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Kim S, Chung S. Causal impact analysis of weir opening on cyanobacterial blooms and water quality in the Yeongsan River, Korea: A bayesian structural time-series analysis and median difference test. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 924:171646. [PMID: 38479532 DOI: 10.1016/j.scitotenv.2024.171646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 02/14/2024] [Accepted: 03/09/2024] [Indexed: 03/17/2024]
Abstract
The construction of weirs in Korea's Four Major Rivers Project has led to an increase in cyanobacterial blooms, posing environmental challenges. To address this, the government began opening weirs in 2017. However, interpreting experimental results has proven to be complex due to the multifaceted nature of blooms. This study aimed to assess the impact of opening the Juksan Weir on cyanobacterial blooms and water quality in the Yeongsan River. Using a median difference test (MDT) and causal impact analysis (CIA) with Bayesian structural time-series (BSTS) models, changes in cyanobacterial cell density (Cyano) and chlorophyll a concentration (Chl-a) before (January 2013 to June 2017) and after (July 2017 to December 2021) the weir-opening event were analyzed. The MDT revealed no significant change in Cyano post-weir opening (p = 0.267), but Chl-a significantly increased by 48.1 % (p < 0.01). As a result of CIA, Cyano decreased, albeit statistically insignificantly (p = 0.454), while Chl-a increased by 59.0 % (p < 0.01). These findings contradict the expectation that Cyano decrease due to the increased flow velocity resulting from weir opening. The absence of changes in Cyano and the increase in Chl-a can be attributed to several factors, including the constrained and inadequate duration of full weir opening combined with conducive conditions for the proliferation of other algae such as diatoms and green algae. These findings suggest that the effectiveness of weir opening in controlling Cyano may have been compromised by factors influencing the overall aquatic ecosystem dynamics. Further analysis revealed that factors such as elevated water temperatures (≥ 30 °C) and reduced flow rates (< 37 m3/s) contributed to the flourishing of cyanobacteria, whose concentrations exceeded 10,000 cells/mL. In analyzing causal relationships in environmental management, especially when there are complex causal interactions, the application of MDT and CIA provides valuable insights.
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Affiliation(s)
- Sungjin Kim
- Department of Environmental Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea
| | - Sewoong Chung
- Department of Environmental Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea.
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2
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Thawabteh AM, Naseef HA, Karaman D, Bufo SA, Scrano L, Karaman R. Understanding the Risks of Diffusion of Cyanobacteria Toxins in Rivers, Lakes, and Potable Water. Toxins (Basel) 2023; 15:582. [PMID: 37756009 PMCID: PMC10535532 DOI: 10.3390/toxins15090582] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 08/10/2023] [Accepted: 09/18/2023] [Indexed: 09/28/2023] Open
Abstract
Blue-green algae, or cyanobacteria, may be prevalent in our rivers and tap water. These minuscule bacteria can grow swiftly and form blooms in warm, nutrient-rich water. Toxins produced by cyanobacteria can pollute rivers and streams and harm the liver and nervous system in humans. This review highlights the properties of 25 toxin types produced by 12 different cyanobacteria genera. The review also covered strategies for reducing and controlling cyanobacteria issues. These include using physical or chemical treatments, cutting back on fertilizer input, algal lawn scrubbers, and antagonistic microorganisms for biocontrol. Micro-, nano- and ultrafiltration techniques could be used for the removal of internal and extracellular cyanotoxins, in addition to powdered or granular activated carbon, ozonation, sedimentation, ultraviolet radiation, potassium permanganate, free chlorine, and pre-treatment oxidation techniques. The efficiency of treatment techniques for removing intracellular and extracellular cyanotoxins is also demonstrated. These approaches aim to lessen the risks of cyanobacterial blooms and associated toxins. Effective management of cyanobacteria in water systems depends on early detection and quick action. Cyanobacteria cells and their toxins can be detected using microscopy, molecular methods, chromatography, and spectroscopy. Understanding the causes of blooms and the many ways for their detection and elimination will help the management of this crucial environmental issue.
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Affiliation(s)
- Amin Mahmood Thawabteh
- Faculty of Pharmacy, Nursing and Health Professions, Birzeit University, Ramallah 00972, Palestine; (A.M.T.); (H.A.N.)
- General Safety Section, General Services Department, Birzeit University, Bir Zeit 71939, Palestine
| | - Hani A Naseef
- Faculty of Pharmacy, Nursing and Health Professions, Birzeit University, Ramallah 00972, Palestine; (A.M.T.); (H.A.N.)
| | - Donia Karaman
- Faculty of Pharmacy, Al-Quds University, Jerusalem 20002, Palestine;
| | - Sabino A. Bufo
- Department of Sciences, University of Basilicata, Via dell’Ateneo Lucano 10, 85100 Potenza, Italy;
- Department of Geography, Environmental Management and Energy Studies, University of Johannesburg, Auckland Park Kingsway Campus, Johannesburg 2092, South Africa
| | - Laura Scrano
- Department of European and Mediterranean Cultures, University of Basilicata, Via Lanera 20, 75100 Matera, Italy;
| | - Rafik Karaman
- Faculty of Pharmacy, Al-Quds University, Jerusalem 20002, Palestine;
- Department of Sciences, University of Basilicata, Via dell’Ateneo Lucano 10, 85100 Potenza, Italy;
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3
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Ma L, Maldonado JFG, Zamyadi A, Dorner S, Prévost M. Monitoring of cyanobacterial breakthrough and accumulation by in situ phycocyanin probe system within full-scale treatment plants. ENVIRONMENTAL MONITORING AND ASSESSMENT 2023; 195:1042. [PMID: 37589790 PMCID: PMC10435606 DOI: 10.1007/s10661-023-11657-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 07/31/2023] [Indexed: 08/18/2023]
Abstract
Worldwide, there has been an increase in the presence of potentially toxic cyanobacterial blooms in drinking water sources and within drinking water treatment plants (DWTPs). The objective of this study is to validate the use of in situ probes for the detection and management of cyanobacterial breakthrough in high and low-risk DWTPs. In situ phycocyanin YSI EXO2 probes were devised for remote control and data logging to monitor the cyanobacteria in raw, clarified, filtered, and treated water in three full-scale DWTPs. An additional probe was installed inside the sludge holding tank to measure the water quality of the surface of the sludge storage tank in a high-risk DWTP. Simultaneous grab samplings were carried out for taxonomic cell counts and toxin analysis. A total of 23, 9, and 4 field visits were conducted at the three DWTPs. Phycocyanin readings showed a 93-fold fluctuation within 24 h in the raw water of the high cyanobacterial risk plant, with higher phycocyanin levels during the afternoon period. These data provide new information on the limitations of weekly or daily grab sampling. Also, different moving averages for the phycocyanin probe readings can be used to improve the interpretation of phycocyanin signal trends. The in situ probe successfully detected high cyanobacterial biovolumes entering the clarification process in the high-risk plant. Grab sampling results revealed high cyanobacterial biovolumes in the sludge for both high and low-risk plants.
