Environmental Quality Control Research Articles

Room-temperature self-calibrating sensor based on CsPbBr3/SnO2 for detecting low-concentration sulfur dioxide

Sulfur dioxide (SO2) is a colorless toxic gas, and accurate monitoring of its concentration is important for safety. However, accuracy is often interfered by humidity. To address the issues, in this work, a self-calibrating SO2 sensor based on CsPbBr3/SnO2 was prepared, the sensor exhibited an excellent response to parts per billion (ppb) levels of SO2 at room temperature, which is rarely achieved by other SO2 sensors. The response sensitivity of the sensor to 600 ppb SO2 reached up to 0.53 with response/recovery times of 17/285 s. In addition, the CsPbBr3/SnO2-based SO2 gas sensor showed a low detection limit, good reproducibility, and high selectivity. Moreover, theoretical calculations show that the gas recognition mechanism is attributed to the significant changes in the band structure and density of states of the heterojunction materials CsPbBr3/SnO2 after gas adsorption, thereby altering the charge carrier transport behavior and electrical properties. A self-calibrating algorithm MobileNetV2 was employed to eliminate humidity interference and effectively identify low-concentration SO2 in humid environments, and the recognition accuracy rate is 0.85. This work demonstrates the production of a high-performance SO2 gas sensor, and provides an effective method to eliminate the interference of humidity, making it significant in environmental monitoring and air quality control.

Optimizing population mean estimation under ranked set sampling with applications to Engineering

This article aims to develop two novel mean estimators of finite populations under ranked set sampling. The suggested estimators result in a high-efficiency gain due to their efficient formulation by taking the linear combination of classical estimators using optimization constants. We also discuss the theoretical properties and derive bias and mean squared error up to the first order of approximation. To theoretically validate our proposed estimators, we derive the conditions under which our estimators prevail over the competing estimators. The findings suggest that when ranked set sampling is implemented effectively, it saves cost and improves precision, serving as a potent instrument for improved decision-making, specifically in engineering applications, such as environmental monitoring, resource management, and quality control. An empirical and simulation study has been conducted using real and synthetic data to validate our methodology empirically. The suggested estimators offer a significant contribution to survey sampling in estimating mean under ranked set sampling. Further, the proposed estimator can be seamlessly adapted in other sampling designs such as simple random sampling, stratified random sampling, cluster sampling, etc, as well as in estimating other parameters such as variance, skewness, coefficient of correlation, etc. in various domains.

Ultra-sensitive nitrate-ion detection via transconductance-enhanced graphene ion-sensitive field-effect transistors

Current potentiometric sensing methods are limited to detecting nitrate at parts-per-billion (sub-micromolar) concentrations, and there are no existing potentiometric chemical sensors with ultralow detection limits below the parts-per-trillion (picomolar) level. To address these challenges, we integrate interdigital graphene ion-sensitive field-effect transistors (ISFETs) with a nitrate ion-sensitive membrane (ISM). The work aims to maximize nitrate ion transport through the nitrate ISM, while achieving high device transconductance by evaluating graphene layer thickness, optimizing channel width-to-length ratio (RWL), and enlarging total sensing area. The captured nitrate ions by the nitrate ISM induce surface potential changes that are transduced into electrical signals by graphene, manifested as the Dirac point shifts. The device exhibits Nernst response behavior under ultralow concentrations, achieving a sensitivity of 28 mV/decade and establishing a record low limit of detection of 0.041 ppt (4.8 × 10−13 M). Additionally, the sensor showed a wide linear detection range from 0.1 ppt (1.2 × 10−12 M) to 100 ppm (1.2 × 10−3 M). Furthermore, successful detection of nitrate in tap and snow water was demonstrated with high accuracy, indicating promising applications to drinking water safety and environmental water quality control.

