# Source apportionment of PM2.5 in a coastal City of Algeria using principal component analysis model

Lyes Rabhi, Abdelkader Lemou, Riad Ladji, Nicolas Bonnaire, Jean Sciare, Noureddine Yassaa

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Lyes Rabhi, Abdelkader Lemou, Riad Ladji, Nicolas Bonnaire, Jean Sciare, et al. Source apportionment of PM2.5 in a coastal City of Algeria using principal component analysis model. Journal of Atmospheric Chemistry, 2025, 82 (2), 10.1007/s10874-025-09477-2. hal-05237523

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# Source apportionment of PM<sub>2.5</sub> in a coastal City of Algeria using principal component analysis model

Lyes Rabhi<sup>1,2,3</sup> · Abdelkader Lemou<sup>1,2,3</sup> · Riad Ladji<sup>1,2</sup> · Nicolas Bonnaire<sup>4</sup> · Jean Sciare<sup>4,5</sup> · Noureddine Yassaa<sup>3</sup>

Received: 9 December 2024 / Accepted: 21 July 2025

© The Author(s), under exclusive licence to Springer Nature B.V. 2025

## Abstract

In this study, the weekly total water-soluble inorganic ions (TWSII) concentrations of PM<sub>2.5</sub> in the coastal city of Algeria, Bou-Ismail, were determined from December 29th, 2013, to June 29th, 2014, under the ChArMEx project. This study aimed to identify the seasonal sources and chemical composition of PM2.5-bound water-soluble inorganic ions (WSIIs) in a coastal city of Algeria using principal component analysis (PCA). The findings indicated that the TWSII concentration was 14.06±0.22 µg m<sup>-3</sup> during the winter and 12.35±0.42 µg m<sup>-3</sup> during the spring. The Na<sup>+</sup>, NH<sub>4</sub><sup>+</sup>, NO<sub>3</sub><sup>-</sup>, and Cl<sup>-</sup> ions were the main TWSII in winter, whilst Na<sup>+</sup>, NH<sub>4</sub><sup>+</sup>, oxalate, and NO<sub>3</sub><sup>-</sup> ions were the main WSII in spring. PCA identified two sources for winter: PC1 is a mix of pollutants from secondary organic traces, marine sources, and stationary emissions from burning, while PC2 encompasses operations, construction materials, and secondary gas-particle transformations. For spring, four sources were identified: PC1, marine aerosol emissions; PC2, stationary emissions, agricultural practices, marine biogenic emissions, and biomass burning; PC3, photochemical response; and PC4, soil dust. The whole sample campaign had a 1.29 cationic-to-anionic regression slope. The [NO<sub>3</sub><sup>-</sup>]/[SO<sub>4</sub><sup>2-</sup>] mass ratio was greater than (1) The findings indicated the strong influence of pollutants from mobile sources over stationary sources. Pathway 1 includes all west and northwest air masses from the sample location. Large air masses traverse the Atlantic via Spain, Portugal, southern France, and western Algeria. An air mass from the south traversed the Algerian Desert and southern Libya in Pathway (2) In pathway 3, northwest Italy and Tunisia across the Mediterranean Sea were the most polluted.

## Highlights

* The mass concentration of TWSII was 26.41±0.35 µg m<sup>-3</sup>.

* NH<sub>4</sub><sup>+</sup> and NO<sub>3</sub><sup>-</sup> were the main cations and anions in the TWSII.

* The [NH<sub>4</sub><sup>+</sup>]/[SO<sub>4</sub><sup>2-</sup>] molar ratio was up to 5 owing to an ammonia-rich environment in Bou-Ismail.

* The [NO<sub>3</sub><sup>-</sup>]/[SO<sub>4</sub><sup>2-</sup>] mass ratio >1; the primary source was mobile.

<footnote>Extended author information available on the last page of the article</footnote>
**Keywords** Water-soluble inorganic ions · Principal component analysis · Mediterranean aerosol · Ionic balance

# 1 Introduction

Over several decades, studies in the North Africa region have looked closely at the particulate matter (PM) rural background aerosol and its chemical compositions (Benchrif et al. 2018, 2022; Lemou et al. 2020; Cherfaoui et al. 2024). Research on the significance of diverse sources has often relied on using specific chemical compositions (Bressi et al. 2014; Petit et al. 2015). Outdoor PM is one of the most hazardous air pollutants in densely populated areas. According to future estimates, it can produce even more severe health consequences (Rabhi et al. 2018; Perrone et al. 2018; Lemou et al. 2024; Cherfaoui et al. 2024). Many scientific investigations have shown that PM has immediate influences on daily mortality rates (Franklin et al. 2008; Ostro et al. 2011; Son et al. 2012; Heo et al. 2014).

Fine particles (PM<sub>2.5</sub>, i.e., particles with an aerodynamic diameter less than or equal to 2.5 µm) have been subject to a more stringent regulatory framework in recent years due to the growing damage they cause to human health. Long-term exposure to elevated levels of fine particulate matter (PM<sub>2.5</sub>) is associated with increased risks of respiratory and cardiovascular diseases, including asthma, chronic obstructive pulmonary disease, heart disease, and premature mortality (Karnae And John 2019). A significant concentration of PM<sub>2.5</sub> particles is recognised to impact human health and the Earth's energy balance, vision impairment, and climate (Seinfeld And Pandis 2006; Huang et al. 2021). According to some findings (Schwarz et al. 2016; Khan et al. 2019), the ageing population has a significantly increased risk of being admitted to a healthcare institution and experiencing more severe health repercussions.

Many studies have revealed that water-soluble inorganic ions (WSIIs) are significant parts of aerosol particles and the key contributors to the indirect climatic influence of aerosols (Zhou et al. 2016; Galindo et al. 2017; Cui et al. 2020). Among WSII, some species predominantly originate from natural sources. Na<sup>+</sup> and Cl<sup>-</sup> are mainly from marine environments, while Ca<sup>2+</sup> primarily originates from crustal sources (Xing et al. 2021). Water-soluble ionic species include mostly ammonium, sulphate, and nitrate. Chemical reactions involving so-called PM precursor gases SO<sub>2</sub>, NO<sub>x</sub>, and NH<sub>3</sub> form secondarily the most significant proportion of these species (Voutsa et al. 2014; Wang et al. 2021b). In the gaseous or aqueous phase, SO<sub>2</sub> is oxidised to sulphate by heterogeneous or homogeneous reactions, respectively (Xing et al. 2021; Zhang et al. 2022). There are two ways to produce nitrate: either by a gas-phase interaction between ammonia and nitric acid or through the reaction of nitric acid with sea salt or crustal aerosols (Pathak et al. 2009).

Because of the complex relationship between emissions and PM<sub>2.5</sub> levels, which are affected by climate, the highest and lowest PM<sub>2.5</sub> levels appear in different cities and change over time. Nevertheless, the dynamic emission-deposition equilibrium governs the principle of mass conservation, which limits the atmosphere's particle capacity to its maximum capacity (Liang et al. 2016). Several investigations on the concentration and water-soluble ion composition of PM<sub>2.5</sub> in the Mediterranean basin have been reported (Sciare et al. 2005; Koçak et al. 2007a; Pey et al. 2009; Theodosi et al. 2011; Benchrif et al. 2018, 2022; Lemou et al. 2020) and worldwide (Bressi et al. 2013; Han et al. 2016; Karnae And John 2019; Wang
et al. 2021b). Western Mediterranean countries, including southern Spain, Morocco, Algeria, and Tunisia, have been understudied despite their unique atmospheric features and pollution sources. Air quality management is complicated by local, regional, and transboundary inputs (Lelieveld et al. 2002). A comprehensive chemical characterisation based on both short-term and long-term campaigns is necessary to address this deficiency and provide information regarding the characteristics of aerosols in the southwestern Mediterranean. This study aimed to identify the seasonal sources and chemical composition of PM2.5-bound water-soluble inorganic ions (WSIIs) in a coastal city of Algeria using principal component analysis (PCA). Certain models help identify aerosol transport pathways; the Hysplit model enables the evaluation and categorisation of atmospheric events impacting the southwestern Mediterranean. Natural events can substantially influence PM2.5 concentrations in the region through dust transport from North Africa. Comprehensive long-term assessments of aerosol chemical composition are therefore essential for mitigating emissions.

The ChArMEx project (Chemistry-Aerosol Mediterranean Experience, http://charmex.lsce.ipsl.fr), is a global scientific attempt focused on investigating air pollution and its impacts in the Mediterranean region. This project employs an interdisciplinary and collaborative methodology to enhance understanding of aerosols' and trace gases' chemical and physical processes and their effects on climate, health, and ecosystems. The primary aim is to investigate the sources, transformations, movements, and effects of atmospheric aerosols and gaseous pollutants in the Mediterranean basin, which is acknowledged as a meteorological and environmental "hot spot".

As part of this project, fine particles (PM<sub>2.5</sub>) were investigated at Bou-Ismail from December 29th to June 29th, 2013–2014 covering two seasons, and eight WSIIs Cl<sup>-</sup>, NO<sub>3</sub><sup>-</sup>, SO<sub>4</sub><sup>2-</sup>, Na<sup>+</sup>, NH<sub>4</sub><sup>+</sup>, K<sup>+</sup>, Mg<sup>2+</sup>, and Ca<sup>2+</sup> and two carboxylic acids (methane sulfonic acid (MSA) and oxalate) were characterised. A reverse trajectory analysis using the HYSPLIT model identified and classified the air masses coming to Bou-Ismail during the campaign, which helped understand the results. We examined the two seasons' worth of PM<sub>2.5</sub> water-soluble ion sources using the PCA model. Understanding these sources, their status, and the characteristics of fine particles is essential for sustainable air quality management in Algeria and other countries in the western Mediterranean basin.

# 2 Methodology

## 2.1 Site description and particle monitoring

The chemical characterisation of PM<sub>2.5</sub> was carried out from December 29th to June 29th, 2013–2014, as part of a comprehensive air monitoring investigation. To meet the study's goals, the investigation site was chosen in the city of Bou-Ismail (Fig. 1), which was well described by (Lemou et al. 2020). Briefly, the sample site (36.383° N, 2.414° E) was about a few metres from the seashore (30 m) and on the roof of the building, which was about 10 m high. The sampling process used a low-volume sampler (SEQ47/50, Leckel) with a flow rate of 2.3 m<sup>3</sup>/h and quartz filters (QMA, Whatman, 47 mm diameter), which are advised for weekly sampling. Ambient air sampling for contaminants measured sequentially follows a predetermined schedule. Generally, a sampling period extends over 24 h, from midnight to midnight, and the process occurs every seven days. A weekly measuring schedule, one week
<figure><img src="image_3.png" /><figcaption>Fig. 1 The sampling site location</figcaption></figure>

at a time, was adopted, which resulted in 55.2 m³ per day and 386.4 m³ for a week. Before collecting the aerosols, the quartz filters were prebaked for forty-eight hours at 480 °C to rule out any possibility of organic contamination (Lemou et al. 2020). Once the samples were collected, the filters were placed in airtight Petri dishes and stored at –20 degrees Celsius until they were subjected to chemical analysis.

## 2.2 Local meteorology and station characteristics

Over the entire period studied, meteorological data were collected at a sampling rate of 1 min using an automatic weather station (Campbell Scientific, Germany). This data included temperature (°C), relative humidity (%), wind speed (m/s) and direction, atmospheric pressure (Pa), and precipitation. Figure 2 shows the meteorological conditions during the campaign period. The temperature ranged from 10.2 °C to 23.1 °C, and the relative humidity ranged from 61.3 to 82.4%. The average temperature was 15.0 °C, with a standard deviation of 4.2 °C. The average wind speed was 4.1±1.2 m s⁻¹, with a range that went to 6.3 m s⁻¹, and the atmospheric pressure was 979.1±2.8 hPa, with a range that went to 985.0 hPa.

## 2.3 Water-soluble inorganic ions

Ion chromatography (IC) was used to analyse several main soluble ions, including chloride, nitrate, sulphate, sodium, ammonium, potassium, magnesium, calcium, and two carboxylic acids, methane sulfonic acid (MSA) and oxalate. The analytical procedure used is detailed by (Bressi et al. 2013; Lemou et al. 2020, 2024). An ultrasonic bath was used to extract the samples in 15 mL of Milli-Q water for 3 * 15 min. Following extraction, the resulting solutions were filtered using 0.45 µm Acrodisc filters to eliminate any remaining insoluble species. To remove any germs that may have been present, 50 µL of chloroform was added to the extract.
<figure><img src="image_4.png" /><figcaption>**Fig. 2** Meteorological parameters during the sampling period ((a) wind speed and wind direction; b relative humidity (RH) and temperature (T)), and (c) the total concentrations of TWSII (µg/m³)</figcaption></figure>

For this purpose, ion chromatography (IC, Dionex D600) equipped with AS11 and CS12 columns for anions and cations analysis, respectively, was employed to determine the concentrations of soluble anions (Cl⁻, NO₃⁻, SO₄²⁻) and cations (Na⁺, NH₄⁺, K⁺, Mg²⁺, Ca²⁺), and two carboxylic acids (MSA and oxalate). Anion and cation concentrations, including
MSA and oxalate, were determined by ion chromatography (IC) following established methods (Bressi et al., 2013; Lemou et al., 2024).

## 2.4 Ion balance

Atmospheric particles often occur in equilibrium. The ion balancing exercise, which involves assessing the equilibrium of electrical charges between anions and cations, identifies potential issues in sampling or ion chromatography analyses. Ionic equilibrium may determine the potential ionic species.

The anion equivalent (AE) and cation equivalent (CE) were used in the ion balance calculations, as they are often used to probe aerosol acidity (Xu et al. 2019; Zhang et al. 2022; Lemou et al. 2024):

$$CE = \frac{Na^{+}}{23} + \frac{NH_{4}^{+}}{18} + \frac{K^{+}}{39} + \frac{Ca^{2+}}{20} + \frac{Mg^{2+}}{23} \quad (1)$$

$$AE = \frac{Cl^{-}}{35.5} + \frac{NO_{3}^{-}}{62} + \frac{SO_{4}^{2-}}{48} \quad (2)$$

where the mass concentrations (µg m⁻³) of Na⁺, NH₄⁺, K⁺, Ca²⁺, Cl⁻, NO₃⁻, and SO₄²⁻ in WSII were calculated. For Ca²⁺ and SO₄²⁻, the divisor values reflect their respective molar masses divided by their charge.

The ratios of the total of equivalent concentrations of cations to anions (CE/AE) are an indication for assessing the acidity of various environments (Wang et al. 2005). Two instances of ions deficiency are probable: an abundance of positive charge corresponds to a deficiency of anions, and an abundance of negative charge corresponds to a deficiency of cations.

