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Article

A Comprehensive Overview of the Hydrochemical Characteristics of Precipitation across the Middle East

1
Faculty of Environment and Resource Studies, Mahidol University, Nakhon Pathom 73170, Thailand
2
Environmental Physics Laboratory (EphysLab), Facultad de Ciencias, Universidade de Vigo, 32004 Ourense, Spain
3
Department of Geography, Ferdowsi University of Mashhad, Mashhad 917794883, Iran
4
Geographic Information Science/System and Remote Sensing Laboratory (GISSRS: Lab), Ferdowsi University of Mashhad, Mashhad 917794883, Iran
5
Department of Geology, Ferdowsi University of Mashhad, Mashhad 917751436, Iran
*
Author to whom correspondence should be addressed.
Water 2022, 14(17), 2657; https://doi.org/10.3390/w14172657
Received: 12 July 2022 / Revised: 23 August 2022 / Accepted: 25 August 2022 / Published: 28 August 2022
(This article belongs to the Section Water Quality and Contamination)

Abstract

:
The Middle East is located in a semiarid and arid region and is faced with an intense water shortage crisis. Therefore, studying the hydrochemical characteristics of precipitation as a main part of the water cycle has great importance in this region. The hydrochemical analyses showed that the quality of precipitation was mainly affected by dust particles originating from terrestrial environments, while marine and anthropogenic sources had a minor role. The statistical studies showed that the dissolution of evaporative and carbonate minerals mainly controlled the hydrochemistry of precipitation. Precipitation had an acidic nature in some stations and a nonacidic nature in others. Ca2+ was the major acid-neutralizing cation in the Middle East precipitation. Various machine learning methods were also used to simulate the TDS values in precipitation. The accuracy of the developed models was validated, showing that the model developed by the Gboost method was more accurate than those developed by other machine learning techniques due to its higher R2 values. To conclude, the hydrochemistry of precipitation showed significant variations across the Middle East. The dissolution of particles with terrestrial origins dominantly controlled the hydrochemistry of precipitation, while marine and anthropogenic sources had minor roles.

1. Introduction

The Middle East is known as one of the main energy hubs in the world due to its large hydrocarbon reservoirs. However, this energy-rich zone has always faced significant water shortage crises due to successive intense droughts [1]. Furthermore, due to significant industrial and population growth in most parts of the Middle East in recent years, the importance of available water supplies and the need for new water resources have dramatically increased in this region [2]. Thus, accurate and reliable methods such as hydrochemical techniques besides statistical analyses should be applied to monitor water resources in this region. This can provide a comprehensive view of the quality and quantity of available water resources. In the hydrological cycle, the quality and quantity of precipitation (as an important element of the water cycle) should be studied very carefully due to its direct interactions with other elements that exist in the water cycle (e.g., surface water and groundwater resources). Although there are numerous studies on the quantity of precipitation and hydrological drought in the Middle East [3,4,5,6,7,8,9], few studies are available on the quality (hydrochemistry) of precipitation in this region [10,11,12].
During the last century, most parts of the world have undergone intense urbanization and industrialization. This has had a direct influence on the production and release of harmful gases (including SO2 and NO2). Therefore, the chemical properties of precipitation have been accurately studied in highly crowded and polluted metropolitan cities across the world, including Beijing in China [13], Tehran in Iran [14], Tokyo in Japan [15], Washington in the USA [16], Rio de Janeiro in Brazil [17], Sydney in Australia [18], and Delhi in India [19]. Studies on the hydrochemical characteristics of precipitation started earlier in the eastern part of Asia in Tokyo, Hanoi, Kolkata, Chennai, Sri Lanka, and East Java [20]. The study of the hydrochemical characteristics of precipitation in the western part of Asia and the Middle East was started by Salameh and Rimawi [11], Schemenauer and Cereceda [21], and Kattan [22] in the last three decades.
To study the hydrochemical characteristics of precipitation more deeply and accurately, statistical and machine learning techniques can be applied. Statistical techniques (such as principal component analysis (PCA), the Pearson correlation coefficient, and cluster analysis (CA)) have been applied in numerous studies to investigate the hydrochemical characteristics of precipitation [11,23,24,25,26,27,28]. In addition to statistical techniques, artificial intelligence (AI) techniques have also been applied in climatological studies, mainly in recent decades. Artificial neural network (ANN) techniques are a significant part of artificial intelligence. They have been designed based on the simulation of the human nervous system and have been applied in many fields of science. Although ANN techniques have existed for over half a century [29], their application in atmospheric sciences commenced in 1986 [30], and they have been mainly applied for forecasting the amount of precipitation [31,32,33,34,35,36]. The multilayer perceptron (MLP) model has one of the simplest and most efficient structures in ANN models and consists of three main parts, including an input layer, a hidden layer, and an output layer, which form a strong network [37]. In addition to simple ANN techniques, deep neural network (DNN) models have also been applied in numerous climatological studies [38,39]. The main difference between ANN and DNN models is the number of hidden layers. ANN models have only one hidden layer, while DNN models contain two or more hidden layers. In addition to neural network methods, data classification models such as decision trees and random forests have also been used to forecast the amount of precipitation [40,41,42,43,44,45]. A decision tree (DT) is a nonparametric supervised learning method. The aim of this method is to anticipate the value of the target parameter based on input parameters. In addition to the decision tree, a random forest, which is a combination of several decision trees and gradient boosting (an ensemble machine learning method), has also been used. The ensemble method is a machine learning technique that combines several basic machine learning methods to obtain an optimal predictive model. Although machine learning techniques have many advantages and capabilities in predicting the target parameter, there are still no studies on the simulation of precipitation quality using machine learning techniques.
This study aimed to investigate the hydrochemical characteristics of precipitation, including ion sources, enrichment factors, and neutralizing capacity, across the Middle East. In addition, statistical approaches and machine learning techniques were also employed to determine the main phenomena affecting the hydrochemistry of precipitation and to simulate the quality of precipitation (the values of total dissolved solids (TDS) in precipitation) using the concentrations of major ions in precipitation.

