Research Outputs
EntropĂa y NeguentropĂa: Una aproximaciĂ³n al proceso de difusiĂ³n de contaminantes y su sostenibilidad
En este estudio se analizan series temporales de partĂculas gruesas y finas, de monĂ³xido de carbono, humedad relativa, velocidad del viento y temperatura, de la ciudad de Santiago de Chile. Se realizaron medidas horarias de evoluciĂ³n de la concentraciĂ³n de contaminantes atmosfĂ©ricos, las que fueron acompañadas con mediciones locales continuas, por sobre tres años, de temperatura, humedad relativa y velocidad del viento a nivel del suelo. Estas series surgen de mediciones efectuadas en seis estaciones de monitoreo durante el periodo de tiempo comprendido entre los años 2010 y 2013 (3.25 años). Su estudio, basado en anĂ¡lisis no lineal, permite obtener sus entropĂas de correlaciĂ³n a partir de las cuales se interpreta el proceso de difusiĂ³n de los contaminantes a travĂ©s del sistema Tierra-atmĂ³sfera. Como resultado, se muestra acuerdo pleno con las dinĂ¡micas de concentraciones observadas. Se discute como la neguentropĂa y el flujo asociado pueden propiciar la dinĂ¡mica dispersiva, segĂºn las variables meteorolĂ³gicas estudiadas, de los contaminantes emitidos acorde a su vida media particular. Se comprueba el carĂ¡cter caĂ³tico de variables de contaminaciĂ³n como las partĂculas atmosfĂ©ricas y algunas variables meteorolĂ³gicas. Estas Ăºltimas podrĂan explicar por quĂ© ciertas localidades en la ciudad de Santiago presentan menos contaminaciĂ³n por partĂculas que otras.
Variables meteorolĂ³gicas y niveles de concentraciĂ³n de material particulado de 10 μm en Andacollo, Chile: Un estudio de dispersiĂ³n y entropĂas
El objetivo de esta investigaciĂ³n es contrastar un modelo Gaussiano de dispersiĂ³n de contaminantes, usando la rosa de los vientos de la ciudad de Andacollo (Chile), con un modelo cuya gĂ©nesis estĂ¡ en la teorĂa del caos y que emplea datos crudos de carĂ¡cter pĂºblico de mediciones de la estaciĂ³n Andacollo del Sistema de InformaciĂ³n Nacional de Calidad del Aire (SINCA) de Chile. Los datos conforman series de tiempo de material particulado, temperatura, humedad relativa, presiĂ³n, radiaciĂ³n solar y magnitud de la velocidad del viento. El tratamiento caĂ³tico de cada serie entrega su exponente de Lyapunov, su coeficiente de Hurst y su entropĂa de correlaciĂ³n. La aproximaciĂ³n caĂ³tica muestra que las entropĂas de las variables meteorolĂ³gicas actĂºan sobre la del contaminante provocando su decaimiento asintĂ³tico segĂºn perdida de persistencia, explicando sus interacciones tĂ©rmicas localizadas. Se concluye que los modelos exhiben predicciones similares al comparar el decaimiento del contaminante PM10.
Probable Relationship between COVID-19, Pollutants and Meteorology: A Case Study at Santiago, Chile
We present here a study about the possible spread of covid-19 pandemic between human’s beings through aerosols contained in urban air polluted by respirable particulate matter and tropospheric ozone, as well as the incidence of local meteorology in an area with orographic basin characteristics and in a certain period of time. Hourly time-series data of three meteorological variables—temperature, relative humidity, wind speed—and three pollutants—PM10, PM2.5 and O3—were considered together with hourly data from the highest number accumulated sick's in seven communes—chosen at random—in Santiago, Chile, studying a probable link between them. From the epidemic perspective, the infected patients number was linked to the hourly time-series of meteorological and pollutant variables, generating new time-series. Nonlinear analysis and the chaos theory formalism was applied to these new time-series, obtaining the largest Lyapunov exponent, correlation dimension, Kolmogorov entropy, Hurst exponent and the Lempel-Ziv complexity. Our preliminary results show meteorological and air pollution variables can be part of the elements fraction that give sustainability to the accumulated growth of infected patients and favor the pandemic spread, making the accumulated sick’s curve chaotic and complex. In addition, environmental pollution could worsen disease conditions like coronavirus (COVID-19) infection. For all time-series, the Lempel-Ziv complexity turned out to be between 0 and 1 which is indicative of connectivity and chaos. The largest Lyapunov exponent as well as the Kolmogorov entropy were positive which also exhibits chaos. The Hurst exponent was found to be greater than 0.5 and less than 1 for all time-series, indicating positive long-term autocorrelation. Finally, the correlation dimension was less than 5, revealing that new time-series constructed are not random. © 2021, AAGR Aerosol and Air Quality Research. All rights reserved.
Urban densification effect on micrometeorology in Santiago, Chile: A comparative study based on Chaos Theory
The concentration distribution of anthropocentric pollutants is favored by urban densification, affecting the micrometeorology in big cities. To examine this condition, chaos theory was applied to time series of measurements of urban meteorology and pollutants of six communes of the Metropolitan Region of Santiago de Chile, in two periods: 2010–2013 and 2017–2020. Each commune contributes, per period, six different time series: three for the meteorological variables (temperature, relative humidity, and magnitude wind speed) and three for the atmospheric pollutant concentrations (PM10, PM2.5, and CO). This qualitative study corroborates that each of the time series is chaotic through the calculation of chaotic parameters: Lyapunov exponent, correlation dimension, Hurst coefficient, correlation entropy, Lempel–Ziv complexity and fractal dimension. The variation in the chaotic parameters between the two periods can be interpreted in relation to the roughness change due to urban densification. More specific parameters, constructed from the Kolmogorov entropies and the fractal dimensions of the time series, show modifications due to the increase in the built surface in the most current period. This variation also extends to micrometeorology, as is clear from the Lempel–Ziv complexity and the Hurst coefficient. The qualitative picture constructed using chaos theory reveals that human interaction with nature affects diversity and sustainability and generates irreversible processes.