Consumption Process Research Articles

Insights on kraft lignin degradation in an anaerobic environment

Lignin is an aromatic macromolecule and one of the main constituents of lignocellulosic materials. Kraft lignin is generated as a residual by-product of the lignocellulosic biomass industrial process, and it might be used as a feedstock to generate low molecular weight aromatic compounds. In this study, we seek to understand and explore the potential of ruminal bacteria in the degradation of kraft lignin. We established two consortia, KLY and KL, which demonstrated significant lignin-degrading capabilities. Both consortia reached maximum growth after two days, with KLY showing a higher growth and decolorization rate. Additionally, SEM analysis revealed morphological changes in the residual lignin from both consortia, indicating significant degradation. This was further supported by FTIR spectra, which showed new bands corresponding to the C-H vibrations of guaiacyl and syringyl units, suggesting structural transformations of the lignin. Taxonomic analysis showed enrichment of the microbial community with members of the Dickeya genus. Seven metabolic pathways related to lignin metabolism were predicted for the established consortia. Both consortia were capable of consuming aromatic compounds such as 4-hydroxybenzoic acid, syringaldehyde, acetovanillone, and syringic acid, highlighting their capacity to convert aromatic compounds into commercially valuable molecules presenting antifungal activity and used as food preservatives as 4-hydroxyphenylacetic, 3-phenylacetic, and phenylacetic acids. Therefore, the microbial consortia shown in the present work are models for understanding the process of lignin degradation and consumption in bacterial anaerobic communities and developing biological processes to add value to industrial processes based on lignocellulosic biomass as feedstock.

Food waste warriors: Indonesian communities engage through workshops and social media education

Food waste is food appropriate for human consumption but discarded in the retail or consumption process. In 2022, food waste accounts for 40.9% of Indonesia’s waste composition. Therefore, food waste community education is critical for reducing food waste. Community education can reach the young generation through workshops and social media. This research aims to evaluate various workshops and social media content in educating teenagers about food waste through community. The PRISMA guidelines become the foundation as a contribution to Sustainable Development Goals. We utilized databases such as EBSCO Host, SAGE, Taylor & Francis, and Wiley Online Library to search for relevant studies. This study’s approach is normative research including secondary data, comprehensive literature review such as workshops, social media, and food waste education journals in Indonesia. This systematic review examines food waste education content of workshops and social media in Indonesia also further content recommendations. This systematic review collected 52 papers, including journals and proceedings. The community education content in Indonesia focuses on recycling with 28.57%. Seminars/online campaigns dominate the education form with sustainable development goals (SDGs) achieved: climate action and zero hunger. This systematic review showed that it is essential to add actions toward food waste in the form of actual activities.

Consumption process manipulation as a means of the emotionalization of modern society

Consumption process manipulation as a means of the emotionalization of modern society

The role of food-related consideration of future consequences, health and environmental concerns in explaining sustainable food (fish) attitudes

PurposeThis study explores the direct and indirect effects of two components of food-related consideration of future consequences (CFCs), including CFC-Immediate and CFC-Future, on sustainable food attitudes (SFA) via food-related health and environmental concerns.Design/methodology/approachPartial least squares structural equation modeling technique was used on a data set of 664 Vietnamese consumers collected in Central Vietnam to evaluate measurement and structural models.FindingsCFC-Immediate and CFC-Future as well as health and environmental concerns have positive effects on SFA. Indirect effects of CFC-Immediate on SFA via health concerns and CFC-Future on SFA via health/environmental concerns are also discovered.Research limitations/implicationsFuture studies should examine the impact of environmental values on CFCs, forming a more comprehensive understanding regarding the relationship between the two variables, especially by including a wider range of sustainable food types to gain diverse knowledge about sustainable food consumption.Practical implicationsCommunicative messages should focus on both health and environmental concerns while emphasizing both immediate and more distant outcomes of sustainable food (fish) consumption for individuals with different dominant temporal orientations.Originality/valueThis study sheds light on the direct and hierarchical relationships among food-related CFCs, health and environmental concerns and SFA to better understand the intricate psychological process of sustainable food consumption.

