Developing a new Bayesian Risk Index for risk evaluation of soil contamination Article Swipe
Teresa Albuquerque
,
Saki Gerassis
,
Carlos Sierra
,
J. Taboada
,
J.E. Martín
,
I.M.H.R. Antunes
,
J.R. Gallego
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1016/j.scitotenv.2017.06.068
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1016/j.scitotenv.2017.06.068
Industrial and agricultural activities heavily constrain soil quality. Potentially Toxic Elements (PTEs) are a threat to public health and the environment alike. In this regard, the identification of areas that require remediation is crucial. In the herein research a geochemical dataset (230 samples) comprising 14 elements (Cu, Pb, Zn, Ag, Ni, Mn, Fe, As, Cd, V, Cr, Ti, Al and S) was gathered throughout eight different zones distinguished by their main activity, namely, recreational, agriculture/livestock and heavy industry in the Avilés Estuary (North of Spain). Then a stratified systematic sampling method was used at short, medium, and long distances from each zone to obtain a representative picture of the total variability of the selected attributes. The information was then combined in four risk classes (Low, Moderate, High, Remediation) following reference values from several sediment quality guidelines (SQGs). A Bayesian analysis, inferred for each zone, allowed the characterization of PTEs correlations, the unsupervised learning network technique proving to be the best fit. Based on the Bayesian network structure obtained, Pb, As and Mn were selected as key contamination parameters. For these 3 elements, the conditional probability obtained was allocated to each observed point, and a simple, direct index (Bayesian Risk Index-BRI) was constructed as a linear rating of the pre-defined risk classes weighted by the previously obtained probability. Finally, the BRI underwent geostatistical modeling. One hundred Sequential Gaussian Simulations (SGS) were computed. The Mean Image and the Standard Deviation maps were obtained, allowing the definition of High/Low risk clusters (Local G clustering) and the computation of spatial uncertainty. High-risk clusters are mainly distributed within the area with the highest altitude (agriculture/livestock) showing an associated low spatial uncertainty, clearly indicating the need for remediation. Atmospheric emissions, mainly derived from the metallurgical industry, contribute to soil contamination by PTEs.
Related Topics
Concepts
Geostatistics
Environmental science
Standard deviation
Environmental remediation
Cluster analysis
Index (typography)
Statistics
Bayesian network
Sampling (signal processing)
Bayesian probability
Contamination
Hydrology (agriculture)
Mathematics
Computer science
Spatial variability
Engineering
Ecology
Filter (signal processing)
World Wide Web
Geotechnical engineering
Computer vision
Biology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.scitotenv.2017.06.068
- https://ars.els-cdn.com/content/image/1-s2.0-S0048969717314729-fx1_lrg.jpg
- OA Status
- bronze
- Cited By
- 39
- References
- 54
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2624775816
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2624775816Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.scitotenv.2017.06.068Digital Object Identifier
- Title
-
Developing a new Bayesian Risk Index for risk evaluation of soil contaminationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2017Year of publication
- Publication date
-
2017-06-16Full publication date if available
- Authors
-
Teresa Albuquerque, Saki Gerassis, Carlos Sierra, J. Taboada, J.E. Martín, I.M.H.R. Antunes, J.R. GallegoList of authors in order
- Landing page
-
https://doi.org/10.1016/j.scitotenv.2017.06.068Publisher landing page
- PDF URL
-
https://ars.els-cdn.com/content/image/1-s2.0-S0048969717314729-fx1_lrg.jpgDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://ars.els-cdn.com/content/image/1-s2.0-S0048969717314729-fx1_lrg.jpgDirect OA link when available
- Concepts
-
Geostatistics, Environmental science, Standard deviation, Environmental remediation, Cluster analysis, Index (typography), Statistics, Bayesian network, Sampling (signal processing), Bayesian probability, Contamination, Hydrology (agriculture), Mathematics, Computer science, Spatial variability, Engineering, Ecology, Filter (signal processing), World Wide Web, Geotechnical engineering, Computer vision, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
39Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 2, 2023: 5, 2022: 3, 2021: 12Per-year citation counts (last 5 years)
- References (count)
-
54Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W2624775816 |
|---|---|
| doi | https://doi.org/10.1016/j.scitotenv.2017.06.068 |
| ids.doi | https://doi.org/10.1016/j.scitotenv.2017.06.068 |
| ids.mag | 2624775816 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/28624637 |
| ids.openalex | https://openalex.org/W2624775816 |
| fwci | 3.43769457 |
| mesh[0].qualifier_ui | |
| mesh[0].descriptor_ui | D001499 |
| mesh[0].is_major_topic | True |
| mesh[0].qualifier_name | |
| mesh[0].descriptor_name | Bayes Theorem |
| mesh[1].qualifier_ui | |
| mesh[1].descriptor_ui | D004784 |
| mesh[1].is_major_topic | True |
| mesh[1].qualifier_name | |
| mesh[1].descriptor_name | Environmental Monitoring |
| mesh[2].qualifier_ui | |
| mesh[2].descriptor_ui | D063366 |
| mesh[2].is_major_topic | False |
| mesh[2].qualifier_name | |
| mesh[2].descriptor_name | Estuaries |
| mesh[3].qualifier_ui | Q000032 |
| mesh[3].descriptor_ui | D019216 |
