The Potential Impact of Smog Spell on Humans' Health Amid COVID-19 Rages

Evaluating the Environmental Impact of Radiation Therapy Using Life Cycle Assessments: A Critical Review

Concurrent increases in global cancer burden and the climate crisis pose an unprecedented threat to public health and human well-being. Today, the health care sector greatly contributes to greenhouse gas emissions, with the future demand for health care services expected to rise. Life cycle assessment (LCA) is an internationally standardized tool that analyzes the inputs and outputs of products, processes, and systems to quantify associated environmental impacts. This critical review explains the use of LCA methodology and outlines its application to external beam radiation therapy (EBRT) with the aim of providing a robust methodology to quantify the environmental impact of radiation therapy care practices today. The steps of an LCA are outlined and explained as defined by the International Organization for Standardization (ISO 14040 and 14044) guidelines: (1) definition of the goal and scope of the LCA, (2) inventory analysis, (3) impact assessment, and (4) interpretation. The existing LCA framework and its methodology is described and applied to the field of radiation oncology. The goal and scope of its application to EBRT is the evaluation of the environmental impact of a single EBRT treatment course within a radiation oncology department. The methodology for data collection via mapping of the resources used (inputs) and the end-of-life processes (outputs) associated with EBRT is explained, with subsequent explanation of the LCA analysis steps. Finally, the importance of appropriate sensitivity analysis and the interpretations that can be drawn from LCA results are reviewed. This critical review of LCA protocol provides and evaluates a methodological framework to scientifically establish baseline environmental performance measurements within a health care setting and assists in identifying targets for emissions mitigation. Future LCAs in the field of radiation oncology and across medical specialties will be crucial in informing best practices for equitable and sustainable care in a changing climate.

Air quality prediction by machine learning models: A predictive study on the indian coastal city of Visakhapatnam

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.

Correction: Javed et al. The Potential Impact of Smog Spell on Humans' Health Amid COVID-19 Rages. Int. J. Environ. Res. Public Health 2021, 18, 11408

There was an error in the original publication [...].

Surface Modification of GdMn2O5 for Catalytic Oxidation of Benzene via a Mild A-Site Sacrificial Strategy

Thermal catalytic oxidation technology is an effective way to eliminate refractory volatile organic pollutants, such as Benzene. Nevertheless, a high reaction temperature is usually an obstacle to practical application. Here, GdMn2O5 mullite (GMO-H) catalyst with disordered surface Gd-deficient and oxygen-vacancy-rich concentrations was synthesized via a controllable low-temperature acid-etching route. Results show that the preferentially broken Gd-O bond is conducive to exposing more Mn-Mn active sites, which Gd species covered. The affluent surface oxygen vacancies supply sufficient adsorption sites for oxygen molecules, facilitating the oxygen cycles during Benzene catalytic oxidation. Furthermore, surface exposed Mn3+ species were oxidized to Mn4+, which is beneficial to increase catalytic activity at a lower temperature. Compared with the conventional GdMn2O5, the reaction temperature for removing 90% Benzene over GMO-H was dropped from 405 to 310 °C with WHSV of 30,000 mL g−1 h−1. Significantly, during a 72 h catalytic test, the catalytic activity remains constant at 90% of the Benzene removal at 300 °C, indicating excellent activity stability. This work reported an efficient approach to preparing manganese-base mullite thermal catalyst, providing insight into the catalytic oxidation of Benzene.

A review on factors influencing fog formation, classification, forecasting, detection and impacts

With the changing climate and environment, the nature of fog has also changed and because of its impact on humans and other systems, study of fog becomes essential. Hence, the study of its controlling factors such as the characteristics of condensation nuclei, microphysics, air–surface interaction, moisture, heat fluxes and synoptic conditions also become crucial, along with research in the field of prediction and detection. The current review expands for the period between 1976 to 2021, however, especially focused on the research articles published in the last two decades. It considers 250 research papers/research letters, 24 review papers, four book chapters/manuals, five news articles, 15 reports, six conference papers and five other online readings. This review is a compilation of the pros and cons of the techniques used to determine the factors influencing fog formation, its classification, tools and techniques available for its detection and forecast. Some recent advanced are also discussed in this review: role of soil properties on fogs, application of microwave communication links in the detection of fog, new class of smog, and how the cognitive abilities of humans are affected by fog. Recently India and China are facing an emergence and repetitions of fog haze/smog and thus their policies initiatives are also briefly discussed. It is concluded that the complexity in fog forecasting is high due to multiple factors playing a role at multiple levels. Most of the researchers have worked upon the role of humidity, temperature, wind, and boundary layer to predict fogs. However, the role of global wind circulations, soil properties, and anthropogenic heat requires further investigations. Literature shows that fog is being harnessed to address water insecurity in various countries, however, coastal areas of Angola, Namibia and South Africa, Kenya, Eastern Yemen, Oman, China, India, Sri Lanka, Mexico, along with the mountainous regions of Peru, Chile, and Ecuador, are some of the potential sites that can benefit from the installation of fog water harvesting systems.