# Ammonia and PM2.5 Air Pollution in Paris during the 2020 COVID Lockdown

Camille Viatte, Jean-Eudes Petit, Shoma Yamanouchi, Martin Van Damme, Carole Doucerain, Emeric Germain-Piaulenne, Valérie Gros, Olivier Favez, Lieven Clarisse, Pierre-François Coheur, et al.

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Camille Viatte, Jean-Eudes Petit, Shoma Yamanouchi, Martin Van Damme, Carole Doucerain, et al. Ammonia and PM2.5 Air Pollution in Paris during the 2020 COVID Lockdown. Atmosphere, 2021, 12, pp.160. 10.3390/atmos12020160. insu-03124156

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Article

# Ammonia and PM<sub>2.5</sub> Air Pollution in Paris during the 2020 COVID Lockdown

Camille Viatte <fnref n="1" />*, Jean-Eudes Petit <fnref n="2" />, Shoma Yamanouchi <fnref n="3" />, Martin Van Damme <fnref n="4" /> ID, Carole Doucerain <fnref n="1" /> ID,
Emeric Germain-Piaulenne <fnref n="1" />, Valérie Gros <fnref n="2" />, Olivier Favez <fnref n="5" /> ID, Lieven Clarisse <fnref n="4" /> ID, Pierre-Francois Coheur <fnref n="4" />,
Kimberly Strong <fnref n="3" /> ID and Cathy Clerbaux <fnref n="1" />,<fnref n="4" /> ID

<figure><img src="image_3.png" /></figure>

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updates

**Citation:** Viatte, C.; Petit, J.-E.; Yamanouchi, S.; Van Damme, M.; Doucerain, C.; Germain-Piaulenne, E.; Gros, V.; Favez, O.; Clarisse, L.; Coheur, P.-F.; et al. Ammonia and PM<sub>2.5</sub> Air Pollution in Paris during the 2020 COVID Lockdown.
*Atmosphere* **2021**, *12*, 160.
https://doi.org/10.3390/atmos12020160

Academic Editor: Olivier Delhomme
Received: 3 December 2020
Accepted: 22 January 2021
Published: 27 January 2021

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**Copyright:** © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

**Abstract:** During the COVID-19 pandemic, the lockdown reduced anthropogenic emissions of NO₂ in Paris. NO₂ concentrations recorded in 2020 were the lowest they have been in the past 5 years. Despite these low-NO₂ levels, Paris experienced PM₂.₅ pollution episodes, which were investigated here based on multi-species and multi-platform measurements. Ammonia (NH₃) measurements over Paris, derived from a mini-DOAS (differential optical absorption spectroscopy) instrument and the Infrared Atmospheric Sounding Interferometer (IASI) satellite, revealed simultaneous enhancements during the spring PM₂.₅ pollution episodes. Using the IASI maps and the FLEXPART model, we show that long-range transport had a statistically significant influence on the degradation of air quality in Paris. In addition, concentrations of ammonium (NH₄⁺) and PM₂.₅ were strongly correlated for all episodes observed in springtime 2020, suggesting that transport of NH₃ drove a large component of the PM₂.₅ pollution over Paris. We found that NH₃ was not the limiting factor for the formation of ammonium nitrate (NH₄NO₃), and we suggest that the conversion of ammonia to ammonium may have been the essential driver.

**Keywords:** particulate matter; ammonia; air pollution; COVID-19; Paris

## 1. Introduction

Lockdowns imposed to decelerate the spread of the 2020 COVID-19 pandemic have led to substantial reductions of anthropogenic air pollutant emissions. Unprecedented temporal reduction of carbon dioxide (CO₂) emissions has been recorded at the global scale [1]. Satellite observations have enabled monitoring the decrease in concentrations of several other gaseous species (https://iasi-ft.eu/covid-19/), such as nitrogen dioxide (NO₂) that is mainly emitted by combustion activities (e.g., traffic, power plants, industries) over China, Western Europe, and the United States [2]. Emission abatements also influenced surface measurements of NO₂ worldwide, in particular in urban environments, such as in Barcelona (Spain, [3]), several cities in China [4], India [5,6], and Morocco [7].

In addition to primary pollutants, air quality can be affected by complex interactions between diverse air pollutants, transformation processes, and meteorological conditions. The decrease in NOₓ concentrations, for instance, caused an increase in surface ozone

<footnote n="1">¹ LATMOS/IPSL, Sorbonne Université, UVSQ, CNRS, 75252 Paris CEDEX 05, France; cdoucerain@clipper.ens.psl.eu (C.D.); 21emeric@live.fr (E.G.-P.); cathy.clerbaux@latmos.ipsl.fr (C.C.)</footnote>

<footnote n="2">² Laboratoire des Sciences du Climat et de l'Environnement, UMR1572, IPSL/CEA/CNRS/UVSQ, L'Orme des Merisiers, CEA Saclay, 91191 Gif sur Yvette CEDEX, France; jean-eudes.petit@lsce.ipsl.fr (J.-E.P.); valerie.gros@lsce.ipsl.fr (V.G.)</footnote>

<footnote n="3">³ Department of Physics, University of Toronto, Toronto, ON M5S 1A7, Canada; syamanou@physics.utoronto.ca (S.Y.); strong@atmosp.physics.utoronto.ca (K.S.)</footnote>

<footnote n="4">⁴ Spectroscopy, Quantum Chemistry and Atmospheric Remote Sensing (SQUARES), Université libre de Bruxelles (ULB), CP160/09 Brussels, Belgium; mvdamme1@ulb.ac.be (M.V.D.); lclariss@ulb.ac.be (L.C.); pfcoheur@ulb.ac.be (P.-F.C.)</footnote>

<footnote n="5">5 Institut National de l'Environnement Industriel et des Risques, Parc Technologique ALATA, 60550 Verneuil en Halatte, France; olivier.favez@ineris.fr</footnote>

<footnote>* Correspondence: camille.viatte@latmos.ipsl.fr</footnote>
(O₃) concentrations in polluted areas on nearly all continents [8], such as over the north of China [4], India [5], Barcelona (Spain, [9]), Rio de Janeiro (Brazil, [10]), and France [11]. Concentrations of fine particulate matter (with an aerodynamic diameter less than 2.5 µm: PM₂.₅) at ground level has decreased in polluted northern China (from 106° E–125° E and 29° N–41° N, [4]), in India [5], as well as in other major cities around the world [12], whereas severe PM₂.₅ pollution episodes have been observed over eastern China [13] and Beijing [14].

