Abstract

Given the wide dependency on surface water used to supply drinking water, agricultural irrigation, and industrial activities, nitrate pollution has posed a serious concern in the Tigris River in recent years. The main objective of this study was to develop an understanding of the spatiotemporal patterns of nitrate distribution in the Tigris River through an integrated approach using hydrological data, physicochemical parameters, and model-based analysis. Eighty-four monthly sampling campaigns from forty monitoring locations along the Tigris River were carried out from January 2011 to December 2018. Obtained results demonstrated that the NO₃^- dynamics were strongly correlated with the length of transport distance and flow rates along the river system (p < 0.05). High flow rates in the upper courses of the river system favored physical transport of NO₃^- and promoted a dilution effect. However, low flow rates in the lower sections favored the accumulation processes of NO₃^- and promoted a concentration effect. High concentration of 7.0±1.96 g NO₃^- m^-3 was observed in February 2018 downstream in the river. No significant seasonal effect in NO₃^- concentrations were observed. These results were supported by the changes in dissolved oxygen concentration and pH in the river system and indicated high nitrification rates and elevated NO₃^- accumulation, particularly downstream in the river. This modeling approach has also confirmed field observations of NO₃^- dynamics with 65% of the variances in the river system being explained by the model.

1 Introduction

Nitrogen dynamics are playing a potential role in biogeochemical cycles in the biosphere (Galloway et al. 2004). Though natural fixation of N is important, anthropogenic processes from transport emissions, industrial activities, biomass combustion, and the use of fertilizers in agricultural practices have doubled the storage of N in the land and water systems from 203 to 413 Teragram N/yr^-1 (Erisman et al. 2008). Moreover, the growth in world population since 1970 (78%) associated with the continuous increase of the different man-made activities has accelerated N fluxes to the natural water resources through sewage and wastewater discharges (Galloway et al. 2008).

Mineral-N is very common in nitrogenous forms in natural ecosystems. Nitrate (NO₃^-), the oxidized form of mineral-N, is one of the major and critical long-term contaminants in aquatic environments (Kadlec and Wallace 2009). With the increase in global food demand and expansion of agricultural practices, emissions of agricultural drainage containing nitrate nitrogen have increased rapidly (Vymazal 2013). Therefore, large quantities of effluents with high NO₃^- levels are produced by these activities. Leakage and advective transport of nitrate from agricultural lands are the principal processes for the global annual flux of 40–70 teragrams of N to the coastal water and the open ocean (Fowler et al. 2013). Since it is a hazard to different aquatic organisms and causes eutrophication of lakes, rivers, and wetlands, NO₃^- pollution poses a serious environmental problem (Camargo and Alonso 2006). High mortality rates of aquatic living organisms associated with nitrate pollution may result in much higher economic burdens on fishery marketing (Cervantes 2009).

The dynamics of NO₃^- in aquatic systems are mainly controlled by different environmental processes along its transport pathway (Galloway et al. 2003). The majority of the transformations of NO₃^- are largely by microbial and plant biochemistry (Fowler et al. 2013). There is a general agreement that microbial denitrification is the major biochemical process for NO₃^- transformations in the water systems (Gruber and Galloway 2008).

The Tigris River in Iraq is particularly vulnerable to nitrate pollution because of the reduced trend of the water flow rates and due to the pressure from agriculture. River systems are complex dynamic systems consisting of a number of interconnected environmental factors that makes understanding their function difficult (Costa et al. 2021). Although our knowledge of the N cycle, at different scales, has developed over the last decades, it is still complex, where the analysis of the dynamics of the most basic chemical of this element is a challenge (Karl and Michaels 2013). To understand the environmental deterioration caused by the rapid increase of nitrate fluxes, it is important to understand the dynamics of NO₃^- over space and time. This can be achieved effectively via the implementation of a modeling approach that can provide an interpretive framework for NO₃^- behavior and fate in the river systems (Al Lami et al. 2021a). A model can be viewed as an extended hypothesis – interpretation of NO₃^- dynamics (Al Lami et al. 2021b). The decision to select an appropriate model to simulate any river system is mainly based on the research needs and objectives and the features of the water body including hydro-morphological conditions and anthropogenic impacts (Burigato Costa et al. 2019). In Iraq, like in many developing countries, scientists still tend to develop simpler models linking processes and structures and build up diagrams of how systems work, which may not reflect reality and undermine well-intended actions, or even lead to informal coping actions (Wolstenholme 2003). Multiple linear regression models can explain the interdependency between dependent and independent variables providing new insights into the dynamic system (Vizcaíno et al. 2016).

The aim of this research is to evaluate the dynamics of nitrate in the Tigris River along its pathway within Iraq, and to improve understanding of the behavior and fate of NO₃^- over space and time of the study period. This research will also address the task of employing a model framework to provide new insights into the NO₃^- dynamics in the river system over the study period.

