Optical property of dissolved organic matter and its correlation with heavy metals in surface water around metal mines to be exploited in southern Anhui Province, China

Agricultural activities and industrial production already significantly impact surface water quality (Wang et al. 2017; Yu et al. 2022). Heavy metals have generated wide concern as pollutants due to their high toxicity, persistence, and refractory degradation (Liu et al. 2022). In mining activities, many heavy metal elements will be released into the surrounding aquatic ecosystems, which will cause harm to human health and the aquatic ecosystem (Chen et al. 2019; Jiang et al. 2021).

Dissolved organic matter (DOM) is a class of organic mixtures with complex structures ubiquitous in rivers, lakes, and ponds, and can directly participate in multiple physicochemical and biogeochemical processes (Dong et al. 2021). In addition, DOM is the world's largest organic carbon pool and plays an essential role in the global carbon cycle (Johnson et al. 2006). DOM can be used as an important water quality and biogeochemical cycle tracer (Lyu et al. 2021). Studies have shown that DOM can form complexes with heavy metals, affecting the form and bioavailability of heavy metals, affecting water quality (Ren et al. 2020; Liu et al. 2022). Dong et al. (2021) studied the distribution and interaction of DOM and heavy metals (Fe, Mn, and Cu) in shallow groundwater in Guanzhong Plain. DOM has a significant impact on the migration and transformation of Mn in groundwater. Yi et al. (2019) analyzed the relationship between DOM and heavy metals in the water of Dianchi Lake and found that DOM and heavy metals have complex effects. Hongxia et al. (2014) using excitation–emission matrix fluorescence spectroscopy (EEM) was combined with parallel factor analysis to analyze the fluorescence components of DOM in surface water of rare earth mining areas, finding there was a significant positive correlation between protein-like components and rare earth metals.

In recent years, due to the continuous development of spectroscopy technology, EEM technology has been widely used in the study of the fluorescence characteristics of DOM in natural water due to its advantages of convenience and fast testing, fewer water samples required, and online monitoring (Yang et al. 2018; Zhang et al. 2021). However, EEM is usually composed of various types of overlapping fluorophores, and traditional ‘peak-finding’ methods cannot accurately reflect the dynamic changes of DOM (Ji et al. 2019). EEM combined with parallel factor analysis (PARAFAC) can separate fluorophores of different origins from overlapping fluorophores and enable quantitative analysis (Hongxia et al. 2014; Zhang et al. 2021). Therefore, many researchers have used EEM-PARAFAC to study DOM fluorescence components and sources in soils, rivers, lakes, and groundwater (Tang et al. 2020, 2021; Yu et al. 2020; Cui et al. 2021). The mining process of metal mines will produce a large amount of acidic mine wastewater, which contains high concentrations of toxic and harmful heavy metals and affects the fluorescence characteristics of DOM in the catchment (Wang et al. 2020). Mining activities will cause irreversible damage to the surrounding aquatic ecological environment. However, little information is known about the relationship between the optical characteristics of DOM and heavy metals in the surface water around the metal mines to be exploited. Many studies only focus on the impact of mining activities on the environment but ignore the background value of the water environment before mining activities. In addition, studying the fluorescence characteristics of DOM in surface water around the exploited metal mines and its relationship with heavy metals will help to clarify the background concentration of DOM and heavy metals in surface water and the internal relationship between them. At the same time, it will help to clarify the degree of impact on the surrounding surface water after the development and utilization of metal mines.

In the dry season (November 2021), 33 surface water samples (Figure 1(c)) were collected from the Chating mining area, and the coordinates of the sampling points were recorded. The water sample was placed in a polyethylene bottle (500 ml) and washed with ultrapure water. After collection, the water samples were sent back to the laboratory and filtered with a 0.45-μm filter within 24 hours. After filtration, the water samples were stored in a refrigerator at 4 °C, and all tests were completed within 48 hours.

Heavy metals (As, Cr, Cu, Cd, Pb, and Zn) were measured by ICP-MS (ICPMS-2030LF, Shimadzu, USA). The UV-Vis absorbance spectrum of DOM was measured by a UV-Vis spectrophotometer (UV-2700, Shimadzu, USA) with a scanning wavelength of 200–800 nm with an interval of 1 nm, with Milli-Q ultrapure water as a reference. The content of DOM was characterized by the absorption coefficient at the wavelength of 254 nm (a254) (Ren et al. 2021). a254:a365 (E2:E3) was calculated by dividing the absorption coefficient at 254 nm by the absorption coefficient at 365 nm. The spectral slope ratio (SR) was calculated by dividing the spectral slope S275-295 by the spectral slope S350-400. E2:E3 and SR were negatively correlated with the molecular size of DOM (Yang et al. 2018).

EEM were measured using a fluorescence spectrometer (F-4700, Hitachi, Tokyo, Japan). The excitation wavelength (Ex) and emission wavelength (Em) slit widths were set to 5 nm, the excitation wavelength scanning range was 200–400 nm and the emission wavelength scanning range was 220–550 nm. The scanning speed was 10,000 nm/min. Ultrapure water was used as the blank to deduct the scattering.

EEM can provide fluorescence information about DOM and cannot give a quantitative interpretation of the fluorescence signal (Ji et al. 2019). PARAFAC is a decomposition algorithm that decomposes higher order data sets (EEMs of multiple water samples) into individual fluorophores (Feng et al. 2022). PARAFAC using MATLAB 2017b (Mathworks, Natick, MA, USA) was combined with drEEM toolbox (Murphy et al. 2013). The reliability of the model was verified by split-half analysis (Ren et al. 2021).

