Aerosols from anthropogenic and biogenic sources and their interactions: modeling aerosol formation, optical properties and impacts over the central Amazon Basin

The Green Ocean Amazon experiment GoAmazon2014/5 explored the interactions between natural biogenic forest emissions from Central Amazonia and urban air pollution from Manaus. Previous GoAmazon2014/5 studies showed that nitrogen oxides (NOx = NO + NO2) and sulfur oxides (SOx) emissions from Manaus strongly interact with biogenic volatile organic compounds (BVOCs), affecting secondary organic aerosol (SOA) formation. In previous studies, ground based and aircraft 5 measurements provided evidence of SOA formation and strong changes in aerosol composition and properties. Aerosol optical properties also evolve, and their impacts on the Amazonian ecosystem can be significant. As particles age, some processes such as SOA production, black carbon (BC) deposition, particle growth, and the BC lensing effect change the aerosol optical properties, affecting the solar radiation flux at the surface. This study analyzes data and models SOA formation using the Weather Research and Forecasting with Chemistry (WRF-Chem) model to assess the spatial variability of aerosol optical properties as 10 the Manaus plumes interact with the natural atmosphere. The following aerosol optical properties are investigated: single scattering albedo (SSA), asymmetry parameter (gaer), absorption Ångström exponent (AAE), and scattering Ångström exponent

January and May), with the Inter-tropical Convergence Zone (ITCZ) extending south over Manaus, it is possible to find one of the lowest particle number concentrations over a continental area in the world (Andreae et al., 2015;Artaxo et al., 1994; highlight the March 13, 2014 as a golden day to study the evolution of the Manaus plume as it advected to the surrounding Amazon tropical forest. Our investigation focuses on a detailed analysis of March 13, 2014, because on that day the plume reached regions downwind of Manaus such as the T2 and T3 sites. During this period mostly sunny skies were observed with little or no precipitation and the interference from biomass burning and cloud processing was negligible. We track the simulated Manaus plume as it ages in order to investigate the evolution of optical properties. Different analyses of atmospheric variables with and without anthropogenic emissions were used to characterize changes in aerosol properties downwind of Manaus due 100 to anthropogenic activity.
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 (Draxler, 2007;Stein et al., 2007). Forward trajectories were calculated starting from 8 points at 200 masl in a circle of radius 0.03 o (∼3.4 km) centered on the plume's initial location at 6:00 LT. Winds and other forcing meteorological fields were taken from our WRF-Chem simulation. Average gas and aerosol concentrations 105 and optical properties were calculated in the volume defined by the maximum and minimum latitude and longitude of the 8 points, and altitude range from 100 to 500m. The averaging region is shown in Figure S12 in the SI, and the altitude of the plume is shown in Figure S14 and S15 in the SI. ∆CO was determined by taking the difference in carbon monoxide (CO) between simulations with anthropogenic emissions turned on and off in the simulated region selected by HYSPLIT. The tracking plume approach was used to track the plume in other days (10 to 14 March 2014) in order to investigate the change 110 in SOA formation due to different NO x concentrations. The days other than the 13 th were not exemplary days for observing the evolution of the Manaus plume due to meteorological factors such as precipitation. Additionally, the plume did not appear until 8 LT. As such, our analysis focuses on March 13, 2014.

