Rapid monitoring of heavy metal pollution in lake water using nitrogen and phosphorus nutrients and physicochemical indicators by support vector machine

Spatio-temporal evolution mechanism and dynamic simulation of nitrogen and phosphorus pollution of the Yangtze River economic Belt in China

Excess nitrogen and phosphorus inputs are the main causes of aquatic environmental deterioration. Accurately quantifying and dynamically assessing the regional nitrogen and phosphorus pollution emission (NPPE) loads and influencing factors is crucial for local authorities to implement and formulate refined pollution reduction management strategies. In this study, we constructed a methodological framework for evaluating the spatio-temporal evolution mechanism and dynamic simulation of NPPE. We investigated the spatio-temporal evolution mechanism and influencing factors of NPPE in the Yangtze River Economic Belt (YREB) of China through the pollution load accounting model, spatial correlation analysis model, geographical detector model, back propagation neural network model, and trend analysis model. The results show that the NPPE inputs in the YREB exhibit a general trend of first rising and then falling, with uneven development among various cities in each province. Nonpoint sources are the largest source of land-based NPPE. Overall, positive spatial clustering of NPPE is observed in the cities of the YREB, and there is a certain enhancement in clustering. The GDP of the primary industry and cultivated area are important human activity factors affecting the spatial distribution of NPPE, with economic factors exerting the greatest influence on the NPPE. In the future, the change in NPPE in the YREB at the provincial level is slight, while the nitrogen pollution emissions at the municipal level will develop towards a polarization trend. Most cities in the middle and lower reaches of the YREB in 2035 will exhibit medium to high emissions. This study provides a scientific basis for the control of regional NPPE, and it is necessary to strengthen cooperation and coordination among cities in the future, jointly improve the nitrogen and phosphorus pollution tracing and control management system, and achieve regional sustainable development.

Industrial effluents and N-nitrosamines in karst aquatic systems: a study on distribution and ecological implications

The discharge of electroplating wastewater, containing high concentrations of N-nitrosamines, poses significant risks to human health and aquatic ecosystems. Karst aquatic environment is easily impacted by N-nitrosamines due to the fragile surface ecosystem. However, it's still unclear in understanding N-nitrosamine transformation in karst water systems. To explore the response and transport of nine N-nitrosamines in electroplating effluent within both karst surface water and groundwater, different river and groundwater samples were collected from both the upper and lower reaches of the effluent discharge areas in a typical karst industrial catchment in Southwest China. Results showed that the total average concentrations of N-nitrosamines (∑NAs) in electroplating effluent (1800 ng/L) was significantly higher than that in the receiving river water (130 ng/L) and groundwater (70 ng/L). The dynamic nature of karst aquifers resulted in comparable average concentrations of ∑NAs in groundwater (70 ng/L) and river water (79 ng/L) at this catchment. Based on the principal component analysis and multiple linear regression analysis, the electroplating effluent contributed 89% and 53% of N-nitrosamines to the river water and groundwater, respectively. The results based on the species sensitivity distribution model revealed N-nitrosodibutylamine as a particularly toxic compound to aquatic organisms. Furthermore, the average N-nitrosamine carcinogenic risk was significantly higher in lower groundwater reaches compared to upper reaches. This study represents a pioneering effort in considering specific N-nitrosamine properties in evaluating their toxicity and constructing species sensitivity curves. It underscores the significance of electroplating effluent as a primary N-nitrosamine source in aquatic environments, emphasizing their swift dissemination and significant accumulation in karst groundwater.

Predictions of heavy metal concentrations by physiochemical water quality parameters in coastal areas of Yangtze river estuary