2 Materials and method

2.1 Study area

The Tigris River is the longest river in Iraq, with a total catchment area of approximately 253 x 10³ km² and a total length of 1300 km (CSO 2019). The river flows through Iraqi territories from north to south, collecting water and wastewater from different natural and man-made sources (Figure 1). The annual mean runoff of the Tigris River from 2011 to 2020 is 22.2 x 10⁹ m³ from (CSO 2020). The Tigris River (latitude 37°04'7'' N, longitude 42°22'37'' E) features many tributaries in the northern and middle regions of Iraq such as Khabur, Greater Zab, Lesser Zab, Adhaim, and Diyala rivers (IMOS 2019). The principal river is interconnected with some important irrigation networks in southern Iraq, including Gharaf, Great Majar, Musharah, Kahlaa, and Majariah canals (Al-Ansari et al. 2019).

Figure 1   Locations of monitoring stations at the Tigris River throughout Iraq.

Iraq, in the middle east region (latitude 33°13'15.34'' N, longitude 43°41'5.13'' E), mainly consists of a vast plain environment, which is geographically sloped from north to south. The region has a relatively hot climate with a mean annual temperature of 24.4 C° and had a mean annual precipitation of 193.2 mm during 2010-2020 (IMOS 2020). Average annual evapotranspiration along the Tigris River is 8.1 mm (IMOS 2019). In Iraq, with a total population of 41.5 million, around 18 million inhabitants are living within the Tigris basin (CSO 2021). Though the Tigris River represents the main source of drinking water production, the river services are also comprised of agricultural irrigation, urban runoff and effluent discharge, fishing, and various recreational activities (CSO 2018). Throughout the region, several water monitoring stations are installed along the river, covering different hydrological characteristics and water quality. Forty stations, located in five megacities, namely Ninawa, Salah AlDin, Baghdad, Wasit, and Missan, were chosen to monitor the dynamics of nitrate alongside the hydrological and physicochemical parameters in the river water (Figure 1). A summary of the study locations is shown in Table 1.

Table 1 Monitoring stations on the Tigris River in Iraq. (s) is the distance between the stations.

Station	Province	Code	Latitude	Longitude	s (km)
Faysh Khabur	Dohuk	DH	37.03° N	42.38° E	0.00
Mosul Dam	Ninawa	NN1	36.63° N	42.82° E	59.26
Regulator Mosul Dam	Ninawa	NN2	36.58° N	42.78° E	6.60
Badoush WTP	Ninawa	NN3	36.42° N	42.95° E	23.39
Al Ayman unified WTP	Ninawa	NN4	36.38° N	43.05° E	10.00
Denden WTP	Ninawa	NN5	36.35° N	43.13° E	7.91
Al Hurriya bridge	Ninawa	NN6	36.33° N	43.13° E	2.22
Al Askari WTP	Ninawa	NN7	36.33° N	43.15° E	1.79
Boussif WTP	Ninawa	NN8	36.28° N	43.23° E	9.08
Al-madina water project	Ninawa	NN9	36.17° N	43.27° E	12.72
Al-qayyara water project	Ninawa	NN10	35.79° N	43.29° E	42.19
Sherqat WTP Intake	Salah AlDin	SD11	35.53° N	43.23° E	29.35
Baiji Thermopower station	Salah AlDin	SD12	35.04° N	43.55° E	61.66
Tikrit unified project intake	Salah AlDin	SD13	34.69° N	43.66° E	38.59
Aouinet Irrigation Project	Salah AlDin	SD14	34.49° N	43.77° E	24.37
Samara district	Salah AlDin	SD15	34.22° N	43.86° E	31.07
Balad WTP	Salah AlDin	SD16	34.07° N	44.09° E	26.95
Dholui'ya district	Salah AlDin	SD17	34.03° N	44.21° E	11.93
Karkh WTP	Baghdad	BG18	33.46° N	44.30° E	63.75
Al-aemmah bridge station	Baghdad	BG19	33.41° N	44.34° E	6.68
Muthanna bridge station	Baghdad	BG20	33.36° N	44.37° E	6.21
Al-shuhada'a bridge station	Baghdad	BG21	33.34° N	44.39° E	2.89
Al-ahrar bridge station	Baghdad	BG22	33.32° N	44.41° E	2.89
Qadisiya WTP intake	Baghdad	BG23	33.29° N	44.45° E	4.99
Al Doura WTP	Baghdad	BG24	33.26° N	44.38° E	7.32
Al Rasheed WTP intake	Baghdad	BG25	33.24° N	44.45° E	7.89
Al-za'afaraniya WTP intake	Baghdad	BG26	33.23° N	44.49° E	3.89
Tigris-Diyala rivers	Baghdad	BG27	33.22° N	44.50° E	1.45
Selman Bek Station	Baghdad	BG28	33.00° N	44.66° E	28.59
Suwaira WTP	Wasit	WS29	33.00° N	44.77° E	10.28
Noumaniya WTP intake	Wasit	WS30	32.58° N	45.41° E	75.89
Kut central water project	Wasit	WS31	32.53° N	45.78° E	35.18
Al karama bridge station	Wasit	WS32	32.53° N	45.86° E	7.51
Ali al Gharbi bridge station	Missan	MS33	32.49° N	46.68° E	77.16
Kumait WTP	Missan	MS34	32.03° N	46.88° E	54.38
Unified Amara project	Missan	MS35	31.85° N	47.11° E	29.52
Al Rafidain WTP	Missan	MS36	31.85° N	47.15° E	3.79
Al-wehda Al-arabiya station	Missan	MS37	31.82° N	47.15° E	3.33
Kahla WTP	Missan	MS38	31.68° N	47.29° E	20
Qalat Saleh WTP	Missan	MS39	31.60° N	47.47° E	17
Al Uzair WTP	Missan	MS40	31.56° N	47.70° E	19.26

