Relationship between non-point source pollution and fluorescence fingerprint of riverine dissolved organic matter is season dependent

Potential of submerged macrophytes restoration for reducing CH4 and CO2 emissions in a typical urban lake

As one of the areas most affected by human activities, urban lakes play a crucial role in global carbon cycle. Currently, submerged macrophyte restoration is a common ecological practice in urban lakes, primarily aimed at improving water quality and enhancing the aesthetic value of lake environments. Despite its widespread application, its contribution to carbon emission reduction has not received sufficient attention. In this study, we quantified the fluxes, concentrations and isotope signatures of CH4 and CO2 in the restoration zone (RL), the unrestored zone (UR) and the inflow rivers (IR) of Lake Xuanwu, along with potential environmental and dissolved organic matter (DOM) factors over the course of a year. The results indicated that the restoration of submerged macrophytes significantly diminished the emission of CH4 and CO2 from the lake. Compared to the UR and IR zone, the CH4 flux in the RL zone was reduced by 82.27 % and 92.18 %, while the CO2 flux decreased by 464.95 % and 133.12 %, respectively. Further investigation revealed distinct eutrophication levels between the RL zone with submerged macrophytes compared to the UR zone, and higher eutrophication levels were associated with reduced carbon sequestration stability. Nitrogen and phosphorus played critical roles in the emission of CH4 and CO2, respectively. Submerged macrophytes directly reduce carbon emissions through photosynthesis and significantly influence the long-term carbon sequestration capacity of lakes by secreting oxygen, modifying the ecological characteristics of the aquatic environment, and altering the production and mineralization processes of CH4 and CO2 in sediment porewater. These results underscore the potential of submerged macrophytes restoration as a viable strategy for reducing local emissions of CH4 and CO2 in urban lakes.

Hydrological connectivity and dissolved organic matter impacts nitrogen and antibiotics fate in river-lake system before and after extreme wet season

The impact and mechanism of hydrological connectivity and dissolved organic matter on the fate of nitrogen and antibiotics are still lack off in a river-lake connected system under climate extreme events. This study examined the fate of NO3--N, 38 antibiotics, and dissolved organic matter (DOM) in Baiyangdian Basin, through dry and wet seasonal (after extreme rainfall) samplings at 2023. In the system, NO3--N and ∑antibiotics average concentrations were higher in the dry season, while the relative abundance of humic-like components was higher in the wet season. Spatial autocorrelation analysis showed that the high-high clusters of pollutants and DOM components were mainly distributed in rivers, and the temporal difference was significant. MixSIAR and PMF model were respectively applied to nitrogen and antibiotics sources apportionment. The results showed that non-point sources (NPS) of nitrogen and antibiotics exhibited an upward trend, while the point sources decreased from dry to wet seasons. Hydrological connectivity was characterized by using δ18O-H2O, which was higher in the wet season. Partial least squares path model revealed that hydrological connectivity directly impacted humic-like components, which were the direct influencing factor of the concentration and NPS for antibiotics and nitrogen in the connected system. Extreme rainfall weaken the impact of hydrological connectivity on the concentration and NPS of pollutants, while enhanced the impact of humic-like components on pollutants NPS. These findings clarified the impact mechanism of hydrological connectivity and DOM on nitrogen and antibiotics fate in the connected system, which plays an important role in future water quality management under extreme events.

Polyethylene microplastics distinctly affect soil microbial community and carbon and nitrogen cycling during plant litter decomposition

Plant litter is an important input source of carbon and nitrogen in soil. While microplastics (MPs) and plant litter are ubiquitously present in soil, their combined impact on soil biogeochemical processes remains poorly understood. To address this gap, we examined the soil changes resulting from the coexistence of plant litter (Alfalfa) and polyethylene microplastics (PE). The soil changes included physicochemical properties, composition of soil dissolved organic matter, and structure of the soil microbial community. The results showed that the addition of polyethylene (PE) inhibited the degradation of humus-like substances and decreased the quantity of humic acid-like compounds in soil dissolved organic matter (DOM). PE negatively impacted plant litter decomposition, disrupted soil organic carbon (SOC) breakdown, interfered with the nitrogen cycle, and significantly altered microbial community structures during the process. By day 35, SOC and total nitrogen (TN) levels were reduced by 39.8% and 10.1%, respectively, in the presence of PE. Furthermore, PE significantly decreased the abundance of nitrogen-fixing microbes, including Streptomyces (43.1%) and Bacillus (45.9%), which play key roles in nitrate reduction to ammonium. This study highlights the environmental effects of MPs on plant litter decomposition and their potential implications for soil biogeochemical processes.

A new view into the characterization of dissolved organic matter composition in lakes and traceability studies

With the booming socio-economic development and accelerated urbanisation, the problem of water pollution is becoming more and more prominent, which not only puts great pressure on nature, but also poses a serious threat to the production and life of human beings. Therefore, the study of dissolved organic matter fractions in lakes and their accurate traceability is the key to alleviate the ecological pressure. In this study, the three-dimensional fluorescence spectral properties, characteristic parameters and fluorescence characteristics of dissolved organic matter in water bodies were analyzed in depth using Changdang Lake in China as an example. Three methods, peak-finding method, Tucker coefficient and self-organised neural network, were prominently used for the analysis. Combined with conventional water quality parameters, Combined with conventional water quality parameters, this method further reveals the correlation between DOM composition and surrounding pollution sources in Changdang Lake. The results showed that there were four main components of dissolved organic matter in the lake body of Changdang Lake, of which C1, C2 and C4 were humic substances and C3 was protein. In addition, the fluorescence characteristic parameters of Changdang Lake, FI, ranged from 1.64 to 1.75, BIX, ranged from 0.95 to 1.05, and HIX, ranged from 0.5 to 0.65, which indicated that the increment of dissolved organic matter in Changdang Lake was mainly a mixture of endogenous and exogenous inputs. Through the joint interpretation of peak discovery, data presentation and result visualisation, it was found that these fluorescence fractions were extremely similar to those of the surrounding aquaculture and textile printing and dyeing. The results of this study not only provide effective data support for the local environmental protection department of Changdang Lake, but also provide a useful reference for pollution traceability in other lake basins.