Intelligent Factory with Environment Quality Control Based on Fuzzy Method through Deep Reinforcement Learning

Intelligent Factory with Environment Quality Control Based on Fuzzy Method through Deep Reinforcement Learning

A review of organometallic compounds as versatile sensors in environmental, medical, and industrial applications

Abstract Sensing technology is gaining attention and continuously advancing, making it a recommended element of individualized healthcare management. This is due to the powers exhibited by organometallic compounds, which are further enhanced by the field of bioengineering. Organometallic compounds have a wide range of biological activity and find uses in industrial and material science fields. Their unique ability to specifically target and overcome constraints faced by traditional counterparts makes them potential contenders for sensor technology. These compounds are highly sensitive to changes in their environment, allowing them to be utilized as sensors for detecting various chemicals or conditions. Additionally, the versatility of organometallic compounds enables their integration into different sensor platforms, making them suitable for environmental monitoring, medical diagnostics, and industrial quality control. This article provides a comprehensive summary of recent advancements in the design and synthesis of organometallic compounds, with a specific emphasis on their potential use as sensors. It also discusses the changes made to the structure, the processes used for functionalization, the incorporation of microfluidics, and the resulting impact on the materials’ sensing capabilities. These biologically derived methods align with sustainability goals and enhance the affordability, applicability, and effectiveness of sensing.

Evaluation of an automatic sample preparer in the development and validation of an analytical method for vial wall sorptive extraction

Automation has transformed the field of analytical chemistry by enhancing efficiency and precision in laboratory processes. A crucial step in analytical workflows, sample preparation, has traditionally relied on manual techniques, leading to variability and inefficiency. Vial wall sorptive extraction (VWSE) is a promising alternative for automating and enhancing sample preparation in analytical processes. This study aims to evaluate the performance of automated VWSE in the analysis of alkanes (C10-C18) in aqueous phases and to explore its potential use in routine laboratory settings for analyzing contaminants in environmental analysis, quality control, and forensics. VWSE devices were developed and characterized, a chromatographic method was established, and the sample preparation process was automated using an automated liquid handling system. The devices manufactured exhibit the best overall response and low standard deviation. Statistical tests confirm the equivalence of the vials prepared. The developed method achieved good results in analytical validation, a wide linear range with a high coefficient of determination (R2 > 0.90), low limits of detection (0.01–1.02 ng mL−1), and low limits of quantification (0.05–1.05 ng mL−1) were achieved. The parameters repeatability (<26.91 %), recovery (91 to 116 %), and intermediate precision (<22.29 %) also achieved a good analytical performance. The developed automated VWSE method proves effective in quantifying alkanes and meets analytical method validation criteria. This research highlights the potential of automated VWSE for sample preparation in various analytical applications.

Highly responsive double-shell ZnO hollow microspheres based gas sensor for acetic acid detection in vinegar

Acetic acid sensing materials for environmental and related food quality control requiring high sensitivity and selectivity, and ppb-level detection limit, remain challenging. Double-shell ZnO hollow microspheres were prepared by a surfactant assisted template method, and the resultant gas sensor showed excellent acetic acid sensing performance. The sensor response reached 116 to 100 ppm acetic acid at 270°C with a short response time of 3.2 s. The relationship between acetic acid concentration and the response was established, and the detection limit reached 100 ppb. DFT theory computational results demonstrated the highest adsorption energy of the ZnO gas sensor to acetic acid among all tested gases, proving its high selectivity to acetic acid. Besides, the unique double-shell hollow structure with abundant oxygen vacancy and large surface area might be the other factors that gave rise to its excellent acetic acid gas sensing performance. Based on the gas sensor, a method for fast quantitative detection of acetic acid content in vinegar was developed. The linear relationship between sensor response and acetic acid gas concentration of vaporized white vinegar was established, and the acetic acid content in the vinegar can be quickly determined. The result proved our acetic acid sensor to be a promising candidate for practical applications.

Exploring Competitive Dynamics, Technological Innovations, and Sustainable Strategies for X Plant-Based Meat Health Technology Co., Ltd.

