*Article* **Organic and Elemental Carbon in the Urban Background in an Eastern Mediterranean City**

**Tareq Hussein 1,2,\* , Xinyang Li <sup>2</sup> , Zaid Bakri 1,3, Andres Alastuey <sup>4</sup> , Sharif Arar <sup>5</sup> , Afnan Al-Hunaiti <sup>5</sup> , Mar Viana <sup>4</sup> and Tuukka Petäjä <sup>2</sup>**


**Abstract:** The Mediterranean region is an important area for air pollution as it is the crossroads between three continents; therefore, the concentrations of atmospheric aerosol particles are influenced by emissions from Africa, Asia, and Europe. Here we concentrate on an eleven-month time series of the ambient concentration of organic carbon (OC) and elemental carbon (EC) between May 2018– March 2019 in Amman, Jordan. Such a dataset is unique in Jordan. The results show that the OC and EC annual mean concentrations in PM2.5 samples were 5.9 <sup>±</sup> 2.8 <sup>µ</sup>g m–3 and 1.7 <sup>±</sup> 1.1 <sup>µ</sup>g m–3 , respectively. It was found that the majority of OC and EC concentrations were within the fine particle fraction (PM2.5). During sand and dust storm (SDS) episodes OC and EC concentrations were higher than the annual means; the mean values during these periods were about 9.6 <sup>±</sup> 3.5 <sup>µ</sup>g m–3 and 2.5 <sup>±</sup> 1.2 <sup>µ</sup>g m–3 in the PM2.5 samples. Based on this, the SDS episodes were identified to be responsible for an increased carbonaceous aerosol content as well as PM2.5 and PM<sup>10</sup> content, which may have direct implications on human health. This study encourages us to perform more extensive measurements during a longer time period and to include an advanced chemical and physical characterization for urban aerosols in the urban atmosphere of Amman, which can be representative of other urban areas in the region.

**Keywords:** urban air quality; PM10; PM2.5; OC; EC

## **1. Introduction**

Carbonaceous aerosols found in particulate matter (PM) are mainly in the form of elemental carbon (EC) and organic carbon (OC) [1–8]. These species of aerosols are of worldwide interest due to their vague origins and complicated source apportionment process [9–11]. On the one hand, incomplete combustion processes and wildfires are the major sources of EC [12,13]. On the other hand, OC sources potentially originate from processes that involve chemical reactions of hydrocarbons [14]. There is a contrast between the effects of OC and EC on the climate; EC is involved in the global warming effect due to its strong light-absorption property [15,16], whereas OC is responsible for cooling the atmosphere mainly because it reflects solar radiation [17]; however, some recent publications have reported that some OC (a newly emerged phrase: brown carbon) can significantly absorb light in the region of 300–400 nm and could hinder and oppose the general cooling action [18–21]. In terms of health effects, it has been suggested that increased mortality rates and respiratory diseases are related to OC and EC content exposure [22–32].

**Citation:** Hussein, T.; Li, X.; Bakri, Z.; Alastuey, A.; Arar, S.; Al-Hunaiti, A.; Viana, M.; Petäjä, T. Organic and Elemental Carbon in the Urban Background in an Eastern Mediterranean City. *Atmosphere* **2022**, *13*, 197. https://doi.org/10.3390/ atmos13020197

Academic Editors: Elena Hristova, Manousos Ioannis Manousakas, Anikó Angyal and Maria Gini

Received: 2 December 2021 Accepted: 24 January 2022 Published: 26 January 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

In urban areas, OC and EC originate from many sources, both local and regional. They can be transported over long distances, reaching thousands of kilometers away from their source [33–42]. For instance, OC comprises thousands of individual molecules that can be directly emitted as primary emissions or can be formed in the atmosphere from semi-volatile and gaseous precursors over the course of minutes to days. EC is directly emitted from combustion processes, such as mobile sources or biomass burning.

