A qualitative study of thermal transfers is carried out from a record of measurements (time series) of meteorological variables (temperature, relative humidity and magnitude of wind speeds) and pollutants (PM₁₀, PM_2.5 and CO) in six localities located at different heights in the geographic basin of Santiago de Chile. The measurements were made in two periods, 2010–2013 and 2017–2020 (a total of 2,049,336 data), the last period coinciding with a process of intense urbanization, especially high-rise construction. The measurements, in the form of hourly time series, are analyzed on the one hand according to the theory of thermal conduction discretizing the differential equation of the temporal variation in the temperature and, on the other hand, through the theory of chaos that provides the entropies (S). Both procedures demonstrate, comparatively, that the last period of intense urbanization presents an increase in thermal transfers and temperature, which affects urban meteorology and makes it more complex. As shown by the chaotic analysis, there is a faster loss of information for the period 2017–2020. The consequences of the increase in temperature on human health and learning processes are studied.

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, PM_2.5, 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.

This work analyzes the evolution of an urban thermal environment using measurements, in the form of time series, of atmospheric pollutants (PM₁₀, PM_2.5, CO) and meteorological variables (temperature (T), relative humidity (RH) and magnitude of wind speed (WS)) of three periods, each of 3.25 years: 2010–2013, 2017–2020 and 2019–2022. The study region is the capital of Chile, Santiago de Chile, located in a rugged basin geography. Of the total communes that make up the capital, six communes that are at different heights from sea level were selected for this study, providing 3,074,004 data records. These communes have been subject to an intensive urban densification process. The time series are analyzed through the chaos theory, demonstrating that they are chaotic through the calculation of the parameters: Lyapunov exponent (λ > 0), correlation dimension (D_C < 5), Kolmogorov entropy (S_K > 0), Hurst exponent (0.5 < H < 1), Lempel–Ziv complexity (LZ > 0). Based on these parameters, the following is constructed for each commune: the C_K ratio, which results from the ratio between the entropies of the meteorological variables and the entropies of the pollutants; the loss of information (<ΔI> < 0) using the Lyapunov exponent; the fractal dimension (D) using the Hurst exponent. It is verified, when comparing the three periods for the six communes, that: C_K evolves declining with height, with a greater influence of pollutants; the loss of information is faster in urban meteorology; an increase in fractality. The estimation of the entropic flows, based on the Clausius equation, confirm the trend. The descriptive framework shows the weakness of the mitigation measures.

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.

This study examines the consequences of human activity on the atmospheric boundary layer considering (i) atmospheric pollution, (ii) urban micrometeorology, (iii) three geographic morphologies (mountain, basin and coast) and (iv) surface change of roughness due to buildings. Qualitative relationships are established between the four issues mentioned using measurements from different periods, urban meteorology and pollutants, in the boundary layer of the three geographic morphologies, all with large urban settlements. The measurements per hour and at ground level correspond to the variables: temperature, magnitude of wind speed, relative humidity and concentration of anthropogenic pollutants (PM₁₀, PM_2.5 and CO). The measurements form time series, demonstrating their chaoticity through the parameters: Lyapunov coefficient, correlation dimension, Hurst coefficient, Lempel–Ziv complexity, information loss, fractal dimension and correlation entropy. The results, according to each parameter, allow us to characterize the effect of human activity on geographical morphologies and its meteorology, showing a lower impact on mountain and coastal areas. Calculating, for each geographical configuration, the quotient between the total correlation entropy of the meteorological variables and that of the pollutants, the basin entropy is less than one, which shows, for the study period, the entropic domain of atmospheric pollutants unlike mountain and coast.