- -

Analysis of incidence of air quality on human health: a case study on the relationship between pollutant concentrations and respiratory diseases in Kennedy, Bogotá

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Analysis of incidence of air quality on human health: a case study on the relationship between pollutant concentrations and respiratory diseases in Kennedy, Bogotá

Mostrar el registro sencillo del ítem

Ficheros en el ítem

Thumbnail
Nombre: Molina-Gomez;Cald ...
Tamaño: 984.2Kb
Formato: PDF
Descripción: Versión del Autor.
Abrir
[Cerrado]
Nombre: Molina-Gómez2021_ ...
Tamaño: 4.515Mb
Formato: PDF
Descripción: Versión editorial
Solicitar una copia al autor
dc.contributor.author Molina-Gomez, Nidia Isabel es_ES
dc.contributor.author Calderón-Rivera, Dayam Soret es_ES
dc.contributor.author Sierra-Parada, Ronal es_ES
dc.contributor.author Díaz Arévalo, Jose Luis es_ES
dc.contributor.author López Jiménez, Petra Amparo es_ES
dc.date.accessioned 2021-03-17T04:31:52Z
dc.date.available 2021-03-17T04:31:52Z
dc.date.issued 2021-01 es_ES
dc.identifier.issn 0020-7128 es_ES
dc.identifier.uri http://hdl.handle.net/10251/163978
dc.description.abstract [EN] Thousands of deaths associated with air pollution each year could be prevented by forecasting the behavior of factors that pose risks to people's health and their geographical distribution. Proximity to pollution sources, degree of urbanization, and population density are some of the factors whose spatial distribution enables the identification of possible influence on the presence of respiratory diseases (RD). Currently, Bogota is among the cities with the poorest air quality in Latin America. Specifically, the locality of Kennedy is one of the zones in the city with the highest recorded concentration levels of local pollutants over the last 10 years. From 2009 to 2016, there were 8619 deaths associated with respiratory and cardiovascular diseases in the locality. Given these characteristics, this study set out to identify and analyze the areas in which the primary socioeconomic and environmental conditions contribute to the presence of symptoms associated with RD. To this end, information collected in field by performing georeferenced surveys was analyzed through geostatistical and machine learning tools which carried out cluster and pattern analyses. Random forests and AdaBoost were applied to establish hot spots where RD could occur, given the conjugation of predictor variables in the micro-territory. It was found that random forests outperformed AdaBoost with 0.63 AUC. In particular, this study's approach applies to densely populated municipalities with high levels of air pollution. In using these tools, municipalities can anticipate environmental health situations and reduce the cost of respiratory disease treatments. es_ES
dc.description.sponsorship Many thanks to the members of the Intelligence and Territorial Analysis Group of the Universidad Santo Tomás for their collaboration in conducting the fieldwork. es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof International Journal of Biometeorology es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Geostatistics es_ES
dc.subject Machine learning es_ES
dc.subject Sustainable development es_ES
dc.subject Air quality es_ES
dc.subject Hot spots es_ES
dc.subject.classification TECNOLOGIA DEL MEDIO AMBIENTE es_ES
dc.subject.classification INGENIERIA HIDRAULICA es_ES
dc.title Analysis of incidence of air quality on human health: a case study on the relationship between pollutant concentrations and respiratory diseases in Kennedy, Bogotá es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s00484-020-01955-4 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Hidráulica y Medio Ambiente - Departament d'Enginyeria Hidràulica i Medi Ambient es_ES