2. The Geography and Climatology of the Middle East

The Middle East is a region extending over a large area, including West Asia and North Africa. A large part of the Middle East is classified as a hot desert climate (BWh) according to the Köppen climate zone. However, other climate zones such as BSk (arid, steppe, and cold), Csa (temperate, dry summer, and hot summer), BWk (arid, desert, and cold), and BSh (arid, steppe, and hot) are also observed in some areas of this region [46]. Significant variations in temperature and precipitation observed across the Middle East also confirm the existence of various climate zones [47,48,49]. Precipitation moisture in the Middle East is transferred by five main air masses, including continental polar (cP), maritime polar (mP), maritime tropical (mT), Mediterranean (MedT), and continental tropical (cT) air masses (Figure 1).
Air mass flux and moisture sources have also been studied using vertically integrated moisture flux (VIMF) (Figure 2). The northward and eastward data of the vertically integrated moisture flux from ERA-Interim reanalysis [50] with a resolution of 1° × 1° were utilized in this study. ERA-Interim reanalysis belongs to the European Center for Medium-Range Weather Forecast (ECMWF). During the dry period (May to October) in the Middle East, a notable VIMF can be observed over the Indian Ocean and the Arabian Sea. During this period, the VIMF divergence can also be seen at degrees higher than 40 °N. In contrast, during the wet period (from November to April), the VIMF convergence prevails over the Middle East and causes precipitation. During this period, moisture mainly originates from the Mediterranean Sea, the Black Sea, the Persian Gulf, and the Arabian Sea. This is in close agreement with previous studies [51,52].

3. Materials and Methods

In this study, the data of hydrochemical analyses (including field parameters (pH, TDS, and electrical conductivity (EC)) and the major and minor ions) were studied in 28 stations across the Middle East. These stations and their hydrochemical analyses in Shiraz [54], Ahvaz [26], Mahshahr [55], Sarcheshmeh [56], Tehran [24], Mashhad [57], Arak [28], Shahroud [58], Haraz [12], and Urmia [59] in Iran; Riyadh [60] and Dhahran [21] in the Kingdom of Saudi Arabia (KSA); Damietta in Egypt [61]; Karachi in Pakistan [62]; Ramallah [63] in Palestine; Tripoli, Hadath, and Nabatieh in Lebanon [64]; Jerdab [65] in Bahrain; Tartus, Palmyra, and Damascus in Syria [22]; Amman [11] and Eshidiya [27] in Jordan; Ankara [66] and Istanbul [67] in Turkey; Baghdad in Iraq [68]; and Faryab [69] in Afghanistan were chosen from the scientific papers in the literature.
In most of the stations, samples from precipitation events were collected in polyethylene bottles. The field parameters, including pH, TDS, and EC, were measured up to 24 h after precipitation. Hydrochemical analyses of the precipitation samples were conducted in various laboratories across the world, including the Acme Analytical Lab in Canada; the physical chemistry laboratory of the Faculty of Sciences at the Ferdowsi University of Mashhad, Iran; the laboratory of the Shiraz Regional Water Authority, Iran; British Geological Survey (BGS) laboratories, England; the environmental engineering laboratory at Fatih University, Turkey; and some other laboratories across the Middle East. The concentrations of most major anions, including NO3, SO42−, and Cl, were measured using an ion chromatography instrument, while the concentrations of most cations, including K+, Ca2+, and Mg2+, were measured by a flame atomic absorption spectrophotometer. The concentration of HCO3 was measured using titration with 0.01 M hydrochloric acid. Using the Nessler method, the concentration of NH4+ was also measured only in a few stations, such as Eshidiya [70].
The differences in the methods of sampling and hydrochemical analysis have a direct effect on the uncertainty of the analyses and the accuracy of the study. Thus, a charge balance error (CBE) index was calculated and used to evaluate the accuracy of the hydrochemical analysis of precipitation as follows:
CBE   % = c a t i o n s a n i o n s c a t i o n s a n i o n s × 100
A hydrochemical analysis with a CBE of <5% confirms that the dataset is reliable. The CBE values show large variations from 0.8% in Faryab station to 6.5% in Ankara station, with an average of 3.99% in all stations across the Middle East. Figure 3 also demonstrates the variations in CBE in the precipitation analyses in the studied stations. Since the concentrations of the major anions and cations were not completely presented in some of the studied stations, the CBE (%) was not calculated for them.
First, in order to estimate the marine and nonmarine origins of precipitation, sea salt fraction (SSF) and nonsea salt fraction (NSSF) indices were calculated in precipitation using Equations (2) and (3), respectively [71].
SSFx = [Na+rain] × [X/Na+]seawater
NSSFx = [Xrain] − [(Na+rain) × (X/Na+)seawater]
Using Equation (4), an enrichment factor (EF) was also calculated to study the origin of the ionic species Ca2+, Na+, K+, HCO3, SO42−, and Cl with respect to Na+ [72].
EFseawater = [X/Na+]rainwater / [X/Na+]seawater
In the above equations, X represents the ion of interest. Since Na+ is assumed to originate totally from the sea [73,74], it is used as a reference for marine sources.
The acidity of precipitation was also considered by calculating the fractional acidity (FA) using Equation (5) [75]:
FA = [H+] / ([NO3] + [NSSSO42−])
NSSSO42− and NO3 (eq/lL) are two dominant acidic components in the above equation, while H+ is calculated by Equation (6) using pH values.
[H+] = 10−pH (eq/l)
In addition to the neutralization process in the studied stations, a neutralization factor (NF), considered an indicator of the neutralization potential in the precipitation samples, was also calculated for some of the major alkaline species (Ca2+, Mg2+, NH4+, and K+) in precipitation using Equations (7)–(10) [73].
NFCa2+ = [NSSCa2+] / ([NO3] + [NSSSO42−])
NFMg2+ = [NSSMg2+] / ([NO3] + [NSSSO42−])
NFK+ = [NSSK+] / ([NO3] + [NSSSO42−])
In the above equations, NSSx is the contribution of a nonsea salt fraction to any desired ion (X) in the precipitation samples.
Finally, statistical analyses and machine learning algorithms were used to comprehensively study the possible sources of ions in the Middle East precipitation. NH4+ and NO3 were omitted from the statistical analyses since there were no data about these ions (NA) in some of the studied stations. However, missing HCO3- concentrations or pH data in some stations were calculated based on the following empirical equation [73,76,77].
[HCO3] = 10−11.24+pH (eq/l)
PCA was applied to the precipitation datasets. PCA is a statistical technique for reducing the dimensionality of large datasets. This increases interpretability and minimizes information loss in datasets [78]. To achieve this goal, PCA uses factor extension with an eigenvalue of >1 after applying varimax rotation. The PCA used in this study was performed in the R programming language by R software version 4.1.3. [79].
In addition, ANN and DNN techniques, as well as other machine learning algorithms such as random forest, decision tree, and gradient boosting (Gboost), were also used for two goals. The first goal was to determine which ions mainly control the hydrochemistry of precipitation (the TDS values) by presenting the importance percentage of each major ion influencing the TDS values. The second goal was to simulate the TDS values in precipitation based on the concentrations of the major ions. The accuracy of the developed models was validated by the coefficient of determination (R2).