WRF-Chem Model Description and Setup
The study region was simulated with the WRF-Chem regional model, version 3.9.1.1 (Grell et al., 2005;Fast et al., 2006)  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: the Rapid Radiative Transfer Model for General Circulation Model applications (RRTMG) scheme for longwave and shortwave radiation (Iacono et al., 2008); the Revised Mesoscale Model 125 version 5 Monin-Obukhov scheme for surface layer (Jiménez et al., 2012); the Unified Noah land-surface model for land surface (Tewari et al., 2004); land use provided by the Moderate-resolution Imaging Spectroradiometer (MODIS) with spatial resolution and 20 different classes; the Yonsei University scheme for the boundary layer (Hong et al., 2006); the Morrison  (Morrison et al., 2009) and the Grell-Freitas ensemble convective scheme (Grell et al., 2014). 130 We simulated atmospheric chemistry using the Regional Atmospheric Chemistry Model (RACM) coupled with the Modal Aerosol Dynamics model for Europe/Volatility Basis Set (MADE/VBS) aerosol scheme, which treats the organic gas/particle partitioning within a spectrum of volatilities (Ahmadov et al., 2012). The RACM includes 21 stable inorganic species (4 being intermediates), 32 stable organic species (4 of which are primarily of biogenic origin). In addition, RACM includes 237 chemical reactions (23 of which are photolysis). MADE/VBS has an advanced SOA module based on VBS approach 135 to simulate concentrations of the main organic and inorganic gas/particle partitions within a spectrum of volatilities using saturation vapor concentrations as surrogates for volatility. It also includes less complex aqueous reactions (sulfate -SO 4 and nitrate -NO 3 wet deposition) following CMAQ methodology (Sarwar et al., 2011). MADE/VBS has a four-bin VBS with the SOA precursor yields based on previous smog chamber studies under both high-and low-NO X conditions (Murphy and Pandis, 2009;Ahmadov et al., 2012). Yields are for four volatility bins with saturation concentrations of 1, 10, 100, and 1000 140 µg m −3 , and represent aerosol modes -Aitken (< 0.1 µ m), accumulation (0.1-1 µm) and coarse (> 1 µm).
We used the approach by Fast et al. (2006), according to Mie theory (Mie, 1908), in order to account for aerosol radiative properties such as absorption and scattering coefficients, SSA and g aer . These properties are then transferred to the RRTMG shortwave radiation scheme in order to calculate the corresponding radiative forcing. In addition, the feedback effects of clouds on aerosol size and composition via aqueous-phase chemistry (Sarwar et al., 2011) as well as wet scavenging processes (Easter 145 et al., 2004) are considered.
Simulations were conducted in order to analyze how Manaus emissions affect SOA production and aerosol optical properties over the Amazon. We considered two scenarios: (i) Manaus on, which represents anthropogenic emissions and background emissions from initial and boundary conditions; (ii) Manaus off, which represents a background scenario, dominated by biogenic emissions, with anthropogenic contribution coming from the boundary conditions.  (Iacono et al., 2008) Land surface Unified Noah land-surface model (Tewari et al., 2004) Surface layer Revised Mesoscale Model version 5 Monin-Obukhov scheme (Jiménez et al., 2012) Boundary layer Yonsei University scheme (Hong et al., 2006) Cloud microphysics Morrison 2-moment (Morrison et al., 2009) Cumulus clouds Grell-Freitas ensemble scheme (Grell et al., 2014) Chemical options Gas-phase chemistry Updated RACM version with chemical reactions for sesquiterpenes (Papiez et al., 2009) Aerosol module MADE/VBS (Ahmadov et al., 2012) Aerosol activation Abdul-Razzak and Ghan scheme (Abdul-Razzak and Ghan, 2000) Photolysis TUV (Madronich, 1987) Meteorological IC and BC National Center for Environmental Prediction Final Analysis (NCEP-FNL) Chemical IC and BC European Centre for Medium-Range Weather Forecasts (ECMWF)

Emissions sources
Biogenic Model of Emissions of Gases and Aerosols from Nature (Guenther et al., 2006) Anthropogenic Emission inventory developed by Rafee et al. (2017) plant functional types, this model estimates the net terrestrial biosphere emission rates for different trace gases and aerosols with a global coverage of ≈ 1 km 2 spatial resolution.

