Convergent Estimates of Biomass Burning-Derived Atmospheric Ammonia in Peninsular Southeast Asia

. Ammonia (NH 3 ) is an important agent involved in atmospheric chemistry and nitrogen cycling. Current estimates of NH 3 emissions from biomass burning (BB) differ by more than a factor of two, impeding a reliable assessment of their environmental consequences. Combining high-resolution satellite observations of NH 3 columns 15 with network measurements of the concentration and stable nitrogen isotope composition ( δ 15 N) of NH 3 , we present coherent estimates on the amount of NH 3 derived from BB in the heartland of Southeast Asia, a tropical monsoon environment. Our results reveal a strong variability of atmospheric NH 3 levels in time and space across different 2 landscapes. All evidence in hand suggests that anthropogenic activities are the most important modulating control with regards to the observed patterns of NH 3 distribution in the study area. N-isotope balance considerations 20 revealed that during the intensive fire period, the atmospheric input from BB accounts for not more than 21±5% (1σ) of the ambient NH 3 , even at the rural sites and in the proximity of burning areas. Our N-isotope based assessment of the variation of the relative contribution of BB-derived NH 3 is further validated independently through the measurements of particulate K + , a chemical tracer of BB. Our findings underscore that BB-induced NH 3 emissions in the tropical monsoon environments can be much lower than previously anticipated, with 25 important implications for future modeling studies to better constrain the climate and air quality effects of wildfires. mass spectrometer (PT-IRMS) (IsoPrime 100, IsoPrime Ltd., Cheadle Hulme, UK) (Liu et al., 2014). In order to correct for any machine drift and procedural blank contribution, international NH 4+ (IAEA N1, USGS 25, and USGS 26) standards were processed in the same way as samples (Liu et al., 2014). The analytical precision for N 115 isotope analyses was better than 0.5‰. can be used to distinguish between specific sources, and to quantify their contribution to the measured total NH 3 pool. As a first step, we examine the spatiotemporal characteristics of the measured δ 15 N-NH 3 relative to the N isotopic source signatures to infer seasonal changes of NH 3 sources. the implications of our to be explored, promise to provide important guidance for revising NH 3 emissions from BB in atmospheric transport models to assess on air quality, human health and climate change.

landscapes. All evidence in hand suggests that anthropogenic activities are the most important modulating control with regards to the observed patterns of NH3 distribution in the study area. N-isotope balance considerations 20 revealed that during the intensive fire period, the atmospheric input from BB accounts for not more than 21±5% (1σ) of the ambient NH3, even at the rural sites and in the proximity of burning areas. Our N-isotope based assessment of the variation of the relative contribution of BB-derived NH3 is further validated independently through the measurements of particulate K + , a chemical tracer of BB. Our findings underscore that BB-induced NH3 emissions in the tropical monsoon environments can be much lower than previously anticipated, with 25 important implications for future modeling studies to better constrain the climate and air quality effects of wildfires.

Introduction
Biomass burning (BB) in tropical vegetation regions due to wildfires has been recognized as a globally important source of trace gases (including CO2, CO, and ozone precursors) and aerosols (mostly black and organic carbon) (Crutzen and Andreae, 1990;Andreae and Merlet, 2001;Shi et al., 2015;Andreae, 2019;Crutzen et al., 1979).

