Contribution to the study of ammonium leaching and its influencing factors in the soils of Al-Sin Lake Catchment, Banias, Syria

Ammonia (NH₃) emissions to the atmosphere increasingly contribute to the occurrence of acid rain (ApSimon et al. 1987). They represent an indirect source of greenhouse gas emissions. Ammonium intrusion into rivers and lakes can also cause eutrophication, which, in turn, leads to excessive growth of aquatic weeds and algae, with adverse effects on the environment (Cameron et al. 2013). Several studies confirmed that the use of nitrogenous (ammonia-based) fertilizers for irrigated crops growth and production is the widest cause of groundwater pollution with ammonium ion although ammonium ion is poorly mobile in soils compared to the nitrate ion (Xiong et al. 2010; Khan et al. 2018; Lwimbo et al. 2019; Singh & Craswel 2021). However, the sandy soil with low content of clay and organic matter (OM) can easily move down the soil section and, in turn, into the groundwater (Lord & Mitchell 2007; Zarabi & Jalali 2012; Rusydi et al. 2021).

There have been numerous studies to determine the factors that affect the leaching and transfer of the ammonium ion into the soil, and they have been divided into several factors, the most important of which are:

1. The effect of added agricultural fertilizers, and the intensity of agricultural activities, which increase the ammonium ion leaching in the soil (Teutscherova et al. 2018).

2. The effect of municipal, agricultural and industrial waste on the accumulation of OM and ammonium ion in the soil and its leaching to the subsurface horizons (Dong et al. 2022).

3. The effect of geographical and climatic conditions, the nature of the soil, its content of clay minerals and pH, on the transport and washout of ammonium (Jabloun et al. 2015; Dong et al. 2022).

Many researchers have studied the sources of ammonium ion in the environment, the problems resulting from the excessive use of fertilizers and the factors affecting this transition (Radersma & Smit 2011; Cameron et al. 2013; Van Grinsven et al. 2015; Chen et al. 2021; Dong et al. 2022). A completed study showed that the ammonium ions after entering the interlayers of clay minerals decreased nitrification, and thus were protected from leaching to the subsurface horizons as a kind of buffer for the ammonium ion in the soil (Radersma & Smit 2011).

A study carried out by Cameron et al. (2013) showed that proper management of ammonium ion levels in the soil using best management practices and newly developed technologies could increase the sustainability of natural resources and reduced their impact on the environment (Cameron et al. 2013).

A study conducted by Van Grinsven et al. (2015) showed that the increased use of ammonium-based nitrogen fertilizers contributed to reducing nitrogen losses, which decreased as a result of the increase in the concentrations of ammonium ions liberated in the soil, and the fixation of the ammonium ion in the soil on clay minerals or adsorption on organic soil components (Van Grinsven et al. 2015). Another study showed that the increased levels of leaching fertilization, significant accumulation of OM and soil content of clay minerals and pH led to the accumulation of high concentrations of ammonium ion and thus increased the risk of leaching into subsurface horizons and ultimately into water sources (Chen et al. 2021). A two-year tracking investigation conducted on a semi-tropical red soil using field lysimeters carried out in China to evaluate the effect of fertilization, climatic data and soil type on ammonium ion leaching and total nitrogen accumulation in the soil concluded that if the levels of fertilization were applied in those areas (200 kg N·ha/yr) ammonium ion accumulation and leaching would be continuous, and decreased with depth. It was recorded that at a depth of 20 and 100 cm the levels of fertilization decreased to 91.5 and 57.9 kg N·ha/yr, respectively. This could pose a potential threat to groundwater quality or the safety of drinking water. In order to reduce ammonium ion leaching, effective management practices should be carried out, and the role of vegetation cover and water drainage system should be evaluated (Dong et al. 2022).

This study may contribute to the environmental assessment resulting from fertilization and human activities and clarify the role of soil properties and climatic factors and their impact on ammonium leaching within the soil horizons. The study aimed to estimate the loss of ammonium ion washing for different soils in terms of physical structure, OM and clay content using statistical methods (correlation and regression) in evaluating ammonium leaching processes.

