# Unraveling Urban NOx Emission Sources in Polluted Arctic Wintertime Using NO2 Nitrogen Isotopes

Sarah Albertin, Slimane Bekki, Joël Savarino, Natalie Brett, Kathy S Law, Meeta Cesler-maloney, James H Flynn, Fangzhou Guo, Brice Barret, Nicolas Caillon, et al.

► To cite this version:

Sarah Albertin, Slimane Bekki, Joël Savarino, Natalie Brett, Kathy S Law, et al. Unraveling Urban NOx Emission Sources in Polluted Arctic Wintertime Using NO2 Nitrogen Isotopes. Journal of Geophysical Research: Atmospheres, 2024, 129 (20), pp.e2024JD041842. 10.1029/2024jd041842. insu-04747838

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# JGR Atmospheres

## RESEARCH ARTICLE

10.1029/2024JD041842

**Key Points:**

* A temporal variability in NO₂ nitrogen isotopes is measured during winter in downtown Fairbanks, Alaska

* Isotope exchange fractionation drives NO₂ nitrogen isotope distribution, at a rate in excellent agreement with theoretical predictions

* A ¹⁵N-based source apportionment indicates vehicle and oil space heating emissions are the main sources of NOₓ in downtown Fairbanks

**Supporting Information:**

Supporting Information may be found in the online version of this article.

**Correspondence to:**

S. Albertin,
sarah.albertin@noaa.gov

**Citation:**

Albertin, S., Bekki, S., Savarino, J., Brett, N., Law, K. S., Cesler-Maloney, M., et al. (2024). Unraveling urban NOₓ emission sources in polluted Arctic wintertime using NO₂ nitrogen isotopes. *Journal of Geophysical Research: Atmospheres*, 129, e2024JD041842. https://doi.org/10.1029/2024JD041842

Received 24 JUN 2024
Accepted 21 SEP 2024

**Author Contributions:**

**Conceptualization:** Sarah Albertin, Slimane Bekki, Joël Savarino
**Data curation:** Natalie Brett, Meeta Cesler-Maloney, James H. Flynn, Fangzhou Guo

**Formal analysis:** Sarah Albertin, Natalie Brett, Soline Richard
**Funding acquisition:** Joël Savarino, Kathy S. Law, Nicolas Caillon, Becky Alexander, Steve R. Arnold, Stefano Decesari, Gilberto J. Fochesatto, Jingqiu Mao, William Simpson

**Investigation:** Sarah Albertin, Alexis Lamothe

**Methodology:** Sarah Albertin, Slimane Bekki, Joël Savarino

© 2024 The Author(s).
This is an open access article under the
terms of the Creative Commons
Attribution-NonCommercial License,
which permits use, distribution and
reproduction in any medium, provided the
original work is properly cited and is not
used for commercial purposes.

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## Unraveling Urban NOₓ Emission Sources in Polluted Arctic Wintertime Using NO₂ Nitrogen Isotopes

Sarah Albertin¹,²,³ ID, Slimane Bekki², Joël Savarino¹ ID, Natalie Brett²,⁴ ID, Kathy S. Law² ID, Meeta Cesler-Maloney⁵ ID, James H. Flynn⁶ ID, Fangzhou Guo⁶,⁷ ID, Brice Barret⁸, Nicolas Caillon¹ ID, Barbara D'Anna⁹, Elsa Dieudonné¹⁰, Alexis Lamothe¹ ID, Soline Richard¹, Brice Temime-Roussel⁹, Becky Alexander¹¹ ID, Steve R. Arnold⁴ ID, Stefano Decesari¹² ID, Gilberto J. Fochesatto¹³, Jingqiu Mao⁵ ID, and William Simpson⁵ ID

¹University Grenoble Alpes, CNRS, IRD, INRAE, Grenoble INP, IGE, Grenoble, France, ²Sorbonne Université, UVSQ, CNRS, LATMOS/IPSL, Paris, France, ³Now at Cooperative Institute for Research in Environmental Sciences, University of Colorado / Chemical Sciences Laboratory, National Oceanic and Atmospheric Administration, Boulder, CO, USA, ⁴School of Earth and Environment, University of Leeds, Leeds, UK, ⁵Geophysical Institute and Department of Chemistry and Biochemistry, University of Alaska Fairbanks, Fairbanks, AK, USA, ⁶Earth & Atmospheric Sciences, University of Houston, Houston, TX, USA, ⁷Now at Aerodyne Research Inc, Billerica, MA, USA, ⁸Université Toulouse III – Paul Sabatier, CNRS, LAERO, Toulouse, France, ⁹Aix Marseille University, CNRS, LCE, Marseille, France, ¹⁰Laboratoire de Physico-Chimie de l'Atmosphère, Université du Littoral Côte d'Opale, Dunkerque, France, ¹¹Department of Atmospheric Sciences, University of Washington, Seattle, WA, USA, ¹²CNR-ISAC, National Research Council of Italy, Institute of Atmospheric Sciences and Climate, Bologna, Italy, ¹³Department of Atmospheric Sciences, College of Natural Science and Mathematics, University of Alaska, Fairbanks, AK, USA

**Abstract** Nitrogen (N) isotopic fractionation during nitrogen oxides (NOₓ) cycling and conversion into atmospheric nitrate alters the original N isotopic composition (δ¹⁵N) of NOₓ emissions. Limited quantification of these isotopic effects in urban settings hampers the δ¹⁵N-based identification and apportionment of NOₓ sources. δ¹⁵N of nitrogen dioxide (NO₂) measured during winter in downtown Fairbanks, Alaska, displayed a large temporal variability, from -10.2 to 24.1‰. δ¹⁵N(NO₂) records are found to be driven by equilibrium isotopic fractionation, at a rate in very close agreement with theoretical predictions. This result confirms that N isotopic partitioning between NO and NO₂ can be accurately predicted over a wide range of conditions. This represents an important step for inferring NOₓ emission sources from isotopic composition measurement of reactive nitrogen species. After correcting our δ¹⁵N(NO₂) measurements for N fractionation effects, a δ¹⁵N-based source apportionment analysis identifies vehicle and space heating oil emissions as the dominant sources of breathing-level NOₓ at this urban site. Despite their large NOₓ emissions, coal-fired power plants with elevated chimney stacks (>26 m) appear to make a small contribution to surface NOₓ levels in downtown Fairbanks (likely less than 18% on average). The combined uncertainties of the δ¹⁵N of NOₓ from heating oil combustion and of the influence of low temperatures on the δ¹⁵N of NOₓ emitted by vehicle exhaust prevent a more detailed partitioning of surface NOₓ sources in Fairbanks.

**Plain Language Summary** Nitrogen (N) stable isotopes measured in atmospheric reactive N (N_r) species can help trace emission sources of nitrogen oxides (NOₓ). However, large uncertainties subsist regarding the factors controlling the variability of N isotopes in N_r species, preventing a precise isotope-based emission source apportionment. This study presents a comprehensive analysis of the enrichment of ¹⁵N–¹⁴N (δ¹⁵N) in atmospheric nitrogen dioxide (NO₂) collected during the Alaskan Layered Pollution And Chemical Analysis (ALPACA) 2022 international winter campaign in Fairbanks, Alaska. Building on in situ meteorological and trace gas, the isotopic fractionation effect driving the significant δ¹⁵N variability of NO₂ observed in downtown Fairbanks is quantified with great precision. The δ¹⁵N records corrected for equilibrium fractionation effects are discussed in light of a local NOₓ emission inventory. In particular, the influence of emissions from coal-fired power plants with high stack heights on surface air pollution is addressed.

## 1. Introduction

Once emitted into the atmosphere, nitrogen oxides (NOₓ ≡ nitrogen monoxide (NO) + nitrogen dioxide (NO₂)) rapidly enter an interconversion photochemical cycle (R1–R4). Briefly, NO is oxidized by ozone (O₃; R1) and
**Project administration:** Kathy S. Law, Nicolas Caillon, Becky Alexander, Steve R. Arnold, Stefano Decesari, Gilberto J. Fochesatto, Jinqiu Mao, William Simpson

**Resources:** Meeta Cesler-Maloney, James H. Flynn, Fangzhou Guo

**Supervision:** Slimane Bekki, Joël Savarino

**Validation:** Nicolas Caillon

**Writing – original draft:** Sarah Albertin

**Writing – review & editing:** Slimane Bekki, Joël Savarino, Natalie Brett, Kathy S. Law, Brice Barret, Barbara D’Anna, Elsa Dieudonné, Alexis Lamothe, Brice Temime-Roussel, Becky Alexander, Steve R. Arnold, Stefano Decesari

peroxy radicals (RO₂ ≡ hydroperoxyl radical (HO₂) + methyl peroxy radical (CH₃O₂); R2), followed by the photolysis of NO₂ (R3), leading back to the production of NO and O₃ (R4) (Crutzen, 1970):

$$NO + O_3 \rightarrow NO_2 + O_2 \quad (R1)$$

$$NO + RO_2 \rightarrow NO_2 + RO \quad (R2)$$

$$NO_2 + h\nu \rightarrow O(^3P) + NO \quad (R3)$$

$$O(^3P) + O_2 \xrightarrow{M} O_3 \quad (R4)$$

During the day, at mid-latitudes, NO and NO₂ interconvert so rapidly that a photostationary steady state is usually established within a few minutes (Leighton, 1961). This NOₓ cycling plays an important role in the tropospheric oxidation capacity through direct and indirect regulation of key atmospheric trace gases such as O₃ and the hydroxyl radical (OH). A large share of NOₓ is ultimately converted into atmospheric nitrate (NO₃⁻ ≡ nitric acid + particulate nitrate), with cascade effects on biodiversity, water quality, human health, and the climate (Galloway et al., 2008; Sutton et al., 2011; Szopa et al., 2021; Vitousek et al., 1997; WHO, 2021).

