Environmental Pollution Management Research Articles

Tunable diode laser absorption spectroscopy for gas detection with a negative curvature anti-resonant hollow-core fiber

In this study, an all-fiber gas sensing technology was proposed, which is based on tunable diode laser absorption spectroscopy in the near infrared. A negative curvature anti-resonant hollow-core fiber was used as the gas chamber and optical channel, and an opto-gas coupling miniature tee was used in the configuration. Down to ppb (parts-per-billion) level noise-equivalent concentration was achieved with a fast-response capability of less than 6 s. The results demonstrated strong long-term stability, with a relative standard deviation of approximately 2.1% over a 12-hour period. This approach demonstrates a simple, robust, fast response and compact sensor configuration that contributes to better management of greenhouse gas emissions and environmental pollution

Three-dimensional intelligent monitoring and early warning technology for tailings ponds based on spatiotemporal fusion of multisource big data.

To solve the difficult problems of tailings dam instability and environmental pollution, multisource information perception, prediction and early warning technology for tailings dams are investigated. Taking a tailings pond in China as an example, a three-dimensional visualization intelligent management platform based on the spatiotemporal fusion of multisource big data is established to realize intelligent real-time monitoring, prediction and early warning of tailings dams. A monitoring system for air-space-ground integration was developed via high-resolution optical image recording, unmanned aerial vehicles (UAVs), radar, video surveillance and displacement sensors. This approach can realize pollution monitoring and efficient identification of high-risk areas. In addition, a machine learning algorithm is used to mine spatiotemporal data in detail. The Alibaba cloud platform was adopted to develop a data integration framework. Multisensor spatiotemporal data from tailings dams and multisource monitoring data from the environment were integrated. The integrated management of tailings dam environmental monitoring, pollution and stability assessment is realized. A stability prediction model for tailings dams based on multisource data is proposed. The temporal and spatial information of various forms of data is analysed from multiple levels and perspectives. Rapid prediction of the disaster situation and stability of tailings ponds is realized. Moreover, a tailings pollution assessment model based on a neural network is integrated with multisource information to establish a multifactor environmental pollution neural network assessment model and a multilevel warning platform. This work can provide technical support for intelligent monitoring and early warning systems for tailings ponds.

Construction site environmental pollution management - initiatives, innovation and research

The management of pollution, and in particular of the impact of noise and vibrations, is very important for construction activities, even more so when it comes to urban construction sites. Indeed, some operations have very significant emissive potential and it is appropriate, for the proper conduct of the activities, to adopt a certain strategy in order to control the impacts and thus ensure good acceptance of the construction by the neighborhood. Environmental monitoring is the key to achieving this dual objective: prevent pollution and communicate. This paper presents our innovative approach to localize, identify and report noise pollution on construction sites for efficient noise mitigation. More precisely, usage of acoustic high resolution beamforming is discussed to both identify predominant sources and estimate reliably their contribution to overall noise pollution captured at a given reference point by à Class I metrological device. By combining the easy-to-deploy ATD directional acoustic sensor with its 10cm aperture microphone array, with video recording for event confirmation and labelling on the highly responsive Cadence interface, we show how we can solve complex use cases for a wide range of users, from site managers to acoustic experts.

A comparative study on the toxic effects of lead pollution and nanoplastic-lead mixed pollution on red drum and their detoxification strategies

Nanoplastics and heavy metals pose various adverse effects on marine organisms. However, the combined toxicity of nanoplastics and lead pollution to marine fish is not fully understood. This study investigates the toxic effects and detoxification strategies of lead pollution (p07) compared to nanoplastic-lead mixed pollution (m07) in red drum during exposure and recovery phases. Under m07 pollution, the maximum lead content in muscle was 22.61 mg/kg, which was significantly higher than the 15.82 mg/kg observed under p07 pollution. This finding demonstrated that nanoplastics significantly enhance lead accumulation, leading to more severe toxic effects on red drum. Histological analyses revealed that lipid droplets in the liver and epithelial lifting in the gills were the primary lesion types. During the exposure periods, red drum primarily detoxified lead through cellular renewal and the removal of damaged proteins under p07 pollution. Conversely, under m07 pollution, detoxification relied on cellular senescence, apoptosis, endocytosis, and the removal of damaged proteins. In the recovery phases, red drum predominantly recovered through cell proliferation and antioxidant responses under p07 pollution. Under m07 pollution, the focus shifted to functional protein synthesis, apoptosis, endocytosis, and lipid metabolism. This study offers valuable insights into the monitoring and management of combined environmental pollution.

