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Bertone E, Ajmar A, Tonolo FG, Dunn RJK, Doriean NJC, Bennett WW, Purandare J. Satellite-based estimation of total suspended solids and chlorophyll-a concentrations for the Gold Coast Broadwater, Australia. Mar Pollut Bull 2024; 201:116217. [PMID: 38520999 DOI: 10.1016/j.marpolbul.2024.116217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 03/01/2024] [Accepted: 03/01/2024] [Indexed: 03/25/2024]
Abstract
Satellite retrieval of total suspended solids (TSS) and chlorophyll-a (chl-a) was performed for the Gold Coast Broadwater, a micro-tidal estuarine lagoon draining a highly developed urban catchment area with complex and competing land uses. Due to the different water quality properties of the rivers and creeks draining into the Broadwater, sampling sites were grouped in clusters, with cluster-specific empirical/semi-empirical prediction models developed and validated with a leave-one-out cross validation approach for robustness. For unsampled locations, a weighted-average approach, based on their proximity to sampled sites, was developed. Confidence intervals were also generated, with a bootstrapping approach and visualised through maps. Models yielded varying accuracies (R2 = 0.40-0.75). Results show that, for the most significant poor water quality event in the dataset, caused by summer rainfall events, elevated TSS concentrations originated in the northern rivers, slowly spreading southward. Conversely, high chl-a concentrations were first recorded in the southernmost regions of the Broadwater.
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Affiliation(s)
- Edoardo Bertone
- School of Engineering and Built Environment, Griffith University, Southport 4215, Queensland, Australia; Cities Research Institute, Griffith University, Southport 4215, Queensland, Australia; Australian Rivers Institute, Griffith University, Nathan 4111, Queensland, Australia.
| | - Andrea Ajmar
- Interuniversity Department of Regional and Urban Studies and Planning (DIST), Politecnico di Torino, Viale Mattioli, 39, 10125 Torino, Italy
| | - Fabio Giulio Tonolo
- Department of Architecture and Design, Politecnico di Torino, Viale Mattioli, 39, 10125 Torino, Italy
| | - Ryan J K Dunn
- Cities Research Institute, Griffith University, Southport 4215, Queensland, Australia; Coastal and Marine Research Centre, Griffith University, Southport 4215, Queensland, Australia
| | - Nicholas J C Doriean
- Cities Research Institute, Griffith University, Southport 4215, Queensland, Australia; Coastal and Marine Research Centre, Griffith University, Southport 4215, Queensland, Australia
| | - William W Bennett
- Cities Research Institute, Griffith University, Southport 4215, Queensland, Australia; Coastal and Marine Research Centre, Griffith University, Southport 4215, Queensland, Australia; School of Environment and Science, Griffith University, Southport 4215, Queensland, Australia
| | - Jemma Purandare
- Cities Research Institute, Griffith University, Southport 4215, Queensland, Australia; Coastal and Marine Research Centre, Griffith University, Southport 4215, Queensland, Australia; School of Environment and Science, Griffith University, Southport 4215, Queensland, Australia; City of Gold Coast, 833 Southport Nerang Road, Nerang 4211, Queensland, Australia
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2
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Lopez Barreto BN, Hestir EL, Lee CM, Beutel MW. Satellite Remote Sensing: A Tool to Support Harmful Algal Bloom Monitoring and Recreational Health Advisories in a California Reservoir. Geohealth 2024; 8:e2023GH000941. [PMID: 38404693 PMCID: PMC10885757 DOI: 10.1029/2023gh000941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 12/08/2023] [Accepted: 01/31/2024] [Indexed: 02/27/2024]
Abstract
Cyanobacterial harmful algal blooms (cyanoHABs) can harm people, animals, and affect consumptive and recreational use of inland waters. Monitoring cyanoHABs is often limited. However, chlorophyll-a (chl-a) is a common water quality metric and has been shown to have a relationship with cyanobacteria. The World Health Organization (WHO) recently updated their previous 1999 cyanoHAB guidance values (GVs) to be more practical by basing the GVs on chl-a concentration rather than cyanobacterial counts. This creates an opportunity for widespread cyanoHAB monitoring based on chl-a proxies, with satellite remote sensing (SRS) being a potentially powerful tool. We used Sentinel-2 (S2) and Sentinel-3 (S3) to map chl-a and cyanobacteria, respectively, classified chl-a values according to WHO GVs, and then compared them to cyanotoxin advisories issued by the California Department of Water Resources (DWR) at San Luis Reservoir, key infrastructure in California's water system. We found reasonably high rates of total agreement between advisories by DWR and SRS, however rates of agreement varied for S2 based on algorithm. Total agreement was 83% for S3, and 52%-79% for S2. False positive and false negative rates for S3 were 12% and 23%, respectively. S2 had 12%-80% false positive rate and 0%-38% false negative rate, depending on algorithm. Using SRS-based chl-a GVs as an early indicator for possible exposure advisories and as a trigger for in situ sampling may be effective to improve public health warnings. Implementing SRS for cyanoHAB monitoring could fill temporal data gaps and provide greater spatial information not available from in situ measurements alone.
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Affiliation(s)
- Brittany N. Lopez Barreto
- Environmental Systems Graduate GroupDepartment of Civil & Environmental EngineeringUniversity of California MercedMercedCAUSA
- Center for Information Technology Research in the Interest of SocietyThe Banatao InstituteUniversity of California MercedMercedCAUSA
| | - Erin L. Hestir
- Environmental Systems Graduate GroupDepartment of Civil & Environmental EngineeringUniversity of California MercedMercedCAUSA
- Center for Information Technology Research in the Interest of SocietyThe Banatao InstituteUniversity of California MercedMercedCAUSA
| | - Christine M. Lee
- NASA Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
| | - Marc W. Beutel
- Environmental Systems Graduate GroupDepartment of Civil & Environmental EngineeringUniversity of California MercedMercedCAUSA
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Fang C, Song C, Wen Z, Liu G, Wang X, Li S, Shang Y, Tao H, Lyu L, Song K. A novel chlorophyll-a retrieval model based on suspended particulate matter classification and different machine learning. Environ Res 2024; 240:117430. [PMID: 37866530 DOI: 10.1016/j.envres.2023.117430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 10/05/2023] [Accepted: 10/15/2023] [Indexed: 10/24/2023]
Abstract
Chlorophyll-a (Chla) in inland waters is one of the most significant optical parameters of aquatic ecosystem assessment, and long-term and daily Chla concentration monitoring has the potential to facilitate in early warning of algal blooms. MOD09 products have multiple observation advantages (higher temporal, spatial resolution and signal-to-noise ratio), and play an extremely important role in the remote sensing of water color. For developing a high accuracy machine learning model of remotely estimating Chla concentration in inland waters based on MOD09 products, this study proposed an assumption that the accuracy of Chla concentration retrieval will be improved after classifying water bodies into three groups by suspended particulate matter (SPM) concentration. A total of 10 commonly used machine learning models were compared and evaluated in this study, including random forest regressor (RFR), deep neural networks (DNN), extreme gradient boosting (XGBoost), and convolutional neural network (CNN). Altogether, 41 basic bands and 820 band ratios between the 41 bands were filtered by measuring their correlation with Ln(Chla) and several bands brought into different machine learning models. Results demonstrated that the construction of Chla concentration remote estimation model based on SPM classification could significantly improve the correlation between Ln(Chla) and 41 basic spectral band combinations, the correlation between Ln(Chla) and 820 band ratios, and the model verification R2 from 0.41 to 0.83. Furthermore, B3, B20, and B32 were finally selected based on correlation with SPM to classify SPM and the classification accuracy could reach 0.9. Finally, we concluded that RFR model performed best via comparing the R2, RMSE, and MAPE. By comparing the relative contribution of input bands in different groups, B3 contributed most to three groups. The model constructed in this study has promising prospects for promotion and application in other inland waters, and could provide systematic research reference for subsequent research.
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Affiliation(s)
- Chong Fang
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
| | - Changchun Song
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China; Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian, 116024, China
| | - Zhidan Wen
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
| | - Ge Liu
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
| | - Xiaodi Wang
- School of Geography and Tourism, Harbin University, Harbin, 150086, China
| | - Sijia Li
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
| | - Yingxin Shang
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
| | - Hui Tao
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Lili Lyu
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Kaishan Song
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China; School of Environment and Planning, Liaocheng University, Liaocheng, 252000, China.