The slope of the regression line for particles, i.e., CE vs. AE, exceeds one (1:1), which may be attributable to the presence of CO₃²⁻ in the particles, which has not been quantified using ion chromatography (IC). The slope of the regression line for the particles is less than one (1:1), likely due to the presence of H⁺ (not quantified). If this is the case, CO₃²⁻ is expected to interact with Ca²⁺, whereas H⁺ is predicted to react with SO₄²⁻ in the particles, respectively (Koçak et al. 2007b).

## 2.5 Quality assessment and quality control

The Bou-Ismail aerosol filters were analysed at the LSCE (Laboratoire des Sciences du Climat et de l'Environnement) using ion chromatography to determine individual anion and cation concentrations. The data quality of WSII is regulated by international inter-laboratory comparison studies conducted by the Quality Assurance Science Activity Centre/Americas in the World Meteorological Organisation Global Atmosphere Watch (WMO/GAW). The principal anions and cations exhibit a change from 2 to 4%. The projected overall inaccuracy in identifying ionic species is around 5%. This inaccuracy accounts for both the variability in filter blank readings and the efficacy of filter extraction (Sciare et al. 2009). WSIIs were detected in the procedure blanks at concentrations above the limit of quantification (LOQ) and were subsequently deducted from the samples of the corresponding
batches. Compounds were identified according to their retention period and quantified using an internal standard method. The LOD and quantification (LOQ) for each congener were determined using field and procedural blanks. LOD is the mean plus three times the blanks' standard deviation. The LOD for congeners not identified in the blanks was determined using the instrumental detection limit (signal-to-noise ratio of 3:1). The method detection limits of (Cl⁻, NO₃⁻, SO₄²⁻ were 0.003, 0.008 and 0.0009 µg m⁻³ respectively), and cations (for Na⁺ and NH₄⁺ 0.003 µg m⁻³, K⁺ 0.009 µg m⁻³, Mg²⁺ and Ca²⁺ were 0.0005 µg m⁻³), and two carboxylic acids (MSA and oxalate were 0.001 and 0.005 µg m⁻³ respectively).

The standard curve had an R² of 0.999, suggesting high linearity. Replicates and blanks were checked every 10 sample runs for quality control. Recovery coefficients ranged from 2.2 to 3.0%, with recoveries ranging from 91.8 to 95.9%. The average range of field blank samples for this ion was 1–6 ng m⁻³.

## 2.6 Mass backward trajectory

The Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT), developed by NOAA/ARL using the global data assimilation system (GDAS) meteorological data, was employed to determine the air mass paths that reached the sample location. This approach has been used to determine the source region and transit pathway of particulate matter at the measurement site. In this instance, 120-hour (5-days) backward trajectories, started every 24 h, were calculated to determine the air masses that most significantly affected the observations at 100 m above ground level (AGL).

## 2.7 Statistical analysis

Jamovi software was used to conduct a principal component analysis (PCA) and Pearson correlation on the TWSII found in PM₂.₅ throughout the winter and spring seasons in Bou-Ismail in 2014. PCA optimises complex data by decreasing its dimensionality and emphasising essential patterns and trends. It helps eliminate noise and identify probable ion sources by categorising related factors. Nevertheless, analysing the primary components may be difficult since they represent linear combinations of the original variables and may not consistently correspond with established processes. Furthermore, some information is necessarily forfeited throughout the dimensionality reduction process since PCA presupposes linearity. A Varimax orthogonal rotation was used to calculate the rotated factor loadings, which resulted in loading values grouped between 0 and 1, making them easier to understand (Meng et al. 2018; Zhao et al. 2024a). Before analysis, we evaluated the data using the Kaiser-Meyer-Olkin criteria and Bartlett's test of sphericity. There are essentially three methods to classify the correlation Pearson loading factor: a loading factor greater than 0.7 indicates a high correlation; between 0.5 and 0.7, a moderate correlation; and less than 0.5, a low correlation. Correlation analysis is interpretable and straightforward, making it effective for discerning correlations between ion concentrations and environmental variables. It offers quantitative assessments of association, indicating possible causal relationships. Nonetheless, it cannot establish causality and presumes linearity, which may fail to include more intricate connections. Moreover, correlation analysis is susceptible to outliers, which may skew findings.

Correlation analysis and PCA provide significant insights into the examination of water-soluble ions.
# 3 Results and discussion

## 3.1 PM<sub>2.5</sub> and seasonal variation of WSIIs concentration

### 3.1.1 WSII Concentration

Table 1 displays the total concentrations of the TWSII throughout the sampling campaign (TWSII is equivalent to the aggregate of the eight ions and the two carboxylic acids). The total concentration of the WSII in this campaign was 26.41±0.35 µg m⁻³. The levels determined in this study were similar to those reported in Athens and Thessaloniki (Greece), Lecce and Venice (Italy), Tetouan (Morocco), Bou-Ismail (Algiers), and Tunis (Tunisia) (Pateraki et al. 2012; Squizzato et al. 2013; Perrone et al. 2014, 2019; Kchih et al. 2015; Diapouli et al. 2017; Benchrif et al. 2022), which were between 22 and 26 µg m⁻³. In Athens (Greece), Bou-Ismail (Algiers), Erdemli (Turkey), and Castillo de Bellver (Spain), the levels were even lower, ranging from 9 to 20 µg m⁻³ (Koçak et al. 2007a; Pey et al. 2009; Remoundaki et al. 2013; Lemou et al. 2020). The values recorded in Bou-Ismail were lower than those recorded in Tetouan, Rabat (Morocco), Venice (Italy), Athens, Goudi, Finoka lia, and Thessaloniki (Greece) (Theodosi et al. 2011; Squizzato et al. 2013; Diapouli et al. 2017; Benchrif et al. 2018; Deabji et al. 2021). Figure 2c shows the concentrations of total WSII during the sampling period. The analysis period may be divided into three distinct segments. Part 1 provides details on the peak concentrations observed during this campaign on February 26, April 9, and May 14, which were 1.49, 1.82, and 1.72 µg m⁻³, respectively. The TWSII contributions for these sampling days were 64.3%, 62.9%, and 79.6% of SNA in the samples, while sea salt exhibited the lowest amounts throughout the campaign, with contributions of 8.4%, 8.2%, and 7.2%. The segment was exhibiting somewhat heightened concentrations over diverse temporal scales. In the first sample period (from the start to January 15th), the recorded contributions indicated a higher proportion of sea salt (51–61%) compared to SNA (19–25%). Lastly, in the section on low concentration, the minimal values were observed between April 23rd and 30th, measuring 0.53 and 0.23 µg m⁻³. During this period, the SNA accounts for around 49–57% of the entire contribution to TWSII species.

### 3.1.2 Water-soluble species

WSII concentrations decrease in the following order: $NH_4^+ > Na^+ > NO_3^- > C_2O_4^{2-} > Cl^-$. $NH_4^+$, $Na^+$, and $NO_3^-$ accounted for most ions in the sample (an individual contribution between 13.75 and 27.25%, Table 1). The maximum contribution of the three ions to the TWSII measured was around 56.7%, with $NH_4^+$, $Na^+$, and $NO_3^-$ averaging 7.20, 4.15, and 3.64 µg m⁻³, respectively. The remaining ions (MSA, $K^+$, and $Mg^{2+}$) had lower mass concentrations (less than 1 µg m⁻³), just 1.82% of the WSII. Figure 4a, b, c, and d show the weekly variation of the water-soluble species during the whole sampling period.

### 3.1.3 SNA

TWSII was mostly made up of $SO_4^{2-}$, $NO_3^-$, and $NH_4^+$, which are all secondary water-soluble (SNA). Changes in their amounts were strongly linked to stationary emissions in the winter (Wang et al. 2021a). These changes come from secondary conversions of gaseous
<table><caption>**Table 1** Summary of the concentrations of WSII species (µg m⁻³) over the sampling campaign</caption><thead><tr><th>Components</th><th>Winter</th><th>Spring</th><th>Overall</th><th>Contribution (%)</th></tr></thead><tbody><tr><td>MSA</td><td>0.13±0.01</td><td>0.45±0.01</td><td>0.58±0.02</td><td>2.12</td></tr><tr><td>Oxalate</td><td>2.04±0.11</td><td>1.50±0.10</td><td>3.54±0.11</td><td>13.42</td></tr><tr><td>Cl<sup>-</sup></td><td>2.49±0.16</td><td>0.57±0.04</td><td>3.07±0.14</td><td>11.62</td></tr><tr><td>NO<sub>3</sub><sup>-</sup></td><td>2.40±0.08</td><td>1.24±0.10</td><td>3.64±0.10</td><td>13.78</td></tr><tr><td>SO<sub>4</sub><sup>2-</sup></td><td>0.46±0.01</td><td>0.92±0.06</td><td>1.38±0.04</td><td>5.23</td></tr><tr><td>Na<sup>+</sup></td><td>2.63±0.12</td><td>1.52±0.04</td><td>4.15±0.10</td><td>15.70</td></tr><tr><td>NH<sub>4</sub><sup>+</sup></td><td>2.39±0.23</td><td>4.81±0.27</td><td>7.20±0.26</td><td>27.25</td></tr><tr><td>K<sup>+</sup></td><td>0.41±0.01</td><td>0.38±0.01</td><td>0.78±0.01</td><td>2.97</td></tr><tr><td>Mg<sup>2+</sup></td><td>0.30±0.01</td><td>0.16±0.00</td><td>0.46±0.01</td><td>1.74</td></tr><tr><td>Ca<sup>2+</sup></td><td>0.81±0.06</td><td>0.81±0.06</td><td>1.61±0.06</td><td>6.11</td></tr><tr><td>Total ions</td><td>14.06±0.22</td><td>12.35±0.42</td><td>26.41±0.35</td><td>100</td></tr></tbody></table>

SO₂, NOₓ, and NH₃ through gas-phase chemical processes and heterogeneous reactions (Han et al. 2016; Huang et al. 2021). These pollutants are thought to have a significant impact on the hygroscopic properties and the acidity of aerosols (Squizzato et al. 2013; Tang et al. 2018; Cui et al. 2020). According to the findings of this work (Fig. 4a), the combined mass concentration of SO₄²⁻, NO₃⁻, and NH₄⁺ at Bou-Ismail was 12.22±0.40 µg m⁻³, which accounted for 46.3% of the WSII. Almost half of the SNA was derived from NH₄⁺ (Fig. 3), with the largest proportion recorded in the spring (69%). Like NH₄⁺, SO₄²⁻ contributed more to TWSII in the spring (57%). NH₄⁺ accounted for 27.3% of the total ion content in the aerosol (17% in the winter against ~39% in the spring). Elevated

<figure><img src="image_5.png" /><figcaption>**Fig. 3** The proportion of each ion component to total water-soluble inorganic ions measured in winter and spring</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>Fig. 4 Concentrations of individual ions and carboxylic acids during the sampling period</figcaption></figure>

levels of $NH_4^+$ were seen in the spring of the winter. Secondary chemical reactions produce $NH_4^+$ when gaseous $NH_3$ combines with gaseous $HNO_3$ or $H_2SO_4$, forming $NH_4NO_3$ or $(NH_4)_2SO_4$ (Zhang et al. 2008; Rabhi et al. 2024). Ammonium-rich conditions influence the production of $NH_4NO_3$, promoting the synthesis of $NH_4NO_3$. Conversely, environments with a lack of $NH_4^+$ would encourage the development of $(NH_4)_2SO_4$ (Priyadharshini et al. 2019). The $NH_4^+$ ion, derived from $NH_3$, plays a crucial role in the aerosol phase by reacting with $SO_4^{2-}$ and $NO_3^-$ to produce secondary particles. $NH_3$ mostly originates from agricultural output, industry pollutants, car exhaust emissions, and other sources (Huang et al. 2016). The significant increase in motor vehicles in Bou-Ismail has led realising substantial nitrogen compounds into the atmosphere via car exhaust. These compounds then undergo a chemical reaction with $NH_3$ to form ammonium nitrate. Additionally, the rise in urban population and the development of industrial sectors, such as thermal power, iron and steel, and cement industries, contribute significantly to the increase in ammonia emissions (Huang et al. 2021). $NO_3^-$ production occurs mostly during the day through the gas-phase oxidation of $NO_2$ by OH radicals and at night by the heterogeneous reaction of $N_2O_5$ (An et al. 2019). Since $HNO_3$ is volatile and $NH_4NO_3$ is thermally stable, both of them undergo thermodynamic partitioning between the gas and particle phases ($NH_3(g)+HNO_3(g) \rightarrow NH_4NO_3(s)$) (Huang et al. 2021). Thus, the low temperature and high relative humidity aid in separating gas and particles. The fractions of $NO_3^-$ in TWSII ranged from 0.02 to 0.39 µg.m⁻³ (Fig. 4a) in Bou-Ismail (17.1 and 10% respectively in winter and spring (Table 1). The large seasonal differences in $NO_3^-$ fractions were due to the strong variations in $NO_3^-$ concentrations. The combination of a relatively low temperature and high emissions of $NO_x$ created favourable conditions for generating $NO_3^-$ aerosol and its subsequent interaction with $NH_4^+$ (Zhang et al. 2011). However, the concentration level of $NO_3^-$ in this study (3.64 µg m⁻³) was higher than that reported in the central Mediterranean (Calzolai et al. 2015; Kchih et al. 2015; Paraskevopoulou et al. 2015; Becagli et al. 2017), in the Western Mediterranean (Ripoll et al. 2015; Pandolfi et al. 2020; Lemou et al. 2020; Deabji et al. 2024). According to Table 1, $NO_3^-$ concentration was lower during the spring (1.24±0.10 µg m⁻³) and greater
during the winter (2.40±0.08 µg m⁻³). Conversely, the average concentration of SO₄²⁻ was higher during the spring (0.92±0.06 µg m⁻³) and lower during the winter (0.46±0.01 µg m⁻³). The contrasting pattern can be attributed to the varying impacts of temperature on the processes involved in creating nitrate and sulphate. Some of the sulphur dioxide (SO₂) released is oxidised to make sulphate aerosols. This can happen through heterogeneous or homogeneous reactions, which can then be deposited by dry or wet processes (Cheng et al. 2000). The recorded concentration of SO₄²⁻ was 1.38 µg m⁻³. The significant role of photochemical activity during this period may be responsible for the rise in sulphate content. The conditions favouring the gas-particle conversion mechanism of sulphur dioxide (SO₂) leading to the formation of sulphate ions (SO₄²⁻), in terms of high temperatures and humidity, were met during the spring season. The combination of SO₄²⁻ with NH₄⁺ often results in the formation of either ammonium sulphate (NH₄₂SO₄) or ammonium bisulfate (NH₄HSO₄). However, unlike NH₄NO₃ or NH₄Cl, the combination of higher temperatures and increased photochemistry during the summer may favour the formation of sulphates, leading to a rise in the levels of SO₄²⁻ and NH₄⁺ concentrations (Xing et al. 2021). The concentration level of sulphate measured in this study (1.38 µg m⁻³) was lower than that reported in other Mediterranean regions. The concentration was 3.37 µg m⁻³ on the island of Crete (Sciare et al. 2008), 3.9 µg m⁻³ in Castillo de Bellver (Pey et al. 2009), 3.7 µg m⁻³ in Lecce (Perrone et al. 2014), 2.2 and 2.8 µg m⁻³ respectively in Marseille and Barcelona (Salameh et al. 2015), 6.68 µg m⁻³ in Athens (Karageorgos And Rapsomanikis 2007), 3.41 µg m⁻³ in Erdemli (Koçak et al. 2007b), and 3.41 µg m⁻³ in Lykourissi and Gouri (Theodosi et al. 2011) and 4 to 5.69 µg m⁻³ at Bou-Ismail (Lemou et al. 2024; Rabhi et al. 2024). During the winter, the combination of decreased temperatures and increased presence of acidic compounds, such as sulphate and nitrate, will promote gas-particle interactions. In contrast, studies have shown that the summer's elevated temperatures hinder the conversion of NH₃ to NH₄⁺. Previous investigations have also shown that concentrations of NH₃ are greater during the summer months, while concentrations of NH₄⁺ are higher during the winter (Cao et al. 2009; Zhang et al. 2011).

