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dc.contributor.author
Diez, Sebastian
dc.contributor.author
Lacy, Stuart
dc.contributor.author
Urquiza, Josefina
dc.contributor.author
Edwards, Pete
dc.date.available
2024-12-16T10:56:45Z
dc.date.issued
2024-08
dc.identifier.citation
Diez, Sebastian; Lacy, Stuart; Urquiza, Josefina; Edwards, Pete; QUANT: a long-term multi-city commercial air sensor dataset for performance evaluation; Nature Publishing Group; Scientific Data; 11; 1; 8-2024; 1-16
dc.identifier.issn
2052-4463
dc.identifier.uri
http://hdl.handle.net/11336/250528
dc.description.abstract
The QUANT study represents the most extensive open-access evaluation of commercial air quality sensor systems to date. This comprehensive study assessed 49 systems from 14 manufacturers across three urban sites in the UK over a three-year period. The resulting open-access dataset captures high time-resolution measurements of a variety of gasses (NO, NO2, O3, CO, CO2), particulate matter (PM1, PM2.5, PM10), and key meteorological parameters (humidity, temperature, atmospheric pressure). The quality and scope of the dataset is enhanced by reference monitors’ data and calibrated products from sensor manufacturers across the three sites. This publicly accessible dataset serves as a robust and transparent resource that details the methods used for data collection and procedures to ensure dataset integrity. It provides a valuable tool for a wide range of stakeholders to analyze the performance of air quality sensors in real-world settings. Policymakers can leverage this data to refine sensor deployment guidelines and develop standardized protocols, while manufacturers can utilize it as a benchmark for technological innovation and product certification. Moreover, the dataset has supported the development of a UK code of practice, and the certification of one of the participating companies, underscoring the dataset’s utility and reliability.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Nature Publishing Group
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by/2.5/ar/
dc.subject
OPEN ACCESS DATA
dc.subject
AIR QUALITY
dc.subject
LOW COST SENSOR
dc.subject.classification
Meteorología y Ciencias Atmosféricas
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Ciencias de la Tierra y relacionadas con el Medio Ambiente
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
QUANT: a long-term multi-city commercial air sensor dataset for performance evaluation
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2024-11-26T11:26:06Z
dc.journal.volume
11
dc.journal.number
1
dc.journal.pagination
1-16
dc.journal.pais
Reino Unido
dc.description.fil
Fil: Diez, Sebastian. Universidad del Desarrollo; Chile
dc.description.fil
Fil: Lacy, Stuart. University of York; Reino Unido
dc.description.fil
Fil: Urquiza, Josefina. Universidad Tecnologica Nacional. Facultad Regional Cordoba. Secretaria de Posgrado.; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina
dc.description.fil
Fil: Edwards, Pete. University of York; Reino Unido
dc.journal.title
Scientific Data
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.nature.com/articles/s41597-024-03767-2
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1038/s41597-024-03767-2
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