Exploration of nitrogen sources and transformation processes in eutrophic estuarine zones based on DOM and stable isotope compositions

Our study examines nitrogen sources and transformations in Xiamen Bay, where eutrophication has increased due to higher nitrogen levels. By analyzing dissolved organic matter (DOM) and nitrate stable isotopes (δ15N-NO3-and δ18O-NO3-), the study finds that nitrate in low salinity areas is influenced by freshwater-seawater mixing and biogeochemical processes, while in high salinity areas, it is mainly affected by physical mixing. Bayesian mixing model (MixSIAR) results show that the primary nitrate sources are fecal matter and sewage, followed by atmospheric deposition. During the high flow period, DOM may facilitate nitrogen transformation and release through processes such as degradation or mineralization. In contrast, during the low flow period, the system is mainly influenced by the physical mixing of saline and freshwater. Studies have shown that DOM can indicate the biogeochemical intensity in water bodies, further identifying the main factors influencing the distribution and transformation processes of nitrate content, providing a basis for mitigating eutrophication in estuarine areas.

Identifying and quantifying multiple pollution sources in estuaries using fluorescence spectra and gradient-based deep learning

This study developed an intelligent method for identifying and quantifying water pollution sources in estuarine areas. It characterized the excitation-emission matrix (EEM) fluorescence spectra from seven end-members, including seawater, rainwater, and five pollution sources typical of these areas. A deep learning model was established to identify and quantify these pollution sources in mixed water bodies. The model was fed either the original EEM or a combined EEM and gradient input. The results indicated that the combined input enhanced classification and quantification accuracy; Although model accuracy declined with an increasing number of mixed pollution sources, the combined input still improved classification accuracy by 3.1 % to 6.8 %; When the proportion of rainwater and seawater was below 70 %, the model maintained a classification accuracy of 57.4 % with original input and 61.3 % with combined input, with root mean square error values for the pollution source proportion being 12.2 % and 11.4 %, respectively.

Declined nutrients stability shaped by water residence times in lakes and reservoirs under climate change

Water quality stability in lakes and reservoirs is essential for drinking water safety and ecosystem health, especially given the frequent occurrence of extreme climate events. However, the relationship between water quality stability and water residence time (WRT) has not been well elucidated. In this study, we explored the relationship based on nitrogen (N) and phosphorus (P) concentrations data in 11 lakes and 49 reservoirs in the Yangtze-Huaihe River basin from 2010 to 2022. Additionally, we examined the effects of hydrometeorological characteristics, the geomorphology of water bodies and catchments, and land use on the WRT, establishing a link between climate change and the stability of N and P in these water bodies. The results showed that a significant correlation between the stability of N and P in lakes and reservoirs and their WRT. The longer WRT tends to coincide with decreased stability and higher nutrient concentrations. Hydrometeorological factors are the primary factors on the WRT, with precipitation exerting the greatest effect, particularly under extreme drought. In recent years, extreme climatic events have intensified the fluctuations of WRT, resulting in a renewed increase in N and P concentrations and deterioration in stability. These findings highlight the importance of incorporating meteorological and hydrological factors alongside reinforcing ecological restoration into lake and reservoir management strategies, and providing a scientific basis for future efforts aimed at enhancing lake and reservoir water quality stability and safeguarding aquatic ecosystems.

Analysis of urban composite non-point source pollution characteristics and its contribution to river DOM based on EEMs and FT-ICR MS

Urban composite non-point source (UCNPS) has an increasing degree of influence on the urban receiving waters. However, there remains a dearth of precise techniques to characterize and evaluate the contribution of UCNPS. Therefore, this study developed a source analytical methodology system based fluorescence excitation-emission matrices spectroscopy (EEMs) and Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS).Specifically, it utilized parallel factor analysis (PARAFAC), two-dimensional correlation spectroscopy (2D-COS), end-member mixing analysis (EMMA), and non-metric multidimensional scaling (NMDS) to analysis UCNPS pollution characteristics and quantify its contributions to river DOM. The results of its application in typical hilly and plain urban within the Yangtze River Basin, China revealed that road and roof runoff exhibited high aromaticity and humic-like content, and the characteristics of pipe sediment was similar with domestic sewage. The component of Rivers had sequences of changes under rainfall perturbations. But terrestrial humic-like represented the initial input in all cases, and it can provide some indication of UCNPS input. The results of EMMA showed that the contribution of road runoff, roof runoff, pipeline sediment and domestic sewage to river DOM was 9.0 %-36.0 %, 2.6 %-19.1 %, 2.3 %-28.8 % and 5.9 %-25.9 %, respectively, and the specific contribution was mainly affected by rainfall level, regional terrain and drainage system. The methodology system of this study can provide technical support for the traceability and precise control of UCNPS pollution.

