Smoke and NOx emission characteristics of in-use construction machinery base on substantial field measurement: A case study in Beijing, China.

To understand the smoke level and NOx emission characteristics of in-use construction machinery in Beijing, we selected 905 construction machines in Beijing from August 2022 to April 2023 to monitor the emission level of smoke and NOx. The exhaust smoke level and excessive emission situation of different machinery types were identified, and their NOx emission levels were monitored according to the free acceleration method. We investigated the correlation of NOx and smoke emission, and proposed suggestions for controlling pollution discharge from construction machinery in the future. The results show that the exhaust smoke level was 0-2.62 m-1, followed a log-normal distribution (µ = -1.73, δ = 1.09, R2 = 0.99), with a 5.64% exceedance rate. Differences were observed among machinery types, with low-power engine forklifts showing higher smoke levels. The NOx emission range was 71-1516 ppm, followed a normal distribution (µ = 565.54, δ = 309.51, R2 = 0.83). Differences among machinery types were relatively small. Engine rated net power had the most significant impact on NOx emissions. Thus, NOx emissions from construction machinery need further attention. Furthermore, we found a weak negative correlation (p < 0.05) between the emission level of smoke and NOx, that is the synergic emission reduction effect is poor, emphasizing the need for NOx emission limits. In the future, the oversight in Beijing should prioritize phasing out China Ⅰ and China Ⅱ machinery, and monitor emissions from high-power engine China Ⅲ machinery.

Model-based prototype design, establishment and operation of ventilation system for underground gymnasium.

Underground small indoor gymnasiums (USIG) are important public places, it is vital to design and build a very economical and efficient ventilation system for effective closed-loop regulation of temperature and gases concentration at prescribed levels. In the article, the model-based prototype design, establishment and operation were proposed and applied to closed-loop control system of the underground small indoor gymnasiums' ventilation system (USIGVS). First of all, the extended Multiphysics model was developed through feedback connecting the 3D Multiphysics model of air flow rate, temperature, O2 and CO2 concentration with a 0D proportional-integral-derivative (PID) controller via Neumann boundary condition, hence a close-loop USIGVS was constructed for feedback control of temperature and gases concentration in ping-pong USIG. Simultaneously, a cost function sufficiently representing the design requirement was formulated. Then global parameter sensitivity analysis (GPSA) was applied for sensitivity ranking of parameters including geometric parameters of USIGVS and tunable parameters of PID controller. The GPSA proved that sensitivity ordering of the cost function to each parameter was proportional gain (k p ) > derivative gain (k d ) > distance from left inlet to bottom (r) > distance from outlet pipe to bottom (d) > integrative gain (k i ) > distance from upper inlet pipe to left (h), respectively, and the k p , k d and r was the parameter influencing the cost function the most. The optimal parameters determined by both GPSA and response optimization were k p  = 3.17 m4 mol-1 s-1, k d  = 1.49 m4 mol-1, r = 2.04 m, d = 3.12 m, k i  = 0.37 m4 mol-1 s-2 and h = 3.85 m. Finally, the closed-loop USIGVS prototype with optimal parameters was designed and established through real-time simulation. The real-time operation confirmed that the temperature and gases concentrations were robust maintained at prescribed levels with desired dynamic response characteristics and lower power consumption, and the expected requirements were achieved for the design, establishment and operation of closed-loop USIGVS control system prototype.

Compost incorporation and wildflowers introduction for stormwater infiltration and erosion-control vegetation cover establishment in post-construction landscapes.

Urban and suburban development frequently disturbs and compacts soils, reducing infiltration rates and fertility, posing challenges for post-development vegetation establishment, and contributing to soil erosion. This study investigated the effectiveness of compost incorporation in enhancing stormwater infiltration and vegetation establishment in urban landscapes. Experimental treatments comprised a split-split plot design of vegetation mix (grass, wildflowers, and grass-wildflowers) as main plot, ground cover (hydro-mulch and excelsior) as subplot, and compost (30% Compost and No-Compost) as sub-subplot factors. Wildflower inclusion was motivated by their recognized ecological benefits, including aesthetics, pollinator habitat, and deep root systems. Vegetation cover was assessed using RGB (Red-Green-Blue) imagery and ArcGIS-based supervised image classification. Over a 24-month period, bulk density, infiltration rate, soil penetration resistance, vegetation cover, and root mass density were assessed. Results highlighted that Compost treatments consistently reduced bulk density by 19-24%, lowered soil penetration resistance to under 2 MPa at both field-capacity and water-stressed conditions, and increased infiltration rate by 2-3 times compared to No-Compost treatments. Vegetation cover assessment revealed rapid establishment with 30% compost and 60:40 grass-wildflower mix, persisting for an initial 12 months. Subsequently, all treatments exhibited similar vegetation coverage from 13 to 24 months, reaching 95-100% cover. Compost treatments had significantly higher root mass density within the top 15 cm than No-Compost, but compost addition did not alter the root profile beyond the 15 cm depth incorporation depth. The findings suggest that incorporating 30% compost and including a wildflower or grass-wildflower mix appears to be effective in enhancing stormwater infiltration and provides rapid erosion control vegetation cover establishment in post-construction landscapes.

