Theotonio Pauliquevis

Rainforest aerosols as biogenic nuclei of clouds and precipitation in the Amazon.

Clean or Dirty Aerosols strongly affect atmospheric properties and processes—including visibility, cloud formation, and radiative behavior. Knowing their effects in both clean and polluted air is necessary in order to understand their influence (see the Perspective by Baltensperger). Clarke and Kapustin (p. 1488) examine vertical atmospheric profiles collected above the Pacific Ocean, where air quality is affected by the transport of polluted air from the west, and find significant regional enhancements in light scattering, aerosol mass, and aerosol number associated with combustion. Aerosol particle concentrations in this region can exceed values in clean, unperturbed regions by over an order of magnitude. Thus combustion affects hemispheric aerosol optical depth and the distribution of cloud condensation nuclei. Pöschl et al. (p. 1513) discuss the composition of aerosols above the Amazon Basin, in the pristine conditions of the rainy season. The aerosols in this region are derived mostly from gaseous biogenic precursors, plants, and microorganisms, and particle concentration is orders of magnitude lower than in polluted continental regions. The majority of cloud condensation nuclei in the Amazon during the wet season are derived from biogenic precursors. The Amazon is one of the few continental regions where atmospheric aerosol particles and their effects on climate are not dominated by anthropogenic sources. During the wet season, the ambient conditions approach those of the pristine pre-industrial era. We show that the fine submicrometer particles accounting for most cloud condensation nuclei are predominantly composed of secondary organic material formed by oxidation of gaseous biogenic precursors. Supermicrometer particles, which are relevant as ice nuclei, consist mostly of primary biological material directly released from rainforest biota. The Amazon Basin appears to be a biogeochemical reactor, in which the biosphere and atmospheric photochemistry produce nuclei for clouds and precipitation sustaining the hydrological cycle. The prevailing regime of aerosol-cloud interactions in this natural environment is distinctly different from polluted regions.

Sources and properties of Amazonian aerosol particles

This review provides a comprehensive account of what is known presently about Amazonian aerosol particles and concludes by formulating outlook and priorities for further research. The review is organized to follow the life cycle of Amazonian aerosol particles. It begins with a discussion of the primary and secondary sources relevant to the Amazonian particle burden, followed by a presentation of the particle properties that characterize the mixed populations present over the Amazon Basin at different times and places. These properties include number and mass concentrations and distributions, chemical composition, hygroscopicity, and cloud nucleation ability. The review presents Amazonian aerosol particles in the context of natural compared to anthropogenic sources as well as variability with season and meteorology. This review is intended to facilitate an understanding of the current state of knowledge on Amazonian aerosol particles specifically and tropical continental aerosol particles in general and thereby to enhance future research in this area. Copyright

Mass spectral characterization of submicron biogenic organic particles in the Amazon Basin

Submicron atmospheric particles in the Amazon Basin were characterized by a high-resolution aerosol mass spectrometer during the wet season of 2008. Patterns in the mass spectra closely resembled those of secondary-organic-aerosol (SOA) particles formed in environmental chambers from biogenic precursor gases. In contrast, mass spectral indicators of primary biological aerosol particles (PBAPs) were insignificant, suggesting that PBAPs contributed negligibly to the submicron fraction of particles during the period of study. For 40% of the measurement periods, the mass spectra indicate that in-Basin biogenic SOA production was the dominant source of the submicron mass fraction, contrasted to other periods (30%) during which out-of-Basin organic-carbon sources were significant on top of the baseline in-Basin processes. The in-Basin periods had an average organic-particle loading of 0.6 mu g m(-3) and an average elemental oxygen-to-carbon (O:C) ratio of 0.42, compared to 0.9 mu g m(-3) and 0.49, respectively, during periods of out-of-Basin influence. On the basis of the data, we conclude that most of the organic material composing submicron particles over the Basin derived from biogenic SOA production, a finding that is consistent with microscopy observations made in a concurrent study. This source was augmented during some periods by aged organic material delivered by long-range transport. Citation: Chen, Q., et al. (2009), Mass spectral characterization of submicron biogenic organic particles in the Amazon Basin, Geophys. Res. Lett., 36, L20806, doi: 10.1029/2009GL039880.

