Abstract

Abstract. The Amazon Basin is a unique region to study atmospheric aerosols, given their relevance for the regional hydrological cycle and the large uncertainty of their sources. Multi-year datasets are crucial when contrasting periods of natural conditions and periods influenced by anthropogenic emissions. In the wet season, biogenic sources and processes prevail, and the Amazonian atmospheric composition resembles preindustrial conditions. In the dry season, the basin is influenced by widespread biomass burning emissions. This work reports multi-year observations of high time resolution submicrometer (10–600 nm) particle number size distributions at a rain forest site in Amazonia (TT34 tower, 60 km NW from Manaus city), between 2008 and 2010 and 2012 and 2014. The median particle number concentration was 403 cm−3 in the wet season and 1254 cm−3 in the dry season. The Aitken mode (∼ 30–100 nm in diameter) was prominent during the wet season, while the accumulation mode (∼ 100–600 nm in diameter) dominated the particle size spectra during the dry season. Cluster analysis identified groups of aerosol number size distributions influenced by convective downdrafts, nucleation events and fresh biomass burning emissions. New particle formation and subsequent growth was rarely observed during the 749 days of observations, similar to previous observations in the Amazon Basin. A stationary 1-D column model (ADCHEM – Aerosol Dynamics, gas and particle phase CHEMistry and radiative transfer model) was used to assess the importance of the processes behind the observed diurnal particle size distribution trends. Three major particle source types are required in the model to reproduce the observations: (i) a surface source of particles in the evening, possibly related to primary biological emissions; (ii) entrainment of accumulation mode aerosols in the morning; and (iii) convective downdrafts transporting Aitken mode particles into the boundary layer mostly during the afternoon. The latter process has the largest influence on the modeled particle number size distributions. However, convective downdrafts are often associated with rain and, thus, act as both a source of Aitken mode particles and a sink of accumulation mode particles, causing a net reduction in the median total particle number concentrations in the surface layer. Our study shows that the combination of the three mentioned particle sources is essential to sustain particle number concentrations in Amazonia.

Highlights

  • The Amazon Basin contains some of the few continental areas in the world where aerosol number concentrations as low as 300–500 particles per cm−3 are routinely observed (Martin et al, 2010b)

  • Forty-six percent of these samples were from the wet season (January to June) and fifty-four percent were from the dry season (July to December), comprising 749 days of observations

  • In spite of the three-fold increase from wet to dry season, observed particle number concentrations are well below observations from the Amazonian state of Rondônia, which is a site heavily affected by biomass burning emissions, where particle number concentration averages are in the range of 5700 to 10 440 cm−3 (Artaxo et al, 2002; Brito et al, 2014; Rissler et al, 2006)

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Summary

Introduction

The Amazon Basin contains some of the few continental areas in the world where aerosol number concentrations as low as 300–500 particles per cm−3 are routinely observed (Martin et al, 2010b). This concentration range represents an upper limit to the natural atmospheric particle loading. This work reports long-term observations of high time resolution aerosol property observations in central Amazonia. This activity was carried out as a part of the following projects: EUCAARI (European Integrated project on Aerosol Cloud Climate and Air Quality Interactions, Kulmala et al, 2011), AEROCLIMA (Direct and indirect effects of aerosols on climate in Amazonia and Pantanal, Artaxo et al, 2013) and GoAmazon2014/5 (Martin et al, 2016). In this work there is a special focus on submicrometer particle number size distributions

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