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Most BB hotspots occur in West Africa and South America (Crutzen and Andreae, 1990;van der Werf et al., 2006;Shi et al., 2015), but recent studies have highlighted the importance also of Southeast (SE) Asia in this regard, mainly because of the much higher population densities near intensive fire burning areas (Huang et al., 2013;Marlier et al., 2013;Lee et al., 2017;Betha et al., 2014). The climate over large parts of SE Asia is governed by a wet (typically May, June, July) and dry (typically February, March, April) season caused by seasonal shifts in the 35 monsoon winds. During the dry season, dry plant materials (e.g., forest, peatland, banana leaf) readily ignite, resulting in large wildfires that can markedly modify the atmospheric composition in the tropics, while the tropical rain belt causes plentiful rainfall during summer, preventing such fires during the rainy season (Lee et al., 2017;Chu et al., 2018). https://doi.org/10.5194/acp-2020-1061 Preprint. Discussion started: 11 January 2021 c Author(s) 2021. CC BY 4.0 License.
Excess NH3 is known to be responsible for several environmental issues: eutrophication of terrestrial and aquatic ecosystem, soil acidification, and loss of plant diversity (Sutton et al., 2008;Aneja et al., 2008;Sutton et al., 2011).

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In the atmosphere, NH3 can neutralize acid gases (mostly sulfuric acid, nitric acid or hydrochloric acid), resulting in the formation of secondary aerosols that in turn negatively affect climate and human health (Wang et al., 2011;Wang et al., 2013;Paulot and Jacob, 2014;Souri et al., 2017).
To assess the environmental impacts of BB (e.g., air quality and climate change), atmospheric chemistry models incorporating BB-related emissions have widely been used over the past decades (Huang et al., 2013;Aouizerats 55 et al., 2015;Wang et al., 2013;Wang et al., 2011;Souri et al., 2017) but are afflicted with a relatively large uncertainty regarding the input parameters used in the models (Hantson et al., 2016;Whitburn et al., 2015;Paulot et al., 2017). The uncertainties, for example, for carbon emissions and for other trace gases (including NH3), can be over 200% (Whitburn et al., 2015;Paulot et al., 2017;Zhu et al., 2013;Pan et al., 2019). In recent years, hyperspectral sounders on board satellites have demonstrated their capabilities to directly measure tropospheric 60 column concentrations of NH3 gas (Van Damme et al., 2018;Van Damme et al., 2014;Van Damme et al., 2015b;Clarisse et al., 2009). Satellite observations therefore offer a "top-down" alternative to the bottom-up estimates.
Still, the biggest challenge of satellite-based NH3 assessments is the requirement for the atmosphere to be cloudfree during observations, and the need for a sizeable temperature difference between land or sea surface and the atmosphere (Van Damme et al., 2015a;Whitburn et al., 2015;Martin, 2008;Streets et al., 2013;Clarisse et al., 65 2010).
Large uncertainties remain regarding global or regional atmospheric budgets of NH3, and the attribution of emissions to specific sources, emphasizing the need for independent verification methods. An impressive body of previous work has studied the BB influence on the concentration and composition of aerosols in SE Asia (Betha et al., 2014;Aouizerats et al., 2015;Lee et al., 2017;Bikkina et al., 2019). However, to our knowledge, there are no 70 reports on the detailed spatiotemporal patterns of atmospheric NH3 concentration and nitrogen isotopic composition (δ 15 N-NH3) associated with BB in this region. Due to isotopic fractionation associated with NH3 production, pyrogenic NH3 displays a distinctly higher δ 15 N-NH3 (δ 15 N defined as (Rsample/Rstandard -1)x1000, where R refers to the 15 N/ 14 N ratio in a sample or a standard) than temperature-dependent volatilized sources (Felix et al., 2013;Chang et al., 2016a). The N isotopic analysis of ambient NH3 has been proven a useful tool to constrain sources of 75 NH3 emissions in the atmosphere, where both natural and anthropogenic activities are relevant (Chang et al., 2019b;Chang et al., 2019a;Elliott et al., 2019). Here, we integrate high-resolution satellite observations with discrete NH3 concentration measurements and δ 15 N-NH3 data obtained from a regional passive monitoring network during and after the dry season of large-scale forest fires in the mountain areas of northern Thailand, SE Asia.