The Mediterranean basin, including the Eastern Mediterranean region, is considered a climate change hotspot due to warming tendencies and decreased precipitation processes [43]. The Eastern Mediterranean region, especially Jordan, is impacted by anthropogenic emissions as well as natural sources (e.g., sand and dust storm episodes (SDS)), which are found to affect PM concentrations in the region [44]. Since there is a lack of information on OC and EC concentrations and ratios in the region, there is a need to monitor and explore their aerosol concentrations, sources, and compositions in this region. Chemical analysis of ambient PM enables the identification of aerosol sources and addressing the relative contributions of different processes. In this study, we aim at characterizing PM<sup>10</sup> and PM2.5 with respect to OC and EC during May 2018–March 2019 in Amman, which is a typical city in the region. This study is important to the Middle East and North Africa region (MENA), especially Jordan, where the measurements took place, as it provides background information for urban aerosol chemical composition. This can be utilized to explore aerosol impacts on climate and health in follow-up analyses.

#### **2. Materials and Methods**

#### *2.1. Aerosol Measurement*

The measurement campaign took place during May 2018–March 2019 on the rooftop (about 20 m above the ground) of the Department of Physics at the campus of the University of Jordan (32◦0129' N, 35◦8738' E) (Figure S1). This was classified as an urban background in the northern part of Amman, Jordan. The surroundings are a mixture of residential areas and road networks [44].

The aerosol measurement instrumentation included two high-volume samplers (model CAV-A/mb, MCV, S.A., Barcelona, Spain) for PM<sup>10</sup> and PM2.5. The cascade head (model PM1025-CAV, MCV, S.A., Barcelona, Spain) was equipped with a filter (Pallflex, PAL-LXQ250ETDS0150, TISSUQUARTZ 2500 QAT-UP, Merek, New Jersy, USA), which has a diameter of 15 cm. The flow rate was 30 m<sup>3</sup> h –1 and the sampler automatically recorded the overall mean temperature and pressure during the sampling session.

The PM<sup>10</sup> and PM2.5 sampling was performed for 24 h every 6 days. We obtained 51 and 48 valid samples for PM<sup>10</sup> and PM2.5, respectively. We also collected several blank samples, which were needed as an accuracy control of the sampling [45].

#### *2.2. Gravimetric and OC/EC Chemical Analysis*

Before performing the chemical analyses (including organic carbon (OC) and elemental carbon (EC)), the PM<sup>10</sup> and PM2.5 mass concentrations were determined by gravimetric analysis, which was performed according to the EN1234-1. Accordingly, the particulate matter concentration can be calculated from the filter's weights (difference between postweight (*mpost*) and pre-weight (*mpre*)) divided by the sampling flow rate (*Q* (30 m<sup>3</sup> h –1)) and sampling period (∆*t* = 24 h).

A quarter of each sampled filter was taken to the OC and EC analysis according to the EUSAAR2 protocol employing a Sunset Laboratory Dual-Optical Carbonaceous Analyzer [46–48]. The uncertainty in our analysis was approximately 0.2, 0.1, 0.3 µg m−<sup>3</sup> , respectively, for OC, EC, and TC.

#### *2.3. Ambient Conditions and Air Mass Trajectories Measurement*

In addition to the aerosol measurement, the ambient conditions (T, P, RH, wind speed, and direction) were monitored with 5 min resolution (Table S1, Figures S2, S3, and S5–S8) by using a weather station (WH-1080, Clas Ohlson: Art. no. 36-3242). We also calculated

the air mass back-trajectories (Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) [49,50]. The trajectories were calculated for the previous four days on an hourly basis at arrival heights 100, 500, and 1500 m above ground level.