Research on Coal Mine Environmental Pollution Management and Ecological Restoration Technology

This paper analyses the current situation of environmental pollution in coal mines, its causes, and its impact on the ecological environment, and systematically researches the management of environmental pollution in coal mines and ecological restoration technology. The paper analyses the main categories of coal mine environmental pollution, including water pollution, air pollution, and solid waste pollution, and discusses the causes of these pollution and their destructive effects on the ecological environment. Immediately after that, the article elaborates on the treatment technologies of coal mine environmental pollution, including physical, chemical and biological treatment technologies for water pollution control, blocking, adsorption and covering technologies for air pollution control, and landfill, incineration and resource utilization strategies for solid waste disposal. This paper also discusses coal mine ecological restoration techniques, including species selection and allocation for vegetation restoration, vegetation restoration models, physical, chemical and biological methods for soil improvement, and engineering, vegetation and agricultural measures for soil and water conservation. Through the research of this paper, the researchers aim to provide scientific basis and technical support for the management of coal mine environmental pollution and ecological restoration and to promote the restoration of the ecological environment in mining areas and the sustainable development of the coal industry.

Hormesis in the Assessment of Toxicity Assessment by Luminescent Bacterial Methods.

The threat posed by water pollutants to aquatic ecosystems and human health cannot be overlooked, and the assessment of the toxicity of these contaminants is paramount to understanding their risks and formulating effective control measures. Luminescent bacteria-based assays, as a vital tool in evaluating contaminant toxicity, encounter a challenge in ensuring accuracy due to the phenomenon of "Hormesis" exhibited by pollutants towards biological entities, which may skew toxicity assessments. This study elucidated the specific effects of pollutants on luminescent bacteria at different concentrations, used modeling to characterize the effects and predict their toxicity trends, and explored the applicable concentration ranges for different pollutants. Research revealed that six typical pollutants, namely PAHs, endocrine disruptors, antibiotics, pesticides, heavy metals, and phytosensory substances, could promote the luminescence intensity of luminescent bacteria at low concentrations, and the promotional effect increased and then decreased. However, when the concentration of the substances reached a certain threshold, the effect changed from promotional to inhibitory, and the rate of inhibition was directly proportional to the concentration. The EC50 values of six types of substances to luminescent bacteria is as follows: endocrine disruptors > pesticides > antibiotics > heavy metals > polycyclic aromatic hydrocarbons > chemosensory agents. The effect curves were further fitted using the model to analyze the maximum point of the promotion of luminescence intensity by different substances, the threshold concentration, and the tolerance of luminescent bacteria to different substances. The maximum promotion of bacterial luminescence intensity was 29% for Bisphenol A at 0.005 mg/L and the minimum threshold concentration of chromium was 0.004 mg/L, and the maximum bacterial tolerance to erythromycin is 6.74. In addition, most of the current environmental concentrations had a positive effect on luminescent bacteria and may still be in the range of concentrations that promote luminescence as the substances continue to accumulate. These findings will enhance the accuracy and comprehensiveness of toxicity assessments, thereby facilitating more informed and effective decision-making in the realms of environmental protection and pollution management.