Site description
Surrounded by the mountain ranges of the northern Thailand highlands, the Chiang Mai Province covers an area of approximately 20107 km 2 , with a total population of over 1.7 million. Chiang Mai is characterized by a tropical https://doi.org/10.5194/acp-2020-1061 Preprint. Discussion started: 11 January 2021 c Author(s) 2021. CC BY 4.0 License. monsoon climate, tempered by the low latitude and moderate elevation, with warm to hot weather year-round. Some 70% of the area is covered by forests, and 13.4% of the area is under agriculture. A continuing environmental 85 issue in Chiang Mai is the smoke pollution from wildfires that primarily occurs every year towards the end of the dry season between February and April (Tsai et al., 2013) before the relatively cool and rainy season from May on.
During the period from March to July 2018, ambient NH3 concentrations and δ 15 N-NH3 values were determined at 9 monitoring stations across the Chiang Mai Province. Figure 1 illustrates the location of sampling sites (with the different land use regimes indicated), while Fig. S1 reports meteorological data for Chiang Mai and Table S1 details 90 the information of each station.

Sampling and laboratory analysis
In order to obtain information regarding the spatial and temporal variability of NH3 concentrations over Chiang Mai, ambient gas-phase NH3 concentrations at each site were collected weekly using passive sampling devices (PSD) (ALPHA -Adapted Low-cost, Passive High Absorption; Centre for Ecology and Hydrology, Edinburg, UK) (Chang et al., 2016a). The ALPHA PSD is a circular polyethylene vial (26 mm height, 27 mm diameter) with one 100 open end. The vial holds a 25 mm phosphorous acid-impregnated filter and a PTFE membrane for gaseous NH3 diffusion. These PSDs have been widely used in Europe, China, and the US, and are capable of detecting NH3 concentrations as low as 0.03 μg m -3 (Chang et al., 2016a;Puchalski et al., 2011;Liu et al., 2013;Tang et al., 2018).
In the laboratory, the ALPHA filter samples were soaked in 10 mL deionized water (18 MΩꞏcm) in a 15 mL vial for 30 min with occasional shaking. Concentrations of NH3-derived NH4 + in extracts were determined using a 105 Dionex TM ICS-5000 + system (Thermo Fisher Scientific, Sunnyvale, USA). The IC system was equipped with an automated sampler (AS-DV), an IonPac CG12A guard column, and a CS12A separation column. Aqueous methanesulfonic acid (MSA, 30 mM L -1 ) served as eluent at a flow rate of 1 mL min -1 . The isotopic analysis of the extracted NH4 + was based on the isotopic analysis of nitrous oxides (N2O) after chemical conversion (Liu et al., 2014). More precisely, dissolved NH4 + in DI extracts was oxidized to NO2by alkaline hypobromite (BrO -), and 110 then reduced to N2O by hydroxylamine hydrochloride (NH2OH.HCl). The produced N2O was analyzed using a purge and cryogenic trap system (Gilson GX-271, IsoPrime Ltd., Cheadle Hulme, UK), coupled to an isotope ratio mass spectrometer (PT-IRMS) (IsoPrime 100, IsoPrime Ltd., Cheadle Hulme, UK) (Liu et al., 2014). In order to correct for any machine drift and procedural blank contribution, international NH4 + (IAEA N1, USGS 25, and USGS 26) standards were processed in the same way as samples (Liu et al., 2014). The analytical precision for N 115 isotope analyses was better than 0.5‰. https://doi.org/10.5194/acp-2020-1061 Preprint. Discussion started: 11 January 2021 c Author(s) 2021. CC BY 4.0 License.