### 3.1.4 Sea Salt

Fig. 4b shows the variation of the Na⁺ and Cl⁻. Both Na⁺ and Cl⁻ are two tracers of sea salt aerosols and are often the result of foam breaking and/or bubbles bursting (Yu et al. 2020; Caumo et al. 2023). Based on the observation that Na⁺ is a sea salt aerosol marker in the marine atmosphere (Yeatman et al. 2001), this indicates that sea salt aerosols significantly altered the chemical composition of aerosols at the Bou-Ismail station. Sources of chloride ions (Cl⁻) include sea salt, stationary emissions, and biomass burning (Liu et al. 2021). Na⁺ (Cl⁻) concentrations were 4.15 µg m⁻³ (3.07 µg m⁻³). The winter and spring demonstrated decreased Cl⁻ concentrations, with mean values ranging from 2.49 µg m⁻³ to 0.57 µg m⁻³, respectively. This implies that the emissions from burning coal also somehow affected these ions (Yao et al. 2002). Na⁺ and Cl⁻ showed the same seasonal fluctuation (Table 1). Marine airflow transports sea salt aerosols to the sample location. Furthermore, the sea salt content of coastal aerosols rises over time because wind speeds are typically greater across the region (Lemou et al. 2020; Rabhi et al. 2024).
### 3.1.5 Soil

The soil is thought to be the major source of $Ca^{2+}$, $Mg^{2+}$, and $K^+$. Road dust, soil dust, and construction dust are the main sources of $Ca^{2+}$, and $Mg^{2+}$, which are used to track terrestrial sources (Li et al. 2018; Caumo et al. 2023). $K^+$ and $Ca^{2+}$, might originate from sources inside the Earth's crust. The stability of these WSII may suggest that the geological sources from which they come are stable (Xing et al. 2021). Throughout the sample period (Fig. 4c), $Ca^{2+}$ and $Mg^{2+}$ total concentration values in TWSII were low, i.e., 1.61 and 0.46 µg m$^{-3}$, respectively. The contents of $Ca^{2+}$, and $Mg^{2+}$ in PM$_{2.5}$ are rather stable throughout urban areas in the Mediterranean region of Europe but show much variation in rural locations in the centre of the continent (Remoundaki et al. 2013; Salameh et al. 2015). Potassium emerged as a relatively minor ionic part of TWSII during these periods. $K^+$ is often used as a tracer in biomass combustion (Zhang et al. 2013; Voutsa et al. 2014). The low mean $K^+$ concentrations (0.03±0.01 µg m$^{-3}$) observed during this period suggested a limited impact of biomass burning on TWSII. The current finding suggests that biomass combustion may have regulated the notable declines in potassium ($K^+$) concentrations recorded over the specified timeframe. This is attributable to the traceability of $K^+$ in fine particulate matter (PM$_{2.5}$) to the combustion of biomass resources.

Oxalate and methane sulfonic acid (MSA), two secondary organic tracers (Arndt et al. 2017; Golly et al. 2019; Rabhi et al. 2024), accounted for 15.6% of WSII in this study (Figs. 3 and 4d). Oxalate is one of the most common species among the chemically recognised water-soluble organics in atmospheric particles (Yang et al. 2014; Farren et al. 2019). Oxalate has been shown to arise from the initial release of biomass burning and the atmospheric transformation of precursor gases, which may be of natural or human-induced origin, resulting in the formation of secondary aerosols (Custódio et al. 2016). In this study, the oxalate compound accounted for 13% of the overall measured content of water-soluble ions. Its concentration ranged from 0.03 to 0.41 µg m$^{-3}$ with a mean concentration of 0.14 µg m$^{-3}$. Oxalate in the particulate matter accounted for 12.2% and 14.5%, respectively, during the spring and winter. The winter and spring samples had almost equivalent levels of abundance. MSA is mostly found in marine and coastal air because dimethylsulfide (DMS) is oxidised in the gas phase. DMS is a by-product of cellular solutes made by some marine phytoplankton (Andreae et al. 2003). The levels of MSA in Bou-Ismail followed a clear seasonal pattern, peaking in the spring and dropping in the winter (Table 1). Our research resulted in a higher concentration of MSA than the concentration previously reported in Bou-Ismail (Algiers), and Fez (Morocco) (Lemou et al. 2020, 2024; Deabji et al. 2021; Rabhi et al. 2024).

### 3.1.6 Dicarboxylic acid

Oxalate and methane sulfonic acid (MSA), two secondary organic tracers (Arndt et al. 2017; Golly et al. 2019; Rabhi et al. 2024), accounted for 15.6% of WSII in this study (Figs. 3 and 4d). Oxalate is one of the most common species among the chemically recognised water-soluble organics in atmospheric particles (Yang et al. 2014; Farren et al. 2019). Oxalate has been shown to arise from the initial release of biomass burning and the atmospheric transformation of precursor gases, which may be of natural or human-induced origin, resulting in the formation of secondary aerosols (Custódio et al. 2016). In this study, the oxalate com-
pound accounted for 13% of the overall measured content of water-soluble ions. Its concen-
tration ranged from 0.03 to 0.41 µg m⁻³ with a mean concentration of 0.14 µg m⁻³. Oxalate
in the particulate matter accounted for 12.2% and 14.5%, respectively, during the spring and
winter. The winter and spring samples had almost equivalent levels of abundance. MSA is
mostly found in marine and coastal air because dimethylsulfide (DMS) is oxidised in the
gas phase. DMS is a by-product of cellular solutes made by some marine phytoplankton
(Andreae et al. 2003). The levels of MSA in Bou-Ismail followed a clear seasonal pattern,
peaking in the spring and dropping in the winter (Table 1). Our research resulted in a higher
concentration of MSA than the concentration previously reported in Bou-Ismail (Algiers),
and Fez (Morocco) (Lemou et al. 2020, 2024; Deabji et al. 2021; Rabhi et al. 2024).

**3.2 Stoichiometric investigations of cations and anions**

3.2.1 Concentration equivalent ratio analysis of [NO₃⁻]/[SO₄²⁻]

Concentration equivalent normality refers to the concentration of a solution measured in
terms of the number of equivalents per litre. The specific reaction in the solution defines an
equivalent. In an acid-base reaction, the term “equivalent” refers to the amount of acid or
base capable of providing or accepting precisely 1 mol of protons (H⁺ ions). Water-soluble
inorganic ions, such as NO₃⁻ and SO₄²⁻, are widely regarded as significant chemical spe-
cies that contribute to visibility loss via light scattering effects (Zhou et al. 2016). NO₃⁻ and
SO₄²⁻ are often produced in the atmosphere by interactions between precursor gases such
as NOₓ and SO₂. There were four pathways for converting SO₂ and NOₓ to SO₄²⁻ and
NO₃⁻ (Meng et al. 2016; Xu et al. 2019; Long et al. 2020). SO₂ and NOₓ dissolve in water
to produce sulphurous acid (H₂SO₃) and either nitrous acid (HNO₂) or nitric acid (HNO₃),
which may further react with aqueous oxidants to provide SO₄²⁻ and NO₃⁻. The second
route involves reactions occurring on the surface of particles already in water, where SO₂
and NOₓ combine with OH radicals, O₃, or H₂O₂ to form SO₄²⁻ and NO₃⁻. The third process
involves changing SO₂ and NOₓ into SO₄²⁻ and NO₂⁻, which are then further changed into
SO₄²⁻ and NO₃⁻. The concluding procedure is the direct photochemical synthesis of SO₄²⁻
and NO₃⁻.

The mass ratio of [NO₃⁻]/[SO₄²⁻] may be an indicator of the relative importance of
mobile (vehicles) vs. stationary (coal) sources of nitrogen and sulphur in the atmosphere.
A high ratio implies that mobile sources are more dominant than stationary ones (Arimoto
et al. 1996). The higher [NO₃⁻]/[SO₄²⁻] ratios indicate a greater contribution from mobile
vehicle sources, but the lower [NO₃⁻]/[SO₄²⁻] ratios indicate a greater contribution from
industrial and combustion sources. Worth noting that factors affecting the [NO₃⁻]/[SO₄²⁻]
ratio include the proportional contributions of primary SO₂ and NOₓ emissions, together
with the chemical processes that govern the generation of secondary inorganic aerosol (Xu
et al. 2019). Throughout the sample period, the NO₃⁻ to SO₄²⁻ ratio had an average value of
3.7±3.1, indicating that the most significant contribution came from mobile vehicles rather
than coal-fuelled enterprises. The elevated value is likely associated with the growth in
vehicle numbers and the reduced use of coal. The findings suggested that the rapid modern-
ization and motorization of Bou-Ismail had exacerbated pollution from car emissions com-
pared to previous years. At Bou-Ismail, the average mass ratios of [NO₃⁻]/[SO₄²⁻] exhibited
small seasonal fluctuations. The ratio was 5.5±2.3 and 2±2.8 in winter and spring, respec-
tively. The $[NO_3^-]/[SO_4^{2-}]$ mass ratio was greater than 1. The findings indicated the strong influence of pollutants from mobile sources over stationary sources. Previous studies in the region have shown a lower ratio value than this study (Lemou et al. 2020, 2024; Rabhi et al. 2024). The amount of $[NO_3^-]/[SO_4^{2-}]$ in PM$_{2.5}$ fine particles in the Mediterranean region often changes with the seasons, different sources of pollution (like cars, factories, and heating), spatial characteristics of the sampling site (like cities, countryside, or near the coast). The values were 0.8 for Kenitra (Morocco), 0.45 to 1.98 for Bou-Ismail (Algeria), 0.18 for Erdemli (Turkey), 0.4–0.7 for Thessaloniki (Greece), and less than 0.2 for Elche (Spain) (Koçak et al. 2007a; Tolis et al. 2015; Bounakhla et al. 2023; El Gourch et al. 2023; Rabhi et al. 2024).

The interpretative significance of aerosol $[NO_3^-]/[SO_4^{2-}]$ mass ratios is constrained, since these ratios do not accurately represent the composition of emissions. The mass ratios of $[NO_3^-]/[SO_4^{2-}]$ are influenced by the nature of primary emissions and the chemical transformations occurring in the atmosphere during each season (Shen et al. 2008; Cvitešić Kušan et al. 2020). As reported in the literature (Yao et al. 2002; Shen et al. 2008), the estimated ratios of NO$_x$ to SO$_x$ from gasoline emissions are 13:1, diesel fuel 8:1, and coal combustion 1:2. Emission factors have shown that $SO_4^{2-}$, a marker for stationary emissions, and $NO_3^-$ are justifiable for mobile emissions.

### 3.2.2 Concentration equivalent ratio analysis of $[NH_4^+]/[SO_4^{2-}]$

It is possible to detect the chemical forms of WSII in PM$_{2.5}$ by analysing the correlation between the different WSII.

Ammonium-rich circumstances ($[NH_4^+]/[SO_4^{2-}] > 1.5$, where $[NH_4^+]$ and $[SO_4^{2-}]$ refer to the molar concentrations of $NH_4^+$ and $SO_4^{2-}$, respectively) through which $SO_4^{2-}$, $NO_3^-$, and $Cl^-$ can form ($NH_4)_2SO_4$, $NH_4NO_3$, and $NH_4Cl$. In the presence of a low ammonium concentration ($[NH_4^+]/[SO_4^{2-}] < 1.5$), $NH_3$ first reacts with $H_2SO_4$ to generate ($NH_4)_2SO_4$ or $NH_4HSO_4$, but in the presence of an excess of ammonium, $NH_3$ reacts with $HNO_3$ or HCl to form $NH_4NO_3$ or $NH_4Cl$ (Cheng et al. 2021). The values of these ratios were higher during the whole period of the investigation campaign (up to 5). The correlation between ammonium and the combined concentration of sulphate and nitrate is crucial in characterising the acidity of aerosols. Ammonia in the surrounding environment neutralises a significant percentage of sulphate and nitrate. If the ratio of $[NH_4^+]/([SO_4^{2-}] + [NO_3^-])$ (expressed as an equivalent concentration, is a widely used metric in atmospheric science to evaluate the neutralization state of acidic species by ammonium ($NH_4^+$) in fine particulate matter) is less than 1, these samples are considered to be deficient in $NH_4^+$. Sulphate and nitrate cannot be neutralised by ammonia in aqueous air aerosol under the conditions described above (Zhou et al. 2018). In the Bou-Ismail samples, the ratio was slightly more than 1 (1.06) throughout the sampling campaign, particularly in the winter (0.37), as opposed to the spring (1.71). These findings suggest that the samples collected in Bou-Ismail significantly lacked $NH_4^+$ ions only during the winter season. This suggests that the atmospheric aerosol was more acidic during the winter, with insufficient $NH_4^+$ to neutralize all $SO_4^{2-}$ and $NO_3^-$. Acidic aerosols can be more harmful to human health and ecosystems. In spring, excess $NH_4^+$ may indicate the presence of organic ammonium salts or measurement uncertainty. The reduction of alkaline ions, particularly ammonia, may be a significant factor in explaining the lower acidity of PM$_{2.5}$ in the coastal samples.
### 3.2.3 Acidity and alkalinity of PM<sub>2.5</sub>

Several studies have shown that anions can increase the acidity levels of particulate matter, while cations can improve the alkalinity of PM. Therefore, the relative proportion of cations to anions can be used as an indicator to assess the acidity or alkalinity of PM (Cheng et al. 2014; Hennigan et al. 2015; Zhou et al. 2016). The molar concentrations of WSII are converted with Eqs. (1) and (2), as described above. Quantifying all the ions in the surrounding aerosol particle results in equal (i.e., balanced) anions and cations. Since H⁺ measurements are missing, we can expect that acidic aerosol particles will show high levels of sulfate and nitrate without enough ammonium or another positive ion to balance them. It is quite probable that the missing anion is carbonate (CO₃²⁻), which is the reason why a significant portion of the fine water-soluble Ca²⁺ that was detected is calcium carbonate (CaCO₃). The scatter plot of the total cation (Na⁺, NH₄⁺, K⁺, Mg²⁺, and Ca²⁺) and anion (Cl⁻, NO₃⁻, and SO₄²⁻) ions micro-equivalents depicted in Fig. 5 resulted in significant R² values (greater than 0.94), suggesting that the measured cations and anions maintained a consistent neutralisation relationship over the sampling period.

