al_local_meteorology.csv</em>)</li> </ul> </li> <li>The ERA5-Land data (<em>Lovric_et_al_era5_recalculated.csv</em>) is subject to the Copernicus licence from following source <a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fcds.climate.copernicus.eu%2Fcdsapp%23!%2Fdataset%2F10.24381%2Fcds.e2161bac%3Ftab%3Doverview&data=05%7C01%7Cmlovric%40know-center.at%7C2ba06457329349623a5608da631632c9%7C0d3c92e977ae4f49bd126ff29e8f1c37%7C0%7C0%7C637931244242754711%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=lt5NcIfbIRGse01Naha8bolxEkdtLmyp2VNcrz38Rk8%3D&reserved=0">https://cds.climate.copernicus.eu/cdsapp#!/dataset/10.24381/cds.e2161bac?tab=overview</a> <ul> <li>it includes following variables : <ul> <li>Cloud_Cover_Mean</li> <li>Temperature_Air_2m_Max_Day_Time</li> <li>Temperature_Air_2m_Min_Night_Time</li> <li>Wind_Speed_10m_Mean</li> </ul> </li> </ul> </li> </ul>
Influence of Large-scale Land-sea Atmosphere Interaction on Ozone Pollution in Coastal Cities in the Northern Bohai Sea
<p><strong>O3_obs </strong>includes ozone observations for Qinhuangdao (QHD), Jinzhou (JZ), Yingkou (YK), Dalian (DL) from 29 August to 5 September 2017, and the information of four sites including station code, longitude and latitude. <strong>O3_sim</strong> includes ozone simulation in the four sites extracted according to location of them. <strong>Met_obs</strong> and <strong>Met_sim</strong> include the observations of 2 m temperature (℃), 2 m relative humidity (RH2) and 10 m wind speed for the 4 stations from 29 August to 5 September 2017, and the information of four stations including station code and their location. <strong>Slp_wind_9km.nc</strong> is mean sea-level pressure and wind in Phase Ⅰ and Phase Ⅱ. <strong>O3_wind_9km.nc</strong> is mean simulated surface ozone mixing ratios and wind at 10 m in 19:00-09:00 LT and 10:00-18:00 LT during Phase Ⅰ and Phase Ⅱ. <strong>Process_contribution </strong>includes mean surface O<sub>3</sub> mixing ratios and O<sub>3</sub> contribution at the bottom level in Phase Ⅰ, Phase Ⅱ, and at different heights (AGL) in Phase Ⅱ in four sites, respectively. <strong>O3_source_site</strong> includes time series of O<sub>3 </sub>source in QHD, JZ, YK, and DL. <strong>Mean_source_base_27km.nc </strong>is the mean O­<sub>3</sub> contribution in Phase Ⅰ and Phase Ⅱ from five primary exogenous source regions. <strong>Mean_source_control_27km.nc</strong> is the O<sub>3</sub> contribution in Phase Ⅱ from the BTH and NEC emissions in Phase I, in which BTH and NEC’s emissions in Phase Ⅱ are set zero. <strong>Trjectory_conc_pa</strong> includes three trajectories analyzed in this work and vertical O<sub>3</sub> and NO<sub>X</sub> mixing ratios, and the chemical generations and consumptions of O<sub>3</sub> within the air masses along the trajectories.</p>
Short-lived air pollutants and climate forcers through the lens of the COVID-19 pandemic
<p>The data in this repository is part of the paper titled "Short-lived air pollutants and climate forcers through the lens of the COVID-19 pandemic". The data is required to obtain a detrended lockdown effects on air quality. The raw data was downloaded from the European Centre for Medium-Range Weather Forecasts Atmospheric Composition Reanalysis 4 (EAC4) product portal. More details of the data are listed below:</p> <p>"ozone_data.nc": Global mixing ratio of ozone (monthly)</p> <p>"pm_data.nc": Global mass concentration of fine particulate matters, and aerosol optical depth (AOD) at 550 nm (monthly)</p> <p>"BAU_clean_latest.csv": The pollution level under a business-as-usual (BAU) scenario, inferred from the historical pollution data by Theil-Sen linear regression (monthly)</p>
First estimation of global trends in nocturnal power emissions reveals acceleration of light pollution
<p>The power emitted by different countries at night is based on DMSP and VIIRS data. Inclued also, some extra data from Spain, Portugal, Italy, UK and Greece.</p>
Demographic factors and the environmental Kuznets curve: global plastic pollution by 2050 could be 2 to 4 times worse than projected