Riverine seasonal rainfall event tracing of organic pollution sources using fluorescence fingerprint difference spectrum

Elucidating the dynamics of dissolved organic matter (DOM) transport and transformation under seasonal rainfall events is essential for the conservation of riverine ecosystems, for mitigating the effects of climate change, and for crafting informed water management strategies. Therefore, this study aimed to investigate the evolutionary characteristics of organic pollution sources during consecutive rainfall events in early spring and to quantify their relative contributions to the process of surface water pollution. The results showed seasonal rainfall induces water quality exceedances in rivers due to the combined impacts of terrestrial inputs and endogenous releases. Humic acid (HA) (region V) and fulvic acid (FA) (region III) emerged as the predominant organic matter in the water column, with their fluorescence intensity altering as rainwater flushed the riverbed. Sources of pollution include agricultural and urban domestic sources (AS + DS) (72.29 %), industrial and urban domestic and microbial sources (IS + DS + MS) (37.71 %), and agricultural and industrial sources (AS + IS) (63.32 %), indicating that agricultural surface pollution discharges contribute significantly. The gas-chromatography-mass spectrometry (GC-MS) further confirmed that exogenous inputs were predominantly comprised of particulate pollutants. This study underscores the efficacy of fluorescence difference spectrometry in delineating the migration and transformation of river pollution sources during seasonal rainfall and facilitating the implementation of targeted management strategies for river ecosystems.

Algal-mediated nitrogen removal and sustainability of algal-derived dissolved organic matter supporting denitrification

Algae-mediated nitrogen removal from low carbon vs. nitrogen (C/N) wastewater techniques has garnered significant attention due to its superior autotrophic assimilation properties. This study investigated the ammonium-N removal potential of four algae species from low C/N synthetic wastewater. Results showed that 95 % and 99 % of ammonium-N are eliminated at initial concentrations of 11.05 ± 0.98 mg/L and 42.51 ± 2.20 mg/L with little nitrate and nitrite accumulation. The compositions of secreted algal-derived dissolved organic matter varied as C/N decreased and showed better bioavailability for nitrate-N removal by Pseudomonas sp. SZF15 without pre-oxidation, achieving an efficiency of 99 %. High-throughput sequencing revealed that the aquatic microbial communities, dominated by Scenedesmus, Kalenjinia, and Micractinium, remain relatively stable across different C/N, aligning with the underlying metabolic pathways. These findings may provide valuable insights into the sustainable elimination of multiple nitrogen contaminants from low C/N wastewater.

Chromophoric dissolved organic matter traces seasonally changing coastal processes in a river-influenced region of the western Bay of Bengal

The optical characteristics of colored dissolved organic matter (CDOM) serve as a convenient tool for evaluating coastal processes, e.g., river runoff, anthropogenic inputs, primary production, and bacterial/photochemical processes. We conducted a study on the seasonal and spatial variability of absorbance and fluorescence characteristics of CDOM and nutrients in the coastal waters near the Gauthami estuary of River Godavari, the largest peninsular river of India, for a year. The surface aCDOM(350) showed a significant inverse relation with salinity in the coastal region, indicating a conservative mixing of marine and terrestrial end members. The aCDOM(350) was not conservative in the offshore (100 m isobath) waters due to enrichment by secondary sources. Seasonal variability in optical properties indicated diverse sources for CDOM, as revealed by principal component analysis. The excitation-emission matrix (EEM) spectra followed by parallel factor analysis (EEM-PARAFAC) revealed four distinct fluorophores. The tyrosine (B) fluorophore showed a predominant increase in the post-monsoon season (October to January), while tryptophan (T) was relatively more enriched, coincident with nutrient enrichment and transparency increase during the early monsoon phase (July). The biological index (BIX), which reflects recent photosynthetic activity, also displayed relatively higher values during the early monsoon. The humic fluorophores A and M, and humification index (HIX) were relatively enriched during the later phase of monsoon (July-October). HIX was > 4 in a few samples of the offshore region (100-m isobath) and indicated a probable contamination from drill-mud (bentonite) used in hydrocarbon exploration. During the monsoon, the relationship between T and B with CDOM was not evident due to the masking of B fluorescence in intact protein. However, during the post-monsoon (POM) and pre-monsoon (PRM) periods, this masking effect was not observed, likely due to protein degradation via bacterial and photochemical processes, respectively. Temporal variability in nutrients indicated that high ammonium levels were produced during POM (OM bacterial degradation), and high nitrite levels were observed during PRM (due to primary production). This study provides foundational insights into the use of CDOM for understanding the impact of diverse environmental, river discharge, and anthropogenic factors on coastal ecosystems.

Heavy metal accumulation analysis using bivalve, sponge, sea urchin, and gastropod species as bioindicators

A comparative assessment of heavy metal accumulation potential in four distinct marine benthic bioindicators: the bivalve Perna perna, the sponge Callyspongia fibrosa, the sea urchin Tripneustes gratilla, and the gastropod Purpura bufo were conducted. These organisms were collected from the same location, and the concentration of ten heavy metals was analyzed in water, sediment and various body parts of the organisms. The bioaccumulation potential was evaluated using the bio-water accumulation factor and bio-sediment accumulation factor. There was significant variation in the bioaccumulation potential of each organism with respect to different metals. The sponge proved to be a reliable indicator of Cd with a highest concentration of 2.60 μg/g. Sea urchin accumulated high concentrations of Cr (16.98 μg/g) and Pb (4.80 μg/g), whereas Cu was predominant (21.05 μg/g) in gastropod, followed by bivalve (17.67 μg/g). The concentration of metals in hard parts was found to be lower than in the tissues.