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Affiliation(s)
- Liya Ma
- Department of Civil, Geological, and Mining Engineering, Polytechnique Montreal, Montreal, QC, H3C 3A7, Canada.
| | | | - Arash Zamyadi
- Department of Civil Engineering, Monash University, Clayton Campus, Melbourne, Australia
| | - Sarah Dorner
- Department of Civil, Geological, and Mining Engineering, Polytechnique Montreal, Montreal, QC, H3C 3A7, Canada
| | - Michèle Prévost
- Department of Civil, Geological, and Mining Engineering, Polytechnique Montreal, Montreal, QC, H3C 3A7, Canada
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Gorgan-Mohammadi F, Rajaee T, Zounemat-Kermani M. Investigating machine learning models in predicting lake water quality parameters as a 3-year moving average. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:63839-63863. [PMID: 37059948 DOI: 10.1007/s11356-023-26830-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 04/03/2023] [Indexed: 04/16/2023]
Abstract
Lake water quality plays a vital role in the lake ecosystem, including biotic (for living creatures, such as plants, animals, and micro-organisms) and abiotic interactions. In this research, various types of machine learning (ML) methodologies, such as classification and regression tree (CART), chi-squared automatic interaction detector (CHAID), C5 tree, quick, unbiased, and efficient statistical tree (QUEST), along with multilayer perceptron (MLP) neural network, and radial basis function (RBF) neural network, are employed to predict the concentration of water quality parameters (P, EC, TDS, pH, DO, NH3, SO4, and θ). Lake Erie is situated at the international border of the USA and Canada. The C5 tree and QUEST tree are used to classify data and predict the number of groups, while the other methods are used to predict the concentration of water quality parameters in the form of a 3-year moving average. The greater matching between the observed and predicted data of dissolved oxygen (NSE = 0.978, bias = 0.126) shows that the CART decision tree has higher accuracy in correctly detecting the concentration of this parameter. The C5 tree could identify 33 groups correctly out of 36 total groups, which shows better accuracy for the C5 tree in classifying the data for this parameter.
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Affiliation(s)
| | - Taher Rajaee
- Department of Civil Engineering, University of Qom, Qom, Iran
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5
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Ai H, Zhang K, Sun J, Zhang H. Short-term Lake Erie algal bloom prediction by classification and regression models. WATER RESEARCH 2023; 232:119710. [PMID: 36801534 DOI: 10.1016/j.watres.2023.119710] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 01/31/2023] [Accepted: 02/04/2023] [Indexed: 06/18/2023]
Abstract
The recent outbreaks of harmful algal blooms in the western Lake Erie Basin (WLEB) have drawn tremendous attention to bloom prediction for better control and management. Many weekly to annual bloom prediction models have been reported, but they only employ small datasets, have limited types of input features, build linear regression or probabilistic models, or require complex process-based computations. To address these limitations, we conducted a comprehensive literature review, complied a large dataset containing chlorophyll-a index (from 2002 to 2019) as the output and a novel combination of riverine (the Maumee & Detroit Rivers) and meteorological (WLEB) features as the input, and built machine learning-based classification and regression models for 10-d scale bloom predictions. By analyzing the feature importance, we identified 8 most important features for the HAB control, including nitrogen loads, time, water levels, soluble reactive phosphorus load, and solar irradiance. Here, both long- and short-term nitrogen loads were for the first time considered in HAB models for Lake Erie. Based on these features, the 2-, 3-, and 4-level random forest classification models achieved an accuracy of 89.6%, 77.0%, and 66.7%, respectively, and the regression model achieved an R2 value of 0.69. In addition, long-short term memory (LSTM) was implemented to predict temporal trends of four short-term features (N, solar irradiance, and two water levels) and achieved the Nash-Sutcliffe efficiency of 0.12-0.97. Feeding the LSTM model predictions for these features into the 2-level classification model reached an accuracy of 86.0% for predicting the HABs in 2017-2018, suggesting that we can provide short-term HAB forecasts even when the feature values are not available.
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Affiliation(s)
- Haiping Ai
- Department of Civil and Environmental Engineering, Case Western Reserve University, Cleveland, OH 44106, United States
| | - Kai Zhang
- Department of Civil and Environmental Engineering, Case Western Reserve University, Cleveland, OH 44106, United States
| | - Jiachun Sun
- Department of Civil and Environmental Engineering, Case Western Reserve University, Cleveland, OH 44106, United States
| | - Huichun Zhang
- Department of Civil and Environmental Engineering, Case Western Reserve University, Cleveland, OH 44106, United States.
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6
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Chlorophyll soft-sensor based on machine learning models for algal bloom predictions. Sci Rep 2022; 12:13529. [PMID: 35941263 PMCID: PMC9360045 DOI: 10.1038/s41598-022-17299-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 07/22/2022] [Indexed: 11/08/2022] Open
Abstract
Harmful algal blooms (HABs) are a growing concern to public health and aquatic ecosystems. Long-term water monitoring conducted by hand poses several limitations to the proper implementation of water safety plans. This work combines automatic high-frequency monitoring (AFHM) systems with machine learning (ML) techniques to build a data-driven chlorophyll-a (Chl-a) soft-sensor. Massive data for water temperature, pH, electrical conductivity (EC) and system battery were taken for three years at intervals of 15 min from two different areas of As Conchas freshwater reservoir (NW Spain). We designed a set of soft-sensors based on compact and energy efficient ML algorithms to infer Chl-a fluorescence by using low-cost input variables and to be deployed on buoys with limited battery and hardware resources. Input and output aggregations were applied in ML models to increase their inference performance. A component capable of triggering a 10 [Formula: see text]g/L Chl-a alert was also developed. The results showed that Chl-a soft-sensors could be a rapid and inexpensive tool to support manual sampling in water bodies at risk.
