Mostrar el registro sencillo del ítem

Information fusion and information quality assessment for environmental forecasting

dc.contributor.authorBecerra M.A
dc.contributor.authorUribe Y
dc.contributor.authorPeluffo-Ordóñez D.H
dc.contributor.authorÁlvarez-Uribe K.C
dc.contributor.authorTobón C.
dc.date.accessioned2022-09-14T14:33:50Z
dc.date.available2022-09-14T14:33:50Z
dc.date.created2021
dc.identifier.issn22120955
dc.identifier.urihttp://hdl.handle.net/11407/7493
dc.descriptionAir pollution is a major environmental threat to human health. Therefore, multiple systems have been developed for early prediction of air pollution levels in large cities. However, deterministic models produce uncertainties due to the complexity of the physical and chemical processes of individual systems and transport. In turn, statistical and machine learning techniques require a large amount of historical data to predict the behavior of a variable. In this paper, we propose a data fusion model to spatially and temporally predict air quality and assess its situation and risk for public health. Our model is based on the Joint Directors of Laboratories (JDL) model and focused on Information Quality (IQ), which allows us to fine tune hyper-parameters in different processes and trace information from raw data to knowledge. Expert systems use the information assessment to select and process data, information, and knowledge. The functionality of our model is tested using an environmental database of the Air Quality Monitoring Network of Área Metropolitana del Valle de Aburrá (AMVA in Spanish) in Colombia. Different levels of noise are added to the data to analyze the effects of information quality on the systems' performance throughout the process. Finally, our system is compared with two conventional machine learning-based models: Deep Learning and Support Vector Regression (SVR). The results show that our proposed model exhibits better performance, in terms of air quality forecasting, than conventional models. Furthermore, its capability as a mechanism to support decision making is clearly demonstrated. © 2021 Elsevier B.V.eng
dc.language.isoeng
dc.publisherElsevier B.V.
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85113323883&doi=10.1016%2fj.uclim.2021.100960&partnerID=40&md5=858f0ca2ed10c41983a25c193945eadb
dc.sourceUrban Climate
dc.titleInformation fusion and information quality assessment for environmental forecasting
dc.typeArticle
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.publisher.programCiencias Básicas
dc.type.spaArtículo
dc.identifier.doi10.1016/j.uclim.2021.100960
dc.subject.keywordAir qualityeng
dc.subject.keywordData fusioneng
dc.subject.keywordInformation qualityeng
dc.subject.keywordJDL modeleng
dc.relation.citationvolume39
dc.publisher.facultyFacultad de Ciencias Básicas
dc.affiliationBecerra, M.A., Instituto Tecnológico Metropolitano, Medellín, Colombia, Universidad de Medellín, Medellín, Colombia
dc.affiliationUribe, Y., Instituto Tecnológico Metropolitano, Medellín, Colombia
dc.affiliationPeluffo-Ordóñez, D.H., Modeling, Simulation and Data Analysis (MSDA) Research Program, Mohammed VI Polytechnic University, Ben Guerir, 47963, Morocco, Faculty of Engineering, Corporación Universitaria Autónoma de Nariño, Carrera 28 No. 19-24, Pasto, 520001, Colombia
dc.affiliationÁlvarez-Uribe, K.C., Instituto Tecnológico Metropolitano, Medellín, Colombia
dc.affiliationTobón, C., Universidad de Medellín, Medellín, Colombia
dc.relation.referencesAbdullah, S., Ismail, M., Fong, S.I.Y., Multiple linear regression (MLR) models for long term PM 10 concentration forecasting during different monsoon seasons (2017) J. Sustain. Sci. Manag., 12 (1), pp. 60-69
dc.relation.referencesAgency, E.P., National ambient air quality standards (NAAQS) — Air and radiation — US EPA (2015) Air Radiat., 4, pp. 1-2
dc.relation.referencesAl-Hajjaj, M.S., Al-Nasser, M.N., Huraib, S.O., Al-Kassimi, F.A., Effect of moderately high altitude on the pattern of lung disease (1994) Ann. Saudi Med., 14 (4), pp. 297-299. , http://www.annsaudimed.net/index.php/vol14/vol14iss4/2378.html