4. Results and Discussion

4.1. The Hydrochemical Characteristics of Precipitation across the Middle East

The hydrochemical parameters across the Middle East show large variations. Among the field parameters, pH varies from 4.7 at Ankara station to 7.8 at Mashhad station. In previous studies [80,81,82], water from a clean and unpolluted atmosphere had a pH of about 5.6 [23]. However, the precipitation events with a pH lower than 5.6 demonstrate anthropogenic acidity mainly caused by NO2 and SO2 gases originating from coal burning, fuel combustion in car engines, and industrial activities [23,81]. Very low pH values at Ankara station are due to the highly polluted atmosphere in this city, mainly owing to automobile fuel combustion, intense industrial activities, and coal burning [83]. Fuel combustion in cars and industrial factories has a direct effect on the production and release of SO2 and NO2 gases, which contribute to precipitation acidity by producing H2SO4 and HNO3, respectively [47]. In other large and crowded metropolitan cities in the Middle East such as Istanbul, Tehran, and Karachi, the pH values are low to moderate (5.1, 6.3, and 6.8, respectively). As another field parameter, EC also shows large variations across the Middle East, ranging from 56 μmho/cm at Shahrood station to 310 μmho/cm at Jerdab (Jurdab) station. The high EC values at Jerdab station may be due to the existence of significant amounts of dust particles in the desert air. There are no anthropogenic sources, such as heavy traffics or industrial zones, in this region. Therefore, the dust particles originating from large deserts in this region are the main reason for high EC values in precipitation. The role of dust particles and aerosols in the quality of the atmosphere in this region has been previously studied in several papers [84,85,86]. The precipitation events at other stations located in the desert such as Riyadh and Damietta also show high EC values (226 μmho/cm and 289 μmho/cm, respectively). TDS, which is another field parameter and is highly correlated to EC, also shows large variations across the Middle East. The TDS values range from 56 mg/l at Tehran station to 156 mg/l at Jerdab station. The high EC and TDS values at Jerdab station, located in the desert, are due to the significant amount of dust particles in the atmosphere at this station. Other stations that are located in the desert or near it such as Palmyra, Baghdad, and Damietta also show high TDS values due to the significant amounts of dust particles in their atmospheres.
In addition to the field parameters, the average concentrations of the major and some minor ions in the Middle East precipitation were also studied (Figure 4). Among the cations, Ca2+ shows the highest average concentration in precipitation, varying from 1.6 mg/l at Eshidiya station to 52.9 mg/l at Ankara station. The Ca2+ ion has a mainly terrestrial origin. This ion mainly originates from evaporative (gypsum (CaSO4·2H2O) and anhydrite (CaSO4)) and/or carbonate minerals (calcite (CaCO3), dolomite (Ca Mg (CO3)2), and aragonite (CaCO3)). Na+ is the second dominant ion in the Middle East precipitation. The concentration of this ion in precipitation ranges from 1.6 mg/l at Sarcheshmeh and Damascus stations to 23.9 and 23.4 mg/l at Mashhad and Mahshahr stations, respectively. The highest concentration of Na+ at Mashhad station is not completely clear yet. However, this ion may originate from Na-containing minerals, such as halite (NaCl) and albite (NaAlSi3O8), which exist in the Ngr Formation in nearby regions [87]. The high concentration of Na+ at the coastal station of Mahshahr is due to its marine sources from the Persian Gulf. K+ and Mg2+ show much lower concentrations in precipitation than Ca2+ and Na+. The concentration of Mg2+ in precipitation varies from 0.4 mg/l at Sarcheshmeh station to 9.7 mg/l at Arak station. The high values of Mg2+ in precipitation are mainly due to its marine origin. However, it can also originate from dust particles with Mg-containing minerals, such as epsomite (MgSO4·7H2O) and magnesite (MgCO3). Since Arak station is located deep in the continent, Mg2+ dominantly originates from dust particles from terrestrial sources. The existence of marl and marl-intercalated limestone near Arak station [88] also confirms this idea. The K+ ion in the Middle East precipitation also varies from 0.4 mg/l at Dhahran station to 9.4 mg/l at Sarcheshmeh station. The K+ ion may have marine and/or terrestrial sources. Because Sarcheshmeh station is located deep in the continent, the K+ ion in the precipitation samples in this station originates from dust particles from the Sarcheshmeh copper mine complex, which is located nearby. NH4+ is another important cation in the precipitation samples. The concentration of this ion was analyzed for some of the stations. It varies from 0.3 mg/l at Ahvaz station to 21.7 mg/l at Ankara station. In contrast to other cations with mainly terrestrial or marine sources, this ion mainly originates from anthropogenic sources. The high concentration of NH4+ in the precipitation at Ankara station is due to intense agricultural activities in the surrounding areas not only in spring but also in autumn and winter. These intense agricultural activities have neutralized precipitation [66].
Among the anions, SO42− and HCO3 have higher concentrations and a more important role in the chemistry of precipitation (Figure 5). The concentration of SO42− in precipitation varies from 1.1 mg/l at Hadath station to 143.5 mg/l at Damietta station. The extremely high concentration of SO42 is due to significant air pollution in the region caused by several factors, such as textile factories, painting and furniture activities, and heavy traffic [61]. The concentration of HCO3, which is another important anion in the Middle East precipitation, varies from 0.4 mg/l at Shiraz station to 146.4 mg/l at Ankara station. The high concentrations of HCO3 are accompanied by high concentrations of Ca2+ in the precipitation samples at Ankara station. This is due to two possible reasons: first, the dissolution of very high amounts of CO2 in precipitation originating from the burning of fossil fuels in automobiles; second, the dissolution of limestone dust particles rich in CaCO3 in the precipitation samples of this station [89]. The concentration of the Cl ion in the Middle East precipitation also varies from 2.2 mg/l at Haraz station to 51.8 mg/l at Ankara station. The Cl ion mainly originates from marine sources, but can also originate from anthropogenic sources, including exhaust fumes, fertilizers, and the burning of coal [13,90]. The high concentrations of Cl at Ankara, as a crowded and polluted city, may have both marine and anthropogenic origins. Finally, the NO3 ion shows large variations, from 0.1 mg/l at Jerdab station to 136.4 mg/l at Ankara station. NO3 is a conservative ion that mainly originates from anthropogenic sources. Among the studied stations in the Middle East, Ankara and Damietta show the highest concentrations of NO3 (136.4 and 103.6 mg/l, respectively). The significantly high concentrations of NO3 in the air of these cities are due to industrial activities as well as heavy traffic, which add large amounts of air pollutants to the atmosphere of Ankara [66] and Damietta [61].
Based on their locations, the precipitation in the studied stations also demonstrates several water types. The stations located on the Mediterranean Sea coast (e.g., Tripoli, Istanbul, and Tartus) or its vicinity (e.g., Nabatieh and Hadath) show a CaCl2 water type. The precipitation events in the stations located on the Persian Gulf coast (e.g., Mahshahr and Dhahran) demonstrate NaSO4 and CaSO4 water types, while those at Ahvaz station (near the Persian Gulf) show a CaSO4 water type. Finally, the precipitation at Karachi station, located on the Oman Sea coast, shows a NaSO4 water type. These water types are normal in coastal areas due to the mainly marine origins of the SO42−, Cl, and Na+ ions. Several water types are observed in the continental stations across the Middle East. At Tehran, Riyadh, Shiraz, Ankara, and Sarcheshmeh stations, the dominant precipitation water type is CaSO4. However, at Damascus, Palmyra, Shahroud, Ahvaz, and Arak stations, the dominant precipitation water type is CaHCO3. The CaSO4 and CaHCO3 water types in the precipitation at most continental stations across the Middle East are due to the dominant role of ions with terrestrial origins. The carbonate aerosol particles originating from carbonate formations increase the concentrations of Ca2+, Mg2+, and HCO3 ions in precipitation. However, the particles originating from evaporative minerals, which have large outcrops in the Middle East, significantly increase the concentrations of Ca2+ and SO42− [26,54,91]. At other continental stations, including Ramallah, Amman, and Eshidiya, NaNO3, NaHCO3, and NH4SO4 are the dominant water types, respectively. Both Ramallah and Amman are located near the Dead Sea in the Jordan Rift Valley. The Na+ ion in the precipitation at these stations originates from salt particles from the highly saline Dead Sea. The NH4SO4 water type in the precipitation at Eshidiya station is due to anthropogenic pollution in the atmosphere of this station, mainly caused by industrial (e.g., by Indo-Jordan Chemicals) and mine activities in this area. Eshidiya is a polluted area, and the approximately high concentrations of NO3, NH4+, and SO42− ions (63.7, 43.0, and 121.5 μeq/l, respectively) in the precipitation at this station show it [27].

