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A víztartó-képesség meghatározása, eszközeit és módszertanát tekintve, jelentős előrelépéseken ment keresztül az elmúlt megközelítőleg 60 évben, mind a közvetlen mérések, mind a modellek területén, például a dinamikus, spektroszkópiai és digitális képazonosítási eljárások kidolgozása, fejlesztése, illetve a mintázatfelismerésen alapuló pedotranszfer típusú becslő eljárások fejlesztése, validálása, beépítése szoftverekbe, almodellekbe. A pórustér hierarchikus hálózati rendszerként való új értelmezésével, illetve a geometriai alapú, a szemifizikus, majd a statisztikus közelítő eljárásokkal lehetővé vált a pórustér és az abban lejátszódó folyamatok mind pontosabb jellemzése.
A talaj víztartó-képességét — és ezáltal a pórusméret-eloszlását is — többmódusú függvénnyel jellemző egyszerű, vagy összetett összefüggések lehetőséget nyújtanak a porozitás, illetve a porozitás-változási jelenségek, folyamatok (aggregátumstabilitás, tömődöttség változása, pórusdeformációs jelenségek stb.) tanulmányozására, valamint a hiszterézis számszerűsítésére és mind pontosabb meghatározására, akár az SWRC teljes nyomástartományában.
A modellezés különböző léptékeiben értelmezett talajjellemzők közötti összefüggések vizsgálatához a hidrológiai talajtulajdonságokat és az áramlási folyamatokat egyaránt meghatározó talajszerkezet szerepének számszerűsítése mind a mai napig kihívást jelent a talajtani, hidrológiai, környezetvédelmi szakterületen dolgozó kutatók számára.
Az NAPL típusú szennyezőanyagok felszín alatti terjedését szimuláló modellek jellemzően a víztartó-képesség használatával állítják fel az NAPL-visszatartó képességre (elsődleges bemeneti paraméter) alkalmazott összefüggéseket. Ezen összefüggések nem veszik figyelembe a különböző fizikai-, kémiai és fizikokémiai tulajdonságokkal jellemezhető folyadékok, illetve a folyadékok és a szilárd fázis között lezajló különböző mértékű kölcsönhatások jelentőségét (duzzadás, dezaggregáció stb). Több különböző környezetvédelmi célú kutatás (hulladéklerakók agyagszigetelésének kompatibilitási tesztjei, mikromorfológiai vizsgálatok, illetve dinamikus NAPL visszatartó- és vezetőképesség mérési módszertan fejlesztésére vonatkozó vizsgálatok stb.) eredményei alapján a víztartó-képességből kiinduló számítási eljárások alkalmazhatósága a talajok NAPL-visszatartó képességének meghatározására megkérdőjelezhető.
Megoldást jelenthet, hogy a víztartó-képesség görbék illesztésére alkalmazott parametrikus eljárásokkal az NAPL-visszatartó képesség görbék is meghatározhatóak. A különböző polaritású folyadékokkal felvett folyadékvisszatartó-képesség görbék alaki jellemzői alapján igazolt a differenciált porozitás különböző mértékű megváltozása a talaj vízzel és nem vizes fázisú szerves folyadékokkal való telítése során. A víztartó-képesség becslésére képzett szemifizikus és empirikus eljárások kidolgozásának és fejlesztésének módszertana szerint az NAPL-visszatartó képesség meghatározására alkalmas PTF típusú becslő összefüggések is képezhetőek, melyek pontossága elsősorban 0–1500 kPa nyomástartományban megfelelő. A porozitásváltozás mértékére vonatkozóan a víz- és NAPL-visszatartó képesség görbék alapján meghatározott pórusméret-eloszlási görbék statisztikai jellemzői nyújthatnak információt. A multimodális függvények alkalmazásával lehetővé válhat az NAPL-visszatartó képesség görbék végponti értékei és a becslésbe vont talajtulajdonságok közötti öszefüggések pontosabb feltérképezése. Szükséges a PTF típusú NAPL-visszatartó képesség becslő eljárások fejlesztése; pl. az NAPL-visszatartó képesség görbék meghatározására alkalmas parametrikus eljárás megválasztása; a többfázisú folyadéktranszport modellezésben kulcsfontosságú telítettségi értékek (pl. maradvány telítettség), illetve az azokhoz rendelhető kapilláris nyomás (pl. belépési küszöbnyomás) meghatározása és a folyadékvisszatartó-képesség görbék függvény paraméterei közötti konverziós lehetőségek kidolgozása.
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| 2022 | |
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not indexed |
| Scimago | |
| Scimago H-index |
10 |
| Scimago Journal Rank |
0.151 |
| Scimago Quartile Score |
Agronomy and Crop Science (Q4) |
| Scopus | |
| Scopus Cite Score |
0.6 |
| Scopus CIte Score Rank |
Agronomy and Crop Science 335/376 (11th PCTL) Soil Science 134/147 (9th PCTL) |
| Scopus SNIP |
0.263 |
| 2021 | |
|---|---|
| Web of Science | |
| Total Cites WoS |
not indexed |
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not indexed |
| Scimago | |
| Scimago H-index |
10 |
| Scimago Journal Rank |
0,138 |
| Scimago Quartile Score | Agronomy and Crop Science (Q4) Soil Science (Q4) |
| Scopus | |
| Scopus Cite Score |
0,8 |
| Scopus CIte Score Rank |
Agronomy and Crop Science 290/370 (Q4) Soil Science 118/145 (Q4) |
| Scopus SNIP |
0,077 |
| 2020 | |
|---|---|
| Scimago H-index |
9 |
| Scimago Journal Rank |
0,179 |
| Scimago Quartile Score |
Agronomy and Crop Science Q4 Soil Science Q4 |
| Scopus Cite Score |
48/73=0,7 |
| Scopus Cite Score Rank |
Agronomy and Crop Science 278/347 (Q4) Soil Science 108/135 (Q4) |
| Scopus SNIP |
0,18 |
| Scopus Cites |
48 |
| Scopus Documents |
6 |
| Days from submission to acceptance | 130 |
| Days from acceptance to publication | 152 |
| Acceptance Rate |
65% |
| 2019 | |
|---|---|
| Scimago H-index |
9 |
| Scimago Journal Rank |
0,204 |
| Scimago Quartile Score |
Agronomy and Crop Science Q4 Soil Science Q4 |
| Scopus Cite Score |
49/88=0,6 |
| Scopus Cite Score Rank |
Agronomy and Crop Science 276/334 (Q4) Soil Science 104/126 (Q4) |
| Scopus SNIP |
0,423 |
| Scopus Cites |
96 |
| Scopus Documents |
27 |
| Acceptance Rate |
91% |
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