Munckcole3085

The identified areas of high fire density are also associated with large coarse particle concentrations at the surface. Moreover, there is a significant contribution of organic carbon to the total coarse particle mass, 60% on average. Finally, while most of the impact in ambient pollution is observed in PNAs located close to the regions with active fires in southern Mexico and Central America, the long-range transport of smoke plumes reaching the USA was also confirmed.This paper investigates the role of economic complexity on energy demand using the panel dataset of 25 Organization for Economic Co-operation and Development (OECD) countries from 1978 to 2016. Both real per capita income level and economy-wide real energy price index are critical determinants in energy demand modeling. NSC 641530 The battery of the cross-sectional dependency test proposed by Pesaran (2004 and 2007) is used, signaling the presence of cross-sectional dependency in the dataset. Thus, the Westerlund (2007) cointegration test is also used, revealing the long-run relationship between the series. Moreover, the results from using the Augmented Mean Group (AMG) estimations illustrate that real per capita income level positively affects energy demand while real energy price and economic complexity negatively influence on it. From a policy perspective, we suggest increasing technological innovation (i.e., higher economic complexity) will reduce the energy demand. The reduction of massive energy usage may be beneficial for the natural environment's health in the OECD countries.Lake surface water temperature (LSWT) is an important factor affecting a lake's ecological environment. In recent decades, LSWT worldwide has shown an increasing trend in the context of global climate change. This rising trend has been more evident in urban lakes. With the rapid development of urbanization, urban lakes are affected not only by climate warming but also by human activities. Among these factors, due to the increase in impervious surface coverage (ISC), the impact of thermal runoff pollution caused by precipitation events on urban lakes cannot be ignored. Therefore, this study used the Dianchi Lake watershed as a study area, and the surface water temperature of Dianchi Lake, the precipitation data, and the land use data were collected and analyzed. Based on these data, the influence of precipitation events on the surface water temperature of Dianchi Lake was analyzed. The research results show that under the background of different ISC levels and different growth rates of impervious surface area (ISA), precipitation events have different effects on the LSWT. When ISC is low and the growth rate of ISA is slow, the annual precipitation is negatively correlated with the annual average surface water temperature of Dianchi Lake (r = - 0.183). When ISC is high and the growth rate of ISA is fast, the annual precipitation is positively correlated with the average annual surface water temperature of Dianchi Lake (r = 0.65). With the increase in ISC, the correlation between seasonal precipitation and the average surface water temperature in Dianchi Lake changed from negative to positive in spring and autumn. Under the action of impervious surfaces, precipitation events have a warming effect on the surface water temperature of the lake, and this effect will be intensified with the increase in ISC.Microfaunal identification and analysis are very complex; thus, an image analysis method was utilized in this paper to overcome the shortcomings of using the number, dominant species, and diversity of population structure of microfauna as activated sludge indicators. Based on a classification of microfaunal movement, the quantitative processing and analysis of the micro-video frame image of microfaunal movement were carried out by using the Image J software. Background subtraction method was utilized to detect target microfauna by matching target area features to track microfaunal movement. Three parameters, namely, motion trajectory (L), consecutive frame of motion paths (Si), and average change rate of extent [Formula see text], were selected to represent the motion trajectory and mass center of microfauna. Four motion-velocity parameters, namely, the left and right rotation angles of adjacent frames (∆αi), instantaneous velocity (Vi), average linear velocity ([Formula see text]), and average angular velocity ([Formula see text]), were selected to characterize the movement modes of microfauna. Finally, a motion analysis method based on the Image J software was established to demonstrate the different motion modes of microfauna in activated sludge. This study provides a methodological foundation for the establishment of a new method of microfauna as indicator. Based on this method, the correlation between the microfaunal motion velocity and activated sludge flocs was analyzed.Based on a comprehensive consideration of waste water (WW) and waste gas (WG), the Tapio decoupling model is constructed to explore the decoupling relationship between industrial growth and industrial pollution in the Circum-Bohai-Sea region (CBSR) of China from 2003 to 2016. By dividing 37 sample cities into three sub-regions, we conduct a comparative analysis to describe the spatial-temporal evolution of the decoupling states of industrial growth and environmental pollution. The results show the following (1) Overall, the industrial WW discharge in 37 key cities has been decoupled from industrial growth, and the industrial development mode is relatively ideal. (2) The decoupling between industrial growth and industrial WW and WG emissions is more ideal in Beijing-Tianjin-Hebei (BTH) than in Midsouthern Liaoning (MSL). (3) There are two nodes for the decoupling between industrial growth and WW and WG in Shandong Peninsula (SDP), and