Identifying the origins of NO₃⁻ gaseous precursors is central to developing effective environmental mitigation policies, particularly in urban settings where NO₃⁻ is a major component of fine particulate matter (PM₂.₅) (R. Zhang et al., 2015). For this purpose, the use of nitrogen (N) stable isotopic enrichments in NO₃⁻, noted δ¹⁵N (NO₃⁻) (δ¹⁵N = (15/¹⁴N)ₐₙ₄ₐ₅/(15/¹⁴N)ₐ₅ₐ₄ - 1) and expressed in per mill (‰), has attracted growing interest in recent years (e.g., Altieri et al., 2022; Lim et al., 2022; Song, Liu, Hu, et al., 2021; Song, Liu, & Liu, 2021; Xiao et al., 2023; W. Zhang et al., 2022). This approach relies on the distinctive δ¹⁵N value of the NOₓ emitted, arising from the different N isotopic fractionations that occur during the NOₓ emission processes (Felix et al., 2012; D. J. Miller et al., 2017, 2018; Walters, Tharp, et al., 2015; Walters, Goodwin, & Michalski, 2015). However, further ¹⁵N partitionings occur during the conversion of NOₓ to NO₃⁻, notably during the NOₓ cycle (R1–R4) (Li et al., 2020). These isotopic effects fall into three categories: (a) The equilibrium isotope effect (EIE), (b) the kinetic isotope effect (KIE), and (c) the photochemical isotope fractionation effect (PHIFE) (C. E. Miller & Yung, 2000; Young et al., 2002). Based on the isotopic mass balance equation, the resulting δ¹⁵N of NO₂ (δ¹⁵N (NO₂)) can be expressed as a function of a factor Fₙ, expressed in ‰, representing the overall N isotopic fractionation effect between NOₓ emissions and NO₂, the fraction of NOₓ in the form of NO₂ (fₙₐ₅ₐ₄ = NO₂/NO₄ₐ₅ₐ₄), and the δ¹⁵N value of ambient NOₓ (δ¹⁵N(NOₓ)) (Albertin et al., 2021; Freyer et al., 1993; Li et al., 2020; Walters et al., 2018):

$$\delta^{15}\text{N}(\text{NO}_2) = F_N \times (1 - f_{\text{NO}_2}) + \delta^{15}\text{N}(\text{NO}_x) \quad (1)$$

δ¹⁵N(NOₓ) can be approximated by a linear combination of the individual source δ¹⁵N values weighted by the relative contributions of individual sources to the total NOₓ as

$$\delta^{15}\text{N}(\text{NO}_x) \approx \sum_i (f_i \times \delta^{15}\text{N}(\text{NO}_x)_i) \quad (2)$$

with fᵢ the relative contribution of the NOₓ emission source i (fᵢ = Eᵢ/Eₕₐ₅ₐ₄ with Eᵢ the NOₓ from the source i and Eₕₐ₅ₐ₄ the total NOₓ, with Σᵢfᵢ = 1) and δ¹⁵N(NOₓ)ᵢ their associated mean isotopic signature. Fₙ is intricately governed by EIE, KIE, and PHIFE, with the importance of each effect determined by local conditions such as temperature, NOₓ-O₃ regime, and solar radiation (Albertin et al., 2021; Freyer et al., 1993; Li et al., 2020; Walters et al., 2016). Therefore, using the δ¹⁵N content of NO₃⁻ for the direct identification of the nature of gaseous precursors after emission requires a clear understanding and quantification of these fractionation effects which remain poorly determined. A large part of this knowledge gap stems from the lack of quantification in the N fractionation by NOₓ cycling, with very few comparisons between laboratory-derived and theoretical N fractionation factors and ambient isotopic observations (Albertin et al., 2021, 2024; Freyer et al., 1993; Li et al., 2020; Walters et al., 2018).
In their pioneering work, Freyer et al. (1993) first identified a N isotope exchange equilibrium between NO and NO₂ under polluted conditions in Germany. They demonstrated that EIE predominantly influenced the atmospheric δ¹⁵N record of NOₓ (varying from ca. -6 to 6‰ over 1 year). The EIE isotope fractionation factor between NO and NO₂ (αEIE(NO₂/NO)) is strongly temperature-dependent and can be theoretically derived using harmonic oscillator frequencies (Begun & Fletcher, 1960; Monse et al., 1969; Walters & Michalski, 2015). Chamber experiments also allow for the determination of αEIE(NO₂/NO) in controlled conditions (Begun & Melton, 1956; Li et al., 2020; Walters et al., 2016). Only three recent studies have addressed the issue of isotopic exchanges between NO and NO₂ in atmospheric conditions (Albertin et al., 2021, 2024; Walters et al., 2018). Notably, as in Freyer et al. (1993), Albertin et al. (2024) found significant EIE between emitted NOₓ and NO₂ in an Alpine urban atmosphere. The authors observed an isotopic exchange of (43.6 ± 3.3)‰ in good agreement with that derived from the Walters and Michalski (2015) theoretical approach at (42.3 ± 1.3)‰. If the uncertainties associated with the use of δ¹⁵N measured in NO₃⁻ for source apportionment are to be reduced, more quantitative studies are required on the N fractionation effects in reactive nitrogen species. Meanwhile, the applicability of quantifying N isotopic fractionation using theoretical calculations needs to be tested over a wide range of temperatures and pollution conditions.

Expanding on Albertin et al. (2024), NO₂ was sampled in an urban Arctic environment in winter 2022 in Fairbanks, Alaska to measure its N isotopic composition. Fairbanks winter conditions (i.e., low temperatures, weak insolation, and high NOₓ) provide an ideal extreme setting to test our understanding of N exchanges in the NOₓ cycle and evaluate theoretical approaches. This study was conducted as part of the joint ALPACA (Alaskan Layered Pollution and Chemical Analysis) and CASPA (Climate-relevant Aerosol Sources and Processes in the Arctic) projects focusing on emission source identification and physicochemical processes leading to severe pollution events during the winter in Fairbanks, as well as their links to boundary layer dynamics (Simpson et al., 2024). Building on meteorological and trace gas observations, this study identifies factors influencing δ¹⁵N measured in NO₂. For the first time, N isotopic fractionations observed during NOₓ cycling in an urban and polar environment are confronted with theoretical estimates. After correction for equilibrium isotope exchange, the dominant NOₓ emission sources influencing the sampling site are examined using isotopic records and a local emission inventory.

## 2. Materials and Methods

### 2.1. Sampling and Isotopic Analysis

Ambient NO₂ was sampled periodically over January–February 2022 in downtown Fairbanks, Alaska near the University of Alaska Fairbanks (UAF) Community and Technical College (CTC) building (64°50'27"N 147° 43'34"W, 135 m above sea level). The CTC site is surrounded by roads, a residential area, the business district, and several power plants providing energy (Figure S1 in Supporting Information S1).

Thirty three samples were collected over 6 distinct sampling periods at ground level on denuder tubes inserted in series within a speciation cartridge (ChemComb® 3500, Thermo Scientific®, USA) connected to an off-line gas sampler (≈10 L min⁻¹ at standard temperature and pressure conditions). Coated with an alkaline guaiacol mixture, denuders allow for quantitative collection of NO₂ (Nash, 1970) under varying temperature and relative humidity conditions while preserving the integrity of NO₂ isotopes (Albertin et al., 2021; Blum et al., 2023; Walters et al., 2018; Zhou et al., 2022). Over six distinctive sampling periods, 33 denuder sets were performed, with collection times ranging from 3 to 9 hr (procedure strictly identical to that detailed in Albertin et al., 2024). Nitrite (NO₂⁻) formed from the reaction of NO₂ with the guaiacol coating was extracted in 10 mL of ultrapure water (18.2 MΩ cm⁻¹) under ultraclean conditions at the UAF laboratory. Sample extractions were stored frozen and shipped to the Institut des Géosciences de l'Environnement (Grenoble, France) isotopic laboratory for subsequent analysis. Trace gases (NO, NO₂ and O₃) and meteorological parameters (ambient temperature at 3 and 11 m) were measured continuously at CTC throughout the field ALPACA-2022 campaign from 17 January to 25 February (see description in Simpson et al. (2024)).