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Fang C, Song C, Wang X, Wang Q, Tao H, Wang X, Ma Y, Song K. A novel total phosphorus concentration retrieval method based on two-line classification in lakes and reservoirs across China. Sci Total Environ 2024; 906:167522. [PMID: 37793448 DOI: 10.1016/j.scitotenv.2023.167522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 09/28/2023] [Accepted: 09/29/2023] [Indexed: 10/06/2023]
Abstract
Phosphorus is widely recognized as a nutrient that restricts growth and is the primary contributor to eutrophication in 80 % of water bodies. Consequently, the Chinese government has consistently prioritized monitoring and controlling total phosphorus (TP) levels. The remote estimation of TP in lakes and reservoirs at a national scale is a challenging task due to TP being a non-optically active parameter. Currently, there is a lack of developed TP inversion models specifically designed for lakes and reservoirs in China. For solving this problem, a novel two-line classification method drawn on scatter plots based on the natural logarithm of TP (Ln(TP)) and B33/B9 was proposed and used to classify 1211 measured samples obtained from field cruises in 105 lakes and reservoirs across China from 2012 to 2022 into three categories, Class 1, Class 2, and Class 3. Results demonstrate that the proposed classification method has the ability to enhance the correlation between Ln(TP) and 43 basic potential single band and band combinations. Specifically, the correlation range improved from (-0.31,0.15) to (-0.77,0.24) in Class 1, (-0.81, 0.36) in Class 2, and (-0.74, 0.52) in Class 3. Additionally, the classification method also improved the correlation range between Ln(TP) and 820 band ratios, from (-0.32, 0.32) to (-0.83, 0.82) in Class 1, (-0.86, 0.86) in Class 2, and (-0.86, 0.86) in Class 3. These datasets were subsequently utilized as input for eXtreme Gradient Boosting (XGBoost) models. Finally, well performing XGBoost models in Class 1 (R2 = 0.76, RMSE = 0.3, MAPE = 12 %), Class 2 (R2 = 0.84, RMSE = 0.49, MAPE = 38 %), and Class 3 (R2 = 0.74, RMSE = 0.46, MAPE = 14 %) were used to map TP of 563 large lakes and reservoirs (≥20 km2) across China using MODIS images from 2005, 2010, 2015, and 2020. This study presents a novel approach for estimating non-optically active parameters through remote sensing on a national scale.
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Affiliation(s)
- Chong Fang
- Northeast Institute of Geography and Agroecology, CAS, Changchun 130102, China
| | - Changchun Song
- Northeast Institute of Geography and Agroecology, CAS, Changchun 130102, China
| | - Xiangyu Wang
- Northeast Institute of Geography and Agroecology, CAS, Changchun 130102, China
| | - Qiang Wang
- Northeast Institute of Geography and Agroecology, CAS, Changchun 130102, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hui Tao
- Northeast Institute of Geography and Agroecology, CAS, Changchun 130102, China
| | - Xiaodi Wang
- School of Geography and Tourism, Harbin University, Harbin 150086, China
| | - Yue Ma
- Jilin Jianzhu University, Changchun, China
| | - Kaishan Song
- Northeast Institute of Geography and Agroecology, CAS, Changchun 130102, China; School of Environment and Planning, Liaocheng University, Liaocheng 252000, China.
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Murray JF, Lavery AM, Schaeffer BA, Seegers BN, Pennington AF, Hilborn ED, Boerger S, Runkle JD, Loftin K, Graham J, Stumpf R, Koch A, Backer L. Assessing the relationship between cyanobacterial blooms and respiratory-related hospital visits: Green bay, Wisconsin 2017-2019. Int J Hyg Environ Health 2024; 255:114272. [PMID: 37871346 DOI: 10.1016/j.ijheh.2023.114272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 09/25/2023] [Accepted: 10/04/2023] [Indexed: 10/25/2023]
Abstract
Potential acute and chronic human health effects associated with exposure to cyanobacteria and cyanotoxins, including respiratory symptoms, are an understudied public health concern. We examined the relationship between estimated cyanobacteria biomass and the frequency of respiratory-related hospital visits for residents living near Green Bay, Lake Michigan, Wisconsin during 2017-2019. Remote sensing data from the Cyanobacteria Assessment Network was used to approximate cyanobacteria exposure through creation of a metric for cyanobacteria chlorophyll-a (ChlBS). We obtained counts of hospital visits for asthma, wheezing, and allergic rhinitis from the Wisconsin Hospital Association for ZIP codes within a 3-mile radius of Green Bay. We analyzed weekly counts of hospital visits versus cyanobacteria, which was modelled as a continuous measure (ChlBS) or categorized according to World Health Organization's (WHO) alert levels using Poisson generalized linear models. Our data included 2743 individual hospital visits and 114 weeks of satellite derived cyanobacteria biomass indicator data. Peak values of ChlBS were observed between the months of June and October. Using the WHO alert levels, 60% of weeks were categorized as no risk, 19% as Vigilance Level, 15% as Alert Level 1, and 6% as Alert Level 2. In Poisson regression models adjusted for temperature, dewpoint, season, and year, there was no association between ChlBS and hospital visits (rate ratio [RR] [95% Confidence Interval (CI)] = 0.98 [0.77, 1.24]). There was also no consistent association between WHO alert level and hospital visits when adjusting for covariates (Vigilance Level: RR [95% CI] 0.88 [0.74, 1.05], Alert Level 1: 0.82 [0.67, 0.99], Alert Level 2: 0.98 [0.77, 1.24], compared to the reference no risk category). Our methodology and model provide a template for future studies that assess the association between cyanobacterial blooms and respiratory health.
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Affiliation(s)
- Jordan F Murray
- University of Wisconsin-Madison School of Medicine and Public Health, 610 Walnut St, Madison, WI, 53726, United States; Wisconsin Department of Health Services, 1 West Wilson St, Madison, WI, 53703, United States.
| | - Amy M Lavery
- Division of Environmental Health Science and Practice, National Center for Environmental Health, Centers for Disease Control and Prevention, 1600 Clifton Road, Atlanta, GA, 30329, United States
| | - Blake A Schaeffer
- Environmental Protection Agency, Office of Research and Development, Research Triangle Park, NC, 27711, United States
| | - Bridget N Seegers
- GESTAR II, Morgan State University, Baltimore, MD, United States; Ocean Ecology Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, United States
| | - Audrey F Pennington
- Division of Environmental Health Science and Practice, National Center for Environmental Health, Centers for Disease Control and Prevention, 1600 Clifton Road, Atlanta, GA, 30329, United States
| | - Elizabeth D Hilborn
- Environmental Protection Agency, Office of Research and Development, Research Triangle Park, NC, 27711, United States
| | - Savannah Boerger
- Oak Ridge Institute for Science and Education, 1299 Bethel Valley Rd, Oak Ridge, TN, 37830, United States
| | - Jennifer D Runkle
- North Carolina Institute for Climate Studies, North Carolina State University, The Cooperative Institute for Satellite Earth Systems Studies, NOAA National Centers for Environmental Information, 151 Patton Ave, Asheville, NC, 28801i, United States; Geological Survey, 1217 Biltmore Dr, Lawrence, KS, 66049, United States
| | - Keith Loftin
- U. S. Geological Survey, 1217 Biltmore Drive, Lawrence, KS, 66049, United States
| | - Jennifer Graham
- U.S. Geological Survey, 425 Jordan Road, Troy, NY, 12180, United States
| | - Richard Stumpf
- National Oceanic and Atmospheric Administration, National Centers for Coastal Ocean Science, 1305 East-West Highway Code N/SCI1, Silver Spring, MD, 20910, United States
| | - Amanda Koch
- Wisconsin Department of Health Services, 1 West Wilson St, Madison, WI, 53703, United States
| | - Lorraine Backer
- Division of Environmental Health Science and Practice, National Center for Environmental Health, Centers for Disease Control and Prevention, 1600 Clifton Road, Atlanta, GA, 30329, United States
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6
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Schaeffer BA, Reynolds N, Ferriby H, Salls W, Smith D, Johnston JM, Myer M. Forecasting freshwater cyanobacterial harmful algal blooms for Sentinel-3 satellite resolved U.S. lakes and reservoirs. J Environ Manage 2024; 349:119518. [PMID: 37944321 PMCID: PMC10842250 DOI: 10.1016/j.jenvman.2023.119518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 10/19/2023] [Accepted: 10/31/2023] [Indexed: 11/12/2023]
Abstract
This forecasting approach may be useful for water managers and associated public health managers to predict near-term future high-risk cyanobacterial harmful algal blooms (cyanoHAB) occurrence. Freshwater cyanoHABs may grow to excessive concentrations and cause human, animal, and environmental health concerns in lakes and reservoirs. Knowledge of the timing and location of cyanoHAB events is important for water quality management of recreational and drinking water systems. No quantitative tool exists to forecast cyanoHABs across broad geographic scales and at regular intervals. Publicly available satellite monitoring has proven effective in detecting cyanobacteria biomass near-real time within the United States. Weekly cyanobacteria abundance was quantified from the Ocean and Land Colour Instrument (OLCI) onboard the Sentinel-3 satellite as the response variable. An Integrated Nested Laplace Approximation (INLA) hierarchical Bayesian spatiotemporal model was applied to forecast World Health Organization (WHO) recreation Alert Level 1 exceedance >12 μg L-1 chlorophyll-a with cyanobacteria dominance for 2192 satellite resolved lakes in the United States across nine climate zones. The INLA model was compared against support vector classifier and random forest machine learning models; and Dense Neural Network, Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Gneural Network (GNU) neural network models. Predictors were limited to data sources relevant to cyanobacterial growth, readily available on a weekly basis, and at the national scale for operational forecasting. Relevant predictors included water surface temperature, precipitation, and lake geomorphology. Overall, the INLA model outperformed the machine learning and neural network models with prediction accuracy of 90% with 88% sensitivity, 91% specificity, and 49% precision as demonstrated by training the model with data from 2017 through 2020 and independently assessing predictions with data from the 2021 calendar year. The probability of true positive responses was greater than false positive responses and the probability of true negative responses was less than false negative responses. This indicated the model correctly assigned lower probabilities of events when they didn't exceed the WHO Alert Level 1 threshold and assigned higher probabilities when events did exceed the threshold. The INLA model was robust to missing data and unbalanced sampling between waterbodies.
