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Current weather monitoring systems often remain out of reach for small-scale users and local communities due to their high costs and complexity. This paper addresses this significant issue by introducing a cost-effective, easy-to-use local weather station. Utilizing low-cost sensors, this weather station is a pivotal tool in making environmental monitoring more accessible and user-friendly, particularly for those with limited resources. It offers efficient in-site measurements of various environmental parameters, such as temperature, relative humidity, atmospheric pressure, carbon dioxide concentration, and particulate matter, including PM 1, PM 2.5, and PM 10. The findings demonstrate the stations capability to monitor these variables remotely and provide forecasts with a high degree of accuracy, displaying an error margin of just 0.67%. Furthermore, the stations use of the Autoregressive Integrated Moving Average (ARIMA) model enables short-term, reliable forecasts crucial for applications in agriculture, transportation, and air quality monitoring. Furthermore, the weather stations open-source nature significantly enhances environmental monitoring accessibility for smaller users and encourages broader public data sharing. With this approach, crucial in addressing climate change challenges, the station empowers communities to make informed decisions based on real-time data. In designing and developing this low-cost, efficient monitoring system, this work provides a valuable blueprint for future advancements in environmental technologies, emphasizing sustainability. The proposed automatic weather station not only offers an economical solution for environmental monitoring but also features a user-friendly interface for seamless data communication between the sensor platform and end users. This system ensures the transmission of data through various web-based platforms, catering to users with diverse technical backgrounds. Furthermore, by leveraging historical data through the ARIMA model, the station enhances its utility in providing short-term forecasts and supporting critical decision-making processes across different sectors.

Residential buildings can contribute to save energy and to decrement electricity consumption in the world. On the other hand, the Internet of Things has allowed the implementation of smart homes that can profile the users. Nevertheless, end-users are not accepting the smart homes due to behavioral problems and usability problems with the Human–Machine Interface (HMI) or with the household appliances. As a solution, social products promote the interaction between the smart home and the consumer by including gamification features in the interface. Thus, smart homes can interact and compete with other houses to reduce energy consumption. Therefore, this paper proposes a three-step framework that takes advantage of social products to promote interaction between smart homes within a smart community to reduce electrical energy consumption. The first step collects from the literature review, the characteristics of the end-users, the behavioral and usability problems, and the most common gamification elements that teach, engage and motivate the user to reduce energy consumption. The second step proposes the gamification elements required for a tailored HMI in each social product and smart home through a fuzzy logic decision. The third step evaluates the interaction between social products in smart homes and the users to test which smart home is reducing more energy consumption. Finally, a three-level tailored gamified mock-up is depicted: level 1 for a single social product, level 2 for the smart home, and level three for the smart community. This mock-up can be implemented in small communities as residential complexes.

Connected thermostats (CTs) often save less energy than predicted because consumers may not know how to use them and may not be engaged in saving energy. Additionally, several models perform contrary to consumers’ expectations and are thus not used the way they are intended to. As a result, CTs save less energy and are underused in households. This paper reviews aspects of gamification and serious games focused on engaging consumers. A gamification and serious games framework is proposed for saving energy that is tailored by a fuzzy logic system to motivate connected thermostat consumers. This intelligent gamification framework can be used to customize the gamification and serious game strategy to each consumer so that fuzzy logic systems can be adapted according to the requirements of each consumer. The framework is designed to teach, engage, and motivate consumers while helping them save electrical energy when using their thermostats. It is described the proposed framework as well as a mockup that can be run on a cellphone. Although this framework is designed to be implemented in CTs, it can be translated to their energy devices in smart homes.

The users' behavior in a house impacts the amount of electrical energy consumption in the electrical products of the household; therefore, energy consumption can be optimized by using sense, smart and sustainable products (S3) as Social Products (SPs) for saving energy in the housing. The SPs communicate between devices and consumers through game design elements known as gamification that can yield behavioral changes in users. Furthermore, behavioral models can shape human behavior to motivate the individual to achieve a specific target, like saving energy. For the energy consumption reduction in households, it is proposed a structure with SPs that uses a gamification Human Machine Interface in each device to communicate between products and consumer in a Smart Home (SH). In this descriptive paper, it is proposed a three-steps structure that applies to every social product. First, it requires the classification of the type of end-user, the behavior, and usability problems when the product is deployed. Then, through the analysis of fuzzy logic, either type 1 or type 2, it is proposed which housing gamification elements best tailors the interface for each appliance. Finally, the application sends stimulus to individuals to test their performance and motivation to use each social product. After every social product has its proper gamification, it is possible to have interconnected SPs to get a Gamified SH. Besides, this Gamified SH could communicate with other Gamified Smart Homes to obtain a Gamified community.

This study evaluated the potential of a biocoagulant produced from prickly pear peel waste valorization and its use as a biocoagulant aid mixed with aluminum sulfate to remove turbidity in domestic wastewater. A central composite design (CCD) and a simplex lattice design (SLD) of two components (biocoagulant and aluminum sulfate) were developed to determine the optimal doses and pH of the biocoagulant and optimal mixing proportions. Both designs optimized the coagulation process from an analysis of variance to fit the experimental data to mathematical models and an optimization analysis to obtain the highest percentage of turbidity removal. The results showed that a water pH of 4 and a biocoagulant dose of 100 mg/L are optimal conditions for a turbidity removal of 76.1%. The potential decreases to 51.7% when the wastewater pH is maintained at 7.8 and a dose of 250 mg/L is used. This efficiency could be increased to 58.2% by using a mixture with optimal proportions of 30% biocoagulant and 70% aluminum sulfate. The experimental data were fitted to two quadratic models, estimating model prediction errors of 0.42% and 2.34%, respectively. Therefore, these results support the valorization of prickly pear peel waste to produce a biocoagulant, which could be used in acid and alkaline wastewater or as a biocoagulant aid mixed with aluminum sulfate. [ABSTRACT FROM AUTHOR]

The present study investigated the effectiveness of two biopolymer coagulants on turbidity and chemical oxygen demand removal in urban wastewater. The biopolymers were produced from vegetal biomass using the mucilage extracted from Opuntia robusta cladodes, and Uncaria tomentosa leaves. Opuntia robusta is an abundant species in Mexico, which is not edible. Uncaria tomentosa is an exotic invasive species in Mexico and other countries, which negatively affects the ecosystems where it is introduced. A combined experimental design of mixture–process was selected to evaluate the effectiveness of both biopolymer coagulants regarding aluminum sulfate (conventional chemical coagulant). Results showed turbidity and chemical oxygen demand removal efficiencies of 42.3% and 69.6% for Opuntia robusta and 17.2% and 39.4% for Uncaria tomentosa biopolymer coagulant, respectively, at a dose of 200 mg/L. Furthermore, optimum conditions from the experimental design to reach the maximum turbidity and chemical oxygen demand removal were obtained at an Opuntia robusta biopolymer coagulant concentration of 10 mg/L, showing removal efficiencies of 68.7 ± 1.7% and 86.1 ± 1.4%, respectively. These results support using Opuntia robusta as an alternative biopolymer coagulant in urban wastewater treatment. [ABSTRACT FROM AUTHOR]