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" Air pollution in Macau has become a serious problem following the Pearl River Delta’s (PRD) rapid industrialization that began in the 1990s. While there has been continual improvement in recent years, harmful air pollutant concentration levels are still common, impacting Macau residents' health and creating long-term medical costs to local society. With this in mind, Macau needs an air quality forecast system that accurately predicts pollutant concentration and an early alert system instead of only daily real-time reminders. Some scholars have previously carried out studies to develop an air quality forecast for Macau by successfully using statistical models. Therefore, pursuant to the outcomes of previous studies, this dissertation aims to build upon research results and explore further possibilities of building a better ML air quality forecast model based on the time series of air pollutants concentration and meteorological data. Four different state-of-the-art ML algorithms were used to create predictive models to forecast PM2.5, PM10, and carbon monoxide (CO) concentrations for the next 24 and 48-hour. These were Support Vector Machine (SVM), Artificial Neural Networks (ANN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). In addition, Multiple Linear Regression MLR, a standard ML model, was used for this dissertation as a baseline reference for performance comparison. The daily measurements of air quality data in Macau from 2016 to 2021 were collected for this dissertation. The 2020 and 2021 datasets were used for model testing while the four-year data prior to 2020 and 2021 were used to build and train the ML models. The results showed that SVM, ANN, RF, and XGBoost were able to provide a very good performance in building up a 24-hour forecast with higher R2 and lower RMSE, MAE, and BIAS. Meanwhile, all ML models in 48-hour forecasting performance were satisfactory enough to be accepted as a two-day continuous forecast even if the R2 value was lower than the 24-hour forecast. The 48-hour forecasting model could be further improved by proper feature selection based on the 24-hour dataset, using the SHAP value test, and the adjusted R2 value of the 48-hour forecasting model."
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Informal recycling plays a crucial role in municiapl solid waste management in many cities, particularly in the global South. This study examines the practices, challenges, and opportunities of informal recycling in Macau, a small city and Self Autonomous Region (S.A.R.) in China. Using qualitative research methods, including semi-structured interviews, this study explores the motivations and strategies of informal recyclers, the challenges they face, and the potential for collaboration with formal waste management systems. The findings of this study reveal that informal recycling in Macau is a complex and multifaceted reality and practice that involves a range of actors, from waste pickers to small-scale processors to exporters, all with their specific challenges. Informal recyclers are motivated by economic necessity, and they employ a variety of strategies to collect and process recyclable materials. However, they also face significant challenges, including high rental and transportation costs, lack of manpower, China’s waste import policies and ensuing restrictions, fluctuating global price rates of materials and the unstable income as serious consequence, accompanied by limited support from the local Government. This study also identifies opportunities for sustainable development of informal recycling in Macau, supported by the analysis of data collected via questionnaire survey regarding Macau citizens’ waste separation habits and their willingness to pay for resource separation and recovering process. The identified oppurtunities include establishing partnerships between informal and formal waste management actors, improving the infrastructure, and introducing environmental levy system and consistent policies and regulations. Overall, this study contributes to a better understanding of the role of informal recycling sector in waste management in Macau and provides insights into potential strategies for improving the sustainability of resource and waste management practices in the city