PM₂.₅ is especially harmful for human health [15] and climate [16]. PM₂.₅ has diverse sources in the Paris region: about 60% of its local budget is considered to be emitted locally, mainly from residential wood burning during winter, vehicle exhaust, and non-exhaust traffic emissions throughout the year [17,18], while the remaining fraction is attributed to transport from other French and European regions [19–21]. The Paris megacity experiences recurrent PM₂.₅ pollution episodes during springtime, and the key role of ammonia (NH₃) has already been highlighted and analyzed in previous studies [22–26]. NH₃ is mainly emitted by the agricultural sector in the region surrounding Paris and can be transported to the city to form secondary aerosols when specific conditions (meteorological and precursor concentrations) are encountered [25]. NH₃ is a precursor of ammonium sulfate and nitrate ((NH₄)₂SO₄, NH₄NO₃) aerosols, which are formed when atmospheric NH₃ reacts with sulfuric acid (H₂SO₄) and nitric acid (HNO₃) [27]. In urban areas, HNO₃ is formed primarily from the oxidation of NOₓ (NOₓ = sum of nitric oxide (NO) and nitrogen dioxide (NO₂)), mostly emitted by traffic [28]. The thermodynamic relationships governing the partitioning between the gas and aerosol phases depend on the quantities of NH₃ and acid precursors (i.e., SO₂ and NOₓ) and on the meteorological conditions (temperature, relative humidity) [29,30].

In this context, cost-effective means of reducing PM₂.₅ levels targeting NH₃ and/or NOₓ and SO₂ emissions have been analyzed globally [31–33], over Europe [34,35], the United States [36–38], and China [39], for instance. While it has been suggested that high concentrations of NH₃ are crucial for aerosol formation [40], recent studies have demonstrated that NH₃ reductions would have mitigation effects only when acidity of the aerosols is low [41,42]. This implies that the fundamental role of NH₃ in regulating aerosol formation is complex and still ambiguous.

Satellite instruments are able to monitor NH₃ from space at global scale [43–47], but the lack of continuous NH₃ measurements over Paris leads to difficulties for reproducing NH₃ temporal variability [48,49] and secondary inorganic aerosol formation [23] in atmospheric chemical transport models (CTMs). Representative and long-term monitoring of NH₃ in urban areas, such as Paris, is therefore needed to determine its role in particulate atmospheric pollution and better advise policy makers. To measure NH₃ continuously over Paris, a mini-DOAS (differential optical absorption spectroscopy) [50] was recently installed in the city center as part of the Qualair instrumental facility [51]. In this study, local NH₃ measurements from this mini-DOAS were coupled with satellite observations of NH₃ derived from the Infrared Atmospheric Sounding Interferometer (IASI) instruments [52] and with aerosol speciation to better comprehend the Parisian particulate matter air pollution that occurred in spring 2020, when very low atmospheric NO₂ levels were recorded.

## 2. Materials and Methods

### 2.1. NH₃ Observations

#### 2.1.1. Mini-DOAS

The mini-DOAS instrument is based on the open-path differential optical absorption spectroscopy (DOAS) technique, which has been optimized to monitor ambient NH₃ concentrations [50,53]. It measures the NH₃ absorption band in the UV between 200 and 230 nm (every 5 min) in Paris. The mini-DOAS consists of two parts: the spectrometer/emitter and the retro-reflector, which are installed 19.8 m apart from each other, both located at 122 m above ground level on the roof of Sorbonne University (at 850 m from Paris city center, see Supplementary Materials S1). By collecting the reflected light back onto a
detector, the integrated concentration of NH₃ along the optical path can be determined. By dividing the integrated concentration by the length of the absorption path, the average NH₃ concentration (in µg/m³) is obtained over the optical path [54]. The mini-DOAS system was evaluated during dedicated field campaigns, which demonstrated its reliability for measuring NH₃ concentrations over an urban area [55] and NH₃ vertical profiles [56] as well as its adequacy for long-term monitoring of NH₃ in an air quality network [57].

The mini-DOAS used in this study was installed at the Qualair facility in December 2019 and has operated since then. This new dataset presented the only current continuous (day and night) NH₃ measurements at high temporal frequency in Paris. Over the period of study (January–June 2020), the mini-DOAS recorded mean hourly NH₃ concentrations up to 22.6 µg/m³ with a mean concentration of 2 µg/m³. An error analysis allowed estimating an average noise error on the hourly measurements of 1.6% and a detection limit of 0.5 µg/m³ [50].

### 2.1.2. IASI

The Infrared Atmospheric Sounding Interferometer (IASI) is a spectrometer launched on board the MetOp polar orbiting satellites A, B, and C in October 2006, September 2012, and November 2018, respectively. It is operated by EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) to measure the thermal infrared radiation of the Earth in the spectral range from 645 to 2760 cm⁻¹ with a spectral resolution of 0.5 cm⁻¹ (apodized). The three IASI instruments (onboard MetOp A, B, and C) have a nadir-viewing geometry and a field of view composed of 2 × 2 pixels of 12 km diameter each at nadir [52]. Measurements are performed at 09:30 and 21:30 local mean solar time for the descending and ascending orbits, respectively.

The IASI NH₃ total column concentrations used in this study were derived by means of the Artificial Neural Network for IASI algorithm [58,59]. Morning observations from the version 3 of the near-real time dataset were used (ANNI-NH₃-v3, see [60]). The number of available NH₃ observations fluctuated with the state of the atmosphere being remotely sensed (e.g., thermal contrast and cloud cover).

In this work, the IASI satellite observations were used to study the transport of NH₃ over Paris to identify the source regions of NH₃ during the pollution episodes and to evaluate the local NH₃ concentrations derived from the mini-DOAS. Therefore, we considered all measurements from IASI on board MetOp A, B, and C jointly to maximize the number of observations within a 50-km radius circle centered in Paris city center (Figure S1 in Supplementary Materials).

## 2.2. Particulate Matter and NO₂ In Situ Measurements

### 2.2.1. PM₂.₅ and NO₂ Concentrations

Hourly PM₂.₅ and NO₂ concentrations were measured using automated measurement systems [61] and chemiluminescence analyzers [62], respectively. Data were obtained via the open access database provided by Airparif, the association responsible for air quality monitoring in the Paris region (https://www.airparif.asso.fr/en/telechargement/telechargement-polluant). Over the studied period (and the investigated pollution events), PM₂.₅ and NO₂ averaged concentrations were 12 (28) and 29 (35) µg/m³, respectively. The spatial variability of hourly concentrations, calculated as the standard deviation (SD) of the datasets available for 13 stations (Figure S1), led to an average value of 4 (7) and 22 (17) µg/m³, respectively. Considering such limited variabilities, mean PM₂.₅ and NO₂ concentrations representative of regional conditions are estimated here as the average of measurements at all of these stations.

