Industrial Pollution Research Articles

Exploration of catalytic activities of Caesalpinia bonduc (L.) Roxb. ash on green-oxidation of small organic substrates: an effective way to reduce the use of harsh chemicals

In this modern era, large-scale industrial use of harsh chemicals is an alarming problem as it can cause serious environmental damage. Among industrial pollutants, harsh chemical oxidizing agents are one of the leading chemicals that can cause severe environmental threats. These harmful harsh chemicals are practically omnipresent in industries and are used to oxidize numerous small organic substrates. Replacement of these harsh chemicals with some plant-based products might be helpful to safeguard our environmental damage. In this study, Caesalpinia bonduc (L.) Roxb. ash is used as a catalyst to activate aerial oxygen for promoting the oxidation of 3,5-di-tert-butylcatechol (3,5-DTBC) and o-aminophenol (OAPH) like small organic substrates. Detailed kinetic studies for these catalytic oxidation reactions have been performed spectrophotometrically, which confirms that catalytic reactions follow Michaelis–Menten kinetics. Several enzyme-kinetics plots such as the Michaelis–Menten plot, Lineweaver–Burk plot, Hanes–Woolf plot, and Eadie–Hofstee plot have been used to determine the maximum rate achieved by the reaction (Vmax) and Michaelis constant (KM) like kinetic parameters. The average values of Vmax and KM for catalytic oxidation of 3,5-DTBC are (8.6669 ± 0.6519) × 10–5 M S−1 and (21.4289 ± 0.4741) × 10–4 M, respectively. Similarly, the average values of Vmax and KM for catalytic oxidation of OAPH are (2.1781 ± 0.2071) × 10–5 M S−1 and (20.1062 ± 1.2690) × 10–3 M, respectively. The mineral composition of Caesalpinia bonduc (L.) Roxb. ash has also been evaluated by using inductively coupled plasma optical emission spectroscopy (ICP-OES).Graphical

Detection of Chemical Contaminants in Water for Irrigation Systems: A Systematic Review

Water pollution is a detrimental issue that occurs when there are bad changes in water quality parameters. It directly disrupts water usage and poses a danger to society, environment, economy, and agriculture. Water quality should be monitored to alert authorities on water pollution, so that action can be taken quickly. Improper water management pollutes rivers and lakes in Malaysia such as Klang River, Semenyih River, Kim Kim River, Slim River, and others. Various techniques are introduced to detect contaminants in water such as electronic sensors, biosensor approaches, laboratory analysis, and optical techniques. The functionality tool varies depending on the specific contaminants or parameters that are targeted, and the resources allocated. Several common contaminant types are found in polluted water including heavy metals, organic and inorganic chemicals, industrial pollutants, suspended solids, and others. The objective of the review is to study various non-optical and optical contaminant detection methods in water to identify the strengths and weaknesses of the methods. In this review, water pollution problems mainly due to the agricultural sector in several countries are discussed. Besides, conventional, and modern methods are compared in terms of parameters, complexity, and reliability. We believe that conventional methods are costly and complex, whereas modern methods are also expensive but simpler with real-time detection. Recent contaminant detection methods in water are also reviewed to study any loopholes in the latest methods. We found that the spectroscopy method based on light propagation theory is suitable and one of the promising methods for chemical contaminants detection for water quality analysis. This method offers fast analysis time, high sensitivity detection, non-invasive analysis, provides reliable data, and others. Undoubtedly, some contaminants in water can be challenging to be identified rapidly and confidently even though there are numerous tools and techniques available for water quality analysis. Thus, the review is important to compare previous methods and to improve current chemical contaminants detection analysis in terms of reliability with a minimum operating system and cost effectiveness.

From policy to practice: Enhancing enterprise productivity through energy transition initiatives

This study investigates the impact of energy transition policies on enterprise Total Factor Productivity (TFP) in China, utilizing a quasi-natural experiment with New Energy Demonstration Cities (NEDC) and panel data from Chinese cities and manufacturing enterprises (2011–2021). Using the difference-in-difference method, the analysis reveals that energy transition policies positively affect enterprise TFP, especially in non-resource-based cities, less polluting industries, and enterprises with strong Environmental, Social, and Governance (ESG) performance. The research identifies two key mechanisms driving productivity gains: green innovation and reduced financing costs. Additionally, it examines the role of Environmental Regulation (ER), finding that formal ER can mitigate the positive impact of energy transition policies on enterprise TFP, while informal ER has a reinforcing effect. This study contributes to the broader understanding of the policy implications of energy transitions, highlighting the nuanced effects of different regulatory environments on enterprise development and providing evidence on sustainable growth in the context of climate imperatives. The findings offer insights into the complex dynamics between energy policy and industrial productivity, relevant to policymakers, industries, and stakeholders interested in the interplay between energy transition and economic sustainability.