The study area is located within the following coordinates: Longitude: (35°54′57.40″ and 36°14′59.41″), Latitude: (35°13′55.54″ and 35°22′49.50″) and forms part of the central region of the coastal mountains. The study area can be divided into two regions: The first region: shallow hills consisting of relatively inclined slopes composed of marl and limestone marl rocks ranging in height between 250 and 700 m and interrupted by transverse valleys in addition to the main streams sloping from the top of the chain, while the second one consists of the coastal plain and semi-flat terraces, slightly inclined to the west as well as the floodplains of the valleys, and the watercourses that are partially intersected by small canyons.

All experiments and measurements were carried out in the laboratories of the Higher Institute of Environmental Research at Tishreen University, and the Agricultural Scientific Research Center in Al-Hennady in Lattakia.

A set of physical and chemical analyses were carried out and summarized as follows:

1. Mechanical analysis of soil by the hydrometer method (Gupta 2007).

2. Determination of saturated water conductivity of soil in the laboratory as well as by applying Darcy's law (Carter & Gregorich 2008).

1. Determining the pH value (pH) in a ratio 1:2.5 soil/water using a field pH device; Metrohm 744 pH Meter (Marx et al. 1999).

2. Determining the electrical conductivity (EC) using an EC meter in an aqueous soil extract 1:5 soil/water (Jones 2001).

3. Determination of calcium carbonate in soil using volume titration of 1 N hydrochloric acid solution (Jackson 2005).

4. Determination of calcium and magnesium concentrations (Bashour & Sayegh 2007).

5. Determining the amount of OM by oxidation of organic carbon with potassium dichromate solution in an acidic medium, then titration of excess dichromate by ferric sulfate (Moore's salt) in the presence of ferroin indicator (Nelson & Sommers 1983).

6. Determination of total nitrogen content in the soil using the Kjeldahl method by digesting soil samples in strong sulfuric acid (Bremner 1960).

The soil infiltration solutions were collected using a set of lysimeters placed in the horizons of the soil at the sampling sites and the following analyses were performed:

1. Determination of the EC of the water samples using an electrical conductivity meter (APHA 1998).

2. Measuring the pH value using a field pH device (Metrohm 744 pH Meter), where this process took place on site immediately after obtaining the water samples (Mhlla 2011).

3. Determination of ammonium ion concentration in the lysimeter solution using a spectrophotometer at a wavelength of 420 nm (Bolleter et al. 1961).

The following findings were resulted in based on the physical and chemical properties of the soil samples obtained from the horizons of the studied sites:

The first horizon was characterized by having moderate acidity, slightly inclined to alkalinity and a high content of calcium carbonate and OM. The soils of the first horizon were characterized by a high percentage of clay, then loamy soils and with a lesser percentage of loamy soils. The saturated water hydraulic conductivity ranged between 0.85 and 1.88 cm/min. Table 1 shows the results of the analysis of the physical and chemical properties of soil of the first horizon for the studied sites.

It can be found from Figure 4 that the leached ammonium ion concentrations in the lysimetric water in the first horizon during the study period, which continued over the entire rainy season for the studied areas, were characterized by high ammonium leaching concentrations in two periods. The first one is high during November and December, when the levels of concentrations in it ranged between 0.97 and 5.74 mg/L, as a result of the effect of two synergistic factors: the first one was due to the accumulation of ammonium ion in the soil as a result of mineralization of OM during the summer period and weak rainfall and the release of ammonium in the soil as a result of autumn fertilization. The second period was March after the spring mineral and organic fertilization at the beginning of February. The level of concentrations ranged between 1.57 and 5.81 mg/L. This was largely due to the accumulation of ammonium ion in the soil as a result of mineralization of OM.

