Industrial Sites Research Articles

Опыт интеграции эвакуированного промышленного оборудования в экономику уральского тыла (осень 1941 – весна 1942 гг.)

The historical experience of industrial evacuation and integration of the arrived equipment into the economy of the rear territories is of undoubted research interest. A historiographical review has shown that the problem of placing and commissioning relocated industrial equipment in evacuation sites has not been a topic of special research. The study was conducted on the basis of archival documents of the Evacuation Council, Gosplan, correspondence of People's Commissariats with the Communist Party and state organizations. The article analyzes the problems faced by the industry of the rear regions in 1941-the spring of 1942 in connection with the arrival of factories and equipment from the front line to the evacuation destinations. During the study, the following problems were identified: incompleteness and sometimes poor safety of the arrived equipment, the unavailability of industrial sites to receive large enterprises due to insufficient industrial infrastructure, and lack of labor for unloading and installing the machinery. It was noted that during the restoration of arrived enterprises, priority was given to the defense industry plants. The article discusses ways to overcome the difficulties that arose: a mechanism was established for obtaining resources stored in evacuation bases; industrial construction was carried out both on the territories of existing factories and on sites occupied by educational, commercial, cultural, etc. organizations; intersectoral cooperation was arranged, involving the exchange of raw materials and equipment; nonstandard management solutions were applied. It is concluded that with a tightly centralized vertical of power, the start-up of evacuated industrial equipment was more successful where the directors’ corps and regional leaders showed independence and initiative.

Selective measurement of Cl2 and HCl based on dopant-assisted negative photoionization ion mobility spectrometry combined with semiconductor cooling.

This study presents an efficient approach for the precise detection of chlorine gas (Cl2) and hydrogen chloride (HCl), harmful pollutants frequently emitted from chlor-alkali and various industrial processes. These substances, even in trace amounts, pose significant health risks. Ion mobility spectrometry (IMS), known for its sensitivity in pollutant detection, traditionally struggles to differentiate between Cl2 and HCl due to the similarity of their product ions, Cl-. To overcome this limitation, we introduce a novel technique combining dopant-assisted negative photoionization ion mobility spectrometry (DANP-IMS) with an automatic semiconductor cooling system. This unique combination utilizes the differential cryogenic removal efficiencies of Cl2 and HCl to segregate these gases before analysis. By applying DANP-IMS, we achieved selective measurement of Cl- ion signal intensities under both standard and cryogenic conditions, facilitating the accurate quantification of total chlorine and Cl2 levels. We then determined HCl concentrations by deducting the Cl2 signal from the total chlorine readings. Our approach demonstrated detection limits of 2.0 parts per billion (ppb) for Cl2 and 0.8 ppb for HCl, across a linear detection range of 0-200 ppb. Moreover, our method's capability for real-time atmospheric monitoring of Cl2 and HCl near industrial sites underscores its utility for environmental monitoring, offering a robust solution for the separate and precise measurement of these pollutants.

Research on Deep learning based Ultrasonic Image Learning to Develop a Gas Leak Detection Model

If a gas leak occurs in an industrial area, identifying the location of the gas leak and predicting the scale of the accident are challenging owing to the invisible nature of the gas. In this study, we developed a deep learning-based gas leak detection model that can obtain not only the gas leak status, but also the gas leak location and flow rate information, by using technology to visualize the ultrasonic waves generated during gas leaks. Research methods are broadly categorized into data collection and model learning methods. First, data was collected using an ultrasonic camera to capture ultrasonic images at different measurement distances (1 and 3 m) and gas leak flow rates (0-8 L/min). YOLO (You Only Look Once) was used for image learning, and the model was trained after setting the class according to the gas-leak flow range. The clarity of the collected ultrasonic images decreased as the measurement distance increased. In addition, there was little difference between the images for each leakage flow rate, posing challenges in distinguishing them with the naked eye. However, the model learning results showed high accuracy, with a precision of 0.960, recall of 0.967, and mAP (IoU (Intersection over Union) 50%) of 0.987. Applying this model as a gas safety management technology at industrial sites, enables the accurate determination of gas leak status, gas leak location, and gas leakage flow. This information is expected to guide appropriate accident responses for workers.