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Particle absorption coefficient (σ a ) was measured at the T3 site with a 7-wavelength Magee AE31 Aethalometer that operates at λ = 370, 430, 470, 520, 565, 700 and 880 nm and was subjected to the correction scheme outlined by Rizzo et al. (2011). The observed (σ a ) values have been interpolated to the nephelometer's wavelengths to allow a proper comparison and calculation of the intensive parameters, such as SSA. The BC mass concentration at the T3 site was estimated using AE31 measurements of the absorption coefficient at 880 nm and a mass absorption cross section (MAC) section of 7.77 m 2 g −1 (Drinovec et al., 2015).
At ATTO, the BC concentration was measured using a Thermo Environment MAAP 5012 (Thermo) using a σ a at 637nm and a MAC of 6.6 m 2 g −1 , the absorption data was corrected according to Müller et al. (2011). Organic and inorganic submicron aerosol mass loadings were measured with a Time of Flight Aerosol Mass Spectrometer (ToF-AMS) (de Sá et al., 2018). Mixing rations of ozone (O 3 ) and CO were obtained with a 49i O 3 Analyzer (Thermo Environment) and a N 2 O/CO Analyzer (Los Gatos Research -LGR). Meteorological observations was measured by a Vaisala WXT520. Observed data was averaged at 1-hour intervals for comparison it with the WRF. Standard temperature and pressure (STP) corrections were also applied to all measurements. We also used aircraft measurements of σ a from the DoE Gulfstream 1 (G-1), as part of the GoAmazon2014/5 experiment (Shilling et al., 2018;Martin et al., 2016), measured using a 3-wavelength (461, 522 and 648nm) Particle/Soot Absorption Photometer (PSAP) from Radiance Research.

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The Observations and Modeling of the Green Ocean Amazon experiment GoAmazon2014/5 was designed to understand how aerosol and cloud life cycles are influenced by the pollutant outflow from Manaus into the tropical rain forest (Martin et al., 2016). The experiment used a set of detailed aerosol, trace gas and cloud measurements at six different sites (see Fig. 1b) in order to better understand the atmospheric processes caused by the interaction between urban pollution emissions with volatile organic compounds (VOCs) emitted from the forest, and the environmental impacts on the natural microphysical properties of 185 clouds and aerosols, such as optical properties and particle size distributions (Gu et al., 2017;Fraund et al., 2017).

Meteorological Analysis
To study the impact that Manaus pollution plume has on SOA production aerosol optical properties in the area downwind of Manaus, meteorological conditions, especially temperature, humidity and PBL height, must be properly characterized and 190 represented in the WRF-Chem model. Comparisons at the T3 site between observed and simulated hourly variations of accumulated total precipitation, 2 m temperature, 2 m relative humidity, 10 m wind speed, and PBL height (SI 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) = -0.5 and -1.6, respectively), with a short delay between peak observed (11:00 LT) and simulated (15:00 LT) values. The simulation has difficulties in obtaining the observed maximum temperature (see SI Fig. S1a).

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According to statistical indices (SI Table 1), the correlation coefficient (r) and Root Mean Square Error (RMSE) show consistent results for relative humidity (r = 0.7 and RMSE = 1.8), temperature (r = 0.8 and RMSE = 0.4) and wind speed (r = 0.7 and RMSE = 0.2). The relative humidity profile agrees well with ground base measurements, but the simulated values exhibit the diurnal minimum with a 3 hour delay. Individual calculations of performance statistics are presented in Supplementary Table   S1. The Central Amazon region has unique topographic characteristics including the Amazon, Negro and Solimões rivers (Marinho et al., 2020), resulting in meteorological systems such as local circulations and the so called friagem events, which occur when a frontal system reaches the Central Amazon basin (Marengo et al., 1997;Lu et al., 2005), that have important influences on the local and mesoscale circulations (dos Santos et al., 2014;Pereira Oliveira and Fitzjarrald, 1993;Silva Dias et al., 2004).
That may affect the wind direction and air subsidence patterns. Figure S3 in the SI compares the simulated vertical wind com-205 ponent during night time at the T3 site. In the early morning hours (05 -11 LT), downdraft movement is not sufficient at the T3 site to inhibit pollutant dispersion. However, during the night time (20 -22 LT), the simulation captured an organic aerosol concentration peak (see Fig. 5a) consistent with the presence of downdraft movement and a temperature inversion at low levels (see SI Fig. S4) observed at the T3 site.

