Hayes Wesley, Jones C Nathan, Osman Khalid K, Eaves Lauren A, Mize Wilson, Fowlkes Jon, Fry Rebecca C, Pieper Kelsey J
Department of Civil and Environmental Engineering, Northeastern University, Boston, Massachusetts 02115, United States.
Department of Biological Sciences, University of Alabama, Tuscaloosa, Alabama 35401, United States.
Environ Sci Technol. 2025 Jan 21;59(2):1232-1242. doi: 10.1021/acs.est.4c05437. Epub 2025 Jan 9.
The natural, built, and social environments shape drinking water quality supplied by private wells. However, the combined effects of these factors are not well understood. Using North Carolina as a case study, we (i) estimate the demographic characteristics of the private well population; (ii) evaluate representation in well testing records; and (iii) demonstrate how spatial scale influences knowledge of well-using household demographics and representation in testing. We leverage a statewide database of 117,960 well testing records collected over 20 years and a national model predicting well locations. An estimated 25% well-using households identify as Black, Indigenous, and Persons of Color (BIPOC) and 15% have incomes below the poverty threshold. While there is robust well sampling (an average of 4,269 wells tested annually), we observed that most testing records were from predominately White block groups (BGs). Well-using households that did not participate in state testing were 2.4 times more likely to be from predominately BIPOC BGs compared predominately White BGs. Due to the spatial heterogeneity of the well population, demographic differences in well populations were more evident using higher resolution data. Multifaceted testing approaches that couple government-driven efforts with localized studies that engage underrepresented communities are needed to facilitate evidence-based management.
自然、建筑和社会环境塑造了私人水井供应的饮用水质量。然而，这些因素的综合影响尚未得到充分理解。以北卡罗来纳州为例，我们（i）估计私人水井用户的人口特征；（ii）评估水井检测记录中的代表性；（iii）展示空间尺度如何影响对使用水井家庭人口特征的了解以及检测中的代表性。我们利用了一个20年来收集的包含117,960条水井检测记录的全州数据库以及一个预测水井位置的全国模型。估计有25%使用水井的家庭为黑人、原住民和有色人种（BIPOC），15%的家庭收入低于贫困线。虽然有大量的水井采样（每年平均检测4269口水井），但我们观察到大多数检测记录来自以白人为主的街区组（BGs）。与以白人为主的BGs相比，未参与州检测的使用水井家庭来自以BIPOC为主的BGs的可能性高出2.4倍。由于水井人口的空间异质性，使用更高分辨率的数据时，水井人口中的人口差异更加明显。需要多方面的检测方法，将政府主导的努力与吸引代表性不足社区参与的本地化研究相结合，以促进基于证据的管理。