| mesh[3].is_major_topic | False |
| mesh[3].qualifier_name | analysis |
| mesh[3].descriptor_name | Metals, Heavy |
| mesh[4].qualifier_ui | |
| mesh[4].descriptor_ui | D018570 |
| mesh[4].is_major_topic | False |
| mesh[4].qualifier_name | |
| mesh[4].descriptor_name | Risk Assessment |
| mesh[5].qualifier_ui | |
| mesh[5].descriptor_ui | D012987 |
| mesh[5].is_major_topic | False |
| mesh[5].qualifier_name | |
| mesh[5].descriptor_name | Soil |
| mesh[6].qualifier_ui | Q000032 |
| mesh[6].descriptor_ui | D012989 |
| mesh[6].is_major_topic | False |
| mesh[6].qualifier_name | analysis |
| mesh[6].descriptor_name | Soil Pollutants |
| mesh[7].qualifier_ui | |
| mesh[7].descriptor_ui | D013030 |
| mesh[7].is_major_topic | False |
| mesh[7].qualifier_name | |
| mesh[7].descriptor_name | Spain |
| mesh[8].qualifier_ui | |
| mesh[8].descriptor_ui | D001499 |
| mesh[8].is_major_topic | True |
| mesh[8].qualifier_name | |
| mesh[8].descriptor_name | Bayes Theorem |
| mesh[9].qualifier_ui | |
| mesh[9].descriptor_ui | D004784 |
| mesh[9].is_major_topic | True |
| mesh[9].qualifier_name | |
| mesh[9].descriptor_name | Environmental Monitoring |
| mesh[10].qualifier_ui | |
| mesh[10].descriptor_ui | D063366 |
| mesh[10].is_major_topic | False |
| mesh[10].qualifier_name | |
| mesh[10].descriptor_name | Estuaries |
| mesh[11].qualifier_ui | Q000032 |
| mesh[11].descriptor_ui | D019216 |
| mesh[11].is_major_topic | False |
| mesh[11].qualifier_name | analysis |
| mesh[11].descriptor_name | Metals, Heavy |
| mesh[12].qualifier_ui | |
| mesh[12].descriptor_ui | D018570 |
| mesh[12].is_major_topic | False |
| mesh[12].qualifier_name | |
| mesh[12].descriptor_name | Risk Assessment |
| mesh[13].qualifier_ui | |
| mesh[13].descriptor_ui | D012987 |
| mesh[13].is_major_topic | False |
| mesh[13].qualifier_name | |
| mesh[13].descriptor_name | Soil |
| mesh[14].qualifier_ui | Q000032 |
| mesh[14].descriptor_ui | D012989 |
| mesh[14].is_major_topic | False |
| mesh[14].qualifier_name | analysis |
| mesh[14].descriptor_name | Soil Pollutants |
| mesh[15].qualifier_ui | |
| mesh[15].descriptor_ui | D013030 |
| mesh[15].is_major_topic | False |
| mesh[15].qualifier_name | |
| mesh[15].descriptor_name | Spain |
| type | article |
| title | Developing a new Bayesian Risk Index for risk evaluation of soil contamination |
| biblio.issue | |
| biblio.volume | 603-604 |
| biblio.last_page | 177 |
| biblio.first_page | 167 |
| topics[0].id | https://openalex.org/T12157 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9991000294685364 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1702 |
| topics[0].subfield.display_name | Artificial Intelligence |
| topics[0].display_name | Geochemistry and Geologic Mapping |
| topics[1].id | https://openalex.org/T10139 |
| topics[1].field.id | https://openalex.org/fields/23 |
| topics[1].field.display_name | Environmental Science |
| topics[1].score | 0.9918000102043152 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2310 |
| topics[1].subfield.display_name | Pollution |
| topics[1].display_name | Heavy metals in environment |
| topics[2].id | https://openalex.org/T10770 |
| topics[2].field.id | https://openalex.org/fields/23 |
| topics[2].field.display_name | Environmental Science |
| topics[2].score | 0.9897000193595886 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2305 |
| topics[2].subfield.display_name | Environmental Engineering |
| topics[2].display_name | Soil Geostatistics and Mapping |
| is_xpac | False |
| apc_list.value | 3870 |
| apc_list.currency | USD |
| apc_list.value_usd | 3870 |
| apc_paid | |
| concepts[0].id | https://openalex.org/C125572338 |
| concepts[0].level | 3 |
| concepts[0].score | 0.5555715560913086 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q1440020 |
| concepts[0].display_name | Geostatistics |
| concepts[1].id | https://openalex.org/C39432304 |
| concepts[1].level | 0 |
| concepts[1].score | 0.5550113320350647 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q188847 |
| concepts[1].display_name | Environmental science |
| concepts[2].id | https://openalex.org/C22679943 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5265341401100159 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q159375 |
| concepts[2].display_name | Standard deviation |
| concepts[3].id | https://openalex.org/C522964758 |
| concepts[3].level | 3 |
| concepts[3].score | 0.4983530044555664 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q2019586 |
| concepts[3].display_name | Environmental remediation |
| concepts[4].id | https://openalex.org/C73555534 |
| concepts[4].level | 2 |
| concepts[4].score | 0.49174001812934875 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q622825 |
| concepts[4].display_name | Cluster analysis |
| concepts[5].id | https://openalex.org/C2777382242 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4862483739852905 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q6017816 |
| concepts[5].display_name | Index (typography) |
| concepts[6].id | https://openalex.org/C105795698 |
| concepts[6].level | 1 |
| concepts[6].score | 0.4578207731246948 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[6].display_name | Statistics |
| concepts[7].id | https://openalex.org/C33724603 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4470135271549225 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q812540 |
| concepts[7].display_name | Bayesian network |
| concepts[8].id | https://openalex.org/C140779682 |
| concepts[8].level | 3 |
