No abstract

No abstract

No abstract

It is estimated that around 600,000 tons of end-of-life tires are generated annually in Thailand. These waste tires will cause danger to the environment and human health if handled improperly. On the other hand, if managed with the proper technology, it will be transformed into valuable products. This research aims to evaluate the potential environmental impacts of a waste tire pyrolysis plant in Thailand by using the Life Cycle Assessment (LCA) method. The functional unit is defined as 1 ton of products from the pyrolysis process of waste tires. The system boundary consists of a pre-treatment and pyrolysis process (gate-to-gate). The LCA calculations were carried out using licensed SimaPro 9.0 software. At the impact assessment step, the ReCiPe2016 method both Midpoint (problem-oriented) and Endpoint (damage-oriented) were applied, and 7 impact categories were selected (global warming, fine particulate matter formation, terrestrial acidification, freshwater eutrophication, terrestrial ecotoxicity, freshwater ecotoxicity, and fossil resource scarcity). If the avoided products from the pyrolysis process, including pyrolysis oil, steel wire, and carbon black were taken into account, the characterization results show that 3 impacts: global warming, terrestrial ecotoxicity, and fossil resource scarcity have a negative value. While the other impacts still have a positive value resulted mainly from electricity consumption. When considering weighting end-point results, it found that human health impact was a major contribution with a totally negative value of -0.947 Pt. As a summary, the outcomes confirm that the utilization of pyrolysis avoided products and the optimization of electricity consumption in the process has the potential to drives pyrolysis to become an environmentally effective technology for end-of-tires management.

Numerous studies have been undertaken to determine the optimal land use/cover classification algorithm. However, there have not been many studies that have compared and evaluated the performance of maximum likelihood (ML), random forest (RF), support vector machine (SVM), and classification and regression trees (CART) using ASTER imagery, especially in a mining district. Therefore, this study aims to investigate land use/cover (LULC) change over three decades (1990–2020), comparing the performance of the ML, RF, SVM, and CART machine learning algorithms. The Landsat and ASTER data were retrieved using Google Earth Engine (GEE). Traditional ML classification was performed on ArcGIS 10.2 software while RF, SVM, and CART classification were undertaken on GEE. Then, thematic accuracy assessments were conducted for the four algorithms and their performances were compared. The results showed that the largest changes in area occurred in forest cover that decreased from 37.8 to 27.3 km2 during the three decades. The remarkable expansion of gold mining occurred during 2005–2010 with the increases of 1.6%. The mining land rose by 2.9% during the study period whereas agricultural land increased significantly by 10.7% between 1990 and 2020. When comparing the four algorithms, the RF algorithm gives the highest accuracy with an overall accuracy of 95.85% while SVM follows RF with 91.69%. This study proved that RF is the best choice for optimal land use/cover classification, particularly in the mining district.