Urbanization shifts long‐term phenology and severity of phytoplankton blooms in an urban lake through different pathways

Global Change Biology, Journal Year: 2023, Volume and Issue: 29(17), P. 4983 - 4999

Published: June 23, 2023

Abstract Climate change can induce phytoplankton blooms (PBs) in eutrophic lakes worldwide, and these severely threaten lake ecosystems human health. However, it is unclear how urbanization its interaction with climate impact PBs, which has implications for the management of lakes. Here, we used multi‐source remote sensing data integrated Virtual‐Baseline Floating macroAlgae Height (VB‐FAH) index OTSU threshold automatic segmentation algorithm to extract area PBs Lake Dianchi, China, been subjected frequent rapid vicinity. We further explored long‐term (2000–2021) trends phenological severity metrics quantified contributions from urbanization, change, also nutrient levels trends. When comparing 2011–2021 2000–2010, found significantly advanced initiation (28.6 days) noticeably longer duration (51.9 but an insignificant trend time disappearance. The enhancement algal use efficiency, likely induced by increased water temperature reduced concentrations, presumably contributed earlier while there was a negative correlation between spring wind speed PBs. Fortunately, that both frequency severe (covering more than 19.8 km 2 ) demonstrated downward trends, could be attributed and/or levels. Moreover, enhanced land surface caused altered thermodynamic characteristics lake, which, turn, possibly increase local temperature, suggesting differently regulate phenology Our findings have significant understanding impacts on PB dynamics improving practices promote sustainable urban development under global change.

Language: Английский

---

Machine learning-based design and monitoring of algae blooms: Recent trends and future perspectives – A short review

Critical Reviews in Environmental Science and Technology, Journal Year: 2023, Volume and Issue: 54(7), P. 509 - 532

Published: Sept. 7, 2023

AbstractMachine learning (ML) models are widely used methods for analyzing data from sensors and satellites to monitor climate change, predict natural disasters, protect wildlife. However, the application of these technologies monitoring managing algal blooms in freshwater environments is relatively new novel. The commonly (ABS) so far artificial neural networks (ANN), random forests (RF), support vector machine (SVM), data-driven modeling, long short-term memory (LSTM). In past, researchers have mostly worked on predicting effluent parameters, nutrients, microculture, area weather conditions, meteorological factors, ground waters, energy optimization, metallic substances using ML models. Most studies employed performance metrics like root mean squared error, peak signal, precision, determination coefficient as their primary model measures accuracy analysis, usage transfer, activation function. While there been some this topic, several research gaps still be addressed. most significant related limited different algae bloom scenarios, interpretability models, lack integration with existing systems. Keeping mind, review article has methodically arranged present an overview past studies, limitations, way forward toward prediction ABS, thus benefitting future area. This aims summarize that available, including benchmarking values.HighlightsReal-time dynamics essential mitigating blooms.Various complexities hinder applications current algorithms ABS.Activation transfer functions can selection ABS.Integrated drive feature engineering control ABS.Keywords: Activation-functionalgae bloomsmonitoringmachine learningperformance predictionHANDLING EDITORS: Hyunjung Kim Scott Bradford Disclosure statementNo potential conflict interest was reported by authors.

Language: Английский

---

Warming surface and Lake heatwaves as key drivers to harmful algal Blooms: A case study of Lake Dianchi, China

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 632, P. 130971 - 130971

Published: Feb. 24, 2024

Language: Английский

---

Distribution, relationship, and environmental driving factors of chlorophyll-a and algal cell density: A national view of China

Global Ecology and Conservation, Journal Year: 2024, Volume and Issue: 54, P. e03084 - e03084