Natural and modified clays as low-cost and ecofriendly materials to remove salinomycin from environmental compartments

Antibiotics in the environment represent a substantial pollution threat. Among these emerging pollutants, ionophore anticoccidials are of special concern due to their potential ecological impact, persistence in the environment, and role in promoting antimicrobial resistance. To investigate the adsorption/desorption of the ionophore antibiotic salinomycin (SAL) on/from raw and modified clay adsorbents, batch-type experiments were performed using 0.5 g of clay adsorbent mixed with 10 mL of increasing doses of SAL solutions for each sample, at room temperature, with a contact time of 24 h. All measurements were conducted in triplicate employing HPLC-UV equipment. Three different natural (raw) and modified clay samples were investigated, which were denominated as follows: AM (with 51% calcite), HJ1 (with 32% kaolinite), and HJ2 (with 32% microcline). The experiments were carried out using three pH ranges: between 3.33 and 4.49 for acid-activated clays, 8.39–9.08 for natural clays, and 9.99–10.18 for base-activated clays. The results indicated that, when low concentrations of the antibiotic were added (from 5 to 20 μmol L−1), more than 98% of SAL was strongly adsorbed by almost all clays, irrespective of the physicochemical and mineralogical composition of the clays or their pH values. When higher SAL concentrations were added (40 and 100 μmol L−1), the adsorption of the antibiotic showed pH-dependent ligand adsorption mechanisms: (i) highly decreased as the pH raised (for the raw and base-activated AM and HJ1 clays), while (ii) slightly decreased as the pH decreased (on the acid-activated clays). Among the adsorption equations tested (Freundlich, Langmuir, and Linear), the Freundlich model was identified as the most suitable for fitting the data corresponding to SAL adsorption onto the studied clays. SAL desorption from clays was consistently below 10% for all the clay samples, especially for the acid-activated clays, due to cation bridging adsorption mechanisms, when the lowest concentration of the antibiotic was added. Additionally, it should be stressed that the desorption values can increase with rising SAL concentrations, but they always remain below 20%. Overall, the clays here investigated (both raw and modified) provide a cost-effective and efficient alternative for the removal of the veterinary anticoccidial antibiotic SAL, with potential positive and practical implications in environmental remediation and antibiotic pollution management, particularly by serving as amendments for contaminated soils to enhance their adsorption capacities against SAL. Additionally, using these clays in water treatment processes could improve the efficiency of mitigating antibiotic contamination in aquatic systems.

G-C3N4-Based Photocatalytic Materials for Converting CO2 Into Energy: A Review.

Environmental pollution management and renewable energy development are humanity's biggest issues in the 21st century. The rise in atmospheric CO2, which has surpassed 400 parts per million, has stimulated research on CO2 reduction and conversion methods. Presently, photocatalytic conversion of CO2 to valuable hydrocarbons enables the transformation of solar energy into chemical energy and offers a novel avenue for energy conversion while regulating the greenhouse effect. This is an ideal strategy for simultaneously addressing environmental issues and the energy crisis. Photocatalysts are essential to photocatalytic processes. Photocatalyst is the core of photocatalytic technology, and graphite carbon nitride (g-C3N4) has attracted much attention because of its nonmetallic characteristics, and it has the characteristics of low cost, tunable electronic structure, easy manufacture and strong reducibility. However, its activity is not only affected by external reaction conditions, but also by the band gap structure, physical and chemical stability, surface morphology and specific surface area of the photocatalyst it. In this paper, the application progress of g-C3N4-based photocatalytic materials in CO2 reduction is reviewed, and the modification strategies of g-C3N4-based catalysts to obtain better catalytic efficiency and selectivity in CO2 photocatalytic reduction are summarized, and the future development of this material is prospected.

Applications of Finite-Time Lyapunov Exponent in detecting Lagrangian Coherent Structures for coastal ocean processes: a review