The second horizon was characterized as having moderate acidity compared to the first horizon, and a high content of calcium carbonate and OM. The content of physical clay was slightly lower compared to the first horizon, and its texture was clay with a high percentage, loamy clay. Also noticed is a decrease in the value of hydro-hydraulic conductivity compared to the first horizon, where its value ranged between 0.74 and 1.70 cm/min. Table 2 shows the analysis of the physical and chemical properties of the second horizon soils for the studied sites.

It can be noticed from Figure 5 that the concentrations of the washed ammonium ion in the lysimetric water from the second horizon during the study period, which lasted for a whole year for the studied areas, were lower than the first horizon due to the adsorption of part of the ammonium ion on the OM and clay minerals within the first horizon. The leached ammonium ion concentrations were high during two periods. The first one was during November and December, ranging between 0.26 and 4.28 mg/L. This was a result of the effect of two synergistic factors. The first factor was due to the accumulation of ammonium ion in the soil as a result of mineralization of OM and weak rainfall during summer, and the release of the ammonium ion in the soil as a result of autumn fertilization. However, the second period was during March, after spring mineral and organic fertilization in the beginning of February, and the level of concentrations was within the range of 1.18–4.81 mg/L. These were due to two synergistic factors. The first one was a result of the increase in the concentration of the ammonium ion liberated from the first horizon in the soil, and the second one was the decrease in the volume of lysimetric water leaching through the soil section in the second horizon. These led to higher concentrations of the washed ammonium ion. The third horizon was distinguished by its alkaline nature compared to the first and second horizons. It had a high content of calcium carbonate and OM. The soil in the region had a high content of physical clay, compared to the first and second horizons. Its texture ranged between clay and lumpy clay. The saturated water hydraulic conductivity ranged between 0.54 and 1.21 cm/min. It was noted that the value of the saturated water hydraulic conductivity of the third horizon was lower than that of the first and second horizons. This could be attributed to the increased amount of physical clay in the third horizon. Table 3 shows the results of the analysis of the physical and chemical properties of the third horizon soils of the studied sites.

Noticed from Figure 6 is a decrease in the concentrations of the leached ammonium ion in the lysimetric water from the third horizon compared to the top horizons of the studied areas during one year of the study period. This can be likely attributed to the occurring adsorption of part of the ammonium ion on the OM and clay minerals within the first and second horizons. The concentrations of the leached ammonium ion were high in two periods. The first one was during November and December, ranging between 0.59 and 3.87 mg/L while the level of concentrations during the second period of March ranged between 1.86 and 4.23 mg/L. This may have been occurred as a result of the accumulation and transformation of the ammonium ion from the first and second horizons to the lysimetric water that was washed to the third part of the horizon.

The soils of the study areas can be divided according to the leached ammonium ion concentrations into two parts:

1. Soils with high levels of concentrations of leached ammonium ion were Beit Aana, Al-Qutailbiya, Quorfase and Al-Waha Spring. These areas were characterized by multiple human activities, whether agricultural, as a result of the diversity of crops, especially green houses, and livestock and cows breeding, in addition to the large number of tourist areas. This might have resulted in high concentrations of ammonium ion in the surrounding soils and waters. The highest values of the washed ammonium ion concentrations were recorded in November and March whereas the lowest was in February.

2. Soils with low concentrations of ammonium leaching were, in contrast, the sites of Bestwair, Beit Al-Alouni, Geiboul and Rahbeyia. The highest of which was recorded in November and March whereas the lowest was recorded in January.

Determining the main parameters of water pollution with ammonium ion is critical in order to determine reasonable agro-environmental indicators that support the design, enforcement and control of regulatory policies. Therefore, it was important to use statistical analysis data to determine the factors affecting the transport of ammonium ion within the soil horizons, and their eventual access to water sources (Schweigert et al. 2004). These factors were divided into three categories:

• (1)

  Soil properties that affect the leaching of the ammonium ion within the soil sections, and its leaching to groundwater.

• (2)

  Climatic factors such as precipitation and temperature.

• (3)

  The predictive ability of ammonium ion leaching.