#### **3. Results and Discussion**

#### *3.1. An Overview of PM Concentrations*

Throughout the measurement period, the 24 h PM<sup>10</sup> was within the range 20–190 µg m–3 (average 64 <sup>±</sup> <sup>39</sup> <sup>µ</sup>g m–3) and the PM2.5 was 15–190 <sup>µ</sup>g m–3 (average 47 <sup>±</sup> <sup>32</sup> <sup>µ</sup>g m–3)—see Table S2 in the Supplementary Material. On average, approximately 80% of the PM<sup>10</sup> was within the PM2.5 fraction. According to the Jordanian standards of ambient air quality (JS-1140/2006: annual mean PM<sup>10</sup> and PM2.5 must not exceed 70 µg m–3 and 15 µg m–3 and 24 h mean must not exceed 120 µg m–3 and 65 µg m–3, respectively), the observed overall mean PM<sup>10</sup> was below its annual limit value but the PM2.5 was three times higher than its limit value. Compared to the World Health Organization (WHO) old air quality guidelines [51] (i.e., before 2021) for PM<sup>10</sup> (annual and 24 h must not exceed 20 µg m–3 and 50 µg m–3, respectively) and PM2.5 (annual and 24 h must not exceed 10 µg m–3 and 25 µg m–3, respectively), the observed annual concentrations here exceeded the annual guideline. By all means, the reported values here exceeded the new WHO air quality guidelines [52] (i.e., after 2021), which was updated to be tighter than the old guidelines for PM<sup>10</sup> (annual and 24 h must not exceed 15 µg m–3 and 45 µg m–3, respectively) and PM2.5 (annual and 24 h must not exceed 5 µg m–3 and 15 µg m–3, respectively). In general, the reported PM<sup>10</sup> concentrations in Jordan were higher than the concentrations reported by the WHO (2018) in urban, suburban, and residential sites in countries around the Mediterranean Sea in 2016, especially Turkey (52 <sup>±</sup> <sup>18</sup> <sup>µ</sup>g m–3), Italy (25 <sup>±</sup> <sup>6</sup> <sup>µ</sup>g m–3), Greece (52 <sup>±</sup> <sup>18</sup> <sup>µ</sup>g m–3), Cyprus (37 <sup>±</sup> <sup>6</sup> <sup>µ</sup>g m–3), and Malta (38 <sup>±</sup> <sup>8</sup> <sup>µ</sup>g m–3). These concentrations were lower than concentrations observed in the following regions: Kuwait (130 <sup>±</sup> <sup>35</sup> <sup>µ</sup>g m–3), Palestine (90 µg m–3), Egypt (249–284 µg m–3), and the United Arab Emirates (122–153 µg m–3).

#### *3.2. Organic and Elemental Carbon Concentrations*

The TC and OC concentrations followed a rather similar temporal variation as that for the PM concentrations (Figures 1 and 2). The OC concentrations observed in the PM2.5 samples were in the range 1.2–17.1 <sup>µ</sup>g m–3 (annual mean 5.9 <sup>±</sup> 2.8 <sup>µ</sup>g m–3), the EC concentrations were 0.45–6.1 <sup>µ</sup>g m–3 (annual mean of 1.7 <sup>±</sup> 1.1 <sup>µ</sup>g m–3), and the TC concentrations were 1.7–23.2 <sup>µ</sup>g m–3 (annual mean 7.6 <sup>±</sup> 3.5 <sup>µ</sup>g m–3). As for the PM10, they were 2.2–17.5 <sup>µ</sup>g m–3 (annual mean of 6.5 <sup>±</sup> 3.0 <sup>µ</sup>g m–3), 0.5–5.5 <sup>µ</sup>g m–3 (annual mean of 1.9 <sup>±</sup> 1.1 <sup>µ</sup>g m–3), and 2.7–22.4 <sup>µ</sup>g m–3 (annual mean 8.4 <sup>±</sup> 3.8 <sup>µ</sup>g m–3), respectively, for the OC, EC, and TC concentrations. On average, the PM2.5 contained about 14%, 4%, and 18% OC, EC, and TC (Figure 3). As for the PM10, it was about 12%, 4%, 15%, respectively. As expected for an urban background, the TC was dominated by OC for both the PM2.5 and PM<sup>10</sup> (Figure 4); this indicates a dominating fraction of organic emissions from anthropogenic activities rather than natural emissions, as also reported elsewhere [53–60].