Construction and verification of a risk prediction model for cognitive frailty in older patients with chronic obstructive pulmonary disease and diabetes mellitus.

OBJECTIVE: We explored risk factors for cognitive frailty in older patients with chronic obstructive pulmonary disease (COPD) and diabetes mellitus to develop and verify a risk prediction model for cognitive frailty. METHODS: This was a cross-sectional study. Convenience sampling was used to randomly select 378 patients hospitalized between February 2022 and December 2023. We allocated 265 patients who visited between February 2022 and February 2023 to a modeling group to analyze risk factors for cognitive frailty and create a logistic regression model for risk prediction. Another 113 patients who visited between March 2023 and December 2023 were included in a validation group for model verification. RESULTS: The cognitive frailty incidence in the 265 patients was 35.09% (93/265). Regression analysis showed that age >80 years (odds ratio [OR] = 4.576), regular exercise (OR = 0.390, polypharmacy (OR = 3.074), depression (OR = 2.395) duration of COPD combined with diabetes (OR = 1.902), Family APGAR index score (OR = 0.428), and chronic pain (OR = 2.156) were factors influencing the occurrence of cognitive frailty in older patients with COPD accompanied by diabetes. CONCLUSIONS: The constructed risk prediction model for cognitive frailty in older patients with COPD and diabetes showed good predictive value, aiding in the clinical identification of high-risk patients and facilitating timely intervention and guidance.

Effect of smart city construction on an urban transition to a low-carbon economy: evidence from China.

China is currently in a new era of an urban transition to a low-carbon economy and digital economic development. Smart cities, as an advanced form of information-based urban development, may be the key to the urban transition to low-carbon emissions. This paper examined the effect of smart city construction (SCC) on urban low-carbon transitions and its transmission mechanisms in China from the dual perspectives of reducing urban total carbon emissions (TCE) and improving urban total-factor carbon emission efficiency (TFCEE). Utilizing a multi-period difference in differences (DID) method, this study was conducted based on panel data of 245 Chinese prefecture-level cities from 2003 to 2021. The results demonstrated that SCC both reduced TCE and enhanced TFCEE. The effects of SCC were stronger in cities with more stringent environmental regulations. SCC achieved the dual effect of reducing TCE and enhancing urban TFCEE by promoting green technological progress and a low-carbon transformation of city residents' lifestyles. Moreover, optimization of the industrial structure was also a transmission mechanism for SCC to improve TFCEE. These conclusions provide an empirical basis for the SCC to empower low-carbon transitions of cities and help countries in different regions to transform the extensive urban development mode and promote urban low-carbon economic development.

Sensory equipment for monitoring and assessing the jumping ability of volleyball players.

Purpose: The objective of this research was to develop a sensor device to control and evaluate the jumping ability of elite volleyball athletes and to test its efficacy in a pedagogical experiment. Methods: The study involved determining the pulsometric and respiratory parameters during test loads, indicative of the endurance and speed-strength aspects essential for volleyball performance. Additionally, the necessity for post-training and post-competition jump performance restoration via short-term relaxation exercises was identified. Results: Through the developed computer program, a method for storing maximal vertical jumps in computer memory was established. Furthermore, a technique was developed to determine the functional significance of maximum vertical jump performance among elite volleyball players. Notably, participants in the experimental group, who performed specialized exercises developed within the experimental framework, exhibited discernible progressive improvements compared to the control group participants. Before the experiment, the maximum number of jumps in the experimental group was 29.2 ± 2.73, with a jump time of 31.7 ± 3.08. Conclusions: The equipment developed for monitoring and assessing volleyball players' jumping ability has proven effective, warranting its incorporation into training regimens.