The measurements of anions and cations in this investigation showed substantial stability. The cations were considerably depleted, as shown by the slope of cationic to anionic elements of the regression curve for the whole sampling campaign, which was 1.29 over 1 (higher than one in two seasons). Previous investigations conducted in the Mediterranean have shown similar findings (Theodosi et al. 2011; Remoundaki et al. 2013; Lemou et al. 2020). This layout indicates that points above the line 1:1 represent alkaline samples (72% of the sample), and points below the line represent acidic samples (28%).

<figure><img src="image_7.png" /><figcaption>Fig. 5 Correlations between anion and cation equivalents</figcaption></figure>
The mean equivalent ratio of total cations to total anions was higher than 1, i.e., 1.56 (with a range of 0.74 to 3.06). This indicates that cations dominated the ionic composition during the sampling period, suggesting a substantial deficit of anions in the aerosol particles. However, PM₂.₅ levels at Bou-Ismail suggest that the aerosol particles were alkaline, often due to dust or sea salt. This deficiency may be attributed to a considerable amount of CO₃²⁻ or HCO₃⁻ in the aerosol particles. Organic acids might contribute to the ionic misbalance.

## 3.3 Statistical analysis of WSII

### 3.3.1 Pearson correlation

There are essentially three ways to classify the correlation loading factor: high correlation, with a loading factor greater than 0.7; moderate correlation, with a loading factor between 0.5 and 0.7; and low correlation, with a loading factor less than 0.5.

The significant link between Na⁺ and Cl⁻, which is not surprising since Bou-Ismail is a coastal location, suggests the formation of NaCl. Similarly, the correlation between Mg²⁺ and Cl⁻ indicates MgCl₂ formation in Bou-Ismail aerosol. There is no association between K⁺ and Ca²⁺ with NO₃⁻ (Table 2), reflected in the relatively weak occurrence of KNO₃ and Ca(NO₃)₂. The link between NO₃⁻ and oxalate is strong (R² up to 0.9) and might be attributed to heterogeneous reactions on the surfaces of alkaline cations (derived from sea salt and mineral dust).

The primary source of methyl sulfonic acid (MSA) was the oxidation of dimethyl sulphide (DMS) generated by marine phytoplankton. Still, the introduction of nutrients from land sources onto the ocean's surface boosts the productivity of marine organisms. This leads to an increase in DMS synthesis and higher concentrations of MSA in the atmosphere. MSA and NH₄⁺ presented good Pearson correlation values of 0.64 and 0.71, P<.01. However, the origins of NH₄⁺ remain complex and uncertain, as biological emissions from the marine environment also influence NH₄⁺ (Yu et al. 2020). The predominant types of WSII observed during the sampling campaign in the coastal region of Bou-Ismail were NaCl and MgCl.

**The chemicals were detected over the winter season** A statistically significant association between NH₄⁺ and NO₃⁻ was only clear during the winter season, as shown in Table 2. During the winter, there is an anticipation of increased levels of NO₃⁻ owing to the greater availability of NOₓ precursors caused by extra emissions that are not present during the spring, such as heating. Due to the sensitivity of the equilibrium dissociation constant for NH₄NO₃ to temperature and humidity, a greater amount of NH₄NO₃ will likely be produced at low temperatures and high relative humidity during the winter season, even when the same amount of ammonia and nitric acid vapour is present. The nitrate on particulates is primarily derived from the reaction between HNO₃ (gas) and NH₃ (gas) in the atmosphere. However, elevated temperatures can lead NH₄NO₃ to dissociate into gaseous HNO₃ and NH₃. The higher correlation coefficient between NH₄⁺ and NO₃⁻ on particulates may be attributed to the composition of NH₄NO₃.

The strong negative correlation (r = -.81) between NH₄⁺ and Cl⁻ suggests a likelihood of NH₄Cl production (Ianniello et al. 2011). In addition, the correlations between Na⁺ and
<table><caption>**Table 2** Seasonal pearson correlation coefficients between the ion species</caption><thead><tr><th rowspan="2"></th><th colspan="8">Spring correlations</th></tr><tr><th>MSA</th><th>Oxalate</th><th>Cl<sup>-</sup></th><th>NO<sub>3</sub><sup>-</sup></th><th>SO<sub>4</sub><sup>2-</sup></th><th>Na<sup>+</sup></th><th>NH<sub>4</sub><sup>+</sup></th><th>K<sup>+</sup></th><th>Mg<sup>2+</sup></th><th>Ca<sup>2+</sup></th></tr></thead><tbody><tr><td>MSA</td><td></td><td>0.164</td><td>-0.140</td><td>0.072</td><td>0.219</td><td>0.342</td><td>0.644*</td><td>0.360</td><td>0.232</td><td>-0.101</td></tr><tr><td>Oxalate</td><td>0.805**</td><td></td><td>0.247</td><td>0.957***</td><td>-0.132</td><td>0.136</td><td>0.308</td><td>0.082</td><td>0.277</td><td>-0.053</td></tr><tr><td>Cl<sup>-</sup></td><td>-0.616*</td><td>-0.819**</td><td></td><td>0.206</td><td>-0.099</td><td>0.757**</td><td>-0.223</td><td>0.017</td><td>0.642*</td><td>0.389</td></tr><tr><td>NO<sub>3</sub><sup>-</sup></td><td>0.742**</td><td>0.906***</td><td>-0.798**</td><td></td><td>-0.226</td><td>0.029</td><td>0.249</td><td>0.118</td><td>0.207</td><td>0.068</td></tr><tr><td>SO<sub>4</sub><sup>2-</sup></td><td>-0.045</td><td>0.053</td><td>-0.197</td><td>-0.106</td><td></td><td>-0.133</td><td>0.666*</td><td>0.199</td><td>-0.292</td><td>0.131</td></tr><tr><td>Na<sup>+</sup></td><td>-0.585*</td><td>-0.719**</td><td>0.917***</td><td>-0.723**</td><td>-0.254</td><td></td><td>-0.106</td><td>0.254</td><td>0.902***</td><td>0.270</td></tr><tr><td>NH<sub>4</sub><sup>+</sup></td><td>0.712**</td><td>0.963***</td><td>-0.806**</td><td>0.908***</td><td>0.048</td><td>-0.765**</td><td></td><td>0.394</td><td>-0.218</td><td>-0.204</td></tr><tr><td>K<sup>+</sup></td><td>0.362</td><td>0.493</td><td>-0.614*</td><td>0.481</td><td>-0.246</td><td>-0.472</td><td>0.470</td><td></td><td>0.261</td><td>0.154</td></tr><tr><td>Mg<sup>2+</sup></td><td>-0.625*</td><td>-0.802**</td><td>0.602*</td><td>-0.683*</td><td>-0.292</td><td>0.471</td><td>-0.692*</td><td>-0.209</td><td></td><td>0.369</td></tr><tr><td>Ca<sup>2+</sup></td><td>-0.060</td><td>-0.223</td><td>0.004</td><td>-0.242</td><td>0.822**</td><td>-0.124</td><td>-0.244</td><td>-0.450</td><td>-0.098</td><td></td></tr></tbody></table>

<footnote>* p < .05, ** p < .01, *** p < .001</footnote>
$NO_3^-$ ($R^2 = -0.72$) indicate that the marine aerosol has likely undergone an ageing process (Contini et al. 2014). Chemical interactions between $NaCl$ and $HNO_3$ or $H_2SO_4$ reduce chlorine, creating $NaNO_3$ and gaseous $HCl$ (Contini et al. 2014). $Mg^{2+}$ and $NO_3^-$ ($R^2 = -0.68$) correlated well. $Ca^{2+}$ correlated only with $SO_4^{2-}$ ($R^2=0.82$) during winter. The high levels of $K^+$, low $Cl^/-K^+$ ratios, and high correlation between $K^+$ and $Cl^-$ have all been proposed as diagnostic tracers for $K^+$ coming from biomass burning (Guo et al. 2020). Potassium ($K^+$) is often used as a tracer for biomass and biofuel combustion, and the correlation coefficients between $K^+$ and ions ($NO_3^-$ and $SO_4^{2-}$, see Table 2) were weaker. $K^+$ gave rise to the formation of $KCl$ in spring, autumn, and winter and $K_2SO_4$ during the summer (with a correlation coefficient between $K^+$ and $SO_4^{2-}$) (Huang et al. 2016). Significant correlations were identified between $K^+$ and $Cl^-$. In contrast, $K^+$ and $SO_4^{2-}$ exhibited no substantial association, suggesting that $KCl$ was the predominant form of potassium, in agreement with the literature (Huang et al. 2016; Turap et al. 2019). Oxalate had a strong positive association, particularly during winter, with $Cl^-$, $Na^+$, $NH_4^+$, and $Mg^{2+}$. The same applies to MSA, which correlated with oxalate species, $Cl^-$, $NO_3^-$, and $Mg^{2+}$. Overall, the winter sampling campaign in the Bou-Ismail coastal environment encountered these primary forms of WSIs: $NH_4NO_3$, $NaNO_3$, $NH_4Cl$, $Ca(SO_4)$, $MgNO_3$, and $KCl$.

**The chemicals were detected over the spring season** The link between $SO_4^{2-}$ and $NH_4^+$ may have resulted from the creation of secondary aerosols, likely originating from anthropogenic precursors. During the aerosol ageing process, the gaseous compound $NH_3$ undergoes a chemical reaction with the gaseous acids $H_2SO_4$ and $HNO_3$, resulting in the formation of solid particles known as particulate salts, namely $(NH_4)_2SO_4$ and $NH_4NO_3$ (Gluščić et al. 2023). Marine is characterized by $Na^+$, $Cl^-$, and $Mg^{2+}$. $Mg^{2+}$ and $Na^+$ exhibited a good correlation (0.90 in winter and 0.64 in spring). It is reported in the literature that this link indicate a contribution from marine aerosols in PM$_{2.5}$, consistent with the measurement site of the coast (Cesari et al. 2019). Typically, sea salt aerosol has a $Mg^{2+}/Na^+$ ratio of around 0.12 (Deng et al. 2016). The ratio in the present study was 0.06 to 0.36, with an average value of 0.12 (0.11 in winter and 0.13 in spring).

Oxalate, the smallest dicarboxylic acid, exists in atmospheric aerosols due to direct releases from biogenic sources, biomass combustion, automobiles, and engine exhaust. The secondary generation of oxalate occurs because of the end products of the photochemical synthesis of organic species (Thepnuan et al. 2019). No correlation was found between oxalate and $K^+$, $NH_4^+$, and $SO_4^{2-}$. The literature indicates that the relationship between Oxalate and $K^+$, $NH_4^+$, and $SO_4^{2-}$ implies that emissions from biomass burning and subsequent transformations are significant contributors to the formation of shorter-chain di-acids (Khedidji et al. 2020). $(NH_4)_2SO_4$ was the main WSII that occurred in the Bou-Ismail coastal environment during the spring period.

### 3.3.2 Principal component analysis

The Bou-Ismail coastal commune in northern Algeria (Wilaya of Tipaza) primarily engages in urbanisation, household waste management, road traffic, preurban agriculture, maritime influences (aerosols), wind, and precipitation. A principal component analysis (PCA) with varimax rotation was conducted to determine the variables contributing to the variation of
the primary ionic constituents in various seasons. This study aimed to gain a deeper understanding of the potential sources of aerosol particles in the Middle West region of the Mediterranean. The study used the different seasons' levels of MSA, oxalate, $Cl^-$, $NO_3^-$, $SO_4^{2-}$, $Na^+$, $NH_4^+$, $K^+$, $Mg^{2+}$, and $Ca^{2+}$ as markers to highlight the differences.

Overview categorised by potential sources of ion inventory in the Bou-Ismail region: positioned in front of a natural marine source: $Na^+$, $Cl^-$, $Mg^{2+}$, $SO_4^{2-}$, MSA: Marine aerosols transported by the wind, shaped by meteorological factors such as prevailing winds, humidity, and tides. The location is in an urban and residential zone: $NH_4^+$, $NO_3^-$, $Ca^{2+}$: wastewater, household discharges, concrete deterioration, and vehicular traffic. Bou Ismail is recognised for its agricultural resources: $K^+$, $NH_4^+$, $NO_3^-$, and $SO_4^{2-}$: fertilisers, irrigation, and runoff. Whether secondary or human-made, sources of atmospheric origin include oxalate, MSA, $NO_3^-$, and $SO_4^{2-}$. Oxalate, MSA, $NO_3^-$, $SO_4^{2-}$: These sources include dry or wet deposition, combustion, and photochemical transformations.