특수산업 현장의 위험요인 관리 애플리케이션을 통한 업무 생산성 증대 연구 : 8대 산업현장 근로자의 애플리케이션 사용 만족도를 중심으로

특수산업 현장의 위험요인 관리 애플리케이션을 통한 업무 생산성 증대 연구 : 8대 산업현장 근로자의 애플리케이션 사용 만족도를 중심으로

Geochemical Assessment of Some Trace Elements in Selected Locations at Thi-Qar Governorate Soil, Southeast Iraq

Trace elements (Co, Cr, Mn, Mo, Ni, Rb, Th, U, and V) in surface soil of Al-Aslah (NH1), Nasiriyah refinery (NH2), Gharraf (NH3), and Tall Al-Laham (NH4) at Thi-Qar governorate, southeast Iraq, were studied. The study aims to detect their distribution and pollution levels to reduce the subtracted from industrial sources and avoid using contaminated areas for agricultural purposes before the soil treatment process. Inductivity Coupled Plasma Mass Spectrometry (ICP-MS) technique was used to determine the concentrations of these elements, and the results showed that there is a high concentration of Cr, Ni, Mo, and Mn in the soil of the study area, which exceeded the international levels compared with other elements (Co, Th, U, and V). The Geo accumulation index (I geo) values showed that the soil area was classified as low contamination in all sites and for all trace elements except for Mo, which is polluted in Al-Gharraf and strongly polluted in Tall Al-Laham. The values of the enrichment factor (EF) were indicated to be minimal enrichment of (Mn and V) in all sites and moderate enrichment of (Co) in all sites except in the Gharraf soil sample, which is considered as minimal enrichment. At the same time, it suffers from significant enrichment of (Cr) in all sites. The values of contamination factor (CF) showed extreme contamination of Cr and Ni in all sites due to the oil industry (Nasiriyah refinery), except for Ni in the Gharraf soil sample, which is considered as high contamination and moderately contamination with Co in all sites, and low contamination with Mn and V. The main cause of the increasing trace element concentrations in the study area that the samples located in industrial sites where trace elements are one of the waste products from fuel combustion from the oil fields and the waste of the brick factories near the study area, also the nature of the study area soil which is clay soil that absorb the trace elements on its surfaces.

Research on Visual Recognition and Localization Methods for Different Feature Parts

Aimed at the problems of complex industrial site environment, difficult identification of assembly features and low positioning accuracy, a new visual identification and positioning method is proposed, which can well identify and locate parts and products with different characteristics. First, combined filtering is used in preprocessing to repair low-quality images. Then, edge detection and contour finding are performed on the preprocessed image. Finally, the internal and external parameters of the camera are calculated through the camera calibration, and the acquired pixel coordinates are converted into world coordinates. The results show that the combined filtering algorithm has good noise reduction effect and can remove image surface noise, which improves the recognition accuracy for the identification and positioning of different feature parts by the subsequent contour search method, and the algorithm can meet the requirements of the actual positioning accuracy of the assembly.

Assessment of herbaceous community structure for identifying metal-tolerant species at different land uses in and around Varanasi city.

Plant community structure under different land uses provides an important understanding of vegetation dynamics to safeguard future restoration programmes and balance ecosystem services. Therefore, this study was carried out to estimate the alterations in soil properties and contamination by potentially toxic metals at different land uses (industrial, brick kiln, highway, and residential areas) compared to the reference (botanical garden area) site coupled with their subsequent influence on herbaceous community structure, bioconcentration, translocation, and extraction amountof metals in different plant species. Most of the total and phytoavailable metals (Co, Cr, Cd, Cu, Ni, Pb, Mn, and Zn) were higher at the contaminated sites compared to the reference site. The number of herbaceous species was highest at the reference site and minimum at the industrial site. Dominant and tolerant species were Cyanodon dactylon, Croton bonaplandianus, Achyranthus aspera, Malvestrum coromendelianum, Dicanthium annulatum, Nicotiana hindostana, Sporobolus virginicus, and Parthenium hysterophorus, found at the industrial, brick kiln, and highway sites. Based on transfer coefficients, C. bonaplandianus, D. annulatum, and Eleusine indica were recognized as potential accumulators, whereas C. dactylon, Commelina benghalensis, A. aspera, Amaranthus sessilis, and M. coromendelianum were found as excluder species for different metals. The identified tolerant herbaceous species could be used for future phytoremediation strategies and the prevention of hazardous risks to living components of contaminated sites.