Isotope-based source apportionment
Isotopic mixing models represent valuable tools to estimate the fractional contributions of multiple sources (emission sources of NH3 in this study) within a mixture (the ambient NH3 in this study) (Layman et al., 2012). By explicitly taking into account the uncertainties associated with the isotopic signatures of single sources and the N 120 isotope fractionation during transformations, the application of Bayesian methods to stable isotope mixing models yields robust probability estimates on source apportionments, and its application to natural systems is more appropriate than the application of simple linear mixing models (Parnell et al., 2010). Here, a novel Bayesian approach using a mixing model, implemented in the software package SIAR (Stable Isotope Analysis in R), was used to resolve multiple NH3 source categories by generating potential solutions of source apportionment as true 125 probability distributions of the single source contribution to the total NH3 pool. The generation of such source contribution probability distributions allows estimating likelihood ranges of source contributions even at underconstrained conditions (i.e., the number of potential sources exceeds the number of different isotope system parameters + 1). The SIAR package is available to download from the packages section of the Comprehensive R Archive Network site (CRAN; http://cran.r-project.org/), which has been widely applied in a number of fields 130 (Chang et al., 2019a;Chang et al., 2019b). Model frame and computing methods are detailed in Text S1.  (Fig. 2b). Intriguingly, from this plot, one is tempted to conclude that fires do play a very important role in NH3 emissions, as the NH3 columns are much higher in March and April (dry season), which is coincident with high number of monthly fire activities (indicated by the number of fire points). Further, using 11 years (2008-2018) of IASI satellite data, Figure S2 presents a climatology of monthly NH3 columns over Chiang Mai at a much 150 finer spatial resolution, which also support the pervasive contribution of BB during dry season (March and April).

Satellite observations of ammonia and fires
Based on the average observed temporal distribution of satellite-constrained wildfires, the sampling period in this study can be divided into two contrasting fire-regime periods, i.e., BB season (March and April) and non-BB season (May and June). Interestingly, however, although the number of fire points in March (43613 points) is significantly (p < 0.01) higher than that in April (27905 points) (Fig. 2b), the average NH3 column in March is nearly the same 155 as that in April (Fig. 2a). This implies that there is not a one-to-one relationship between BB and NH3 emissions, and in turn that other sources or factors (e.g., soil dryness, agricultural emissions, precipitation, temperature dependence, etc.) must also play a significant role.

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Given that the average monthly temperature varies only little, in contrast to the drastic change of rainfall during our study period (Fig. 2c), it is reasonable to assume that temperature-dependent NH3 volatilization is not the main driver of changes in the NH3 columns. The amount of rainfall, on the other hand, can have multi-faceted impact on NH3 emissions. First of all, there is an obvious link between precipitation rates and the number of wildfires, and, if BB is a major NH3 emission source, we can also expect a relationship between the NH3 columns and monthly 170 rainfall rates. Secondly, and maybe more importantly, rain will dissolve atmospheric particulate NH4 + , and will act to clean the air from NH3, which may partly explain the low NH3 levels during May and June. On the other hand, https://doi.org/10.5194/acp-2020-1061 Preprint. Discussion started: 11 January 2021 c Author(s) 2021. CC BY 4.0 License. comparison between March and April reveals higher NH3 levels in April despite higher rain rates, suggesting that other processes than BB and rain-scavenging of BB-derived NH3 must be relevant factors. In Fig. 3a and 3b, we superimposed the orography at the scale of the study area (Chiang Mai and surrounding mountains) onto the images 175 of year-long averaged MODIS FRP (Fire Radiative Power) and IASI-NH3 for 2018, respectively. Just by visual evaluation it seems obvious that there is no strong correlation between fire intensity/number of fires and the observed IASI-NH3, suggesting only limited influence of BB on NH3. Yet more strikingly, the IASI-NH3 distribution matches that of the population density quite well (Fig. 3c). More precisely, hot spots of atmospheric NH3 (Fig. 3b)

