Amazon savannization and climate change are projected to increase dry season length and temperature extremes over Brazil.

Land use change and atmospheric composition, two drivers of climate change, can interact to affect both local and remote climate regimes. Previous works have considered the effects of greenhouse gas buildup in the atmosphere and the effects of Amazon deforestation in atmospheric general circulation models. In this study, we investigate the impacts of the Brazilian Amazon savannization and global warming in a fully coupled ocean-land-sea ice-atmosphere model simulation. We find that both savannization and global warming individually lengthen the dry season and reduce annual rainfall over large tracts of South America. The combined effects of land use change and global warming resulted in a mean annual rainfall reduction of 44% and a dry season length increase of 69%, when averaged over the Amazon basin, relative to the control run. Modulation of inland moisture transport due to savannization shows the largest signal to explain the rainfall reduction and increase in dry season length over the Amazon and Central-West. The combined effects of savannization and global warming resulted in maximum daily temperature anomalies, reaching values of up to 14 °C above the current climatic conditions over the Amazon. Also, as a consequence of both climate drivers, both soil moisture and surface runoff decrease over most of the country, suggesting cascading negative future impacts on both agriculture production and hydroelectricity generation.

Modeling the Climatic Suitability of COVID-19 Cases in Brazil.

Studies have shown that climate may affect the distribution of coronavirus disease (COVID-19) and its incidence and fatality rates. Here, we applied an ensemble niche modeling approach to project the climatic suitability of COVID-19 cases in Brazil. We estimated the cumulative incidence, mortality rate, and fatality rate of COVID-19 between 2020 and 2021. Seven statistical algorithms (MAXENT, MARS, RF, FDA, CTA, GAM, and GLM) were selected to model the climate suitability for COVID-19 cases from diverse climate data, including temperature, precipitation, and humidity. The annual temperature range and precipitation seasonality showed a relatively high contribution to the models, partially explaining the distribution of COVID-19 cases in Brazil based on the climatic suitability of the territory. We observed a high probability of climatic suitability for high incidence in the North and South regions and a high probability of mortality and fatality rates in the Midwest and Southeast regions. Despite the social, viral, and human aspects regulating COVID-19 cases and death distribution, we suggest that climate may play an important role as a co-factor in the spread of cases. In Brazil, there are regions with a high probability that climatic suitability will contribute to the high incidence and fatality rates of COVID-19 in 2020 and 2021.

Short-term association between ambient air pollution and cardio-respiratory mortality in Rio de Janeiro, Brazil.

BACKGROUND: Several epidemiological studies have reported associations between ambient air pollution and mortality. However, relatively few studies have investigated this relationship in Brazil using individual-level data. OBJECTIVES: To estimate the short-term association between exposure to particulate matter <10 µm (PM10) and ozone (O3), and cardiovascular and respiratory mortality in Rio de Janeiro, Brazil, between 2012 and 2017. METHODS: We used a time-stratified case-crossover study design with individual-level mortality data. Our sample included 76,798 deaths from cardiovascular diseases and 36,071 deaths from respiratory diseases. Individual exposure to air pollutants was estimated by the inverse distance weighting method. We used data from seven monitoring stations for PM10 (24-hour mean), eight stations for O3 (8-hour max), 13 stations for air temperature (24-hour mean), and 12 humidity stations (24-hour mean). We estimated the mortality effects of PM10 and O3 over a 3-day lag using conditional logistic regression models combined with distributed lag non-linear models. The models were adjusted for daily mean temperature and daily mean absolute humidity. Effect estimates were presented as odds ratios (OR) with their 95% confidence interval (CI) associated with a 10 µg/m3 increase in each pollutant exposure. RESULTS: No consistent associations were observed for both pollutant and mortality outcome. The cumulative OR of PM10 exposure was 1.01 (95% CI 0.99-1.02) for respiratory mortality and 1.00 (95% CI 0.99-1.01) for cardiovascular mortality. For O3 exposure, we also found no evidence of increased mortality for cardiovascular (OR 1.01, 95% CI 1.00-1.01) or respiratory diseases (OR 0.99, 95% CI 0.98-1.00). Our findings were similar across age and gender subgroups, and different model specifications. CONCLUSION: We found no consistent associations between the PM10 and O3 concentrations observed in our study and cardio-respiratory mortality. Future studies need to explore more refined exposure assessment methods to improve health risk estimates and the planning and evaluation of public health and environmental policies.