Ecological Imperialism and Indigenous Resistance: A Postcolonial Ecocritical Analysis of Selected Indian English Novels

This paper examines the representation of environmental degradation and critique of postcolonial development paradigms in two contemporary Indian English novels: Amitav Ghosh's The Hungry Tide (2004) and Indra Sinha's Animal's People (2007). Employing a postcolonial ecocritical framework, the study analyzes how these works articulate the environmental consequences of development in India, challenge Western models of progress, and highlight indigenous environmental perspectives. The research reveals that both novels effectively portray multi-faceted environmental issues, from biodiversity loss in the Sundarbans to long-term impacts of industrial pollution in urban areas. They offer pointed critiques of anthropocentric development models while presenting indigenous knowledge as a valuable alternative for sustainable human-nature relationships. The study contributes to postcolonial ecocriticism by demonstrating how Indian English literature engages with and critiques the environmental costs of postcolonial development. It underscores the potential of literature to provide nuanced, culturally-specific insights into complex environmental issues, challenging dominant narratives and proposing alternative visions of progress.

Chromium Selectively Accumulates in the Rat Hippocampus after 90 Days of Exposure to Cr(VI) in Drinking Water and Induces Age- and Sex-Dependent Metal Dyshomeostasis.

Hexavalent chromium (Cr[VI]) is a widespread environmental pollutant in air and water that is primarily attributed to industrial pollution. The current maximum contaminant levels (MCLs) for drinking water from the World Health Organization and the U.S. Environmental Protection Agency (0.05 and 0.1 mg/L, respectively) were set based on contact dermatitis and warrant further toxicological investigation. While Cr(VI) is neurotoxic and accumulates in the brain, most animal studies only report whole-brain Cr, leaving large knowledge gaps. Few studies consider differences between ages or sexes, and fewer consider essential metal dyshomeostasis. We sought to investigate where Cr accumulates in the brain, considering sex and age differences, following a 90-day drinking water exposure to current MCLs. Here, we report Cr levels in six brain regions of rats exposed to drinking water Cr(VI). We observed Cr only accumulated in the hippocampus, and only in older females. We further assessed changes to essential metals in the hippocampus, observing opposite effects across sexes and between young rats compared to older rats. In sum, our data indicate drinking water Cr(VI) selectively targeted the hippocampus, with geriatric females accumulating the most Cr, and induced significant essential metal dyshomeostasis even in tissues lacking evident Cr accumulation.

Using synchrotron based ATR-FTIR, EXAFS, and XRF to characterize the chemical compositions of TSP in industrial estate area

In Thailand, the Map Ta Phut Industrial Estate (MTPIE), a prominent industrial hub, has substantial environmental and health issues caused by industrial pollution. This study uses advanced synchrotron-based techniques, such as Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR), Extended X-ray Absorption Fine Structure (EXAFS), and X-ray Fluorescence (XRF), to fully examine the chemical make-up of total suspended particulate (TSP) in the given area. Notable findings include the detection of remarkably high enrichment factors for magnesium and sulfur, indicating the presence of industrial operations. Additionally, we found that magnetite, which accounts for an average of 40 % of the total iron oxides in the samples, is the main iron oxide. The study also highlights about how calcium carbonate and different organic functional groups are found in large amounts, which shows that industrial emissions and natural sources are connected in a complex way. The findings underscore the susceptibility of children to TSP exposure, revealing increased rates of inhalation and significant health hazards. In order to safeguard public health in industrial areas such as MTPIE, it is imperative to implement more sophisticated pollution control techniques and maintain ongoing environmental monitoring.