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7
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A Spatial Long-Term Trend Analysis of Estimated Chlorophyll-a Concentrations in Utah Lake Using Earth Observation Data. REMOTE SENSING 2022. [DOI: 10.3390/rs14153664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
We analyzed chlorophyll-a (chl-a) concentrations in shallow, turbid Utah Lake using Landsat data from 1984 to 2021. Utah Lake is ~40 km by 21 km, has a surface area of ~390 km2, an average depth of ~3 m, and loses ~50% of inflow to evaporation. This limits spatial mixing, allowing us to evaluate impacts on smaller lake regions. We evaluated long-term trends at the pixel level and for areas related to boundary conditions. We created 17 study areas based on differences in shoreline development and nutrient inflows. We expected impacted areas to exhibit increasing chl-a trends, as population growth and development in the Utah Lake watershed have been significant. We used the non-parametric Mann–Kendall test to evaluate trends. The majority of the lake exhibited decreasing trends, with a few pixels in Provo and Goshen Bays exhibiting slight increasing or no trends. We estimated trend magnitudes using Sen’s slope and fitted linear regression models. Trend magnitudes in all pixels (and regions), both decreasing and increasing, were small; with the largest decreasing and increasing trends being about −0.05 and −0.005 µg/L/year, and about 0.1 and 0.005 µg/L/year for the Sen’s slope and linear regression slope, respectively. Over the ~40 year-period, this would result in average decreases of 2 to 0.2 µg/L or increases of 4 and 0.2 µg/L. All the areas exhibited decreasing trends, but the monthly trends in some areas exhibited no trends rather than decreasing trends. Monthly trends for some areas showed some indications that algal blooms are occurring earlier, though evidence is inconclusive. We found essentially no change in algal concentrations in Utah Lake at either the pixel level or for the analysis regions since the 1980′s; despite significant population expansion; increased nutrient inflows; and land-use changes. This result matches prior research and supports the hypothesis that algal growth in Utah Lake is not limited by direct nutrient inflows but limited by other factors.
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Tan F, Xiao P, Yang JR, Chen H, Jin L, Yang Y, Lin TF, Willis A, Yang J. Precision early detection of invasive and toxic cyanobacteria: A case study of Raphidiopsis raciborskii. HARMFUL ALGAE 2021; 110:102125. [PMID: 34887005 DOI: 10.1016/j.hal.2021.102125] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 10/13/2021] [Accepted: 10/16/2021] [Indexed: 06/13/2023]
Abstract
Blooms of the toxic cyanobacterium, Raphidiopsis raciborskii (basionym Cylindrospermopsis raciborskii), are becoming a major environmental issue in freshwater ecosystems globally. Our precision prevention and early detection of R. raciborskii blooms rely upon the accuracy and speed of the monitoring method. A duplex digital PCR (dPCR) monitoring approach was developed and validated to detect the abundance and toxin-producing potential of R. raciborskii simultaneously in both laboratory spiked and environmental samples. Results of dPCR were strongly correlated with traditional real time quantitative PCR (qPCR) and microscopy for both laboratory and environmental samples. However, discrepancies between methods were observed when measuring R. raciborskii at low abundance (1 - 105 cells L - 1), with dPCR showing a higher precision compared to qPCR at low cell concentration. Furthermore, the dPCR assay had the highest detection rate for over two hundred environmental samples especially under low abundance conditions, followed by microscopy and qPCR. dPCR assay had the advantages of simple operation, time-saving, high sensitivity and excellent reproducibility. Therefore, dPCR would be a fast and precise monitoring method for the early warning of toxic bloom-forming cyanobacterial species and assessment of water quality risks, which can improve prediction and prevention of the impacts of harmful cyanobacterial bloom events in inland waters.
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Affiliation(s)
- Fengjiao Tan
- Aquatic EcoHealth Group, Fujian Key Laboratory of Watershed Ecology, Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Peng Xiao
- Aquatic EcoHealth Group, Fujian Key Laboratory of Watershed Ecology, Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
| | - Jun R Yang
- Aquatic EcoHealth Group, Fujian Key Laboratory of Watershed Ecology, Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; Engineering Research Center of Ecology and Agricultural Use of Wetland (Ministry of Education), College of Agriculture, Yangtze University, Jingzhou 434025, China
| | - Huihuang Chen
- Aquatic EcoHealth Group, Fujian Key Laboratory of Watershed Ecology, Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lei Jin
- Aquatic EcoHealth Group, Fujian Key Laboratory of Watershed Ecology, Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yigang Yang
- Aquatic EcoHealth Group, Fujian Key Laboratory of Watershed Ecology, Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tsair-Fuh Lin
- Department of Environmental Engineering, National Cheng Kung University, Tainan 70101, Taiwan
| | - Anusuya Willis
- Australian National Algae Culture Collection, CSIRO, Hobart 7000, Tasmania, Australia
| | - Jun Yang
- Aquatic EcoHealth Group, Fujian Key Laboratory of Watershed Ecology, Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China.
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9
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Hong SM, Baek SS, Yun D, Kwon YH, Duan H, Pyo J, Cho KH. Monitoring the vertical distribution of HABs using hyperspectral imagery and deep learning models. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 794:148592. [PMID: 34217087 DOI: 10.1016/j.scitotenv.2021.148592] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 05/13/2021] [Accepted: 06/17/2021] [Indexed: 06/13/2023]
Abstract
Remote sensing techniques have been applied to monitor the spatiotemporal variation of harmful algal blooms (HABs) in many inland waters. However, these studies have been limited to monitor the vertical distribution of HABs due to the optical complexity of inland water. Therefore, this study applied a deep neural network model to monitor the vertical distribution of Chlorophyll-a (Chl-a), phycocyanin (PC), and turbidity (Turb) using drone-borne hyperspectral imagery, in-situ measurement, and meteoroidal data. The pigment concentrations were measured between depths of 0 m and 5.0 m with 0.05 m intervals. Here, four state-of-the-art data-driven model structures (ResNet-18, ResNet-101, GoogLeNet, and Inception v3) were adopted for estimating the vertical distributions of the harmful algal pigments. Among the four models, the ResNet-18 model showed the best performance, with an R2 value of 0.70. In addition, Gradient-weighted Class Activation Mapping (Grad-CAM) substantially provided informative reflectance band ranges near 490 nm and 620 nm in the hyperspectral image for the vertical estimation of pigments. Therefore, this study demonstrated that the explainable deep learning model with drone-borne hyperspectral images has the potential to estimate Chl-a, PC, and Turb vertical distributions and to show influential features that contribute to describing the vertical profile phenomena.
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Affiliation(s)
- Seok Min Hong
- School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 689-798, Republic of Korea
| | - Sang-Soo Baek
- School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 689-798, Republic of Korea
| | - Daeun Yun
- School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 689-798, Republic of Korea
| | - Yong-Hwan Kwon
- Electronics and Telecommunication Research Institute, 218 Gajeong-ro, Yeseong-gu, Daejeon 305-700, Republic of Korea
| | - Hongtao Duan
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
| | - JongCheol Pyo
- Center for Environmental Data Strategy, Korea Environment Institute, Sejong 30147, Republic of Korea.
| | - Kyung Hwa Cho
- School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 689-798, Republic of Korea.
