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Air pollution has serious environmental and human health-related consequences; however, little work seems to be undertaken to address the harms in Middle Eastern countries, including Saudi Arabia. We installed a continuous air quality monitoring station in Jeddah, Saudi Arabia and monitored several air pollutants and meteorological parameters over a 2-year period (2018–2019). Here, we developed two supervised machine learning models, known as quantile regression models, to analyze the whole distribution of the modeled pollutants, not only the mean values. Two pollutants, namely NO2 and O3, were modeled by dividing their concentrations into several quantiles (0.05, 0.25, 0.50, 0.75, and 0.95) and the effect of several pollutants and meteorological variables was analyzed on each quantile. The effect of the explanatory variables changed at different segments of the distribution of NO2 and O3 concentrations. For instance, for the modeling of O3, the coefficients of wind speed at quantiles 0.05, 0.25, 0.5, 0.75, and 0.95 were 1.40, 2.15, 2.34, 2.31, and 1.56, respectively. Correlation coefficients of 0.91 and 0.92 and RMSE values of 14.41 and 8.96, which are calculated for the cross-validated models of NO2 and O3, showed an acceptable model performance. Quantile analysis aids in better understanding the behavior of air pollution and how it interacts with the influencing factors. [ABSTRACT FROM AUTHOR]

Proper treatment and disposal of industrial pollutants of all kinds are a global issue that presents significant techno-economical challenges. The presence of pollutants such as heavy metal ions (HMIs) and organic dyes (ODs) in wastewater is considered a significant problem owing to their carcinogenic and toxic nature. Additionally, industrial gaseous pollutants (GPs) are considered to be harmful to human health and may cause various environmental issues such as global warming, acid rain, smog and air pollution, etc. Conductive polymer-based nanomaterials have gained significant interest in recent years, compared with ceramics and metal-based nanomaterials. The objective of this review is to provide detailed insights into different conductive polymers (CPs) and their nanocomposites that are used as adsorbents for environmental remediation applications. The dominant types of CPs that are being used as adsorbent materials include polyaniline (PANI), polypyrrole (Ppy), and polythiophene (PTh). The various adsorption mechanisms proposed for the removal of ODs, HMIs, and other GPs by the different CPs are presented, together with their maximum adsorption capacities, experimental conditions, adsorption, and kinetic models reported. [ABSTRACT FROM AUTHOR]

Climate finance is growing popular in addressing challenges of climate change because it controls the funding and resources to emission entities and promotes green manufacturing. In this study, we determined that PM 2.5 , PM 10 , SO 2 , NO 2 , CO, and O 3 are the target pollutant in the atmosphere and we use a deep neural network to enhance the regression analysis in order to investigate the relationship between air pollution and stock prices of the targeted manufacturer. We also conduct time series analysis based on air pollution and heavy industry manufacturing in China, as the country is facing serious air pollution problems. Our study uses Convolutional-Long Short Term Memory in 2 Dimension (ConvLSTM2D) to extract the features from air pollution and enhance the time series regression in the financial market. The main contribution in our paper is discovering a feature term that impacts the stock price in the financial market, particularly for the companies that are highly impacted by the local environment. We offer a higher accurate model than the traditional time series in the stock price prediction by considering the environmental factor. The experimental results suggest that there is a negative linear relationship between air pollution and the stock market, which demonstrates that air pollution has a negative effect on the financial market. It promotes the manufacturer's improving their emission recycling and encourages them to invest in green manufacture—otherwise, the drop in stock price will impact the company funding process. [ABSTRACT FROM AUTHOR]

This study examines point and non-point sources of air pollution and particulate matter and their associated socioeconomic and health impacts in South Asian countries, primarily India, China, and Pakistan. The legislative frameworks, policy gaps, and targeted solutions are also scrutinized. The major cities in these countries have surpassed the permissible limits defined by WHO for sulfur dioxide, carbon monoxide, particulate matter, and nitrogen dioxide. As a result, they are facing widespread health problems, disabilities, and causalities at extreme events. Populations in these countries are comparatively more prone to air pollution effects because they spend more time in the open air, increasing their likelihood of exposure to air pollutants. The elevated level of air pollutants and their long-term exposure increases the susceptibility to several chronic/acute diseases, i.e., obstructive pulmonary diseases, acute respiratory distress, chronic bronchitis, and emphysema. More in-depth spatial-temporal air pollution monitoring studies in China, India, and Pakistan are recommended. The study findings suggest that policymakers at the local, national, and regional levels should devise targeted policies by considering all the relevant parameters, including the country's economic status, local meteorological conditions, industrial interests, public lifestyle, and national literacy rate. This approach will also help design and implement more efficient policies which are less likely to fail when brought into practice. [Display omitted] • Air pollution and particulate matter are emerging challenges in South Asia. • Legislative and policy gaps on air pollution in South Asia were scrutinized. • South Asia has surpassed the permissible limits of air quality. • India, China and Pakistan have severe health issues due to air pollution. • Spatial-temporal monitoring studies in China, India, and Pakistan are needed. [ABSTRACT FROM AUTHOR]