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This study is aimed to qualitatively analyze the impact of remote sources on air pollution in the Metropolitan Area of São Paulo (MASP). Air-mass back trajectories from June to August of 1999 were calculated using a three-dimensional kinematic trajectory model and grouped into trajectory clusters. Correlations of individual trajectory clusters with O3, CO and PM10 concentrations were determined. In this model, trajectories were obtained using the means of the three wind velocity components (U, V and W). The three-dimensional wind field was derived from the Regional Atmospheric Modeling System, and downscaling was employed. Coarse and fine nested grids (64-km and 16-km horizontal resolution, respectively) were used. Every 12 h (at 00 and 12 UTC), a back-trajectory ensemble, using the 64-km grid, was calculated for five defined endpoints at intervals of 0.5° N, S, E and W of the MASP (λ = 23° 33′S, ϕ = 46° 45′W), that last endpoint being centered in the MASP. To analyze cluster trajectories, the five trajectory ensembles from each day were allocated into one of four clusters (northeast, southeast, southwest or northwest quadrant) based on the origin of the trajectory over 4 days. Days on which all five trajectories originated from the same quadrant were classified as “core” days. Core day concentrations of CO, O3 and PM10 during the study period were evaluated. The results show that, during the study period, air-mass back trajectories in the MASP originated from all four quadrants: northeast (32%), southeast (12%), southwest (19%) and northwest (37%). Our analysis of back-trajectory clusters in the MASP suggests a transport to ambient air of O3 precursors and O3 from the northeast region, which is associated with agricultural activities involving biomass burning. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) has been linked to air pollution, especially particulate matter (PM). PM comprises various elements, including iron-rich particles that may reach the brain through inhalation. Lima, Peru is one of the most polluted cities in Latin America, with a high rate of AD. The study aims to evaluate the association between iron (Fe) trace elements in PM 10 and AD cases in Lima, Peru. This retrospective ecological study used monthly Fe concentration data from the Peruvian Ministry of Health. AD cases (ICD-10-G30) and dementia in AD cases (DAD, ICD-10-F00) were obtained from the Peruvian CDC. Fe trace element data were available for six districts in Lima for the years 2017-2019 and 2022. Cases were standardized based on ≥60-year-old populations of each district. Hierarchical mixed-effects models of Gaussian and negative binomial families were constructed to evaluate both outcomes jointly (AD + DAD) and separately (AD, and DAD). A sensitivity analysis was conducted by excluding data from Lima's downtown district. In the complete model, log-Fe concentration was associated with a higher rate of AD + DAD and DAD, and with a higher IRR for the three outcomes. After controlling for other metals, a higher DAD rate was observed (β-coeff = 6.76, 95%CI 0.07; 13.46, p = 0.048), and a higher IRR for AD + DAD (1.55, 95%CI 1.09; 2.20, p = 0.014) and DAD (1.83, 95%CI 1.21; 2.78, p = 0.004). The association was not significant in the sensitivity analysis. In conclusion, exposure to Fe through PM 10 inhalation may be associated with the presence of AD in Lima.

A total of 188,859 meteorological-PM[Formula: see text] data validated before (2019) and during the COVID-19 pandemic (2020) were used. In order to predict PM[Formula: see text] in two districts of South Lima in Peru, hourly, daily, monthly and seasonal variations of the data were analyzed. Principal Component Analysis (PCA) and linear/nonlinear modeling were applied. The results showed the highest annual average PM[Formula: see text] for San Juan de Miraflores (SJM) (PM[Formula: see text]-SJM: 78.7 [Formula: see text]g/m[Formula: see text]) and the lowest in Santiago de Surco (SS) (PM[Formula: see text]-SS: 40.2 [Formula: see text]g/m[Formula: see text]). The PCA showed the influence of relative humidity (RH)-atmospheric pressure (AP)-temperature (T)/dew point (DP)-wind speed (WS)-wind direction (WD) combinations. Cool months with higher humidity and atmospheric instability decreased PM[Formula: see text] values in SJM and warm months increased it, favored by thermal inversion (TI). Dust resuspension, vehicular transport and stationary sources contributed more PM[Formula: see text] at peak times in the morning and evening. The Multiple linear regression (MLR) showed the best correlation (r = 0.6166), followed by the three-dimensional model LogAP-LogWD-LogPM[Formula: see text] (r = 0.5753); the RMSE-MLR (12.92) exceeded that found in the 3D models (RMSE [Formula: see text]) and the NSE-MLR criterion (0.3804) was acceptable. PM[Formula: see text] prediction was modeled using the algorithmic approach in any scenario to optimize urban management decisions in times of pandemic., (© 2022. The Author(s).)

The observed behavior of pollution concentrations to the prevailing meteorological conditions has been studied for the period from June 13 to September 2, 1994, for the Metropolitan Area of Sao Paulo (MASP). The synoptic conditions, which prevailed during the period, have been identified. The behavior of these large-scale systems namely the relative positions, the nature and the types of the anticyclone or cold front (CF) has been investigated, and for each identified synoptic situation, trace elements concentrations have been determined. During the CF synoptic condition, the elements presented low concentrations associated with intense ventilation, precipitation and high relative humidity. It is seen that for both the synoptic conditions namely the South Atlantic Subtropical High and Polar High, high values of concentrations prevailed due to weak ventilation, low relative humidity and absence of precipitation. The micro and mesoscale meteorological conditions have also been studied for a day with low and a day with high concentration of particulate pollutants. Finally, applying receptor modeling to the fine aerosol mass concentration data set, five categories of emission sources in the MASP were identified: vehicles, garbage incineration, vegetation, suspend soil dust and burning of fuel oil.