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Affiliation(s)
| | | | | | - Wilson Salls
- US EPA, Office of Research and Development, Durham, NC, USA
| | - Deron Smith
- US EPA, Office of Research and Development, Athens, GA, USA
| | | | - Mark Myer
- US EPA, Office of Chemical Safety and Pollution Prevention, Durham, NC, USA
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7
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Bansal S, Creed IF, Tangen BA, Bridgham SD, Desai AR, Krauss KW, Neubauer SC, Noe GB, Rosenberry DO, Trettin C, Wickland KP, Allen ST, Arias-Ortiz A, Armitage AR, Baldocchi D, Banerjee K, Bastviken D, Berg P, Bogard MJ, Chow AT, Conner WH, Craft C, Creamer C, DelSontro T, Duberstein JA, Eagle M, Fennessy MS, Finkelstein SA, Göckede M, Grunwald S, Halabisky M, Herbert E, Jahangir MMR, Johnson OF, Jones MC, Kelleway JJ, Knox S, Kroeger KD, Kuehn KA, Lobb D, Loder AL, Ma S, Maher DT, McNicol G, Meier J, Middleton BA, Mills C, Mistry P, Mitra A, Mobilian C, Nahlik AM, Newman S, O’Connell JL, Oikawa P, van der Burg MP, Schutte CA, Song C, Stagg CL, Turner J, Vargas R, Waldrop MP, Wallin MB, Wang ZA, Ward EJ, Willard DA, Yarwood S, Zhu X. Practical Guide to Measuring Wetland Carbon Pools and Fluxes. Wetlands (Wilmington) 2023; 43:105. [PMID: 38037553 PMCID: PMC10684704 DOI: 10.1007/s13157-023-01722-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 07/24/2023] [Indexed: 12/02/2023]
Abstract
Wetlands cover a small portion of the world, but have disproportionate influence on global carbon (C) sequestration, carbon dioxide and methane emissions, and aquatic C fluxes. However, the underlying biogeochemical processes that affect wetland C pools and fluxes are complex and dynamic, making measurements of wetland C challenging. Over decades of research, many observational, experimental, and analytical approaches have been developed to understand and quantify pools and fluxes of wetland C. Sampling approaches range in their representation of wetland C from short to long timeframes and local to landscape spatial scales. This review summarizes common and cutting-edge methodological approaches for quantifying wetland C pools and fluxes. We first define each of the major C pools and fluxes and provide rationale for their importance to wetland C dynamics. For each approach, we clarify what component of wetland C is measured and its spatial and temporal representativeness and constraints. We describe practical considerations for each approach, such as where and when an approach is typically used, who can conduct the measurements (expertise, training requirements), and how approaches are conducted, including considerations on equipment complexity and costs. Finally, we review key covariates and ancillary measurements that enhance the interpretation of findings and facilitate model development. The protocols that we describe to measure soil, water, vegetation, and gases are also relevant for related disciplines such as ecology. Improved quality and consistency of data collection and reporting across studies will help reduce global uncertainties and develop management strategies to use wetlands as nature-based climate solutions. Supplementary Information The online version contains supplementary material available at 10.1007/s13157-023-01722-2.
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Affiliation(s)
- Sheel Bansal
- U.S. Geological Survey, Northern Prairie Wildlife Research Center, Jamestown, ND USA
| | - Irena F. Creed
- Department of Physical and Environmental Sciences, University of Toronto Scarborough, Toronto, ON Canada
| | - Brian A. Tangen
- U.S. Geological Survey, Northern Prairie Wildlife Research Center, Jamestown, ND USA
| | - Scott D. Bridgham
- Institute of Ecology and Evolution, University of Oregon, Eugene, OR USA
| | - Ankur R. Desai
- Department of Atmospheric and Oceanic Sciences, University of Wisconsin-Madison, Madison, WI USA
| | - Ken W. Krauss
- U.S. Geological Survey, Wetland and Aquatic Research Center, Lafayette, LA USA
| | - Scott C. Neubauer
- Department of Biology, Virginia Commonwealth University, Richmond, VA USA
| | - Gregory B. Noe
- U.S. Geological Survey, Florence Bascom Geoscience Center, Reston, VA USA
| | | | - Carl Trettin
- U.S. Forest Service, Pacific Southwest Research Station, Davis, CA USA
| | - Kimberly P. Wickland
- U.S. Geological Survey, Geosciences and Environmental Change Science Center, Denver, CO USA
| | - Scott T. Allen
- Department of Natural Resources and Environmental Science, University of Nevada, Reno, Reno, NV USA
| | - Ariane Arias-Ortiz
- Ecosystem Science Division, Department of Environmental Science, Policy and Management, University of California, Berkeley, CA USA
| | - Anna R. Armitage
- Department of Marine Biology, Texas A&M University at Galveston, Galveston, TX USA
| | - Dennis Baldocchi
- Department of Environmental Science, Policy and Management, University of California, Berkeley, CA USA
| | - Kakoli Banerjee
- Department of Biodiversity and Conservation of Natural Resources, Central University of Odisha, Koraput, Odisha India
| | - David Bastviken
- Department of Thematic Studies – Environmental Change, Linköping University, Linköping, Sweden
| | - Peter Berg
- Department of Environmental Sciences, University of Virginia, Charlottesville, VA USA
| | - Matthew J. Bogard
- Department of Biological Sciences, University of Lethbridge, Lethbridge, AB Canada
| | - Alex T. Chow
- Earth and Environmental Sciences Programme, The Chinese University of Hong Kong, Shatin, Hong Kong SAR China
| | - William H. Conner
- Baruch Institute of Coastal Ecology and Forest Science, Clemson University, Georgetown, SC USA
| | - Christopher Craft
- O’Neill School of Public and Environmental Affairs, Indiana University, Bloomington, IN USA
| | - Courtney Creamer
- U.S. Geological Survey, Geology, Minerals, Energy and Geophysics Science Center, Menlo Park, CA USA
| | - Tonya DelSontro
- Department of Earth and Environmental Sciences, University of Waterloo, Waterloo, ON Canada
| | - Jamie A. Duberstein
- Baruch Institute of Coastal Ecology and Forest Science, Clemson University, Georgetown, SC USA
| | - Meagan Eagle
- U.S. Geological Survey, Woods Hole Coastal & Marine Science Center, Woods Hole, MA USA
| | | | | | - Mathias Göckede
- Department for Biogeochemical Signals, Max Planck Institute for Biogeochemistry, Jena, Germany
| | - Sabine Grunwald
- Soil, Water and Ecosystem Sciences Department, University of Florida, Gainesville, FL USA
| | - Meghan Halabisky
- School of Environmental and Forest Sciences, University of Washington, Seattle, WA USA
| | | | | | - Olivia F. Johnson
- U.S. Geological Survey, Northern Prairie Wildlife Research Center, Jamestown, ND USA
- Departments of Biology and Environmental Studies, Kent State University, Kent, OH USA
| | - Miriam C. Jones
- U.S. Geological Survey, Florence Bascom Geoscience Center, Reston, VA USA
| | - Jeffrey J. Kelleway
- School of Earth, Atmospheric and Life Sciences and Environmental Futures Research Centre, University of Wollongong, Wollongong, NSW Australia
| | - Sara Knox
- Department of Geography, McGill University, Montreal, Canada
| | - Kevin D. Kroeger
- U.S. Geological Survey, Woods Hole Coastal & Marine Science Center, Woods Hole, MA USA
| | - Kevin A. Kuehn
- School of Biological, Environmental, and Earth Sciences, University of Southern Mississippi, Hattiesburg, MS USA
| | - David Lobb
- Department of Soil Science, University of Manitoba, Winnipeg, MB Canada
| | - Amanda L. Loder