The research objectives were as follows: 1) to analyze the competitive environment of X Plant-Based Meat Health Technology Co., Ltd.; 2) to determine the technologies and innovation capabilities of X Plant-Based Meat Health Technology Co., Ltd.; and 3) to formulate sustainable strategies for X Plant-Based Meat Health Technology Co., Ltd. This research adopted qualitative research and document study. Twelve key informants, including top-level management, product development teams, head of marketing departments, and consumer groups within the company, were involved. The data analyzed the current market competition status of X Plant-Based Meat Technology Co., Ltd. using PEST, Porter’s Five Forces, and SWOT models, thus proposing sustainable development strategies. The research findings revealed that: 1) X Plant-Based Meat Health Technology Co., Ltd. operated in a competitive market driven by government initiatives and increasing environmental awareness. Challenges included intensified competition and price sensitivity. However, the company had opportunities for growth in China's expanding plant-based food sector. Success depended on strategic differentiation, technological innovation, and adaptability to evolving market trends amidst competition from diverse players; 2) X Plant-Based Meat Health Technology Co., Ltd. excelled in technology and innovation, operating eight cutting-edge soybean processing facilities and maintaining extensive non-GMO soybean fields. With over 250 patents, it was a global leader, prioritizing environmental sustainability and quality control. Its integrated supply chain, diverse product portfolio, and strategic partnerships contributed to its ongoing success and growth; and 3) X Plant-Based Meat Health Technology Co., Ltd. pursued a comprehensive strategy for sustainable growth in a competitive market. Key elements included niche market penetration, organizational excellence, channel expansion, production capacity enhancement, technical expertise development, and market protection through innovation. These strategies aimed to position the company for long-term success in the plant-based food industry.

Emotion-oriented recommender system for personalized control of indoor environmental quality

Indoor Environmental Quality (IEQ) significantly influences health, cognitive abilities, and even moods. With the rise of smart homes and the Internet of Things (IoT), the need for personalized IEQ control has become paramount. Traditional systems, primarily focusing on explicit feedback (i.e., preference), often neglect the role of emotions in shaping this feedback. Therefore, this study presents the Emotion-oriented Recommender System for personalized control of IEQ (ERS-IEQ), based on the R-E-C-S ontology, focusing on (i) Recognizing user's continuous emotional states, (ii) Estimating emotional similarity between users, (iii) Collecting user's feedback on IEQ conditions, and (iv) Systemizing an emotion-oriented recommender system using a graph neural network. In order to confirm the predictive performance of the ERS-IEQ and verify its higher level of excellence compared to traditional recommender systems, a private dataset composed of IEQ conditions, users' explicit feedback, and users' emotional states was built via a human participant experiment using a climate chamber. As a result, the ERS-IEQ significantly enhances the recommender system's predictive performance, particularly in thermal preferences. The number of linguistic terms of emotional similarity has a profound effect on the system's predictions, with four terms proving most effective. In the near future, ERS-IEQ will play a role as a personal assistant in smart home automation, offering emotion-based recommendations. It addresses key challenges in traditional recommender systems, such as the cold start problem and rating sparsity, and ensures personalized adjustments to IEQ conditions in both private and public spaces.

Impact of COVID-19 on urban environment in developing countries: Case study and environmental sustainability strategy in Bangladesh

As of 2023, Bangladesh stands as the seventh most populous nation globally, confronting challenges arising from limited resources and rapid urbanization. The emergence of the COVID-19 pandemic has led to unprecedented disruptions on a global scale, necessitating a detailed examination of its effects, particularly within urban environments. This study meticulously scrutinizes the repercussions of COVID-19 on the urban environment, focusing specifically on Dhaka City. In this comprehensive exposition, we explore the multifaceted impact of the COVID-19 outbreak, spanning environmental and non-environmental aspects like urban water quality, air quality, municipal waste management, and noise pollution. We analyze urban environmental data across distinct timeframes, employing the Indicators, Impact, and Strategy (IIS) framework for environmental improvement and urban sustainability. The study reveals substantial shifts in the urban environment, poised to exert a profound influence on the city’s inhabitants and overall urban ecology. Notably, findings indicate a marked improvement in air and water quality, attributed partly to reduced industrial activity and diminished vehicular emissions during lockdowns. However, municipal waste management faced significant challenges, resulting in a deterioration of the overall waste disposal system. Of particular concern was medical waste disposal due to the increased generation during the pandemic. The study concludes by proposing a robust policy framework addressing key concerns, including environmental quality, urban health, and pollution control. These policy recommendations aim to bolster sustainable environmental progress within the unique exigencies of the COVID-19 era. As Bangladesh grapples with the pandemic’s aftermath, this research offers valuable insights and a roadmap for ensuring a more resilient and environmentally sustainable urban future in Dhaka City.

