Statistical Analysis of PM10 Concentration in the Monterrey Metropolitan Area, Mexico (2010–2018)

Abstract

Air-quality monitoring and analysis are initial parts of a comprehensive strategy to prevent air pollution in cities. In such a context, statistical tools play an important role in determining the [d=R1]time-series trendstrend of time series, locating areas with high [d=R1]pollutant concentrationsconcentration of pollutants, and building predictive models. In this work, we analyzed the spatio-temporal behavior of the pollutant PM10 in the Monterrey Metropolitan Area (MMA), Mexico during the period 2010–2018 by applying statistical analysis to the time series of seven environmental stations. [d=R1]First, weWe used experimental variograms and scientific visualization to determine the general trends and variability in time. Then, fractal exponents (the Hurst rescaled range and Higuchi algorithm) were used to analyze the long-term dependence of the time series and characterize the study area by correlating that dependence with the geographical parameters of each environmental station. The results suggest a linear decrease in PM10 concentration, which showed an annual cyclicity[d=R1]. The, with the autumn-winter period [d=R1]wasas the most polluted and the spring-summer period [d=R1]wasas the least. Furthermore, it was found that the highest average concentrations are located in the western and high-altitude zones of the MMA, and that average concentration is related in a quadratic way to the Hurst and Higuchi exponents, which in turn are related to some geographic parameters. Therefore, [d=R1]in additionadditionally to the results for the MMA, the present paper shows three [d=R1]practicaluseful statistical methods for analyzing the spatio-temporal behavior of air quality.

Deep Learning with Pretrained Framework Unleashes the Power of Satellite-Based Global Fine-Mode Aerosol Retrieval

“…At the national scale of Mexico, GMDL-modeled fAODs were generally lower than those modeled with DLFE-Satellite (Figure f). The decrease in fAOD over time was captured by both modeled sets (Figure g), corresponding well to the reduction in air pollutants in Mexico due to the establishment of the Environmental State Plan 1995–2020 (Plan Estatal de Medio Ambiente 1995–2020). , The GDLM model overestimated the declining trend over Mexico by 16.6% per year compared with that estimated by the DLFE-Satellite model (−1.45 × 10 –3 /yr). GMDL-modeled fAODs over Bangladesh were generally higher than DLFE-Satellite-modeled values (Figure h).…”