### 2.2.2. Chemical Speciation of Submicron Aerosols

The major submicron chemical components (with an aerodynamic diameter less than 1 µm: PM₁) have been continuously characterized from 2012 onward at the SIRTA facility located about 20 km southwest of Paris city center ([63]; see Figure S1) and integrated in
the European ACTRIS research infrastructure. Measurements are based on online mass spectrometry with the Aerosol Chemical Speciation Monitor (ACSM, [64]). The ACSM provides concentrations of Organic Matter (OM), nitrate, sulfate, ammonium, and chloride collected at 30-min time intervals. Details of its onsite deployment and quality control and quality assurance procedures can be found elsewhere [18,65,66]. Measurements of these chemical species at SIRTA are assessed as representative of air quality conditions in the Paris region [65,67]. Hourly PM<sub>2.5</sub> and PM<sub>1</sub> concentrations measured at SIRTA were strongly correlated with the average of measurements at all the Airparif stations with *R* = 0.94 and *R* = 0.92 (*p*-value < 0.05), respectively, for the period of this study (see Figure S2).

## 2.3. *The Lagrangian Particle Dispersion Model FLEXPART*

The Flexible Particle (FLEXPART) model [68,69] has been used to compute airmasses' back-trajectories in order to investigate the geographical sources of pollution transported over Paris. FLEXPART is a Lagrangian tracer dispersion model, which compares well with other widely used models such as HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory) or STILT (Stochastic Time-Inverted Lagrangian Transport), for instance [70,71]. To determine the emission sensitivity response functions to various regions (i.e., influence of a particular geographical region to the measurements made in Paris), meteorological data from the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS v.2) 6-h product [72] were used. The model runs involved releasing an ensemble of 160,000 tracer particles in a 0.5° × 0.5° grid box centered around the Paris city-center at altitudes ranging from 200 to 300 m.

# 3. Results

## 3.1. NO₂ and PM₂.₅ Concentrations during the 2020 Lockdown

The lockdown imposed in France from 17 March 2020 resulted in a significant reduction of emission of air pollutants. Over Paris, the usual 15.5 million daily motorized transport trips were highly limited, halving the daily NO₂ emissions for the first 3 weeks after lockdown [73]. Consequently, concentrations of NO₂ diminished by 24% (averaged over 17 March–1 June, compared to 1 January–17 March 2020, see top right panel of Figure 1). This value was lower than that for emission reductions (24% compared to 50%) because of complexity of the atmospheric chemical system [74] and also because of the drier meteorological conditions, i.e., unusual precipitation rate in March and April (Figure S3), which favored accumulation of pollutants in the atmosphere. Before lockdown, NO₂ and PM₂.₅ concentrations were also much lower than usual (−28 and −39%, respectively) due to enhanced dispersion conditions associated with a high occurrence of oceanic air masses, along with precipitation and high wind speed (Figure S3). Nevertheless, NO₂ concentrations recorded during the 2020 lockdown were at their lowest than in the last 5 years.

On the other end, concentrations of PM₂.₅ appeared to have been barely affected by the lockdown and, contrasting with previous years at the same time periods, a 16% increase was observed after the start of lockdown (averaged over 17 March–1 June compared to 1 January–17 March 2020, see bottom right panel of Figure 1). The high interannual variability of PM₂.₅ concentrations for the first five months of the year (box plot's length in lower panels of Figure 1) seemed to indicate no significant patterns (reduction or increase) compared to the mean of the five previous years. Meteorological conditions highly influence PM variability in Paris [75].

## 3.2. Identification of PM₂.₅ Pollution Episodes

Despite the exceptional lockdown situation that significantly reduced NO₂ concentrations, the Paris megacity experienced several fine particulate pollution episodes in 2020. Here we chose to use a threshold of 25 µg/m³ on hourly concentrations to identify and investigate all five PM₂.₅ pollution episodes occurring in Paris. We note that four of the five episodes were observed during the lockdown period. Table 1 summarizes the dates of
these episodes, along with the associated mean and 1-σ (standard deviation) of PM<sub>2.5</sub> and NH<sub>3</sub> concentrations measured in Paris by Airparif and at the Qualair facility, respectively. Averaged PM<sub>2.5</sub> and NH<sub>3</sub> concentrations over the period of study (Jan–June 2020) were 12 µg/m<sup>3</sup> and 2 µg/m<sup>3</sup>. Pollution episodes differed in duration: 165 h, 96 h, 41 h, 13 h, and 37 h for event 1, 2, 3, 4, and 5, respectively, and composition: event 1 with high PM<sub>2.5</sub> (31 µg/m<sup>3</sup>) but low NH<sub>3</sub> concentrations (below detection limit: 0.5 µg/m<sup>3</sup>) whereas event 3 exhibited both high PM<sub>2.5</sub> (36 µg/m<sup>3</sup>) and NH<sub>3</sub> (11 µg/m<sup>3</sup>) concentrations.

<figure><img src="image_5.png" /><figcaption>**Figure 1.** Statistical distribution of NO<sub>2</sub> (upper panels) and PM<sub>2.5</sub> (lower panels) concentrations (µg/m<sup>3</sup>) measured by Airparif over Paris for the 1 January to 17 March (17 March to 1 June) time period in red (blue) from 2015 to 2020 (left to right panels). The medians and the quartiles are represented by center lines and borders of the boxes, respectively. The outliers are indicated in blue points. Numbers are the percentage of concentrations change between 17 March–1 June and 1 January–17 March time periods.</figcaption></figure>