Due to the degradation-resistant and strong toxicity, heavy metals pose a serious threat to the safety of water environment and aquatic ecology. Rapid acquisition and prediction of heavy metal concentrations are of paramount importance for water resource management and environmental preservation. In this study, heavy metal concentrations (Cr, Ni, Cu, Pb, Zn, Cd) and physicochemical parameters of water quality including Temperature (Temp), pH, Oxygen redox potential (ORP), Dissolved oxygen (DO), Electrical conductivity (EC), Electrical resistivity (RES), Total dissolved solids (TDS), Salinity (SAL), Cyanobacteria (BGA-PE), and turbidity (NTU) were measured at seven stations in the Yangtze river estuary. Principal Component Analysis (PCA) and Spearman correlation analysis were employed to analyze the main factors and sources of heavy metals. Results of PCA revealed that the main sources of Cr, Ni, Zn, and Cd were steel industry wastewater, domestic and industrial sewage, whereas shipping and vessel emissions were typically considered sources of Pb and Cu. Spearman correlation analysis identified Temp, pH, ORP, EC, RES, TDS, and SAL as the key physicochemical parameters of water quality, exhibiting the strongest correlation with heavy metal concentrations in sediment and water samples. Based on these results, multiple linear regression as well as non-linear models (SVM and RF) were constructed for predicting heavy metal concentrations. The results showed that the results of the nonlinear model were more suitable for predicting the concentrations of most heavy metals than the linear model, with average R values of the SVM test set and RF test set being 0.83 and 0.90. The RF model showed better applicability for simulating the concentration of heavy metals along the Yangtze river estuary. It was demonstrated that non-linear research methods provided efficient and accurate predictions of heavy metal concentrations in a simple and rapid manner, thereby offering decision-making support for watershed managers.

Interannual variation and machine learning simulation of organophosphate esters in Taihu Lake

Organophosphate esters (OPEs) are widespread in water bodies and have attracted public attention due to their hazards. This study investigated the presence of OPEs in surface water of Taihu Lake from 2012 and 2021-2022. The OPEs concentration was compared ten years ago and ten years later. Water and meteorological parameters were ranked using the random forest (RF) model, and OPEs concentration in lakes was simulated using selected parameters as inputs. The concentration of Σ7OPEs was higher ten years ago compared to ten years later. There was no significant seasonal difference in Σ7OPEs from 2021-2022, while the concentration of Σ7OPEs in 2012 was lower in summer than in other seasons. The spatial distribution of the two interannual Σ7OPEs exhibited a decreasing trend from the northwest region. The results of RF importance ranking and redundancy analysis showed that NH3-N, TN, TP, water temperature and relative humidity were the most influential factors affecting OPEs concentrations. RF models performed better for TnBP, as indicated by training R and test R values are excellent and relatively low errors. Our results demonstrated that machine learning models were useful in facilitating efficient monitoring and assessment of OPEs contamination in lakes.

Estimating the temporal and spatial distribution and threats of bisphenol A in temperate lakes using machine learning models

Bisphenol A (BPA) is easily enriched in many human-disturbed watersheds, particularly lakes with poor water mobility, which is posing a threat to aquatic biota. While previous studies have focused on the concentration of BPA in water and its toxicity to aquatic organisms, a small amount of measured data is not enough to reveal the temporal and spatial distribution and threats of BPA, and estimate the ecological risk in watersheds. Therefore, we collected 164 measured BPA data points from Taihu Lake to develop machine learning models using random forest (RF), support vector machine (SVM) and least square regression (LSR) and created month-by-month watershed prediction maps in temperate lakes to estimate the spatiotemporal distribution and threats of BPA. Due to RF's superior robustness to noisy data, the RF model exhibits the best performance among the three algorithms. The RF model showed acceptable predictive performance on the modeling dataset (coefficients of determination and root-mean-square error for the training set were 0.927 and 17.499, respectively, and 0.607, 39.645 for the validation set, respectively). The maps indicated that areas susceptible to anthropogenic activities were more severely polluted by BPA, and rainy climate may favor the migration of BPA to aquatic ecosystems. The model was also applied to predict 42 data points of BPA collected from Dianchi Lake, and the results showed that most predicted data were within a factor of 10 of the measured data, but the prediction accuracy of the model has declined. The ecological risks in the two lakes were evaluated and attention should be paid to the regions with higher risks. Our study provided a novel idea for comprehensive monitoring of an unconventional trace pollutant with endocrine disrupting effects in aquatic ecosystems and analyzing their spatiotemporal distribution, which will contribute to the scientific assessment of the ecological risk of BPA.

Synthesis, Sensing Performance and DFT Studies of a Novel Coumarin-based Schiff Base As a Turn-on Fluorescence Probe for Zinc Ion Detection

The design and development of novel efficient fluorescent chemical sensors for the selective detection of ions is currently of significant importance in supramolecular chemistry. Since zinc is a ubiquitous, indispensable and the second most abundant metal ion in the human body, developing chemosensors that can accurately discriminate between Zn2+ and Cd2+ ions has been a challenge due to their similar properties as they are in the same group of the periodic table. Therefore, a technique to trace and visualize free zinc ions is demanded. In this study, an innovative coumarin-based Schiff base (L) was synthesized and characterized by 1H NMR, 13C NMR and mass spectroscopy. A novel "Turn On" fluorescence chemosensor platform was developed for trace amounts of Zn2+ ions. The fluorescence Job's plot measurement was used to determine the complexation ratio between the probe and Zn2+ ion, which showed a maximum point indicating the formation of a ML2 adduct. Additionally, the geometrical parameters calculated using DFT and TD-DFT calculations were in close agreement with the experimentally observed values.

