Terrestrial humic-like fluorescence peak of chromophoric dissolved organic matter as a new potential indicator tracing the antibiotics in typical polluted watershed.

Natural surface waters are threatened globally by antibiotics pollution. In this study, we analyzed antibiotics and CDOM (Chromophoric dissolved organic matter) fluorescence in different water bodies using HPLC method and Excitation Emission Matrix- Parallel factor analysis, respectively. A combination of field studies in the Yinma River Watershed were conducted in rivers, reservoirs and urban rivers, and 65 CDOM and antibiotic samples were taken in April, May, July, and August 2016. EEM-PARAFAC analysis identified two components; a humic-like (C1) component and a tryptophan-like (C2) component. The redundancy analysis (RDA) demonstrated that CDOM could explain 38.2% (two axes) of the five antibiotics in reservoirs (N = 31), and 26.0% (two axes) of those in rivers and urban water (N = 30). Furthermore, the Pearson correlation coefficient between Sulfamethoxazole and C1 in reservoir water was 0.91 (t-test, 2-tailed, p < 0.01), and that between Sulfamethoxazole and C2 was 0.68 (t-test, 2-tailed, p < 0.01). This indicated that the humic-like component of CDOM PARAFAC fluorescence could detect Sulfamethoxazole contamination levels in the homogenized reservoir waters. Our results identified Sulfamethoxazole and Quinolones (Norfloxacin, 16.5 ng L-1; Enrofloxacin, 0.3 ng L-1; Ciprofloxacin, 30.9 ng L-1) at mean concentrations of 369.5 ng L-1 and 15.9 ng L-1, respectively, which were the higher levels in natural surface waters. The FTIR spectroscopy of the mixture of humic acid and sulfamethoxazole showed that the absorbance at 3415 cm-1 linked to OH stretching of OH groups and at 1386 cm-1 because of OH bending and vibration of COOH groups became weaker, indicating that COOH groups of humic acid can adsorb and react with -NH2 of sulfamethoxazole. The CDOM PARAFAC components can be adapted for online or in situ fluorescence measurements as an early warning of Sulfamethoxazole distribution and contamination in similar aquatic environments.

Dynamics and ecological risk assessment of chromophoric dissolved organic matter in the Yinma River Watershed: Rivers, reservoirs, and urban waters.

The extensive use of a geographic information system (GIS) and remote sensing in ecological risk assessment from a spatiotemporal perspective complements ecological environment management. Chromophoric dissolved organic matter (CDOM), which is a complex mixture of organic matter that can be estimated via remote sensing, carries and produces carcinogenic disinfection by-products and organic pollutants in various aquatic environments. This paper reports the first ecological risk assessment, which was conducted in 2016, of CDOM in the Yinma River watershed including riverine waters, reservoir waters, and urban waters. Referring to the risk formation theory of natural disaster, the entropy evaluation method and DPSIR (driving force-pressure-state-impact-response) framework were coupled to establish a hazard and vulnerability index with multisource data, i.e., meteorological, remote sensing, experimental, and socioeconomic data, of this watershed. This ecological vulnerability assessment indicator system contains 23 indicators with respect to ecological sensitivity, ecological pressure, and self-resilience. The characteristics of CDOM absorption parameters from different waters showed higher aromatic content and molecular weights in May because of increased terrestrial inputs. The assessment results indicated that the overall ecosystem risk in the study area was focused in the extremely, heavily, and moderately vulnerable regions. The ecological risk assessment results objectively reflect the regional ecological environment and demonstrate the potential of ecological risk assessment of pollutants over traditional chemical measurements.

Analysis of the Electricity Consumption in Municipal Wastewater Treatment Plants in Northeast China in Terms of Wastewater Characteristics.

