Synthesis and Characterization of Poly(o-chloroaniline)/TiO2 Nanocomposites for Photocatalytic Degradation of Direct Yellow 50 Dye in Textile Wastewater

<p>The nanocomposites of poly(o-chloroaniline) with titanium dioxide have been prepared via chemical oxidative polymerization technique using o-chloroaniline monomer and titanium dioxide nanoparticles for photocatalytic application. The different composites were prepared by varying the load percentage of titanium oxide nanoparticles (TiO2 NPs) in polyorthochloroaniline (POCA) matrix. The synthesized composite materials were characterized by Scanning electron microscopy (SEM), X-Rays diffraction (XRD), and Fourier transform infrared spectroscopy (FTIR) techniques. The POCA/TiO2 nanocomposites were further applied to evaluate the photocatalytic degradation potential towards direct yellow 50 (DY50) dye in an aqueous medium under ultraviolet radiations.</p>

Total and bioavailable heavy metals in the soils of two adjacent forests

<p>The concentrations and comparisons of total and available metals Cd, Cr, Ni, Pb and the metalloid As were examined in two adjacent acid forest soils in Greece under oak and beech together with the dependency of their availability. It was found that the soil in the beech plot had higher concentrations of total elements with the exception of the litter layer (L) where most metals did not differ. It is probable that the parent material of the beech soil contained some metamorphic mafic material. The surface soils for both stands were moderately enriched with Pb, Cd and As, whereas for Cr and Ni the enrichment was minimal. The concentrations of available elements (extracted with DTPA) were higher in the beech soil. The availability of most metals was affected by the pH, the organic C, the ratio of C/N and the total concentration of the metals. Through a Principal Component Analysis (PCA) analysis, it was found that 63-75% of the concentrations variance of the available metals was explained. The percentages of available metals with regard to their total concentrations in soils were higher in the beech plot in the FH layer but in the mineral layers, they did not differ apart from Pb. The concentrations of the metals in the leaves of both species in three consecutive years did not differ with the exception of Cd, the concentration of which was higher in the beech leaves.</p>

Numerical Simulation of the Effect of Construction Dust Emission on Air Quality in Jinan, a Central City in the North China Plain

<p>Numerical simulation was conducted to assess the impact of dust emission on typical environmental sites in Jinan City. The CALPUFF model was applied to five simulation scenarios. The results showed that dust emission had a significant impact on air quality in Jinan. The impact of dust emission on the average concentration of PM10 at 15 monitoring sites was 19.8 μg/m3, accounting for 14.9% of the annual total. The impact of dust emission on the average concentration of PM2.5 was 5.2 μg/m3, accounting for 8.1% of the annual total. Adoption of yellow warning measures in the emission reduction scenarios had insignificant environmental effects due to unfavorable meteorological conditions. Compared with the baseline scenario, the average concentrations of PM10 and PM2.5 decreased by 13.6% and 1.9%, respectively. After adoption of orange and red warning measures, the impact of site dust emission on air quality at the monitoring site was reduced significantly. Significant environmental effects were achieved after all construction sites within a 2-km radius of the monitoring site were closed. Compared with the baseline scenario, the average concentrations of PM10 and PM2.5 were reduced by 45.5% and 42.3%, respectively. The results showed that under adverse meteorological conditions, higher-level warning measures should be undertaken to reduce the impact of site emissions on environmental quality. Considering the economic and social effects of emission reduction, temporary construction stoppage within 2 km of the monitoring site is a feasible plan that is in accordance with the goals of comprehensive environmental management.</p>

Seasonal and interannual coastal wind variability off the central Maluku Islands revealed by satellite oceanography

<p>The Maluku Islands (henceforth MI) are situated in the northeastern Indonesia. Ocean region off the central MI is pivotal as it provides a course for the Indonesian Throughflow (ITF) through the Lifamatola passage. However, sea surface dynamics off the central MI is unknown until recently due to inadequate measurements. The current fact motivates the present study to decipher the coastal wind variability off the central MI and its effect on the sea surface by analysing long-term datasets (2007-2019) of satellite-derived sea surface wind, sea surface temperature (SST), and surface chlorophyll-a concentration. Possible influence of extreme climate events of the 2015 El Niño-Southern Oscillation (ENSO) and the 2019 Indian Ocean Dipole (IOD) on all oceanographic parameters was also examined. Results show that the prevailing southeasterly winds over the central MI induce SST cooling and phytoplankton bloom. Correlation analysis revealed that the ENSO and IOD play significant roles in defining spatial distribution of the coastal wind, SST, and phytoplankton bloom in the research area. In addition, the anomaly analysis exhibits distinct oceanographic features during the climate extreme events of 2015 and 2019. Collectively, results of the present research highlight the importance of coastal wind variability and extreme events in shaping the ocean surface characteristics and perhaps regional fisheries production.</p>

The Carbon Footprint of a Public Sector University Before and During the COVID-19 Lockdown.

