key: cord-0959509-q5zh5ixx authors: Wang, Leyun; Zhang, Xian; Chen, Shanshan; Meng, Fanbin; Zhang, Dayi; Liu, Yi; Li, Miao; Liu, Xiang; Huang, Xia; Qu, Jiuhui title: Spatial variation of dissolved organic nitrogen in Wuhan surface waters: Correlation with the occurrence of disinfection byproducts during the COVID-19 pandemic date: 2021-04-11 journal: Water Res DOI: 10.1016/j.watres.2021.117138 sha: c5d77f7c8eaf8af4066e3ade6d4de2898583951a doc_id: 959509 cord_uid: q5zh5ixx Intensified sanitization practices during the recent coronavirus disease-2019 (COVID-19) led to the release of chlorine-based disinfectants in surface water, potentially triggering the formation of disinfection byproducts (DBPs) in the presence of dissolved organic nitrogen (DON). Thus, a comprehensive investigation of DON's spatial distribution and its association with DBP occurrence in the surface water is urgently needed. In this study, a total of 51 water samples were collected from two rivers and four lakes in May 2020 in Wuhan to explore the regional variation of nitrogen (N) species, DON's compositional characteristics, and the three classes of DBP occurrence. In lakes, 53.0% to 86.3% of N existed as DON, with its concentration varying between 0.3–4.0 mg N/L. In contrast, NO(3)(−)-N was the dominant N species in rivers. Spectral analysis revealed that DON in the lakes contained higher humic and fulvic materials with higher A(254), A(253)/A(203,) SUVA(254), and P(III+IV)/P(I+II+V) ratios, while rivers had higher levels of hydrophilic compounds. Trihalomethanes (THMs) were the most prevalent DBPs in the surface waters, followed by N-nitrosamines and haloacetonitriles (HANs). The levels of N-nitrosamines (23.1–97.4 ng/L) increased significantly after the outbreak of the COVID-19 pandemic. Excessive DON in the surface waters was responsible for the formation of N-nitrosamines. This study confirmed that the presence of DON in surface water could result in DBP formation, especially N-nitrosamines, when disinfectants were discharged into surface water during the COVID-19 pandemic. Highlights  Remarkable differences in N species were observed in Wuhan surface waters.  Lakes had higher DON contents with more humic substances compared to rivers.  N-nitrosamines significantly increased in surface water after COVID-19 epidemic.  Both quantity and chemical properties of DON influence DBP occurrence. Dissolved organic nitrogen (DON) refers to the nitrogenous fraction of dissolved organic matter (DOM). This fraction comprises easily decomposable, mineralizable nitrogen (N), which provides available substrates for microbial utilization (Wang et al., 2018) . As the available pool of N, DON has a rapid turnover and plays a vital role in the N cycle. DON is ubiquitous in surface water. Typical DON concentration in surface water lies in the range of 0.02-10.0 mg N/L with a mean of approximately 0.3 mg N/L (Seitzinger and Sanders, 1997; Westerhoff and Mash, 2002; Xu et al., 2010; Yao et al., 2020) . Excessive DON can lead to eutrophication and acidification (Yao et al., 2020) . Furthermore, when surface water is treated for drinking purposes, DON can react with chlorinated disinfectants to form disinfection byproducts (DBPs) (Gu et al., 2011; Mazhar et al., 2020) . The presence of DON in surface water will proliferate the formation potential of DBPs, posing a considerable threat to human health. Population growth and large-scale anthropogenic activities have increased the discharge of exogenous DON, such as chemical fertilizer, animal and human excrement, sewage, and litter amount in surface water (rivers and lakes) Hu et al., 2016) . Considerable variations are expected in the amount and compositional characteristics of DON in surface water due to diverse sources. Although the concentration of DON has been reported in a few surface water bodies, data on the spatial variation of DON at a regional scale are still limited. Besides, identification of the chemical components of DON is challenging, owing to its extremely complicated structural composition. As is well-known, determining the composition and structure of DON is the characterization of DOM with a particular focus on the N fraction (He et al., 2015; Hu et al., 2020) . Multiple technologies are often used in conjunction to analyze the compositional structure of DOM (Zhang et al., 2020c) . Specific ultraviolet-visible (UV-vis) adsorption spectrum is widely used as an index to reflect the aromatic content of DOM (He et al., 2011; Hudson et al., 2008; Li et al., 2000) . Recent studies have demonstrated the use of three-dimensional fluorescence excitation-emission matrix (3D-EEM) to observe the fluorescence peaks of DOM (Carstea et al., 2019; Maqbool et al., 2020) . The fluorescence peaks represent five typical fluorophores, including tyrosine-like, tryptophan-like, fulvic acid-like, soluble microbial product-like, and humic acid-like. Wuhan, a metropolitan, is the capital of the Hubei Province, with a population of 11.2 million (Fu et al., 2020) . This city is located in the middle reaches of the Yangtze River, specifically at the convergence point of the Yangtze River and its largest tributary (the Han River). In Wuhan, the centralized surface water for drinking is mainly procured from the Yangtze River, the Han River, and some lakes, which is crucial for human survival. However, rapid urbanization poses a significant risk to the quality of water in these pristine water bodies. properties, and DBP occurrence. The study area is located in Wuhan City (Hubei