Monitoring urban air pollution using low-cost sensor devices

Ilustraciones, gráficas, tablas

Autores:: Rios Martinez, Jenny Rocio

Tipo de recurso:: Doctoral thesis

Fecha de publicación:: 2023

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: eng

id	UNACIONAL2_679c87ed39134fdb72a8295a1c375281
oai_identifier_str	oai:repositorio.unal.edu.co:unal/85696
network_acronym_str	UNACIONAL2
network_name_str	Universidad Nacional de Colombia
repository_id_str
dc.title.eng.fl_str_mv	Monitoring urban air pollution using low-cost sensor devices
dc.title.translated.spa.fl_str_mv	Monitoreo de la contaminación del aire urbano utilizando dispositivos sensores de bajo costo
title	Monitoring urban air pollution using low-cost sensor devices
spellingShingle	Monitoring urban air pollution using low-cost sensor devices 000 - Ciencias de la computación, información y obras generales::003 - Sistemas Contaminación del aire Minería de datos Contaminación de aire Aprendizaje de máquinas Sensores de bajo costo Calibración de sensores Air pollution Particulate matter Low-cost sensors Sensor calibration Machine learning Data mining Sensores
title_short	Monitoring urban air pollution using low-cost sensor devices
title_full	Monitoring urban air pollution using low-cost sensor devices
title_fullStr	Monitoring urban air pollution using low-cost sensor devices
title_full_unstemmed	Monitoring urban air pollution using low-cost sensor devices
title_sort	Monitoring urban air pollution using low-cost sensor devices
dc.creator.fl_str_mv	Rios Martinez, Jenny Rocio
dc.contributor.advisor.none.fl_str_mv	Yris, Olaya Morales
dc.contributor.author.none.fl_str_mv	Rios Martinez, Jenny Rocio
dc.contributor.researchgroup.spa.fl_str_mv	Ciencias de la Decision
dc.subject.ddc.spa.fl_str_mv	000 - Ciencias de la computación, información y obras generales::003 - Sistemas
topic	000 - Ciencias de la computación, información y obras generales::003 - Sistemas Contaminación del aire Minería de datos Contaminación de aire Aprendizaje de máquinas Sensores de bajo costo Calibración de sensores Air pollution Particulate matter Low-cost sensors Sensor calibration Machine learning Data mining Sensores
dc.subject.lemb.none.fl_str_mv	Contaminación del aire Minería de datos
dc.subject.proposal.none.fl_str_mv	Contaminación de aire Aprendizaje de máquinas Sensores de bajo costo Calibración de sensores
dc.subject.proposal.eng.fl_str_mv	Air pollution Particulate matter Low-cost sensors Sensor calibration Machine learning Data mining
dc.subject.wikidata.none.fl_str_mv	Sensores
description	Ilustraciones, gráficas, tablas
publishDate	2023
dc.date.issued.none.fl_str_mv	2023-02-13
dc.date.accessioned.none.fl_str_mv	2024-02-13T18:22:10Z
dc.date.available.none.fl_str_mv	2024-02-13T18:22:10Z
dc.type.spa.fl_str_mv	Trabajo de grado - Doctorado
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/doctoralThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.coar.spa.fl_str_mv	http://purl.org/coar/resource_type/c_db06
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TD
format	http://purl.org/coar/resource_type/c_db06
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/85696
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/85696 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	eng
language	eng
dc.relation.indexed.spa.fl_str_mv	LaReferencia
dc.relation.references.spa.fl_str_mv	Agarwal, A., Kaushik, A., Kumar, S., & Mishra, R. K. (2020). Comparative study on air quality status in Indian and Chinese cities before and during the COVID-19 lockdown period. Air Quality, Atmosphere & Health, 13(10), 1167–1178. https://doi.org/10.1007/s11869-020-00881-z Aggarwal, C. C. (2015). Data Mining. Springer International Publishing. https://doi.org/10.1007/978-3-319-14142-8 Alcaldía Mayor de Bogotá D.C., S. de A. (2022). Red de Monitoreo de Calidad del Aire de Bogotá—RMCAB. Secretaría Distrital de Ambiente. https://ambientebogota.gov.co/red-de-monitoreo-de-calidad-del-aire-de-bogota-rmcab Alfano, B., Barretta, L., Del Giudice, A., De Vito, S., Di Francia, G., Esposito, E., Formisano, F., Massera, E., Miglietta, M. L., & Polichetti, T. (2020). A Review of Low-Cost Particulate Matter Sensors from the Developers’ Perspectives. Sensors, 20(23), Article 23. https://doi.org/10.3390/s20236819 AMVA. (2018). Plan de acción para la implementación del Plan Operacional para enfrentar Episodios de Contaminación Atmosférica (POECA) en el área Metropolitana del Valle de Aburrá. https://www.metropol.gov.co/ambiental/calidad-del-aire/Documents/POECA/Plan-de-Accion-POECA-2019.pdf AMVA. (2019). Condiciones especiales del valle de aburrá. https://www.metropol.gov.co:443/ambientales/calidad-del-aire/generalidades/condiciones-especiales AMVA. (2021). Ciudadanos Científicos. Programa local de ciencia, educación y tecnología. https://www.metropol.gov.co:443/ambiental/siata/Paginas/ciudadanos-cientificos.aspx AMVA. (2022). Encuesta origen destino. https://www.metropol.gov.co:443/observatorio/Paginas/encuestaorigendestino.aspx Apte, J. S., Marshall, J. D., Cohen, A. J., & Brauer, M. (2015). Addressing Global Mortality from Ambient PM 2.5. Environmental Science & Technology, 49(13), 8057–8066. https://doi.org/10.1021/acs.est.5b01236 Área Metropolitana del Valle de Aburrá. (2022). Encuesta de Origen—Destino. https://www.metropol.gov.co/observatorio/Paginas/encuestaorigendestino.aspx Arfire, A., Marjovi, A., & Martinoli, A. (2016). Enhancing measurement quality through active sampling in mobile air quality monitoring sensor networks. 2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), 1022–1027. https://doi.org/10.1109/AIM.2016.7576904 Astudillo, G. D., Garza-Castanon, L. E., & Minchala Avila, L. I. (2020). Design and Evaluation of a Reliable Low-Cost Atmospheric Pollution Station in Urban Environment. IEEE Access, 8, 51129–51144. https://doi.org/10.1109/ACCESS.2020.2980736 Austin, E., Novosselov, I., Seto, E., & Yost, M. G. (2015). Laboratory Evaluation of the Shinyei PPD42NS Low-Cost Particulate Matter Sensor. PLOS ONE, 10(9), e0137789. https://doi.org/10.1371/journal.pone.0137789 Badura, M., Batog, P., Drzeniecka-Osiadacz, A., & Modzel, P. (2019). Regression methods in the calibration of low-cost sensors for ambient particulate matter measurements. SN Applied Sciences, 1(6), 622. https://doi.org/10.1007/s42452-019-0630-1 Bai, L., Huang, L., Wang, Z., Ying, Q., Zheng, J., Shi, X., & Hu, J. (2020). Long-term field Evaluation of Low-cost Particulate Matter Sensors in Nanjing. Aerosol and Air Quality Research, 20(2), 242–253. https://doi.org/10.4209/aaqr.2018.11.0424 BAM-1020 Continuous Particulate Monitor. (2021). Met One Instruments. https://metone.com/products/bam-1020/ Barcelo-Ordinas, J. M., Doudou, M., Garcia-Vidal, J., & Badache, N. (2019). Self-calibration methods for uncontrolled environments in sensor networks: A reference survey. Ad Hoc Networks, 88, 142–159. https://doi.org/10.1016/j.adhoc.2019.01.008 Báthory, C., Dobó, Z., Garami, A., Palotás, Á., & Tóth, P. (2022). Low-cost monitoring of atmospheric PM—development and testing. Journal of Environmental Management, 304, 114158. https://doi.org/10.1016/j.jenvman.2021.114158 Benabbas, A., Geißelbrecht, M., Nikol, G. M., Mahr, L., Nähr, D., Steuer, S., Wiesemann, G., Müller, T., Nicklas, D., & Wieland, T. (2019). Measure particulate matter by yourself: Data-quality monitoring in a citizen science project. Journal of Sensors and Sensor Systems, 8(2), 317–328. https://doi.org/10.5194/jsss-8-317-2019 Berghmans, P., Bleux, N., Panis, L. I., Mishra, V. K., Torfs, R., & Van Poppel, M. (2009). Exposure assessment of a cyclist to PM10 and ultrafine particles. Science of The Total Environment, 407(4), 1286–1298. https://doi.org/10.1016/j.scitotenv.2008.10.041 Bergmann, M. L., Andersen, Z. J., Amini, H., Khan, J., Lim, Y. H., Loft, S., Mehta, A., Westendorp, R. G., & Cole-Hunter, T. (2022). Ultrafine particle exposure for bicycle commutes in rush and non-rush hour traffic: A repeated measures study in Copenhagen, Denmark. Environmental Pollution, 294, 118631. https://doi.org/10.1016/j.envpol.2021.118631 Boniardi, L., Borghi, F., Straccini, S., Fanti, G., Campagnolo, D., Campo, L., Olgiati, L., Lioi, S., Cattaneo, A., Spinazzè, A., Cavallo, D. M., & Fustinoni, S. (2021). Commuting by car, public transport, and bike: Exposure assessment and estimation of the inhaled dose of multiple airborne pollutants. Atmospheric Environment, 262, 118613. https://doi.org/10.1016/j.atmosenv.2021.118613 Borghi, F., Fanti, G., Cattaneo, A., Campagnolo, D., Rovelli, S., Keller, M., Spinazzè, A., & Cavallo, D. M. (2020). Estimation of the inhaled dose of airborne pollutants during commuting: Case study and application for the general population. International Journal of Environmental Research and Public Health, 17(17), 1–14. Scopus. https://doi.org/10.3390/ijerph17176066 Božilov, A., Tasić, V., Živković, N., Lazović, I., Blagojević, M., Mišić, N., & Topalović, D. (2022). Performance assessment of NOVA SDS011 low-cost PM sensor in various microenvironments. Environmental Monitoring and Assessment, 194(9), 595. https://doi.org/10.1007/s10661-022-10290-7 Brunekreef, B., & Holgate, S. T. (2002). Air pollution and health. The Lancet, 360(9341), 1233–1242. https://doi.org/10.1016/S0140-6736(02)11274-8 Builes-Jaramillo, A., Gómez-Bedoya, J., Lopera-Uribe, S., & Fajardo-Castaño, V. (2020). Hotspots, daily cycles and average daily dose of PM2.5 in a cycling route in Medellin. Revista Facultad de Ingeniería Universidad de Antioquia, 96, 87–99. https://doi.org/10.17533/udea.redin.20191153 Bulot, F. M. J., Johnston, S. J., Basford, P. J., Easton, N. H. C., Apetroaie-Cristea, M., Foster, G. L., Morris, A. K. R., Cox, S. J., & Loxham, M. (2019). Long-term field comparison of multiple low-cost particulate matter sensors in an outdoor urban environment. Scientific Reports, 9(1), 7497. https://doi.org/10.1038/s41598-019-43716-3 Burnett, R. T., Pope, C. A., Ezzati, M., Olives, C., Lim, S. S., Mehta, S., Shin, H. H., Singh, G., Hubbell, B., Brauer, M., Anderson, H. R., Smith, K. R., Balmes, J. R., Bruce, N. G., Kan, H., Laden, F., Prüss-Ustün, A., Turner, M. C., Gapstur, S. M., … Cohen, A. (2014). An Integrated Risk Function for Estimating the Global Burden of Disease Attributable to Ambient Fine Particulate Matter Exposure. Environmental Health Perspectives, 122(4), 397–403. https://doi.org/10.1289/ehp.1307049 Bulot, F. M. J., Johnston, S. J., Basford, P. J., Easton, N. H. C., Apetroaie-Cristea, M., Foster, G. L., Morris, A. K. R., Cox, S. J., & Loxham, M. (2019). Long-term field comparison of multiple low-cost particulate matter sensors in an outdoor urban environment. Scientific Reports, 9(1), 7497. https://doi.org/10.1038/s41598-019-43716-3 Bulot, F. M. J., Ossont, S. J., Morris, A. K. R., Basford, P. J., Easton, N. H. C., Mitchell, H. L., Foster, G. L., Cox, S. J., & Loxham, M. (2023). Characterisation and calibration of low-cost PM sensors at high temporal resolution to reference-grade performance. Heliyon, 9(5), e15943. https://doi.org/10.1016/j.heliyon.2023.e15943 Camprodon, G., González, Ó., Barberán, V., Pérez, M., Smári, V., de Heras, M. Á., & Bizzotto, A. (2019). Smart Citizen Kit and Station: An open environmental monitoring system for citizen participation and scientific experimentation. HardwareX. https://doi.org/10.1016/j.ohx.2019.e00070 CanAirIO. (2021). CanAirIO. Citizen Network for Monitoring Air Quality. https://canair.io/index.html Carvalho, H. (2017). The global burden of air pollution-associated deaths—How many are needed for countries to react? The Lancet Planetary Health, 1(5), e179. https://doi.org/10.1016/S2542-5196(17)30076-1 Castell, N., Dauge, F. R., Schneider, P., Vogt, M., Lerner, U., Fishbain, B., Broday, D., & Bartonova, A. (2017). Can commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates? Environment International, 99, 293–302. https://doi.org/10.1016/j.envint.2016.12.007 Cavaliere, A., Carotenuto, F., Di Gennaro, F., Gioli, B., Gualtieri, G., Martelli, F., Matese, A., Toscano, P., Vagnoli, C., & Zaldei, A. (2018). Development of Low-Cost Air Quality Stations for Next Generation Monitoring Networks: Calibration and Validation of PM2.5 and PM10 Sensors. Sensors, 18(9), 2843. https://doi.org/10.3390/s18092843 Cepeda, M., Schoufour, J., Freak-Poli, R., Koolhaas, C. M., Dhana, K., Bramer, W. M., & Franco, O. H. (2017). Levels of ambient air pollution according to mode of transport: A systematic review. The Lancet Public Health, 2(1), e23–e34. https://doi.org/10.1016/S2468-2667(16)30021-4 Chen, C.-H., Wu, C.-D., Chiang, H.-C., Chu, D., Lee, K.-Y., Lin, W.-Y., Yeh, J.-I., Tsai, K.-W., & Guo, Y.-L. L. (2019). The effects of fine and coarse particulate matter on lung function among the elderly. Scientific Reports, 9(1), 14790. https://doi.org/10.1038/s41598-019-51307-5 Chen, H., Covert, I. C., Lundberg, S. M., & Lee, S.-I. (2023). Algorithms to estimate Shapley value feature attributions. Nature Machine Intelligence, 5(6), Article 6. https://doi.org/10.1038/s42256-023-00657-x Chen, L.-J., Ho, Y.-H., Lee, H.-C., Wu, H.-C., Liu, H.-M., Hsieh, H.-H., Huang, Y.-T., & Lung, S.