The distance between neighbouring monitoring stations on one hand, and the distance from the first station (Faysh Khabur) to the subsequent stations on the other hand, was extracted using GIS software (ArcGIS 9.2). Time-series data of nitrate concentrations, dissolved oxygen, pH, and flow rate of river water at all study locations for a period between 2011 and 2018 were obtained from the Tigris River Basin Authority data logging system. Data series for precipitation and evapotranspiration (IMOS 2019) along with flow rate data were employed to estimate the changes in the water budget of the Tigris River throughout the study period.

The water quality parameters examined are summarized in Table 2. Due to the dramatic military activities in some parts of Iraq, including Ninawa and Salah AlDin cities, as part of the war against terrorism between 2014 and 2018, some important data regarding water quality parameters, flow data, and meteorological measurements of the Tigris River were unfortunately missed. Therefore, the data series for NO₃^- DO, pH, Q, rainfall, and evapotranspiration (ET) for the city of Ninawa and Salah AlDin only represent the datasets of the monitoring period between 2011 and 2014, as compared to other study locations.

Table 2   Summary of studied parameters in the Tigris River.

Parameter	Acronym	Units
Nitrate	NO3-	g m-3
Dissolved oxygen	DO	g m-3
Potential of hydrogen ion	pH	[H+]
Flow rate	Q	m3 sec-1
Precipitation	-	m3 sec-1
Evapotranspiration	ET	m3 sec-1

2.2 Modeling analysis

To understand how the Tigris River behaves over space and time in terms of NO₃^-  dynamics, a model-based analysis for water quality is required. The dynamics of NO₃^-  along the river system were estimated using a multiple linear regression model:

(1)

Where:

NO3Intercept	=	intercept of the regression equation (g m-3)
Q	=	flow rate (m3sec-1)
DO	=	dissolved oxygen concentration (g m-3)
pH	=	potential of hydrogen (-)
s	=	distance from each station to the first one (km)
t	=	sampling time (month)
k1, k2, k3, k4, k5	=	coefficients for linear regression

The conceptual model proposed to simulate the behavior of the river system is comprised of the changes in NO₃^-  concentration, which is controlled by the physical and hydrological boundaries of the river. The modeling process consisted of datasets of key parameters through a full-scale monitoring system per unit of space and time. The dynamic system of the river being simulated is divided into defined spaces between monitoring stations at units of time. The model can forecast more than one influence in any unit of length, at any unit of time. Thus, the diffused runoff features the effect of flow rate and water quality at the same units of space and time in the model. Measured data of different examined parameters were used to develop, calibrate, and validate the linear model. The key independent variables used in the model simulation are flow rate, dissolved oxygen, pH, the distance between stations, and sampling time. The changes in the water balance of the river system throughout the study are assumed to be negligible in the model due to the insignificant effects of meteorological parameters (e.g., rainfall and ET) on the entire water budget, and NO₃^- concentration (as shown in the next section).

The model was calibrated to fit simulated NO₃^- dynamics to the behavioral patterns observed in the river water, assuming steady-state conditions. Rate coefficients of the key independent variables were adjusted until a good match between measured and simulated data was obtained using the Microsoft Excel solver tool. The optimization procedure is made by minimizing the root mean squared error (RMSE) but maximizes the coefficient of determination (R²) values between the predicted and observed data (Abadi et al. 2015). Model validation to verify the accuracy of the model performance was performed by comparing the best fit of the observed and simulated dataset of NO₃^-  concentration using RMSE, R², and the slope of linear regression.

2.3 Data analysis

Before any calculation, all datasets of the examined variables were log-transformed to approximate a normal distribution of the data, and to avoid any misclassification of the differences in data dimensionality. A two-way analysis of variance (ANOVA), and simple linear regression were performed to analyze spatiotemporal variation in NO₃^-  concentration. Pearson’s correlations were used to explore the relationships between NO₃^- concentration and flow rate, dissolved oxygen, pH, the distance between monitoring stations, and sampling time. A multiple linear regression model was applied to simulate and predict the dynamic patterns of NO₃^- in the Tigris River. Significant differences between examined variables were set to be at p < 0.05. The RMSE, slope of the regression line, and R² were used to determine the statistical differences between the measured and predicted data series of NO₃^- in the model performance. All data analysis was conducted using Microsoft Excel and R (RStudio PBC, version 1.4.1717).

