Total Nitrogen Load Research Articles

Development of a deep learning–based feature stream network for forecasting riverine harmful algal blooms from a network perspective

Global increases in the occurrence of harmful algal blooms (HABs) are of major concern in water quality and resource management. A predictive model capable of quantifying the spatiotemporal associations between HABs and their influencing factors is required for effective preventive management. In this study, a feature stream network (FSN) model is proposed to provide daily forecasts of cyanobacteria abundance at multiple monitoring sites simultaneously in a river network. The spatial connectivity between monitoring sites was expressed as a directed acyclic graph comprising edges and nodes representing flows and monitoring sites, respectively. Furthermore, a segment-wise node connection structure was developed to extract the latent features of a river segment comprising individual nodes and sequentially transfer them to the downstream segment(s). In addition, a feature engineering–attention hybrid mechanism was employed to address temporal mismatches among different monitoring schemes while adding explainability to the model. Consequently, the FSN showed improved predictive performance, temporal resolution, and explainability for multi-site forecasts of HAB in a single model framework. The developed model was applied to a bloom-prone middle course of the Nakdong River, South Korea. Various hydrological, environmental, and biological factors were utilized for forecasting the cyanobacteria abundance. The FSN exhibited a high degree of accuracy across the sites for the test data with a coefficient of determination in the range of 0.64–0.71 and root mean square error in the range of 2.06–2.26 cells/mL on natural log scales. Although the relative importance of input features varied across the sites, the features extracted from nearby nodes consistently exhibited high importance in forecasting the cyanobacteria abundance. These explanations indicate that the proposed model can successfully characterize the spatial hierarchy of a river network. A scenario analysis suggested that reduced total nitrogen loads in the effluents from the wastewater treatment plant and the combined operations of upstream and downstream weirs were effective in managing HABs.

Correlation between Different Factors of Non-point Source Pollution in Yangtze River Basin

This research used the improved export coefficient model to estimate non-point source load in sub-watersheds and a self-organizing map model to identify the correlation relationship of factors, that can be affected by non-point source load in Yangtze River Basin. Research results indicate that total nitrogen load is 4.87 - 15.53 kg/ha; total phosphorus load is 0.18 - 0.51 kg/ha; Dong Ting Lake sub-basin has the lowest contribution value of total nitrogen and phosphorus; Tai Lake sub-basin has the highest contribution load of total nitrogen and total phosphorus; Jinsha River sub-basin reveals the highest value of total nitrogen and phosphorus contribution on grassy land and desert land; Tai Lake sub-basin has the highest total nitrogen and total phosphorus load on forest, agricultural land, and urban construction land. The correlation relationship of factors reveals a very close correlation with each other as precipitation, total nitrogen load, and total phosphorus load factors have correlation in the highest value, in which total nitrogen load and total phosphorus load factors have a close correlation with each other from the lowest value to highest value; wetland and barren land factors correlate with the lowest value and the highest value; agricultural land, forestland, and grassy land factors correlate with one another in the high value and the highest value. The forest cover rate factor, as well as the population factor, does not correlate with other factors.

Combining SWAT with Machine Learning to Identify Primary Controlling Factors and Their Impacts on Non-Point Source Pollution

Non-point source (NPS) pollution has a complex formation mechanism, and identifying its primary controlling factors is crucial for effective pollution treatment. In this study, the Baixi Reservoir Watershed, characterized by low-intensity development, was selected as the study area. A new methodology combining the Soil and Water Assessment Tool (SWAT) with the Random Forest (RF) algorithm was proposed to comprehensively identify the primary controlling factors of NPS pollution and analyze the interaction between factors. The results of the validated SWAT model showed that the annual intensity of total nitrogen (TN) load range was 0.677–11.014 kg ha−1 yr−1, and the total phosphorus (TP) load per unit area range was 0.020–0.110 kg ha−1 yr−1. Loads of sediment, TP, and TN exhibited significant seasonal variations, particularly in the Baixi basin, where sediment yield had the highest absolute change rate, with a value of up to 232.26. Random Forest models for TN and TP displayed high accuracy (R2 > 0.99) and robust generalization ability. Fertilization, sediment yield, and terrain slope were identified through RF models as the primary factors affecting TN and TP. By graphing partial dependency plots (PDPs) based on the results of the RF models to analyze the interaction between factors, the findings suggest a strong synergistic effect of two combined factors: fertilization and sediment yield. When fertilizer application exceeds 15 kg ha−1 yr−1 and sediment yield exceeds 3 kg ha−1 yr−1, there is a sharp increase in nitrogen and phosphorus load. Through the identification and analysis of the primary controlling factors of NPS pollution, this study provides a solid scientific foundation for developing effective watershed management strategies.