Dominant Factors Influencing the Implementation of Safety Protocols in Building Project Sites

Despite establishing extensive safety regulations and protocols, the construction sector finds it challenging to consistently uphold these norms. This study attempts to investigate the dominant factors influencing the implementation of safety protocols with a view to safeguarding the health and safety of workers while reducing the likelihood of accidents and guaranteeing the reliability and durability of built infrastructure. The study employed a cross-sectional research design and Lagos State served as the research area. The population of the study comprised built environment professionals and site operatives who were conveniently selected. The study utilized two sets of structured questionnaires that were self-administered to the two groups of respondents. Descriptive and inferential statistical approaches were employed to analyze the data collected. The findings revealed that the dominant factors influencing the implementation of safety protocols on building project sites among the built industry professionals and site operatives include management support, regulatory requirements, safety awareness, employee training and education, recognition and encouragement, workers’ attitudes, and penalties for non-compliance. The study concludes that regulatory requirement has a profound influence on how safety protocols are being implemented in building project sites. This implies that stringent adherence to regulatory requirements reduces risks and promotes a safety culture, which sustains the safety of building project sites. The study therefore recommends that the management of construction organizations should keep employees abreast of local safety requirements and provide guidelines for its implementation for compliance to be easily incorporated into building site operations. This may be accomplished by maintaining up-to-date information, performing frequent audits, and putting in place robust mechanisms aimed at guaranteeing compliance.

Automatic non-contact grinding surface roughness measurement based on multi-focused sequence images and CNN

Microscopic images of surfaces can be used for non-contact roughness measurement by visual methods. However, the images are usually acquired manually and need to be as sharp as possible, which limits the general application of the method. This manuscript provides an automatic roughness measurement method that can apply to automatic industrial sites. This method first automatically acquires the sharpest image and then feeds the image into a convolutional neural network (CNN) model for roughness measurement. In this method, the weighted window enhanced sharpness evaluation algorithm based on the sharpness evaluation function is proposed to automatically extract the sharpest image. Then, a CNN model, CFEN, suitable for the roughness measurement task was designed and pre-trained. The results demonstrate that the measurement accuracy of the method reaches 91.25% and the time is within a few seconds. It is proved that the method has high accuracy and efficiency and is feasible in practical applications.

Risk perception among residents living near industries in Godawari Municipality of Lalitpur, Nepal.

The industrial sector stands out as a significant contributor to environmental pollution. Those who reside in close proximity to industrial areas commonly harbor concerns about potential health and environmental hazards. This study aimed to find out the perception of risk and self-reported health impacts among individuals living near industries in Godawari Municipality, Lalitpur, Nepal. Conducted as a community-based cross-sectional study, it involved 270 households. Face-to-face interviews were employed, utilizing a pretested structured questionnaire. The study zone encompassed the communities of Godawari Municipality within a 3-kilometer radius of industrial sites. Specifically, stone mines, stone crushers, and brick kilns were purposefully selected, while study participants were randomly sampled using a random table. Data analysis was performed using IBM SPSS, incorporating both univariate and bivariate techniques. Among those residing near industrial zones, a mere 9.6 % reported experiencing wheezing or whistling in the past 12 months. A substantial 36.3% consistently felt stressed due to industrial activities in their vicinity. Approximately half (51.9 %) of the participants indicated that the contaminated air in the area had adverse effects on human health. Furthermore, a palpable perception of elevated risk was associated with the proximity of industries (p<0.001). Over half of the participants perceived a notable risk stemming from the presence of industries near their homes, largely due to pollutants. These individuals also disclosed various health repercussions and expressed significant apprehension regarding their future well-being in the area. The implications of these findings are substantial, particularly for local-level planning and the development of industrial sites. Addressing the concerns surrounding people's heightened perception of risk from nearby industries is pivotal in fostering harmonious coexistence and informed decision-making.