<table><caption>**Table 1.** Periods identified as particulate pollution episodes over Paris in the frame of the present study, along with associated mean and 1-σ standard deviation of PM<sub>2.5</sub>, NH<sub>3</sub>, and major PM<sub>1</sub> chemical species (OM: organic matter, SO<sub>4</sub><sup>2-</sup>: sulfate, NO<sub>3</sub><sup>-</sup>: nitrate, and NH<sub>4</sub><sup>+</sup>: ammonium) concentrations (µg/m<sup>3</sup>).</caption><thead><tr><th>Event #</th><th>Date</th><th>PM<sub>2.5</sub></th><th>NH<sub>3</sub></th><th>OM</th><th>SO<sub>4</sub><sup>2-</sup></th><th>NO<sub>3</sub><sup>-</sup></th><th>NH<sub>4</sub><sup>+</sup></th></tr></thead><tbody><tr><td>1</td><td>20 January 15:00–27 January 00:00</td><td>31.19 ± 13.34</td><td>below &lt; 0.5</td><td>11.93 ± 4.66</td><td>2.18 ± 0.99</td><td>8.18 ± 4.21</td><td>3.26 ± 1.54</td></tr><tr><td>2</td><td>17 March 12:00–21 March 12:00</td><td>22.55 ± 8.38</td><td>8.22 ± 5.19</td><td>7.42 ± 3.13</td><td>1.32 ± 0.78</td><td>7.07 ± 3.65</td><td>2.71 ± 1.35</td></tr><tr><td>3</td><td>27 March 10:00–29 March 03:00</td><td>36.03 ± 15.61</td><td>11.13 ± 4.65</td><td>10.04 ± 4.77</td><td>2.17 ± 0.67</td><td>13.33 ± 7.25</td><td>4.93 ± 2.45</td></tr><tr><td>4</td><td>13 April 3:00–13 April 16:00</td><td>20.89 ± 12.03</td><td>7.08 ± 1.50</td><td>6.05 ± 2.62</td><td>2.13 ± 1.21</td><td>8.23 ± 9.67</td><td>3.34 ± 3.25</td></tr><tr><td>5</td><td>18 April 23:00–20 April 12:00</td><td>21.76 ± 10.30</td><td>6.06 ± 1.33</td><td>7.13 ± 1.40</td><td>2.25 ± 1.10</td><td>8.52 ± 5.03</td><td>3.60 ± 1.98</td></tr></tbody></table>

NH<sub>3</sub> concentrations derived from both the mini-DOAS and IASI, as well as the speciation of aerosols, are shown in Figure 2 (for Jan–Jun 2020) and Figure S4 (pollution episodes).
<figure><img src="image_6.png" /><figcaption>**Figure 2.** Time series of hourly PM<sub>2.5</sub> and NO<sub>2</sub> concentrations (µg/m<sup>3</sup>) derived from the Airparif network (**a**, **b**), hourly NH<sub>3</sub> concentrations (µg/m<sup>3</sup>) derived from the mini-DOAS (**c**), daily Infrared Atmospheric Sounding Interferometer (IASI) NH<sub>3</sub> total column (molecules/cm<sup>2</sup>) measured in a 50 km radius circle from Paris city center (**d**), and mean relative contributions of major PM<sub>1</sub> chemical species (OM: organic matter, SO<sub>4</sub><sup>2-</sup>: sulfate, NO<sub>3</sub><sup>-</sup>: nitrate, and NH<sub>4</sub><sup>+</sup>: ammonium) measured at SIRTA from January to June 2020, and for the 5 investigated periods (**e**).</figcaption></figure>

Simultaneous increases of PM<sub>2.5</sub> and NH<sub>3</sub> hourly concentrations were observed during most of pollution episodes (Figure 2). The aerosol composition differed, however, between episodes, with varying dominating contribution of organic matter (OM), sulfate (SO<sub>4</sub><sup>2-</sup>), nitrate (NO<sub>3</sub><sup>-</sup>), or ammonium (NH<sub>4</sub><sup>+</sup>). The IASI total columns averaged within a 50-km circle radius centered on the Paris city center were also higher during the pollution episodes. In addition, the mini-DOAS NH<sub>3</sub> measurements were in strong agreement with IASI data, with Pearson's correlation coefficient *R* = 0.75 (*p*-value < 1 × 10<sup>-16</sup>) over 85 days of coincident observations (see Figure S5), illustrating the high quality of the new mini-DOAS dataset and its representativeness of the Paris area.

### 3.3. *Sources and Transport of NH<sub>3</sub> during Pollution Episodes*

Complex equilibrium between contributions of long-range transport, local emission and dispersion, and chemical transformations in the atmosphere control the air quality in megacities [76] such as Greater Paris. To attribute the transport input to pollution episodes detected over Paris in winter and spring 2020, the FLEXPART Lagrangian particle dispersion model was used. Figure 3 shows the sensitivity (i.e., influence) to various regions for air parcels reaching the Paris region during the five pollution episodes.
<figure><img src="image_7.png" /><figcaption>**Figure 3.** IASI NH₃ total column (molecules/cm²) average distributions over Paris (blue cross) for (a) January to June 2020, and (b–f) the five periods investigated.</figcaption></figure>

The NH₃ source regions that could have affected the air quality over Paris during pollution episodes were identified with the IASI NH₃ maps (see Figure 3). NH₃ hotspots averaged over the whole period of study (Figure 3a) were very similar to those detected using the 10-year average of IASI observations [25]. These included the French Champagne-Ardennes region as well as the Netherlands and the north of Belgium and also Piedmont and Lombardy areas in the northern Italy.

Concerning event 1, no significant NH₃ enhancement was observed from the IASI and mini-DOAS observations (Figure 2). During this event, the pollution was transported from three directions (east, south, and west) according to the FLEXPART map (Figure 4).

For episodes 2, 3, 4 and 5, FLEXPART maps show that the pollution plumes were transported from the northeast with a higher sensitivity (red and yellow colors, Figure 4) to the nearby surrounding region on the east-southeast of Paris, over which major NH₃ enhancements were observed by IASI. High NH₃ concentrations for episodes 2 and 5 were located over Central Europe, mainly over Germany and the northeast of France (Figure 3c,f). In contrast, high NH₃ concentrations were located over France, the Netherlands, and south of United Kingdom during event 3 (Figure 3d). During event 4, which lasted only thirteen hours over Paris, high NH₃ concentrations were located across Europe (east-west) and over the Atlantic Ocean (Figure 3e), and fast transport of the NH₃ plume from the east-northeast was supported by the transport of air parcels seen in the FLEXPART map.