Seasonally varying contributions of contemporaneous and lagged sources of instream total nitrogen and phosphorus load across the Illinois River basin

Quantifying nutrient sources in streams, their temporal and spatial variability, and drivers of that variability can support effective water resources management. Yet a lack of data and modeling capabilities has previously prevented comprehensive quantification across both space and time. Here a dynamic SPARROW (Spatially Referenced Regressions on Watershed attributes) model that accounts for a lagged delivery of nutrients to streams was developed and applied to simulate seasonal and source-specific total nitrogen (TN) and total phosphorus (TP) loads in streams across the Illinois River basin (IRB). Dynamic load predictions from 2000 through 2020 revealed that a third of the TN and a quarter of the TP instream load originated from non-point sources that were lagged in their delivery from land-application to streams by more than a season. This lagged mass was the largest overall TN source—which was estimated as a lagged expression of previous seasonal non-point sources including fertilizer, manure, atmospheric deposition and fixation, and urban land use. Treated wastewater effluent was the largest TP source exported from the basin, contributing 39 % of the TP load and 15 % of the TN load, and dominated the load in the upper Illinois River near Chicago. Loads in the lower river during this period, conversely, were attributed primarily to a mix of agricultural sources and their lagged fractions from headwater tributaries. Instream processes removed 10 % of the TN load while only 4 % of the TP load was removed during instream transport. With appropriate datasets, the models could be extended to other basins or time periods and used to forecast future seasonal nutrient loads.

Predicting aeration time and nitrite accumulation rate variations for Partial Nitritation: A model incorporating nitrogen oxidation rate dynamics

This study aimed to develop a two-step nitrification model to predict variations in aeration time and nitrite accumulation rate (NAR) under fluctuating operational conditions in mainstream partial nitritation (PN) processes. Lab-scale sequencing batch reactors (SBRs) were used to evaluate the ammonia oxidation rate (AOR) and nitrite oxidation rate (NOR) under different solids retention times (SRT) (10, 15, 20, 30, and 50 days) and total volumetric nitrogen loadings (TVNL) (20–60 mg N/L per cycle). A static model was developed to predict consistent AOR and NOR values in the steady state, whereas a dynamic model was established to capture the growth dynamics of ammonia-oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB) under unsteady-state conditions. The static model accurately predicted the AOR, NOR, and aeration time at steady state. The dynamic model quantified the relationship between specific growth rates (μ) and food-to-microorganism ratios (F/M) through exponential fitting, successfully capturing AOB and NOB growth dynamics. Validation experiments (SRT = 10 d, TVNL = 60 mg/L per cycle) demonstrated the ability of the dynamic model to predict trends in NAR and aeration time accurately. This study emphasizes the importance of accurately modeling AOR and NOR variations to predict aeration time and NAR, thereby providing valuable insights for aeration control and precise management of AOB and NOB populations in mainstream PN processes.

Sewage leakage challenges urban wastewater management as evidenced by the Yangtze River basin of China

Sewage leakage, including uncollected, collected but exfiltrated, and collected but discharged with stormwater in combined sewer overflows, becomes a key challenge to clean water around the world. This study proposed a mass balance model to quantify sewage leakage and analyzed its influencing factors. Approximately 56% (53%–59%) of domestic total nitrogen loads in the Yangtze River basin of China leak out without treatment, two-thirds are exfiltrated or overflowed, significantly impacting receiving water quality. Cities with higher precipitation have greater leakage, indicating overflows are important. Pipe density has limited impact, showing higher importance of pipe quality over quantity. The per capita leakage rate first increases with city size and then decreases, showing the economies of scale. Yet uneven distribution of population and economy within megacities leads to greater leakage. Hence, separated sewage systems of rainwater and wastewater, regular maintenance and adapting centralized and decentralized systems are recommended, tailored to city-specific leakage characteristics.

Impacts of climate change on urban stormwater runoff quantity and quality in a cold region

Climate change poses significant challenges to urban environments affecting both flood risks and stormwater pollutant loadings. However, studies on variations in stormwater runoff quantity and quality in cold regions, which are highly sensitive to climate change, are notably limited. Integrating climatic, hydrologic, and hydraulic modelling, the study assesses the potential impacts of climate change on stormwater runoff volume and pollutant dynamics in a Canadian urban watershed (Calgary). A two-year field program was conducted to support the calibration and validation of the Storm Water Management Model (SWMM). Intensity–duration–frequency curves were employed to evaluate the impacts of climate change on peak flow rate and flooding duration. In addition, typical dry, average, and wet years were applied to continuously simulate stormwater runoff quantity and quality during the 2050s and 2080s. The results suggest substantial increases in peak flow rates and flooding durations, particularly for the 5-year return period rainfall, with 1-h, 4-h, and 24-h peak inflow rates increasing by 74.3% (170.7%), 89.2% (158.4%), and 64.1% (102.8%) in the 2050s (2080s) Furthermore, the runoff quantity is projected to rise by 2.4–10.2% in the 2050s and 11.8–17.5% in the 2080s. Total suspended solids (TSS), total nitrogen (TN), and total phosphorus (TP) loadings are anticipated to increase by 2.0–36.1%, 3.1–21.4%, and 4.1–20.7%, respectively. As a result, the current stormwater system could overload and stormwater quality is likely to deteriorate under the impact of climate change. The findings are beneficial for cold regions to develop adaptive strategies that enhance urban water security and environmental sustainability under climate change.

Efficient partial denitrification-anammox process enabled by a novel denitrifier with truncated nitrite reduction pathway

Partial denitrification coupled with anaerobic ammonium oxidation (PD-anammox) is a promising technology for cost-effective nitrogen removal from wastewater. Nitrite availability is crucial to anammox performance but often limited by the slow partial denitrification process. Here we report an efficient PD-anammox system driven by the novel denitrifier Bacillus velezensis C1–3 with truncated nitrite reduction pathway. Whole-genome sequencing analysis revealed that the lack of nitrite reductase genes nirS/nirK and norBC in strain C1–3 enabled nitrite accumulation without the need for precise control of carbon dosage. By coupling it with anammox sludge, over 79 % total nitrogen (TN) removal was stably achieved, under a TN loading rate of 660 mg/L/d and a carbon/nitrogen ratio below 1.0. Mechanism explorations indicate that the niche differentiation of C1–3 and anammox bacteria facilitated their mutualism while avoiding nitrite competition. This study demonstrates a novel strategy for establishing efficient PD-anammox process by harnessing the unique metabolic deficiency of denitrifiers, shedding light on the development of stable and sustainable biological nitrogen removal technologies with minimal carbon footprint.

One third of African rivers fail to meet the ’good ambient water quality’ nutrient targets

The ambition of Sustainable Development Goal (SDG) target 6.3 is to improve global water quality by 2030. SDG indicator 6.3.2 monitors progress towards this target by assessing water bodies against ‘good’ ambient water quality criteria, with nutrients (nitrogen and phosphorus) as part of the key metrics. However, large data gaps present a fundamental challenge, especially for Africa on how to assess the progress being made with respect to both the current and desired future situations. Here, a continental water quality model for Africa is presented to simulate river sediment load, Total Nitrogen (TN) and Total Phosphorus (TP) loads and concentrations. Furthermore, critical areas and hotspots of TN and TP pollution are mapped for the period 2017 – 2019, in relation to the United Nations Environment Programme (UNEP) target thresholds used for the assessment of SDG indicator 6.3.2. Utilizing the UNEP’s criteria, which designates a water body as having “good ambient water quality” if 80% or more of its monitored values meet their targets, it is estimated that 44 % and 15 % of African rivers fail to meet the set water quality thresholds for simulated TP and TN, respectively. When synthesizing data for both TP and TN, 34 % of the rivers do not qualify as having “good ambient water quality”. Geographically, the most pronounced nutrient pollution hotspots were in North Africa, Niger River Delta, Nile River basin, Congo River basin and specific zones in Southern Africa. These areas correlate with regions characterized by high inputs of fertilizers, manure and wastewater discharge.

Atmospheric deposition patterns in bulk open field precipitation and throughfall in Aleppo pine forest and black pine forest on the eastern Adriatic coast

The Mediterranean region, with its unique ecological characteristics, is particularly sensitive to global environmental changes, including climate change and impact of air pollution.Although Aleppo pine and black pine forests are the most abundant on the eastern Adriatic coast, atmospheric deposition in these forests is poorly studied. Changes in the chemical composition of precipitation as it passes through the tree canopy can lead to soil and groundwater eutrophication, and soil acidification, which affects plant vitality. In this study, the dynamics of ion deposition in Aleppo pine forest (Pinus halepensis Mill.) and black pine forest (Pinus nigra Arnold) on the eastern Adriatic coast are investigated, focusing on throughfall and bulk open field depositions.The aim of our research was to fill the gaps in understanding the influence of tree canopies on deposition fluxes in two different Mediterranean pine stands and to compare total inorganic nitrogen loads with critical loads. Over a period of two years, bulk open field precipitation and throughfall were sampled, measured and analysed using the ICP Forest methodology.The results indicate significant differences in ion deposition between bulk open field and throughfall, with throughfall showing higher values for almost all ions. The highest enrichment ratio was determined for K+. The comparison of the actual inorganic nitrogen load with the critical nitrogen load for Mediterranean pine forests revealed that the inorganic nitrogen load exceeded the critical load in the Aleppo pine forest. Ion deposition increased in the throughfall compared to bulk precipitation, which can be attributed to the seasonality of precipitation, including leaching and long dry periods. These findings enhance our understanding of ion deposition fluxes in vulnerable Mediterranean pine ecosystems and emphasize the need for long-term research on this topic in the actual changing environmental conditions.

Double layer packaging based on active black chickpea protein isolate electrospun nanofibers and intelligent salep film containing black chickpea peel anthocyanins for seafood products

In this study, a double-layer active and intelligent packaging system was developed based on two main natural macromolecules i.e. protein and carbohydrate with green perspective. Firstly, the salep-based films containing different concentrations (0–8 % w/w) of the inclusion complex of β-cyclodextrin/black chickpea anthocyanins (βCD/BCPA) were produced. The salep film containing 8 % of βCD/BCPA complex was specified as the optimized film sample based on its performance as a color indicator. The electrospinning of black chickpea protein isolate nanofibers (BCPI NFs) containing citral nanoliposomes (NLPs) was done on the optimized salep film. The cross-sectional field emission scanning electron microscopy approved the creation of double-layer structure of the developed film. The study of chemical and crystalline structure, as well as the thermal properties of the film exhibited the physical attachment of BCPI electrospun NFs on salep film. The effectiveness of the developed system was studied in detection of spoilage and increasing the shelf life of seafood products, including shrimp and fish fillet. The performance of the intelligent layer in detection of freshness/spoilage was acceptable for both seafood products. In addition, the active layer of the film controlled the changes of pH, total volatile basic nitrogen, oxidation, and microbial load in samples during storage time.

Lab-Scale Treatment of Anaerobic Co-Digestion Liquor from Kitchen Waste, Human Feces, and Municipal Sludge Using Partial Nitritation-Anammox Process

Effective nitrogen removal from anaerobic co-digestion is a major challenge to achieving dual-carbon goals. This study explored the acclimatization process of a lab-scale two-stage partial nitritation and anammox process of a stepwise increase in the percentage of raw anaerobic co-digestion liquor from kitchen waste, human feces, and municipal sludge in a venous industrial park in China, which has not been reported yet. Under limited dissolved oxygen (below 0.5 mg/L) and high ammonia levels (200–1500 mg/L), based on adjusting aeration rates, partial nitritation rapidly started up in 50 days. After acclimatization, partial nitritation still performed efficiently and stably, with the final total nitrogen loading rate (TNLR) of 1.24 ± 0.09 gN/L/d, nitrite accumulation rate of 99 ± 4%, and ratio of eff. nitrite/ammonia of 1.32 ± 0.13. In the anammox process, the final total nitrogen removal efficiency, total nitrogen removal rate, and TNLR reached 94 ± 5%, 1.27 ± 0.03 gN/L/d, and 1.36 ± 0.05 gN/L/d, respectively. Chemical oxygen demand (COD) was also reduced in both reactors, with COD removal rates of 0.7 gCOD/L/d in the partial nitritation and 0.4 gCOD/L/d in the anammox process. Overall, the PNA system demonstrated its feasibility in adapting to high ammonia, salinity, and iron levels, when treating anaerobic co-digestion liquor, particularly regarding resource recovery in venous industrial parks.

Dynamics and correlations of maximum water age zones and nutrients in the Changjiang Estuary, China

The hydrological process dominates the spatial and temporal concentrations of nutrients and other biogeochemical compounds in the large estuary where river and ocean meet. Water age is a measure of water renewal timescale and a characteristic parameter of hydrodynamics and contaminant transport. A large-scale river-sea linked and multi-process coupled 2D EFDC model was constructed from Datong hydrometric station of Changjiang River to the East China Sea. The effects of changes in river discharge on hydrodynamics and nutrients in the Changjiang Estuary were simulated and analyzed. Pollutant loads to the Changjiang Estuary are mainly from upstream inflows and local inputs. The proportion of total phosphorus (TP) loads from upstream is greater than the total nitrogen (TN) loads, accounting for about 90% and 78%, respectively. As the river discharge decreases, TN concentrations decrease in most regions, except for near large sewage discharges where TN concentrations increase while TP concentrations show a decreasing trend. When the river flow drops from over 60000 m3/s to a relatively low value of 10000 m3/s, the average TN concentration in the southern channel of the Shanghai section of the Changjiang Estuary increases by nearly 13%, while TP concentration decreases by nearly 5%. The model results show that the water age inside the river mouth is inversely proportional to the flow rate. In contrast, the water age outside the mouth is negatively with the river discharge. The water age maximum zones (WAMZ) are found in the estuary, ranging from within the estuary to 90 km from the mouth, and are likely to coincide with the positions of the turbidity maximum zones (TMZ), which move seawards or landwards with the increase or decrease of river discharge in the Changjiang Estuary. In addition to location, the value of water age maxima varies negatively with river discharge, which is also relevant to turbidity intensity likely. Water age and WAMZ can be the indicators to predict the changes in the water environment and nutrient sinks in the Changjiang Estuary.

Improvements in the InVEST water purification model for detecting spatio-temporal changesintotal nitrogen and total phosphorus discharges in the Huai river watershed, China

Improving water quality to provide freshwater is an urgent requirement for regional and even global social development. More accurate simulation of non-point sources pollution, monitored mainly by total nitrogen (TN) and total phosphorus (TP), has always been a challenge for InVEST water purification model, particularly in agricultural areas. This can be attributed to the fact that there is no reference data for TN and TP to rectify the outcomes modelled by this model. This paper provided these data to rectify simulation results of TN and TP to ensure their accuracy. The Huai River watershed (HRW) is an important grain production area with slow economic development, and non-point source pollution has exceeded point-source pollution. There is an urgent need for water management authorities to obtain complete spatio-temporal data on TN and TP loads and their exports to improve water quality. The reference data onloads and exports of TN and TP were estimated for the entire watershed and its sub-watersheds through an investigation-evaluation technique during 1980–2018. TN and TP loads generated from the agricultural sector were the major pollution sources in the HRW and had similar time trends during the same period. The spatial distribution of TN and TP exports was modelled byusingthe InVEST water purification model, and it was found that the temporal trends for the final exports of TN and TP into river systems were similar to those for TN and TP loads in the HRW for 1980–2018. Key driving factors were detected using the Geo-detector method to quantify the contribution rates of factors to the spatiotemporal exports of TN and TP. Our results showed that individual factors, such as precipitation and land use/cover, were the most important factors driving spatio-temporal variations in TN and TP exports in the HRW from 1980 to 2018. Meanwhile, the contribution rates of interactions between land use/cover and other factors were consistently highest in this watershed during the same period. In this study, we estimated the loads and exports of TN and TP, and modelled their spatial patterns in this watershed from 1980 to 2018, providing important information on TN and TP for water-related management authorities. We also provide a method for other river systems to calibrate the parameters in the biophysical table of InVEST water purification model based on final exports of TN and TP.

Total nitrogen and phosphorus loads in surface runoff from urban land use (city of Lublin) under climate change

An expansion of impervious surfaces in urban areas leads to increases of nutrient loads discharged with the surface runoff to receivers. A study of a different density of urban development impact on total nitrogen (TN) and phosphorus (TP) loads from the city of Lublin (eastern Poland) with the use of the SWAT (Soil & Water Assessment Tool) model was performed. To distinguish between areas with high and low density of urban development (UHD and ULD), a special analysis of hydrological parameters has been proposed. Moreover, to investigate the impact of climate change, four variant scenarios were taken into account, combining the RCP (representative concentration pathway) 4.5 and 8.5 forecasts and the adopted time horizons (2026–2035 and 2046–2055). The results showed a much higher share of TN and TP from UHD compared to ULD (86%—32 022 kg/year and 89%—2574 kg/year, respectively). In addition, the variant scenarios showed that the forecasted increase in precipitation and temperature will result in increased loads of nutrients from UHD and ULD up to 30%. Furthermore, the current increase of inhabitant number, due to the Ukrainian war migration and the common tendency to convert agricultural land to residential areas, could contribute to further expansion of UHD and ULD areas and an additional increase of nutrient loads.Graphical abstract

A dataset of riverine nitrogen yield across watersheds in the Conterminous United States

Riverine nitrogen is a pivotal determinant influencing water quality in inland and coastal waters. Despite the recognized utility, no spatially-explicit data on riverine nitrogen yield is available for large parts of the world, thus hindering our ability to identify the contributors to riverine nitrogen and understand aquatic nitrogen cycling. To fill the data gap for the United States, here we (1) compiled 294,996 total nitrogen (TN), 225,827 nitrate (NO3−), 204,015 ammonium (NH4+), and 158,837 total organic nitrogen (TON) concentrations, with concurrent streamflow data, across the Conterminous United States (CONUS), (2) estimated riverine nitrogen loads for over 1,800 hydrological stations, (3) derived the spatial distribution of annual riverine nitrogen yield by leveraging river and catchment connectivity information contained in the National Hydrography Dataset plus (NHDPlus), and (4) characterized nonpoint-source TN loads by excluding point-source loads. This new spatial dataset quantifies spatial sources of nitrogen yield from point and non-point sources (e.g., up to 36% from point sources across the U.S.) and serves as ground-truthing to validate water quality models.

Can the best management practices resist the combined effects of climate and land-use changes on non-point source pollution control?

Climate and land-use changes have an overlying impact on non-point source (NPS) pollution in river basins. However, the control effect of Best Management Practices (BMPs) for NPS pollution is not yet clear under future scenarios. The Soil and Water Assessment Tool (SWAT) model was coupled with the entropy-weighted method, global climate patterns and land-use data to explore the dynamic variations in total nitrogen (TN) and total phosphorus (TP) loads in the Jing River Basin during the baseline (2000−2020) and future periods (2021–2065), evaluate the pollution reduction effectiveness of individual and combined BMPs, and propose practical BMP configurations. Results indicate that a future trend of urban land expansion, particularly in the economic scenario (LU_SSP585), leads to weakened environmental ecosystems, while the sustainable scenario (LU_SSP126) exhibits more balanced land development. The MIROC-ES2L model demonstrates higher Taylor skill scores, forecasted significant increases in precipitation, maximum, and minimum temperatures under the SSP585 scenario. Spatial heterogeneity in TN and TP loads is notable, showing an upward trajectory in the future. The interaction between land-use and climate change has complex effects on TN and TP loads, with land-use-induced TN changes being relatively small (4.6 %) and TP changes substantial (24.3 %). The spatial distribution, under overlying effects, leans towards the influence of climate change, emphasizing its dominant role in TN and TP load variations. Distinct differences exist in the reduction of NPS pollution loads among different BMPs, with combined BMPs demonstrating superior effectiveness. The environmental-cost effectiveness trends of BMPs remain consistent across various future scenarios. RG (Return agricultural land to grass), RG + TT (Terracing), and RG + FR10 (Fertilizer reduction: 10 %) + GW (Grassed waterway) + FS (Filter strip) + TT emerge as the most effective single, double, and multiple BMP combinations, respectively. The results offer valuable insights for preventing and mitigating future NPS pollution risks, optimizing land-use layouts, and enhancing watershed management decisions.

Assessment and driving factor analysis of total nitrogen loads: a case study of counties from 2000 to 2020 in Henan Province, China

Assessment and driving factor analysis of total nitrogen loads: a case study of counties from 2000 to 2020 in Henan Province, China

Optimizing Sampling Strategies for Estimating Riverine Nutrient Loads in the Yiluo River Watershed, China

Accurately estimating nutrient loads is crucial for effective management and monitoring of aquatic ecosystems. This study evaluated the uncertainty in different sampling frequencies and calculation methods for estimating total nitrogen (TN) and total phosphorus (TP) loads in the Yiluo River watershed, a tributary of the Yellow River in China. Using daily TN and TP concentration data from 2019 to 2020, we conducted a bootstrapping analysis to evaluate the accuracy of nine different load estimation methods at different sampling frequencies. Our results showed that Method 3 (M_3, constant concentration interpolation) and Method 7 (M_7, flow-weighted concentration method), when used with a biweekly sampling frequency, had the lowest Standard Deviation of the Percentage errors (STD) (7.70% and 8.60% for TN, 12.0% and 18.8% for TP, respectively) and Mean Relative Error (MRE) values (0.078% and −1.60% for TN, 0.305% and 2.33% for TP, respectively) on an annual scale. For monthly TN and TP load estimates, M_7 can control the MRE within ±20% at a biweekly sampling frequency. Furthermore, the uncertainty in TN and TP load estimates was generally larger during the summer months (June–September), emphasizing the important role of storm events in nutrient export. Extreme events (<10% of the time) contributed approximately 50% of the annual nutrient loads. The findings of this study provide a scientific basis for optimizing water quality monitoring schemes and management strategies in agricultural watersheds.

Complete long-term monitoring of greenhouse gas emissions from a full-scale industrial wastewater treatment plant with different cover configurations

The greenhouse gas (GHG) emissions from wastewater treatment plants (WWTPs), consisting mainly of methane (CH4) and nitrous oxide (N2O), have been constantly increasing and become a non-negligible contributor towards carbon neutrality. The precise evaluation of plant-specific GHG emissions, however, remains challenging. The current assessment approach is based on the product of influent load and emission factor (EF), of which the latter is quite often a single value with huge uncertainty. In particular, the latest default Tier 1 value of N2O EF, 0.016 ± 0.012 kgN2O–N kgTN−1, is estimated based on the measurement of 30 municipal WWTPs only, without involving any industrial wastewater. Therefore, to resolve the pattern of GHG emissions from industrial WWTPs, this work conducted a 14-month monitoring campaign covering all the process units at a full-scale industrial WWTP in Shanghai, China. The total CH4 and N2O emissions from the whole plant were, on average, 447.7 ± 224.5 kgCO2-eq d−1 and 1605.3 ± 2491.0 kgCO2-eq d−1, respectively, exhibiting a 5.2- or 3.9-times more significant deviation than the influent loads of chemical oxygen demand (COD) or total nitrogen (TN). The resulting EFs, 0.00072 kgCH4 kgCOD−1 and 0.00211 kgN2O–N kgTN−1, were just 0.36% of the IPCC recommended value for CH4, and 13.2% for N2O. Besides, the parallel anoxic-oxic (A/O) lines of this industrial WWTP were covered in two configurations, allowing the comparison of GHG emissions from different odor control setup. Unit-specific analysis showed that the replacement of enclosed A/open O with enclosed A/O reduced the CH4 EF by three times, from 0.00159 to 0.00051 kgCH4 kgCOD−1, and dramatically decreased the N2O EF by an order of magnitude, from 0.00376 to 0.00032 kgN2O–N kgTN−1, which was among the lowest of all full-scale WWTPs.