Addressing the threats of climate change, pollution, and overfishing to marine ecosystems necessitates a deeper understanding of coastal and oceanic fluid dynamics. Within this context, Lagrangian Coherent Structures (LCS) emerge as essential tools for elucidating the complexities of marine fluid dynamics. Methods used to detect LCS include geometric, probabilistic, cluster-based and braid-based approaches. Advancements have been made to employ Finite-time Lyapunov Exponents (FTLE) to detect LCS due to its high efficacy, reliability and simplicity. It has been proven that the FTLE approach has provided invaluable insights into complex oceanic phenomena like shear, confluence, eddy formations, and oceanic fronts, which also enhanced the understanding of tidal-/wind-driven processes. Additionally, FTLE-based LCS were crucial in identifying barriers to contaminant dispersion and assessing pollutant distribution, aiding environmental protection and marine pollution management. FTLE-based LCS has also contributed significantly to understanding ecological interactions and biodiversity in response to environmental issues. This review identifies pressing challenges and future directions of FTLE-based LCS. Among these are the influences of external factors such as river discharges, ice formations, and human activities on ocean currents, which complicate the analysis of ocean fluid dynamics. While 2D FTLE methods have proven effective, their limitations in capturing the full scope of oceanic phenomena, especially in 3D environments, are evident. The advent of 3D LCS analysis has marked progress, yet computational demands and data quality requirements pose significant hurdles. Moreover, LCS extracted from FTLE fields involves establishing an empirical threshold that introduces considerable variability due to human judgement. Future efforts should focus on enhancing computational techniques for 3D analyses, integrating FTLE and LCS into broader environmental models, and leveraging machine learning to standardize LCS detection.

Collaborative management of environmental pollution and carbon emissions drives local green growth: An analysis based on spatial effects

Collaborative management of environmental pollution and carbon emissions (CMPC) has been a major policy instrument to promote Sustainable Development Goals (SDG) in recent years. However, the relationship between the benefits and drawbacks of this environmental management practice for green growth in and around a local area remains to be clarified. Using 30 provinces in China during 2001–2019 as the object of analysis, we assessed the efficiency of local CMPC practices using the nonradial directional distance function (NDDF) model, predicted local green growth using the frontier green complexity index (GCI), and empirically examined the spatial effects, locational heterogeneity, and threshold characteristics of the relationship using the spatial Durbin model and the panel threshold model. Our study finds that although efficient CMPC does drive local green growth, the promotion effect is nonlinear with decreasing marginal effect. This effect is particularly obvious in economically developed regions with higher CMPCs, which will absorb resources from neighboring regions and create a "siphoning" effect. It was found that local financial support and foreign direct investment (FDI) can radiate green growth to neighboring regions; therefore, CMPC practice needs to pay more attention to the effect of joint governance, supplemented by financial and foreign investment policy tools, to better promote the green transformation of local economy.

Assessing Multifaceted Effects of Speed Humps and Bumps: Travel Time, Safety, and Environmental Considerations

This study focuses on investigating the significant impacts of speed breakers on various parameters, including travel time delays, vehicle speeds, fuel consumption, pavement maintenance costs, and vehicular exhaust emissions. Field data was collected and analyzed to assess the effects of different types of traffic calming measures on these parameters. The findings provide valuable insights into the implications of speed breakers on road safety, environmental pollution, and overall road infrastructure management. The results reveal that the implementation of speed humps, speed bumps, and triple bumps effectively slows down vehicles, as evidenced by considerable reductions in the 85th percentile speeds. The reduction percentages were 41.65% for speed humps, 73.52% for speed bumps, and 86.27% for triple bumps. This indicates the effectiveness of these traffic calming measures in improving road safety by reducing vehicle speeds. However, the presence of speed breakers also leads to increased travel time delays. On average, traversing stretches with speed humps, speed bumps, and triple bumps resulted in delays of 9.31, 16.42, and 29.51 seconds, respectively. While the individual delay times may appear relatively short, the cumulative effect of multiple speed obstacles along a road needs to be considered. Another significant impact observed is the increased fuel consumption associated with speed breakers. The study found that for every 100 km of travel, motorcycles and passenger cars consumed approximately 12.07 km and 27.37 km of additional fuel, respectively, when the density of speed breakers was 1.33/km. This translates to a fuel consumption increase of 13.73% for motorcycles and 37.74% for passenger cars. Furthermore, the presence of speed humps was found to contribute to pavement deterioration, as indicated by decreased Pavement Condition Index (PCI) values. The study also revealed that sections with speed humps incurred significantly higher maintenance costs compared to sections without speed humps. The increase in maintenance cost ranged from 100 to 264% across different road sections, with higher traffic volumes leading to greater cost escalation. Additionally, the study confirms that lower vehicle speeds, particularly between 0-15 km/hr, are associated with higher emissions of pollutants, including carbon monoxide (CO) and other pollutants. This highlights the environmental implications of speed breakers and their contribution to urban air pollution. Doi: 10.28991/CEJ-2024-010-07-07 Full Text: PDF