- Department of Geography, University of Toronto, Toronto, ON Canada
| | - Shizhou Ma
- School of Environment and Sustainability, University of Saskatchewan, Saskatoon, SK Canada
| | - Damien T. Maher
- Faculty of Science and Engineering, Southern Cross University, Lismore, NSW Australia
| | - Gavin McNicol
- Department of Earth and Environmental Sciences, University of Illinois Chicago, Chicago, IL USA
| | - Jacob Meier
- U.S. Geological Survey, Northern Prairie Wildlife Research Center, Jamestown, ND USA
| | - Beth A. Middleton
- U.S. Geological Survey, Wetland and Aquatic Research Center, Lafayette, LA USA
| | - Christopher Mills
- U.S. Geological Survey, Geology, Geophysics, and Geochemistry Science Center, Denver, CO USA
| | - Purbasha Mistry
- School of Environment and Sustainability, University of Saskatchewan, Saskatoon, SK Canada
| | - Abhijit Mitra
- Department of Marine Science, University of Calcutta, Kolkata, West Bengal India
| | - Courtney Mobilian
- O’Neill School of Public and Environmental Affairs, Indiana University, Bloomington, IN USA
| | - Amanda M. Nahlik
- Office of Research and Development, Center for Public Health and Environmental Assessments, Pacific Ecological Systems Division, U.S. Environmental Protection Agency, Corvallis, OR USA
| | - Sue Newman
- South Florida Water Management District, Everglades Systems Assessment Section, West Palm Beach, FL USA
| | - Jessica L. O’Connell
- Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, CO USA
| | - Patty Oikawa
- Department of Earth and Environmental Sciences, California State University, East Bay, Hayward, CA USA
| | - Max Post van der Burg
- U.S. Geological Survey, Northern Prairie Wildlife Research Center, Jamestown, ND USA
| | - Charles A. Schutte
- Department of Environmental Science, Rowan University, Glassboro, NJ USA
| | - Changchun Song
- Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China
| | - Camille L. Stagg
- U.S. Geological Survey, Wetland and Aquatic Research Center, Lafayette, LA USA
| | - Jessica Turner
- Freshwater and Marine Science, University of Wisconsin-Madison, Madison, WI USA
| | - Rodrigo Vargas
- Department of Plant and Soil Sciences, University of Delaware, Newark, DE USA
| | - Mark P. Waldrop
- U.S. Geological Survey, Geology, Minerals, Energy and Geophysics Science Center, Menlo Park, CA USA
| | - Marcus B. Wallin
- Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Zhaohui Aleck Wang
- Department of Marine Chemistry and Geochemistry, Woods Hole Oceanographic Institution, Woods Hole, MA USA
| | - Eric J. Ward
- U.S. Geological Survey, Wetland and Aquatic Research Center, Lafayette, LA USA
| | - Debra A. Willard
- U.S. Geological Survey, Florence Bascom Geoscience Center, Reston, VA USA
| | - Stephanie Yarwood
- Environmental Science and Technology, University of Maryland, College Park, MD USA
| | - Xiaoyan Zhu
- Key Laboratory of Songliao Aquatic Environment, Ministry of Education, Jilin Jianzhu University, Changchun, China
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8
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Mishra S, Stumpf RP, Schaeffer BA, Werdell PJ. Recent changes in cyanobacteria algal bloom magnitude in large lakes across the contiguous United States. Sci Total Environ 2023; 897:165253. [PMID: 37394074 PMCID: PMC10835736 DOI: 10.1016/j.scitotenv.2023.165253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 06/25/2023] [Accepted: 06/29/2023] [Indexed: 07/04/2023]
Abstract
Cyanobacterial blooms in inland lakes produce large quantities of biomass that impact drinking water systems, recreation, and tourism and may produce toxins that can adversely affect public health. This study analyzed nine years of satellite-derived bloom records and compared how the bloom magnitude has changed from 2008-2011 to 2016-2020 in 1881 of the largest lakes across the contiguous United States (CONUS). We determined bloom magnitude each year as the spatio-temporal mean cyanobacteria biomass from May to October and in concentrations of chlorophyll-a. We found that bloom magnitude decreased in 465 (25 %) lakes in the 2016-2020 period. Conversely, there was an increase in bloom magnitude in only 81 lakes (4 %). Bloom magnitude either didn't change, or the observed change was in the uncertainty range in the majority of the lakes (n = 1335, 71 %). Above-normal wetness and normal or below-normal maximum temperature over the warm season may have caused the decrease in bloom magnitude in the eastern part of the CONUS in recent years. On the other hand, a hotter and dryer warm season in the western CONUS may have created an environment for increased algal biomass. While more lakes saw a decrease in bloom magnitude, the pattern was not monotonic over the CONUS. The variations in temporal changes in bloom magnitude within and across climatic regions depend on the interactions between land use land cover (LULC) and physical factors such as temperature and precipitation. Despite expectations suggested by recent global studies, bloom magnitude has not increased in larger US lakes over this time period.
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Affiliation(s)
- Sachidananda Mishra
- Consolidated Safety Services Inc., Fairfax, VA 22030, USA; National Oceanic and Atmospheric Administration, National Centers for Coastal Ocean Science, Silver Spring, MD 20910, USA.
| | - Richard P Stumpf
- National Oceanic and Atmospheric Administration, National Centers for Coastal Ocean Science, Silver Spring, MD 20910, USA
| | - Blake A Schaeffer
- U.S. Environmental Protection Agency, Office of Research and Development, Durham, NC 27709, USA
| | - P Jeremy Werdell
- Ocean Ecology Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
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9
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Zhao H, Zhou Y, Wu H, Kutser T, Han Y, Ma R, Yao Z, Zhao H, Xu P, Jiang C, Gu Q, Ma S, Wu L, Chen Y, Sheng H, Wan X, Chen W, Chen X, Bai J, Wu L, Liu Q, Sun W, Yang S, Hu M, Liu C, Liu D. Potential of Mie-Fluorescence-Raman Lidar to Profile Chlorophyll a Concentration in Inland Waters. Environ Sci Technol 2023; 57:14226-14236. [PMID: 37713595 DOI: 10.1021/acs.est.3c04212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/17/2023]
Abstract
Vertical distribution of phytoplankton is crucial for assessing the trophic status and primary production in inland waters. However, there is sparse information about phytoplankton vertical distribution due to the lack of sufficient measurements. Here, we report, to the best of our knowledge, the first Mie-fluorescence-Raman lidar (MFRL) measurements of continuous chlorophyll a (Chl-a) profiles as well as their parametrization in inland water. The lidar-measured Chl-a during several experiments showed good agreement with the in situ data. A case study verified that MFRL had the potential to profile the Chl-a concentration. The results revealed that the maintenance of subsurface chlorophyll maxima (SCM) was influenced by light and nutrient inputs. Furthermore, inspired by the observations from MFRL, an SCM model built upon surface Chl-a concentration and euphotic layer depth was proposed with root mean square relative difference of 16.5% compared to MFRL observations, providing the possibility to map 3D Chl-a distribution in aquatic ecosystems by integrated active-passive remote sensing technology. Profiling and modeling Chl-a concentration with MFRL are expected to be of paramount importance for monitoring inland water ecosystems and environments.