NO₂⁻ concentration in denuder extractions was estimated using UV-Vis spectrometry at 544 nm (Griess-Saltzmann reaction). The contribution of NO₂⁻ from blank denuders was almost always found to be negligible ((mean ± 1 standard deviation) = (0.9 ± 1.4)%). NO₂⁻ from the denuders in second position represented on average (1.8 ± 1.4)% of the total NO₂⁻ measured on the first denuders. N isotope ratios (¹⁵N/¹⁴N) of NO₂⁻ were
measured using the azide method (McIlvin & Altabet, 2005). $NO_2^-$ was converted into nitrous oxide ($N_2O$) using a sodium azide/acetic acid buffer solution. $N_2O$ was then thermally decomposed into $N_2$ and $O_2$ in a gold tube (Kaiser et al., 2007) and analyzed for isotopic composition on a Thermo Finnigan MAT253 Isotope Ratio Mass Spectrometer (continuous flow mode) equipped with gas chromatography (GasBench II™) (Morin et al., 2009). When possible, $NO_2^-$ analytes were analyzed several times (Table S1 in Supporting Information S1). International standard salts used to calibrate $\delta^{15}N$ isotopic ratios (Table S2 in Supporting Information S1) had an average standard deviation of $\pm 0.2\%$. The analytical procedure used for this study is strictly identical to the description given in Albertin et al. (2021).

## 2.2. ADEC/EPA NOₓ Emission Inventory

The Alaska Department of Conservation (ADEC)/Environmental Protection Agency (EPA) reproduced an updated emission inventory for the duration of the ALPACA-2022 campaign, including $NO_x$ emissions at the surface within the Fairbanks nonattainment area at hourly time resolution and emissions from power plants located in the Fairbanks area (Figure S1 in Supporting Information S1). The contribution from power plant emissions to surface pollution is a key issue in Fairbanks during winter, as the exhaust chimneys are often above the surface inversion layer (Simpson et al., 2024). We distinguish here between emissions from diesel and coal-fired power plants, which are referred to as diesel-PP and coal-PP. For our study, surface $NO_x$ emissions are grouped by sectors for: space heating (residential and commercial) and fuel type combustion: oil, wood, and fossil gas (negligible emissions from coal combustion for space heating are excluded). Vehicle exhaust combines on-road and non-road emissions. Surface emissions from industrial waste oils, airport activities, and minor point sources resulting from a combination of oil and gas combustion grouped under the term “other sources”. See ADEC (2019a) and Brett et al. (2024) for details about the emission inventory.

# 3. Results and Discussion

## 3.1. Atmospheric Observations

Classified as a serious $PM_{2.5}$ nonattainment area in 2017 (ADEC, 2019b), Fairbanks-North Star Borough experiences severe winter air pollution (Cesler-Maloney et al., 2022; Fochesatto et al., 2015; Mayfield & Fochesatto, 2013; Robinson et al., 2023; Tran & Mölders, 2011). High winter pollution is linked to weak vertical mixing, especially during surface-based temperature inversions (SBIs) events, when pollutant emissions can accumulate for hours to days near the surface. Strong SBIs have been defined by temperature differences between 3 and 11 m ($dT_{11m-3m}$) greater than 0.5°C (Cesler-Maloney et al., 2022). In such conditions, surface $O_3$ is generally fully titrated by NO (R1). In weakly stable conditions, SBIs collapse (i.e., $dT_{11m-3m} < 0.5°C$) and $O_3$ increases due to transport from aloft and $NO_x$ dilution. During the ALPACA-2022 field study, two extreme pollution episodes were observed, from January 29 to February 3, and from February 23 to February 25 (Simpson et al., 2024).

The $NO_2$ sampling strategy aimed to cover distinct pollution conditions during the campaign. Four $NO_2$ sampling periods correspond to moderately polluted (MP) periods (from January 25 to 26 = MP#1, from February 8 to 9 = MP#2, from February 11 to 12 = MP#3, and from February 16 to 17 = MP#4; Figure 1). During these MP periods, $NO_2$, NO, and $O_3$ mixing ratios fluctuate, with no clear diurnal patterns but in a correlated way. In short, when NO and $NO_2$ increase, $O_3$ decreases substantially, and vice versa (Figure 1). $NO_2$ mixing ratios range from ca. 1 to 49 nmol mol$^{-1}$ during the MP periods with a mean $\pm 1$ standard deviation of (22.5 $\pm$ 15.2) nmol mol$^{-1}$. $O_3$ mixing ratios reach a maximum of 35.2 nmol mol$^{-1}$ in the early morning during MP#1, typical of wintertime background air at high latitudes (Whaley et al., 2023). $NO_2$ sampling was also carried out during the two extremely polluted (EP) periods (from January 29 to February 2 = EP#1, and from February 24 to 25 = EP#2). EP#1 is considered the “cold pollution event” of the field campaign (Simpson et al., 2024). The temperature drops to -35°C and $PM_{2.5}$ levels reach their maximum (Figure S2 in Supporting Information S1). In contrast, although still highly polluted, EP#2 is much warmer, with temperatures above freezing. $NO_x$ mixing ratios are elevated during both periods (mean of (130.9 $\pm$ 38.1) nmol mol$^{-1}$) with NO being the dominant $NO_x$ component (i.e., low $f_{NO_x}$), and $O_3$ being almost or fully titrated (Figure 1). Covering different temperature and pollution conditions, the six $NO_2$ sampling periods can be considered representative of the diverse conditions encountered throughout the ALPACA-2022 campaign (Figure S2 in Supporting Information S1).
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Panel (a) displays temperature at 3 m (black line) and the temperature difference between 11 and 3 m (gray dashed line) at CTC. Panel (b) displays the 2-hr rolling mean of NO₂ (black line), O₃ (cyan dashed line), and NO (red line) mixing ratios measured at CTC during 6 non continuous periods (MP = moderately polluted and EP = extremely polluted). Panel (c) displays δ¹⁵N of atmospheric NO₂ (white and black dots for daytime and nighttime values, respectively) measured at CTC and the ratio fNO₂ (orange line). The horizontal bars of the isotopic data cover the NO₂ collection time and the dots represent the middle of the sampling period. Gray backdrop shaded areas represent the night duration. δ¹⁵N(NO₂)ctc-obs values and atmospheric observations averaged over each denuder collection interval are reported in Table S1 and S3 in Supporting Information S1, respectively.</figcaption></figure>

## 3.2. N Fractionation Effects

δ¹⁵N of atmospheric NO₂ at CTC (δ¹⁵N(NO₂)ctc-obs) fluctuates markedly over the six sampling periods (Figure 1b), ranging from -10.2 to 24.1‰ (weighted average of (12.3 ± 11.1)‰). Day (09:00–17:20 local time) and night (17:20–09:00 local time) δ¹⁵N values do not differ very significantly (independent samples t-test p-value of 0.8). δ¹⁵N(NO₂)ctc-obs values mainly fall within the range of previous measurements in suburban/urban environments, ranging from -31 to 20‰ (Albertin et al., 2021, 2024; Freyer et al., 1993; Walters et al., 2018).

It is now well established that the variability in δ¹⁵N(NO₂) can be attributed to two different causes: (a) Changes in the nature and relative contribution of NOₓ emission sources and (b) partitioning of ¹⁵N between NO and NO₂ due to fractionation effects. As expected from Equation 1, δ¹⁵N(NO₂)ctc-obs correlates well with (1 - fNO₂), the fraction of NOₓ under the form of NO (R² = 0.9, p << 0.05, n = 33; Figure 2a). This linear relationship indicates that the variability of δ¹⁵N(NO₂) at CTC is primarily driven by isotopic fractionation effects. Additionally, the
<figure><img src="image_4.png" /><figcaption>**Figure 2.** (a) $\delta^{15}\text{N}$ of atmospheric NO$_2$ (vertical axis, in $\%\text{e}$) as a function of $(1 - f_{\text{NO}_2})$ (horizontal axis) from observations at the CTC site, Fairbanks, Alaska, in January–February 2022. $f_{\text{NO}_2}$ is averaged over the collection period of each NO$_2$ sample. White and black dots represent daytime and nighttime data, respectively. The gray shading is the 95% confidence interval. Panel (b) shows theoretical $\alpha_{\text{EIE(NO}_2/\text{NO})}$ during individual NO$_2$ collection periods, derived from Walters et al. (2016) approach (Equation 4) and observed temperatures in Fairbanks (blue stars). The spread of the theoretical $\alpha_{\text{EIE(NO}_2/\text{NO})}$ values results from the temperature variability during NO$_2$ sampling periods (from -32.5°C to -1.6°C). The brown diamond represents the mean observed $\alpha_{\text{EIE(NO}_2/\text{NO})}$ during the NO$_2$ sampling periods, derived from the slope of panel (a) and Equation 3.</figcaption></figure>

$\delta^{15}\text{N}$ value of NO$_x$ emissions, represented by the intercept of the fit ($\delta^{15}\text{N}(\text{NO}_x)_{\text{ctc-obs}} = (-10.8 \pm 1.0)\%\text{e}$), remains constant throughout the campaign. As this linear dependency between $\delta^{15}\text{N}(\text{NO}_2)_{\text{ctc-obs}}$ and $(1 - f_{\text{NO}_2})$ applies to both day and night samples, similar isotopic processes governing the $\delta^{15}\text{N}(\text{NO}_2)_{\text{ctc-obs}}$ variability are expected over the diurnal cycle.