Table 3 presents the WSII factor loadings obtained from PCA conducted throughout the winter and spring seasons in Bou-Ismail. Based on the selection criteria, this research investigated factors with eigenvalues greater than 1.0. Distinctions corresponded to a variety of sources of contamination. Each part can be categorised as either a source of emissions or a chemical reaction, and their relative amounts show differences in the sources of emissions and the processes that affect aerosol composition. PCA produced two winter varimax rotated variables (PC1 and PC2) accounting for 79.59% of the variance. PC1 accounted for 58.25% of the variation and had significant $Na^+$, $K^+$, $NH_4^+$, $Mg^{2+}$, $Cl^-$, $NO_3^-$, and two carboxylic loadings. $NH_4^+$ mainly originates from the heterogeneous neutralisation reaction of $NH_3$ in the atmosphere on the particles (Rabhi et al. 2024). $NH_3$ is primarily produced through agricultural fertilisation (Golly et al. 2019). Nitrogen oxides ($NO_x$) often serve as the precursor for $NO_3^-$: $NO_x$ undergoes photochemical reactions to produce $HNO_3$, which then reacts with $NH_3$ to make $NO_3^-$, a secondary aerosol (Hsu et al. 2008). $Na^+$, $Cl^-$, $Mg^{2+}$, and $K^+$ were classified as a combination of marine sources ($Na^+$, $Cl^-$, and $Mg^{2+}$) (Kumar And Yadav 2013), and biomass and stationary emissions ($K^+$ and $Cl^-$) (Zhao et al. 2024b). However, several sources, such as potassium from biomass and wood burning, chloride from coal burning, and magnesium from emissions originating from the Earth's crust, may

<table><caption>**Table 3** Factor loading from PCA in PM$_{2.5}$ at four Bouismail in winter and spring</caption><thead><tr><th>Season</th><th>Winter</th><th>Spring</th><th></th><th></th><th></th></tr></thead><tbody><tr><td>Principale componant</td><td>PC1</td><td>PC2</td><td>PC1</td><td>PC2</td><td>PC3</td><td>PC4</td></tr><tr><td>MSA</td><td><strong>0.802</strong></td><td>-0.078</td><td>0.256</td><td><strong>0.790</strong></td><td>0.025</td><td>-0.371</td></tr><tr><td>Oxalate</td><td><strong>0.964</strong></td><td>-0.116</td><td>0.128</td><td>0.115</td><td><strong>0.971</strong></td><td>-0.051</td></tr><tr><td>Cl<sup>-</sup></td><td><strong>-0.918</strong></td><td>-0.041</td><td><strong>0.717</strong></td><td>-0.149</td><td>0.195</td><td>0.430</td></tr><tr><td>NO<sub>3</sub><sup>-</sup></td><td><strong>0.916</strong></td><td>-0.208</td><td>0.066</td><td>0.032</td><td><strong>0.989</strong></td><td>0.024</td></tr><tr><td>SO<sub>4</sub><sup>2-</sup></td><td>0.151</td><td><strong>0.927</strong></td><td>-0.361</td><td><strong>0.672</strong></td><td>-0.193</td><td>0.483</td></tr><tr><td>Na<sup>+</sup></td><td><strong>-0.853</strong></td><td>-0.151</td><td><strong>0.961</strong></td><td>0.166</td><td>-0.022</td><td>0.061</td></tr><tr><td>NH<sub>4</sub><sup>+</sup></td><td><strong>0.938</strong></td><td>-0.132</td><td>-0.285</td><td><strong>0.891</strong></td><td>0.255</td><td>-0.038</td></tr><tr><td>K<sup>+</sup></td><td><strong>0.542</strong></td><td>-0.493</td><td>0.253</td><td><strong>0.626</strong></td><td>0.030</td><td>0.086</td></tr><tr><td>Mg<sup>2+</sup></td><td><strong>-0.778</strong></td><td>-0.216</td><td><strong>0.945</strong></td><td>0.034</td><td>0.134</td><td>0.070</td></tr><tr><td>Ca<sup>2+</sup></td><td>-0.067</td><td><strong>0.955</strong></td><td>0.310</td><td>-0.030</td><td>-0.019</td><td><strong>0.825</strong></td></tr><tr><td>% Variance</td><td>58.25</td><td>21.34</td><td>30.90</td><td>24.49</td><td>18.17</td><td>10.89</td></tr><tr><td>Eigenvalue</td><td>5.82</td><td>2.13</td><td>3.09</td><td>2.45</td><td>1.82</td><td>1.09</td></tr></tbody></table>

Loadings > 0.5 are shown in bold
change the use of some markers, which is rather puzzling (Jain et al. 2017). This research identified oxalate and MSA in PM<sub>2.5</sub>. Oxalate and MSA are typical secondary organic tracers derived from marine biogenic sources and the oxidation of several precursors (Golly et al. 2019). They may also induce inflammatory reactions. The first component, PC1, is a mix of pollutants from secondary organic traces, marine sources, and burning and stationary emissions. According to the finding, potential health impacts of K<sup>+</sup>, NO<sub>3</sub><sup>-</sup>, NH<sub>4</sub><sup>+</sup>, and Oxalate can be elevated respiratory toxicity, inflammatory reaction, and oxidative injury, while Na<sup>+</sup>, Mg<sup>2+</sup>, Cl<sup>-</sup>, and MSA typically exhibit less toxicity, although they may promote pulmonary deposition and interact with more deleterious species. This aligns with the position of the sampling site. Secondary pollutants are generated in the atmosphere by the oxidation of volatile organic compounds (VOCs), which can originate from biogenic or anthropogenic sources. Marine aerosols, originating from sea spray, oceanic biological processes, and marine boundary layer chemistry, significantly affect the atmospheric composition above the seas. Simultaneously, terrestrial emissions from wildfires, household heating, agricultural burning, fossil fuel combustion, and persistent emissions from industrial smokestacks substantially contribute to air pollution. In the PC2, the primary sources of Ca<sup>2+</sup> and SO<sub>4</sub><sup>2-</sup> are building operations, construction materials, and secondary gas-particle transformations. Calcium sulphate (CaSO<sub>4</sub>) indicates urban-industrial interactions, highlighting the convergence of mineral dust and sulphur emissions (Huang et al. 2014). Ca<sup>2+</sup> and SO<sub>4</sub><sup>2-</sup> augmented pulmonary irritation, inflammation, and systemic repercussions. Examining the air masses passing the sample site reveals that they primarily originate from the north, northeast, and northwest, forming the combination of sources approaching the sampling location.

PCA found four main parts (PC1, PC2, PC3, and PC4) based on the rotational loading matrix during spring (Table 3). These parts explained 84.45% of the total variation during spring (30.90%, 24.49%, 18.17%, and 10.89%, respectively). PC1 exhibited significant loads for Na<sup>+</sup>, Cl<sup>-</sup>, and Mg<sup>2+</sup> ions; in elevated concentrations, particularly when combined with acidic species, they may elevate airway irritation, especially in susceptible individuals (e.g., those with asthma), contribute to particle mass, and enhance the respiratory deposition of more harmful compounds. The latter is also a typical marine part (magnesium sulphate). It was hence recognised as the contribution of marine aerosols. This component was linked to maritime components located at a short distance from the sample place. This finding indicates that PC1 originated from marine aerosol emissions.

PC2 (24.49%), with the presence of SO₄²⁻ and NH₄⁺, indicated a potential influence from stationary emissions and agricultural practices (Allen et al. 2015). MSA is usually prevalent in marine and coastal air because DMS oxidises in gas. Some marine phytoplankton produce DMS from cellular solutes (Andreae et al. 2003). SO₄²⁻ often co-occurs due to marine sulfate production from the same oxidation pathway. Burning may produce NH₄⁺, although agricultural ammonia reacting with acids is the most prevalent source. At the same time, K⁺ is often used as a tracer for biomass burning (Thepnuan et al. 2019). PC2 originates from stationary emissions, agricultural practices, marine biogenic emissions, and biomass burning. NO₃⁻ is a secondary inorganic aerosol, highly influenced by traffic and combustion sources. It was identified as the primary component that significantly increased during biomass burning. According to Guo's research, oxalate is mostly found in small particles because of a photochemical reaction in the gas phase that creates gaseous oxalic acid and then condenses on already present particles (Guo et al. 2010). The simultaneous elevation of NO₃⁻ and oxalate may indicate an interaction of urban or traffic emissions alongside
oxidative atmospheric processing (Benchrif et al. 2022). Potential combined health impacts may lead to increased aerosol acidity, respiratory irritation, immunological disruption, and oxidative damage. Facilitates oxidative stress and may interact with transition metals. PC3 was indicative of a photochemical reaction source in the gas phase. PC4 had a significant factor loading of 0.881 for Ca²⁺, suggesting a potential origin in soil dust. While Ca²⁺ is not intrinsically very hazardous, its presence in PM₂.₅ exacerbates physical and inflammatory responses, particularly in conjunction with other particulate matter constituents. Air masses were affected by movement from the eastern Mediterranean, central Mediterranean, western Mediterranean, and southern Algeria.

Figure 6 examines explicitly the seasonal contributions of the sources. Winter exhibits a predominant emission regime characterised by heating and local combustion, which results in significant atmospheric stability that facilitates pollutant accumulation. Spring exhibits increased source complexity, influenced by natural phenomena such as marine air and dust resuspension, as well as human activities including farming and burning, in conjunction with heightened photochemical activity.

### 3.3.3 Air mass back trajectories

We executed 25 backward trajectories in alignment with the weekly sample interval during the 2013–2014 monitoring period. We illustrated the 120-hour backward trajectory for this weekly sample pollution period (Fig. 7). The initial height for trajectory prediction was established at 100 m. The trajectory analysis indicated three important grouping trajectories designated as 1, 2, and 3, oriented towards the southwest, south, and northwest, respectively. Pathway 1 is the predominant air mass trajectory reaching the sample location, accounting for about 72% of the air masses. Pathway 1 encompasses all air masses originating from the west and northwest of the sample location. The substantial air masses initially traverse the Atlantic Ocean via Spain and Portugal, northern Morocco, southern France, and ultimately western Algeria.

Several air masses from the eastern Atlantic Ocean cross urban centres, automobile traffic, and industrial activities (such as oil refineries, power plants, and shipping ports) that emit NOₓ, SOₓ, PM₂.₅, and VOCs in Spain and Portugal. Dust resuspension from dry soils also raises PM₁₀ levels. Southern France's chemical sector, heavy manufacturing, and transport networks, especially Marseille and Lyon, create fine particles, ozone precursors, and heavy metals. Pollution from petrochemical complexes, cement factories, and high traffic

<figure><img src="image_8.png" /><figcaption>Fig. 6 Differentiates the seasonal inputs from various PC sources</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>**Fig. 7** Back-trajectory of air mass from the HYSPLIT model for every weekly sample</figcaption></figure>

in coastal and interior cities like Oran, Tlemcen, and Sidi Bel Abbès in western Algeria includes PM, SO₂, NOₓ. Air masses travelling inland near Bou-Ismail include a diverse mix of human and natural aerosols: Aged secondary aerosols from industry and transportation go far. Urban pollution may include Atlantic and Mediterranean marine aerosols and sea
salt particles. The confluence of dust from the southern flow occurs in the Sahara. Due to its regularity, pathway 1 dominates Bou-Ismail's background air quality, impacting particulate matter and trace gas levels without local pollution.

Pathway 2, represented by 4% of the trajectory, was characterised by an air mass trajectory that primarily originated from the south, moving across the southern region of Libya and over the vast Algerian Desert. Southern pathway 2 showed a sluggish flow in Bou-Ismail Province, resulting in elevated pollution concentrations.

Air masses passing across dry and semi-arid regions likely picked up natural desert dust and manmade emissions from southern Libya and Algeria's oil exploitation, flaring, and industrial activity. NOx, SOx, VOCs, and PM emissions are included. Pathway 2's airflow slowed or stagnated in Bou-Ismail Province, limiting contaminant dispersal. This caused toxins to accumulate and raise regional contamination. Low wind speeds and thermal inversion likely trapped pollutants at the surface, worsening air quality.

For pathway 3, the predominant source of pollution came from northwest Italy and Tunisia via the Mediterranean Sea, which represented 24% of the total trajectory. These pollutants traversed central Italy, northern France, northeastern Algeria, and the Mediterranean Basin before reaching the city of Bou-Ismail. Following the flight over densely populated metropolitan regions, chilly air infused with particulate matter permeated Bou-Ismail.

The air mass flew over heavily industrialised and urbanised areas. Vehicle emissions, industrial pollution, and thermal power plant discharges produce high NOₓ, SO₂, PM₂.₅, PM₁₀, and ozone precursors in Northwestern and Central Italy. Industrial centres, oil refineries, and agricultural zones in northern France generate combustion-related pollutants and secondary aerosols, such as nitrates, sulphates, and ammonia-derived particles. In northeastern Algeria, industrial regions around Annaba and Skikda, including petrochemical complexes, cement mills, and ships, contribute to air pollution with PM, SO₂, and heavy metals. Over the Mediterranean Sea, air masses likely transport contaminants, particularly fine particulate matter (PM₂.₅), which stays suspended in the atmosphere for lengthy durations. Due to maritime humidity and colder temperatures, gas-phase pollutants may have condensed and formed secondary aerosols. The arrival of chilly, particle-laden air increased pollution in Bou-Ismail. Transboundary pollution transfer and local climatic variables, including temperature inversions and coastal breeze stagnation, may have increased ground-level pollution.

## 4 Conclusion

Under the framework of the ChArMEx project, to assess the chemical composition of the Mediterranean region's air, including aerosols, to learn how it affects local weather, air quality, and biogeochemistry on land and sea, as well as to foretell what's to come. The PM₂.₅ weekly aerosol mass concentration of water-soluble inorganic ions (WSII) in Bou-Ismail, Algeria, was investigated from December 29th, 2013, to June 29th, 2014.

The high water-soluble ions, including Cl⁻, NO₃⁻, SO₄²⁻, Na⁺, NH₄⁺, K⁺, Mg²⁺, and Ca²⁺, along with two carboxylic acids, MSA and oxalate, found in Bou-Ismail, were examined. The mean WSII mass concentrations in this campaign were 26.41±0.35 µg m⁻³. NH₄⁺>Na⁺>NO₃⁻ were the main ions in the PM₂.₅ particle fractions, accounting for 56.7% of the WSII, with mean mass concentrations of 7.20, 4.15, and 3.64 µg m⁻³, respectively. The other ions (MSA, K⁺, and Mg²⁺) comprised only 1.82% of the WSII, with mass con-
centrations below 1 µg m⁻³. The winter TWSII concentration was 14.06±0.22 µg m⁻³. Na⁺, NH₄⁺, NO₃⁻, and Cl⁻ were the primary WSII.

NH₄⁺ levels were higher in spring than in winter. Secondary chemical reactions create H₂SO₄ from SO₂ oxidation or NH₄⁺ from gaseous NH₃ and HNO₃. Spring NO₃⁻ levels were lower, and winter levels were greater. In contrast, Spring had higher SO₄²⁻ levels than winter. Temperature impacts nitrate and sulphate production, creating the discrepancy. Heterogeneous or homogeneous reactions and dry or moist deposition may occur. Two PCAs reflected 79.59% of the total variation (PC1: secondary organic traces, marine sources, biomass, and stationary emissions, PC2: building and secondary gas-particle transformations). The WSII occurred in winter in the form of NaCl, MgCl, NH₄NO₃, NaNO₃, NH₄Cl, Ca(SO₄), MgNO₃, and KCl. The findings suggest that K⁺, NO₃⁻, NH₄⁺, and oxalate may cause pulmonary toxicity, inflammation, and oxidative damage. While Na⁺, Mg²⁺, Cl⁻, MSA are less hazardous but may induce pulmonary deposition and interact with more harmful species, Ca²⁺ and SO₄²⁻ increased lung inflammation and systemic effects.

On the other hand, the spring TWSII concentration was 12.35±0.42 µg m⁻³. Na⁺, NH₄⁺, oxalate, and NO₃⁻ were the primary WSII. Four PCAs explained 84.45% of the overall variation: PC1: marine aerosol emissions, PC2: from stationary emissions, agricultural practices, marine biogenic emissions, and biomass burning, PC3: photochemical response, and PC4: soil dust. The WSII occurred in spring in the form of NaCl, MgCl, and (NH₄)₂SO₄. High levels of Na⁺, Cl⁻, and Mg²⁺, particularly when coupled with acidic species, might irritate asthmatics' airways, increase dangerous chemical particle mass and respiratory deposition. Oxalate and NO₃⁻ may lead to aerosol acidity, respiratory discomfort, immunological disruption, and oxidative damage and increase oxidative stress. Although Ca²⁺ is harmless, its presence in PM₂.₅ might trigger physical and inflammatory effects, especially when mixed with other particulates.