A municipal wastewater treatment plant plays an important role in treating urban sewage and reducing the quantity of pollutants discharged into rivers. However, the energy consumption of the municipal wastewater treatment industry is large. High energy consumption indirectly produces ecological damage, accelerates the energy crisis, and increases carbon emissions. For energy conservation and emission reduction in wastewater treatment plants, it is first necessary to identify the main factors influencing energy consumption. Electricity consumption accounts for more than 80% of the energy consumption of wastewater treatment plants. Wastewater quantity and wastewater quality have become the key influencing factors of energy conservation and consumption reduction in wastewater treatment plants. In this study, a municipal wastewater treatment plant in Northeast China was selected as the research object, and the measured data, such as air temperature, wastewater quantity, wastewater quality, and electricity consumption of the plant from 2017 to 2020 were statistically analyzed to explore the influences of temperature and wastewater quantity and wastewater quality indicators of influent and effluent on energy consumption. Firstly, the range of influent quantity in the wastewater treatment plant was large. The influent quantity in summer was high because some rainwater entered the sewage treatment plant. In winter, average daily electricity consumption (ADEC) was higher than that in summer. The relationship between ADEC and the wastewater quantity showed a positive correlation, and ADEC slowly increased with the increase in wastewater quantity. Electricity consumption per unit of wastewater (UEC) was negatively correlated with the wastewater quantity, but the correction coefficient in winter was larger than that in summer. Secondly, the ranges of chemical oxygen demand (CODCr) and ammonia nitrogen in influent were large, and the ranges of CODCr and ammonia nitrogen in effluent were small. Influent CODCr concentration was negatively correlated with influent ammonia nitrogen concentration. ADEC increased slightly with the increase in influent CODCr concentration. In winter, the increasing trend of ADEC with the influent CODCr concentration was higher than that in the summer. The increasing trend of UEC with the increase in influent COD concentration in summer was more significant than that in winter. Thirdly, influent CODCr in 11.6% of the samples exceeded the corresponding designed value, and influent ammonia nitrogen concentration in 41.4% of the samples exceeded the corresponding designed value. Effluent CODCr in 10.6% of the samples exceeded the First Level Class B standard in "Discharge Standard of Pollutants for Municipal Wastewater Treatment Plants (GB18918-2002)", and unqualified CODCr in 94% of the effluent samples was ascribed to the unqualified ammonia nitrogen concentration in the influent samples. The electricity consumption level under abnormal conditions was higher than that under normal conditions. Fourthly, ADEC was positively correlated with the average daily CODCr reduction. The correction coefficient of ADEC with average daily CODCr reduction was greater in winter than that in summer. Fifthly, the average electricity consumption per unit of wastewater was close to the national average energy consumption, displaying the characteristics of high energy consumption in winter and low energy consumption in summer. The correlation analysis results of unit electricity consumption and temperature showed that when it was below 0 °C, the lower the temperature, the higher the electricity consumption. In Northeast China, the influences of seasons and temperatures on the electricity consumption of sewage plants were obvious. Accordingly, it is necessary to implement the diversion of rainwater and sewage, reduce the discharge of unqualified wastewater from enterprises, and take thermal insulation measures in winter. In addition, activated sludge microorganisms suitable for a low temperature area and the optimal scheduling of sewage pipe networks can also improve the operation and management of sewage treatment plants.

Quantification of chlorophyll-a in typical lakes across China using Sentinel-2 MSI imagery with machine learning algorithm.