3 Results and discussion

3.1 Water balance

The water budget in the Tigris River responds to the seasonal and spatial variation of the hydrological loads. Changes in the water budget of the Tigris River over the course of the study are shown in Figure 2a. The highest water budget (4694.9 m³ sec^-1) was observed in May 2014 at the upper courses, while the lowest water balance was 65.66 m³ sec^-1 in December 2016 at the lower courses of the river. The average water loss from the river system is estimated to be 11.14 m³ sec^-1 which represents only 3.54% of the total hydrological load passing through the river.

Figure 2   (a) Changes in the water balance of the Tigris River throughout the study period, (b) Relationship between NO₃^- concentration and flow rate. (ET) evapotranspiration, (Q) flow rate. 

The water budget of the Tigris River is mainly limited by the flow rate compared to the effect of the meteorological parameters such as rainfall and ET. Higher flow rates (1586-4701 m³ sec^-1) were observed upstream of the river, which could be associated with the high capacity of natural attenuation to nitrate contamination. However, lower flows (69-142 m³ sec^-1) were found downstream of the river, which might indicate a high tendency for nitrate to accumulate. Lower flow rates, combined with the feeding of surface water for several irrigation channels at the lower sections of the river (stations WS29 to MS40) from September to December, could explain the reduced water budget of the river and therefore make it more vulnerable to NO₃^- pollution. However, the upper sections of the river (stations NN1 to SD17) maintained a greater water balance because of the inputs from the tributary rivers. A reverse relationship between NO₃^- concentrations and flow rate (Q) was observed, where nitrate levels are likely to be increased when the water flow rates are reduced, and vice versa (Figure 2b). This fact has been widely described by Knight et al. (1999) and Kadlec and Wallace (2009), who reported that the low hydrological load is almost always associated with the increased concentration effect, therefore resulting in high nitrogen concentration in the water. In contrast, the high flow velocity is related to the N level reduction via dilution effects in the water body.

3.2 Spatiotemporal analysis of nitrate

Data analysis revealed that spatial patterns significantly influence the variance of all measured datasets; however, the effects of seasonality were not substantial (p < 0.05). Spatial distribution of NO₃^- concentrations have shown a space-dependent trend, where nitrate levels tended to increase by increasing the transport distance along the river pathway. Lower NO₃^- concentrations were observed in the headwaters, but higher levels were detected in the lower courses (Figure 3a).

 

Figure 3   (a) Spatial distribution of nitrate concentrations at sampling locations on the Tigris River from January 2011 to December 2018, (b) Relationship between NO₃^-  concentration and distance from each station with respect to the first one.

Average NO₃^- concentrations in the upper courses of the river, including Ninawa and Salah AlDin cities, varied from 1.71 to 2.43 g m³ for the study locations (NN1-SD17). Here, it should be noted that the measured NO₃^- concentrations at these sites represent only datasets for between 2011 and 2014, as discussed previously. Mean NO₃^- levels in the mid-section of the Tigris River, including the metropolitan region of Baghdad city, varied from 2.69 to 4.69 g m³ for the study locations (BG18-BG28). The average NO₃^-  concentration ranged between 4.73 and 5.94 g m³ in the lower courses, covering sampling locations from WS29 to MS40 in the southern Wasit and Missan cities. These results widely agree with those of Willett et al. (2004) in some European rivers.

Findings presented in this research indicated a positive relationship between nitrate levels and the distance between sampling stations along the Tigris River (Figure 4b). Spatial distribution of the NO₃^- concentrations explained 40% of the variance of the different groups of parameters examined. The influence of space dimension on NO₃^- dynamics was significant (p <0.05), with some affected locations showing some remarkable values. The highest level of NO₃^- (11.05 g m^-3) was measured in location WS30 within Wasit City, downstream of large natural and agricultural lands. Although higher levels of NO₃^- were observed in the lower courses, the Tigris River has not exceeded the recommended standards for public health and ecosystem functionality (25 g m^-3) at all study locations along the river.

Nitrate concentrations have not shown significant dependency on seasonality, though they may also reach river waters from different land sources and are highly influenced by the prevailing natural conditions in the watershed. Nitrate content in the Tigris River presented neither significant intra-annual variability (between seasons), nor substantial inter-annual differences (between years). Nevertheless, obtained results indicated that the time patterns of NO₃^- dynamics are likely affected by the variation of hydrological loads on the river, and by the impact of frequent wastewater discharges with high fluxes of nitrate. Overall, an approximate steady-state condition of NO₃^- dynamics was observed over the course of the study periods, except for the last period, when nitrate concentrations tended to increase in February 2018 in the lower courses of the river. Mean concentrations of NO₃^- in the Tigris River between 2011 and 2017 were 4.11±0.50 g m^-3, while an average concentration of 7.0±1.96 g NO₃^- m^-3 was observed in February 2018, particularly downstream in the river (Figure 4a). Combined impacts of the river hydrology conditions and the additions of nitrate loads from natural and anthropogenic sources generally explain these results (Arauzo et al. 2011). This could also be interpreted by the fact that the lower courses of the river, where several irrigation channels are connected, have been continuously fed with contaminated irrigative effluents and therefore have increased NO₃^- levels.