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Generally, global and regional models contain uncertainties associated with the wet/dry deposition scheme (Wang et al., 2015).
For example, the BC residence time in the atmosphere is typically larger in global models than in the real atmosphere. During the wet season, the T0a site is upwind of Manaus and so has low anthropogenic influence. However, the T0a site receives sporadic air masses loaded with marine aerosol transported from the Atlantic Ocean, and dust outflows from the Sahara desert, ). During the 10 th and the 11 th , the simulation also follows the BC variability shown in the observed data. The simulation appears to do a reasonable job of representing the BC transport from West Africa. Figure 2 shows that the global model BC concentrations are also representative of the hours with the largest values during the 10 th and 11 th .
March 13 th shows good agreement between simulated and observed data at T3. We can assess the simulations ability to represent Amazonian background conditions comparing observed and simulated data from the region with very little anthro-

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To better understand the impact of the Manaus urban plume on SOA formation and mixing ratios at the T3 site during March 13, 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 (Palm et al., 2017;de Sá et al., 2018;Cirino et al., 2018).
In our analysis, with observed data from the GoAmazon2014/5 experiment (T3 site), adjusted cluster centroids were used 240 to analyze clean and polluted conditions, during two months in the wet season (February and March 2014). We define three different clusters (i) low pollution (Low Pol), (ii) middle pollution (Mid Pol) and (iii) high pollution (High Pol) (see Table   2). We chose three different clusters at the T3 site because the pollution conditions arriving are heterogeneous. Our cluster analysis (see Fig. 3) was made with a fuzzy c-means (FCM) clustering algorithm (Bezdek et al., 1984). On March 13, 2014, our analysis shows a day with mostly polluted conditions (at 10-17 LT). Previous work (Palm et al., 2017;de Sá et al., 2018) 245 reported the same polluted conditions during this day.   (Fig. 4a). Because NO x and isoprene emissions vary in different regions, our results suggest that NO x in southeastern Manaus  has important impacts on the O 3 concentration in the Manaus urban area. This is primarily due to the rapid reactions of radicals with NO x , which deplete the radicals.
The O 3 values are highest during the day as VOC production peaks and solar radiation is available for the photo-chemical  duction with in the city and providing a great enhancement downwind of Manaus (Fig. 4a). The wind direction is predominantly from the northeast, which allows the plume be transported to the T2 and T3 sites and allowing the pollution plume to have a great impact on the surrounding areas . Interestingly, our results show that when O 3 concentrations change by a factor of between 2 and 4, oxidation and NO x levels may be affected, and consequently, the rate and SOA production efficiency may be impacted, by decreasing the reacted BVOCs and SOA formation downwind of Manaus.

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According to Figure 5a, the simulated organic PM 2.5 at the T3 site has one of the highest values during the first hours of March 13, 2014 (2 to 4, LT), with the largest contribution coming from primary anthropogenic organic aerosol (POA).
We suggest that the large contributions of BC and CO emissions, coming from Manaus (Fig. S7   Between 10 and 16 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 (Shilling et al., 2018). This increase is mostly due to a sharp increase in anthropogenic SOA (ASOA) peaking at 15 A third total organic aerosol simulated peak is observed between 20 and 21 LT (see Fig. 5a). The simulated peak may be explained by the transport of air pollutants from the regions south of the T3 site ( Fig. S8 in the SI). We propose two possible 315 explanations for this phenomenon. Our first explanation involves the Negro River breeze effect. Since most thermal power plants and the Issac Sabbá refiner REMAN are located near the banks of the Negro and Solimões rivers , the plume transport could be influenced by the river breeze circulation, which defines the trajectory of pollutants. It may be that, between 19 to 21 LT (Fig. S8 in the SI), the wind direction was affected by the Negro River breeze effect due the horizontal thermal gradient caused by the different energy partitioning of the water and land surfaces. Consistent with dos Santos et al.