Statistical analysis of data and plotting are implemented by R Studio (1.4) software to analyze the correlation between heavy metals and the DOM using SPSS 17.0 and filter out the significant correlation levels greater than 0.05 on both sides. Co-occurrence networks are built by Gephi (0.9.2), and the detailed steps of the co-occurrence network construction can be found in other articles (Hu et al. 2021).

a254 is mainly used to characterize the content of the DOM (Ren et al. 2021; Zhang et al. 2021). As shown in Figure 5, the a254 of ponds and rivers in the study area had a good positive correlation with the fluorescent components C1, C2, and C3, which was also consistent with previous relevant studies (Yang et al. 2018; Zhang et al. 2021).

The content characteristics of six heavy metals (Fe, Cu, Zn, As, Cd and Pb) in the surface water are shown in Table 2. The average contents of Fe, Cu, Zn, As, Cd and Pb in the pond water were 176.78, 11.48, 27.07, 0.35, 0.03 and 1.11 μg/L, respectively. The average contents of Fe, Cu, Zn, As, Cd and Pb in the river water were 32.10, 9.84, 17.17, 0.86, 0.0048 and 0.47 μg/L, respectively.

In the study area, ponds and rivers each have a water sample with an Fe content of more than 300 μg/L. Drinking high levels of Fe in water samples can cause harm to human health (Sharma et al. 2021). The coefficient of variation (CV) is an important indicator to describe the degree of dispersion of data. It is generally believed that the higher the CV of pollutants in the environmental medium, the more likely the contaminant may be affected by human activities (Chen et al. 2019; Chen et al. 2022). In this study, Fe in surface water (ponds and rivers) and Cd in ponds in the study area had significant CVs, indicating that these heavy metals may be affected by human activities.

The co-occurrence network analysis results of DOM and heavy metals in river water are shown in Figure 7(b), It can be seen that the relationship between the nodes in the co-occurrence network graph of rivers was more complex than that of ponds. Fe with fluorescent components C1, C2 and C3 showed strong positive correlations, and the correlation coefficients were 0.98, 0.92 and 0.64, respectively. Fe is generally believed to be adsorb and complex with DOM and can effectively prevent microorganisms from decomposing DOM (Dong et al. 2021). There was a weak negative correlation between Cd and SR, and the correlation coefficient is −0.44. The correlation coefficients between the fluorescent components C1, C2 and C3 were all greater than 0.8, indicating that these fluorescent components have the same source.

In this study, we aimed to clarify the optical characteristics of DOM and the content characteristics of heavy metals in the surface water surrounding the metal mine to be exploited. The surface water (rivers and ponds) surrounding the metal mine was taken as the research object. We found that the content of DOM in the river was higher than that in the pond, and that there were significant differences (P < 0.05). In addition, the molecular weight of DOM in river water was smaller than that in ponds. Three fluorescent components (C1, C2 and C3) were obtained by the method EEM-PARAFAC, of which C1 and C2 were humic-like substances, and C3 were protein-like substances, respectively. The content of humus-like substances (C1 and C2) in the river was higher than in the pond. In contrast, the protein-like component C3 had no significant difference between the river and the pond. The a254 and fluorescent components C1 in the surface water of the aquaculture area and the #1 village are more significant than those of other areas indicating that, in addition to agricultural non-point source pollution, aquaculture and village sewage discharge are also potential sources of pollution.

The average contents of Fe, Cu, Zn, As, Cd, and Pb in the pond water were 176.78, 11.48, 27.07, 0.35, 0.03, and 1.11 μg/L, respectively. The average contents of Fe, Cu, Zn, As, Cd, and Pb in the river water were 32.10, 9.84, 17.17, 0.86, 0.0048, and 0.47 μg/L, respectively. One water sample in each pond and the river Fe content exceeded the Chinese surface water quality standard (300 μg/L). The content of As in river water is higher than that in ponds, while the rest of the heavy metals (Fe, Cu, Zn, Cd, and Pb) were higher in ponds than in rivers. The relatively high levels of As in surface water near aquaculture areas suggest that aquaculture may increase As levels.

The results of the co-occurrence network analysis showed that the molecular weight of DOM in pond water had a certain correlation with heavy metals (Cu and Fe), and the content of Fe in river water had a good positive correlation with fluorescent components (C1, C2 and C3). These conclusions will help to clarify the relationship between DOM and heavy metals in the surface water around the metal mines to be exploited.

In general, the results of this study clarified the optical properties of DOM and the background value of heavy metal content in the surface water around the metal mine to be exploited. It will provide background references for evaluating the impact of metal mining on the surface water environment. In addition, due to the significant difference in rainfall between the rainy and dry seasons, DOM optical characteristics and heavy metal content in surface water samples collected in the dry season are bound to be different from those in the rainy season. In the future study, we will collect surface water samples in the rainy season and use multivariate statistical analysis to clarify the background values of DOM and heavy metals in surface water around metal mines to be exploited.

This work was financially supported by the Demonstration Teaching Organization of Anhui Education Department (2020SJSFJXZZ416), the Dualabillity Teaching Team Project of Suzhou University (2020XJSN06), the Green Mine Research Center of Suzhou University (2021XJPT53), and Anhui Coal Mine Exploration Engineering Technology Research Center (2022ykf11).

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.