Chronic toxic effects of erythromycin and its photodegradation products on microalgae Chlorella pyrenoidosa.

The photodegradation products (PDPs) of antibiotics in the aquatic environment received increasing concern, but their chronic effects on microalgae remain unclear. This study initially focused on examining the acute effects of erythromycin (ERY), then explored the chronic impacts of ERY PDPs on Chlorella pyrenoidosa. ERY of 4.0 - 32 mg/L ERY notably inhibited the cell growth and chlorophyll synthesis. The determined 96 h median effective concentration of ERY to C. pyrenoidosa was 11.78 mg/L. Higher concentrations of ERY induced more serious oxidative damage, antioxidant enzymes alleviated the oxidative stress. 6 PDPs (PDP749, PDP747, PDP719, PDP715, PDP701 and PDP557) were identified in the photodegradation process of ERY. The predicted combined toxicity of PDPs increased in the first 3 h, then decreased. Chronic exposure showed a gradual decreasing inhibition on microalgae growth and chlorophyll content. The acute effect of ERY PDPs manifested as growth stimulation, but the chronic effect manifested as growth inhibition. The malonaldehyde contents decreased with the degradation time of ERY at 7, 14 and 21 d. However, the malonaldehyde contents of ERY PDPs treatments were elevated compared to those in the control group after 21 d. Risk assessment still need to consider the potential toxicity of degradation products under long-term exposure.

Cascade damming impacts on microbial mediated nitrogen cycling in rivers.

Rivers display essential role in nitrogen (N) cycling in terrestrial and aquatic ecosystems, but now they are suffering from damming worldwide, especially from cascade damming. Despite of the importance of microorganisms in biogeochemical nutrient cycling, little attention has been paid to microbial functional biogeography under damming disturbances. Here, the Geochip microarray was applied to investigate the microbial mediated N cycling across the single-dammed Yarlung Tsangpo-Brahmaputra River and the cascade-dammed Lancang-Mekong River in southwest China. Our results showed that the N cycling processes (nitrogen fixation, ammonification, denitrification, nitrification and anammox) were stimulated in reservoirs in both rivers and the enhancement was inversely coupled with hydraulic retention time, but the recovery of N-cycling gene abundance in downstream of dam was intervened by cascade damming. Moreover, N-cycling gene composition was significantly altered in the single-dammed river, while no remarkable change was found in the cascade-dammed reaches. However, different from the unvaried gene composition, cascade damming intervened the recovery of N-cycling gene flow connectivity and resulted in the continuous decrease of connectivity in cascade damming reaches. In addition, in the single-dammed river, nutrients were the important drivers for variation in gene abundance, while they did not influence gene composition. Meanwhile, the abundance and composition of N-cycling genes in the cascade-dammed river were both significantly correlated to geographical parameters and water physical characteristics. Therefore, our study has vital implications for anticipating microbial functional response and biogeochemical feedback to ongoing cascade damming, contributing to the protection of river ecosystems under river regulation.

Monitoring the particulate phosphorus concentration of inland waters on the Yangtze Plain and understanding its relationship with driving factors based on OLCI data.

Tracking the spatiotemporal dynamics of particulate phosphorus concentration (CPP) and understanding its regulating factors is essential to improve our understanding of its impact on inland water eutrophication. However, few studies have assessed this in eutrophic inland lakes, owing to a lack of suitable bio-optical algorithms allowing the use of remote sensing data. Herein, a novel semi-analytical algorithm of CPP was developed to estimate CPP in lakes on the Yangtze Plain, China. The independent validations of the proposed algorithm showed a satisfying performance with the mean absolute percentage error and root mean square error less than 27% and 27 µg/L, respectively. The Ocean and Land Color Instrument observations revealed a remarkable spatiotemporal heterogeneity of CPP in 23 lakes on the Yangtze Plain from 2016 to 2020, with the lowest value in December (62.91 ± 34.59 µg/L) and the highest CPP in August (114.9 ± 51.69 µg/L). Among the 23 examined lakes, the highest mean CPP was found in Lake Poyang (124.58 ± 44.71 µg/L), while the lowest value was found in Lake Qiandao (33.51 ± 4.71 µg/L). Additionally, 13 lakes demonstrated significant decreasing or increasing trends (P < 0.05) of annual mean CPP during the observation period. The driving factor analysis revealed that four natural factors (wind speed, air temperature, precipitation, and sunshine duration) and two anthropogenic factors (the normalized difference vegetation index and nighttime light) combined explained more than 91% of the variation in CPP, while the impacts of these factors on CPP showed considerable differences among lakes. This study offered a novel and scalable algorithm for the study of the spatiotemporal variation of CPP in inland waters and provided new insights into the regulating factors in water eutrophication.