Accounting for the microbial assembly of each process in wastewater treatment plants (WWTPs): study of four WWTPs receiving similar influent streams

We evaluated a unique model in which four full-scale wastewater treatment plants (WWTPs) with the same treatment schematic and fed with similar influent wastewater were tracked over an 8-month period to determine whether the community assembly would differ in the activated sludge (AS) and sand filtration (SF) stages. For each WWTP, AS and SF achieved an average of 1-log10 (90%) and <0.02-log10 (5%) reduction of total cells, respectively. Despite the removal of cells, both AS and SF had a higher alpha and beta diversity compared to the influent microbial community. Using the Sloan neutral model, it was observed that AS and SF were individually dominated by different assembly processes. Specifically, microorganisms from influent to AS were predominantly determined by the selective niche process for all WWTPs, while the microbial community in the SF was relatively favored by a stochastic, random migration process, except two WWTPs. AS also contributed more to the final effluent microbial community compared with the SF. Given that each WWTP operates the AS independently and that there is a niche selection process driven mainly by the chemical oxygen demand concentration, operational taxonomic units unique to each of the WWTPs were also identified. The findings from this study indicate that each WWTP has its distinct microbial signature and could be used for source-tracking purposes.IMPORTANCEThis study provided a novel concept that microorganisms follow a niche assembly in the activated sludge (AS) tank and that the AS contributed more than the sand filtration process toward the final microbial signature that is unique to each treatment plant. This observation highlights the importance of understanding the microbial community selected by the AS stage, which could contribute toward source-tracking the effluent from different wastewater treatment plants.

Dissolved organic matter (DOM) is independently stratified in thermally stratified water bodies

Thermal stratification often occurs in deep-water bodies, including oceans, lakes, and reservoirs. Dissolved organic matter (DOM) plays a critical role in regulating the dynamics of aquatic food webs and water quality in aquatic ecosystems. In the past, thermal stratification boundaries have been sometimes used exclusively to analyze the vertical distribution of DOM in thermally stratified water bodies. However, the validity of this practice has been challenged. Currently, there is limited understanding of the formation mechanism and stratification of the vertical distribution of DOM in thermally stratified water bodies, which hinders the analysis of the interactions between DOM and vertical aquatic ecological factors. To address this gap, we conducted a comprehensive study to extensively collect the vertical distribution of DOM in thermally stratified water bodies and identify the primary factors influencing this distribution. We found that DOM was independently stratified in thermally stratified water bodies (including two cases in unstratified water bodies), and that the formation mechanisms and statuses of DOM stratification were different from those of thermal stratification. The boundaries and numbers of DOM stratification were generally inconsistent with those of thermal stratification. Therefore, it is more accurate to divide DOM into different layers according to its own vertical profile, and analyze DOM characteristics of each layer based on its own stratification instead of thermal stratification. This study sheds light on the relationship between DOM and thermal stratification and provides a novel approach for analyzing DOM vertical distribution characteristics and their impact on aquatic ecosystems. This finding also holds significant implications for the design and implementation of environmental management programs aimed at preserving the health and functionality of aquatic ecosystems.

Review: The application of source analysis methods in tracing urban non-point source pollution: categorization, hotspots, and future prospects

The contribution of urban non-point source (NPS) pollution to surface water pollution has gradually increased, analyzing the sources of urban NPS pollution is of great significance for precisely controlling surface water pollution. A bibliometric analysis of relevant research literature from 2000 to 2021 reveals that the main methods used in the source analysis research of urban NPS pollution include the emission inventory approach, entry-exit mass balance approach, principal component analysis (PCA), positive matrix factorization (PMF) model, etc. These methods are primarily applied in three aspects: source analysis of rainfall-runoff pollution, source analysis of wet weather flow (WWF) pollution in combined sewers, and analysis of the contribution of urban NPS to the surface water pollution load. The application of source analysis methods in urban NPS pollution research has demonstrated an evolution from qualitative to quantitative, and further towards precise quantification. This progression has transitioned from predominantly relying on on-site monitoring to incorporating model simulations and employing mathematical statistical analyses for traceability. This paper reviews the principles, advantages, disadvantages, and the scope of application of these methods. It also aims to address existing problems and analyze potential future development directions, providing valuable references for subsequent related research.

Characteristics of DOM and Their Relationships with Potentially Toxic Elements in the Inner Mongolia Section of the Yellow River, China

The characterization of dissolved organic matter (DOM) is important for better understanding of the migration and transformation mechanisms of DOM in water bodies and its interaction with other contaminants. In this work, fluorescence characteristics and molecular compositions of the DOM samples collected from the mainstream, tributary, and sewage outfall of the Inner Mongolia section of the Yellow River (IMYR) were determined by using fluorescence spectroscopy and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). In addition, concentrations of potentially toxic elements (PTEs) in the relevant surface water and their potential relationships with DOM were investigated. The results showed that the abundance of tyrosine-like components increased significantly in downstream waters impacted by outfall effluents and was negatively correlated with the humification index (HIX). Compared to the mainstream, outfall and tributaries have a high number of molecular formulas and a higher proportion of CHOS molecular formulas. In particular, the O5S class has a relative intensity of 41.6% and the O5-7S class has more than 70%. Thirty-eight PTEs were measured in the surface water samples, and 12 found above their detective levels at all sampling sites. Protein-like components are positively correlated with Cu, which is likely indicating the source of Cu in the aquatic environment of the IMYR. Our results demonstrated that urban wastewater discharges significantly alter characteristics and compositions of DOM in the mainstream of IMYR with strongly anthropogenic features. These results and conclusions are important for understanding the role and sources of DOM in the Yellow River aquatic environment.