dc.relation.referencesAlvarez, H.B., Echeverria, R.S., Alvarez, P.S., Krupa, S., Air quality standards for particulate matter (PM) at high altitude cities (2013) Environ. Pollut., 173, pp. 255-256
dc.relation.referencesAMVA, CAI, Estrategias Ambientales Integradas Proyecto: Desarrollo de una Estrategia Ambiental el Área Metropolitana del Valle de Aburra Fase (2011), 1
dc.relation.referencesAriyajunya, B., Chen, Y., Chen, V.C.P., Bum, S., Data mining for state space orthogonalization in adaptive dynamic programming (2017) Expert Syst. Appl., 76, pp. 49-58
dc.relation.referencesBadaloni, C., Cesaroni, G., Cerza, F., Davoli, M., Brunekreef, B., Forastiere, F., Effects of long-term exposure to particulate matter and metal components on mortality in the Rome longitudinal study (2017) Environ. Int., 109 (September), pp. 146-154
dc.relation.referencesBadia, A., Jorba, O., Voulgarakis, A., Dabdub, D., García-pando, C.P., Description and evaluation of the multiscale online nonhydrostatic atmosphere chemistry model (NMMB-MONARCH) version 1. 0: gas-phase chemistry at global scale (2017) Geosci. Model Dev., 2, pp. 609-638
dc.relation.referencesBecerra, M., Sánchez, M., Carvajal, J., Luna, J., Peluffo-Ordóñez, D., Tobón, C., Data fusion from multiple stations for estimation of PM2.5 in specific geographical location (2017) LNCS, 10125
dc.relation.referencesBecerra, M.A., Tobón, C., Castro-Ospina, A.E., Peluffo-Ordóñez, D.H., Information quality assessment for data fusion systems (2021) Data, 6 (6), p. 60. , https://www.mdpi.com/2306-5729/6/6/60, 10.3390/data6060060
dc.relation.referencesBecker, P., Zum Nachweis der Abtragung von Vertikalkräften bei Verbauwänden (2017) Bautechnik, 94 (3), pp. 190-199
dc.relation.referencesBianco, F., Busilacchio, M., Verdecchia, M., Tomassetti, B., Aruffo, E., Bianco, S., Di, S., Di, P., Recursive neural network model for analysis and forecast of PM10 and PM2.5 (2017) Atmos. Poll. Res., 8, pp. 652-659
dc.relation.referencesBlasch, E., Plano, S., JDL Level 5 Fusion Model “User Refinement” Issues and Applications in Group Tracking (2002), 4729, pp. 270-279
dc.relation.referencesBourdrel, T., Bind, M.A., Béjot, Y., Morel, O., Cardiovascular Effects of Air Pollution (2017), 2, pp. 634-642
dc.relation.referencesCarmona, R., Linares, C., Recio, A., Ortiz, C., Díaz, J., Science of the Total Environment Emergency multiple sclerosis hospital admissions attributable to chemical and acoustic pollution: Madrid (Spain), 2001–2009 (2018) Sci. Total Environ., 612, pp. 111-118
dc.relation.referencesCarnevale, C., Angelis, E.D., Finzi, G., Turrini, E., Volta, M., Application of data fusion techniques to improve air quality forecast: a case study in the Northern Italy (2020) Atmosphere, 11 (3), p. 244. , https://www.mdpi.com/2073-4433/11/3/244
dc.relation.referencesChaudhuri, S., Dutta, D., Mann – Kendall Trend of Pollutants, Temperature and Humidity Over an Urban Station of India With Forecast Verification Using Different ARIMA Models (2014), 2, pp. 4719-4742
dc.relation.referencesChen, C., Xia, Z., Wu, M., Zhang, Q., Concentrations, source identification, and lung cancer risk associated with springtime PM 2.5 -bound polycyclic aromatic hydrocarbons (PAHs) in Nanjing, China (2017) Arch. Environ. Contam. Toxicol., 73 (3), pp. 391-400
dc.relation.referencesCobourn, W.G., An enhanced PM2.5 air quality forecast model based on nonlinear regression and back-trajectory concentrations (2010) Atmos. Environ., 44 (25), pp. 3015-3023
dc.relation.referencesDietmüller, S., Garny, H., Plöger, F., Jöckel, P., Cai, D., Effects of Mixing on Resolved and Unresolved Scales on Stratospheric Age of Air (2017), pp. 7703-7719
dc.relation.referencesDonnelly, A., Misstear, B., Broderick, B., Real time air quality forecasting using integrated parametric and non- parametric regression techniques (2015) Atmos. Environ., 103 (2), pp. 53-65
dc.relation.referencesDonnelly, A., Naughton, O., Broderick, B., Misstear, B., Short-Term Forecasting of Nitrogen Dioxide (NO 2) Levels Using a Hybrid Statistical and Air Mass History Modelling Approach (2017), 2, pp. 231-241