| concepts[8].score | 0.4330633878707886 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q210868 |
| concepts[8].display_name | Sampling (signal processing) |
| concepts[9].id | https://openalex.org/C107673813 |
| concepts[9].level | 2 |
| concepts[9].score | 0.4272933304309845 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q812534 |
| concepts[9].display_name | Bayesian probability |
| concepts[10].id | https://openalex.org/C112570922 |
| concepts[10].level | 2 |
| concepts[10].score | 0.404867023229599 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q60528603 |
| concepts[10].display_name | Contamination |
| concepts[11].id | https://openalex.org/C76886044 |
| concepts[11].level | 2 |
| concepts[11].score | 0.33724087476730347 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q2883300 |
| concepts[11].display_name | Hydrology (agriculture) |
| concepts[12].id | https://openalex.org/C33923547 |
| concepts[12].level | 0 |
| concepts[12].score | 0.3295632302761078 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[12].display_name | Mathematics |
| concepts[13].id | https://openalex.org/C41008148 |
| concepts[13].level | 0 |
| concepts[13].score | 0.306915819644928 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[13].display_name | Computer science |
| concepts[14].id | https://openalex.org/C94747663 |
| concepts[14].level | 2 |
| concepts[14].score | 0.281232088804245 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q7574086 |
| concepts[14].display_name | Spatial variability |
| concepts[15].id | https://openalex.org/C127413603 |
| concepts[15].level | 0 |
| concepts[15].score | 0.17239540815353394 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[15].display_name | Engineering |
| concepts[16].id | https://openalex.org/C18903297 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q7150 |
| concepts[16].display_name | Ecology |
| concepts[17].id | https://openalex.org/C106131492 |
| concepts[17].level | 2 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q3072260 |
| concepts[17].display_name | Filter (signal processing) |
| concepts[18].id | https://openalex.org/C136764020 |
| concepts[18].level | 1 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q466 |
| concepts[18].display_name | World Wide Web |
| concepts[19].id | https://openalex.org/C187320778 |
| concepts[19].level | 1 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q1349130 |
| concepts[19].display_name | Geotechnical engineering |
| concepts[20].id | https://openalex.org/C31972630 |
| concepts[20].level | 1 |
| concepts[20].score | 0.0 |
| concepts[20].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[20].display_name | Computer vision |
| concepts[21].id | https://openalex.org/C86803240 |
| concepts[21].level | 0 |
| concepts[21].score | 0.0 |
| concepts[21].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[21].display_name | Biology |
| keywords[0].id | https://openalex.org/keywords/geostatistics |
| keywords[0].score | 0.5555715560913086 |
| keywords[0].display_name | Geostatistics |
| keywords[1].id | https://openalex.org/keywords/environmental-science |
| keywords[1].score | 0.5550113320350647 |
| keywords[1].display_name | Environmental science |
| keywords[2].id | https://openalex.org/keywords/standard-deviation |
| keywords[2].score | 0.5265341401100159 |
| keywords[2].display_name | Standard deviation |
| keywords[3].id | https://openalex.org/keywords/environmental-remediation |
| keywords[3].score | 0.4983530044555664 |
| keywords[3].display_name | Environmental remediation |
| keywords[4].id | https://openalex.org/keywords/cluster-analysis |
| keywords[4].score | 0.49174001812934875 |
| keywords[4].display_name | Cluster analysis |
| keywords[5].id | https://openalex.org/keywords/index |
| keywords[5].score | 0.4862483739852905 |
| keywords[5].display_name | Index (typography) |
| keywords[6].id | https://openalex.org/keywords/statistics |
| keywords[6].score | 0.4578207731246948 |
| keywords[6].display_name | Statistics |
| keywords[7].id | https://openalex.org/keywords/bayesian-network |
| keywords[7].score | 0.4470135271549225 |
| keywords[7].display_name | Bayesian network |
| keywords[8].id | https://openalex.org/keywords/sampling |
| keywords[8].score | 0.4330633878707886 |
| keywords[8].display_name | Sampling (signal processing) |
| keywords[9].id | https://openalex.org/keywords/bayesian-probability |
| keywords[9].score | 0.4272933304309845 |
| keywords[9].display_name | Bayesian probability |
| keywords[10].id | https://openalex.org/keywords/contamination |
| keywords[10].score | 0.404867023229599 |
| keywords[10].display_name | Contamination |
| keywords[11].id | https://openalex.org/keywords/hydrology |
| keywords[11].score | 0.33724087476730347 |
| keywords[11].display_name | Hydrology (agriculture) |
| keywords[12].id | https://openalex.org/keywords/mathematics |
| keywords[12].score | 0.3295632302761078 |
| keywords[12].display_name | Mathematics |
| keywords[13].id | https://openalex.org/keywords/computer-science |
| keywords[13].score | 0.306915819644928 |
| keywords[13].display_name | Computer science |
| keywords[14].id | https://openalex.org/keywords/spatial-variability |
| keywords[14].score | 0.281232088804245 |
| keywords[14].display_name | Spatial variability |
| keywords[15].id | https://openalex.org/keywords/engineering |
| keywords[15].score | 0.17239540815353394 |
| keywords[15].display_name | Engineering |
| language | en |
| locations[0].id | doi:10.1016/j.scitotenv.2017.06.068 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S86852077 |