Optimization path of primary public health service talent team construction: a largescale survey in Huaihai Economic Zone, China.

Background: The primary public health service system is indispensable for the implementation of the "Healthy China 2030" strategy, and primary healthcare workers, as the key drivers of this system, play a pivotal role in its development and establishment to ensure population well-being. In developing countries, such as China, primary public health systems are still weak, and in order to address this phenomenon, health system reform is needed, and primary public health personnel are crucial to health system reform. The current situation of primary public health workers in low-income and developing countries is characterized by varying degrees of problems that need improvement. Objectives: The purpose of this study is to understand the current situation of primary public health service workforce building, analyze the existing problems of the workforce, put forward suggestions for improvement and explore countermeasures, and provide Chinese wisdom and a reference basis for primary public health workforce building in the world, especially in developing countries. Methods: Combining the Work-Family Conflict Scale, Copenhagen Burnout Inventory, Minnesota Satisfaction Questionnaire, and Turnover Intention Scale, a relevant survey questionnaire was designed to quantitatively investigate the baseline characteristics of primary public health service institutions and their staff in four representative cities in the Huaihai Economic Zone: Xuzhou in Jiangsu Province, Linyi in Shandong Province, Shangqiu in Henan Province, and Huaibei in Anhui Province. The collected data were analyzed and processed using SPSS 25.0 statistical analysis software through univariate analysis and logistic regression analyses. Methods such as one-way ANOVA, Logistic regression analysis, and independent samples t-test were used to analyze the influencing factors of primary public health workforce development. Results: The current work intensity at the primary public health level is currently high, the salary and benefits cannot meet the needs of most primary public health personnel, and the competition between work and family in terms of time and resources is pronounced, and the majority of primary public health personnel are dissatisfied with the status quo of "doing more work for less reward" and the poor social security. Emotional exhaustion, depersonalization, and a sense of personal accomplishment were positively correlated with the tendency to leave (all p < 0.01), and the burnout and emotional exhaustion of primary public health workers were intense. Conclusion: Primary public health personnel play an important role in providing primary public health services. However, the current working conditions of junior public health personnel in the Huaihai Economic Zone are influenced by factors such as workload, income level, and employment situation improvement, leading to low job satisfaction, significant work-family conflicts, and high turnover intention. In this context, based on the opinions of grassroots administrative departments and internationally relevant experiences, a series of suggestions have been proposed to improve the professional service level, job satisfaction, and occupational identity of staff members. These suggestions make valuable contributions to both the Huaihai Economic Zone and countries worldwide in safeguarding individual health and promoting national primary healthcare reform.

"Track style" children's fundamental movement skills test: construction and verification of an efficient evaluation system.

Objectives: This study aimed to develop an efficient tool for assessing children's fundamental motor skills, the "Track style" Children's Fundamental Movement Skills Test (TCFMST), based on theories of motor development integrated with Chinese cultural context and physical education teaching situations. Methods: Starting from a literature analysis, the study selected items from existing fundamental movement skill (FMS) assessments, textbooks, physical education and health standards, and children's movement guidelines to construct a pool of test items. Subsequently, the items were screened and optimized using the Delphi method. Finally, the feasibility, discrimination, difficulty, reliability, and validity of the constructed test were examined using testing methods. Results: The TCFMST includes three dimensions: locomotive skills, body control skills, and manipulative skills, with a total of 10 items. The difficulty and discrimination of each item are appropriate; the correlation coefficients for retest reliability range from 0.789 to 0.943 (p < 0.01). The results of exploratory factor analysis indicate that the common factors align with the hypothesized three dimensions, indicating good structural validity of the test. The concurrent validity results show a correlation coefficient of -0.510 (p < 0.01) between the TCFMST and the total score of TGMD-3, indicating a moderate correlation between the two tests. Conclusion: The TCFMST developed in this study has good difficulty, discrimination, reliability, and validity. It also features strong operability, a short duration, and high interest. It can serve as an important tool for monitoring children's fundamental motor skill levels.

Constructing a Hospital Department Development-Level Assessment Model: Machine Learning and Expert Consultation Approach in Complex Hospital Data Environments.

