Water, Air, & Soil Pollution最新文献

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.

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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.

Microplastic (MP) pollution has become a significant environmental concern due to its persistent nature and toxic environmental effects. MPs pose threats to the aquatic and terrestrial environment by impacting plant habitats and altering soil properties. Wastewater treatment plants (WWTPs) are one of the most prominent channels for MP pollution in both aquatic and terrestrial ecosystems. MP particles separated during various treatment processes are trapped in the sludge and subsequently enter the terrestrial agroecosystem when sludge is used as fertilizer. In addition, sludge incineration releases tiny MP particles into the atmosphere. Although MPs have been extensively studied in aquatic environments, limited studies are available regarding their transport to agricultural ecosystems and the pathways through which they pose risk to food security and soil fertility. This review aims to evaluate the prevalence of MPs within sewage sludge and their subsequent translocation into terrestrial agroecosystems. A critical investigation has been carried out to address the challenges in detecting MPs in complex sludge samples to provide guidance for developing standardized and widely accepted methodologies. Furthermore, the impacts of MPs on soil fertility and biota were assessed and investigated from the perspective of agricultural production. This review aims to compile evidence of the presence and transport of MPs through sewage sludge applied to agricultural lands and help policymakers and environmentalists in developing viable strategies to protect agricultural land from MP pollution.

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The removal of pharmaceutical pollutants from the aqueous environment is a significant challenge that human beings are facing. In the current study, electro-Fenton (EF) and Anodic oxidation (AO) processes were applied separately to remove doxycycline (DOX) antibiotic from polluted water. For the EF process, carbon cloth cathode and Pt anode were used, and for the AO process, Ti/RuO₂-IrO₂ anode, and active carbon cathode were used. Raman spectroscopy, X-ray diffraction, atomic force microscopy, scanning electron microscopy, mapping by energy dispersive X-ray spectroscopy, and cyclic voltammetry analyses were done to study the electrodes of the EF and AO processes. The investigated variables in the EF and AO processes for DOX removal were current density (CD, mA/cm²), pH, DOX concentration (mg/L), and reaction time (min). For this purpose, the response surface methodology was applied. According to the results, the highest DOX removal efficiency was achieved at a CD of 16 mA/cm², pH = 3, DOX concentration of 20 mg/L, and reaction time of 120 min for the EF process. The optimal values of the AO process for CD, pH, DOX concentration, and process time were 24 mA/cm², 3, 20 mg/L, and 120 min, respectively. Finally, the amount of DOX mineralization and the intermediates of DOX removal were determined via total organic carbon and gas chromatography–mass spectrometry analyses for both the EF and AO methods.

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Soil pH is a key attribute regulating biogeochemical processes, with its changes significantly influenced by land use patterns and acid deposition. While extensive research has focused on the causes of soil acidification, comprehensively understanding the mechanisms and extent of pH recovery in acidified soils is crucial for developing effective ecological management strategies. This study investigated the dynamic changes in soil pH recovery through 13 years of field sampling across different land use patterns, including woodland, paddy, and upland. Results indicate that acidified soils under various land use patterns exhibit varying degrees of pH recovery trends, with an overall upward trend in pH values. Notably, forest soils exhibited the most pronounced recovery, with average pH rising from 3.70 to 4.81. This was followed by paddy and upland. The calculated reduction in free acidity confirmed this recovery sequence: woodland (-0.46 mmol/kg) > upland (-0.32 mmol/kg) > paddy (-0.225 mmol/kg). The study identified key drivers of soil pH recovery, highlighting the decisive role of nitrogen and sulfur deposition alongside fertilizer application. Among these, reduced sulfur deposition emerged as the most influential factor, explaining the largest proportion of pH recovery across all land-use patterns. Furthermore, the study explored strategies to promote sustainable recovery of acidified soils in subtropical watersheds, emphasizing the importance of emission reductions. These findings provide valuable insights for regional applications and lay the groundwork for long-term ecosystem management, particularly in mitigating pollutant emissions and restoring soil health.