Heat waves and mortality in the Brazilian Amazon: Effect modification by heat wave characteristics, population subgroup, and cause of death.

BACKGROUND: The Brazilian Amazon faces overlapping socio-environmental, sanitary, and climate challenges, and is a hotspot of concern due to projected increases in temperature and in the frequency of heat waves. Understanding the effects of extreme events on health is a central issue for developing climate policies focused on the population's health. OBJECTIVES: We investigated the effects of heat waves on mortality in the Brazilian Amazon, examining effect modification according to various heat wave definitions, population subgroups, and causes of death. METHODS: We included all 32 Amazonian municipalities with more than 100,000 inhabitants. The study period was from 2000 to 2018. We obtained mortality data from the Information Technology Department of the Brazilian Public Healthcare System, and meteorological data were derived from the ERA5-Land reanalysis dataset. Heat waves were defined according to their intensity (90th; 92.5th; 95th; 97.5th and 99th temperature percentiles) and duration (≥2, ≥3, and ≥4 days). In each city, we used a time-stratified case-crossover study to estimate the effects of each heat wave definition on mortality, according to population subgroup and cause of death. The lagged effects of heat waves were estimated using conditional Poisson regression combined with distributed lag non-linear models. Models were adjusted for specific humidity and public holidays. Risk ratios were pooled for the Brazilian Amazon using a univariate random-effects meta-analysis. RESULTS: The pooled relative risks (RR) for mortality from total non-external causes varied between 1.03 (95% CI: 1.01-1.06), for the less stringent heat wave definition, and 1.18 (95% CI: 1.04-1.33) for the more stringent definition. The mortality risk rose as the heat wave intensity increased, although the increase from 2 to 3, and 3-4 days was small. Although not statistically different, our results suggest a higher mortality risk for the elderly, this was also higher for women than men, and for cardiovascular causes than for non-external or respiratory ones. CONCLUSIONS: Heat waves were associated with a higher risk of mortality from non-external causes and cardiovascular diseases. Heat wave intensity played a more important role than duration in determining this risk. Suggestive evidence indicated that the elderly and women were more vulnerable to the effects of heat waves on mortality.

Projections of excess cardiovascular mortality related to temperature under different climate change scenarios and regionalized climate model simulations in Brazilian cities.

BACKGROUND: There is an urgent need for more information about the climate change impact on health in order to strengthen the commitment to tackle climate change. However, few studies have quantified the health impact of climate change in Brazil and in the Latin America region. In this paper, we projected the impacts of temperature on cardiovascular (CVD) mortality according to two climate change scenarios and two regionalized climate model simulations in Brazilian cities. METHODS: We estimated the temperature-CVD mortality relationship in 21 Brazilian cities, using distributed lag non-linear models in a two-stage time-series analysis. We combined the observed exposure-response functions with the daily temperature projected under two representative concentration pathways (RCP), RCP8.5 and RCP4.5, and two regionalized climate model simulations, Eta-HadGEM2-ES and Eta-MIROC5. RESULTS: We observed a trend of reduction in mortality related to low temperatures and a trend of increase in mortality related to high temperatures, according to all the investigated models and scenarios. In most places, the increase in mortality related to high temperatures outweighed the reduction in mortality related to low temperatures, causing a net increase in the excess temperature-related mortality. These trends were steeper according to the higher emission scenario, RCP8.5, and to the Eta-HadGEM2-ES model. According to RCP8.5, our projections suggested that the temperature-related mortality fractions in 2090-99 compared to 2010-2019 would increase by 8.6% and 1.7%, under Eta-HadGEM2-ES and Eta-MIROC5, respectively. According to RCP4.5, these values would be 0.7% and -0.6%. CONCLUSIONS: For the same climate model, we observed a greater increase trend in temperature-CVD mortality according to RCP8.5, highlighting a greater health impact associated with the higher emission scenario. Our results may be useful to support public policies and strategies for mitigation of and adaptation to climate change, particularly in the health sector.

