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Dr. Kun Shan

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Guest Editor

Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
Interests: inland waters; harmful algae blooms; ecological modelling; machine learning; Bayesian statistics
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Lin Li

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Guest Editor

Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, China
Interests: phytoplankton ecology; ecological informatics; water monitoring; ecological modelling and engineering

Prof. Dr. Jianming Deng

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Guest Editor

Nanjing Institute of Geography & Limnology, Chinese Academy of Sciences, Nanjing, China
Interests: inland waters; global change; ecology of phytoplankton; cyanobacterial blooms; biostatistics

Special Issue Information

Dear Colleagues,

Recent evolutions in sensor technology and big data have provided the environmental community with continuously expanding resources for data collection. Traditionally, biological, chemical, and physical paramters of waterbodies are measured monthly, weekly, or biweekly. Nowadays, more and more inland waters are well monitored by online automatic instruments, giving access to long-term datasets with high monitoring frequencies. Many data-driven methodologies have been presented to address this issue, including linear and nonlinear models. This enables new strategies in water quality management.

An important feature of the field-based study is that we use variables operating at different spatiotemporal scales. The water quality response may be driven both by local changes in the catchment and by regional variations of parameters within a lake. Therefore, cross-scale interactions defined as patterns or processes at one scale that affect driver-response relationships taking place at a different scale may account for model performance.

This special issue aims to find promising and new machine learning methods for future water management. We welcome studies using field-based data to environmental and ecological problems. We also accept the studies which use statistical or mathematical models to analyse water quality and biological responses in both catchment and regional scales.

Prof. Dr. Kun Shan
Prof. Dr. Lin Li
Prof. Dr. Jianming Deng
Guest Editors

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Keywords

machine learning
statistical analysis
on-line monitoring
eutrophication
phytoplankton
nutrients
contamination
food web
inland water

Published Papers (2 papers)

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Research

16 pages, 3316 KiB

Open AccessArticle

Data-Driven Models for Evaluating Coastal Eutrophication: A Case Study for Cyprus

by Ekaterini Hadjisolomou, Maria Rousou, Konstantinos Antoniadis, Lavrentios Vasiliades, Ioannis Kyriakides, Herodotos Herodotou and Michalis Michaelides

Water 2023, 15(23), 4097; https://0-doi-org.brum.beds.ac.uk/10.3390/w15234097 - 26 Nov 2023

Viewed by 1087

Abstract

Eutrophication is a major environmental issue with many negative consequences, such as hypoxia and harmful cyanotoxin production. Monitoring coastal eutrophication is crucial, especially for island countries like the Republic of Cyprus, which are economically dependent on the tourist sector. Additionally, the open-sea aquaculture industry in Cyprus has been exhibiting an increase in recent decades and environmental monitoring to identify possible signs of eutrophication is mandatory according to the legislation. Therefore, in this modeling study, two different types of artificial neural networks (ANNs) are developed based on in situ data collected from stations located in the coastal waters of Cyprus. These ANNs aim to model the eutrophication phenomenon based on two different data-driven modeling procedures. Firstly, the self-organizing map (SOM) ANN examines several water quality parameters’ (specifically water temperature, salinity, nitrogen species, ortho-phosphates, dissolved oxygen, and electrical conductivity) interactions with the Chlorophyll-a (Chl-a) parameter. The SOM model enables us to visualize the monitored parameters’ relationships and to comprehend complex biological mechanisms related to Chl-a production. A second feed-forward ANN model is also developed for predicting the Chl-a levels. The feed-forward ANN managed to predict the Chl-a levels with great accuracy (MAE = 0.0124; R = 0.97). The sensitivity analysis results revealed that salinity and water temperature are the most influential parameters on Chl-a production. Moreover, the sensitivity analysis results of the feed-forward ANN captured the winter upwelling phenomenon that is observed in Cypriot coastal waters. Regarding the SOM results, the clustering verified the oligotrophic nature of Cypriot coastal waters and the good water quality status (only 1.4% of the data samples were classified as not good). The created ANNs allowed us to comprehend the mechanisms related to eutrophication regarding the coastal waters of Cyprus and can act as useful management tools regarding eutrophication control. Full article

(This article belongs to the Special Issue Application of Data-Driven Methods for Analyzing Complex Environmental and Ecological Data)

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14 pages, 4162 KiB

Open AccessFeature PaperArticle

Responses of Net Anthropogenic N Inputs and Export Fluxes in the Megacity of Chengdu, China

by Yao Ding, Chengyue Lai, Qing Shi, Lili Ouyang, Zhaoli Wang, Gang Yao and Binyang Jia

Water 2021, 13(24), 3543; https://0-doi-org.brum.beds.ac.uk/10.3390/w13243543 - 11 Dec 2021

Cited by 3 | Viewed by 2140

Abstract

Anthropogenic N inputs have become progressively more problematic and have profoundly affected the water quality in megacities throughout China. Thus, to design and implement appropriate megalopolis watershed management, it is important to understand the relationship between N inputs and exports and to identify the N pollution sources. To that end, in this work, the net anthropogenic N inputs (NANI) in Chengdu City were estimated based on statistical data collected between 1970 and 2019. N input fluxes and pollution sources were estimated through sample collection and field measurements that were performed between 2017 and 2019, while nitrate (

{NO}_{3}^{-}

) was identified using stable isotope and Bayesian model (SIAR) analysis. The NANI was found to be affected primarily by livestock and poultry consumption of N rich feed. Moreover, the N export fluxes and runoff showed a high degree of correlation. Notably,

{NO}_{3}^{-}

fluxes exhibited a significant increase over the course of the study period, such that, by 2019, the total N fluxes (18,883.85 N kg/

{km}^{2}

) exceeded the NANI (17,093.87 N kg/

{km}^{2}

). The results indicate that although livestock and poultry farming were the original primary sources of NANI, their contributions declined on an annual basis. Moreover, with the emphasis placed on point source management in Chengdu City, domestic sewage discharge has been significantly reduced. Therefore, N retention in groundwater is thought to be the factor driving the N flux increase. These findings are pivotal to solving the N pollution problem in megacities like Chengdu (China). Full article

(This article belongs to the Special Issue Application of Data-Driven Methods for Analyzing Complex Environmental and Ecological Data)

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