All measured NO₃^- data versus time (season) of sampling are shown in Figure 4b. Nitrate distribution in the Tigris River exhibited a weak positive correlation from 2011 to 2018. This can be explained by the transport processes and biogeochemical transformations of NO₃^- within the river system, which is mainly conditioned by the flow rate (Arenas-Sánchez et al. 2019). Weak coefficient of determination values between NO₃^- concentrations and the study duration confirms the insignificant dependency of nitrate dynamics on seasonality.

Figure 4   (a) Temporal patterns of nitrate concentrations in the Tigris River from January 2011 to December 2018 (where bars are average values of measured NO₃^-, and dotted line is standard deviation), (b) Relationship between NO₃^- concentration and sampling time.

3.3 Physicochemical responses

Changes in physicochemical parameters are highly dependent on environmental processes along the river system. Dissolved oxygen concentration and pH in the Tigris River have shown relatively steady-state conditions over the course of the study period. For most sampling sites, DO concentration in the river water was between 70% and 90% saturation (Figure 5a). This indicates that there was no significant depletion in oxygen content throughout the study period. However, low DO levels (less than 70%) were observed in some study locations, probably due to the high rates of O₂ consumed processes, particularly microbial activities, and to some extent to the influence of the land uses surrounding the water basin and wastewater effluents. Nevertheless, no significant impact from seasonality on the oxygen content of the river system was determined (p < 0.05).

Figure 5   (a) Measured DO concentration in the sampling locations on the Tigris River from January 2011 to December 2018, (b) Relationship between NO₃^- and DO concentrations.

High oxygenation status during the study suggested the presence of a significant nitrification process, and therefore resulted in NO₃^- production along the river pathway. Elevated nitrate production along the river appeared to be highly correlated with oxygen content in the river water (Figure 6a). Although nitrification is generally considered a cause of a potential drop in DO in the fresh water system, regeneration of oxygen content by moving currents could overcome the DO shortage, and re-balance oxidative conditions in the water table (Kannel et al. 2011).

Changes in water pH were in the range of 6.66-8.98, which can be considered within the regular limits for freshwater (Bundschuh et al. 2016). Like the temporal patterns of DO, pH dynamics have shown relatively steady-state conditions over time, with some exceptions where the values are slightly higher or lower. Since high pH values (> 7.5) are likely to be a result of high photosynthetic activity, more importantly, lower pH (< 7.5) values could indicate higher nitrification rates and therefore high NO₃^- production (Figure 6b).

Figure 6   (a) Measured pH concentration in the sampling locations on the Tigris River from January 2011 to December 2018, (b) Relationship between NO₃^-  and pH.

Nitrification is generally associated with the depression of pH as protons are released to the medium when the oxidative pathways from NH₄⁺ pass through NO₂^- and to NO₃^-  (Alzate et al. 2016; Thakur and Medhi 2019). These results also support the explanation of the continuous accumulation of NO₃^- along the river system. Continuous increases in NO₃^- concentrations, particularly in the lower sections of the Tigris River, mean that these watercourses could be restricted for drinking supply or agronomic irrigation in the near future.

3.4 Model analysis

A multiple linear regression model was implemented to analyze NO₃^- dynamics in the Tigris River over the course of the study. Five parameters (k₁, k₂, k₃, k₄, and k₅) for the key independent variables examined (Q, DO, pH, s, and t, respectively) were used in model calibration. These parameters were optimized to obtain best fit between predicted and measured NO₃^- concentrations. Calibrated values of k₁, k₂, k₃, k₄, and k₅ were 0.0004 (m³ sec^-1), 0.135 (g m^-3), 0.554, 0.005 (km) and 0.004 (year), respectively. Measured and predicted concentrations of NO₃^- in the modeled river system are shown in Figure 7.

Figure 7   Changes in measured and predicted concentrations of NO₃^- in the Tigris River from January 2011 to December 2018. Each time step (year) represents NO₃^- concentrations in all study locations along the river system.

The performance of the model showed a reasonable agreement between measured and predicted nitrate concentrations, where the simulated data generally followed spatial and temporal patterns of the measured dataset. The RMSE, slope, and R² values of the model performance were 1.52 g m^-3, 1 g m^-3 and 0.65, respectively. These values indicate that the model can reasonably reproduce the spatial and temporal distribution of the NO₃^- concentrations. Given the R² value, comparison of modeled and measured NO₃^- concentrations shows that 65% of the variances in the river system can be described by the model.

For all study locations along the river per each unit of time (year), NO₃^- was always at low levels in the upper courses of the river compared to mid and lower courses, which exhibited high concentrations. Average concentrations of NO₃^- were 2.14±1.15, 4.03±1.75, and 5.45±1.51 g m^-3, in the upper, mid, and lower sections of the river, respectively. However, mean NO₃^- concentrations in the summertime were 4.0±2.0 g m^-3, and in the winter were 3.93±1.77 g m^-3, which revealed insignificant seasonal variation of nitrate dynamics in the Tigris River between 2011 and 2017. An exception of temporal patterns of nitrate was observed during 2018, where the levels of NO₃^- tended to increase along the river downstream, as discussed previously.