As mentioned above, N fractionation effects, grouped under the term $F_N$ and represented here by the linear regression slope in Figure 2a, encompass the different natures of isotopic partitioning in the NO$_x$ cycle, that is, PHIFE, KIE and EIE. As detailed in Li et al. (2020), the relative contribution of each isotopic fractionation effect is expected to differ between a remote and a heavily polluted atmosphere. On the one hand, in clean environments, NO$_x$ is mainly in the form of NO$_2$ due to efficient oxidation, notably by O$_3$. Hence, the NO$_2$ chemical lifetime (where $\tau_{\text{chem-NO}_2} = \frac{1}{J_{\text{NO}_2}}$, with $J_{\text{NO}_2}$ the NO$_2$ photolysis rate), is generally shorter than its lifetime with respect to isotopic exchange (where $\tau_{\text{exchange-NO}_2} = \frac{1}{k_{\text{NO+NO}_2}[\text{NO}]}$, with $k_{\text{NO+NO}_2} = 8.4 \times 10^{-14} \text{ cm}^3 \text{ mol}^{-1} \text{ s}^{-1}$ the rate constant for the N isotopic exchange between NO and NO$_2$; Sharma et al., 1970). Therefore, $^{15}\text{N}$ partitioning between NO and NO$_2$ is limited and mainly determined by a combination of PHIFE and KIE, namely the Leighton cycle isotopic effect (LCIE). On the other hand, in polluted environments such as Fairbanks in winter, NO and NO$_2$ are more likely to be in isotopic equilibrium than in remote areas due to a low O$_3$/NO ratio (Li et al., 2020). Typically, during the daytime NO$_2$ collection intervals, $\tau_{\text{exchange-NO}_2}$ was much shorter than $\tau_{\text{chem-NO}_2}$ during the field campaign (Table S4 in Supporting Information S1). At night, when photolysis ceases, the EIE controls the N fractionation between NO and NO$_2$, as the NO oxidation is slower. Besides, in theory, lower temperatures exacerbate the $^{15}\text{N}$ enrichment of NO$_2$ relative to NO$_x$ emissions (Walters & Michalski, 2015). Between a LCIE- and EIE-dominated regime, all isotopic effects can be significant. In this context, and as suggested by Figure 2a, substantial N fractionation between NO and NO$_2$ is expected under Fairbanks winter conditions (i.e., low temperatures, weak insolation, and high-NO$_x$), controlled by EIE.

Under an EIE dominant regime, $F_N$ can be expressed in a simplified form as follows (see Albertin et al., 2021 and references therein for derivation):

$$F_N \approx \frac{\alpha_{\text{EIE(NO}_2/\text{NO})} - 1}{\alpha_{\text{EIE(NO}_2/\text{NO})}} \quad (3)$$
$α_{EIE(NO_2/NO)}$ can be calculated using an expression derived from the Bigeleisen-Mayer equation in the harmonic oscillator approximation (Walters et al., 2016):

$$ (α_{EIE(NO_2/NO)} - 1) \times 1000 = \frac{3.9968}{T^4} \times 10^{10} + \frac{-7.9646}{T^3} \times 10^8 + \frac{6.2144}{T^2} \times 10^6 + \frac{-0.2911}{T} \times 10^4 \quad (4) $$

with T the ambient temperature in Kelvin. We calculate theoretically predicted $α_{EIE(NO_2/NO)}$ values at CTC using the mean surface temperature during each NO₂ collection interval (Table S5 in Supporting Information S1). Theoretical $α_{EIE(NO_2/NO)}$ values range from 1.0411 to 1.0499 (Figure 2b), corresponding to the observed minimum and maximum temperatures of -32.5°C and -1.8°C, respectively, with a mean of 1.0461 ± 0.0024. This value matches well with the value of 1.0471 ± 0.0512 derived from the observed $F_N$ at our sampling site (45.0‰; slope of the regression line in Figure 2a and Equation 3 ($α_{EIE(NO_2/NO)} = \frac{1}{F_N}$)). This excellent agreement between the theoretical and observation-derived $α_{EIE(NO_2/NO)}$, assuming an EIE-dominated regime, demonstrates that the ¹⁵N partitioning between NO and NO₂ at our site is indeed mainly determined by isotopic equilibrium. Importantly, while laboratory and theoretical studies still disagree on the magnitude of $α_{EIE(NO_2/NO)}$ (see Li et al., 2020 for a review), our results confirm the applicability of the Walters et al. (2016) theoretical expression for $α_{EIE(NO_2/NO)}$ in a polar winter environment.

Since the first work of Freyer et al. (1993), a few similar investigations have been carried out at mid-latitudes. Walters et al. (2018) and Albertin et al. (2021) conducted NO₂ sampling in summer and spring, respectively, in moderately polluted atmospheres. They revealed that δ¹⁵N(NO₂) was primarily driven by the variability in the δ¹⁵N value of NOₓ emissions. N fractionation effects were found to have little influence (below ca. 2‰) with NOₓ being overwhelmingly in the form of NO₂. In contrast, in an Alpine urban area during winter, Albertin et al. (2024) observed significant N fractionation between NOₓ emissions and NO₂, which largely explained their δ¹⁵N(NO₂) record. Our results are consistent with these previous studies (see summary of δ¹⁵N(NO₂) measurements in Table S6 in Supporting Information S1) and provide further observational evidence that as pollution levels rise (i.e., low $f_{NO_2}$), the disparity in δ¹⁵N between primary NOₓ and NO₂ increases due to equilibrium fractionation effects.

### 3.3. Identification of NOₓ Emission Sources

Following the results from the previous section, the δ¹⁵N value of NOₓ emissions at CTC (δ¹⁵N(NOₓ)ctc-obs-corrr) can be accurately derived for each of the 33 NO₂ collection intervals (14 daytime and 19 nighttime) as follows: $δ¹⁵N(NOₓ)ctc-obs-corrr = δ¹⁵N(NO₂)ctc-obs - F_N \times (1 - 2021f_{NO_2})$ with $F_N$ calculated from Equations 3 and 4 and using ambient temperatures at CTC (Table S3 in Supporting Information S1). $δ¹⁵N(NOₓ)ctc-obs-corrr$ is found to vary between -16.3‰ and -7.3‰ (mean propagated error of 2.6‰, Table S5 in Supporting Information S1), with a weighted mean (by the mean ambient NO₂ mixing ratio over the collection intervals) of (-10.3 ± 2.0)% (Table 1). Using the relative contribution of NOₓ emission sources given by the ADEC/EPA inventory over the non attainment area (Section 2.2) and δ¹⁵N values reported in the literature, one can calculate the expected δ¹⁵N of NOₓ over this region ($δ¹⁵N(NOₓ)nonatt-calc$; Equation 2 with $i = coal-PP, diesel-PP, wood, vehicle exhaust, gas, oil, and others$) during each NO₂ collection interval. Power plants are large emitters of NOₓ in Fairbanks (Figure S3 in Supporting Information S1). Their contribution to surface pollution is expected to vary according to atmospheric mixing conditions. According to the inventory, vehicle exhaust and oil for space heating dominated surface NOₓ sources during the ALPACA-2022 winter campaign (Figure S3 in Supporting Information S1).

The literature reports a wide range of δ¹⁵N values for NOₓ generated by vehicle exhaust, depending on the type of fuel, the presence of an emission control system, and the engine running time (Heaton, 1990; Ammann et al., 1999; Felix & Elliott, 2014; Walters, Tharp, et al., 2015; Walters, Goodwin, et al., 2015; Miller et al., 2017; Zong et al., 2017, 2020). Reviewing δ¹⁵N measurements in diverse conditions (warm-start, cold-start, running) with various vehicle types (diesel- and gasoline-powered), Song et al. (2022) reported a mean vehicle-emitted δ¹⁵N(NOₓ) of (-7.1 ± 4.2)%c. Fossil gas combustion also emits ¹⁵N-depleted NOₓ, with a mean of (-16.5 ± 1.7)%c (Walters, Tharp, et al., 2015). The δ¹⁵N of NOₓ emitted from oil combustion (heating oil and diesel-fired boilers) is not reported in the literature. However, as for gasoline, diesel, and fossil gas, the N content of distillate oil is negligible. Therefore, NOₓ emitted from oil combustion falls into the category of thermal production (i.e., NOₓ produced by the breaking of N₂ at high temperature), in contrast to NOₓ originating from the
<caption>**Table 1**</caption>

Mean δ¹⁵N of NOₓ at the CTC Site Fairbanks, Alaska (δ¹⁵N(NOₓ)ctc-obs_corr) and Mean δ¹⁵N of NOₓ Derived From the ADEC/EPA NOₓ Emission Inventory (δ¹⁵N(NOₓ)nonatt-calc). δ¹⁵N(NOₓ)ctc-obs_corr Values Were Derived From 33 Measured δ¹⁵N(NO₂) Values Corrected for Equilibrium Isotopic Effects (Section 3.2). δ¹⁵N(NOₓ)nonatt-calc Values Were Derived From Equation 2 and the ADEC/EPA NOₓ Emission Inventory for Two Emission Scenarios: (a) 100% of Power Plant Emissions Mixes With Surface NOₓ Emitted in the Fairbanks Non Attainment Area (All Emissions) and (b) Emissions From Coal-Fired Power Plant do Not Mix With Surface NOₓ Emitted in the Fairbanks Non Attainment Area (No Coal-PP)