The Spatial Distribution and Influencing Factors of Heavy Metals in Soil in Xinjiang, China

Heavy metal pollution has been a problem of concern in soil ecology in recent decades. This study investigated the spatial distribution of heavy metals and their pollution levels in the soil of Xinjiang, based on the data of heavy metals published in the literature in the past five years, by using a geostatistical method, pollution index method, and geographic information system (GIS)-based spatial analysis. Additionally, the effects of five economic development indicators, such as population and industrial activities on the accumulation of heavy metals in soil, were explored by correlation analysis. The results showed that the average contents of Cd, Cr, Cu, Ni, Pb, and Zn in the soils were 2.858, 1.062, 1.194, 1.159, 1.192, and 1.086 times higher than the background values in Xinjiang, respectively. The semi-variance functions indicated that the Cd and Pb block gold coefficients of soils were greater than 25% and less than 50%, with an obvious spatial correlation. The spatial patterns showed that the high values of Cd, Cr, Cu, Ni, Pb, and Zn were mainly distributed in Karamay, Changji, Tacheng, and Kashi areas, with an overall decreasing trend from north to south, and the pollution index showed that the pollution of heavy metal Cd in soil was the most serious. Furthermore, Karamay, Changji, and Kashi areas were at heavy pollution levels. Correlation analysis showed that heavy metal Pb in the soil was significantly positively correlated with the agricultural GDP in Xinjiang, while Cd was correlated significantly and positively with comprehensive energy consumption and more significantly with industrial GDP. Thus, this study could provide a scientific basis for local evaluation of soil environmental quality and prevention and control of soil heavy metal pollution, which is of great significance for understanding the impact of human activities.

Luminescent 3D-Europium-based metal-organic framework: A brilliant sensor for quercetin detection. unveiling the path to portable, selective, and sensitive sensing

In this study, we present the synthesis and characterization of a metal-organic framework (MOF) named ZEuMOF, which was prepared through a solvothermal method using pamoic acid and Eu(NO3)3·6H2O. The MOF was characterized using various analytical techniques and computational studies. One of the noteworthy findings of this study is that the ZEuMOF exhibits excellent selectivity and sensitivity towards quercetin, an important bioflavonoid. The limit of detection (LOD) of quercetin was found to be as low as 9.86 × 10−7 M. In addition, we compared the performance of ZEuMOF with that of high-performance liquid chromatography with ultraviolet detection (HPLC–UV) for determining the quercetin content in a green tea sample. The results were satisfactory, and the detection of quercetin could even be visualized by the naked eye under 254 nm UV light using a test filter paper. These findings suggest that ZEuMOF could serve as a portable luminescent sensor for the detection of quercetin. Overall, this study sheds light on the potential application of ZEuMOF as a highly selective, sensitive, recyclable, and portable sensor for quercetin, which could have significant implications in various fields, including environmental monitoring and food quality control.

Streamlining Pesticide Regulation Across International River Basins for Effective Transboundary Environmental Management.

Pesticide standard values (PSVs) are critical for environmental management, environmental quality control, and remediation. Some countries or regions share river basins; however, their pesticide regulations are inconsistent, which could create a barrier to transboundary environmental management. To address this issue, we propose PSV scores for neighboring countries in order to promote pesticide regulatory harmonization within international river basins. Representative pesticides were selected to define PSV scores, including chemicals that are currently and historically widely used. Countries or regions from five international river basins were chosen for analysis: the Amazon, Mekong-Lancang, Rhine-Meuse, Danube, and Great Lakes. PSV scores were calculated for each of four environmental compartments: soil, surface freshwater, groundwater, and drinking water. The results revealed that current regulatory agencies lack PSVs of current used pesticides for surface freshwater. With the exception of the member states of the European Union and the Great Lakes states of the United States, the majority of basin countries or regions lack uniform pesticide regulations in environmental compartments to facilitate transboundary environmental management. In addition, PSVs have not been established for a large number of pesticides currently used in agriculture, which could lead to water contamination by pesticides used in upstream environmental compartments (e.g., croplands). Also, current PSVs do not align across environmental compartments, which could cause inter-environmental contamination by pesticides used in upstream compartments. In light of the fact that current river basins lack uniform pesticide regulations, the following recommendations are provided to promote transboundary environmental management: (1) river basin regions should collaborate on pesticide regulation establishment, (2) pesticide regulations should be aligned across environmental compartments, (3) current-use pesticides should receive more attention, and (4) quantitative approaches should be proposed for linking PSVs across environmental compartments. This study provides a regulatory tool to identify possible gaps in transboundary environmental management and improve the pesticide regulatory policies. It is expected to establish cooperation organizations to enhance regulatory communications and collaborations for transboundary environmental pesticide management.