dc.relation.referencesDunea, D., Pohoata, A., Iordache, S., Using Wavelet – Feedforward Neural Networks to Improve Air Pollution Forecasting in Urban Environments (2015), 2
dc.relation.referencesFasbender, D., Brasseur, O., Bogaert, P., Bayesian data fusion for space – time prediction of air pollutants: The case of NO 2 in Belgium (2009) Atmos. Environ., 43 (30), pp. 4632-4645
dc.relation.referencesFeng, H., Zou, B., Wang, J., Gu, X., Dominant variables of global air pollution-climate interaction: geographic insight (2019) Ecol. Indic., 99, pp. 251-260
dc.relation.referencesFenger, J., Air pollution in the last 50 years - From local to global (2009) Atmos. Environ., 43 (1), pp. 13-22
dc.relation.referencesGómez-arias, R.D., Bonmatí, A.N., Pereyra-zamora, P., Arias-valencia, S., León, F., Daniel, R.O., Aguirre, C., Diseño y análisis comparativo de un inventario de indicadores de mortalidad evitable adaptado a las condiciones sanitarias de Colombia (2009) Rev. Panam. Salud Publica, 26 (6), pp. 385-397
dc.relation.referencesGrange, S.K., Carslaw, D.C., Science of the total environment using meteorological normalisation to detect interventions in air quality time series (2019) Sci. Total Environ., 653, pp. 578-588
dc.relation.referencesGressent, A., Malherbe, L., Colette, A., Rollin, H., Scimia, R., Data fusion for air quality mapping using low-cost sensor observations: feasibility and added-value (2020) Environ. Int., 143. , https://linkinghub.elsevier.com/retrieve/pii/S0160412020319206
dc.relation.referencesGulia, S., Nagendra, S.M.S., Khare, M., A system Based Approach to Develop Hybrid Model Predicting Extreme Urban NOx and PM2.5 Concentrations (2017), p. 2017
dc.relation.referencesHenneman, L.R.F., Liu, C., Hu, Y., Mulholland, J.A., Russell, A.G., Air quality modeling for accountability research: operational, dynamic, and diagnostic evaluation (2017) Atmos. Environ., 166, pp. 551-565
dc.relation.referencesHernandez, W., Air pollution Analysis with a PFCM Clustering Algorithm Applied in a Real Database of Salamanca (Mexico) (2010), pp. 1297-1302
dc.relation.referencesHuang, W., Li, T., Liu, J., Xie, P., Du, S., Teng, F., An overview of air quality analysis by big data techniques: monitoring, forecasting, and traceability (2021) Inform. Fusion, 75, pp. 28-40. , https://linkinghub.elsevier.com/retrieve/pii/S1566253521000658
dc.relation.referencesJane, L., Patrick, M., Russell, A., Applied geochemistry an odyssey of environmental pollution: The rise, fall and remobilisation of industrial lead in Australia (2017) Appl. Geochem., 83, pp. 3-13
dc.relation.referencesKanda, I., Wakamatsu, S., Okazaki, Y., Analysis of Criteria Air Pollutant Trends in Three Mexican Metropolitan Areas (2014), pp. 806-829
dc.relation.referencesKanjo, E., Younis, E.M.G., Sherkat, N., Towards unravelling the relationship between on-body, environmental and emotion data using sensor information fusion approach (2018) Inform. Fusion, 40, pp. 18-31
dc.relation.referencesKononenko, I., Estimating attributes: Analysis and extensions of RELIEF (1994) Machine Learning: ECML-94, pp. 171-182. , F. Bergadano L. De Raedt Springer Berlin Heidelberg, Berlin, Heidelberg
dc.relation.referencesKonovalov, I.B., Beekmann, M., Meleux, F., Dutot, A., Foret, G., Combining deterministic and statistical approaches for PM10 forecasting in Europe (2009) Atmos. Environ., 43 (40), pp. 6425-6434
dc.relation.referencesKwok, L.K., Lam, Y.F., Tam, C., Developing a statistical based approach for predicting local air quality in complex terrain area (2017) Atmos. Pollut. Res., 8 (1), pp. 114-126
dc.relation.referencesLightstone, S.D., Moshary, F., Gross, B., Comparing CMAQ Forecasts with a Neural Network Forecast Model for PM 2.5 in New York (2017)
dc.relation.referencesLim, Y.H., Bae, H.J., Yi, S.M., Park, E., Lee, B.E., Hong, Y.C., Vascular and cardiac autonomic function and PM2.5 constituents among the elderly: a longitudinal study (2017) Sci. Total Environ., 607-608, pp. 847-854
dc.relation.referencesLondoño-ciro, L.A., Cañón-barriga, J.E., Imputation of Spatial Air Quality Data using Gis-spline and the Index of Agreement in Sparse Urban Monitoring Networks (2015), 76, pp. 73-81