According to PM2.5 observations elsewhere in the region (mostly outside of Jordan; see Table 1), the OC and EC concentrations reported herein are comparable. A greater interest might be the extremely high OC concentrations in Beijing (29.1 µg m–3) and Tehran (15.35 <sup>±</sup> 6.05 <sup>µ</sup>g m–3) with corresponding PM2.5 concentrations of 115 <sup>µ</sup>g m–3 and 41.2 µg m–3, respectively [61,62]. As for PM<sup>10</sup> observations elsewhere (Table 2), the OC concentrations reported here are generally lower than those reported in other regions in the world.

The PM<sup>10</sup> records in Table 2 show a clear contrast with the OC concentrations. A relatively high OC<sup>10</sup> concentration record was observed in Lahore, with a value of 63 µg m–3 , while a very low concentration was observed in Spain, with a value of 4 µg m–3. For both PM2.5 and PM<sup>10</sup> the OC to EC ratio was generally higher in the summertime than that in the wintertime (Figure 5). The OC/EC ratio in the PM2.5 was approximately 5.6 ± 1.5

(**a**)

(**b**)

during May–August and was approximately 2.8 ± 0.9 during November–March. As for the PM10, the ratio was approximately 4.9 ± 1.2 and 3.1 ± 1.4, respectively. *Atmosphere* **2022**, *13*, x FOR PEER REVIEW 4 of 15

During the measurement period, the ratio PM2.5/PM<sup>10</sup> was close to one on some days. This was basically due to the domination of fine aerosols, i.e., the absence of sand and dust storm (SDS) episodes. On these occasions, the OC2.5 was 4.0–17.1 µg m–3 and the OC<sup>10</sup> was 2.4–15.9 µg m–3. The corresponding EC2.5 was 0.7–6.1 µg m–3 and EC<sup>10</sup> was 0.9–5.5 µg m–3 . *Atmosphere* **2022**, *13*, x FOR PEER REVIEW 4 of 15

**Figure 1.** PM and corresponding OC, EC, and TC concentrations: (**a**) fine fraction; (**b**) coarse fraction.

**Figure 1.** PM and corresponding OC, EC, and TC concentrations: (**a**) fine fraction; (**b**) coarse fraction.

**Figure 1.** PM and corresponding OC, EC, and TC concentrations: (**a**) fine fraction; (**b**) coarse fraction.

**Figure 2.** Boxplot of OC, EC, and TC within the PM2.5 and PM10. The box plot represents 25th and 75th percentiles and median (red line), the bars represent the 5th and 95th percentiles, and the plus symbols indicate outlayers. **Figure 2.** Boxplot of OC, EC, and TC within the PM2.5 and PM10. The box plot represents 25th and 75th percentiles and median (red line), the bars represent the 5th and 95th percentiles, and the plus symbols indicate outlayers. **Figure 2.** Boxplot of OC, EC, and TC within the PM2.5 and PM10. The box plot represents 25th and 75th percentiles and median (red line), the bars represent the 5th and 95th percentiles, and the plus symbols indicate outlayers.

(**a**)

(**b**)

*Atmosphere* **2022**, *13*, x FOR PEER REVIEW 5 of 15

**Figure 3.** OC, EC, and TC contents (mass concentrations, %) in the corresponding PM concentrations: (**a**) fine fraction; (**b**) coarse fraction. **Figure 3.** OC, EC, and TC contents (mass concentrations, %) in the corresponding PM concentrations: (**a**) fine fraction; (**b**) coarse fraction. **Figure 3.** OC, EC, and TC contents (mass concentrations, %) in the corresponding PM concentrations: (**a**) fine fraction; (**b**) coarse fraction.