Discrete concentration measurements confirm urban areas as hot spots of NH3 emissions
A total of 153 passive samples were collected in this study for analyzing NH3 concentrations and reported in Fig.   4. Considering all weekly samples, the range of atmospheric NH3 concentrations over Chiang Mai was from 2.5 to 46.4 μg m -3 , with a mean (± 1σ) and median value of 14.5 (± 9.2) and 11.4 μg m -3 , respectively. Consistent with 190 the IASI satellite-based NH3 assessment, the weighted average NH3 concentration ( max min mean 1  ) was significantly (p < 0.01) higher during the dry season when there were significantly (p < 0.01) more wild fires ( 46.4 6.8 20.6 9.8  μg m -3 ) than that during the rainy season ( 31.9 2.5 10.2 5.7  μg m -3 ). Again, it is tempting to conclude that there is a direct link between higher atmospheric NH3 levels and the higher number of BB events.
However, there are several aspects that appear to preclude BB at least as the main or only driver of ambient NH3 195 concentrations. Firstly, from a global perspective, the ambient NH3 concentrations we measured in northern Thailand are generally lower than in tropical regions with dense population or intensive agricultural production (also see Fig. 2a) (Carmichael et al., 2003;Chang et al., 2016b). Secondly, within the study area, large spatial differences in NH3 concentrations were found (Fig. 4). Yet, despite their proximity to wildfires at the time, the three rural sites always displayed the lowest NH3 concentrations (  The error bar indicates one standard deviation. Although afflicted with large uncertainties, it is well accepted that, globally, atmospheric NH3 is primarily emitted 205 by agricultural activities and biomass burning (Asman et al., 1998;Bouwman et al., 1997). One could expect the NH3 concentrations in the atmosphere over rural environments with lush vegetation and agricultural land-use to be higher than those in (sub)urban areas, where agricultural activities are mostly absent. In our study, the average NH3 Such gradient can be taken as evidence that non-agricultural activities (including on-road traffic), at least in some regions, can overtake agriculture and/or BB as the dominant NH3 source in urban areas.

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Indeed, a growing body of studies confirm that the urban atmosphere can be a hot spot of NH3 release. Nonagricultural activities like wastewater treatment, coal combustion, solid garbage, vehicular exhaust, and urban green space also contribute to NH3 emissions (Chang et al., 2016a;Chang et al., 2019b;Teng et al., 2017;Chang et al., 2015;Li et al., 2016;Sun et al., 2017). For example, high vehicular NH3 emissions from noble metal-based threeway catalysts (TWCs) have been demonstrated in chassis dynamometer vehicle experiments, road tunnel tests, and 220 through ambient air measurements (Huang et al., 2018;Chang et al., 2016b;Chang et al., 2019b).

N isotopic constraints on the sources of natural and anthropogenic NH3
The correlative analysis of spatiotemporal concentration patterns with variations in land use effects provides first qualitative constraints with regards to the relative importance of natural/BB and anthropogenic NH3 emissions, but it is insufficient when a more quantitative assessment is required. The N-isotopic composition of NH3 (i.e., δ 15 N-225 NH3 ) can provide help in this regard, as it is sensitive to changes of NH3 sources with distinct isotopic composition (Elliott et al., 2019;Felix et al., 2013 show a relatively large variability in time and space (Fig. 5). NH3 emitted from the five major NH3 sources displays distinct isotopic signatures (N-fertilizer application, -50.0 ± 1.8‰; urban waste volatilized sources, -37.8 ± 3.6‰; livestock breeding, -29.1 ± 1.7‰; on-road traffic, -12.0 ± 1.8‰; biomass burning, 12‰) (see colored bars in Fig.   230 4) (Chang et al., 2016a;Kawashima and Kurahashi, 2011;Chang and Ma, 2016). Thus, the measurement of δ 15 N-NH3 can be used to distinguish between specific sources, and to quantify their contribution to the measured total NH3 pool. As a first step, we examine the spatiotemporal characteristics of the measured δ 15 N-NH3 relative to the N isotopic source signatures to infer seasonal changes of NH3 sources.
https://doi.org/10.5194/acp-2020-1061 Preprint. Discussion started: 11 January 2021 c Author(s) 2021. CC BY 4.0 License. increased δ 15 N-NH3 during the non-BB (i.e., rainy) period can probably be explained by the fact that agricultural 245 NH3 emissions with low δ 15 N-NH3 can be dramatically lowered by continuous and heavy rainfall (Zheng et al., 2018;Chang et al., 2019a) so that at low levels, local sources can become more important (e.g., residential kitchens, nearby burning of biofuels for cooking).
https://doi.org/10.5194/acp-2020-1061 Preprint. , n = 40), after the pronounced decrease 255 of NH3 concentrations due to wet removal. This rather minor difference can hardly be ascribed to the influence of BB emissions, given the large seasonal fluctuation of wildfire intensity mentioned above. Based on the absolute δ 15 N-NH3 values in the urban settings, and their rather invariant temporal trends, we argue that vehicle/transportrelated is a more important and apparently steady source of pyrogenic NH3 in the studied urban areas.
The two suburban sites (S1 and S2) are located geographically within the transition zone between the urban and 260 rural environments, and this transitional character seems also indicated by their