Overall, the highest sensitivity from FLEXPART corresponded to NH₃ hotspots derived from the IASI maps for each pollution event except event 1 (Figures 3 and 4). This analysis confirms the predominant transboundary transport of pollutants from the east and northeast of France over Paris, which was already indicated by previous studies [20,77]. This highlights the substantial influence of long-range transport on fine particulate matter pollution over Paris.
<figure><img src="image_8.png" /><figcaption>**Figure 4.** FLEXPART emission sensitivity response functions to air parcels reaching the Paris region (red dot) during the five pollution events.</figcaption></figure>

### 3.4. *Correlations between Hourly PM<sub>2.5</sub>, NH<sub>3</sub>, and PM<sub>1</sub> Major Components*

To better understand the origin of the particles and their composition over Paris during the period studied, the Spearman's rank correlation coefficients between hourly surface concentration of particulate matter (PM<sub>2.5</sub>), ammonia (NH<sub>3</sub>), and PM<sub>1</sub> major components (OM: organic matter, SO<sub>4</sub><sup>2-</sup>: sulfate, NO<sub>3</sub><sup>-</sup>: nitrate, and NH<sub>4</sub><sup>+</sup>: ammonium) were calculated for the period of study (January–June 2020) and for the five pollution episodes (Table 2).

The identified PM<sub>2.5</sub> pollution episodes occurring over Paris seem to have different origins (transport/chemistry). Indeed, during event 3, the Spearman's rank correlation coefficients were higher than 0.71 for all the species (PM<sub>2.5</sub>, NH<sub>3</sub>, and PM<sub>1</sub> major components) concentrations, which suggests the build-up of pollution downwind of Paris induced by permissible meteorological conditions, e.g., lack of wind/rain that prevent the venting/raining out of pollutants (see Figure S3). The different origins of the PM<sub>2.5</sub> pollution episodes were also related to differences in the chemical composition of particles. During event 1, PM<sub>2.5</sub> and OM concentrations, which was mainly emitted within Paris by wood burning, were strongly correlated ($r_s = 0.90$) suggesting a more typical winter event driven by local emission of pollutants. For the other episodes, the highest correlation was not found with OM but between PM<sub>2.5</sub> and NO<sub>3</sub><sup>-</sup> or NH<sub>4</sub><sup>+</sup> concentrations. Moreover, OM dominated the PM<sub>1</sub> composition over the whole time period and during event 1, whereas NO<sub>3</sub><sup>-</sup> and NH<sub>4</sub><sup>+</sup> accounted for more than one-half of PM<sub>1</sub> composition during the four spring pollution episodes (see Figure 2d). Thus, the sources, including transformation processes of particulate pollution, were different, which confirms the findings obtained from observations made between mid-2011 and mid-2013 in Paris [65].
<table><caption>**Table 2.** Spearman's rank correlation coefficients ($r_s$) between particulate matter (PM<sub>2.5</sub>), ammonia (NH<sub>3</sub>), and particulate species (OM: organic matter, SO<sub>4</sub><sup>2-</sup>: sulfate, NO<sub>3</sub><sup>-</sup>: nitrate, and NH<sub>4</sub><sup>+</sup>: ammonium) for the period of study (January–June 2020, and the five pollution events). $r_s > 0.85$ are in bold. NH<sub>3</sub> concentrations derived from the mini-DOAS (differential optical absorption spectroscopy) below detection limits were discarded.</caption><thead><tr><th>January–June</th><th>PM<sub>2.5</sub></th><th>NH<sub>3</sub></th><th>OM</th><th>SO<sub>4</sub><sup>2-</sup></th><th>NO<sub>3</sub><sup>-</sup></th><th>NH<sub>4</sub><sup>+</sup></th></tr></thead><tbody><tr><td>PM<sub>2.5</sub></td><td><strong>1.00</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>NH<sub>3</sub></td><td>0.61</td><td><strong>1.00</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>OM</td><td>0.82</td><td>0.70</td><td><strong>1.00</strong></td><td></td><td></td><td></td></tr><tr><td>SO<sub>4</sub><sup>2-</sup></td><td>0.71</td><td>0.58</td><td>0.79</td><td><strong>1.00</strong></td><td></td><td></td></tr><tr><td>NO<sub>3</sub><sup>-</sup></td><td>0.78</td><td>0.60</td><td>0.79</td><td>0.76</td><td><strong>1.00</strong></td><td></td></tr><tr><td>NH<sub>4</sub><sup>+</sup></td><td>0.75</td><td>0.63</td><td>0.78</td><td><strong>0.87</strong></td><td><strong>0.92</strong></td><td><strong>1.00</strong></td></tr><tr><td>Event 1</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>PM<sub>2.5</sub></td><td><strong>1.00</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>NH<sub>3</sub></td><td>n/a</td><td><strong>1.00</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>OM</td><td><strong>0.90</strong></td><td>n/a</td><td><strong>1.00</strong></td><td></td><td></td><td></td></tr><tr><td>SO<sub>4</sub><sup>2-</sup></td><td>0.56</td><td>n/a</td><td>0.55</td><td><strong>1.00</strong></td><td></td><td></td></tr><tr><td>NO<sub>3</sub><sup>-</sup></td><td>0.73</td><td>n/a</td><td>0.65</td><td>0.83</td><td><strong>1.00</strong></td><td></td></tr><tr><td>NH<sub>4</sub><sup>+</sup></td><td>0.67</td><td>n/a</td><td>0.60</td><td><strong>0.88</strong></td><td><strong>0.98</strong></td><td><strong>1.00</strong></td></tr><tr><td>Event 2</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>PM<sub>2.5</sub></td><td><strong>1.00</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>NH<sub>3</sub></td><td>0.48</td><td><strong>1.00</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>OM</td><td>0.69</td><td>0.51</td><td><strong>1.00</strong></td><td></td><td></td><td></td></tr><tr><td>SO<sub>4</sub><sup>2-</sup></td><td>0.59</td><td>0.69</td><td>0.40</td><td><strong>1.00</strong></td><td></td><td></td></tr><tr><td>NO<sub>3</sub><sup>-</sup></td><td><strong>0.87</strong></td><td>0.47</td><td>0.50</td><td>0.61</td><td><strong>1.00</strong></td><td></td></tr><tr><td>NH<sub>4</sub><sup>+</sup></td><td>0.84</td><td>0.48</td><td>0.43</td><td>0.67</td><td><strong>0.98</strong></td><td><strong>1.00</strong></td></tr><tr><td>Event 3</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>PM<sub>2.5</sub></td><td><strong>1.00</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>NH<sub>3</sub></td><td><strong>0.88</strong></td><td><strong>1.00</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>OM</td><td>0.80</td><td>0.75</td><td><strong>1.00</strong></td><td></td><td></td><td></td></tr><tr><td>SO<sub>4</sub><sup>2-</sup></td><td>0.71</td><td>0.66</td><td><strong>0.87</strong></td><td><strong>1.00</strong></td><td></td><td></td></tr><tr><td>NO<sub>3</sub><sup>-</sup></td><td><strong>0.96</strong></td><td><strong>0.85</strong></td><td><strong>0.89</strong></td><td>0.83</td><td><strong>1.00</strong></td><td></td></tr><tr><td>NH<sub>4</sub><sup>+</sup></td><td><strong>0.94</strong></td><td>0.82</td><td><strong>0.91</strong></td><td><strong>0.88</strong></td><td><strong>0.99</strong></td><td><strong>1.00</strong></td></tr><tr><td>Event 4</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>PM<sub>2.5</sub></td><td><strong>1.00</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>NH<sub>3</sub></td><td>0.69</td><td><strong>1.00</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>OM</td><td>0.76</td><td>0.71</td><td><strong>1.00</strong></td><td></td><td></td><td></td></tr><tr><td>SO<sub>4</sub><sup>2-</sup></td><td><strong>0.95</strong></td><td>0.75</td><td>0.80</td><td><strong>1.00</strong></td><td></td><td></td></tr><tr><td>NO<sub>3</sub><sup>-</sup></td><td><strong>0.97</strong></td><td>0.74</td><td>0.73</td><td><strong>0.95</strong></td><td><strong>1.00</strong></td><td></td></tr><tr><td>NH<sub>4</sub><sup>+</sup></td><td><strong>0.97</strong></td><td>0.71</td><td>0.72</td><td><strong>0.95</strong></td><td><strong>0.98</strong></td><td><strong>1.00</strong></td></tr><tr><td>Event 5</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>PM<sub>2.5</sub></td><td><strong>1.00</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>NH<sub>3</sub></td><td>0.33</td><td><strong>1.00</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>OM</td><td>0.59</td><td>0.40</td><td><strong>1.00</strong></td><td></td><td></td><td></td></tr><tr><td>SO<sub>4</sub><sup>2-</sup></td><td><strong>0.85</strong></td><td>0.52</td><td>0.82</td><td><strong>1.00</strong></td><td></td><td></td></tr><tr><td>NO<sub>3</sub><sup>-</sup></td><td><strong>0.86</strong></td><td>0.50</td><td>0.58</td><td><strong>0.85</strong></td><td><strong>1.00</strong></td><td></td></tr><tr><td>NH<sub>4</sub><sup>+</sup></td><td><strong>0.86</strong></td><td>0.51</td><td>0.60</td><td><strong>0.86</strong></td><td><strong>0.99</strong></td><td><strong>1.00</strong></td></tr></tbody></table>