4.2. The Origins of the Major Ions in Precipitation in the Middle East

The chemistry of precipitation is controlled by different factors, including terrestrial, anthropogenic, and marine sources. Studying the ratio of major ions to Na+ and Ca2+ shows marine and crust sources, respectively, whereas the SO42−/NO3 ratio can provide valuable information about the anthropogenic sources of ions in precipitation (Supplementary Table S1). The SO42−/Na+, K+/Na+, Ca2+/Na+, and Mg2+/Na+ ratios in precipitation at the studied stations across the Middle East (Figure 6) are higher than seawater ratios [92]. The higher average ratios in precipitation compared with seawater ratios demonstrate that in addition to marine sources, anthropogenic and terrestrial sources also affect the chemical characteristics of precipitation.
The ratios of major ions to Ca2+ (including SO42−/Ca2+, K+/Ca2+, Na+/Ca2+, and Mg2+/Ca2+) present important information about the role of ions from the crust in precipitation (Figure 7). The X/Ca2+ ratio has lower values at continental stations than at marine stations. This is due to the fact that the role of the crust in the chemistry of precipitation increases toward the continental stations; the concentration of Ca2+ as an indicator of crust/terrestrial origin increases, while the X/Ca2+ ratio decreases toward the continent. There are some exceptions among the continental stations, such as Eshidiya station. Although it is located on the continent, it shows high X/Ca2+ ratios. This is perhaps due to severe anthropogenic pollution at this station, as was mentioned above.
Furthermore, the SO42−/NO3 ratio is a good indicator for studying and monitoring anthropogenic sources in precipitation [93]. In the Middle East precipitation, this ratio varies from 0.37 to 21.77, with an average of 3.77. Both SO42− and NO3 contribute to precipitation acidity by producing H2SO4 and HNO3, respectively [94]. At most stations across the Middle East, the SO42−/NO3 ratio is very low. For instance, at Tehran and Ankara stations, which are affected by severe air pollution, the SO42−/NO3 ratio is 1.94 and 0.88, respectively. Atmospheric pollution due to fuel combustion in vehicles and the lower usage of other air pollutants, such as coal, result in low SO42−/NO3 ratios [26]. However, higher values of SO42− compared with NO3 in precipitation acidity have also been reported in areas where coal is used to provide energy [10,95].

4.2.1. The Sea Salt and Nonsea Salt Contributions to the Middle East Precipitation

To estimate the nonmarine and marine contributions of various ionic species to the Middle East precipitation, a nonsea salt fraction (NSSF) and sea salt fraction (SSF) were respectively calculated, and the results are presented in Table 1. The results demonstrate high NSS values for the Ca2+ and HCO3 ions at the studied stations due to the terrestrial origins of these ions. However, Cl- shows much lower NSSF values than SSF values. In addition, the negative values of NSSF in some of the studied ions such as Cl-, Mg2+, and K+ show mainly marine origins of these ions. The other ions in the precipitation samples of the studied stations show both marine and continental sources.

4.2.2. Calculating the Enrichment Factor in the Middle East Precipitation

An enrichment factor (EF) was used to determine and examine the origins and contributions of the dominant ions in the Middle East precipitation [96,97]. The enrichment factor is calculated by comparing the elemental ratio between some major ions measured in the Middle East precipitation with a similar ratio in marine sources [98]. The calculated EF values show that the Ca2+, SO42−, and HCO3 ions have terrestrial origins. However, the lower EF values for K+, Cl, and Mg2+ show the important role of marine sources in these ions (Table 2).

