Rossi Robert J, Tisherman Rebecca A, Jaeger Jessie M, Domen Jeremy, Shonkoff Seth B C, DiGiulio Dominic C
PSE Healthy Energy, Oakland, California 94612, United States.
Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, California 94720, United States.
Environ Sci Technol. 2023 May 16;57(19):7559-7567. doi: 10.1021/acs.est.3c01219. Epub 2023 May 5.
Oil and gas development generates large amounts of wastewater (i.e., produced water), which in California has been partially disposed of in unlined percolation/evaporation ponds since the mid-20th century. Although produced water is known to contain multiple environmental contaminants (e.g., radium and trace metals), prior to 2015, detailed chemical characterizations of pondwaters were the exception rather than the norm. Using a state-run database, we synthesized samples ( = 1688) collected from produced water ponds within the southern San Joaquin Valley of California, one of the most productive agricultural regions in the world, to examine regional trends in pondwater arsenic and selenium concentrations. We filled crucial knowledge gaps resulting from historical pondwater monitoring by constructing random forest regression models using commonly measured analytes (boron, chloride, and total dissolved solids) and geospatial data (e.g., soil physiochemical data) to predict arsenic and selenium concentrations in historical samples. Our analysis suggests that both arsenic and selenium levels are elevated in pondwaters and thus this disposal practice may have contributed substantial amounts of arsenic and selenium to aquifers having beneficial uses. We further use our models to identify areas where additional monitoring infrastructure would better constrain the extent of legacy contamination and potential threats to groundwater quality.
石油和天然气开发产生大量废水(即采出水),自20世纪中叶以来,加利福尼亚州的采出水部分被排放到无衬砌的渗滤/蒸发池中。尽管已知采出水中含有多种环境污染物(如镭和微量金属),但在2015年之前,对池塘水进行详细的化学表征是例外情况而非普遍做法。我们利用一个国家运营的数据库,综合了从加利福尼亚州圣华金河谷南部的采出水池塘收集的样本((n = 1688)),该地区是世界上最具生产力的农业地区之一,以研究池塘水砷和硒浓度的区域趋势。我们通过使用常见测量分析物(硼、氯和总溶解固体)和地理空间数据(如土壤理化数据)构建随机森林回归模型来预测历史样本中的砷和硒浓度,填补了历史池塘水监测所导致的关键知识空白。我们的分析表明,池塘水中的砷和硒含量均有所升高,因此这种处置方式可能已向具有有益用途的含水层中大量输入了砷和硒。我们进一步利用我们的模型来确定哪些区域增加监测基础设施将能更好地限制遗留污染的范围以及对地下水质量的潜在威胁。