A curated review of recent literature of biomarkers used for assessing air pollution exposures and effects in humans.

This is a cross-sectional review of biomarkers used in air pollution research from January 2009 through December 2012. After an initial keyword search in PubMed retrieving 426 articles, a comprehensive abstract review identified 54 articles of experimental design that used biomarkers of exposure or effect in human studies in the area of air pollution research during this specified time period. A thorough bibliographic search of the included articles retrieved an additional 65 articles meeting the inclusion criteria. This review presents these 119 studies and the 234 biomarkers employed in these air pollution research investigations. Data presented are 70 biomarkers of exposure with 54% relating to polycyclic aromatic hydrocarbons, 36% volatile organic carbons, and 10% classified as other. Of the 164 biomarkers of effect, 91 and 130 were used in investigating effects of short-term and chronic exposure, respectively. Results of biomarkers used in short-term exposure describe different lag times and pollutant components such as primary and secondary pollutants, and particle number associated with corresponding physiological mechanisms including airway inflammation, neuroinflammation, ocular, metabolic, early endothelial dysfunction, coagulation, atherosclerosis, autonomic nervous system, oxidative stress, and DNA damage. The review presents three different exposure scenarios of chronic, occupational, and extreme exposure scenarios (indoor cooking) with associated biomarker findings presented in three broad categories of (1) immune profile, (2) oxidative stress, and (3) DNA damage. This review offers a representation of the scope of data being explored by air pollution researchers through the use of biomarkers and has deliberately been restricted to this particular subject rather than an extensive or in-depth review. This article provides a contextualization of air pollution studies conducted with biomarkers in human subjects in given areas while also integrating this complex body of information to offer a useful review for investigators in this field of study.

Risk assessment of PM(2.5) to child residents in Brazilian Amazon region with biofuel production.

BACKGROUND: Exposure to fine fractions of particulate matter (PM(2.5)) is associated with increased hospital admissions and mortality for respiratory and cardiovascular disease in children and the elderly. This study aims to estimate the toxicological risk of PM(2.5) from biomass burning in children and adolescents between the age of 6 and 14 in Tangará da Serra, a municipality of Subequatorial Brazilian Amazon. METHODS: Risk assessment methodology was applied to estimate the risk quotient in two scenarios of exposure according to local seasonality. The potential dose of PM(2.5) was estimated using the Monte Carlo simulation, stratifying the population by age, gender, asthma and Body Mass Index (BMI). RESULTS: Male asthmatic children under the age of 8 at normal body rate had the highest risk quotient among the subgroups. The general potential average dose of PM(2.5) was 1.95 µg/kg.day (95% CI: 1.62 - 2.27) during the dry scenario and 0.32 µg/kg.day (95% CI: 0.29 - 0.34) in the rainy scenario. During the dry season, children and adolescents showed a toxicological risk to PM(2.5) of 2.07 µg/kg.day (95% CI: 1.85 - 2 .30). CONCLUSIONS: Children and adolescents living in the Subequatorial Brazilian Amazon region were exposed to high levels of PM(2.5) resulting in toxicological risk for this multi-pollutant. The toxicological risk quotients of children in this region were comparable or higher to children living in metropolitan regions with PM(2.5) air pollution above the recommended limits to human health.