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Affiliation(s)
- Hongkai Zhao
- Ningbo Innovation Center, State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou 310027, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 311200, China
| | - Yudi Zhou
- Ningbo Innovation Center, State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou 310027, China
| | - Hongda Wu
- Ningbo Innovation Center, State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou 310027, China
| | - Tiit Kutser
- Estonian Marine Institute, University of Tartu, Mäealuse 14, Tallinn 10619, Estonia
| | - Yicai Han
- Institute of Environmental Protection Science, Hangzhou 310014, China
| | - Ronghua Ma
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
| | - Ziwei Yao
- State Environmental Protection Key Laboratory of Coastal Ecosystem, Dalian 116023, China
| | - Huade Zhao
- State Environmental Protection Key Laboratory of Coastal Ecosystem, Dalian 116023, China
| | - Peituo Xu
- Ningbo Innovation Center, State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou 310027, China
| | - Chengchong Jiang
- Ningbo Innovation Center, State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou 310027, China
| | - Qiuling Gu
- Ningbo Innovation Center, State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou 310027, China
| | - Shizhe Ma
- Ningbo Innovation Center, State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou 310027, China
| | - Lingyun Wu
- Ningbo Innovation Center, State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou 310027, China
| | - Yang Chen
- Ningbo Innovation Center, State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou 310027, China
| | - Haiyan Sheng
- Institute of Environmental Protection Science, Hangzhou 310014, China
| | - Xueping Wan
- Wuxi CAS Photonics Co., Ltd., Wuxi 214135, China
| | - Wentai Chen
- Wuxi CAS Photonics Co., Ltd., Wuxi 214135, China
| | | | - Jian Bai
- Ningbo Innovation Center, State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou 310027, China
| | - Lan Wu
- Ningbo Innovation Center, State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou 310027, China
| | - Qun Liu
- Ningbo Innovation Center, State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou 310027, China
- International Research Center for Advanced Photonics, Zhejiang University, Jiaxing 314400, China
| | - Wenbo Sun
- Donghai Laboratory, Zhoushan 316021, China
| | - Suhui Yang
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
| | - Miao Hu
- College of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Chong Liu
- Ningbo Innovation Center, State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou 310027, China
| | - Dong Liu
- Ningbo Innovation Center, State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou 310027, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 311200, China
- International Research Center for Advanced Photonics, Zhejiang University, Jiaxing 314400, China
- Jiaxing Key Laboratory of Photonic Sensing & Intelligent Imaging, Jiaxing 314000, China
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10
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Duan H, Xiao Q, Qi T, Hu C, Zhang M, Shen M, Hu Z, Wang W, Xiao W, Qiu Y, Luo J, Lee X. Quantification of Diffusive Methane Emissions from a Large Eutrophic Lake with Satellite Imagery. Environ Sci Technol 2023; 57:13520-13529. [PMID: 37651621 DOI: 10.1021/acs.est.3c05631] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
Lakes are major emitters of methane (CH4); however, a longstanding challenge with quantifying the magnitude of emissions remains as a result of large spatial and temporal variability. This study was designed to address the issue using satellite remote sensing with the advantages of spatial coverage and temporal resolution. Using Aqua/MODIS imagery (2003-2020) and in situ measured data (2011-2017) in eutrophic Lake Taihu, we compared the performance of eight machine learning models to predict diffusive CH4 emissions and found that the random forest (RF) model achieved the best fitting accuracy (R2 = 0.65 and mean relative error = 21%). On the basis of input satellite variables (chlorophyll a, water surface temperature, diffuse attenuation coefficient, and photosynthetically active radiation), we assessed how and why they help predict the CH4 emissions with the RF model. Overall, these variables mechanistically controlled the emissions, leading to the model capturing well the variability of diffusive CH4 emissions from the lake. Additionally, we found climate warming and associated algal blooms boosted the long-term increase in the emissions via reconstructing historical (2003-2020) daily time series of CH4 emissions. This study demonstrates the great potential of satellites to map lake CH4 emissions by providing spatiotemporal continuous data, with new and timely insights into accurately understanding the magnitude of aquatic greenhouse gas emissions.
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Affiliation(s)
- Hongtao Duan
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, Jiangsu 210008, People's Republic of China
- University of Chinese Academy of Sciences, Nanjing, Jiangsu 211135, People's Republic of China
| | - Qitao Xiao
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, Jiangsu 210008, People's Republic of China
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing, Jiangsu 210044, People's Republic of China
| | - Tianci Qi
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, Jiangsu 210008, People's Republic of China
| | - Cheng Hu
- College of Biology and the Environment, Joint Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, Jiangsu 210037, People's Republic of China
| | - Mi Zhang
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing, Jiangsu 210044, People's Republic of China
| | - Ming Shen
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, Jiangsu 210008, People's Republic of China
| | - Zhenghua Hu
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing, Jiangsu 210044, People's Republic of China
| | - Wei Wang
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing, Jiangsu 210044, People's Republic of China
| | - Wei Xiao
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing, Jiangsu 210044, People's Republic of China
| | - Yinguo Qiu
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, Jiangsu 210008, People's Republic of China
| | - Juhua Luo
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, Jiangsu 210008, People's Republic of China
| | - Xuhui Lee
- School of the Environment, Yale University, New Haven, Connecticut 06511, United States
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11
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Cook KV, Beyer JE, Xiao X, Hambright KD. Ground-based remote sensing provides alternative to satellites for monitoring cyanobacteria in small lakes. Water Res 2023; 242:120076. [PMID: 37352675 DOI: 10.1016/j.watres.2023.120076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 05/11/2023] [Accepted: 05/12/2023] [Indexed: 06/25/2023]
Abstract
Cyanobacteria are the most prevalent bloom-forming harmful algae in freshwater systems around the world. Adequate sampling of affected systems is limited spatially, temporally, and fiscally. Remote sensing using space- or ground-based systems in large water bodies at spatial and temporal scales that are cost-prohibitive to standard water quality monitoring has proven to be useful in detecting and quantifying cyanobacterial harmful algal blooms. This study aimed to identify a regional 'universal' multispectral reflectance model that could be used for rapid, remote detection and quantification of cyanoHABs in small- to medium-sized productive reservoirs, such as those typical of Oklahoma, USA. We aimed to include these small waterbodies in our study as they are typically overlooked in larger, continental wide studies, yet are widely distributed and used for recreation and drinking water supply. We used Landsat satellite reflectance and in-situ pigment data spanning 16 years from 38 reservoirs in Oklahoma to construct empirical linear models for predicting concentrations of chlorophyll-a and phycocyanin, two key algal pigments commonly used for assessing total and cyanobacterial algal abundances, respectively. We also used ground-based hyperspectral reflectance and in-situ pigment data from seven reservoirs across five years in Oklahoma to build multispectral models predicting algal pigments from newly defined reflectance bands. Our Oklahoma-derived Landsat- and ground-based models outperformed established reflectance-pigment models on Oklahoma reservoirs. Importantly, our results demonstrate that ground-based multispectral models were far superior to Landsat-based models and the Cyanobacteria Index (CI) for detecting cyanoHABs in highly productive, small- to mid-sized reservoirs in Oklahoma, providing a valuable tool for water management and public health. While satellite-based remote sensing approaches have proven effective for relatively large systems, our novel results indicate that ground-based remote sensing may offer better cyanoHAB monitoring for small or highly dendritic turbid lakes, such as those throughout the southern Great Plains, and thus prove beneficial to efforts aimed at minimizing public health risks associated with cyanoHABs in supply and recreational waters.
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Affiliation(s)
- Katherine V Cook
- Plankton Ecology and Limnology Laboratory, Department of Biology, University of Oklahoma, Norman, USA; Program in Ecology and Evolutionary Biology, Department of Biology, University of Oklahoma, Norman, USA
| | - Jessica E Beyer
- Plankton Ecology and Limnology Laboratory, Department of Biology, University of Oklahoma, Norman, USA; Program in Ecology and Evolutionary Biology, Department of Biology, University of Oklahoma, Norman, USA
| | - Xiangming Xiao
- Center for Earth Observation and Modeling, Department of Microbiology and Plant Biology, University of Oklahoma, Norman, USA
| | - K David Hambright
- Plankton Ecology and Limnology Laboratory, Department of Biology, University of Oklahoma, Norman, USA; Program in Ecology and Evolutionary Biology, Department of Biology, University of Oklahoma, Norman, USA; Geographical Ecology, Department of Biology, University of Oklahoma, Norman, USA.
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12
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Soto Ramos IM, Crooke B, Seegers B, Cetinić I, Cambazoglu MK, Armstrong B. Spatial and temporal characterization of cyanobacteria blooms in the Mississippi Sound and their relationship to the Bonnet Carré Spillway openings. Harmful Algae 2023; 127:102472. [PMID: 37544672 DOI: 10.1016/j.hal.2023.102472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 06/07/2023] [Accepted: 06/10/2023] [Indexed: 08/08/2023]
Abstract
During the spring and summer of 2019, an unprecedented cyanobacterial harmful algal bloom (cyanoHAB) was responsible for beach advisories on 25 beaches along the Mississippi Sound for over 3 months. Due to the preceding heavy rainfall and flooding within the Mississippi River watershed, for the first time in history, the Bonnet Carré Spillway (BCS) opened twice in one year during 2019. The coastal cyanoHAB coincided with the second BCS opening. The main objectives of this study were: (1) to investigate the potential for using the National Aeronautics and Space Administration (NASA) ocean color standard Cyanobacteria Index (CIcyano) algorithm to characterize the spatial and temporal extent of the 2019 cyanoHAB; (2) to couple the CIcyano data with river discharge, salinity, and modeled-wind data to study the conditions leading to the cyanoHAB and factors aiding the advection and persistence of the bloom within the Mississippi Sound, including a possible relationship to the BCS; (3) to further investigate the relationship with the BCS by repeating the methods using data from 2018, which was a year when the BCS was opened but no evidence of cyanoHABs was reported along the Mississippi coast. Weekly means and monthly frequency CIcyano images, river discharge, salinity, and modeled-wind data from February to September of 2018 and 2019 were analyzed, which coincide with three BCS openings. In March 2018, a cyanobacteria bloom was observed within Lake Pontchartrain coinciding with the BCS opening; however, the month-long bloom was contained to the lake. Two distinct cyanoHABs were observed in 2019 and both blooms were advected into the Mississippi Sound, and likely contributed to the 3-month-long beach water advisories of 2019 along the Mississippi coastline. From March to mid-July 2019, salinity at stations within the Mississippi Sound was consistently near zero indicating high levels of freshwater. During that time, winds were predominantly northwestward, preventing the BCS waters from flushing into the Mississippi Shelf and resulting in BCS waters remaining longer within the estuarine lakes and Mississippi Sound. Although the BCS had an undeniable impact on the presence of the coastal cyanoHAB of 2019, other variables including wind direction, water flow, mixing, and persistence of freshwater within the Sound can determine the intensity and extent of the cyanoHABs. Coupling in situ phytoplankton information from freshwater water bodies to the marine continuum along with water flow, wind data, and satellite imagery could help identify cyanoHABs at early stages and forecast their trajectory and potential impacts on coastal areas.