A highly specific and ultrasensitive approach to detect Prymnesium parvum based on RPA-CRISPR-LbaCas12a-LFD system

BackgroundHarmful algal blooms (HABs), caused by the rapid proliferation or aggregation of microorganisms, are catastrophic for the environment. The Prymnesium parvum is a haptophyte algal species that is found worldwide and is responsible for extensive blooms and death of larval amphibians and bivalves, causing serious negative impacts on the ecological environment. For the prevention and management of environmental pollution, it is crucial to explore and develop early detection strategies for HABs on-site using simple methods. The major challenge related to early detection is the accurate and sensitive detection of algae present in low abundance. ResultsHerein, recombinase polymerase amplification (RPA) was combined with clustered regularly interspaced short palindromic repeats and Cas12a protein (CRISPR-LbaCas12a) systems, and the lateral flow dipstick (LFD) was used for the first time for early detection of P. parvum. The internal transcribed spacer (ITS) of P. parvum was selected as the target sequence, and the concentration of single-strand DNA reporters, buffer liquid system, reaction time, and amount of gold particles were optimized. The RPA-CRISPR-LbaCas12a-LFD approach demonstrated highly specificity during experimental testing, with no cross-reaction against different microalgae used as controls. In addition, the lowest detection limit was 10,000 times better than the lowest detection limit of the standalone RPA approach. The feasibility and robustness of this approach were further verified by using the different environmental samples. It also observed that P. parvum are widely distributed in Chinese Sea, but the cell density of P. parvum is relatively low (<0.1 cells/mL). SignificanceThe developed approach has an excellent specificity and offers 10,000 times better sensitivity than the standalone RPA approach. These advantages make this approach suitable for early warning detection and prevention of HAB events in environmental water. Also, the outcomes of this study could promote a shift from traditional laboratory-based detection to on-site monitoring, facilitating early warning against HABs.

Interpretable Time Series Models for Wastewater Modeling in Combined Sewer Overflows

Climate change poses increasingly complex challenges to our society. Extreme weather events such as floods, wild fires or droughts are becoming more frequent, spontaneous and dificult to foresee or counteract. In this work we specifically address the problem of sewage water polluting surface water bodies after spilling over from rain tanks as a consequence of heavy rain events. We investigate to what extent state-of-the-art interpretable time series models can help predict such critical water level points, so that the excess can promptly be redistributed across the sewage network. Our results indicate that modern time series models can contribute to better waste water management and prevention of environmental pollution from sewer systems. All the code and experiments can be found in our repository:https://github.com/TeodorChiaburu/RIWWERTimeSeries.

GIS platform ecological water environment pollution automatic monitoring and analysis

This paper summarizes the application of GIS platform for automatic monitoring of ecological water environmental pollution. Firstly, the function of GIS platform and its combination with environmental monitoring are briefly introduced, and then the pollution status of ecological water environment is analyzed, including the source of pollution, classification and the impact on environment and human beings. This paper focuses on the application of GIS in data collection and integration, spatial distribution analysis of pollutants, simulation and prediction of pollution diffusion, design and implementation of monitoring system, etc. Through the introduction of GIS technology, the monitoring and management of ecological water environmental pollution can be realized more effectively, which provides a strong support for environmental protection.