4.2.3. The Acidity and Neutralizing Capacity of the Middle East Precipitation

The acidity of precipitation is normally affected by the existence of HNO3, H2SO4, and organic acids in precipitation, while NH3 and CaCO3 in the environment neutralize the acidity of precipitation [80,99]. To test the acidity of precipitation after the neutralization process in the atmosphere, calculating fractional acidity (FA) using the equation H+/(SO42−+NO3) can provide valuable results. This index has been calculated and used in many previous studies [23,97,100]. In the studied stations, FA ranges from 0.0007 to 1.17 and has an average of 0.16. To study the relationship between acidity and neutralization more deeply, FA was plotted against pH (Figure 8). A strong positive correlation between FA and pH according to the coefficient of determination (R2) value of 0.72 shows that SO42− and NO3 are the main elements controlling the acidity of precipitation. Nevertheless, a large proportion of the species causing acidity are neutralized in the atmosphere [23].
The neutralizing capacity of the major ions can also show the acidity potential of precipitation in the Middle East (Table 3). Calculating the neutralizing factor (NF) in the precipitation samples shows the following descending trend: Ca2+ (0.29) > Mg2+ (0.25) > K+ (0.24). Therefore, Ca2+ is considered the major acid-neutralizing cation among the ions. This may be due to the terrestrial origin of this ion in controlling the chemistry of precipitation as well as the abundance of dust particles in the atmosphere of the Middle East. In most cases, the NF is much lower than unity, which confirms the fact that the acidic species have a more important role than the neutralizing ones. The NF is more than unity only at Baghdad station, where the neutralizing species are dominant.
Finally, the relative contributions of nitric and sulfuric acids (the main acidic elements in precipitation) to the Middle East precipitation were studied using some ion ratios. Firstly, the SO42−/NO3 ratio in precipitation ranges from 0.38 to 22.0, with an average of 3.76. This demonstrates that sulfuric acid has a stronger role in the acidity of precipitation than nitric acid. Secondly, the (NO3 + Cl)/(SO42−) ratio was studied in precipitation. This ratio ranges from 0.27 to 20.73, with an average of 3.35. At some of the stations, including Haraz, Urmia, Tripoli, Hadath, Nabatieh, Tartus, Palmyra, Amman, Dhahran, Ankara, and Faryab, the (NO3 + Cl)/(SO42−) ratio shows values higher than unity. However, the (NO3 + Cl)/(SO42−) ratio at Mahshahr, Sarcheshmeh, Tehran, Damietta, Damascus, and Eshidiya stations shows values lower than unity. At the stations with a (NO3 + Cl)/(SO42−) ratio lower than unity, the role of sulfuric acid is more dominant. However, at the stations with a (NO3 + Cl)/(SO42−) ratio more than unity, the role of hydrochloric and nitric acids in the acidity of water is significant.
On the other hand, comparing the NH4+/NO3 ratios at a few stations in the Middle East (NH4+ was analyzed at a few stations) shows that ammonium (NH4+) cannot neutralize the acidic N-containing species in the Middle East precipitation. This is because the NH4+/NO3 ratio is lower than unity in most cases and ranges from 0.07 to 5, with an average of 0.77. The high value of the NH4+/NO3 ratio (~5) at Eshidiya station is due to the significant amount of ammonium produced by chemical manufacturing industries in this area. A high amount of ammonium completely neutralizes N-containing acidic compounds and possibly forms NH4NO3 in the atmosphere [101].

4.3. Applying Statistical and Machine Learning Techniques in Hydrochemical Studies of the Middle East Precipitation

To study the sources of ions in the Middle East precipitation more deeply, statistical techniques such as PCA were applied (Text Code S2). The varimax rotation of the PCA, which covers more than 71% of the total variance, is depicted in Table 4. The first factor has a high loading for Ca2+, HCO3, Cl, SO42−, EC, and TDS, which explains 52% of the total variance. This factor shows the main role of the ions with terrestrial origins in precipitation. They mainly originate from the dissolution of carbonate-containing (such as calcite, dolomite, and aragonite) and/or evaporative minerals (such as gypsum, anhydrite, and hydrophilite). The second factor, which covers 19.0% of the total variance, has a significant loading for Mg2+, Na+, K+, and pH. The second factor shows the importance of the concentrations of some ions and their correlation with the pH/acidity of precipitation. The K+ ion mainly originates from terrestrial sources and is inversely correlated with pH. However, Mg2+ and Na+ have a mainly marine origin and are directly correlated with pH.
In addition to statistical techniques, machine learning algorithms were also used to simulate the TDS values in precipitation. Various machine learning algorithms, including ANN, DNN, decision tree, and gradient boosting (Gboost), were also applied to the precipitation data to simulate the TDS values. First, the importance percentage of each major ion affecting the TDS values in precipitation was determined (Figure 9). Studying the importance percentage of various ions controlling the TDS values in precipitation by different machine learning algorithms shows that Ca2+ and Na+ are the main ions controlling the TDS values in precipitation according to the Gboost and DNN models. The Cl and HCO3 ions were the major ions controlling the TDS values in precipitation in the ANN model, as are SO42− and Cl in the decision tree model. Among the cations, Ca2+ mainly originates from terrestrial sources, while Na+ mainly originates from marine sources. Among the anions, Cl and SO42 have mainly marine origins, while HCO3 mainly originates from terrestrial sources.
Furthermore, as an indicator of the hydrochemistry of precipitation, TDS was simulated by these machine learning algorithms. Then, the accuracy of the developed models was validated by R2 values (Table 5).
It can be observed that among the studied machine learning algorithms, gradient boosting (Gboost) has the highest R2 values both in the train and test sets. This confirms the higher accuracy of the model developed by Gboost compared with those developed by other machine learning methods. Normally, boosting machine learning algorithms, such as gradient boosting (Gboost), extra gradient boosting (XGboost), Ada boosting, and cat boosting, improve weak regression or decision tree models to obtain more accurate machine learning models. This is why the model developed by Gboost is more accurate and shows higher R2 values.