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Affiliation(s)
- Inia M Soto Ramos
- GESTAR II, Morgan State University, Baltimore, MD, USA; Ocean Ecology Laboratory, NASA/Goddard Space Flight Center, Greenbelt, MD, USA.
| | - Benjamin Crooke
- Skidmore College, NASA Goddard Space Flight Center Office of STEM Engagement (OSTEM) Internship Program, Greenbelt, MD, USA
| | - Bridget Seegers
- GESTAR II, Morgan State University, Baltimore, MD, USA; Ocean Ecology Laboratory, NASA/Goddard Space Flight Center, Greenbelt, MD, USA
| | - Ivona Cetinić
- GESTAR II, Morgan State University, Baltimore, MD, USA; Ocean Ecology Laboratory, NASA/Goddard Space Flight Center, Greenbelt, MD, USA
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13
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Olson NE, Boaggio KL, Rice RB, Foley KM, LeDuc SD. Wildfires in the western United States are mobilizing PM 2.5-associated nutrients and may be contributing to downwind cyanobacteria blooms. Environ Sci Process Impacts 2023; 25:1049-1066. [PMID: 37232758 PMCID: PMC10585592 DOI: 10.1039/d3em00042g] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Wildfire activity is increasing in the continental U.S. and can be linked to climate change effects, including rising temperatures and more frequent drought conditions. Wildfire emissions and large fire frequency have increased in the western U.S., impacting human health and ecosystems. We linked 15 years (2006-2020) of particulate matter (PM2.5) chemical speciation data with smoke plume analysis to identify PM2.5-associated nutrients elevated in air samples on smoke-impacted days. Most macro- and micro-nutrients analyzed (phosphorus, calcium, potassium, sodium, silicon, aluminum, iron, manganese, and magnesium) were significantly elevated on smoke days across all years analyzed. The largest percent increase was observed for phosphorus. With the exception of ammonium, all other nutrients (nitrate, copper, and zinc), although not statistically significant, had higher median values across all years on smoke vs. non-smoke days. Not surprisingly, there was high variation between smoke impacted days, with some nutrients episodically elevated >10 000% during select fire events. Beyond nutrients, we also explored instances where algal blooms occurred in multiple lakes downwind from high-nutrient fires. In these cases, remotely sensed cyanobacteria indices in downwind lakes increased two to seven days following the occurrence of wildfire smoke above the lake. This suggests that elevated nutrients in wildfire smoke may contribute to downwind algal blooms. Since cyanobacteria blooms can be associated with the production of cyanotoxins and wildfire activity is increasing due to climate change, this finding has implications for drinking water reservoirs in the western United States, and for lake ecology, particularly alpine lakes with otherwise limited nutrient inputs.
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Affiliation(s)
- Nicole E Olson
- U.S. Environmental Protection Agency, Office of Research and Development, Research Triangle Park, NC, USA.
| | - Katie L Boaggio
- U.S. Environmental Protection Agency, Office of Air and Radiation, Research Triangle Park, NC, USA
| | - R Byron Rice
- U.S. Environmental Protection Agency, Office of Research and Development, Research Triangle Park, NC, USA.
| | - Kristen M Foley
- U.S. Environmental Protection Agency, Office of Research and Development, Research Triangle Park, NC, USA.
| | - Stephen D LeDuc
- U.S. Environmental Protection Agency, Office of Research and Development, Research Triangle Park, NC, USA.
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14
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Handler AM, Compton JE, Hill RA, Leibowitz SG, Schaeffer BA. Identifying lakes at risk of toxic cyanobacterial blooms using satellite imagery and field surveys across the United States. Sci Total Environ 2023; 869:161784. [PMID: 36702268 PMCID: PMC10018780 DOI: 10.1016/j.scitotenv.2023.161784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 01/18/2023] [Accepted: 01/19/2023] [Indexed: 06/18/2023]
Abstract
Harmful algal blooms caused by cyanobacteria are a threat to global water resources and human health. Satellite remote sensing has vastly expanded spatial and temporal data on lake cyanobacteria, yet there is still acute need for tools that identify which waterbodies are at-risk for toxic cyanobacterial blooms. Algal toxins cannot be directly detected through imagery but monitoring toxins associated with cyanobacterial blooms is critical for assessing risk to the environment, animals, and people. The objective of this study is to address this need by developing an approach relating satellite imagery on cyanobacteria with field surveys to model the risk of toxic blooms among lakes. The Medium Resolution Imaging Spectrometer (MERIS) and United States (US) National Lakes Assessments are leveraged to model the probability among lakes of exceeding lower and higher demonstration thresholds for microcystin toxin, cyanobacteria, and chlorophyll a. By leveraging the large spatial variation among lakes using two national-scale data sources, rather than focusing on temporal variability, this approach avoids many of the previous challenges in relating satellite imagery to cyanotoxins. For every satellite-derived lake-level Cyanobacteria Index (CI_cyano) increase of 0.01 CI_cyano/km2, the odds of exceeding six bloom thresholds increased by 23-54 %. When the models were applied to the 2192 satellite monitored lakes in the US, the number of lakes identified with ≥75 % probability of exceeding the thresholds included as many as 335 lakes for the lower thresholds and 70 lakes for the higher thresholds, respectively. For microcystin, the models identified 162 and 70 lakes with ≥75 % probability of exceeding the lower (0.2 μg/L) and higher (1.0 μg/L) thresholds, respectively. This approach represents a critical advancement in using satellite imagery and field data to identify lakes at risk for developing toxic cyanobacteria blooms. Such models can help translate satellite data to aid water quality monitoring and management.
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Affiliation(s)
- Amalia M Handler
- Center for Public Health and Environmental Assessment, Office of Research and Development, U.S. Environmental Protection Agency, Corvallis, OR 97333, United States of America.
| | - Jana E Compton
- Center for Public Health and Environmental Assessment, Office of Research and Development, U.S. Environmental Protection Agency, Corvallis, OR 97333, United States of America
| | - Ryan A Hill
- Center for Public Health and Environmental Assessment, Office of Research and Development, U.S. Environmental Protection Agency, Corvallis, OR 97333, United States of America
| | - Scott G Leibowitz
- Center for Public Health and Environmental Assessment, Office of Research and Development, U.S. Environmental Protection Agency, Corvallis, OR 97333, United States of America
| | - Blake A Schaeffer
- Center for Environmental Measurement and Modeling, Office of Research and Development, U.S. Environmental Protection Agency, Durham, NC 27711, United States of America
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15
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Reynolds N, Schaeffer BA, Guertault L, Nelson NG. Satellite and in situ cyanobacteria monitoring: Understanding the impact of monitoring frequency on management decisions. J Hydrol (Amst) 2023; 619:1-14. [PMID: 38273893 PMCID: PMC10807294 DOI: 10.1016/j.jhydrol.2023.129278] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2024]
Abstract
Cyanobacterial harmful algal blooms (cyanoHABs) in reservoirs can be transported to downstream waters via scheduled discharges. Transport dynamics are difficult to capture in traditional cyanoHAB monitoring, which can be spatially disparate and temporally discontinuous. The introduction of satellite remote sensing for cyanoHAB monitoring provides opportunities to detect where cyanoHABs occur in relation to reservoir release locations, like canal inlets. The study objectives were to assess (1) differences in reservoir cyanoHAB frequencies as determined by in situ and remotely sensed data and (2) the feasibility of using satellite imagery to identify conditions associated with release-driven cyanoHAB export. As a representative case, Lake Okeechobee and the St. Lucie Estuary (Florida, USA), which receives controlled releases from Lake Okeechobee, were examined. Both systems are impacted by cyanoHABs, and the St. Lucie Estuary experienced states of emergency for extreme cyanoHABs in 2016 and 2018. Using the European Space Agency's Sentinel-3 OLCI imagery processed with the Cyanobacteria Index (CI cyano ), cyanoHAB frequencies across Lake Okeechobee from May 2016-April 2021 were compared to frequencies from in situ data. Strong agreement was observed in frequency rankings between the in situ and remotely sensed data in capturing intra-annual variability in bloom frequencies across Lake Okeechobee (Kendall's tau = 0.85, p-value = 0.0002), whereas no alignment was observed when evaluating inter-annual variation (Kendall's tau = 0, p-value = 1). Further, remotely sensed observations revealed that cyanoHABs were highly frequent near the inlet to the canal connecting Lake Okeechobee to the St. Lucie Estuary in state-of-emergency years, a pattern not evident from in situ data alone. This study demonstrates how remote sensing can complement traditional cyanoHAB monitoring to inform reservoir release decision making.
