(1)
A Machine Learning Based Spatio-Temporal Data Mining Approach for Detection of Harmful Algal Blooms in the Gulf of Mexico | IEEE Journals & Magazine | IEEE Xplore. https://ieeexplore.ieee.org/abstract/document/5701668 (accessed 2024-02-21).
(2)
A Review of Water Quality Responses to Air Temperature and Precipitation Changes 2: Nutrients, Algal Blooms, Sediment, Pathogens - Coffey - 2019 - JAWRA Journal of the American Water Resources Association - Wiley Online Library. https://onlinelibrary.wiley.com/doi/10.1111/1752-1688.12711 (accessed 2024-04-16).
(3)
Alkon, A. Commentary: Stockton addresses historic redlining by Going Green. Stocktonia News. https://stocktonia.org/news/opinion/2023/04/26/commentary-stockton-addresses-historic-redlining-by-going-green/ (accessed 2024-03-06).
(4)
Interagency Ecological Program (IEP); Adams, J.; Pien, C. Interagency Ecological Program: Discrete Water Quality and Phytoplankton Data from the Sacramento River Floodplain and Yolo Bypass Tidal Slough, Collected by the Yolo Bypass Fish Monitoring Program, 1998 - 2022, 2023. https://doi.org/10.6073/PASTA/5791D7EACA09FB9471C5589C66F86863.
(5)
US EPA, O. Learn about Cyanobacteria and Cyanotoxins. https://www.epa.gov/cyanohabs/learn-about-cyanobacteria-and-cyanotoxins (accessed 2024-02-26).
(6)
Mission & History – Restore the Delta. https://restorethedelta.org/mission-history/ (accessed 2024-03-14).
(7)
Yu, P.; Gao, R.; Zhang, D.; Liu, Z.-P. Predicting Coastal Algal Blooms with Environmental Factors by Machine Learning Methods. Ecological Indicators 2021, 123, 107334. https://doi.org/10.1016/j.ecolind.2020.107334.
(8)
Liu, M.; Hu, J.; Huang, Y.; He, J.; Effiong, K.; Tang, T.; Huang, S.; Perianen, Y. D.; Wang, F.; Li, M.; Xiao, X. Probabilistic Prediction of Algal Blooms from Basic Water Quality Parameters by Bayesian Scale-Mixture of Skew-Normal Model. Environ. Res. Lett. 2023, 18 (1), 014034. https://doi.org/10.1088/1748-9326/acaf11.
(9)
Kimambo, O. N.; Chikoore, H.; Gumbo, J. R.; Msagati, T. A. M. Retrospective Analysis of Chlorophyll-a and Its Correlation with Climate and Hydrological Variations in Mindu Dam, Morogoro, Tanzania. Heliyon 2019, 5 (11), e02834. https://doi.org/10.1016/j.heliyon.2019.e02834.
(10)
Plaas, H. E.; Yan, J.; Christensen, C.; Chang, S.; Cortez, C.; Fern, S.; Nelson, L.; Sabo, A.; Armstrong, N. C.; Turpin, B. J.; Zhang, Y.; Paerl, H. W.; Surratt, J. D. Secondary Organic Aerosol Formation from Cyanobacterial-Derived Volatile Organic Compounds. ACS Earth Space Chem. 2023, 7 (9), 1798–1813. https://doi.org/10.1021/acsearthspacechem.3c00177.
(11)
US EPA, O. Sources and Solutions. https://www.epa.gov/nutrientpollution/sources-and-solutions (accessed 2024-03-06).
(12)
Reader, R. The Biggest Dead Zones in America’s Waterways. Vice. https://www.vice.com/en/article/znqjx4/the-biggest-dead-zones-in-americas-waterways (accessed 2024-04-08).
(13)
The Story of Chesapeake Bay. Department of the Environment. https://mde.maryland.gov/programs/water/TMDL/TMDLImplementation/Pages/default.aspx (accessed 2024-04-08).
(14)
Merlo, P. This Day in History: 2,750,000 Gallons of Sewage in Mormon Slough. Stocktonia News. https://stocktonia.org/news/history/2022/06/03/this-day-in-history-2750000-gallons-of-sewage-in-mormon-slough/ (accessed 2024-02-21).
(15)
Keith, D. J.; Milstead, B.; Walker, H.; Snook, H.; Szykman, J. J.; Wusk, M.; Kagey, L.; Howell, C.; Mellanson, C.; Drueke, C. Trophic Status, Ecological Condition, and Cyanobacteria Risk of New England Lakes and Ponds Based on Aircraft Remote Sensing. JARS 2012, 6 (1), 063577. https://doi.org/10.1117/1.JRS.6.063577.