The few seasonal fluctuations encountered in the average mass ratios of [NO₃⁻] and [SO₄²⁻] in Bou-Ismail (greater than 1) may indicate the influence of pollutants from mobile sources over stationary sources. The ratio (NH₄⁺/SO₄²⁻) was greater for the measuring PM during the whole period (up to 5). The link between ammonium, sulphate, and nitrate concentrations is often used to characterise aerosol acidity. Environmental ammonia neutralises a significant amount of sulphate and nitrate. The ratio [NH₄⁺]/([SO₄²⁻] + [NO₃⁻]) was generally smaller than 1, especially in winter (0.4) compared to spring (1.7). These data reveal that Bou-Ismail samples only have low NH₄⁺ ions in winter. The slope of the cationic to anionic regression curve was 1.29 for the entire sample campaign. The mean ratio of total cations to total anions was 1.56, suggesting that cations dominated the ionic composition and the aerosol particles were poor in anions. This insufficiency may be due to high aerosol CO₃²⁻ or HCO₃⁻ levels.

The Hysplit model allows for the assessment and classification of atmospheric events affecting the southern Mediterranean. Trajectories 1, 2, and 3 were grouped and pointed southwest, south, and northwest. About 72% of air masses reach the sample site via Pathway 1. All air masses from the west and northwest of the sample site are in Pathway 1. The large air masses cross the Atlantic Ocean via Spain, Portugal, northern Morocco, southern France, and western Algeria. Pathway 2, with 4% of the trajectory, had an air mass trajectory from the south that crossed the Algerian Desert and southern Libya. This region is recognised for its oil extraction, gas flaring, and industrial operations. In pathway 3, northwest Italy and Tunisia via the Mediterranean Sea were the most polluted.
Although not all chemicals were evaluated in this study, the concentration assessment of carbon (both organic and elemental), PAHs (polycyclic aromatic hydrocarbons), heavy metals, and saccharides warrants future investigations.

**Author contributions** Lyes Rabhi: From sample-to-sample processing, analysis and data processing. As well as the writing.Abdelkader Lemou: From sample-to-sample processing, analysis and data processing. As well as the writing.Riad Ladji: Discussed results, analysed data and commented on the paper.Nicolas Bonnaire: Validation of instrumental analysis data.Jean Sciare: Supervising the sampling campaigns and chemical characterisations.Noureddine Yassaa: Structuring of the manuscript and approved the version to be published.

**Funding** financial support from CMEP-TASSILI Algeria-France, EARLINET-ASOS Network by the European Commission under grant RICA-025991.

**Data availability** No datasets were generated or analysed during the current study.

**Declarations**

**Competing interests** The authors declare no competing interests.

**Consent to participate** Not applicable in this section.

**Consent to publish** 'Not applicable' in this section.

**Ethics approval** This material is the authors' own original work, which has not been previously published elsewhere.

**References**

Allen, H.M., Draper, D.C., Ayres, B.R., Ault, A., Bondy, A., Takahama, S., Modini, R.L., Baumann, K., Edgerton, E., Knote, C., Laskin, A., Wang, B., Fry, J.L.: Influence of crustal dust and sea spray supermicron particle concentrations and acidity on inorganic NO3- aerosol during the 2013 Southern oxidant and aerosol study. Atmos. Chem. Phys. **15**, 10669–10685 (2015). https://doi.org/10.5194/acp-15-10669-2015

An, Z., Huang, R.-J., Zhang, R., Tie, X., Li, G., Cao, J., Zhou, W., Shi, Z., Han, Y., Gu, Z., Ji, Y.: Severe haze in Northern China: A synergy of anthropogenic emissions and atmospheric processes. Proceedings of the National Academy of Sciences **116**, 8657–8666 (2019). https://doi.org/10.1073/pnas.1900125116

Andreae, M.O., Andreae, T.W., Meyerdierks, D., Thiel, C.: Marine sulfur cycling and the atmospheric aerosol over the springtime North Atlantic. Chemosphere. **52**, 1321–1343 (2003). https://doi.org/10.1016/S0045-6535(03)00366-7

Arimoto, R., Duce, R.A., Savoie, D.L., Prospero, J.M., Talbot, R., Cullen, J.D., Tomza, U., Lewis, N.F., Ray, B.J.: Relationships among aerosol constituents from Asia and the North Pacific during PEM-West A. J. Geophys. Research: Atmos. **101**, 2011–2023 (1996). https://doi.org/10.1029/95JD01071

Arndt, J., Sciare, J., Mallet, M., Roberts, G.C., Marchand, N., Sartelet, K., Sellegri, K., Dulac, F., Healy, R.M., Wenger, J.C.: Sources and mixing state of summertime background aerosol in the north-western mediterranean basin. Atmos. Chem. Phys. **17**, 6975–7001 (2017). https://doi.org/10.5194/acp-17-6975-2017

Becagli, S., Anello, F., Bommarito, C., Cassola, F., Calzolai, G., Di Iorio, T., di Sarra, A., Gómez-Amo, J.-L., Lucarelli, F., Marconi, M., Meloni, D., Monteleone, F., Nava, S., Pace, G., Severi, M., Sferlazzo, D.M., Traversi, R., Udisti, R.: Constraining the ship contribution to the aerosol of the central mediterranean. Atmos. Chem. Phys. **17**, 2067–2084 (2017). https://doi.org/10.5194/acp-17-2067-2017

Benchrif, A., Guinot, B., Bounakhla, M., Cachier, H., Damnati, B., Baghdad, B.: Aerosols in Northern Morocco: Input pathways and their chemical fingerprint. Atmos. Environ. **174**, 140–147 (2018). https://doi.org/10.1016/j.atmosenv.2017.11.047

Benchrif, A., Tahri, M., Guinot, B., Chakir, E.M., Zahry, F., Bagdhad, B., Bounakhla, M., Cachier, H., Costabile, F.: Aerosols in Northern Morocco-2: Chemical characterization and PMF source apportionment of ambient PM2.5. Atmosphere. **13**, 1701 (2022). https://doi.org/10.3390/atmos13101701
Bounakhla, Y., Benchrif, A., Costabile, F., Tahri, M., El Gourch, B., Hassan, E., Zahry, E.K., Bounakhla, F.: Overview of PM10, PM2.5 and BC and their dependent relationships with meteorological variables in an urban area in Northwestern Morocco. Atmosphere. **14** (2023). https://doi.org/10.3390/atmos14010162

Bressi, M., Sciare, J., Ghersi, V., Bonnaire, N., Nicolas, J.B., Petit, J.-E., Moukhtar, S., Rosso, A., Mihalopoulos, N., Féron, A.: A one-year comprehensive chemical characterisation of fine aerosol (PM2.5) at urban, suburban and rural background sites in the region of Paris (France). Atmos. Chem. Phys. **13**, 7825–7844 (2013). https://doi.org/10.5194/acp-13-7825-2013

Bressi, M., Sciare, J., Ghersi, V., Mihalopoulos, N., Petit, J.-E., Nicolas, J.B., Moukhtar, S., Rosso, A., Féron, A., Bonnaire, N., Poulakis, E., Theodosi, C.: Sources and geographical origins of fine aerosols in Paris (France). Atmos. Chem. Phys. **14**, 8813–8839 (2014). https://doi.org/10.5194/acp-14-8813-2014

Calzolai, G., Nava, S., Lucarelli, F., Chiari, M., Giannoni, M., Becagli, S., Traversi, R., Marconi, M., Frosini, D., Severi, M., Udisti, R., di Sarra, A., Pace, G., Meloni, D., Bommarito, C., Monteleone, F., Anello, F., Sferlazzo, D.M.: Characterization of PM10 sources in the central mediterranean. Atmos. Chem. Phys. **15**, 13939–13955 (2015). https://doi.org/10.5194/acp-15-13939-2015

Cao, J.-J., Zhang, T., Chow, J.C., Watson, J.G., Wu, F., Li, H.: Characterization of atmospheric ammonia over xi'an, China. Aerosol Air Qual. Res. **9**, 277–289 (2009). https://doi.org/10.4209/aaqr.2008.10.0043

Caumo, S., Yera, A.B., Alves, C., Casotti Rienda, I., Kováts, N., Hubai, K., De Castro Vasconcellos, P.: Assessing the chemical composition, potential toxicity and cancer risk of airborne fine particulate matter (PM2.5) near a petrochemical industrial area. Environ. Toxicol. Pharmacol. **101**, 104170 (2023). https://doi.org/10.1016/j.etap.2023.104170

Cesari, D., Merico, E., Grasso, F.M., Decesari, S., Belosi, F., Manarini, F., De Nuntiis, P., Rinaldi, M., Volpi, F., Gambaro, A., Morabito, E., Contini, D.: Source apportionment of PM2.5 and of its oxidative potential in an industrial suburban site in South Italy. Atmosphere (2019). https://doi.org/10.3390/atmos10120758

Cheng, Z.L., Lam, K.S., Chan, L.Y., Wang, T., Cheng, K.K.: Chemical characteristics of aerosols at coastal station in Hong kong. I. Seasonal variation of major ions, halogens and mineral dusts between 1995 and 1996. Atmos. Environ. **34**, 2771–2783 (2000). https://doi.org/10.1016/S1352-2310(99)00343-X

Cheng, H., Gong, W., Wang, Z., Zhang, F., Wang, X., Lv, X., Liu, J., Fu, X., Zhang, G.: Ionic composition of submicron particles (PM1.0) during the long-lasting haze period in January 2013 in wuhan, central China. J. Environ. Sci. **26**, 810–817 (2014). https://doi.org/10.1016/S1001-0742(13)60503-3

Cheng, C., Shi, M., Liu, W., Mao, Y., Hu, J., Tian, Q., Chen, Z., Hu, T., Xing, X., Qi, S.: Characteristics and source apportionment of water-soluble inorganic ions in PM2.5 during a wintertime haze event in huanggang, central China. Atmospheric Pollution Research **12**, 111–123 (2021). https://doi.org/10.1016/j.apr.2020.08.026

Cherfaoui, B., Lemou, A., Rabhi, L., Cherifi, N., Ladji, R.: Water-soluble organic compounds (WSOC) from atmospheric aerosols in Bou-Ismail (Algeria). Environ. Sci. Pollut. Res. (2024). https://doi.org/10.1007/s11356-024-34520-2

Contini, D., Cesari, D., Genga, A., Siciliano, M., Ielpo, P., Guascito, M.R., Conte, M.: Source apportionment of size-segregated atmospheric particles based on the major water-soluble components in Lecce (Italy). Sci. Total Environ. **472**, 248–261 (2014). https://doi.org/10.1016/j.scitotenv.2013.10.127

Cui, Y., Yin, Y., Chen, K., Zhang, X., Kuang, X., Jiang, H., Wang, H., Zhen, Z., He, C.: Characteristics and sources of WSI in North China plain: A simultaneous measurement at the summit and foot of Mount Tai. J. Environ. Sci. **92**, 264–277 (2020). https://doi.org/10.1016/j.jes.2020.02.017

Custódio, D., Cerreira, M., Alves, C., Nunes, T., Pio, C., Esteves, V., Frosini, D., Lucarelli, F., Querol, X.: A one-year record of carbonaceous components and major ions in aerosols from an urban Kerbside location in oporto, Portugal. Sci. Total Environ. **562**, 822–833 (2016). https://doi.org/10.1016/j.scitotenv.2016.04.012

Cvitešić Kušan, A., Kroflič, A., Grgić, I., Ciglenečki, I., Frka, S.: Chemical characterization of fine aerosols in respect to water-soluble ions at the Eastern middle Adriatic Coast. Environ. Sci. Pollut. Res. **27**, 10249–10264 (2020). https://doi.org/10.1007/s11356-020-07617-7

Deabji, N., Fomba, K.W., El Hajjaji, S., Mellouki, A., Poulain, L., Zeppenfeld, S., Herrmann, H.: First insights into Northern Africa high-altitude background aerosol chemical composition and source influences. Atmos. Chem. Phys. **21**, 18147–18174 (2021). https://doi.org/10.5194/acp-21-18147-2021

Deabji, N., Fomba, K.W., Dos Santos Souza, E.J., Mellouki, A., Herrmann, H.: Influence of anthropogenic activities on metals, sugars and PAHs in PM10 in the City of fez, morocco: Implications on air quality. Environ. Sci. Pollut. Res. (2024). https://doi.org/10.1007/s11356-024-32740-0

Deng, X., Shi, C., Wu, B., Yang, Y., Jin, Q., Wang, H., Zhu, S., Yu, C.: Characteristics of the water-soluble components of aerosol particles in hefei, China. J. Environ. Sci. **42**, 32–40 (2016). https://doi.org/10.1016/j.jes.2015.07.010
Diapouli, E., Manousakas, M., Vratolis, S., Vasilatou, V., Maggos, T., Saraga, D., Grigoratos, T., Argyropoulos, G., Voutsa, D., Samara, C., Eleftheriadis, K.: Evolution of air pollution source contributions over one decade, derived by PM10 and PM2.5 source apportionment in two metropolitan urban areas in Greece. Atmos. Environ. **164**, 416–430 (2017). https://doi.org/10.1016/j.atmosenv.2017.06.016

El Gourch, B., Ihssane, B., Tahri, M., Zahry, F., Acil, G., Saffaj, T., Benchrif, A.: Seasonal variations and composition of soluble ions in PM2.5 at an urban location in kenitra, Morocco. Environ. Sci. Proc. **27** (2023). https://doi.org/10.3390/ecas2023-16341

Farren, N.J., Dunmore, R.E., Mead, M.I., Mohd Nadzir, M.S., Samah, A.A., Phang, S.-M., Bandy, B.J., Sturges, W.T., Hamilton, J.F.: Chemical characterisation of water-soluble ions in atmospheric particulate matter on the East Coast of Peninsular Malaysia. Atmos. Chem. Phys. **19**, 1537–1553 (2019). https://doi.org/10.5194/acp-19-1537-2019

Franklin, M., Koutrakis, P., Schwartz, J.: The role of particle composition on the association between PM 2.5 and mortality. Epidemiology. **19**, 680–689 (2008)

Galindo, N., Yubero, E., Nicolás, J.F., Crespo, J., Varea, M., Gil-Moltó, J.: Regional and long-range transport of aerosols at mt. Aitana, southeastern Spain. Sci. Total Environ. **584–585**, 723–730 (2017). https://doi.org/10.1016/j.scitotenv.2017.01.108