the decoupling state between industrial growth and WG is better than the decoupling state between industrial growth and WW from 2003 to 2016. (4) From 2003 to 2016, the decoupling state between industrial growth and WW and WG in MSL is not ideal. The conclusions show that the decoupling relationship between industrial growth and environmental pollution in the CBSR is still quite variable and unstable; thus, differential treatment measures should be taken. To enhance the effectiveness of these measures, we will further study the main factors affecting the decoupling relationship, and conduct a comparative study in a larger scale.With the growing awareness of the linkage among open defecation (OD), environment, and health, it is important to understand the factors responsible for OD. link2 It is a necessary step toward developing a strategy to end open defecation for ensuring a better environment and human health. There is no such study available for Pakistan. The study, therefore, aims to bridge this gap. Using household data of Pakistan Demographic and Health Survey (PDHS) 2017-2018, an association of OD with potential predictors, analysis of variance, and a logistic regression model are employed to develop the evidence. The results suggest that place of residence, education, poverty status, social norms, geopolitical regions, and living space significantly predict the OD behavior in Pakistan. This study recommends two things first is to facilitate the households and communities to own latrines, second is to change the behavior through intervention. However, political commitment and effective administration will be key to ascertain ending OD.Globally, urban has been the major contributor to greenhouse gas (GHG) emissions and thus plays an increasingly important role in its efforts to reduce CO2 emissions. However, quantifying city-level CO2 emissions is generally a difficult task due to lacking or lower quality of energy-related statistics data, especially for some underdeveloped areas. To address this issue, this study used a set of open access data and machine learning methods to estimate and predict city-level CO2 emissions across China. Two feature selection technologies including Recursive Feature Elimination and Boruta were used to extract the important critical variables and input parameters for modeling CO2 emissions. Finally, 18 out of 31 predictor variables were selected to establish prediction models of CO2 emissions. We found that the statistical indicators of urban environment pollution (such as industrial SO2 and dust emissions per capita) are the most important variables for predicting the city-level CO2 emissions in China. The XGBduction goal.As well known, mercury is a toxic trace element due to its bioaccumulation and volatility which results in severe effects in health of ecosystems and humans' life. Herein, for the first time, the synthesis of a N and S dual-doped waste-derived graphene-like nanoporous carbon via a facile and single-step route is presented and its capability in mercury vapor removal from gas streams is investigated. To prepare a modified adsorbent, thiourea was utilized as the doping agent to induce nitrogen and sulfur dopants into the nanoporous carbon structure derived from pyrolysis of cabbage (Capitat. var. Brassica oleracea) waste from Brassicaceae family as an inherently S, N-containing precursor, which is produced in noticeable amounts annually. The prepared adsorbents were characterized through FTIR, XRD, BET, SEM, TEM, and CHNOS techniques to get an insight into the structure, morphology, and chemical characteristics of the adsorbents. The structural characterization revealed the successful synthesis of a graphene-like nanoporous carbon sheet which was doped with nitrogen and sulfur atoms. The S, N dual-doped graphene-like carbon nanosheets showed an enhanced activity toward mercury vapor adsorption. link3 For this end, two different dopant to carbon source ratios were considered and it was found that the higher dopant amount results in a better performance. From the adsorption experiments, it was revealed that the pristine graphene-like carbon had a less performance in mercury removal (71%) compared with doped samples (more than 90%) which shows the necessity of reinforcement and surface modification of as mentioned cabbage base graphene. However, the best sample which was prepared with the dopant to carbon ratio of 10 had a performance of 94.5% removal (2100 μg/g) compared with 89% (1980 μg/g) for mercury removal by the sulfur-impregnated commercial activated carbon.In this study, 18S rRNA high-throughput sequencing was applied to investigate the eukaryotic community in a full-scale drinking water treatment plant. Eukaryotic species and microbial functions in raw water and filter biofilms were identified by metagenomic sequencing. The eukaryotic species richness and diversity presented declining trends throughout the treatment process. The lowest eukaryotic species richness was observed in disinfected water. Arthropoda, Ciliophora, Ochrophyta, and Rotifera were the dominant eukaryotic phyla and exhibited high variations in relative abundance among the different treatment units. Sedimentation significantly decreased the abundance of all eukaryotes except Arthropoda. Biological activated carbon (BAC) filtration and chlorine disinfection exerted strong effects on community composition. The eukaryotic communities in water were distinct from those in filter biofilms, as were the communities of different filter biofilms from each other. In contrast, communities were functionally similar among different filter biofilms, with the category metabolism being the dominant category represented, within which amino acid transport and metabolism (E) and energy production and conversion (C) dominated among subcategories.