Although NH<sub>3</sub> and PM<sub>2.5</sub> concentrations were moderately well correlated over the whole time period (R = 0.61), important variability existed within the correlation values (see Figure S6). The correlations between concentrations of NH<sub>3</sub> and PM<sub>2.5</sub> were not expected to be close to unity since NH<sub>3</sub> can react in the atmosphere, especially over cities when the availability of acidic species (mainly sulfuric and nitric acid) is sufficient to form ammonium salts. Nevertheless, because the atmospheric lifetimes of NH<sub>3</sub> and ammonium salts are on the order of one to a few days [78], high correlation between NH<sub>3</sub> and PM<sub>2.5</sub>
concentrations can be found when a portion of NH₃ has condensed onto acids downwind of a city and been transported in a plume containing both NH₃ and ammonium salts.

NH₄⁺ concentrations were strongly correlated with PM₂.₅ concentrations for all episodes occurring in spring (r_s > 0.84 compared to r_s = 0.68 during non-events period) when NH₃ concentrations were high (episodes 2, 3, 4 and 5, Table 1). This suggests that NH₄⁺ drove a large part of PM₂.₅ pollution over Paris in spring.

In addition, NO₃⁻ and NH₄⁺ concentrations presented similar correlation values with PM₂.₅ concentrations and were almost perfectly correlated with each other (r_s ≥ 0.98 and 0.89 during pollution events and non-events period, respectively), to a larger extent than between SO₄²⁻ and NH₄⁺, confirming the important abundance of ammonium nitrate (NO₃NH₄) over Paris [20,65,77].

Finally, the contributions of SNA (Sulfate: SO₄²⁻, Nitrate: NO₃⁻, and Ammonium: NH₄⁺) in Paris were correlated with PM₂.₅ concentrations over the period of study (r_s = 0.71–0.78) and for episodes 2, 3, 4 and 5, with r_s ranging from 0.59 to 0.97, showing the importance of the inorganic secondary aerosol, especially in spring pollution episodes. SNA are key species that can make up more than 50% of PM₂.₅ mass loadings [79–81].

These findings confirm the persistent role of ammonia and ammonium nitrate in particulate pollution episodes over Paris in springtime [23,25]. The response of SNA to ammonia concentrations is detailed in the Section 4.

**4. Discussion**

In this section, we discuss the formation processes of SNA in Paris during COVID-19 lockdown despite the low NO₂ loadings. Previous studies have reported the need for better understanding of the processes involved in driving ammonium nitrate formation over Paris [65,82]. Despite the usual high NOₓ emissions in the Parisian city, the local formation of ammonium nitrate has been found to be minor overall [23], although it can be significant for specific episodes [24]. Here we aim to evaluate the potential formation of SNA under the exceptional lockdown situation that significantly reduced concentrations of ammonium nitrate precursors (e.g., NOₓ = NO₂ + NO). Indeed, it has been shown that based on observations in California, a reduction of NOₓ levels can affect the acidic content of the atmosphere and change the ammonium nitrate chemistry [83].

In the troposphere, NH₃ neutralizes acidic sulfate before reacting with nitric acid to condense onto aerosols as ammonium nitrate [84]. Thus, NH₃f, the free total ammonia concentrations, defined as NH₃f = NH₃ + NH₄⁺ - 2 × SO₄²⁻ [85], is an indicator of the excess NH₃ available to form ammonium nitrate (NH₄NO₃) if all SO₄²⁻ has been neutralized (ratio NH₄⁺/SO₄²⁻ higher than 2); in other words, if NH₃f is positive and NH₄⁺/SO₄²⁻ higher than 2, NH₃ concentrations are sufficient to form NH₄NO₃. Over the time period of study, molar ratios of concentrations of NH₄⁺ to SO₄²⁻ showed considerable variability with a mean of 2.09 ± 230.90 (Figure S7, upper panel). During spring pollution episodes, this ratio was significantly higher (7.96 ± 0.04), indicating a complete neutralization of SO₄²⁻ by NH₄⁺. Over the time period January–March 2020, the mean hourly NH₃f was 0.15 ± 0.21 µg/m³ (Figure S7, lower panel). During event 1, NH₃f was close to zero (0.08 ± 0.04 µmole/m³), whereas for all the other episodes the NH₃f were significantly positive (event 2: 0.61 ± 0.34 µmole/m³; event 3: 0.88 ± 0.38 µmole/m³; event 4: 0.53 ± 0.23 µmole/m³; event 5: 0.51 ± 0.14 µmole/m³). This indicates that NH₄NO₃ could not be formed through NH₃ reaction in the January pollution episode, whereas NH₃ was not the limiting species for NH₄NO₃ formation during spring pollution episodes. These observation-based results verify the findings based on numerical simulations that aerosol formation is not immediately limited by the availability of ammonia over Europe [33], at least in spring.

