Marginal quality water arbitrated essential oil contents in metal hoarded flower petals of scented roses
Bibliographic record
Abstract
Climate change and the consequent alteration in agricultural circumstances enhance the susceptibility of fresh water use particularly in water-scarce regions. Marginal quality water reuse is a common alternative practice but possible perils of metal accretion in plant parts are mostly ignored. The present research aimed to probe the impact of treated wastewater (TWW) and untreated wastewater (UTWW) on metal accumulation in flower petals and their influence on essential oil contents of fragrant Rosa species (R. Gruss-an-teplitz, R. bourboniana, R. centifolia, R. damascena) in a peri-urban area of Faisalabad, Pakistan during January, 2017 to December, 2018. The mineral and chemical contents in canal water (CW) and TWW were less than recommended levels of national environmental quality standards (NEQS) for wastewater of Pakistan. The experimentally UTWW possessed higher electrical conductivity (EC), biological and chemical oxygen demand (BOD and COD), and some metals (Pb, Co, Cr) that were above the permissible levels. The experimental data revealed that except Cr other metals contents in the flower petals were less than the WHO recommended limits (for medicinal plants) under experimental irrigation regimes. Rosa centifolia and R. damascena possessed higher metal i.e. Zn, Cu, Pb, Cr, Co contents while Fe and Ni contents were higher in R. Gruss-an-Teplitz and R. bourboniana respectively. There were twelve constituents which were detected in essential oil by gas chromatography. Major constituents were phenyl ethyl alcohol, citronellol, geranyl acetate, γ- undelactone, methyl eugenol, and limonene whose share was 48.17%, 41.11%, 8.46%, 4.82%, 4.44%, and 4.15% respectively whereas concentrations of other 06 constituents were less than 3.7%. Phenyl ethyl alcohol, lion shared constituent of essential oil was found highest (48.17%) in R. Gruss-an-Teplitz whereas minimum level was recorded in R. damascena (28.84%) under CW. In contrast, citronellol (chief component of fragrance) was highest in R. damascena (41.11%) in UTWW while the lowest level was found in R. Gruss-an-Teplitz (17.41%) in CW. This study confirmed the variations in metal concentrations of Rosa species due to different absorbability of each metal in flower petals. It also indicates that wastewater did not affect the composition but there were quantitative differences in aroma constituents and chemical composition of essential oil.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".