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14 results for “air quality sensors”
Air Quality Index (AQI) data from PurpleAir sensor at H.J. Andrews Experimental Forest LTER
This dataset contains hourly air quality and meteorological measurements collected from a PurpleAir sensor deployed at the H.J. Andrews Experimental Forest Long Term Ecological Research (LTER) site. The data includes four variables: timestamp (in Pacific Time), relative humidity (%), temperature (°C), and particulate matter concentrations (PM2.5 in µg/m³ using the CF=1 correction factor). The sensor provides continuous monitoring of local air quality conditions, with particular focus on fine particulate matter that can impact ecosystem health and visibility. Data are recorded at hourly intervals and timestamped in ISO 8601 format with UTC offset. PM2.5 values are reported using PurpleAir's CF=1 (Correction Factor 1) algorithm, which is optimized for atmospheric particulate matter. This dataset supports long-term environmental monitoring objectives at the Andrews Forest LTER and provides baseline air quality data for research on atmospheric conditions, wildfire smoke impacts, and climate-ecosystem interactions in Pacific Northwest forest ecosystems.
Minneapolis-St. Paul Air Quality Sensor output 2024-2025
In the summers since field research at the MSP LTER began, wildfire smoke has led to record highs in Minnesota's Air Quality Index and there has been increasing public concern over the acute and long-term impacts of exposure to toxins in the air. In 2024, the MSP LTER acquired a small network of PurpleAir sensors to place in key areas alongside transplanted lichens that are also used in air quality research. In addition to Particulate Matter, the sensor models used here also are equipped with experimental VOC detection (Volatile Organic Compounds). PurpleAir sensors were collocated with lichens at two locations in St. Paul MN, one in Roseville MN, and one at Cedar Creek Ecosystem Reserve in Northern Anoka County, MN. After lichen material was collected, the sensors remained at the site to keep reporting to community air quality tracking maps. Data from the sensors is being regularly appended to a file on the MSP LTER GitHub page and is planned to be archived long-term.
COMPAIR traffic and air quality sensor data
<p>Sensor data regarding traffic and air quality was gathered as part of the <a href="https://cordis.europa.eu/project/id/101036563">EU Horizon2020 COMPAIR project</a> in Europe. The pilot cities/regions are Berlin, Athens, Sofia, Plovdiv, and Flanders.<br><br>During the project, the data was published through an <a href="https://sensorthings.wecompair.eu/FROST-Server/v1.1/Things">OGC SensorThings API</a>. To persist after the project, the air quality related are available as CSV exports, with the retention of the API's structure (Location, Thing, Datastream, Sensor, ObservedProperty, and Observation). Observations about air quality contain sensor readings regarding nitrodioxide (NO2), black carbon (BC), particulate matter (PM1.0, PM2.5 and PM10), humidity and temperature. The NO2 observations are calibrated data streams.<br><br>The traffic observations remain available through the <a href="https://app.swaggerhub.com/apis-docs/telraam/Telraam-API/1.2.0">API of the Telraam platform</a>.<br><br><br></p>
Dataset for "LoRa Sensor Network Development for Air Quality Monitoring or Detecting Gas Leakage Events; DOI: 10.3390/s20216225"
<p>This excel file contains the raw data used in the paper " LoRa Sensor Network Development for Air Quality Monitoring or Detecting Gas Leakage Events; DOI: 10.3390/s20216225 " In particular it comprises sensor measurements and pollutant data from the automated air quality monitoring stations in the Tarragona area.</p>
AirHeritage Datalake: Multi-site, Multi-season, Multi Unit dataset including Fixed and Mobile Citizen science data from networked Air Quality Low-Cost Multi-Sensors devices and reference stations
<p>This datalake comprises several datasets from <strong>37 networked low cost air quality multisensors</strong> (<strong>30</strong> <strong>mobile</strong> ENEA MONICA(tm) + <strong>7</strong> <strong>fixed</strong>) along with <strong>3</strong> (fixed) + <strong>1</strong> (mobile) <strong>reference stations</strong> operated by Campania Regional Envronmental Protection Agency. The datalake is organized in 3 main directories respectively related to fixed nodes, mobile nodes and nearby reference stations including a mobile laboratory used for colocation campaigns; each subdirectory include its own metadata description file.</p> <p>Data, curated by Energy and Data Science Laboratory of ENEA, include multi-weeks colocation periods when low cost devices have been colocated with reference stations as well as operational periods during which sensors are deployed for fixed or mobile monitoring campaigns. Data have been recorded during 2021 and 2022 in a<strong> pervasive, multi-site, multi-seasonal deployment</strong> in Portici, a densely populated small area city (4km2, 55k + inhabitants) located 7km south of Naples, Italy.