Thermal equalization design for the battery energy storage system (BESS) of a fully electric ship

The adoption of fully electric ships represents a significant step forward in addressing the environmental challenges of climate change and pollution in the shipping industry. This research details the optimized design of a battery energy storage system (BESS) and its air-cooling thermal management system for a 2000-ton bulk cargo ship. In comparison to the conventional flow splitter (FS-I), which divides airflow into 8 transverse branches, two novel designs—FS-II (dividing airflow into 2, 4, and 8 branches) and FS-III (dividing into 2, 4, 8, and 16 longitudinal and transverse branches)—were introduced. FS-II improves flow uniformity from 0.42 (FS-I) to 0.86 but results in an increased pressure drop up to 881.47 Pa. FS-III further enhances flow uniformity to 0.97 while reducing pressure drop to 191.99 Pa. The thermal performance of battery boxes with FS-I, FS-II, and FS-III was evaluated under extreme conditions and a complete voyage cycle. FS-III exhibited superior thermal regulation, maintaining Tmax consistently below 33.9°C during the entire voyage. These findings provide valuable insights for designing thermal management systems in electric ships.

Toxicity prediction based on PAHs in fruits and vegetables − A machine learning approach

This study analyses the toxicity levels of fruits and vegetables by identifying the presence of polycyclic aromatic hydrocarbons (PAHs), particularly in areas affected by industrial and vehicle pollution. The presence of PAHs on plant surfaces, caused mostly by air pollution particulate matter, raises serious concerns about the nutritional value of these foods. Traditional techniques for measuring PAH levels include Gas Chromatography/Mass Spectrometry (GC/MS) and High-Performance Liquid Chromatography (HPLC). Although successful, these procedures are expensive and time-consuming. Toxicity detection is often based on expert knowledge or experimental analysis relative to the European Food Safety Authority’s (EFSA) limits. In response to these challenges, this work uses artificial intelligence approaches to determine toxicity levels using 16 PAHs. Data on PAH concentrations in fruits and vegetables were collected from a variety of sources, classified as safe or harmful, and verified using statistical analysis. The validated dataset was subsequently classified using several machine learning techniques. The level of toxicity is scaled and compared to the expected outputs using the results obtained from the neural network. An experimental investigation validates the promising findings of level of toxicity classification using artificial intelligence approaches, which are then evaluated statistically. The study shows that machine learning algorithms obtained classification accuracies of over 90 %, including the evaluation of harmfulness. The accuracy for the two-class (Safe and Unsafe) category was 98.27 %, while the accuracy for the four-class (No harm, Low harm, Moderate Harm, and Severe harm) category was 98.68 %. These findings highlight the potential of artificial intelligence in improving food safety and public health by presenting an innovative multidisciplinary method to assessing the impacts of environmental pollutants on the edibility of fruits and vegetables in the context of climate change.

How does the state capital participation optimize the corporate green innovation structure: Evidence from listed private firms

This study draws on data from A-share listed businesses in China from 2011 to 2022. The presence of state capital significantly increases business participation in green innovation and improves its structure, transforming strategic innovation into meaningful innovation. The mechanism test demonstrates that using information governance, resource support, and corporate governance, including state capital participation, can enhance companies' green innovation structure. Heterogeneity research reveals that companies' industry pollution levels and greening goals determine whether state capital participation affects corporate green innovation. This link is critical for companies with fewer greening goals and lower pollution levels.

Recycling and breakdown photodegradation processes cost of BEN-TiQD via different photodegradation processes of Brilliant Green S dye and industrial effluents