5. Conclusions

The hydrochemical analyses showed that the particles originating from terrestrial sources controlled the quality of precipitation across the Middle East. This was due to the significant amount of dust particles in the atmosphere of the Middle East originating from dry and bare lands. The dust particles originating from evaporative and carbonate minerals were the main sources affecting precipitation. In addition, marine and anthropogenic sources also had a minor effect on the quality of precipitation in this region. Precipitation in some of the stations had an acidic nature, while it had a nonacidic or neutralized nature in some other stations. SO42− and NO3 were the main ions controlling the acidity of precipitation. On the other hand, the major acid-neutralizing cation in the Middle East precipitation was Ca2+. Finally, studying the importance percentages of various ions in controlling the TDS values in precipitation and simulating the TDS values using machine learning algorithms (DNN and Gboost models) showed that Ca2+ and Na+ were the dominant ions controlling the TDS values in precipitation. Cl and HCO3 were the main ions in the ANN model, whereas SO42− and Cl were the major ions in the decision tree model. The validation of the developed models showed that Gboost was the most accurate model in simulating the TDS values in precipitation due to its higher R2 values in both the train and test sets.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/w14172657/s1, Table S1: The ratio of ions to Na+ and Ca2+, as well as the SO42/NO3 ratio in precipitation at the studied stations, Text Code S2: code for PCA of precipitation data in the studied stations across the Middle East.

Author Contributions

Conceptualization, M.H., H.G.M. and M.M.; methodology, L.G.; software, M.H.; validation, M.H., M.M. and S.A.; formal analysis, M.H. and H.G.M.; investigation, M.H.; resources, M.H. and M.M.; data curation, M.H.; writing—original draft preparation, M.H.; visualization, S.A.; supervision, L.G.; project administration, L.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The data of this study are available from the first author [M.H.] upon reasonable request.

