Statistical analysis for biogeochemical processes in a sandy column with dynamic hydrologic regimes using spectral induced polarization (SIP) and self-potential (SP)
Notice bibliographique
Résumé
SUMMARY The capillary fringe (CF) is characterized by transient and steep redox gradients and is thought to be a hot spot for biogeochemical processes. Understanding chemical fate and transport in the CF is significant, however, biogeochemical dynamics at the CF are poorly understood because of the difficulty to measure representatively with high spatio-temporal resolution at depths under dynamic hydrologic regimes. Hydrogeophysics is a developing field that uses minimally intrusive and quick response methods to monitor hydrological properties. Two geoelectrical methods [spectral induced polarization (SIP) and self-potential (SP)], which are sensitive to the solid–liquid interfaces (SIP) and biogeochemical processes (SP) can address the above difficulty. The challenge lies on linking the geoelectrical responses with biogeochemical processes, where many different processes contribute to the signals. We conducted a soil column experiment under five hydrologic regimes focusing on nitrogen transformations with SIP and SP measurements: (1) a static regime with a stable water level; (2) an infiltration regime with periodic pulse infiltration events with a constant water level and (3) fluctuating regimes with water level fluctuations under three drying-wetting frequencies (6/12/18-day-cycle). This is the first large lab-scale work in a well-controlled and highly instrumented soil column. The dynamic hydrologic conditions stimulated complex biogeochemical processes at the CF, and therefore the SIP and SP signals result from many physical and biogeochemical processes. Therefore, we relied on statistical analysis in this study for a novel interpretation. Spearman correlation analysis supported water content played the most important role in real conductivity (σ′) dynamics in the vadose zone, whereas fluid conductivity dominated σ′ in the saturated zone. Both correlation analysis and spatial moment analysis implicated that water content was the driving factor for both σ′ and imaginary conductivity (σ″). A multiple linear regression model indicated the gradient of redox potential, the gradient of soil matric potential and water content were the three main influencing factors for the SP signals. We proposed that the water level fluctuation can efficiently facilitate microbial electron transfer through ions transport between the different redox zones, and aggregate redox processes to create SP signal gradients. Depth zonation analysis, using six environmental indexes (Eh and nitrogen species; water content; real conductivity; imaginary conductivity; SP signal; microbial community composition), suggested that water content induced by soil hydrology was the most dominant factor, captured by all the indexes. In turn, it led to indirect inference on biogeochemical processes and resultant geoelectrical signals. Applying geoelectrical methods to such biogeochemical processes will not only lead to a better understanding of the mechanistic meanings of the geoelectrical signals, but also build relationships between geoelectrical signals and biogeochemical parameters to facilitate a novel way to monitor biogeochemical processes.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».