Design and Application of an IVD Reagent Management Information Systems Based on Unique Code

The management of in vitro diagnostic (IVD) reagents in hospitals often faces issues such as the lack of a unified coding system, unclear consumption patterns, and unknown cost-to-income ratios. It is necessary to employ information systems to achieve comprehensive, detailed, and traceable management of IVD reagents. An information management system for IVD reagents based on unique coding is introduced, which integrates admission, acceptance, and consumption processes through unique codes. The system calculates the income per experimental item based on the consumption of IVD reagents and the charge for each experimental item. The system enhances the efficiency of the IVD reagent supply chain management and promotes detailed oversight of IVD reagent usage.

СИСТЕМНО-ПАРАМЕТРИЧНЕ ОЦІНЮВАННЯ ЕФЕКТИВНОСТІ ЕЛЕКТРОСПОЖИВАННЯ ЗАЛІЗОРУДНИМИ ПІДПРИЄМСТВАМИ

The components of electricity consumption by iron ore enterprises are generalized and studied. On the basis of systematicity, a mathematical model has been built that allows developing a methodology for assessing the efficiency of electricity consumption under a three-zone tariff grid. The mathematical modeling of electricity consumption by iron ore enterprises as a complex object makes it possible to numerically determine the impact of the value of electricity consumption on the cost of electricity consumption by an iron ore enterprise. This approach made it possible to obtain quantitative indicators of the efficiency of electricity consumption at specific iron ore enterprises. The methodology for assessing the efficiency of electricity consumption at iron ore enterprises was further developed. For the first time, a model for assessing the efficiency of electricity consumption under a three-zone tariff grid was formulated and built. The proposed methodology can be used as a basis for unifying the quantitative assessment of electricity consumption efficiency. The analysis of the results makes it possible to conduct a quantitative and qualitative assessment of the efficiency of electricity consumption processes on the basis of system-parametric modeling for iron ore enterprises. The developed methodology can be unified for implementation at iron ore enterprises of the national economy. It is advisable to include the developed model in the practice of relevant calculations of the economic activity of iron ore enterprises. It has been established that in the current conditions of functioning of iron ore enterprises it is advisable to study the consumption of electricity as a systemic complex object. The carried out analysis made it possible to build an objective function that takes into account the consumption of electricity by ore intake devices depending on the time of day

Kinetics of Vegetable Oils (Rice Bran, Sunflower Seed, and Soybean) Extracted by Pressurized Liquid Extraction in Intermittent Process

The research focuses on optimizing vegetable oil production processes for human consumption, emphasizing green and efficient extraction methods using renewable solvents with minimal toxic residues. Pressurized liquid extraction (PLE), especially with ethanol, is studied for its efficiency and low solvent usage in intermittent processes. By evaluating extraction parameters and kinetics, the study aims to determine optimal conditions for higher extraction rates and yields, providing insights into production costs and other factors. Specifically, the research examines the behavior of extraction kinetics for vegetable oils like rice bran, sunflower seeds, and rolled soybeans. It also seeks to determine mass diffusivity in semi-continuous processes and to model PLE in intermittent processes using Fick’s Law and Mathematica Wolfram Software v11.2. The effective diffusivity (Deff) for rice bran oil in pressurized ethanol varied between 13.09 and 15.70 × 10−12 m2/s, and the Deff value of sunflower seed oil was between 8.10 and 12.60 × 10−12 m2/s. For rolled soybean oil, the Deff value ranged from 17.25 to 31.29 × 10−12 m2/s. The mass diffusivity values of vegetable oils in pressurized ethanol remained within the same order of magnitude. The mass diffusivity for PLE in an intermittent process presented values of 5.97 × 10−12 m2/s for rice bran oil with 3 extraction cycles. The Deff value for sunflower seed oil in pressurized ethanol was 1.38 × 10−12 m2/s, with 4 cycles, and for rolled soybeans, the Deff value was 1.77 × 10−12 m2/s in 3 cycles. The Deff value found in the intermittent extraction process was lower than that in the semi-continuous process. The total solvent renewal in the semi-continuous extraction process significantly impacted the diffusivity values for all extracted oils, as this process utilizes much more solvent compared to the intermittent process for all matrices studied. Various factors, including geometry, average particle diameter, extraction temperature, and rinse solvent volume, can affect the differences in curve behavior between the semi-continuous and intermittent processes. Despite these factors, the intermittent process is considered more viable for implementation due to its favorable economic and environmental characteristics, primarily because it requires a much smaller amount of solvent.

