Pande, Chaitanya Baliram; Katipoğlu, Okan Mert; Radwan, Neyara; Sidek, Lariyah Mohd; Pal, Subodh Chandra; Yaseen, Zaher Mundher; Ali, Tarig; Chakrabortty, Rabin; Refadah, Samyah Salem; Khan, Mohd Yawar Ali

Urban population growths, expansion of the industrial economy, burning stubble, and other major sources have significantly contributed to the increasing levels of air pollution in urban areas. Therefore, accurate prediction of the Air Quality Index (AQI) is essential for effective monitoring and planning within cities. In this research, an ensemble machine learning (ML) called Gradient Boosting (GB) model with 5- and 10-folds’ cross-validation was developed to achieve more precise and operative prediction outcomes based on important air quality related input variables. The results demonstrated that the suggested hybrid model attained the best accuracy in comparison with other benchmark ML models for predicting AQI values of Delhi city. Statistically, GB model reported second best model based on statistical metrics (R2 = 0.96, R = 0.98, RMSE = 23.03, MSE = 530.71, MAE = 15.16 and MARE = 11.96). Whereas Light Gradient Boosting Machine (LightGBM) model third best accuracy reported (R2 = 0.96, R = 0.98, RMSE = 23.21, MSE = 538.74, MAE = 14.68, and MARE = 11.33). The AQI prediction values are presented as spatial thematic maps for better understanding, highlighting city areas with high AQI levels to support air quality planning. The daily AQI prediction maps were prepared using various ML models. Further, the current research forecasted five days of AQI values and compared with the observed values. The results of five-day forecasting of GB model again indicated the best match with observed data of AQI values. Finally, forecasting accuracy results of GB (Boosting) models was attained 90% match with the observed values of AQI as compared with hybrid model. In conclusion, the reported results provided better understanding of AQI modeling for values and classes of spatial maps.

Çetinkaya, Ozlem Akguc; Çatik, Abdurrahman Nazif; Balli, Esra; Manga, Muge; Destek, Mehmet Akif

This article examines the effect of environmental policy stringency and green innovation on CO2 emissions in the BRICS nations, using annual data from 1990 to 2019 utilizing panel FMOLS and DOLS estimators and Method of Moments Quantile Regression (MMQR). To this end, we estimate an equation in which CO2 emissions are explained by GDP, trade openness, nonrenewable and renewable energy consumption, the environmental stringency index, and green innovation, as measured by the number of patent applications for environmentally related inventions. FMOLS and DOLS results reveal that GDP, nonrenewable energy consumption, and trade openness have a positive effect on environmental pollution, whereas improvements in renewable energy consumption and environmental regulations lead to a drop in CO2 emissions. However, green innovation does not have a significant effect on CO2 emissions. MMQR estimates demonstrate that the GDP has a positive effect on CO2 emissions across all quantiles, suggesting that a higher degree of economic growth is associated with higher emissions. Based on findings, empirical evidence suggests that BRICS countries should follow the policies encouraging the reduction of nonrenewable energy consumption in the region without harming the development of the economy. Besides, policymakers should promote renewable energy consumption and enhance investment in green innovation to achieve sustainable development and environmental quality.