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(2014), the water surface temperature of the Negro River starts to increase in the afternoon (13 LT), affecting the vertical heat and mass transport. Our second explanation is that there is an air subsidence pattern at the T3 site between 19 and 22 LT (see SI

Variability of Amazonian Aerosol Optical Properties
Understanding how optical properties such as SSA and g aer vary downwind of Manaus is key to understanding the impact of the pollution plume on radiative forcing, its contributions to the local radiative budget, its impacts on the hydrological cycle and unknown indirect consequences on photosynthesis rates. These effects suggest the possibility of investigating aerosol direct radiative effects (DREs) by examining g aer , which presents, in general, higher values associated with stronger forward 345 scattering of radiation by atmospheric aerosols (Korras-Carraca et al., 2015). Figure 6 shows that the simulation overestimates the observed scattering coefficient by a factor 6. The overestimate in the observed scattering coefficient is due the fact that our WRF-Chem simulations are producing more SO 4 than in the real atmosphere, with 30% of the observed PM1 attributed to SO 4 in the accumulation mode (Fig. S11

Calculations and measurements of SSA
According to our simulation results, the Manaus plume interferes with the amount of radiation absorbed by the atmosphere, 360 being responsible for an SSA reduction of approximately 10% at Manaus, 12% at the T2 site and 5.3% at the T3 site (see Fig. 7d). This indicates a large fraction of absorbing material present in the Manaus plume, potentially warming the local atmosphere. These regions are associated with thermal power plants , indicating that the vehicular emissions and stationary sources (refineries) are dominated by small absorbing particles like BC, while biogenic particles are mostly found in the coarse mode and efficiently scatter radiation due to their organic carbon-dominated composition.  Figure 8 shows the simulated and observed SAE and AAE distributions from 9 to 14 March 2014. The simulation with an-375 thropogenic emissions is mostly characterized by 1.0 < AAE < 1.3 and 1.0 < SAE < 2.0, corresponding to a large OC particle contribution, including primary and secondary components (POC and SOC, respectively) (Cazorla et al., 2013). Additionally, the simulated SAE (Manaus on) when variability ranges between 1 to 1.8, indicates a contribution of fine and absorbing particles, which increases the SAE (see Fig. 8).

Calculations of AAE and SAE
In general, these SAE and AAE values show that the values in simulation with anthropogenic emissions are, on average, 380 associated with the fine fraction of PM 2.5 sampled particles. In contrast, some values are mostly associated with large-size PM 2.5 particles (SAE < 1), consistent with the Manaus plume not having a strong influence on the T3 site during those days.
Conversely, the SAE with anthropogenic emissions (see Fig. 8b

Asymmetry Parameter
G aer is an important optical property in radiative transfer, climate and general circulation models (Korras-Carraca et al. (2015).
The, g aer describes the angular distribution of scattered radiation and determines whether the particles scatter radiation prefer-395 entially forwards or backwards (Boucher (2015)).
Figure 9 (a) shows low 600nm g aer values (0.64) that could be associated with industrial activities such as TTPs as well as biomass burning in nearby areas. A region of special interest is between Manaus and T3, since it hosts a large variety of mixing interactions between anthropogenic, biogenic and dust aerosols (e.g., Artaxo et al., 2002;Saturno et al., 2018;Martin et al., 2016;Rizzo et al., 2013). In this region it can be seen that g aer decreases by 8% compared when there is no anthropogenic 400 emissions (see Fig. 9d). This is associated with the presence of fine anthropogenic aerosols transported from adjacent urban and industrial areas in the northwest, especially from central Manaus Rafee et al., 2017;Shrivastava et al., 2019). Those smaller g aer values are seen in places where a significant fraction of the aerosol loading comes from small size particles of anthropogenic origin, with the smallest values appearing over the regions containing industrial activities.
Previous studies (Cirino et al., 2018) have shown a period in the late afternoon around T3 in which particles with the smallest 405 geometric diameter (ca. 50 nm) were observed, and the same period coincides with smaller g aer found in simulations for the (a)