In addition, complete neutralization of SO₄²⁻ by NH₄⁺ was observed in Paris from January to June since the ratio of molar concentrations of 2xSO₄²⁻ with NH₄⁺ was relatively correlated (r² = 0.68) and significantly lower than 1.0 (0.19). During spring pollution events (black marker edges in Figure 5), the ratio of molar concentrations of 2xSO₄²⁻ with NH₄⁺
was even lower (0.14), suggesting that $NH_4^+$ was mainly condensed in the $(NH_4)_2SO_4$ in the aerosols. Moreover, the regression yielded a strong linear correlation between $2xSO_4^{2-} + NO_3^-$ with $NH_4^+$ ($r^2 = 0.99$ and a slope of 0.94 for the time period of study and 0.95 during pollution events), with the sum of $2xSO_4^{2-} + NO_3^-$ sometimes exceeding the $NH_4^+$ concentrations, implying that $NH_4^+$ might have been present in forms other than $NH_4NO_3$ over Paris.

<figure><img src="image_9.png" /><figcaption>**Figure 5.** Molar concentrations of $2xSO_4^{2-} + NO_3^-$ (triangles) and $2xSO_4^{2-}$ (squares) versus $NH_4^+$, color coded by $NH_3$ molar concentrations measured in Paris from January to June 2020. Concentrations measured during the five pollution events are represented with black marker edges.</figcaption></figure>

To further investigate the relationship between atmospheric SNA formation and $NH_3$ concentrations during the pollution episodes occurring in spring over Paris (episodes 2, 3, 4 and 5), the conversion rate of $NH_3$ to $NH_4^+$, which is described by the ratio of concentrations of $NH_4^+$ to reduced inorganic nitrogen $NH_x$ ($=[NH_3] + [NH_4^+]$), where $[NH_3]$ and $[NH_4^+]$ are concentrations of $NH_3$ and $NH_4^+$, and its complement, which expresses the ammonia gas fraction ($AGF = [NH_3]/[NH_x]$), were used [42,55]. A value of AGF higher than 0.5 means that the total ammonia $NH_x$ is dominated by gaseous $NH_3$ rather than by the aerosol load [86]. In our study, the AGF derived from hourly measurements were highly variable with a mean of $1.01 \pm 7.25$, whereas during spring pollution episodes the mean AGF value was $0.71 \pm 0.12$, suggesting that $NH_3$ remained predominantly in the gas phase rather than the particle phase during spring particulate episodes in 2020 over Paris.

On the other hand, the relationships between the conversion rate of ammonia to ammonium, SNA, and $NH_3$ concentrations were nonlinear since higher $NH_3$ concentrations (red dots, Figure S8) can correspond to very low SNA concentrations. Thus, this suggests
that high concentrations of precursors (NH₃) do not necessarily result in high secondary aerosols (SNA) formation, which is supported by the AGF values within spring pollution episodes. Therefore, we conclude that the conversion rate of ammonia to ammonium, rather than ambient NH₃ concentrations, played an important role in the SNA formation in Paris during spring pollution events in 2020.

The conversion rate of ammonia to ammonium has been reported to be strongly affected mainly by meteorological conditions (temperature and humidity) and concentrations of primary acid gas (total nitrate NO₃ (HNO₃ + NO₃) and total sulfate (SO₂ + SO₄)) [79,87,88]. Low temperature and high relative humidity conditions are shown by thermodynamic equilibrium models such as ISORROPIA II [89] and observations in several cities (Beijing [90]; Shanghai [55]; Paris [25,91] to facilitate multi-phase reactions for aerosol formation in urban areas.

A recent study has attributed urban PM₂.₅ pollution during COVID-19 lockdown to enhanced efficiency of nitrate aerosol formation promoted by high relative humidity despite low atmospheric NO₂ levels in China [14]. However, Chang et al. (2020) argued that regional long-range transport, rather than meteorological conditions, mainly drove the enhanced nitrate formation in Shanghai during the 2020 lockdown. In Paris, relative humidity levels in 2020 were similar to, if not lower than, normal conditions, with a change of -8 ± 24% in 2020 compared to the 2015–2019 average (see Figure S9). Our results therefore tend to corroborate more the explanation of Chang et al. (2020) and indicate that relative humidity did not enhance ammonium nitrate formation over Paris in 2020.

The high SNA concentrations in spring could also be due to the nonlinear production chemistry and titration of tropospheric ozone. Indeed, the anomalously elevated ozone levels found in urban areas of France as a consequence of reduced NOₓ concentrations during lockdown [11] could have enhanced atmospheric oxidizing capacity (by the OH radical) and further facilitated SNA formation in Paris in 2020.

**5. Conclusions**

The COVID-19 lockdown measures reduced anthropogenic emissions and affected atmospheric composition in the urban area of Paris. Consequently, NO₂ concentrations recorded in the first half of 2020 were the lowest they have been in the last 5 years. Despite a change of NO₂ concentrations by -24% (averaged over 17 March–1 June compared to 1 January–17 March 2020), fine particles (PM₂.₅) concentrations increased by about 16% over the same period. Five PM₂.₅ pollution events, identified here when PM₂.₅ concentrations were higher than 25 µg/m³, over the Paris region, occurring in January, March, and April 2020, were investigated in this study.

Analysis of NH₃ and PM₂.₅ concentrations as well as PM₁ chemical composition were performed to better understand the origin of these episodes. Multi-platform NH₃ observations were derived from a new in situ (mini-DOAS) instrument located in Paris city center and from the IASI satellite instrument.

Atmospheric composition of the five pollution episodes was different, with a large contribution from organic matter in winter, in contrast to nitrate and ammonium in spring.

