The Green Ocean Amazon experiment – GoAmazon 2014–2015 – explored the interactions between natural biogenic forest emissions from central Amazonia and urban air pollution from Manaus. Previous GoAmazon 2014–2015 studies showed that nitrogen oxide (NO
Aerosol particles are present in the atmosphere in highly variable types and concentrations, which contribute differently to climate forcing, cloud formation and development, as well as ecosystem impacts. Particles may have a cooling or heating effect on the atmosphere, and their climatic roles are defined by their interactions with solar and terrestrial radiation fluxes, which strongly depend on their optical properties (extinction coefficient, single scattering albedo (SSA),
Recent studies in Amazonia that integrated data from ground-based sensors
A previous study conducted over the Amazonian region during the GoAmazon 2014–2015 experiment found strong secondary organic aerosol (SOA) production, with an enhancement of biogenic SOA (BSOA) formation in both the Manaus plume and its outflow by a factor of 100 %–400 % on average during the afternoon of 13 March 2014
A possible strategy for improving estimates of the urban plume impact on optical properties downwind of Manaus is to create regional scenario models with and without anthropogenic emissions and compare them to analyze how the emissions affect aerosol properties. Other studies have used sensitivity scenarios to understand how aerosol optical properties and secondary formation can be affected by events such as biomass burning
Different aerosol optical properties have been used to study aerosol impacts on ecosystems and the radiation balance, such as SSA
The objective of this work is to model secondary aerosol formation in central Amazonia, comparing modeled scenarios with and without anthropogenic emission, examining the interactions between natural biogenic emissions and urban air pollution from Manaus and investigating their impact on aerosol optical properties. We have extensively validated the model predictions with ground-based measurements and estimated how the optical properties may be affected by the plume-aging process (see Fig.
The Amazonian region has an annual mean temperature of around
Manaus is a city located in central Amazonia (3
Our WRF-Chem simulation was performed over 7 d between 8 and 14 March 2014. This period is part of the wet season in the region
The choice of the simulated days was made based upon ground-based data availability, which is necessary for evaluating the performance of the model, and the suggestions of
To track the plume as it ages, its approximate location and extent over time were determined using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model
Forward trajectories were calculated starting from eight points defined at 200 m a.s.l. (above sea level), defining a disk with a radius of 0.03
We used CO as a passive tracer of the plumes. It is a common choice as a tracer because has a long residence time in the atmosphere (much longer than the transport time of the Manaus plume), and it is almost entirely anthropogenic in origin, as it is emitted during combustion and other anthropogenic processes. In addition, it is significantly enhanced in urban plumes relative to the background, and it is routinely and robustly measured
The HYSPLIT plume tracking approach was used on the morning plumes of 10–14 March 2014 in order to investigate the change in SOA formation due to different NO
The study region was simulated with the WRF-Chem regional model, version 3.9.1.1
The physics, chemistry and emission options used in this study, as well as their corresponding references, are listed in Table 1. The most significant ones for this application are as follows: the Rapid Radiative Transfer Model for General Circulation Model applications (RRTMG) scheme for longwave and shortwave radiation
We used the approach by
Simulations were conducted in order to analyze how Manaus emissions affect SOA production and aerosol optical properties over the Amazon. We considered the following two scenarios: (i) Manaus on, which represents anthropogenic emissions and background emissions from initial and boundary conditions, and (ii) Manaus off, which represents a background scenario, dominated by biogenic emissions, with any anthropogenic contributions coming from the boundary conditions.
WRF-Chem simulations configuration used in this study.
Anthropogenic emissions were calculated using the
Biogenic emissions were calculated online using the Model of Emissions of Gases and Aerosols from Nature (MEGAN) version 2
We used in situ real-time measurements at several GoAmazon 2014–2015 surface sites (see Fig.
