A novel statistical approach for analyzing environmental pollutant data with detection limits: atmospheric organochloride pesticide concentrations near Tibet's Namco Lake as a case study

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61388971%3A_____%2F25%3A00640781" target="_blank" >RIV/61388971:_____/25:00640781 - isvavai.cz</a>

Výsledek na webu

<a href="https://pubs.rsc.org/en/content/articlelanding/2025/ea/d5ea00005j" target="_blank" >https://pubs.rsc.org/en/content/articlelanding/2025/ea/d5ea00005j</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1039/d5ea00005j" target="_blank" >10.1039/d5ea00005j</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A novel statistical approach for analyzing environmental pollutant data with detection limits: atmospheric organochloride pesticide concentrations near Tibet's Namco Lake as a case study

Popis výsledku v původním jazyce

In the analysis of pollutant data, concentrations below the analytical detection limit are commonly handled by substituting a constant value between zero and the limit of detection (LOD). However, this substitution can introduce significant bias under certain conditions. To address this issue, we have derived weight expressions that eliminate bias for lognormal and gamma data. These weights, applied to LOD/2 substitutions, can be calculated using available ranges of means, standard deviations and censoring proportions. We evaluated the performance of our weighted substitution (ωLOD/2) method using both simulated datasets with censoring proportions ranging from 5% to 50% and actual atmospheric α-HCH and HCB data from Tibet's Namco Lake. The ωLOD/2 method was compared against LOD/2 substitution, maximum likelihood estimation (MLE), and regression on order statistics (ROS). The results demonstrate that with small sample sizes (<160), although MLE and ROS did not show larger bias, ωLOD/2 outperforms both methods in estimating arithmetic and geometric means in most scenarios. It is also worth noting that ROS is currently limited to estimating summary statistics under the assumption of a lognormal distribution and cannot be applied to gamma-distributed data. In addition, ωLOD/2 provides standard deviation estimates comparable to those from MLE, with biases remaining within 5% in the majority of cases. Therefore, the proposed method is particularly suitable for situations involving small sample sizes. The application of our method to six censored atmospheric organochloride pesticide concentrations from Namco Lake further highlights its advantages in practical settings. To facilitate easy adoption by researchers, a free web app was developed that integrates our proposed weighting method with censored data distribution fitting.

Název v anglickém jazyce

A novel statistical approach for analyzing environmental pollutant data with detection limits: atmospheric organochloride pesticide concentrations near Tibet's Namco Lake as a case study

Popis výsledku anglicky

In the analysis of pollutant data, concentrations below the analytical detection limit are commonly handled by substituting a constant value between zero and the limit of detection (LOD). However, this substitution can introduce significant bias under certain conditions. To address this issue, we have derived weight expressions that eliminate bias for lognormal and gamma data. These weights, applied to LOD/2 substitutions, can be calculated using available ranges of means, standard deviations and censoring proportions. We evaluated the performance of our weighted substitution (ωLOD/2) method using both simulated datasets with censoring proportions ranging from 5% to 50% and actual atmospheric α-HCH and HCB data from Tibet's Namco Lake. The ωLOD/2 method was compared against LOD/2 substitution, maximum likelihood estimation (MLE), and regression on order statistics (ROS). The results demonstrate that with small sample sizes (<160), although MLE and ROS did not show larger bias, ωLOD/2 outperforms both methods in estimating arithmetic and geometric means in most scenarios. It is also worth noting that ROS is currently limited to estimating summary statistics under the assumption of a lognormal distribution and cannot be applied to gamma-distributed data. In addition, ωLOD/2 provides standard deviation estimates comparable to those from MLE, with biases remaining within 5% in the majority of cases. Therefore, the proposed method is particularly suitable for situations involving small sample sizes. The application of our method to six censored atmospheric organochloride pesticide concentrations from Namco Lake further highlights its advantages in practical settings. To facilitate easy adoption by researchers, a free web app was developed that integrates our proposed weighting method with censored data distribution fitting.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10606 - Microbiology

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Environmental Science: Atmospheres

ISSN

2634-3606

e-ISSN

2634-3606

Svazek periodika

5

Číslo periodika v rámci svazku

8

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

12

Strana od-do

921-932

Kód UT WoS článku

001517058000001

EID výsledku v databázi Scopus

2-s2.0-105009436813