Gluščić, V., Žužul, S., Pehnec, G., Jakovljević, I., Smoljo, I., Godec, R., Bešlić, I., Milinković, A., Alempijević, S.B., Frka, S.: Sources, ionic composition and acidic properties of bulk and wet atmospheric deposition in the Eastern middle Adriatic region. Toxics. **11**, 551 (2023). https://doi.org/10.3390/toxics11070551

Golly, B., Waked, A., Weber, S., Samake, A., Jacob, V., Conil, S., Rangognio, J., Chrétien, E., Vagnot, M.-P., Robic, P.-Y., Besombes, J.-L., Jaffrezo, J.-L.: Organic markers and OC source apportionment for seasonal variations of PM2.5 at 5 rural sites in France. Atmos. Environ. **198**, 142–157 (2019). https://doi.org/10.1016/j.atmosenv.2018.10.027

Guo, S., Hu, M., Wang, Z.B., Slanina, J., Zhao, Y.L.: Size-resolved aerosol water-soluble ionic compositions in the summer of beijing: Implication of regional secondary formation. Atmos. Chem. Phys. **10**, 947–959 (2010). https://doi.org/10.5194/acp-10-947-2010

Guo, W., Zhang, Z., Zheng, N., Luo, L., Xiao, H., Xiao, H.: Chemical characterization and source analysis of water-soluble inorganic ions in PM2.5 from a plateau city of Kunming at different seasons. Atmos. Res. **234**, 104687 (2020). https://doi.org/10.1016/j.atmosres.2019.104687

Han, X., Guo, Q., Liu, C., Strauss, H., Yang, J., Hu, J., Wei, R., Tian, L., Kong, J., Peters, M.: Effect of the pollution control measures on PM 2.5 during the 2015 China victory day parade: Implication from water-soluble ions and sulfur isotope. Environ. Pollut. **218**, 230–241 (2016). https://doi.org/10.1016/j.envpol.2016.06.038

Hennigan, C.J., Izumi, J., Sullivan, A.P., Weber, R.J., Nenes, A.: A critical evaluation of proxy methods used to estimate the acidity of atmospheric particles. Atmos. Chem. Phys. **15**, 2775–2790 (2015). https://doi.org/10.5194/acp-15-2775-2015

Heo, J., Schauer, J.J., Yi, O., Paek, D., Kim, H., Yi, S.-M.: Fine particle air pollution and mortality: Importance of specific sources and chemical species. Epidemiology. **25**, 379–388 (2014)

Hsu, Y.-C., Lai, M.-H., Wang, W.-C., Chiang, H.-L., Shieh, Z.-X.: Characteristics of Water-Soluble ionic species in fine (PM2.5) and coarse particulate matter (PM10–2.5) in kaohsiung, Southern Taiwan. J. Air Waste Manag. Assoc. **58**, 1579–1589 (2008). https://doi.org/10.3155/1047-3289.58.12.1579

Huang, R.-J., Zhang, Y., Bozzetti, C., Ho, K.-F., Cao, J.-J., Han, Y., Daellenbach, K.R., Slowik, J.G., Platt, S.M., Canonaco, F., Zotter, P., Wolf, R., Pieber, S.M., Bruns, E.A., Crippa, M., Ciarelli, G., Piazzalunga, A., Schwikowski, M., Abbaszade, G., Schnelle-Kreis, J., Zimmermann, R., An, Z., Szidat, S., Baltensperger, U., Haddad, I.E., Prévôt, A.S.H.: High secondary aerosol contribution to particulate pollution during haze events in China. Nature. **514**, 218–222 (2014). https://doi.org/10.1038/nature13774

Huang, T., Chen, J., Zhao, W., Cheng, J., Cheng, S.: Seasonal variations and correlation analysis of Water-Soluble inorganic ions in PM2.5 in wuhan, 2013. Atmosphere. **7**, 49 (2016). https://doi.org/10.3390/atmos7040049

Huang, X., Tang, G., Zhang, J., Liu, B., Liu, C., Zhang, J., Cong, L., Cheng, M., Yan, G., Gao, W., Wang, Y.: Characteristics of PM2.5 pollution in Beijing after the improvement of air quality. J. Environ. Sci. **100**, 1–10 (2021). https://doi.org/10.1016/j.jes.2020.06.004

Ianniello, A., Spataro, F., Esposito, G., Allegrini, I., Hu, M., Zhu, T.: Chemical characteristics of inorganic ammonium salts in PM2.5 in the atmosphere of Beijing (China). Atmos. Chem. Phys. **11**, 10803–10822 (2011). https://doi.org/10.5194/acp-11-10803-2011

Jain, S., Sharma, S.K., Choudhary, N., Masiwal, R., Saxena, M., Sharma, A., Mandal, T.K., Gupta, A., Gupta, N.C., Sharma, C.: Chemical characteristics and source apportionment of PM2.5 using PCA/APCS, UNMIX, and PMF at an urban site of delhi, India. Environ. Sci. Pollut. Res. **24**, 14637–14656 (2017). https://doi.org/10.1007/s11356-017-8925-5
Karageorgos, E.T., Rapsomanikis, S.: Chemical characterization of the inorganic fraction of aerosols and mechanisms of the neutralization of atmospheric acidity in athens, Greece. Atmos. Chem. Phys. **7**, 3015–3033 (2007). https://doi.org/10.5194/acp-7-3015-2007

Karnae, S., John, K.: Source apportionment of PM2.5 measured in South Texas near U.S.A.–Mexico border. Atmospheric Pollution Research **10**, 1663–1676 (2019). https://doi.org/10.1016/j.apr.2019.06.007

Kchih, H., Perrino, C., Cherif, S.: Investigation of desert dust contribution to source apportionment of PM10 and PM2.5 from a Southern mediterranean Coast. Aerosol Air Qual. Res. **15**, 454–464 (2015). https://doi.org/10.4209/aaqr.2014.10.0255

Khan, R., Konishi, S., Ng, C.F.S., Umezaki, M., Kabir, A.F., Tasmin, S., Watanabe, C.: Association between short-term exposure to fine particulate matter and daily emergency room visits at a cardiovascular hospital in dhaka, Bangladesh. Sci. Total Environ. **646**, 1030–1036 (2019). https://doi.org/10.1016/j.scitotenv.2018.07.288

Khedidji, S., Müller, K., Rabhi, L., Spindler, G., Fomba, K.W., van Pinxteren, D., Yassaa, N., Herrmann, H.: Chemical characterization of marine aerosols in a South mediterranean coastal area located in Bou ismail, Algeria. Aerosol Air Qual. Res. **20**, 2448–2473 (2020). https://doi.org/10.4209/aaqr.2019.09.0458

Koçak, M., Mihalopoulos, N., Kubilay, N.: Contributions of natural sources to high PM10 and PM2.5 events in the Eastern mediterranean. Atmos. Environ. **41**, 3806–3818 (2007a). https://doi.org/10.1016/j.atmosenv.2007.01.009

Koçak, M., Mihalopoulos, N., Kubilay, N.: Chemical composition of the fine and coarse fraction of aerosols in the Northeastern mediterranean. Atmos. Environ. **41**, 7351–7368 (2007b). https://doi.org/10.1016/j.atmosenv.2007.05.011

Kumar, P., Yadav, S.: Factors and sources influencing ionic composition of atmospheric condensate during winter season in lower troposphere over delhi, India. Environ. Monit. Assess. **185**, 2795–2805 (2013). https://doi.org/10.1007/s10661-012-2749-z

Lelieveld, J., Berresheim, H., Borrmann, S., Crutzen, P.J., Dentener, F.J., Fischer, H., Feichter, J., Flatau, P.J., Heland, J., Holzinger, R., Korrmann, R., Lawrence, M.G., Levin, Z., Markowicz, K.M., Mihalopoulos, N., Minikin, A., Ramanathan, V., de Reus, M., Roelofs, G.J., Scheeren, H.A., Sciare, J., Schlager, H., Schultz, M., Siegmund, P., Steil, B., Stephanou, E.G., Stier, P., Traub, M., Warneke, C., Williams, J., Ziereis, H.: Global air pollution crossroads over the mediterranean. Science **298**, 794 (2002). https://doi.org/10.1126/science.1075457

Lemou, A., Rabhi, L., Merabet, H., Ladji, R., Nicolas, J.B., Bonnaire, N., Mustapha, M.A., Dilmi, R., Sciare, J., Mihalopoulos, N., Yassaa, N.: Chemical characterization of fine particles (PM2.5) at a coastal site in the South Western mediterranean during the ChArMex experiment. Environ. Sci. Pollut. Res. **27**, 20427–20445 (2020). https://doi.org/10.1007/s11356-020-08168-7

Lemou, A., Rabhi, L., Ladji, R., Nicolas, J.B., Bonnaire, N., Sciare, J., Yassaa, N.: Sources of PM10 ionic species in the South-West mediterranean (Algeria). Environ. Sci. Pollut. Res. (2024). https://doi.org/10.1007/s11356-024-34449-6

Li, Y., Hao, Q., Wen, T., Ji, D., Liu, Z., Wang, Y., Li, X., He, X., Jiang, C.: Pollution characteristics of Water-soluble ions in aerosols in the urban area in Beibei of Chongqing. Aerosol Air Qual. Res. **18**, 1531–1544 (2018). https://doi.org/10.4209/aaqr.2017.11.0500

Liang, C.-S., Duan, F.-K., He, K.-B., Ma, Y.-L.: Review on recent progress in observations, source identifications and countermeasures of PM2.5. Environ. Int. **86**, 150–170 (2016). https://doi.org/10.1016/j.envint.2015.10.016

Liu, X., Wang, M., Pan, X., Wang, X., Yue, X., Zhang, D., Ma, Z., Tian, Y., Liu, H., Lei, S., Zhang, Y., Liao, Q., Ge, B., Wang, D., Li, J., Sun, Y., Fu, P., Wang, Z., He, H.: Chemical formation and source apportionment of PM2.5 at an urban site at the Southern foot of the Taihang mountains. J. Environ. Sci. **103**, 20–32 (2021). https://doi.org/10.1016/j.jes.2020.10.004

Long, T., Peng, B., Yang, Z., Ishimwe, C.S., Tang, C., Zhao, N., Lin, H., Zhong, K., Zhong, S.: Spatial distribution and formation mechanism of Water-soluble inorganic ions in PM2.5 during a typical winter haze episode in guilin, China. Arch. Environ. Contam. Toxicol. **78**, 367–376 (2020). https://doi.org/10.1007/s00244-019-00699-6

Meng, C.C., Wang, L.T., Zhang, F.F., Wei, Z., Ma, S.M., Ma, X., Yang, J.: Characteristics of concentrations and water-soluble inorganic ions in PM2.5 in Handan city, Hebei province, China. Atmos. Res. **171**, 133–146 (2016). https://doi.org/10.1016/j.atmosres.2015.12.013

Meng, L., Zuo, R., Wang, J., Yang, J., Teng, Y., Shi, R., Zhai, Y.: Apportionment and evolution of pollution sources in a typical riverside groundwater resource area using PCA-APCS-MLR model. J. Contam. Hydrol. **218**, 70–83 (2018). https://doi.org/10.1016/j.jconhyd.2018.10.005

Ostro, B., Tobias, A., Querol, X., Alastuey, A., Amato, F., Pey, J., Pérez, N., Sunyer, J.: The effects of particulate matter sources on daily mortality: A Case-Crossover study of Barcelona, Spain. Environ. Health Perspect. **119**, 1781–1787 (2011)
Pandolfi, M., Mooibroek, D., Hopke, P., van Pinxteren, D., Querol, X., Herrmann, H., Alastuey, A., Favez, O., Hüglin, C., Perdrix, E., Rifault, V., Sauvage, S., van der Swaluw, E., Tarasova, O., Colette, A.: Long-range and local air pollution: What can we learn from chemical speciation of particulate matter at paired sites? Atmos. Chem. Phys. **20**, 409–429 (2020). https://doi.org/10.5194/acp-20-409-2020

Paraskevopoulou, D., Liakakou, E., Gerasopoulos, E., Mihalopoulos, N.: Sources of atmospheric aerosol from long-term measurements (5years) of chemical composition in athens, Greece. Sci. Total Environ. **527–528**, 165–178 (2015). https://doi.org/10.1016/j.scitotenv.2015.04.022

Pateraki, S., Assimakopoulos, V.D., Bougiatioti, A., Kouvarakis, G., Mihalopoulos, N., Vasilakos, C.: Carbonaceous and ionic compositional patterns of fine particles over an urban mediterranean area. Sci. Total Environ. **424**, 251–263 (2012). https://doi.org/10.1016/j.scitotenv.2012.02.046

Pathak, R.K., Wu, W.S., Wang, T.: Summertime PM2.5 ionic species in four major cities of china: Nitrate formation in an ammonia-deficient atmosphere. Atmos. Chem. Phys. **9**, 1711–1722 (2009). https://doi.org/10.5194/acp-9-1711-2009

Perrone, M.R., Dinoi, A., Becagli, S., Udisti, R.: Chemical composition of PM1 and PM2.5 at a suburban site in Southern Italy. Int. J. Environ. Anal. Chem. **94**, 127–150 (2014). https://doi.org/10.1080/0306791978

Perrone, M.G., Vratolis, S., Georgieva, E., Török, S., Šega, K., Veleva, B., Osán, J., Bešlić, I., Kertész, Z., Pernigotti, D., Eleftheriadis, K., Belis, C.A.: Sources and geographic origin of particulate matter in urban areas of the Danube macro-region: The cases of Zagreb (Croatia), Budapest (Hungary) and Sofia (Bulgaria). Sci. Total Environ. **619–620**, 1515–1529 (2018). https://doi.org/10.1016/j.scitotenv.2017.11.092

Perrone, M.R., Vecchi, R., Romano, S., Becagli, S., Traversi, R., Paladini, F.: Weekly cycle assessment of PM mass concentrations and sources, and impacts on temperature and wind speed in Southern Italy. Atmos. Res. **218**, 129–144 (2019). https://doi.org/10.1016/j.atmosres.2018.11.013

Petit, J.-E., Favez, O., Sciare, J., Crenn, V., Sarda-Estève, R., Bonnaire, N., Močnik, G., Dupont, J.-C., Haefelin, M., Leoz-Garziandia, E.: Two years of near real-time chemical composition of submicron aerosols in the region of Paris using an aerosol chemical speciation monitor (ACSM) and a multi-wavelength aethalometer. Atmos. Chem. Phys. **15**, 2985–3005 (2015). https://doi.org/10.5194/acp-15-2985-2015

Pey, J., Querol, X., Alastuey, A.: Variations of levels and composition of PM10 and PM2.5 at an insular site in the Western mediterranean. Atmos. Res. **94**, 285–299 (2009). https://doi.org/10.1016/j.atmosres.2009.06.006

