Matches in SemOpenAlex for { <https://semopenalex.org/work/W4327571694> ?p ?o ?g. }
- W4327571694 endingPage "131231" @default.
- W4327571694 startingPage "131231" @default.
- W4327571694 abstract "Pollution threshold and high-risk area determination for heavy metals is important for effectively developing pollution control strategies. Based on heavy metal contents in 3627 dense samples, an integrated framework combining the finite mixture distribution model and Bayesian maximum entropy (BME) theory was proposed to assess pollution thresholds, contamination levels and risk areas in an uncertain environment for soil heavy metals. The results showed that the average heavy metal contents were in the order Zn > Cr > Pb > Cu > Ni > As > Cd > Hg, with strong/moderate variation, and the corresponding pollution thresholds were 158.39, 84.29, 47.84, 49.75, 28.95, 18.01, 0.49 and 0.16 mg/kg, respectively. The thresholds were consistently greater than the Zhejiang Province backgrounds but lower than the national risk screening values, except for Cd. Approximately 27.9% of the samples were classified as contaminated at various levels, and they were distributed in the northern, northwestern and eastern regions of the study area. Additionally, 3.73%, 5.34% and 8.22% of the total area were classified as at-risk areas under confidence levels of 95%, 90% and 75%, respectively, through BME theory. The findings provide a reasonable classification system and suggestions for heavy metal pollution management and control." @default.
- W4327571694 created "2023-03-17" @default.
- W4327571694 creator A5002534655 @default.
- W4327571694 creator A5038849741 @default.
- W4327571694 creator A5046574691 @default.
- W4327571694 creator A5047054646 @default.
- W4327571694 creator A5084804962 @default.
- W4327571694 date "2023-06-01" @default.
- W4327571694 modified "2023-10-14" @default.
- W4327571694 title "Pollution threshold assessment and risk area delineation of heavy metals in soils through the finite mixture distribution model and Bayesian maximum entropy theory" @default.
- W4327571694 cites W1068040093 @default.
- W4327571694 cites W1971204840 @default.
- W4327571694 cites W1971866367 @default.
- W4327571694 cites W1975381045 @default.
- W4327571694 cites W1983835937 @default.
- W4327571694 cites W2000996445 @default.
- W4327571694 cites W2003400262 @default.
- W4327571694 cites W2037334621 @default.
- W4327571694 cites W2049781224 @default.
- W4327571694 cites W2054176843 @default.
- W4327571694 cites W2071913886 @default.
- W4327571694 cites W2099099327 @default.
- W4327571694 cites W2131586477 @default.
- W4327571694 cites W2267118147 @default.
- W4327571694 cites W2334825407 @default.
- W4327571694 cites W2344929405 @default.
- W4327571694 cites W2726382703 @default.
- W4327571694 cites W2744595592 @default.
- W4327571694 cites W2756085834 @default.
- W4327571694 cites W2784260830 @default.
- W4327571694 cites W2883818011 @default.
- W4327571694 cites W2890080750 @default.
- W4327571694 cites W2896476191 @default.
- W4327571694 cites W2904699764 @default.
- W4327571694 cites W2909711122 @default.
- W4327571694 cites W2960095593 @default.
- W4327571694 cites W2977988034 @default.
- W4327571694 cites W2989643546 @default.
- W4327571694 cites W2995022178 @default.
- W4327571694 cites W3004785865 @default.
- W4327571694 cites W3015070040 @default.
- W4327571694 cites W3037847278 @default.
- W4327571694 cites W3044283162 @default.
- W4327571694 cites W3044459621 @default.
- W4327571694 cites W3045281143 @default.
- W4327571694 cites W3086366127 @default.
- W4327571694 cites W3093587779 @default.
- W4327571694 cites W3107298396 @default.
- W4327571694 cites W3115140263 @default.
- W4327571694 cites W3117422876 @default.
- W4327571694 cites W3118512449 @default.
- W4327571694 cites W3134469759 @default.
- W4327571694 cites W3166722341 @default.
- W4327571694 cites W3172238819 @default.
- W4327571694 cites W3172805721 @default.
- W4327571694 cites W3177926592 @default.
- W4327571694 cites W4207061529 @default.
- W4327571694 cites W4210641016 @default.
- W4327571694 cites W4210864744 @default.
- W4327571694 cites W4212772811 @default.
- W4327571694 cites W4240659544 @default.
- W4327571694 cites W4282018720 @default.
- W4327571694 cites W4283585162 @default.
- W4327571694 cites W4283735039 @default.
- W4327571694 cites W4311764026 @default.
- W4327571694 cites W4312204529 @default.
- W4327571694 cites W3204531697 @default.
- W4327571694 doi "https://doi.org/10.1016/j.jhazmat.2023.131231" @default.
- W4327571694 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36934631" @default.
- W4327571694 hasPublicationYear "2023" @default.
- W4327571694 type Work @default.
- W4327571694 citedByCount "3" @default.
- W4327571694 countsByYear W43275716942023 @default.
- W4327571694 crossrefType "journal-article" @default.
- W4327571694 hasAuthorship W4327571694A5002534655 @default.
- W4327571694 hasAuthorship W4327571694A5038849741 @default.
- W4327571694 hasAuthorship W4327571694A5046574691 @default.
- W4327571694 hasAuthorship W4327571694A5047054646 @default.
- W4327571694 hasAuthorship W4327571694A5084804962 @default.
- W4327571694 hasConcept C105795698 @default.
- W4327571694 hasConcept C107872376 @default.
- W4327571694 hasConcept C112570922 @default.
- W4327571694 hasConcept C159390177 @default.
- W4327571694 hasConcept C185592680 @default.
- W4327571694 hasConcept C18903297 @default.
- W4327571694 hasConcept C2776053758 @default.
- W4327571694 hasConcept C33923547 @default.
- W4327571694 hasConcept C39432304 @default.
- W4327571694 hasConcept C521259446 @default.
- W4327571694 hasConcept C86803240 @default.
- W4327571694 hasConcept C87717796 @default.
- W4327571694 hasConcept C9679016 @default.
- W4327571694 hasConceptScore W4327571694C105795698 @default.
- W4327571694 hasConceptScore W4327571694C107872376 @default.
- W4327571694 hasConceptScore W4327571694C112570922 @default.
- W4327571694 hasConceptScore W4327571694C159390177 @default.
- W4327571694 hasConceptScore W4327571694C185592680 @default.
- W4327571694 hasConceptScore W4327571694C18903297 @default.