<p>Carbon footprint (CF) is a measure of greenhouse gas emissions generated from daily human-induced activities as carbon dioxide equivalent. This study is an attempt to represent a consumption-based CF study from the scope of transportation, electricity, and waste generation for University of the Punjab (PU), Lahore under the WRI/WBCSD greenhouse gas protocol corporate standards. Data acquired through fieldwork, questionnaire surveys, direct sampling, and existing records for the year 2019-20 suggested that electricity is the greatest contributor of CO2 emissions at 59%, followed by transportation at 36%, and waste generation at approximately 5%. The total CF(CO2_eq) generated from different sources is about 18360.62MT for one year. The recent COVID-19 lockdown has offered inimitable prospect to compare the carbon footprint of one of the largest higher education institutes of Pakistan before and during this pandemic. The data can serve for tracking, assessing, and setting goals for greenhouse gas emission reduction programs in future.</p>

Study of Chinese carbon emission trading market mechanism based on the game theory

<p>In order to provide corresponding suggestions for the establishment and development of China's carbon trading market mechanism, the three-party game model of the competent government departments, carbon emission enterprises and third-party verification institution in the initial allocation of carbon emission rights and the rotation bargaining game model in the secondary carbon trading market are solved and analyzed in this paper. The results show that the competent government departments should improve the review efficiency effectively to reduce cost by outsourcing the review work to universities, research institutes and other scientific research units and increasing punishment for the collusion behavior between the carbon emission enterprises and third-party verification institution. At the same time, the competent government departments should adopt the regular regulatory policies to deal with collusion behavior and reduce the sampling proportion to cut cost of government review. The trading center should directly determine transaction price in combination with the forces of buyers and sellers, and make matchmaking trading directly by selecting the qualified buyers and sellers at the secondary carbon trading market in process of bilateral open bidding.</p>

A comprehensive study of the high temperature pyrolysis of sewage sludge: kinetics, energy analysis and products formation

<p>This study evaluates the pyrolysis of sewage sludge until 960 °C using heating rates between 3 K/min and 12 K/min in a macro TG/EGA. Mass and energy balances and kinetic parameters are determined. Thermal decomposition is divided into a low temperature zone (until 550 °C to 590 °C), for decomposition of organic matter, and a high temperature zone, for decomposition of inorganic matter and secondary reactions of the residual organic matter. In dry basis at 570 °C solid, liquid and gaseous products amount to 69.2 wt.-%, 29.2 wt.-% and 1.6 wt.-%, respectively. An increment in the final temperature to 960 °C causes a successive decrease of the solid residue to 56.9 wt.-%. The solid product contains more than 80 wt.-% mineral matter with high amounts of valuable elements, such as Ca and P. An energy requirement of 2.18 MJ/kg of dry sewage sludge is calculated for the pyrolysis until 570 °C. At this temperature, 58.5 % of the energy entering the process is concentrated in the liquid product and 40.0 % in the solid. A suitable set of kinetic parameters is determined through a formal independent parallel reactions model with six-pseudo components, using a combination of isoconversional and fitting methods.</p>

Improving Water Quality Assessment through Anomaly Detection Using Hybrid Convolutional Neural Network Approach

<p>Water being a precious commodity for every person around the world needs to be quality monitored continuously for ensuring safety whilst usage. The water data collected from sensors in water plants are used for water quality assessment. The anomaly present in the water data seriously affects the performance of water quality assessment. Hence it needs to be addressed. In this regard, water data collected from sensors have been subjected to various anomaly detection approaches guided by Machine Learning (ML) and Deep Learning framework. Standard machine learning algorithms have been used extensively in water quality analysis and these algorithms in general converge quickly. Considering the fact that manual feature selection has to be done for ML algorithms, Deep Learning (DL) algorithm is proposed which involve implicit feature learning. A hybrid model is formulated that takes advantage of both and presented it is data invariant too. This novel Hybrid Convolutional Neural Network (CNN) and Extreme Learning Machine (ELM) approach is used to detect presence of anomalies in sensor collected water data. The experiment of the proposed CNN-ELM model is carried out using the publicly available dataset GECCO 2019. The findings proved that the model has improved the water quality assessment of the sensor water data collected by detecting the anomalies efficiently and achieves F1 score of 0.92. This model can be implemented in water quality assessment.</p>

Spatial and temporal variations of hydrodynamics and sediment dynamics in Indus River Estuary,Pakistan

<p>Field investigations were conducted to study the seasonal variation of hydrodynamics and sediment transport in Indus River Estuary (IRE), Pakistan. The data of water levels, currents, salinity, and suspended sediment concentration (SSC) were collected hourly covering both wet and dry seasons. Tidal amplitudes were higher near the mouth than those at the middle and upper estuary. The ebb phase lasted longer than that of the flood during the wet season. The asymmetric tidal pattern with higher ebb velocity was observed during the wet season. A slight difference in current velocity was found during the dry season. The flood currents were higher at middle estuary than those in wet season. During the wet season, salinity variation within a tidal cycle slightly increased from the upper estuary to the mouth. Salinity was substantially higher during the dry season than the wet season at all three stations, with the absence of the flood-ebb variation, showing a strong saltwater intrusion. The SSC data revealed that the sediments were mainly brought into the estuary by freshwater discharge during the wet season. Sediment re-suspension process persists during the dry season, due to the tidal currents. A stronger saltwater intrusion occurred in the dry season due to weak river discharge. An estuarine turbidity maximum zone was formed near station-2 due to the combined effects of tides, river discharge and saltwater intrusion. Overall, field observations have shown a significant spatial and temporal variation in flood/ebb and wet/dry seasons for hydrodynamics and sediment transport in IRE.</p>

Estimation of changes in the Dead Sea surface water area through multiple water indices and geospatial techniques

<p>The study calculated the changes in the Dead Sea surface area from 1984 to 2019. The satellite images of 1985, 1990, 2000, 2010, and 2019 were classified by applying four different methods to estimate the changes in the Dead Sea surface water area. The methods included normalized difference water index (NDWI), modified normalized differences water index (MNDWI), automated water extraction Index (AWEI), and ISO cluster unsupervised classification. The results revealed a decrease of 76.63 sq. km area that accounts for an average of 11.27% sea area. The statistical model predicted that the Dead Sea surface area will shrink by half within the next 143 years, and the sea will be completely dried by 2305 if appropriate measures are not taken by decision-makers to avoid further reduction of the surface area.</p>