dc.description.bibliographicCitation Molina-Gomez, NI.; Calderón-Rivera, DS.; Sierra-Parada, R.; Díaz Arévalo, JL.; López Jiménez, PA. (2021). Analysis of incidence of air quality on human health: a case study on the relationship between pollutant concentrations and respiratory diseases in Kennedy, Bogotá. International Journal of Biometeorology. 65(1):119-132. https://doi.org/10.1007/s00484-020-01955-4 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1007/s00484-020-01955-4 es_ES
dc.description.upvformatpinicio 119 es_ES
dc.description.upvformatpfin 132 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 65 es_ES
dc.description.issue 1 es_ES
dc.identifier.pmid 32661801 es_ES
dc.relation.pasarela S\415803 es_ES
dc.description.references Altman DG, Bland JM (1994) Diagnostic tests 3: receiver operating characteristic plots. BMJ 309(6948):188. https://doi.org/10.1136/bmj.309.6948.188 es_ES
dc.description.references Billionnet C, Sherrill D, Annesi-Maesano I (2012) Estimating the health effects of exposure to multi-pollutant mixture. Ann Epidemiol 22:126–141. https://doi.org/10.1016/J.ANNEPIDEM.2011.11.004 es_ES
dc.description.references Bobb JF, Valeri L, Claus Henn B, Christiani DC, Wright RO, Mazumdar M, Godleski JJ, Coull BA (2015) Bayesian kernel machine regression for estimating the health effects of multi-pollutant mixtures. Biostatistics 16:493–508. https://doi.org/10.1093/biostatistics/kxu058 es_ES
dc.description.references Borja-Aburto VH (2000) Ecological studies. Salud Publica Mex 42:533–538 es_ES
dc.description.references Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324 es_ES
dc.description.references CRAN Comprensive R Archive Network (2018) R-3.5.2 for Windows (32/64 bit). https://cran.r-project.org/bin/windows/base/old/3.5.2/. Accessed 10 June 2019. Accessed 10 Jun 2019 es_ES
dc.description.references DANE National Administrative Department of Statistics (2018) Multi-purpose survey -MS 2017. Bogotá, Colombia es_ES
dc.description.references DHS (2019) SALUDATA- Health Observatory of Bogota http://saludata.saludcapital.gov.co/osb/index.php/datos-de-salud/salud-ambiental/consultaurgencias14anios/. Accessed 11 April 2019. Accessed 11 April 2019 es_ES
dc.description.references Franceschi F, Cobo M, Figueredo M (2018) Discovering relationships and forecasting PM10 and PM2.5 concentrations in Bogotá, Colombia, using artificial neural networks, principal component analysis, and k-means clustering. Atmos Pollut Res 9:912–922. https://doi.org/10.1016/j.apr.2018.02.006 es_ES
dc.description.references Galindo WG (2013) Construction dynamics by use, the locality of Kennedy 2002/2012. Bogotá es_ES
dc.description.references García-Ubaque JC, García-Ubaque CA, Vaca-Bohórquez ML (2011) Medical consultation in productive age population related with air pollution levels in Bogota city. Procedia Environ Sci 4:165–169. https://doi.org/10.1016/j.proenv.2011.03.020 es_ES
dc.description.references Gorai AK, Tchounwou PB, Biswal S, Tuluri F (2018) Spatio-temporal variation of particulate matter (PM2.5) concentrations and its health impacts in a mega city, Delhi in India. Environ Health Insights 12:1–9. https://doi.org/10.1177/1178630218792861 es_ES
dc.description.references Habibi R, Alesheikh AA, Mohammadinia A, et al (2017) An assessment of spatial pattern characterization of air pollution: a case study of CO and PM2.5 in Tehran, Iran. ISPRS Int J Geo-Inf 6:270. https://doi.org/10.3390/ijgi6090270 es_ES
dc.description.references Hand DJ (2009) Measuring classifier performance: a coherent alternative to the area under the ROC curve. Mach Learn 77:103–123. https://doi.org/10.1007/s10994-009-5119-5 es_ES
dc.description.references Hernández B, Velasco-Mondragón HE (2000) Cross-sectional surveys. Salud Publica Mex 42:447–455 es_ES