Streamlining IoT-driven Data Using Blockchain

The integration of Internet of Things (IoT) technology has revolutionized data collection and analytics, creating vast networks of interconnected devices generating large volumes of data. However, this rapid expansion of IoT ecosystems introduces significant challenges related to data security, integrity, scalability, and privacy. Blockchain technology, with its decentralized and immutable nature, emerges as a compelling solution to address these challenges. This paper explores the synergy between IoT and blockchain, examining how blockchain can streamline IoT-driven data by providing secure data exchanges, enhancing transparency, and facilitating scalability. The study investigates the key mechanisms through which blockchain can mitigate common IoT data issues. It considers blockchain's role in ensuring data integrity by utilizing cryptographic methods, enabling traceability through distributed ledgers, and protecting against unauthorized data manipulation. Furthermore, it explores the potential of blockchain-based smart contracts to automate processes and improve interoperability among IoT devices. The paper delves into specific use cases, such as smart cities, demonstrating how blockchain can effectively streamline IoT data across various industries. The findings highlight that while blockchain offers substantial benefits, it also introduces unique challenges, including high energy consumption, latency concerns, and complex integration processes. This concludes with a discussion of Security concerns, Comparison with existing solutions. By addressing these challenges, blockchain can become a pivotal technology in streamlining IoT-driven data, paving the way for more secure, efficient, and trustworthy IoT ecosystems.

A light - Driven acidic positive feedback mechanism of sulfate formation

As the main source of atmospheric sulfate aerosol, the aqueous-phase oxidation of SO2 has been of great concern. Traditionally, the aqueous-phase oxidation of SO2 is considered a consumption process of atmospheric oxidation capacity; however, we report here that •OH radicals and other reactive oxygen species (ROS) can be generated in the photochemical reactions of bulk HSO3− solution. This •OH production pathway results in the fast sulfate formation in the photooxidation of dissolved SO2. The photochemical process is determined to be pH-dependent in which sulfate formation is promoted by decreasing pH. Hence, a light - driven acidic positive feedback mechanism is proposed that the oxidation of dissolved SO2 and the formation of •OH radicals have a synergistic promoting effect. These results indicate that the photochemistry of dissolved SO2 could be a missing source of aqueous •OH radicals as well as sulfate at regional to global scales.

Carbon Dioxide Capturing in Temperature/Pressure Swing‐Commercial Processes Using Ionic Liquids

AbstractCapturing carbon dioxide is vital for mitigating global warming and supporting chemical processes. Ionic liquids (ILs) have emerged as promising solvents for CO2 capture. Using ASPEN simulation software, this study explores three specific ILs: [emim][triflate], [bmim][MeSO3], and [bmim][NTf2]. Their selection is based on exploitable differences in energy. The study models CO2 solubility and validates it against published data. It also considers the decomposition of ILs using a more accurate vapor loss model. Simulated equations predict CO2 and IL behavior more accurately than published ASPEN studies, optimizing the process for minimal energy consumption. Commercial considerations and rigorous engineering calculations guide the analysis, encompassing energy and economic factors.

Enhanced boron removal without pre-pH adjustment via redox-mediated electrodialysis assisted by ion-exchange resins

Boron presents challenges in desalination owing to its small size and lack of charge. While various methods, such as reverse osmosis, capacitive deionization, and electrodeionization, have been utilized to control boron, they often suffer from high levels of energy consumption and complex processes requiring pH adjustment to generate the charged form of boron. Herein, we introduce a novel approach, i.e., redox-mediated electrodialysis (redox-ED) assisted by ion-exchange resins, aimed at energy-efficient boron control without requiring pH adjustment. Our system displays a remarkable boron removal efficiency of 99% with a low energy consumption of 0.009 kWh/g (0.09 kWh/m3). These results are likely due to the dissociation of water on the surface of the ion-exchange resins, which led to a local pH gradient and reduced systemic resistance through additional ion flux. Furthermore, via techno-economic analysis, the total cost of the redox-ED for boron removal revealed a 50% reduction with the scale-up strategy of electrode stacking, compared to those of traditional EDI systems. Overall, this study demonstrates the new potential of a sustainable boron removal technology without pH adjustment through the redox-ED system assisted by ion-exchange resins, providing an attractive option applicable to the boron removal industry.

