Assessment of the impact of reforestation on soil and river water quality based on organic chemical pollutants.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Forests are a natural resource and are influential in most countries as they are a source of food, clothing, and form of shelter for many organisms. These sections of forested land have been sacrificed for the development of urban areas, making way for agriculture, cities and the ever increasing human population. Some of the detrimental effects associated with deforestation are as follows: loss of wildlife and fish habitats, increased nutrient and sediment loads in nearby rivers, and ultimately increases in greenhouse gas emissions. Reforestation refers to the planting of trees so as to replenish an area that was previously a forest but due to anthropogenic effects, such as land deforestation, resulted in its deterioration. This project aimed to assess the impact of the eThekwini Municipality reforestation project on the quality of the soil within the reforestation sites, and water from the nearby rivers (White and Black Mhlasini Rivers) situated at the reforested Buffelsdraai area in KwaZulu-Natal. The levels of organic pollutants were assessed from the analysis of soil, sediment and river water. Selected polyaromatic hydrocarbons (PAHs) and pesticides, which had been previously utilised at this site when it was a sugarcane farm, were analysed. The sixteen PAHs analysed were naphthalene, acenaphthylene, acenaphthene, fluorine, phenanthrene, anthracene, fluoranthene, pyrene, chrysene, benzo[a]anthracene, benzo[b]fluoranthene, benzo[k]fluoranthene, benzo[i]fluoranthene, benzo[a]pyrene, benzo[e]pyrene, indeno[1,2,3-cd]pyrene, dibenzo[a,h]anthracene, and benzo[g,h,i]perylene which are on the Environmental Protection Agency (EPA) list of priority pollutants. The pesticides analysed were hexazinone, oxamyl, and acetochlor. The soil and sediment samples were extracted using ultrasonication, and liquid-liquid extraction was utilised for the water samples. Gas chromatography mass spectrometry (GCMS) and liquid chromatography mass spectrometry (LCMS) were used to analyse the PAHs and pesticides, respectively. PAH recoveries on the GCMS ranged between 60-110%, and pesticide recoveries on the LCMS were between 83-113%. The PAH LOD values were between 0.30–0.69 μg g-1 and between 0.17-0.32 μg g-1 for pesticides. PAH LOQ values ranged between 0.99-1.9 μg g-1 and between 0.56-1.33 μg g-1 for pesticides. The total PAH concentrations determined were between 4.258 – 6.426 μg g-1 in the soil samples, 2.210 – 13.900 μg g-1 in sediment, and 6.360 – 85.468 ng L-1 in river water. The total pesticide concentration was between 1.271 – 1.742 μg g-1 in soil, 0.197 – 1.175 μg g-1 in sediment, and 0.792 – 12.950 ng L-1 in river water. A comparison between the soil samples and the control,showed that reforestation is potentially reducing the concentration of organic chemical pollutants. The water and sediment samples also provided potential evidence of the positive impact of reforestation, as it revealed the concentration of pollutants to be lower within the reforestation boundaries and higher outside the reforestation boundary. The most abundant PAH determined in the samples was fluoranthene, which could possibly be due to this hydrocarbon being the most abundant aerosol in the atmosphere. Source apportionment analysis showed that most PAHs originated from pyrolytic sources, which was from burning of sugarcane. The total concentration for specific PAHs was above the threshold value for most sampling sites according to Canadian environmental guidelines. However, reforestation was shown to potentially be reducing these pollutant concentrations. The findings from this study will assist the neighbouring communities and eThekwini in future planning for the extension of existing or development of new reforestation sites.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it