Evaluating the reliability of environmental concentration data to characterize exposure in environmental risk assessments

可靠性(半导体) 计算机科学 环境数据 样品(材料) 风险评估 数据质量 数据挖掘 风险分析(工程) 工程类 量子力学 医学 色谱法 物理 计算机安全 功率(物理) 公制(单位) 化学 运营管理 法学 政治学
作者
Michelle L. Hladik,Arjen Markus,Dennis R. Helsel,Lisa H. Nowell,Stefano Polesello,Heinz Rüdel,Drew Szabo,Iain Wilson
出处
期刊:Integrated Environmental Assessment and Management [Wiley]
卷期号:20 (4): 981-1003 被引量:9
标识
DOI:10.1002/ieam.4893
摘要

Abstract Environmental risk assessments often rely on measured concentrations in environmental matrices to characterize exposure of the population of interest—typically, humans, aquatic biota, or other wildlife. Yet, there is limited guidance available on how to report and evaluate exposure datasets for reliability and relevance, despite their importance to regulatory decision‐making. This paper is the second of a four‐paper series detailing the outcomes of a Society of Environmental Toxicology and Chemistry Technical Workshop that has developed Criteria for Reporting and Evaluating Exposure Datasets (CREED). It presents specific criteria to systematically evaluate the reliability of environmental exposure datasets. These criteria can help risk assessors understand and characterize uncertainties when existing data are used in various types of assessments and can serve as guidance on best practice for the reporting of data for data generators (to maximize utility of their datasets). Although most reliability criteria are universal, some practices may need to be evaluated considering the purpose of the assessment. Reliability refers to the inherent quality of the dataset and evaluation criteria address the identification of analytes, study sites, environmental matrices, sampling dates, sample collection methods, analytical method performance, data handling or aggregation, treatment of censored data, and generation of summary statistics. Each criterion is evaluated as “fully met,” “partly met,” “not met or inappropriate,” “not reported,” or “not applicable” for the dataset being reviewed. The evaluation concludes with a scheme for scoring the dataset as reliable with or without restrictions, not reliable, or not assignable, and is demonstrated with three case studies representing both organic and inorganic constituents, and different study designs and assessment purposes. Reliability evaluation can be used in conjunction with relevance evaluation (assessed separately) to determine the extent to which environmental monitoring datasets are “fit for purpose,” that is, suitable for use in various types of assessments. Integr Environ Assess Manag 2024;20:981–1003. © 2024 Society of Environmental Toxicology & Chemistry (SETAC). This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
曾婉之小汁完成签到,获得积分10
1秒前
能干发卡完成签到,获得积分10
1秒前
舒心魂幽完成签到,获得积分10
1秒前
YQY完成签到,获得积分10
1秒前
Szw666发布了新的文献求助10
1秒前
2秒前
3秒前
3秒前
夨艺应助科研通管家采纳,获得20
3秒前
Ava应助科研通管家采纳,获得10
3秒前
3秒前
狄仁杰克应助科研通管家采纳,获得10
4秒前
4秒前
kaisa发布了新的文献求助10
5秒前
5秒前
ch发布了新的文献求助10
6秒前
6秒前
wyy发布了新的文献求助10
6秒前
未完成完成签到,获得积分0
10秒前
Winky发布了新的文献求助10
11秒前
huzj发布了新的文献求助10
12秒前
朴素如之完成签到,获得积分10
12秒前
ale应助幸福代柔采纳,获得20
13秒前
小懒完成签到,获得积分10
14秒前
wyx完成签到 ,获得积分10
14秒前
FashionBoy应助343采纳,获得10
15秒前
minjeong完成签到 ,获得积分10
15秒前
马尧完成签到,获得积分20
15秒前
16秒前
火星上的菲鹰完成签到,获得积分0
16秒前
20秒前
IceWater完成签到,获得积分10
20秒前
科研通AI6.3应助ch采纳,获得10
22秒前
航仔发布了新的文献求助10
22秒前
小鱼完成签到 ,获得积分10
22秒前
huzj完成签到,获得积分10
22秒前
ZHOU完成签到,获得积分10
23秒前
Sasioverlxrd发布了新的文献求助20
23秒前
kaisa完成签到,获得积分10
23秒前
IceWater发布了新的文献求助10
24秒前
高分求助中
Markov Chain Monte Carlo 10000
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Common Foundations of American and East Asian Modernisation: From Alexander Hamilton to Junichero Koizumi 2000
Weaponeering: An Introduction Fourth Edition, Volume 1 1000
Advanced Weaponeering Fourth Edition, Volume 2 1000
悉尼大学博士学位论文,题目:Modelling and testing of one-sided stitched laminated composites. 作者:Kristopher P. Plain 700
Matrix Methods in Data Mining and Pattern Recognition Second Edition 610
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7515680
求助须知:如何正确求助?哪些是违规求助? 9103901
关于积分的说明 19433842
捐赠科研通 7121064
什么是DOI,文献DOI怎么找? 3253687
关于科研通互助平台的介绍 2422495
邀请新用户注册赠送积分活动 2240476