Table 4 shows the values of the correlation coefficients of the parameters in the first horizon. It is noticed from the correlation matrix that the leached ammonium ion is associated with the variables OM, saturated hydraulic conductivity (SHC), pH and EC as follows: 0.760, 0.741, 0.851 and 0.846, respectively. When applying multiple regression according to Enter to find the equation that linked the variables, the following values were obtained as shown in Table 5.

Table 4

Parameter correlation matrix of the first horizon

Correlations .
.		leaching .	N−total .	CaCO3 .	EC .	OM .	pH .	SHC .	Clay .	Silt .	Sand .	Ca .
leaching	Pearson correlation	1	−0.138	−0.556	0.760*	−0.846**	−0.741*	0.851**	0.015	−0.426	0.539	−0.004
	Sig. (two-tailed)		0.745	0.152	0.029	0.008	0.035	0.007	0.971	0.293	0.168	0.992
	N	8	8	8	8	8	8	8	8	8	8	8
EC	Pearson correlation	0.760*	−0.520	−0.520	1	−0.617	−0.533	0.682	0.350	−0.554	0.187	0.228
	Sig. (two-tailed)	0.029	0.187	0.186		0.103	0.174	0.062	0.396	0.154	0.657	0.587
	N	8	8	8	8	8	8	8	8	8	8	8
OM	Pearson correlation	−0.846**	0.069	0.362	−0.617	1	0.557	−0.931**	0.313	0.108	−0.633	0.317
	Sig. (two-tailed)	0.008	0.871	0.379	0.103		0.152	0.001	0.450	0.799	0.092	0.445
	N	8	8	8	8	8	8	8	8	8	8	8
pH	Pearson correlation	−0.741*	−0.043	0.862**	−0.533	0.557	1	−0.587	−0.160	0.215	−0.034	−0.301
	Sig. (two-tailed)	0.035	0.919	0.006	0.174	0.152		0.126	0.704	0.609	0.937	0.469
	N	8	8	8	8	8	8	8	8	8	8	8
SHC	Pearson correlation	0.851**	0.030	−0.360	0.682	−0.931**	−0.587	1	0.013	−0.404	0.514	−0.018
	Sig. (two-tailed)	0.007	0.944	0.382	0.062	0.001	0.126		0.975	0.321	0.192	0.966
	N	8	8	8	8	8	8	8	8	8	8	8

Correlations .
.		leaching .	N−total .	CaCO3 .	EC .	OM .	pH .	SHC .	Clay .	Silt .	Sand .	Ca .
leaching	Pearson correlation	1	−0.138	−0.556	0.760*	−0.846**	−0.741*	0.851**	0.015	−0.426	0.539	−0.004
	Sig. (two-tailed)		0.745	0.152	0.029	0.008	0.035	0.007	0.971	0.293	0.168	0.992
	N	8	8	8	8	8	8	8	8	8	8	8
EC	Pearson correlation	0.760*	−0.520	−0.520	1	−0.617	−0.533	0.682	0.350	−0.554	0.187	0.228
	Sig. (two-tailed)	0.029	0.187	0.186		0.103	0.174	0.062	0.396	0.154	0.657	0.587
	N	8	8	8	8	8	8	8	8	8	8	8
OM	Pearson correlation	−0.846**	0.069	0.362	−0.617	1	0.557	−0.931**	0.313	0.108	−0.633	0.317
	Sig. (two-tailed)	0.008	0.871	0.379	0.103		0.152	0.001	0.450	0.799	0.092	0.445
	N	8	8	8	8	8	8	8	8	8	8	8
pH	Pearson correlation	−0.741*	−0.043	0.862**	−0.533	0.557	1	−0.587	−0.160	0.215	−0.034	−0.301
	Sig. (two-tailed)	0.035	0.919	0.006	0.174	0.152		0.126	0.704	0.609	0.937	0.469
	N	8	8	8	8	8	8	8	8	8	8	8
SHC	Pearson correlation	0.851**	0.030	−0.360	0.682	−0.931**	−0.587	1	0.013	−0.404	0.514	−0.018
	Sig. (two-tailed)	0.007	0.944	0.382	0.062	0.001	0.126		0.975	0.321	0.192	0.966
	N	8	8	8	8	8	8	8	8	8	8	8

**Correlation is significant at the 0.01 level (two-tailed).

*Correlation is significant at the 0.05 level (two-tailed).