The observations and modeling of the Green Ocean Amazon experiment, GoAmazon 2014–2015, were designed to understand how aerosol and cloud life cycles are influenced by the pollutant outflow from Manaus into the tropical rain forest
To study the impact the Manaus pollution plume has on SOA production and aerosol optical properties in the area downwind of Manaus, meteorological conditions, especially temperature, humidity and PBL height, must be properly characterized and represented in the WRF-Chem model. Comparisons at the T3 site between observed and simulated hourly variations in accumulated total precipitation, temperature and relative humidity at 2 m, wind speed at 10 m, and PBL height (Figs. S1 and S2) show that the model performs well in terms of diurnal representation and trends. Simulated temperature and humidity tend to be underestimated (mean bias, MB, is equal to
The model is indeed underestimating the total amount of the precipitation during the simulated days (Fig. S2). However, the 4 d that were simulated which we focus on show little precipitation compared to the average during the wet season. Because of this, precipitation had quite a small impact on the chemistry during these days, and we do not expect this precipitation bias to affect our atmospheric chemistry simulations very much. Individual calculations of performance statistics for meteorological and chemical variables are presented in Table S1.
Figure S3 compares the simulated and observed vertical wind component during nighttime at the T3 site. In the early morning hours (05:00–11:00 LT), downdraft movement is not sufficient at the T3 site to inhibit pollutant dispersion. However, during the nighttime (20:00–22:00 LT), the simulation captured an organic aerosol concentration peak (see Fig.
Verifying model background conditions is important as it allows us to use comparisons between simulations with local anthropogenic emissions turned on and off to calculate enhancement factors. BC is an ideal aerosol for determining background anthropogenic conditions because, other than biomass burning sources, it is entirely anthropogenic.
Outside of local emissions plumes, average observed BC values are influenced by biogenic aerosol absorption, the global BC background and by long-range transport of BC from Saharan dust and African biomass burning. The BC transported from Africa is episodic, depending on the ITCZ positioning, and the air mass trajectories from Africa to the central Amazon. As we have several years of BC background measurements at the ATTO tower, it is possible to separate African episodic events from the rather constant regional BC concentrations that are relevant when comparing with modeled values not under anthropogenic influences
Generally, global and regional models contain uncertainties associated with the wet/dry deposition scheme
Figure
On 12 and 13 March, when no long-range transport effects are present, both simulations are consistent with observation, suggesting that our models accurately captured the background behavior. This, combined with successful modeling of regions down wind of Manaus, allows the successful calculation of aerosol and other enhancement factors in the plume region.
Observed and simulated surface black carbon (BC) concentration from 10 to 13 March 2014 at the T0a site. Standard deviation bars are shown for each set of measurements. Events due to long-range transport of Saharan dust and biomass burning emissions from West Africa are visible on 10 and 11 March
To better understand the impact of the Manaus urban plume on SOA formation and mixing ratios at the T3 site during 13 March 2014, we must be able to separate time periods representing clean and polluted episodes and compare observed and simulated values. Previous studies have developed methods to separate these episodes in the Amazon region
In our analysis, with observed data from the GoAmazon 2014–2015 experiment (T3 site), adjusted cluster centroids were used to analyze clean and polluted conditions during 2 months in the wet season (February and March 2014). We define three different clusters, i.e., (i) low pollution (low pol), (ii) middle pollution (mid pol) and (iii) high pollution (high pol; see Table
Because the concentration values of high pol and mid pol, episodes are substantially larger than those at low pol, and we distinguish time periods representing clean episodes as low pol and polluted episodes as high pol and mid pol. Quantitatively, we separated clean from polluted episodes with the degree of cluster membership. When membership for low pol is
Results of FCM clusters analysis during 13 March 2014 from 10:00 to 20:00 LT (local time) at the T3 site.
Cluster centroids used to analyze clean and polluted conditions. Note: ppbv – parts per billion by volume.
Given the abundance of BVOCs in the Amazon region
Temporal mean (06:00 to 15:00 LT; 13 March) spatial distribution of simulated surface level concentrations of
The O
In regions downwind of Manaus, the simulations showed O
Our results imply that the high NO
Time series and box plot comparison of measured and WRF-Chem-simulated surface-level gases and aerosols at the T3 site. Contributions from simulated primary anthropogenic organic aerosol (POA), biogenic SOA (BSOA) and anthropogenic SOA (ASOA) to total organic aerosol (OA), as simulated by WRF-Chem, with
According to Fig.