**Figure 4.** The ratio OC/TC in the PM2.5 and PM10 concentrations.

**Figure 4.** The ratio OC/TC in the PM2.5 and PM10 concentrations. **Figure 4.** The ratio OC/TC in the PM2.5 and PM<sup>10</sup> concentrations.

**Figure 5. Figure 5.** Variation of OC and EC ratio in PM Variation of OC and EC ratio in PM 2.5 and PM10 during the measurement period**.** 2.5 and PM<sup>10</sup> during the measurement period.


*Atmosphere* **2022**, *13*, 197




m–3*.* 

#### *3.3. Changes during Sand and Dust Storms (SDS)* As per our previous analysis [44], the SDS episodes were classified into three catego-

*3.3. Changes during Sand and Dust Storms (SDS)* 

*Atmosphere* **2022**, *13*, x FOR PEER REVIEW 9 of 15

As per our previous analysis [44], the SDS episodes were classified into three categories based on their origin: S (Sahara), SL (Saharan and Levant), SA (Sahara and Arabia), and SLA (Sahara, Levant, and Arabia); kindly see more information in the Supplementary Materials. During SDS episodes, the mean OC2.5 and EC2.5 were 9.6 <sup>±</sup> 3.5 <sup>µ</sup>g m–3 and 2.5 <sup>±</sup> 1.2 <sup>µ</sup>g m–3, respectively (Figure 6). During non-dust episodes, the OC2.5 and EC2.5 were 5.4 <sup>±</sup> 1.8 <sup>µ</sup>g m–3 and 1.7 <sup>±</sup> 0.9 <sup>µ</sup>g m–3, respectively (Figure 7). As for PM10, the OC<sup>10</sup> and EC<sup>10</sup> were 8.0 <sup>±</sup> 3.5 <sup>µ</sup>g m–3 and 2.4 <sup>±</sup> 1.3 <sup>µ</sup>g m–3 during dust episodes and 5.2 <sup>±</sup> 2.0 <sup>µ</sup>g m–3 and 2.4 <sup>±</sup> 1.3 <sup>µ</sup>g m–3 during non-dust episodes, respectively. ries based on their origin: S (Sahara), SL (Saharan and Levant), SA (Sahara and Arabia), and SLA (Sahara, Levant, and Arabia); kindly see more information in the Supplementary Materials. During SDS episodes, the mean OC2.5 and EC2.5 were 9.6 ± 3.5 µg m–3 and 2.5 ± 1.2 µg m–3, respectively (Figure 6). During non-dust episodes, the OC2.5 and EC2.5 were 5.4 ± 1.8 µg m–3 and 1.7 ± 0.9 µg m–3, respectively (Figure 7). As for PM10, the OC10 and EC10 were 8.0 ± 3.5 µg m–3 and 2.4 ± 1.3 µg m–3 during dust episodes and 5.2 ± 2.0 µg m–3 and 2.4 ± 1.3 µg m–3 during non-dust episodes, respectively.

During the measurement period, the ratio PM2.5/PM10 was close to one on some days. This was basically due to the domination of fine aerosols, i.e., the absence of sand and dust storm (SDS) episodes. On these occasions, the OC2.5 was 4.0–17.1 µg m–3 and the OC10 was 2.4–15.9 µg m–3. The corresponding EC2.5 was 0.7–6.1 µg m–3 and EC10 was 0.9–5.5 µg

**Figure 6.** OC and EC versus the corresponding PM concentrations on days (13 in total) with sand and dust storm (SDS) episodes: (**a**) the OC content (here called OC2.5) versus its corresponding PM2.5, (**b**) the EC versus (here called EC2.5) within its corresponding PM2.5, (**c**) the OC content (here called OC10) versus its corresponding PM10, and (**d**) the EC versus (here called EC10) within its corresponding PM10. **Figure 6.** OC and EC versus the corresponding PM concentrations on days (13 in total) with sand and dust storm (SDS) episodes: (**a**) the OC content (here called OC2.5) versus its corresponding PM2.5, (**b**) the EC versus (here called EC2.5) within its corresponding PM2.5, (**c**) the OC content (here called OC10) versus its corresponding PM10, and (**d**) the EC versus (here called EC10) within its corresponding PM10.