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It is noted from Table 5 that the statistical significance values for each of the independent and constant variables are greater than the significance level (0.05), which indicates that the equation cannot be adopted to predict changes since the parameters are not significant, although the value of the correlation coefficient reached the value R = 0.932 and the coefficient of determination R² = 86.9%. This can be attributed to the fact that the values of the correlation coefficient between the variables are large, which causes a problem in predicting the washout process. To solve the problem, the multiple regression test was used via a stepwise method. Therefore, the following values were tabulated as shown in Table 6.

It is noticed from the table of parameters that the statistical significance values of the independent variable (SHC) are less than the significance level (0.05), which indicates that the equation is well represented and can be adopted to predict changes in the ammonium leaching.

Table 7 shows the values of the correlation coefficients of the parameters in the second horizon.

Table 7

Parameter correlation matrix of the second horizon

Correlations .
.		leaching .	N−total .	CaCO3 .	EC .	OM .	pH .	SHC .	Clay .	Silt .	Sand .	Ca .
leaching	Pearson correlation	1	0.076	−0.333	0.821*	−0.894**	−0.419	0.844**	0.191	−0.458	0.180	0.191
	Sig. (two-tailed)		0.858	0.420	0.012	0.003	0.302	0.008	0.651	0.253	0.670	0.650
	N	8	8	8	8	8	8	8	8	8	8	8
EC	Pearson correlation	0.821*	−0.042	−0.435	1	−0.599	−0.435	0.494	0.570	−0.641	−0.273	0.497
	Sig. (two-tailed)	0.012	0.922	0.281		0.116	0.282	0.213	0.140	0.087	0.514	0.210
	N	8	8	8	8	8	8	8	8	8	8	8
OM	Pearson correlation	−0.894**	−0.033	0.343	−0.599	1	0.434	−0.915**	−0.037	0.407	−0.387	−0.067
	Sig. (two-tailed)	0.003	0.938	0.406	0.116		0.282	0.001	0.931	0.317	0.343	0.876
	N	8	8	8	8	8	8	8	8	8	8	8
SHC	Pearson correlation	0.844**	0.416	−0.320	0.494	−0.915**	−0.430	1	0.039	−0.308	0.275	0.168
	Sig. (two-tailed)	0.008	0.306	0.440	0.213	0.001	0.288		0.927	0.457	0.510	0.691
	N	8	8	8	8	8	8	8	8	8	8	8

Correlations .
.		leaching .	N−total .	CaCO3 .	EC .	OM .	pH .	SHC .	Clay .	Silt .	Sand .	Ca .
leaching	Pearson correlation	1	0.076	−0.333	0.821*	−0.894**	−0.419	0.844**	0.191	−0.458	0.180	0.191
	Sig. (two-tailed)		0.858	0.420	0.012	0.003	0.302	0.008	0.651	0.253	0.670	0.650
	N	8	8	8	8	8	8	8	8	8	8	8
EC	Pearson correlation	0.821*	−0.042	−0.435	1	−0.599	−0.435	0.494	0.570	−0.641	−0.273	0.497
	Sig. (two-tailed)	0.012	0.922	0.281		0.116	0.282	0.213	0.140	0.087	0.514	0.210
	N	8	8	8	8	8	8	8	8	8	8	8
OM	Pearson correlation	−0.894**	−0.033	0.343	−0.599	1	0.434	−0.915**	−0.037	0.407	−0.387	−0.067
	Sig. (two-tailed)	0.003	0.938	0.406	0.116		0.282	0.001	0.931	0.317	0.343	0.876
	N	8	8	8	8	8	8	8	8	8	8	8
SHC	Pearson correlation	0.844**	0.416	−0.320	0.494	−0.915**	−0.430	1	0.039	−0.308	0.275	0.168
	Sig. (two-tailed)	0.008	0.306	0.440	0.213	0.001	0.288		0.927	0.457	0.510	0.691
	N	8	8	8	8	8	8	8	8	8	8	8

**Correlation is significant at the 0.01 level (two-tailed).

*Correlation is significant at the 0.05 level (two-tailed).