Between 10:00 and 16:00 LT, there is an increase in the total organic aerosol concentration, which was successfully reproduced by our simulation. This evolution of the organic aerosol concentration was expected on that day due to the Manaus plume arriving at the T3 site
A third total organic aerosol simulated peak is observed between 20:00 and 21:00 LT (see Fig.
An example of the differences between the measured and modeled concentration distributions is shown for organics, BC, CO and O
Comparisons between observed and simulated aerosol optical properties. Box plot of simulated and observed single scattering albedo (SSA), scattering Ångström exponent (SAE), absorption Ångström exponent (AAE), scattering (
Understanding how optical properties such as SSA and
Figure
WRF-Chem simulated values of single scattering albedo (SSA) in the presence or absence of Manaus emissions.
According to our simulation results, the Manaus plume modifies the amount of radiation absorbed by the atmosphere and is responsible for an SSA reduction of approximately 10 % at Manaus, 12 % at the T2 site and 5.3 % at the T3 site (see Fig.
During simulations with the Manaus pollution plume component turned on, average SSA values vary between 0.75 and 0.90 in regions downwind of Manaus. This represents the contribution from the interactions of urban aerosols with biogenic components of the forest. Similar results were found by
Absorption Ångström exponents (AAEs) at the wavelength pair 470 and 660 nm, as a function of the corresponding scattering Ångström exponents (SAEs) at the wavelength pair 470 and 660 nm, which are color coded using the related SSA at the wavelength pair 470 and 660 nm. The 1 h averaged instantaneous observed data values
Figure
In general, these SAE and AAE values show that the simulated values with anthropogenic emissions are, on average, associated with the fine fraction of PM
The observed AAE values in the simulation without anthropogenic emissions express a large variability (1.1 to 1.8) compared to the ones from simulation with anthropogenic emissions (1 to 1.3). This behavior is assumed to be caused by the lack of a brown carbon component in the aerosol population in our simulation. When the anthropogenic emissions are off, the SAE variability is mostly related to the significant contribution from large aerosols, as already mentioned
The
WRF-Chem simulated values of 600 nm asymmetry parameter (
Figure
WRF-Chem-simulated mean incoming solar radiation (instantaneous downwelling clear-sky shortwave flux at the bottom – SWDNBC), in watts per square meter, in the presence and absence of Manaus emissions.
In regions like the Amazon with sufficiently high levels of NO
In our simulations, we considered both direct and indirect aerosol effects during the wet season in the Amazon region. Incoming shortwave radiation at the surface is predicted to drop by up to ca. 40 W m
Averaged aerosol concentrations and optical properties within simulated plume between 06:00 and 15:00 LT on 13 March 2014.
In this section, we examine how aging of the Manaus plume may affect its optical properties. SSA initially has low values of ca. 0.91, then increases after the plume is 1 h old (07:00 LT). Some processes which affect SSA values as the plume ages are dilution, BC deposition, SOA formation and the lensing effect
During plume aging, a decrease in anthropogenic primary organic aerosol and an increase in SOA was observed, similar to results reported by
Figure
BC simulations at an altitude of ca. 500 m above the ground were evaluated using aircraft measurements from the Manaus plume on 13 March 2014. For the most part, our simulation shows good agreement with the G1 measurements (Fig. S19), particularly for background conditions. The offset in the third and fourth peaks is due to differences between the meteorological conditions of the simulation and reality. Similar offsets between simulations and observations were found by
As the plume ages, SAE begins to increase at 08:00 LT (after 2 h of plume aging) and remains constant with values of ca. 1.17 until 13:00 LT (after 7 h of plume aging). During this period, AAE is mostly close to 1, which can be explained by increased concentrations of fine (SOA
Numerical simulations with the WRF-Chem model were performed in order to investigate the impact of the Manaus plume on secondary organic aerosol production and aerosol optical properties downwind of Manaus. This study also shows how the plume-aging process can affect aerosol optical properties. Modeling the interactions between anthropogenic and biogenic emissions allows us to better understand the effects of demographic changes taking place in areas surrounded by tropical forests. We used the simulations to investigate the impact of anthropogenic emissions on SOA formation over the Amazon region during the wet season and the effect of anthropogenic NO