dc.description.references Huang K, Xiao Q, Meng X, Geng G, Wang Y, Lyapustin A, Gu D, Liu Y (2018) Predicting monthly high-resolution PM2.5 concentrations with random forest model in the North China plain. Environ Pollut 242:675–683. https://doi.org/10.1016/j.envpol.2018.07.016 es_ES
dc.description.references IDEAM Institute of Hydrology,Meteorology and Environmental Studies (2016) State of air quality in Colombia, 2011–2015 Report. Bogotá D.C. es_ES
dc.description.references Ivanov A, Voynikova D, Stoimenova M et al (2018) Random forests models of particulate matter PM10: a case study, in: AIP conference proceedings 2025, 030001. https://doi.org/10.1063/1.5064879 es_ES
dc.description.references Jenks GF (1967) The data model concept in statistical mapping. International Yearbook of Cartography 7:186–190 es_ES
dc.description.references Kami JA (2019) A random forest partition model for predicting NO2 concentrations from traffic flow and meteorological conditions. Sci Total Environ 651:475–483. https://doi.org/10.1016/j.scitotenv.2018.09.196 es_ES
dc.description.references Kassomenos P, Petrakis M, Sarigiannis D, Gotti A, Karakitsios S (2011) Identifying the contribution of physical and chemical stressors to the daily number of hospital admissions implementing an artificial neural network model. Air Qual Atmos Health 4:263–272. https://doi.org/10.1007/s11869-011-0139-2 es_ES
dc.description.references Kestenbaum B (2019) Epidemiology and biostatistics. Seattle, USA. https://doi.org/10.1007/978-3-319-96644-1 es_ES
dc.description.references Kuhn M, Johnson K (2016) Applied predictive modeling. New York, USA. https://doi.org/10.1007/978-1-4614-6849-3 es_ES
dc.description.references Lazcano-Ponce E, Fernández E, Salazar-Martínez E, Hernández-Avila M (2000) Cohort studies. Methodology, biases and application. Salud Publica Mex 42:230–241 es_ES
dc.description.references Li S, Batterman S, Wasilevich E, Elasaad H, Wahl R, Mukherjee B (2011) Asthma exacerbation and proximity of residence to major roads: a population-based matched case-control study among the pediatric Medicaid population in Detroit, Michigan. Environ Health 10:34. https://doi.org/10.1186/1476-069X-10-34 es_ES
dc.description.references Jin L, Heap Andrew D (2014) Spatial interpolation methods applied in the environmental sciences: a review. Environ Model Softw 53:173–189 https://doi.org/10.1016/j.envsoft.2013.12.008 es_ES
dc.description.references Ly S, Charles C, Degr A (2011) Geostatistical interpolation of daily rainfall at catchment scale: the use of several variogram models in the Ourthe and Ambleve catchments, Belgium. Hydrol Earth Syst Sci 15:2259–2274. https://doi.org/10.5194/hess-15-2259-2011 es_ES
dc.description.references MAVDT Ministry of Environment, Housing and Territorial Development (2010) Protocol for air quality monitoring. Bogota, Colombia es_ES
dc.description.references Mazenq J, Dubus J-C, Gaudart J, Charpin D, Viudes G, Noel G (2017) City housing atmospheric pollutant impact on emergency visit for asthma: a classification and regression tree approach. Respir Med 132:1–8. https://doi.org/10.1016/j.rmed.2017.09.004 es_ES
dc.description.references Pandey G, Zhang B, Jian L (2013) Predicting submicron air pollution indicators: a machine learning approach. Environ Sci Processes Impacts 15:996–1005. https://doi.org/10.1039/c3em30890a es_ES
dc.description.references Polezer G, Tadano YS, Siqueira HV, Godoi AFL, Yamamoto CI, de André PA, Pauliquevis T, Andrade MF, Oliveira A, Saldiva PHN, Taylor PE, Godoi RHM (2018) Assessing the impact of PM2.5 on respiratory disease using artificial neural networks. Environ Pollut 235:394–403. https://doi.org/10.1016/j.envpol.2017.12.111 es_ES
