利用多元分析了解尼翁河红树林河口水质参数的季节和空间变化(喀麦隆南大西洋海岸)

A. Mama, Willy Karol Abouga Bodo, Gisèle Flodore Youbouni Ghepdeu, G. Ajonina, Jules Rémi Ngoupayou Ndam
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引用次数: 7

摘要

利用多元统计技术(主成分分析)对宁戎河口水质参数的季节和空间变化规律进行了分析,以评价宁戎河口水质的实际状况,明确主要污染源。在河口4个地面站对9个环境变量进行了2个季节周期的监测。实地调查于2018年至2019年进行,每次调查均在涨潮和退潮期间进行。共测量了64个样本的原位物理参数(每个潮汐32个样本)。实验室工作包括一些物理化学分析,并通过描述性和多维统计分析处理这些数据。温度、悬浮颗粒物、硝酸盐、亚硝酸盐和磷酸盐随季节变化显著(p p > 0.05)。主成分分析结果表明,温度、盐度、pH、铵态氮是枯水期尼龙口表层水质波动的最主要影响因子,而悬浮颗粒物、硝酸盐和磷酸盐是雨季尼龙口表层水质波动的最主要影响因子。基于空间变异,主成分分析发现,悬浮物、硝酸盐和磷酸盐对河口上游地表水水质参数波动的影响最大,而下游盐度、pH和铵对地表水水质参数波动的影响最大。这项研究向我们展示了用于评估水质数据集的多元统计技术的有用性,这将有助于我们了解水质参数的季节和空间变化,以管理河口系统。
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Understanding Seasonal and Spatial Variation of Water Quality Parameters in Mangrove Estuary of the Nyong River Using Multivariate Analysis (Cameroon Southern Atlantic Coast)
To evaluate the actual status of water quality and conclude on the mains source of pollution in the Nyong estuary River, seasonal and spatial variation of water quality parameters was interpreted by multivariate statistical techniques (Principal Component analysis). Nine (09) environmental variables were monitored at four surface stations in the estuary for two seasonal cycles. The fieldwork was conducted from 2018 to 2019 during high tide and low tide for each survey. In situ physical parameters were measured for a total of 64 samples (32 samples for each tide). The laboratory works consisted of some physicochemical analyses and processing of these data by descriptive and multidimensional statistical analyses. Temperature, suspended particle matter, nitrate, nitrite and phosphate change significantly in the estuary with season (p p > 0.05). Principal Component analysis found temperature, salinity, pH, ammonium to be the most important parameters contributing to the fluctuations of surface water quality in the Nyong estuary during the dry seasons whereas suspended particle matter, nitrate, and phosphate are the most important parameters contributing to the fluctuation of surface water quality in the Nyong estuary during the rainy seasons. Based on spatial variation, the Principal Component analysis found that, suspended particle matter, nitrate and phosphate contribute to the fluctuation of surface water quality parameters upstream of the estuary while downstream salinity, pH, and ammonium contribute the most to the fluctuation of surface water quality. This study shows us the usefulness of multivariate statistical techniques used in assessing water quality data sets that would help us in understanding seasonal and spatial variations of water quality parameters to manage estuarine systems.
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