From model experiments, we conclude that the influence of the Manaus plume can reach areas up to 300 km downwind of Manaus, and we also provide a quantitative assessment of the effects urban pollution can cause in Amazonian forests surrounding urban centers. Overall, the simulations show that the aerosol impact of the Manaus plume is an increase in ground irradiance values by 20 % near the T3 site. We also separated the contributions of the different aerosol components from our estimate of the total aerosol mass concentration and their impact on optical properties. Especially striking is the impact on O
According to our results, the lowest
This study contributes to the investigation of the optical properties of PM
Investigating the urban plume as it changes in time is a challenge, due to the complex meteorology, particularly in determining the effects of emissions from both Manaus and the surrounding tropical forest. The approach used in this study was able to show interesting results, quantifying OA formation in a source plume as it ages. The simulations showed an increase in the total organics normalized by
The atmospheric behaviors described in this study are applicable to other urban areas in the Amazon and may be mirrored in cities located in tropical forests around the world. Medium-sized cities, such as Belém, Santarém, Rio Branco, Porto Velho and others, can impact SOA and O
The GoAmazon 2014–2015 experiment data are available from the ARM website (
The simulations and analysis code generated for this study are available upon request from the corresponding author, Janaína P. Nascimento.
The supplement related to this article is available online at:
JPN, MMB and PA conceptualized and defined the methodology. JPN carried out the formal analysis and the investigation of the model results, with support from BM, MMB, ALB, HG, LVR, SC, HJB, MAF, MT, SAM and PA. ALVV, SAAR, HG and MMB supported the design and running of the simulations. GGC, PA, LVR, MLB, BM and RAFS collected and curated the experimental data. JPN wrote the original draft, and all authors discussed the results and commented on the paper.
The authors declare that they have no conflict of interest.
We acknowledge support from the central office of the Large-Scale Biosphere–Atmosphere Experiment in Amazonia (LBA), coordinated by the National Institute of Amazonian Research (INPA) and the Amazonas State University (UEA), Amazonas, Brazil. JPN thanks the Brazilian Federal Agency for Support and Evaluation of Graduate Education (CAPES) for a graduate fellowship, linked to the doctoral program in Climate and Environment (CLIAMB), and for supporting 7 months of a visiting graduate student program at the NOAA Earth System Research Laboratory. Janaína P. Nascimento also thanks the Institute of Physics of the University of São Paulo (IFUSP), for student mobility and logistical support, and CIRES and NOAA ESRL for financial and logistical support. We thank Michael Trainer for providing support and knowledge during the research. We thank Manish Shrivastava for providing WRF-Chem simulation output for comparison with this work. We thank Gilberto Fish for providing the planetary boundary layer observed data. We thank Steven Jefferts, Stefania Romisch and Samuel Brewer, for facilitating communication between members of this collaboration. We are grateful to Bruno Takeshi, Luiz Cândido, Renata Teixeira and Delano Campos, for instrument operation and data analysis. Finally, we thank Richard Tisinai, for IT support. Marco A. Franco acknowledges a scholarship from CNPq (grant no. 169842/2017-7), for supporting his doctoral studies at the IFUSP, São Paulo, Brazil, and CAPES (grant no. 88887.368025/2019-00), for supporting 6 months of a visiting graduate student program at the Max Planck Institute for Chemistry, Mainz, Germany. Bruno Meller acknowledges a scholarship from CNPq (grant no. 133393/2019-4), for supporting his Masters studies at the IFUSP, São Paulo, Brazil. Helber Gomes acknowledges funding from CAPES (grant no. 757/2017). Paulo Artaxo acknowledges funding from FAPESP (grant no. 2017/17047-0).
This research has been supported by the Fundação de Amparo à Pesquisa do Estado de São Paulo (grant no. 2017/17047-0) and the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (grant nos. 88882.444345/2018-01 and 88881.190103/2018-01).
This paper was edited by David Topping and reviewed by two anonymous referees.