An approach for retrieval of horizontal and vertical distribution of total suspended matter concentration from GOCI data over Lake Hongze.

There are a few studies working on the vertical distribution of TSM, however, understanding the underwater profile of TSM is of great benefit to the study of biogeochemical processes in the water column that still require further research. In this study, three data-gathering expeditions were conducted in Lake Hongze (HZL), China, between 2016 and 2018. Based on the in situ optical and biological data, a multivariate linear stepwise regression method was applied for retrieval of the surface horizontal distribution of TSM (TSM0.2) using GOCI (Geostationary Ocean Color Imager) data. Then, the estimation model of vertical structure of underwater TSM was constructed using layer-by-layer recursion. This study drew several crucial findings: (1) the approach proposed in this paper generated very high goodness of fit results, with determination coefficients (R2) of 0.83 (p < 0.001, N = 54), and with smaller prediction errors (the mean absolute percentage error is determined to be 16.34%, the root mean square error is 9.01 mg l-1, and the mean ratio is 1.00, N = 26). (2) The monthly surface TSM and the column mass of suspended matter (CMSM) are affected by both wind speed and precipitation in HZL. In addition, the hourly variation of surface TSM and CMSM are driven by local wind, most especially in spring and winter. (3) Compared with the non-uniform hypothesis, the CMSM derived by conventional vertical uniformity hypothesis was underestimated by almost 10% in HZL during 2016. This should warrant the attention of lake managers and lake environmental evolution researchers.

Long-term observation of cyanobacteria blooms using multi-source satellite images: a case study on a cloudy and rainy lake.

High-frequency and reliable data on cyanobacteria blooming over a long time period is crucial to identify the outbreak mechanism of blooms and to forecast future trends. However, in cloudy and rainy areas, it is difficult to retrieve useful satellite images, especially in the rainy season. To address this problem, we used data from the HJ-1/CCD (Chinese environment and disaster monitoring and forecasting satellite/charge coupled device), GF-1/WFV (Chinese high-resolution satellite/wide field of view), and Landsat-8/OLI (Operational Land Imager) satellites to generate a time series of the bloom area from 2009 to 2016 in Dianchi Lake, China. We then correlated the responses of bloom dynamics to meteorological factors. Several findings can be drawn: (1) a higher bloom frequency and a larger bloom area occurred in 2011, 2013, and 2016, compared to the other years; (2) the frequency of blooms peaked in April, August, and November each year and expanded from north to south starting in July; (3) air temperature in spring and sunshine hours in summer greatly correlated to the yearly bloom area; (4) wind speed and sunshine hours strongly affected the short-term expansion of blooms and thereafter influenced the monthly bloom scale; and (5) rainfall had a strong short-term influence on the occurrence of blooms. Cyanobacteria blooms often occurred when wind speeds were less than 2.35 ± 0.78 m/s in the dry season and 2.01 ± 0.75 m/s in the rainy season, when there were 48 to 72 h of sunshine in the dry season and 35 to 57 h of sunshine in the rainy season, and when there was more than 10 mm of daily precipitation.

Remote observation of water clarity patterns in Three Gorges Reservoir and Dongting Lake of China and their probable linkage to the Three Gorges Dam based on Landsat 8 imagery.

The Secchi disk depth (ZSD) plays a critical role in describing water clarity. Several studies have shown linkages between Three Gorges Dam (TGD) and the downstream lacustrine ecosystem in the middle and lower Yangtze River basin. However, the potential influence on the ZSD fluctuation in the entire anthropogenic reservoirs of Three Gorges (ER) and Dongting Lake (DTL) has not been reported, possibly due to technical obstacles in obtaining statistically significant spatial and temporal results. We addressed this challenge by using remote sensing technology: the Landsat 8 Operational Land Imager (OLI). We proposed a new, robust remote-sensing algorithm to estimate ZSD from OLI imagery using red and green band-ratio, leading to MAPE of 21.68% and RMSE of 0.076m for ZSD ranging from 0.1m to 1.05m. After satisfactory image-based validation, the algorithm was implemented on OLI data to derive ZSD patterns over ER and DTL from 2013 to 2017. Several crucial findings can be drawn: 1) Spatial-temporal patterns of ZSD exhibited notable fluctuations over both ER and DTL, and they also demonstrated a significant correlation with each other because of the opposite temporal cycle of ZSD fluctuations between ER and DTL; 2) Temporally, monthly fluctuations of ZSD between ER and DTL had opposite temporal cycles, which was mainly attributed to the surface runoff and sediment discharge driven by the outbound runoff variations of TGD. Spatially, the heterogeneity of the ZSD pattern in ER might have resulted from the different geographical regions being divided by large anthropologic hydrological facilities, such as TGD; 3) The relationship between ZSD and total suspended matter (TSM) showed a significant negative correlation, as did the relationship between ZSD and Kd(490). These findings demonstrate that TSM often plays a principal role in light attenuation of extremely turbid inland waters; 4) An inversed phenomenon of water clarity was observed at the intersection of DTL and the Yangtze River around Chenglingji site (YRAC), which was due to the opposite temporal cycle of ZSD fluctuations between DTL and ER after the impoundment of TGD; and 5) Owing to the analysis of noise-equivalent ZSD, OLI data can be used to derive ZSD, since the imagery uncertainty is 0.07m by means of our band-ratio algorithm, which demonstrates similar results to MODIS. The proposed ZSD-derived algorithm in this study could be suitable for other turbid lakes or reservoirs to formulate related strategies of water quality management in the middle and lower Yangtze River basin, and the unveiled findings here improve our understanding of ZSD spatiotemporal fluctuations in large river-connected lakes, such as Poyang Lake.

Tempo-spatial dynamics of water quality and its response to river flow in estuary of Taihu Lake based on GOCI imagery.

Knowledge of tempo-spatial dynamics of water quality and its response to river flow is important for the management of lake water quality because river discharge associated with rainstorms can be an important source of pollutants to the estuary. Total phosphorus (TP), chlorophyll a (Chl-a), and total suspended matter (TSM) are important indexes of water quality and important factors influencing eutrophication and algal blooms. In this study, remote sensing was used to monitor these indexes to investigate the effects of river discharge on the estuary of Taihu Lake by the largest inflow river which is Chendong River using a total of 136 Geostationary Ocean Color Images (GOCI). In situ datasets collected during the four cruise experiments on Taihu Lake between 2011 and 2015 were used to develop the TP, Chl-a, and TSM inversion models based on simple empirical algorithms: 154 points for TP (mg/L), 114 for Chl-a (µg/L), and 181 for TSM (mg/L). The spatial and temporal changes of the concentration of the three parameters in the Chendong River estuary were analyzed by combining the GOCI data, the flow of the Chendong River, and meteorological data throughout the year in 2014. The several key findings are as follows: (1) In summer and autumn, TP, Chl-a, and TSM contents were significantly higher than in winter and spring. TP and Chl-a have a few similar distribution characteristics. And organic suspended matter in summer was the main reason for the increase of the TSM concentration. (2) The severe surface erosion in the rivers cannot be ignored; the high erodibility is an important factor in the increase of TP and TSM concentrations in the estuary. The concentration of the water quality parameter showed exponential decay with distance from the shore. The concentration decreased slowly after 12 km and then remained essentially constant. (3) TP content in the Chendong River estuary decreased under steady flow inputs and dramatically increased when the flow became large. The increase in Chl-a content was linked to higher levels of TP and good weather conditions after the rain event. Higher flow rates mainly play a dilution role for the Chl-a concentration. Erosion of the surface soil via rainfall is a major source of TSM to the estuary. This paper firstly analyzes tempo-spatial dynamics of water quality and its response to river flow in estuary of Taihu Lake, helps to further understand the impact of river input on lake water quality, and is important for lake eutrophication.

Remote sensing of diffuse attenuation coefficient patterns from Landsat 8 OLI imagery of turbid inland waters: A case study of Dongting Lake.

The diffuse attenuation coefficient, Kd(λ), is an important optical property. Environmental change and anthropogenic activities, however, have made it challenging to accurately assess Kd(λ) patterns in the extremely turbid inland waters. We addressed this challenge by using new Landsat 8 Operational Land Imager (OLI) imagery. For the bio-optical complexity of water, we proposed an empirical band-ratio algorithm for estimating Kd(490) using our in situ measurements. Based on the acceptable performance of an OLI image-based atmospheric correction and Kd(490) validation, the algorithm was then applied to OLI images to estimate Kd(490) patterns from April 2013 to April 2016, leading to several key findings: (1) Spatial-temporal patterns of Kd(490) varied significantly in Dongting Lake. The temporal heterogeneity of Kd(490) could be explained primarily by surface-runoff changes driven by regional precipitation. The spatial heterogeneity was due to sediment resuspension, resulting from sand dredging and shipping activities; (2) Kd(490) values that were inversed at the intersection of Dongting Lake and Yangtze River were observed for the first time near the Chengliji site and resulted from the opposing temporal cycle of Kd(490) variations between Dongting Lake and the Yangtze River; (3) There was a significant positive correlation between Kd(490) and total suspended matter (TSM). This confirms that TSM often plays a principal role in the attenuation of light in extremely turbid water bodies; (4) The empirical band-ratio algorithm worked well, not only for the broader Landsat archives, but also for the narrower Sentinel-2/3 for Kd(490) estimation, which demonstrates that the algorithm could be used to quantitatively monitor multi-decade records of Landsat observations and future applications of inland water quality in turbid inland waters, such as Dongting Lake and Poyang Lake.

[Inversion Model and Daily Variation of Total Phosphorus Concentrations in Taihu Lake Based on GOCI Data].

The TP concentration is an important index of water quality and an important influencing factor of eutrophication and algae blooms. Remote sensing technology has advantages of wide scope and high time limited efficacy. Monitoring the concentration of TP by satellite remote sensing is important for the study of water quality and eutrophication. In situ datasets collected during the three times of experiments in Taihu Lake between 2013 and 2014 were used to develop the TP inversion model based on GOCI data. The GOCI data in spring, summer, autumn and winter in 2014 were selected to analyze the time and space changes of TP concentration in Taihu Lake. The results showed that the TP algorithm was built up based on the variables, which was to use the eight band combination of GOCI data as variable, and build model using Multi factor linear regression method. The algorithm achieved more accurate TP estimation with R² = 0.898, MAPE = 14.296%, RMSE = 0.026 mg · L⁻¹. Meantime, a analysis on the precision of the model by using the measured sample points and the synchronous satellite images with MAPE = 33.642%, 22.551%, RMSE = 0.076 mg · L⁻¹, 0.028 mg · L⁻¹ on August 5, 2014 and October 24, 2014. Through the analysis of the 30 images on the four days of the four seasons, it showed that the absolute concentration of total phosphorus was different in different seasons. But temporal and spatial distribution of total phosphorus concentration was similar in the morning and afternoon. In spatial distribution, the TP concentration in Meiliang Bay, Zhushan Bay, Gonghu Bay, Xiaomei Port and Changdou Port in the southwest coast was at a continuously high position. The TP concentration change in different regions was influenced by wind direction, wind speed and other factors. The TP concentration highest in the morning, and then gradually decreased, this phenomenon reflected that the TP concentration was affected by temperature and light.

[Muti-model collaborative retrieval of chlorophyll a in Taihu lake based on data assimilation ].

Under the efforts of many scholars, large amount of remote retrieval models of water quality parameters have been developed. However, each model could only reflect the "true value" from one level because of the natural limitation of remote sensing. To get the relatively true value by combining various retrieval models, in this work, we developed a multi-model collaborative retrieval algorithm for retrieving the concentration of Chlorophyll a based on data assimilation. We measured water quality parameters and water reflectance spectra in Taihu Lake during 2006 to 2009. There were seven retrieve models established and six models were selected to participate in the multi-model collaborative retrieval algorithm. Then these selected models were combined to establish a multi-model for retrieving the concentration of Chlorophyll a. The results indicated: (1) the accuracy of multi-model retrieval algorithm was better than that of single-model retrieval method, with an optimal MAPE of only 22. 4% ; (2) with more models participating in the multi-model collaborative retrieval algorithm, the accuracy became better, the average MAPE was decreased from 25. 6% to 23. 4% , the average RMSE was decreased from 15. 082 µg.L-1 to 14. 575 µg.L-1, and the average correlation coefficient was improved from 0.91 to 0. 92; (3) the accuracy and errors of retrieval products could be effective evaluated through calculating the confidence interval, which makes possible the acquirement of spatial and temporal error distribution of Chlorophyll a concentration retrieval in Taihu Lake.