Fine blue-coke and direct liquefaction residue of coal are byproducts in the process of coal chemical production. They were taken as raw materials for the preparation of activated coke by the activation of carbon dioxide. The conditions (activation temperature, activation time and carbon dioxide flow rate) for activated coke preparation were optimized by response surface methodology (RSM). Results showed that activation temperature and activation time had a significant effect on the activated coke iodine adsorption value. The synergistic effect of activation time and carbon dioxide flow had a great influence on iodine adsorption value of activated coke. RSM optimization experiment obtained the optimum activation conditions were activation temperature of 850°C, activation time of 90min and carbon dioxide flow rate of 60 mL/min. Under these conditions, the obtained activated coke iodine adsorption value can reach 401 mg/g, which could meet the needs of industrial desulphurization.

Microplastic pollution is widely reported in different marine environments from shorelines to seabed of deep seas which pose an emerging threat to entire marine ecosystems. As the world’s secondlargest microplastics polluter, an understanding of the distribution of this type of pollution is important for the measurement of the magnitude of environmental risk. In the present study, the abundance and distribution of microplastics in coastal sediments of Bama Resort, Baluran National Park were measured at depths of 0-10 cm, 10-20 cm, and 20-30 cm. Microplastics characterization was performed using a modified flotation method while a sieve analysis was used to assess the particle size of the sediments. Results showed that there were 484 particles with a total average abundance of 116.41 ± 80.78 particles kg-1 DW. Fibres shared 37.8% of the total microplastics found with overall average of 43.71 ± 36.52 particles kg-1 DW. Overall, Tukey’s multiple comparisons test showed significant differences (P< 0.01) in vertical distribution of microplastics in which 55.46% of particles were found at the depths of 0-10 cm, whereas at the depths of 20-30 cm, the proportion was only 15.95%. There were two types of sediments, sandy gravel and gravelly sand in which the former type of sediments holds higher microplastic particles due to its grain dominance in upper sediments. These results imply that microplastics pollute coastal sediments of Bama Resorts, BNP, and their deposition increase over time as greater microplastics frequencies were observed in upper and more recent sediment.

Crown profile models are key components of growth and yield models and are crucial for estimating the crown volume and constructing 3D visualization of trees. We used a total of 431 trees collected from 98 pure even-aged temporary sample plots established in Fujian Province to develop crown profile models of Chinese fir (Cunninghamia lanceolata).To describe the shape of tree crowns more accurately, significance tests of the effects of different stand conditions (stand age, site index, and stand density) on crown shape were conducted with one-way analysis of variance (ANOVA). Multiple comparisons based on the ANOVA results were used to classify the crown data into three groups according to stand age: Group I (young forest), Group II (medium forest), and Group III (nearly mature and mature forest). We analysed the relationships between the crown variables and stand variables and used the reparameterization approach to develop three optimal crown profile models for different age groups. Stand variables (such as stand density) further improved the prediction efficacy of the models. Considering the correlation between repeated measurement data for the same tree crown, the non-linear mixed-effects modelling (NLME) method was used to account for autocorrelation. The determination coefficients (R2) of the above three optimal models fitted by the non-linear mixed-effects approach were 0.9214, 0.9398 and 0.9129, and their Root Mean Squared Errors (RMSEs) were 0.1246, 0.1409 and 0.1786, respectively. The determinant coefficients (R2) of the three models fitted by the nonlinear least squares (NLS) approach were 0.9015, 0.8794 and 0.8930, and their RMSEs were 0.1395, 0.2102 and 0.1878, respectively. The results indicated that the predicted accuracy was significantly increased by using non-linear mixed effects modelling compared with the NLS method.

Machine learning and data mining are the two important tools for extracting useful information and knowledge from large datasets. In machine learning, classification is a widely used technique to predict qualitative variables and is generally preferred over regression from an operational point of view. Due to the enormous increase in air pollution in various countries especially China, air quality classification has become one of the most important topics in air quality research and modelling. This study aims at introducing a new hybrid classification model based on information theory and support vector machine (SVM) using the air quality data of 4 cities in China namely Beijing, Guangzhou, Shanghai and Tianjin from January 1, 2014 to April 30, 2016. China’s Ministry of Environmental Protection has classified the daily air quality into 6 levels, namely, serious pollution, severe pollution, moderate pollution, light pollution, good and excellent based on their respective air quality index (AQI) values. Using the information theory, information gain (IG) is calculated and feature selection is done for both categorical features and continuous numeric features. Then SVM machine learning algorithm is implemented on the selected features with cross-validation. The final evaluation reveals that the IG and SVM hybrid model performs better than SVM (alone), artificial neural network (ANN) and K-nearest neighbours (KNN) models in terms of accuracy as well as complexity.

The global concerns about the rise in anthropogenic gases have resulted in alternative clean energy sources. Biomass is one of the most prominent renewable energy sources, which can be found in wood and wood wastes, agricultural crops and their waste byproducts, municipal solid waste (MSW), animal wastes, food processing, aquatic plants and algae. Wood and by-products obtained from forest biomass stand at the centre of Renewable Energy Source (RES) due to its availability and usefulness in most developing countries. Sawdust is one of the wood processing residues that is in excess of local demand because of the near absence of its industrial demand in Kwara State. Data relating to its availability, industrial usage and energy potential are rarely available in this study area. This study investigates its availability and inherent energy potential that can be a vital tool for energy policy, planning and development. Wood wastes generated in the state were estimated to be 8012.8 m3/yr with inherent energy potential of 31298 GJ. By putting sawdust, seen as wastes in most wood processing plants, into efficient use will help reduce the competition for wood as a source of heat for cooking and heating.