The development of efficient photocatalytic materials for environmental remediation is of paramount importance. This study reports the synthesis and characterization of novel bentonite-titanium dioxide quantum dot (BEN-TiQD) nanocomposites for the photodegradation of Brilliant Green S, a prominent industrial pollutant. The nanocomposites were prepared via a co-precipitation method, ensuring uniform dispersion of TiO2 nanoparticles on the bentonite (BEN) surface, followed by heat treatment to achieve optimal crystallinity and surface properties. Comprehensive characterization techniques, including FTIR, XRD, SEM, BET surface area analysis, and optical spectroscopy, were employed to elucidate the structural, morphological, and photophysical characteristics of the composites. The BEN-TiQD nanocomposites exhibited an augmented surface area of 277.75 m2/g, surpassing BEN alone by more than threefold, and an intermediate bandgap of 3.17 eV, rendering them suitable for visible light absorption and photocatalytic applications. The photocatalytic efficacy of BEN-TiQD was evaluated by monitoring the degradation of Brilliant Green S under various operational parameters. The nanocomposites demonstrated superior photocatalytic performance, with a rate constant of 22.24 × 10−3 s−1, significantly only lower than TiQD (28.32 × 10−3 s−1) by nearly about 21.5 %. The enhanced activity was attributed to the quantum size effect of the incorporated TiO2 quantum dots, optimizing light absorption and charge separation. Mechanistic studies revealed the generation of reactive oxygen species, particularly hydroxyl radicals, as the primary drivers of dye degradation. The reusability of BEN-TiQD was evaluated through multiple photocatalytic cycles, exhibiting remarkable stability for up to six consecutive cycles. However, a gradual decline in photodegradation efficiency was observed after prolonged sunlight exposure, likely due to particle agglomeration and reduced surface area. This research contributes to the field of environmental remediation by developing a novel composite material that synergistically combines the adsorptive properties of BEN with the photocatalytic capabilities of titanium dioxide quantum dots. The findings pave the way for further exploration and optimization of these composites for sustainable and efficient treatment of industrial dyes and effluents, addressing critical environmental challenges.

Sustainable degradation of AZO dyes using green synthesized lead nanoparticles and solar energy

This study explores the green synthesis of lead nanoparticles and their application in degrading the AZO dye Nicoracine under solar irradiation. UV-Visible spectroscopy confirmed nanoparticle formation with a peak at 248 nm, indicating SPR. FTIR revealed functional groups from plant extracts aiding stabilization. XRD analysis showed a crystalline structure, while SEM and AFM indicated irregular shape and rough surface. The nanoparticles exhibited significant catalytic activity, enhancing Nicoracine degradation via solar light, facilitated by ROS generation. Kinetic analysis suggested a pseudo-first-order reaction model. This green synthesis method offers a sustainable solution for wastewater treatment and industrial pollution mitigation.

AUTONOMOUS MONITORING ROBOT SYSTEM THAT MEASURES AIR POLLUTION

Air pollution poses a significant threat to human health, ecosystems, and climate stability, necessitating effective monitoring and control measures. Traditional air quality monitoring stations, while accurate, are static, expensive, and limited in coverage. The Autonomous Monitoring Robot System (AMRS) provides a dynamic solution, offering real-time air quality monitoring through mobile robotic platforms. Equipped with advanced sensors, these robots can measure various pollutants, such as particulate matter (PM), nitrogen dioxide (NO2), carbon monoxide (CO), and volatile organic compounds (VOCs), across large and complex areas. By employing AI-based navigation and mapping technologies, AMRS can autonomously traverse urban and industrial environments, collecting high-resolution pollution data and creating real-time air quality maps. This approach allows for better spatial coverage, cost-efficiency, and access to hard-to-reach locations compared to traditional methods. The system can be deployed in diverse use cases, including urban air quality monitoring, industrial pollution control, and disaster management. While challenges such as battery life, sensor calibration, and data processing remain, AMRS represents a promising technological advancement in environmental monitoring and pollution control.

Industrial Pollution in Patna City : Physico-Chemical Analysis of Effluents and Their Effects on Water and Soil

Industrial pollution is a significant concern in Patna City, with rapid urbanization and industrial growth contributing to the degradation of natural resources. This study focuses on the physico-chemical analysis of industrial effluents and their effects on the water and soil quality in and around Patna. Effluents from various industries, including textile, sugar, rice mills, and small-scale manufacturing units, are released into nearby water bodies and soils, posing serious environmental risks. The research aims to assess the levels of pollutants by analysing key physico-chemical parameters such as pH, total dissolved solids (TDS), biochemical oxygen demand (BOD), chemical oxygen demand (COD), heavy metals (lead, chromium, cadmium), and organic contaminants in industrial effluents. Samples were collected from various locations near industrial zones and water bodies. The study also evaluated the impact of these pollutants on soil and groundwater quality. It was found that prolonged exposure to industrial effluents led to the accumulation of toxic metals in soil, reducing its fertility and altering its structure. Additionally, groundwater sources were contaminated with hazardous chemicals, making them unfit for drinking and irrigation purposes. The implementation of advanced treatment technologies and sustainable industrial practices is crucial for safeguarding the environment and public health in Patna City.