-C. C. (2017). An Open Framework for Participatory PM2.5 Monitoring in Smart Cities. IEEE Access, 5, 14441–14454. https://doi.org/10.1109/ACCESS.2017.2723919 Chen, Z., Barros, C. P., & Gil-Alana, L. A. (2016). The persistence of air pollution in four mega-cities of China. Habitat International, 56, 103–108. https://doi.org/10.1016/j.habitatint.2016.05.004 Clements, A., Duvall, R., & Dye, T. (2022). The Enhanced Air Sensor Guidebook. https://cfpub.epa.gov/si/si_public_record_Report.cfm?dirEntryId=356426&Lab=CEMM Cohen, A. J., Brauer, M., Burnett, R., Anderson, H. R., Frostad, J., Estep, K., Balakrishnan, K., Brunekreef, B., Dandona, L., Dandona, R., Feigin, V., Freedman, G., Hubbell, B., Jobling, A., Kan, H., Knibbs, L., Liu, Y., Martin, R., Morawska, L., … Forouzanfar, M. H. (2017). Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: An analysis of data from the Global Burden of Diseases Study 2015. The Lancet, 389(10082), 1907–1918. https://doi.org/10.1016/S0140-6736(17)30505-6 Cole-Hunter, T., Jayaratne, R., Stewart, I., Hadaway, M., Morawska, L., & Solomon, C. (2013). Utility of an alternative bicycle commute route of lower proximity to motorised traffic in decreasing exposure to ultra-fine particles, respiratory symptoms and airway inflammation – a structured exposure experiment. Environmental Health, 12(1), 29. https://doi.org/10.1186/1476-069X-12-29 Collier-Oxandale, A., Feenstra, B., Papapostolou, V., Zhang, H., Kuang, M., Der Boghossian, B., & Polidori, A. (2020). Field and laboratory performance evaluations of 28 gas-phase air quality sensors by the AQ-SPEC program. Atmospheric Environment, 220, 117092. https://doi.org/10.1016/j.atmosenv.2019.117092 Commodore, A., Wilson, S., Muhammad, O., Svendsen, E., & Pearce, J. (2017). Community-based participatory research for the study of air pollution: A review of motivations, approaches, and outcomes. Environmental Monitoring and Assessment, 189(8), 378. https://doi.org/10.1007/s10661-017-6063-7 Correia, C., Martins, V., Cunha-Lopes, I., Faria, T., Diapouli, E., Eleftheriadis, K., & Almeida, S. M. (2020). Particle exposure and inhaled dose while commuting in Lisbon. Environmental Pollution, 257, 113547. https://doi.org/10.1016/j.envpol.2019.113547 Crilley, L. R., Shaw, M., Pound, R., Kramer, L. J., Price, R., Young, S., Lewis, A. C., & Pope, F. D. (2018). Evaluation of a low-cost optical particle counter (Alphasense OPC-N2) for ambient air monitoring. Atmospheric Measurement Techniques, 11(2), 709–720. https://doi.org/10.5194/amt-11-709-2018 Cross, E. S., Williams, L. R., Lewis, D. K., Magoon, G. R., Onasch, T. B., Kaminsky, M. L., Worsnop, D. R., & Jayne, J. T. (2017). Use of electrochemical sensors for measurement of air pollution: Correcting interference response and validating measurements. Atmospheric Measurement Techniques, 10(9), 3575–3588. https://doi.org/10.5194/amt-10-3575-2017 Davies, L., Fradera, R., Riesch, H., & Lakeman-Fraser, P. (2016). Surveying the citizen science landscape: An exploration of the design, delivery and impact of citizen science through the lens of the Open Air Laboratories (OPAL) programme. BMC Ecology, 16(S1), 17. https://doi.org/10.1186/s12898-016-0066-z DC1700-PM PM2.5/PM10 AQM. (2022). http://www.dylosproducts.com/dcpmaqm.html de Nazelle, A., Fruin, S., Westerdahl, D., Martinez, D., Ripoll, A., Kubesch, N., & Nieuwenhuijsen, M. (2012). A travel mode comparison of commuters’ exposures to air pollutants in Barcelona. Atmospheric Environment, 59, 151–159. https://doi.org/10.1016/j.atmosenv.2012.05.013 de Nazelle, A., Seto, E., Donaire-Gonzalez, D., Mendez, M., Matamala, J., Nieuwenhuijsen, M. J., & Jerrett, M. (2013). Improving estimates of air pollution exposure through ubiquitous sensing technologies. Environmental Pollution, 176, 92–99. https://doi.org/10.1016/j.envpol.2012.12.032 Delaney, D. G., Sperling, C. D., Adams, C. S., & Leung, B. (2008). Marine invasive species: Validation of citizen science and implications for national monitoring networks. Biological Invasions, 10(1), 117–128. https://doi.org/10.1007/s10530-007-9114-0 Deng, Q., Deng, L., Miao, Y., Guo, X., & Li, Y. (2019). Particle deposition in the human lung: Health implications of particulate matter from different sources. Environmental Research, 169, 237–245. https://doi.org/10.1016/j.envres.2018.11.014 Deng, Q., Lu, C., Norbäck, D., Bornehag, C.-G., Zhang, Y., Liu, W., Yuan, H., & Sundell, J. (2015). Early life exposure to ambient air pollution and childhood asthma in China. Environmental Research, 143, 83–92. https://doi.org/10.1016/j.envres.2015.09.032 Duvall, R., Clements, A., Hagler, G., Kamal, A., Vasu Kilaru, Goodman, L., Frederick, S., Barkjohn, K. J., VonWald, D., Greene, D., & Dye, T. (2021). Performance Testing Protocols, Metrics, and Target Values for Fine Particulate Matter Air Sensors: Use in Ambient, Outdoor, Fixed Site, Non-Regulatory Supplemental and Informational Monitoring Applications (EPA/600/R-20/280). U.S. EPA Office of Research and Development. Dylos Corporation. (2022, October 10). Dylos Corporation. Air quality monitoring innovation. http://www.dylosproducts.com/ EAFIT. (2020). Informe Anual de Calidad de Aire 2020. https://www.metropol.gov.co/ambiental/calidad-del-aire/informes_red_calidaddeaire/Informe-Anual-Aire-2020.pdf EAFIT. (2021). Informe Anual de la Calidad de Aire 2021. https://www.metropol.gov.co/ambiental/calidad-del-aire/informes_red_calidaddeaire/Informe-Anual-Aire-2021.pdf Eitzel, M. V., Cappadonna, J. L., Santos-Lang, C., Duerr, R. E., Virapongse, A., West, S. E., Kyba, C. C. M., Bowser, A., Cooper, C. B., Sforzi, A., Metcalfe, A. N., Harris, E. S., Thiel, M., Haklay, M., Ponciano, L., Roche, J., Ceccaroni, L., Shilling, F. M., Dörler, D., … Jiang, Q. (2017). Citizen Science Terminology Matters: Exploring Key Terms. Citizen Science: Theory and Practice, 2(1), 1. https://doi.org/10.5334/cstp.96 El Colombiano. (2023, April 27). Encuesta en Medellín y el Aburrá revela que se camina más, se usa mucho la moto y el transporte público pierde protagonismo. www.elcolombiano.com. https://www.elcolombiano.com/antioquia/resultados-encuesta-origen-destino-area-metropolitana-2023-HD21226562 EPA. (2014). Air Quality Index. A Guide to Air Quality and your Health. U.S. Environmental Protection Agency. Office of Air Quality Planning and Standards. https://www3.epa.gov/airnow/aqi_brochure_02_14.pdf ERG - King’s College London. (2017). Air Pollution Guide. http://www.londonair.org.uk/LondonAir/guide/WhatIsLAQN.aspx European Environment Agency. (2021). Air quality standards—European Environment Agency [Page]. https://www.eea.europa.eu/themes/air/air-quality-concentrations/air-quality-standards Fanti, G., Borghi, F., Spinazzè, A., Rovelli, S., Campagnolo, D., Keller, M., Cattaneo, A., Cauda, E., & Cavallo, D. M. (2021). Features and Practicability of the Next-Generation Sensors and Monitors for Exposure Assessment to Airborne Pollutants: A Systematic Review. Sensors, 21(13), Article 13. https://doi.org/10.3390/s21134513 Feenstra, B., Papapostolou, V., Hasheminassab, S., Zhang, H., Boghossian, B. D., Cocker, D., & Polidori, A. (2019). Performance evaluation of twelve low-cost PM2.5 sensors at an ambient air monitoring site. Atmospheric Environment, 216, 116946. https://doi.org/10.1016/j.atmosenv.2019.116946 Ferrer-Cid, P., Barcelo-Ordinas, J. M., Garcia-Vidal, J., Ripoll, A., & Viana, M. (2019). A Comparative Study of Calibration Methods for Low-Cost Ozone Sensors in IoT Platforms. IEEE Internet of Things Journal, 6(6), 9563–9571. https://doi.org/10.1109/JIOT.2019.2929594 Ferrer-Cid, P., Barcelo-Ordinas, J. M., Garcia-Vidal, J., Ripoll, A., & Viana, M. (2020). Multisensor Data Fusion Calibration in IoT Air Pollution Platforms. IEEE Internet of Things Journal, 7(4), 3124–3132. https://doi.org/10.1109/JIOT.2020.2965283 Franco, J. F., Segura, J. F., & Mura, I. (2016). Air Pollution alongside Bike-Paths in Bogotá-Colombia. Frontiers in Environmental Science, 4. https://doi.org/10.3389/fenvs.2016.00077 Gao, M., Cao, J., & Seto, E. (2015). A distributed network of low-cost continuous reading sensors to measure spatiotemporal variations of PM2.5 in Xi’an, China. Environmental Pollution, 199, 56–65. https://doi.org/10.1016/j.envpol.2015.01.013 Garcia, D., Parra, M. A., Gómez, D., Alzate, C. A., Herrera, L., & Hoyos Ortiz, C. D. (2018). Citizen Scientists of the Aburrá Valley: The results of an education, communication, and science strategy. 2018, ED54A-02. Goel, A., & Kumar, P. (2014). A review of fundamental drivers governing the emissions, dispersion and exposure to vehicle-emitted nanoparticles at signalised traffic intersections. Atmospheric Environment, 97, 316–331. https://doi.org/10.1016/j.atmosenv.2014.08.037 González, R. (2020, June 25). Arenas del Sahara afectan calidad del aire en el Valle de Aburrá. https://territoriossostenibles.com/cambio-climatico/arenas-del-sahara-afectan-calidad-del-aire-en-el-valle-de-aburra/ Guevara, M., Tena, C., Soret, A., Serradell, K., Guzmán, D., Retama, A., Camacho, P., Jaimes-Palomera, M., & Mediavilla, A. (2017). An emission processing system for air quality modelling in the Mexico City metropolitan area: Evaluation and comparison of the MOBILE6.2-Mexico and MOVES-Mexico traffic emissions. Science of The Total Environment, 584–585, 882–900. https://doi.org/10.1016/j.scitotenv.2017.01.135 Gulia, S., Shiva Nagendra, S. M., Khare, M., & Khanna, I. (2015). Urban air quality management-A review. Atmospheric Pollution Research, 6(2), 286–304. https://doi.org/10.5094/APR.2015.033 HabitatMap. (2023). Airbeam—User’s Guide. HabitatMap Is an Environmental Tech Org and Maker of AirBeam. https://www.habitatmap.org/airbeam/users-guide Hagan, D. H., Isaacman-VanWertz, G., Franklin, J. P., Wallace, L. M. M., Kocar, B. D., Heald, C. L., & Kroll, J. H. (2018). Calibration and assessment of electrochemical air quality sensors by co-location with regulatory-grade instruments. Atmospheric Measurement Techniques, 11(1), 315–328. https://doi.org/10.5194/amt-11-315-2018 Vineis, P., Forastiere, F., Saldiva, P., Yorifuji, T., & Loomis, D. (2014). Outdoor Particulate Matter Exposure and Lung Cancer: A Systematic Review and Meta-Analysis. Environmental Health Perspectives. https://doi.org/10.1289/ehp.1408092 Han, J., & Kamber, M. (2012). Data mining: Concepts and techniques (3rd ed). Elsevier. Hankey, S., & Marshall, J. D. (2015). On-bicycle exposure to particulate air pollution: Particle number, black carbon, PM 2.5 , and particle size. Atmospheric Environment, 122, 65–73. https://doi.org/10.1016/j.atmosenv.2015.09.025 Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning. Springer New York. https://doi.org/10.1007/978-0-387-84858-7 Heimann, I., Bright, V. B., McLeod, M. W., Mead, M. I., Popoola, O. A. M., Stewart, G. B., & Jones, R. L. (2015). Source attribution of air pollution by spatial scale separation using high spatial density networks of low cost air quality sensors. Atmospheric Environment, 113, 10–19. https://doi.org/10.1016/j.atmosenv.2015.04.057 Hermelin, M. (2007, August). Valle de Aburrá: ¿Quo vadis? Gestión y Ambiente, 10(2), 7–16. Hernández, M. A., Ramírez, O., Benavides, J. A., & Franco, J. F. (2021). Urban cycling and air quality: Characterizing cyclist exposure to particulate-related pollution. Urban Climate, 36. Scopus. https://doi.org/10.1016/j.uclim.2020.100767 Hernández-Paniagua, I. Y., Andraca-Ayala, G. L., Diego-Ayala, U., Ruiz-Suarez, L. G., Zavala-Reyes, J. C., Cid-Juárez, S., Torre-Bouscoulet, L., Gochicoa-Rangel, L., Rosas-Pérez, I., & Jazcilevich, A. (2018). Personal Exposure to PM2.5 in the Megacity of Mexico: A Multi-Mode Transport Study. Atmosphere, 9(2), Article 2. https://doi.org/10.3390/atmos9020057 Herrera-Mejía, L., & Hoyos, C. D. (2019). Characterization of the atmospheric boundary layer in a narrow tropical valley using remote-sensing and radiosonde observations and the WRF model: The Aburrá Valley case-study. Quarterly Journal of the Royal Meteorological Society, 145(723), 2641–2665. https://doi.org/10.1002/qj.3583 Holstius, D. M., Pillarisetti, A., Smith, K. R., & Seto, E. (2014). Field calibrations of a low-cost aerosol sensor at a regulatory monitoring site in California. Atmospheric Measurement Techniques, 7(4), 1121–1131. https://doi.org/10.5194/amt-7-1121-2014 Hoyos, C. D., Herrera-Mejía, L., Roldán-Henao, N., & Isaza, A. (2020). Effects of fireworks on particulate matter concentration in a narrow valley: The case of the Medellín metropolitan area. Environmental Monitoring and Assessment, 192(1), 6. https://doi.org/10.1007/s10661-019-7838-9 Hu, K., Rahman, A., Bhrugubanda, H., & Sivaraman, V. (2017). HazeEst: Machine Learning Based Metropolitan Air Pollution Estimation From Fixed and Mobile Sensors. IEEE Sensors Journal, 17(11), 3517–3525. https://doi.org/10.1109/JSEN.2017.2690975 Hubbell, B. J., Kaufman, A., Rivers, L., Schulte, K., Hagler, G., Clougherty, J., Cascio, W., & Costa, D. (2018). Understanding social and behavioral drivers and impacts of air quality sensor use. Science of The Total Environment, 621, 886–894. https://doi.org/10.1016/j.scitotenv.2017.11.275 Int Panis, L., de Geus, B., Vandenbulcke, G., Willems, H., Degraeuwe, B., Bleux, N., Mishra, V., Thomas, I., & Meeusen, R. (2010). Exposure to particulate matter in traffic: A comparison of cyclists and car passengers. Atmospheric Environment, 44(19), 2263–2270. https://doi.org/10.1016/j.atmosenv.2010.04.028 