Abstract

Developing a solid understanding of the nitrogen dynamics across the Tigris River is critical to evaluate the environmental degradation of the increased N fluxes. Nitrite, nitrate, and total oxidized N (nitrite+nitrate) were monitored from April 2018 to August 2019. Plug flow reactors and continuously stirred tank reactors in series models were implemented to explore N behavior in the river system. The results indicated that the total oxidized N decreased over the first half of the study period, then was followed by a high rate of nitrate production. These findings are also supported by changes of the river flow rates, dissolved oxygen, pH, and chemical oxygen demand. The models have the capacity to simulate N dynamics, with varied prediction errors. Root mean squared errors between measured and predicted nitrite, nitrate, and total oxidized N concentrations were 0.118, 2.595, and 2.560 g m^-3, respectively, for the PFR model, while these values were 0.05, 0.175 g m^-3, and 0.176 g m^-3, respectively, for the CSTRS model. The correlation coefficients were 0.012, 0.925, and 0.922 for nitrite, nitrate, and total oxidized N, respectively, when the PFR model was applied. These values were 0.92, 0.99, and 0.99, respectively, after the application of the CSTRS model. Obtained results revealed that the modeling approach can provide a useful framework to improve understanding of N dynamics, which helps to develop mitigation strategies for sustaining water quality in the Tigris River.

1 Introduction

In the last few decades, global attention on nitrogen pollution has increased due to the accelerated fluxes of these contaminants in the biosphere, which influence natural sources, public health, and climate change (Vymazal 2010). Nitrogen is well known as a principal contributor to plant growth, however, high N levels can cause eutrophication (Kadlec and Wallace 2009). Also, biogeochemical cycling of N can affect oxygen content in the water systems and results in various toxic effects in aquatic organisms through anoxia and hypoxia (Camargo and Alonso 2006). Key microbial processes for nutrient cycling, and therefore overall system function, are influenced by acidification effects associated with the metabolic pathways of N transformations (Cervantes 2009). A relationship between some health issues (e.g., blue baby syndrome, birth defects, and mutagenicity) and high levels of NO₃^- in drinking water is evident (Powlson et al. 2008). Nitrogen oxides like NO and N₂O are considered some of the greenhouse gases that act a potential role in global warming and ozone layer destruction (Cervantes 2009).

Oxidized-N (NO₂^- and NO₃^-) is a very common form of elemental nitrogen observed in river waters (Arenas-Sánchez et al. 2019). Since it exists in water systems, oxidized N is transformed into different forms using various environmental mechanisms as its moving pathway (Galloway et al. 2003). Chemical mechanisms and transport processes between system components are the primary biogeochemical responses of oxidized N in riverine systems (Stüeken et al. 2016). The chemical changes are mainly physicochemical and biochemical processes (Marimon et al. 2013). Most biochemical reactions include metabolic pathways by the microbial community (Vymazal 2007). Microbial processes like nitrification and denitrification are considered the dominant mechanisms for N dynamics in water systems (Coban et al. 2015). In nitrification, ammonium ion can be nitrified to nitrite and subsequently to nitrate by specialized autotrophic oxidizing bacteria (Vymazal 2007). However, nitrate can be reduced to dinitrogen (N₂) by anaerobic heterotrophic bacteria in a process, namely denitrification (Kadlec and Wallace 2009).

Though knowledge of the N dynamics has improved dramatically during the last few decades, it is still complex, where understanding the behavior and fate of this element is challenging (Kadlec and Wallace 2009). In order to understand the consequences of the accelerated N fluxes in aquatic systems, it is critical to understand the changes in the N dynamics over time (Al Lami et al. 2021a).

Numerical models are deemed one of the best approaches for understanding N behavior in aquatic systems (Langergraber 2008). The concept of dynamic modeling depends on the multidisciplinary field of N dynamics, system hydrology, and ecological characteristics (Kadlec and Wallace 2009). Most environmental processes are first order reactions (Kadlec and Wallace 2009). Therefore, the first order kinetics model, and its derivatives, have been widely used to represent N dynamics in different aquatic systems (Costa et al. 2021). Process-based modeling can effectively be used to develop a solid understanding of N dynamics and highlight new insights into the function of black-box systems and provide important information which helps to develop efficient mitigation strategies sustaining water quality (Langergraber 2008; Al Lami et al. 2021b).

This research is dedicated to investigating the dynamics of oxidized-N forms (NO₂^-, NO₃^- and NO_x^-) in the Tigris River within the city of Baghdad, and to develop a better understanding of the behavior and fate of the constituents examined during the study period. Oxidized-N (NO_x^-) is used here to indicate the total oxidized nitrogen present as nitrite (NO₂^-) and nitrate (NO₃^-). First order models are used as a framework to interpret the behavior of the oxidized-N forms and to develop a robust understanding of the dynamics of the variables investigated.

2 Materials and method

2.1 Study area

The Tigris River is one of the largest rivers in Iraq with a total length of 1290 km and an annual mean water flow rate is 272.14 billion m³ (IES 2019). Baghdad, the capital of Iraq (latitude 33°18'46.0980'' N, longitude 44°21'41.3568'' E), where Tigris River is passing through along 72.80 km from north to south as depicted in Figure 1.

Figure 1 Study area at the Tigris River within Baghdad City.

Baghdad City is located in a vast plain environment with a hot climate. Average annual temperature is 24.2 C°, with the lowest and highest temperatures of 16.0 and 33.0 C°, respectively during 2009-2019 (IMOS 2019). Annual mean rainfall is 148.5 mm, of which more than 85% is concentrated in the wet season period from November to March (IES 2019). The annual mean of evapotranspiration (ET) within Baghdad City is 7.9 mm, with a mean annual relative humidity of 42% (IMOS 2019). The metropolitan region is densely populated with a total population of approximately 8 million inhabitants (CSO 2018). Through runs within the region, the Tigris River is collecting wastewater from different domestic, industrial, and agricultural activities. A diversified socio-economic sector (e.g., urban, industry, retailers, services, and agriculture lands) are largely distributed along the river pathway. Water supply for human consumption, industrial production, agricultural irrigation, and sewage dilution are the predominant water uses in the Tigris River within Baghdad City. Several water quality monitoring stations are installed along the river pathway within Baghdad City. For this research, one station was chosen in the midstream section of the river, located near the water intake of one of the central water treatment plants (33°43'' N, longitude 44°35'' E), to monitor nitrogen dynamics in the river water (Figure 1). Water quality parameters studied in this research are described in Table 1.

Table 1 Summary of water quality parameters studied in Tigris River.

High resolution time-series data for the time period from April 2018 to August 2019, with detection intervals for every 15 minutes using an automated data logging system, were used in this research. Seven water quality parameters were selected for the modeling analysis of N dynamics in this research, including nitrite, nitrate, total oxidized nitrogen, water flow rate, dissolved oxygen, pH, and chemical oxygen demand. In addition, local meteorological parameters including rainfall (m³) and evapotranspiration (m³ sec^-1) combined with flow rates were used to calculate water balance (m³ sec^-1) of the Tigris River passing through Baghdad City.

2.2 Models

Two first-order models, including a plug flow reactor model (PFR) and a continually stirred tank reactors in series model (CSTRS) as shown in Equations 1 and 2, respectively (Kadlec and Wallace 2009), were used to describe N behavior and were therefore employed as an interpretive framework to understand the dynamics of N in the Tigris River within Baghdad City.

and

(1)

and

(2)

where:

Nitrogen dynamics in water systems were assumed to follow first order kinetics. It is well known that the dynamics of most contaminants in aquatic systems can be described using a 1^st order kinetics model, though inadequacy has been reported (Kadlec 2000). Therefore, 1^st order rate constants were used in this research to simulate N concentrations over the time of the study. Both models assumed perfect and instantaneous mixing, with solute moving at the same velocity as the water. For the two models, changes in water budget throughout the study period were estimated to be negligible (shown in the next section), therefore, the effect of meteorological parameters on the water balance and N concentrations was negated. 

The river system being modeled represents series of data for selected parameters through the monitoring station per unit of time. The models can represent more than one influence at any unit of time. Therefore, water runoff of the river system can represent the influence of the hydraulic flow rate and water quality at the same unit of time in the model scheme. Model performance was verified by examining the goodness of fit of the measured and predicted time-series data of N concentration in the river system. Root mean squared error (RMSE) and Correlation coefficient (r²) were used to compare observed and simulated datasets.

3 Results and discussion

3.1 Water balance

The changes in the water balance of the Tigris River within Baghdad City throughout the study are shown in Figure 2a, which shows water gain and loss via rainfall and evapotranspiration as well as the flow rate of the river system.

The water budget of the Tigris River within Baghdad City was relatively high between June 2018 and January 2019 with a highest hydraulic rate of 376 m³ sec^-1. However, the lowest water balance (368 m³ sec^-1) was observed in April 2019. The mean water loss was estimated to be 4.77 m³ sec^-1 with a percentage of 0.94 of the total hydrological mass moving in the river system. The differences in the water budget are mainly controlled by the flow rate passing through the river system rather than by the effect of meteorological parameters measured over the course of the study. Water gain or loss in the river system within the study location via precipitation and evapotranspiration, respectively, have not indicated significant influence in the water budget (p < 0.05). Therefore, it can be suggested that the flow rate could have influence on the N concentrations through either dilution effects or concentration effects. Further, it is proposed that the biogeochemical processes of N, and therefore its dynamics patterns, are highly bounded by the hydrological characteristics of the water system (Knight et al. 1999).

NO_x^- concentrations versus river flow rates (Q) over time are shown in Figure 2b. Obtained results indicated that the concentrations of NO_x^- were inversely correlated to flow rates of the river system. NO_x^- levels tended to decrease as the river flow rates increased (r² = 0.35), which is consistent with physical expectations. Consequently, a higher flow rate caused a higher rate to dilute NO_x^- brought into the river, but a lower flow rate caused a concentration of NO_x^- levels. This could also agree with the general concept that an inverse relationship is likely to be observed between the rate constant of the chemical reactions and water flow rate (Kadlec and Wallace 2009).

3.2 Model performance

A plug flow reactor model (PFR) and continuously stirred tank reactors in a series model (CSTRS) were employed to analyze N dynamics in the Tigris River within Baghdad City. Figure 3 compares time-series measured and predicted concentrations of NO₂^-, NO₃^- and NO_x^- in the river system using a PFR model. Generally, model predictions follow the trend of measured N data, and the dynamics of oxidized N were intimately related to the 1^st order kinetics. Measured and predicted data have shown reasonable agreement with some exceptions.

Data from field observations indicated that the concentrations of NO₂^- were at relatively high levels at the start of the study (0.42±0.05 g m^-3), however, these concentrations tended to decline over time to be under minimum detection limits (Figure 3a). Accumulated NO₂^- concentrations in the surface water, suggests that nitrification was the most plausible cause of such increase. Here, it should be pointed out that NO₂^- is an intermediate form in two-step nitrification, therefore the concentrations represent the net balance between NH₄⁺ oxidation and NO₂^- oxidation (Park et al. 2015). Predicted and analyzed NO₂^- concentrations appeared to be in poor agreement when the PRF model is used. Unsystematic NO₂^- behavior was observed when the measured data were outside the model line. The ideal steady-state pattern of NO₂^- dynamic in such systems was foreseen in the model but seems to be of minor importance.

Obtained data revealed that NO₃^- is the most dominant species of the oxidized N forms in the Tigris River. Steady-state concentrations of NO₃^- and NO_x^- were at low levels from the start of the monitoring period to November 2018 (1.25±0.43 and 1.37±0.51 g m^-3, respectively), but these concentrations appeared to increase over the rest of the study (Figure 3b, c). High levels of NO₃^- and therefore NO_x^- (5.72±1.02 and 5.73±1.01 g m^-3, respectively) could be attributed to the high nitrification rates versus the inhibited microbial activity for denitrification, possibly because of the lack of biodegradable organics (Gao et al. 2017).

Increased concentrations of NO₃^- and NO_x^- could also be explained as a result of the lower flow rates of the river system, particularly from November 2018 to August 2019 (as shown in Figure 2). As a general hydrological concept, water systems with low flow rates are basically shallow systems with greater oxygenation (García et al. 2005). Lower flow rates could have an important effect on the biochemical reactions causing increase in nitrification rates and NO₃^- accumulation in the river system, mainly because of the influence of the redox status on the function of the microbial community and also because of better hydraulic efficiency (Holland et al. 2004; Matamoros and Bayona 2006).

Measured and simulated data of NO₃^- and NO_x^- have shown good agreement, except for the period when nitrate production is increased while river flow rates dropped at the end of the study. Therefore, systematic behavior of NO₃^- and NO_x^- was observed when measured and simulated data was best fitted, but unsystematic dynamics was observed when substantial differences between analytical and predicted data was observed.

Overall, simulated concentrations of NO₂^-, NO₃^-, and NO_x^- show reasonable fit with measured data when the PFR model is applied. However, the lower predicted concentrations of N forms seem to reflect the ideal pattern of the constituent’s dynamics in the river system, where no substantial changes in the hydrological and physicochemical parameters were observed. The model seems to underestimate NO₂^- concentration at the start of the study, and NO₃^- concentration at the end of the study, by some 0.4 and 4 g m^-3, respectively. This might indicate that the river system has quicker nitrification capacity and is more sensitive to the flow rate changes than what the model predicts.

Model performance for the N dynamics in the river system is shown in Figure 4. The root mean squared errors (RMSEs) between the observed and simulated steady-state concentrations were 0.118, 2.595 and 2.560 g m^-3 for NO₂^-, NO₃^- and NO_x^-, respectively. The correlation coefficients (r²) values for NO₂^-, NO₃^- and NO_x^- were 0.012, 0.925 and 0.922, respectively. Therefore, data from the 1^st order fits showed that 92% of the measured NO₃^- and NO_x^- data were represented by the PFR model, while only 1% of NO₂^- dynamics were described. It is worth noting that some of the observations have shown different standard errors from regression lines, which deviated from the linear fits and therefore affect model performance. This was obvious in model performance for NO₂^- when the regression line exhibited skewed validation with an insignificant r² value, which likely resulted from constraint to fit the regression line through the data set.

The steady state observed and simulated concentrations of NO₂^-, NO₃^- and NO_x^- in the Tigris River using a CSTRS model is illustrated in Figure 5. The changes in the dynamics of N forms in the river system are proven through modeled and observed data sets. Although there are changes in N behavior over time (e.g., decreased concentrations of NO₂^- and increased production of NO₃^-), the model could reasonably predict the dynamics of examined variables.

Simulated concentrations of NO₂^-, NO₃^-, and NO_x^- show the best fit with the measured data from the river system. The least squared errors were observed between predicted and measured concentrations of N constituents. Therefore, systematic behavior of predicted and measured NO₂^-, NO₃^-, and NO_x^- concentrations was observed, which indicated certainty of N dynamics in the river system. Both field observation and model simulation of N dynamics were supported by the findings of the physicochemical responses obtained (as shown in the next section). High DO associate and depressed COD concentrations, as well as pH, were consistent with the N behavior during the study period.

Measured and modeled oxidized N concentrations are compared in Figure 6. Also, Figure 6 shows the best fit regression lines of the measured and modeled NO₂^-, NO₃^-, and NO_x^- data along with simple linear equations and r² values.

For all N forms, the data group along the regression lines with a slope of the best fit is close to unity, despite some of the outliers. Simple linear regression for NO₂^- dynamics is (Y_model = 0.958x_observation), for NO₃^- is (Y_model = 0.997x_observation) and for NO_x^- is (Y_model = 0.996x_observation), which confirms model validation. Absolute errors between measured and predicted concentrations are low. The RMSE for NO₂^- was 0.05 g m^-3, the RMSE for NO₃^- was 0.175 g m^-3, and the RMSE for NO_x^- was 0.176 g m^-3. The r² between measured and simulated steady state concentrations were 0.92, 0.99, and 0.99 for NO₂^-, NO₃^-, and NO_x^-, respectively. Some of the measured data sets introduced in the model exhibited different standard deviations, therefore reducing the performance of the model. The “type X” outliers may otherwise result in skewed validation, and are identified as data outside the range of the pattern in the data set, or distance from the average (Marimon et al. 2013). Overall, data from the simulation procedure showed that 92-99% of the measured NO₂^-, NO₃^-, and NO_x^- were represented by the CSTRS model. Therefore, these findings show that the CSTRS model is well able to describe and predict the trend of N dynamics in the Tigris River system.