<table><thead><tr><th rowspan="2"></th><th rowspan="2">(Mean δ¹⁵N(NOₓ)ctc-obs-corr ± 1σ) %o</th><th colspan="2">(Mean δ¹⁵N(NOₓ)nonatt-calc ± 1σ) %o</th></tr><tr><th>All emissions</th><th>No coal-PP</th></tr></thead><tbody><tr><td>Over the sampling period</td><td>-10.3 ± 2.0</td><td>3.4 ± 2.0</td><td>-12.4 ± 1.4</td></tr><tr><td>Daytime</td><td>-9.7 ± 1.6</td><td>3.5 ± 0.8</td><td>-11.0 ± 0.5</td></tr><tr><td>Nighttime</td><td>-10.71 ± 2.0</td><td>3.4 ± 2.6</td><td>-13.4 ± 0.8</td></tr></tbody></table>

Note. Emission data are reported in Table S8 and S9 in Supporting Information S1 for the “All emissions” and “No coal-PP” scenarios, respectively. The mean δ¹⁵N(NOₓ)nonatt-calc values are weighted by the mean ambient NO₂ mixing ratio over the collection intervals. Daytime: 09:00–17:00 LT and nighttime: 17:00–09:00 LT. The mean calculation propagation error on δ¹⁵N(NOₓ)nonatt-calc over the sampling period is 1.7 and 2.5%o for emission scenario 1 (All emissions) and 2 (No coal-PP), respectively. Individual δ¹⁵N(NOₓ)nonatt-calc values and propagation errors are reported in Table S11 in Supporting Information S1.

N in the fuel (Miller & Bowman, 1989). While it is difficult to estimate the δ¹⁵N value of NOₓ emitted by oil-fired burners (fueled with heating oil for space heating and diesel for power generation), we may expect it to lie close to diesel-powered vehicles and fossil gas burner exhausts, somewhere between ca. -20%o and -13%o (Walters, Tharp, et al., 2015, Walters et al., 2018). Therefore, it is reasonable to assume that the δ¹⁵N value of NOₓ emitted by oil combustion is approximately of (-16.5 ± 4.0)%o, as for gas combustion. Coal-PP with selective catalytic reduction technology emit NOₓ with the highest δ¹⁵N content, averaging (19.5 ± 2.3)%o (Felix et al., 2012). We set arbitrarily δ¹⁵N(NOₓ)others at -16.5%o because these minor sources correspond to stationary emissions of gas and oil combustion. Based on the mean δ¹⁵N of temperate forest ((-2.8 ± 2.0)%o; Martinelli et al., 1999) and using the empirical relationship between the δ¹⁵N of burnt biomass and the δ¹⁵N of NOₓ determined by Chai et al. (2019), δ¹⁵N(NOₓ)wood is estimated at (-0.1 ± 1.3)%o.

In a first approach, 100% of diesel-PP and coal-PP emissions are included in the calculation of δ¹⁵N(NOₓ)nonatt-calc for the 33 NO₂ collection intervals, resulting in a weighted mean value of (3.4 ± 2.0)%o (Table 1). It is worth pointing out that the δ¹⁵N(NOₓ) signature of coal combustion (ca. 20%o) is very distinct from the isotopic signatures of the other important NOₓ sources (varying between ca. -7 and -17%o). As a result, the δ¹⁵N(NOₓ) in Fairbanks is expected to be quite sensitive to the coal-PP contribution. The Aurora and Doyon power plants are the largest emitters of NOₓ from coal combustion in downtown Fairbanks (Table S7 in Supporting Information S1). With stack heights of 26 and 48 m, respectively, the top of the power plant chimneys are often above the winter surface boundary layer (ca. 25 m; Brett et al., 2024). Consequently, only a fraction of the NOₓ emissions from these high stacks may be expected to reach the surface, particularly during very stable meteorological conditions with SBIs (Moon et al., 2024; Simpson et al., 2024). This is less the case for diesel-PP with lower stack heights, such as Zehnder near CTC emitting NOₓ closer to the surface (18 m). Thus, when assuming 100% of coal-PP emissions mixes with surface NOₓ (representing ca. 52% of the total NOₓ, Table S8 in Supporting Information S1), it is not surprising that δ¹⁵N(NOₓ)nonatt-calc values are biased high with respect to δ¹⁵N(NOₓ)ctc-obs-corr values. This result implies that the average coal-PP contribution to surface NOₓ level was likely below ca. 50% during the campaign.

In contrast, if the fraction of coal-PP emissions reaching the Fairbanks surface is assumed to be negligible (i.e., no mixing down of coal-PP plumes, Table S9 in Supporting Information S1), the weighted mean δ¹⁵N(NOₓ)nonatt-calc over the NO₂ sampling periods is (-12.4 ± 1.4)%o, matching well with the mean δ¹⁵N(NOₓ)ctc-obs-corr (Table 1). Note that over the entire ALPACA-2022 campaign, the mean δ¹⁵N(NOₓ)nonatt-calc is similar (-12.1%o). These results suggest that the inventory of NOₓ emissions for the Fairbanks nonattainment area is reasonably representative of emissions at CTC, including emissions from diesel-PP, and that NOₓ emissions from coal-PP may only be contributing marginally to breathing NOₓ levels in downtown Fairbanks. Note that the sample of δ¹⁵N(NOₓ)nonatt-calc values is not significantly different from that of δ¹⁵N(NOₓ)ctc-obs-corr values (p value > 0.05), until more than 5% of coal-PP are included. In other words, if, on average, during the NO₂ collection intervals, up to 5%
of coal-PP emissions had reached the surface at CTC, the impact on the ambient NOₓ isotopic signature is too small to be detected robustly because of the dispersion in the comparison between observed and calculated values. In the scenario where 5% of coal-PP emissions reach the surface layer, coal-PP emissions account for (5.0 ± 1.3)% of total NOₓ emissions, while vehicle exhaust and heating oil combustion contribute (34.0 ± 15.1)% and (29.0 ± 7.5)%, respectively (Table S10 in Supporting Information S1). It is interesting to note that, without coal-PP contribution to surface NOₓ, the weighted mean daytime and nighttime δ¹⁵N(NOₓ)nonatt-calc is slightly different ((-11.0 ± 0.5)%c and (-13.4 ± 0.8)%c, respectively), as for δ¹⁵N(NOₓ)ctc-obs-corr ((-9.7 ± 1.6)%c and (-10.7 ± 2.0)%c, respectively). This diurnal sensitivity of δ¹⁵N(NOₓ) values likely reflects the increase in the oil-combustion contribution (mainly from space heating) to total NOₓ emissions at night, while the vehicle exhaust contribution decreases (Figure S3 in Supporting Information S1). These results support our hypothesis that the δ¹⁵N(NOₓ)oil is rather low compared to the δ¹⁵N(NOₓ)vehicle.

Quantifying the amount of power plant emissions reaching the surface in downtown Fairbanks is relevant to the design of pollution control strategies. The fraction of coal-PP emissions ($x_{coal-PP}$) that reached the CTC surface site during the NO₂ sampling periods can be derived from Equation 2 (see Text S1 in Supporting Information S1) following

$$x_{\text{coal-PP\_ctc}} = \frac{\sum_{j \neq \text{coal-PP}} (E_j \times \delta^{15}\text{N}(\text{NO}_x)_j) - \sum_{j \neq \text{coal-PP}} E_j \times \delta^{15}\text{N}(\text{NO}_x)_{\text{ctc-obs\_corr}}}{E_{\text{coal-PP}} \times (\delta^{15}\text{N}(\text{NO}_x)_{\text{ctc-obs\_corr}} - \delta^{15}\text{N}(\text{NO}_x)_{\text{coal-PP}})} \quad (5)$$

with $E_{coal-PP}$ the total amount of NOₓ emitted by coal-fired power plants in the vicinity of CTC. However, it is not possible to precisely estimate $x_{coal-PP\_ctc}$ at the CTC site from our analysis, as the error propagation in Equation 5 of isotopic fractionation and δ¹⁵N values of individual emission sources generates a large uncertainty. For example, δ¹⁵N(NOₓ) of vehicle exhaust, the reported dominant source, could be overestimated in our analysis due to the unusual winter environmental conditions prevailing in Fairbanks. Cold engines emit ¹⁵N-depleted NOₓ, generally below ca. -9‰, compared to running vehicles (Walters, Goodwin, et al., 2015; Zong et al., 2020). During winter in Fairbanks, low ambient temperatures, typically below -15°C (Figure S2 in Supporting Information S1), engender short vehicle trips with frequent cold starts. Given that 60%-80% of NOₓ from a typical vehicle is emitted during the first 200 s of cold start operation (Walters, Goodwin, et al., 2015), δ¹⁵N(NOₓ)vehicle in Fairbanks could lie in the lower range of isotopic signatures reported for motor engines. The proportion of diesel and gasoline vehicle is also a source of uncertainty for δ¹⁵N(NOₓ)vehicle. Indeed, more negative δ¹⁵N(NOₓ)vehicle have been reported for diesel (from -23.3 to -15.9‰) compared to gasoline vehicle emissions (from -15.1 to 10.5‰) (Walters, Goodwin, et al., 2015; Walters, Tharp, et al., 2015; Zong et al., 2020). Diesel vehicles accounted for an estimated 10% of the total fleet in the Fairbanks nonattainment area in winter 2022 but contributed 59% of on-road emissions (Table S12 in Supporting Information S1). In addition, according to Brett et al. (2024), diesel vehicle NOₓ emissions may be underestimated in very cold conditions during the winter in Fairbanks. Although observed δ¹⁵N(NOₓ) at CTC is quite well reproduced using a mean δ¹⁵N(NOₓ)vehicle of -7.1‰, specific conditions in Fairbanks could punctually lower this value during rush hours and in cold conditions.