HERITAGE REQUALIFICATION PROCESS THROUGH SOCIAL PERCEPTIONS: THE CASE OF "HALLE VOLTA" IN ALGIERS

Urban industrial sites in Algeria constitute a colonial heritage that often undergoes a process of degradation and abandonment, leading to ruptures in the urban fabric. The question of their preservation is conditioned by their integration into a contemporaneity useful to the community to which they represent the heritage.&#x0D; Therefore, their future cannot be dissociated from the physical and social context they are part of, emphasizing the relationship between the object and the subject. Engaged in their epistemic posi-tions, this article proposes expertise on the heritage potential of Halle Volta, one of the industrial architectural structures from the late 19th and early 20th centuries in Algiers.&#x0D; This expertise makes it possible to understand the appreciations and perceptions engaged by a community towards this relic of the past, with the perspective of its management and transformation. This approach highlights the connection between the social and urban dimensions that can be raised by a heritage requalification project. The knowledge gained from this aims to address the issue of the requalification of Halle Volta, and, to a larger extent, the future of urban industrial heritage in Algeria.

Neurophysiological and psychological indicators of liquidators of chemical pollution of the environment

Introduction. Elimination of the consequences of emergency situations (ES) involves increased physical and psychological stress, as well as the direct aggressive impact of chemical and physical factors. Currently, the problem of environmental hazard is environmental pollution from the activities of «Usolyekhimprom», where on an area of 610 hectares there are tens of thousands of tons of harmful substances, including metallic mercury. On the territory of this enterprise, employees of the Federal Environmental Operator and the Ministry of Emergency Situations are currently engaged in eliminating environmental pollution. Over the past two years, a number of measures have been taken at the industrial site of «Usoliekhimprom» to dismantle buildings and demercurize. For 2023–2024 reconstruction, dismantling and disposal of the destroyed buildings of the enterprise are planned, in this regard, the liquidators may be affected by an even wider range of adverse factors. Employees of the East-Siberian Institute of Medical and Ecological Research conduct dynamic monitoring of the liquidators.&#x0D; The purpose of the study was to evaluate the neurophysiological and psychological indicators in liquidators in eliminating environmental pollution associated with the production of chemical products to prevent the occurrence of occupational pathology.&#x0D; Materials and methods. Forty males were examined. Electroneuromyographic examination (ENMG) was performed with testing of the sensory and motor components of the nerves of the upper extremities, electroencephalography (EEG), and psychological testing.&#x0D; Results. According to data of obtained ENMG parameters in the group of liquidators, a subthreshold decrease in the speed of impulse conduction in the area of the elbow joint was established during stimulation of the motor and sensory components of the ulnar nerve (up to 50 m/s). Analysis of interhemispheric coherence in the α-range in the group of liquidators revealed an increase in coherence in the right temporal leads (0.6 (0.4–0.7) G) compared with the standard values (0.35 (0.3–0.4) D) and weakening of coherent connections in the left frontal leads (0.35 (0.17–0.4) D). Changes in the mnestic sphere and the quality of life of the subjects were not revealed.&#x0D; Limitations. The limitation of the study was the lack of data on testing the motor and sensory components of the lower extremities during an electroneuromyographic examination and the lack of registration of evoked potentials during electroencephalography.&#x0D; Conclusion. The changes detected by ENMG indicate the manifestations of polyneuropathy at the preclinical stage. The analysis of interhemispheric coherence in the α-band revealed an increase in coherence in the right temporal leads and a weakening of coherent connections in the left frontal leads. The results of the medical and psychological study did not reveal any changes on the part of the mnestic sphere and the quality of life in the group of liquidators.

Fault diagnosis of wind turbine gearbox under limited labeled data through temporal predictive and similarity contrast learning embedded with self-attention mechanism

Data-driven models for wind turbine (WT) gearbox health monitoring have garnered significant attention. However, these models usually depend on extensive manually labeled data, which is costly and time-consuming to obtain at industrial sites. The model performance is also heavily affected by volatile operating conditions of WT gearbox. To overcome these issues, this paper proposes a self-supervised fault diagnosis method based on temporal predictive and similarity contrast learning (TPSCL) embedded with self-attention mechanism. This method aims to extract latent fault features from unlabeled vibration signals, thereby improving diagnostic performance under limited labeled data and volatile working conditions. Firstly, a data augmentation combination strategy is proposed to improve the variety of input data and enhance the generalization. Furthermore, a temporal predictive and similarity contrastive learning model embedded with self-attention mechanism is proposed to extract representations related to WT gearbox health conditions. In this case, the intrinsic characteristics of unlabeled vibration signals are learned, and the operating environment disturbance can be eliminated. Finally, the fault separability is realized by fine-tuning the model with few labeled data, which can finally achieve accurate fault identification of WT gearbox. Compared with existing methods, the proposed method can fully utilize existing monitoring data and achieve better performance in WT gearbox status monitoring and diagnosis under limited labeled data and variable operating conditions, contributing to WT predictive maintenance and efficiency improvement.

The presence of polluting sites in urban contexts: an analysis of the effects on the dynamics of the residential real estate market

IntroductionThe industrial activity constitutes one of the most driving sectors of a nation, as it is often considered at the center of public debate as the fulcrum of development policies. However, considering the climate impacts generated, many industries have necessarily changed, relocated, and/or interrupted their production. Due to the connection of industrial sites with the socio-economic, environmental, and health systems of cities, the real estate market dynamics can be differently affected by the presence of polluting industrial sites. With reference to the Italian context, the goal of the research is to, firstly, verify whether there is a functional relationship between the presence of polluting industrial sites and the residential real estate market dynamics, for then determining its type and the extent of purchase price affection.MethodThe proposed logical-evaluative model is based on the application of an econometric technique for investigating the significance of the industrial sites on the residential real estate dynamics through a set of technological, locational, and health variables.ResultsThe obtained results show that the total surface area of the polluting sites, the activity level and the industrial plant opening date are among the most relevant variables, instead the accessibility to the industrial site does not influence the real estate market dynamics.Discussion and practical implicationsThe main practical implication of the present research consists of the possibility to provide a support for the activation of strategies aimed at adopting sustainable development models in the industrial sector.

High-Sensitivity Hydrogen Sulfide Electrochemical Gas Sensor Based on CNT/Pt Cluster Electrode

Recently, due to the development of industrial technology, harmful gases discharged from industrial sites are gradually increasing, and as a result, inconvenience and civil complaints due to odor in the community are rapidly increasing. Among several types of harmful gases, hydrogen sulfide (H2S) is a colorless representative harmful gas that can cause pain through irritation of the mucous membranes of the eyes and respiratory tract, and can cause central nerve paralysis and suffocation at the same time when exposed to high concentrations. In addition, H2S is not only harmful to the human body, but also can create an unpleasant feeling by causing an odor even in a very small amount. Therefore, in order to manage H2S, research on a gas sensor capable of quickly and stably detecting H2S has been actively conducted. Most of the sensors that have been studied in the past are metal oxide-based semiconductor H2S gas sensors, which have high sensitivity, but have disadvantages in that low selectivity and high-temperature operating conditions are required. On the other hand, the electrochemical gas sensor can measure H2S gas with high selectivity because a chemical reaction occurs with respect to a specific gas depending on the type of electrolyte and electrode, and a potential difference due to electrons generated thereby is measured. Therefore, in this study, a CNT/Pt-based electrochemical H2S gas sensor capable of detecting at room temperature with high selectivity for H2S was developed. After forming a CNT thin film on a PTFE membrane, Pt was deposited through an E-beam to fabricate a working electrode capable of selectively detecting only H2S, and applied to an electrochemical H2S gas sensor using sulfuric acid (H2SO4) electrolyte. H2S was measured at a concentration of 100 to 10 ppm in units of 10 ppm using the manufactured CNT/Pt-based electrochemical H2S gas sensor. As a result, the amount of current change was confirmed to be 100uA at a concentration of 100 ppm, and the response time and recovery time were confirmed to be about 10 seconds.This work was supported by the Korea Innovation Foundation(INNOPOLIS) grant funded by the Korea government(MSIT) (2020-DD-UP-0348). This study has been conducted with the support of the Korea Institute of Industrial Technology as "Development of a thermal convection-type flow sensor with high-sensitivity for emergency patients breathing monitoring in real-time" (Kitech UI-23-0012).