It was apparent that the concentrations of OC were greatly influenced by the type of aerosols (i.e., coarse dust particles) but the EC was not affected as much. The OC2.5/PM2.5 during the SDS episodes carried the highest correlation coefficient of 0.89, which was expected since OC mostly exists in accumulation mode (0.1–1µm) particles in PM2.5 [80]; it also confirmed the dominance of OC2.5 in the total carbon content. By comparing both the ECx/PM<sup>x</sup> in the corresponding PM2.5 and PM10, we observed that, during SDS episodes, the EC and PM in the fine particle size range were more correlated (r = 0.556, Figure 6b), whereas they were much less correlated (r = 0.32, Figure 6d) in the coarse particle size range. On the other hand, the r values for EC and PM on both particle size ranges were very close on non-SDS days (Figure 7b,d). This result suggests that, during SDS episodes, aerosols have a high potential of transporting OC, which dramatically increased the concentration of OC to the measurement site from other urban centers in the region. As previously mentioned, the regular atmospheric EC concentrations remain at around 2–3 µg m–3 .

**Figure 7.** OC and EC versus the corresponding PM concentrations on days (39 in total) without sand and dust storm (SDS) episodes: (**a**) the OC content (here called OC2.5) versus its corresponding PM2.5, (**b**) the EC versus (here called EC2.5) within its corresponding PM2.5, (**c**) the OC content (here called OC10) versus its corresponding PM10, and (**d**) the EC versus (here called EC10) within its corresponding PM10. **Figure 7.** OC and EC versus the corresponding PM concentrations on days (39 in total) without sand and dust storm (SDS) episodes: (**a**) the OC content (here called OC2.5) versus its corresponding PM2.5, (**b**) the EC versus (here called EC2.5) within its corresponding PM2.5, (**c**) the OC content (here called OC10) versus its corresponding PM10, and (**d**) the EC versus (here called EC10) within its corresponding PM10.

#### **4. Conclusions**

It was apparent that the concentrations of OC were greatly influenced by the type of aerosols (i.e., coarse dust particles) but the EC was not affected as much. The OC2.5/PM2.5 during the SDS episodes carried the highest correlation coefficient of 0.89, which was expected since OC mostly exists in accumulation mode (0.1–1µm) particles in PM2.5 [80]; it also confirmed the dominance of OC2.5 in the total carbon content. By comparing both the ECx/PMx in the corresponding PM2.5 and PM10, we observed that, during SDS episodes, the EC and PM in the fine particle size range were more correlated (r = 0.556, Figure 6b), General monitoring requirements for air quality and assessment include ambient air PM2.5 and PM10, in addition to some gaseous pollutants; however, there is a lack of monitoring of OC and EC concentrations in many parts of the world. Unfortunately, there has been a lack of information regarding OC, EC, and TC in the Eastern Mediterranean region. In this study, we characterized the PM<sup>10</sup> and PM2.5 with respect to OC/EC during an eleven-month time series (May 2018–March 2019) in the urban atmosphere of Amman, Jordan, which is a typical Eastern Mediterranean city.