PSO-PSP-Net + InceptionV3: An optimized hyper-parameter tuned Computer-Aided Diagnostic model for liver tumor detection using CT scan slices

An automated diagnostic system leads to a supreme requirement in medical image analysis, greatly impacting the death rate due to the high spreading rate of liver tumors. However, the traditional histopathological diagnostic processes cause more disruption in the medical detection framework because of the procedure of manual estimation of CT scans and medical labor-intensive, which lead to the time consumption process. It is a highly challenging task for the segmentation of liver tumor because of the irregular appearance, distinct densities, patchy edges, and tumor sizes. To overcome this issue, an automated computer-aided diagnosis framework has been utilized to diagnose the liver cancer quickly and accurately with the less workload of expert radiologists.Presently, Deep learning-based techniques have been applied to identify the tumor regions and assist radiologists in the initial diagnosis and scrutinizing the severity level. The proposed work presents a modified pyramid scene parsing network (PSP-Net) model based on InceptionV3 deep network architecture with a particle swarm optimization algorithm, which increases the efficiency and effectiveness of the liver diagnostic model. Three publicly accessible benchmark datasets have been engaged with the pre-processing task accomplished by image normalization and data augmentation techniques. Moreover, a hyper-parameter tuned improved PSO-PSP-Net algorithm has analyzed the optimal set of gathered features. In the experimental evaluation, the proposed model outperformed than grey wolf optimization (GWO-PSP-Net), PSO-U-Net, GWO-U-Net, opposition based spotted hyena optimization (O-SHO-CRNN), and grey wolf-class topper optimization (GW-CTO) + U-Net by 8.42 %, 4.9 %, 9.54 %, 5.12 %, and 1.75 %, respectively in terms of accuracy for LiTS dataset. The developed PSO-PSP-Net model attained better accuracy than GWO-PSP-Net, PSO-U-Net, GWO-U-Net, and Residual Atrous U-Net (RA-U-Net) for the segmentation of Type-I and Type-II liver tumors by 6.88 %, 8.44 %, 13.75 %, 5.99 % and 6.12 %, 9.74 %, 17.3 %, and 5.11 %, respectively for 3DIRCAD-b1 dataset. The obtained accuracy of the developed model is 14.09 %, 7.54 %, 16.36 %, 2.64 %, and 14.9 %, 8.86 %, 17.76 %, and 4.87 % maximized than GWO-PSP-Net, PSO-U-Net, GWO-U-Net, and RA-U-Net, respectively, for the segmentation of the infected arteries and bones in the 3DIRCAD-b1 dataset. For the CHAOS dataset, the improved PSO-PSP-Net model outperformed GWO-PSP-Net, PSO-U-Net, and GWO-U-Net, respectively, in terms of overall accuracy by 7.44 %, 8.85 %, and 17.2 %, respectively. The investigational analysis validates the proposed PSO-PSP-Net model's effectiveness and performance in obtaining high segmentation accuracy in comparison with other state-of-the-art approaches.

DEVELOPING HUMAN RESOURCES OF ETHNIC MINORITIES IN THE MOUNTAINOUS AREA OF NGHE AN PROVINCE, VIETNAM IN THE CONTEXT OF THE IMPACT OF THE 4.0 INDUSTRIAL REVOLUTION

The Fourth Industrial Revolution has been developing strongly, with a profound and comprehensive impact on all aspects and areas of social life. The development of science and technology and production forces on a global scale with new speed and quality has been promoting the world economy to shift to a knowledge economy, bringing new scientific achievements - technology is applied to optimize the process of production, distribution, exchange, consumption, management, and administration. That poses new requirements for developing human resources in general and training and using high-quality ethnic minority human resources in Vietnam. In recent years, developing and improving the quality of human resources of ethnic minorities in mountainous areas has always been of concern to Nghe An province, Vietnam. In the coming time, the development of human resources for ethnic minorities in the mountainous regions of Nghe An province, Vietnam, will be an essential and urgent issue that requires feasible strategies and solutions to meet the requirements, facing the increasing challenges of the revolution industry 4.0.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0711/a.php" alt="Hit counter" /></p>

METHODS AND TOOLS FOR IN-DEPTH ANALYSIS OF ENERGY CHARACTERISTICS AND MANAGEMENT OF ENERGY CONSUMPTION IN EDUCATIONAL INSTITUTIONS