Despite these uncertainties, we attempt to estimate an upper limit of $x_{coal-PP\_ctc}$ using the lower limits in the uncertainty ranges of NOₓ emission source δ¹⁵N-signature (i.e., δ¹⁵N(NOₓ) = mean value - 1σ) in order to maximize the coal-PP contribution to CTC. Using δ¹⁵N(NOₓ)ctc-obs-corr values and average emission rates over corresponding NO₂ collection intervals, the mean maximum $x_{coal-PP\_ctc}$ over the sampling campaign is of ca. 22%. Mixed with surface emissions, it represents an average maximum contribution of coal-PP to the surface NOₓ at CTC of ca. 18%. Note that a higher contribution of vehicle exhaust emissions to CTC would give a lower contribution of coal-PP. This maximum coal-PP contribution is derived with emissions averaged over the NO₂ collection intervals. It is therefore likely that, during shorter periods of time, and depending on the meteorological conditions, coal combustion emissions from elevated power plants could have mixed more with ambient NOₓ at the surface, as suggested by Brett et al. (2024). Complementary investigations are required to quantify the high-time resolved contribution of power plant emissions on the surface NOₓ budget in downtown Fairbanks in winter.

## 4. Conclusions

Consistent with prior studies in mid-latitude urban areas, the N isotopic composition of atmospheric NO₂ in polluted wintertime Fairbanks exhibited large temporal variations driven by N fractionation between NO and
NO₂. The N isotopic fractionation is found to be driven by the equilibrium effect between NO and NO₂, at rates in excellent agreement with the theoretical predictions. This result confirms that N partitioning between NO and NO₂ is well-constrained across diverse polluted environments and that the resulting N fractionation can be accurately predicted. This holds significant implications for understanding δ¹⁵N dynamics in the atmospheric Nₓ cycle, in particular for the accuracy of δ¹⁵N-based identification and apportionment of NOₓ emission sources. Nonetheless, uncertainties pertain to the δ¹⁵N of individual emission sources, representing a primary limitation in δ¹⁵N-based source apportionment. Efforts should focus on measuring accurately these δ¹⁵N-signatures, notably for vehicle exhaust and heating oil combustion, while accounting for environmental conditions and emission control technologies.

Our isotopic analysis indicates that the very large emissions from coal-fired power plants with elevated stacks do not appear to contribute substantially to NOₓ levels in the Fairbanks urban center, likely well below 18% on average. To our knowledge, this is the first quantitative estimate of the coal-power plants contribution to NOₓ surface levels in this area. This result suggests that, on average, there is relatively little mixing between the surface boundary layer and above in winter Fairbanks. In addition, this study shows that vehicle exhaust and oil combustion from space heating were the dominant NOₓ sources at our sampling site. Our results align well with previous findings indicating that Fairbanks downtown pollution is largely influenced by surface emissions (Moon et al., 2024; Simpson et al., 2024).

We wish to emphasize that such an analysis of the NO₂ isotopic composition is very valuable and even necessary for the interpretation of δ¹⁵N records of NO₃⁻ in urban atmospheres. Given its critical contribution to air quality, atmospheric samples of NO₃⁻ are widely used in the literature to trace NOₓ emission sources using δ¹⁵N measurements. However, to date, there is a limited understanding of possible N isotopic effects between NO₂ and NO₃⁻. Chang et al. (2018) estimated that NO₃⁻ isotope-based source apportionment studies conducted in China overestimated the contribution of coal combustion by ca. 30% on average when N isotopic fractionation effects were not accounted for. Although most recent studies do apply some isotopic fractionation correction to δ¹⁵N (NO₃⁻) records (e.g., Bekker et al., 2023; Fan et al., 2023; Li et al., 2022; Lim et al., 2022; Zhang et al., 2022), we currently suffer from a too limited number of simultaneous δ¹⁵N measurements in atmospheric NO₂ and NO₃⁻ to assess the applicability of these theoretical corrections to various ambient conditions (see Albertin et al., 2024). Therefore, relying only on ¹⁵N measurements of NO₃⁻ to trace NOₓ emission sources in urban settings remains uncertain. In parallel with NO₂ sampling, we collected atmospheric NO₃⁻ in and outside Fairbanks throughout the ALPACA-2022 campaign (n = 95 at sampling time resolution ranging from half a day to a few hours). An in-depth analysis of the factors influencing the NO₃⁻ isotopic composition, notably the NO₂ isotopic composition, fₘO₂, and temperature, will be conducted in a forthcoming study. Implications for the traceability of NO₃⁻ chemistry processes and sources from isotopic records will also be assessed. At the same time, we encourage other groups to carry out more simultaneous δ¹⁵N measurements of NO₂ and NO₃⁻ under other atmospheric conditions to improve further our understanding of N isotopic fractionation effects and be more confident in accounting for them in quantitative apportionment of Nₓ emission sources and processes from NO₃⁻ records.

## Data Availability Statement

Hourly meteorological and trace gas data from the ALPACA-2022 study are available to the scientific community through the ALPACA data portal hosted by Arcticdata.io (https://arcticdata.io/catalog/portals/ALPACA/Data). Data from this study (i.e., hourly NOₓ emission rates from the updated ADEC/EPA inventory and isotopic measurements) are available at https://doi.org/10.18739/A2XG9FC9F (Albertin et al., 2024). A copy of isotopic measurements as well as meteorological and trace gas measurements, and ADEC/EPA emission data averaged over each denuder collection interval is available in Supporting Information S1.

## References

* ADEC. (2019a). In *Alaska department of environmental conservation (ADEC) - amendments to: State air quality control plan* (Vol. III). Retrieved from https://dec.alaska.gov/air/anpms/communities/fbks-pm2-5-serious-sip/

* ADEC. (2019b). Alaska department of environmental conservation - state air quality control plan. *Executive summary*. Retrieved from https://dec.alaska.gov/air/anpms/communities/fbks-pm2-5-serious-sip/

* Albertin, S., Bekki, S., Savarino, J., Brett, N., Law, K. S., Cesler-Maloney, M., et al. (2024). *Nitrogen isotope measurements of nitrogen dioxide in Fairbanks, Alaska, during the Alaskan Layered Pollution And Chemical Analysis (ALPACA) 2022 field study*. Arctic Data Center. https://doi.org/10.18739/A2XG9FC9F

## Acknowledgments

This research benefited from national and IGE infrastructures and laboratory platforms, RéGEF and PANDA respectively and has received funding from: The Agence Nationale de la Recherche (ANR) via contract ANR-21-CE01-0017 CASPA (coordinated by Kathy Law), the Institut polaire français Paul-Emile Victor (IPEV) (Grant 1215), INSU-CNRS (National Institute of Sciences of the Universe) via its national LEFE program (Les Enveloppes Fluide et l'Environnement), the Labex OSUG@2020 (Investissements d'avenir - ANR10 LABX56), and IDEX-UGA ANR project ANR-15-IDEX-02 (coordinated by Joël Savarino). BA acknowledges NOAA Grant NA20OAR4310295. The authors thank the power plant facilities for providing their emission data. The authors particularly acknowledge Deanna Huff (ADEC) and Kathleen Fahey (EPA) for providing the updated ALPACA-2022 emission inventory. The authors are very grateful to the University of Alaska Fairbanks and the Geophysical Institute, the ALPACA participants, and the technical and administrative staff of IGE and LATMOS for assistance.
Albertin, S., Savarino, J., Bekki, S., Barbero, A., & Caillon, N. (2021). Measurement report: Nitrogen isotopes (δ¹⁵N) and first quantification of oxygen isotope anomalies (Δ¹⁷O, δ¹⁸O) in atmospheric nitrogen dioxide. *Atmospheric Chemistry and Physics*, *21*(13), 10477–10497. https://doi.org/10.5194/acp-21-10477-2021