Irradiance
In regions like the Amazon with sufficiently high levels of NO x , and VOCs such as isoprene and monoterpene, an enhanced formation of near surface O 3 is expected. Solar radiation is another element that contributes to photochemical activity and, 415 20 https://doi.org/10.5194/acp-2020-1002 Preprint. Diff - ( consequently, the formation of O 3 . According to Figure 10c, it is possible to notice that even in regions presenting average decreased surface downward shortwave flux values of ca. 20 W m −2 due to the presence of anthropogenic emissions near T2 and T3, not enough to reduce the enhanced formation of near surface O 3 (see Fig. 4a), which more than compensates for the effect of the comparatively reduced solar radiation there. The lower solar radiation over the west side of Manaus seen in simulations with anthropogenic emissions (see Fig. 10a) is accompanied by a general increase in mean O 3 values (see Fig. 4a). 420 Studies of regional direct and indirect aerosol effects are important and still challenging due to their complexity making an accurate determination of the direct and indirect effects difficult (Forkel et al., 2012;Wang et al., 2015;Zhang et al., 2010).
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 −2 due the direct aerosol effect. In regions within and up to ca. 100 km south-west of Manaus, Figure 10c shows an aerosol cooling effect with maximum SWDNBC values 425 ca. -40 W m −2 . The same behavior can also be seen in the region north-west of the T3 site. The aerosol cooling effect is mostly related with SOA production caused by the interaction between VOCs and NO x . When the Manaus plume reaches regions downwind of the city, as seen on March 13, 2014, with few clouds, low precipitation and biomass burning, the plume has a cooling effect on the region as the plume evolves. The SWDNBC (clear sky) variable was used in this study to investigate the aerosol radiative effect on the surface due limitations for simulate the cloud coverage on Amazonian region. Also, the results founded in this paper have to be investigated deeply in order to better understand the effects on the diffuse and direct radiation.

Aging Plume Impact on the Optical Properties
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 plume age 1 (7 LT). Some processes which affect SSA values as the plume ages are dilution, BC deposition, SOA formation and the lensing effect (Holanda et al., 2020;Shrivastava et al., 2019;Cirino et al., 2018). The SSA 435 values in the plume continue to increase during the plume aging process, consistent with SOA (ASOA + BSOA) production in the aging plume (see Fig. 11d). Our simulations show that, on March 13, 2014, the increase of SSA as the plume ages is mostly related to a combination of an increase in SOA formation and BC dilution. Figure 11f shows that BC and CO diluted in similar proportions, suggesting that, at this time scale, dilution is more important than deposition. When the plume is 3 hours old, total organics reach ca. 11 µg m −3 and at that time the plume is north of the T3 site (see Fig. S12d  During plume aging, a decrease of anthropogenic primary organic aerosol and a increase in SOA was observed, similar to results reported by Shilling et al. (2018). The biggest contribution to total SOA during the plume aging comes from anthropogenic emissions, ca. 70% of the total SOA. SOA production increases rapidly and saturates at plume age 4, indicating that it is a challenge to represent these processes in tropical regions with global models, especially without correct treatments of 445 sub-grid effects, such as the production of SOA. The simulated plume used in the tracking analysis traveled 160 km from Manaus ( Fig. S13 in the SI). The distance between T1 and T3 is around 70 km, so when the plume reaches that distance from Manaus, it is ca. 3 hours old. After 3 hours of plume aging, incoming solar radiation is reduced by ca. -15 W m −2 and is further reduced by about -30 W 450 m −2 after the plume is 7 hours old. This reduction by 3% of solar flux and the resulting increase in diffuse radiation results in a significant increase in net primary productivity (Cirino et al., 2014;Rap et al., 2015). As the plume ages and dilutes, its impact on the solar radiation remains constant. Between hours 7 and 9, although the plume's attenuation of incoming solar radiation decreases in absolute terms, from ca. -32 W m −2 to -27 W m −2 , as a percentage, the plume's attenuation remains constant at ca. 3%.

