Search Results (101)

Despite the immense potential in environmental remediation, the translation of magnetic nanomaterials (MNMs) from laboratory innovations to practical, field-scale applications remains hindered by significant technical and environmental challenges. This is particularly evident in soil environments—which are inherently more complex than aquatic systems and have received comparatively less research attention. Beginning with an outline of the fundamental properties that make iron-based MNMs effective as adsorbents and catalysts for heavy metals and organic pollutants, this review systematically examines their core contaminant removal mechanisms. These include adsorption, catalytic degradation (e.g., via Fenton-like reactions), and magnetic recovery. However, the practical implementation of MNMs is constrained by several key limitations, such as particle agglomeration, oxidative instability, and reduced efficacy in multi-pollutant systems. More critically, major uncertainties persist regarding their long-term environmental fate and biocompatibility. In light of these challenges, we propose that future efforts should prioritize the rational design of stable, selective, and intelligent MNMs through advanced surface engineering and interdisciplinary collaboration. Full article

Air pollution has emerged as one of the most critical public health challenges globally, with an astonishing 96% of the world’s population breathing air below the health standards. This study investigates the amount and distribution of six major air pollutants, PM₁₀, PM_2.5, O₃, SO₂, NO₂, and CO, at numerous air monitoring stations across Iran from 2016 to 2021. The primary objectives were to identify the cities with the highest pollution levels, and to assess the spatiotemporal evolution of air pollution across the country, aiming to provide a comprehensive overview and climatology of air quality. The results indicate that cities such as Zabol and Ahvaz consistently rank among the most polluted, with annual average PM₁₀ concentrations exceeding 190 µg m⁻³ and PM_2.5 reaching alarming levels up to 116.7 µg m⁻³. Furthermore, O₃ and SO₂ amounts were high in Zabol too, classifying it as the most polluted city in Iran. In addition, Tehran exhibits high NO₂, SO₂, and CO concentrations due to high industrial activity and vehicular emissions. Seasonal analysis reveals significant variations in pollutant levels, with PM concentrations peaking during specific months over various parts of the country, particularly driven by local and distant dust events. By integrating MERRA-2 reanalysis pollution data and ground measurements, this research provides a robust framework for understanding pollution dynamics, thereby facilitating more effective policy-making and public health interventions. The results underscore the necessity for immediate action to mitigate the adverse effects of air pollution on public health, particularly in areas prone to industrial activities (i.e., Tehran, Isfahan) and dust events (Zabol, Ahvaz). Full article

Piezoelectric catalytic technology has attracted much attention in the field of dye wastewater treatment, in which inorganic piezoelectric materials have been widely studied. Its core mechanism involves utilizing the piezoelectric effect to generate positive and negative charges, which react with oxygen ions and hydroxyl radicals, respectively, to generate reactive oxygen species to degrade organic pollutants. Currently, while organic piezoelectric catalysts theoretically offer significant advantages such as low cost and high processability, there has been a notable lack of research in this area, which presents an innovative opportunity for the exploration of new organic piezoelectric catalytic materials. In this study, new research using natural nanocellulose (FC) suspension as an efficient organic piezoelectric catalyst is reported for the first time. The experimental results showed that the catalyst exhibited excellent degradation performance for Rhodamine B (RhB), Acid Orange 7 (AO7), and Methyl Orange (MO) under ultrasonic vibration (40 kHz, 200 W): the degradation rates reached 95.4%, 72.4%, and 31.2%, respectively, for 150 min, and the corresponding first-order reaction kinetic constants were 0.0205, 0.00858, and 0.00249 min⁻¹, respectively. It is noteworthy that the RhB solution can achieve the optimal degradation efficiency without adjustment under neutral initial pH conditions, which significantly enhances the practical application feasibility. The experimental results showed that the catalyst, with a measurable piezoelectric coefficient (d33 = 4.4 pm/V), exhibited excellent degradation performance for Rhodamine B (RhB), Acid Orange 7 (AO7), and Methyl Orange (MO) under ultrasonic vibration (40 kHz, 200 W). This organic piezoelectric catalyst, based on renewable biomass, innovatively converts mechanical vibration energy in the environment into the power to degrade pollutants. It not only expands the application boundaries of organic piezoelectric materials but also provides a new solution for sustainable water treatment technology, demonstrating extremely promising application prospects in the field of green and environmentally friendly water treatment. Full article