Fluorescence fingerprint as an indicator to identify urban non-point sources in urban river during rainfall period

Nowadays, the urban non-point source (NPS) pollution gradually evolved as the main contributor to urban water contamination since the point source pollution was effectively controlled. It was imperative to perform urban NPS identification in urban river to meet the requirements of precise source governance. In this study, the real-time detection about water quality parameters and fluorescence fingerprints (FFs) was performed for BX River and its outlets during rainfall period. EEM-PARAFAC and component similarity analyses discovered that the pollution encountered by BX River mainly came from road runoff and untreated municipal wastewater (UMWW) overflow. The C1 (tryptophan-like) and C3 (terrestrial humic-like) components located at Ex/Em = ∼230(280)/340 and ∼275/430 nm were both detected in these two kinds of urban NPS. The C2 components of road runoff and UMWW overflow displayed remarkable differences, which located at Ex/Em = 250/385 and 245/365 nm, respectively, thus could be served as indicators for distinguishing them. During rainfall period, the outflow from rainwater outlets (RWOs) constantly showed similar FF features to road runoff, while the FFs of outflow from combined sewer outlets (CSOs) alternated between those of road runoff and UMWW overflow. The FF features of sections in BX River changed in response to the dynamic variations in FFs of the outlets, which revealed real-time pollution causes of BX River. This work not only realized the identification and differentiation of urban NPS, but also elucidated the dynamic variations of pollution characteristics throughout the entire process of "urban NPS-outlets-urban river", and demonstrated the feasibility of FF technique in quickly diagnosing the pollution causes of urban river during rainfall period, which provided important guidance for urban NPS governance.

Humification levels of dissolved organic matter in the eastern plain lakes of China based on long-term satellite observations

Under the influence of intensive human activities and global climate change, the sources and compositions of dissolved organic matter (DOM) in the eastern plain lake (EPL) region in China have fluctuated sharply. It has been successfully proven that the humification index (HIX), which can be derived from three-dimensional excitation-emission matrix fluorescence spectroscopy, can be an effective proxy for the sources and compositions of DOM. Therefore, combined with remote sensing technology, the sources and compositions of DOM can be tracked on a large scale by associating the HIX with optically active components. Here, we proposed a novel HIX remote sensing retrieval (IRHIX) model suitable for Landsat series sensors based on the comprehensive analysis of the covariation mechanism between HIX and optically active components in different water types. The validation results showed that the model runs well on the independent validation dataset and the satellite-ground synchronous sampling dataset, with an uncertainty ranging from 30.85 % to 36.92 % (average ± standard deviation = 33.6 % ± 3.07 %). The image-derived HIX revealed substantial spatiotemporal variations in the sources and compositions of DOM in 474 lakes in the EPL during 1986-2021. Subsequently, we obtained three long-term change modes of the HIX trend, namely, significant decline, gentle change, and significant rise, accounting for 74.68 %, 17.09 %, and 8.23 % of the lake number, respectively. The driving factor analysis showed that human activities had the most extensive influence on the DOM humification level. In addition, we also found that the HIX increased slightly with increasing lake area (R2 = 0.07, P < 0.05) or significantly with decreasing trophic state (R2 = 0.83, P < 0.05). Our results provide a new exploration for the effective acquisition of long-term dynamic information about the sources and compositions of DOM in inland lakes and provide important support for lake water quality management and restoration.

Dissolved organic matter evolution and straw decomposition rate characterization under different water and fertilizer conditions based on three-dimensional fluorescence spectrum and deep learning

Straw returning is a sustainable way to utilize agricultural solid waste resources. However, incomplete decomposition of straw will cause harm to crop growth and soil quality. Currently, there is a lack of technology to timely monitor the rate of straw decomposition. Dissolved organic matter (DOM) is the most active organic matter in soil and straw is mainly immersed in the soil in the form of DOM. In order to formulate reasonable straw returning management measures , a timely monitoring method of straw decomposition rate was developed in the study. Three water treatment (60%-65%, 70%-75% and 80%-85% maximum field capacity) and two fertilizer (organic fertilizer and chemical fertilizer) were set up in the management of straw returning to the field. Litterbag method was used to monitor the weight loss rate of straw decomposition under different water and fertilizer conditions in strawberry growth stage. The changes of DOM components were determined by three-dimensional fluorescence spectroscopy (3D-EEM). From the faster decomposition period to the slower decomposition period, the main components of DOM changed from protein-like components to humus-like components. At the end of the experiment, the relative content of humus-like components under the treatment of organic fertilizer and moderate water was the highest. Convolutional neural network (CNN) combined with 3D-EEM was used to identify the decomposition speed of straw. The classification precision of neural network validation set and test are 85.7% and 81.2%, respectively. In order to predict the decomposition rate of straw under different water and fertilizer conditions, 3D-EEM data of DOM were used as the input of CNN, parallel factor analysis (PARAFAC) and fluorescence region integral (FRI), and dissolved organic carbon data were used as the input of dissolved organic carbon linear prediction. The prediction model based on CNN had the best effect (R2 = 0.987). The results show that this method can effectively identify the spectral characteristics and predict the decomposition rate of straw under different conditions of water and fertilizer, which is helpful to promote the efficient decomposition of straw.

Remote sensing identification of urban water pollution source types using hyperspectral data

Owing to accelerated urbanisation, increased pollutants have degraded urban water quality. Timely identification and control of pollution sources enable relevant departments to effectively perform water treatment and restoration. To achieve this goal, a remote sensing identification method for urban water pollution sources applicable to unmanned aerial vehicle (UAV) hyperspectral images was established. First, seven fluorescent components were obtained through three-dimensional excitation-emission matrix fluorescence spectroscopy of dissolved organic matter (DOM) combined with parallel factor analysis. Based on the hierarchical cluster analysis of the seven fluorescence components and three spectral indices, four pollution source (PS) types were determined, namely, domestic sewage, terrestrial input, agricultural and algal, and industrial wastewater sources. Second, several water colour and optical parameters, including the absorption coefficient of chromophoric DOM at 254 nm, humification index, chlorophyll-a concentration, and hue angle, were utilised to develop an identification method with a recognition accuracy exceeding 70% for the four PSs that is suitable for UAV hyperspectral data. This study demonstrated the potential of identifying PSs by combining the fluorescence characteristics of DOM with the optical properties of water, thus expanding the application of remote sensing technologies and providing more comprehensive and reliable information for urban water quality management.