Sustainable waste management practices in the informal sector: Towards industrial symbiosis

Industrial pollution is considered to be routed in the waste and byproducts of the production process. Traditional pollution control approaches try to eliminate and/or treat the pollutants which are often technically complicated and expensive. In this regard, industrial ecology and industrial symbiosis have emerged as effective strategy to eliminate industrial pollution. This principle requires the generated waste/by-products absorbed in the same or other industrial process cycles and thus the material cycle remains closed. Industrial pollution appears as a big problem in the global south countries, where industrialization is considered as the main thrust of economic development. Usually, in such countries formal pollution control approaches are primarily directed to the formal sectors (such as state owned and legally registered industries), informal sectors are often left behind. Although the role of informal sector is increasingly being recognized for sustainable development, their significance in pollution abatement is a less discussed topic. This article attempts to investigate the informal industrial sector in Dhaka, Bangladesh with empirical evidence. Adopting a qualitative approach with field investigation of the informal industries and detail interviews, this study identified that the informal industries are closely linked in clusters according to the manufacturing process and continue material/byproduct/waster exchange primarily from the need to minimize cost. The studied patterns of waste management practice indicate existence of industrial symbiosis without adequate academic/technical knowledge and designed efforts. This suggests that the informal sector can meaningfully contribute to sustainable development offering insights for the application of similar approaches in the formal sector.

Klímasemleges energiatermelés – Kilátások Magyarországon 2024-ben

In order to protect our planet's climate, it is important to reduce humanity's carbon dioxide emissions significantly. The energy sector is one of the most polluting industries, but the rapid spread of renewable energy sources over the past 20 years can significantly contribute to the reduction of carbon dioxide emissions. The study examined which energy sources are used to produce electricity in Hungary, and how the proportion of renewable energy has developed in recent years compared to more polluting energy production methods. There is a strong negative exponential relationship between the share of renewable energy and the carbon dioxide emissions of the energy sector, based on which the carbon dioxide emissions of the energy sector can be significantly reduced by increasing the share of renewable energy. A forecast was made for the renewable share based on past data and the revised state of the National Energy and Climate Plan in 2023, and a linear forecast was made for the energy sector's emissions. There is a strong exponential relationship between the data series. Both models examined in the study had strong explanatory power, but neither model supports the possibility of climate-neutral energy production by 2030.

Use of Machine Learning and Indexing Techniques for Identifying Industrial Pollutant Sources: A Case Study of the Lower Kelani River Basin, Sri Lanka

With the recent acceleration in urbanisation and industrialisation, industrial pollution has severely impacted inland water bodies and ecosystem services globally, causing significant restrains to freshwater availability and myriad damages to benthic species. The Kelani River Basin in Sri Lanka, covering only ~3.6% of the land but hosting over a quarter of its population and many industrial zones, is identified as the most polluted watershed in the country. This study used unsupervised learning (UL) and an indexing approach to identify potential industrial pollutant sources along the Kelani River. The UL results were compared with those obtained from a novel Industrial Pollution Index (IPI). Three latent variables related to industrial pollution were identified via Factor Analysis of monthly water quality data from 17 monitoring stations from 2016 to 2020. The developed IPI was validated using a Long Short-Term Memory Artificial Neural Network model (NSE = 0.98, RMSE = 0.81), identifying Cd, Zn, and Fe as the primary parameters influencing river pollution status. The UL method identified five stations with elevated concentrations for the developed latent variables, and the IPI confirmed four of them. Based on the findings from both methods, the industrial zones along the Kelani River have emerged as a likely source of pollution in the river’s water. The results suggest that the proposed method effectively identifies industrial pollution sources, offering a scalable methodology for other river basins to ensure sustainable water resource management.

Tracking progress toward Sustainable Development Goal 12 using Canadian industrial pollutants in waste

Canada's National Pollutant Release Inventory (NPRI) is part of a global network of over 50 national Pollutant Release and Transfer Registries. These registries track industrial pollutant releases into the air, water, and land, and transfer and disposal of pollutants to various waste management practices. Despite the NPRI being a long-standing public dataset that can speak directly to Canadian progress on various reductions in chemical releases and waste generation sought by the United Nations’ Sustainable Development Goal (SDG) 12, its data on pollutant transfers and disposals is so far overlooked in tracking SDG progress. This study aims to uncover the potential of this waste-related data to provide meaningful measures of progress toward SDG 12 using two case studies (on the Basel Convention on the Control of Transboundary Movements of Hazardous Wastes and their Disposal and the Minamata Convention on Mercury) and a framework by the Organization for Economic Cooperation and Development to do so. The findings challenge the premise that progress in the current SDG 12.4 indicator (number of parties to international chemical agreements that transmit required information) also leads to progress on overall SDG 12.4 waste-related aims. This analysis sets a precedent for using publicly available PRTR data from any country to monitor progress toward SDG 12 waste-related objectives, opening up new possibilities for more accurate global tracking of this SDG.

The Impact of Fintech on Urban Industrial Pollution: A Quasi-Natural Experiment Based on Pilot Policies Integrating Technology and Finance

Against the backdrop of accelerating global industrialization and the increasingly severe issue of industrial pollution, reducing industrial pollution has become a key topic in sustainable development. Fintech, with its advantages of high efficiency, low cost, inclusiveness, and ability to reduce information asymmetry, has gained widespread attention and rapid development globally. Given the swift advancement of fintech, it is important to study its impact on urban industrial pollution. This paper, based on data from 281 Chinese cities between 2007 and 2021, selects cities included in the 2011 and 2016 "Technology and Finance Integration" pilot policies as the treatment group. Using a difference-in-differences model, it systematically analyzes the relationship between the development of fintech and urban industrial pollution. The study further explores the variations in fintech's impact on industrial pollution across different regions and types of cities, revealing the potential of fintech in promoting green transformation and reducing industrial pollution.

Effect of different set of electrodes for degradation of distillery spent wash promoted by electrocoagulation

ABSTRACT One of the most polluting industries in the world is the distillery industry, which produces strong distillery wastewater that has an adverse impact on the environment since it contains high organic matter. Using various combinations of electrodes in the electrocoagulation process, to examined the operational variables, viz., total dissolved solids (TDS), electrical conductivity (EC) and turbidity from the distillery spent wash. With a constant pH of 7, an agitation speed of 500 RPM, and optimizing the operating parameters, viz., varying the electrode distance (2, 3, 4, 5, and 6 cm), voltage (5–25 volts) current density of 1.5 A/cm2, and electrolysis time (30–150 min). Aluminium (Al) electrodes were observed to perform slightly better than iron (Fe) and zinc (Zn) electrodes, with punched Al electrodes outperforming plane Al electrodes. The highest removal efficiencies were achieved with punched Al electrodes in an optimized condition of voltage 25 volts, electrode distance (2 cm), and electrolysis time 150 minutes, with removal efficiencies of 91% for TDS, 92% for EC and 92% for turbidity. This study highlights the potential of electrocoagulation with optimized parameters for improving the purification of distillery spent wash and enhancing the removal of TDS, EC, and turbidity.

Effect of chemical pollution on the fertility of male rodents from natural populations: сomparing the response of sperm morphology, motility, and concentration

The results of studies of epididymal spermatozoa of three species of rodents (bank vole Clethrionomys glareolus, northern red-backed vole Cl. rutilus, and herb field mouse Sylvaemus uralensis) living under long-term exposure to atmospheric emissions from two large copper smelters in the Middle Urals are summarized (Middle Ural and Kirovgrad copper smelters). The impact of pollution (including at the individual level on the accumulation of Cu, Zn, Pb, and Cd in the liver) was assessed for indicators characterizing the quality of sperm from different aspects: morphology (proportion of cells with head and tail defects), motility (proportion of motile cells, velocity, and straightness of movement) and concentration. Sperm motility responds to pollution: in impact zones, the proportion of motile cells and their velocity were lower than in background zones. The occurrence of abnormal cells and sperm concentration were not statistically significantly different between impact and background zones. The reaction of sperm to chemical pollution is species-specific: voles react more strongly than the herb field mouse. The consistency of changes in sperm parameters (in the direction of their deterioration) in response to increased pollution was found only in the bank vole. Effect sizes for sperm parameters are much smaller compared to those for liver Cd accumulation and animal abundance. In general, the reaction of sperm to pollution turned out to be weak, none of the studied indicators can be a reliable marker of industrial pollution.