Jiang, Q., Kresin, F., Bregt, A. K., Kooistra, L., Pareschi, E., van Putten, E., Volten, H., & Wesseling, J. (2016). Citizen Sensing for Improved Urban Environmental Monitoring. Journal of Sensors, 2016, 1–9. https://doi.org/10.1155/2016/5656245 Jiao, W., Hagler, G., Williams, R., Sharpe, R., Brown, R., Garver, D., Judge, R., Caudill, M., Rickard, J., Davis, M., Weinstock, L., Zimmer-Dauphinee, S., & Buckley, K. (2016). Community Air Sensor Network (CAIRSENSE) project: Evaluation of low-costsensor performance in a suburban environment in the southeastern UnitedStates. Atmospheric Measurement Techniques, 9(11), 5281–5292. https://doi.org/10.5194/amt-9-5281-2016 Jovašević-Stojanović, M., Bartonova, A., Topalović, D., Lazović, I., Pokrić, B., & Ristovski, Z. (2015). On the use of small and cheaper sensors and devices for indicative citizen-based monitoring of respirable particulate matter. Environmental Pollution, 206, 696–704. https://doi.org/10.1016/j.envpol.2015.08.035 Kaneko, H. (2023). Interpretation of Machine Learning Models for Data Sets with Many Features Using Feature Importance. ACS Omega, 8(25), 23218–23225. https://doi.org/10.1021/acsomega.3c03722 Kang, Y., Aye, L., Ngo, T. D., & Zhou, J. (2022). Performance evaluation of low-cost air quality sensors: A review. Science of The Total Environment, 818, 151769. https://doi.org/10.1016/j.scitotenv.2021.151769 Karagulian, F., Barbiere, M., Kotsev, A., Spinelle, L., Gerboles, M., Lagler, F., Redon, N., Crunaire, S., & Borowiak, A. (2019). Review of the Performance of Low-Cost Sensors for Air Quality Monitoring. Atmosphere, 10(9), 506. https://doi.org/10.3390/atmos10090506 Kaur, K., & Kelly, K. E. (2023). Laboratory evaluation of the Alphasense OPC-N3, and the Plantower PMS5003 and PMS6003 sensors. Journal of Aerosol Science, 171, 106181. https://doi.org/10.1016/j.jaerosci.2023.106181 Kelly, K. E., Whitaker, J., Petty, A., Widmer, C., Dybwad, A., Sleeth, D., Martin, R., & Butterfield, A. (2017). Ambient and laboratory evaluation of a low-cost particulate matter sensor. Environmental Pollution, 221, 491–500. https://doi.org/10.1016/j.envpol.2016.12.039 Kim, K.-H., Kabir, E., & Kabir, S. (2015). A review on the human health impact of airborne particulate matter. Environment International, 74, 136–143. https://doi.org/10.1016/j.envint.2014.10.005 Kobernus, M., Berre, A. J., Gonzalez, M., Liu, H.-Y., Rombouts, R., & Bartanova, A. (2015). A Practical Approach to an Integrated Citizens’ Observatory: The CITI-SENSE Framework. https://ceur-ws.org/Vol-1322/paper_1.pdf Kosmidis, E., Syropoulou, P., Tekes, S., Schneider, P., Spyromitros-Xioufis, E., Riga, M., Charitidis, P., Moumtzidou, A., Papadopoulos, S., Vrochidis, S., & others. (2018). HackAIR: Towards raising awareness about air quality in Europe by developing a collective online platform. ISPRS International Journal of Geo-Information, 7(5), 187. Kumar, P., Hama, S., Nogueira, T., Abbass, R. A., Brand, V. S., Andrade, M. de F., Asfaw, A., Aziz, K. H., Cao, S.-J., El-Gendy, A., Islam, S., Jeba, F., Khare, M., Mamuya, S. H., Martinez, J., Meng, M.-R., Morawska, L., Muula, A. S., Shiva Nagendra, S. M., … Salam, A. (2021). In-car particulate matter exposure across ten global cities. Science of The Total Environment, 750, 141395. https://doi.org/10.1016/j.scitotenv.2020.141395 Kumar, P., Morawska, L., Martani, C., Biskos, G., Neophytou, M., Di Sabatino, S., Bell, M., Norford, L., & Britter, R. (2015). The rise of low-cost sensing for managing air pollution in cities. Environment International, 75, 199–205. https://doi.org/10.1016/j.envint.2014.11.019 Kumar, P., Rivas, I., Singh, A. P., Ganesh, V. J., Ananya, M., & Frey, H. C. (2018). Dynamics of coarse and fine particle exposure in transport microenvironments. Npj Climate and Atmospheric Science, 1(1), 11. https://doi.org/10.1038/s41612-018-0023-y Landrigan, P. J., Fuller, R., Acosta, N. J. R., Adeyi, O., Arnold, R., Basu, N. (Nil), Baldé, A. B., Bertollini, R., Bose-O’Reilly, S., Boufford, J. I., Breysse, P. N., Chiles, T., Mahidol, C., Coll-Seck, A. M., Cropper, M. L., Fobil, J., Fuster, V., Greenstone, M., Haines, A., … Zhong, M. (2017). The Lancet Commission on pollution and health. The Lancet. https://doi.org/10.1016/S0140-6736(17)32345-0 Levy Zamora, M., Xiong, F., Gentner, D., Kerkez, B., Kohrman-Glaser, J., & Koehler, K. (2019). Field and Laboratory Evaluations of the Low-Cost Plantower Particulate Matter Sensor. Environmental Science & Technology, 53(2), 838–849. https://doi.org/10.1021/acs.est.8b05174 Li, S.-T., & Shue, L.-Y. (2004). Data mining to aid policy making in air pollution management. Expert Systems with Applications, 27(3), 331–340. https://doi.org/10.1016/j.eswa.2004.05.015 Liu, H.-Y., Schneider, P., Haugen, R., & Vogt, M. (2019). Performance Assessment of a Low-Cost PM2.5 Sensor for a near Four-Month Period in Oslo, Norway. Loomis, D., Huang, W., & Chen, G. (2014). The International Agency for Research on Cancer (IARC) evaluation of the carcinogenicity of outdoor air pollution: Focus on China. Chinese Journal of Cancer, 33(4), 189–196. https://doi.org/10.5732/cjc.014.10028 Luengo-Oroz, J., & Reis, S. (2019). Assessment of cyclists’ exposure to ultrafine particles along alternative commuting routes in Edinburgh. Atmospheric Pollution Research, 10(4), 1148–1158. https://doi.org/10.1016/j.apr.2019.01.020 Lundberg, S. M., & Lee, S.-I. (2017). Consistent feature attribution for tree ensembles. Proceedings of the 34 Th International Conference on Machine Learning. Ma, Y., Richards, M., Ghanem, M., Guo, Y., & Hassard, J. (2008). Air Pollution Monitoring and Mining Based on Sensor Grid in London. Sensors, 8(6), 3601–3623. https://doi.org/10.3390/s8063601 Maag, B., Zhou, Z., & Thiele, L. (2018). A Survey on Sensor Calibration in Air Pollution Monitoring Deployments. IEEE Internet of Things Journal, 5(6), 4857–4870. https://doi.org/10.1109/JIOT.2018.2853660 Mahajan, S., Chung, M.-K., Martinez, J., Olaya, Y., Helbing, D., & Chen, L.-J. (2022). Translating citizen-generated air quality data into evidence for shaping policy. Humanities and Social Sciences Communications, 9(1), Article 1. https://doi.org/10.1057/s41599-022-01135-2 Mahajan, S., & Kumar, P. (2020). Evaluation of low-cost sensors for quantitative personal exposure monitoring. Sustainable Cities and Society, 57, 102076. https://doi.org/10.1016/j.scs.2020.102076 Mahajan, S., Kumar, P., Pinto, J. A., Riccetti, A., Schaaf, K., Camprodon, G., Smári, V., Passani, A., & Forino, G. (2020). A citizen science approach for enhancing public understanding of air pollution. Sustainable Cities and Society, 52, 101800. https://doi.org/10.1016/j.scs.2019.101800 Mahajan, S., Luo, C.-H., Wu, D.-Y., & Chen, L.-J. (2021). From Do-It-Yourself (DIY) to Do-It-Together (DIT): Reflections on designing a citizen-driven air quality monitoring framework in Taiwan. Sustainable Cities and Society, 66, 102628. https://doi.org/10.1016/j.scs.2020.102628 Makri, A., & Stilianakis, N. I. (2008). Vulnerability to air pollution health effects. International Journal of Hygiene and Environmental Health, 211(3–4), 326–336. https://doi.org/10.1016/j.ijheh.2007.06.005 Mannucci, P., & Franchini, M. (2017). Health Effects of Ambient Air Pollution in Developing Countries. International Journal of Environmental Research and Public Health, 14(9), 1048. https://doi.org/10.3390/ijerph14091048 Manojkumar, N., Monishraj, M., & Srimuruganandam, B. (2021). Commuter exposure concentrations and inhalation doses in traffic and residential routes of Vellore city, India. Atmospheric Pollution Research, 12(1), 219–230. https://doi.org/10.1016/j.apr.2020.09.002 Manojkumar, N., & Srimuruganandam, B. (2021). Investigation of on-road fine particulate matter exposure concentration and its inhalation dosage levels in an urban area. Building and Environment, 198, 107914. https://doi.org/10.1016/j.buildenv.2021.107914 Marjovi, A., Arfire, A., & Martinoli, A. (2015). High Resolution Air Pollution Maps in Urban Environments Using Mobile Sensor Networks. 2015 International Conference on Distributed Computing in Sensor Systems, 11–20. https://doi.org/10.1109/DCOSS.2015.32 McKercher, G. R., Salmond, J. A., & Vanos, J. K. (2017). Characteristics and applications of small, portable gaseous air pollution monitors. Environmental Pollution, 223, 102–110. https://doi.org/10.1016/j.envpol.2016.12.045 Mead, M. I., Popoola, O. A. M., Stewart, G. B., Landshoff, P., Calleja, M., Hayes, M., Baldovi, J. J., McLeod, M. W., Hodgson, T. F., Dicks, J., Lewis, A., Cohen, J., Baron, R., Saffell, J. R., & Jones, R. L. (2013). The use of electrochemical sensors for monitoring urban air quality in low-cost, high-density networks. Atmospheric Environment, 70, 186–203. https://doi.org/10.1016/j.atmosenv.2012.11.060 Medellín Cómo Vamos. (2021). Informe de de Calidad de Vida de Medellín, 2020. https://www.medellincomovamos.org/system/files/2021-09/docuprivados/Movilidad%20y%20espacio%20p%C3%BAblico%20Informe%20de%20Calidad%20de%20Vida%20de%20Medell%C3%ADn%2C%202020.pdf Resolución 2254 de 2017, (2017). https://www.minambiente.gov.co/documento-entidad/resolucion-2254-de-2017/ Ministerio de Ambiente y Desarrollo Sostenible. (2021). Contaminación Atmosférica. Ministerio de Ambiente y Desarrollo Sostenible. https://www.minambiente.gov.co/asuntos-ambientales-sectorial-y-urbana/contaminacion-atmosferica/ Moore, E., Chatzidiakou, L., Kuku, M.-O., Jones, R. L., Smeeth, L., Beevers, S., Kelly, F. J., Barratt, B., & Quint, J. (2016). Global Associations between Air Pollutants and Chronic Obstructive Pulmonary Disease Hospitalizations: A Systematic Review. Annals of the American Thoracic Society, AnnalsATS.201601-064OC. https://doi.org/10.1513/AnnalsATS.201601-064OC Morawska, L., Thai, P. K., Liu, X., Asumadu-Sakyi, A., Ayoko, G., Bartonova, A., Bedini, A., Chai, F., Christensen, B., Dunbabin, M., Gao, J., Hagler, G. S. W., Jayaratne, R., Kumar, P., Lau, A. K. H., Louie, P. K. K., Mazaheri, M., Ning, Z., Motta, N., … Williams, R. (2018). Applications of low-cost sensing technologies for air quality monitoring and exposure assessment: How far have they gone? Environment International, 116, 286–299. https://doi.org/10.1016/j.envint.2018.04.018 Mukherjee, A., Stanton, L., Graham, A., & Roberts, P. (2017). Assessing the Utility of Low-Cost Particulate Matter Sensors over a 12-Week Period in the Cuyama Valley of California. Sensors, 17(8), 1805. https://doi.org/10.3390/s17081805 NABEL. (2017). National Air Pollution Monitoring Network (NABEL). https://www.bafu.admin.ch/bafu/en/home/topics/air/state/data/national-air-pollution-monitoring-network--nabel-.html Noble, C. A., Vanderpool, R. W., Peters, T. M., McElroy, F. F., Gemmill, D. B., & Wiener, R. W. (2001). Federal Reference and Equivalent Methods for Measuring Fine Particulate Matter. Aerosol Science and Technology, 34(5), 457–464. https://doi.org/10.1080/02786820121582 OCL. (2019). People-centered informed decision making for participatory democracy. https://opencitieslab.org/odd/home OpenSeneca. (2019). Open-Seneca in Nairobi. https://centreforglobalequality.org/news/open-seneca-in-nairobi/ Papapostolou, V., Zhang, H., Feenstra, B. J., & Polidori, A. (2017). Development of an environmental chamber for evaluating the performance of low-cost air quality sensors under controlled conditions. Atmospheric Environment, 171, 82–90. https://doi.org/10.1016/j.atmosenv.2017.10.003 Perera, F. (2017). Pollution from Fossil-Fuel Combustion is the Leading Environmental Threat to Global Pediatric Health and Equity: Solutions Exist. International Journal of Environmental Research and Public Health, 15(1), 16. https://doi.org/10.3390/ijerph15010016 Pope, C. A., & Dockery, D. W. (2006). Health Effects of Fine Particulate Air Pollution: Lines that Connect. Journal of the Air & Waste Management Association, 56(6), 709–742. https://doi.org/10.1080/10473289.2006.10464485 Pope III, C. A. (2002). Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate Air Pollution. JAMA, 287(9), 1132. https://doi.org/10.1001/jama.287.9.1132 Popoola, O. A. M., Carruthers, D., Lad, C., Bright, V. B., Mead, M. I., Stettler, M. E. J., Saffell, J. R., & Jones, R. L. (2018). Use of networks of low cost air quality sensors to quantify air quality in urban settings. Atmospheric Environment, 194, 58–70. https://doi.org/10.1016/j.atmosenv.2018.09.030 PurpleAir. (2023). PurpleAir \| Real-time Air Quality Monitoring. PurpleAir, Inc. https://www2.purpleair.com/ Ramírez, O., Mura, I., & Franco, J. F. (2017). How Do People Understand Urban Air Pollution? Exploring Citizens’ Perception on Air Quality, Its Causes and Impacts in Colombian Cities. Open Journal of Air Pollution, 6(1), Article 1. https://doi.org/10.4236/ojap.2017.61001 Ramos, C. A., Silva, J. R., Faria, T., Wolterbeek, T. H., & Almeida, S. M. (2017). Exposure assessment of a cyclist to particles and chemical elements. Environmental Science and Pollution Research, 24(13), 11879–11889. https://doi.org/10.1007/s11356-016-6365-2 Ramos, C. A., Wolterbeek, H. T., & Almeida, S. M. (2016). Air pollutant exposure and inhaled dose during urban commuting: A comparison between cycling and motorized modes. Air Quality, Atmosphere & Health, 9(8), 867–879. https://doi.org/10.1007/s11869-015-0389-5 Rendón, A. M., Salazar, J. F., Palacio, C. A., & Wirth, V. (2015). Temperature Inversion Breakup with Impacts on Air Quality in Urban Valleys Influenced by Topographic Shading. Journal of Applied Meteorology and Climatology, 54(2), 302–321. https://doi.org/10.1175/JAMC-D-14-0111.1 Rendón, A. M., Salazar, J. F., Palacio, C. A., Wirth, V., & Brötz, B. (2014). Effects of Urbanization on the Temperature Inversion Breakup in a Mountain Valley with Implications for Air Quality. Journal of Applied Meteorology and Climatology, 53(4), 840–858. Riesch, H., & Potter, C. (2014). Citizen science as seen by scientists: Methodological, epistemological and ethical dimensions. Public Understanding of Science, 23(1), 107–120. https://doi.org/10.1177/0963662513497324 Riojas-Rodríguez, H., Texcalac-Sangrador, J. L., & Moreno-Banda, G. L. (2016). Air pollution management and control in Latin America and the Caribbean: Implications for climate change. Rev Panam Salud Publica, 10. Roldán Henao, N., Isaza, A., Herrera, L., & Hoyos Ortiz, C. D. (2018). Aburrá Valley (Colombia) spatio-temporal air quality assessment from a monitoring network using low-cost sensors and citizen science. 