</p> <p>The datalake consists in actual sensors and reference intrumentations timeseries along with metadata description files with deployment dates and location data. The dataset files include high sampling frequency raw sensor data of quality-controlled sensor network along with co-located reference stations data sets. Sensor data include electrochemical sensors data (intended target pollutants: NO2, O3, CO), Optical sensor data (PM2.5, PM10, PM1) readings along with meteorological parameters. .</p> <p>Further description of sensors and reference instruments are reported in the accompanying paper (see citation request).</p> <p>The dataset can be used for </p> <ul> <li> <strong>advanced (remote/universal/in field) data driven calibration strategies</strong> test or development including <strong>machine learning </strong>models</li> <li><strong>mobile opportunistic data fusion</strong> methods development</li> <li><strong>geomatics and data assimilation</strong> models studies</li> </ul> <p>as well as low cost sensor characterization performance studies. </p>
Unraveling a black box: An open-source methodology for the field calibration of small air quality sensors
<p>This repository contains data for the manuscript: "Unraveling a black box: An open-source methodology for the field calibration of small air quality sensors."</p> <p> </p> <p>This includes:</p> <p>Raw data from the low-cost prototype EarthSense Zephyrs, as well as raw data from reference instrumentation.</p> <p>SC stands for "Summer Campaign" and WC stands for "Winter Campaign", denoting the two different campaigns assessed in this study.</p> <p> </p> <p><strong>Abstract</strong></p> <p>The last two decades have seen substantial technological advances in the development of low-cost air pollution instruments using small sensors. While their use continues to spread across the field of atmospheric chemistry, challenges remain in ensuring data quality and comparability of calibration methods. This study introduces a seven-step methodology for the field calibration of low-cost sensors using reference instrumentation with user-friendly guidelines, open access code, and a discussion of common barriers to such an approach. The methodology has been developed and is applicable for gas-phase pollutants, such as for the measurement of nitrogen dioxide (NO<sub>2</sub>) or ozone (O<sub>3</sub>). A full example of the application of this methodology to a case study in an urban environment using both Multiple Linear Regression (MLR) and the Random Forest (RF) machine-learning technique is presented with relevant R code provided, including error estimation. In this case, we have applied it to the calibration of metal oxide gas-phase sensors (MOS). Results reiterate previous findings that MLR and RF are similarly accurate, though with differing limitations. The methodology presented here goes a step further than most studies by including explicit, transparent steps for addressing model selection, validation, and tuning, as well as addressing the common issues of autocorrelation and multicollinearity. We also highlight the need for standardized reporting of methods for data cleaning and flagging, model selection and tuning, and model metrics. In the absence of a standardized methodology for the calibration of low-cost sensors, we suggest a number of best practices for future studies using low-cost sensors to ensure greater comparability of research.</p>
Data from multi-sensor devices and reference station to monitoring urban air quality
<p>Data from electrochemical and optical sensors.</p> <h3>Files names</h3> <ul> <li>ECT01, ECT02, ECT06, ECT07 = device name</li> <li>ISSEP = reference station <ul> <li>"c" = calibration data</li> <li>"v" = validation data</li> </ul> </li> </ul> <h3>Variable description</h3> <table> <tbody> <tr> <td><strong>Electrochemical sensor</strong></td> <td><strong>Optical sensor</strong></td> <td><strong>Probe</strong></td> <td><strong>Reference</strong></td> </tr> <tr> <td> <p>AE = auxiliary electrode (mV)</p> <p>WE = working electrode (mV)</p> <p>N = temperature correction </p> <ul> <li>ch0 = CO sensor</li> <li>ch1 = OX sensor</li> <li>ch2 = NO2 sensor</li> <li>ch3 = NO sensor</li> </ul> </td> <td> <p>PM1, PM2.5 and PM10 in µg/m³</p> </td> <td> <p>Prs_mbar = pressure (mbar)</p> <p>Temp = temperature (°C)</p> <p>RH = relative humidity (%)</p> </td> <td> <p>DV30 = wind direction @ 30m (°)</p> <p>HR = relative humidity (%)</p> <p>NO, NO2, O3, PM10 and PM2.5 (µg/m³)</p> <p>Precipita = precipitation (mm)</p> <p>TC3 = temperature @ 3m (°C)</p> <p>VV30 = wind speed @ 30m (m/s)</p> </td> </tr> </tbody> </table> <p> </p>
SensEURCity: A multi-city air quality dataset collected using networks of open low-cost sensor systems
<p>We provide a unique curated dataset of urban air quality measurements acquired using dense networks of low-cost sensor systems in three European cities for the years 2020 and 2021. The dataset includes the raw sensor data of quality-controlled sensor networks along with co-located reference data sets. Sensor data are collected using the AirSensEUR sensor system, including sensors to monitor NO, NO2, O3, CO, PM2.5, PM10, PM1, CO2, and meteorological parameters. In total, 85 sensor systems were deployed throughout the years 2020 and 2021 in three European cities (Antwerp , Oslo and Zagreb), resulting in a dataset comprising different meteorological and ambient conditions. The main data collection included two co-location campaigns in different seasons at an air quality monitoring station in each city and a deployment at different locations in each city (including also locations at other air quality monitoring stations). The dataset consists of data files with sensor and reference data, and metadata files with description of locations, deployment dates and description of sensors and reference instruments. </p>