| locations[0].source.issn | 0048-9697, 1879-1026 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 0048-9697 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | The Science of The Total Environment |
| locations[0].source.host_organization | https://openalex.org/P4310320990 |
| locations[0].source.host_organization_name | Elsevier BV |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320990 |
| locations[0].license | |
| locations[0].pdf_url | https://ars.els-cdn.com/content/image/1-s2.0-S0048969717314729-fx1_lrg.jpg |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Science of The Total Environment |
| locations[0].landing_page_url | https://doi.org/10.1016/j.scitotenv.2017.06.068 |
| locations[1].id | pmh:oai:repositorium.uminho.pt:1822/48237 |
| locations[1].is_oa | True |
| locations[1].source | |
| locations[1].license | other-oa |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | info:eu-repo/semantics/article |
| locations[1].license_id | https://openalex.org/licenses/other-oa |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://hdl.handle.net/1822/48237 |
| locations[2].id | pmid:28624637 |
| locations[2].is_oa | False |
| locations[2].source.id | https://openalex.org/S4306525036 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | PubMed |
| locations[2].source.host_organization | https://openalex.org/I1299303238 |
| locations[2].source.host_organization_name | National Institutes of Health |
| locations[2].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[2].license | |
| locations[2].pdf_url | |
| locations[2].version | publishedVersion |
| locations[2].raw_type | |
| locations[2].license_id | |
| locations[2].is_accepted | True |
| locations[2].is_published | True |
| locations[2].raw_source_name | The Science of the total environment |
| locations[2].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/28624637 |
| locations[3].id | pmh:oai:repositorium.sdum.uminho.pt:1822/72511 |
| locations[3].is_oa | True |
| locations[3].source.id | https://openalex.org/S4306400354 |
| locations[3].source.issn | |
| locations[3].source.type | repository |
| locations[3].source.is_oa | False |
| locations[3].source.issn_l | |
| locations[3].source.is_core | False |
| locations[3].source.is_in_doaj | False |
| locations[3].source.display_name | RepositóriUM (Universidade do Minho) |
| locations[3].source.host_organization | https://openalex.org/I99682543 |
| locations[3].source.host_organization_name | University of Minho |
| locations[3].source.host_organization_lineage | https://openalex.org/I99682543 |
| locations[3].license | other-oa |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | conference object |
| locations[3].license_id | https://openalex.org/licenses/other-oa |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | |
| locations[3].landing_page_url | http://hdl.handle.net/1822/72511 |
| locations[4].id | pmh:oai:digibuo.uniovi.es:10651/44636 |
| locations[4].is_oa | True |
| locations[4].source.id | https://openalex.org/S4306402334 |
| locations[4].source.issn | |
| locations[4].source.type | repository |
| locations[4].source.is_oa | False |
| locations[4].source.issn_l | |
| locations[4].source.is_core | False |
| locations[4].source.is_in_doaj | False |
| locations[4].source.display_name | Consultation of the Doctoral Thesis Database (TESEO) (Ministerio de Educación, Cultura y Deporte) |
| locations[4].source.host_organization | https://openalex.org/I2801976130 |
| locations[4].source.host_organization_name | Ministerio de Educación Cultura y Deporte |
| locations[4].source.host_organization_lineage | https://openalex.org/I2801976130 |
| locations[4].license | cc-by-nc-nd |
| locations[4].pdf_url | |
| locations[4].version | submittedVersion |
| locations[4].raw_type | info:eu-repo/semantics/article |
| locations[4].license_id | https://openalex.org/licenses/cc-by-nc-nd |
| locations[4].is_accepted | False |
| locations[4].is_published | False |
| locations[4].raw_source_name | Scopus |
| locations[4].landing_page_url | http://hdl.handle.net/10651/44636 |
| locations[5].id | pmh:oai:repositorium.sdum.uminho.pt:1822/48237 |
| locations[5].is_oa | True |
| locations[5].source.id | https://openalex.org/S4306400354 |
| locations[5].source.issn | |
| locations[5].source.type | repository |
| locations[5].source.is_oa | False |
| locations[5].source.issn_l | |
| locations[5].source.is_core | False |
| locations[5].source.is_in_doaj | False |
| locations[5].source.display_name | RepositóriUM (Universidade do Minho) |
| locations[5].source.host_organization | https://openalex.org/I99682543 |
| locations[5].source.host_organization_name | University of Minho |
| locations[5].source.host_organization_lineage | https://openalex.org/I99682543 |
| locations[5].license | other-oa |
| locations[5].pdf_url | |
| locations[5].version | submittedVersion |
| locations[5].raw_type | info:eu-repo/semantics/article |
| locations[5].license_id | https://openalex.org/licenses/other-oa |
| locations[5].is_accepted | False |
| locations[5].is_published | False |
| locations[5].raw_source_name | |
| locations[5].landing_page_url | http://hdl.handle.net/1822/48237 |
| indexed_in | crossref, pubmed |
| authorships[0].author.id | https://openalex.org/A5030233029 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-8782-6133 |
| authorships[0].author.display_name | Teresa Albuquerque |
| authorships[0].countries | PT |
| authorships[0].affiliations[0].raw_affiliation_string | CERENA/FEUP Research Center, Portugal |
