Humanity has developed recycling activities over time due to their benefits, the shortage of raw materials, or the footprint with regard to the environment. The absence of a recycling culture in Mexico has not allowed its development and growth despite the benefits. In 2012, Mexico only recycled less than 10% of urban solid waste. Most recycling activities are focused on plastic, paper, and cardboard products due to their prices in local markets. This article presents a semi-automated prototype focused on recycling glass bottles using the thermal shock phenomenon. It aims to develop a sustainable glass recycling culture by creating a new branch for the integral glass recycling process and a proposal base on Integrated Sustainable Waste Management (ISWM) and the Quintuple Helix Model. It helps to reduce waste and resource recovery from recycling and upcycling glass bottles. The products obtained from upcycling fulfill new uses and acquire new value, while glass leftovers continue the integral recycling process for glass. Additionally, this paper demonstrates the relation between the ISWM and the Quintuple Helix Model and the opportunity to implement the twelfth Sustainable Development Goal (SDG).

Water quality (WQ) monitoring is critical for Mexico and the world due to water pollution and scarcity problems in recent years. In this article, a systematic review was conducted considering only forecasting models focused on lentic freshwater bodies (to specialize the analysis of variables, problems, considerations, etc.) from 2019 to 2025 (to ensure the inclusion of the most relevant and new studies). This review analyzes 52 articles focused on the monitoring place, predictors, forecasted variables, configuration of each forecasting model, results with or without multiple forecast horizons, monitoring conditions, forecasting horizon, data availability, and model replicability. Our review shows that the main models documented used to predict WQ are based on machine learning (where RFs are the most used), AI (where ANNs are the most used and LSTM-based architectures are the most implemented), and statistical methods (where MLR is the most used). The principal forecasted WQ variables are Chl-α, DO, and TP. In comparison, the most used predictors are TP, temperature, and Chl-α. Furthermore, only 10 articles have made their databases available, and nine articles share the configuration of their models. Future research should investigate the real impact of data (quantity and inputs) variation in forecasting values for multiple forecast horizons.