Dataset for "Exposure and environmental engagement: A pilot integrating wearable sensors, air quality and citizen science"
<p>The dataset contains anonymised readings of 7 citizens taking air quality measurements using PlumeLabs Flow 2 monitor. Data is for Falmouth/Penryn, and Bristol and it was collected between January 26, 2022 and March 9, 2022.</p> <p>CSV file:</p> <ul> <li>latitude: unit degrees, positive values indicate North hemisphere.</li> <li>longitude, unit degrees, positive values indicate East.</li> <li>AQI: PlumeLabs' Air Quality Index.</li> <li>site: A refers to Falmouth/Penryn(UK), B refers to Bristol (UK).</li> <li>count: auxiliary variable that indicates that the record was comprised of a single reading.</li> </ul> <p>Jupyter notebook: The air quality analysis was conducted with Python 3.9.16 alongside numpy 1.24.3, pandas 2.0.2, matplotlib 3.7.1, and cartopy 0.21.1 (background tiles by OpenStreetMaps).</p>
Updated Smoke Exposure Estimate for Indonesian Peatland Fires using a Network of Low-cost PM2.5 sensors and a regional air quality model - Model Simulation Data
<p>WRF-Chem simulated daily mean PM2.5 concentrations for:</p> <p>1) with fires </p> <p>2) without fires</p> <p>simulations. </p>
Updated Smoke Exposure Estimate for Indonesian Peatland Fires using a Network of Low-cost PM2.5 sensors and a regional air quality model - Purple Air data
<p>Daily mean PM2.5 concentrations collected by Purple Air sensors between 2023-08-16 and 2023-12-01. Concentrations have been RH adjusted using the Nilson et al (2022) adjustment. </p>
Dataset for "Improving data quality of low-cost light-scattering PM sensors: Towards automatic air quality monitoring in urban environments"
<p>The dataset contains the data used in the article "Improving data quality of low-cost light-scattering PM sensors: Towards automatic air quality monitoring in urban environments".</p> <p>A low-cost monitoring system composed of 14 monitoring stations was positioned at the official monitoring station of Torino Rubino in the city of Turin (Italy). The official station is managed by the environmental agency ARPA Piemonte.</p> <p>Each low-cost station contains four low-cost light-scattering PM sensors (Honeywell HPMA115S0-XXX), one temperature and relative humidity sensor (DHT22), and one atmospheric pressure sensor (BME/BMP280).<br>The sampling time of the PM sensors was set to one second, while the other sensors generated measurements every 3-4 seconds.</p> <p>The official monitoring station uses both a gravimetric and a beta attenuation instrument for measuring PM.</p> <p>The data contained in this dataset was collected from October 2020 to November 2021. It contains the PM2.5, relative humidity, and temperature measurements of the low-cost monitoring system and the official measurements of the beta attenuation device.</p> <p>Measurements of low-cost sensors are expressed in UTC, while official measurements are expressed in UTC+1.</p> <p>Official PM measurements can be also found at https://aria.ambiente.piemonte.it/qualita-aria/dati.</p>
The Effectiveness of Air Quality Sensor in Elderly Residential Setting
ClinicalTrials.gov study NCT05837585. IPD Sharing: YES. Countries: 1. Publications: 0.
Field evaluation of low-cost electrochemical air quality gas sensors under extreme temperature and relative humidity conditions
<p>1. Files starting with Alphasense (i.e. Alphasense_raw_CO, Alphasense_raw_NO2, Alphasense_raw_O3, Alphasense_raw_SO2) correspond to the raw measurements obtained from the Alphasense low-cost sensors for CO, NO2, O3 and SO2 respectively. These files have five (5) columns each. The first column is the output of the working electrode (mV), the second column is the output of the auxiliary electrode (mV), the third column is the temperature measured by the reference instrument (C), the fourth column is the relative humidity measured by the reference instrument (%) and the fifth column is the matlab time.</p> <p>2. Files starting with Winsen (i.e. Winsen _raw_CO, Winsen _raw_NO2, Winsen _raw_O3, Winsen _raw_SO2) correspond to the raw measurements obtained from the Winsen low-cost sensors for CO, NO2, O3 and SO2 respectively. These files have four (4) columns each. The first column is the concentration output (ppb), the second column is the temperature measured by the reference instrument (C), the third column is the relative humidity measured by the reference instrument (%) and the fourth column is the matlab time.</p> <p>3. The file "reference_data" corresponds to the gas measurements obtained from the reference instruments. This file has five (5) columns. The first column is the CO concentration (ppb), the second column is the NO2 concentration (ppb), the third column is the O3 concentration (ppb), the fourth column is the SO2 concentration and the fifth column is the matlab time.</p>
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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