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Record W4409799959 · doi:10.11159/iceptp25.157

Characterizing VOC Emissions and Retention in Recovered Water during Solar Drying of Wastewater Sludge under Variable Conditions

2025· article· en· W4409799959 on OpenAlexvenueno aff
Lidia Nuño-Sánchez, Virginia Perez-Lopez, Luis Saul Esteban-Pascual, Nuria Ortuño-Garcia, Adoración Carratalá

Bibliographic record

VenueProceedings of the World Congress on Civil, Structural, and Environmental Engineering · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Reuse
Canadian institutionsnot available
Fundersnot available
KeywordsWastewaterEnvironmental scienceWaste managementSewage treatmentVariable (mathematics)Environmental engineeringPulp and paper industryEngineeringMathematics

Abstract

fetched live from OpenAlex

Photochemical processes are a significant source of secondary organic aerosols (SOAs), many of which are watersoluble and remain in the condensed phase [1], [2], [3].Water recovery from wastewater sludge is feasible, as previously discussed in this congress ("Greenhouse Gas Emissions and Water Recovery in Solar Drying of Wastewater Sludge: Insights from LIFE-DRY4GASr").However, the characterization of VOC emissions during solar drying of wastewater sludge (WWS) under varying light intensities has not been explored.This study provides the first identification of VOCs emitted into the air and retained in water during laboratory-scale solar drying of WWS.A laboratory-scale solar dryer with controllable heat sources was designed for this purpose.Experiments were conducted using two independent heat sources: artificial solar light and heated plates.Light intensities of 625 W, 400 W and 200 W corresponded to dryer temperatures of 50 C, 45 C and 35 C, respectively, while heated plates were set to 60 C, 50 C, 40 C, yielding internal dryer temperatures of 45 C, 37.8 C, and 28 C.To simulate the dryness levels achieved in the LIFE-DRY4GAS prototype [4], each experiment continued until the WWS weight decreased from 100 to 15 %.VOC emissions during drying were measured in both the air and the recovered water.Airflow was directed sequentially through the sludge, water recovery system, and air emissions sampling setup.Hydrophobic VOCs were collected using two Tenax sorbent tubes in series.Recovered water was stored at varying pH levels to evaluate its influence on VOC retention.In one set of experiments, samples were preserved at their original pH (8-10) or acidified to pH 2. In another setup, the impingers were acidified with HSO before condensation to maintain a pH of 2 throughout condensation.Recovered water was analysed by ion chromatography for F -, CH3OO -, HCOO -, ClO2 -, NO2 -, NO3 -, PO4 3-, SO4 2-, and C2O4 2-.VOCs in water were further analysed using headspace solid-phase micro extraction (HS-SPME), while VOCs retained in Tenax tubes were identified using a thermal desorption system coupled to gas chromatography-mass spectrometry (TDS-GC-MS).The results revealed that VOC emissions were higher when temperature increases were driven by artificial solar light compared to heated plates.The dominant chemical families included alkanes, non-oxygenated terpenes, and aromatic hydrocarbons.Recovered water showed acetate concentrations around 300 ppm, while other analytes remained below 1 ppm, consistent with results from the LIFE-DRY4GAS prototype.VOC analysis of recovered water indicated two predominant chemical families: carboxylic acids and aromatic hydrocarbons.Carboxylic acids were more concentrated at acidic pH, whereas aromatic hydrocarbons dominated at the original pH (8-9).Acidification of water after recovery increased the concentration of carboxylic acids, while pre-acidification of impingersresulted in greater VOC diversity, particularly esters.Aromatic compounds in water were primarily toluene and phenols, whereas the carboxylic acids identified included butanoic acid, propanoic acid, and pentatonic acid.These findings demonstrate the interplay between temperature, light intensity, and pH in determining VOC emissions and their retention in recovered water, providing valuable insights for optimizing sustainable WWS drying processes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score0.508

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.181
Teacher spread0.177 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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Same venueProceedings of the World Congress on Civil, Structural, and Environmental EngineeringSame topicWastewater Treatment and ReuseFrench-language works237,207