Human milk has been used for over 70 years to monitor pollutants such as polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs). Despite the growing body of data, our understanding of the pollutant exposome, particularly co-exposure patterns and their interactions, remains limited. Artificial intelligence (AI) offers considerable potential to enhance biomonitoring efforts through advanced data modelling, yet its application to pollutant dynamics in complex biological matrices such as human milk remains underutilized. This study applied an AI-based framework, integrating machine learning, metaheuristic hyperparameter optimization, explainable AI, and postprocessing, to analyze PCB-170 levels in breast milk samples from 186 mothers in Zadar, Croatia. Among 24 analyzed POPs, the most influential predictors of PCB-170 concentrations were hexa- and hepta-chlorinated PCBs (PCB-180, -153, and -138), alongside p,p’-DDE. Maternal age and other POPs exhibited negligible global influence. SHAP-based interaction analysis revealed pronounced co-behavior among highly chlorinated congeners, especially PCB-138–PCB-153, PCB-138–PCB-180, and PCB-180–PCB-153. These findings highlight the importance of examining pollutant interactions rather than individual contributions alone. They also advocate for the revision of current monitoring strategies to prioritize multi-pollutant assessment and focus on toxicologically relevant PCB groups, improving risk evaluation in real-world exposure scenarios. Full article

The presence of recalcitrant organic compounds in oilfield-produced-water poses significant challenges for conventional biological treatment technologies. In this study, an Fe³⁺-augmented composite bioreactor was developed to enhance the multi-pollutant removal performance and to elucidate the associated microbial community dynamics. The Fe³⁺-augmented system achieved efficient removal of oil (99.18 ± 0.91%), suspended solids (65.81 ± 17.55%), chemical oxygen demand (48.63 ± 15.15%), and polymers (57.72 ± 14.87%). The anaerobic compartment served as the core biotreatment unit, playing a pivotal role in microbial pollutant degradation. High-throughput sequencing indicated that Fe³⁺ supplementation strengthened syntrophic interactions between iron-reducing bacteria (Trichococcus and Bacillus) and methanogenic archaea (Methanobacterium and Methanomethylovorans), thereby facilitating the biodegradation of long-chain hydrocarbons (e.g., eicosane and nonadecane). Further metabolic function analysis identified long-chain-fatty-acid CoA ligase (EC 6.2.1.3) as a key enzyme mediating the interplay between hydrocarbon degradation and nitrogen cycling. This study elucidated the ecological mechanisms governing Fe³⁺-mediated multi-pollutant removal in a composite bioreactor and highlighted the potential of this approach for efficient, sustainable, and adaptable management of produced water in the petroleum industry. Full article

Real-time, high-resolution monitoring of chemically diverse water pollutants remains a critical challenge for smart water management. Here, we report a fully integrated, multi-modal nano-sensor array, combining graphene field-effect transistors, Ag/Au-nanostar surface-enhanced Raman spectroscopy substrates, and CdSe/ZnS quantum dot fluorescence, coupled to an edge-deployable CNN-LSTM architecture that fuses raw electrochemical, vibrational, and photoluminescent signals without manual feature engineering. The 45 mm × 20 mm microfluidic manifold enables continuous flow-through sampling, while 8-bit-quantised inference executes in 31 ms at <12 W. Laboratory calibration over 28,000 samples achieved limits of detection of 12 ppt (Pb²⁺), 17 pM (atrazine) and 87 ng L⁻¹ (nanoplastics), with R² ≥ 0.93 and a mean absolute percentage error <6%. A 24 h deployment in the Cherwell River reproduced natural concentration fluctuations with field R² ≥ 0.92. SHAP and Grad-CAM analyses reveal that the network bases its predictions on Dirac-point shifts, characteristic Raman bands, and early-time fluorescence-quenching kinetics, providing mechanistic interpretability. The platform therefore offers a scalable route to smart water grids, point-of-use drinking water sentinels, and rapid environmental incident response. Future work will address sensor drift through antifouling coatings, enhance cross-site generalisation via federated learning, and create physics-informed digital twins for self-calibrating global monitoring networks. Full article