Acknowledgments

The first author acknowledges the postdoctoral fellowship (no. MU-PD-2021-13) from the Faculty of Environment and Resource Studies, Mahidol University. The authors also express their gratitude to their colleagues at Mahidol University in Thailand and Vigo University (Ourense campus) in Spain for their support during this study.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. The studied stations and main air masses affecting the Middle East.
Figure 1. The studied stations and main air masses affecting the Middle East.
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Figure 2. The northward and eastward data of the vertically integrated moisture flux (VIMF) with a 1° × 1° resolution from 1981 to 2021 are derived from ERA-Interim reanalysis [53].
Figure 2. The northward and eastward data of the vertically integrated moisture flux (VIMF) with a 1° × 1° resolution from 1981 to 2021 are derived from ERA-Interim reanalysis [53].
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Figure 3. The variations in CBE (%) calculated for the precipitation analyses in the studied stations across the Middle East.
Figure 3. The variations in CBE (%) calculated for the precipitation analyses in the studied stations across the Middle East.
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Figure 4. The variations in major cations in the precipitation at the studied stations across the Middle East.
Figure 4. The variations in major cations in the precipitation at the studied stations across the Middle East.
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Figure 5. The variations in the major anions in the precipitation at the studied stations across the Middle East.
Figure 5. The variations in the major anions in the precipitation at the studied stations across the Middle East.
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Figure 6. The distribution of major ion ratios in precipitation to Na+ and SO42-/NO3- in the studied stations.
Figure 6. The distribution of major ion ratios in precipitation to Na+ and SO42-/NO3- in the studied stations.
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Figure 7. The distribution of major ion ratios in precipitation to Ca2+ in the studied stations.
Figure 7. The distribution of major ion ratios in precipitation to Ca2+ in the studied stations.
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Figure 8. Regression analysis of FA and pH in the Middle East precipitation.
Figure 8. Regression analysis of FA and pH in the Middle East precipitation.
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Figure 9. The percentage importance of different major ions in the Middle East precipitation (obtained from various machine learning models).
Figure 9. The percentage importance of different major ions in the Middle East precipitation (obtained from various machine learning models).
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Table 1. The SSF and NSSF of the major ions in the Middle East precipitation.
Table 1. The SSF and NSSF of the major ions in the Middle East precipitation.
RowStationSea Salt Fraction (SSF)Non Sea Salt Fraction (NSSF)
Ca2+K+Mg2+SO42ClHCO3Ca2+K+Mg2+SO42ClHCO3
Continental stations
1Shiraz0.110.110.350.755.250.0312.491.590.6613.45−0.150.33
2Ahvaz0.320.310.992.0914.680.0815.83−0.31−0.2133.020.2140.62
3Shahroud0.310.30.952.0114.110.0821.592.50.4520.99−6.1144.12
4Haraz0.070.070.230.483.40.0212.351.020.284.41−1.2143.79
5Sarcheshmeh0.060.060.20.422.930.025.689.290.1819.391.565.01
6Tehran0.110.110.340.725.080.039.391.011.8111.6−2.6123.02
7Mashhad0.940.912.926.1543.30.2512.82−0.21−1.4619.87−14.9828.97
8Arak0.750.732.334.9234.60.224.970.297.3538.19−30.1762.27
9Urmia_________2.344.04_
10Riyadh0.240.230.741.5711.040.0631.261.570.7615.73−1.54−0.06
11Eshidiya0.150.150.470.996.960.041.471.811.538.24−1.134.27
12Ankara0.480.461.483.1322.040.1352.424.971.43116.9729.72146.27
13Faryab1.090.550.570.312.9_6.991.150.235.19−0.10_
14Hadath0.290.280.891.8713.170.088.581.873.21−0.796.7134.09
15Nabatieh0.280.270.871.8412.960.088.561.91.290.436.1528.7
16Palmyra0.080.080.260.563.910.0229.250.753.7413.771.4277.31
17Damascus0.060.060.20.422.930.0222.940.441.819.331.5745.73