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10
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Akyol Ç, Ozbayram EG, Accoroni S, Radini S, Eusebi AL, Gorbi S, Vignaroli C, Bacchiocchi S, Campacci D, Gigli F, Farina G, Albay M, Fatone F. Monitoring of cyanobacterial blooms and assessing polymer-enhanced microfiltration and ultrafiltration for microcystin removal in an Italian drinking water treatment plant. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 286:117535. [PMID: 34119863 DOI: 10.1016/j.envpol.2021.117535] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 05/22/2021] [Accepted: 06/02/2021] [Indexed: 06/12/2023]
Abstract
The water intake of a drinking water treatment plant (DWTP) in Central Italy was monitored over six bloom seasons for cyanotoxin severity, which supplies drinking water from an oligo-mesotrophic lake with microcystin levels up to 10.3 μg/L. The historical data showed that the water temperature did not show extreme/large seasonal variation and it was not correlated either with cyanobacterial growth or microcystin concentration. Among all parameters, the cyanobacteria growth was negatively correlated with humidity and manganese and positively correlated with atmospheric temperature. No significant correlation was found between microcystin concentration and the climatic parameters. Polymer(chitosan)-enhanced microfiltration (PEMF) and ultrafiltration (PEUF) were further tested as an alternative microcystin removal approach from dense cyanobacteria-rich flows. The dominant cyanobacteria in the water intake, Planktothrix rubescens, was isolated and enriched to simulate cyanobacterial blooms in the lake. The PEMF and PEUF were separately applied to enriched P. rubescens culture (PC) (microcystin = 1.236 μg/L) as well as to the sand filter backwash water (SFBW) of the DWTP where microcystin concentration was higher than 12 μg/L. The overall microcystin removal rates from the final effluent of PC (always <0.15 μg/L) were between 90.1-94.7% and 89.5-95.4% using 4 and 20 mg chitosan/L, respectively. Meanwhile, after the PEMF and PEUF of SFBW, the final effluent contained only 0.099 and 0.057 μg microcystin/L with an overall removal >99%. The presented results are the first from the application of chitosan to remove P. rubescens as well as the implementation of PEMF and PEUF on SFBW to remove cyanobacterial cells and associated toxins.
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Affiliation(s)
- Çağrı Akyol
- Department of Science and Engineering of Materials, Environment and Urban Planning-SIMAU, Marche Polytechnic University, 60131, Ancona, Italy
| | - E Gozde Ozbayram
- Department of Marine and Freshwater Resources Management, Faculty of Aquatic Sciences, Istanbul University, Fatih, 34134, Istanbul, Turkey.
| | - Stefano Accoroni
- Department of Life and Environmental Sciences, Marche Polytechnic University, 60131, Ancona, Italy; Istituto Zooprofilattico Umbria e Marche, Via Cupa di Posatora, 3, 60100, Ancona, Italy
| | - Serena Radini
- Department of Science and Engineering of Materials, Environment and Urban Planning-SIMAU, Marche Polytechnic University, 60131, Ancona, Italy
| | - Anna Laura Eusebi
- Department of Science and Engineering of Materials, Environment and Urban Planning-SIMAU, Marche Polytechnic University, 60131, Ancona, Italy
| | - Stefania Gorbi
- Department of Life and Environmental Sciences, Marche Polytechnic University, 60131, Ancona, Italy
| | - Carla Vignaroli
- Department of Life and Environmental Sciences, Marche Polytechnic University, 60131, Ancona, Italy
| | - Simone Bacchiocchi
- Istituto Zooprofilattico Umbria e Marche, Via Cupa di Posatora, 3, 60100, Ancona, Italy
| | - Debora Campacci
- Istituto Zooprofilattico Umbria e Marche, Via Cupa di Posatora, 3, 60100, Ancona, Italy
| | - Fabiola Gigli
- Acquambiente Marche S.r.l., Via Recanatese 27/I, 60022, Castelfidardo, Italy
| | - Giuseppe Farina
- Acquambiente Marche S.r.l., Via Recanatese 27/I, 60022, Castelfidardo, Italy
| | - Meric Albay
- Department of Marine and Freshwater Resources Management, Faculty of Aquatic Sciences, Istanbul University, Fatih, 34134, Istanbul, Turkey
| | - Francesco Fatone
- Department of Science and Engineering of Materials, Environment and Urban Planning-SIMAU, Marche Polytechnic University, 60131, Ancona, Italy
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11
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Ho JC, Michalak AM, Pahlevan N. Reply to: Concerns about phytoplankton bloom trends in global lakes. Nature 2021; 590:E48-E50. [PMID: 33597758 DOI: 10.1038/s41586-021-03255-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Jeff C Ho
- Department of Global Ecology, Carnegie Institution for Science, Stanford, CA, USA. .,Department of Civil & Environmental Engineering, Stanford University, Stanford, CA, USA.
| | - Anna M Michalak
- Department of Global Ecology, Carnegie Institution for Science, Stanford, CA, USA.
| | - Nima Pahlevan
- NASA Goddard Space Flight Center, Greenbelt, MD, USA.,Science Systems and Applications Inc., Lanham, MD, USA
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12
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Del Giudice D, Fang S, Scavia D, Davis TW, Evans MA, Obenour DR. Elucidating controls on cyanobacteria bloom timing and intensity via Bayesian mechanistic modeling. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 755:142487. [PMID: 33035987 DOI: 10.1016/j.scitotenv.2020.142487] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 09/17/2020] [Accepted: 09/17/2020] [Indexed: 06/11/2023]
Abstract
The adverse impacts of harmful algal blooms (HABs) are increasing worldwide. Lake Erie is a North American Great Lake highly affected by cultural eutrophication and summer cyanobacterial HABs. While phosphorus loading is a known driver of bloom size, more nuanced yet crucial questions remain. For example, it is unclear what mechanisms are primarily responsible for initiating cyanobacterial dominance and subsequent biomass accumulation. To address these questions, we develop a mechanistic model describing June-October dynamics of chlorophyll a, nitrogen, and phosphorus near the Maumee River outlet, where blooms typically initiate and are most severe. We calibrate the model to a new, geostatistically-derived dataset of daily water quality spanning 2008-2017. A Bayesian framework enables us to embed prior knowledge on system characteristics and test alternative model formulations. Overall, the best model formulation explains 42% of the variability in chlorophyll a and 83% of nitrogen, and better captures bloom timing than previous models. Our results, supported by cross validation, show that onset of the major midsummer bloom is associated with about a month of water temperatures above 20 °C (occurring 19 July to 6 August), consistent with when cyanobacteria dominance is usually reported. Decreased phytoplankton loss rate is the main factor enabling biomass accumulation, consistent with reduced zooplankton grazing on cyanobacteria. The model also shows that phosphorus limitation is most severe in August, and nitrogen limitation tends to occur in early autumn. Our results highlight the role of temperature in regulating bloom initiation and subsequent loss rates, and suggest that a 2 °C increase could lead to blooms that start about 10 days earlier and grow 23% more intense.
