FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3036687438> ?p ?o ?g. }

• W3036687438 endingPage "140383" @default.
• W3036687438 startingPage "140383" @default.
• W3036687438 abstract "The quality of groundwater in a region is regarded as a function of natural and anthropogenic factors. Receptor models have advantages in source identification and source apportionment by testing the physicochemical properties of receptor samples and emission sources. In our study, receptor models PMF and PCA-APCS-MLR were developed to qualitatively identify the latent sources of groundwater pollution in the study area and quantitatively evaluate the contribution of each source to groundwater quality. The performances of PMF and APCS-MLR models were compared to test their applicability on the assessment of groundwater pollution sources. Results showed that both of the models identified five sources of groundwater contamination with similar main load species of each potential source. The comparable source apportionment of species NO2− and NO3− with two models indicated the reliable source estimation for these species, whereas the contributions of sources to species Fe, Mn, Cl−, SO42− and NH4+ were significantly different due to the large variability of data, difference of uncertainty analysis and algorithm of unexplained variability in the two models. R-squared value between observation and model prediction was 0.603–0.931 in PMF and 0.497–0.859 in PCA-APCS-MLR. The significant disagreement of average source contribution was detected in agricultural source and unexplained variability using PMF and PCA-APCS-MLR models. Average contributions of other sources to groundwater quality parameters had similar estimates between the two models. Higher R2 and smaller proportion of unexplained variability in the PMF model suggested that PMF approach could provide more physically plausible source apportionment in the study area and a more realistic representation of groundwater pollution than solutions from PCA-APCS-MLR model. The study showed the advantages of application of multiple receptor models on achieving reliable source identification and apportionment, particularly, providing a better understanding of applicability of PMF and PCA-APCS-MLR models on the assessment of groundwater pollution sources." @default.
• W3036687438 created "2020-06-25" @default.
• W3036687438 creator A5019793006 @default.
• W3036687438 creator A5050852420 @default.
• W3036687438 creator A5063590142 @default.
• W3036687438 creator A5067284848 @default.
• W3036687438 creator A5083357085 @default.
• W3036687438 date "2020-11-01" @default.
• W3036687438 modified "2023-10-16" @default.
• W3036687438 title "Groundwater pollution source identification and apportionment using PMF and PCA-APCA-MLR receptor models in a typical mixed land-use area in Southwestern China" @default.
• W3036687438 cites W1975983855 @default.
• W3036687438 cites W1993760488 @default.
• W3036687438 cites W2007788579 @default.
• W3036687438 cites W2017040932 @default.
• W3036687438 cites W2033923757 @default.
• W3036687438 cites W2037570227 @default.
• W3036687438 cites W2054739003 @default.
• W3036687438 cites W2056857971 @default.
• W3036687438 cites W2059745395 @default.
• W3036687438 cites W2073063498 @default.
• W3036687438 cites W2083274400 @default.
• W3036687438 cites W2083669033 @default.
• W3036687438 cites W2114357073 @default.
• W3036687438 cites W2164303307 @default.
• W3036687438 cites W2170013960 @default.
• W3036687438 cites W2171223272 @default.
• W3036687438 cites W2186382271 @default.
• W3036687438 cites W2236330127 @default.
• W3036687438 cites W2252286236 @default.
• W3036687438 cites W2273205165 @default.
• W3036687438 cites W2281207681 @default.
• W3036687438 cites W2294472071 @default.
• W3036687438 cites W2295210637 @default.
• W3036687438 cites W2306844976 @default.
• W3036687438 cites W2385630391 @default.
• W3036687438 cites W2420056736 @default.
• W3036687438 cites W2515375067 @default.
• W3036687438 cites W2518204448 @default.
• W3036687438 cites W2566090657 @default.
• W3036687438 cites W2590501409 @default.
• W3036687438 cites W2602874887 @default.
• W3036687438 cites W2761206195 @default.
• W3036687438 cites W2765977318 @default.
• W3036687438 cites W2774297910 @default.
• W3036687438 cites W2777700026 @default.
• W3036687438 cites W2886297025 @default.
• W3036687438 cites W2887538677 @default.
• W3036687438 cites W2895983842 @default.
• W3036687438 cites W2896609553 @default.
• W3036687438 cites W2900057276 @default.
• W3036687438 cites W2940934984 @default.
• W3036687438 cites W2942651265 @default.
• W3036687438 cites W2947314730 @default.
• W3036687438 cites W2964502799 @default.
• W3036687438 cites W2969625178 @default.
• W3036687438 cites W2972174684 @default.
• W3036687438 cites W2973190090 @default.
• W3036687438 cites W2988134734 @default.
• W3036687438 cites W2998002788 @default.
• W3036687438 cites W3004912848 @default.
• W3036687438 cites W3009731638 @default.
• W3036687438 cites W3011191651 @default.
• W3036687438 doi "https://doi.org/10.1016/j.scitotenv.2020.140383" @default.
• W3036687438 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32610237" @default.
• W3036687438 hasPublicationYear "2020" @default.
• W3036687438 type Work @default.
• W3036687438 sameAs 3036687438 @default.
• W3036687438 citedByCount "99" @default.
• W3036687438 countsByYear W30366874382020 @default.
• W3036687438 countsByYear W30366874382021 @default.
• W3036687438 countsByYear W30366874382022 @default.
• W3036687438 countsByYear W30366874382023 @default.
• W3036687438 crossrefType "journal-article" @default.
• W3036687438 hasAuthorship W3036687438A5019793006 @default.
• W3036687438 hasAuthorship W3036687438A5050852420 @default.
• W3036687438 hasAuthorship W3036687438A5063590142 @default.
• W3036687438 hasAuthorship W3036687438A5067284848 @default.
• W3036687438 hasAuthorship W3036687438A5083357085 @default.
• W3036687438 hasConcept C127313418 @default.
• W3036687438 hasConcept C159390177 @default.
• W3036687438 hasConcept C17744445 @default.
• W3036687438 hasConcept C187320778 @default.
• W3036687438 hasConcept C18903297 @default.
• W3036687438 hasConcept C199539241 @default.
• W3036687438 hasConcept C2778337684 @default.
• W3036687438 hasConcept C39432304 @default.
• W3036687438 hasConcept C521259446 @default.
• W3036687438 hasConcept C76177295 @default.
• W3036687438 hasConcept C76886044 @default.
• W3036687438 hasConcept C86803240 @default.
• W3036687438 hasConcept C87717796 @default.
• W3036687438 hasConceptScore W3036687438C127313418 @default.
• W3036687438 hasConceptScore W3036687438C159390177 @default.
• W3036687438 hasConceptScore W3036687438C17744445 @default.
• W3036687438 hasConceptScore W3036687438C187320778 @default.
• W3036687438 hasConceptScore W3036687438C18903297 @default.
• W3036687438 hasConceptScore W3036687438C199539241 @default.
• W3036687438 hasConceptScore W3036687438C2778337684 @default.

• next