Spatial and Temporal Analysis of Nitrate Dynamics along the Tigris River
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.
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.
|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.
|Dissolved oxygen||DO||g m-3|
|Potential of hydrogen ion||pH||[H+]|
|Flow rate||Q||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:
|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.
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.
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.
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