3.3 Physicochemical responses

Changes in the dissolved oxygen (DO), pH, and chemical oxygen demand (COD) in river water over the course of the study are shown in Figure 7

Physicochemical parameters have shown relative steady state conditions over most of the study period. Mean DO concentration was 7.19±1.55 g m^-3 during the study period. Though the river system appeared to be in approximate physical steady state, there were fluctuations in DO concentrations throughout the study period, which may influence biogeochemical processes through control redox status in the system (Figure 7a).

High DO concentrations were sufficient to increase nitrification rates (oxidation of NO₂^- to NO₃^-) throughout the study period. Increased concentration of nitrite at the start of the study is consistent with the concept that increased nitrification is correlated to DO levels (Figure 8a).

It is well known that nitrification is regarded as an important sink [LLF1] for DO in aquatic systems (Kannel et al. 2011); however, continuous reaeration in the river water could tackle this limitation and maintain high levels of DO. Half saturation is a constant for the conversion of NO₂^- to NO₃^- is 1.75 mg O₂ L^-1 (Guisasola et al. 2005). This means that below about 2 mg O₂ L^-1 could partially inhibit nitrification (Al Lami et al. 2021c), although this was not observed in the findings presented in this research. Data from changes in pH over time (Figure 7b) also support the explanation of the high concentrations of oxidized nitrogen. The relative depression in pH seen between April to November 2018 is likely related to the high nitrification activity (Figure 8b) where the oxidative pathway of this process includes the release of protons as ammonium is oxidized to NO₂^- and subsequently to NO₃^- (Alzate et al. 2016; Thakur and Medhi 2019).

Mean COD concentration was 24.70±6.87 g m^-3 over the course of the study (Figure 7c). Accumulated COD in the river system is an indicator for carbon source consumption to perform complete denitrification, particularly under low NO₃^- loading rates from April to November 2018. Nitrate concentrations appear to be decreased more efficiently in this period, which is consistent with our hypothesis that accelerated denitrification is correlated to COD concentrations (Figure 8c). High denitrification rates were associated with high nitrification rates over the same time frame. Simultaneous nitrification/denitrification could be observed in the presence of DO and organic matter gradients (e.g., high DO at water surface is essential for nitrification, and high organic content on the river bed is essential for denitrification (Ding et al. 2021). However, when a high nitrate loading rate was observed at the end of the study, NO₃^- concentrations tended to increase significantly (p < 0.05). This is because denitrification rates could be inhibited because of the lack of biodegradable organics. The consumption rate of organic matters by denitrifying microorganisms could not afford to reduce the high NO₃^- loading.

4 Conclusions

A model-based study was carried out to develop an understanding of nitrogen behavior in the Tigris River within Baghdad City. A plug flow reactor model (PRF) and continuously stirred tank reactors in a series model (CSTRS) was used as a descriptive framework to interpret the dynamics of oxidized nitrogen forms over time in the river system. The performance of the models was investigated using measured datasets of NO₂^-, NO₃^-, and NO_x^- collected from the river. The dynamics of the N forms examined were proven by measured and predicted data. Nitrite and nitrate concentrations have exhibited a gradual reduction from the start of the study, then were followed by high NO₃^- production to the end of the study. Simultaneous nitrification and denitrification at the start of the study, followed by denitrification inhibition at the end of the study, are the most likely drivers for the N dynamics in the river system. These findings are also supported by the changes of the river flow rates and physicochemical parameters (DO, pH, and COD). The model analysis revealed a reasonable agreement, with some exceptions, between measured and predicted data for N dynamics, which supports the implementation of the models. Bearing in mind that the uncertainties of the N fractionation and the measured data could have associated sampling and analytical errors, it can be suggested that the models examined were reasonably able to describe and predict N behavior in the Tigris River. Therefore, the models can provide a framework for understanding N dynamics within complex systems and obtain insights into the different interactions over time.

Acknowledgments

This research is supported by the technical team of the Ministry of Municipalities and Public Works in Baghdad. The authors are happy to extend deep thanks to the invaluable feedback from editors and reviewers.

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