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Affiliation(s)
- Dario Del Giudice
- Department of Civil, Construction & Environmental Engineering, NC State University, Raleigh, NC 27695, USA.
| | - Shiqi Fang
- Department of Civil, Construction & Environmental Engineering, NC State University, Raleigh, NC 27695, USA
| | - Donald Scavia
- School for Environment and Sustainability, University of Michigan, Ann Arbor, MI 48104, USA
| | - Timothy W Davis
- Department of Biological Sciences, Bowling Green State University, Bowling Green, OH 43403, USA
| | - Mary Anne Evans
- U.S. Geological Survey, Great Lakes Science Center, Ann Arbor, MI 48105, USA
| | - Daniel R Obenour
- Department of Civil, Construction & Environmental Engineering, NC State University, Raleigh, NC 27695, USA; Center for Geospatial Analytics, NC State University, Raleigh, NC 27695, USA
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13
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Chen N, Wang S, Zhang X, Yang S. A risk assessment method for remote sensing of cyanobacterial blooms in inland waters. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 740:140012. [PMID: 32569911 DOI: 10.1016/j.scitotenv.2020.140012] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 05/27/2020] [Accepted: 06/04/2020] [Indexed: 06/11/2023]
Abstract
The widespread occurrence of Cyanobacterial blooms (CABs) in inland waters is a typical and severe challenge for water resources management and environment protection. An accurate and spatially continuous risk assessment of CABs is critical for prediction and preparedness in advance. In this study, a multivariate integrated risk assessment (MIRA) method of CABs in inland waters was proposed. MIRA was simplified with the trophic levels, cyanobacterial and other aquatic plant condition using remote sensing indexes, including the Trophic State Index (TSI), Floating Algae Index (FAI) and Cyanobacteria and Macrophytes Index (CMI). First, the dates of risk assessment were carefully selected based on TSI. Then, we obtained the trophic levels, cyanobacterial, and other aquatic plant condition of water using TSI, CMI and FAI on the selected date, and further scored them pixel by pixel to quantify the risk value. Finally, the risk of CABs in water was accurately assessed based on the pixel risk value. Based on Landsat 8 OLI dataset, MIRA was executed and validated in three different lakes of Wuhan urban agglomeration (WUA) with different trophic states. The results demonstrated that the risk of CABs in Lake LongGan was overall higher than that in Lake LiangZi and Lake FuTou. And the risk of CABs in the east part of Lake LongGan was higher than the other parts. Seasonally, the risk level ranking in Lake LiangZi was the highest in summer, while lowest in winter. However, the seasonal risk ranking was spring, summer, autumn, and winter in Lake LongGan. Based on the comparisons with monthly water quality classification data and results of the existing study, including trophic level, ecology risk, and algal extent, the MIRA method was valuable for accurate and spatially continuous identifying the risk of CABs in inland waters with potential eutrophication trends.
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Affiliation(s)
- Nengcheng Chen
- State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China.; Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China
| | - Siqi Wang
- State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China
| | - Xiang Zhang
- State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China..
| | - Shangbo Yang
- State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China
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14
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Oleksy IA, Beck WS, Lammers RW, Steger CE, Wilson C, Christianson K, Vincent K, Johnson G, Johnson PTJ, Baron JS. The role of warm, dry summers and variation in snowpack on phytoplankton dynamics in mountain lakes. Ecology 2020; 101:e03132. [PMID: 32628277 PMCID: PMC7583380 DOI: 10.1002/ecy.3132] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Revised: 02/28/2020] [Accepted: 05/21/2020] [Indexed: 11/08/2022]
Abstract
Climate change is altering biogeochemical, metabolic, and ecological functions in lakes across the globe. Historically, mountain lakes in temperate regions have been unproductive because of brief ice-free seasons, a snowmelt-driven hydrograph, cold temperatures, and steep topography with low vegetation and soil cover. We tested the relative importance of winter and summer weather, watershed characteristics, and water chemistry as drivers of phytoplankton dynamics. Using boosted regression tree models for 28 mountain lakes in Colorado, we examined regional, intraseasonal, and interannual drivers of variability in chlorophyll a as a proxy for lake phytoplankton. Phytoplankton biomass was inversely related to the maximum snow water equivalent (SWE) of the previous winter, as others have found. However, even in years with average SWE, summer precipitation extremes and warming enhanced phytoplankton biomass. Peak seasonal phytoplankton biomass coincided with the warmest water temperatures and lowest nitrogen-to-phosphorus ratios. Although links between snowpack, lake temperature, nutrients, and organic-matter dynamics are increasingly recognized as critical drivers of change in high-elevation lakes, our results highlight the additional influence of summer conditions on lake productivity in response to ongoing changes in climate. Continued changes in the timing, type, and magnitude of precipitation in combination with other global-change drivers (e.g., nutrient deposition) will affect production in mountain lakes, potentially shifting these historically oligotrophic lakes toward new ecosystem states. Ultimately, a deeper understanding of these drivers and pattern at multiple scales will allow us to anticipate ecological consequences of global change better.