Isotope-based quantification of BB contribution to ambient NH3
There are several challenges that need to be overcome when trying to more accurately quantify the contribution of BB emissions to ambient NH3 based on N isotope data. Firstly, given the use of only one isotope parameter (δ 15 N; 270 in contrast to NOx where also the δ 18 O can be analyzed), more than three potential NH3 sources (e.g., urban and suburban sites) will introduce large uncertainties in isotopic endmember mixing models in terms of quantifying their relative contributions to the ambient NH3 (Chang et al., 2015). Secondly, atmospheric wet scavenging could further compromise/alter the primary NH3 N isotopic signatures (Elliott et al., 2019;Zheng et al., 2018;Chang et al., 2019a). For these reasons, we focus here on the samples collected at the three rural sites during the dry BB 275 season (lasting seven weeks) to isotopically examine the contribution of BB emissions to ambient NH3. We separated these samples into seven groups based on the week of their sampling, and we integrated the measured δ 15 N-NH3 values as well as the N isotopic signatures of potential NH3 sources (i.e., biomass burning, livestock breeding, fertilizer application) into the Bayesian isotopic mixing model (see Text S1 for details). The results of NH3 source apportionment are reported in Fig. 6. With a certain degree of variability, the contribution of BB to the 280 ambient NH3 in the rural areas during the seven weeks of sampling in the dry season was only 21.0% (± 4.7%).
Hence, NH3 emission from BB significantly less important than from livestock breeding (37.1 ± 7.1%) and fertilizer application (41.8 ± 5.9%). This comes at a surprise, given the fact that the study area belongs to one of the most important BB regions in SE Asia, or even in the world, and the samples used for isotopic source apportionment were collected during the season of intensive BB.  During the dry season, we also analyzed particulate potassium (K + ), a chemical tracer of biomass combustion, at two rural sites (S4, S5; in 39 daily fine-particle (PM2.5) samples). The particulate K + data offer a valuable 290 opportunity to validate our isotope-based source apportionment results. Again, we divided the dry-season data set into seven groups based on the week of NH3 passive sampling. The correlation between the particulate K + concentration and the total NH3 concentration at the rural sites was rather poor (r 2 = 0.43; blue symbols in Fig. 7a).
Such a weak correlation supports our conclusion regarding the isotope-based source apportionment results (see above), providing additional independent evidence that BB can hardly be the dominant source of NH3 during the 295 sampling period at the studied rural areas. In contrast, the correlation between the particulate K + concentration and the estimated BB-derived NH3 concentration (instead of total NH3) is much better (r 2 = 0.76; Fig. 7a), and thus further validates our modeling approach. While the independent particulate K + data further increase our confidence in the N-isotope based assessment, still some uncertainty remains with regards to the robustness of the endmember source δ 15 N values, potential source-altering effects, and in turn our estimates on the BB-associated NH3 300 contribution. In other words, the latter is probably sensitive to the considered range in the δ 15 N of potential NH3 https://doi.org/10.5194/acp-2020-1061 Preprint. Discussion started: 11 January 2021 c Author(s) 2021. CC BY 4.0 License. emission sources, and this range may be quite large/uncertain at least for some of the sources. The δ 15 N-NH3 from BB, in particular, is only poorly constrained, with hardly any reports from the literature (e.g., (Kawashima and Kurahashi, 2011)). In recent chamber experiments, we found that the δ 15 N-NH3 produced by combustion of a variety of biomass types (subtropical trees and agricultural residues) ranged between -11.8‰ and -4.6‰ (Chang et 305 al., 2016(Chang et 305 al., , 2019a, which is distinctly lower than the N isotopic signature of BB-emitted NH3 (12‰) determined previously (Kawashima and Kurahashi, 2011), and adopted in this study. Assuming that the true N isotopic signatures of BB-emitted NH3 in the study area falls somewhere within the range of -12‰ and 12‰ (based on our published data (Chang et al., 2016(Chang et al., ,2019a and the value reported in (Kawashima and Kurahashi, 2011)), we recalculated the source apportionment estimates as function of the different δ 15 N values for BB-emitted NH3 (Fig.   310 7b). The estimates are not sensitive to the choice of the N isotopic composition of the BB-associated NH3 source.
Specifically, independent of the chosen δ 15 N-NH3 value, BB is always the least important of the three main NH3 sources in rural areas, contributing not more than 29.6%. This is because that although the isotopic signatures of BB-emitted NH3 have a wide range of δ 15 N values, their δ 15 N-NH3 values are still significantly (p < 0.01) higher (i.e., without overlap as shown in Fig. 4) than the measured δ 15 N values of ambient NH3 at the rural sites. source apportionment results of ambient NH3 at the rural sites during dry season, as function of the assumed N isotopic signatures of BB-emitted NH3.

