Linking Meteorological Variables and Particulate Matter PM2.5 in the Aburrá Valley, Colombia

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dc.contributor.affiliationParra J.C., Research Group GIS, Politécnico Colombiano Jaime Isaza Cadavid, Facultad de Ingeniería, Medellín, 050022, Colombia
dc.contributor.affiliationGómez M., Research Group GHYCAM, Politécnico Colombiano Jaime Isaza Cadavid, Facultad de Ingeniería, Medellín, 050022, Colombia
dc.contributor.affiliationSalas H.D., Instituto Tecnológico Metropolitano, Facultad de Ciencias Exactas y Aplicadas, Medellín, 050034, Colombia
dc.contributor.affiliationBotero B.A., Research Group GICI, Facultad de Ingeniería, Universidad de Medellín, Medellín, 050026, Colombia
dc.contributor.affiliationPiñeros J.G., Research Group Salud y Ambiente, Universidad de Antioquia, Medellín, 050011, Colombia
dc.contributor.affiliationTavera J., Research Group GHYCAM, Politécnico Colombiano Jaime Isaza Cadavid, Facultad de Ingeniería, Medellín, 050022, Colombia
dc.contributor.affiliationVelásquez M.P., Research Group GHYCAM, Politécnico Colombiano Jaime Isaza Cadavid, Facultad de Ingeniería, Medellín, 050022, Colombia
dc.contributor.authorParra J.C.; Gómez M.; Salas H.D.; Botero B.A.; Piñeros J.G.; Tavera J.; Velásquez M.P.
dc.date.accessioned2025-04-28T22:09:50Z
dc.date.available2025-04-28T22:09:50Z
dc.date.issued2024
dc.descriptionEnvironmental pollution indicated by the presence of (Formula presented.) particulate matter varies based on prevailing atmospheric conditions described by certain meteorological variables. Consequently, it is important to understand atmospheric behavior in areas such as the Aburrá Valley, which experiences recurrent pollution events twice a year. This study examines the behavior of specific meteorological variables and (Formula presented.) particulate matter in the Aburrá Valley. By using statistical analysis tools such as correlation coefficients, principal component analysis (PCA), and multiple linear regression models, the research identifies relationships between (Formula presented.) and daily cycles of temperature, rainfall, radiation, and wind speed and direction. Datasets were analyzed considering periods before and after the COVID-19 lockdown (pre-pandemic and pandemic, respectively), and specific pollution events were also analyzed. Furthermore, this work considers the relationships between (Formula presented.) and meteorological variables, contrasting the pre-pandemic and pandemic periods. This study characterizes diurnal cycles of meteorological variables and their relationship with (Formula presented.). There are consistent patterns among temperature, atmospheric boundary layer (ABL) height, and solar radiation, whereas precipitation and relative humidity show the opposite behavior. (Formula presented.) exhibits similar relative frequency functions during both daytime and nighttime, regardless of rainfall. An inverse relationship is noted between (Formula presented.) levels and ABL height at different times of the day. Moreover, the PCA results show that the first principal component explains around 60% of the total variance in the hydrometeorological data. The second PC explains 10%, and the rest of the variance is distributed among the other three to eight PCs. In this sense, there is no significant difference between the two PCAs with hydrometeorological data from a pre-pandemic period and a COVID-19 pandemic period. Multiple regression analysis indicates a significant and consistent dependence of (Formula presented.) on temperature and solar radiation across both analyzed periods. The application of Generalized Additive Models (GAMs) to our dataset yielded promising results, reflecting the complex relationship between meteorological variables and (Formula presented.) concentrations. The metrics obtained from the GAM were as follows: Mean Squared Error (MSE) of 98.04, Root Mean Squared Error (RMSE) of 9.90, R-squared (R2) of 0.24, Akaike Information Criterion (AIC) of 110,051.34, and Bayesian Information Criterion (BIC) of 110,140.63. In comparison, the linear regression model exhibited slightly higher MSE (100.49), RMSE (10.02), and lower R-squared (0.22), with AIC and BIC values of 110,407.45 and 110,460.67, respectively. Although the improvement in performance metrics from GAM over the linear model is not conclusive, they indicate a better fit for the complexity of atmospheric dynamics influencing (Formula presented.) levels. These findings underscore the intricate interplay of meteorological factors and particulate matter concentration, reinforcing the necessity for advanced modeling techniques in environmental studies. This work presents new insights that enhance the diagnosis, understanding, and modeling of environmental pollution, thereby supporting informed decision-making and strengthening management efforts. © 2024 by the authors.
dc.identifier.doi10.3390/su162310250
dc.identifier.instnameinstname:Universidad de Medellínspa
dc.identifier.issn20711050
dc.identifier.reponamereponame:Repositorio Institucional Universidad de Medellínspa
dc.identifier.repourlrepourl:https://repository.udem.edu.co/
dc.identifier.urihttp://hdl.handle.net/11407/8855
dc.language.isoeng
dc.publisher.facultyFacultad de Ingenieríasspa
dc.publisher.programIngeniería Civilspa
dc.relation.citationissue23
dc.relation.citationvolume16
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85211794855&doi=10.3390%2fsu162310250&partnerID=40&md5=e05ada1c67535892dde06f628b3ea275
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dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.sourceSustainability (Switzerland)
dc.sourceSustainability
dc.sourceScopus
dc.subjectAir quality
dc.subjectCorrelation coefficients
dc.subjectDiurnal cycle
dc.subjectGeneralized Additive Models (GAM)
dc.subjectMultiple linear regression (MLR)
dc.subjectParticulate matter PM<sub>2.5</sub>
dc.subjectPollution
dc.subjectPrecipitation
dc.subjectPrincipal component analysis (PCA)
dc.subjectTropical meteorology
dc.subjectColombia
dc.subjectAtmospheric dynamics
dc.subjectBoundary layer
dc.subjectCOVID-19
dc.subjectData set
dc.subjectParticulate matter
dc.subjectPrincipal component analysis
dc.subjectRegression analysis
dc.subjectRelative humidity
dc.subjectSolar radiation
dc.subjectWind velocity
dc.subjectAir quality
dc.subjectCorrelation coefficients
dc.subjectDiurnal cycle
dc.subjectGeneralized Additive Models (GAM)
dc.subjectMultiple linear regression (MLR)
dc.subjectParticulate matter PM<sub>2.5</sub>
dc.subjectPollution
dc.subjectPrecipitation
dc.subjectPrincipal component analysis (PCA)
dc.subjectTropical meteorology
dc.titleLinking Meteorological Variables and Particulate Matter PM2.5 in the Aburrá Valley, Colombia
dc.typeArticle
dc.type.localArtículo revisado por paresspa
dc.type.versioninfo:eu-repo/semantics/publishedVersion

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