An integrated risk assessment framework using information theory-based coupling methods for basin-scale water quality management: A case study in the Danjiangkou Reservoir Basin, China

As the second largest reservoir in China, the Danjiangkou Reservoir (DJKR) serves as the water source of the Middle Route of the South-to-North Water Diversion Project of China (MRSNWDPC), i.e., the currently longest (1273 km) inter-basin water diversion project in the world, for more than eight years. The water quality status of the DJKR basin has been receiving worldwide attention because it is related to the health and safety of >100 million people and the integrity of an ecosystem covering >92,500 km2. In this study, basin-scale water quality sampling campaigns were conducted monthly at 47 monitoring sites in river systems of the DJKRB from the year 2020 to 2022, covering nine water quality indicators, i.e., water temperature (WT), pH, dissolved oxygen (DO), permanganate index (CODMn), five-day biochemical oxygen demand (BOD5), ammonia nitrogen (NH3-N), total phosphorus (TP), total nitrogen (TN), and fluoride (F-). The water quality index (WQI) and multivariate statistical techniques were introduced to comprehensively evaluate water quality status and understand the corresponding driving factors of water quality variations. An integrated risk assessment framework simultaneously considered intra and inter-regional factors using information theory-based and the SPA (Set-Pair Analysis) methods were proposed for basin-scale water quality management. The results showed that the water quality of the DJKR and its tributaries stably maintained a "good" status, with all the average WQIs >60 of river systems during the monitoring period. The spatial variations of all WQIs in the basin showed significantly different (Kruskal-Wallis tests, P < 0.01), while no seasonal differences were found. The increase in built-up land use and agricultural water consumption revealed the highest contributions (Mantel's r > 0.5, P < 0.05) to the rise of nutrient loadings of all river systems, showing the intensive anthropogenic activities can eclipse the power of natural processes on water quality variations to some extent. The risks of specific sub-basins that may cause water quality degradation on the MRSNWDPC were effectively quantified and identified into five classifications based on transfer entropy and the SPA methods. This study provides an informative risk assessment framework that was relatively easy to be applied by professionals and non-experts for basin-scale water quality management, thus providing a valuable and reliable reference for the administrative department to conduct effective pollution control in the future.

Field evidences of fluorescent dissolved organic matter (FDOM) as potential fingerprints for agricultural and urban sources in river environment

Field evidences of the fluorescence differences between agricultural and urban river reaches are still lack. In this study, the middle reaches of Danhe River (DH) and Mihe River (MH) in Shouguang, China, were designed as agricultural and urban river reaches, respectively, to compare the the fluorescence differences in disparate river reaches using excitation-emission matrix coupled with parallel factor analysis (EEM-PARAFAC). Three fluorescence components were identified. C1 (Ex/Em=230,255,295 nm/420 nm) was categorized as humic-like fluorophores, C2 (Ex/Em=230,275 nm/330 nm) was recognized as tryptophan-like substances, and C3 (Ex/Em=215 nm/290 nm) was noted as tyrosine-like and phenylalanine-like compounds. The results showed that the FDOM posed significant differences between agricultural and urban river reaches (P < 0.001). The monitoring sites in DH were rich in C2 (1.90 ± 0.62 Raman Unit (RU), mean ± standard deviation), and the monitoring sites in MH were rich in C3 (1.32 ± 0.51 RU). Redundancy analysis revealed that C2 could be regarded as a fluorescence indicator of agricultural sewage in river environment, while C3 was recognized as a fluorescence indicator of domestic sewage in river environment. In conclusion, this study provided field evidences of FDOM as potential fingerprints for agricultural and urban sources in river environment.

How does anthropogenic activity influence the spatial distribution of dissolved organic matter in rivers of a typical basin located in the Loess Plateau, China

River ecosystems interact strongly with adjacent terrestrial environments and receive dissolved organic matter (DOM) from a variety of sources, all of which are vulnerable to human activities and natural processes. However, it is unclear how and to what extent human and natural factors drive DOM quantity and quality changes in river ecosystems. Here, three fluorescence components were identified via optical techniques, including two humic-like substances and one protein-like component. The protein-like DOM was mainly accumulated in anthropogenically impacted regions, while humic-like components exhibit the opposite trend. Furthermore, the driving mechanisms of both natural and anthropogenic factors on the variations in DOM composition were investigated using partial least squares structural equation modelling (PLS-SEM). Human activities, especially agriculture, positively influence the protein-like DOM directly by enhancing anthropogenic discharge with protein signals and also indirectly by affecting water quality. Water quality directly influences the DOM composition by stimulating in-situ production through a high nutrient load from anthropogenic discharge and inhibiting the microbial humification processes of DOM due to higher salinity levels. The microbial humification processes can also be restricted directly by a shorter water residence time during the DOM transport processes. Furthermore, protein-like DOM was more sensitive to direct anthropogenic discharge than indirect in-situ production (0.34 vs. 0.25), especially from non-point source input (39.1%), implying that agricultural industry optimization may be an efficient way to improve water quality and reduce protein-like DOM accumulation.