| authorships[0].affiliations[1].institution_ids | https://openalex.org/I144926016 |
| authorships[0].affiliations[1].raw_affiliation_string | Instituto Politécnico de Castelo Branco, 6001-909 Castelo Branco, Portugal |
| authorships[0].institutions[0].id | https://openalex.org/I144926016 |
| authorships[0].institutions[0].ror | https://ror.org/004s18446 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I144926016 |
| authorships[0].institutions[0].country_code | PT |
| authorships[0].institutions[0].display_name | Polytechnic Institute of Castelo Branco |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | M.T.D. Albuquerque |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | CERENA/FEUP Research Center, Portugal, Instituto Politécnico de Castelo Branco, 6001-909 Castelo Branco, Portugal |
| authorships[1].author.id | https://openalex.org/A5004289357 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-9249-6407 |
| authorships[1].author.display_name | Saki Gerassis |
| authorships[1].countries | ES |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I6289922 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Natural Resources and Environmental Engineering, Univ. of Vigo, Lagoas Marcosende, 36310 Vigo, Spain |
| authorships[1].institutions[0].id | https://openalex.org/I6289922 |
| authorships[1].institutions[0].ror | https://ror.org/05rdf8595 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I6289922 |
| authorships[1].institutions[0].country_code | ES |
| authorships[1].institutions[0].display_name | Universidade de Vigo |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | S. Gerassis |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Natural Resources and Environmental Engineering, Univ. of Vigo, Lagoas Marcosende, 36310 Vigo, Spain |
| authorships[2].author.id | https://openalex.org/A5006875014 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-6820-2499 |
| authorships[2].author.display_name | Carlos Sierra |
| authorships[2].countries | ES |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I13134134 |
| authorships[2].affiliations[0].raw_affiliation_string | Departamento de Transportes, Tecnología de Procesos y Proyectos, Universidad de Cantabria, Campus de Torrelavega, Spain |
| authorships[2].institutions[0].id | https://openalex.org/I13134134 |
| authorships[2].institutions[0].ror | https://ror.org/046ffzj20 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I13134134 |
| authorships[2].institutions[0].country_code | ES |
| authorships[2].institutions[0].display_name | Universidad de Cantabria |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | C. Sierra |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Departamento de Transportes, Tecnología de Procesos y Proyectos, Universidad de Cantabria, Campus de Torrelavega, Spain |
| authorships[3].author.id | https://openalex.org/A5007114572 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-9181-766X |
| authorships[3].author.display_name | J. Taboada |
| authorships[3].countries | ES |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I6289922 |
| authorships[3].affiliations[0].raw_affiliation_string | Department of Natural Resources and Environmental Engineering, Univ. of Vigo, Lagoas Marcosende, 36310 Vigo, Spain |
| authorships[3].institutions[0].id | https://openalex.org/I6289922 |
| authorships[3].institutions[0].ror | https://ror.org/05rdf8595 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I6289922 |
| authorships[3].institutions[0].country_code | ES |
| authorships[3].institutions[0].display_name | Universidade de Vigo |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | J. Taboada |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Department of Natural Resources and Environmental Engineering, Univ. of Vigo, Lagoas Marcosende, 36310 Vigo, Spain |
| authorships[4].author.id | https://openalex.org/A5104505548 |
| authorships[4].author.orcid | https://orcid.org/0000-0003-0157-2644 |
| authorships[4].author.display_name | J.E. Martín |
| authorships[4].countries | ES |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I6289922 |
| authorships[4].affiliations[0].raw_affiliation_string | Department of Natural Resources and Environmental Engineering, Univ. of Vigo, Lagoas Marcosende, 36310 Vigo, Spain |
| authorships[4].institutions[0].id | https://openalex.org/I6289922 |
| authorships[4].institutions[0].ror | https://ror.org/05rdf8595 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I6289922 |
| authorships[4].institutions[0].country_code | ES |
| authorships[4].institutions[0].display_name | Universidade de Vigo |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | J.E. Martín |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Department of Natural Resources and Environmental Engineering, Univ. of Vigo, Lagoas Marcosende, 36310 Vigo, Spain |
| authorships[5].author.id | https://openalex.org/A5044093748 |
| authorships[5].author.orcid | https://orcid.org/0000-0003-3456-5926 |
| authorships[5].author.display_name | I.M.H.R. Antunes |
| authorships[5].countries | PT |
| authorships[5].affiliations[0].raw_affiliation_string | CERENA/FEUP Research Center, Portugal |
| authorships[5].affiliations[1].institution_ids | https://openalex.org/I99682543 |
| authorships[5].affiliations[1].raw_affiliation_string | ICT/University of Minho, Braga, Portugal |
| authorships[5].institutions[0].id | https://openalex.org/I99682543 |
| authorships[5].institutions[0].ror | https://ror.org/037wpkx04 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I99682543 |