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Affiliation(s)
- Isabella A. Oleksy
- Natural Resource Ecology LaboratoryColorado State UniversityFort CollinsColorado80526USA
- Cary Institute of Ecosystem StudiesMillbrookNew York12545USA
| | - Whitney S. Beck
- Department of BiologyColorado State UniversityFort CollinsColorado80526USA
| | | | - Cara E. Steger
- Cary Institute of Ecosystem StudiesMillbrookNew York12545USA
| | - Codie Wilson
- Department of GeosciencesColorado State UniversityFort CollinsColorado80526USA
| | - Kyle Christianson
- Department of Fish, Wildlife, and Conservation BiologyColorado State UniversityFort CollinsColorado80526USA
| | - Kim Vincent
- Department of Ecology and Evolutionary BiologyUniversity of ColoradoBoulderColorado80309USA
| | - Gunnar Johnson
- Department of GeologyPortland State UniversityPortlandOregon97201USA
| | - Pieter T. J. Johnson
- Department of Ecology and Evolutionary BiologyUniversity of ColoradoBoulderColorado80309USA
| | - J. S. Baron
- Natural Resource Ecology LaboratoryColorado State UniversityFort CollinsColorado80526USA
- U.S. Geological SurveyFort CollinsColorado80526USA
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15
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Rousso BZ, Bertone E, Stewart R, Hamilton DP. A systematic literature review of forecasting and predictive models for cyanobacteria blooms in freshwater lakes. WATER RESEARCH 2020; 182:115959. [PMID: 32531494 DOI: 10.1016/j.watres.2020.115959] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Revised: 05/06/2020] [Accepted: 05/17/2020] [Indexed: 06/11/2023]
Abstract
Cyanobacteria harmful blooms (CyanoHABs) in lakes and reservoirs represent a major risk for water authorities globally due to their toxicity and economic impacts. Anticipating bloom occurrence and understanding the main drivers of CyanoHABs are needed to optimize water resources management. An extensive review of the application of CyanoHABs forecasting and predictive models was performed, and a summary of the current state of knowledge, limitations and research opportunities on this topic is provided through analysis of case studies. Two modelling approaches were used to achieve CyanoHABs anticipation; process-based (PB) and data-driven (DD) models. The objective of the model was a determining factor for the choice of modelling approach. PB models were more frequently used to predict future scenarios whereas DD models were employed for short-term forecasts. Each modelling approach presented multiple variations that may be applied for more specific, targeted purposes. Most models reviewed were site-specific. The monitoring methodologies, including data frequency, uncertainty and precision, were identified as a major limitation to improve model performance. A lack of standardization of both model output and performance metrics was observed. CyanoHAB modelling is an interdisciplinary topic and communication between disciplines should be improved to facilitate model comparisons. These shortcomings can hinder the adoption of modelling tools by practitioners. We suggest that water managers should focus on generalising models for lakes with similar characteristics and where possible use high frequency monitoring for model development and validation.
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Affiliation(s)
- Benny Zuse Rousso
- Griffith School of Engineering and Built Environment, Griffith University, Parklands Drive, Southport, Queensland, 4222, Australia; Cities Research Institute, Griffith University, Parklands Drive, Southport, Queensland, 4222, Australia
| | - Edoardo Bertone
- Griffith School of Engineering and Built Environment, Griffith University, Parklands Drive, Southport, Queensland, 4222, Australia; Cities Research Institute, Griffith University, Parklands Drive, Southport, Queensland, 4222, Australia; Australian Rivers Institute, Griffith University, 170 Kessels Road, Nathan, Queensland, 4111, Australia.
| | - Rodney Stewart
- Griffith School of Engineering and Built Environment, Griffith University, Parklands Drive, Southport, Queensland, 4222, Australia; Cities Research Institute, Griffith University, Parklands Drive, Southport, Queensland, 4222, Australia
| | - David P Hamilton
- Australian Rivers Institute, Griffith University, 170 Kessels Road, Nathan, Queensland, 4111, Australia
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16
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Francy DS, Brady AMG, Stelzer EA, Cicale JR, Hackney C, Dalby HD, Struffolino P, Dwyer DF. Predicting microcystin concentration action-level exceedances resulting from cyanobacterial blooms in selected lake sites in Ohio. ENVIRONMENTAL MONITORING AND ASSESSMENT 2020; 192:513. [PMID: 32666330 PMCID: PMC7360538 DOI: 10.1007/s10661-020-08407-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Accepted: 06/03/2020] [Indexed: 06/11/2023]
Abstract
Cyanobacterial harmful algal blooms and the toxins they produce are a global water-quality problem. Monitoring and prediction tools are needed to quickly predict cyanotoxin action-level exceedances in recreational and drinking waters used by the public. To address this need, data were collected at eight locations in Ohio, USA, to identify factors significantly related to observed concentrations of microcystins (a freshwater cyanotoxin) that could be used in two types of site-specific regression models. Real-time models include easily or continuously-measured factors that do not require that a sample be collected; comprehensive models use a combination of discrete sample-based measurements and real-time factors. The study sites included two recreational sites and six water treatment plant sites. Real-time models commonly included variables such as phycocyanin, pH, specific conductance, and streamflow or gage height. Many real-time factors were averages over time periods antecedent to the time the microcystin sample was collected, including water-quality data compiled from continuous monitors. Comprehensive models were useful at some sites with lagged variables for cyanobacterial toxin genes, dissolved nutrients, and (or) nitrogen to phosphorus ratios. Because models can be used for management decisions, important measures of model performance were sensitivity, specificity, and accuracy of estimates above or below the microcystin concentration threshold standard or action level. Sensitivity is how well the predictive tool correctly predicts exceedance of a threshold, an important measure for water-resource managers. Sensitivities > 90% at four Lake Erie water treatment plants indicated that models with continuous monitor data were especially promising. The planned next steps are to collect more data to build larger site-specific datasets and validate models before they can be used for management decisions.
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Affiliation(s)
- Donna S Francy
- U.S. Geological Survey, Ohio-Kentucky-Indiana Water Science Center, 6460 Busch Blvd, Columbus, OH, 43229, USA.
