Predicting health impacts of wildfire smoke in Amazonas basin, Brazil.

Worldwide, smoke from forest fires has deleterious health effects. Even so, because of the complexity of fire mechanics, public health authorities face challenges in forecasting and thus mitigating population exposure to smoke. The population in the Amazon basin regularly suffers from fire smoke tied to agriculture and land-use change.

Hybrid unorganized machines to estimate the number of hospital admissions caused by PM[Formula: see text] concentration.

Air pollution levels exceeding the recommended limit can be the main cause of illnesses that affect human health, mainly diseases of the respiratory system. Consequently, this high exposure can impact public health management, given the increase in hospital admissions. One of the most influential air pollution parameters related to respiratory diseases is particulate matter (PM) concentrations.

Dynamic model to predict the association between air quality, COVID-19 cases, and level of lockdown.

Studies have reported significant reductions in air pollutant levels due to the COVID-19 outbreak worldwide global lockdowns. Nevertheless, all of the reports are limited compared to data from the same period over the past few years, providing mainly an overview of past events, with no future predictions. Lockdown level can be directly related to the number of new COVID-19 cases, air pollution, and economic restriction.

Air pollution epidemiology: A simplified Generalized Linear Model approach optimized by bio-inspired metaheuristics.

Studies in air pollution epidemiology are of paramount importance in diagnosing and improve life quality. To explore new methods or modify existing ones is critical to obtain better results. Most air pollution epidemiology studies use the Generalized Linear Model, especially the default version of R, Splus, SAS, and Stata softwares, which use maximum likelihood estimators in parameter optimization.