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As illustrated by the pie chart in Fig. 6, the average contribution of BB to ambient NH3 at the rural sites during the season of intensive fire events is 2.4 μg m -3 , which can be regarded as the maximum possible concentration of BBemitted NH3 for the urban and rural sites, which are much further away from the fire areas. Based on the total NH3 concentrations measured at the other sites, we calculate, in turn, that the contribution of BB to the ambient NH3 in the urban and suburban areas are on the order of 9.6% (ranging from 5.2% to 14.8%) and 12.3% (ranging from 6.1% 325 to 19.9%), respectively.

Conclusion
In this study, we integrated satellite constraints on atmospheric NH3 levels and fire intensity, discrete NH3 concentration measurement, and N isotopic analysis of NH3 in order to assess the regional-scale contribution of BB to ambient NH3 in the heartland of Southeast Asia. The combined approach provides a cross-validation 330 framework for source apportioning of NH3 in the lower atmosphere and will thus help to ameliorate predictions of BB emissions beyond the tropics, particularly in areas of high vegetation fire risk. Our results suggest that during the dry wildfire season, BB emissions represent a ubiquitous but comparatively small NH3 source, which accounts for 9.6%, 12.3%, and 21.0% of ambient NH3 in urban, suburban, and rural environments, respectively. While we do not claim that our results necessarily apply also at the global scale, and we do not question that globally BB is 335 one of the most important NH3 sources, we find that at least in the heartland of SE Asia, BB related NH3 emissions to the atmosphere are rather moderate, and vary significantly in time and space. Both satellite observations and field/ground-based measurements capture these variations. Our findings underscore that BB-induced NH3 emissions in tropical monsoon environments can be much lower than previously anticipated. Existing atmospheric transport models may overestimate current, and likely future, NH3 emissions under changing climate conditions. 340 https://doi.org/10.5194/acp-2020-1061 Preprint. Discussion started: 11 January 2021 c Author(s) 2021. CC BY 4.0 License.
While the full implications of our results remain to be explored, they promise to provide important guidance for revising NH3 emissions from BB in atmospheric transport models to assess on air quality, human health and climate change.