whereas they were much less correlated (r = 0.32, Figure 6d) in the coarse particle size range. On the other hand, the r values for EC and PM on both particle size ranges were very close on non-SDS days (Figure 7b,d). This result suggests that, during SDS episodes, aerosols have a high potential of transporting OC, which dramatically increased the concentration of OC to the measurement site from other urban centers in the region. As previously mentioned, the regular atmospheric EC concentrations remain at around 2–3 µg The OC found in the PM2.5 fraction (i.e., OC2.5) was within the range of 1.2–17.1 µg m–3 . The corresponding EC2.5 was within the range of 0.6–6.1 µg m–3. As for the PM<sup>10</sup> fraction, the OC<sup>10</sup> and EC<sup>10</sup> were within the range of 2.2–17.5 µg m–3 and 0.5–5.5 µg m–3, respectively. In percentages, about 14% and 3.9% of the PM2.5 were OC and EC, respectively. In the PM10, 11.6% and 3.5% were OC and EC, respectively. These results indicate the domination of anthropogenic activities' emissions over natural sources' emissions.

m–3. **4. Conclusions**  General monitoring requirements for air quality and assessment include ambient air PM2.5 and PM10, in addition to some gaseous pollutants; however, there is a lack of monitoring of OC and EC concentrations in many parts of the world. Unfortunately, there has been a lack of information regarding OC, EC, and TC in the Eastern Mediterranean region. Sand and dust storm (SDS) episodes were observed during the measurement campaign. During SDS episodes, the mean OC2.5 was approximately 9.6 <sup>±</sup> 3.5 <sup>µ</sup>g m–3, which is much higher than the annual mean (i.e., 5.9 <sup>±</sup> 2.8 <sup>µ</sup>g m–3). Similarly, the EC2.5 approached a mean concentration of 2.5 <sup>±</sup> 1.2 <sup>µ</sup>g m–3 during the SDS episodes, while it had an annual mean of 1.7 <sup>±</sup> 1.1 <sup>µ</sup>g m–3. This is evidence of the SDS episodes' role in introducing particulate phase pollutants other than the coarse mode dust particles to the measurement site, or more generally, to the whole region.

In this study, we characterized the PM10 and PM2.5 with respect to OC/EC during an eleven-month time series (May 2018–March 2019) in the urban atmosphere of Amman, Jordan, which is a typical Eastern Mediterranean city. The OC found in the PM2.5 fraction (i.e., OC2.5) was within the range of 1.2–17.1 µg m–3. The corresponding EC2.5 was within the range of 0.6–6.1 µg m–3. As for the PM10 frac-This study indicated that a large fraction of carbonaceous aerosol mass most likely originates from anthropogenic activities rather than natural sources. Future studies with detailed source apportionment tools [81,82] are needed to verify this result. Accurate classification of the origins of this type of aerosols can be useful in regulating the involved activities.

tion, the OC10 and EC10 were within the range of 2.2–17.5 µg m–3 and 0.5–5.5 µg m–3,