Overall, the linear regression model was able to predict spatial and temporal patterns of NO₃^- using the interdependency of some independent variables over the course of space and time. Figure 8 illustrates the complex interactions of the variables examined in this study, particularly the relationship between NO₃^- behavior (as a dependent variable) and Q, DO, pH, s, and t (as independent variables).

Figure 8   Correlation matrix of variables examined in the Tigris River. (s) distance between study locations, (t) sampling time.

A strong positive correlation between NO₃^- concentrations and the distance between study locations was observed; however, nitrate was slightly correlated with sampling time and DO. Negative correlations were detected between NO₃^- and flow rate and pH. These results suggest that the NO₃^- levels are proportional to the length of the river and to some extent, the time of sampling and DO. Nonetheless, NO₃^- concentrations are inversely related to flow rate and pH. Obtained results have also indicated negative correlations between flow rate and distance between study locations, as well as sampling time. Water flow rate is decreased by the increase of the distance from the first study location at the upper section, and downward to the lower section of the river, and by the increase in sampling time intervals. Here, the flow rate of the Tigris River is the main hydrological influence on NO₃^- concentrations through dilution and concentration effects as compared to the other parameters studied (as discussed in the water balance section). Therefore, these results could also explain the tendency for high NO₃^- concentrations at the lower courses of the river, where the concentration effects of the reduced flow rate are dominant, compared to the mid and upper courses, where the dilution effects of high flow are evident. A similar tendency of elevated NO₃^- concentrations was also confirmed at the end of the study duration (2018) which is also associated with the reduced discharges of the river system.

A further explanation of this phenomenon is that the biochemical transformations of nitrate are directly connected to the flow rate characteristics and physicochemical conditions of the water system (Schlesinger 2020). Low removal rates associated with high half-life of NO₃^- concentrations under low flow rate conditions were proposed by Kadlec and Wallace (2009). A low flow rate is almost always associated with shallow systems with high O₂ content that improve NO₃^- production via nitrification but inhibit denitrification (García-Lledó et al. 2011). Such shallow systems with low flow rates also promote high redox potential as a result of the high ratio of air-water interface area to water volume, which boosts nitrification rates and NO₃^- production (Nivala et al. 2013). In addition, river systems with low water volume have a greater bed surface area per unit of volume encouraging nitrifiers in fixed biofilm to produce NO₃^- more effectively (Al Lami et al. 2021a). For these reasons, nitrate accumulation in the Tigris River tends to be higher when the flow rate of the river system is reduced, and vice versa when water flow is increased.

4 Conclusions

Spatio-temporal analysis was performed to develop an understanding of nitrate dynamics in the Tigris River from 2011 to 2018. Obtained results show that the distribution of NO₃^- concentrations in the Tigris River mainly depends on the length of the transport pathway along the river system. This dependency was found to be strongly correlated with the variation in the flow rate of the river system. Spatial patterns of NO₃^- in the Tigris River have shown higher concentrations in the lower courses than in the upper courses. Combined effects of lower flow rates, land use, and insufficiently treated wastewater discharges are likely the main drivers for the increased levels of NO₃^- in the river downstream. Findings presented in this research revealed that the flow rates have shown a reduced trend from the upper courses of the river to the lower ones, therefore influencing the distribution of nitrate through concentration effects. Furthermore, the changes in some physicochemical parameters, such as dissolved oxygen and pH, have indicated high nitrification rates, therefore high NO₃^- accumulation in the lower sections of the river. No clear seasonal variations in nitrate dynamics were observed over the course of the study. Model-based analysis, to improve understanding of NO₃^- behavior and fate in the river system, has shown a reasonable agreement between measured and predicted datasets. Simulation results have supported field observations and confirmed the major spatial differences and minor seasonal variations of the NO₃^- concentrations in the river system. Although NO₃^- levels have not exceeded the recommended standards for human consumption and ecological services of the river system (25 g m^-3) in all study locations along the river pathway, reverse effects of the reduced flow rate, alterations of land use, and wastewater discharges could result in the health and ecological burdens of nitrate pollution in the near future. Research is critically needed for continuous monitoring and evaluation of nitrate contamination in the Tigris River basin. Future studies should be dedicated to investigating the negative impacts of water flow rate reduction and studying the impact of land use on the dynamics of NO₃^- in the river basin after 2018.

Acknowledgments

This research is supported by the Ministry of the Environment in Iraq. The author would like to dedicate special thanks to Prof. Thaer AlGhezi and Prof. Imzahim Alwan from the Department of Civil Engineering at the University of Technology-Iraq for their assistance. I would also like to extend my thanks for the invaluable feedback from editors and reviewers.