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Affiliation(s)
- Natalie Reynolds
- ORISE Fellow at U.S. Environmental Protection Agency, Office of Research and Development, Durham, NC, USA
- Department of Biological and Agricultural Engineering, North Carolina State University, Raleigh, NC, USA
| | - Blake A Schaeffer
- Office of Research and Development, U.S. Environmental Protection Agency, Durham, NC, USA
| | - Lucie Guertault
- Department of Biological and Agricultural Engineering, North Carolina State University, Raleigh, NC, USA
| | - Natalie G Nelson
- Department of Biological and Agricultural Engineering, North Carolina State University, Raleigh, NC, USA
- Center for Geospatial Analytics, North Carolina State University, Raleigh, NC, USA
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Schaeffer BA, Urquhart E, Coffer M, Salls W, Stumpf RP, Loftin KA, Werdell PJ. Satellites quantify the spatial extent of cyanobacterial blooms across the United States at multiple scales. Ecol Indic 2022; 140:1-14. [PMID: 36425672 PMCID: PMC9680831 DOI: 10.1016/j.ecolind.2022.108990] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Previous studies indicate that cyanobacterial harmful algal bloom (cyanoHAB) frequency, extent, and magnitude have increased globally over the past few decades. However, little quantitative capability is available to assess these metrics of cyanoHABs across broad geographic scales and at regular intervals. Here, the spatial extent was quantified from a cyanobacteria algorithm applied to two European Space Agency satellite platforms-the MEdium Resolution Imaging Spectrometer (MERIS) onboard Envisat and the Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3. CyanoHAB spatial extent was defined for each geographic area as the percentage of valid satellite pixels that exhibited cyanobacteria above the detection limit of the satellite sensor. This study quantified cyanoHAB spatial extent for over 2,000 large lakes and reservoirs across the contiguous United States (CONUS) during two time periods: 2008-2011 via MERIS and 2017-2020 via OLCI when cloud-, ice-, and snow-free imagery was available. Approximately 56% of resolvable lakes were glaciated, 13% were headwater, isolated, or terminal lakes, and the rest were primarily drainage lakes. Results were summarized at national-, regional-, state-, and lake-scales, where regions were defined as nine climate regions which represent climatically consistent states. As measured by satellite, changes in national cyanoHAB extent did have a strong increase of 6.9% from 2017 to 2020 (|Kendall's tau (τ)| = 0.56; gamma (γ) = 2.87 years), but had negligible change (|τ| = 0.03) from 2008 to 2011. Two of the nine regions had moderate (0.3 ≤ |τ| < 0.5) increases in spatial extent from 2017 to 2020, and eight of nine regions had negligible (|τ| < 0.2) change from 2008 to 2011. Twelve states had a strong or moderate increase from 2017 to 2020 (|τ| ≥ 0.3), while only one state had a moderate increase and two states had a moderate decrease from 2008 to 2011. A decrease, or no change, in cyanoHAB spatial extent did not indicate a lack of issues related to cyanoHABs. Sensitivity results of randomly omitted daily CONUS scenes confirm that even with reduced data availability during a short four-year temporal assessment, the direction and strength of the changes in spatial extent remained consistent. We present the first set of national maps of lake cyanoHAB spatial extent across CONUS and demonstrate an approach for quantifying past and future changes at multiple spatial scales. Results presented here provide water quality managers information regarding current cyanoHAB spatial extent and quantify rates of change.
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Affiliation(s)
- Blake A. Schaeffer
- Office of Research and Development, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Durham, NC 27709, United States
| | - Erin Urquhart
- Science Systems and Applications, Inc., Ocean Ecology Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, United States
| | - Megan Coffer
- Oak Ridge Institute for Science and Education (ORISE), U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Durham, NC 27709, United States
| | - Wilson Salls
- Office of Research and Development, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Durham, NC 27709, United States
| | - Richard P. Stumpf
- National Oceanic and Atmospheric Administration, National Centers for Coastal Ocean Science, 1305 East-West Highway Code N/SCI1, Silver Spring, MD 20910, United States
| | - Keith A. Loftin
- U.S. Geological Survey, Organic Geochemistry Research Laboratory, Kansas Water Science Center, 1217 Biltmore Drive, Lawrence, KS 66049, United States
| | - P. Jeremy Werdell
- Ocean Ecology Laboratory, NASA Goddard Space Flight Center, 8800 Greenbelt Road, Greenbelt, MD 20771, United States
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Ignatius AR, Purucker ST, Schaeffer BA, Wolfe K, Urquhart E, Smith D. Satellite-derived cyanobacteria frequency and magnitude in headwaters & near-dam reservoir surface waters of the Southern U.S. Sci Total Environ 2022; 822:153568. [PMID: 35114225 DOI: 10.1016/j.scitotenv.2022.153568] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 01/27/2022] [Accepted: 01/27/2022] [Indexed: 06/14/2023]
Abstract
Reservoirs are dominant features of the modern hydrologic landscape and provide vital services. However, the unique morphology of reservoirs can create suitable conditions for excessive algae growth and associated cyanobacteria blooms in shallow in-flow reservoir locations by providing warm water environments with relatively high nutrient inputs, deposition, and nutrient storage. Cyanobacteria harmful algal blooms (cyanoHAB) are costly water management issues and bloom recurrence is associated with economic costs and negative impacts to human, animal, and environmental health. As cyanoHAB occurrence varies substantially within different regions of a water body, understanding in-lake cyanoHAB spatial dynamics is essential to guide reservoir monitoring and mitigate potential public exposure to cyanotoxins. Cloud-based computational processing power and high temporal frequency of satellites enables advanced pixel-based spatial analysis of cyanoHAB frequency and quantitative assessment of reservoir headwater in-flows compared to near-dam surface waters of individual reservoirs. Additionally, extensive spatial coverage of satellite imagery allows for evaluation of spatial trends across many dozens of reservoir sites. Surface water cyanobacteria concentrations for sixty reservoirs in the southern U.S. were estimated using 300 m resolution European Space Agency (ESA) Ocean and Land Colour Instrument (OLCI) satellite sensor for a five year period (May 2016-April 2021). Of the reservoirs studied, spatial analysis of OLCI data revealed 98% had more frequent cyanoHAB occurrence above the concentration of >100,000 cells/mL in headwaters compared to near-dam surface waters (P < 0.001). Headwaters exhibited greater seasonal variability with more frequent and higher magnitude cyanoHABs occurring mid-summer to fall. Examination of reservoirs identified extremely high concentration cyanobacteria events (>1,000,000 cells/mL) occurring in 70% of headwater locations while only 30% of near-dam locations exceeded this threshold. Wilcoxon signed-rank tests of cyanoHAB magnitudes using paired-observations (dates with observations in both a reservoir's headwater and near-dam locations) confirmed significantly higher concentrations in headwater versus near-dam locations (p < 0.001).
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Affiliation(s)
- Amber R Ignatius
- Institute for Environmental and Spatial Analysis, University of North Georgia, 3820 Mundy Mill Road, Oakwood, GA 30566, USA.
| | - S Thomas Purucker
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, 109 TW Alexander Drive, Durham, NC 27711, USA.
| | - Blake A Schaeffer
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Environmental Measurement and Modeling, 109 TW Alexander Drive, Durham, NC 27711, USA.
| | - Kurt Wolfe
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Environmental Measurement and Modeling, 960 College Station Road, Athens, GA 30605, USA.
| | - Erin Urquhart
- Science Systems and Applications, Inc., Ocean Ecology Laboratory, NASA Goddard Space Flight Center, 8800 Greenbelt Road, Greenbelt, MD 20771, USA.
| | - Deron Smith
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Environmental Measurement and Modeling, 960 College Station Road, Athens, GA 30605, USA.
