环境•农林最新文献

Valorization of industrial by-products like sodium lignosulfonate (SL) offers a sustainable strategy for environmental remediation. This study provides a systematic evaluation of the effects of SL addition on humification and copper (Cu) behavior during municipal sludge composting. The effects of SL addition at 0% (CK), 3% (T3), and 9% (T9) dry weight over a 60-day composting period were investigated. The 9% SL treatment (T9) significantly accelerated compost maturity, achieving a germination index of 96.4% compared to 85.9% in the control. EEM-PARAFAC analysis revealed that SL addition promoted the transformation of protein-like and quinone-like intermediates into stable HA-like substances. Consequently, the final HA content and degree of polymerization in T9 were substantially higher than in the control. Concurrently, enhanced humification was accompanied by a significant reduction in Cu bioavailability. The diethylenetriaminepentaacetic acid-extractable (bioavailable) Cu in T9 was reduced to 96.40 mg/kg, a 66% reduction from its initial value and significantly lower than the 150.73 mg/kg in the final control compost. Pearson analysis confirmed a strong negative correlation between HA content and bioavailable Cu. The findings suggest that SL addition is an effective amendment for producing safer, higher-quality compost, offering a novel strategy for the synergistic valorization of industrial and municipal waste streams.

This study investigated formaldehyde emissions from iron and steel industry processes in the Zonguldak-Ereğli urban development region, one of the major industrial centers of Europe and Türkiye, using tropospheric measurement data collected using the Sentinel 5p Tropospheric Measurement Instrument between (WHO 2019) and 2024. Taking into the cloudiness of tropospheric measurement data, 1408 positive values from the 1736 measurements obtained with Google Earth Engine geospatial web applications were examined and evaluated as Sentinel 5p observation data. Measurements in mol/m² were converted to mass/volume, and outdoor exposure values were compared with international standard threshold values to determine risk levels for workers in three major industrial areas and those living near industrial facilities. In the study, a weighted daily average emission value based on population was estimated as 0.001 mg/m³. The map-based study shows widespread formaldehyde formation in industrial facilities and urban areas. When the findings were evaluated in terms of health risks, the average daily concentration for chronic cancer risk was 0.9589 μg/m³, the hazard ratio was 2.5, and the individual's probability of developing cancer was 0.04. The hazard ratio being greater than one and the probability of cancer remaining above the chronic exposure limits indicate that formaldehyde exposure poses a negative health risk to both workers and residents in the area. The findings highlight the need for strict environmental regulations and enforcement, particularly regarding formaldehyde.

Absorbent hygiene products (AHPs), including infant care items, feminine hygiene products, and nursing materials, generate over 20 million tons of non-biodegradable waste annually. The widespread practice of open burning exacerbates environmental and public health risks due to complex material compositions and harmful emissions. This study presents an intelligent, scalable incineration system for sustainable AHP waste management, integrating Internet of Things (IoT)-enabled gas sensing, real-time emission monitoring, and machine learning (ML)-based pollutant classification. The system comprises a nichrome-wire-activated combustion chamber and a multi-stage gas purification unit incorporating limestone slurry, activated carbon, and baghouse filtration. Real-time concentrations of CO₂, NH₃, VOCs, SO₂, and Nox were continuously tracked using embedded sensors. A Random Forest classifier achieved classification accuracies of 86–90% across different AHP waste types. Experimental evaluations demonstrated a 92% reduction in waste volume, an 88% average decrease in toxic gas emissions, and thermal energy recovery of approximately 2.5 kWh per cycle. The residual ash was assessed for potential reuse in construction and agriculture, contributing to circular economy goals. By enabling adaptive combustion control, intelligent emission profiling, and resource valorization, the proposed framework addresses key limitations in conventional AHP disposal methods. Aligned with sustainable Development Goals (SDGs) 3,12, and 13, this system provides a practical and scalable approach to cleaner production and environmentally responsible waste management, particularly in healthcare, municipal, and decentralized sanitation contexts.

To overcome challenges of energy-intensive nitrification and nitrite-limited anammox, this study developed a biochar-assisted anaerobic NH₄⁺-N oxidation process using bicarbonate (HCO₃^⁻) as a potential electron acceptor. While the biochar-free system primarily accumulated NO₂^⁻-N, bamboo biochar enabled the complete nitrification to NO₃^⁻-N, even under low-temperature stress (12–15 °C). The prolonged operation under lower temperatures (< 12 °C) diminished this enhancement and reduced nitrification efficiency, with persistent effects even after temperature recovery to 25 °C. Morphological and 16S rRNA sequencing results revealed distinct microbial communities in this bicarbonate-driven system compared to conventional anammox sludge. Biochar enhanced the resilience of the system against low-temperature stress by selectively enriching specific taxa, such as nitrifying bacterium Nitrobacter and the functionally associated nxrB gene, both of which were critical for complete nitrification. Machine learning with XGBoost modeling effectively predicted the nitrite accumulation ratio (NAR) and nitrogen removal efficiency (NRE), identifying operating temperature as the significant positive factor. The negative contribution of biochar dosage to NAR prediction further confirmed its role in prompting complete nitrification. Overall, this study presents a promising complete nitrification process to address low-temperature stress and electron acceptor limitations in NH₄⁺-N removal.

Graphical Abstract

Highlights

• NH₄ ⁺ -N oxidation using HCO₃ ⁻ as an alternative electron acceptor is acclimated

• Byproducts distribution and NAR depend on biochar and operation temperature

• Biochar promotes the complete nitrification under lower-temperature stress

• Biochar selectively enriches Nitrobacter and upregulates nxrB gene

• Machine learning identifies temperature as the key factor in predicting NAR and

NRE

Time series forecasting is essential in do-mains such as environmental monitoring, particularly for predicting air pollutant levels like PM(_{2.5}). This study introduces a deep ensemble framework, termed Feature Engineered LSTM-BiLSTM-GRU-RNN-CNN (FE-LBGRC), that integrates multiple deep learning architectures, including Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN). Advanced feature engineering techniques, such as rolling window statistics and Savitzky-Golay smoothing, are applied to enhance input quality. The ensemble leverages XGBoost as a meta-learner to combine individual model predictions, improving accuracy and robustness. The framework is evaluated using PM(_{2.5}) data collected from 20 monitoring stations across major Indian cities (Bengaluru, Chennai, Mumbai, Pune, Lucknow, Delhi, and Hyderabad) between 2016 and January 2025. The proposed FE-LBGRC framework achieved the maximum predictive accuracy among all examined models, yielding an MAE of 2.87, MSE of 23.23, RMSE of 4.42, and an R(^2) of 0.9906. These results highlight the superiority of the proposed ensemble architecture in understanding complex temporal and spatial patterns, demonstrating that the integration of deep learning models with feature engineering and ensemble learning leads to more accurate and generalized PM(_{2.5}) forecasting.