Dr. Salini-Calderon, Giovanni

En este estudio se analizan series temporales de partículas gruesas y finas, de monóxido de carbono, humedad relativa, velocidad del viento y temperatura, de la ciudad de Santiago de Chile. Se realizaron medidas horarias de evolución de la concentración de contaminantes atmosféricos, 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 análisis no lineal, permite obtener sus entropías de correlación a partir de las cuales se interpreta el proceso de difusión de los contaminantes a través del sistema Tierra-atmósfera. Como resultado, se muestra acuerdo pleno con las dinámicas de concentraciones observadas. Se discute como la neguentropía y el flujo asociado pueden propiciar la dinámica dispersiva, según las variables meteorológicas estudiadas, de los contaminantes emitidos acorde a su vida media particular. Se comprueba el carácter caótico de variables de contaminación como las partículas atmosféricas y algunas variables meteorológicas. Estas últimas podrían explicar por qué ciertas localidades en la ciudad de Santiago presentan menos contaminación por partículas que otras.

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.

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.

We have made a comparative study about the nonlinear behaviour of PM2.5 hourly average concentrations, which were measured at some of the most polluted mid-sized cities located in the South of Chile. The chosen cities were Chillán, Coyhaique and Temuco where high PM2.5 concentrations concentrated in the winter season are caused by the intensive use of wood for heating. The city of Cochabamba, Bolivia, has also been included in this study, due to its very high level of atmospheric pollution by PM10 (especially in the winter season).This city is at a greater height compared to the Chilean cities. Using nonlinear tools, as Wavelet, Recurrence Plots, and Phase Portrait we have investigated the behaviour of PM2.5 and PM10 (hourly) concentrations. Wavelet spectrum and global amplitude for the more polluted cities in study was calculated. Spectral descomposition was performed in time-frecuency through Morlet’s wavelet transform and their global amplitud in time and energy, concentrated around the most importants peaks. On the other hand, a graphical tool that shows typical patterns of dynamic behaviour is the recurrence graph allowing extraction of qualitative characteristics from time series. This method was applied for all cities in study showing patterns that differ from a noisy or random signal. Also the technique of phase-portrait analysis was implemented, showing typical dynamical patterns of non-linear time series, different to a noisy signal pattern. Finally, it was found that hourly airborne particle concentrations exhibit a possible chaotic behaviour, related to short-term predictability some hours ahead.

We present here a study about the limitations found when trying to develop an accurate atmospheric particulate matter forecasting model based on real data, and evidence that the time series of fine particulate matter concentration exhibit deterministic chaotic behavior. We have calculated the Lyapunov exponents of PM2.5 time series obtained from measurements from four monitoring stations located in the city of Santiago, Chile, in recent years. Values obtained for the largest Lyapunov exponents turned out to be positive and ranging between 0.3 and 0.5 which, according to the theory of chaos, is a condition for the presence of deterministic chaos and random behavior in time series. Given the shape of decay of autocorrelation functions and values of correlation dimension and Hurst exponents, random behavior can be discarded: we therefore conclude that the series are chaotic and very sensitive to initial conditions. The study presented here can be replicated in other mid-sized cities that present similar situations to the city of Santiago, where complexity of topography, meteorology and seasonal trends favor the generation of high concentration episodes of atmospheric particulate matter and where a reliable air quality forecasting model may be important for environmental management.

En este documento se presenta un estudio de series temporales de PM10 que muestran la mala calidad del aire en Cochabamba, mediante parámetros estadísticos usados en estudios sobre dinámica no lineal. El promedio diario de PM10 sigue patrones similares al de grandes ciudades que poseen altos índices de contaminación ambiental. Uno de los parámetros resultó del mismo orden y característica que los presentados en trabajos similares sobre el estudio de caoticidad en variables de contaminación como ozono, PM2.5, y CO, demostrando el origen caótico de estos datos. Nuestros resultados contribuyen a la literatura puesto que introducen un análisis metodológico más detallado de la naturaleza no lineal del contaminante PM10.

Desde la perspectiva de la teoría del caos, se analizan series de tiempo de contaminantes como material particulado fino y grueso y de monóxido de carbono junto a las variables meteorológicas, humedad relativa, velocidad del viento y temperatura. Las series surgen de mediciones en seis estaciones de monitoreo ubicadas en Santiago de Chile, de las cuales se seleccionaron dos, para un periodo de 3.25 años. Aplicando la segunda ley de la termodinámica, que es un principio general que impone restricciones a la dirección de la transferencia de calor, y a la eficiencia posible de la denominada máquina térmica (natural y artificial), se estudia la actividad antropogénica y su conexión con la dinámica meteorológica atmosférica. Las entropías de correlación permiten explicar esta conectividad, así como, el efecto de la proximidad geográfica en el proceso de difusión de los contaminantes.

El objetivo de esta investigación es contrastar un modelo Gaussiano de dispersión de contaminantes, usando la rosa de los vientos de la ciudad de Andacollo (Chile), con un modelo cuya génesis está en la teoría del caos y que emplea datos crudos de carácter público de mediciones de la estación Andacollo del Sistema de Información Nacional de Calidad del Aire (SINCA) de Chile. Los datos conforman series de tiempo de material particulado, temperatura, humedad relativa, presión, radiación solar y magnitud de la velocidad del viento. El tratamiento caótico de cada serie entrega su exponente de Lyapunov, su coeficiente de Hurst y su entropía de correlación. La aproximación caótica muestra que las entropías de las variables meteorológicas actúan sobre la del contaminante provocando su decaimiento asintótico según perdida de persistencia, explicando sus interacciones térmicas localizadas. Se concluye que los modelos exhiben predicciones similares al comparar el decaimiento del contaminante PM10.