Linking Meteorological Variables and Particulate Matter PM 2.5 in the Aburrá Valley, Colombia

Juan C. Parra (), Miriam Gómez, Hernán D. Salas, Blanca A. Botero, Juan G. Piñeros, Jaime Tavera and María P. Velásquez
Additional contact information
Juan C. Parra: Research Group GIS, Politécnico Colombiano Jaime Isaza Cadavid, Facultad de Ingeniería, Medellín 050022, Colombia
Miriam Gómez: Research Group GHYCAM, Politécnico Colombiano Jaime Isaza Cadavid, Facultad de Ingeniería, Medellín 050022, Colombia
Hernán D. Salas: Instituto Tecnológico Metropolitano, Facultad de Ciencias Exactas y Aplicadas, Medellín 050034, Colombia
Blanca A. Botero: Research Group GICI, Facultad de Ingeniería, Universidad de Medellín, Medellín 050026, Colombia
Juan G. Piñeros: Research Group Salud y Ambiente, Universidad de Antioquia, Medellín 050011, Colombia
Jaime Tavera: Research Group GHYCAM, Politécnico Colombiano Jaime Isaza Cadavid, Facultad de Ingeniería, Medellín 050022, Colombia
María P. Velásquez: Research Group GHYCAM, Politécnico Colombiano Jaime Isaza Cadavid, Facultad de Ingeniería, Medellín 050022, Colombia

Sustainability, 2024, vol. 16, issue 23, 1-30

Abstract: Environmental pollution indicated by the presence of P M 2.5 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 P M 2.5 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 P M 2.5 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 P M 2.5 and meteorological variables, contrasting the pre-pandemic and pandemic periods. This study characterizes diurnal cycles of meteorological variables and their relationship with P M 2.5 . There are consistent patterns among temperature, atmospheric boundary layer (ABL) height, and solar radiation, whereas precipitation and relative humidity show the opposite behavior. P M 2.5 exhibits similar relative frequency functions during both daytime and nighttime, regardless of rainfall. An inverse relationship is noted between P M 2.5 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 P M 2.5 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 P M 2.5 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 (R 2 ) 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 P M 2.5 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.

Keywords: diurnal cycle; precipitation; pollution; air quality; particulate matter PM 2.5; tropical meteorology; correlation coefficients; principal component analysis (PCA); multiple linear regression (MLR); Generalized Additive Models (GAM) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/23/10250/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/23/10250/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:23:p:10250-:d:1527568

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().