Joycehelbo9764

During the day, the vast majority of the interviewees did not complain of any annoyance during the morning (45.5%), afternoon (39.6%), and evening (31%) with only  less then  4% of residents have reported a very high degree of annoyance of during COVID-19 pandemic period. Very few people (17%) did complain of experiencing general health problems while 29% did not know of any potential health effects that could be attributed to aircraft noise exposures. Aircraft noise annoyance complaints among the As-Seeb residents during the pre-COVID-19 pandemic periods were reported to be extremely high reaching about 84% compared to 41% during this current COVID-19 pandemic period. These findings support the need to develop future sustainable noise mitigation policies in order to help reduce noise exposures and improve human health during post-COVID-19 pandemic periods.Based on provincial data pertaining to China from 2003 to 2018, this paper empirically analyzes the impact of the industrial structure on haze pollution by constructing static and dynamic spatial econometric models. The marginal contribution of this paper lies in the analysis based on two indicators the upgrading and rationalization of the industrial structure. The results indicate that at the overall level, haze pollution in China exhibits a significant positive spatial correlation and remains relatively stable, upgrading and rationalization of the industrial structure can significantly reduce haze pollution, the control variables of technological progress and trade openness yield obvious haze reduction effects, and the market-oriented haze reduction effect is better that of the government behavior. In terms of the robustness, the effect of industrial structure upgrading is not obvious in the eastern regions and even aggravates haze pollution in the central and western regions, while industrial structure rationalization can play a role in haze reduction in all regions. Industrial structure upgrading and rationalization achieve better effects in the southern region but can aggravate haze pollution in the northern region. Based on the results of the time period test, the effect is very obvious at the first stage but not that at the second stage because of the diminishing marginal effect. The robustness results of the replacement of the core variables and dynamic spatial Durbin model further validate the empirical results in this paper. Finally, according to the empirical results, we propose corresponding policy implications.Rapid progress of industrial development, urbanization and traffic has caused air quality reduction that negatively affects human health and environmental sustainability, especially among developed countries. Numerous studies on the development of air quality forecasting model using machine learning have been conducted to control air pollution. As such, there are significant numbers of reviews on the application of machine learning in air quality forecasting. Shallow architectures of machine learning exhibit several limitations and yield lower forecasting accuracy than deep learning architecture. Deep learning is a new technology in computational intelligence; thus, its application in air quality forecasting is still limited. This study aims to investigate the deep learning applications in time series air quality forecasting. Owing to this, literature search is conducted thoroughly from all scientific databases to avoid unnecessary clutter. This study summarizes and discusses different types of deep learning algorithms applied in air quality forecasting, including the theoretical backgrounds, hyperparameters, applications and limitations. Hybrid deep learning with data decomposition, optimization algorithm and spatiotemporal models are also presented to highlight those techniques' effectiveness in tackling the drawbacks of individual deep learning models. Androgen Receptor Antagonist It is clearly stated that hybrid deep learning was able to forecast future air quality with higher accuracy than individual models. At the end of the study, some possible research directions are suggested for future model development. The main objective of this review study is to provide a comprehensive literature summary of deep learning applications in time series air quality forecasting that may benefit interested researchers for subsequent research.E-waste generation has become a serious environmental challenge worldwide. Taizhou of Zhejiang Province, situated on the southeast coastline of China, has been one of the major e-waste dismantling areas in China for the last 40 years. In this review, we focused on the polychlorinated biphenyl (PCB) trends in environmental compartments, burden and impact to humans, food safety, and health risk assessment from Taizhou, China. The review suggested that PCBs showed dynamic trends in air, soil, water, biodiversity, and sediments. Soils and fish samples indicated higher levels of PCBs than sediments, air, water, and food items. PCB levels decreased in soils with the passage of time. Agriculture soils near the e-waste recycling sites showed more levels of total PCBs than industrial soils and urban soils. Dioxin-like PCB levels were higher in humans near Taizhou, suggesting that e-waste pollution could influence humans. Compared with large-scale plants, simple household workshops contributed more pollution of PCBs to the environment. Pollution index, hazard quotient, and daily intake were higher for PCBs, suggesting Taizhou should be given priority to manage the e-waste pollution. The elevated body burden may have health implications for the next generation. The areas with stricter control measures, strengthened laws and