dc.relation.referencesMa, Y., Zhao, Y., Yang, S., Zhou, J., Xin, J., Wang, S., Yang, D., Short-term effects of ambient air pollution on emergency room admissions due to cardiovascular causes in Beijing, China
dc.relation.references(2017) Environ. Pollut., 230 (2), pp. 974-980
dc.relation.referencesMao, Y.H., Liao, H., Chen, H.S., Impacts of East Asian Summer and Winter Monsoons on Interannual Variations of Mass Concentrations and Direct Radiative Forcing of Black Carbon Over Eastern China (2017), pp. 4799-4816
dc.relation.referencesMartínez-jaramillo, J.E., Arango-aramburo, S., Álvarez-uribe, K.C., Jaramillo-álvarez, P., Assessing the impacts of transport policies through energy system simulation: the case of the Medellin Metropolitan Area, Colombia (2017) Energy Policy, 101 (November 2016), pp. 101-108
dc.relation.referencesMishra, D., Goyal, P., Environmental technology & innovation neuro-fuzzy approach to forecasting ozone episodes over the urban area of Delhi, India (2016) Environ. Technol. Innov., 5, pp. 83-94
dc.relation.referencesMoradi, A.A.A.M., Lubitz, D.C.W.D., Flow and temperature dynamics in an urban canyon under a comprehensive set of wind directions, wind speeds, and thermal stability conditions (2018) Environ. Fluid Mech.
dc.relation.referencesMurillo-Escobar, J., Sepulveda-Suescun, J., Correa, M., Orrego-Metaute, D., Forecasting concentrations of air pollutants using support vector regression improved with particle swarm optimization: Case study in Aburrá Valley, Colombia (2019) Urban Clim., 29, p. 100473. , https://linkinghub.elsevier.com/retrieve/pii/S2212095518302438
dc.relation.referencesOlawoyin, R., Schweitzer, L., Zhang, K., Okareh, O., Slates, K., Ecotoxicology and Environmental Safety Index analysis and human health risk model application for evaluating ambient air-heavy metal contamination in Chemical Valley Sarnia (2018) Ecotoxicol. Environ. Saf., 148 (September 2017), pp. 72-81
dc.relation.referencesPalacio-baena, J.A., Genotoxicity in Human Lymphocytes Exposed to PM10 from Three Sites in the Valle de Aburrá (Antioquia) (2013), 152, pp. 294-306
dc.relation.referencesPavani, M., Rao, P.T., Monitoring Real-Time Urban Carbon Monoxide (CO) 2(Ictis 2017) (2018)
dc.relation.referencesPeyvandi, S., Hernandez, J., Olmo, F., Nieves, J., Romero, J., Colorimetric analysis of outdoor illumination across varieties of atmospheric conditions (2016) J. Opt. Soc. Am. A Opt. Image Sci. Vis., 33 (6), pp. 1049-1059
dc.relation.referencesPires, C.M., Pino-mejías, R., Characterization of Background Air Pollution Exposure in Urban Environments using a Metric Based on Hidden Markov Models (2016), 127, pp. 255-261
dc.relation.referencesPitchika, A., Hampel, R., Wolf, K., Kraus, U., Cyrys, J., Babisch, W., Peters, A., Schneider, A., Science of the Total Environment Long-term associations of modeled and self-reported measures of exposure to air pollution and noise at residence on prevalent hypertension and blood pressure (2017) Sci. Total Environ., 593-594 (2), pp. 337-346
dc.relation.referencesPolychronopoulos, A., Amditis, A., Scheunert, U., Tatschke, T., Revisiting JDL Model for Automotive Safety Applications: The PF2 Functional Model (2006)
dc.relation.referencesReynolds, C.C., Escobedo, F.J., Clerici, N., Zea-camaño, J., Does “Greening” of Neotropical Cities Considerably Mitigate Carbon Dioxide Emissions? The Case of Medellin, Colombia (2017)
dc.relation.referencesRivera-gonzález, L.O., Zhang, Z., Sánchez, B.N., Zhang, K., Brown, D.G., Rojas-bracho, L., Osornio-vargas, A., Neill, M.S.O., An assessment of air pollutant exposure methods in Mexico City, Mexico An assessment of air pollutant exposure methods in Mexico City, Mexico (2015) J. Air Waste Manage. Assoc., 65 (5), pp. 581-591
dc.relation.referencesRyan, W.F., Ryan, W.F., The Air Quality Forecast Rote: Recent Changes and Future Challenges The Air Quality Forecast Rote: Recent Changes and Future Challenges (2017), 2247October)
dc.relation.referencesSingh, H., Dilbag, P., Avleen, S., Malhi, K., Multi-objective particle swarm optimization-based adaptive neuro-fuzzy inference system for benzene monitoring (2017) Neural Comput. & Applic.