Ecological water replenishment to the Yongding River, China: effects of different water sources on inorganic ions and organic matter characteristics

Ecological water replenishment is considered to be an important project to adjust river ecosystems with water shortages and degradation, and its impact on the water quality of the target river section deserves attention. By investigating the characteristics of inorganic ions and organic components of the Beijing section of the Yongding River (YDR) from upstream to downstream, the sources of inorganic ions and dissolved organic matter (DOM) during an ecological water replenishment event were analysed and discussed. This study illustrated the hydrochemical response to different supplemental water sources in three sections of the YDR (mountain gorge section (MGS), urban plain section (UPS), and suburb plain section (SPS)). The results showed that the total dissolved solids (TDS) and ion concentrations were significantly different (p < 0.001) in the three river sections due to different supplemental water sources. The highest concentration of TDS was found in the UPS (870.92 mg/L) replenished by reclaimed water, while the lowest concentration of TDS was found in the SPS (306.95 mg/L) replenished by the water of the South-to-North Water Diversion Project (SNWD). Despite the differences in the water sources of replenishment, the optical parameters of DOM and fluorescent components showed similar protein-like dominated endogenous source characteristics in the three river sections of the YDR, which was due to the influence of reservoir water (MGS and SPS) and reclaimed water (UPS). Our study emphasizes the importance of understanding the impact of different water sources on the water replenishment process, which provides a scientific reference for the management of ecological water replenishment.

Using fluorescence index (FI) of dissolved organic matter (DOM) to identify non-point source pollution: The difference in FI between soil extracts and wastewater reveals the principle

Traceability and quantification of agricultural non-point source pollution are of great significance to water pollution management in watersheds. In this study, fluorescence components and indices of dissolved organic matter (DOM) in the river, wastewater and soil extracts from different land use types were analyzed to screen indicators that can identify non-point source pollution in 15 independent small watersheds located at the southern Qinling piedmont (China). The results showed that DOM fluorescence components in soil extracts among different land uses didn't have significant differences. The values of humification index (HIX) did not vary obviously between soil extracts and wastewater, with the mean values ranging from 3.4 to 3.9. However, the average value of fluorescence index (FI) of effluent wastewater was about 2.1 and did not change significantly through treatment. The FI values of soil extracts were generally between 1.5 and 1.7. The FI values in most river waters were just between the FI values of wastewater and soil extracts. This phenomenon indicated that FI could be used as an indicator to distinguish point source and non-point source pollution. Besides, the correlation analysis showed a significant positive relationship between the non-point source pollution calculated by FI and δ¹⁵N. The relationship was different in January and July, but further confirmed the reliability of using FI to quantify non-point source pollution. This study demonstrated the feasibility of using FI to identify non-point source pollution. When combined with handheld fluorescence spectrometers and unmanned aerial vehicle-mounted fluorescence spectrometers, this method may be adopted more widely.

Innovative approach to reveal source contribution of dissolved organic matter in a complex river watershed using end-member mixing analysis based on spectroscopic proxies and multi-isotopes

Dissolved organic matter (DOM) in river watersheds dynamically changes based on its source during a monsoon period with storm event. However, the variations in DOM in urban and rural river watersheds that are dominated by point and non-point sources have not been adequately explored to date. We developed an innovative approach to reveal DOM sources in complex river watershed systems during pre-monsoon, monsoon, and post-monsoon periods using end-member mixing analysis (EMMA) by combining multi-isotope values (δ¹³C-DOC, δ¹⁵N-NO₃ and δ¹⁸O-NO₃) and spectroscopic indices (fluorescence index [FI], biological index [BIX], humification index [HIX], and specific UV absorbance [SUVA]). Several potential end-members of DOM sources were collected from watersheds, including top-soils, groundwater, plant group (fallen leaves, riparian plants, suspended algae), and different effluents (cattle and pig livestock, agricultural land, urban, industry facility, swine treatment facility and wastewater treatment facility). Concentrations of dissolved organic carbon, dissolved organic nitrogen, NO₃-N, and NH₄-N increased during the monsoon period with an increase in the input of anthropogenic DOM, which have higher HIX values owing to the flushing effect. The results of EMMA indicate that soil and agricultural effluents accounted for a substantial contribution of anthropogenic DOM at varying rates based on seasons. We also found that results of EMMA based on combining spectroscopic indices and δ¹³C-DOC isotope values were more accurate in tracing DOM sources with respect to land-use characteristics compared to applying only spectroscopic indices. The positive relationship between FI, BIX and δ¹⁵N-NO₃ were revealed that nitrate would be decomposed from DOM affected by intensive agricultural activities. In addition, consistent with the EMMA results, the molecular composition of the DOM was clearly evidenced by a large number of CHON formulas, accounting for over 50% of the total characterized compounds, including pesticides and pharmaceuticals used in agriculture farmland and livestock. Our results clearly demonstrated that EMMA based on combing multi-stable isotopes and spectroscopic indices could be trace the DOM source, which is important for understanding changes in the DOM quality, and application of nitrate isotopes and molecular analysis supports in-depth interpretation. This study provides easy and intuitive techniques for the estimation of the relative impacts of DOM sources in complex river watersheds, which can be verified in various ways rather than relying on a single technique approach.

Predicting phosphorus and nitrate loads by using SWAT model in Vamanapuram River Basin, Kerala, India

Evaluations of probable environmental impacts of point and diffuse source pollution at regional sizes are essential to achieve sustainable development of natural resources such as land and water. This research focused on how nitrate and phosphorus load varied over time and space in the Vamanapuram River Basin (VRB). Phosphorus and nitrate loads have been evaluated in the VRB using the semi-distributed Soil and Water Assessment Tool (SWAT) hydrological model. SWAT Calibration and Uncertainty Programs (SWAT-CUP) have simulated the developed model using the Sequential Uncertainty Fitting, version 2(SUFI-2). The developed model was simulated for 2001 to 2008, and it was split into two-phase calibration and validation phases. Model performance was evaluated by the percentage of bias (PBAIS) and Nash-Sutcliffe efficiency coefficient (NSE). The simulated performance of nitrate was indicated as NSE = 0.22-0.59 and PBIAS = 51.86-65.88. The simulated performance of phosphorus showed NSE = 0.06-0.33 and PBIAS = 15.14-33.97. Total Phosphorus load was most sensitive to the organic Phosphorus enrichment ratio (ERORGP) and CH_N2 for streamflow simulation. This study concluded that the South-western region was a high potential for nutrient loads. This study will explain the nutrient load and guidelines for land management practice in the study area.