2018, A12E-08. Roldán-Henao, N., Hoyos, C. D., Herrera-Mejía, L., & Isaza, A. (2020). An Investigation of the Precipitation Net Effect on the Particulate Matter Concentration in a Narrow Valley: Role of Lower-Troposphere Stability. Journal of Applied Meteorology and Climatology, 59(3), 401–426. https://doi.org/10.1175/JAMC-D-18-0313.1 Roy, H. E., & Brown, P. M. J. (2015). Ten years of invasion: Harmonia axyridis (Pallas) (Coleoptera: Coccinellidae) in Britain. Ecological Entomology, 40(4), 336–348. Scopus. https://doi.org/10.1111/een.12203 Samad, A., & Vogt, U. (2021). Mobile air quality measurements using bicycle to obtain spatial distribution and high temporal resolution in and around the city center of Stuttgart. Atmospheric Environment, 244, 117915. https://doi.org/10.1016/j.atmosenv.2020.117915 Sayahi, T., Kaufman, D., Becnel, T., Kaur, K., Butterfield, A. E., Collingwood, S., Zhang, Y., Gaillardon, P.-E., & Kelly, K. E. (2019). Development of a calibration chamber to evaluate the performance of low-cost particulate matter sensors. Environmental Pollution, 255, 113131. https://doi.org/10.1016/j.envpol.2019.113131 Schaefer, T., Kieslinger, B., & Fabian, C. M. (2020). Citizen-Based Air Quality Monitoring: The Impact on Individual Citizen Scientists and How to Leverage the Benefits to Affect Whole Regions. Citizen Science: Theory and Practice, 5(1), 6. https://doi.org/10.5334/cstp.245 Science, R. L. (2020). Transforming air quality monitoring through a microscopic lens. https://shaktifoundation.in/wp-content/uploads/2020/01/Impacts_Respirer.pdf Semple, S., Sweeting, H., Demou, E., Logan, G., O’Donnell, R., Hunt, K., & on behalf of the Tobacco in Prisons (TIPs) Research Team. (2017). Characterising the Exposure of Prison Staff to Second-Hand Tobacco Smoke. Annals of Work Exposures and Health, 61(7), 809–821. https://doi.org/10.1093/annweh/wxx058 Sensirion. (2021). SPS30—PM2.5 Sensor for HVAC and air quality applications SPS30. https://sensirion.com/products/catalog/SPS30/ Sensors.Africa. (2021). Citizen science initiative that uses sensors to monitor air, water and sound pollution. https://sensors.africa/ Shah, A. S., Langrish, J. P., Nair, H., McAllister, D. A., Hunter, A. L., Donaldson, K., Newby, D. E., & Mills, N. L. (2013). Global association of air pollution and heart failure: A systematic review and meta-analysis. The Lancet, 382(9897), 1039–1048. https://doi.org/10.1016/S0140-6736(13)60898-3 Si, M., Xiong, Y., Du, S., & Du, K. (2020). Evaluation and calibration of a low-cost particle sensor in ambient conditions using machine-learning methods. Atmospheric Measurement Techniques, 13(4), 1693–1707. https://doi.org/10.5194/amt-13-1693-2020 Siata. (2017). Monitoreo de la calidad del aire. https://siata.gov.co/sitio_web/index.php/calidad_aire Spinelle, L., Gerboles, M., Kok, G., Persijn, S., & Sauerwald, T. (2017). Review of Portable and Low-Cost Sensors for the Ambient Air Monitoring of Benzene and Other Volatile Organic Compounds. Sensors, 17(7), 1520. https://doi.org/10.3390/s17071520 Sun, J., Zhang, N., Yan, X., Wang, M., & Wang, J. (2020). The effect of ambient fine particulate matter (PM2.5) on respiratory diseases in China: A systematic review and meta-analysis. Stochastic Environmental Research and Risk Assessment, 34(3–4), 593–610. https://doi.org/10.1007/s00477-020-01786-0 Supasri, T., & Sampattagul, S. (2019). Haze Problem in the North of Thailand and DustBoy PM 2.5 Monitoring Systems. Tagle, M., Rojas, F., Reyes, F., Vásquez, Y., Hallgren, F., Lindén, J., Kolev, D., Watne, Å. K., & Oyola, P. (2020). Field performance of a low-cost sensor in the monitoring of particulate matter in Santiago, Chile. Environmental Monitoring and Assessment, 192(3), 171. https://doi.org/10.1007/s10661-020-8118-4 Thiel, M., Penna-Díaz, M. A., Luna-Jorquera, G., Salas, S., Sellanes, J., & Stotz, W. (2014). Citizen scientists and marine research: Volunteer participants, their contributions, and projection for the future. Oceanography and Marine Biology: An Annual Review, 52, 257–314. Scopus. https://doi.org/10.1201/b17143 UPB & AMVA. (2018). Actualización inventario de emisiones atmosféricas del Valle de Aburrá, 2016. Informe final. https://www.metropol.gov.co/ambiental/calidad-del-aire/Documents/Inventario-de-emisiones/Inventario_FuentesM%C3%B3viles2016.pdf U.S. Environmental Protection Agency (EPA). (2009). Metabolically Derived Human Ventilation Rates: A Revised Approach Based Upon Oxygen Consumption Rates (EPA/600/R-06/129F). National Center for Environmental Assessment. https://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=202543 US EPA. (2016a). List of Designated Reference and Equivalent Methods. https://www.epa.gov/system/files/documents/2021-12/designated-referene-and-equivalent-methods-12152021.pdf US EPA. (2016b). Technical Assistance Document for the Reporting of Daily Air Quality \| AirNow.gov. https://www.airnow.gov/publications/air-quality-index/technical-assistance-document-for-reporting-the-daily-aqi/ US EPA. (2021). The National Ambient Air Quality Standards for Particulate Matter. https://www.epa.gov/sites/default/files/2020-04/documents/fact_sheet_pm_naaqs_proposal.pdf US EPA, O. (2018, January 23). Instruction Guide and Macro Analysis Tool: Evaluating Air Sensors by Collocation with Federal Reference Monitors [Data and Tools]. https://www.epa.gov/air-research/instruction-guide-and-macro-analysis-tool-evaluating-air-sensors-collocation-federal Valencia, J.-C., & Fonseca, O. (2019). AIR POLLUTION, CITIZEN DATA COLLECTIVES AND COMMUNICATION AGENDA SETTING IN COLOMBIA. 33–43. https://doi.org/10.2495/AIR190041 Varaden, D., Leidland, E., Lim, S., & Barratt, B. (2021). “I am an air quality scientist”– Using citizen science to characterise school children’s exposure to air pollution. Environmental Research, 201, 111536. https://doi.org/10.1016/j.envres.2021.111536 von Gönner, J., Bowler, D. E., Gröning, J., Klauer, A.-K., Liess, M., Neuer, L., & Bonn, A. (2023). Citizen science for assessing pesticide impacts in agricultural streams. Science of the Total Environment, 857. Scopus. https://doi.org/10.1016/j.scitotenv.2022.159607 Wang, W.-C. V., Lung, S.-C. C., & Liu, C.-H. (2020). Application of Machine Learning for the in-Field Correction of a PM2.5 Low-Cost Sensor Network. Sensors, 20(17), 5002. https://doi.org/10.3390/s20175002 Wang, W.-C. V., Lung, S.-C. C., Liu, C.-H., Wen, T.-Y. J., Hu, S.-C., & Chen, L.-J. (2021). Evaluation and Application of a Novel Low-Cost Wearable Sensing Device in Assessing Real-Time PM2.5 Exposure in Major Asian Transportation Modes. Atmosphere, 12(2), Article 2. https://doi.org/10.3390/atmos12020270 Wang, Y., Li, J., Jing, H., Zhang, Q., Jiang, J., & Biswas, P. (2015). Laboratory Evaluation and Calibration of Three Low-Cost Particle Sensors for Particulate Matter Measurement. Aerosol Science and Technology, 49(11), 1063–1077. https://doi.org/10.1080/02786826.2015.1100710 Wesseling, J., de Ruiter, H., Blokhuis, C., Drukker, D., Weijers, E., Volten, H., Vonk, J., Gast, L., Voogt, M., Zandveld, P., van Ratingen, S., & Tielemans, E. (2019). Development and Implementation of a Platform for Public Information on Air Quality, Sensor Measurements, and Citizen Science. Atmosphere, 10(8), Article 8. https://doi.org/10.3390/atmos10080445 Wesseling, J., Hendricx, W., de Ruiter, H., van Ratingen, S., Drukker, D., Huitema, M., Schouwenaar, C., Janssen, G., van Aken, S., Smeenk, J., Hof, A., & Tielemans, E. (2021). Assessment of PM2.5 Exposure during Cycle Trips in The Netherlands Using Low-Cost Sensors. International Journal of Environmental Research and Public Health, 18(11), 6007. https://doi.org/10.3390/ijerph18116007 Williams, R., Kilaru, V., Snyder, E., Kaufman, A., Dye, T., Rutter, A., Russell, A., & Hafner, H. (2014). Air Sensor Guidebook (EPA/600/R-14/159). United States Environmental Protection Agency. file:///C:/Users/SVS13115FLS/Downloads/GUIDEBOOK%20-%20FINAL.PDF World Health Organization. (2006). WHO Air quality guidelines for particulate matter, ozone, nitrogen dioxide and sulfur dioxide. Global update 2005. Summary of risk assessment (Occupational and Environmental Health Team). World Health Organization. http://apps.who.int/iris/bitstream/10665/69477/1/WHO_SDE_PHE_OEH_06.02_eng.pdf World Health Organization. (2021). WHO global air quality guidelines: Particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. World Health Organization. https://apps.who.int/iris/handle/10665/345329 Xing, Y. F., Xu, Y. H., Shi, M. H., & Lian, Y. X. (2016). The impact of PM2.5 on the human respiratory system. Journal of Thoracic Disease, 8(1). https://doi.org/10.3978/j.issn.2072-1439.2016.01.19 Yatkin, S., Gerboles, M., Belis, C. A., Karagulian, F., Lagler, F., Barbiere, M., & Borowiak, A. (2020). Representativeness of an air quality monitoring station for PM2.5 and source apportionment over a small urban domain. Atmospheric Pollution Research, 11(2), 225–233. https://doi.org/10.1016/j.apr.2019.10.004 Zamora, M. L., Rice, J., & Koehler, K. (2020). One year evaluation of three low-cost PM2.5 monitors. Atmospheric Environment, 235, 117615. https://doi.org/10.1016/j.atmosenv.2020.117615 Zikova, N., Masiol, M., Chalupa, D., Rich, D., Ferro, A., & Hopke, P. (2017). Estimating Hourly Concentrations of PM2.5 across a Metropolitan Area Using Low-Cost Particle Monitors. Sensors, 17(8), 1922. https://doi.org/10.3390/s17081922 Zimmerman, N., Presto, A. A., Kumar, S. P. N., Gu, J., Hauryliuk, A., Robinson, E. S., Robinson, A. L., & R. Subramanian. (2018). A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring. Atmospheric Measurement Techniques, 11(1), 291–313. https://doi.org/10.5194/amt-11-291-2018 Malings, C., Tanzer, R., Hauryliuk, A., Kumar, S. P. N., Zimmerman, N., Kara, L. B., Presto, A. A., & Subramanian, R. (2019). Development of a general calibration model and long-term performance evaluation of low-cost sensors for air pollutant gas monitoring. Atmospheric Measurement Techniques, 12(2), 903–920. https://doi.org/10.5194/amt-12-903-2019 Snyder, E. G., Watkins, T. H., Solomon, P. A., Thoma, E. D., Williams, R. W., Hagler, G. S. W., Shelow, D., Hindin, D. A., Kilaru, V. J., & Preuss, P. W. (2013). The Changing Paradigm of Air Pollution Monitoring. Environmental Science & Technology, 47(20), 11369–11377. https://doi.org/10.1021/es4022602 Spinelle, L., Gerboles, M., Villani, M. G., Aleixandre, M., & Bonavitacola, F. (2015). Field calibration of a cluster of low-cost available sensors for air quality monitoring. Part A: Ozone and nitrogen dioxide. Sensors and Actuators B: Chemical, 215, 249–257. https://doi.org/10.1016/j.snb.2015.03.031
dc.rights.coar.fl_str_mv	http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv	Atribución-NoComercial 4.0 Internacional
dc.rights.uri.spa.fl_str_mv	http://creativecommons.org/licenses/by-nc/4.0/
dc.rights.accessrights.spa.fl_str_mv	info:eu-repo/semantics/openAccess
rights_invalid_str_mv	Atribución-NoComercial 4.0 Internacional http://creativecommons.org/licenses/by-nc/4.0/ http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv	openAccess
dc.format.extent.spa.fl_str_mv	196 páginas
dc.format.mimetype.spa.fl_str_mv	application/pdf
dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Medellín - Minas - Doctorado en Ingeniería - Sistemas
dc.publisher.faculty.spa.fl_str_mv	Facultad de Minas
dc.publisher.place.spa.fl_str_mv	Medellín, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Medellín
institution	Universidad Nacional de Colombia
bitstream.url.fl_str_mv	https://repositorio.unal.edu.co/bitstream/unal/85696/1/license.txt https://repositorio.unal.edu.co/bitstream/unal/85696/2/63560781.2023.pdf.pdf https://repositorio.unal.edu.co/bitstream/unal/85696/3/63560781.2023.pdf.pdf.jpg
bitstream.checksum.fl_str_mv	eb34b1cf90b7e1103fc9dfd26be24b4a 21cece1afd7f7d1ac48d7ceb59d68b21 e129e58763a5b9af81d9a584cf31692a
bitstream.checksumAlgorithm.fl_str_mv	MD5 MD5 MD5
repository.name.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
repository.mail.fl_str_mv	repositorio_nal@unal.edu.co
_version_	1806886514785779712
spelling	Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Yris, Olaya Morales110f54339555420e316d32df97ad67afRios Martinez, Jenny Rocio6d0f23048a80bbfd3898a7a2f9cfc437Ciencias de la Decision2024-02-13T18:22:10Z2024-02-13T18:22:10Z2023-02-13https://repositorio.unal.edu.co/handle/unal/85696Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/Ilustraciones, gráficas, tablasThe study of urban air pollution holds paramount significance within the realms of environmental science and public health. Urban areas are epicenters of diverse human activities, industrial operations, and vehicular traffic, collectively contributing to elevated concentrations of air pollutants. As the use of LCS devices becomes more prevalent in citizen science initiatives, educational purposes, rise of information and awareness, it is crucial to establish their performance characteristics and evaluation metrics for air pollution monitoring. This thesis focuses on evaluating the performance of Low-cost sensors (LCS) in the monitoring of PM2.5 concentrations in outdoor urban environments in Colombia using data mining and machine learning models. The results show that the polynomial regression and Artificial Neural Networks models present a better enhancing in the accuracy and precision of the measurements of the different models of LCS used in this study compared with simple linear regression and other machine learning models. Lastly, the project endeavors to demonstrate the applicability of LCS devices for monitoring PM2.5 concentration in various transportation modes within the city of Medellin. This research contributes to the broader understanding of LCS devices' potential in enhancing air quality monitoring and their suitability for citizen-driven initiatives in regions lacking regulatory-grade instruments.El estudio de la contaminación del aire urbano tiene una importancia fundamental en los ámbitos de la ciencia ambiental y la salud pública. Las áreas urbanas son epicentros de diversas actividades humanas, operaciones industriales y tráfico vehicular, contribuyendo colectivamente a concentraciones elevadas de contaminantes atmosféricos. A medida que el uso de dispositivos LCS se vuelve más frecuente en iniciativas de ciencia ciudadana, con fines educativos, aumento de información y conciencia, es crucial establecer sus características de rendimiento y métricas de evaluación para el monitoreo de la contaminación del aire. Esta tesis se centra en evaluar el rendimiento de los sensores de bajo costo (LCS) en el monitoreo de las concentraciones de PM2.5 en entornos urbanos al aire libre en Colombia mediante la minería de datos y modelos de aprendizaje automático. Los resultados muestran que los modelos de regresión polinómica y redes neuronales artificiales mejoran la precisión y la exactitud de las mediciones de los diferentes modelos de LCS utilizados en este estudio en comparación con la regresión lineal simple y otros modelos de aprendizaje de máquinas. Por último, el proyecto pretende demostrar la aplicabilidad de los dispositivos LCS para monitorear la concentración de PM2.5 en diversos modos de transporte dentro de la ciudad de Medellín. Esta investigación contribuye a una comprensión más amplia del potencial de los dispositivos LCS para mejorar el monitoreo de la calidad del aire y su idoneidad para iniciativas impulsadas por ciudadanos en regiones que carecen de instrumentos de calidad regulatoria. (text tomado de la fuente)Colciencias Convocatoria 727 doctorados nacionalesDoctoradoDoctor en IngenieríaInvestigación de operacionesÁrea Curricular de Ingeniería de Sistemas e Informática196 páginasapplication/pdfengUniversidad Nacional de ColombiaMedellín - Minas - Doctorado en Ingeniería - SistemasFacultad de MinasMedellín, ColombiaUniversidad Nacional de Colombia - Sede Medellín000 - Ciencias de la computación, información y obras generales::003 - SistemasContaminación del aireMinería de datosContaminación de aireAprendizaje de máquinasSensores de bajo costoCalibración de sensoresAir pollutionParticulate matterLow-cost sensorsSensor calibrationMachine learningData miningSensoresMonitoring urban air pollution using low-cost sensor devicesMonitoreo de la contaminación del aire urbano utilizando dispositivos sensores de bajo costoTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06Texthttp://purl.org/redcol/resource_type/TDLaReferenciaAgarwal, A., Kaushik, A., Kumar, S., & Mishra, R. K. (2020). Comparative study on air quality status in Indian and Chinese cities before and during the COVID-19 lockdown period. Air Quality, Atmosphere & Health, 13(10), 1167–1178. https://doi.org/10.1007/s11869-020-00881-zAggarwal, C. C. (2015). Data Mining. Springer International Publishing. https://doi.org/10.1007/978-3-319-14142-8Alcaldía Mayor de Bogotá D.C., S. de A. (2022). Red de Monitoreo de Calidad del Aire de Bogotá—RMCAB. Secretaría Distrital de Ambiente. https://ambientebogota.gov.co/red-de-monitoreo-de-calidad-del-aire-de-bogota-rmcabAlfano, B., Barretta, L., Del Giudice, A., De Vito, S., Di Francia, G., Esposito, E., Formisano, F., Massera, E., Miglietta, M. L., & Polichetti, T. (2020). A Review of Low-Cost Particulate Matter Sensors from the Developers’ Perspectives. Sensors, 20(23), Article 23. https://doi.org/10.3390/s20236819AMVA. (2018). Plan de acción para la implementación del Plan Operacional para enfrentar Episodios de Contaminación Atmosférica (POECA) en el área Metropolitana del Valle de Aburrá. https://www.metropol.gov.co/ambiental/calidad-del-aire/Documents/POECA/Plan-de-Accion-POECA-2019.pdfAMVA. (2019). Condiciones especiales del valle de aburrá. https://www.metropol.gov.co:443/ambientales/calidad-del-aire/generalidades/condiciones-especialesAMVA. (2021). Ciudadanos Científicos. Programa local de ciencia, educación y tecnología. https://www.metropol.gov.co:443/ambiental/siata/Paginas/ciudadanos-cientificos.aspxAMVA. (2022). Encuesta origen destino. https://www.metropol.gov.co:443/observatorio/Paginas/encuestaorigendestino.aspxApte, J. S., Marshall, J. D., Cohen, A. J., & Brauer, M. (2015). Addressing Global Mortality from Ambient PM 2.5. Environmental Science & Technology, 49(13), 8057–8066. https://doi.org/10.1021/acs.est.5b01236Área Metropolitana del Valle de Aburrá. (2022). Encuesta de Origen—Destino. https://www.metropol.gov.co/observatorio/Paginas/encuestaorigendestino.aspxArfire, A., Marjovi, A., & Martinoli, A. (2016). Enhancing measurement quality through active sampling in mobile air quality monitoring sensor networks. 2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), 1022–1027. https://doi.org/10.1109/AIM.2016.7576904Astudillo, G. D., Garza-Castanon, L. E., & Minchala Avila, L. I. (2020). Design and Evaluation of a Reliable Low-Cost Atmospheric Pollution Station in Urban Environment. IEEE Access, 8, 51129–51144. https://doi.org/10.1109/ACCESS.2020.2980736Austin, E., Novosselov, I., Seto, E., & Yost, M. G. (2015). Laboratory Evaluation of the Shinyei PPD42NS Low-Cost Particulate Matter Sensor. PLOS ONE, 10(9), e0137789. https://doi.org/10.1371/journal.pone.0137789Badura, M., Batog, P., Drzeniecka-Osiadacz, A., & Modzel, P. (2019). Regression methods in the calibration of low-cost sensors for ambient particulate matter measurements. SN Applied Sciences, 1(6), 622. https://doi.org/10.1007/s42452-019-0630-1Bai, L., Huang, L., Wang, Z., Ying, Q., Zheng, J., Shi, X., & Hu, J. (2020). Long-term field Evaluation of Low-cost Particulate Matter Sensors in Nanjing. Aerosol and Air Quality Research, 20(2), 242–253. https://doi.org/10.4209/aaqr.2018.11.0424BAM-1020 Continuous Particulate Monitor. (2021). Met One Instruments. https://metone.com/products/bam-1020/Barcelo-Ordinas, J. M., Doudou, M., Garcia-Vidal, J., & Badache, N. (2019). Self-calibration methods for uncontrolled environments in sensor networks: A reference survey. Ad Hoc Networks, 88, 142–159. https://doi.org/10.1016/j.adhoc.2019.01.008Báthory, C., Dobó, Z., Garami, A., Palotás, Á., & Tóth, P. (2022). Low-cost monitoring of atmospheric PM—development and testing. Journal of Environmental Management, 304, 114158. https://doi.org/10.1016/j.jenvman.2021.114158Benabbas, A., Geißelbrecht, M., Nikol, G. M., Mahr, L., Nähr, D., Steuer, S., Wiesemann, G., Müller, T., Nicklas, D., & Wieland, T. (2019). Measure particulate matter by yourself: Data-quality monitoring in a citizen science project. Journal of Sensors and Sensor Systems, 8(2), 317–328. https://doi.org/10.5194/jsss-8-317-2019Berghmans, P., Bleux, N., Panis, L. I., Mishra, V. K., Torfs, R., & Van Poppel, M. (2009). Exposure assessment of a cyclist to PM10 and ultrafine particles. Science of The Total Environment, 407(4), 1286–1298. https://doi.org/10.1016/j.scitotenv.2008.10.041Bergmann, M. L., Andersen, Z. J., Amini, H., Khan, J., Lim, Y. H., Loft, S., Mehta, A., Westendorp, R. G., & Cole-Hunter, T. (2022). Ultrafine particle exposure for bicycle commutes in rush and non-rush hour traffic: A repeated measures study in Copenhagen, Denmark. Environmental Pollution, 294, 118631. https://doi.org/10.1016/j.envpol.2021.118631Boniardi, L., Borghi, F., Straccini, S., Fanti, G., Campagnolo, D., Campo, L., Olgiati, L., Lioi, S., Cattaneo, A., Spinazzè, A., Cavallo, D. M., & Fustinoni, S. (2021). Commuting by car, public transport, and bike: Exposure assessment and estimation of the inhaled dose of multiple airborne pollutants. Atmospheric Environment, 262, 118613. https://doi.org/10.1016/j.atmosenv.2021.118613Borghi, F., Fanti, G., Cattaneo, A., Campagnolo, D., Rovelli, S., Keller, M., Spinazzè, A., & Cavallo, D. M. (2020). Estimation of the inhaled dose of airborne pollutants during commuting: Case study and application for the general population. International Journal of Environmental Research and Public Health, 17(17), 1–14. Scopus. https://doi.org/10.3390/ijerph17176066Božilov, A., Tasić, V., Živković, N., Lazović, I., Blagojević, M., Mišić, N., & Topalović, D. (2022). Performance assessment of NOVA SDS011 low-cost PM sensor in various microenvironments. Environmental Monitoring and Assessment, 194(9), 595. https://doi.org/10.1007/s10661-022-10290-7Brunekreef, B., & Holgate, S. T. (2002). Air pollution and health. The Lancet, 360(9341), 1233–1242. https://doi.org/10.1016/S0140-6736(02)11274-8Builes-Jaramillo, A., Gómez-Bedoya, J., Lopera-Uribe, S., & Fajardo-Castaño, V. (2020). Hotspots, daily cycles and average daily dose of PM2.5 in a cycling route in Medellin. Revista Facultad de Ingeniería Universidad de Antioquia, 96, 87–99. https://doi.org/10.17533/udea.redin.20191153 Bulot, F. M. J., Johnston, S. J., Basford, P. J., Easton, N. H. C., Apetroaie-Cristea, M., Foster, G. L., Morris, A. K. R., Cox, S. J., & Loxham, M. (2019). Long-term field comparison of multiple low-cost particulate matter sensors in an outdoor urban environment. Scientific Reports, 9(1), 7497. https://doi.org/10.1038/s41598-019-43716-3Burnett, R. T., Pope, C. A., Ezzati, M., Olives, C., Lim, S. S., Mehta, S., Shin, H. H., Singh, G., Hubbell, B., Brauer, M., Anderson, H. R., Smith, K. R., Balmes, J. R., Bruce, N. G., Kan, H., Laden, F., Prüss-Ustün, A., Turner, M. C., Gapstur, S. M., … Cohen, A. (2014). An Integrated Risk Function for Estimating the Global Burden of Disease Attributable to Ambient Fine Particulate Matter Exposure. Environmental Health Perspectives, 122(4), 397–403. https://doi.org/10.1289/ehp.1307049Bulot, F. M. J., Johnston, S. J., Basford, P. J., Easton, N. H. C., Apetroaie-Cristea, M., Foster, G. L., Morris, A. K. R., Cox, S. J., & Loxham, M. (2019). Long-term field comparison of multiple low-cost particulate matter sensors in an outdoor urban environment. Scientific Reports, 9(1), 7497. https://doi.org/10.1038/s41598-019-43716-3Bulot, F. M. J., Ossont, S. J., Morris, A. K. R., Basford, P. J., Easton, N. H. C., Mitchell, H. L., Foster, G. L., Cox, S. J., & Loxham, M. (2023). Characterisation and calibration of low-cost PM sensors at high temporal resolution to reference-grade performance. Heliyon, 9(5), e15943. https://doi.org/10.1016/j.heliyon.2023.e15943Camprodon, G., González, Ó., Barberán, V., Pérez, M., Smári, V., de Heras, M. Á., & Bizzotto, A. (2019). Smart Citizen Kit and Station: An open environmental monitoring system for citizen participation and scientific experimentation. HardwareX. https://doi.org/10.1016/j.ohx.2019.e00070CanAirIO. (2021). CanAirIO. Citizen Network for Monitoring Air Quality. https://canair.io/index.htmlCarvalho, H. (2017). The global burden of air pollution-associated deaths—How many are needed for countries to react? The Lancet Planetary Health, 1(5), e179. https://doi.org/10.1016/S2542-5196(17)30076-1Castell, N., Dauge, F. R., Schneider, P., Vogt, M., Lerner, U., Fishbain, B., Broday, D., & Bartonova, A. (2017). Can commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates? Environment International, 99, 293–302. https://doi.org/10.1016/j.envint.2016.12.007Cavaliere, A., Carotenuto, F., Di Gennaro, F., Gioli, B., Gualtieri, G., Martelli, F., Matese, A., Toscano, P., Vagnoli, C., & Zaldei, A. (2018). Development of Low-Cost Air Quality Stations for Next Generation Monitoring Networks: Calibration and Validation of PM2.5 and PM10 Sensors. Sensors, 18(9), 2843. https://doi.org/10.3390/s18092843Cepeda, M., Schoufour, J., Freak-Poli, R., Koolhaas, C. M., Dhana, K., Bramer, W. M., & Franco, O. H. (2017). Levels of ambient air pollution according to mode of transport: A systematic review. The Lancet Public Health, 2(1), e23–e34. https://doi.org/10.1016/S2468-2667(16)30021-4Chen, C.