dc.relation.referencesSociety, C., Great, L., Smoke, B., London, A., Cities, H.S., Cancer, A.C.S., Ii, P., Is smog innocuous? (2017) Air Pollut. Cardiovasc. Dis., 69, pp. 425-429
dc.relation.referencesSteinberg, A.N., Bowman, C.L., White, F.E., Revisions to the JDL Data Fusion. Data Fusion Lexicon by JDL (1991)
dc.relation.referencesStockfelt, L., Andersson, E.M., Molnár, P., Gidhagen, L., Segersson, D., Rosengren, A., Barregard, L., Sallsten, G., Long-term e ff ects of total and source-speci fi c particulate air pollution on incident cardiovascular disease in Gothenburg, Sweden (2017) Environ. Res., 158 (April), pp. 61-71
dc.relation.referencesSun, W., Zhang, H., Palazoglu, A., Singh, A., Zhang, W., Liu, S., Science of the Total Environment Prediction of 24-hour-average PM 2.5 concentrations using a hidden Markov model with different emission distributions in Northern California (2013) Sci. Total Environ., 443, pp. 93-103
dc.relation.referencesSynnergren, J., Gamalielsson, J., Olsson, B., Mapping of the JDL data fusion model to bioinformatics (2007), pp. 1506-1511
dc.relation.referencesTacnet, J.M., Dezert, J., Curt, C., Batton-Hubert, M., Chojnacki, E., How to manage natural risks in mountain areas in a context of imperfect information? New frameworks and paradigms for expert assessments and decision-making (2014) Environ. Syst. Decis., 34 (2), pp. 288-311
dc.relation.referencesTaylan, O., Modelling and analysis of ozone concentration by arti fi cial intelligent techniques for estimating air quality (2017) Atmos. Environ., 150, pp. 356-365
dc.relation.referencesThe JDL, Model of Data Fusion Applied to Cyber-defence
dc.relation.referencesA Review Paper (2012), pp. 116-119
dc.relation.referencesTimmermans, R., Kranenburg, R., Manders, A., Hendriks, C., Segers, A., Dammers, E., Zhang, Q., Schaap, M., Source apportionment of PM2.5 across China using LOTOS-EUROS (2017) Atmos. Environ., 164, pp. 370-386
dc.relation.referencesTimonen, J., Laaperi, L., Rummukainen, L., Puuska, S., Vankka, J., Situational Awareness and Information Collection from Critical Infrastructure (2014), pp. 157-173
dc.relation.referencesTodoran, I.G., Lecornu, L., Khenchaf, A., Caillec, J.M.L., A methodology to evaluate important dimensions of information (2015) ACM J. Data Inform. Qual., 6 (2-3), p. 23. , Article 11
dc.relation.referencesToro-gómez, M.V., Quiceno-rendón, D.M., Energy Demand and Vehicle Emissions Stimate in Aburra Valley from 2000 to 2010 using LEAP Model (2015), 82189, pp. 45-51
dc.relation.referencesVapnik, V.N., The Nature of Statistical Learning Theory (2000), Springer
dc.relation.referencesVidale, S., Arnaboldi, M., Bosio, V., Corrado, G., Guidotti, M., Sterzi, R., Campana, C., Short-term air pollution exposure and cardiovascular events: A 10-year study in the urban area of Como, Italy (2017) Int. J. Cardiol., 248, pp. 389-393
dc.relation.referencesWang, J., Niu, T., Wang, R., Research and Application of an Air Quality Early Warning System Based on a Modified Least Squares Support Vector Machine and a Cloud Model (2017)
dc.relation.referencesWang, J., Zhang, X., Guo, Z., Lu, H., Developing an early-warning system for air quality prediction and assessment of cities in China (2017) Expert Syst. Appl.
