Open AccessMultitemporal analysis with statistical models: influence of the atmospheric condition on urban concentrations of particulate matter
Author(s) -
L Rodríguez-Garavito,
K J Romero-Corredor,
Carlos Alfonso Zafra-Mejía
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2159/1/012003
Subject(s) - particulates , environmental science , atmospheric instability , atmospheric sciences , pollutant , air pollution , atmospheric pollution , pollution , atmospheric model , meteorology , geography , chemistry , geology , wind speed , ecology , organic chemistry , biology
This paper shows a multitemporal analysis with autoregressive integrated moving average models of the influence of atmospheric condition on concentrations of particulate matter ≤ 10 µm in Bogotá city, Colombia. Information was collected from six monitoring stations distributed throughout the city. The study period was nine years. Autoregressive component of the models suggests that urban areas with greater atmospheric instability show a lower hourly persistence of particulate matter (one hour) compared to urban areas with lower atmospheric instability (two hours). Moving average component of the models hints those urban areas with greater atmospheric instability show greater hourly variability in particulate matter concentrations (5-10 hours). The models also suggest that a high degree of air pollution decreases the temporal influence of the atmospheric condition on particulate matter concentrations; in this case, the temporal behavior of particulate matter possibly depends on the urban emission sources of this pollutant rather than on the existing atmospheric condition. This study is relevant to deepen the knowledge in relation to the following aspects of atmospheric physics: The use of statistical models for the time series analysis of atmospheric condition, and the analysis by statistical models of the influence of atmospheric condition on air pollutant concentrations.