Lake eutrophication has attracted the attention of the government and general public. Chlorophyll-a (Chl-a) is a key indicator of algal biomass and eutrophication. Many efforts have been devoted to establishing accurate algorithms for estimating Chl-a concentrations. In this study, a total of 273 samples were collected from 45 typical lakes across China during 2017-2019. Here, we proposed applicable machine learning algorithms (i.e., linear regression model (LR), support vector machine model (SVM) and Catboost model (CB)), which integrate a broad scale dataset of lake biogeochemical characteristics using Multispectral Imager (MSI) product to seamlessly retrieve the Chl-a concentration. A K-means clustering approach was used to cluster the 273 normalized water leaving reflectance spectra [Rrs (λ)] extracted from MSI imagery with Case 2 Regional Coast Colour (CR2CC) processor into three groups. The pH, electrical conductivity (EC), total suspended matter (TSM) and dissolved organic carbon (DOC) from three clustering groups had significant differences (p < 0.05**), indicating that water quality parameters have an integrated impact on Rrs(λ)-spectra. The results of machine learning algorithms integrating demonstrated that SVM obtained a better degree of measured- and derived- fitting (calibration: slope = 0.81, R2 = 0.91; validation: slope = 1.21, R2 = 0.88). On the contrary, the documented nine Chl-a algorithms gave poor results (fitting 1:1 linear slope < 0.4 and R2 < 0.70) with synchronous train and test datasets. It demonstrated that machine learning provides a robust model for quantifying Chl-a concentration. Further, considering three Rrs(λ) clustering groups by k-means, Chl-a SVM model indicated that cluster 1 group gave a better retrieving performance (slope = 0.71, R2 = 0.78), followed by cluster 3 group (slope = 0.77, R2 = 0.64) and cluster 2 group (slope = 0.67, R2 = 0.50). These are related to the low TSM and high DOC levels for cluster-1 and cluster-3 Rrs(λ) spectra, which reduce the influence of particle in red bands for Rrs(λ) signal. Our results highlighted the quantification of lake Chl-a concentrations using MSI imagery and SVM, which can realize the large-scale monitoring and more appropriate for medium/low Chl-a level. The remote estimation of Chl-a based on artificial intelligence can provide an effective and robust way to monitor the lake eutrophication on a macro-scale; and offer a better approach to elucidate the response of lake ecosystems to global change.

Evaluation of CDOM sources and their links with antibiotics in the rivers dividing China and North Korea using fluorescence spectroscopy.

Rivers act as carriers where active cycling of chromophoric dissolved organic matter (CDOM) affected by anthropogenic activities and land cover variation may occur. Little is known about the optical properties of CDOM in relation to antibiotics; these properties could provide valuable insights into CDOM transformation processes and biogeochemical reactivity. The spatial distribution of CDOM absorption, fluorescence intensities of CDOM components, and correlations between fluorescence indices (FI370, humification index (HIX)) and biological index (BIX)) with water quality and antibiotics were investigated in three rivers, namely, two rivers (Yalu River and Tumen River) dividing China and North Korea, and the Songhua River. Four humic-like components (C1 and C3-5), and one tryptophan-like component (C2), were identified via fluorescence excitation-emission matrices-parallel factor analysis (EEM-PARAFAC). The correlation between Fmax for five EEM-PARAFAC extracted CDOM components, C1-C5, FI370, HIX, BIX, and water quality parameters, and four antibiotics (i.e., Norfloxacin, Enrofloxacin, Sulfamethoxazole and Metronidazole), were determined through a redundancy analysis (RDA), with species-environment correlations of 0.887 and 0.833, respectively. The results showed that spatial variation in land cover, pollution sources, and terrestrial contribution in water quality affected Fmax for the fluorescent components C1-C5 and the fluorescence indices, indicating a high diverse chemical composition and transformation history. The Fmax for terrestrial humic-like components, C4 and C5, showed spatial variation depending on land cover and anthropogenic impacts. Further correlation and regression analyses indicated that CDOM soil fulvic-like component C5 correlated with Sulfamethoxazole and Metronidazole (t test, p < 0.01). Our results indicate that the spatial distributions of Fmax for CDOM fluorescent terrestrial components, evaluated by EEM-PARAFAC, have potential implications for the monitoring of Sulfamethoxazole and Metronidazole in surface waters. Further, these findings can be used to understand the biogeochemical cycling of CDOM and its effects on antibiotics pollution in the environment.

The relationship of chromophoric dissolved organic matter parallel factor analysis fluorescence and polycyclic aromatic hydrocarbons in natural surface waters.

Polycyclic aromatic hydrocarbons (PAHs), a large group of persistent organic pollutants (POPs), have caused wide environmental pollution and ecological effects. Chromophoric dissolved organic matter (CDOM), which consists of complex compounds, was seen as a proxy of water quality. An attempt was made to understand the relationships of CDOM absorption parameters and parallel factor analysis (PARAFAC) components with PAHs under seasonal variation in the riverine, reservoir, and urban waters of the Yinma River watershed in 2016. These different types of water bodies provided wide CDOM and PAHs concentration ranges with CDOM absorption coefficients at a wavelength of 350 nm (a CDOM(350)) of 1.17-20.74 m-1 and total PAHs of 0-1829 ng/L. CDOM excitation-emission matrix (EEM) presented two fluorescent components, e.g., terrestrial humic-like (C1) and tryptophan-like (C2) were identified using PARAFAC. Tryptophan-like associated protein-like fluorescence often dominates the EEM signatures of sewage samples. Our finding is that seasonal CDOM EEM-PARAFAC and PAHs concentration showed consistent tendency indicated that PAHs were un-ignorable pollutants. However, the disparities in seasonal CDOM-PAH relationships relate to the similar sources of CDOM and PAHs, and the proportion of PAHs in CDOM. Overlooked and poorly appreciated, quantifying the relationship between CDOM and PAHs has important implications, because these results simplify ecological and health-based risk assessment of pollutants compared to the traditional chemical measurements.

[Absorption Characteristics of Particulates and CDOM in Waters of Chagan Lake and Xinlicheng Reservoir in Autumn].

Field surveys and laboratory analysis were carried out in Chagan Lake and Xinlicheng Reservoir under different salinity conditions in September 2012. In the laboratory, the absorption coefficients of particulates and chromophoric dissolved organic matter (CDOM) were measured, aiming to compare the absorption features, source of optical active substances and relative contribution of optical active constituents over the range of PAR (400-700 nm) in Chagan Lake and Xinlicheng Reservoir. The results showed that the Chagan Lake and Xinlicheng Reservoir were water bodies with medium eutrophication in autumn by TAL nutrient index and the absorption spectra of particulates matters were similar to those of phytoplankton. For the Chagan Lake with high salinity( EC = 988. 87 micro S x cm(-1)), the total particulate absorption was dominated by the nonalgal particles, and the contribution rate was in the order of nonalgal particles > phytoplankton > CDOM. For the Xinlicheng Reservoir with low salinity (EC = 311.67 microS x -cm(-1)), the total particulate absorption was dominated by the phytoplankton, and the contribution rate was ranked as phytoplankton > nonalgal particles > CDOM. Positive correlation was observed between a(p) (440), a(p) (675), a(d) (440) and total suspended matter (TSM), inorganic suspended matter (ISM), organic suspended matter (OSM) and Chl-a respectively in Chagan Lake, with correlation coefficients all above 0.55. Positive correlation was observed between a(p)(440), a(p) (675) and Chl-a (0.77 and 0.85, P < 0.05) , so did a(d) (440) and ISM (0.74, P < 0.01), while negative correlation was observed between a(p) (440) and OSM in the Xinlicheng Reservoir. In terms of Chagan Lake, negative correlation was merely observed between a(g) (440) and OSM (-0.54, P < 0.05) , but not in the Xinlicheng Reservoir. Both Sg, which was calculated by the fitting absorption curve from 250 to 400 nm, and relative molecular weight M showed that Sg[ (0.021 +/- 0.001) m(-1)] in Chagan Lake was greater than that in the Xinlicheng Reservoir [(0.0176 +/- 0.001) m(-1)], and Mr, in Chagan Lake was 11.44 +/- 2.00 (7.5-15.09), which was greater than that in Xinlicheng Reservoir 7.53 +/- 0.79 (6.17-8.89), indicating that the relative molecular weight of CDOM in the Chagan Lake was less than that in the Xinlicheng Reservoir. The Chagan Lake was greatly affected by wind speed and shore collapse to produce suspended mineral and sediment particles. Thereby the total particulate absorption was dominated by the nonalgal particles. The waters in the Xinlicheng Reservoir were greatly impacted by terrestrial inorganic matter, and the growth of phytoplankton was weakened and microbes activities were strengthened simultaneously, which led to the negative correlations between a(g)(lamda) and OSM.