Albertin, S., Savarino, J., Bekki, S., Barbero, A., Grilli, R., Fournier, Q., et al. (2024). Diurnal variations in oxygen and nitrogen isotopes of atmospheric nitrogen dioxide and nitrate: Implications for tracing NOₓ oxidation pathways and emission sources. *Atmospheric Chemistry and Physics*, *24*(2), 1361–1388. https://doi.org/10.5194/acp-24-1361-2024

Altieri, K. E., Burger, J., Language, B., & Piketh, S. J. (2022). A case study in the wintertime Vaal Triangle Air-Shed Priority Area on the utility of the nitrogen stable isotopic composition of aerosol nitrate to identify NOₓ sources. *Clean Air Journal*, *32*(1). https://doi.org/10.17159/caj/2022/32/1.12505

Ammann, M., Siegwolf, R., Pichlmayer, F., Suter, M., Saurer, M., & Brunold, C. (1999). Estimating the uptake of traffic-derived NO₂ from ¹⁵N abundance in Norway spruce needles. *Oecologia*, *118*(2), 124–131. https://doi.org/10.1007/s004420050710

Begun, G. M., & Fletcher, W. H. (1960). Partition function ratios for molecules containing nitrogen isotopes. *The Journal of Chemical Physics*, *33*(4), 1083–1085. https://doi.org/10.1063/1.1731338

Begun, G. M., & Melton, C. E. (1956). Nitrogen isotopic fractionation between NO and NO₂ and mass discrimination in mass analysis of NO₂. *The Journal of Chemical Physics*, *25*(6), 1292–1293. https://doi.org/10.1063/1.1743215

Bekker, C., Walters, W. W., Murray, L. T., & Hastings, M. G. (2023). Nitrate chemistry in the northeast US – Part 1 Nitrogen isotope seasonality tracks nitrate formation chemistry. *Atmospheric Chemistry and Physics*, *23*(7), 4185–4201. https://doi.org/10.5194/acp-23-4185-2023

Blum, D. E., Walters, W. W., Eris, G., Takeuchi, M., Huey, L. G., Tanner, D., et al. (2023). Collection of nitrogen dioxide for nitrogen and oxygen isotope determination—laboratory and environmental chamber evaluation. *Analytical Chemistry*, *95*(6), 3371–3378. https://doi.org/10.1021/acs.analchem.2c04672

Brett, N., Law, K. S., Arnold, S. R., Fochesatto, J. G., Raut, J.-C., Onishi, T., et al. (2024). *Investigating processes influencing simulation of local Arctic wintertime anthropogenic pollution in Fairbanks, Alaska during ALPACA-2022* (pp. 1–55). EGUSphere. https://doi.org/10.5194/egusphere-2024-1450

Cesler-Maloney, M., Simpson, W. R., Miles, T., Mao, J., Law, K. S., & Roberts, T. J. (2022). Differences in ozone and particulate matter between ground level and 20 m aloft are frequent during wintertime surface-based temperature inversions in Fairbanks, Alaska. *Journal of Geophysical Research: Atmospheres*, *127*(10), e2021JD036215. https://doi.org/10.1029/2021JD036215

Chai, J., Miller, D. J., Scheuer, E., Dibb, J., Selimovic, V., Yokelson, R., et al. (2019). Isotopic characterization of nitrogen oxides (NOₓ), nitrous acid (HONO), and nitrate (pNO₃⁻) from laboratory biomass burning during FIREX. *Atmospheric Measurement Techniques*, *12*(12), 6303–6317. https://doi.org/10.5194/amt-12-6303-2019

Chang, Y., Zhang, Y., Tian, C., Zhang, S., Ma, X., Cao, F., et al. (2018). Nitrogen isotope fractionation during gas-to-particle conversion of NOₓ to NO₃⁻ in the atmosphere – Implications for isotope-based NOₓ source apportionment. *Atmospheric Chemistry and Physics*, *18*(16), 11647–11661. https://doi.org/10.5194/acp-18-11647-2018

Crutzen, P. J. (1970). The influence of nitrogen oxides on the atmospheric ozone content. *Quarterly Journal of the Royal Meteorological Society*, *96*(408), 320–325. https://doi.org/10.1002/qj.49709640815

Fan, M.-Y., Zhang, W., Zhang, Y.-L., Li, J., Fang, H., Cao, F., et al. (2023). Formation mechanisms and source apportionments of nitrate aerosols in a megacity of eastern China based on multiple isotope observations. *Journal. Geophysical Research Atmospheres*, *128*(6), e2022JD038129. https://doi.org/10.1029/2022JD038129

Felix, J. D., & Elliott, E. M. (2014). Isotopic composition of passively collected nitrogen dioxide emissions: Vehicle, soil and livestock source signatures. *Atmospheric Environment*, *92*, 359–366. https://doi.org/10.1016/j.atmosenv.2014.04.005

Felix, J. D., Elliott, E. M., & Shaw, S. L. (2012). Nitrogen isotopic composition of coal-fired power plant NOₓ: Influence of emission controls and implications for global emission inventories. *Environmental Science & Technology*, *46*(6), 3528–3535. https://doi.org/10.1021/es203355v

Fochesatto, G. J., Mayfield, J. A., Starkenburg, D. P., Gruber, M. A., & Conner, J. (2015). Occurrence of shallow cold flows in the winter atmospheric boundary layer of interior of Alaska. *Meteorology and Atmospheric Physics*, *127*(4), 369–382. https://doi.org/10.1007/s00703-013-0274-4

Freyer, H. D., Kley, D., Volz-Thomas, A., & Kobel, K. (1993). On the interaction of isotopic exchange processes with photochemical reactions in atmospheric oxides of nitrogen. *Journal of Geophysical Research*, *98*(D8), 14791–14796. https://doi.org/10.1029/93JD00874

Galloway, J. N., Townsend, A. R., Erisman, J. W., Bekunda, M., Cai, Z., Freney, J. R., et al. (2008). Transformation of the nitrogen cycle: Recent trends, questions, and potential solutions. *Science*, *320*(5878), 889–892. https://doi.org/10.1126/science.1136674

Heaton, T. H. E. (1990). ¹⁵N/¹⁴N ratios of NOₓ from vehicle engines and coal-fired power stations. *Tellus B: Chemical and Physical Meteorology*, *42*(3), 304–307. https://doi.org/10.1034/j.1600-0889.1990.00007.x-i1

Kaiser, J., Hastings, M. G., Houlton, B. Z., Röckmann, T., & Sigman, D. M. (2007). Triple oxygen isotope analysis of nitrate using the denitrifier method and thermal decomposition of N₂O. *Analytical Chemistry*, *79*(2), 599–607. https://doi.org/10.1021/ac061022s

Leighton, P. A. (1961). *Photochemistry of air pollution* (Vol. 66). Academic Press.

Li, J., Zhang, X., Orlando, J., Tyndall, G., & Michalski, G. (2020). Quantifying the nitrogen isotope effects during photochemical equilibrium between NO and NO₂: Implications for δ¹⁵N in tropospheric reactive nitrogen. *Atmospheric Chemistry and Physics*, *20*(16), 9805–9819. https://doi.org/10.5194/acp-20-9805-2020

Li, Y., Shi, G., Chen, Z., Lan, M., Ding, M., Li, Z., & Hastings, M. G. (2022). Significant latitudinal gradient of nitrate production in the marine atmospheric boundary layer of the northern hemisphere. *Geophysical Research Letters*, *49*(23), e2022GL100503. https://doi.org/10.1029/2022GL100503

Lim, S., Lee, M., Savarino, J., & Laj, P. (2022). Oxidation pathways and emission sources of atmospheric particulate nitrate in seoul: Based on δ¹⁵N and Δ¹⁷O measurements. *Atmospheric Chemistry and Physics*, *22*(8), 5099–5115. https://doi.org/10.5194/acp-22-5099-2022

Martinelli, L. A., Piccolo, M. C., Townsend, A. R., Vitousek, P. M., Cuevas, E., McDowell, W., et al. (1999). Nitrogen stable isotopic composition of leaves and soil: Tropical versus temperate forests. *Biogeochemistry*, *46*(1/3), 45–65. https://doi.org/10.1023/A:1006100128782

Mayfield, J. A., & Fochesatto, G. J. (2013). The layered structure of the winter atmospheric boundary layer in the interior of Alaska. *Journal of Applied Meteorology and Climatology*, *52*(4), 953–973. https://doi.org/10.1175/JAMC-D-12-01.1

McIlvin, M. R., & Altabet, M. A. (2005). Chemical conversion of nitrate and nitrite to nitrous oxide for nitrogen and oxygen isotopic analysis in freshwater and seawater. *Analytical Chemistry*, *77*(17), 5589–5595. https://doi.org/10.1021/ac050528s

Miller, C. E., & Yung, Y. L. (2000). Photo-induced isotopic fractionation. *Journal of Geophysical Research*, *105*(D23), 29039–29051. https://doi.org/10.1029/2000JD900388

Miller, D. J., Chai, J., Guo, F., Dell, C. J., Karsten, H., & Hastings, M. G. (2018). Isotopic composition of in situ soil NOₓ emissions in manure-fertilized cropland. *Geophysical Research Letters*, *45*(21), 12058–12066. https://doi.org/10.1029/2018GL079619
Miller, D. J., Wojtal, P. K., Clark, S. C., & Hastings, M. G. (2017). Vehicle NOₓ emission plume isotopic signatures: Spatial variability across the eastern United States. *Journal of Geophysical Research: Atmospheres*, 122(8), 4698–4717. https://doi.org/10.1002/2016JD025877

