The leachability of pollutants from asbestos-containing waste, previously used for roofing was investigated. Laboratory tests were performed under static conditions (tests 1–20) in accordance with the TCLP methodology (with the use of acetic acid as the leaching medium, initial pH = 3.15). The maintaining of constant leaching conditions proved to be impossible at the experimental stage. Following the stabilization of conditions, the pH range for the obtained solutions increased to an average value of 8.3. Aluminum, boron, barium, cadmium, chromium, copper, iron, nickel, lead, strontium, zinc, and mercury were identified in the eluate. The low leachability of individual metals under the planned conditions was observed. In general, no leaching of such metals as cadmium, nickel, and lead was observed. The mercury content in the eluates is below the quantification limit, but the obtained values fall to around the limit of detection for the element. As compared with leaching with the use of distilled water (Klojzy-Karczmarczyk et al. 2021), zinc and boron additionally appear in eluates. The determined value of leachability for the individual analyzed elements increases from double to a few times with the use of the TCLP method. The value of leaching for barium is on average 5.56 mg/kg, for chromium it is 1.10 mg/kg, for copper 0.26 mg/kg, and for iron 0.80 mg/kg. In addition, the leaching of boron of around 3.00 mg/kg and of zinc 1.84 mg/kg was found. Higher leachability values were found only for strontium and aluminum. The leaching of strontium is on average around 62 mg/kg. While the leaching of aluminum is lower than values identified in the previous tests with the use of distilled water and is around 2.76 mg/kg. Products of leaching contain mainly pollutants characteristic of cement (aluminum, strontium, and iron).

The disposal of industrial steel mill sludge in landfills has frequently received significant concern as the sludge has a very notable potential to contaminate soil surface and groundwater in the long run. Recently, the incorporation of industrial steel mill sludge into fired clay brick has become one of the promising alternative methods as it could produce a lightweight product while minimizing the environmental impact of the waste used. In this study, fired clay bricks as the most common building material were incorporated with 0%, 5%, 10% and 15% of steel mill sludge and fired at 1050°C (heating rate of 1°C/min). The manufactured bricks were subjected to physical and mechanical properties such as firing shrinkage, dry density, and compressive strength while the Toxicity Characteristic Leaching Procedure (TCLP) was conducted to analyze leaching behavior from the manufactured bricks. The results demonstrated that incorporation up to 15% of steel mill sludge reduces the properties up to 27.3% of firing shrinkage, 8.1% of dry density and 67.3% of compressive strength. The leaching behavior of Zn and Cu from steel mill sludge was reduced up to 100% from 7414 to 9.22 ppm (Zn) and 16436 to 4.654 ppm (Cu) after 15% of sludge incorporation. It was observed that high temperature during the firing process would improve the properties of bricks while immobilizing the heavy metals from the waste. Therefore, recycling steel mill sludge into construction building materials could not only alleviate the disposal problems but also promote alternative new raw materials in building industries.

Missing data in test result tables can significantly impact the analysis quality, especially in relation to technical sciences, where the mechanism generating missing data is often non-random, and their presence depends on the non-observed part of studied variables. In such cases, the application of an inappropriate method for dealing with missing data will lead to bias in the estimated distribution parameters.
The article presents a relatively simple method to implement in dealing with missing data generated as a result of the MNAR mechanism, which utilizes the censored random variable. This procedure does not modify the variable distribution form, which is why it ensures objective and efficient estimation of distribution parameters within studies affected by certain restrictions of technical or physical nature (censored distribution), with a relatively low workload. Furthermore, it does not require the application of specialized software. A prerequisite for using this method is the knowledge of the frequency and cause of missing data.
The method for estimating the random variable censored distribution parameters was shown based on the example of studying the leachability of selected heavy metals from a hardening slurry. The analysis results were compared with classical methods for dealing with missing data, such as, ignoring missing data observations (listwise or pairwise deletion), single imputation and stochastic regressive imputation.