BACKGROUND: Every hospital manager aims to build harmonious, mutually beneficial, and steady-state departments. Therefore, it is important to explore a hospital department development assessment model based on objective hospital data. OBJECTIVE: This study aims to use a novel machine learning algorithm to identify key evaluation indexes for hospital departments, offering insights for strategic planning and resource allocation in hospital management. METHODS: Data related to the development of a hospital department over the past 3 years were extracted from various hospital information systems. The resulting data set was mined using neural machine algorithms to assess the possible role of hospital departments in the development of a hospital. A questionnaire was used to consult senior experts familiar with the hospital to assess the actual work in each hospital department and the impact of each department's development on overall hospital discipline. We used the results from this questionnaire to verify the accuracy of the departmental risk scores calculated by the machine learning algorithm. RESULTS: Deep machine learning was performed and modeled on the hospital system training data set. The model successfully leveraged the hospital's training data set to learn, predict, and evaluate the working and development of hospital departments. A comparison of the questionnaire results with the risk ranking set from the departments machine learning algorithm using the cosine similarity algorithm and Pearson correlation analysis showed a good match. This indicates that the department development assessment model and risk score based on the objective data of hospital systems are relatively accurate and objective. CONCLUSIONS: This study demonstrated that our machine learning algorithm provides an accurate and objective assessment model for hospital department development. The strong alignment of the model's risk assessments with expert opinions, validated through statistical analysis, highlights its reliability and potential to guide strategic hospital management decisions.

Scale Construction of "Breathing" Bi/N-CNSs Quasi-Array Structure with Hierarchical Bi Distribution for Sodium-Ion Battery.

Sodium ion batteries (SIBs) are promising postlithium battery technologies with high safety and low cost. However, their development is hampered by complicated electrode material preparation and unsatisfactory sodium storage performance. Here, a bismuth/N-doped carbon nanosheets (Bi/N-CNSs) composite featuring a quasi-array structure (alternated porous Bi layers and N-CNSs) with hierarchical Bi distribution (large particles of ∼35 nm in Bi layers and ultrafine Bi of ∼8 nm on N-CNSs) is prepared. Bi/N-CNSs delivers an ultralong-lifespan of 26000 cycles at 5 A g-1 and prominent rate capability of 91.5% capacity retention at 100 A g-1. Even at -40 °C, it exhibits a high rate capability of 161 mAh g-1 at 5 A g-1. Notably, the involved preparation method is characterized by a high yield of 14.53 g in a single laboratory batch, which can be further scaled up, and such a method can also be extended to synthesize other metallic-based materials.

Eco-friendly concrete: Combining treated wastewater and recycled aggregates.

The concrete industry is a significant consumer of drinking water and natural aggregates, such as sand and gravel. However, the scarcity of water and aggregate resources and the challenges associated with the disposal of construction and demolition waste prompted the exploration of alternative materials. This study investigates the feasibility of incorporating secondary treated wastewater from UASB reactors followed by trickling filters and mixed recycled aggregates as potential alternatives. To assess the viability of these alternatives, the study considered the replacement of 100% potable water with treated wastewater, as well as varying proportions of recycled gravel (20, 40, 60, 80, and 100%) and recycled sand (10, 20, 30, 40, and 100%). Physical and mechanical properties were negatively affected, but it was possible to reach compressive results over 40 MPa and splitting tensile strength over 4 MPa for almost all mixes. Regarding physical properties, the use of alternative materials caused poorer outcomes for density, water absorption, and air-void ratio. The limited magnitude of these detrimental effects indicates the potential of manufacturing concrete with the addition of combined treated wastewater and recycled aggregate as a viable strategy while enhancing reuse practices.

Evaluating the effectiveness of strategies for mitigating fatigue among construction workers in Abuja, Nigeria: a quantitative analysis.

Fatigue is one of the menaces that contribute to the rising number of construction-related accidents and fatalities in projects. Therefore, the purpose of this study is to identify several strategies that can be adopted to mitigate fatigue in construction projects. This was achieved through a quantitative study. A questionnaire was the main instrument for data collection in the quantitative study. The study revealed that frustration/depression or work pressure is one of the significant causes of fatigue in construction projects. Fatigue risk management education and bonding among workers are some of the underlying strategies that can be used to mitigate the identified causes. Depending on the nature of the fatigue causative elements, the study further established that some mitigation strategies are more effective than others. Therefore, further studies should be conducted by prospective researchers on a range of attributes that may influence the success of fatigue mitigation strategies in construction projects.

Lane Attribute Classification Based on Fine-Grained Description.