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Lima ST, Fallon TR, Cordoza JL, Chekan JR, Delbaje E, Hopiavuori AR, Alvarenga DO, Wood SM, Luhavaya H, Baumgartner JT, Dörr FA, Etchegaray A, Pinto E, McKinnie SMK, Fiore MF, Moore BS. Biosynthesis of Guanitoxin Enables Global Environmental Detection in Freshwater Cyanobacteria. J Am Chem Soc 2022; 144:9372-9379. [PMID: 35583956 DOI: 10.1021/jacs.2c01424] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Harmful cyanobacterial blooms (cyanoHABs) cause recurrent toxic events in global watersheds. Although public health agencies monitor the causal toxins of most cyanoHABs and scientists in the field continue developing precise detection and prediction tools, the potent anticholinesterase neurotoxin, guanitoxin, is not presently environmentally monitored. This is largely due to its incompatibility with widely employed analytical methods and instability in the environment, despite guanitoxin being among the most lethal cyanotoxins. Here, we describe the guanitoxin biosynthesis gene cluster and its rigorously characterized nine-step metabolic pathway from l-arginine in the cyanobacterium Sphaerospermopsis torques-reginae ITEP-024. Through environmental sequencing data sets, guanitoxin (gnt) biosynthetic genes are repeatedly detected and expressed in municipal freshwater bodies that have undergone past toxic events. Knowledge of the genetic basis of guanitoxin biosynthesis now allows for environmental, biosynthetic gene monitoring to establish the global scope of this neurotoxic organophosphate.
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Affiliation(s)
- Stella T Lima
- Center for Nuclear Energy in Agriculture, University of São Paulo, Piracicaba, Sao Paulo 13416-000, Brazil.,Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California 92093, United States
| | - Timothy R Fallon
- Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California 92093, United States
| | - Jennifer L Cordoza
- Department of Chemistry and Biochemistry, University of California, Santa Cruz, California 95064, United States
| | - Jonathan R Chekan
- Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California 92093, United States.,Department of Chemistry and Biochemistry, University of North Carolina at Greensboro, Greensboro, North Carolina 27402, United States
| | - Endrews Delbaje
- Center for Nuclear Energy in Agriculture, University of São Paulo, Piracicaba, Sao Paulo 13416-000, Brazil
| | - Austin R Hopiavuori
- Department of Chemistry and Biochemistry, University of California, Santa Cruz, California 95064, United States
| | - Danillo O Alvarenga
- Department of Biology, University of Copenhagen, Copenhagen, DK 2100, Denmark
| | - Steffaney M Wood
- Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California 92093, United States
| | - Hanna Luhavaya
- Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California 92093, United States
| | - Jackson T Baumgartner
- Department of Chemistry and Biochemistry, University of California, Santa Cruz, California 95064, United States
| | - Felipe A Dörr
- School of Pharmaceutical Sciences, University of São Paulo, São Paulo, Ribeirao Preto, Sao Paulo 05508-000, Brazil
| | - Augusto Etchegaray
- Center for Life Sciences, Graduate Program in Health Sciences, Pontifical Catholic University of Campinas (PUC-Campinas), Campinas, Sao Paulo 13087-571, Brazil
| | - Ernani Pinto
- Center for Nuclear Energy in Agriculture, University of São Paulo, Piracicaba, Sao Paulo 13416-000, Brazil
| | - Shaun M K McKinnie
- Department of Chemistry and Biochemistry, University of California, Santa Cruz, California 95064, United States
| | - Marli F Fiore
- Center for Nuclear Energy in Agriculture, University of São Paulo, Piracicaba, Sao Paulo 13416-000, Brazil
| | - Bradley S Moore
- Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California 92093, United States.,Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, California 92093, United States
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19
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Wang Q, Sun L, Zhu Y, Wang S, Duan C, Yang C, Zhang Y, Liu D, Zhao L, Tang J. Hysteresis effects of meteorological variation-induced algal blooms: A case study based on satellite-observed data from Dianchi Lake, China (1988-2020). Sci Total Environ 2022; 812:152558. [PMID: 34952086 DOI: 10.1016/j.scitotenv.2021.152558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 11/23/2021] [Accepted: 12/16/2021] [Indexed: 06/14/2023]
Abstract
As one of three top-priority eutrophic lakes in China, Dianchi Lake has received national attention due to its severe eutrophication in recent decades. Meteorological factors are the main factors driving the formation and persistence of algae blooms. In addition, meteorological variation-induced algal blooms usually have a hysteresis effect. However, there have been few quantitative studies on this hysteresis effect. In the present study, Landsat images were used to extract the dynamic characteristics of changes in algal blooms in Dianchi Lake from 1988 to 2020. The hysteresis effect of meteorological factors driving algal blooms was studied by employing the modified lag-correlation method. The results showed that the algal blooms in Dianchi Lake were most severe between 1998 and 2008. During the periods of algal blooms, the values of air temperature (AT) and precipitation (PP) were significantly higher, while those wind velocity (WV) and sunshine duration (SSD) were obviously lower, than the corresponding annual mean values. AT and PP were significantly positively correlated with algal bloom factors in both the formation and persistence stages of algal blooms, while SSD and WV both promoted their regression, but these effects were less significant in the persistence period than in the formation period. Moreover, rainfall led to a decrease in SSD and WV, indirectly contributing to algal blooms. Furthermore, AT, PP and SSD are the main factors impacting the duration of persistent blooms. The time periods during which each meteorological factor was most influential were as follows: 1) AT - 25-30 days before the maximum bloom. 2) PP - within the first 10 days before the maximum bloom. 3) Both SSD and WV - 15-20 days before the maximum bloom. The results of this study support the prediction of algal blooms in Dianchi Lake.
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Affiliation(s)
- Quan Wang
- College of Chemistry, Biology and Environment, Yuxi Normal University, Yuxi 653100, China.
| | - Liu Sun
- School of Mathematics and Information Technology, Yuxi Normal University, Yuxi 653100, China
| | - Yi Zhu
- College of Chemistry, Biology and Environment, Yuxi Normal University, Yuxi 653100, China
| | - Shuaibing Wang
- College of Chemistry, Biology and Environment, Yuxi Normal University, Yuxi 653100, China
| | - Chunyu Duan
- College of Chemistry, Biology and Environment, Yuxi Normal University, Yuxi 653100, China
| | - Chaojie Yang
- College of Chemistry, Biology and Environment, Yuxi Normal University, Yuxi 653100, China
| | - Yumeng Zhang
- College of Chemistry, Biology and Environment, Yuxi Normal University, Yuxi 653100, China; Institute of Environmental and Ecological Engineering, Guangdong University of Technology, Guangzhou 510006, China
| | - Dejiang Liu
- College of Geography and Land Engineering, Yuxi Normal University, Yuxi 653100, China
| | - Lin Zhao
- College of Geography and Land Engineering, Yuxi Normal University, Yuxi 653100, China
| | - Jinli Tang
- College of Geography and Land Engineering, Yuxi Normal University, Yuxi 653100, China
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Makarov M, Aslamov I, Gnatovsky R. Environmental Monitoring of the Littoral Zone of Lake Baikal Using a Network of Automatic Hydro-Meteorological Stations: Development and Trial Run. Sensors (Basel) 2021; 21:7659. [PMID: 34833734 PMCID: PMC8620454 DOI: 10.3390/s21227659] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 11/11/2021] [Accepted: 11/16/2021] [Indexed: 11/16/2022]
Abstract
An automatic hydro-meteorological station (AHMS) was designed to monitor the littoral zone of Lake Baikal in areas with high anthropogenic pressure. The developed AHMS was installed near the Bolshiye Koty settlement (southern basin). This AHMS is the first experience focused on obtaining the necessary competencies for the development of a monitoring network of the Baikal natural territory. To increase the flexibility of adjustment and repeatability, we developed AHMS as a low-cost modular system. AHMS is equipped with a weather station and sensors measuring water temperature, pH, dissolved oxygen, redox potential, conductivity, chlorophyll-a, and turbidity. This article describes the main AHMS functions (hardware and software) and measures taken to ensure data quality control. We present the results of the first two periods of its operation. The data acquired during this periods have demonstrated that, to obtain accurate measurements and to detect and correct errors that were mainly due to biofouling of the sensors and calibration bias, a correlation between AHMS and laboratory studies is necessary for parameters such as pH and chlorophyll-a. The gained experience should become the basis for the further development of the monitoring network of the Baikal natural territory.
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Affiliation(s)
- Mikhail Makarov
- Limnological Institute, Siberian Branch of the Russian Academy of Sciences, 664033 Irkutsk, Russia; (I.A.); (R.G.)
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