18Amman0.560.541.743.6625.790.155.070.33−0.751.53−20.0610.95
19Baghdad2.51.241.290.716.61_17.952.577.1151.919.59_
20Ramallah0.430.421.342.8219.850.11−0.434.56−1.34−2.82−19.85−0.11
21Jerdab____________
22Tartus0.320.310.992.0814.660.088.080.791.813.12−0.6624.12
23Dhahran0.120.110.360.775.410.039.150.280.7310.4−0.334.45
24Istanbul0.070.060.210.433.060.027.112.652.738.973.88−0.02
25Tripoli0.260.260.821.7412.210.077.452.83.851.192.7919.93
26Damietta_________143.5437.13_
27Karachi0.440.421.362.8620.140.126.234.74−0.1816.21−11.569.89
28Mahshahr0.920.892.856.0142.270.2412.61−0.303.2752.15−23.9533.83
Table 2. The enrichment factor (EF) of the major ions in the Middle East precipitation.
Table 2. The enrichment factor (EF) of the major ions in the Middle East precipitation.
RowStationCa2+K+Mg2+SO42−ClHCO3
1Shiraz110.7315.432.8619.040.9711.85
2Ahvaz50.75_0.7916.841.01479.04
3Shahroud71.569.451.4711.470.57540.91
4Haraz168.3715.262.2310.120.642224.4
5Sarcheshmeh90.3151.881.9147.551.53296.38
6Tehran86.1610.496.2817.050.49783
7Mashhad14.650.770.54.230.65116.56
8Arak34.281.44.158.770.13311.87
9Urmia______
10Riyadh131.617.772.0211.030.86_
11Eshidiya10.7513.374.269.330.84106.84
12Ankara110.6911.731.9638.352.351147.34
13Faryab64.6922.011.4117.60.96_
14Hadath31.057.774.620.581.51448.03
15Nabatieh31.477.982.471.231.47383.68
16Palmyra346.0610.1115.1925.81.363417.36
17Damascus361.838.1210.1347.411.542695.71
18Amman10.071.610.571.420.2274.35
19Baghdad72.7321.726.4972.843.96_
20Ramallah_11.95____
Coastal stations
21Jerdab______
22Tartus26.433.572.842.50.96285.19
23Dhahran79.013.432.9914.530.94143.02
24Istanbul108.2842.214.2721.632.27_
25Tripoli29.1111.935.681.691.23282.83
26Damietta______
27Karachi15.2712.20.876.660.4385.85
28Mahshahr14.760.662.159.680.43139.22
Table 3. The NFs of the major ions in the Middle East precipitation.
Table 3. The NFs of the major ions in the Middle East precipitation.
RowStationCa2+K+Mg2+
Continental stations
1Shiraz___
2Ahvaz___
3Shahroud___
4Haraz0.0130.0420.013
5Sarcheshmeh0.0450.1520.045
6Tehran0.0160.0480.016
7Mashhad0.9520.0471.61
8Arak0.6650.0251.115
9Urmia___
10Riyadh0.1530.4710.146
11Eshidiya0.0840.2630.084
12Ankara0.0030.0110.003
13Faryab0.2860.150.144
14Hadath0.0610.1870.059
15Nabatieh0.0360.1110.034
16Palmyra0.0070.0240.007
17Damascus0.0070.0230.007
18Amman0.0870.2690.084
19Baghdad3.5211.8171.746
20Ramallah0.020.0620.019
Coastal stations
21Jerdab___
22Tartus0.0320.0980.031
23Dhahran0.0120.0370.011
24Istanbul0.1630.4880.14
25Tripoli0.0590.1850.059
26Damietta___
27Karachi0.1540.4760.147
28Mahshahr0.0890.2760.086
Table 4. The PCA varimax rotation of hydrochemical parameters of the Middle East precipitation.
Table 4. The PCA varimax rotation of hydrochemical parameters of the Middle East precipitation.
ElementComponent
12
Ca2+0.85−0.04
Mg2+0.34−0.61
Na+0.43−0.57
K+0.230.72
SO42−0.940.03
HCO30.88−0.06
Cl0.82−0.01
pH−0.21−0.81
EC0.960.1
TDS0.870.084
Table 5. The validation of the developed models in simulating the TDS values in precipitation based on R2 values.
Table 5. The validation of the developed models in simulating the TDS values in precipitation based on R2 values.
Method DNNANNDecision TreeGradient Boosting (Gboost)
Train setR20.490.590.640.71
Test setR20.450.520.510.67
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Heydarizad, M.; Gimeno, L.; Amiri, S.; Minaei, M.; Mohammadabadi, H.G. A Comprehensive Overview of the Hydrochemical Characteristics of Precipitation across the Middle East. Water 2022, 14, 2657. https://doi.org/10.3390/w14172657

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Heydarizad M, Gimeno L, Amiri S, Minaei M, Mohammadabadi HG. A Comprehensive Overview of the Hydrochemical Characteristics of Precipitation across the Middle East. Water. 2022; 14(17):2657. https://doi.org/10.3390/w14172657

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Heydarizad, Mojtaba, Luis Gimeno, Somayeh Amiri, Masoud Minaei, and Hamid Ghalibaf Mohammadabadi. 2022. "A Comprehensive Overview of the Hydrochemical Characteristics of Precipitation across the Middle East" Water 14, no. 17: 2657. https://doi.org/10.3390/w14172657

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