| authorships[5].institutions[0].country_code | PT |
| authorships[5].institutions[0].display_name | University of Minho |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | I.M.H.R. Antunes |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | CERENA/FEUP Research Center, Portugal, ICT/University of Minho, Braga, Portugal |
| authorships[6].author.id | https://openalex.org/A5056089668 |
| authorships[6].author.orcid | https://orcid.org/0000-0002-8243-3883 |
| authorships[6].author.display_name | J.R. Gallego |
| authorships[6].countries | ES |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I165339363 |
| authorships[6].affiliations[0].raw_affiliation_string | INDUROT and Environmental Technology, Biotechnology, and Geochemistry Group, Universidad de Oviedo, Campus de Mieres, Asturias, Spain |
| authorships[6].institutions[0].id | https://openalex.org/I165339363 |
| authorships[6].institutions[0].ror | https://ror.org/006gksa02 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I165339363 |
| authorships[6].institutions[0].country_code | ES |
| authorships[6].institutions[0].display_name | Universidad de Oviedo |
| authorships[6].author_position | last |
| authorships[6].raw_author_name | J.R. Gallego |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | INDUROT and Environmental Technology, Biotechnology, and Geochemistry Group, Universidad de Oviedo, Campus de Mieres, Asturias, Spain |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://ars.els-cdn.com/content/image/1-s2.0-S0048969717314729-fx1_lrg.jpg |
| open_access.oa_status | bronze |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Developing a new Bayesian Risk Index for risk evaluation of soil contamination |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T12157 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9991000294685364 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1702 |
| primary_topic.subfield.display_name | Artificial Intelligence |
| primary_topic.display_name | Geochemistry and Geologic Mapping |
| related_works | https://openalex.org/W2365452842, https://openalex.org/W2384253261, https://openalex.org/W2387912620, https://openalex.org/W2381029319, https://openalex.org/W4238586177, https://openalex.org/W2356808522, https://openalex.org/W4238710278, https://openalex.org/W2363883631, https://openalex.org/W603236851, https://openalex.org/W4247721244 |
| cited_by_count | 39 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 2 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 2 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 5 |
| counts_by_year[3].year | 2022 |
| counts_by_year[3].cited_by_count | 3 |
| counts_by_year[4].year | 2021 |
| counts_by_year[4].cited_by_count | 12 |
| counts_by_year[5].year | 2020 |
| counts_by_year[5].cited_by_count | 4 |
| counts_by_year[6].year | 2019 |
| counts_by_year[6].cited_by_count | 3 |
| counts_by_year[7].year | 2018 |
| counts_by_year[7].cited_by_count | 7 |
| counts_by_year[8].year | 2017 |
| counts_by_year[8].cited_by_count | 1 |
| locations_count | 6 |
| best_oa_location.id | doi:10.1016/j.scitotenv.2017.06.068 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S86852077 |
| best_oa_location.source.issn | 0048-9697, 1879-1026 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 0048-9697 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | The Science of The Total Environment |
| best_oa_location.source.host_organization | https://openalex.org/P4310320990 |
| best_oa_location.source.host_organization_name | Elsevier BV |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320990 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://ars.els-cdn.com/content/image/1-s2.0-S0048969717314729-fx1_lrg.jpg |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Science of The Total Environment |
| best_oa_location.landing_page_url | https://doi.org/10.1016/j.scitotenv.2017.06.068 |
| primary_location.id | doi:10.1016/j.scitotenv.2017.06.068 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S86852077 |
| primary_location.source.issn | 0048-9697, 1879-1026 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 0048-9697 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | The Science of The Total Environment |
| primary_location.source.host_organization | https://openalex.org/P4310320990 |
| primary_location.source.host_organization_name | Elsevier BV |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320990 |
| primary_location.license | |
| primary_location.pdf_url | https://ars.els-cdn.com/content/image/1-s2.0-S0048969717314729-fx1_lrg.jpg |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Science of The Total Environment |
| primary_location.landing_page_url | https://doi.org/10.1016/j.scitotenv.2017.06.068 |
| publication_date | 2017-06-16 |
| publication_year | 2017 |
