Modelling the Effect of COVID-19 Lockdown on Air Pollution in Makkah Saudi Arabia with a Supervised Machine Learning Approach

Sustainable Plasma Gasification Treatment of Plastic Waste: Evaluating Environmental, Economic, and Strategic Dimensions

The conversion of waste to energy is an essential process in the Middle East, especially in Saudi Arabia, where it plays a crucial role in waste management. The annual analysis of the decomposition hazards of the landfills in the city of Makkah showed that they ranged from 168,000 Mg in 1994 to 297,000 Mg in 2022. The emission costs of pollution ranged from 4 million in 1994 to 7 million in 2022. The number of gas emissions of plastic waste accumulating in the analyzed landfill has increased from 128,000 Mg in 1994 to 227,000 Mg in 2022. The thermal plasmas used were produced by air plasma torches, which are an essential part of plasma reactors. The operational characteristics of air plasma torches were investigated at a flow rate of 10-30 g/s, including current-voltage characteristics, applied power, applied flow rate, average temperatures of the exiting plasma jet ranging from 15,000 to 19,000 K, average velocity of the exiting plasma jet (1677.3-2763.2) m/s, wavelength of the emitted radiation ranging from 153 to 193 nm, and the corresponding beam lengths ranging from 400 to 450 mm. The proposed plasma gasification is the greenest method for processing plastic waste in the light of ecological, economic, and strategic visions with an amount of recovered pyrolysis oil in the year 2022 of 3.17 × 105 tons with an equivalent energy of 12.55 × 109 MJ, with an output efficiency value equal to 81%. A roadmap for the economic and environmental visions was introduced, where the economic return on investment (ROR%) was 80%, the payback period (PBP) was 1.2 years, and the gross profit reached 129%.

Machine learning insights into PM2.5 changes during COVID-19 lockdown: LSTM and RF analysis in Mashhad

This study seeks to investigate the impact of COVID-19 lockdown measures on air quality in the city of Mashhad employing two strategies. We initiated our research using basic statistical methods such as paired sample t-tests to compare hourly PM2.5 data in two scenarios: before and during quarantine, and pre- and post-lockdown. This initial analysis provided a broad understanding of potential changes in air quality. Notably, a low reduction of 2.40% in PM2.5 was recorded when compared to air quality prior to the lockdown period. This finding highlights the wide range of factors that impact the levels of particulate matter in urban settings, with the transportation sector often being widely recognized as one of the principal causes of this issue. Nevertheless, throughout the period after the quarantine, a remarkable decrease in air quality was observed characterized by distinct seasonal patterns, in contrast to previous years. This finding demonstrates a significant correlation between changes in human mobility patterns and their influence on the air quality of urban areas. It also emphasizes the need to use air pollution modeling as a fundamental tool to evaluate and understand these linkages to support long-term plans for reducing air pollution. To obtain a more quantitative understanding, we then employed cutting-edge machine learning methods, such as random forest and long short-term memory algorithms, to accurately determine the effect of the lockdown on PM2.5 levels. Our models' results demonstrated remarkable efficacy in assessing the pollutant concentration in Mashhad during lockdown measures. The test set yielded an R-squared value of 0.82 for the long short-term memory network model, whereas the random forest model showed a calculated cross-validation R-squared of 0.78. The required computational cost for training the LSTM and the RF models across all data was 25 min and 3 s, respectively. In summary, through the integration of statistical methods and machine learning, this research attempts to provide a comprehensive understanding of the impact of human interventions on air quality dynamics.

Energy Recovery and Economic Evaluation for Industrial Fuel from Plastic Waste

Plasma gasification is considered an environmentally friendly process to convert plastic waste into fuel oil; a prototype system is described to test and validate the plasma treatment of plastic waste as a strategic vision. The proposed plasma treatment project will deal with a plasma reactor with a waste capacity of 200 t/day. The annual plastic waste production in tons in all regions of Makkah city during 27 years for all months in the years 1994 to 2022 is evaluated. A statistics survey of plastic waste displays the average rate generation ranging from 224 thousand tons in the year 1994 to 400 thousand tons in the year 2022, with an amount of recovered pyrolysis oil; 3.17 × 10⁵ t with the equivalent energy; 12.55 × 10⁹ MJ, and an amount of recovered diesel oil; 2.7 × 10⁵ t with an amount of electricity for sale 2.96 × 10⁶ MW.h. The economic vision will be estimated, using the results of energy generated from diesel oil as an industrial fuel extracted from plastic waste equivalent to 0.2 million barrels of diesel oil, with sales revenue and cash recovery of USD 5 million, considering the sale of each one barrel of diesel extracted from plastic waste in the range of USD 25. It is important to consider that the equivalent barrels of petroleum cost, according to the organization of the petroleum-exporting countries' basket prices, up to USD 20 million. The sales profit (2022) is as follows: for diesel with a sales revenue of diesel oil, USD 5 million, with a rate of return of 4.1%, and a payback period of 3.75 years. The generated electricity reached USD 32 million for households and USD 50 million for factories.