| | - Amie M G Brady
- U.S. Geological Survey, Ohio-Kentucky-Indiana Water Science Center, 6460 Busch Blvd, Columbus, OH, 43229, USA
| | - Erin A Stelzer
- U.S. Geological Survey, Ohio-Kentucky-Indiana Water Science Center, 6460 Busch Blvd, Columbus, OH, 43229, USA
| | - Jessica R Cicale
- U.S. Geological Survey, Ohio-Kentucky-Indiana Water Science Center, 6460 Busch Blvd, Columbus, OH, 43229, USA
| | - Courtney Hackney
- U.S. Geological Survey, Ohio-Kentucky-Indiana Water Science Center, 6460 Busch Blvd, Columbus, OH, 43229, USA
| | - Harrison D Dalby
- U.S. Geological Survey, Ohio-Kentucky-Indiana Water Science Center, 6460 Busch Blvd, Columbus, OH, 43229, USA
| | | | - Daryl F Dwyer
- Lake Erie Center, University of Toledo, Oregon, OH, USA
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17
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Fang S, Del Giudice D, Scavia D, Binding CE, Bridgeman TB, Chaffin JD, Evans MA, Guinness J, Johengen TH, Obenour DR. A space-time geostatistical model for probabilistic estimation of harmful algal bloom biomass and areal extent. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 695:133776. [PMID: 31426003 DOI: 10.1016/j.scitotenv.2019.133776] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Revised: 08/02/2019] [Accepted: 08/03/2019] [Indexed: 05/12/2023]
Abstract
Harmful algal blooms (HABs) have been increasing in intensity worldwide, including the western basin of Lake Erie. Substantial efforts have been made to track these blooms using in situ sampling and remote sensing. However, such measurements do not fully capture HAB spatial and temporal dynamics due to the limitations of discrete shipboard sampling over large areas and the effects of clouds and winds on remote sensing estimates. To address these limitations, we develop a space-time geostatistical modeling framework for estimating HAB intensity and extent using chlorophyll a data sampled during the HAB season (June-October) from 2008 to 2017 by five independent monitoring programs. Based on the Bayesian information criterion for model selection, trend variables explain bloom northerly and easterly expansion from Maumee Bay, wind effects over depth, and variability among sampling methods. Cross validation results demonstrate that space-time kriging explains over half of the variability in daily, location-specific chlorophyll observations, on average. Conditional simulations provide, for the first time, comprehensive estimates of overall bloom biomass (based on depth-integrated concentrations) and surface areal extent with quantified uncertainties. These new estimates are contrasted with previous Lake Erie HAB monitoring studies, and deviations among estimates are explored and discussed. Overall, results highlight the importance of maintaining sufficient monitoring coverage to capture bloom dynamics, as well as the benefits of the proposed approach for synthesizing data from multiple monitoring programs to improve estimation accuracy while reducing uncertainty.
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Affiliation(s)
- Shiqi Fang
- Department of Civil, Construction, & Environmental Engineering, North Carolina State University, Campus Box 7908, Raleigh, NC 27695, USA.
| | - Dario Del Giudice
- Department of Civil, Construction, & Environmental Engineering, North Carolina State University, Campus Box 7908, Raleigh, NC 27695, USA
| | - Donald Scavia
- School for Environment and Sustainability, University of Michigan, 440 Church St., Ann Arbor, MI 48104, USA
| | - Caren E Binding
- Water Science and Technology Directorate, Environment and Climate Change Canada, 867 Lakeshore Rd, Burlington, Ontario L7S 1A1, Canada
| | - Thomas B Bridgeman
- Department of Environmental Sciences and Lake Erie Center, University of Toledo, 6200 Bayshore Drive, Oregon, OH 43616, USA
| | - Justin D Chaffin
- F. T. Stone Laboratory and Ohio Sea Grant, The Ohio State University, 878 Bayview Ave, Put-in-Bay, OH 43456, USA
| | - Mary Anne Evans
- U.S. Geological Survey, Great Lakes Science Center, 1451 Green Rd, Ann Arbor, MI 48105, USA
| | - Joseph Guinness
- Department of Statistics and Data Science, Cornell University, 1178 Comstock Hall, Ithaca, NY 14853, USA
| | - Thomas H Johengen
- Cooperative Institute for Great Lakes Research (CIGLR), University of Michigan, 4840 South State Road, Ann Arbor, MI 48108, USA
| | - Daniel R Obenour
- Department of Civil, Construction, & Environmental Engineering, North Carolina State University, Campus Box 7908, Raleigh, NC 27695, USA; Center for Geospatial Analytics, North Carolina State University, Campus Box 7106, Raleigh, NC 27695, USA
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18
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Zhao CS, Shao NF, Yang ST, Ren H, Ge YR, Feng P, Dong BE, Zhao Y. Predicting cyanobacteria bloom occurrence in lakes and reservoirs before blooms occur. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 670:837-848. [PMID: 30921717 DOI: 10.1016/j.scitotenv.2019.03.161] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Revised: 02/28/2019] [Accepted: 03/11/2019] [Indexed: 06/09/2023]
Abstract
With increased global warming, cyanobacteria are blooming more frequently in lakes and reservoirs, severely damaging the health and stability of aquatic ecosystems and threatening drinking water safety and human health. There is an urgent demand for the effective prediction and prevention of cyanobacterial blooms. However, it is difficult to effectively reduce the risks and loss caused by cyanobacterial blooms because most methods are unable to successfully predict cyanobacteria blooms. Therefore, in this study, we proposed a new cyanobacterial bloom occurrence prediction method to analyze the probability and driving factors of the blooms for effective prevention and control. Dominant cyanobacterial species with bloom capabilities were initially determined using a dominant species identification model, and the principal driving factors of the dominant species were then analyzed using canonical correspondence analysis (CCA). Cyanobacterial bloom probability was calculated using a newly-developed model, after which, the probable mutation points were identified and thresholds for the principal driving factors of cyanobacterial blooms were predicted. A total of 141 phytoplankton data sets from 90 stations were collected from six large-scale hydrology, water-quality ecology, integrated field surveys in Jinan City, China in 2014-2015 and used for model application and verification. The results showed that there were six dominant cyanobacterial species in the study area, and that the principal driving factors were water temperature, pH, total phosphorus, ammonia nitrogen, chemical oxygen demand, and dissolved oxygen. The cyanobacterial blooms corresponded to a threshold water temperature range, pH, total phosphorus (TP), ammonium nitrogen level, chemical oxygen demand, and dissolved oxygen levels of 19.5-32.5 °C, 7.0-9.38, 0.13-0.22 mg L-1, 0.38-0.63 mg L-1, 10.5-17.5 mg L-1, and 4.97-8.28 mg L-1, respectively. Comparison with research results from other global regions further supported the use of these thresholds, indicating that this method could be used in habitats beyond China. We found that the probability of cyanobacterial bloom was 0.75, a critical point for prevention and control. When this critical point was exceeded, cyanobacteria could proliferate rapidly, increasing the risk of cyanobacterial blooms. Changes in driving factors need to be rapidly controlled, based on these thresholds, to prevent cyanobacterial blooms. Temporal and spatial scales were critical factors potentially affecting the selection of driving factors. This method is versatile and can help determine the risk of cyanobacterial blooms and the thresholds of the principal driving factors. It can effectively predict and help prevent cyanobacterial blooms to reduce the global probability of occurrence, protect the health and stability of water ecosystems, ensure drinking water safety, and protect human health.