Housing and Indoor Factor Influencing Spread of COVID-19

There has been growing recognition linking spread of COVID-19 with environmental factors. One of the environmental factors with robust epidemiological literature supporting its role in diseases is the housing or built environment. COVID-19 spread has been found to occur mostly at homes through secondary household transmission. As most people spend more times inside homes during the pandemic, household remains an important site of COVID-19 spread. The aim of this study is to examine how housing and indoor factors affect the transmission and spread of COVID-19. This article summarizes the housing indoor factors involved in COVID-19 transmission, including the role of transmission from contaminated household surfaces. Indoor transmission of COVID-19 is found to be more likely due to contact transmission and close-contact aerosol transmission in a crowded, confined, and poorly ventilated indoor environment, related to poor housing condition. Whilst role of spread through contaminated household surfaces is of low probability. Based on this review, it can be suggested that besides the existing measures including avoiding crowding, close contacts and proper ventilation, specific standards for indoor environmental quality control and housing condition might be required. Housing is a public health issue and healthy housing is of universal concern.

Energy modeling and predictive control of environmental quality for building energy management using machine learning

Heating, Ventilation, and Air Conditioning (HVAC) systems play a vital role in building energy management by controlling the indoor temperature and ensuring the occupant's comfort. However, the energy consumption of HVACs contributes significantly towards overall energy usage of a building and carbon footprint. To address this challenge, this research proposes the development of a predictive model for HVAC temperature forecasting using Machine Learning (ML) algorithms to optimize energy efficiency while maintaining thermal comfort in buildings. The study focuses on comparing the performance of Transformer Neural Networks and CNN-LSTM, a seq2seq model combining Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM) on multiple forecasting horizons using data obtained from multiple devices deployed in a room verified by feedback survey forms filled by occupants. The transformer model outperformed, achieving an R2 score of 0.936 at a 1-minute forecasting horizon, surpassing the performance of CNN-LSTM model at all tested forecasting horizons. The transformer model yielded significant energy savings thereby reducing energy consumption by almost 50 % compared to the non-AI conventional methods, particularly at forecasting horizons of 1 min and 60 min, while the occupant survey also favoured a 60-minute forecasting horizon. The performance of transformer model particularly with a 60-minute forecasting horizon underscores its potential to optimize energy efficiency while ensuring thermal comfort in building energy management systems.

Concrete 3D Printing: Process Parameters for Process Control, Monitoring and Diagnosis in Automation and Construction

In Singapore, there is an increasing need for independence from manpower within the Building and Construction (B&amp;C) Industry. Prefabricated Prefinished Volumetric Construction (PPVC) production is mainly driven by benefits in environmental pollution reduction, improved productivity, quality control, and customizability. However, overall cost savings have been counterbalanced by new cost drivers like modular precast moulds, transportation, hoisting, manufacturing &amp; holding yards, and supervision costs. The highly modular requirements for PPVC places additive manufacturing in an advantageous position, due to its high customizability, low volume manufacturing capabilities for a faster manufacturing response time, faster production changeovers, and lower inventory requirements. However, C3DP has only just begun to move away from its early-stage development, where there is a need to closely evaluate the process parameters across buildability, extrudability, and pumpability aspects. As many parameters have been identified as having considerable influence on C3DP processes, monitoring systems for feedback applications seem to be an inevitable step forward to automation in construction. This paper has presented a broad analysis of the challenges posed to C3DP and feedback systems, stressing the admission of process parameters to correct multiple modes of failure.

A Review of Quartz Crystal Microbalance for Chemical and Biological Sensing Applications.