Evidence of elevated heavy metals concentrations in wild and farmed sugar kelp (Saccharina latissima) in New England

Seaweed farming in the United States is gaining significant financial and political support due to prospects to sustainably expand domestic economies with environmentally friendly products. Several networks are seeking appropriate synthesis of available science to both inform policy and substantiate the sector's sustainability claims. Significant knowledge gaps remain regarding seaweed-specific food hazards and their mitigation; a resource-intensive challenge that can inhibit sustainable policies. This is particularly concerning for rapidly expanding Saccharina latissima (sugar kelp) crops, a brown seaweed that is known to accumulate heavy metals linked to food hazards. Here, we present baseline information about concentrations of arsenic, cadmium, lead, and mercury, in both wild and farmed sugar kelp from the New England region. We interpret our findings based on proximity to potential sources of contamination, location on blade, and available heavy metals standards. Contrary to our expectations, high concentrations were widespread in both wild and farmed populations, regardless of proximity to contamination. We find, like others, that cadmium and arsenic consistently reach levels of regulatory concern, and that dried seaweeds could harbor higher concentrations compared to raw products. We also share unique findings that suggest some toxins concentrate at the base of kelp blades. Our results are one step towards aggregating vital data for the region to expand its seaweed farming footprint.

Surface water quality for irrigation and industrial purposes: a comparison between the south and north sides of the Wei River Plain (northwest China)

Surface water is extensively used for irrigation and industrial purposes in the Wei River Plain. However, the surface water shows different characteristics in the southern and northern zones of the Wei River Plain. This study aims to investigate the differences in surface water quality between the southern and northern zones of the Wei River Plain and their influencing factors. To ascertain the hydrochemistry and its governing factors, graphical methods, ion plots, and multivariate statistical analyses were employed. The quality of the irrigation water was assessed using various irrigation water quality indices. In addition, water foaming, corrosion, scaling, and incrustation risks were determined to evaluate water quality for industrial uses. The spatial distribution of water quality was done using GIS models. This research revealed that the concentrations of EC, TH, TDS, HCO₃^-, Na⁺, Mg²⁺, SO₄^2- and Cl^- on the north side of the plain were twice as high as those on the south side. On both sides of the Wei River Plain, water‒rock interactions, ion exchange, and considerable evaporation were observed. Gypsum, halite, calcite, and dolomite all dissolve to produce significant anions and cations in the water, according to ion correlation analysis. However, additional sources of contaminants led to higher concentrations in the surface water on the north side than on the south side. Surface water in the south of the Wei River Plain has superior quality to that in the north, according to the overall findings of irrigation water and industrial water quality assessments. The findings of this study will boost better water resource management policies for the plain.

Improving the efficiency of machine learning in simulating sedimentary heavy metal contamination by coupling preposing feature selection methods

Sediment cores were collected from Taihu Lake in China. The chronology was determined by radionuclide. Heavy metals and magnetic properties of each core slice were assessed, respectively. The concentrations of most heavy metals in sediments surged at 20 cm from the surface, accompanying the increase in the concentrations of single-domain magnetic particles. This may be resulted from the influence of anthropic activities on the lake's environment after the 1970s. Two feature selection methods, random forest (RF) and maximal information coefficient (MIC), were combined with support vector machine (SVM) model to simulate heavy metals, with the inclusion of selected magnetic and physicochemical parameters. Compared with the modeling results obtained with the full set of parameters, a reasonable simulation performance was obtained with RF and MIC. RF performed better than MIC by increasing the R² of simulation models for Cd, Cr, Cu, Pb, and Sb. For heavy metals with high ecological risks (As, Cd, Cr, Hg, Pb, Sb), the correlation coefficients for observed and predicted data ranged from 0.73 to 0.97 with only 14-27% of the parameters selected by RF as input variables. The RF-RBF-SVM enabled heavy metal predictions based on the magnetic properties of the lake sediments.

A soft sensor for simulating algal cell density based on dynamic response to environmental changes in a eutrophic shallow lake

There is a great need for timely monitoring and rapid water quality assessment to control the algal blooms that often occur in eutrophic lakes. While algal cell density (ACD) is a critical indicator of algal growth, field monitoring is laborious and time-consuming, and rapid assessment of algal blooms based on ACD is often not possible. To address the limitations of conventional ACD detection, we proposed a soft sensor approach that uses surrogate indicators to simulate ACD in machine learning models. We conducted a case study using monitoring data from Chaohu Lake collected between 2016 and 2019. We found that ensemble learning models, especially extreme gradient boosting (XGBoost), outperformed traditional machine learning algorithms by comparing various machine learning algorithms. Also, considering the influence of input variable selection on model performance, we combined the results of different filter methods in the multi-stage variable selection process. Finally, we screened out seven key variables out of the 43 initial candidate variables, including dissolved oxygen (DO), chlorophyll-a (Chl-a), Secchi disk depth (SD), pH, permanganate index (COD_Mn), week of the year (WOY), and wind velocity (WV). Their inclusion substantially improved data accessibility and supported the development of a rapid simulation model. The final model was capable of reliable spatiotemporal generalization, with an overall R² value of 0.761. On the theoretical side, our study makes a new attempt to simulate ACD values in a eutrophic lake. For practical purposes, the soft sensor can facilitate the rapid assessment of bloom conditions, which helps the local administration with emergency prevention and control.

A sensitive and rapid determination of zinc ion (Zn2+) using electrochemical sensor based on f-MWCNTs/CS/PB/AuE in drinking water

An electrochemical method for detecting the presence of zinc (Zn²⁺) ions in drinking water was developed using functionalized multi-walled carbon nanotubes (f-MWCNTs) and chitosan (CS). Numerous cylinder-shaped graphene molecules make up f-MWCNTs, which have a high mechanical and electrical conductivity. CS benefits from nanomaterials include biocompatibility, biodegradability, and low toxicity, which are excellent in capacity absorption of metal ions. Dangerous levels of metal ions such as zinc are currently present in drinking water as a result of human and natural activity. Zinc toxicity is associated with a variety of disorders, including Alzheimer's, Parkinson's, diabetes, and cancer. This study incorporated f-MWCNTs and CS with Prussian blue (PB) immobilised on a gold electrode (AuE). Several parameters, including as buffers, pH, scan rate, redox indicator, accumulation time, and volume, were optimised using the cyclic voltammetry (CV) method. According to the CV method, the optimal parameters were phosphate buffered saline (0.1 M, pH 2), 5 mM Prussian blue, 200 mVs^-1 scan rate, and 5 s accumulation time. Under ideal circumstances, the differential pulse voltammetry (DPV) method was used to determine the Zn²⁺ ions concentration range of 0.2-7.0 ppm. The limit of detection (LOD) was 2.60 × 10^-7 mol L^-1 with a correlation coefficient of R² = 0.9777. The recovery rate of the developed sensor (f-MWCNTs/CS/PB/AuE) ranged from 95.78 to 98.96%. The developed sensor showed a variety of advantages for detecting Zn²⁺ in drinking water, including a quick setup process, quick detection, high sensitivity, and mobility. This study developed the essential sensor for monitoring Zn²⁺ levels in drinking water in the future.

Regional-scale investigation of the molecular weight distribution and metal-binding behavior of dissolved organic matter from a shallow macrophytic lake using multispectral techniques

In this work, based on excitation-emission matrix spectroscopy combined with parallel factor analysis (EEM-FARAFAC) and two-dimensional correlation analysis of synchronous fluorescence spectroscopy, Fourier transform infrared spectroscopy, and combination of two spectra (2D-SF-COS, 2D-FTIR-COS, and Hetero-2D-COS), the characteristics and metal-binding behaviors of DOM in Dongping Lake were explored for molecular weight (MW), fluorescence components, and functional groups. The results showed that the entire lake was governed by protein-like materials with low MW(< 1 kDa). The complexation occurred preferentially in protein-like materials for bulk DOM after adding copper (Cu²⁺) and lead (Pb²⁺), which were changed by fractionation for MW. The active points were aliphatic C－OH for DOM-Cu and phenol －OH or polysaccharide for DOM-Pb from 2D-FTIR-COS. The protein-like components possessed higher LogK than humic-like component during binding to Cu²⁺ or Pb²⁺. Moreover, the complexing affinities of DOM-Cu (LogK_Cu: 3.26 ± 0.87-4.04 ± 0.49) were higher than those of DOM-Pb (LogK_Pb: 2.66 ± 0.52-3.78 ± 0.36). On a spatial scale, high LogK_Cu and LogK_Pb were found in the center and entrance of the lake, respectively. Humic-like component C2 in the LMW fraction possessed a stronger binding capacity with Cu²⁺. This study affords new insights into the migration and conversion of HMs in lakes.

Distribution characteristics, source identification, and risk assessment of heavy metals in surface sediments of the salt lakes in the Ordos Plateau, China

Salt lakes considerably affect the regional climate, environment, and ecology of semiarid regions characterized by low rainfall and high evaporation. However, under the stresses of global change and human disturbance, anthropogenic pollution is the primary factor threatening the lake's ecological environment. Surface sediment samples collected from four salt lakes in the Ordos Plateau were used to investigate the salinity, concentration, pollution status, potential sources of heavy metals, and influencing factors. The surface sediments of Beida Pond and Gouchi Pond were weakly alkaline (pH < 9) due to the presence of Na₂SO₄, whereas those of Chaigannaoer and Hongjiannao were strongly alkaline (pH > 9) due to the presence of Na₂CO₃. The concentration range of Cr, Ni, Cu, Zn, As, Cd, and Pb in the sediment samples collected from the salt lakes in the Ordos Plateau followed the order of Cr > Zn > Ni > Pb > Cu > As > Cd. The Cr concentration values were higher in Chagannaoer and Hongjiannao; however, the Ni, Cu, and Zn values were higher in Beida Pond and Gouchi Pond. The geoaccumulation index (I_geo) and enrichment factor (EF) consistently indicated that Cr posed the greatest potential ecological risk and that Ni, Cu, and Zn pollution was more severe in Beida Pond and Gouchi Pond than in Chagannaoer or Hongjiannao. However, the ecological risk index and potential ecological risk value indicated that these heavy metals posed low risks to the environment. The risk assessment code (RAC) revealed that Pb and Cr exhibited no mobility and had low potential bioavailability risk. Meanwhile, Zn, Ni, and As were categorized as medium risk. Cu had the highest mobility and was categorized as high risk. Principal component analysis for the four salt lakes revealed that the source of Ni, Cu, Zn, and Cd might be associated with water-soluble elements associated with aqueous migration, while the source of Cr, Pb, and As might be the lithospheric minerals carried by dust storms. Pearson's correlation analysis indicated that clay minerals were the primary adsorbers of Ni, Cu, Zn, and Cd. Moreover, pH was identified as the main environmental factor controlling the distribution of heavy metals in the salt lakes.

Chronological deposition record of trace metals in sediment cores from Chaohu Lake, Anhui Province, China

Lakes located in the mid-low reaches of the Yangtze River watershed have been subjected to various degrees of human perturbation that would have resulted in toxic metal concentrations and would pose potential risk to the natural habitats. Therefore, in the present study, two sediment cores from Chaohu Lake were collected to determine any such concentration, expressed as the enrichment characteristics of major and trace metals (Al, Fe, Mn, Cu, Pb, Zn, Cr, Cd, As, Hg, and Ni) in response to natural and anthropogenic changes. An approximate 180-year (1840-2021) deposition record of trace metals in sediment cores was obtained on the basis of ¹³⁷Cs and ²¹⁰Pb dating. Enrichment factors (EFs) and the geo-accumulation index (I_geo) were adopted to evaluate the enrichment state and pollution status of trace metals. The results showed that Cu and Zn were persistently more enriched in sediment, whereas Mn, Pb, Cd, and Hg has shown remarkable increasing trends in the west lake since the 1970s. Evaluation of the I_geo confirmed that sediment was moderately polluted with Cu and Zn within the whole lake and with Cd in the west lake area, whereas it was uncontaminated with Cr and slightly contaminated with other metals. Furthermore, source identification based on multivariate statistical analysis including correlation analysis, principal component analysis, and cluster analysis suggested similar pollution sources for the studied metals. Combined with the natural and anthropogenic changes within the watershed, enhanced soil erosion due to population expansion and agricultural intensification was a major contributor to sedimentary metals before the 1970s while industrial wastewater, urban runoff, and domestic sewage were predominant inputs of trace metals after the 1970s.

Study on spatial distribution, potential sources and ecological risk of heavy metals in the surface water and sediments at Shanghai Port, China

Heavy metal concentrations in surface waters and sediments of Shanghai port were measured to analyze the spatial distribution characteristics, sources and pollution degrees. The southern Shanghai port was heavily polluted by Cd, Pb, Cr and Cu in water, and the concentrations of Pb, Hg and Zn in sediments exceeded Effects Range Low. Cu, Pb, Cd and Cr in water were positively correlated in group, and they probably derived from industrial and domestic sewage, fossil fuel burning and vehicular pollution and represented 82.47 % of the contribution. Zn, Hg and As contaminations represented industrial and agricultural effluent sources and accounted for the remaining 17.53 %. Heavy metal pollution of Yangshan Port water was at high pollution, and the remaining sites were at low pollution. Most of the sampling sites were at moderate ecological risk in sediments, and the pollution of Cd, Hg and Zn were serious.

Deep learning model based on urban multi-source data for predicting heavy metals (Cu, Zn, Ni, Cr) in industrial sewer networks

The high concentrations of heavy metals in municipal industrial sewer networks will seriously impact the microorganisms of the activated sludge in the wastewater treatment plant (WWTP), thus deteriorating the effluent quality and destroying the stability of sewage treatment. Therefore, timely prediction and early warning of heavy metal concentrations in industrial sewer networks is crucial. However, due to the complex sources of heavy metals in industrial sewer networks, traditional physical modeling and linear methods cannot establish an accurate prediction model. Herein, we developed a Gated Recurrent Unit (GRU) neural network model based on a deep learning algorithm for predicting the concentrations of heavy metals in industrial sewer networks. To train the GRU model, we used low-cost and easy-to-obtain urban multi-source data, including socio-environmental indicator data, air environmental indicator data, water quantity indicator data, and easily measurable water quality indicator data. The model was applied to predict the concentrations of heavy metals (Cu, Zn, Ni, and Cr) in the sewer networks of an industrial area in southern China. The results are compared with the commonly used Artificial Neural Network (ANN) model. In this study, it was shown that the GRU had better prediction performance for Cu, Zn, Ni, and Cr concentrations, with the average R² significantly increased by 12.35%, 11.94%, 9.21%, and 8.13%, respectively, compared to ANN predictions. The sensitivity analysis based on Shapley (SHAP) values revealed that conductivity (σ), temperature (T), pH, and sewage flow (Flow) contributed significantly to the prediction results of the model. Furthermore, the three input variables including air pressure (AP), land area (A), and population (Pop.) were removed without affecting the prediction performance of the model, which maximized the modeling efficiency and reduced the operational cost. This study provides an economical and feasible technical method for early warning of abnormal heavy metal concentrations in urban industrial sewer networks.

Multi-spectroscopic investigation of the molecular weight distribution and copper binding ability of dissolved organic matter in Dongping Lake, China

The properties and metal-binding abilities of dissolved organic matter (DOM) rely on its molecular weight (MW) structure. In this study, the spatial differences of DOM in compositions, MW structures, and binding mechanisms with copper (Cu²⁺) in Dongping Lake were investigated by applying excitation-emission matrix combining parallel factor analysis (EEM-PARAFAC), synchronous fluorescence (SF) spectra, two-dimensional correlation spectra (2D-COS), and Fourier transform infrared (FTIR) spectra. The EDOM for the entrance of the Dawen River and PDOM for the macrophyte-dominated region were divided from DOM of Dongping Lake based on hierarchical clustering analysis (HCA) and principal component analysis (PCA) and were size-fractioned into MW < 500 kDa and <100 kDa fractions. According to EEM-PARAFAC, Dongping Lake was dominated by tryptophan-like substances with MW < 500 kDa. The concentration of PDOM was higher than that of EDOM (p < 0.05). 2D-COS showed that protein-like components preceded humic-like components binding to Cu²⁺ regardless of sample type (215 nm > 285 nm > 310-360 nm). The Cu²⁺ binding capacity of DOM exhibited specific differences in space, components, and molecular weights. The humic-like component 1 (C1) and tryptophan-like component 4 (C4) of PDOM showed stronger binding abilities than those of EDOM. Endogenous tryptophan-like component 4 (C4) had a higher binding affinity for Cu²⁺ than humic-like components (logK_a: C4 > C1 > C2) in PDOM irrespective of MW. Humic-like components with MW < 500 kDa displayed higher binding potentials for Cu²⁺. FTIR spectra showed that the main participants of DOM-Cu complexation included aromatic hydrocarbons, aliphatic groups, amide Ⅰ bands, and carboxyl functional groups. This study provides spatial-scale insights into the molecular weight structure of DOM in influencing the behavior, fate, and bioavailability of heavy metals in lakes.