A factor of the state's national security is reducing energy dependence and taking measures to increase energy efficiency, especially in the budgetary sphere, because about 90% of such buildings do not meet modern energy efficiency requirements. Creating a reliable energy consumption management system, choosing measures to reduce consumption and diversifying energy sources is a difficult task that requires consideration of building energy consumption processes as a complex problem with the use of modern tools of the energy management system, modeling, and the use of modern approaches to automation and measurements. The object of research is energy consumption processes in educational institutions. The subject of the research is energy characteristics and methods of energy consumption management in educational institutions. Research methods. analytical methods, system analysis, synthesis, modeling, systematization, method of comparative and structural analysis. One of the tasks of this study is to review the capabilities of software products that can be used by energy managers of educational institutions to monitor, analyze and forecast energy consumption, as well as to create reports. Also in the work, an analysis of the state of affairs regarding energy consumption management was carried out on the example of KPI named after Igor Sikorskyi; proposals for the development of the automated energy monitoring system were provided, in particular, the architecture of the web application was developed taking into account the support of existing databases to improve the energy monitoring process.

Sustained bacterial N2O reduction at acidic pH

Nitrous oxide (N2O) is a climate-active gas with emissions predicted to increase due to agricultural intensification. Microbial reduction of N2O to dinitrogen (N2) is the major consumption process but microbial N2O reduction under acidic conditions is considered negligible, albeit strongly acidic soils harbor nosZ genes encoding N2O reductase. Here, we study a co-culture derived from acidic tropical forest soil that reduces N2O at pH 4.5. The co-culture exhibits bimodal growth with a Serratia sp. fermenting pyruvate followed by hydrogenotrophic N2O reduction by a Desulfosporosinus sp. Integrated omics and physiological characterization revealed interspecies nutritional interactions, with the pyruvate fermenting Serratia sp. supplying amino acids as essential growth factors to the N2O-reducing Desulfosporosinus sp. Thus, we demonstrate growth-linked N2O reduction between pH 4.5 and 6, highlighting microbial N2O reduction potential in acidic soils.

Effects of Antheraea pernyi on Parasitization of Kriechbaumerella dendrolimi by Using Immunology and Metabolomics

Kriechbaumerella dendrolimi (Hymenoptera, Chalcididae) is a dominant pupal parasitoid species of various significant pine caterpillars, including Dendrolimus houi Lajonquiere (Lepidoptera, Lasiocampidae), with great potential for utilization. So far, the mass rearing of K. dendrolimi has been successfully established using Antheraea pernyi (Lepidoptera, Saturniidae) pupae as alternative hosts and released in the forest to suppress D. houi populations. However, the outcome is still expected to be improved due to lower parasitism rates, which might be related to the autonomous immune function of A. pernyi pupae. In our study, we investigated the effects of K. dendrolimi parasitization on the immune responses of A. pernyi pupae by measuring the expression of key immune factors: superoxide dismutase (SOD), polyphenol oxidases (PPOs), Attacin, Lysozymes (LYSs), and serine proteases (PRSSs). Our results show that parasitization significantly upregulated these immune factors, with distinct temporal patterns observable between 4 and 48 h post-parasitization. This upregulation highlights a robust immune response, adapting over time to the parasitic challenge. These findings suggest that specific immune mechanisms in A. pernyi pupae are activated in response to K. dendrolimi, shedding light on potential targets for enhancing host resistance. Metabolomic analyses complemented these findings by illustrating the broader metabolic shifts associated with the immune response. Specifically, Attacin was significantly upregulated twice, hypothesizing that the parasitoid’s venom contains at least two parasitic factors. Metabolomics analysis revealed a significant metabolite difference within parasitized A. pernyi pupae. The highest number of differential expression metabolites (DEMs) was observed at 16 h post-parasitism (1184 metabolites), with fewer DEMs at 8 h (568 metabolites) and 32 h (693 metabolites), suggesting a close relationship between parasitism duration and the number of DEMs. These fluctuations reflected the fundamental process of immune interaction. KEGG enrichment results showed that the DEMs were mainly enriched in energy metabolism and immune-related pathways, indicating that parasitism is a process of continuous consumption and immune interaction in the host. These DEMs could also become future targets for regulating the immune functions of A. pernyi pupae and could provide reference data for optimizing mass-rearing techniques.

Lightweight Hash-Based Authentication Protocol for Smart Grids.

Smart grids integrate information and communications technology into the processes of electricity production, transportation, and consumption, thereby enabling interactions between power suppliers and consumers to increase the efficiency of the power grid. To achieve this, smart meters (SMs) are installed in households or buildings to measure electricity usage and allow power suppliers or consumers to monitor and manage it in real time. However, SMs require a secure service to address malicious attacks during memory protection and communication processes and a lightweight communication protocol suitable for devices with computational and communication constraints. This paper proposes an authentication protocol based on a one-way hash function to address these issues. This protocol includes message authentication functions to address message tampering and uses a changing encryption key for secure communication during each transmission. The security and performance analysis of this protocol shows that it can address existing attacks and provides 105,281.67% better computational efficiency than previous methods.