Humans are fundamentally interested in monitoring and understanding interactions that occur in and around our bodies. Biological interactions within the body determine our physical condition and can be used to improve medical treatments and develop new drugs. Daily life involves contact with numerous chemicals, ranging from household elements, naturally occurring scents from common plants and animals, and industrial agents. Many chemicals cause adverse health and environmental effects and require regulation to prevent pollution. Chemical detection is critically important for food and environmental quality control efforts, medical diagnostics, and detection of explosives. Thus, sensitive devices are needed for detecting and discriminating chemical and biological samples. Compared to other sensing devices, the Quartz Crystal Microbalance (QCM) is well-established and has been considered and sufficiently sensitive for detecting molecules, chemicals, polymers, and biological assemblies. Due to its simplicity and low cost, the QCM sensor has potential applications in analytical chemistry, surface chemistry, biochemistry, environmental science, and other disciplines. QCM detection measures resonate frequency changes generated by the quartz crystal sensor when covered with a thin film or liquid. The quartz crystal is sandwiched between two metal (typically gold) electrodes. Functionalizing the electrode's surface further enhances frequency change detection through to interactions between the sensor and the targeted material. These sensors are sensitive to high frequencies and can recognize ultrasmall masses. This review will cover advancements in QCM sensor technologies, highlighting in-sensor and real-time analysis. QCM-based sensor function is dictated by the coating material. We present various high-sensitivity coating techniques that use this novel sensor design. Then, we briefly review available measurement parameters and technological interventions that will inform future QCM research. Lastly, we examine QCM's theory and application to enhance our understanding of relevant electrical components and concepts.

Environmental noise modeling, quality control, verification, and validation concepts

Over the past 30 + year's noise modeling has developed in all engineering fields. ISO 9001 has developed detail simulation validation criteria's for variouse engineering fields. Engineering software application for environmental noise modeling are just one of many fields that offer advanced simulation options. Unfortunately, the validation of environmental noise simulation is still under development. With the increase of environmental noise projects the model validation between test and simulation is scrutinized by the public or in court. The presentation discusses the definition of Model, Mathematical Model, Computational Model and Conceptual Models and the definition of Verification, Validation and Updating. What are the tools for quality assurance (QA) for the noise modeling software, software verification and project quality control (QC)? What is the state of noise modeling standards in North America? What are the uncertainties and variabilities that the environmental noise engineer can control? Are we sure that we are modeling a worst-case scenario? What can consultants do to improve the quality of noise simulations.

The role of institutional quality in assessing the environmental externality of financial inclusion: A DCCE approach

Controlling environmental contamination requires the use of environmental regulation. The growth of green finance depends on digital finance. The objectives of the study are threefold: first, to explore the impact of digital financial inclusion in deriving climate change; second, to trace the shape of the financial inclusion-based environmental Kuznets curve; and third, to investigate the intersecting effect of digital financial inclusion and institutional quality on environmental quality. Using panel data from 48 Asian economies between 1996 and 2020, heterogeneity, non-stationarity, and cross-sectional dependence are addressed using an econometric method called “dynamic common correlated effects (DCCE).” The empirical evidence confirms a significant relationship between environmental performance and financial inclusiveness. Furthermore, the findings also validated the inverted U-shape environmental Kuznets curve based on financial inclusiveness. Our research suggests that a strong institutional framework has the potential to mitigate the long-term negative consequences of financial inclusion on the environment. To establish coordinated control of environmental quality, the government fully utilizes the environmental regulation and digital inclusive finance environmental governance. Consequently, to achieve environmental sustainability, policymakers in Asian countries should develop policies that enhance financial inclusion and institutional quality.

Determining River Water Quality Status based on the Pollution Index Method as Control of Environmental Quality: The Laeya River, South Konawe Regency

This study aims to analyze and determine the water quality status of the Laeya River for the provision of raw water in South Konawe Regency. This research was conducted in South Konawe Regency during 2022, using one method, namely the Pollution Index method. The Pollution Index method is used to determine the level of pollution from the Laeya River for the provision of raw water in South Konawe Regency. The results showed that the Laeya River at Station-I, Station-II, and Station-III obtained Pollution Index values of 2.511, 3.768, and 2.963, of the three stations whose parameters were analyzed, all of them met the quality standards except Total Suspended Solids (TSS), Chemical Oxygen Demand (COD), and Total of Coliform at Station-I. TSS, Biological Oxygen Demand (BOD), COD and Total of Coliform at Station-II, and TSS, Dissolved Oxygen (DO), BOD, COD, Detergent and Total of Coliform at Station-III, so that the water quality status of the Laeya River belonged to the classification of mild contaminants for Station-I, Station-II, and Station-III.