With the accelerating industrialization and urbanization in China, the energy consumption of the transportation industry in the country is increasing quickly, and its proportion to the total social energy consumption is significantly growing. The transportation industry is a main source of carbon emission in urban areas. The unreasonable structure of energy consumption, the low proportion of new-energy use, and low energy utilization influence the energy-saving and emission reduction in the transportation industry. Thus, in this work, the influencing factors of transportation-induced carbon emission were estimated to analyze the environmental pollution caused by the transportation industry further. Regression analysis was performed on the environmental pollution caused by the transportation industry and the influencing factors of carbon emission in Jiangxi Province, China. Subsequently, a random STIRPAT model was constructed, and the influencing factors of carbon emission from the transportation industry in Jiangxi Province from 2007 to 2017 were analyzed through the partial least squares (PLS) method. Regression results based on the PLS method were relatively ideal. Increases in gross domestic product per capita, population size, passenger person kilometers, rotation freight transport kilometers, and the number of car ownerships can intensify transportation-induced carbon emission. This emission is increased when transportation energy intensity declines, but can be significantly inhibited by increased energy prices. The conclusions of this study can provide references for the continuous optimization of the energy use structure in the transportation industry, saving of energy resources, reduction of greenhouse gas and pollutant emission, and acceleration of low carbonization in the transportation industry.

Industrialization being the main force of development has caused many changes not only in the global phenomena but also on a regional level through its ill effects on plants and animals. The present study was thus undertaken to assess the biochemical alterations in plants subjected to polluted (industrial) and non-polluted (control) environments. The results revealed that all the studied biochemical parameters (ascorbic acid, protein, carbohydrate, total chlorophyll, catalase, and peroxidase activities) showed significant variation with respect to sites (p < 0.05). Excepting the peroxidase activity, all other biochemical parameters showed a decline in their concentration in the polluted environment as compared to their counterparts in a non-polluted environment. The highest concentration of biochemical parameters in plants of polluted sites were: ascorbic acid (4.85 mg/g), carbohydrate (0.905 mg/g), protein (28.07 mg/g), total chlorophyll (1.13 mg/g), catalase (0.394 μmoles/H2O2 decomposed/ min/g) and peroxidase (433.76 μmoles/GDHP/min/g) while that in the control site, the highest value of all the biochemical parameters were: ascorbic acid (8.97 mg/g), carbohydrate (1.283 mg/g), protein (48.68 mg/g), total chlorophyll (1.17 mg/g), catalase (0.434 μmoles/H2O2 decomposed/min/g) and peroxidase (271.25 μmoles/GDHP/min/g) respectively. Hence, it can be concluded that plants do undergo physiological stress when exposed to polluted environments and their biochemical synthesis is severely altered by pollution. However, they develop an inbuilt mechanism to counter the pollution and protect themselves in polluted or stressed environment. In the present study, peroxidase activity was primarily responsible to protect the plant in the stressed environment.

Several methods of treatment have been suggested for the removal of chromium from raw water, which include chemical precipitation, reverse osmosis, ion exchange, foam formation, etc. The main disadvantages of the above processes are that they produce a large amount of sludge and there are no possibilities of metal recovery as they are very costly. The use of plants and other plant materials for the removal of the heavy metals has already been reported in the literature as the non-conventional adsorbents. In the present work, an attempt has been made to check the suitability of Terminalia chebula powder for removing chromium from raw water by adsorption and for suggesting an environmental friendly as well as economically feasible solution to overcome the problems due to the presence of toxic pollutants like chromium in drinking water. Batch experiments were conducted using aqueous solution of chromium to determine the chromium removal. Terminalia chebula powder (commonly known as karakkaya locally) has been collected locally and used as an adsorbent for all the batch experiments. Variation of chromium removal with dosage of adsorbent and initial pollution concentration is studied.

Monsoon is a typical wind system, which influences a quarter of continental area on the earth and is closely bound up with the life of one half of the earth’s population. Therefore, it is important to explore the information on monsoon activities. In the present study, samples of precipitation and surface water collected in the summer of 2018 were analysed to reveal the variation of stable isotopes influenced by summer monsoon and its relationship with the sources of water vapour. The temporal variation of stable isotopes in precipitation is great during the monsoon period, which is primarily the result of the varying proportions of water vapour from continental and oceanic sources. The heavy isotopes in precipitation grew gradually as the proportion of ocean-source water vapour increased from May to August. The meteorological parameters of temperature and precipitation amount are not the main factors that influence the isotopic composition in precipitation, for the determination coefficient (R2 value) is low. The isotopic characteristics of river water are similar to those of precipitation, indicating that the rivers are mainly recharged by precipitation. The temporal-spatial variations of isotopes surface water are complex for the joint influence of the distribution of isotopes in precipitation, isotopic compositions of the river source, rainfall amount and evaporation, which can be considered as the indirect effects of monsoon activities.

The present study is based on the conversion from biomass to biofuels of karanja (Pongamia glabra) seeds via solar thermochemical pyrolysis process. Karanja seeds were pyrolysed at a cavity type reactor temperature of 280-340°C. The pyrolysis process was occurring in the range of 210-550°C. The ultimate and proximate analysis of the pyrolysed bio-oil was performed based on ASTM standards. The FTIR (Fourier transform infrared spectroscopy) analysis of the liquid product indicated the presence of alkenes, alkanes, ketones, carboxylic acids and aromatic rings. GC-MS (gas chromatography-mass spectrometry) demonstrated the presence of hydrocarbons having between 15 and 34 carbon atoms in a chain.