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/ 10.3390/atmos13020197/s1, Figure S1:Maps showing (a) the Mediterranean Sea region with Jordan highlighted in red, (b) Jordan with highlights on the geographical locations of main cities, (c) road network and the campus of the University of Jordan (red shaded area) inside Amman, and (d) details of the campus of the University of Jordan with the sampling location (red shaded area) at the middle of the campus, Figure S2:Time series of weather conditions during 1 May 2018—19 March 2019 presented as hourly, daily, and monthly means for (a) ambient temperature, (b) relative humidity, (c) absolute pressure, and (d) wind speed. (e) The rainfall was presented as hourly cumulative precipitation, Figure S3: Back trajectories (96 h) crossing maps at arrival heights (a) 100 m and (b) 1500 m. The arrival location was the campus of the University of Jordan, Amman, Jordan. These maps were generated from the hourly trajectories during 1 May 2018—31 March 2019, Figure S4: Time series of PM10 and PM2.5 concentrations with markups for sand and dust episodes (SDS) and clean air periods (i.e. PM10 concentrations <70 µg/m<sup>3</sup> ), Figure S5: Back trajectories (96 h) crossing maps during S-type SDS-episodes (indicated on Figure S4) at arrival heights (a) 100 m, (b) 500 m, and (c) 1500 m. The arrival location was the campus of the University of Jordan, Amman, Jordan. These maps were generated from the hourly back trajectories during the sampling dates (+ following day), Figure S6: Back trajectories (96 h) crossing maps during SL-type SDS-episodes (indicated on Figure S4) at arrival heights (a) 100 m, (b) 500 m, and (c) 1500 m. The arrival location was the campus of the University of Jordan, Amman, Jordan. These maps were generated from the hourly back trajectories during the sampling dates (+ following day), Figure S7: Back trajectories (96 h) crossing maps during SLA-type SDS-episodes (indicated on Figure S4) at arrival heights (a) 100 m, (b) 500 m, and (c) 1500 m. The arrival location was the campus of the University of Jordan, Amman, Jordan. These maps were generated from the hourly back trajectories during the sampling dates (+ following day), Figure S8: Back trajectories (96 h) crossing maps during low PM10 concentrations (indicated on Figure S4) at arrival heights (a) 100 m, (b) 500 m, and (c) 1500 m. The arrival location was the campus of the University of Jordan, Amman, Jordan. These maps were generated from the hourly back trajectories during the sampling dates (+ following day), Table S1: Particulate matter concentrations and overall average temperature and pressure according to the sampling schedule. The aerosol sampler reported the 24-h mean temperature (T) and pressure (P) during sampling sessions, Table S2: PM2.5 and PM10 concentrations (µg m–3) and corresponding OC and EC concentrations (µg m–3), Table S3: Sand and Dust Storm (SDS) episodes according to type and observation during the sampling period. The type of SDS is denoted as: Saharan (S); Saharan and Levant (SL); Saharan, Arabian, and Levant (SAL); Saharan, Arabian, Levant, and Ahvaz (SALA). The source region was verified according to the back trajectories analysis for crossing maps on the sampling day (+ following day). The date here indicates the start of the sampling day.

**Author Contributions:** Conceptualization, T.H. and M.V.; methodology, T.H., A.A.-H., S.A., A.A., and M.V.; validation, T.H., M.V., A.A., and Z.B.; formal analysis, T.H., Z.B., and X.L.; investigation, T.H., A.A.-H., T.P., S.A., and M.V.; resources, T.H., T.P., and M.V.; data curation, X.L., and Z.B.; writing—original draft preparation, T.H., M.V., A.A.-H., Z.B., and X.L.; writing—review and editing, T.H., X.L., Z.B., A.A., S.A., A.A.-H., M.V., and T.P.; visualization, X.L., Z.B., and T.H.; supervision, T.H., S.A., A.A.-H., and M.V.; project administration, T.H. and M.V.; funding acquisition, T.H. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Deanship of Academic Research (DAR, project number 2015) at the University of Jordan. Academy of Finland Center of Excellence (project No. 272041). eCOST action (inDUST, project number CA16202) support via the Short Term Scientific Mission (STSM) mobility grant.

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

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data will be available upon request.

**Acknowledgments:** This research was part of a close collaboration between the University of Jordan, the Institute of Environmental Assessment and Water Research (IDAEA-CSIC), and the Institute for Atmospheric and Earth System Research (INAR/Physics, University of Helsinki). We acknowledge the financial support provided by the Deanship of Academic Research (DAR, project No. 2015) at the University of Jordan, Academy of Finland Center of Excellence (project No. 272041), ERA- PLANET, trans-national project SMURBS (grant agreement n. 689443 funded under the EU Horizon 2020 Framework Programme and Academy of Finland via the Center of Excellence in Atmospheric sciences and NanoBioMass (project number 1307537). The eCOST action (inDUST, project number CA16202) is acknowledged for supporting this research via the Short Term Scientific Mission (STSM) mobility grant. The work was supported by the Spanish Ministry of Science and Innovation (Project CEX2018-000794-S) and by AGAUR (project 2017 SGR41). This manuscript was written and completed during the sabbatical leave of the first author (Tareq Hussein) that was spent at the University of Helsinki and supported by the University of Jordan during 2019.

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

## **References**

