Today, air pollution is the biggest environmental health problem in the world. Air pollution leads to adverse effects on human health, climate and ecosystems. Air is contaminated by toxic gases released by industry, vehicle emissions and the increased concentration of harmful gases and particulate matter in the atmosphere. Air pollution can cause many serious health problems such as respiratory, cardiovascular and skin diseases in humans. Nowadays, where air pollution has become the largest environmental health risk, the interest in monitoring air quality is increasing. Recently, mobile technologies, especially the Internet of Things, data and machine learning technologies have a positive impact on the way we manage our health. With the production of IoT-based portable air quality measuring devices and their widespread use, people can monitor the air quality in their living areas instantly. In this study, e-nose, a real-time mobile air quality monitoring system with various air parameters such as CO₂, CO, PM₁₀, NO₂ temperature and humidity, is proposed. The proposed e-nose is produced with an open source, low cost, easy installation and do-it-yourself approach. The air quality data measured by the GP2Y1010AU, MH-Z14, MICS-4514 and DHT22 sensor array can be monitored via the 32-bit ESP32 Wi-Fi controller and the mobile interface developed by the Blynk IoT platform, and the received data are recorded in a cloud server. Following evaluation of results obtained from the indoor measurements, it was shown that a decrease of indoor air quality was influenced by the number of people in the house and natural emissions due to activities such as sleeping, cleaning and cooking. However, it is observed that even daily manual natural ventilation has a significant improving effect on air quality.

Muestras de las aleaciones 6082 A1 y 7075 A1 se sometieron a un tratamiento térmico en horno de inducción de ultraalta frecuencia (UHFIHS) a 580 ºC durante 1 min y suministro de agua al final del proceso. El envejecimiento artificial se llevó a cabo a 190 °C durante 2, 4, 6 y 8 min. En ambas aplicacio-nes, el calentamiento se llevó a cabo utilizando un sistema de inducción con una frecuencia de 900 kHz y una potencia de 2,8 kW. Para estas series y diseños de aluminio, se calcularon los costos del tratamiento térmico. Adicionalmente, se compararon los valores de dureza de las muestras de 6082 A1 y 7075 A1 envejecidas artificialmente a 190 °C durante 10 h con los métodos convencionales 540 °C durante 5 h. Los resultados de dureza de la muestra 6082 Al obtenidos en 10 h mediante métodos convencionales, tardaron sólo 8 min mediante el envejecimiento artificial con el sistema de inducción.

Air pollution has become a severe problem in most of the world and is among the governments' prior subjects. In the present time, urban dwellers spend most of their time in confined spaces such as home, office, school, shopping malls, and gyms. Contaminant gases (CO2, CO, NO2) and particulate matters arising from occupant activities such as exercise, sleeping, cooking, smoking, and cleaning are among the most critical factors which influence indoor air quality. Such gasses and particulate matter depend on human activities lower indoor air quality; hence, they cause many serious health problems, especially respiratory tract, cardiovascular and dermatological diseases. In this study, the indoor air quality of housing is examined depending upon occupant activities. Air quality parameters of temperature, humidity, CO2, CO, PM10, NO2, which are collected from the bathroom, kitchen, living room and bedroom of the housing, are measured using 32-bit ESP32 controller and a set of air quality sensors Obtained air quality data is saved to cloud server by the help of mobile user interface developed through Blynk IoT platform. As a result of the analysis, it is observed that occupant activities like sleeping, shower, laundry, and cooking adversely affect indoor air quality.

The high installation and operational costs of reference-grade air quality monitoring systems have accelerated the widespread adoption of low-cost sensors (LCS). However, their susceptibility to environmental influences, temporal drift, and measurement uncertainty necessitates robust calibration approaches to ensure reliable measurements. Although machine learning (ML)-based calibration methods have been widely investigated, most existing implementations rely on static analytical workflows and require programming expertise, which limits their accessibility for many domain specialists. To simplify and standardize the calibration process for low-cost air quality sensors, this study presents Air Quality Multi-Model Calibration (AQ-MultiCal), an interactive, no-code platform. The platform provides a unified environment for evaluating 14 regression models, performing automated hyperparameter optimization, and conducting comparative performance analysis through an intuitive graphical interface supported by interactive visualization tools. The platform is validated using CO₂ measurements collected from January and February 2025. Experimental results indicate that the optimized k-nearest neighbors (kNN) model achieved the best performance, with a coefficient of determination of R² = 0.990 with low prediction error. These results demonstrate that AQ-MultiCal enables accurate sensor calibration and systematic comparison of ML models while improving the accessibility of ML-based calibration through an open-source platform designed for domain experts without programming expertise.

In this study, an integrated machine learning (ML)-based calibration approach was employed for the data-driven calibration of a low-cost PM_2.5 sensor. The primary objective was to systematically evaluate the relative contributions of location-specific environmental covariates, auxiliary gaseous pollutants, and Feature Engineering (FE) strategies in the calibration of a low-cost PM_2.5 sensor deployed in an outdoor residential environment. For this purpose, a multi-stage experimental framework based on an ablation study design incorporating different environmental information groups was implemented, and the models were evaluated using a leakage-safe time-based validation approach. The results demonstrated that FE strategies significantly improved model performance across all experimental configurations. The highest performance was obtained under the configuration using raw PM_2.5 sensor data together with FE-derived features, where the RF model increased the Test R² value from 0.7237 to 0.8467 and reduced the RMSE from 6.96 µg/m³ to 5.19 µg/m³. Feature importance analyses indicated that humidity-based interaction features and cyclic temporal encoding structures provided substantial contributions to model performance. The findings suggest that meaningful PM_2.5 calibration performance can be achieved using location-specific environmental covariates and data-driven FE strategies in scenarios where continuous co-location is not operationally feasible.