Equity-centered adaptive sampling in sub-sewershed wastewater surveillance using census data.

IF 3.5 4区 环境科学与生态学 Q3 ENGINEERING, ENVIRONMENTAL Environmental Science: Water Research & Technology Pub Date : 2024-10-24 DOI:10.1039/d4ew00552j
Amita Muralidharan, Rachel Olson, C Winston Bess, Heather N Bischel
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Abstract

Sub-city, or sub-sewershed, wastewater monitoring for infectious diseases offers a data-driven strategy to inform local public health response and complements city-wide data from centralized wastewater treatment plants. Developing strategies for equitable representation of diverse populations in sub-city wastewater sampling frameworks is complicated by misalignment between demographic data and sampling zones. We address this challenge by: (1) developing a geospatial analysis tool that probabilistically assigns demographic data for subgroups aggregated by race and age from census blocks to sub-city sampling zones; (2) evaluating representativeness of subgroup populations for COVID-19 wastewater-based disease surveillance in Davis, California; and (3) demonstrating scenario planning that prioritizes vulnerable populations. We monitored SARS-CoV-2 in wastewater as a proxy for COVID-19 incidence in Davis (November 2021-September 2022). Daily city-wide sampling and thrice-weekly sub-city sampling from 16 maintenance holes covered nearly the entire city population. Sub-city wastewater data, aggregated as a population-weighted mean, correlated strongly with centralized treatment plant data (Spearman's correlation 0.909). Probabilistic assignment of demographic data can inform decisions when adapting sampling locations to prioritize vulnerable groups. We considered four scenarios that reduced the number of sampling zones from baseline by 25% and 50%, chosen randomly or to prioritize coverage of >65-year-old populations. Prioritizing representation increased coverage of >65-year-olds from 51.1% to 67.2% when removing half the zones, while increasing coverage of Black or African American populations from 67.5% to 76.7%. Downscaling had little effect on correlations between sub-city and centralized data (Spearman's correlations ranged from 0.875 to 0.917), with strongest correlations observed when prioritizing coverage of >65-year-old populations.

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利用普查数据在分流域废水监测中开展以公平为中心的适应性采样。
针对传染病的次城市或次流域废水监测提供了一种数据驱动型策略,可为当地公共卫生应对措施提供信息,并对来自集中式废水处理厂的全市范围数据进行补充。由于人口统计数据与采样区域不一致,因此制定策略以公平代表次级城市废水采样框架中的不同人群变得非常复杂。我们通过以下方法应对这一挑战(1) 开发一种地理空间分析工具,以概率方式将按种族和年龄从人口普查区块汇总的亚群人口数据分配到次级城市采样区;(2) 评估加州戴维斯 COVID-19 废水疾病监测亚群人口的代表性;(3) 展示优先考虑易感人群的情景规划。我们监测了戴维斯废水中的 SARS-CoV-2 作为 COVID-19 发病率的替代指标(2021 年 11 月至 2022 年 9 月)。每天在全市范围内采样,每周三次在 16 个维护孔的子城市采样,几乎覆盖了全市人口。作为人口加权平均值汇总的次级城市污水数据与集中处理厂数据密切相关(斯皮尔曼相关性为 0.909)。人口统计数据的概率分配可为调整取样位置以优先考虑弱势群体提供决策依据。我们考虑了四种方案,分别将采样区的数量从基线减少 25% 和 50%,随机选择或优先覆盖年龄大于 65 岁的人群。在去掉一半采样区的情况下,优先覆盖 65 岁以上人群的比例从 51.1% 增加到 67.2%,而黑人或非裔美国人的覆盖率从 67.5% 增加到 76.7%。缩小比例对次级城市和集中数据之间的相关性影响不大(斯皮尔曼相关性在 0.875 到 0.917 之间),当优先覆盖年龄大于 65 岁的人口时,相关性最强。
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来源期刊
Environmental Science: Water Research & Technology
Environmental Science: Water Research & Technology ENGINEERING, ENVIRONMENTALENVIRONMENTAL SC-ENVIRONMENTAL SCIENCES
CiteScore
8.60
自引率
4.00%
发文量
206
期刊介绍: Environmental Science: Water Research & Technology seeks to showcase high quality research about fundamental science, innovative technologies, and management practices that promote sustainable water.
期刊最新文献
Back cover Equity-centered adaptive sampling in sub-sewershed wastewater surveillance using census data. Different routes of bismuth mineral transformation during pertechnetate and perrhenate uptake for subsurface remediation† Environmental and private property contamination following the Norfolk Southern chemical spill and chemical fires in Ohio† Back cover
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