References

1. Abadi, L.S.K., A. Shamsai, and H. Goharnejad. 2015. "An Analysis of the Sustainability of Basin Water Resources Using Vensim Model." KSCE Journal of Civil Engineering 19 (6): 1941-1949. https://doi.org/10.1007/s12205-014-0570-7
2. Al-Ansari, N., N. Adamo, and V. Sissakian. 2019. "Hydrological Characteristics of the Tigris and Euphrates Rivers." Journal of Earth Sciences and Geotechnical Engineering 9 (4): 1-26.
3. Al Lami, M.H.M., M.J. Whelan, A. Boom, and D.M. Harper. 2021a. "Ammonia Removal in Free-surface Constructed Wetlands Employing Synthetic Floating Islands." Baghdad Science Journal 18 (2): 253-267. https://doi.org/10.21123/bsj.2021.18.2.0253
4. Al Lami, M.H.M., M.J. Whelan, A. Boom, and D.M. Harper. 2021b. "Mechanistic Understanding of Nitrogen Behaviour in Floating Treatment Wetlands: Abatement of Ammonia Flux." IOP Conference Series: Earth and Environmental Science 779 (1): 012093. https://doi.org/10.1088/1755-1315/779/1/012093
5. Alzate Marin, J.C., A.H. Caravelli, and N.E. Zaritzky. 2016. "Nitrification and Aerobic Denitrification in Anoxic–aerobic Sequencing Batch Reactor." Bioresource Technology 200: 380-387. https://doi.org/10.1016/j.biortech.2015.10.024
6. Arauzo, M., M. Valladolid, and J.J. Martínez-Bastida. 2011. "Spatio-temporal Dynamics of Nitrogen in River-alluvial Aquifer Systems Affected by Diffuse Pollution from Agricultural Sources: Implications for the Implementation of the Nitrates Directive." Journal of Hydrology 411 (1-2): 155-168. https://doi.org/10.1016/j.jhydrol.2011.10.004
7. Arenas-Sánchez, A., A. Rico, D. Rivas-Tabares, A. Blanco, P. Garcia-Doncel, A. Romero-Salas, L. Nozal, and M. Vighi. 2019. "Identification of Contaminants of Concern in the Upper Tagus River Basin (central Spain). Part 2: Spatio-temporal Analysis and Ecological Risk Assessment." Science of the Total Environment 667: 222-233. https://doi.org/10.1016/j.scitotenv.2019.02.286
8. Bundschuh, M., A. Weyers, M. Ebeling, D. Elsaesser, and R. Schulz. 2016. "Narrow pH Range of Surface Water Bodies Receiving Pesticide Input in Europe." Bulletin of Environmental Contamination and Toxicology 96 (1): 3-8. https://doi.org/10.1007/s00128-015-1665-7
9. Burigato Costa, C., M. da Silva, L.D. Marques, A.K. Almeida, I.R. Leite, and I.K. de Almeida. 2019. "Applicability of Water Quality Models Around the World—a Review." Environmental Science and Pollution Research 26 (36): 36141-36162. https://doi.org/10.1007/s11356-019-06637-2
10. Camargo, J.A., and Á. Alonso. 2006. "Ecological and Toxicological Effects of Inorganic Nitrogen Pollution in Aquatic Ecosystems: A Global Assessment." Environment International 32 (6): 831-849. https://doi.org/10.1016/j.envint.2006.05.002
11. Cervantes, F.J. 2009. Environmental Technologies to Treat Nitrogen Pollution: Principles and Engineering.  IWA Publishing, London.
12. Costa, D. de Andrade, L.C.S. da Silva Junior, J.P.S. de Azevedo, M.A. dos Santos, and R.S.F.V. Assumpção. 2021. "From Monitoring and Modeling to Management: How to Improve Water Quality in Brazilian Rivers? A Case Study: Piabanha River Watershed." Water 13 (2): 176. https://doi.org/10.3390/w13020176
13. CSO. 2018. "Iraqi’s Environmental Statistics: water quantity and quality." Ministry of Planning, Central Statistical Organisation, Republic of Iraq. 1-94.
14. CSO. 2019. "Iraqi’s Environmental Statistics: water quantity and quality." Ministry of Planning, Central Statistical Organisation, Republic of Iraq. 1-72.
15. CSO. 2020. "Iraqi’s Environmental Statistics: water quantity and quality." Ministry of Planning, Central Statistical Organisation, Republic of Iraq. 1-72.
16. CSO. 2021. "Iraqi’s Environmental Statistics: water quantity and quality." Ministry of Planning, Central Statistical Organisation, Republic of Iraq. 1-72.
17. Erisman, J.W., M.A. Sutton, J. Galloway, Z. Klimont, and W. Winiwarter. 2008. "How a Century of Ammonia Synthesis Changed the World." Nature Geoscience 1 (10): 636-639. https://doi.org/10.1038/ngeo325