Miller, J. A., & Bowman, C. T. (1989). Mechanism and modeling of nitrogen chemistry in combustion. *Progress in Energy and Combustion Science*, 15(4), 287–338. https://doi.org/10.1016/0360-1285(89)90017-8

Monse, E. U., Spindel, W., & Stern, M. J. (1969). Analysis of isotope-effect calculations illustrated with exchange equilibria among oxynitrogen compounds. In *Isotope effects in chemical processes* (Vol. 89, pp. 148–184). https://doi.org/10.1021/ba-1969-0089.ch009

Moon, A., Jongebloed, U., Dingilian, K. K., Schauer, A. J., Chan, Y.-C., Cesler-Maloney, M., et al. (2024). Primary sulfate is the dominant source of particulate sulfate during winter in Fairbanks, Alaska. *ACS ES&T Air*, 1(3), 139–149. https://doi.org/10.1021/acsestair.3c00023

Morin, S., Savarino, J., Frey, M. M., Domine, F., Jacobi, H.-W., Kaleschke, L., & Martins, J. M. F. (2009). Comprehensive isotopic composition of atmospheric nitrate in the Atlantic Ocean boundary layer from 65°S to 79°N. *Journal of Geophysical Research*, 114(D5). https://doi.org/10.1029/2008JD010696

Nash, T. (1970). An efficient absorbing reagent for nitrogen dioxide. *Atmospheric Environment*, 4(6), 661–665. https://doi.org/10.1016/0004-6981(70)90039-9

Robinson, E. S., Cesler-Maloney, M., Tan, X., Mao, J., Simpson, W., & DeCarlo, P. F. (2023). Wintertime spatial patterns of particulate matter in Fairbanks, AK during ALPACA 2022. *Environmental Sciences: Atmosphere*, 3(3), 568–580. https://doi.org/10.1039/D2EA00140C

Sharma, H. D., Jervis, R. E., & Wong, K. Y. (1970). Isotopic exchange reactions in nitrogen oxides. *The Journal of Physical Chemistry A*, 74(4), 923–933. https://doi.org/10.1021/j1006999a044

Simpson, W. R., Mao, J., Fochesatto, G. J., Law, K. S., DeCarlo, P. F., Schmale, J., et al. (2024). Overview of the alaskan layered pollution and chemical analysis (ALPACA) field experiment. *ACS ES&T Air*, 1(3), 200–222. https://doi.org/10.1021/acsestair.3c00076

Song, W., Liu, X., & Liu, C. (2021). New constraints on isotopic effects and major sources of nitrate in atmospheric particulates by combining δ¹⁵N and Δ¹⁷O signatures. *Journal of Geophysical Research: Atmospheres*, 126(16). https://doi.org/10.1029/2020JD034168

Song, W., Liu, X.-Y., Houlton, B. Z., & Liu, C.-Q. (2022). Isotopic constraints confirm the significant role of microbial nitrogen oxides emissions from the land and ocean environment. *National Science Review*, 9(9), nwac106. https://doi.org/10.1093/nsw/nswac106

Song, W., Liu, X.-Y., Hu, C.-C., Chen, G.-Y., Liu, X.-J., Walters, W. W., et al. (2021). Important contributions of non-fossil fuel nitrogen oxides emissions. *Nature Communications*, 12(1), 243. https://doi.org/10.1038/s41467-020-20356-0

Sutton, E. M. A., Howard, C. M., Erisman, J. W., Billen, G., & Bleeker, A. (2011). *The European nitrogen assessment, sources, effects and policy perspectives* (Vol. 30). Cambridge University Press.

Szopa, S., Naik, V., Adhikary, B., Artaxo, P., Berntsen, T., Collins, W. D., et al. (2021). Short-lived climate forcers. In V. Masson-Delmotte, P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, et al. (Eds.), *Climate change 2021: The physical science basis. Contribution of working group I to the sixth assessment report of the intergovernmental panel on climate change* (pp. 817–922). Cambridge University Press. https://doi.org/10.1017/9781009157896.008

Tran, H. N. Q., & Mölders, N. (2011). Investigations on meteorological conditions for elevated PM2.5 in Fairbanks, Alaska. *Atmospheric Research*, 99(1), 39–49. https://doi.org/10.1016/j.atmosres.2010.08.028

Vitousek, P. M., Aber, J. D., Howarth, R. W., Likens, G. E., Matson, P. A., Schindler, D. W., et al. (1997). Human alteration of the global nitrogen cycle: Sources and consequences. *Ecological Applications*, 7(3), 737–750. https://doi.org/10.1890/1051-0761(1997)007[0737:HAOTGN]2.0. CO;2

Walters, W. W., Fang, H., & Michalski, G. (2018). Summertime diurnal variations in the isotopic composition of atmospheric nitrogen dioxide at a small midwestern United States city. *Atmospheric Environment*, 179, 1–11. https://doi.org/10.1016/j.atmosenv.2018.01.047

Walters, W. W., Goodwin, S. R., & Michalski, G. (2015). Nitrogen stable isotope composition (δ¹⁵N) of vehicle-emitted NOₓ. *Environmental Science & Technology*, 49(4), 2278–2285. https://doi.org/10.1021/es50580v

Walters, W. W., & Michalski, G. (2015). Theoretical calculation of nitrogen isotope equilibrium exchange fractionation factors for various NOₓ molecules. *Geochimica et Cosmochimica Acta*, 164, 284–297. https://doi.org/10.1016/j.gca.2015.05.029

Walters, W. W., Simonini, D. S., & Michalski, G. (2016). Nitrogen isotope exchange between NO and NOₓ and its implications for δ¹⁵N variations in tropospheric NOₓ and atmospheric nitrate. *Geophysical Research Letters*, 43(1), 440–448. https://doi.org/10.1002/2015GL066438

Walters, W. W., Tharp, B. D., Fang, H., Kozak, B. J., & Michalski, G. (2015). Nitrogen isotope composition of thermally produced NOₓ from various fossil-fuel combustion sources. *Environmental Science & Technology*, 49(19), 11363–11371. https://doi.org/10.1021/acs.est.5b02769

Whaley, C. H., Law, K. S., Hjorth, J. L., Skov, H., Arnold, S. R., Langner, J., et al. (2023). Arctic tropospheric ozone: Assessment of current knowledge and model performance. *Atmospheric Chemistry and Physics*, 23(1), 637–661. https://doi.org/10.5194/acp-23-637-2023

WHO. (2021). World health organization global air quality guidelines: Particulate matter (PM₂.₅ and PM₁₀), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. World Health Organization.

Xiao, H., Ding, S.-Y., Ji, C.-W., Li, Q.-K., & Li, X.-D. (2023). Strict control of biomass burning inhibited particulate matter nitrate pollution over Tianjin: Perspective from dual isotopes of nitrate. *Atmospheric Environment*, 293, 119460. https://doi.org/10.1016/j.atmosenv.2022.119460

Young, E. D., Galy, A., & Nagahara, H. (2002). Kinetic and equilibrium mass-dependent isotope fractionation laws in nature and their geochemical and cosmochemical significance. *Geochimica et Cosmochimica Acta*, 66(6), 1095–1104. https://doi.org/10.1016/S0016-7037(01) 00832-8

Zhang, R., Wang, G., Guo, S., Zamora, M. L., Ying, Q., Lin, Y., et al. (2015). Formation of urban fine particulate matter. *Chemical Reviews*, 115(10), 3803–3855. https://doi.org/10.1021/acs.chemrev.5b00067

Zhang, W., Bi, X., Zhang, Y., Wu, J., & Feng, Y. (2022). Diesel vehicle emission accounts for the dominate NOₓ source to atmospheric particulate nitrate in a coastal city: Insights from nitrate dual isotopes of PM₂.₅. *Atmospheric Research*, 278, 106328. https://doi.org/10.1016/j.atmosres.2022.106328

Zhou, T., Jiang, Z., Zhou, J., Zhao, W., Wu, Y., Yu, H., et al. (2022). Fast and efficient atmospheric NO₂ collection for isotopic analysis by a 3D-printed denuder system. *Analytical Chemistry*, 94(38), 13215–13222. https://doi.org/10.1021/acs.analchem.2c02839

Zong, Z., Sun, Z., Xiao, L., Tian, C., Liu, J., Sha, Q., et al. (2020). Insight into the variability of the nitrogen isotope composition of vehicular NOₓ in China. *Environmental Science & Technology*, 54(22), 14246–14253. https://doi.org/10.1021/acs.est.0c04749

Zong, Z., Wang, X., Tian, C., Chen, Y., Fang, Y., Zhang, F., et al. (2017). First assessment of NOₓ sources at a regional background site in north China using isotopic analysis linked with modeling. *Environmental Science and Technology*, 51(11), 5923–5931. https://doi.org/10.1021/acs. est.6b06316