Overview of the inorganic and organic composition of size-segregated aerosol in Rondônia, Brazil, from the biomass-burning period to the onset of the wet season

The aerosol characterization experiment performed within the Large-Scale Biosphere-Atmosphere Experiment in Amazonia-Smoke, Aerosols, Clouds, Rainfall and Climate (LBA-SMOCC) field experiment carried out in Rondonia, Brazil, in the period from September to November 2002 provides a unique data set of size-resolved chemical composition of boundary layer aerosol over the Amazon Basin from the intense biomass-burning period to the onset of the wet season. Three main periods were clearly distinguished on the basis of the PM10 concentration trend during the experiment: (1) dry period, with average PM10 well above 50 mu g m(-3); (2) transition period, during which the 24-hour-averaged PM10 never exceeded 40 mu g m(-3) and never dropped below 10 mg m(-3); (3) and wet period, characterized by 48-hour-averaged concentrations of PM10 below 12 mu g m(-3) and sometimes as low as 2 mu g m(-3). The trend of PM10 reflects that of CO concentration and can be directly linked to the decreasing intensity of the biomass- burning activities from September through November, because of the progressive onset of the wet season. Two prominent aerosol modes, in the submicron and supermicron size ranges, were detected throughout the experiment. Dry period size distributions are dominated by the fine mode, while the fine and coarse modes show almost the same concentrations during the wet period. The supermicron fraction of the aerosol is composed mainly of primary particles of crustal or biological origin, whereas submicron particles are produced in high concentrations only during the biomass-burning periods and are mainly composed of organic material, mostly water-soluble, and similar to 10% of soluble inorganic salts, with sulphate as the major anion. Size-resolved average aerosol chemical compositions are reported for the dry, transition, and wet periods. However, significant variations in the aerosol composition and concentrations were observed within each period, which can be classified into two categories: (1) diurnal oscillations, caused by the diurnal cycle of the boundary layer and the different combustion phase active during day (flaming) or night (smouldering); and (2) day-to-day variations, due to alternating phases of relatively wet and dry conditions. In a second part of the study, three subperiods representative of the conditions occurring in the dry, transition, and wet periods were isolated to follow the evolution of the aerosol chemical composition as a function of changes in rainfall rate and in the strength of the sources of particulate matter. The chemical data set provided by the SMOCC field experiment will be useful to characterize the aerosol hygroscopic properties and the ability of the particles to act as cloud condensation nuclei.

Dust and smoke transport from Africa to South America: Lidar profiling over Cape Verde and the Amazon rainforest

[1] Quasi-simultaneous vertically resolved multiwavelength aerosol Raman lidar observations were conducted in the near field (Praia, Cape Verde, 15°N, 23.5°W) and in the far field (Manaus, Amazon basin, Brazil, 2.5°S, 60°W) of the long-range transport regime between West Africa and South America. Based on a unique data set (case study) of spectrally resolved backscatter and extinction coefficients, and of the depolarization ratio a detailed characterization of aerosol properties, vertical stratification, mixing, and aging behavior during the long-distance travel in February 2008 (dry season in western Africa, wet season in the Amazon basin) is presented. While highly stratified aerosol layers of dust and smoke up to 5.5 km height were found close to Africa, the aerosol over Manaus was almost well-mixed, reached up to 3.5 km, and mainly consisted of aged biomass burning smoke.

Química atmosférica na Amazônia: a floresta e as emissões de queimadas controlando a composição da atmosfera amazônica

The understanding of the natural processes that regulate atmospheric composition in Amazonia is critical to the establishment of a sustainable development strategy in the region. The large emissions of trace gases and aerosols during the dry season, as a result of biomass burning, profoundly change the composition of the atmosphere in most of its area. The concentration of trace gases and aerosols increases by a factor of 2 to 8 over large areas, affecting the natural mechanisms of several key atmospheric processes in the region. Cloud formation mechanisms, for instance, are strongly affected when the concentration of cloud condensation nuclei (CCN) changes from 200-300 CCN/cc in the wet season to 5,000-10,000 CCN/cc in the dry season. The cloud droplet radius is reduced from values of 18 to 25 micrometers in the wet season to 5 to 10 micrometers in the dry season, suppressing cloud formation and the occurrence of precipitation under some conditions. Ozone is a key trace gas for changes in the forest health, with concentrations increasing from 12 parts per billion (ppb), at the wet season, to values as high as 100 ppb (in the dry season in areas strongly affected by biomass burning emissions). At this level, ozone could be damaging the vegetation in regions far from the emissions. The atmospheric radiation balance is also strongly affected, with a net loss of up to 70% of photosynthetic active radiation at the surface.

The Green Ocean Amazon Experiment (GoAmazon2014/5) Observes Pollution Affecting Gases, Aerosols, Clouds, and Rainfall over the Rain Forest

AbstractThe Observations and Modeling of the Green Ocean Amazon 2014–2015 (GoAmazon2014/5) experiment took place around the urban region of Manaus in central Amazonia across 2 years. The urban pollution plume was used to study the susceptibility of gases, aerosols, clouds, and rainfall to human activities in a tropical environment. Many aspects of air quality, weather, terrestrial ecosystems, and climate work differently in the tropics than in the more thoroughly studied temperate regions of Earth. GoAmazon2014/5, a cooperative project of Brazil, Germany, and the United States, employed an unparalleled suite of measurements at nine ground sites and on board two aircraft to investigate the flow of background air into Manaus, the emissions into the air over the city, and the advection of the pollution downwind of the city. Herein, to visualize this train of processes and its effects, observations aboard a low-flying aircraft are presented. Comparative measurements within and adjacent to the plume followed t...

The Amazon Dense GNSS Meteorological Network: A New Approach for Examining Water Vapor and Deep Convection Interactions in the Tropics

AbstractThe complex interactions between water vapor fields and deep atmospheric convection remain one of the outstanding problems in tropical meteorology. The lack of high spatial–temporal resolution, all-weather observations in the tropics has hampered progress. Numerical models have difficulties, for example, in representing the shallow-to-deep convective transition and the diurnal cycle of precipitation. Global Navigation Satellite System (GNSS) meteorology, which provides all-weather, high-frequency (5 min), precipitable water vapor estimates, can help. The Amazon Dense GNSS Meteorological Network experiment, the first of its kind in the tropics, was created with the aim of examining water vapor and deep convection relationships at the mesoscale. This innovative, Brazilian-led international experiment consisted of two mesoscale (100 km × 100 km) networks: 1) a 1-yr (April 2011–April 2012) campaign (20 GNSS meteorological sites) in and around Manaus and 2) a 6-week (June 2011) intensive campaign (15 G...

Influence on the oxidative potential of a heavy-duty engine particle emission due to selective catalytic reduction system and biodiesel blend.

Although the particulate matter (PM) emissions from biodiesel fuelled engines are acknowledged to be lower than those of fossil diesel, there is a concern on the impact of PM produced by biodiesel to human health. As the oxidative potential of PM has been suggested as trigger for adverse health effects, it was measured using the Electron Spin Resonance (OP(ESR)) technique. Additionally, Energy Dispersive X-ray Fluorescence Spectroscopy (EDXRF) was employed to determine elemental concentration, and Raman Spectroscopy was used to describe the amorphous carbon character of the soot collected on exhaust PM from biodiesel blends fuelled test-bed engine, with and without Selective Catalytic Reduction (SCR). OP(ESR) results showed higher oxidative potential per kWh of PM produced from a blend of 20% soybean biodiesel and 80% ULSD (B20) engine compared with a blend of 5% soybean biodiesel and 95% ULSD (B5), whereas the SCR was able to reduce oxidative potential for each fuel. EDXRF data indicates a correlation of 0.99 between concentration of copper and oxidative potential. Raman Spectroscopy centered on the expected carbon peaks between 1100cm(-1) and 1600cm(-1) indicate lower molecular disorder for the B20 particulate matter, an indicative of a more graphitic carbon structure. The analytical techniques used in this study highlight the link between biodiesel engine exhaust and increased oxidative potential relative to biodiesel addition on fossil diesel combustion. The EDXRF analysis confirmed the prominent role of metals on free radical production. As a whole, these results suggest that 20% of biodiesel blends run without SCR may pose an increased health risk due to an increase in OH radical generation.

Assessing the impact of PM2.5 on respiratory disease using artificial neural networks

Understanding the impact on human health during peak episodes in air pollution is invaluable for policymakers. Particles less than PM2.5 can penetrate the respiratory system, causing cardiopulmonary and other systemic diseases. Statistical regression models are usually used to assess air pollution impacts on human health. However, when there are databases missing, linear statistical regression may not process well and alternative data processing should be considered. Nonlinear Artificial Neural Networks (ANN) are not employed to research environmental health pollution even though another advantage in using ANN is that the output data can be expressed as the number of hospital admissions. This research applied ANN to assess the impact of air pollution on human health. Three well-known ANN were tested: Multilayer Perceptron (MLP), Extreme Learning Machines (ELM) and Echo State Networks (ESN), to assess the influence of PM2.5, temperature, and relative humidity on hospital admissions due to respiratory diseases. Daily PM2.5 levels were monitored, and hospital admissions for respiratory illness were obtained, from the Brazilian hospital information system for all ages during two sampling campaigns (2008-2011 and 2014-2015) in Curitiba, Brazil. During these periods, the daily number of hospital admissions ranged from 2 to 55, PM2.5 concentrations varied from 0.98 to 54.2 μg m-3, temperature ranged from 8 to 26 °C, and relative humidity ranged from 45 to 100%. Of the ANN used in this study, MLP gave the best results showing a significant influence of PM2.5, temperature and humidity on hospital attendance after one day of exposure. The Anova Friedmans test showed statistical difference between the appliance of each ANN model (p < .001) for 1 lag day between PM2.5 exposure and hospital admission. ANN could be a more sensitive method than statistical regression models for assessing the effects of air pollution on respiratory health, and especially useful when there is limited data available.