Urban heritage materials face accelerated decay due to the synergistic effects of air pollution and climate change. Dose–response functions (DRFs) have emerged as a key tool to quantify and predict these risks. This review synthesizes the scientific development of DRFs, their application in Europe and China, and their role in policy and heritage management. European initiatives have refined DRFs to incorporate multi-pollutant and climate interactions, providing spatial risk maps and informing pollution control measures. In China, recent applications adapt European insights to local contexts, revealing strong influences of particulate matter. While DRFs offer clear quantitative estimates, their empirical nature and simplified assumptions necessitate complementary methods, including sensor networks, remote sensing, and machine learning models. Future research should integrate multivariate modelling, expand empirical data, and couple DRFs with real-time monitoring to better protect urban heritage materials amid environmental change. Full article

Ozone (O₃) and fine particulate matter (PM_2.5) are critical atmospheric pollutants whose complex chemical coupling presents significant challenges for multi-pollutant control strategies. This study investigated the spatiotemporal variations and driving mechanisms of O₃ and PM_2.5 in Jiaxing, China, during different COVID-19 lockdown periods from November 2019 to January 2024. Using high-resolution monitoring data, random forest modeling, and HYSPLIT backward trajectory analysis, we quantified the relative contributions of anthropogenic emissions, meteorological conditions, and regional transport to the formation and variation of O₃ and PM_2.5 concentrations. The results revealed a distinct inverse relationship between O₃ and PM_2.5, with meteorologically normalized PM_2.5 decreasing significantly (−5.0 μg/m³ compared to the pre-lockdown baseline of 0.6 μg/m³), while O₃ increased substantially (15.2 μg/m³ compared to the baseline of 5.3 μg/m³). Partial dependency analysis revealed that PM_2.5-O₃ relationships evolved from linear to non-linear patterns across lockdown periods, while NO₂-O₃ interactions indicated shifts from VOC-limited to NO_x-limited regimes. Regional transport patterns exhibited significant temporal variations, with source regions shifting from predominantly northern areas pre-lockdown to more diverse directional contributions afterward. Notably, the partial lockdown period demonstrated the most balanced pollution control outcomes, maintaining reduced PM_2.5 levels while avoiding O₃ increases. These findings provide critical insights for developing targeted multi-pollutant control strategies in the Yangtze River Delta region and similar urban environments. Full article

Under China’s “dual carbon” goals (carbon peaking and carbon neutrality), the utilization of high-chlorine coal faces significant challenges due to its abundant reserves in regions such as Xinjiang and its notable environmental impacts. This study systematically investigates the combustion characteristics, environmental risks, and control strategies for high-chlorine coal. Key findings reveal that chlorine release occurs in three distinct stages, namely low-temperature desorption, medium-temperature organic bond cleavage, and high-temperature inorganic decomposition, with release kinetics governed by coal metamorphism and the reaction atmosphere. Chlorine synergistically enhances mercury oxidation through low-activation-energy pathways but exacerbates boiler corrosion via chloride–sulfate interactions. Advanced control technologies—such as water washing, calcium-based sorbents, and integrated pyrolysis–gasification systems—demonstrate substantial emission reductions. However, challenges remain in addressing high-temperature corrosion and optimizing multi-pollutant synergistic control. This study provides critical insights into the clean utilization of high-chlorine coal, supporting sustainable energy transitions. Full article

This study investigates the performance of different demand-controlled ventilation strategies for improving indoor air quality while optimizing energy efficiency. The experimental research was conducted at the Indoor Live Lab at the University of Coimbra using a smart window equipped with mechanical ventilation boxes, occupancy sensors, and a real-time CO₂ monitoring system. Several occupancy-based and CO2-based ventilation control strategies were implemented and tested to dynamically adjust ventilation rates according to real-time indoor conditions, including (1) occupancy period-based control, (2) occupancy level-based control, (3) ON-OFF CO₂-based control, (4) multi-level CO₂-based control, and (5) modulating CO₂-based control. The results indicate that intelligent control strategies can significantly reduce energy consumption while maintaining indoor air quality within acceptable limits. Among the CO₂-based controls, strategy 5 achieved optimal performance, reducing energy consumption by 60% compared to the simple ON-OFF strategy, while maintaining satisfactory indoor air quality. Regarding occupancy-based strategies, strategy 2 showed 58% energy savings compared to the simple occupancy period-based control, but with greater CO₂ concentration fluctuation. The results demonstrate that intelligent DCV systems can simultaneously reduce ventilation energy use by 60% and maintain compliant indoor air quality levels, with modulating CO₂-based control proving most effective. The findings highlight the potential of integrating sensor-based ventilation controls in office spaces to achieve energy savings, enhance occupant comfort, and contribute to the development of smarter, more sustainable buildings. Future research should explore the integration of predictive analytics and multi-pollutant sensing to further optimize demand-controlled ventilation performance. Full article