Climate change poses significant challenges to global food security, exacerbating crop-yield reductions, water scarcity, soil degradation, and pest proliferation. To address these emerging threats, nanotechnology has emerged as a promising solution in agriculture, offering innovations that optimize resource use, enhance crop resilience, and improve productivity. Nanomaterials such as nano-fertilizers, nano-pesticides, and nanosensors are revolutionizing agricultural practices by enabling precise nutrient delivery, efficient pest control, and real-time monitoring of soil health. Furthermore, nanotechnology facilitates sustainable practices by improving water use efficiency, enhancing soil health, and supporting precision farming. However, the adoption of nanotechnology in agriculture is not without concerns, particularly regarding the environmental and health risks of nanoparticles. While existing literature extensively catalogs individual applications, a critical synthesis that compares mechanisms across nanomaterial classes and evaluates their field scalability within a climate-specific context is lacking. This review addresses this gap by providing a mechanistic framework to differentiate how major nanomaterial classes (e.g., metallic, polymeric, carbon-based) confer stress resilience and resource efficiency through distinct pathways, ranging from reactive oxygen species (ROS) scavenging and phytohormone modulation to structural reinforcement and targeted antimicrobial action. We critically synthesize current research on the role of nanotechnology in mitigating the impacts of climate change on agriculture, focusing on its applications to enhance water-use efficiency, promote crop stress tolerance, and ensure sustainable food production. Beyond cataloging effects, we propose a comparative analysis of material-specific pathways and trade-offs, including a systematic evaluation of contradictory findings across nanomaterial types, experimental conditions, and field contexts. We also discuss the need for eco-friendly, biodegradable nanomaterials and robust regulatory frameworks to guide safe and equitable implementation. By aligning with the United Nations’ ‘sustainable development goals’, nanotechnology offers a transformative pathway for climate-resilient agriculture, provided that its development and application are conducted responsibly.

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Many studies have focused on improving the adsorption capacity and selectivity of activated carbons (ACs), while fewer studies emphasize the correlation between the physical–chemical properties of activated carbon and the adsorption capacity, as well as the activation energy of desorption. To explore the mechanisms behind the elimination of m-xylene, which is one of the major oil gas VOCs species contributing synergistically to O₃ and SOA, a total of six kinds of commercially available ACs made of shell, coal, and wood materials were selected from the market. The characterization results showed that the ACs belonged to the micro-mesoporous structure and possessed good thermal stability. The surface alkaline functional groups were dominant for shell carbon and coal carbon, while the surface acidic oxygen-containing functional groups were rich for wooden carbon. Among them, wooden carbon W-2# had the lowest desorption activation energy, which was conducive to m-xylene desorption. Dynamic adsorption experiments conducted at room temperature (298 K) showed that the saturation adsorption capacity towards m-xylene of six ACs agreed well with the results of specific surface area and pore volume. Noteworthy the correlation between pore volume and adsorption capacity was stronger, especially the pore volume in the range of 4.2–6.3 nm. Notably, among three oxygen-containing functional groups, the relevance of carboxyl groups to equilibrium adsorption was as high as 0.94 due to the strong Π-electron donor–acceptor effect, which could form a complex with m-xylene to improve the adsorption capacity. Furthermore, m-xylene desorption process was also analyzed for the six ACs materials. The obtained results revealed that the desorption capacity was lower than the adsorption capacity, meanwhile W-2# exhibited the minimum activation energy (2.76 kJ/mol) for m-xylene desorption, which is consistent with the fact that the larger of Δd, the worse of the adsorption affinity, and the lower of the activation energy for desorption. In addition, W-2# demonstrated good cyclic stability. Modeling of dynamics and equilibrium fitting revealed that the mechanism of adsorbent-adsorbate interaction was monolayer physical adsorption controlled by inter-particle diffusion during adsorption process.

The fly ash being a significant byproduct of coal combustion in the thermal power plants (TPP) has caused much concern to the environment and disposal in India. New government-driven moves have managed to reinvent it as a secondary resource that is worth using. The paper provides a critical review of policies, programs, and regulatory frameworks in managing fly ash in a sustainable manner with specific reference to Fly Ash Mission and major guidelines by the Ministry of Environment, Forest and Climate Change (MoEFCC), Central Electricity Authority (CEA), and Central Pollution Control Board (CPCB). Relying on data that started in (Ministry 1996) and went through 2022, the paper records that the percentage of fly ash use has increased dramatically, that is, the percentages are now in the single-digit range in the mid-1990s to almost 100 percent in the recent years due to the increased usage of fly ash in the cement manufacturing sector, mine filling, road construction, production of bricks, and reclamation of land. The discussion shows that there have been major sectoral and regional advances as well as comparable challenges such as the inconsistencies in enforcement, regional imbalances, and underutilization in some states. Combining a policy assessment with empirical utilization patterns, this review gives a detailed view of the changing system of fly ash management in India and suggests the strategic options to firm up the practice of a circular economy, minimize environmental hazards, and improve sustainable industrial development.