dc.description.references Ramírez O, Sánchez de la Campa AM, Amato F, Catacolí RA, Rojas NY, de la Rosa J (2018) Chemical composition and source apportionment of PM10 at an urban background site in a high–altitude Latin American megacity (Bogota, Colombia). Environ Pollut 233:142–155. https://doi.org/10.1016/j.envpol.2017.10.045 es_ES
dc.description.references Reid CE, Jerrett M, Tager IB, Petersen ML, Mann JK, Balmes JR (2016) Differential respiratory health effects from the 2008 northern California wildfires: a spatiotemporal approach. Environ Res 150:227–235. https://doi.org/10.1016/J.ENVRES.2016.06.012 es_ES
dc.description.references Rodríguez-Villamizar LA, Rojas-Roa NY, Blanco-Becerra LC, Herrera-Galindo V, Fernández-Niño J (2018) Short-term effects of air pollution on respiratory and circulatory morbidity in Colombia 2011−2014: a multi-city, time-series analysis. Int J Environ Res Public Health 15:2–12. https://doi.org/10.3390/ijerph15081610 es_ES
dc.description.references Rokach L, Maimon O (2015) Data mining with decision trees: theory and applications, 2nd edn. World Scientific Publishing Co. Pte. Ltd, Singapore, p 5 es_ES
dc.description.references Salam MT, Islam T, Gilliland FD (2008) Recent evidence for adverse effects of residential proximity to traffic sources on asthma. Curr Opin Pulm Med 14:3–8. https://doi.org/10.1097/MCP.0b013e3282f1987a es_ES
dc.description.references Sajjadia SA, Zolfagharib G, Adabc H et al (2017) Measurement and modeling of particulate matter concentrations: applying spatial analysis and regression techniques to assess air quality. MethodsX 4:372–390. https://doi.org/10.1016/j.mex.2017.09.006 es_ES
dc.description.references Schapire RE, Freund Y (2012) Boosting: foundations and algorithms, adaptive computation and machine learning. MIT Press, London es_ES
dc.description.references SDA District Secretariat for the Environment (2017) Air quality annual report of Bogota, 2016. Bogotá, Colombia es_ES
dc.description.references SDP District Planning Secretariat (2018) Monograph 2017 assessment of the main territorial, infrastructure, demographic and socio-economic aspects of the locality of Kennedy 08. Bogotá, Colombia es_ES
dc.description.references Valle Benavides AR del (2017) ROC curves (receiver-operating-characteristic) and their applications. Universidad de Sevilla es_ES
dc.description.references Weizhen H, Zhengqiang L, Yuhuan Z, et al (2014) Using support vector regression to predict PM10 and PM2.5, in: IOP conference series: Earth and Environmental Science. IOP. https://doi.org/10.1088/1755-1315/17/1/012268 es_ES
dc.description.references Westerlund J, Urbain JP, Bonilla J (2014) Application of air quality combination forecasting to Bogota. Atmos Environ 89:22–28. https://doi.org/10.1016/j.atmosenv.2014.02.015 es_ES
dc.description.references WHO (2006) WHO Air quality guidelines for particulate matter, ozone, nitrogen dioxide and sulfur dioxide. Global update (2005) Geneva. Switzerland es_ES
dc.description.references Yu Y, Yao S, Dong H, Wang L, Wang C, Ji X, Ji M, Yao X, Zhang Z (2019) Association between short-term exposure to particulate matter air pollution and cause-specific mortality in Changzhou, China. Environ Res 170:7–15. https://doi.org/10.1016/j.envres.2018.11.041 es_ES
dc.description.references Zhan Y, Luo Y, Deng X, Chen H, Grieneisen ML, Shen X, Zhu L, Zhang M (2017) Spatiotemporal prediction of continuous daily PM2.5 concentrations across China using a spatially explicit machine learning algorithm. Environ Pollut 233:464–473. https://doi.org/10.1016/j.atmosenv.2017.02.023 es_ES
dc.subject.ods 03.- Garantizar una vida saludable y promover el bienestar para todos y todas en todas las edades es_ES
dc.subject.ods 11.- Conseguir que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem