Nitrogen in the Yangtze River Basin: Pollution Reduction through Coupling Crop and Livestock Production

Enhanced nitrogen removal in constructed wetlands filled with iron-carbon substrates: Reexploring unique roles of iron-cycling and electroactive microorganisms

Bacteria involved in iron cycle and electroactivity are commonly found in intensified constructed wetlands (CWs) with iron-carbon substrates. However, their roles in nitrogen (N) removal remain unclear. Here, two types of CWs with different filling modes (separate and mixed) of sponge iron and biochar substrates under micro-oxygen regulation (2 h/d, 60 mL min-1) were constructed for nitrogen removal for 240 days. The results revealed that CWs amended with iron-biochar substrates separately (CW-D) achieved a higher total nitrogen removal performance (81 %) and lower greenhouse gas emission (global warming potential reduced by 3.07 × 105 μg CO2-eq m-2h-1) compared with Fe-C micro-electrolysis CWs (CW-E). In this process, although 41 genera of nitrogen-transforming bacteria (NTB) were detected in CWs, no NTB members had a significant difference (P < 0.05) in relative abundance between CW-D and CW-E. However, 11 genera of iron-cycling bacteria (ICB, e.g. Pseudomonas) with electroactive and 5 genera of electroactive bacteria (EAB, e.g. Tetrasphaera) were significantly enriched in CW-D and CW-E, respectively, both showing significant negative correlations (P < 0.05) with NO2--N content. It indicated that ICB and EAB rather than specific NTB members were decisive in N removal in iron and carbon CWs in low C/N ratio wastewater treatment and regulated by filling modes. Our findings expand the knowledge of the application of iron and carbon substrates in CWs and provide an initial assessment of the effect of different filling modes of iron and carbon on nitrogen removal in CWs.

Hotspots of nitrogen losses from anthropogenic sources in the Huang-Huai-Hai Basin, China

Poor management of nitrogen (N) can lead to serious environmental problems, such as air and water pollution. The accurate identification of priority control areas and emission sources is critical for making effective decisions regarding sustainable N management. This study aimed to identify hotspots for N losses and quantitatively analyze the relative contributions of different emission sources in the Huang-Huai-Hai Basin at the county scale. Specifically, it focused on N losses from agricultural production (crop production, livestock production, and freshwater aquaculture) and residential sources (urban sewage and rural domestic wastewater). To achieve this, we developed the Model to assess Anthropogenic Nitrogen losses to the Environment, following the mass flow analysis approach. The total N losses from agricultural production and residential sources in the Huang-Huai-Hai Basin were approximately 6.4 Tg per year in 2017, with 56% emitted to water and 44% to the air. Crop production and livestock production accounted for 46% and 45% of the total N losses, respectively. The main pathways of N loss were NH3 emissions to the air (41%) and direct nitrogen discharge to water (28%). Hotspots of total N losses were identified in 202 out of 910 counties, primarily located in the Henan and Hebei provinces. Although these hotspots cover less than 7% of the total basin area, they account for 51% of the total N losses. The main driving factors leading to these hotspots are the number of livestock, followed by the use of chemical N fertilizers and the extent of cultivated area. The findings of this study provide important insights for the targeted control of anthropogenic reactive N loss in the Huang-Huai-Hai Basin.

Post-Fishing Ban Period: The Fish Diversity and Community Structure in the Poyang Lake Basin, Jiangxi Province, China

Between 2022 and 2023, four systematic fish surveys were carried out in the Poyang Lake basin (PLB), capturing 49,192 fish (7017 kg) and identifying 120 species from 10 orders, 21 families, and 70 genera. Cypriniformes were the most dominant, accounting for 79 species. The spring and autumn surveys collected 25,734 and 23,458 individuals, respectively, with corresponding biomasses of 3978 kg and 3038 kg. Dominant species (IRI > 1000) in the study area included Hemiculter leucisculus, Megalobrama skolkovii, Hypophthalmichthys molitrix, and Aristichthys nobilis. Additionally, critically endangered species such as Ochetobius elongatus, Myxocyprinus asiaticus, and Acipenser sinensis as well as exotic species like Cirrhinus mrigala and euryhaline species like Cynoglossus gracilis and Hyporhamphus intermedius were observed. Hierarchical clustering grouped the survey stations into three distinct areas (PYS, XBMS, and XBUS), with the ANOSIM analysis showing highly significant differences (R = 0.893, p < 0.01). Redundancy analysis (RDA) indicated that in spring, total phosphorus (TP) and temperature were the main factors influencing variability (80.50%), while in autumn, temperature, oil, and pH were the key factors (75.20%). This study emphasizes the predictable changes in fish community composition caused by environmental gradients and highlights the need for ongoing monitoring to effectively manage and protect the ecosystem, particularly in the post-fishing ban period.

The measurement and insight of bacterial community structure succession in cyanobacteria biochar co-composting based on basic carbon and nitrogen indices

The effects of cyanobacteria biochar (CB) amendment on microbial community succession (MCS) during pig manure co-composting was evaluated. Conventional composting (T1) and different concentrations of CB co-composting were set up here (T2: 2.5% CB, T3: 5% CB, T4: 7.5% CB, T5: 10% CB, and T6: 20% CB). Core substrate indicators and microbial information were used to gain insight into microbial community succession structure (MCSS) by CB treatments. Low concentrations of CB show higher organic degradation rates (2.4% vs 2.2%; and Y = C ∗(1- e(-k∗x))), while high concentrations increased the content of TKN (T5: 54.40%). An innovative diversity quantization method (pan-γ-diversity T5:42.275, T1: 40.642, and T2: 34.285) was proposed through linear simulation and integration. CB optimized Bacillus and Thermobacillus were key organic degradation genera during succession (collaborate with Caldicoprobacter) and increased the abundance of important nitrogen fixation genera Chelativorans (Day 42: minimun 4.8 times; and Day 72: minimum 1.3 times) and Longispora (Max 10.0%). The existence of bacteria Caldicoprobacter (2.0-9.3%) on mineralization process showed the synergy and co-assembly effects of CB on MCSS. Moreover, mantel test also shows the assembled and cooperation of Firmicutes and Actinobacteria.

Unravelling nitrate transformation mechanisms in karst catchments through the coupling of high-frequency sensor data and machine learning

Nitrate dynamics within a catchment are critical to the earth's system process, yet the intricate details of its transport and transformation at high resolutions remain elusive. Hydrological effects on nitrate dynamics in particular have not been thoroughly assessed previously and this knowledge gap hampers our understanding and effective management of nitrogen cycling in watersheds. Here, machine learning (ML) models were employed to reconstruct the annual variation trend in nitrate dynamics and isotopes within a typical karst catchment. Random forest model demonstrates promising potential in predicting nitrate concentration and its isotopes, surpassing other ML models (including Long Short-term Memory, Convolutional Neural Network, and Support Vector Machine) in performance. The ML-modeled NO3--N concentrations, δ15N-NO3-, and δ18O-NO3- values were in close agreement with field data (NSE values of 0.95, 0.80, and 0.53, respectively), which are notably challenging to achieve for process models. During the transition from dry to wet period, approximately 23.0 % of the annual precipitation (∼269.1 mm) was identified as the threshold for triggering a rapid response in the wet period. The modeled nitrate isotope values were significantly supported by the field data, suggesting seasonal variations of nitrogen sources, with precipitation as the primary driving force for fertilizer sources. Mixing of multiple sources appeared to be the main control of the transport and transformation of nitrate during the rising limb in the wet period, whereas process control (denitrification) took precedence during the falling limb, and the fate of nitrate was controlled by biogeochemical processes during the dry period.

Whole-chain intensification of pig and chicken farming could lower emissions with economic and food production benefits

Intensified monogastric livestock management could conserve feed inputs and mitigate some of the environmental and climate challenges associated with animal production. In this study, we used data from 166 countries to model the environmental, climate and economic impacts of pig and chicken intensification. We found that whole-chain intensification could reduce annual nitrogen and greenhouse gas emissions by 49% (4.6 Tg) and 68% (554 Tg CO2-equivalent), respectively. These changes translate to 5.0 Tg lower nitrogen fertilizer input for feed production, resulting in an overall benefit of US$93 billion. Integrated crop-livestock optimization under intensive management could release 27 Mha of cropland and provide additional food for 310 million people. A judicious promotion of intensification could alleviate global pressures related to food security, environment and climate change.

Increasing cascade dams in the upstream area reduce nutrient inputs to the Three Gorges Reservoir in China

The upstream cascade dams play an essential role in the nutrient cycle in the Yangtze. However, there is little quantitative information on the effects of upstream damming on nutrient retention in the Three Gorges Reservoir (TGR) in China. Here, we aim to assess the impact of increasing cascade dams in the upstream area of the Yangtze on Dissolved Inorganic Nitrogen and Phosphorus (DIN and DIP) inputs to the TGR and their retention in the TGR and to draw lessons for other large reservoirs. We implemented the Model to Assess River Inputs of Nutrients to seAs (MARINA-Nutrients China-2.0 model). We ran the model with the baseline scenario in which river damming was at the level of 2009 (low) and alternative scenarios with increased damming. Our scenarios differed in nutrient management. Our results indicated that total water storage capacity increased by 98 % in the Yangtze upstream from 2009 to 2022, with 17 new large river dams (>0.5 km3) constructed upstream of the Yangtze. As a result of these new dams, the total DIN inputs to the TGR decreased by 15 % (from 768 Gg year-1 to 651 Gg year-1) and DIP inputs decreased by 25 % (from 70 Gg year-1 to 53 Gg year-1). Meanwhile, the molar DIN:DIP ratio in inputs to the TGR increased by 13 % between 2009 and 2022. In the future, DIN and DIP inputs to the TGR are projected to decrease further, while the molar DIN:DIP ratio will increase. The Upper Stem contributed 39 %-50 % of DIN inputs and 63 %-84 % of DIP inputs to the TGR in the past and future. Our results deepen our knowledge of nutrient loadings in mainstream dams caused by increasing cascade dams. More research is needed to understand better the impact of increased nutrient ratios due to dam construction.

High resolution spatiotemporal modeling of long term anthropogenic nutrient discharge in China

High-resolution integration of large-scale and long-term anthropogenic nutrient discharge data is crucial for understanding the spatiotemporal evolution of pollution and identifying intervention points for pollution mitigation. Here, we establish the MEANS-ST1.0 dataset, which has a high spatiotemporal resolution and encompasses anthropogenic nutrient discharge data collected in China from 1980 to 2020. The dataset includes five components, namely, urban residential, rural residential, industrial, crop farming, and livestock farming, with a spatial resolution of 1 km and a temporal resolution of monthly. The data are available in three formats, namely, GeoTIFF, NetCDF and Excel, catering to GIS users, researchers and policymakers in various application scenarios, such as visualization and modelling. Additionally, rigorous quality control was performed on the dataset, and its reliability was confirmed through cross-scale validation and literature comparisons at the national and regional levels. These data offer valuable insights for further modelling the interactions between humans and the environment and the construction of a digital Earth.

Decoding China's anthropogenic typical pollutant discharge patterns: Long-term dynamics and hotspot transitions driven by population, diet, and sanitation

Human activities have led to an alarming increase in pollution, resulting in widespread water contamination. A comprehensive understanding of the quantitative relationship between anthropogenic pollutant discharges and the escalating anthropogenic disturbances and environmental efforts is crucial for effective water quality management. Here we establish a Model for Estimating Anthropogenic pollutaNts diScharges (MEANS) and simulate the long-term dynamics of various types of anthropogenic discharges in China based on an unprecedented spatio-temporal dynamic parameter dataset. Our findings reveal that from 1980 to 2020, anthropogenic discharges exhibited an overall trend of initially increasing and subsequently decreasing, with the peak occurring around 2005. During this period, the dominant pollution sources in China shifted from urban to rural areas, thereby driving the transition of hotspot pollutants from nitrogen to phosphorus in the eastern regions. The most significant drivers of anthropogenic pollutant discharges gradually shifted from population size and dietary structure to wastewater treatment and agricultural factors. Furthermore, we observed that a significant portion of China's regions still exceed the safety thresholds for pollutant discharges, with excessive levels of total phosphorus (TP) being particularly severe. These findings highlight the need for flexible management strategies in the future to address specific pollution levels and hotspots in different regions. Our study underscores the importance of considering the complex interplay between anthropogenic disturbances, environmental efforts, and long-term anthropogenic pollutant discharges for effective water pollution control.

Unraveling spatial patterns and source attribution of nutrient transport: Towards optimal best management practices in complex river basin

A comprehensive understanding of nutrient transport patterns and clarification of pollutant sources' load contributions are critical prerequisites for developing scientific pollution control strategies in complex river basins. Here, we focused on the Minjiang River Basin (MRB) and employed the Soil and Water Assessment Tool (SWAT) model to systematically investigate the nitrogen (N) and phosphorus (P) loads from both point and non-point sources. Results revealed that the key source areas of N and P pollution in the MRB were predominantly located along the riverbanks, influenced by a combination of sediment, precipitation, agricultural activities such as fertilization. Our analysis indicated that soil nutrient loss, fertilization, and livestock farming were the major contributors to N and P inputs, accounting for over 70 % of the total input, followed by rural residential and urban point sources. Based on the identification of non-point source pollution as the primary load source, a multi-objective optimization was conducted using response surface methodology (RSM) coupled with the non-dominated sorting genetic algorithm-II (NSGA-II), resulting in the identification of optimal best management practices (BMPs) that achieve a reduction of 40.04 % in N load, 39.22 % in P load, and a net economic benefit of -1.13 billion yuan per year. Compared to the RSM and automated optimization results, the proposed management measures exhibited significant improvements in N and P load reduction and net benefits. Overall, the findings provide important insights for formulating agricultural management policies in the MRB and offering valuable implications for pollution management in other complex river basins.

N-containing dissolved organic matter promotes dissolved inorganic carbon supersaturation in the Yangtze River, China

Dissolved inorganic carbon (DIC) represents a major global carbon pool and the flux from rivers to oceans has been observed to be increasing. The effect of weathering with respect to increasing DIC has been widely studied in recent decades; however, the influence of dissolved organic matter (DOM) on increasing DIC in large rivers remains unclear. This study employed stable carbon isotopes and Fourier transform ion cyclotron mass spectrometry (FT-ICR MS) to investigate the effect of the molecular composition of DOM on the DIC in the Yangtze River. The results showed that organic matter is an important source of DIC in the Yangtze River, accounting for 40.0 ± 12.1 % and 32.0 ± 7.2 % of DIC in wet and dry seasons, respectively, and increased along the river by approximately three times. Nitrogen (N)-containing DOM, an important composition in DOM with a percentage of ∼40 %, showed superior oxidation state than non N-containing DOM, suggesting that the presence of N could improve the degradable potential of DOM. Positive relationship between organic sourced DIC (DICOC) and N-containing DOM formulae indicated that N-containing DOM is crucial to facilitate the mineralization of DOM to DICOC. N-containg molecular formular with low H/C and O/C ratio were positively correlated with DICOC further verified these energy-rich and biolabile compounds are preferentially decomposed by bacteria to produce DIC. N-containing components significantly accelerated the degradation of DOM to DICOC, which is important for understanding the CO2 emission and carbon cycling in large rivers.

Important Role of Overland Flows and Tile Field Pathways in Nutrient Transport

Nitrogen and phosphorus pollution is of great concern to aquatic life and human well-being. While most of these nutrients are applied to the landscape, little is known about the complex interplay among nutrient applications, transport attenuation processes, and coastal loads. Here, we enhance and apply the Spatially Explicit Nutrient Source Estimate and Flux model (SENSEflux) to simulate the total annual nitrogen and phosphorus loads from the US Great Lakes Basin to the coastline, identify nutrient delivery hotspots, and estimate the relative contributions of different sources and pathways at a high resolution (120 m). In addition to in-stream uptake, the main novelty of this model is that SENSEflux explicitly describes nutrient attenuation through four distinct pathways that are seldom described jointly in other models: runoff from tile-drained agricultural fields, overland runoff, groundwater flow, and septic plumes within groundwater. Our analysis shows that agricultural sources are dominant for both total nitrogen (TN) (58%) and total phosphorus (TP) (46%) deliveries to the Great Lakes. In addition, this study reveals that the surface pathways (sum of overland flow and tile field drainage) dominate nutrient delivery, transporting 66% of the TN and 76% of the TP loads to the US Great Lakes coastline. Importantly, this study provides the first basin-wide estimates of both nonseptic groundwater (TN: 26%; TP: 5%) and septic-plume groundwater (TN: 4%; TP: 2%) deliveries of nutrients to the lakes. This work provides valuable information for environmental managers to target efforts to reduce nutrient loads to the Great Lakes, which could be transferred to other regions worldwide that are facing similar nutrient management challenges.

Effects of point and nonpoint source pollution on urban rivers: From the perspective of pollutant composition and toxicity

Wastewater discharge is considered to be one of the anthropogenic factors affecting the water quality of urban rivers. The source and composition of wastewater are complex and diverse, and it is difficult to evaluate its effect on water quality and ecological health of receiving waters. Environmental DNA method can determine all species living in waters by examining DNA sequences, reflecting the impact of water quality changes on aquatic systems. In this study, water samples from two urban rivers were collected in dry and wet seasons, and the composition of pollutants was investigated by nontarget screening. Based on the pollutant composition, compound toxicity prediction and concentration addition model were used to predict the toxicity changes of pollutants in the urban rivers. More than 1500 suspect organic pollutants were nontarget screened, and silafluofen was found to be a major toxicity contributor. Environmental DNA analysis was combined with water quality measure and pollutant toxicity prediction to reveal the effects of pollutants from different sources on aquatic ecosystems. Fish diversity was negatively correlated with the mixed toxicity of organic pollutants, suggesting potential ecological risk in these two urban rivers. Our study developed a water quality assessment method based on pollutant composition and toxicity, and the potential risk of nonpoint source pollutants on aquatic ecosystems should not be neglected.

Animal production predominantly contributes to antibiotic profiles in the Yangtze River

Human-induced antibiotic pollution in the world's large rivers poses significant risk to riverine ecosystems, water quality, and human health. This study identified geophysical and socioeconomic factors driving antibiotic pollution in the Yangtze River by quantifying 83 target antibiotics in water and sediment samples collected in its 6300-km-long reach, followed by source apportionment and statistical modeling. Total antibiotic concentrations ranged between 2.05-111 ng/L in water samples and 0.57-57.9 ng/g in sediment samples, contributed predominantly by veterinary antibiotics, sulfonamides and tetracyclines, respectively. Antibiotic compositions were clustered according to three landform regions (plateau, mountain-basin-foothill, and plains), resulting from varying animal production practices (cattle, sheep, pig, poultry, and aquaculture) in the sub-basins. Population density, animal production, total nitrogen concentration, and river water temperature are directly associated with antibiotic concentrations in the water samples. This study revealed that the species and production of food animals are key determinants of the geographic distribution pattern of antibiotics in the Yangtze River. Therefore, effective strategies to mitigate antibiotic pollution in the Yangtze River should include proper management of antibiotic use and waste treatment in animal production.