During lockdown, simultaneous enhancements of PM₂.₅, NH₃, and SNA surface concentrations were monitored. Hence, we confirm the role of NH₃ and inorganic secondary aerosols in spring particulate episodes in Paris.

The NH₃ hotspots revealed by IASI outside of the city were found to be consistent with the highest sensitivity regions obtained with the FLEXPART model. This suggests the contribution from regional sources rather than local pollution from Paris.

Moreover, we have shown that sulfate was completely associated with ammonium and that NH₃ was not the limiting species for the formation of ammonium nitrate in 2020 over Paris. We have further demonstrated that the conversion rate of ammonia to ammonium, which mainly depends on atmospheric temperature and humidity, might be the main driver, rather than NH₃ concentrations, for SNA formation over Paris.
Finally, our results clearly illustrate the need for a better assessment of the origins of springtime PM<sub>2.5</sub> pollution episodes in the Paris region, which could eventually be considered by policy makers with relevance to mitigation strategies. Future investigations will focus on the impact of the exceptionally low NO<sub>2</sub> levels in 2020 on the relationships between NH<sub>3</sub>, NH<sub>4</sub><sup>+</sup>, and SNA over Paris using state-of-the-art and optimized CTMs.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/2073-4333/12/2/160/s1, Figure S1: The domain of study around Paris (white cross) showing locations of the PM<sub>2.5</sub> and NO<sub>2</sub> observations from the Airparif stations (red pins), PM composition observations at SIRTA (yellow pin), as well as NH<sub>3</sub> observations derived from the mini-DOAS instrument (blue pin) and from the IASI satellite instrument (inside the red circle). Map provided by Google Earth V7.3.2.5776, US Dept. of State Geographer, copyright Google, 2020, Image Landsat/Copernicus, Data SIO, NOAA, US Navy, NGA, and GEBCO. Figure S2: Correlation plots between hourly PM2.5 concentrations derived from the mean of the 13 Airparif stations and PM2.5 (red, left panel) and PM1 (blue, right panel) concentrations measured at SIRTA. PM2.5 and PM1 concentrations measured at SIRTA were strongly correlated with the average of measurements at all the Airparif stations with R = 0.94 and *P* = 0.92 (*p*-value < 0.05). Figure S3: Timeseries of daily meteorological parameters over Paris from January to June 2020 and for the five investigated periods in shaded grey: boundary layer height (in meter; dark cyan, upper panel), temperature at 2 m (in Kelvin; red, upper middle panel), total precipitation (in meter; black, lower middle panel), and wind speeds and directions (blue and orange in lower panel) derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-5 (C3S, 2021). Figure S4: Timeseries of NH<sub>3</sub>, PM<sub>2.5</sub> and major submicron chemical species concentrations over Paris during the five periods investigated. Figure S5: IASI and mini-DOAS NH<sub>3</sub> comparison. Figure S6: Correlation plot (using Spearman's rank correlation coefficient) between hourly PM<sub>2.5</sub> and NH<sub>3</sub> concentrations over Paris during the period of study (January–June 2020 in grey), color-coded according to the five periods investigated. NH<sub>3</sub> concentrations derived from the mini-DOAS below detection limits were discarded. Figure S7: Hourly (blue) and daily (black) NH<sub>4</sub><sup>+</sup> to SO<sub>4</sub><sup>2-</sup> molar concentrations ratio (upper panel), and hourly (green) and daily (black) free total ammonia concentrations (µmole/m<sup>3</sup>) defined as NH<sub>3</sub>f = NH<sub>3</sub> + NH<sub>4</sub><sup>+</sup> − 2 × SO<sub>4</sub><sup>2-</sup> (lower panel) as function of time. Figure S8: Relationship between the conversion rate of ammonia to ammonium (NH<sub>4</sub><sup>+</sup>/NH<sub>x</sub>) and SNA (sulfate, nitrate, and ammonium (µmole/m<sup>3</sup>)) during pollution episodes occurring in spring 2020. Figure S9: Daily concentrations of Relative Humidity (RH) (%) measured in Paris from January to June of 2020 (solid lines) and average from 2015 to 2019 (dashed lines). Light blue area represents the 1-σ standard deviation around the 5-year average. The vertical line corresponds to the start of the lockdown period in France.

**Author Contributions:** Formulated the research goals and aims, developed the methodology, conducted the research, and wrote the initial draft, C.V.; produced and provided data used in this study, C.V., J.-E.P., S.Y. and M.V.D.; helped analyzing the datasets, C.D. and E.G.-P.; reviewed and edited the manuscript, J.-E.P., S.Y., M.V.D., V.G., O.F., L.C., P.-F.C. and C.C.; provided financial support leading to this publication, C.C., P.-F.C., and K.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** In situ aerosol measurements at the SIRTA facility have benefited from the support of the French research infrastructure ACTRIS-FR, as well as national observation service CLAP.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data for PM<sub>2.5</sub> and NO<sub>2</sub> concentrations over Paris are available at the database of Airparif (https://www.airparif.asso.fr/en/telechargement/telechargement-polluant). The IASI Level-1C data and Level-2 NH<sub>3</sub> data are provided by the Aeris data infrastructure (http://iasi.aeris-data.fr/NH3/).
**Acknowledgments:** IASI is a joint mission of Eumetsat and the Centre National d'Etudes Spatiales (CNES, France). The IASI Level-1C data are distributed in near real time by Eumetsat through the EumetCast system distribution. The FLEXPART Lagrangian dispersion model is available at https://www.flexpart.eu/wiki/FpDownloads. The Pflexible Python module (available at https://bitbucket.org/jfburkhart/pflexible) developed by John F. Burkhart was modified to generate the sensitivity plots from the FLEXPART outputs. The authors would like to thank Arjan Hensen, Daan Swart, and their teams at TNO for their constructive advice on the mini-DOAS instrument and CNES and AC-SAF for their financial support. This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 and innovation programme (grant agreement No 742909, IASI-FT advanced ERC grant). Part of the research at the ULB has been supported by the IASI Flow Prodex arrangement (ESA-BELSPO). Lieven Clarisse and Martin Van Damme are respectively a research associate and a postdoctoral researcher supported by the F.R.S.-FNRS. In situ aerosol measurements at the SIRTA facility are part of the European Aerosol, Clouds and Trace gases Research InfraStructure (ACTRIS) and has benefited from the support of the research infrastructure ACTRIS-FR, registered on the Roadmap of the French Ministry of Research.

**Conflicts of Interest:** The authors declare that they have no conflict of interest.

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