regulations, and more environmentally friendly techniques indicated reduced levels of PCBs. For environment protection and health safety, proper e-waste dismantling techniques, environmentally sound management, awareness, and regular monitoring are very important.Taking industrial waste gas control as an example, this study explores the impact of fiscal decentralization on the efficiency of local governments' environmental governance under the pressure of competition, and analyzes the role of competition among local governments and support for R&D activities. The results demonstrate that fiscal decentralization can significantly promote the efficiency of local governance on control of waste gas, and this positive effect is mainly realized through the support from local government for R&D activities. As for the influence of inter-governmental competitions, only the competition in terms of governance efficiency delivers a significant U-shaped impact, and competition in expenditure scale and economy area weakly inhibits the positive influence of decentralization, meaning that the evaluation system centered on economic growth and the imitation strategy of local expenditure will not lead to a great decline in the efficiency of the use of environmental governance. Overall, this study suggests that further fiscal decentralization, emphases on government support for R&D activities, and enhanced environmental assessment in official evaluation are all the important directions for boosting the efficiency of governance.The phytoremediation efficiency is largely depends on the bioavailability of heavy metal in soil. The activity of earthworms and oxidation of elemental sulfur (S0) in soil has influence on heavy metal speciation transformation in soil. By conducting pot experiment, we examined the possibility of enhancing phytoextraction efficiency of lead (Pb) in soil by ryegrass (Lolium perenne L.) with application of both S0 and earthworms. Results showed that the addition of S0 decreased soil pH and increased soil CEC, while a slight trend of decrease for soil pH and increase for CEC was found with earthworm application. In soil treated with earthworms, the addition of S0 increased the concentration of DTPA-extractable Pb by 9.9~20.8%. The concentration of diffusive gradients in thin film (DGT)-extractable Pb was increased by 26.31~32.9% with S0 and earthworm addition. In soil treated with earthworms, the addition of S0 increased the concentration of Pb in shoots of ryegrass by 55.7~110.4% and the translocation factor of Pb in ryegrass was also increased by S0 addition. Our results suggested that the combination application of earthworms and S0 could be an effective way to enhance the remediation efficiency of ryegrass for Pb-contaminated soil.Seawater intrusion in coastal aquifers is a major concern due to geogenic and anthropogenic activities leading to declining groundwater quality. The present study focuses on deciphering the sea water intruded zones and its extent in the Quaternary alluvial aquifer system in the coastal belt of Digha, West Bengal, India. In this study, 36 groundwater samples were collected during pre-monsoon (2020). Subsequently, an integrated approach of hydrogeological, hydrogeochemistry, bulk magnetic susceptibility, isotopic, multivariate statistical, and geochemical modeling is adopted. Spatial distribution maps of hydrological parameters (salinity, conductivity, TDS) and major ion concentration (Na+, K+, Ca2+, Mg2+, Cl-, SO42-, F-, and Br-) suggest that the northern, south-west, and eastern parts of the study area are largely affected by saltwater intrusion and are corroborated with seawater mixing index (SMI). Based on sodium adsorption ratio (SAR), sodium percentage (Na%), and Permeability index (PI) distribution maps,ineating seawater intruded zones is elaborated. Saturation index (SI) shows that groundwater is saturated (> 0) with calcite, dolomite, and aragonite, plausibly due to seawater ingression. Stable isotopic analysis of δ2H (- 53.979 to - 16.9578‰) and δ18O (- 7.00183 to - 1.37 ‰) suggests precipitation recharge/paleo-water at some locations and evaporation enrichment of groundwater. It is recommended to increase groundwater recharge, reduce groundwater extraction at critically affected locations, and have regular monitoring and management to control seawater intrusion.The Middle Route (MR) of the South-to-North Water Diversion Project (SNWDP) of China is one of the world's largest inter-basin water diversion projects. As an important source of drinking water in North China, its water quality safety determines the success or failure of a sizable water supply. At present, there is a lack of in-depth and systematic understanding of the interaction between hydrodynamics and the water environment as well as water ecological processes in the main canal at the early stages of operation. It is not currently possible to accurately predict water quality and algae status at the early stage of canal ecosystem succession. Change trends and distribution characteristics of the main water ecological environment elements in the main canal at the early MR stage are analyzed in this study. The main factors influencing algae are investigated by principal component analysis (PCA) to characterize the water quality and algae transport distribution in the main MR canal under the complex multi-slu change reaches 22.13% and 29.55% under double sluice and four sluice scheduling. Algae control effects grow significantly as the number of control sluices increases. The results of this work may provide technical support for water quality forecasting and algae risk warning in the SNWDP MR as well as a workable reference for similar projects.