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Affiliation(s)
- C S Zhao
- College of Water Sciences, Beijing Normal University, Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, Beijing 100875, PR China; ICube, UdS, CNRS (UMR 7357), 300 Bld Sebastien Brant, CS 10413, 67412 Illkirch, France
| | - N F Shao
- School of Geography, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, PR China.
| | - S T Yang
- College of Water Sciences, Beijing Normal University, Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, Beijing 100875, PR China; Guizhou Normal University, Guiyang 550001, PR China.
| | - H Ren
- Administration of Yanma Reservoir, Zaozhuang 277200, PR China
| | - Y R Ge
- Jinan Survey Bureau of Hydrology and Water Resources, Jinan 250013, PR China
| | - P Feng
- Jinan Survey Bureau of Hydrology and Water Resources, Jinan 250013, PR China
| | - B E Dong
- Dongying Bureau of Hydrology and Water Resources, Dongying 257000, PR China
| | - Y Zhao
- Jinan Survey Bureau of Hydrology and Water Resources, Jinan 250013, PR China
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19
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Picardo M, Filatova D, Nuñez O, Farré M. Recent advances in the detection of natural toxins in freshwater environments. Trends Analyt Chem 2019. [DOI: 10.1016/j.trac.2018.12.017] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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20
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Wen Z, Song K, Liu G, Shang Y, Fang C, Du J, Lyu L. Quantifying the trophic status of lakes using total light absorption of optically active components. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2019; 245:684-693. [PMID: 30500747 DOI: 10.1016/j.envpol.2018.11.058] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 10/01/2018] [Accepted: 11/19/2018] [Indexed: 06/09/2023]
Abstract
Eutrophication of lakes has become one of the world's most serious environmental problems, resulting in an urgent need to monitor and provide safeguards to control water quality. Results from analysis of lake trophic status based on calculated throphic state index (TSI) showed that 69.5% of the surveyed 277 lakes were in a state of eutrophication. Significant logarithmic relationships between light absorption of optically active components (aOACs) and TSI (R2 = 0.78) existed: TSI = 13.64 × ln(aOACs)+43.24, and the regression relationship between aOACs and TSI had a better degree of fit (R2) than the currently used reflectance-TSI relationship. aOACs appeared to be a good predictor of TSI estimation in lake ecosystems. The relationship coefficient (aOACs-TSI) slightly varied with lake type, and relationships in saline lakes and phy-type lakes were shown to be more robust than the relationship with the total lake data. This study highlights the quantification of the trophic status in lakes using aOACs, which realized the monitoring of trophic status in lakes using inherent optical properties on a large-scale. To our knowledge this is the first investigation to assess the variability of trophic status in lakes across China. The assessment trophic state of lakes based on aOACs provides a new way to monitor the trophic status of lakes, and findings may have applications for monitoring large-scale and long-term trophic patterns in lakes using remote sensing techniques.
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Affiliation(s)
- Zhidan Wen
- Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
| | - Kaishan Song
- Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China.
| | - Ge Liu
- Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
| | - Yingxin Shang
- Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Chong Fang
- Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jia Du
- Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
| | - Lili Lyu
- Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
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21
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Alcántara I, Piccini C, Segura A, Deus S, González C, Martínez de la Escalera G, Kruk C. Improved biovolume estimation of Microcystis aeruginosa colonies: A statistical approach. J Microbiol Methods 2018; 151:20-27. [DOI: 10.1016/j.mimet.2018.05.021] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2018] [Revised: 05/24/2018] [Accepted: 05/24/2018] [Indexed: 01/05/2023]
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22
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An Automatic Monitoring System for High-Frequency Measuring and Real-Time Management of Cyanobacterial Blooms in Urban Water Bodies. Processes (Basel) 2018. [DOI: 10.3390/pr6020011] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
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23
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Dörnhöfer K, Klinger P, Heege T, Oppelt N. Multi-sensor satellite and in situ monitoring of phytoplankton development in a eutrophic-mesotrophic lake. THE SCIENCE OF THE TOTAL ENVIRONMENT 2018; 612:1200-1214. [PMID: 28892864 DOI: 10.1016/j.scitotenv.2017.08.219] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Revised: 08/17/2017] [Accepted: 08/21/2017] [Indexed: 05/24/2023]
Abstract
Phytoplankton indicated by its photosynthetic pigment chlorophyll-a is an important pointer on lake ecology and a regularly monitored parameter within the European Water Framework Directive. Along with eutrophication and global warming cyanobacteria gain increasing importance concerning human health aspects. Optical remote sensing may support both the monitoring of horizontal distribution of phytoplankton and cyanobacteria at the lake surface and the reduction of spatial uncertainties associated with limited water sample analyses. Temporal and spatial resolution of using only one satellite sensor, however, may constrain its information value. To discuss the advantages of a multi-sensor approach the sensor-independent, physically based model MIP (Modular Inversion and Processing System) was applied at Lake Kummerow, Germany, and lake surface chlorophyll-a was derived from 33 images of five different sensors (MODIS-Terra, MODIS-Aqua, Landsat 8, Landsat 7 and Sentinel-2A). Remotely sensed lake average chlorophyll-a concentration showed a reasonable development and varied between 2.3±0.4 and 35.8±2.0mg·m-3 from July to October 2015. Match-ups between in situ and satellite chlorophyll-a revealed varying performances of Landsat 8 (RMSE: 3.6 and 19.7mg·m-3), Landsat 7 (RMSE: 6.2mg·m-3), Sentinel-2A (RMSE: 5.1mg·m-3) and MODIS (RMSE: 12.8mg·m-3), whereas an in situ data uncertainty of 48% needs to be respected. The temporal development of an index on harmful algal blooms corresponded well with the cyanobacteria biomass development during summer months. Satellite chlorophyll-a maps allowed to follow spatial patterns of chlorophyll-a distribution during a phytoplankton bloom event. Wind conditions mainly explained spatial patterns. Integrating satellite chlorophyll-a into trophic state assessment resulted in different trophic classes. Our study endorsed a combined use of satellite and in situ chlorophyll-a data to alleviate weaknesses of both approaches and to better characterise and understand phytoplankton development in lakes.
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Affiliation(s)
- Katja Dörnhöfer
- Christian-Albrechts-Universität zu Kiel, Department of Geography, Earth Observation and Modelling, Ludewig-Meyn-Str. 14, 24098 Kiel, Germany.
| | - Philip Klinger
- EOMAP GmbH & Co.KG, Castle Seefeld, Schlosshof 4a, 82229 Seefeld, Germany
| | - Thomas Heege
- EOMAP GmbH & Co.KG, Castle Seefeld, Schlosshof 4a, 82229 Seefeld, Germany
| | - Natascha Oppelt
- Christian-Albrechts-Universität zu Kiel, Department of Geography, Earth Observation and Modelling, Ludewig-Meyn-Str. 14, 24098 Kiel, Germany
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