Scripps Institution of Oceanography, La Jolla, California 92037, United States.
University of California, Santa Barbara, Santa Barbara, California 93106, United States.
Environ Sci Technol. 2023 Nov 21;57(46):18162-18171. doi: 10.1021/acs.est.3c01256. Epub 2023 Jun 15.
Disposal of industrial and hazardous waste in the ocean was a pervasive global practice in the 20th century. Uncertainty in the quantity, location, and contents of dumped materials underscores ongoing risks to marine ecosystems and human health. This study presents an analysis of a wide-area side-scan sonar survey conducted with autonomous underwater vehicles (AUVs) at a dump site in the San Pedro Basin, California. Previous camera surveys located 60 barrels and other debris. Sediment analysis in the region showed varying concentrations of the insecticidal chemical dichlorodiphenyltrichloroethane (DDT), of which an estimated 350-700 t were discarded in the San Pedro Basin between 1947 and 1961. A lack of primary historical documents specifying DDT acid waste disposal methods has contributed to the ambiguity surrounding whether dumping occurred via bulk discharge or containerized units. Barrels and debris observed during previous surveys were used for ground truth classification algorithms based on size and acoustic intensity characteristics. Image and signal processing techniques identified over 74,000 debris targets within the survey region. Statistical, spectral, and machine learning methods characterize seabed variability and classify bottom-type. These analytical techniques combined with AUV capabilities provide a framework for efficient mapping and characterization of uncharted deep-water disposal sites.
在 20 世纪,向海洋倾倒工业和危险废物是一种普遍存在的全球做法。倾倒物的数量、位置和内容的不确定性突出了对海洋生态系统和人类健康的持续风险。本研究分析了在加利福尼亚州圣佩德罗盆地的一个倾倒场使用自主水下航行器(AUV)进行的宽幅侧扫声纳调查。先前的摄像调查定位了 60 个桶和其他碎片。该地区的沉积物分析显示出杀虫剂滴滴涕(DDT)的浓度不同,据估计,1947 年至 1961 年间,有 350-700 吨滴滴涕在圣佩德罗盆地被丢弃。缺乏明确指定滴滴涕酸废物处理方法的主要历史文件,导致围绕倾倒是通过散装排放还是集装箱单元进行的问题存在模糊性。在前几次调查中观察到的桶和碎片被用于基于大小和声学强度特征的地面真实分类算法。图像和信号处理技术在调查区域内识别出超过 74000 个碎片目标。统计、光谱和机器学习方法描述了海底的可变性并对底部类型进行分类。这些分析技术与 AUV 能力相结合,为未开发的深水处置场的高效测绘和特征描述提供了框架。