18. Fowler, D., M. Coyle, U. Skiba, M.A. Sutton, J.N. Cape, S. Reis, L.J. Sheppard, et al. 2013. "The Global Nitrogen Cycle in the Twenty-first Century." Philosophical transactions of the Royal Society of London. Series B, Biological sciences 368 (1621): 164. https://doi.org/10.1098/rstb.2013.0164
19. Galloway, J.N., J.D. Aber, J.W. Erisman, S.P. Seitzinger, R.W. Howarth, E.B. Cowling, and B.J. Cosby. 2003. "The Nitrogen Cascade." BioScience 53 (4): 341-356. https://doi.org/10.1641/0006-3568(2003)053[0341:TNC]2.0.CO;2
20. Galloway, J.N., F.J. Dentener, D.G. Capone, E.W. Boyer, R.W. Howarth, S.P. Seitzinger, G.P. Asner, et al. 2004. "Nitrogen Cycles: Past, Present, and Future." Biogeochemistry 70 (2): 153-226. 
21. Galloway, J.N., A.R. Townsend, J.W. Erisman, M. Bekunda, Z. Cai, J.R. Freney, L.A. Martinelli, et al. 2008. "Transformation of the Nitrogen Cycle: Recent Trends, Questions, and Potential Solutions." Science 320 (5878): 889-892. https://doi.org/10.1126/science.1136674
22. García-Lledó, A., O. Ruiz-Rueda, A. Vilar-Sanz, L. Sala, and L. Bañeras. 2011. "Nitrogen Removal Efficiencies in a Free Water Surface Constructed Wetland in Relation to Plant Coverage." Ecological Engineering 37 (5): 678-684. https://doi.org/10.1016/j.ecoleng.2010.06.034
23. Gruber, N., and J.N. Galloway. 2008. "An Earth-system Perspective of the Global Nitrogen Cycle." Nature 451 (7176): 293-296. https://doi.org/10.1038/nature06592
24. IMOS. 2019. "Iraqi Meteorological Organization and Seismology." Ministry of Transportation. Republic of Iraq. pp. 26.
25. IMOS. 2020. "Iraqi Meteorological Organization and Seismology." Ministry of Transportation. Republic of Iraq. pp. 26.
26. Kadlec, R.H., and S.D. Wallace. 2009. Treatment Wetlands, Second Edition. New York, USA: CRC Press, Taylor and Francis Group, Boca Raton, New York, USA.
27. Kannel, P.R., S.R Kanel, S. Lee, Y.S. Lee, and T.Y Gan. 2011. "A Review of Public Domain Water Quality Models for Simulating Dissolved Oxygen in Rivers and Streams." Environmental Modeling & Assessment 16 (2): 183-204. https://doi.org/10.1007/s10666-010-9235-1
28. Karl, D.M., and A.F. Michaels. 2013. Nitrogen Cycle. Elsevier Inc.
29. Knight, R.L., R.H. Kadlec, and H.M. Ohlendorf. 1999. "The Use of Treatment Wetlands for Petroleum Industry Effluents." Environmental Science and Technology 33 (7): 973-980. https://doi.org/10.1021/es980740w
30. Nivala, J., S. Wallace, T. Headley, K. Kassa, H. Brix, M. van Afferden, and R. Müller. 2013. "Oxygen Transfer and Consumption in Subsurface Flow Treatment Wetlands." Ecological Engineering 61 (Part B): 544-554. https://doi.org/10.1016/j.ecoleng.2012.08.028
31. Schlesinger, W.H., and E.S. Bernhardt. 2020. Biogeochemistry: An Analysis of Global Change. 4^th ed. London Wall, United Kingdom: Academic Press. https://doi.org/10.1016/C2017-0-00311-7
32. Thakur, I.S., and K. Medhi. 2019. "Nitrification and Denitrification Processes for Mitigation of Nitrous Oxide from Wastewater Treatment Plants for Valorization: Challenges and Opportunities." Bioresource Technology 282: 502-513. https://doi.org/10.1016/j.biortech.2019.03.069
33. Vizcaíno, I.P, E.V. Carrera, M.S. Junquera, S.M. Romero, J.L. Rojo-Álvarez, and L.H. Cumbal. 2016. " Spatio-Temporal Analysis of Water Quality Parameters in Machángara River with Nonuniform Interpolation Methods." Water 8 (11): 507. https://doi.org/10.3390/w8110507
34. Vymazal, J. 2013. "The Use of Hybrid Constructed Wetlands for Wastewater Treatment with Special Attention to Nitrogen Removal: A Review of a Recent Development." Water Research 47 (14): 4795-4811. https://doi.org//10.1016/j.watres.2013.05.029
35. Willett, V.B., B.A. Reynolds, P.A. Stevens, S.J. Ormerod, and D.L. Jones. 2004. "Dissolved Organic Nitrogen Regulation in Freshwaters." Journal of Environmental Quality 33 (1): 201-209. https://doi.org/10.2134/jeq2004.2010
36. Wolstenholme, E.F. 2003. "Towards the Definition and Use of a Core Set of Archetypal Structures in System Dynamics." System Dynamics Review 19 (1): 7-26. https://doi.org/10.1002/sdr.259