-H., Wu, C.-D., Chiang, H.-C., Chu, D., Lee, K.-Y., Lin, W.-Y., Yeh, J.-I., Tsai, K.-W., & Guo, Y.-L. L. (2019). The effects of fine and coarse particulate matter on lung function among the elderly. Scientific Reports, 9(1), 14790. https://doi.org/10.1038/s41598-019-51307-5Chen, H., Covert, I. C., Lundberg, S. M., & Lee, S.-I. (2023). Algorithms to estimate Shapley value feature attributions. Nature Machine Intelligence, 5(6), Article 6. https://doi.org/10.1038/s42256-023-00657-xChen, L.-J., Ho, Y.-H., Lee, H.-C., Wu, H.-C., Liu, H.-M., Hsieh, H.-H., Huang, Y.-T., & Lung, S.-C. C. (2017). An Open Framework for Participatory PM2.5 Monitoring in Smart Cities. IEEE Access, 5, 14441–14454. https://doi.org/10.1109/ACCESS.2017.2723919Chen, Z., Barros, C. P., & Gil-Alana, L. A. (2016). The persistence of air pollution in four mega-cities of China. Habitat International, 56, 103–108. https://doi.org/10.1016/j.habitatint.2016.05.004Clements, A., Duvall, R., & Dye, T. (2022). The Enhanced Air Sensor Guidebook. https://cfpub.epa.gov/si/si_public_record_Report.cfm?dirEntryId=356426&Lab=CEMMCohen, A. J., Brauer, M., Burnett, R., Anderson, H. R., Frostad, J., Estep, K., Balakrishnan, K., Brunekreef, B., Dandona, L., Dandona, R., Feigin, V., Freedman, G., Hubbell, B., Jobling, A., Kan, H., Knibbs, L., Liu, Y., Martin, R., Morawska, L., … Forouzanfar, M. H. (2017). Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: An analysis of data from the Global Burden of Diseases Study 2015. The Lancet, 389(10082), 1907–1918. https://doi.org/10.1016/S0140-6736(17)30505-6Cole-Hunter, T., Jayaratne, R., Stewart, I., Hadaway, M., Morawska, L., & Solomon, C. (2013). Utility of an alternative bicycle commute route of lower proximity to motorised traffic in decreasing exposure to ultra-fine particles, respiratory symptoms and airway inflammation – a structured exposure experiment. Environmental Health, 12(1), 29. https://doi.org/10.1186/1476-069X-12-29Collier-Oxandale, A., Feenstra, B., Papapostolou, V., Zhang, H., Kuang, M., Der Boghossian, B., & Polidori, A. (2020). Field and laboratory performance evaluations of 28 gas-phase air quality sensors by the AQ-SPEC program. Atmospheric Environment, 220, 117092. https://doi.org/10.1016/j.atmosenv.2019.117092Commodore, A., Wilson, S., Muhammad, O., Svendsen, E., & Pearce, J. (2017). Community-based participatory research for the study of air pollution: A review of motivations, approaches, and outcomes. Environmental Monitoring and Assessment, 189(8), 378. https://doi.org/10.1007/s10661-017-6063-7Correia, C., Martins, V., Cunha-Lopes, I., Faria, T., Diapouli, E., Eleftheriadis, K., & Almeida, S. M. (2020). Particle exposure and inhaled dose while commuting in Lisbon. Environmental Pollution, 257, 113547. https://doi.org/10.1016/j.envpol.2019.113547Crilley, L. R., Shaw, M., Pound, R., Kramer, L. J., Price, R., Young, S., Lewis, A. C., & Pope, F. D. (2018). Evaluation of a low-cost optical particle counter (Alphasense OPC-N2) for ambient air monitoring. Atmospheric Measurement Techniques, 11(2), 709–720. https://doi.org/10.5194/amt-11-709-2018Cross, E. S., Williams, L. R., Lewis, D. K., Magoon, G. R., Onasch, T. B., Kaminsky, M. L., Worsnop, D. R., & Jayne, J. T. (2017). Use of electrochemical sensors for measurement of air pollution: Correcting interference response and validating measurements. Atmospheric Measurement Techniques, 10(9), 3575–3588. https://doi.org/10.5194/amt-10-3575-2017Davies, L., Fradera, R., Riesch, H., & Lakeman-Fraser, P. (2016). Surveying the citizen science landscape: An exploration of the design, delivery and impact of citizen science through the lens of the Open Air Laboratories (OPAL) programme. BMC Ecology, 16(S1), 17. https://doi.org/10.1186/s12898-016-0066-zDC1700-PM PM2.5/PM10 AQM. (2022). http://www.dylosproducts.com/dcpmaqm.htmlde Nazelle, A., Fruin, S., Westerdahl, D., Martinez, D., Ripoll, A., Kubesch, N., & Nieuwenhuijsen, M. (2012). A travel mode comparison of commuters’ exposures to air pollutants in Barcelona. Atmospheric Environment, 59, 151–159. https://doi.org/10.1016/j.atmosenv.2012.05.013de Nazelle, A., Seto, E., Donaire-Gonzalez, D., Mendez, M., Matamala, J., Nieuwenhuijsen, M. J., & Jerrett, M. (2013). Improving estimates of air pollution exposure through ubiquitous sensing technologies. Environmental Pollution, 176, 92–99. https://doi.org/10.1016/j.envpol.2012.12.032Delaney, D. G., Sperling, C. D., Adams, C. S., & Leung, B. (2008). Marine invasive species: Validation of citizen science and implications for national monitoring networks. Biological Invasions, 10(1), 117–128. https://doi.org/10.1007/s10530-007-9114-0Deng, Q., Deng, L., Miao, Y., Guo, X., & Li, Y. (2019). Particle deposition in the human lung: Health implications of particulate matter from different sources. Environmental Research, 169, 237–245. https://doi.org/10.1016/j.envres.2018.11.014Deng, Q., Lu, C., Norbäck, D., Bornehag, C.-G., Zhang, Y., Liu, W., Yuan, H., & Sundell, J. (2015). Early life exposure to ambient air pollution and childhood asthma in China. Environmental Research, 143, 83–92. https://doi.org/10.1016/j.envres.2015.09.032Duvall, R., Clements, A., Hagler, G., Kamal, A., Vasu Kilaru, Goodman, L., Frederick, S., Barkjohn, K. J., VonWald, D., Greene, D., & Dye, T. (2021). Performance Testing Protocols, Metrics, and Target Values for Fine Particulate Matter Air Sensors: Use in Ambient, Outdoor, Fixed Site, Non-Regulatory Supplemental and Informational Monitoring Applications (EPA/600/R-20/280). U.S. EPA Office of Research and Development.Dylos Corporation. (2022, October 10). Dylos Corporation. Air quality monitoring innovation. http://www.dylosproducts.com/EAFIT. (2020). Informe Anual de Calidad de Aire 2020. https://www.metropol.gov.co/ambiental/calidad-del-aire/informes_red_calidaddeaire/Informe-Anual-Aire-2020.pdfEAFIT. (2021). Informe Anual de la Calidad de Aire 2021. https://www.metropol.gov.co/ambiental/calidad-del-aire/informes_red_calidaddeaire/Informe-Anual-Aire-2021.pdfEitzel, M. V., Cappadonna, J. L., Santos-Lang, C., Duerr, R. E., Virapongse, A., West, S. E., Kyba, C. C. M., Bowser, A., Cooper, C. B., Sforzi, A., Metcalfe, A. N., Harris, E. S., Thiel, M., Haklay, M., Ponciano, L., Roche, J., Ceccaroni, L., Shilling, F. M., Dörler, D., … Jiang, Q. (2017). Citizen Science Terminology Matters: Exploring Key Terms. Citizen Science: Theory and Practice, 2(1), 1. https://doi.org/10.5334/cstp.96El Colombiano. (2023, April 27). Encuesta en Medellín y el Aburrá revela que se camina más, se usa mucho la moto y el transporte público pierde protagonismo. www.elcolombiano.com. https://www.elcolombiano.com/antioquia/resultados-encuesta-origen-destino-area-metropolitana-2023-HD21226562EPA. (2014). Air Quality Index. A Guide to Air Quality and your Health. U.S. Environmental Protection Agency. Office of Air Quality Planning and Standards. https://www3.epa.gov/airnow/aqi_brochure_02_14.pdfERG - King’s College London. (2017). Air Pollution Guide. http://www.londonair.org.uk/LondonAir/guide/WhatIsLAQN.aspxEuropean Environment Agency. (2021). Air quality standards—European Environment Agency [Page]. https://www.eea.europa.eu/themes/air/air-quality-concentrations/air-quality-standardsFanti, G., Borghi, F., Spinazzè, A., Rovelli, S., Campagnolo, D., Keller, M., Cattaneo, A., Cauda, E., & Cavallo, D. M. (2021). Features and Practicability of the Next-Generation Sensors and Monitors for Exposure Assessment to Airborne Pollutants: A Systematic Review. Sensors, 21(13), Article 13. https://doi.org/10.3390/s21134513Feenstra, B., Papapostolou, V., Hasheminassab, S., Zhang, H., Boghossian, B. D., Cocker, D., & Polidori, A. (2019). Performance evaluation of twelve low-cost PM2.5 sensors at an ambient air monitoring site. Atmospheric Environment, 216, 116946. https://doi.org/10.1016/j.atmosenv.2019.116946Ferrer-Cid, P., Barcelo-Ordinas, J. M., Garcia-Vidal, J., Ripoll, A., & Viana, M. (2019). A Comparative Study of Calibration Methods for Low-Cost Ozone Sensors in IoT Platforms. IEEE Internet of Things Journal, 6(6), 9563–9571. https://doi.org/10.1109/JIOT.2019.2929594Ferrer-Cid, P., Barcelo-Ordinas, J. M., Garcia-Vidal, J., Ripoll, A., & Viana, M. (2020). Multisensor Data Fusion Calibration in IoT Air Pollution Platforms. IEEE Internet of Things Journal, 7(4), 3124–3132. https://doi.org/10.1109/JIOT.2020.2965283Franco, J. F., Segura, J. F., & Mura, I. (2016). Air Pollution alongside Bike-Paths in Bogotá-Colombia. Frontiers in Environmental Science, 4. https://doi.org/10.3389/fenvs.2016.00077Gao, M., Cao, J., & Seto, E. (2015). A distributed network of low-cost continuous reading sensors to measure spatiotemporal variations of PM2.5 in Xi’an, China. Environmental Pollution, 199, 56–65. https://doi.org/10.1016/j.envpol.2015.01.013Garcia, D., Parra, M. A., Gómez, D., Alzate, C. A., Herrera, L., & Hoyos Ortiz, C. D. (2018). Citizen Scientists of the Aburrá Valley: The results of an education, communication, and science strategy. 2018, ED54A-02.Goel, A., & Kumar, P. (2014). A review of fundamental drivers governing the emissions, dispersion and exposure to vehicle-emitted nanoparticles at signalised traffic intersections. Atmospheric Environment, 97, 316–331. https://doi.org/10.1016/j.atmosenv.2014.08.037González, R. (2020, June 25). Arenas del Sahara afectan calidad del aire en el Valle de Aburrá. https://territoriossostenibles.com/cambio-climatico/arenas-del-sahara-afectan-calidad-del-aire-en-el-valle-de-aburra/Guevara, M., Tena, C., Soret, A., Serradell, K., Guzmán, D., Retama, A., Camacho, P., Jaimes-Palomera, M., & Mediavilla, A. (2017). An emission processing system for air quality modelling in the Mexico City metropolitan area: Evaluation and comparison of the MOBILE6.2-Mexico and MOVES-Mexico traffic emissions. Science of The Total Environment, 584–585, 882–900. https://doi.org/10.1016/j.scitotenv.2017.01.135Gulia, S., Shiva Nagendra, S. M., Khare, M., & Khanna, I. (2015). Urban air quality management-A review. Atmospheric Pollution Research, 6(2), 286–304. https://doi.org/10.5094/APR.2015.033HabitatMap. (2023). Airbeam—User’s Guide. HabitatMap Is an Environmental Tech Org and Maker of AirBeam. https://www.habitatmap.org/airbeam/users-guideHagan, D. H., Isaacman-VanWertz, G., Franklin, J. P., Wallace, L. M. M., Kocar, B. D., Heald, C. L., & Kroll, J. H. (2018). Calibration and assessment of electrochemical air quality sensors by co-location with regulatory-grade instruments. Atmospheric Measurement Techniques, 11(1), 315–328. https://doi.org/10.5194/amt-11-315-2018Vineis, P., Forastiere, F., Saldiva, P., Yorifuji, T., & Loomis, D. (2014). Outdoor Particulate Matter Exposure and Lung Cancer: A Systematic Review and Meta-Analysis. Environmental Health Perspectives. https://doi.org/10.1289/ehp.1408092Han, J., & Kamber, M. (2012). Data mining: Concepts and techniques (3rd ed). Elsevier.Hankey, S., & Marshall, J. D. (2015). On-bicycle exposure to particulate air pollution: Particle number, black carbon, PM 2.5 , and particle size. Atmospheric Environment, 122, 65–73. https://doi.org/10.1016/j.atmosenv.2015.09.025Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning. Springer New York. https://doi.org/10.1007/978-0-387-84858-7Heimann, I., Bright, V. B., McLeod, M. W., Mead, M. I., Popoola, O. A. M., Stewart, G. B., & Jones, R. L. (2015). Source attribution of air pollution by spatial scale separation using high spatial density networks of low cost air quality sensors. Atmospheric Environment, 113, 10–19. https://doi.org/10.1016/j.atmosenv.2015.04.057Hermelin, M. (2007, August). Valle de Aburrá: ¿Quo vadis? Gestión y Ambiente, 10(2), 7–16.Hernández, M. A., Ramírez, O., Benavides, J. A., & Franco, J. F. (2021). Urban cycling and air quality: Characterizing cyclist exposure to particulate-related pollution. Urban Climate, 36. Scopus. https://doi.org/10.1016/j.uclim.2020.100767Hernández-Paniagua, I. Y., Andraca-Ayala, G. L., Diego-Ayala, U., Ruiz-Suarez, L. G., Zavala-Reyes, J. C., Cid-Juárez, S., Torre-Bouscoulet, L., Gochicoa-Rangel, L., Rosas-Pérez, I., & Jazcilevich, A. (2018). Personal Exposure to PM2.5 in the Megacity of Mexico: A Multi-Mode Transport Study. Atmosphere, 9(2), Article 2. https://doi.org/10.3390/atmos9020057Herrera-Mejía, L., & Hoyos, C. D. (2019). Characterization of the atmospheric boundary layer in a narrow tropical valley using remote-sensing and radiosonde observations and the WRF model: The Aburrá Valley case-study. Quarterly Journal of the Royal Meteorological Society, 145(723), 2641–2665. https://doi.org/10.1002/qj.3583Holstius, D. M., Pillarisetti, A., Smith, K. R., & Seto, E. (2014). Field calibrations of a low-cost aerosol sensor at a regulatory monitoring site in California. Atmospheric Measurement Techniques, 7(4), 1121–1131. https://doi.org/10.5194/amt-7-1121-2014Hoyos, C. D., Herrera-Mejía, L., Roldán-Henao, N., & Isaza, A. (2020). Effects of fireworks on particulate matter concentration in a narrow valley: The case of the Medellín metropolitan area. Environmental Monitoring and Assessment, 192(1), 6. https://doi.org/10.1007/s10661-019-7838-9Hu, K., Rahman, A., Bhrugubanda, H., & Sivaraman, V. (2017). HazeEst: Machine Learning Based Metropolitan Air Pollution Estimation From Fixed and Mobile Sensors. IEEE Sensors Journal, 17(11), 3517–3525. https://doi.org/10.1109/JSEN.2017.2690975Hubbell, B. J., Kaufman, A., Rivers, L., Schulte, K., Hagler, G., Clougherty, J., Cascio, W., & Costa, D. (2018). Understanding social and behavioral drivers and impacts of air quality sensor use. Science of The Total Environment, 621, 886–894. https://doi.org/10.1016/j.scitotenv.2017.11.275Int Panis, L., de Geus, B., Vandenbulcke, G., Willems, H., Degraeuwe, B., Bleux, N., Mishra, V., Thomas, I., & Meeusen, R. (2010). Exposure to particulate matter in traffic: A comparison of cyclists and car passengers. Atmospheric Environment, 44(19), 2263–2270. https://doi.org/10.1016/j.atmosenv.2010.04.028Jiang, Q., Kresin, F., Bregt, A. K., Kooistra, L., Pareschi, E., van Putten, E., Volten, H., & Wesseling, J. (2016). Citizen Sensing for Improved Urban Environmental Monitoring. Journal of Sensors, 2016, 1–9. https://doi.org/10.1155/2016/5656245Jiao, W., Hagler, G., Williams, R., Sharpe, R., Brown, R., Garver, D., Judge, R., Caudill, M., Rickard, J., Davis, M., Weinstock, L., Zimmer-Dauphinee, S., & Buckley, K. (2016). Community Air Sensor Network (CAIRSENSE) project: Evaluation of low-costsensor performance in a suburban environment in the southeastern UnitedStates. Atmospheric Measurement Techniques, 9(11), 5281–5292. https://doi.org/10.5194/amt-9-5281-2016Jovašević-Stojanović, M., Bartonova, A., Topalović, D., Lazović, I., Pokrić, B., & Ristovski, Z. (2015). On the use of small and cheaper sensors and devices for indicative citizen-based monitoring of respirable particulate matter. Environmental Pollution, 206, 696–704. https://doi.org/10.1016/j.envpol.2015.08.035Kaneko, H. (2023). Interpretation of Machine Learning Models for Data Sets with Many Features Using Feature Importance. ACS Omega, 8(25), 23218–23225. https://doi.org/10.1021/acsomega.3c03722Kang, Y., Aye, L., Ngo, T. D., & Zhou, J. (2022). Performance evaluation of low-cost air quality sensors: A review. Science of The Total Environment, 818, 151769. https://doi.org/10.1016/j.scitotenv.2021.151769Karagulian, F., Barbiere, M., Kotsev, A., Spinelle, L., Gerboles, M., Lagler, F., Redon, N., Crunaire, S., & Borowiak, A. (2019). Review of the Performance of Low-Cost Sensors for Air Quality Monitoring. Atmosphere, 10(9), 506. https://doi.org/10.3390/atmos10090506Kaur, K., & Kelly, K. E. (2023). Laboratory evaluation of the Alphasense OPC-N3, and the Plantower PMS5003 and PMS6003 sensors. Journal of Aerosol Science, 171, 106181. https://doi.org/10.1016/j.jaerosci.2023.106181Kelly, K. E., Whitaker, J., Petty, A., Widmer, C., Dybwad, A., Sleeth, D., Martin, R., & Butterfield, A. (2017). Ambient and laboratory evaluation of a low-cost particulate matter sensor. Environmental Pollution, 221, 491–500. https://doi.org/10.1016/j.envpol.2016.12.039Kim, K.-H., Kabir, E., & Kabir, S. (2015). A review on the human health impact of airborne particulate matter. Environment International, 74, 136–143. https://doi.org/10.1016/j.envint.2014.10.005Kobernus, M., Berre, A. J., Gonzalez, M., Liu, H.-Y., Rombouts, R., & Bartanova, A. (2015). A Practical Approach to an Integrated Citizens’ Observatory: The CITI-SENSE Framework. https://ceur-ws.org/Vol-1322/paper_1.pdfKosmidis, E., Syropoulou, P., Tekes, S., Schneider, P., Spyromitros-Xioufis, E., Riga, M., Charitidis, P., Moumtzidou, A., Papadopoulos, S., Vrochidis, S., & others. (2018). HackAIR: Towards raising awareness about air quality in Europe by developing a collective online platform. ISPRS International Journal of Geo-Information, 7(5), 187.Kumar, P., Hama, S., Nogueira, T., Abbass, R. A., Brand, V. S., Andrade, M. de F., Asfaw, A., Aziz, K. H., Cao, S.-J., El-Gendy, A., Islam, S., Jeba, F., Khare, M., Mamuya, S. H., Martinez, J., Meng, M.-R., Morawska, L., Muula, A. S., Shiva Nagendra, S. M., … Salam, A. (2021). In-car particulate matter exposure across ten global cities. Science of The Total Environment, 750, 141395. https://doi.org/10.1016/j.scitotenv.2020.141395Kumar, P., Morawska, L., Martani, C., Biskos, G., Neophytou, M., Di Sabatino, S., Bell, M., Norford, L., & Britter, R. (2015). The rise of low-cost sensing for managing air pollution in cities. Environment International, 75, 199–205. https://doi.org/10.1016/j.envint.2014.11.019Kumar, P., Rivas, I., Singh, A. P., Ganesh, V. J., Ananya, M., & Frey, H. C. (2018). Dynamics of coarse and fine particle exposure in transport microenvironments. Npj Climate and Atmospheric Science, 1(1), 11. https://doi.org/10.1038/s41612-018-0023-yLandrigan, P. J., Fuller, R., Acosta, N. J. R., Adeyi, O., Arnold, R., Basu, N. (Nil), Baldé, A. B., Bertollini, R., Bose-O’Reilly, S., Boufford, J. I., Breysse, P. N., Chiles, T., Mahidol, C., Coll-Seck, A. M., Cropper, M. L., Fobil, J., Fuster, V., Greenstone, M., Haines, A., … Zhong, M. (2017). The Lancet Commission on pollution and health. The Lancet. https://doi.org/10.1016/S0140-6736(17)32345-0Levy Zamora, M., Xiong, F., Gentner, D., Kerkez, B., Kohrman-Glaser, J., & Koehler, K. (2019). Field and Laboratory Evaluations of the Low-Cost Plantower Particulate Matter Sensor. Environmental Science & Technology, 53(2), 838–849. https://doi.org/10.1021/acs.est.8b05174Li, S.-T., & Shue, L.-Y. (2004). Data mining to aid policy making in air pollution management. Expert Systems with Applications, 27(3), 331–340. https://doi.org/10.1016/j.eswa.2004.05.015Liu, H.-Y., Schneider, P., Haugen, R., & Vogt, M. (2019). Performance Assessment of a Low-Cost PM2.5 Sensor for a near Four-Month Period in Oslo, Norway.Loomis, D., Huang, W., & Chen, G. (2014). The International Agency for Research on Cancer (IARC) evaluation of the carcinogenicity of outdoor air pollution: Focus on China. Chinese Journal of Cancer, 33(4), 189–196. https://doi.org/10.5732/cjc.014.10028Luengo-Oroz, J., & Reis, S. (2019). Assessment of cyclists’ exposure to ultrafine particles along alternative commuting routes in Edinburgh. Atmospheric Pollution Research, 10(4), 1148–1158. https://doi.org/10.1016/j.apr.2019.01.020Lundberg, S. M., & Lee, S.-I. (2017). Consistent feature attribution for tree ensembles. Proceedings of the 34 Th International Conference on Machine Learning.Ma, Y., Richards, M., Ghanem, M., Guo, Y., & Hassard, J. (2008). Air Pollution Monitoring and Mining Based on Sensor Grid in London. Sensors, 8(6), 3601–3623. https://doi.org/10.3390/s8063601Maag, B., Zhou, Z., & Thiele, L. (2018). A Survey on Sensor Calibration in Air Pollution Monitoring Deployments. IEEE Internet of Things Journal, 5(6), 4857–4870. https://doi.org/10.1109/JIOT.2018.2853660Mahajan, S., Chung, M.-K., Martinez, J., Olaya, Y., Helbing, D., & Chen, L.-J. (2022). Translating citizen-generated air quality data into evidence for shaping policy. Humanities and Social Sciences Communications, 9(1), Article 1. https://doi.org/10.1057/s41599-022-01135-2Mahajan, S., & Kumar, P. (2020). Evaluation of low-cost sensors for quantitative personal exposure monitoring. Sustainable Cities and Society, 57, 102076. https://doi.org/10.1016/j.scs.2020.102076Mahajan, S., Kumar, P., Pinto, J. A., Riccetti, A., Schaaf, K., Camprodon, G., Smári, V., Passani, A., & Forino, G. (2020). A citizen science approach for enhancing public understanding of air pollution. Sustainable Cities and Society, 52, 101800. https://doi.org/10.1016/j.scs.2019.101800Mahajan, S., Luo, C.-H., Wu, D.-Y., & Chen, L.-J. (2021). From Do-It-Yourself (DIY) to Do-It-Together (DIT): Reflections on designing a citizen-driven air quality monitoring framework in Taiwan. Sustainable Cities and Society, 66, 102628. https://doi.org/10.1016/j.scs.2020.102628Makri, A., & Stilianakis, N. I. (2008). Vulnerability to air pollution health effects. International Journal of Hygiene and Environmental Health, 211(3–4), 326–336. https://doi.org/10.1016/j.ijheh.2007.06.005Mannucci, P., & Franchini, M. (2017). Health Effects of Ambient Air Pollution in Developing Countries. International Journal of Environmental Research and Public Health, 14(9), 1048. https://doi.org/10.3390/ijerph14091048Manojkumar, N., Monishraj, M., & Srimuruganandam, B. (2021). Commuter exposure concentrations and inhalation doses in traffic and residential routes of Vellore city, India. Atmospheric Pollution Research, 12(1), 219–230. https://doi.org/10.1016/j.apr.2020.09.002Manojkumar, N., & Srimuruganandam, B. (2021). Investigation of on-road fine particulate matter exposure concentration and its inhalation dosage levels in an urban area. Building and Environment, 198, 107914. https://doi.org/10.1016/j.buildenv.2021.107914Marjovi, A., Arfire, A., & Martinoli, A. (2015). High Resolution Air Pollution Maps in Urban Environments Using Mobile Sensor Networks. 2015 International Conference on Distributed Computing in Sensor Systems, 11–20. https://doi.org/10.1109/DCOSS.2015.32McKercher, G. R., Salmond, J. A., & Vanos, J. K. (2017). Characteristics and applications of small, portable gaseous air pollution monitors. Environmental Pollution, 223, 102–110. https://doi.org/10.1016/j.envpol.2016.12.045Mead, M. I., Popoola, O. A. M., Stewart, G. B., Landshoff, P., Calleja, M., Hayes, M., Baldovi, J. J., McLeod, M. W., Hodgson, T. F., Dicks, J., Lewis, A., Cohen, J., Baron, R., Saffell, J. R., & Jones, R. L. (2013). The use of electrochemical sensors for monitoring urban air quality in low-cost, high-density networks. Atmospheric Environment, 70, 186–203. https://doi.org/10.1016/j.atmosenv.2012.11.060Medellín Cómo Vamos. (2021). Informe de de Calidad de Vida de Medellín, 2020. https://www.medellincomovamos.org/system/files/2021-09/docuprivados/Movilidad%20y%20espacio%20p%C3%BAblico%20Informe%20de%20Calidad%20de%20Vida%20de%20Medell%C3%ADn%2C%202020.pdf Resolución 2254 de 2017, (2017). https://www.minambiente.gov.co/documento-entidad/resolucion-2254-de-2017/Ministerio de Ambiente y Desarrollo Sostenible. (2021). Contaminación Atmosférica. Ministerio de Ambiente y Desarrollo Sostenible. https://www.minambiente.gov.co/asuntos-ambientales-sectorial-y-urbana/contaminacion-atmosferica/Moore, E., Chatzidiakou, L., Kuku, M.-O., Jones, R. L., Smeeth, L., Beevers, S., Kelly, F. J., Barratt, B., & Quint, J. (2016). Global Associations between Air Pollutants and Chronic Obstructive Pulmonary Disease Hospitalizations: A Systematic Review. Annals of the American Thoracic Society, AnnalsATS.201601-064OC. https://doi.org/10.1513/AnnalsATS.201601-064OCMorawska, L., Thai, P. K., Liu, X., Asumadu-Sakyi, A., Ayoko, G., Bartonova, A., Bedini, A., Chai, F., Christensen, B., Dunbabin, M., Gao, J., Hagler, G. S. W., Jayaratne, R., Kumar, P., Lau, A. K. H., Louie, P. K. K., Mazaheri, M., Ning, Z., Motta, N., … Williams, R. (2018). Applications of low-cost sensing technologies for air quality monitoring and exposure assessment: How far have they gone? Environment International, 116, 286–299. https://doi.org/10.1016/j.envint.2018.04.018Mukherjee, A., Stanton, L., Graham, A., & Roberts, P. (2017). Assessing the Utility of Low-Cost Particulate Matter Sensors over a 12-Week Period in the Cuyama Valley of California. Sensors, 17(8), 1805. https://doi.org/10.3390/s17081805NABEL. (2017). National Air Pollution Monitoring Network (NABEL). https://www.bafu.admin.ch/bafu/en/home/topics/air/state/data/national-air-pollution-monitoring-network--nabel-.htmlNoble, C. A., Vanderpool, R. W., Peters, T. M., McElroy, F. F., Gemmill, D. B., & Wiener, R. W. (2001). Federal Reference and Equivalent Methods for Measuring Fine Particulate Matter. Aerosol Science and Technology, 34(5), 457–464. https://doi.org/10.1080/02786820121582OCL. (2019). People-centered informed decision making for participatory democracy. https://opencitieslab.org/odd/homeOpenSeneca. (2019). Open-Seneca in Nairobi. https://centreforglobalequality.org/news/open-seneca-in-nairobi/Papapostolou, V., Zhang, H., Feenstra, B. J., & Polidori, A. (2017). Development of an environmental chamber for evaluating the performance of low-cost air quality sensors under controlled conditions. Atmospheric Environment, 171, 82–90. https://doi.org/10.1016/j.atmosenv.2017.10.003Perera, F. (2017). Pollution from Fossil-Fuel Combustion is the Leading Environmental Threat to Global Pediatric Health and Equity: Solutions Exist. International Journal of Environmental Research and Public Health, 15(1), 16. https://doi.org/10.3390/ijerph15010016Pope, C. A., & Dockery, D. W. (2006). Health Effects of Fine Particulate Air Pollution: Lines that Connect. Journal of the Air & Waste Management Association, 56(6), 709–742. https://doi.org/10.1080/10473289.2006.10464485Pope III, C. A. (2002). Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate Air Pollution. JAMA, 287(9), 1132. https://doi.org/10.1001/jama.287.9.1132Popoola, O. A. M., Carruthers, D., Lad, C., Bright, V. B., Mead, M. I., Stettler, M. E. J., Saffell, J. R., & Jones, R. L. (2018). Use of networks of low cost air quality sensors to quantify air quality in urban settings. Atmospheric Environment, 194, 58–70. https://doi.org/10.1016/j.atmosenv.2018.09.030PurpleAir. (2023). PurpleAir \| Real-time Air Quality Monitoring. PurpleAir, Inc. https://www2.purpleair.com/Ramírez, O., Mura, I., & Franco, J. F. (2017). How Do People Understand Urban Air Pollution? Exploring Citizens’ Perception on Air Quality, Its Causes and Impacts in Colombian Cities. Open Journal of Air Pollution, 6(1), Article 1. https://doi.org/10.4236/ojap.2017.61001Ramos, C. A., Silva, J. R., Faria, T., Wolterbeek, T. H., & Almeida, S. M. (2017). Exposure assessment of a cyclist to particles and chemical elements. Environmental Science and Pollution Research, 24(13), 11879–11889. https://doi.org/10.1007/s11356-016-6365-2Ramos, C. A., Wolterbeek, H. T., & Almeida, S. M. (2016). Air pollutant exposure and inhaled dose during urban commuting: A comparison between cycling and motorized modes. Air Quality, Atmosphere & Health, 9(8), 867–879. https://doi.org/10.1007/s11869-015-0389-5Rendón, A. M., Salazar, J. F., Palacio, C. A., & Wirth, V. (2015). Temperature Inversion Breakup with Impacts on Air Quality in Urban Valleys Influenced by Topographic Shading. Journal of Applied Meteorology and Climatology, 54(2), 302–321. https://doi.org/10.1175/JAMC-D-14-0111.1Rendón, A. M., Salazar, J. F., Palacio, C. A., Wirth, V., & Brötz, B. (2014). Effects of Urbanization on the Temperature Inversion Breakup in a Mountain Valley with Implications for Air Quality. Journal of Applied Meteorology and Climatology, 53(4), 840–858.Riesch, H., & Potter, C. (2014). Citizen science as seen by scientists: Methodological, epistemological and ethical dimensions. Public Understanding of Science, 23(1), 107–120. https://doi.org/10.1177/0963662513497324Riojas-Rodríguez, H., Texcalac-Sangrador, J. L., & Moreno-Banda, G. L. (2016). Air pollution management and control in Latin America and the Caribbean: Implications for climate change. Rev Panam Salud Publica, 10.Roldán Henao, N., Isaza, A., Herrera, L., & Hoyos Ortiz, C. D. (2018). Aburrá Valley (Colombia) spatio-temporal air quality assessment from a monitoring network using low-cost sensors and citizen science. 2018, A12E-08.Roldán-Henao, N., Hoyos, C. D., Herrera-Mejía, L., & Isaza, A. (2020). An Investigation of the Precipitation Net Effect on the Particulate Matter Concentration in a Narrow Valley: Role of Lower-Troposphere Stability. Journal of Applied Meteorology and Climatology, 59(3), 401–426. https://doi.org/10.1175/JAMC-D-18-0313.1Roy, H. E., & Brown, P. M. J. (2015). Ten years of invasion: Harmonia axyridis (Pallas) (Coleoptera: Coccinellidae) in Britain. Ecological Entomology, 40(4), 336–348. Scopus. https://doi.org/10.1111/een.12203Samad, A., & Vogt, U. (2021). Mobile air quality measurements using bicycle to obtain spatial distribution and high temporal resolution in and around the city center of Stuttgart. Atmospheric Environment, 244, 117915. https://doi.org/10.1016/j.atmosenv.2020.117915Sayahi, T., Kaufman, D., Becnel, T., Kaur, K., Butterfield, A. E., Collingwood, S., Zhang, Y., Gaillardon, P.-E., & Kelly, K. E. (2019). Development of a calibration chamber to evaluate the performance of low-cost particulate matter sensors. Environmental Pollution, 255, 113131. https://doi.org/10.1016/j.envpol.2019.113131Schaefer, T., Kieslinger, B., & Fabian, C. M. (2020). Citizen-Based Air Quality Monitoring: The Impact on Individual Citizen Scientists and How to Leverage the Benefits to Affect Whole Regions. Citizen Science: Theory and Practice, 5(1), 6. https://doi.org/10.5334/cstp.245Science, R. L. (2020). Transforming air quality monitoring through a microscopic lens. https://shaktifoundation.in/wp-content/uploads/2020/01/Impacts_Respirer.pdfSemple, S., Sweeting, H., Demou, E., Logan, G., O’Donnell, R., Hunt, K., & on behalf of the Tobacco in Prisons (TIPs) Research Team. (2017). Characterising the Exposure of Prison Staff to Second-Hand Tobacco Smoke. Annals of Work Exposures and Health, 61(7), 809–821. https://doi.org/10.1093/annweh/wxx058Sensirion. (2021). SPS30—PM2.5 Sensor for HVAC and air quality applications SPS30. https://sensirion.com/products/catalog/SPS30/Sensors.Africa. (2021). Citizen science initiative that uses sensors to monitor air, water and sound pollution. https://sensors.africa/Shah, A. S., Langrish, J. P., Nair, H., McAllister, D. A., Hunter, A. L., Donaldson, K., Newby, D. E., & Mills, N. L. (2013). Global association of air pollution and heart failure: A systematic review and meta-analysis. The Lancet, 382(9897), 1039–1048. https://doi.org/10.1016/S0140-6736(13)60898-3Si, M., Xiong, Y., Du, S., & Du, K. (2020). Evaluation and calibration of a low-cost particle sensor in ambient conditions using machine-learning methods. Atmospheric Measurement Techniques, 13(4), 1693–1707. https://doi.org/10.5194/amt-13-1693-2020Siata. (2017). Monitoreo de la calidad del aire. https://siata.gov.co/sitio_web/index.php/calidad_aireSpinelle, L., Gerboles, M., Kok, G., Persijn, S., & Sauerwald, T. (2017). Review of Portable and Low-Cost Sensors for the Ambient Air Monitoring of Benzene and Other Volatile Organic Compounds. Sensors, 17(7), 1520. https://doi.org/10.3390/s17071520Sun, J., Zhang, N., Yan, X., Wang, M., & Wang, J. (2020). The effect of ambient fine particulate matter (PM2.5) on respiratory diseases in China: A systematic review and meta-analysis. Stochastic Environmental Research and Risk Assessment, 34(3–4), 593–610. https://doi.org/10.1007/s00477-020-01786-0Supasri, T., & Sampattagul, S. (2019). Haze Problem in the North of Thailand and DustBoy PM 2.5 Monitoring Systems.Tagle, M., Rojas, F., Reyes, F., Vásquez, Y., Hallgren, F., Lindén, J., Kolev, D., Watne, Å. K., & Oyola, P. (2020). Field performance of a low-cost sensor in the monitoring of particulate matter in Santiago, Chile. Environmental Monitoring and Assessment, 192(3), 171. https://doi.org/10.1007/s10661-020-8118-4Thiel, M., Penna-Díaz, M. A., Luna-Jorquera, G., Salas, S., Sellanes, J., & Stotz, W. (2014). Citizen scientists and marine research: Volunteer participants, their contributions, and projection for the future. Oceanography and Marine Biology: An Annual Review, 52, 257–314. Scopus. https://doi.org/10.1201/b17143UPB & AMVA. (2018). Actualización inventario de emisiones atmosféricas del Valle de Aburrá, 2016. Informe final. https://www.metropol.gov.co/ambiental/calidad-del-aire/Documents/Inventario-de-emisiones/Inventario_FuentesM%C3%B3viles2016.pdfU.S. Environmental Protection Agency (EPA). (2009). Metabolically Derived Human Ventilation Rates: A Revised Approach Based Upon Oxygen Consumption Rates (EPA/600/R-06/129F). National Center for Environmental Assessment. https://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=202543US EPA. (2016a). List of Designated Reference and Equivalent Methods. https://www.epa.gov/system/files/documents/2021-12/designated-referene-and-equivalent-methods-12152021.pdfUS EPA. (2016b). Technical Assistance Document for the Reporting of Daily Air Quality \| AirNow.gov. https://www.airnow.gov/publications/air-quality-index/technical-assistance-document-for-reporting-the-daily-aqi/US EPA. (2021). The National Ambient Air Quality Standards for Particulate Matter. https://www.epa.gov/sites/default/files/2020-04/documents/fact_sheet_pm_naaqs_proposal.pdfUS EPA, O. (2018, January 23). Instruction Guide and Macro Analysis Tool: Evaluating Air Sensors by Collocation with Federal Reference Monitors [Data and Tools]. https://www.epa.gov/air-research/instruction-guide-and-macro-analysis-tool-evaluating-air-sensors-collocation-federalValencia, J.-C., & Fonseca, O. (2019). AIR POLLUTION, CITIZEN DATA COLLECTIVES AND COMMUNICATION AGENDA SETTING IN COLOMBIA. 33–43. https://doi.org/10.2495/AIR190041Varaden, D., Leidland, E., Lim, S., & Barratt, B. (2021). “I am an air quality scientist”– Using citizen science to characterise school children’s exposure to air pollution. Environmental Research, 201, 111536. https://doi.org/10.1016/j.envres.2021.111536von Gönner, J., Bowler, D. E., Gröning, J., Klauer, A.-K., Liess, M., Neuer, L., & Bonn, A. (2023). Citizen science for assessing pesticide impacts in agricultural streams. Science of the Total Environment, 857. Scopus. https://doi.org/10.1016/j.scitotenv.2022.159607Wang, W.-C. V., Lung, S.-C. C., & Liu, C.-H. (2020). Application of Machine Learning for the in-Field Correction of a PM2.5 Low-Cost Sensor Network. Sensors, 20(17), 5002. https://doi.org/10.3390/s20175002Wang, W.-C. V., Lung, S.-C. C., Liu, C.-H., Wen, T.-Y. J., Hu, S.-C., & Chen, L.-J. (2021). Evaluation and Application of a Novel Low-Cost Wearable Sensing Device in Assessing Real-Time PM2.5 Exposure in Major Asian Transportation Modes. Atmosphere, 12(2), Article 2. https://doi.org/10.3390/atmos12020270Wang, Y., Li, J., Jing, H., Zhang, Q., Jiang, J., & Biswas, P. (2015). Laboratory Evaluation and Calibration of Three Low-Cost Particle Sensors for Particulate Matter Measurement. Aerosol Science and Technology, 49(11), 1063–1077. https://doi.org/10.1080/02786826.2015.1100710Wesseling, J., de Ruiter, H., Blokhuis, C., Drukker, D., Weijers, E., Volten, H., Vonk, J., Gast, L., Voogt, M., Zandveld, P., van Ratingen, S., & Tielemans, E. (2019). Development and Implementation of a Platform for Public Information on Air Quality, Sensor Measurements, and Citizen Science. Atmosphere, 10(8), Article 8. https://doi.org/10.3390/atmos10080445Wesseling, J., Hendricx, W., de Ruiter, H., van Ratingen, S., Drukker, D., Huitema, M., Schouwenaar, C., Janssen, G., van Aken, S., Smeenk, J., Hof, A., & Tielemans, E. (2021). Assessment of PM2.5 Exposure during Cycle Trips in The Netherlands Using Low-Cost Sensors. International Journal of Environmental Research and Public Health, 18(11), 6007. https://doi.org/10.3390/ijerph18116007Williams, R., Kilaru, V., Snyder, E., Kaufman, A., Dye, T., Rutter, A., Russell, A., & Hafner, H. (2014). Air Sensor Guidebook (EPA/600/R-14/159). United States Environmental Protection Agency. file:///C:/Users/SVS13115FLS/Downloads/GUIDEBOOK%20-%20FINAL.PDFWorld Health Organization. (2006). WHO Air quality guidelines for particulate matter, ozone, nitrogen dioxide and sulfur dioxide. Global update 2005. Summary of risk assessment (Occupational and Environmental Health Team). World Health Organization. http://apps.who.int/iris/bitstream/10665/69477/1/WHO_SDE_PHE_OEH_06.02_eng.pdfWorld Health Organization. (2021). WHO global air quality guidelines: Particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. World Health Organization. https://apps.who.int/iris/handle/10665/345329Xing, Y. F., Xu, Y. H., Shi, M. H., & Lian, Y. X. (2016). The impact of PM2.5 on the human respiratory system. Journal of Thoracic Disease, 8(1). https://doi.org/10.3978/j.issn.2072-1439.2016.01.19Yatkin, S., Gerboles, M., Belis, C. A., Karagulian, F., Lagler, F., Barbiere, M., & Borowiak, A. (2020). Representativeness of an air quality monitoring station for PM2.5 and source apportionment over a small urban domain. Atmospheric Pollution Research, 11(2), 225–233. https://doi.org/10.1016/j.apr.2019.10.004Zamora, M. L., Rice, J., & Koehler, K. (2020). One year evaluation of three low-cost PM2.5 monitors. Atmospheric Environment, 235, 117615. https://doi.org/10.1016/j.atmosenv.2020.117615Zikova, N., Masiol, M., Chalupa, D., Rich, D., Ferro, A., & Hopke, P. (2017). Estimating Hourly Concentrations of PM2.5 across a Metropolitan Area Using Low-Cost Particle Monitors. Sensors, 17(8), 1922. https://doi.org/10.3390/s17081922Zimmerman, N., Presto, A. A., Kumar, S. P. N., Gu, J., Hauryliuk, A., Robinson, E. S., Robinson, A. L., & R. Subramanian. (2018). A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring. Atmospheric Measurement Techniques, 11(1), 291–313. https://doi.org/10.5194/amt-11-291-2018Malings, C., Tanzer, R., Hauryliuk, A., Kumar, S. P. N., Zimmerman, N., Kara, L. B., Presto, A. A., & Subramanian, R. (2019). Development of a general calibration model and long-term performance evaluation of low-cost sensors for air pollutant gas monitoring. Atmospheric Measurement Techniques, 12(2), 903–920. https://doi.org/10.5194/amt-12-903-2019Snyder, E. G., Watkins, T. H., Solomon, P. A., Thoma, E. D., Williams, R. W., Hagler, G. S. W., Shelow, D., Hindin, D. A., Kilaru, V. J., & Preuss, P. W. (2013). The Changing Paradigm of Air Pollution Monitoring. Environmental Science & Technology, 47(20), 11369–11377. https://doi.org/10.1021/es4022602Spinelle, L., Gerboles, M., Villani, M. G., Aleixandre, M., & Bonavitacola, F. (2015). Field calibration of a cluster of low-cost available sensors for air quality monitoring. Part A: Ozone and nitrogen dioxide. Sensors and Actuators B: Chemical, 215, 249–257. https://doi.org/10.1016/j.snb.2015.03.031ColcienciasEstudiantesLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/85696/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL63560781.2023.pdf.pdf63560781.2023.pdf.pdfTesis de Doctorado en Ingenieria de Sistemasapplication/pdf5319819https://repositorio.unal.edu.co/bitstream/unal/85696/2/63560781.2023.pdf.pdf21cece1afd7f7d1ac48d7ceb59d68b21MD52THUMBNAIL63560781.2023.pdf.pdf.jpg63560781.2023.pdf.pdf.jpgGenerated Thumbnailimage/jpeg4296https://repositorio.unal.edu.co/bitstream/unal/85696/3/63560781.2023.pdf.pdf.jpge129e58763a5b9af81d9a584cf31692aMD53unal/85696oai:repositorio.unal.edu.co:unal/856962024-02-13 23:03:35.72Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.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

Monitoring urban air pollution using low-cost sensor devices

Publicaciones similares