Efficient removal of environmental pollutants by green synthesized metal nanoparticles of Clitoria ternatea

Prevention and management of water pollution are becoming a great challenge in the present scenario. Different conventional methods like carbon adsorption, ion exchange, chemical precipitation, evaporation, and biological treatments remove water pollutants. Nowadays, the requirement for effective, non-toxic and safe waste management strategies is very high. Nanomaterials have been explored in various fields due to their unique characteristics. Green synthesis of nanomaterial is becoming more popular due to their safety, non-toxicity, and ease of scale-up technology. Metal nanoparticles can be synthesized using a green synthesis method using biological sources provided by eco-friendly, non-hazardous nanomaterials with superior properties to bulk metals. Hence, this study has designed a green synthesis of magnetic (cobalt oxide) and noble (gold) nanoparticles from the fresh flowers of Clitoria ternatea. The flavonoids and polyphenols in the extract decreased the energy band gap of cobalt oxide and gold nanoparticles; hence, the capping of the natural constituents in Clitoria ternatea helped form stable metal nanoparticles. The cobalt oxide and gold nanoparticles are evaluated for their potential for eliminating organic pollutants from industrial effluent. The novelty of this present work represents the application of cobalt oxide nanoparticles in the removal of organic pollutants and a comparative study of the catalytic behaviour of both metal nanoparticles. The degradation of bromophenol blue, bromocresol green, and 4–nitrophenol in the presence of gold nanoparticles was completed in 120, 45, and 20 min with rate constants of 3.7 × 10−3/min, 6.9 × 10−3/min, and 16.5 × 10−3/min, respectively. Similarly, the photocatalysis of bromophenol blue, bromocresol green, and 4–nitrophenol in the presence of cobalt oxide nanoparticles was achieved in 60, 90, and 40 min with rate constants of 2.3 × 10−3/min, 1.8 × 10−3/min, and 1.7 × 10−3/min, respectively. The coefficient of correlation (R2) values justify that the degradation of organic pollutants follows first-order kinetics. The significance of the study is to develop green nanomaterials that can be used efficiently to remove organic pollutants in wastewater using a cost-effective method with minimal toxicity to aquatic animals. It has proved to be useful in environmental pollution management.

Research on the Impact of Digital Economy on Environmental Pollution Management – A Quasi-Natural Experiment from the “Broadband China” Pilot Policy

Research on the Impact of Digital Economy on Environmental Pollution Management – A Quasi-Natural Experiment from the “Broadband China” Pilot Policy

Novel methods to monitor the biodegradation of polylactic acid (PLA) by Amycolatopsis orientalis and Amycolatopsis thailandensis.

Plastics are essential in modern life, but their conventional production is problematic due to environmental pollution and waste management issues. Polylactic acid (PLA) is a widely used bioplastic that is bio-based and biodegradable, making it a key player in the bioeconomy. PLA has been proven to be degradable in various settings, including aqueous, soil, and compost environments. However, monitoring and optimizing PLA biodegradation remains challenging. This study proposes methods to improve the quantification of PLA biodegradation by Amycolatopsis spp. Ultrasound treatments (10s) significantly improved the enumeration of viable Amycolatopsis cells by breaking the pellets into quantifiable individual cells. A separation technique combining ultrasound (120s) and 40μm cell strainers effectively isolated PLA particles from biomass to quantify PLA weight loss. This enabled the monitoring of PLA biofragmentation. Finally, CO2 production was measured according to ISO 14852 to quantify mineralization. Integrating these methods provides an improved quantification for PLA biodegradation along its different stages. In a case study, this led to the construction of a carbon balance where 85.1% of initial carbon content was successfully tracked. The developed techniques for monitoring of PLA biodegradation are essential to design future waste management strategies for biodegradable plastics.

Investigating pharmaceuticals and personal care products in Musi River: A exploratory study in Hyderabad, India