<p>These data are made of two files. One file provides the observed data we collected and cleaned from the World Bank database. The second file provides the simulation results from the STIRPAT model we designed based on the observed data abovementioned. Our results can be summarised as follows:</p> <p>Since 2015, the detrimental effects of plastic pollution have attracted media, public, and governmental attention. Considering economic growth is inevitable and a key driver of plastic contamination, it is worthwhile to analyze the environmental Kuznets curve (EKC) relationship between economic development and plastic pollution. To this end, we contribute by being the first to (i) use the Stochastic Impacts by Regression on Population, Affluence, and technology model (STIRPAT model) to investigate this EKC relationship; (ii) provide a comprehensive analysis of how demographic factors affect plastic pollution; and (iii) use panel model techniques to examine the drivers of plastic pollution. Our empirical results support an inverted U-shaped relationship between plastic pollution and income. They show that at current trends, global plastic pollution (that is, annual discard of inadequately managed plastic waste) is expected to grow from 52 million tons per year in 2020 to 257 million tons per year in 2050.</p>
Air pollution datasets
<p>Processed air pollution datasets originally obtained from the <a href="https://datos.madrid.es/portal/site/egob/menuitem.c05c1f754a33a9fbe4b2e4b284f1a5a0/?vgnextoid=f3c0f7d512273410VgnVCM2000000c205a0aRCRD&vgnextchannel=374512b9ace9f310VgnVCM100000171f5a0aRCRD&vgnextfmt=default">open data portal of the Madrid City Hall</a>. The pollutants include:</p> <ul> <li>Fine particulate matter: <strong>PM<sub>2.5</sub></strong></li> <li>Coarse particulate matter: <strong>PM<sub>10</sub></strong></li> <li>Ozone: <strong>O<sub>3</sub></strong></li> <li>Nitrogen monoxide: <strong>NO</strong></li> <li>Nitrogen dioxide: <strong>NO<sub>2</sub></strong></li> <li>Nitrogen oxides: <strong>NOx</strong></li> <li>Sulfur dioxide: <strong>SO<sub>2</sub></strong></li> <li>Carbon monoxide: <strong>CO</strong></li> <li>Toluene: <strong>TOL</strong></li> <li>Benzene: <strong>BEN</strong></li> <li>Ethylbenzene: <strong>EBE</strong></li> </ul> <p>The period covered goes from the 1<sup>st</sup> of January 2010 to the 30<sup>th</sup> of April 2022. Each pollutant is recorded by a variable number of sensors, between 6 and 24 of them (additional information <a href="https://datos.madrid.es/portal/site/egob/menuitem.c05c1f754a33a9fbe4b2e4b284f1a5a0/?vgnextoid=9e42c176313eb410VgnVCM1000000b205a0aRCRD&vgnextchannel=374512b9ace9f310VgnVCM100000171f5a0aRCRD&vgnextfmt=default">here</a>). They cover Madrid city and surroundings (see <a href="https://datos.madrid.es/egob/new/detalle/auxiliar/mapa.jsp?geoUrl=/egob/catalogo/212629-2-estaciones-control-aire.geo">this map</a>). Additional information about the pollutants can be found <a href="https://datos.madrid.es/FWProjects/egob/Catalogo/MedioAmbiente/Aire/Ficheros/Interprete_ficheros_%20calidad_%20del_%20aire_global.pdf">here</a>.</p> <p>The specific datasets in HDF5 format are:</p> <ol> <li><strong>01h_flat_raw.h5</strong>: Hourly raw data, one table per pollutant. The first column is the timestamp, which are in <a href="https://en.wikipedia.org/wiki/Unix_time">time-since-epoch</a>. 108049 rows and between 7 and 25 columns.</li> <li><strong>01h_35x30_norm_linear_J0.0.h5</strong>: Hourly mesh-grid data, normalized and linearly interpolated. Single table of shape: (108049, 35, 30, 11).</li> <li><strong>01h_35x30_norm_nearest_J0.0.h5</strong>: Hourly mesh-grid data, normalized and nearest-neighbors interpolated. Single table of shape: (108049, 35, 30, 11).</li> <li><strong>01h_35x30_raw_linear_J0.0.h5</strong>: Hourly mesh-grid data, linearly interpolated. Single table of shape: (108049, 35, 30, 11).</li> <li><strong>01h_35x30_raw_nearest_J0.0.h5</strong>: Hourly mesh-grid data, nearest-neighbors interpolated. Single table of shape: (108049, 35, 30, 11).</li> <li><strong>01h_35x30_stand_linear_J0.0.h5</strong>: Hourly mesh-grid data, standardized and linearly interpolated. Single table of shape: (108049, 35, 30, 11).</li> <li><strong>01h_35x30_stand_nearest_J0.0.h5</strong>: Hourly mesh-grid data, standardized and nearest-neighbors interpolated. Single table of shape: (108049, 35, 30, 11).</li> </ol> <p>This datasets are prepared to work with the framework published at <a href="https://github.com/iipr/air-quality">https://github.com/iipr/air-quality</a></p>
Supplementary Material no. 2 to the manuscript: Reemission of inorganic pollution from permafrost? – a freshwater hydrochemistry study in the lower Kolyma basin (North-East Siberia)
<p>A dataset on the inorganic chemistry of permafrost-related creeks and ice, thermokarst lakes and the Kolyma river and its tributaries in late July 2021.<br> Companion dataset to the manuscript: "Reemission of inorganic pollution from permafrost? – a freshwater hydrochemistry study in the lower Kolyma basin (North-East Siberia)".<br> Current abstract of the manuscript (prior to peer review):</p> <p>Permafrost regions are under particular pressure from climate change resulting in widespread landscape changes, which impact also freshwater chemistry. We investigated a snapshot of hydrochemistry in various freshwater environments in the lower Kolyma river basin (North-East Siberia, continuous permafrost zone) to explore the mobility of metals, metalloids and non-metals resulting from permafrost thaw. Particular attention was focused on heavy metals as contaminants potentially released from the secondary source in the permafrozen Yedoma complex. Permafrost creeks represented the Mg-Ca-Na-HCO<sub>3</sub>-Cl-SO<sub>4</sub> ionic water type (with mineralisation in the range 600-800 mg/L), while permafrost ice and thermokarst lake waters were the HCO<sub>3</sub>-Ca-Mg type. Multiple heavy metals (As, Cu, Co, Mn and Ni) showed much higher dissolved phase concentrations in permafrost creeks and ice than in Kolyma and its tributaries, and only in the permafrost samples and one Kolyma tributary have we detected dissolved Ti or Hg. In thermokarst lakes, several metal and metalloid dissolved concentrations increased with water depth (Fe, Mn, Ni and Zn - in both lakes; Al, Cu, K, Sb, Sr and Pb in either lake), reaching 1370 µg/L Cu, 4610 µg/L Mn, and 687 µg/L Zn in the bottom water layers. Permafrost-related waters were also enriched in dissolved phosphorus (up to 512 µg/L in Yedoma-fed creeks). The impact of permafrost thaw on river and lake water chemistry is a complex problem which needs to be considered both in the context of legacy permafrost shrinkage and the interference of the deepening active layer with newly deposited antropogenic contaminants.</p>
Data and code for the publication: "Deforestation as an anthropogenic driver of mercury pollution"
<p>A. Feinberg, Sep 2023<br> arifeinberg@gmail.com</p> <p>Essential data and code for the publication: Feinberg et al. : Deforestation as an anthropogenic driver of mercury pollution</p> <p>The directories include:<br> 1) analysis_scripts/ - all analysis scripts used to produce input data and figures for paper<br> 2) Erosion_data/ - Erosion model (GloSEM) output<br> 3) GC_code/ - Archived GEOS-Chem code used to simulate the runs in this paper<br> 4) GC_data/ - GEOS-Chem simulation data and run scripts can be found here for the following runs:<br> HIST - run0311<br> BAU - run0312<br> GOV- run0313<br> SAV - run0315<br> RFR - run0314<br> Deforesting different regions for EF calculations:<br> DFR_Afrotropic - run0321<br> DFR_Indomalayan - run0322<br> DFR_China - run0323<br> DFR_Neotropic - run0324<br> DFR_Palearctic - run0325<br> DFR_Australasia - run0326<br> DFR_Nearctic - run0327<br> DFR_Amazon = SAV - run0315</p> <p>5) input_data/ - input data used to run GEOS-Chem</p> <p>Please refer to other README.md files within sub-directories and contact me for any questions</p>
Trophic Interactions, Habitat Use, and Pollution Loads of Bottlenose Dolphins (Tursiops Truncatus) in the Florida Coastal Everglades, Florida, USA, 2013-2019
Cetaceans can feed at upper trophic levels and occur from freshwater to open-ocean ecosystems. Due to their abundance, mobility, and high metabolic rates, they have the potential to affect the structure and function of ecosystems through both top-down and bottom-up pathways. To better understand what ecological roles they may play in a system, it is important to understand patterns and drivers of their abundance, habitat use, and trophic interactions. I investigated the trophic interactions and pollutant exposure of common bottlenose dolphins (Tursiops truncatus) of the Florida Coastal Everglades. Based on bulk stable isotope analysis of tissue samples collected using biopsy sampling, it appears that despite their high mobility, bottlenose dolphins restrict their foraging within the habitats where they were sampled. Trophic position and foraging locations affected exposure to pollutants, with high levels of mercury found in dolphins estimated to forage at higher trophic levels and feeding within an inland bay. Mercury levels also varied with age and sex. Dolphins and their prey both contained substantial mercury levels and dolphins’ health could be impacted by this exposure, but the selenium levels we measured might counteract these negative effects.
Characteristic and spatiotemporal variation of air pollution in Northern China based on correlation analysis and clustering analysis of five air pollutants
<p>original daily data for 'Characteristic and spatiotemporal variation of air pollution in Northern China based on correlation analysis and clustering analysis of five air pollutants'</p>
Polluting the pair-instability mass gap for binary black holes through super-Eddington accretion in isolated binaries
<p>These are the results from:</p> <p>"Polluting the pair-instability mass gap for binary black holes through super-Eddington accretion in isolated binaries"<br> Authors: L.A.C. van Son, S. E. de Mink, F. S. Broekgaarden, M. Renzo, S. Justham, E. Laplace, J. Moran-Fraile, D. D. Hendriks, and R. Farmer</p> <p>ADS: https://ui.adsabs.harvard.edu/abs/2020arXiv200405187V/abstract<br> arXiv: https://arxiv.org/abs/2004.05187</p> <p>If you use (part of) these results in a scientific publication, we would greatly appreciate it if you would cite the source paper.</p> <p>This work uses <a href="https://compas.science/">COMPAS</a> to compute binary population properties (<a href="http://https://github.com/TeamCOMPAS/COMPAS/tree/master/docs">https://github.com/TeamCOMPAS/COMPAS/tree/master/docs</a>).</p> <p>*****************************</p> <p>For each of our 4 model variations (0. Fiducial, 1. Stable accretion, 2. Common envelope accretion and 3. Combined) we provide 2 files:</p> <p>1.) pythonSubmit.py file describing the initial conditions that were used to run the simulations</p> <p>2.) COMPASOutput.h5 file, which contains the following datasets resulting from our simulations :<br> ['systems',<br> 'doubleCompactObjects',<br> 'commonEnvelopes',<br> ]</p> <p>Detailed descriptions of these groups can be found in the accompanying README file.</p>
Alternate Results Dataset in Comparative Study of Four Scenarios Measuring Visual Pollution in Intramuros, Manila
<p>Alternate results dataset tabulated in all the methods and procedures done in the study 'Comparative Study of Four Scenarios Measuring Visual Pollution in Intramuros, Manila'.</p>
Air pollution in a tropical city: the relationship between wind direction and lichen bio-indicators in San José, Costa Rica
<p>Lichens are good bio-indicators of air pollution, but in most tropical countries there are few studies on the subject; however, in the city of San José, Costa Rica, the relationship between air pollution and lichens has been studied for decades. In this article we evaluate the hypothesis that air pollution is lower where the wind enters the urban area (Northeast) and higher where it exits San José (Southwest). We identified the urban parks with a minimum area of approximately 5 000m² and randomly selected a sample of 40 parks located along the passage of wind through the city. To measure lichen coverage, we applied a previously validated 10 x 20cm template with 50 random points to five trees per park (1.5m above ground, to the side with most lichens). Our results (years 2008 and 2009) fully agree with the generally accepted view that lichens reflect air pollution carried by circulating air masses. The practical implication is that the air enters the city relatively clean by the semi-rural and economically middle class area of Coronado, and leaves through the developed neighborhoods of Escazú and Santa Ana with a significant amount of pollutants. In the dry season, the live lichen coverage of this tropical city was lower than in the May to December rainy season, a pattern that contrasts with temperate habitats; but regardless of the season, pollution follows the pattern of wind movement through the city</p>
Air pollution in a tropical city: the relationship between wind direction and lichen bioindicators in San Jose, Costa Rica
<p>Lichens are good bio-indicators of air pollution, but in most tropical countries there are few studies on the subject; however, in the city of San José, Costa Rica, the relationship between air pollution and lichens has been studied for decades. In this article we evaluate the hypothesis that air pollution is lower where the wind enters the urban area (Northeast) and higher where it exits San José (Southwest). We identified the urban parks with a minimum area of approximately 5 000m² and randomly selected a sample of 40 parks located along the passage of wind through the city. To measure lichen coverage, we applied a previously validated 10 x 20cm template with 50 random points to five trees per park (1.5m above ground, to the side with most lichens). Our results (years 2008 and 2009) fully agree with the generally accepted view that lichens reflect air pollution carried by circulating air masses. The practical implication is that the air enters the city relatively clean by the semi-rural and economically middle class area of Coronado, and leaves through the developed neighborhoods of Escazú and Santa Ana with a significant amount of pollutants. In the dry season, the live lichen coverage of this tropical city was lower than in the May to December rainy season, a pattern that contrasts with temperate habitats; but regardless of the season, pollution follows the pattern of wind movement through the city</p>
Air pollution in a tropical city: the relationship between wind direction and lichen bio-indicators in San José, Costa Rica
<p>Lichens are good bio-indicators of air pollution, but in most tropical countries there are few studies on the subject; however, in the city of San José, Costa Rica, the relationship between air pollution and lichens has been studied for decades. In this article we evaluate the hypothesis that air pollution is lower where the wind enters the urban area (Northeast) and higher where it exits San José (Southwest). We identified the urban parks with a minimum area of approximately 5 000m² and randomly selected a sample of 40 parks located along the passage of wind through the city. To measure lichen coverage, we applied a previously validated 10 x 20cm template with 50 random points to five trees per park (1.5m above ground, to the side with most lichens). Our results (years 2008 and 2009) fully agree with the generally accepted view that lichens reflect air pollution carried by circulating air masses. The practical implication is that the air enters the city relatively clean by the semi-rural and economically middle class area of Coronado, and leaves through the developed neighborhoods of Escazú and Santa Ana with a significant amount of pollutants. In the dry season, the live lichen coverage of this tropical city was lower than in the May to December rainy season, a pattern that contrasts with temperate habitats; but regardless of the season, pollution follows the pattern of wind movement through the city</p>
Star Trail in the Southern Hemisphere with Bortle 4 Scale Light Pollution
<p>Winner in the 2023 IAU OAE Astrophotography Contest, category Still images with smartphones-mobile devices: Star Trail in the Southern Hemisphere with Bortle 4 Scale Light Pollution, by Slamat Riyadi.</p> <p>This breathtaking photo, captured under the clear night sky of Linggamekar Village, Cilimus, Kuningan, West Java, Indonesia on 25 June 2020, displays star trails sweeping across the southern hemisphere’s heavens. The star trails are due to Earth’s rotation causing the apparent motion of stars, creating these mesmerising arcs of light when followed over extended periods. Here, the point the stars rotate around (the South Celestial Pole) is close to the horizon, as the image was taken close to the equator. The photographer used the star trail feature on a smartphone, which captured a series of images over an extended period and stacked them together. The striking tree in the foreground adds depth to the image, contrasting the celestial motion above with its Earthly stillness, while also masking some of the surrounding light pollution. Different parts of the world offer diverse and stunning perspectives on the night sky, emphasising the importance of preserving dark skies everywhere.</p> <p>Credit: Slamat Riyadi/IAU OAE (<a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC BY 4.0</a>)</p>
Data of "Aerosol-cloud interactions near cloud base deteriorating the haze pollution in East China"
<p><span>The attached data is observations from ground to 1200 m a.g.l. using a tethered airship in Yangtze Rive Delta of China. The data is for analysis and figures in the study of "Aerosol-cloud interactions near cloud base deteriorating the haze pollution in East China".</span></p>
Dataset for article: Adsorption of the hydrophobic organic pollutant hexachlorobenzene to phyllosilicate minerals
<p>This repository contains data obtained from first principle DFT calculations at the PBE-D3 DFT level<br>by the program VASP for the research article </p> <p><br>Title: "Adsorption of the hydrophobic organic pollutant hexachlorobenzene to phyllosilicate minerals"<br>published in Environmental Science and Pollution Research (2023) 30:36824–36837.</p> <p>Authors: Leonard Böhm, Peter Grančič, Eva Scholtzová, Benjamin Justus Heyde, Rolf-Alexander Düring, Jan Siemens, Martin H. Gerzabek & Daniel Tunega.</p> <p>Please cite that article when using this dataset.</p> <p>The systems in the dataset are models of Me-montmorillonite layers (Me = Li, Na, K, Rb, Cs, Mg, Ca, Sr, Ba)<br>interacting with hexachlorobenzene (HCB) molecule. Calculated are interaction energies of optimized geometries of HCB...Me-Mnt complexes. Dateset contains tables with collected calculated adsorption energies and main geometrical paramters.<br>The structure of dataset is following:<br>Rep_Ads_I directory contains directories for HCB molecule, and for complexes of HCB with Li-Mnt to Rb-Mnt. Each directory of complexes contains corresponding directory of isolated Me-Mnt layer. The second directory, Rep_ads_II has the same structure as Rep_Ads_I directory for Me=Mg, Ca, Sr, and Ba.<br>In each directory are the main files for VASP calculations:<br>input geometry data (POSCAR.norm file)<br>optimized geometry (CONTCAR.norm file)<br>input parameters for VASP (INCAR file)<br>k-points (KPOINT file)<br>complete output files (OUTCAR.norm and vasprun.xml.norm files)<br>POTCAR file with pseudopotentials are not provided due to copyright restrictions. Their type can be found in OUTCAR file or vasprun.xml file.</p> <p>Funding: This work has been supported by German Research Foundation (Deutsche Forschungsgemeinschaft, DFG), grant number 443637168, BO5388/1–1 and Austrian Science Fund (Fonds zur Förderung der Wissenschaftlichen Forschung, FWF), grant number I 4876–N in the bilateral project ”Clay minerals as sorbents for hydrophobic organic chemicals – ClayHOC”. The results<br>presented have been achieved using the Vienna Scientific Cluster (VSC), project number 70544.</p> <p>Terms of use: These data are provided "as is", without any warranty. This dataset is provided under the Creative Commons Attribution 4.0 International license.</p>
The more microplastic types pollute the soil, the stronger the growth suppression of invasive alien and native plants
<p>The ecological consequences of microplastic pollution for plants remain largely unknown, and the few studies that tested the effects usually focused on a single type of microplastic and a single plant species. However, most plants will be exposed to multiple microplastic types simultaneously, and the effects may vary among species.</p> <p>To test the effects of microplastic diversity on plants, we grew single plants of eight invasive and eight native species in pots with substrate polluted with 0, 1, 3 and 6 types of microplastics.</p> <p>We found that the growth suppression by microplastic pollution became stronger with the number of microplastic types the plants were exposed to. This tended to be particularly the case for invasive species, as their biomass advantage over natives diminished with the number of microplastic types. The biomass responses coincided with a positive effect of the number of microplastic types on root allocation and thickness, which was also stronger for invasive than for native species. In addition, the results of hierarchical diversity-interaction models suggest that the negative impact of microplastic diversity on the total biomass of invasive plant species was influenced by both the identities of the microplastic and certain types of microplastic with strong pairwise interactions. In contrast, the effect on native species was determined solely by the microplastic identities.</p> <p><em>Synthesis: </em>Our multi-species study thus shows for the first time that the negative effects of microplastic pollution on plant growth increase with the number of microplastic types. We also found tentative evidence that the negative impacts of microplastic diversity were more pronounced for invasive plants compared to native plants, and that this might be due to differences in the responses of root allocation and thickness.</p>
Data from: Do the health benefits of boiling drinking water outweigh the negative impacts of increased indoor air pollution exposure?
<p><strong>Background: </strong>Billions of the world's poorest households are faced with the lack of access to both safe drinking water and clean cooking. One solution to microbiologically contaminated water is boiling, often promoted without acknowledging the additional risks incurred from indoor air degradation from using solid fuels.</p> <p><strong>Objectives: </strong>This modeling study explores the tradeoff of increased air pollution from boiling drinking water under multiple contamination and fuel use scenarios typical of low-income settings.</p> <p><strong>Methods: </strong>We calculated the total change in disability-adjusted life years (DALYs) from indoor air pollution (IAP) and diarrhea from fecal contamination of drinking water for scenarios of different source water quality, boiling effectiveness, and stove type. We used Uganda and Vietnam, two countries with a high prevalence of water boiling and solid fuel use, as case studies. </p> <p><strong>Results: </strong>Boiling drinking water reduced the diarrhea disease burden by a mean of 1110 DALYs and 368 DALYs per 10,000 people for adults and children <5 years in Uganda, respectively, for high-risk water quality and the most efficient (lab-level) boiling scenario, with smaller reductions for less contaminated water and ineffective boiling. Similar results were found in Vietnam, apart from fewer avoided DALYs in children due to different demographics. In both countries, for households with high baseline IAP from existing solid fuel use, adding water boiling to cooking on a given stove was associated with a limited increase in IAP DALYs due to the log-linear dose-response curves. Boiling, even at low effectiveness, was associated with <em>net </em>DALY reductions for medium- and high-risk water, even if using unclean stoves/fuels. Replacing traditional stoves with improved stoves coupled with effective boiling practices significantly reduced total DALYs. </p> <p><strong>Discussion: </strong>Boiling water generally resulted in a net decrease in DALYs. Future efforts should empirically measure health outcomes from IAP vs. diarrhea associated with boiling drinking water using field studies with different boiling methods and stove types.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.