Clean air is critical component for health and survival of human and wildlife, as atmospheric pollution is associated with a number of significant diseases including cancer. However, due to rapid industrialization and population growth, activities such as transportation, household, agricultural, and industrial processes contribute to air pollution. As a result, air pollution has become a significant problem in many cities, especially in emerging countries like India. To maintain ambient air quality, regular monitoring and forecasting of air pollution is necessary. For that purpose, machine learning has emerged as a promising technique for predicting the Air Quality Index (AQI) compared to conventional methods. Here we apply the AQI to the city of Visakhapatnam, Andhra Pradesh, India, focusing on 12 contaminants and 10 meteorological parameters from July 2017 to September 2022. For this purpose, we employed several machine learning models, including LightGBM, Random Forest, Catboost, Adaboost, and XGBoost. The results show that the Catboost model outperformed other models with an R2 correlation coefficient of 0.9998, a mean absolute error (MAE) of 0.60, a mean square error (MSE) of 0.58, and a root mean square error (RMSE) of 0.76. The Adaboost model had the least effective prediction with an R2 correlation coefficient of 0.9753. In summary, machine learning is a promising technique for predicting AQI with Catboost being the best-performing model for AQI prediction. Moreover, by leveraging historical data and machine learning algorithms enables accurate predictions of future urban air quality levels on a global scale.
Over the past decades, increasing research in metal-organic frameworks (MOFs) being a large family of highly tunable porous materials with intrinsic physical properties, show propitious results for a wide range of applications in adsorption, separation, electrocatalysis, and electrochemical sensors. MOFs have received substantial attention in electrochemical sensors owing to their large surface area, active metal sites, high chemical and thermal stability, and tunable structure with adjustable pore diameters. Benefiting from the superior properties, MOFs and MOF-derived carbon materials act as promising electrode material for the detection of food contaminants. Although several reviews have been reported based on MOF and its nanocomposites for the detection of food contaminants using various analytical methods such as spectrometric, chromatographic, and capillary electrophoresis. But there no significant review has been devoted to MOF/and its derived carbon-based electrodes using electrochemical detection of food contaminants. Here we review and classify MOF-based electrodes over the period between 2017 and 2022, concerning synthetic procedures, electrode fabrication process, and the possible mechanism for detection of the food contaminants which include: heavy metals, antibiotics, mycotoxins, and pesticide residues. The merits and demerits of MOF as electrode material and the need for the fabrication of MOF and its composites/derivatives for the determination of food contaminants are discussed in detail. At last, the current opportunities, key challenges, and prospects in MOF for the development of smart sensing devices for future research in this field are envisioned.
•Microplastic pollution threats environmental and human health.•The resolution of End Plastic Pollution promotes the global strategy against plastic pollution.•The governments should launch relevant policies to implement this resolution.