This study investigated the presence of 46 pharmaceuticals and personal care products (PPCPs), including antibiotics, anti-inflammatory, antifungal, surfactant, antidepressants, and anticonvulsants, in wastewater samples collected from 11 sites along the Musi River in Hyderabad, India. These samples were obtained from diverse points along the river, representing urban, industrial, and hospital-related sources. This strategic selection ensures a comprehensive assessment of water quality, covering areas of varying population density and industrial activity. The samples were extracted for PPCPs using solid-phase extraction and quantified using ultra-performance liquid chromatography-tandem mass spectrometry in multiple reaction mode. Of the 46, 16 target analytes were detected at concentrations ranging from 0.01 ± 0.001 μg/L (for fluoxetine) to 19.295 ± 4.16 μg/L (for ciprofloxacin). An ecological risk assessment was conducted in terms of risk quotient (RQ) for acute and chronic effects for three trophic levels of aquatic organisms (algae, fish, and invertebrates) for each detected contaminant at every site. In the case of acute toxicity, high RQ values (>>1) were obtained for the antibiotics, naproxen, diclofenac, caffeine, fluconazole, propyl paraben, fluoxetine, and benzalkonium chloride C-12. In the case of chronic toxicity, high RQ values (>>1) were estimated for some of the commonly used PPCPs. This study aimed to investigate the contamination levels of PPCPs in various wastewater discharge points along the Musi River in Hyderabad, a densely populated urban center with a population density of up to 45,000/sq km. Additionally, the research conducted an ecotoxicological risk assessment to evaluate the risk posed by these pollutants to three trophic levels (algae, fish, and invertebrates) using RQ analysis. Notably, this study represents the first attempt to conduct such an assessment in this region, providing valuable insights into environmental health and pollution management.

Artificial intelligence and IoT driven technologies for environmental pollution monitoring and management

Detecting hazardous substances in the environment is crucial for protecting human wellbeing and ecosystems. As technology continues to advance, artificial intelligence (AI) has emerged as a promising tool for creating sensors that can effectively detect and analyze these hazardous substances. The increasing advancements in information technology have led to a growing interest in utilizing this technology for environmental pollution detection. AI-driven sensor systems, AI and Internet of Things (IoT) can be efficiently used for environmental monitoring, such as those for detecting air pollutants, water contaminants, and soil toxins. With the increasing concerns about the detrimental impact of legacy and emerging hazardous substances on ecosystems and human health, it is necessary to develop advanced monitoring systems that can efficiently detect, analyze, and respond to potential risks. Therefore, this review aims to explore recent advancements in using AI, sensors and IOTs for environmental pollution monitoring, taking into account the complexities of predicting and tracking pollution changes due to the dynamic nature of the environment. Integrating machine learning (ML) methods has the potential to revolutionize environmental science, but it also poses challenges. Important considerations include balancing model performance and interpretability, understanding ML model requirements, selecting appropriate models, and addressing concerns related to data sharing. Through examining these issues, this study seeks to highlight the latest trends in leveraging AI and IOT for environmental pollution monitoring.

Investigating the evolution of reactive species in the CuO-mediated peroxymonosulfate activation process

The utilization of copper oxide (CuO) as a catalyst in the peroxymonosulfate (PMS) activation process holds great promise for effectively degrading aqueous organic pollutants, while the relevant mechanism remains inadequately understood. In this study, we delve into the evolution pathways of reactive species in the CuO/PMS system through a comprehensive series of experimental analyses. Our findings indicate that various reactive species are generated in the CuO/PMS system with the specific sequence, where the decomposition of surface Cu(II)-OOSO3− leads to the formation of surface Cu(III) species, which are responsible for the subsequent generation of HO•. The reactivity of these reactive species and the sequence of their generation explain the distinct oxidation behaviors of pollutants with different values of ionization potential (IP). In addition, singlet oxygen (1O2) may be produced during the PMS activation process, while its involvement in the oxidation of substrates is deemed negligible. This investigation presents a novel perspective, enhancing our comprehension of the mechanism underlying transition metal-mediated PMS activation processes. Environmental implicationThe removal of refractory organic contaminations in water constitutes a fundamental concern within the realm of environmental pollution management. Peroxymonosulfate activation induced by transition metal oxides has garnered significant recognition as a promising technological approach for the degradation of aqueous organic contaminants, while the underlying mechanism remains enigmatic. In this study, we systematically investigate the evolution pathways of reactive species in the CuO/peroxymonosulfate system to reveal the mystery of the reaction mechanism between CuO and peroxymonosulfate. The outcomes of our study contribute to enhancing the practical applicability of transition metal-triggered PMS activation processes.