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BC simulations at an altitude of ca. 500 m above the ground were evaluated using aircraft measurements from the Manaus plume on March 13, 2014. For the most part, our simulation shows good agreement with the G1 measurements (SI Fig. S16, in the SI), 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 Shrivastava et al. (2019).

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As the plume ages SAE, begins to increase at 8 LT (after 2 hours of plume aging) and remains constant with values of ca.  have many impacts that could influence ecosystems on a regional scale. We selected March 13 th , 2014 as a "golden day" (Shilling et al., 2018), to analyze the Manaus plume's influence at the T3 site and regions further downwind. During this day, the transport event brought elevated gas and aerosol concentrations from Manaus, associated with favorable meteorological conditions. According to our results, downwind of Manaus at the T3 site, the total organic aerosol mass increased by ca. 75% (0.5 -2.0 µg m −3 ) when anthropogenic emissions were turned on. This increase in organic aerosol mass suggests the Manaus 475 plume is primarily responsible for the changes in the physical and chemical aerosol population characteristics in those regions.
From model experiments, we conclude that the influence of the Manaus plume can reach areas up to 300 km downwind of Manaus, and provide a quantitative assessment of the effects urban pollution could cause to Amazonian forests surrounding urban centers. Overall, our simulations indicate that the aerosol impact of the Manaus plume increases irradiance values by 20% near the T3 site. We also separated the contributions of the different aerosol chemical components that contribute to our 480 estimate of the total aerosol mass concentration and their impact on optical properties. Especially striking is the impact on O 3 formation. Due to the high NO x concentrations present in Manaus, the simulations showed that increased O 3 production mostly occurs in the regions to the south-west of Manaus, where an atmosphere conducive to O 3 enhancement can be found.
According to our results, the lowest g aer values were generally found in regions with a significant fraction of the aerosol load coming from small size particles of anthropogenic origin, e.g., from TPPs and refineries in the Manaus region. Conversely, the 485 largest g aer values were observed over regions with aerosol dominated by large particles of biogenic origin (T0a site). Further investigations are necessary to determine if different sulfate amounts from anthropogenic emissions may change the strong direct effect for high aerosol particle concentrations. More ground-based aerosol and trace gas observations over the western Amazon region could help to evaluate the magnitude of the aerosol effect in this area.
This study contributes to the investigation of the optical properties of PM 2.5 over the Amazon region during the wet season.

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To assess the impact of Manaus emissions on SOA production, and consequently, on aerosol optical properties, WRF-Chem model runs were conducted with and without anthropogenic emissions. Assuming only biogenic emissions with boundary conditions from the global model, OA production decreased by 75% at the T3 site. This study also shows that on March 13, 2014, the aerosol aging process caused a gradual increase in SSA. Additionally, due to the deposition process, significantly decreasing concentrations of BC are found during plume evolution. This process, combined with SOA formation, contributes 495 to the increase in SSA as the plume ages. The results of this study demonstrate that uncertainties in coating processes of organic aerosols involving BC particles also warrant additional study to better account for a possible decrease in SSA during the plume aging process. The results here also demonstrate that in order to precisely calculate the radiative forcing impact, it is important to take into account all SOA formation mechanisms, including VOC oxidation, especially for tropical forest regions like the Amazon. One action that may improve SOA model accuracy is to update some of the MEGAN model inputs when new data 500 such as emission factors and vegetation coverage data becomes available. In addition, there are very few long term aircraft based ASOA and BSOA observations data and more observations could help validate the models and improve their accuracy.