TY - GEN
T1 - Correlation Research between Blue-green Algae and Water Quality Indicators Using Unmanned Surface Vehicle
AU - Wei, Yichen
AU - Zhang, Zixian
AU - Zhu, Xiaohui
AU - Yue, Yong
N1 - Funding Information:
This research was supported by the Suzhou Science and Technology Projects (SYG202006 and SYG202122); the Key Program Special Fund of Xi’an Jiaotong-Liverpool University (XJTLU) (KSF-A-19, KSF-E-65, KSF-P-02 and KSF-E-54); the Research Development Fund of XJTLU (RDF-19-02-23), National Natural Science Foundation of China (62002296) and Natural Science Foundation of Jiangsu Province (BK20200250).
Funding Information:
This research was supported by the Suzhou Science and Technology Projects (SYG202006 and SYG202122); the Key Program Special Fund of Xi an Jiaotong-Liverpool University (XJTLU) (KSF-A-19, KSF-E-65, KSFP- 02 and KSF-E-54); the Research Development Fund of XJTLU (RDF-19- 02-23), National Natural Science Foundation of China (62002296) and Natural Science Foundation of Jiangsu Province (BK20200250).
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The harmful algal blooms in fresh waters have led to severe environmental problems such as mass mortalities of wild and cultured fish and shellfish, and human illnesses, which hamper the sustainability of fisheries and aquaculture. Blue-green algae (BGA) are commonly the dominant species in harmful algal blooms. Thus, studying the correlation between BGA and water quality indicators can contribute to establishing data-driven models when predicting the outbreaks of BGA. Previous studies typically used data from fixed-point sampling for correlation analysis. For specific waters, fixed-point sampling has the defects of small coverage, low sampling frequency, and poor flexibility, which is one of the reasons affecting the reliability of the analysis results. This paper uses an unmanned surface vehicle (USV) for water quality data collection. Spearman's correlation coefficient and statistical methods are used to conduct correlation analysis between BGA biomass (measured by phycocyanin) and water quality indicators. The results show a significant positive correlation between BGA biomass and chlorophyll-a, pH, water temperature, and dissolved oxygen. The results are consistent with most correlation studies and demonstrate the feasibility of using the massive sampling data collected by unmanned surface vehicle to analyze the correlation between BGA and water quality indicators.
AB - The harmful algal blooms in fresh waters have led to severe environmental problems such as mass mortalities of wild and cultured fish and shellfish, and human illnesses, which hamper the sustainability of fisheries and aquaculture. Blue-green algae (BGA) are commonly the dominant species in harmful algal blooms. Thus, studying the correlation between BGA and water quality indicators can contribute to establishing data-driven models when predicting the outbreaks of BGA. Previous studies typically used data from fixed-point sampling for correlation analysis. For specific waters, fixed-point sampling has the defects of small coverage, low sampling frequency, and poor flexibility, which is one of the reasons affecting the reliability of the analysis results. This paper uses an unmanned surface vehicle (USV) for water quality data collection. Spearman's correlation coefficient and statistical methods are used to conduct correlation analysis between BGA biomass (measured by phycocyanin) and water quality indicators. The results show a significant positive correlation between BGA biomass and chlorophyll-a, pH, water temperature, and dissolved oxygen. The results are consistent with most correlation studies and demonstrate the feasibility of using the massive sampling data collected by unmanned surface vehicle to analyze the correlation between BGA and water quality indicators.
KW - blue-green algae
KW - correlation analysis
KW - sustainable development
KW - unmanned surface vehicle
KW - water quality indicators
UR - http://www.scopus.com/inward/record.url?scp=85166985679&partnerID=8YFLogxK
U2 - 10.1109/ICESGE56040.2022.10180367
DO - 10.1109/ICESGE56040.2022.10180367
M3 - Conference Proceeding
AN - SCOPUS:85166985679
T3 - Proceedings - 2022 International Conference on Environmental Science and Green Energy, ICESGE 2022
SP - 7
EP - 13
BT - Proceedings - 2022 International Conference on Environmental Science and Green Energy, ICESGE 2022
A2 - Shi, Fanian
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 International Conference on Environmental Science and Green Energy, ICESGE 2022
Y2 - 9 December 2022 through 11 December 2022
ER -
