LIFE CYCLE ASSESSMENT (LCA) OF IRON-OXIDE MODIFIED BIOCHAR ADSORBENTS PREPARED FOR REMOVAL OF ARSENIC FROM WATER: MICROWAVE VS. CONVENTIONAL PYROLYSIS
Bibliographic record
Abstract
Arsenic contamination in water originates from anthropogenic and natural sources, occurring in many locations all over the world. Due to its very high toxicity, exposure to low concentrations can lead to critical consequences such as cancer in humans and ecosystem contamination. Thus, regulatory limits for drinking water have been set by the World Health Organization at 10 μg/L or less as a safe threshold. However, achieving this limit can be challenging. Location, water source, concentration, pH levels, and other factors have great influence in the arsenic removal process. Many removal methods have been studied and promoted in the past and the selection of the appropriate removal method may depend on the factors previously mentioned. However, two overall significant components are the focus of industry for treatment including economic and environmental considerations. While there are many arsenic removal method alternatives, few studies evaluate its realistic economic and environmental burdens in large scale. This type of analysis must also take into consideration the applicable location and data for more appropriate and accurate outcomes. This study investigated the potential of using biochar for adsorption as its main arsenic removal method. The biomass chosen was canola straw due to its abundance in the province of Saskatchewan, Canada. Biochar adsorption has become popular due to its potentially increased environmentally friendliness as well as lower cost due to biomass availability. The pyrolysis type for biochar production was also studied, comparing conventional and microwave pyrolysis and its effect on the biochar adsorption efficiency and economic and environmental burdens. For satisfactory adsorption results, the biomass should be treated and modified for enhancement. A wide range of activating and modifying agents have been used in the past. For this project, an acid and base were studied for its activating effect and iron was utilized as a modifying agent due to its previously reported high efficiency rate. Additionally, the modification that occurred to the biomass and biochar due to these treatments were also explored to better comprehend their roles. Characterization analyses to study the chemical and physical modifications were carried out including Brunauer-Emmett-Teller (BET), Scanning Electron Microscopy (SEM), Energy Dispersive X-ray Spectroscopy (EDS), and X-ray Diffraction (XRD). iv Furthermore, different factors were also analyzed, including, pH, adsorbent dosage, temperature and equilibrium time. Different biochars were synthesized with optimized adsorption capacities of 825 and 893 μg/g for As(III) and 929 and 771 μg/g for As(V). For further analysis of the biochar’s economic and environmental performance, a Life Cycle Assessment (LCA) and Life Cycle Cost Assessment (LCCA) were performed for the chosen optimal biochars. These assessments allowed a more objective look by using SimaPro and comparing processes utilizing the same method and materials, generating less discrepancies between comparisons. It was found that pyrolysis types, although not significant on adsorption capacity, does have a significant impact on both environmental and economic factors. Furthermore, microwave pyrolysis biochar has a great potential for environmental efficiency, considering its CO2 production which was found to be -0.298 CO2/kg of biochar. In an economic perspective, produced biochars averaged at a range of price from $5.14 to $6.95 USD per kg of biochar. A weighting analysis was also carried out to further analyze the economic and environmental factors, since few studies report this data for biochar adsorbents. Thus, an adsorbent can be chosen based on personal goals and preferences of the industry or company and its application. Biochars were synthesized and showed high arsenic removal rates in the circumstances studied. Distinct factors showed their impact on the arsenic removal capacity and characterization experiments showed the influencing modifications and resulting biochar characteristics that lead to increased adsorption capacities. Although the LCA and LCCA was carried out with its weighting scenarios, further research and analyses can be done for greater data comparison and continued enhancement of biochar for arsenic adsorption.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".