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16 results for “particulate pollution”
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>
Laboratory Comparison of Low-Cost Particulate Matter Sensors to Measure Transient Events of Pollution - Part B - Particle Number Concentrations - Dataset
<p>This repository contains the data used for the analysis of the paper "Laboratory Comparison of Low-Cost Particulate Matter Sensors to Measure Transient Events of Pollution - Part B - Particle Number Concentrations (PNC)" which is under submission.</p> <p> </p> <p>The experimental conditions and the instruments used are detailed in 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. https://doi.org/10.3390/s20082219</p> <p>The files are available in .csv and in .rds (for R) formats. For details about the measurement equipment used<br> during this study, please refer to the methods section of the paper.</p> <p> </p> <p>sensors_raw.csv contains the following headers:</p> <ul> <li>Bin0 to Bin15: Alphasense OPC-R1 particle number concentrations for different size bins</li> <li>Bin[1-3-5-7]MToF: mean time of flight of particles within the corresponding size bins of the Alphasense OPC-R1</li> <li>Checksum: checksum of the Alphasense OPC-R1</li> <li>SFR: sample flow rate of the Alphasense OPC-R1</li> <li>Humidity: relative humidity measured by the Alphasense OPC-R1</li> <li>Temperature: temperature measured by the Alphasense OPC-R1</li> <li>SamplingPeriod: sampling period of the Alphasense OPC-R1</li> <li>gr03um, gr05um, gr10um, gr25um, gr50um, gr100um: PNC measured by the Plantower PMS5003</li> <li>n05, n1, n25, n4, n10: PNC measured by the Sensirion SPS30</li> <li>humidity: relative humidity measured by a Sensirion SHT-3x</li> <li>temperature: temperature measured by a Sensirion SHT-3x</li> <li>sensor: id of the sensors</li> <li>site: name of the air quality monitor hosting the sensors</li> <li>exp: name of the experiment conducted</li> <li>source: source used to generate PM (incense or candle)</li> <li>variation: whether the sensors were exposed to stable or peak concentrations of PM pollution</li> <li>date: date in format yyyy-mm-dd HH:MM:SS</li> </ul> <p>For more explanations about the fields of individual sensors, please refer to their manual (Alphasense OPC-R1: https://kolegite.com/EE_library/datasheets_and_manuals/sensors/OPC/072-0500_OPC-R1_manual_issue_1_250219.pdf ; Plantower PMS5003: https://www.aqmd.gov/docs/default-source/aq-spec/resources-page/plantower-pms5003-manual_v2-3.pdf ; Sensirion SPS30: https://sensirion.com/products/catalog/SPS30/)</p> <p> </p> <p>ops.csv and ops.rds contains the readings from the OPS with the following cut sizes for the bins:</p> <ul> <li>Bin 1 Cut Point (um),0.300</li> <li>Bin 2 Cut Point (um),0.374</li> <li>Bin 3 Cut Point (um),0.465</li> <li>Bin 4 Cut Point (um),0.579</li> <li>Bin 5 Cut Point (um),0.721</li> <li>Bin 6 Cut Point (um),0.897</li> <li>Bin 7 Cut Point (um),1.117</li> <li>Bin 8 Cut Point (um),1.391</li> <li>Bin 9 Cut Point (um),1.732</li> <li>Bin 10 Cut Point (um),2.156</li> <li>Bin 11 Cut Point (um),2.685</li> <li>Bin 12 Cut Point (um),3.343</li> <li>Bin 13 Cut Point (um),4.162</li> <li>Bin 14 Cut Point (um),5.182</li> <li>Bin 15 Cut Point (um),6.451</li> <li>Bin 16 Cut Point (um),8.031</li> <li>Bin 17 Cut Point (um),10.000</li> </ul> <p>nanotracer.csv and nanotracer.rds contain the measurements from the Nanotracer:</p> <ul> <li>N.1.: particles/cm3</li> <li>dp_avg.1.: mean diameter of the particles (nm)</li> <li>P.1.:</li> <li>S_al.1.: Lung Deposited Surface Area in um2/cm3</li> </ul> <p> </p> <p>experimental_conditions.csv and experimental_conditions.rds contain the end dates and start dates of each of the experiment conducted.</p> <p> </p> <p> </p> <p>"pm100_cf1","pm10_cf1","pm25_cf1"</p> <p> </p> <p> </p>
Regional E-Atlas of the Greater Phoenix Region: Particulate Matter (2.5) pollution contours
These data represent the spatial distribution of one-year average particulate matter (2.5) concentration as pollution contours. Concentration is in micrograms per cubic meter.
Sources of particulate matter air pollution and its oxidative potential in Europes
<p>Data presented in the manuscript "Sources of particulate matter air pollution and its oxidative potential in Europe" (https://doi.org/10.1038/s41586-020-2902-8) by Daellenbach et al. (2020).</p>
Aerosol light absorption alleviates particulate pollution during wintertime haze events
<p>Observed and simulated data presented in Figs. 1-4.</p>
Biomagnetic characterisation of air pollution particulates in Lahore, Pakistan
<p>This dataset includes raw magnetic data measured at room and low temperatures. </p>
Staggered-peak production is a mixed blessing in the control of particulate matter pollution
<p>This is the data of our article named "Staggered-peak production is a mixed blessing in the control of particulate matter pollution"</p>
Patterns discovery dataset for particulate matter (pm2.5) pollution trends in Japan
Open the record for dataset details and reuse information.
Input data and analysis codes for "Reversal of trends in global fine particulate matter air pollution"
<p>This dataset contains Input data and analysis codes used for the following article:</p> <p>Li, C., A. van Donkelaar, M. S. Hammer, E. E. McDuffie, R. T. Burnett, J. V. Spadaro, D. Chatterjee, A. J. Cohen, J. S. Apte, V. A. Southerland, S. C. Anenberg, M. Brauer, & R. V. Martin, Reversal of trends in global fine particulate matter air pollution, submitted, 2023.</p> <p> </p> <p><strong>Input Data</strong></p> <p>Baseline mortality data (204 countries and territories, 17 age groups, 6 diseases, 22 years)</p> <p>Concentration-response functions (GEMM and MRBRT)</p> <p>PM<sub>2.5</sub> exposure for 204 territories and 22 years</p> <p>Age-specific population for 204 territories and 22 years</p> <p> </p> <p><strong>Derived Data</strong></p> <p>Age- and disease-specific PM<sub>2.5</sub>-attributable Mortality estimates for 204 territories and 22 years.</p> <p>Sensitivity of PM<sub>2.5</sub>-attributable Mortality to marginal PM<sub>2.5</sub> reduction for 204 territories and 22 years.</p> <p>Attributable of changes in PM<sub>2.5</sub>-attributable Mortality (and its sensitivity to marginal PM<sub>2.5</sub> reduction) to four driving factors.</p> <p> </p> <p><strong>Code</strong></p> <p>Necessary python scripts to verify and replicate analysis results in the manuscript.</p>
Developmental exposure to air pollution particulates increases asthma severity, alters the RNA transcriptome, and DNA methylation in female adult progeny [RNA-Seq]
GEO Series GSE285128. Mus musculus. 23 samples. Type: Expression profiling by high throughput sequencing.
Cerebral cortex and blood transcriptome changes in mouse neonates prenatally exposed to air pollution particulate matter
GEO Series GSE142453. Mus musculus. 24 samples. Type: Expression profiling by high throughput sequencing.
Exposure to particulate air pollution disrupts erythrocyte turnover
GEO Series GSE262906. Mus musculus. 18 samples. Type: Expression profiling by high throughput sequencing.
Evaluation of optical particulate matter sensors under realistic conditions of strong and mild urban pollution
<p>In this paper we evaluate characteristics of three optical particulate matter sensors/sizers (OPS): high-end spectrometer 11-D (Grimm, Germany), low-cost sensor OPC-N2 (Alphasense, United Kingdom) and in-house developed MAQS which is based on another low-cost sensor - PMS5003 (Plantower, China), under realistic conditions of strong and mild urban pollution. Results were compared against a reference gravimetric system, based on Gemini (Dadolab, Italy), 2.3 m3/h air sampler, with two channels (simultaneously measuring PM2.5 and PM10 concentrations). The measurements were performed in Sarajevo, the capital of Bosnia-Herzegovina, from December 2019 until May 2020. This interval is divided into period 1 - strong pollution (December 2019 - March 2020) and period 2 - mild pollution (March 2020 - May 2020). The city of Sarajevo is one of the most polluted cities in Europe in terms of aerosols: the average concentration of PM2.5 during the period 1 was 83 ug/m3, with daily aveverage values exceeding 500 ug/m3. During period 2, the average concentration of PM2.5 was 20 ug/m3. These conditions represent a good opportunity to<br> test optical devices against reference instrument in a wide range of ambient particulate matter (PM) concentrations. The effect of an in-house developed diffusion dryer for 11-D is discussed as well. In order to analyze the mass distribution of aerosols, a scanning mobility particle sizer (SMPS) spectrometer, which together with the 11-D spectrometer gives the full spectrum from nanoparticles of diameter 10 nm to coarse particles of diameter 35 um, was used. All tested devices showed excellent correlation with the reference instrument in period 1, with R^2 values between 0.90 and 0.99 for daily average PM concentrations. However, in period 2, where the range of concentrations was much narrower, R^2 values decreased significantly, to values from 0.28 to 0.92. We have also included results of a 13.5 month long-term comparison of our MAQS sensor with a nearby beta attenuation monitor (BAM) 1020 (Met One Instruments, USA) operated by the United States Environmental Protection Agency (US EPA), which showed similar correlation and no observable change of performance over time.</p> <p>This dataset contains:<br> 1. preliminary.csv - hourly average PM2.5 and PM10 values for 8 MAQS sensors during the preliminary test period<br> 2. meteo.csv - ambient air temperature and relative humidity, hourly average values<br> 3. PM_reference_timeseries.csv - timeseries of data from reference air sampler, including PM2.5, PM10, average air temperature, average air pressure, actual air volume and mass difference (loaded-blank) of each filter<br> 4. main.csv - comparisons of 11-D, OPC-N2 and MAQS sensor against the reference during the campaign<br> 5. dryer_effect.csv - hourly average data of ambient air vs 11-D internal, temperature and relative humidity. Dryer was installed on 03/12/2020 15:30<br> 6. SMPS.zip (4 files) - wide-range spectrometer (SMPS with 11-D) csv files for density plots: time, Log(Dp) and concentration values (both absolute and normalized), based on hourly average data<br> 7. histograms.zip (8 files) - selected wide-range histograms, based on hourly average data from SMPS and 11-D<br> 8. BAM.zip (5 files) - long-term comparisons of MAQS sensor with a nearby BAM. Hourly (BAM1.csv), daily (BAM2.csv) and monthly (BAM3.csv) average values and comaparison of two MAQS sensors 300 meters apart, hourly (BAM4.csv) and daily (BAM5.csv) values<br> </p>
Developmental exposure to air pollution particulates increases asthma severity, alters the RNA transcriptome, and DNA methylation in female adult progeny [WGMS]
GEO Series GSE285243. Mus musculus. 29 samples. Type: Methylation profiling by high throughput sequencing.
Particulate air pollution enhanced dry and moist heat stress during the 2015 mega heatwave in India
<p>The dataset originates from two simulations: one defined as the Warming Aerosol Effect (BASELINE) and the other as no Warming Aerosol Effect (SENSITIVE). The short name of each variable is provided in brackets as it appears in the file name. The simulation name is also included in the file name to distinguish between the two simulations. The list of processed output variables is as follows: </p> <p>1) Temperature (ta)</p> <p>2) Relative Humidity (hurs)</p> <p>3) Wet bulb temperature (wbt)</p> <p>4) Surface black carbon concentrations (sconcbc)</p> <p>5) Surface shortwave flux (rss)</p> <p>6) Winds (uas/vas)</p> <p>7) Potential temperature (tpot)</p> <p> </p>
Asthma & Exposure to Peaks in Particulate Air Pollution
ClinicalTrials.gov study NCT00013728. IPD Sharing: Not stated. Countries: 0. Publications: 0.
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