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According to the correlation matrix shown in Table 7, it is clear that ammonium leaching is associated with the independent variables: OM, SHC and EC where the statistical significance is less than 0.05. The correlation values were 0.821, 0.894 and 0.844, respectively.

When applying the multiple regression analysis in order to obtain the equation that links the variables, the values shown in Table 8 were obtained:

It is noticed from the table of parameters that the values of statistical significance for each of the independent variables: OM and SHC are greater than the significance level (0.05), which indicates that the equation cannot be adopted to predict changes because the parameters are not significant, although the value of the correlation coefficient R reached 0.970 and the coefficient of determination R² = 94.2%. This can be attributed to the fact that the values of the correlation coefficient between the variables are large, which causes a problem in predicting the washout process, to solve the problem a multiple regression test in a stepwise method was applied to obtain the values shown in Table 9.

It is noted from Table 9 that there are two corrected linear models:

The value of the correlation coefficient was R = 0.894, and the coefficient of determination R² = 76.5%.

The value of the correlation coefficient R and the coefficient of determination R² were 0.962 and 92.6%, respectively. It is also noted from the table of parameters that the significance values of the variables and the constant were less than the significance level (0.05), which indicates that the two equations are a very well representative for the study of predicting the process of ammonium washout.

Table 10 shows the values of the correlation coefficients of the parameters in the third horizon. It is noticed from the correlation matrix that the variable is associated with the variables: OM, SHC and EC, with the following values: 0.709, 0.796 and −0.816, respectively.

Table 10

Parameter correlation matrix of the third horizon

Correlations .
.		leaching .	N− total .	CaCO3 .	EC .	OM .	pH .	SHC .	Clay .	Silt .	Sand .	Ca .
leaching	Pearson correlation	1	0.387	−0.432	0.709*	−0.816*	−0.319	0.796*	0.219	−0.504	0.180	0.274
	Sig. (two-tailed)		0.344	0.286	0.049	0.014	0.440	0.018	0.602	0.203	0.669	0.512
	N	8	8	8	8	8	8	8	8	8	8	8
EC	Pearson correlation	0.709*	0.258	−0.655	1	−0.574	−0.589	0.415	0.723*	−0.785*	−0.378	0.664
	Sig. (two-tailed)	0.049	0.537	0.078		0.137	0.125	0.307	0.042	0.021	0.356	0.072
	N	8	8	8	8	8	8	8	8	8	8	8
OM	Pearson correlation	−0.816*	−0.192	0.652	−0.574	1	0.584	−0.799*	−0.241	0.560	−0.204	−0.216
	Sig. (two-tailed)	0.014	0.649	0.080	0.137		0.128	0.017	0.565	0.149	0.627	0.607
	N	8	8	8	8	8	8	8	8	8	8	8
SHC	Pearson correlation	0.796*	0.518	−0.587	0.415	−0.799*	−0.513	1	0.126	−0.313	0.130	0.261
	Sig. (two-tailed)	0.018	0.188	0.126	0.307	0.017	0.194		0.767	0.451	0.758	0.533
	N	8	8	8	8	8	8	8	8	8	8	8