Priyadharshini, B., Verma, S., Chatterjee, A., Sharma, S.K., Mandal, T.K.: Chemical characterization of fine atmospheric particles of Water-Soluble ions and carbonaceous species in a tropical urban atmosphere over the Eastern Indo-Gangetic plain. Aerosol Air Qual. Res. **19**, 129–147 (2019). https://doi.org/10.4209/aaqr.2017.12.0606

Rabhi, L., Lemou, A., Cecinato, A., Balducci, C., Cherifi, N., Ladji, R., Yassaa, N.: Polycyclic aromatic hydrocarbons, phthalates, Parabens and other environmental contaminants in dust and suspended particulates of algiers, Algeria. Environ. Sci. Pollut Res. **25**, 24253–24265 (2018). https://doi.org/10.1007/s11356-018-2496-y

Rabhi, L., Lemou, A., Ladji, R., Khedidji, S., Bonnaire, N., Sciare, J., Yassaa, N.: Size distribution of water-soluble ions and carbon content in the Southwest mediterranean basin: Results from the ChArMEx project. Air Qual. Atmos. Health. (2024). https://doi.org/10.1007/s11869-024-01614-2

Remoundaki, E., Kassomenos, P., Mantas, E., Mihalopoulos, N., Tsezos, M., Athens, Greece: Aerosol Air Qual. Res. **13**, 72–82 (2013). https://doi.org/10.4209/aaqr.2012.03.0054

Ripoll, A., Minguillón, M.C., Pey, J., Pérez, N., Querol, X., Alastuey, A.: Joint analysis of continental and regional background environments in the Western mediterranean: PM₁ and PM₁₀ concentrations and composition. Atmos. Chem. Phys. **15**, 1129–1145 (2015). https://doi.org/10.5194/acp-15-1129-2015

Salameh, D., Detournay, A., Pey, J., Pérez, N., Liguori, F., Saraga, D., Bove, M.C., Brotto, P., Cassola, F., Massabò, D., Latella, A., Pillon, S., Formenton, G., Patti, S., Armengaud, A., Piga, D., Jaffrezo, J.L., Bartzis, J., Tolis, E., Prati, P., Querol, X., Wortham, H., Marchand, N.: PM2.5 chemical composition in five European mediterranean cities: A 1-year study. Atmos. Res. **155**, 102–117 (2015). https://doi.org/10.1016/j.atmosres.2014.12.001

Schwarz, J., Cusack, M., Karban, J., Chalupničková, E., Havránek, V., Smolik, J., Ždímal, V.: PM2.5 chemical composition at a rural background site in central europe, including correlation and air mass back trajectory analysis. Atmos. Res. **176–177**, 108–120 (2016). https://doi.org/10.1016/j.atmosres.2016.02.017

Sciare, J., Oikonomou, K., Cachier, H., Mihalopoulos, N., Andreae, M.O., Maenhaut, W., Sarda-Estève, R.: Aerosol mass closure and reconstruction of the light scattering coefficient over the Eastern mediterranean sea during the MINOS campaign. Atmos. Chem. Phys. **5**, 2253–2265 (2005). https://doi.org/10.5194/acp-5-2253-2005

Sciare, J., Oikonomou, K., Favez, O., Liakakou, E., Markaki, Z., Cachier, H., Mihalopoulos, N.: Long-term measurements of carbonaceous aerosols in the Eastern mediterranean: Evidence of long-range transport of biomass burning. Atmos. Chem. Phys. **8**, 5551–5563 (2008). https://doi.org/10.5194/acp-8-5551-2008
Sciare, J., Favez, O., Sarda-Estève, R., Oikonomou, K., Cachier, H., Kazan, V.: Long-term observations of carbonaceous aerosols in the Austral ocean atmosphere: Evidence of a biogenic marine organic source. Journal of Geophysical Research: Atmospheres (2009). https://doi.org/10.1029/2009JD011998

Seinfeld, J.H., Pandis, S.N.: Atmospheric Chemistry and Physics: from Air Pollution To Climate Change. John Wiley & Sons, Inc, New Jersey (2006)

Shen, Z., Arimoto, R., Cao, J., Zhang, R., Li, X., Du, N., Okuda, T., Nakao, S.: Seasonal variations and evidence for the effectiveness of pollution controls on Water-Soluble inorganic species in total suspended particulates and fine particulate matter from xi'an, China. J. Air Waste Manag. Assoc. 58, 1560-1570 (2008). https://doi.org/10.3155/1047-3289.58.12.1560

Son, J.-Y., Lee, J.-T., Kim, K.-H., Jung, K., Bell, M.L.: Characterization of fine particulate matter and associations between particulate chemical constituents and mortality in seoul, Korea. Environ. Health Prospect. 120, 872-878 (2012)

Squizzato, S., Masiol, M., Brunelli, A., Pistollato, S., Tarabotti, E., Rampazzo, G., Pavoni, B.: Factors determining the formation of secondary inorganic aerosol: A case study in the Po Valley (Italy). Atmos. Chem. Phys. 13, 1927-1939 (2013). https://doi.org/10.5194/acp-13-1927-2013

Tang, Y.S., Braban, C.F., Dragosits, U., Simmons, I., Leaver, D., van Dijk, N., Poskitt, J., Thacker, S., Patel, M., Carter, H., Pereira, M.G., Keenan, P.O., Lawlor, A., Conolly, C., Vincent, K., Heal, M.R., Sutton, M.A.: Acid gases and aerosol measurements in the UK (1999-2015): Regional distributions and trends. Atmos. Chem. Phys. 18, 16293-16324 (2018). https://doi.org/10.5194/acp-18-16293-2018

Theodosi, C., Grivas, G., Zarmpas, P., Chaloulakou, A., Mihalopoulos, N.: Mass and chemical composition of size-segregated aerosols (PM1, PM2.5, PM10) over athens, greece: Local versus regional sources. Atmos. Chem. Phys. 11, 11895-11911 (2011). https://doi.org/10.5194/acp-11-11895-2011

Thepnuan, D., Chantara, S., Lee, C.-T., Lin, N.-H., Tsai, Y.I.: Molecular markers for biomass burning associated with the characterization of PM2.5 and component sources during dry season haze episodes in upper South East Asia. Sci. Total Environ. 658, 708-722 (2019). https://doi.org/10.1016/j.scitotenv.2018.12.201

Tolis, E.I., Saraga, D.E., Filiou, K.F., Tziavos, N.I., Tsiaousis, C.P., Dinas, A., Bartzis, J.G.: One-year intensive characterization on PM2.5 nearby Port area of thessaloniki, Greece. Environ. Sci. Pollut. Res. 22, 6812-6826 (2015). https://doi.org/10.1007/s11356-014-3883-7

Turap, Y., Rekefu, S., Wang, G., Talifu, D., Gao, B., Aierken, T., Hao, S., Wang, X., Tursun, Y., Maihemuti, M., Nuerla, A.: Chemical characteristics and source apportionment of PM2.5 during winter in the Southern part of urumqi, China. Aerosol Air Qual. Res. 19, 1325-1337 (2019). https://doi.org/10.4209/aqr.2018.12.0454

Voutsa, D., Samara, C., Manoli, E., Lazarou, D., Tzoumaka, P.: Ionic composition of PM2.5 at urban sites of Northern greece: Secondary inorganic aerosol formation. Environ. Sci. Pollut. Res. 21, 4995-5006 (2014). https://doi.org/10.1007/s11356-013-2445-8

Wang, Y., Zhuang, G., Tang, A., Yuan, H., Sun, Y., Chen, S., Zheng, A.: The ion chemistry and the source of PM2.5 aerosol in Beijing. Atmos. Environ. 39, 3771-3784 (2005). https://doi.org/10.1016/j.atmosenv.2005.03.013

Wang, B., Tang, Z., Cai, N., Niu, H.: The characteristics and sources apportionment of water-soluble ions of PM2.5 in suburb tangshan, China. Urban Clim 35, 100742 (2021a). https://doi.org/10.1016/j.uclim.2020.100742

Wang, H., Zhang, L., Cheng, I., Yao, X., Dabek-Zlotorzynska, E.: Spatiotemporal trends of PM2.5 and its major chemical components at urban sites in Canada. J. Environ. Sci. 103, 1-11 (2021b). https://doi.org/10.1016/j.jes.2020.09.035

Xing, W., Yang, L., Zhang, H., Zhang, X., Wang, Y., Bai, P., Zhang, L., Hayakawa, K., Nagao, S., Tang, N.: Variations in traffic-related water-soluble inorganic ions in PM2.5 in kanazawa, japan, after the implementation of a new vehicle emission regulation. Atmospheric Pollution Research 12, 101233 (2021). https://doi.org/10.1016/j.apr.2021.101233

Xu, W., Liu, X., Liu, L., Dore, A.J., Tang, A., Lu, L., Wu, Q., Zhang, Y., Hao, T., Pan, Y., Chen, J., Zhang, F.: Impact of emission controls on air quality in Beijing during APEC 2014: Implications from water-soluble ions and carbonaceous aerosol in PM2.5 and their precursors. Atmos. Environ. 210, 241-252 (2019). https://doi.org/10.1016/j.atmosenv.2019.04.050

Yang, F., Gu, Z., Feng, J., Liu, X., Yao, X.: Biogenic and anthropogenic sources of oxalate in PM2.5 in a mega City. Shanghai. Atmospheric Res. 138, 356-363 (2014). https://doi.org/10.1016/j.atmosres.2013.12.006

Yao, X., Chan, C.K., Fang, M., Cadle, S., Chan, T., Mulawa, P., He, K., Ye, B.: The water-soluble ionic composition of PM2.5 in Shanghai and beijing, China. Atmospheric Environment 36, 4223-4234 (2002). https://doi.org/10.1016/S1352-2310(02)00342-4

Yeatman, S.G., Spokes, L.J., Jickells, T.D.: Comparisons of coarse-mode aerosol nitrate and ammonium at two polluted coastal sites. Atmos. Environ. 35, 1321-1335 (2001). https://doi.org/10.1016/S1352-2310(00)00452-0
Yu, C., Yan, J., Zhang, H., Lin, Q., Zheng, H., Zhong, X., Zhao, S., Zhang, M., Zhao, S., Li, X.: Characteristics of aerosol WSI with High-Time-Resolution observation over Arctic ocean. Earth Space Sci. 7, 1–12 (2020). https://doi.org/10.1029/2020EA001227

Zhang, L., Vet, R., Wiebe, A., Mihele, C., Sukloff, B., Chan, E., Moran, M.D., Iqbal, S.: Characterization of the size-segregated water-soluble inorganic ions at eight Canadian rural sites. Atmos. Chem. Phys. 8, 7133–7151 (2008). https://doi.org/10.5194/acp-8-7133-2008

Zhang, T., Cao, J.J., Tie, X.X., Shen, Z.X., Liu, S.X., Ding, H., Han, Y.M., Wang, G.H., Ho, K.F., Qiang, J., Li, W.T.: Water-soluble ions in atmospheric aerosols measured in xi’an, china: Seasonal variations and sources. Atmospheric Research 102, 110–119 (2011). https://doi.org/10.1016/j.atmosres.2011.06.014

Zhang, Y., Obrist, D., Zielinska, B., Gertler, A.: Particulate emissions from different types of biomass burning. Atmos. Environ. 72, 27–35 (2013). https://doi.org/10.1016/j.atmosenv.2013.02.026

Zhang, Y., Tang, A., Wang, C., Ma, X., Li, Y., Xu, W., Xia, X., Zheng, A., Li, W., Fang, Z., Zhao, X., Peng, X., Zhang, Y., Han, J., Zhang, L., Collett, J.L., Liu, X.: PM2.5 and water-soluble inorganic ion concentrations decreased faster in urban than rural areas in China. J. Environ. Sci. 122, 83–91 (2022). https://doi.org/10.1016/j.jes.2021.09.031

Zhao, J., Xu, Y., Xu, J., Ji, Y.: The characteristics of Water-Soluble inorganic ions in PM1.0 and their impact on visibility at a typical coastal airport. Atmosphere (2024a). https://doi.org/10.3390/atmos15111367

Zhao, Z., Li, H., Wei, Y., Fang, G., Jiang, Q., Pang, Y., Huang, W., Tang, M., Jing, Y., Feng, X., Luo, X.-S., Berkemeier, T.: Airborne environmentally persistent free radicals (EPFRs) in PM2.5 from combustion sources: Abundance, cytotoxicity and potential exposure risks. Sci. Total Environ. 927, 172202 (2024b). https://doi.org/10.1016/j.scitotenv.2024.172202

Zhou, J., Xing, Z., Deng, J., Du, K.: Characterizing and sourcing ambient PM2.5 over key emission regions in China I: Water-soluble ions and carbonaceous fractions. Atmos. Environ. 135, 20–30 (2016). https://doi.org/10.1016/j.atmosenv.2016.03.054

Zhou, M., Zhang, Y., Han, Y., Wu, J., Du, X., Xu, H., Feng, Y., Han, S.: Spatial and Temporal characteristics of PM 2.5 acidity during autumn in marine and coastal area of Bohai sea, china, based on two-site contrast. Atmos. Res. 202, 196–204 (2018). https://doi.org/10.1016/j.atmosres.2017.11.014

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## Authors and Affiliations

Lyes Rabhi<fnref n="1" />,<fnref n="2" />,<fnref n="3" /> · Abdelkader Lemou<fnref n="1" />,<fnref n="2" />,<fnref n="3" /> · Riad Ladji<fnref n="1" />,<fnref n="2" /> · Nicolas Bonnaire<fnref n="4" /> · Jean Sciare<fnref n="4" />,<fnref n="5" /> · Noureddine Yassaa<fnref n="3" />

<figure><img src="image_10.png" /></figure>

Lyes Rabhi
rabhilyes@live.fr

<footnote n="1">¹ Centre de Recherche Scientifique et Technique en Analyses Physico-chimiques (CRAPC), Zone Industrielle, BP 384 Bou-Ismail, Tipaza, Algeria</footnote>

<footnote n="2">² Unité de Recherche en Analyses Physico-Chimiques des Milieux Fluides et Sols– (URAPC-MFS/CRAPC), 11 Chemin Doudou Mokhtar Ben Aknoun, Algiers, Algeria</footnote>

<footnote n="3">³ Laboratoire d'Analyse Organique Fonctionnelle, Faculté de Chimie, Université des Sciences et de la Technologie Houari Boumediene, BP 32, El-Alia, Bab Ezzouar, Algiers 16111, Algeria</footnote>

<footnote n="4">⁴ Laboratoire des Sciences du Climat et de l'Environnement, CEA Orme Des Merisiers, Gif-Sur-Yvette Cedex, Paris 91191, France</footnote>

<footnote n="5">⁵ Climate and Atmosphere Research Center, The Cyprus Institute, 20 Konstantinou Kavafi Street, Nicosia 2121, Cyprus</footnote>