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Air pollution is a major issue all over the world because of its impacts on the environment and human beings. The present review discussed the sources and impacts of pollutants on environmental and human health and the current research status on environmental pollution forecasting techniques in detail; this study presents a detailed discussion of the Artificial Intelligence methodologies and Machine learning (ML) algorithms used in environmental pollution forecasting and early-warning systems; moreover, the present work emphasizes more on Artificial Intelligence techniques (particularly Hybrid models) used for forecasting various major pollutants (e.g., PM2.5, PM10, O3, CO, SO2, NO2, CO2) in detail; moreover, focus is given to AI and ML techniques in predicting chronic airway diseases and the prediction of climate changes and heat waves. The hybrid model has better performance than single AI models and it has greater accuracy in prediction and warning systems. The performance evaluation error indexes like R2, RMSE, MAE and MAPE were highlighted in this study based on the performance of various AI models.

Geopolymer concrete, because of its less embodied energy as compared to conventional cement concrete, has paved the way for achieving sustainable development goals. In this study, an effort was made to optimize its quality characteristics or responses, namely, workability, and the compressive and flexural strengths of Ground Granulated Blast-furnace Slag (GGBS)-based geopolymer concrete incorporated with polypropylene (PP) fibers by Taguchi’s method. A three-factor and three-level design of experiments was adopted with the three factors and their corresponding levels as alkali ratio (NaOH:Na2SiO3) (1:1.5 (8 M NaOH); 1:2 (10 M NaOH); 1:2.5 (12 M NaOH)), percentage of GGBS (80%, 90%, and 100%) and PP fibers (1.5%, 2%, and 2.5%). M25 was taken as the control mix for gauging and comparing the results. Nine mixes were obtained using an L9 orthogonal array, and an analysis was performed. The analysis revealed the optimum levels as 1:2 (10 molar) alkali ratio, 80% GGBS, and 2% PP fibers for workability; 1:2 (10 molar) alkali ratio, 80% GGBS, and 2.5% PP fibers for compressive strength; and 1:2 (10 molar) alkali ratio, 80% GGBS, and 1.5% PP fibers for flexural strength. The percentage of GGBS was found to be the most effective parameter for all three responses. The analysis also revealed the ranks of all the factors in terms of significance in determining the three responses. ANOVA conducted on the results validated the reliability of the results obtained by Taguchi’s method. The optimized results were further verified by confirmation tests. The confirmation tests revealed the compressive and flexural strengths to be quite close to the strengths of the control mix. Thus, optimum mixes with comparable strengths were successfully achieved by replacing cement with GGBS and thereby providing a better path for sustainable development.

The large size of a glazed component allows greater access to natural light inside and a wider view of the outdoors while protecting the inside from extreme weather conditions. However, glazed components make buildings energy inefficient compared to opaque components if not designed suitably, and sometimes they create glare discomforts too. In order to protect against excessive natural light and direct sunlight and for privacy, dynamic shading devices are integrated into the glazed façade. In this study, the impact of various glazing and shading design parameters has been investigated by performing uncertainty and sensitivity analyses. The uncertainty analysis indicates that the variance coefficients for the source energy use, lighting energy use, useful daylight illuminance (UDI), and shade-deployed time fraction are in the ranges of 15.04–30.47, 39.05–45.06, 40.57–49.92, and 19.35–52%, respectively. The dispersion in the energy and indoor visual performance is evident by the large variation in the source energy consumption and UDI (500–2000), which vary in the ranges of 250–450 kWh/(m2-year) and 5–90%. Furthermore, a sensitivity analysis identified the window-to-wall ratio (WWR), aspect ratio (ASR), glazing type (Gt), absorptance of the wall (Aw), and shade transmittance (ST) as major influences of the parameters. Each of the identified parameters has a different proportionate impact depending on the façade orientation and performance parameters.