Published: July 11, 2024

Chlorophyll-a (Chl-a) and algal cell density (ACD) are vital for assessing proliferation eutrophication in aquatic ecosystems. Although Chl-a is often used as a proxy ACD, its accuracy requires validation, studies on their linear correlation scarce. Additionally, ACD influenced by various factors, including nutritional, economic, biochemical, physicochemical, meteorological factors. However, the relative importance of these factors remains insufficiently studied. This study analyzed data from 57 lakes reservoirs across China March 2021 to February 2023, investigating distribution patterns different regions seasons. The employed regression model explore between seasons throughout China. Furthermore, Mantel test generalized were evaluated nutritional (total nitrogen (TN), total phosphorus (TP), TN/TP ratio (TN/TP), ammonia (NH3-N)), economic (gross domestic product), (surface pressure, net solar radiation, air temperature, wind speed (WS), rainfall (RF)), well biochemical physicochemical (turbidity (TUR), pH value, water temperature (WT), permanganate index, dissolved oxygen) ACD. Results showed that average concentrations South highest, at 12 μg/L 19.5 × 106 cells/L, respectively. Seasonally, peaked spring was lowest winter, while summer winter. Significant seasonal regional variations observed, with showing strongest relationship. In Central China, significantly correlated four seasons, whereas correlations less distinct Eastern Western regions. Therefore, caution. Nutrient (TN, TP, TN/TP, NH3-N) identified primary drivers Meteorological (WS, RF), along (WT, TUR) also emerged critical predictors spatial variations. validates analyzes spatiotemporal distribution, assesses influence enhancing our understanding dynamics Chinese reservoirs.

Language: Английский

---

Dynamics and drivers of phytoplankton in Lake Hulun

Journal of Lake Sciences, Journal Year: 2025, Volume and Issue: 37(1), P. 50 - 60

Published: Jan. 1, 2025

Language: Английский

---

Numerical modeling of water diversion impacts on water quality improvement in Lake Dianchi

Environmental Modelling & Software, Journal Year: 2025, Volume and Issue: unknown, P. 106375 - 106375

Published: Feb. 1, 2025

Language: Английский

---

Prediction of outpatient visits for allergic rhinitis using an artificial intelligence LSTM model - a study in Eastern China

BMC Public Health, Journal Year: 2025, Volume and Issue: 25(1)

Published: April 9, 2025

Allergic rhinitis is a common disease that can affect the health of patients and bring huge social economic burdens. In this study, we developed model to predict incidence rate allergic so as provide accurate information for treatment, prevention, control rhinitis. We Long Short-Term Memory effectively predicting daily outpatient visits based on air pollution meteorological data. collected data from departments otolaryngology, emergency medicine, pediatrics, respiratory medicine at Affiliated Hospital Hangzhou Normal University, January 2022 August 2024. The were stratified by gender age separately input into evaluation. A total 25,425 samples assessed in study. Based obtained males (n = 13,943), females 11,482), adults 17,473), minors 7,952), normalized mean squared errors 0.4674976, 0.3812502, 0.418301, 0.4322124, respectively. By comparing NMSE prediction results ARIMA LSTM models dataset, was found outperform terms stability accuracy. presented here could data, thereby offering valuable data-driven support hospital management potentially improving societal prevention

Language: Английский

---

Exploring spatiotemporal patterns of algal cell density in lake Dianchi with explainable machine learning

Environmental Pollution, Journal Year: 2024, Volume and Issue: 356, P. 124395 - 124395

Published: June 18, 2024

Language: Английский

---

Characteristics and Influencing Factors of Intra-Annual Water Quality Variation of the Central Yunnan Plateau Lakes

Advances in Environmental Protection, Journal Year: 2025, Volume and Issue: 15(02), P. 167 - 175

Published: Jan. 1, 2025

Language: Английский

---

Mapping satellite-derived chlorophyll-a concentrations from 2013 to 2023 in Western Lake Ontario using Landsat 8 and 9 imagery

The Science of The Total Environment, Journal Year: 2025, Volume and Issue: 968, P. 178881 - 178881

Published: Feb. 21, 2025

Language: Английский

---