Synthesis of Mesoporous SiO2 coating containing chlorine phenol formaldehyde resin (Cl-PFR) composites for effective fingerprint detection

As a kind of non-metals fluorescent reagent, the containing chlorine phenol-formaldehyde resin (Cl-PFR) nanoparticles (NPs) were synthesized with the facile method. The as-synthesized Cl-PFR nanoparticles can emit strong green fluorescence emission under the irradiation of 365nm UV light. Since mesoporous silica nanoparticles (MSNs) NPs have a large specific area, strong adsorption, and uniform dispersion, the MSN coating Cl-PFR composites were prepared by mixing Cl-PFR and MSN NPs together. Thus, the as-synthesized multifunctional composites combine the advantages of green fluorescence Cl-PFR, and strong adhesion MSN was applied to detect the potential fingerprint. Different bases fingerprints (glass, paper, aluminum sheets, rough stones, tape) can be clearly observed in the presence of the Cl-PFR@MSN-NH₂ composites. Furthermore, the aging three months and washed with water several times fingerprint can also be clearly displayed with the multifunctional composites. This study provided a simple, economical, and non-toxic fluorescent reagent for the application in fingerprint detection.

The Distribution of DOM in the Wanggang River Flowing into the East China Sea

Dissolved organic matter (DOM) is a central component in the biogeochemical cycles of marine and terrestrial carbon pools, and its structural features greatly impact the function and behavior of ecosystems. In this study, the Wanggang River, which is a seagoing river that passes through Yancheng City, was selected as the research object. Three-dimensional (3D) fluorescence spectral data and UV−visible spectral data were used for component identification and source analysis of DOM based on the PARAFAC model. The results showed that the DOM content of the Wanggang River during the dry season was significantly higher than during the wet season; the DOM content increased gradually from the upper to lower reaches; the proportion of terrigenous components was higher during the wet season than during the dry. UV−Vis spectral data a280 and a355 indicated that the relative concentrations of protein-like components in the DOM of the Wanggang River were higher than those of humic-like components, and the ratio of aromatic substances in the DOM of the Wanggang River water was higher during the wet season. The DOM in the Wanggang River was dominated by protein-like components (>60%), and the protein-like components were dominated by tryptophan proteins (>40%). This study showed that the temporal and spatial distributions of DOM in rivers can be accurately determined using 3D fluorescence spectroscopy combined with the PARAFAC model. This provides useful insight into the biogeochemical process of DOM in rivers of coastal areas.

The Impacts of Precipitation on Fluorescent Dissolved Organic Matter (FDOM) in an Urban River System

Precipitation is considered a key factor influencing the fluorescent dissolved organic matter (FDOM) of urban rivers. However, the multiple effects of precipitation on FDOM in urban rivers and the long-term impacts of precipitation on the spatial patterns of FDOM are seldom known. Spatiotemporal variations of FDOM at 36 sites from the urban rivers of Jinan City during dry and wet seasons were investigated in this study. Four components were identified using an excitation–emission matrix and parallel factor analysis. Overall, the total fluorescence intensities in dry and wet seasons ranged from 6.59 to 35.7 quinine sulfate units (QSU) and 3.42 to 69.3 QSU, respectively. Significant variations were found for different components that C2 and C3 declined but C4 increased in the wet season (p < 0.05). The temporal variations for different components could be explained by the different combined effects of precipitation dilution and flushing. Three different reference FDOM sources, including background water, spring water, and wastewater treatment plant (WWTP) outlets, were illustrated using principal coordinate analysis (PCoA). The places of FDOM in most sites were more closed to the PCoA location of WWTP outlets in the dry season while central shifted in the wet season. The changes of FDOM sources in the wet season could be explained by the mixed effect of precipitation. In conclusion, this study provided new insights into the multiple impacts of precipitation on FDOM in urban river systems, and also data support for precise pollution discharge and water resource management.

A 3D Fluorescence Classification and Component Prediction Method Based on VGG Convolutional Neural Network and PARAFAC Analysis Method

Three-dimensional fluorescence is currently studied by methods such as parallel factor analysis (PARAFAC), fluorescence regional integration (FRI), and principal component analysis (PCA). There are also many studies combining convolutional neural networks at present, but there is no one method recognized as the most effective among the methods combining convolutional neural networks and 3D fluorescence analysis. Based on this, we took some samples from the actual environment for measuring 3D fluorescence data and obtained a batch of public datasets from the internet species. Firstly, we preprocessed the data (including two steps of PARAFAC analysis and CNN dataset generation), and then we proposed a 3D fluorescence classification method and a components fitting method based on VGG16 and VGG11 convolutional neural networks. The VGG16 network is used for the classification of 3D fluorescence data with a training accuracy of 99.6% (as same as the PCA + SVM method (99.6%)). Among the component maps fitting networks, we comprehensively compared the improved LeNet network, the improved AlexNet network, and the improved VGG11 network, and finally selected the improved VGG11 network as the component maps fitting network. In the improved VGG11 network training, we used the MSE loss function and cosine similarity to judge the merit of the model, and the MSE loss of the network training reached 4.6 × 10−4 (characterizing the variability of the training results and the actual results), and we used the cosine similarity as the accuracy criterion, and the cosine similarity of the training results reached 0.99 (comparison of the training results and the actual results). The network performance is excellent. The experiments demonstrate that the convolutional neural network has a great application in 3D fluorescence analysis.