Province) in central China (29°58′ N-31°22′ N; 113°41′ E-115°05′ E) (Fig. 1) . The Yangtze River catchment of Wuhan city with one main tributary (Han River) is the most important river with an annual water supply of 8.4×10 8 m 3 . Out of the hundreds of lakes in this city, four lakes (Tangxun Lake, East lakes, Hou Lake and Liangzi Lake) were selected for further assessment. Tangxun Lake and East Lake are the largest and second-largest urban lakes in China, with water areas of 47.6 and 33.0 km 2 . Hou Lake and Liangzi Lake are located in the northwest and southeast of Wuhan City. The study area is characterized by a subtropical monsoon climate with four distinct seasons. The mean annual temperature is 15.8℃-17.5℃, with 40% of total rainfall occurring during the rainy season from June to August. Water samples were collected from 51 sampling sites located in two rivers (the Yangtze River catchment of Wuhan and Han River) and four lakes (Tangxun Lake, Dong lake, Liangzi Lake and Hou Lake) in May 2020 (Fig. 2) . We ensured that the sampling points covered the entire study area. The Yangtze River and Han River contained a total of 10 and 6 sampling locations, respectively. The number of sampling sites in Tangxun Lake, Dong lakes, Liangzi Lake, and Hou Lake was 10, 8, 11, and 6, respectively. The samples were packed in amber glass bottles without allowing any headspace and refrigerated immediately. Later, the samples were brought back to the laboratory, filtered using Whatman nylon membrane filters (pore size of 0.2 μm; 47 mm diameter; Germany), and stored at 4℃ for subsequent analysis. TBM, BDCM, NMOR, and NMEA were not detected in the samples. The concentration of each DBPs below the method detection limit (MDL) was treated as zero to avoid incorrect conclusion and bias. The results were reported as the mean and the standard deviation computed from three replicates. Statistical analysis was performed using IBM SPSS statistics 26.0 software package for Windows 10. The significance of the differences among the distinct water bodies was ascertained using the analysis of variance (ANOVA) followed by Duncan's multiple range test. Spearman's rank correlation coefficients were calculated to identify the relationships among the levels of DBPs, DON-related indices, and physicochemical characteristics. For this study, a correlation coefficient greater than or equal to 0.7 (r ≥ 0.7) was defined as a strong correlation. Correlation coefficient values between 0.4 and 0.7 (0.4 ≤ r < 0.7) were considered moderately strong, and below 0.4 (r < 0.4) were considered weak (Haldar et al., 2020) . Principal component analysis (PCA) was also performed to provide a rough overview of the reduced dimensions of parameters and sample clustering using Origin 2021. The differences were considered statistically significant at p < 0.05. 3.1 Spatial variation of water quality in Wuhan surface water resources The physical and chemical properties of water samples collected from two rivers and four lakes, including pH, EC, DO, turbidity, COD, DOC, TOCl, TOBr and N species, were summarized in Table S1 and Fig. 3 . The results indicated different levels of variation in water quality among different water resources. A significant variation was demonstrated by statistical analysis (ANOVA, p < 0.05) for pH, EC, Turbidity, COD, DOC, TOCl, and TOBr. Most water quality parameters in lakes exhibited significantly higher levels than in rivers. For example, the results showed that DOC concentrations were 1.5-4.4 mg/L in rivers and 1.5-9.8 mg/L in lakes, respectively. Average DOC concentrations in different surface water resources were in the order Hou Lake > Tangxun Lake > East Lake > Liangzi Lake > Han River > Yangtze River. Yangtze River had the lowest DOC concentrations (1.5-3.3 mg/L), while Hou Lake had the highest DOC values (3.4-9.8 mg/L) among all water resources. Generally, the lakes in this study were characterized by higher EC, turbidity, COD, DOC, and TOCl values, indicating that their water quality was worse than rivers. This could be mainly attributed to discharge from different pollutant sources such as rainfall, surface runoff, overuse of fertilizers, sewage, and domestic effluents (Hu et al., 2016; Maqbool et al., 2020) . To further identify primary sources of spatial variability in water quality, PCA of the water quality parameters was performed (Fig. 4) . Detailed information about pollutants was summarized in Table S2 -S6. As shown in Fig. 4 , the different types of water samples showed distinct clusters with the two principal components accounting for 62.7% of the variances. Rivers and lakes clustered in different quadrants due to their distinct pollutant sources. PC1 explained 42.3% of the total variance with strong positive loadings of DON, COD and NH 4 + . Tangxun Lake and Hou Lake exhibited higher values on the PC1 axes, which can be associated to large discharges of sewage and industrial waste directly unloaded into lakes. Especially in Hou Lake (Table S5) Table S2 also showed that the amount of NH 4 + and COD from agricultural runoff was higher than from other sources. The different predominant N forms ( The chemical properties of DON can affect its bioavailability and transformation in aquatic ecosystems (Carstea et al., 2019 According to the results mentioned above, the contents and constitutes of DON in the same water resource did not differ significantly. Thus, we chose the sampling station with the highest DON content in each water resource to characterize the 3D-EEM spectra (Fig. 6 ). According to excitation and emission wavelength boundaries, the 3D-EEM spectra were divided into five areas (Jacquin et al., 2017; Zhang et al., 2020b) . In summary, peaks at shorter emission wavelengths (< 380 nm) and shorter excitation wavelengths (< 250 nm) were attributed to region I and region II (P I and P II ), indicating simple aromatic protein-like substances, such as tyrosine-like and tryptophan-like substances. Peaks at shorter excitation wavelengths (< 250 nm) and longer emission wavelengths (> 380 nm) were related to region III (P III ), representing fulvic acid-like compounds. Peaks in region IV (P IV ), indicating soluble microbial product-like substances, occurred at shorter emission wavelengths (< 380 nm) and longer excitation (> 250 nm). Peaks of region V (P V ) characterized by longer emission wavelengths (> 380 nm) and longer excitation wavelengths (> 250 nm) indicated humic acid-like compounds. The percent distribution of the five regions for DON based on the fluorescence peak characteristics has been presented in Table 1 . acid-like substances with fewer soluble microbial product-like and humic acid-like compounds, reflected by its high P I+II+IV of 57.7%-75.1%. This suggested that microorganisms efficiently utilized the dominant component of DON in Wuhan's surface water resources. Notably, Tangxun Lake and Hou Lake had higher P III+V than others. In addition, P III+V /P I+II+IV ratios of DON and SUVA 254 were also calculated to reflect the humification degree (Table 1 ). The P III+V /P I+II+IV ratios were 0.6 and 0.7 in Tangxun Lake and Hou Lake, which were higher than those in other water resources. The results illustrated that these two lakes had a higher humification degree China regulates the levels of some of the DBPs in centralized surface water resources. For instance, the regulatory standards for TCM and TBM are 60 and 100 μg/L, respectively (Zhou et al., 2019) . In this study, TCM and TBM concentrations complied with the standards even after the COVID-19 pandemic. Notably, most countries, including China, have not developed guidelines to limit DBPs in surface water (Kristiana et al., 2017) . Until now, the levels of DBPs in drinking water have garnered attention, while their occurrence in natural water bodies has been assumed to be insignificant. The current understanding of the DBP distribution in China's surface water is highly inadequate. To address this knowledge gap, we gathered the observation data on the concentrations of DBPs in surface water in China from published reports (Table S7) . Data from previous research showed that DBP concentrations in surface water remained steady at a very low level before the COVID-19 pandemic. The levels of most DBPs after the COVID-19 pandemic were within the range of data previously reported, while some DBPs were higher than the reported data. For example, previous studies showed that the total nitrosamines concentration in rivers was in the range of 1.6-62.4 ng/L. A recent study reported that the mean concentration of N-nitrosamines in the surface water was 29.2 ng/L after COVID-19, which was not higher than the reported data (Li et al., 2021) . In contrast, the concentration of N-nitrosamines varied from 23.1 to 97.4 ng/L in the current study. Increased levels of N-nitrosamines may be associated with the elevated use of disinfectants during the COVID-19 pandemic. Increased N-nitrosamines could come from two sources. (1) Wastewater treatment plant effluents and industrial/domestic wastewaters containing N-nitrosamines may directly be discharged into the surface water (Wang et al., 2016) . (2) N-nitrosamines could be generated when the surface water received residual chlorine. Although most DBPs did not increase significantly in surface water after the COVID-19 pandemic, their potential health risks in surface water can not be overlooked. In precursor, which agreed with reported studies (Chang et al., 2013; Hua et al., 2020) . With regard to optical properties, SUVA 254 exhibited strong negative correlations with total N-nitrosamines and some specific species of N-nitrosamines (including NPIP, NDPA, and NDBA) (r = -0.7, p < 0.05; r = -0.7, p < 0.05; r = -0.8, p < 0.05; r = -0.8, p < 0.05). The results implied that hydrophilic substances with low SUVA 254 values could be precursors for NPIP, NDPA, NDBA or total N-nitrosamines. Further, THMs showed a significant positive with P V (humic acid-like components) (r = 0.7, p < 0.05), indicating that the humidified DON was a dominant THMs precursor. Unexpectedly, total N-nitrosamines had no significant relationship with DON but had a strong positive correlation with P Ⅱ (protein-like substances) (r = 0.9, p < 0.05). This suggested that DON fraction containing more protein-like components favored the formation of N-nitrosamines. Krasner et al. (2013) has proposed that amine precursor is the dominant mechanism responsible for the formation of nitrosamines. For protein-like fraction, amines, carboxylic and aliphatic structures are considered as basic fluorescent units (Chen et al., 2015; Li et al., 2014a; Liu et al., 2017) . In this study, the amines structures in the protein-like components may serve as N-nitrosamines precursors. Overall, the key factor influencing the occurrence of The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The clusters of dots indicate the sampling sites with the same water quality pattern. Han River; TX: Tangxun Lake; DH: Dong lakes; HH: Hou Lake; LZH: Liangzi Lake. 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