| referenced_works | https://openalex.org/W2001391324, https://openalex.org/W1964183674, https://openalex.org/W1998508622, https://openalex.org/W2092191717, https://openalex.org/W1966658892, https://openalex.org/W2122209610, https://openalex.org/W416053696, https://openalex.org/W6634267634, https://openalex.org/W2126974166, https://openalex.org/W6680300913, https://openalex.org/W2144731007, https://openalex.org/W2063833515, https://openalex.org/W1985045569, https://openalex.org/W2131586477, https://openalex.org/W2582684192, https://openalex.org/W2140910096, https://openalex.org/W2067065647, https://openalex.org/W2058515858, https://openalex.org/W1517366800, https://openalex.org/W2133521487, https://openalex.org/W2012333061, https://openalex.org/W2017547066, https://openalex.org/W2079087023, https://openalex.org/W2022869344, https://openalex.org/W1983539533, https://openalex.org/W6617412644, https://openalex.org/W168288856, https://openalex.org/W2076367034, https://openalex.org/W2026852428, https://openalex.org/W1884131190, https://openalex.org/W2060492880, https://openalex.org/W2143075689, https://openalex.org/W6708156130, https://openalex.org/W2510083130, https://openalex.org/W2145923836, https://openalex.org/W2154167907, https://openalex.org/W1995875735, https://openalex.org/W608962888, https://openalex.org/W2111574971, https://openalex.org/W2129999935, https://openalex.org/W2161048175, https://openalex.org/W2470050080, https://openalex.org/W6734157668, https://openalex.org/W2045759663, https://openalex.org/W2566265410, https://openalex.org/W2590658888, https://openalex.org/W1571196848, https://openalex.org/W3138949179, https://openalex.org/W2396252827, https://openalex.org/W2312470061, https://openalex.org/W585685318, https://openalex.org/W2606321545, https://openalex.org/W2138857742, https://openalex.org/W2369222871 |
| referenced_works_count | 54 |
| abstract_inverted_index.3 | 180 |
| abstract_inverted_index.A | 137 |
| abstract_inverted_index.G | 249 |
| abstract_inverted_index.a | 13, 38, 86, 104, 193, 203 |
| abstract_inverted_index.14 | 44 |
| abstract_inverted_index.Al | 58 |
| abstract_inverted_index.As | 169 |
| abstract_inverted_index.In | 22, 34 |
| abstract_inverted_index.Mn | 171 |
| abstract_inverted_index.S) | 60 |
| abstract_inverted_index.V, | 55 |
| abstract_inverted_index.an | 271 |
| abstract_inverted_index.as | 174, 202 |
| abstract_inverted_index.at | 93 |
| abstract_inverted_index.be | 157 |
| abstract_inverted_index.by | 68, 212, 294 |
| abstract_inverted_index.in | 78, 120 |
| abstract_inverted_index.is | 32 |
| abstract_inverted_index.of | 27, 83, 107, 111, 147, 206, 244, 254 |
| abstract_inverted_index.on | 162 |
| abstract_inverted_index.to | 15, 102, 156, 188, 291 |
| abstract_inverted_index.Ag, | 49 |
| abstract_inverted_index.As, | 53 |
| abstract_inverted_index.BRI | 219 |
| abstract_inverted_index.Cd, | 54 |
| abstract_inverted_index.Cr, | 56 |
| abstract_inverted_index.Fe, | 52 |
| abstract_inverted_index.For | 178 |
| abstract_inverted_index.Mn, | 51 |
| abstract_inverted_index.Ni, | 50 |
| abstract_inverted_index.One | 223 |
| abstract_inverted_index.Pb, | 47, 168 |
| abstract_inverted_index.The | 115, 231 |
| abstract_inverted_index.Ti, | 57 |
| abstract_inverted_index.Zn, | 48 |
| abstract_inverted_index.and | 1, 18, 59, 75, 96, 170, 192, 234, 251 |
| abstract_inverted_index.are | 12, 259 |
| abstract_inverted_index.for | 141, 280 |
| abstract_inverted_index.key | 175 |
| abstract_inverted_index.low | 273 |
| abstract_inverted_index.the | 19, 25, 35, 79, 108, 112, 145, 150, 158, 163, 182, 207, 213, 218, 235, 242, 252, 263, 266, 278, 287 |
| abstract_inverted_index.was | 61, 91, 117, 186, 200 |
| abstract_inverted_index.(230 | 41 |
| abstract_inverted_index.(Cu, | 46 |
| abstract_inverted_index.Mean | 232 |
| abstract_inverted_index.PTEs | 148 |
| abstract_inverted_index.Risk | 198 |
| abstract_inverted_index.Then | 85 |
| abstract_inverted_index.area | 264 |
| abstract_inverted_index.best | 159 |
| abstract_inverted_index.each | 100, 142, 189 |
| abstract_inverted_index.fit. | 160 |
| abstract_inverted_index.four | 121 |
| abstract_inverted_index.from | 99, 131, 286 |
| abstract_inverted_index.long | 97 |
| abstract_inverted_index.main | 70 |
| abstract_inverted_index.maps | 238 |
| abstract_inverted_index.need | 279 |
| abstract_inverted_index.risk | 122, 209, 246 |
| abstract_inverted_index.soil | 6, 292 |
| abstract_inverted_index.that | 29 |
| abstract_inverted_index.then | 118 |
| abstract_inverted_index.this | 23 |
| abstract_inverted_index.used | 92 |
| abstract_inverted_index.were | 172, 229, 239 |
| abstract_inverted_index.with | 265 |
| abstract_inverted_index.zone | 101 |
| abstract_inverted_index.(Low, | 124 |
| abstract_inverted_index.(SGS) | 228 |
| abstract_inverted_index.Based | 161 |
| abstract_inverted_index.High, | 126 |
| abstract_inverted_index.Image | 233 |
| abstract_inverted_index.PTEs. | 295 |
| abstract_inverted_index.Toxic | 9 |
| abstract_inverted_index.areas | 28 |
| abstract_inverted_index.eight | 64 |
| abstract_inverted_index.heavy | 76 |
| abstract_inverted_index.index | 196 |
| abstract_inverted_index.their | 69 |
| abstract_inverted_index.these | 179 |
| abstract_inverted_index.total | 109 |
| abstract_inverted_index.zone, | 143 |
| abstract_inverted_index.zones | 66 |
| abstract_inverted_index.(Local | 248 |
| abstract_inverted_index.(North | 82 |
| abstract_inverted_index.(PTEs) | 11 |
| abstract_inverted_index.alike. | 21 |
| abstract_inverted_index.direct | 195 |
| abstract_inverted_index.health | 17 |
| abstract_inverted_index.herein | 36 |
| abstract_inverted_index.linear | 204 |
| abstract_inverted_index.mainly | 260, 284 |
| abstract_inverted_index.method | 90 |
| abstract_inverted_index.obtain | 103 |
| abstract_inverted_index.point, | 191 |
| abstract_inverted_index.public | 16 |
| abstract_inverted_index.rating | 205 |
| abstract_inverted_index.short, | 94 |
| abstract_inverted_index.threat | 14 |
| abstract_inverted_index.values | 130 |
| abstract_inverted_index.within | 262 |
| abstract_inverted_index.(SQGs). | 136 |
| abstract_inverted_index.Avilés | 80 |
| abstract_inverted_index.Estuary | 81 |
| abstract_inverted_index.Spain). | 84 |
| abstract_inverted_index.allowed | 144 |