dc.relation.referencesWang, J., Li, S., Li, H., Qian, X., Li, X., Liu, X., Lu, H., Sun, Y., Trace metals and magnetic particles in PM 2.5: Magnetic identification and its implications (2017) Sci. Rep., March, pp. 1-11
dc.relation.referencesWei, X., Wang, X., Zhu, T., Gong, Z., Fusion prediction model of atmospheric pollutant based on self-organized feature (2021) IEEE Access, 9, pp. 8110-8120. , https://ieeexplore.ieee.org/document/9316297/
dc.relation.referencesWhite, F.E., A model for data fusion (1988) Proc. 1st National Symposium on Sensor Fusion, 2
dc.relation.referencesWoo, M., Byoung, L., Choi, G., Wook, S., Rha, K.S.W., Air Pollution and Short-term Clinical Outcomes of Patients with Acute Myocardial Infarction (September 2016) (2017), pp. 631-638
dc.relation.referencesWorld, H.O, Air Quality Guidelines for Particulate Matter, Ozone, Nitrogen Dioxide and Sulfur Dioxide (2006), 4, pp. 1-2. , WHO Press, World Health Organization
dc.relation.referencesXu, J., Chang, L., Yan, F., He, J., Science of the total environment role of climate anomalies on decadal variation in the occurrence of wintertime haze in the Yangtze River Delta, China (2017) Sci. Total Environ., 599-600, pp. 918-925
dc.relation.referencesXu, Y., Yang, W., Wang, J., Air quality early-warning system for cities in China (2017) Atmos. Environ., 148
dc.relation.referencesXu, Z., Cao, Y., Kang, Y., Deep spatiotemporal residual early-late fusion network for city region vehicle emission pollution prediction (2019) Neurocomputing, 355, pp. 183-199. , https://linkinghub.elsevier.com/retrieve/pii/S0925231219306228
dc.relation.referencesYu, Z., Qu, Y., Wang, Y., Ma, J., Cao, Y., Application of machine-learning-based fusion model in visibility forecast: a case study of Shanghai, China (2021) Remote Sens., 13 (11), p. 2096. , https://www.mdpi.com/2072-4292/13/11/2096
dc.relation.referencesZhang, J., Ding, W., Prediction of air pollutants concentration based on an extreme learning machine: the case of Hong Kong (2017) Int. J. Environ. Res. Public Health, pp. 1-19
dc.relation.referencesZhang, Y., Bocquet, M., Mallet, V., Seigneur, C., Baklanov, A., Real-time air quality forecasting, part I: history, techniques, and current status (2012) Atmos. Environ., 60, pp. 632-655
dc.relation.referencesZhang, Y., Zhang, R., Ma, Q., Wang, Y., Wang, Q., Huang, Z., Huang, L., A feature selection and multi-model fusion-based approach of predicting air quality (2020) ISA Trans., 100, pp. 210-220. , https://linkinghub.elsevier.com/retrieve/pii/S0019057819305038
dc.relation.referencesZhao-bin, S., Hua, W., Chunwei, G., Jin, W., Tiantao, C., Zi-ming, L., Barrier effect of terrain on cold air and return flow of dust air masses (2019) Atmos. Res., 220, pp. 81-91. , https://www.sciencedirect.com/science/article/pii/S0169809518313577?via%3Dihub
dc.relation.referencesZhu, L., Ge, X., Chen, Y., Zeng, X., Pan, W., Zhang, X., Ben, S., Zhang, Z., Short-term Effects of Ambient Air Pollution and Childhood Lower Respiratory Diseases (May) (2017), pp. 1-7. , 10.1038/s41598-017-04310-7
dc.type.coarhttp://purl.org/coar/resource_type/c_6501
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.type.driverinfo:eu-repo/semantics/article
dc.identifier.reponamereponame:Repositorio Institucional Universidad de Medellín
dc.identifier.repourlrepourl:https://repository.udem.edu.co/
dc.identifier.instnameinstname:Universidad de Medellín


Ficheros en el ítem

FicherosTamañoFormatoVer

No hay ficheros asociados a este ítem.

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

Mostrar el registro sencillo del ítem