As an indispensable part of the vehicle environment perception task, road traffic marking detection plays a vital role in correctly understanding the current traffic situation. However, the existing traffic marking detection algorithms still have some limitations. Taking lane detection as an example, the current detection methods mainly focus on the location information detection of lane lines, and they only judge the overall attribute of each detected lane line instance, thus lacking more fine-grained dynamic detection of lane line attributes. In order to meet the needs of intelligent vehicles for the dynamic attribute detection of lane lines and more perfect road environment information in urban road environment, this paper constructs a fine-grained attribute detection method for lane lines, which uses pixel-level attribute sequence points to describe the complete attribute distribution of lane lines and then matches the detection results of the lane lines. Realizing the attribute judgment of different segment positions of lane instances is called the fine-grained attribute detection of lane lines (Lane-FGA). In addition, in view of the lack of annotation information in the current open-source lane data set, this paper constructs a lane data set with both lane instance information and fine-grained attribute information by combining manual annotation and intelligent annotation. At the same time, a cyclic iterative attribute inference algorithm is designed to solve the difficult problem of lane attribute labeling in areas without visual cues such as occlusion and damage. In the end, the average accuracy of the proposed algorithm reaches 97% on various types of lane attribute detection.

A Multi-Sensing IoT System for MiC Module Monitoring during Logistics and Operation Phases.

Modular integrated construction (MiC) is now widely adopted by industry and governments. However, its fragile and delicate logistics are still a concern for impeding project performance. MiC logistic operations involve rigorous multimode transportation, loading-unloading, and stacking during storage. Such processes may induce latent and intrinsic damage to the module. This damage causes safety hazards during assembly and deteriorates the module's structural health during the building use phase. Also, additional inspection and repairs before assembly cause uncertainties and can delay the whole supply chain. Therefore, continuous monitoring of the module's structural response during MiC logistics and the building use phase is vital. An IoT-based multi-sensing system is developed, integrating an accelerometer, gyroscope, and strain sensors to measure the module's structural response. The compact, portable, wireless sensing devices are designed to be easily installed on modules during the logistics and building use phases. The system is tested and calibrated to ensure its accuracy and efficiency. Then, a detailed field experiment is demonstrated to assess the damage, safety, and structural health during MiC logistic operations. The demonstrated damage assessment methods highlight the application for decision-makers to identify the module's structural condition before it arrives on site and proactively avoid any supply chain disruption. The developed sensing system is directly helpful for the industry in monitoring MiC logistics and module structural health during the use phase. The system enables the researchers to investigate and improve logistic strategies and module design by accessing detailed insights into the dynamics of MiC logistic operations.

Non-Destructive Testing for Documenting Properties of Structural Concrete for Reuse in New Buildings: A Review.

Reuse in new buildings of structural concrete components from demolitions holds the potential for avoiding the use of raw materials to produce new components, including cement for new castings. Reuse rates are high in the circular economy; however, reusing structural components requires documentation of the properties to equate the safety of using reused and new components. Yet, there is no structured or recognized way to perform the documentation. This paper discusses a framework for the documentation requirements for structural concrete, stating the need for documenting the mechanical properties, concrete heterogeneity, and corrosion status of the reinforcement. The possibility is explored for documenting the required properties while the components are in the donor building by use of non-destructive test (NDT) methods. Such use of NDT methods is new. A comprehensive literature survey on the indirect literature, where NDT methods are used to demonstrate similar concrete properties though related to other purposes, is conducted. The overall conclusion is that the use of NDT methods has the potential to document the requested properties before reuse. The next steps towards implementation of NDT for documenting the properties of structural concrete components for reuse involve research in combined NDT methods and the development of AI systems for data interpretation.

Technology for Automated Production of High-Performance Building Compounds for 3D Printing.

Three-dimensional printing technology in construction is a rapidly growing field that offers innovative opportunities for design and construction execution. A key component of this process is the automated production of high-performance construction mixtures that meet specific requirements for strength, fluidity, and setting speed. This overview article outlines the history and development of 3D printing technology in the construction industry, describes various printing technologies, and discusses the properties and requirements for construction mixes. Special attention is given to automated systems for batching and mixing ingredients, which increase the precision and efficiency of production. The different types of construction mixes used in 3D printing and the main technical and operational challenges associated with their application are also presented. The article's conclusions highlight the potential of this technology to revolutionize the construction industry by improving efficiency and reducing costs and project lead times.