| abstract_inverted_index.classes | 123, 210 |
| abstract_inverted_index.clearly | 276 |
| abstract_inverted_index.dataset | 40 |
| abstract_inverted_index.derived | 285 |
| abstract_inverted_index.heavily | 4 |
| abstract_inverted_index.highest | 267 |
| abstract_inverted_index.hundred | 224 |
| abstract_inverted_index.medium, | 95 |
| abstract_inverted_index.namely, | 72 |
| abstract_inverted_index.network | 153, 165 |
| abstract_inverted_index.picture | 106 |
| abstract_inverted_index.proving | 155 |
| abstract_inverted_index.quality | 134 |
| abstract_inverted_index.regard, | 24 |
| abstract_inverted_index.require | 30 |
| abstract_inverted_index.several | 132 |
| abstract_inverted_index.showing | 270 |
| abstract_inverted_index.simple, | 194 |
| abstract_inverted_index.spatial | 255, 274 |
| abstract_inverted_index.Bayesian | 138, 164 |
| abstract_inverted_index.Elements | 10 |
| abstract_inverted_index.Finally, | 217 |
| abstract_inverted_index.Gaussian | 226 |
| abstract_inverted_index.High/Low | 245 |
| abstract_inverted_index.Standard | 236 |
| abstract_inverted_index.allowing | 241 |
| abstract_inverted_index.altitude | 268 |
| abstract_inverted_index.clusters | 247, 258 |
| abstract_inverted_index.combined | 119 |
| abstract_inverted_index.crucial. | 33 |
| abstract_inverted_index.elements | 45 |
| abstract_inverted_index.gathered | 62 |
| abstract_inverted_index.industry | 77 |
| abstract_inverted_index.inferred | 140 |
| abstract_inverted_index.learning | 152 |
| abstract_inverted_index.observed | 190 |
| abstract_inverted_index.obtained | 185, 215 |
| abstract_inverted_index.quality. | 7 |
| abstract_inverted_index.research | 37 |
| abstract_inverted_index.samples) | 42 |
| abstract_inverted_index.sampling | 89 |
| abstract_inverted_index.sediment | 133 |
| abstract_inverted_index.selected | 113, 173 |
| abstract_inverted_index.weighted | 211 |
| abstract_inverted_index.(Bayesian | 197 |
| abstract_inverted_index.Deviation | 237 |
| abstract_inverted_index.High-risk | 257 |
| abstract_inverted_index.Moderate, | 125 |
| abstract_inverted_index.activity, | 71 |
| abstract_inverted_index.allocated | 187 |
| abstract_inverted_index.analysis, | 139 |
| abstract_inverted_index.computed. | 230 |
| abstract_inverted_index.constrain | 5 |
| abstract_inverted_index.different | 65 |
| abstract_inverted_index.distances | 98 |
| abstract_inverted_index.elements, | 181 |
| abstract_inverted_index.following | 128 |
| abstract_inverted_index.industry, | 289 |
| abstract_inverted_index.modeling. | 222 |
| abstract_inverted_index.obtained, | 167, 240 |
| abstract_inverted_index.reference | 129 |
| abstract_inverted_index.structure | 166 |
| abstract_inverted_index.technique | 154 |
| abstract_inverted_index.underwent | 220 |
| abstract_inverted_index.Index-BRI) | 199 |
| abstract_inverted_index.Industrial | 0 |
| abstract_inverted_index.Sequential | 225 |
| abstract_inverted_index.activities | 3 |
| abstract_inverted_index.associated | 272 |
| abstract_inverted_index.comprising | 43 |
| abstract_inverted_index.contribute | 290 |
| abstract_inverted_index.definition | 243 |
| abstract_inverted_index.emissions, | 283 |
| abstract_inverted_index.guidelines | 135 |
| abstract_inverted_index.indicating | 277 |
| abstract_inverted_index.previously | 214 |
| abstract_inverted_index.stratified | 87 |
| abstract_inverted_index.systematic | 88 |
| abstract_inverted_index.throughout | 63 |
| abstract_inverted_index.Atmospheric | 282 |
| abstract_inverted_index.Potentially | 8 |
| abstract_inverted_index.Simulations | 227 |
| abstract_inverted_index.attributes. | 114 |
| abstract_inverted_index.clustering) | 250 |
| abstract_inverted_index.computation | 253 |
| abstract_inverted_index.conditional | 183 |
| abstract_inverted_index.constructed | 201 |
| abstract_inverted_index.distributed | 261 |
| abstract_inverted_index.environment | 20 |
| abstract_inverted_index.geochemical | 39 |
| abstract_inverted_index.information | 116 |
| abstract_inverted_index.parameters. | 177 |
| abstract_inverted_index.pre-defined | 208 |
| abstract_inverted_index.probability | 184 |
| abstract_inverted_index.remediation | 31 |
| abstract_inverted_index.variability | 110 |
| abstract_inverted_index.Remediation) | 127 |
| abstract_inverted_index.agricultural | 2 |
| abstract_inverted_index.probability. | 216 |
| abstract_inverted_index.remediation. | 281 |
| abstract_inverted_index.uncertainty, | 275 |
| abstract_inverted_index.uncertainty. | 256 |
| abstract_inverted_index.unsupervised | 151 |
| abstract_inverted_index.contamination | 176, 293 |
| abstract_inverted_index.correlations, | 149 |
| abstract_inverted_index.distinguished | 67 |
| abstract_inverted_index.metallurgical | 288 |
| abstract_inverted_index.recreational, | 73 |
| abstract_inverted_index.geostatistical | 221 |
| abstract_inverted_index.identification | 26 |
| abstract_inverted_index.representative | 105 |
| abstract_inverted_index.characterization | 146 |
| abstract_inverted_index.agriculture/livestock | 74 |
| abstract_inverted_index.(agriculture/livestock) | 269 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5030233029 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 7 |
| corresponding_institution_ids | https://openalex.org/I144926016 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/2 |
| sustainable_development_goals[0].score | 0.6000000238418579 |
| sustainable_development_goals[0].display_name | Zero hunger |
| citation_normalized_percentile.value | 0.92977387 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |