The adverse effect of air pollution has always been a problem for human health. The presence of a high level of air pollutants can cause severe illnesses such as emphysema, chronic obstructive pulmonary disease (COPD), or asthma. Air quality prediction helps us to undertake practical action plans for controlling air pollution. The Air Quality Index (AQI) reflects the degree of concentration of pollutants in a locality. The average AQI was calculated for the various cities in China to understand the annual trends. Furthermore, the air quality index has been predicted for ten major cities across China using five different deep learning techniques, namely, Recurrent Neural Network (RNN), Bidirectional Gated Recurrent unit (Bi-GRU), Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Network BiLSTM (CNN-BiLSTM), and Convolutional BiLSTM (Conv1D-BiLSTM). The performance of these models has been compared with a machine learning model, eXtreme Gradient Boosting (XGBoost) to discover the most efficient deep learning model. The results suggest that the machine learning model, XGBoost, outperforms the deep learning models. While Conv1D-BiLSTM and CNN-BiLSTM perform well among the deep learning models in the estimation of the air quality index (AQI), RNN and Bi-GRU are the least performing ones. Thus, both XGBoost and neural network models are capable of capturing the non-linearity present in the dataset with reliable accuracy.

Hybrid power systems are technically suitable for providing continuous and quality power to remotely located dwellings and have the potential of reducing the greenhouse gases emissions. The present study aims at finding an optimal hybrid power system based on wind, solar, diesel, and battery backup, which can address the load requirements of a village (Muhavoor, India). The present study utilizes the HOMER software for sizing the major power components, performing economic analysis and estimating greenhouse gas emission. It uses the minimum cost of energy as the basis for optimum system selection. Local load, wind speed, and solar radiation data along with technical and financial input of wind turbines, solar photovoltaic panels, diesel generators, inverters, and fuel are considered in the simulation. Several options have been considered in the study including: diesel, PV/diesel/battery, wind/diesel, and PV/wind/diesel/battery hybrid power systems. Based on the simulation results and analysis, the PV/diesel/battery system with 53MW capacity of PV, 16.55MW diesel generator capacity, 3,520MWh of battery backup, and 15.5MW of converter is recommended as the best option for the concerned village. This system is capable of meeting the entire load of the village with annual energy yield of 92,549MWh with an excess energy of 7,262MWh. Of the total electricity produced, PV panels generate 92.71% with a renewable fraction of 89.2% at an energy cost of 0.117US$/kWh. The total cost of the proposed hybrid power system can be recovered in 11 or 8 years with positive cash flows at the end of year 12 or 9 based on sale of electricity alone or with additional income from fuel savings and greenhouse gases credit incentive.

The increasing population density and industrialization are adversely affecting the environment globally. The contamination of the soil, agricultural lands, and water bodies with petroleum wastes and other hydrocarbon pollutants has become a serious environmental concern as perceived by the impacts on the aquatic and marine ecosystem. Various investigations have provided novel insights into the significant roles of microbial activities in the cleanup of hydrocarbon contaminants. However, the burden of these pollutants is expected to increase many folds in the next decade. Therefore, it is necessary to investigate and develop low-cost technologies rapidly, focusing on eco-sustainable development. An understanding of the details of biodegradation mechanisms paves the way for enhancing the efficiency of bioremediation technology. The current article reviews the applicability of various bioremediation processes, biodegradation pathways, and treatments, and the role of microbial activities in achieving efficient eco-sustainable bioremediation of hydrocarbon pollutants. It is envisaged that an integrated bioremediation approach, including biostimulation and bioaugmentation is preferably advocated for the cost-effective removal of toxic petroleum hydrocarbons and their derivatives.