Correlations .
.		leaching .	N− total .	CaCO3 .	EC .	OM .	pH .	SHC .	Clay .	Silt .	Sand .	Ca .
leaching	Pearson correlation	1	0.387	−0.432	0.709*	−0.816*	−0.319	0.796*	0.219	−0.504	0.180	0.274
	Sig. (two-tailed)		0.344	0.286	0.049	0.014	0.440	0.018	0.602	0.203	0.669	0.512
	N	8	8	8	8	8	8	8	8	8	8	8
EC	Pearson correlation	0.709*	0.258	−0.655	1	−0.574	−0.589	0.415	0.723*	−0.785*	−0.378	0.664
	Sig. (two-tailed)	0.049	0.537	0.078		0.137	0.125	0.307	0.042	0.021	0.356	0.072
	N	8	8	8	8	8	8	8	8	8	8	8
OM	Pearson correlation	−0.816*	−0.192	0.652	−0.574	1	0.584	−0.799*	−0.241	0.560	−0.204	−0.216
	Sig. (two-tailed)	0.014	0.649	0.080	0.137		0.128	0.017	0.565	0.149	0.627	0.607
	N	8	8	8	8	8	8	8	8	8	8	8
SHC	Pearson correlation	0.796*	0.518	−0.587	0.415	−0.799*	−0.513	1	0.126	−0.313	0.130	0.261
	Sig. (two-tailed)	0.018	0.188	0.126	0.307	0.017	0.194		0.767	0.451	0.758	0.533
	N	8	8	8	8	8	8	8	8	8	8	8

**Correlation is significant at the 0.01 level (two-tailed).

*Correlation is significant at the 0.05 level (two-tailed).

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When applying multiple regression analysis according to the Enter method for obtaining the equation that links the variables, the values shown in Table 11 were obtained as follows:

It is noted from the table of parameters (Table 11) that the values of the statistical significance (Sig.) for each of the independent and constant variables are greater than the significance level (0.05), which indicates that the equation cannot be adopted to predict changes since the parameters are not significant, although the value of the correlation coefficient reached the value R = 0.907 and the coefficient of determination R² = 82.3%. This can be attributed to the fact that the values of the correlation coefficient between the variables are large, which causes a problem in predicting the leaching process. To solve the problem, we apply the multiple regression test by stepwise method to obtain the values shown in Table 12.

It was noticed from the table of the parameters that the significant values of the independent variable (OM) were less than the significance level (0.05). This may indicate that the equation is very well represented and can be adopted to predict the potential changes in the ammonium ion leaching.

• (1)

  When studying the ammonium ion leaching according to the soil horizons of the studied sites, the leaching levels were decreasing with depth, as the highest concentrations of the leached ammonium ion were in the first horizon, due to the positive charge of the ammonium ion and its adsorption on OM and clay minerals.

• (2)

  When studying the ammonium ion leaching according to the studied area, it was found that the sites of Beit Anna, Qutaibiya, Qurafis and the Oasis Spring were characterized by their soil and horizons with high leaching levels. The highest concentration of ammonium ion leaching was obtained in two periods, the first in December and the second in March, The lowest concentration was recorded in February, while it was in the sites: Pastoer, Beit Al-Alouni, Jebol and Al-Rahbeya, which had lower levels of leaching compared to the previous sites, and the highest concentration of ammonium ion leaching was recorded in the first two periods in December and the second in March, and the lowest concentration was recorded in the month of January.

• (3)

  The statistical study showed that the washed ammonium ion concentration is a statistically significant indicator of ammonium pollution, and the main factors for the increase in ammonium ion transfer within soil horizons are the OM and the saturated water hydraulic conductivity. Hydro-conductivity and pH, the predictive power can be improved if average rainfall is taken into account.

• (4)

  The regression equations, which were devised in the study, can be used as a representative mathematical model of the ammonium ion leaching in the three horizons, and its role in evaluating the leaching of the ammonium ion in the soil horizons.

• (1)

  Follow-up on conducting laboratory experiments to develop mathematical models, in order to identify agricultural environmental indicators that support the design of management and control policies.

• (2)

  Studying the effect of climatic data separately on the ammonium ion leaching from soil to groundwater.

• (3)

  Study of the different levels of fertilization and its effect, in relation to soil factors, on the process of transporting and washing away the ammonium ion and its accumulation in the soil and its transfer to groundwater and water bodies.

• (4)

  Studying the effect of human activities, especially industrial and sewage water, on the ammonium ion leaching.

• (5)

  Adding urea fertilizer to the soil, with higher frequency and smaller quantities.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.