Dataset Information

The Predominant Sources of Heavy Metals in Different Types of Fugitive Dust Determined by Principal Component Analysis (PCA) and Positive Matrix Factorization (PMF) Modeling in Southeast Hubei: A Typical Mining and Metallurgy Area in Central China.

ABSTRACT: To develop accurate air pollution control policies, it is necessary to determine the sources of different types of fugitive dust in mining and metallurgy areas. A method integrating principal component analysis and a positive matrix factorization model was used to identify the potential sources of heavy metals (HMs) in five different types of fugitive dust. The results showed accumulation of Mn, Fe, and Cu can be caused by natural geological processes, which contributed 38.55% of HMs. The Ni and Co can be released from multiple transport pathways and accumulated through local deposition, which contributed 29.27%. Mining-related activities contributed 20.11% of the HMs and showed a relatively high accumulation of As, Sn, Zn, and Cr, while traffic-related emissions contributed the rest of the HMs and were responsible for the enrichment in Pb and Cd. The co-applied source-identification models improved the precision of the identification of sources, which revealed that the local geological background and mining-related activities were mainly responsible for the accumulation of HMs in the area. The findings can help the government develop targeted control strategies for HM dispersion efficiency.

SUBMITTER: Chen H

PROVIDER: S-EPMC9602615 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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The Predominant Sources of Heavy Metals in Different Types of Fugitive Dust Determined by Principal Component Analysis (PCA) and Positive Matrix Factorization (PMF) Modeling in Southeast Hubei: A Typical Mining and Metallurgy Area in Central China.

Chen Hongling H Wu Dandan D Wang Qiao Q Fang Lihu L Wang Yanan Y Zhan Changlin C Zhang Jiaquan J Zhang Shici S Cao Junji J Qi Shihua S Liu Shan S

International journal of environmental research and public health 20221014 20

To develop accurate air pollution control policies, it is necessary to determine the sources of different types of fugitive dust in mining and metallurgy areas. A method integrating principal component analysis and a positive matrix factorization model was used to identify the potential sources of heavy metals (HMs) in five different types of fugitive dust. The results showed accumulation of Mn, Fe, and Cu can be caused by natural geological processes, which contributed 38.55% of HMs. The Ni and ...[more]

PMID: 36293808

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Project description:BackgroundGender differences in dietary patterns and physical activity are known to influence metabolic health, but research exploring these differences using principal component analysis (PCA) is limited. This study aims to identify distinct patterns of eating behaviour, body composition and physical activity between men and women, in order to develop tailored interventions.MethodsA cross-sectional study was conducted on 2,509 adults attending a metabolic health centre. Data on eating habits, physical activity and body composition were collected by means of questionnaires and bioimpedance analysis. PCA was used to identify patterns of eating behaviour and physical activity. Statistical analyses were performed to explore gender-specific differences.ResultsBased on the PCA, five distinct groups of participants were identified: Balanced Eaters, Focused on Home Cooking, Routine Eaters, Restaurant Lovers and Varied Eaters. Significant gender differences in food preferences were observed, with men consuming more meat and women more vegetables. Men also reported greater participation in strength and endurance sports, while women showed a more structured eating routine.ConclusionsThis study, using principal component analysis (PCA), revealed gender-specific patterns in diet, physical activity and body composition. PCA identified five distinct behavioural groups, revealing that men tended to consume more meat and engage in strength training, while women adhered to structured, vegetable-rich diets. The application of PCA provided more insight than traditional analysis, highlighting the complexity of gender-specific behaviour. These results emphasize the need for tailored interventions, focusing on increasing vegetable intake in men and encouraging strength training in women. Future research should exploit PCA to explore behavioural patterns longitudinally for more refined and personalised health strategies.Clinical trials registeredThis study is registered on ClinicalTrials.gov (NCT06654674).

Dataset Information

The Predominant Sources of Heavy Metals in Different Types of Fugitive Dust Determined by Principal Component Analysis (PCA) and Positive Matrix Factorization (PMF) Modeling in Southeast Hubei: A Typical Mining and Metallurgy Area in Central China.

Publications

The Predominant Sources of Heavy Metals in Different Types of Fugitive Dust Determined by Principal Component Analysis (PCA) and Positive Matrix Factorization (PMF) Modeling in Southeast Hubei: A Typical Mining and Metallurgy Area in Central China.

Similar Datasets

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