Under increasingly stringent global policies aimed at reducing emissions from shipping, the impact of maritime activities on air quality has garnered significant attention. However, the absence of comprehensive macro-evaluation methods and a limited understanding of regional-scale pollutant emissions introduce substantial uncertainties in assessing emission reduction effectiveness and identifying pollution sources. In this study, we utilized Sentinel-5P satellite data from 2019 to 2024 to examine the spatiotemporal characteristics of six air pollutants (SO₂, NO₂, HCHO, O₃, CO, and CH₄) in China’s coastal areas. We further investigated the correlation between ship density and pollutant concentrations and analyzed the distribution of pollutant concentrations in major coastal ports across China. The results indicate the following: (1) The concentrations of SO₂, HCHO, and CH₄ exhibited a continuous increasing trend, whereas NO₂, CO, and O₃ remained relatively stable or showed a slight decline. All six pollutants demonstrated obvious seasonal variations, with NO₂ and HCHO following a double-peak pattern and O₃, SO₂, CH₄, and CO exhibiting a single-peak pattern. (2) Pollutant concentrations were higher along the northern coast (Yellow Sea and Bohai Sea) and relatively lower in the South China Sea region. Specifically, NO₂, SO₂, and O₃ were higher in the Bohai Sea region; HCHO and CO were more concentrated in the northern coastal area; and CH₄ was elevated in the north and certain ports of the Yangtze River Delta. (3) Ship density displayed a significant positive correlation with NO₂, SO₂, HCHO, CO, and CH₄, indicating that ship emissions are an important source of these pollutants. Although O₃ is not directly emitted by ships, a positive correlation was observed in certain ship-dense areas, primarily due to photochemical reactions involving NO₂ and volatile organic compounds (VOCs). (4) Higher concentrations of NO₂, SO₂, HCHO, CO, and CH₄ were observed in northern ports (e.g., Tianjin Xingang, Qinhuangdao, Tangshan, and Dalian), whereas southern Chinese ports (e.g., Shenzhen, Xiamen, and Haikou) exhibited lower pollution levels. These findings provide a scientific foundation for coastal air pollution control and highlight the necessity of ship emission regulation and integrated multi-pollutant management. Full article

Background: Ambient air pollution is a modifiable determinant of lung cancer survival, affecting early-stage Non-Small Cell Lung Cancer (NSCLC) incidence and mortality. Methods: This retrospective cohort study examined the association between all-cause mortality and exposure to air pollution among stage 1A NSCLC-treated patients from the U.S. National Cancer Registry from 1988 to 2015. The Cox hazard model and Kaplan–Meier survival plots were provided. Air pollutants were included separately and together in the models, accounting for spatiotemporal weather variability affecting air pollution exposure levels pre and post lung cancer diagnosis. Results: NO₂ (above the median sample mean = 25.66 ppb; 12.97 ppb below median), SO₂ (above median sample mean = 3.98 ppb; 1.81 ppb below median), and CO (above median sample mean = 1010.84 ppb; 447.91 ppb below median) air pollutant levels and weather conditions were calculated for county-day units. The median months of survival for those exposed to above-median NO₂ were 27 months (SD = 17.61 months), while the median was 30 months (SD = 15.93 months) for those exposed to below-median levels. Multipollutant analyses indicated that an average monthly NO₂ increase of 1 part per billion (ppb) in the county of NSCLC diagnosis was associated with increases of 4%, 6%, and 9% in the all-cause mortality rate one, three, and five years after diagnosis, respectively; an equivalent increase in SO₂ was associated with increases of 16%, 17%, and 17%; and an increase in CO was associated with increases of 53%, 51%, and 42% Conclusion: It is vital to implement environmental policies that control emissions to reduce preventable deaths in stage 1A NSCLC patients with adenocarcinoma or squamous cell carcinoma histology types who reside in metropolitan areas. Full article

Fine particulate matter (PM) and sulfur trioxide (SO₃) from coal-fired flue gas pose significant environmental and health risks. While low-low temperature electrostatic precipitators (LLT-ESPs) enhance PM and SO₃ removal by cooling flue gas below the acid dew point, their efficiency is limited by incomplete agglomeration. This study proposes integrating turbulent agglomeration technology into LLT-ESP systems to improve collision and adhesion between droplets and particles. Experiments were conducted under three conditions: flue gas containing SO₃ alone, fly ash alone, and their mixture. Particle size distributions, mass concentrations, and removal efficiencies were analyzed using ELPI⁺ and PM samplers. Results showed that turbulent agglomeration reduced the number concentration of sulfuric acid droplets by 21.4% from 1.59 × 10⁷ cm⁻³ to 1.25 × 10⁷ cm⁻³ (SO₃-only case) and fine fly ash particles by 19.5% from 5.79 × 10⁶ cm⁻³ to 4.66 × 10⁶ cm⁻³ (fly-ash-only case). Although LLT-ESP combined with turbulent agglomeration has a certain removal effect in the case of individual pollutants, the overall effect is not unsatisfactory, especially for SO₃, whose mass-based removal efficiency was merely 16.2%. The value of the fly-ash-only case was 92.1%. Synergistic effects in the coexistence scenario (fly ash and SO₃) significantly enhanced agglomeration, increasing SO₃ and PM removal efficiencies to 82.9% and 97.6%, respectively, compared to 69.7% and 90.1% without turbulent agglomeration. The mechanism behind the efficiency improvement involved droplet–particle collisions, sulfate deposition, and improved particle charging. This work demonstrates that turbulent agglomeration optimizes multi-pollutant control in LLT-ESP systems, offering a feasible strategy for achieving ultra-low emissions in coal-fired power plants. Full article

This study investigated the relationships between air pollutants (PM₁₀, SO₂, NO₂, O₃, CO) and meteorological parameters (wind speed, relative humidity, ambient temperature) across urban, suburban, and industrial areas in Malaysia from 2017 to 2021. Using data from six monitoring stations, this research employed descriptive analysis, trend analysis, and Granger causality testing to uncover complex interactions. The results revealed distinct patterns: suburban areas showed strong ambient temperature-ozone (p-value = 0.0063) and relative humidity–nitrogen dioxide relationships (p-value = 0.0411); industrial zones exhibited bidirectional causality between SO₂ and PM₁₀ and had a strong nitrogen dioxide–PM₁₀ relationship (p-value = 0.0292); urban areas exhibited complex multi-pollutant interactions. Notably, the 2020 Movement Control Order significantly improved air quality. This research provides crucial insights for targeted air quality management strategies, contributing to public health improvements and aligning with global sustainability goals. Full article

Ambient air pollution has been associated with gestational diabetes mellitus (GDM); however, evidence regarding trimester-specific effects from China remains limited. This case–control study study analyzed data from pregnant women who delivered in Wuhan, China, between 2017 and 2022 (164 GDM cases and 731 controls), integrating geographic information, air quality measurements, and maternal characteristics. Using Inverse Distance Weighting interpolation and Generalized Linear Mixed Models (GLMM), we assessed associations between air pollutant exposure and GDM across different gestational periods. Results indicated that NO₂ demonstrated the strongest association with GDM compared to other pollutants. Specifically, increased NO₂ exposure was consistently associated with higher GDM risk throughout pregnancy. PM_2.5 exposure showed significant associations during early and mid-pregnancy, while SO₂ exposure was significantly associated with GDM risk exclusively in early pregnancy. Sensitivity analyses stratified by urban maternity status and maternal age revealed the stability of the study’s findings. These findings underscore the importance of reducing air pollution exposure during pregnancy and implementing targeted interventions for high-risk populations to prevent GDM development. Full article