Medical waste represents a significant health risk and an environmental pollution concern due to its hazardous characteristics. The knowledge and practice of healthcare personnel in respect of the disposal of medical waste is essential to perform effective medical waste management. Therefore, the aim of this study was to assess the knowledge, attitudes, and practices related to medical waste management among healthcare workers in clinics (medical and dental clinics, specialized medical, laboratory clinics, polyclinics, and midwifery clinics) in Phuket, Thailand. A cross-sectional study was designed with stratified-random sampling used to select the sample of 344 respondents from 172 clinics of which data were collected using face-to-face interviews. The results showed that the majority of respondents (87.2) were female of whom 36.9 were aged (2029), 52.0 had more than 5 years working experience, and 51.2 had participated in at least one training course regarding medical waste management. The overall scores for knowledge, attitude, and practice were at a high level (89.5, 91.9, and 92.2, respectively). Significant and positive correlations were found between knowledge and attitude (r=0.464), knowledge and practice (r=0.396), and practice and attitude (r=0.519). Statistical analysis using t tests and one-way analysis of variance showed that working experience and its duration were significant factors influencing good medical waste management practice. However, local authorities should implement a well-planned collection and transfer process for medical waste in order to reduce the risk of environmental pollution and the risk of infection or injury to healthcare workers and the general public.

The beach sediment samples from Kalim, Tri Trang, and Patong Beaches, Phuket Province, southern of Thailand were collected for preliminary investigation on coastal debris. A total of 9 samples were taken from a 1 x 1 m quadrat at the depth of 5 cm, parallel with the shore during the period of maximum low tides in November, 2018. Microplastics (particles size < 5 mm) were extracted using sodium chloride (1.15 g mL-1) and their characteristics- including abundance (items m-2), weight (g), density (itemsm-2), color and shape- were determined using a stereo microscope. The results showed that microplastic debris varied in abundance from 1 to 35 items m-2 at the three selected beaches, depending on sampling location. In comparison, abundance of macroplastic (> 2.5 cm) and mesoplastic debris (5 mm to 2.5 cm) varied from 1 item m-2 quadrat (Kalim Beach) to 38 items (Patong Beach), 9 items (Tri Trang Beach) and 68 items (Patong Beach) m-2. Blue fibers were predominant among microplastic debris found at Kalim Beach; these are likely to have originated from pieces of rope, safeguard lines and fishing materials. In contrast, microplastic debris found in Patong and Tri Trang Beaches were predominantly green in color. The results of this study provide an overview of the sources of microplastics in coastal environments, which provides a basis for developing long term plastic management plans for these highly popular beaches in Phuket Province.

The accurate prediction of water quality parameters is essential for effective pollution control and resource management. This study presents a hybrid AI-remote sensing framework for forecasting water quality in the Gulf of Thailand, which combines Sentinel-2 imagery with Support Vector Machine (SVM) and Autoregressive Integrated Moving Average (ARIMA) models. Our approach achieves a 5.4× increase in data coverage over traditional methods, demonstrating the effectiveness of machine learning in environmental monitoring. Predictive accuracy was evaluated across Support Vector Machine (SVM), ARIMA, and Amazon Forecast models. Results indicate that SVM, optimised through RBF kernel and grid search, outperforms other models for Chlorophyll-a (RMSE: 1.8), while ARIMA exhibits superior performance for Secchi Depth (RMSE: 0.2) and Trophic State Index (RMSE: 0.8). The study also introduces Aqua Sight, a web-based visualisation tool built on Google Earth Engine, enabling stakeholders to access real-time water quality forecasts. These findings highlight the potential of integrating satellite-derived data with machine learning to enhance early warning systems and support environmental decision making in coastal ecosystems.