The Influence of Weld Interface Characteristics on the Bond Strength of Collision Welded Aluminium-Steel Joints.

Collision welding is a promising approach for joining conventional materials in identical or dissimilar combinations without heat-related strength loss, thereby opening up new lightweight potential. Widespread application of this technology is still limited by an insufficient state of knowledge with respect to the underlying joining mechanisms. This paper applies collision welding to a material combination of DC04 steel and EN AW 6016 aluminium alloy. Firstly, the welding process window for the combination is determined by varying the collision speed and the collision angle, the two main influencing variables in collision welding, using a special model test rig. The process window area with the highest shear tensile strength of the welded joint is then determined using shear tensile tests and SEM images of the weld zone. The SEM investigations reveal four distinct metallographic structures in the weld zones, the area fractions of which are determined and correlated with collision angle and shear tensile strength.

Facile Construction Engineering of Pr6O11@C with Efficient Photocatalytic Activity.

In this study, facile construction engineering of Pr6O11@C with efficient photocatalytic activity was established. Taking advantage of the flocculation of Pr3+ in the base medium, acid red 14 (AR14) was flocculated together with Pr(OH)3 precipitate, in which Pr(OH)3 and AR14 mixed highly uniformly. Calcinated at high temperature in N2, a novel Pr6O11@C was successfully synthesized. The resulting materials were characterized by XRD, SEM, FT-IR, Raman, and XPS techniques. The results show that the cubic Pr6O11@C with Fm3m space group, similar to that of Pr6O11, was obtained. From the results of the photodegradation of AR14, it is found that the photocatalytic efficiency of Pr6O11@C is higher than that of pure Pr6O11 due to the formation of abundant carbon bonds and oxygen vacancies. Compared with pure Pr6O11 and other carbon-based composites, the acid resistance of Pr6O11@C is greatly improved due to the highly uniform dispersion of Pr6O11 and C, which lays a solid foundation for the practical application of Pr6O11@C. Moreover, the role of NH3·H2O and NaOH used as precipitants for the photocatalytic efficiency of Pr6O11 was investigated in detail.

Enhancing Clay-Based 3D-Printed Mortars with Polymeric Mesh Reinforcement Techniques.

Additive manufacturing (AM) technologies, including 3D mortar printing (3DMP), 3D concrete printing (3DCP), and Liquid Deposition Modeling (LDM), offer significant advantages in construction. They reduce project time, costs, and resource requirements while enabling free design possibilities and automating construction processes, thereby reducing workplace accidents. However, AM faces challenges in achieving superior mechanical performance compared to traditional methods due to poor interlayer bonding and material anisotropies. This study aims to enhance structural properties in AM constructions by embedding 3D-printed polymeric meshes in clay-based mortars. Clay-based materials are chosen for their environmental benefits. The study uses meshes with optimal geometry from the literature, printed with three widely used polymeric materials in 3D printing applications (PLA, ABS, and PETG). To reinforce the mechanical properties of the printed specimens, the meshes were strategically placed in the interlayer direction during the 3D printing process. The results show that the 3D-printed specimens with meshes have improved flexural strength, validating the successful integration of these reinforcements.

Adsorption Dynamics of Disodium Octaborate Tetrahydrate on Clay Minerals: Implications for Construction Wood Protection.

The wood preservative disodium octaborate tetrahydrate (DOT) migration is studied in clay. Using boron analysis by inductively coupled plasma optical emission spectroscopy (ICP-OES), DOT spatial and temporal dynamics are surveyed to show how DOT permeates into the wood and the clay using concentration profiles as a function of depth, initial wood moisture, and direction of filling. Atomic force microscopy and chemical imaging using photoinduced force microscopy are used to show the morphology of the wood samples and the distribution of DOT on their surface. ICP-OES results show that the average DOT concentration in the wood samples is originally 0.8 and 1.5 wt% in the bulk and at the surface, respectively. Conditioning of the wood to a moisture content of 19% in a climatic chamber reduces DOT concentration by 8% for the fir and 17% for the spruce. After one week of contact with the clays, the results showed a rapid decrease of 25-40% in DOT concentration in wood. On longer periods (5 months), the spruce shows a tendency to reabsorb the DOT from the clay and the DOT migration stabilizes at 20%. These results contribute to defining the dosage of DOT when the wood is exposed to clay.