An evolutionary game and system dynamics approach for the production and consumption of carbon-labeled products-based on a media monitoring perspective

Carbon label is acknowledged as an effective way to combat the problem of global warming. As a powerful way to encourage individuals to adjust their consumption patterns and to promote the development of green consumption, carbon-labeled products are widely developed in China. To reveal the production and consumption process of carbon-labeled products, the present study constructs a tripartite game model consisting of the government, carbon-labeled products manufacturers and consumers based on a media monitoring perspective. The evolutionary stability strategy (ESS) is firstly determined by solving the replication dynamic equations and stability analysis of the equilibrium point, followed by the strategy analysis and sensitivity analysis through numerical simulation. The results show that media supervision can effectively complement and constrain government supervision. In addition, it can promote enterprises to standardize production and enhance consumers' trust and willingness to buy carbon-labeled products. The introduction of media supervision can well realize the ideal equilibrium of "effective government supervision, enterprise compliance and consumer support for purchase".

Resource-efficient federated learning over IoAT for rice leaf disease classification

Rice is an important staple food in Asia. It is produced and consumed in large quantities. It contributes to 15 % of protein intake and 21 % of total per capita energy intake in the region, underscoring its important role as a primary global food source. Conversely, rice plants are heavily affected by bacterial, fungal and other microbial diseases, resulting in reduced plant health and crop yields and posing a major challenge for rice farmers. Manual diagnosis of these diseases is particularly problematic in regions with a shortage of agricultural experts. Farmers with insufficient experience sometimes incorrectly identify these diseases by hand. Recent advances in deep learning (DL) models offer a promising solution through automatic image recognition systems that can be very helpful in accurately identifying these diseases. This manuscript presents a resource-efficient federated learning IoAT (Internet of Agriculture Things) approach for rice leaf disease classification. This approach incorporates two key strategies, namely federated transfer learning and feature extraction, and evaluates their performance on various metrics such as accuracy, loss, precision, recall, AUC, and resource-related parameters such as GPU memory consumption, virtual memory, CPU and GPU process utilization. The dataset used in the study includes 5932 images of rice leaf diseases categorized as bacterial leaf blight, blast, brown spot and tungro. The research investigates the application of federated transfer learning and federated feature extraction techniques to classify rice leaf disease images. It performs a comparative analysis of their performance and resource utilization. EfficientNetB3, with an impressive validation accuracy of 99 %, is identified as the base model for the federated learning (FL) environment. Furthermore, we implement FL using both transfer learning and feature extraction methods and compare their results on a number of performance and resource related parameters. It is shown that the federated feature extraction approach has lower GPU memory and process utilization compared to the federated transfer learning approach and is thus more resource efficient. Extracting features before model training results in a lightweight model, which is especially beneficial for FL, IoAT devices that require efficient execution on edge devices. Therefore, the proposed approach is presented as a lightweight, privacy-friendly and resource-efficient solution for rice leaf disease classification.

The Effect of Water during the Compaction Process on Surface Characteristics of HMA Pavement.

During the compaction process of HMA pavement, it is common to spray cold water on the wheel of a road roller to prevent the mixture from sticking to the wheel, which might deteriorate the bonding strength between the asphalt binder and aggregate, and consequently lead to surface polishing of the pavement. This paper aims to demonstrate whether the water used during the compaction process affects the surface performance of HMA pavement. In this study, the black pixel ratio and mass loss ratio were used to evaluate the water effect on the surface performance of asphalt pavement, considering the water consumption, molding temperature and long-term ageing process. The test results indicated that the water used during the compaction process would increase the risk of surface polishing of HMA pavement. This adverse effect became more significant if the HMA samples were prepared using greater water consumption, a greater molding temperature and a long-term ageing process. Moreover, there exists a certain correlation between the black pixel ratio and mass loss ratio, and their relationships were demonstrated by the experimental results in this study. It is recommended that further research concentrates on the influencing mechanism and the treatment strategy for the adverse effect caused by the water used during the compaction process. The use of more types of asphalt binders, aggregate and methodologies is also recommended in further studies.