Heinrich’s Law for Traffic Incidents – Using the Digital Tachograph Data to Identify Traffic Accident Hotspots

Heinrich’s Law indicates an empirical ratio between serious accidents, minor accidents and near misses in industrial sites, but has not been discussed in the context of road traffic accidents. Digital tachographs (DTG), a type of IoT device collecting spatiotemporal big data of vehicle trajectories, allow0 for examining a linkage between abnormal driving behaviours and the prevalence of road traffic incidents. According to the Traffic Safety Act implemented in 2011, DTG has been mandatorily pre-installed on most commercial vehicles in South Korea. The data have been analysed to evaluate the data processing method or promote eco-driving or safe driving, but only a few studies have examined an association between driving behaviours and actual traffic accidents using the limited data. We obtained 7,785,124 DTG sensing records from 1,523 commercial taxis driving within the city limits of Seoul at least once in 2013 and integrated them with 57,139 traffic accident cases during the same period. Using the integrated GIS database, we performed a grid-based spatiotemporal mapping and analysis to calculate a ratio among abnormal driving events, minor and major traffic accidents by road type. The findings suggest a potential for enhancing road safety by monitoring and controlling abnormal driving patterns as a precursor for accidents.

Spatial Variation, Ecological Risk, and Point Sources of Environmental Trace Metals in Lacustrine Ecosystems: An Assessment of Natural and Urban Inputs

ABSTRACT To gain insights into the spatial distribution, ecological risk and origins of heavy metals in surface sediments of lacustrine ecosystems in Cameroon, the content of 7 trace metals (Fe, Cr, Zn, Cu, Ni, Pb and Cd) in sediments from 60 sampling stations in 5 lacustrine environments of different formation contexts. The selected metals were investigated based on inductively coupled plasma mass spectrometry. The results revealed that the average concentrations of Cr, Cu and Cd were higher than the geochemical Threshold values. On the basis of the spatial variation, the studied metals vary quantitatively in the following order: Fe > Cr > Zn > Cu > Ni > Pb > Cd. The calculated enrichment factor (EF) and geo-accumulation index (Igeo) showed that, sediments from large cities and industrial sites had moderate enrichment (3 < EF ≤ 5) and were moderately to severely contaminated (2 < Igeo < 3) while those from low population and rural areas had minor enrichment (1 < EF ≤ 3) and were moderately contaminated (1 < Igeo < 2). With regards to cumulative pollution, the pollution load index (PLI) and the sediment pollution index (SPI) coupled with the potential ecological risk factor (Er) and the potential ecological risk index (RI), the sediments of the lakes are polluted (SPI and PLI > 1) to some extent but with a low ecological risk (Er < 40 and RI < 150). The results of Pearson correlation, principal component analysis (PCA), and cluster analysis (CA) identified three main sources: Ni would have a natural source; Pb from anthropogenic sources, and the other metals (Fe, Cr, Zn, Cu, and Cd) could have a mixed source (anthropogenic and natural). These findings serve as a scientific foundation for the advancement and use of these lake environments, which are the most attractive sites.

Toward a better understanding of collaborative research, development, and innovation (R&amp;D&amp;I) - exploring virtual, physical, and cognitive structures

Objective of the study: The research in this paper contributes to the understanding of how physical, virtual, and cognitive structures support innovation ecosystems’ multi-actor research, development, and innovation (R&amp;D&amp;I) collaboration in its different phases. Methodology/Approach: The research’s methodological approach is based on a qualitative case study research strategy. It is done by exploring three innovation ecosystem cases. The case data comprises the case ecosystems’ existing documentation that was supplemented with five semi-structured interviews. Originality/Relevance: Based on the findings of this research, it was possible to explore how industry and academy partners are collaborating through virtual, physical, and cognitive structures. Our cases also provide empirical evidence on how physical industrial sites can be used as environments for collaborative industry-academy R&amp;D&amp;I work. Main Results: As a result, the paper presents lessons learned from three different innovation ecosystem cases that involve industrial, technology, and academy partners to tackle industrial use cases through virtual, physical, and cognitive structures. An example of such lessons learned is assembling dynamic teams to solve industrial problems. Theoretical/Methodological Contributions: This article builds an understanding of how virtual, physical, and cognitive structures support collaboration between different participants in their joint R&amp;D&amp;I work covering industry-academy collaboration. The article also explains practical examples of this using innovation ecosystem cases. Management/Social Contributions: The findings of this study may benefit professionals and managers who have an interest in understanding collaborative R&amp;D&amp;I and how physical, virtual, and cognitive structures can support it. Furthermore, the results provide means and experiences for innovation ecosystem managers to facilitate the definition of operational models suitable for the context of their innovation ecosystems.

Prediction models for effective PVD-enhanced in-situ flushing remediation of multi-layered soils

The re-utilization of original industrial sites poses a significant challenge in the remediation of contaminated soils, driven by the transformation of urban industrial spaces. In this study, a prediction model is proposed to address this challenge by focusing on the in-situ flushing remediation of multi-layered soils enhanced with PVDs. The analytical model is versatile for various scenarios where the boundaries have different diffusion capacities by introducing imperfect diffusion boundaries. It incorporates the effects of the sandy silt layer and the decay characteristics of initial contaminant concentrations with depth on remediation efficiency of multi-layered contaminated soils. To validate the model, the solutions derived from the matrix analysis were compared with experimental data and numerical simulation results from COMSOL Multiphysics. Parametric analysis reveals that the model effectively captures different boundary conditions ranging from fully non-diffusive to completely diffusive by utilizing the imperfect diffusion capacity coefficient within the range of 0 to 50. Furthermore, the remediation of the upper and lower silty clay layers of the sandy silt layer was different but both decreased in efficiency as the thickness of the sandy silt layer increased. Notably, the concentration of the contaminant exhibits more pronounced variations with increasing radial dispersivity compared to vertical dispersivity.

Biomonitoring of PAHs and PCBs in industrial, suburban, and rural areas using snails as sentinel organisms.

There is a worldwide concern about the presence of persistent organic pollutants (POPs) in the environment because of their toxicity, bioaccumulation, and resistance to degradation. Various conventional monitoring techniques have been used to assess their presence in diverse environmental compartments. Most currently available methods, however, have limitations with regards to long-term monitoring. In the present work, juvenile Cornu aspersum (O. F. Müller, 1774) snails were tested in field microcosms as biomonitors for two major classes of organic pollutants, namely, polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs). The study assessed their deployment in one suburban, one rural, and two industrial sites over an 18-week period and monitored for temporal variations of 16 PAHs and 22 PCBs. Sampling was conducted once every 3weeks. Targeted pollutants were extracted from the caged snails using the QuEChERS extraction procedure and subsequently analyzed using gas chromatography coupled to tandem mass spectrometry (GC-MS/MS). The results showed that the bioaccumulation of specific pollutants was site dependent; significantly higher levels of PCBs were observed at the industrial sites as compared to the suburban and rural ones. PAHs were bioaccumulated by the snails via ingestion of air and soil whereas PCBs were mainly bioaccumulated via soil contact and ingestion. The findings of this study indicate that C. aspersum is a reliable model organism for the biomonitoring of organic pollutants in air and soil compartments and can be used as part of an integrated environmental assessment.