Predicting of Daily PM2.5 Concentration Employing Wavelet Artificial Neural Networks Based on Meteorological Elements in Shanghai, China

Anthropogenic sources of fine particulate matter (PM_2.5) threaten ecosystem security, human health and sustainable development. The accuracy prediction of daily PM_2.5 concentration can give important information for people to reduce their exposure. Artificial neural networks (ANNs) and wavelet-ANNs (WANNs) are used to predict daily PM_2.5 concentration in Shanghai. The PM_2.5 concentration in Shanghai from 2014 to 2020 decreased by 39.3%. The serious COVID-19 epidemic had an unprecedented effect on PM_2.5 concentration in Shanghai. The PM_2.5 concentration during the lockdown in 2020 of Shanghai is significantly reduced compared to the period before the lockdown. First, the correlation analysis is utilized to identify the associations between PM_2.5 and meteorological elements in Shanghai. Second, by estimating twelve training algorithms and twenty-one network structures for these models, the results show that the optimal input elements for daily PM_2.5 concentration predicting models were the PM_2.5 from the 3 previous days and fourteen meteorological elements. Finally, the activation function (tansig-purelin) for ANNs and WANNs in Shanghai is better than others in the training, validation and forecasting stages. Considering the correlation coefficients (R) between the PM_2.5 in the next day and the input influence factors, the PM_2.5 showed the closest relation with the PM_2.5 1 day lag and closer relationships with minimum atmospheric temperature, maximum atmospheric pressure, maximum atmospheric temperature, and PM_2.5 2 days lag. When Bayesian regularization (trainbr) was used to train, the ANN and WANN models precisely simulated the daily PM_2.5 concentration in Shanghai during the training, calibration and predicting stages. It is emphasized that the WANN1 model obtained optimal predicting results in terms of R (0.9316). These results prove that WANNs are adept in daily PM_2.5 concentration prediction because they can identify relationships between the input and output factors. Therefore, our research can offer a theoretical basis for air pollution control.

The lock-down effects of COVID-19 on the air pollution indices in Iran and its neighbors

Introduction

The COVID-19 restrictions have a lot of various peripheral negative and positive effects, like economic shocks and decreasing air pollution, respectively. Many studies showed NO2 reduction in most parts of the world.

Methods

Iran and its land and maritime neighbors have about 7.4% of the world population and 6.3% and 5.8% of World COVID-19 cases and deaths, respectively. The air pollution indices of them such as CH4 (Methane), CO_1 (CO), H2O (Water), HCHO (Tropospheric Atmospheric Formaldehyde), NO2 (Nitrogen oxides), O3 (ozone), SO2 (Sulfur Dioxide), UVAI_AAI [UV Aerosol Index (UVAI)/Absorbing Aerosol Index (AAI)] are studied from the First quarter of 2019 to the fourth quarter of 2021 with Copernicus Sentinel 5 Precursor (S5P) satellite data set from Google Earth Engine. The outliers are detected based on the depth functions. We use a two-sample t test, Wilcoxon test, and interval-wise testing for functional data to control the familywise error rate.

Result

The adjusted p value comparison between Q2 of 2019 and Q2 of 2020 in NO2 for almost all countries is statistically significant except Iraq, UAE, Bahrain, Qatar, and Kuwait. But, the CO and HCHO are not statistically significant in any country. Although CH4, O3, and UVAI_AAI are statistically significant for some countries. In the Q2 comparison for NO2 between 2020 and 2021, only Iran, Armenia, Turkey, UAE, and Saudi Arabia are statistically significant. However, Ch4 is statistically significant for all countries except Azerbaijan.

Conclusions

The comparison with and without adjusted p values declares the decreases in some air pollution in these countries.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40808-022-01528-x.