Characterization of Demolition Construction Waste Containing Asbestos, and the Release of Fibrous Dust Particles

Clear and simple detection of asbestos stained with two dyes for building materials collected from disaster and demolition sites using a stereomicroscope

Whenever houses are demolished or disasters occur, large quantities of building materials are discharged, which may contain asbestos. To prevent the damage caused by asbestos exposure, a rapid asbestos presence confirmation method is required at demolition sites or temporary disaster storage sites. It is difficult to confirm the presence of asbestos in waste building materials by simple observation. However, it can be confirmed by staining the materials with two dyes: methylene blue (MB) with positive charge, and erythrosine (RED-3) with negative charge, and using a stereomicroscope. The method was applied to samples collected from disaster and demolition sites. Asbestos was stained violet or reddish-purple, and the base material of the building materials remained blue. Using this method, even amateur workers can detect asbestos by means of an image in a different color than the building substrate. Furthermore, the present method detected asbestos more explicitly than the official method (JIS A 1482, 1483; detection limit is < 0.1%) recommended by the Japanese government. This cost-effective method is suitable for detecting asbestos at disaster and demolition sites. The mixture of MB and RED-3 formed nanoparticles of size 151 nm and surface charge of -34 mV that selectively stained asbestos. The staining mechanism was discussed.

Measurements of airborne asbestos fibres during refurbishing

Although the use of asbestos fibres in building materials has been prohibited in Norway since 1985, asbestos-containing materials (ACMs) are still found in many buildings. Lack of knowledge and awareness of these materials may lead to exposure during refurbishing. The aim of this study was to investigate the airborne fibre concentration and classify fibres found during the abatement of various ACMs. The release of fibres during short-term work tasks, such as drilling and sawing, was also investigated. Parallel air samples were collected during asbestos abetment of different building materials and analysed with scanning electron microscope (SEM) and phase-contrast microscope (PCM), respectively. Material samples were analysed with SEM. A real-time fibre monitor was used to measure asbestos during short-term work. The highest fibre concentrations were measured for samples collected during the removal of asbestos insulating boards (1.5-4.5 fibres/cm3 [f/cm3]), and the numbers were relatively similar for SEM and PCM. A large difference in asbestos concentrations was found between SEM and PCM when analysing floor materials, which were probably caused by a high number of gypsum fibres that the PCM operator counted. Thin fibres (<0.2 µm in width) were included in the SEM count and constituted up to 50% of the total fibre concentration for the asbestos cement materials. The presence of other inorganic and organic fibres on these samples probably led to similar results between SEM and PCM. Short-term work led to peak concentrations above 30 f/cm3.

Cancer risk assessment of exposure to asbestos during old building demolition

BACKGROUND: Years ago, the use of asbestos in construction materials was common. Although asbestos has been recently banned in many countries, exposure to asbestos during old building demolition is not unexpected. OBJECTIVE: The aim of this study is to assess the concentration of exposure to asbestos and estimate its cancer risk among old building demolition workers. METHODS: In this study, personal air samples were collected during building demolition. The number of asbestos fibers in collected samples were determined according to the NIOSH-7400 standard method. Chemical compositions of fibers were assessed using scanning electron microscopy (SEM). The carcinogenic risk of exposure to asbestos was determined based on the recommended United State Environmental Protection Agency (USEPA) method and Monte-Carlo simulation used to estimate the probability of cancer. RESULTS: Chemical analysis confirmed the presence of asbestos in collected air samples, and 67% of counted fibers were asbestos. In a number of buildings, workers had exposed to asbestos that was higher than occupational exposure limit (0.10 f/ml). Results of cancer risk estimation showed that cancer risk were considerable among workers. CONCLUSION: Implementation of asbestos risk management program such as separation of asbestos containing material, personal protective equipment’s and use of wet method in demolition could minimize asbestos exposure during old building demolition.

Analyzed and Simulated Prediction of Emission Characteristics of Construction Dust Particles under Multiple Pollution Sources

Dust pollution in construction sites is an invisible hazard that is often ignored as a nuisance. Regulatory and engineering control methods are predominantly used for its mitigation. To control dust, dust-generating activities and their magnitudes need to be established. While researchers have comprehensively studied dust emissions of construction work, prediction of dust concentrations based on work phases and climatic conditions is still lacking. To overcome the above knowledge gap, this article selected two construction stages of a project to monitor dust generation using the HXF-35 dust sampler. Based on the collected data, dust emission characteristics of these two stages are studied, and dust emission characteristics under multiple pollution sources are analyzed. Based on the results, a BP neural network model is built to perform simulations of dust emission concentrations in different work areas and predict construction dust concentrations under different conditions. Except few, the majority of the work areas monitored have exceeded the allowable upper limit of TSP concentration stipulated by relevant standards. In addition, dust emission differences of work areas are pronounced. The results verified that the BP neural network dust concentration prediction model is feasible to be used to predict dust concentration changes in different work faces under different climate conditions and to provide a scientific base for pollution control. This study provides several practical solutions where the prediction of dust concentrations at designated work areas will allow construction companies early warning to implement mitigation measures before it becomes a serious health hazard. In addition, it provides an opportunity to re-evaluate those hazardous work in the light of these revelations. The outcome of this study is both original and useful for both construction companies and regulatory agencies. It can better predict the concentration of construction dust in different operating areas and different weather conditions and provide a guide for the prevention and control of construction dust.