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910 results for “pollution”
Perceptions of heat and air pollution among older adults experiencing homelessness in Phoenix, Arizona (USA) (June 2024)
This dataset consists of survey responses from 40 older adults experiencing homelessness in Phoenix, Arizona (USA), assessing the perceptions of environmental hazards—specifically heat and air pollution—and attitudes toward coping resources and behaviors. The survey includes 51 questions co-created with community members across five categories: demographics and behavior, movement/transportation, climate perceptions, resource availability, and local knowledge mapping. Surveys were conducted indoors at a local service provider over two days in June 2024, when outdoor temperatures reached 42 degrees C and 45 degrees C. The dataset offers insights into potential public service reforms to mitigate heat and air pollution risks among Arizona’s unhoused population. The survey was approved by the Institutional Review Board of Arizona State University (IRB approval number: STUDY00018399).
Concentrations and Surface Exchange of Air Pollutants at Harvard Forest EMS Tower since 1990
In North America, anthropogenic activities such as fossil fuel combustion and high-intensity agriculture have increased the inputs of nitrogen oxides in the atmosphere far above natural, biogenic inputs. The effect of this excess N depends on how it is distributed through the environment. If fixed N is deposited as nitrate in forests, it may act as a "fertilizer", stimulating growth and thus enhancing carbon sequestration. But when accumulated deposition exceeds the nutritional needs of the ecosystem, nitrogen saturation may result. Soil fertility declines due to leaching of cations and thus, carbon uptake diminishes. The balance between fertilization and saturation depends on the spatial and temporal extent of nitrogen deposition. Measurements of nitrogen oxide concentrations and fluxes made at Harvard Forest are intended to quantify the deposition of nitrogen oxides and to examine the rates for oxidation and deposition of reactive nitrogen that are critical in controlling how far the influence of nitrogen oxide emission sources extends. Measurements made to date indicate that dry deposition of NOy to the Harvard Forest canopy is controlled by advection from source regions, vertical mixing, and chemical reaction. The input is about equally divided between wet and dry deposition depending on the amount of precipitation. Southwesterly winds bring air from the major urban areas along the mid-Atlantic coast, whereas northwesterly wind bring air from less populated regions of northern New England and Canada. As a result, southwesterly winds transport higher concentrations and fluxes of NOx and NOy than northwesterly winds. In the summer, aerodynamically rough forests intercept NOx and emit reactive hydrocarbons that accelerate the oxidation of NOx to rapidly depositing species. As a result, much of the NOx emitted by North America is retained by the region in the summer. This deposition leads to a summertime decrease in reactive nitrogen concentrations and fluxes relati
A unified dataset of co-located sewage pollution, periphyton, and benthic macroinvertebrate community and food web structure from Lake Baikal (Siberia)
Sewage released from lakeside development can introduce nutrients and micropollutants that can restructure aquatic ecosystems. Lake Baikal, the world's most ancient, biodiverse, and voluminous lake, has been experiencing localized sewage pollution from lakeside settlements. Increasing filamentous algal abundance suggests benthic communities are responding to this localized pollution. We surveyed 40-km of Lake Baikal's southwestern shoreline 19-23 August 2015 for sewage indicators, including pharmaceuticals, personal care products, and microplastics with co-located periphyton, macroinvertebrate, stable isotope, and fatty acid sampling. Unique identifiers corresponding to sampling locations are retained throughout all data files to facilitate interoperability among the dataset's 150+ variables. The data are structured in a tidy format (a tabular arrangement familiar to limnologists) to encourage future reuse. For Lake Baikal studies, these data can support continued monitoring and research efforts. For global studies of lakes, these data can help characterize sewage prevalence and ecological consequences of anthropogenic disturbance across spatial scales.
Genome-Wide DNA Methylation in Peripheral Blood and Long-Term Exposure to Source-Specific Transportation Noise and Air Pollution: The SAPALDIA Study (Supplementary Data)
<p>The zip file contains supplementary data for the publication - Genome-Wide DNA Methylation in Peripheral Blood and Long-Term Exposure to Source-Specific Transportation Noise and Air Pollution: The SAPALDIA Study, accepted for publication in Environmental Health Perspectives (DOI: 10.1289/EHP6174).</p> <p>The description of the files are noted below:</p> <p><strong>1. Readme File for SAPALDIA Noise and Air Pollution EWAS Single Exposure.zip </strong></p> <p>This zip file contains all the results of the association between source-specific transportation noise (aircraft, railway and road traffic), air pollution (NO<sub>2</sub> and PM<sub>2.5</sub>), and genome-wide DNA methylation, derived from multi-exposure models.</p> <p><strong>SAPALDIA_EWAS_SingleExposure_AircraftLden.txt</strong> contains the results for aircraft noise</p> <p><strong>SAPALDIA_EWAS_SingleExposure_RailwayLden.txt</strong> contains the results for railway noise</p> <p><strong>SAPALDIA_EWAS_SingleExposure_RoadtrafficLden.txt</strong> contains the results for road traffic noise</p> <p><strong>SAPALDIA_EWAS_SingleExposure_NO2.txt</strong> contains the results for nitrogen dioxide</p> <p><strong>SAPALDIA_EWAS_SingleExposure_PM25.txt</strong> contains the results for fine particulate matter</p> <p> </p> <p><strong>General footnote for all files:</strong>SAPALDIA: Swiss cohort study on air pollution and lung and heart diseases in adults. CpG: Cytosine-phosphate-Guanine. CHR: chromosome. SE: standard error. Lden: day-evening-night noise level. NO<sub>2</sub>: nitrogen dioxide. PM<sub>2.5</sub>: particulate matter with aerodynamic diameter <2.5 µm. Beta coefficients represent increase or decrease in DNA methylation per 10 dB increase in aircraft, railway or road traffic Lden or 10 µg/m<sup>3</sup> increase in NO<sub>2</sub> or PM<sub>2.5</sub>. All estimates were from single exposure epigenome-wide linear mixed models, with random intercept at the level of participant. Each model was adjusted for age, sex, educational level, area, and neighborhood socio-economic status, greenness index, smoking status and pack years, exposure to passive smoke, consumption of fruits, vegetables and alcohol, nested study, asthma status, survey, source-specific noise truncation indicator (for Lden models) and leukocyte composition. In a preliminary step, DNA methylation β-values were regressed on the Illumina control probe-derived first 30 principal components to correct for correlation structures and technical bias, and residuals of these regressions covering 430,477 CpGs were used as the technical bias-corrected methylation level at the CpG sites.</p> <p>Extreme values of the residuals (lying beyond three times the interquartile range below the first quartile and above the third quartile at each CpG site) were replaced with their corresponding detection threshold value (“modified winsorization”). The “winsorized” data were then used as the dependent variables in the epigenome-wide association study.</p> <p> </p> <p><strong>2. Readme File for SAPALDIA Noise and Air Pollution EWAS Multi Exposure.zip </strong></p> <p>This zip file contains all the results of the association between source-specific transportation noise (aircraft, railway and road traffic), air pollution (NO<sub>2</sub> and PM<sub>2.5</sub>), and genome-wide DNA methylation, derived from multi-exposure models.</p> <p><strong>SAPALDIA_EWAS_MultiExposure_AircraftLden.txt</strong> contains the results for aircraft noise</p> <p><strong>SAPALDIA_EWAS_MultiExposure_RailwayLden.txt</strong> contains the results for railway noise</p> <p><strong>SAPALDIA_EWAS_MultiExposure_RoadtrafficLden.txt</strong> contains the results for road traffic noise</p> <p><strong>SAPALDIA_EWAS_MultiExposure_NO2.txt</strong> contains the results for nitrogen dioxide</p> <p><strong>SAPALDIA_EWAS_MultiExposure_PM25.txt</strong> contains the results for fine particulate matter</p> <p><strong>General table footnotes: </strong>SAPALDIA: Swiss cohort study on air pollution and lung and heart diseases in adults. CpG: Cytosine-phosphate-Guanine. CHR: chromosome. SE: standard error. Lden: day-evening-night noise level. NO<sub>2</sub>: nitrogen dioxide. PM<sub>2.5</sub>: particulate matter with aerodynamic diameter <2.5 µm. Beta coefficients represent increase or decrease in DNA methylation per 10 dB increase in aircraft, railway or road traffic Lden or 10 µg/m<sup>3</sup> increase in NO<sub>2</sub> or PM<sub>2.5</sub>. All estimates were from multi-exposure epigenome-wide linear mixed models, with random intercept at the level of participant, and were adjusted for age, sex, educational level, area, and neighborhood socio-economic status, greenness index, smoking status and pack years, exposure to passive smoke, consumption of fruits, vegetables and alcohol, nested study, asthma status, survey, source-specific noise truncation indicator and leukocyte composition. Multi-exposure models included all five exposures (Aircraft, railway, road traffic Lden and respective truncation indicators, NO<sub>2</sub> and PM<sub>2.5</sub>) at the same time. In a preliminary step, DNA methylation β-values were regressed on the Illumina control probe-derived first 30 principal components to correct for correlation structures and technical bias, and residuals of these regressions covering 430,477 CpGs were used as the technical bias-corrected methylation level at the CpG sites. Extreme values of the residuals (lying beyond three times the interquartile range below the first quartile and above the third quartile at each CpG site) were replaced with their corresponding detection threshold value (“modified winsorization”). The “winsorized” data were then used as the dependent variables in the epigenome-wide association study.</p>
Inter-Chemical Correlation results for the study: HHEARx2017-1729 (Air Pollution, Placenta Function, and Birth Outcomes in Los Angeles)
Title: Air Pollution, Placenta Function, and Birth Outcomes in Los Angeles <br>Species: Homo sapiens <br>Number of samples: 450 <br>Number of named analytes: 14 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=48 <br>
Dataset for simulation studies of fleet vehicle selection in terms of pollutant emissions
<p>The purpose of this dataset is to enable the replication of the research results presented in the article: Szczepański E, Jachimowski R, Rudyk T. Simulation studies of fleet vehicle selection in terms of pollutant emissions. Combustion Engines. 2024;196(1):80-88. https://doi.org/10.19206/CE-169802 - published online: 2023-08-10, which discusses the application of simulation in solving the problem of vehicle selection and determining optimal approaches considering pollutant emissions.</p> <p>Dataset contains:</p> <ul> <li>Readme.txt: description of the dataset</li> <li>InputData.csv: contains the input data used in the model, including data from the COPERT model.</li> <li>OutputOptimization.csv: contains output data</li> <li>OutputSummary.xlsx: contains output data</li> <li>imulation_model_xml.fsx: contains the code of the model in XML format.</li> </ul> <p>The dataset was created as part of the E-Laas project (Energy optimal urban logistics As A Service).<br>Project implemented as part of the call ERA-NET Cofund Urban Accessibility and Connectivity (ENUAC China Call) organized by JPI Urban Europe and the National Natural Science Foundation of China (NSFC). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875022.<br> E-Laas project is carried out in an international consortium. Project coordinator in Europe: Chalmers University of Technology (Sweden), project coordinator in China: Shanghai University (China), consortium members: Tsinghua University (China), Warsaw University of Technology (Poland), cooperation partners: Stockholms stad, Trafikkontoret (Sweden), ParkUnload (Spain), Metropolis GZM (Poland), Shanghai Urban-Rural Construction and Transportation Department (China), Volvo Group Trucks Technology and Operations (Sweden).<br>- The Chinese part of the project is funded by National Natural Science Foundation of China.<br>- The Swedish part of the project is funded by Swedish Energy Agency.<br>- The Polish part of the project is funded by the National Science Centre, Poland (project no. 2022/04/Y/ST8/00134). The value of the co-financing is PLN 878,107.00. Project duration 27/04/2023 - 26/04/2026 (36 months).</p>
Predicted locations and nitrate pollution of groundwater discharge from D3pl karst aquifer in Latvia
<p><strong>Description</strong></p> <p>A georeferenced raster data layer [5_predicted_D3pl_GW_discharge_zone.tif] showing predicted likelihood that groundwater polluted with nitrate (NO<sub>3</sub><sup>-</sup>) is discharging as springs or diffuse seepage from the Upper Devonian Pļaviņas (<em>D<sub>3</sub>pl</em>) dolomite karst aquifer in Latvia is presented. The cell value indicates the likelihood (0 – low, 1 - high) that groundwater with nitrate contamination is discharging from the <em>D<sub>3</sub>pl</em> aquifer at this location. Value of 0 means that no groundwater is discharging there. The BalticTM 93 (EPSG:25884) references system is used.</p> <p>The rationale and methodology for elaborating the map of groundwater discharge as springs or diffuse seepage from the <em>D<sub>3</sub>pl</em> dolomite karst aquifer is described in the main article (Kalvāns et al. under review). In short, a 3D regional geological model (Popovs et al. 2015), land surface elevation model and bedrock surface elevation model (Popovs et al. under review) were combined to identify locations where aquitard at the base of <em>D<sub>3</sub>pl</em> aquifer is outcropping at bedrock surface and in the nearby depressions (in a distance up to 0.25 km) the land surface was below the surface of this aquitard. The likely contamination with NO<sub>3</sub><sup>-</sup> was estimated from proportion of arable land (European Environment Agency 2018) within 4.75 km window. It is assumed that the NO<sub>3</sub><sup>-</sup> contamination in the <em>D<sub>3</sub>pl</em> karst aquifer is likely only close to its distribution margins, where groundwater table is deeper than the top of the aquifer.</p> <p>This work was supported by the EU Interreg Est–Lat program project GroundEco No. Est-Lat62, and base funding grant from the Latvian Ministry of Education and Science to the University of Latvia, No. ZD2016/AZ03.</p> <p><strong>References</strong></p> <p>European Environment Agency (2018) Corine Land Cover 2018. https://land.copernicus.eu/pan-european/corine-land-cover/clc2018?tab=download (CLC). Accessed 1 Jun 2020</p> <p>Popovs K, Kalvāns A, Jemeljanova M, et al (under review) Bedrock surface topography map of Latvia. J Maps</p> <p>Popovs K, Saks T, Jātnieks J (2015) A comprehensive approach to the 3D geological modelling of sedimentary basins: example of Latvia, the central part of the Baltic Basin. Est J Earth Sci 64:173–188. https://doi.org/10.3176/earth.2015.25</p> <p> </p>
Hourly air pollution data for Graz, Austria
<p>The dataset spans from January 1, 2014, to March 15, 2020, with measurements recorded on an hourly basis.</p> <p> </p> <ul> <li> <p>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></p> <ul> <li> <p>Air quality by means of NO2, NO, NOx, PM10 and O3 was measured at five sites in Graz, Austria (Süd (eng. South) - S, Nord (eng. North) - N, West (eng. West) - W, Don Bosco – D, Ost (eng. East) – O). </p> </li> <li> <p>Temperature, precipitation, relative humidity, pressure, and wind speed are among the weather conditions considered. To represent wind direction, the wind speed was multiplied by the sine and cosine of the wind direction.</p> </li> <li> <p>Lags were generated using weather data, considering the last 12 data points. The mean of these 12 values was then calculated to represent an hourly metric.</p> </li> </ul> </li> <li> <p>The ERA5-Land data 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> </p> <ul> <li> <p>it includes following variables :</p> <ul> <li> <p>Snowfall - sf</p> </li> <li> <p>Surface latent heat flux - slhf</p> </li> <li> <p>Snowmelt - smlt</p> </li> <li> <p>Snow cover - snowc</p> </li> <li> <p>Windspeed - speed</p> </li> <li> <p>Surface latent heat flux sshf</p> </li> <li> <p>Soil temperature level 4 - stl4</p> </li> <li> <p>Skin temperature - str</p> </li> <li> <p>Surface thermal radiation downwards - strd</p> </li> <li> <p>Total precipitation - tp</p> </li> <li> <p>Temperature of snow layer - tsn</p> </li> <li> <p>10m u-component of wind - u10</p> </li> <li> <p>10m v-component of wind - v10</p> </li> <li> <p>Surface net radiation - rsn</p> </li> <li> <p>Snow depth - sd</p> </li> <li> <p>Snow depth water equivalent - sde</p> </li> <li> <p>2m dewpoint temperature - d2m</p> </li> <li> <p>Forecast albedo - fal</p> </li> </ul> </li> </ul> </li> <li> <p>Temporal values are also incorporated into this dataset, values such as holidays, weekdays, seasons, and months.</p> </li> <li> <p>The dataset includes Prophet values for all pollutants, which were determined by considering various metrics such as trend, seasonality (weekly, yearly, and daily), as well as yhat lower and upper bounds.</p> </li> </ul>
Data for Figures 4, A-F and Table J of Publication "Blue skies over China: The effect of pollution-control on solar power generation and revenues"
<p>This repository contains the data to produce Figures 4, A-F and Table J and emission data in the paper:</p> <p>"Labordena M, Neubauer D, Folini D, Patt A, Lilliestam J (2018) Blue skies over China: The effect of pollution-control on solar power generation and revenues. PLoS ONE 13(11): e0207028. https://doi.org/10.1371/journal.pone.0207028"</p> <p>Note that the scripts are to be found in the accompanying package (https://doi.org/10.5281/zenodo.8130726)</p>
A route to school informational intervention for air pollution exposure reduction
<p>iSCAPE Dataset Reference No. = DS_PD_020</p> <p>Following datasets are gathered during the implementation of route to school intervention study in Antwerp (Belgium)</p> <ol> <li>Introductory Questionnaire Responses</li> <li>Feedback Questionnaire Responses</li> </ol>
Undirected Node Attributed Social Network Graph of Twitter Users interested in plastic pollution - created in the framework of the PlasticTwist project
<p>This dataset has been created in the framework of the Plastic Twist project (<a href="https://ptwist.eu/">Ptwist</a>) and more specifically using the Ptwist crowdsourcing application (<a href="https://crowdsourcing.plastictwist.com/">crowdsourcing.plastictwist.com/</a>). We are sharing the edge list and specific node attributes (hashtags) of Twitter users posting about plastic pollution. The dataset can be used for community detection,clustering, node importance, influence maximization tasks, etc. Each user is represented by a unique integer which has nothing to do with the official Twitter user ID. The dataset contains three (3) files: </p> <ul> <li>ptwist.edgelist: A list containing all the 1,362,863 edges between the users. When loaded they create an undirected graph of 800K+ users.</li> <li>node_attributes.txt: This file contains information about the hashtags used by each user. (e.g. "652003": ["SingleUsePlastic"] -> user 6529003 has used the hashtag SingleUsePlastic) </li> <li>annotated_graph: A pickle file which, when loaded, returns a <a href="https://networkx.github.io/">NetworkX</a> node attributed undirected graph.</li> </ul> <p> </p> <p> </p>
Sonic Kayaks geolocated air pollution, water turbidity, temperature and hydrophone analysis
<p>These data sets are the result of five trips using <a href="https://fo.am/activities/kayaks/">Sonic Kayaks</a> to collect data as part of the <a href="https://actionproject.eu/">ACTION Project</a>. The sampling was carried out in the Penryn river, around Falmouth docks and the Helford estuary. A variety of sensors were used:</p> <ol> <li>Thermometer recording water temperature.</li> <li>PMS7003 air pollution sensor recording a variety of particulate sizes.</li> <li>DolphinEar DE PRO hydrophone for detecting noise pollution and biological signals.</li> <li>A custom turbidity sensor to detect changes in water cloudiness.</li> </ol> <p>The sound has been processed in this data set in order to classify sound sources from different boat engines. More information, source code and <a href="https://github.com/fo-am/sonic-kayaks/wiki">open hardware plans for construction can be found here</a>.</p>
Air pollution exposure fields for 2020
<p>Air pollution exposure fields for the year 2020 created with a chemical transport model and landuse regression models. ASCII data files in zip form. Windows assignment program that calcualtes exposure concentrations based on location, start date, end date. R codes that perform statistical analysis based on assigned exposures. Note that confidential patient data is <em>not</em> included in the archive.</p>
Weekly county-level pollution data for China from Zhang, Carleton, Lin, and Zhou (accepted, Nature Sustainability), "Estimating the role of air quality improvements in the decline of suicide rates in China"
<p>This dataset contains weekly, county-level air pollution data for 2,839 counties from 2013 to early 2018. These data are used and described in Zhang, Carleton, Lin, and Zhou (accepted, <em>Nature Sustainability</em>), "Estimating the role of air quality improvements in the decline of suicide rates in China". When the paper is published a link to the manuscript will be added here. </p> <p>The manuscript Methods section details data construction. In summary, these county-level observations are obtained from monitoring stations maintained by the China National Environmental Monitoring Center (CNEMC), which is affiliated with the Ministry of Ecology and Environment of China. CNEMC began publishing hourly air pollution data in 2013, including the Air Quality Index, PM2.5, PM10, ozone, sulfur dioxide, nitrogen dioxide, and carbon monoxide. We average hourly data to the station-day level and use inverse-distance weighting with a radius of 200km to convert data from station to the county level. We average across days to generate county-level weekly values. Any missing station-hour observations in the raw data are omitted in this spatial and temporal aggregation. Our main analysis relies on PM2.5, but all pollutants are released here.</p>
High-resolution air pollution emission inventory for the Nordic countries
<p>This common Nordic (Denmark, Finland, Iceland, Norway, and Sweden) air pollution emission inventory was compiled using country total emissions from national emission inventories that the countries submit to the CLRTAP. Our inventory was based on the 2016-2018 submissions. The inventory contains annual emissions for 1990, 1995, 2000, 2005, 2010, 2012 and 2014. Components included in the inventory are: particulate matter (PM10 and PM2.5), black carbon (BC), organic carbon (OC), sulphur oxides (SOx), nitrogen oxides (NOx), carbon monoxide (CO), non-methane volatile organic compounds (NMVOC) and ammonia (NH3). The gridding was done separately for each country, using national data and gridding methods. The emissions were harmonized to the same sector nomenclature, i.e. SNAP, and to the EEA reference grid. Spatial resolution for the inventory is 1 km × 1 km in the European grid ETRS89-LAEA (EPSG: 3035). Large point source emissions are provided with locations and stack heights included. Two modifications to the CLRTAP submissions were made: (1) road transport non-exhaust PM emissions were adjusted to better conform with Nordic traffic dust assessments; and (2) for OC emission, that are not included in the inventories, rough estimates were calculated based on expert estimates on OC/PM2.5-ratios on main SNAP level. The inventory was originally created for the NordicWelfAir-project (<a href="https://projects.au.dk/nordicwelfair">https://projects.au.dk/nordicwelfair</a>). The main aim of developing this new inventory was to provide air pollution modelers and health scientists a harmonized dataset to be used for studies on the link between air pollution exposure and negative impacts on the human health.<br>Description of the data can be found in this data article, which can be referenced when using the data: <a href="https://doi.org/10.5194/essd-16-1453-2024">https://doi.org/10.5194/essd-16-1453-2024</a>.</p>
NETTAG+ Data set on adsorption of inorganic (Cu and Pb) and organic (PAHs) pollutants in fishing nets
<p>This data set includes the raw data associated with the article in <em>Marine Pollution Bulletin</em><strong> "</strong>Potential of fishing nets for adsorption of inorganic (Cu and Pb) and organic (PAHs) pollutants"</p>
Source Data and ambient ozone dataset generated in "Substantially underestimated global health risks of current ozone pollution"
<p>Existing assessments might have underappreciated ozone-related health impacts worldwide. Here our study assesses current global ozone pollution using the high-resolution (0.05°) estimation from a geo-ensemble learning model, with key focuses on population exposure and all-cause mortality burden. Our model demonstrates strong performance, achieving a mean bias of less than -1.5 parts per billion against in-situ measurements. We estimate that 66.2% of the global population is exposed to excess ozone for short term (> 30 days per year), and 94.2% suffers from long-term exposure. Furthermore, severe ozone exposure levels are observed in Cropland areas, particularly over Asia. Importantly, the all-cause ozone-attributable deaths significantly surpass previous recognition from specific diseases worldwide. Notably, mid-latitude Asia (30°N) and the western United States show high mortality burden, contributing substantially to global ozone-attributable deaths. Our study highlights current significant global ozone-related health risks and may benefit the ozone-exposed population in the future.</p>
Laboratory comparison of low-cost particulate matter sensors to measure transient events of pollution - Dataset
<p>This repository contains the data associated with the paper: Laboratory comparison of low-cost particulate matter sensors to measure transient events of pollution.</p> <p>Bulot, F.M.J.; Russell, H.S.; Rezaei, M.; Johnson, M.S.; Ossont, S.J.J.; Morris, A.K.R.; Basford, P.J.; Easton, N.H.C.; Foster, G.L.; Loxham, M.; Cox, S.J. Laboratory Comparison of Low-Cost Particulate Matter Sensors to Measure Transient Events of Pollution. <em>Sensors</em> <strong>2020</strong>, <em>20</em>, 2219.</p> <p><a href="https://doi.org/10.3390/s20082219">https://doi.org/10.3390/s20082219</a> </p> <p>It contains:</p> <p>- DHT22.csv measurements from the DHT22 humidity and temperature sensor</p> <p>- dusttrak.csv measurements from the DustTrak</p> <p>- ops.csv measurements from the OPS TSI 3330</p> <p>- sensors.csv measurement from the low-cost PM sensors</p> <p>- sensors_blank.csv measurements from the low-cost PM sensors during the blank test</p> <p> </p> <p>sensors_blank.csv contains the following variables:</p> <ul> <li>Bin1 to Bin15: particle numbers for different bin sizes reported by the Alphasense OPCR1, as defined by its user's manual available here https://www.alphasense.com/products/optical-particle-counter/</li> <li>SamplingPeriod: sampling period of the Alphasense OPCR1 in seconds</li> <li>SFR: sampling flow rate of the Alphasense OPCR1 in ml/s</li> <li>PM1, PM25, PM4, PM10: PM concentrations reported by the sensors in ug/m3.</li> <li>gr03um to gr100um: particle number concentrations for different bin sizes for the Plantower PMS5003, in particle per 100ml, as defined by its user's manual https://aqicn.org/air/view/sensor/spec/pms5003-manual_v2-3</li> <li>n05 to n10: particle number concentrations for different bin sizes for the Sensirion SPS30, in particles per cm3, as defined by its user's manual: https://www.sensirion.com/fileadmin/user_upload/customers/sensirion/Dokumente/9.6_Particulate_Matter/Datasheets/Sensirion_PM_Sensors_Datasheet_SPS30.pdf</li> <li>humidity and temperature: relative humidity (%) and temperature (Celsius) recorded by the SHT35 sensors</li> <li>sensor: sensor identifier</li> <li>site: name of the air quality monitor containing the sensors</li> <li>exp: name of the experiment considered</li> <li>source: source of PM used</li> <li>variation: peak or stable concentration</li> <li>date: date and time of the experiment</li> </ul>
Stormwater runoff pollution of an existing catchment consisting essentially of apartment buildings in Braunschweig/Germany.
<p>This dataset includes stormwater runoff concentrations of pollutants (COD, TP, DP, NO<sub>3</sub>, NH<sub>4</sub>, TSS) taken from a sampling point in Braunschweig, Germany. The total catchment size is 5 ha with approximately 1.8 ha total imperviousness consisting essentially of apartment buildings from the 1970s and roads. This catchment was chosen for comparatively clear delimitation to different land uses considering location-independent results. Samples were taken as part of the research project TransMiT (https://www.transmit-zukunftsstadt.de/). More information including sampling and analytical methods are detailed in the corresponding journal paper "Dynamization of Urban Runoff Pollution and Quantity" and supplementary data, submitted to the MDPI-journal Water.</p> <p>Description of fields:</p> <p>- SamplingPoint:<br> - storm sewer: stormwater runoff sampled during individual rain events in a storm water sewer manhole receiving runoff from the entire catchment<br> - SamplingTime: time of sampling during individual rain event (CET)<br> - COD: measured value of chemical oxygen demand (COD)<br> - TP: measured value of total phosphorus (TP)<br> - DP: measured value of dissovled phosphorus (COD)<br> - NO<sub>3</sub>: measured value of nitrate (NO3)<br> - NH<sub>4</sub>: measured value of ammonium (NH4)<br> - TSS: measured value of total suspended solids (TSS)<br> - N/A: data not available<br> - UnitsAbbreviation: all data given in milligram per litre (mg/L)</p> <p>The data file is provided in comma separated format ("TransMiT.csv") and contains concentrations of all samples.</p> <p> </p>
Data for Korner et al. "Birds and the Post Tower in Bonn: A case study of light pollution"
<p><strong>Abstract</strong></p> <p>During six consecutive autumn seasons we registered birds that were attracted to an illuminated 41-storey building in Bonn, Germany. Casualties on the ground were disoriented by the light and in most cases collided with the building. All-night observations with numbers of casualties, effective light sources, moon, and weather parameters registered hourly allowed for analyses of the role of these factors for the attraction and disorientation of numerous migratory birds. As expected, the conspicuous façade illumination was responsible for many casualties (fatal or non-fatal). Additionally, the illuminated roof logos and even faint light sources like the emergency lights were attractive and led to casualties. Moon and rain were negatively correlated with casualties, but there was no clear correlation with other weather parameters. Turning off lights was key, but effects of other <em>ex post</em> mitigation measures were limited: shutters were not originally intended for the attenuation of light emissions, control technology was insufficient, and there was a lack of willingness of the building owner to reduce light emissions consistently, even during core bird migration periods. Conservation recommendations are derived from this case study.</p> <p> </p> <p><strong>Vögel und der „Postturm“ in Bonn: Eine Fallstudie zur Lichtverschmutzung</strong></p> <p>In sechs aufeinanderfolgenden Herbstsaisons erfassten wir die Vögel, die an ein beleuchtetes, 41stöckiges Hochhaus in Bonn (Deutschland), den sog. „Postturm “ angelockt wurden. Die Opfer am Boden waren aufgrund der Beleuchtung desorientiert und in den meisten Fällen mit dem Gebäude kollidiert. Basierend auf Beobachtungen während des gesamten Nachtverlaufes wurden die registrierten Opfer, die in Betrieb befindlichen Lichtquellen, Mond und Wettervariablen stundenweise dargestellt, um die Bedeutung dieser Faktoren für die Anlockung und Desorientierung zahlreicher Zugvögel zu analysieren. Die auffällige Fassadenbeleuchtung war erwartungsgemäß für die meisten der Todesfälle und Verletzungen verantwortlich. Zusätzlich führten die beleuchteten Firmenlogos auf dem Dach und sogar schwache Lichtquellen wie die Notbeleuchtung zu Opfern durch Anlockung, auch bei ausgeschalteter Fassadenbeleuchtung. Mond und Regen korrelierten negativ mit den Opferzahlen, aber mit anderen Wettervariablen fehlten klare Korrelationen. Das Abschalten der Beleuchtung war ausschlaggebend, während andere nachträgliche Abhilfemaßnahmen wenig wirksam waren: Sonnenschutzlamellen waren ursprünglich beim Einbau nicht dafür konzipiert, Lichtabstrahlung zu reduzieren, die Steuerungstechnik war fehleranfällig und die Bereitschaft der Gebäudeeigentümerin, Lichtemissionen selbst während der Kernzeiten des Vogelzugs konsequent zu reduzieren, war begrenzt. Aus dieser Fallstudie werden Empfehlungen für Schutzmaßnahmen abgeleitet.</p>
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Allen Brain Atlas
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