Olive mill wastewater (OMW) is a major problem in the olive oil producing countries, because of its highly polluting power. Its physico-chemical characterization showed that this effluent has an acidic character (pH = 4.9) and it is rich in organic and mineral matter (chemical and biochemical oxygen demands, polyphenols, chlorides, sulphates, nitrites, nitrates, etc.). In this work, the corrosion behaviour of carbon steel (X70) immersed in olive mill wastewater sample solution, collected from an agro-industry, was studied. The obtained results show that the rate of corrosion increases because of the attack of acids exist in the OMW (Cl-, polyphenols, Ni, Fe). For two days of immersion, the potential stretches toward more positive values due to the oxidization of carbon steel. After the extension of the immersion until 7 days, we recorded the formation of the corrosion product on the surface of the working electrode. On the other hand, the analysis of surface samples by scanning electron microscope coupled with EDX confirms the formation of a layer obtained after the corrosion process.

Based on the systematic collection of the shallow groundwater samples in the urban area of Suzhou, Anhui province, China, the concentrations of zinc have been analysed by statistical and spatial methods. The results show that the zinc concentrations in the groundwater samples are low, and all the samples can meet the national groundwater quality standard of China with Class I and II, which means that the groundwater can be used for drinking purpose. However, the zinc concentration has high coefficient of variation and low p-values of normal distribution, implying that it has been affected by anthropogenic activities, which was also demonstrated by the consistency of the distribution of the samples with high zinc concentrations and the areas with high density of human and transportation, as well as the high-high cluster of the spatial autocorrelation analysis of zinc. Based on the statistical analysis, the environmental background has been calculated to be 0-62.6 μg/L, whereas the environmental background has been calculated to be 0-69.8 μg/L with spatial analysis. They are different because of the different basis of the two methods, the former based on the hypothesis of normal distribution of the background values, whereas the latter do not need such a hypothesis.

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.

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.

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.

The cell-phone radiation is a potential health hazard not only to the singular user but to the whole population through the environmental pollution. The environmental radiation density may vary from place to place depending upon its absorption or reflection by surroundings, e.g., trees, buildings, human population, water bodies, etc. The present work was taken to study the effect of population density and surroundings on the environmental radiation. The power density values of radiation were measured using a hand-held portable power density meter TES 593, and specific absorption rates (SAR) were estimated from the measured values. The SAR values were compared with the safe limit of 1 mW/kg above which biological system of humans and animals starts getting affected. The studies indicated that the average environmental radiation power density values were higher in unpopulated and open areas than densely populated regions by 300%.

Mango seed kernel, a byproduct of the mango processing industry, is known to be a rich source of phenolic compounds. Phenolic compounds are bioactive in nature and highly valuable due to a number of potential health and therapeutic benefits making it a good component in functional food formulations and for the nutraceutical industry. Through spray drying, the bioactive fraction from an industrial waste mango seed kernel extract was recovered through encapsulation using maltodextrin (MD), gum Arabic (GA), and starch (ST) as encapsulating agents. The encapsulating agent type used alongside inlet drying air temperature was varied and observed to have an influence on encapsulation yield and on the powder qualities measured such as total phenolic content, antioxidant activity, moisture content, bulk density and water solubility index. Among the encapsulating agents used, MD was found to be the most desirable encapsulating material with regards to the desirable properties of spray-dried powders in terms of functionality and applicability.

In the present research work, batch adsorption study was carried out to optimize the fluoride removal capacity of guar gum hydrogel beads from aqueous solution. The adsorption capacity was found to be 71.2 mg/g. The FTIR spectrum revealed the presence of functional groups that might be involved in fluoride adsorption. Adsorption of fluoride onto bio-sorbent was investigated as a function of pH, adsorbent dosage and time. The experimental equilibrium sorption data well fitted to the Langmuir mo del and the sorption kinetics for the bio-sorbent was found to follow second order rate expression. The negative values of ΔG° suggest that the sorption of fluoride onto the bio-sorbent was spontaneous and exothermic due to the negative value of ΔH°. The negative ΔS° value for the guar gum was found to be decreasing in randomness at the solid-liquid intersection.

The major and overriding factors affecting water quality and the aquatic ecosystems in the coastal areas are sewage and nutrient inputs from municipal and industrial wastewater, depletion of seaside contrivances, risks of public health as well as loss of biodiversity. The coastal area of the Karachi harbour is most heavily polluted due to these reasons. In this study, we proposed the artificial neural network (ANN) models to monitor and control the sea surface water quality of the Karachi coastal area along the harbour. Recently, various types of ANN have been successfully applied in hydrological fields. In this study, Nonlinear Auto Regressive eXogenous Neural Network (NARX-NN) shall be applied to predict the concentration of heavy metals in coastal sea surface water of the Karachi harbour area. This method provides significant insight into the comparative study of two different training functions of NARX-NN, namely, Levenberg-Marquardt (LM) and Scale Conjugate Gradient (SCG). The physical parameters like sea surface temperature (SST), salinity, tides and pH are taken as an input and the chemical parameters chromium, copper, lead, nickel and zinc are taken as output individually for all six locations. The performance of the model was evaluated by statistical criteria that include a correlation coefficient (r) and mean square error (MSE). The prediction results indicated that the LM training function is superior to SCG training function. Hope this study is helpful for local authorities and policy makers to develop a new infrastructure and install a water treatment plant to reduce the water pollution of the harbour area.

Civil buildings are among the structures that consume the most energy in society. Reducing the energy consumption of civil buildings has become an important component of China’s energy policy. Identifying the key influencing factors of the energy consumption of civil buildings and formulating effective energy-saving countermeasures are important to enhance the energy efficiency of civil buildings. This study conducted a systematic analysis of the influencing factors of the energy consumption of civil buildings in Hubei Province, China. First, existing studies on the influencing factors of the energy consumption of buildings in foreign developed countries were reviewed. The status of the population, economic life, and social life that influenced the energy consumption of civil buildings in Hubei Province in the past 10 years was summarized. Relevant influencing factors were estimated via ridge regression. Results demonstrated that developed countries generally investigated the influencing factors of the energy consumption of civil buildings from the energy supply-demand relation, family factors, and out-of-family factors. Gross domestic product (GDP), total population, tertiary industry development, per capita floor space, household consumption level, and urbanization facilitated the energy consumption of civil buildings in Hubei Province during the aforementioned period, and the regression coefficients were significant at the 5% level. Lastly, policy countermeasures were proposed to accelerate the energy-saving development of civil buildings. Research conclusions are important to enrich theories regarding the energy consumption reduction of civil buildings, to help relevant government sectors of Hubei Province recognize action points for the energy-saving development of civil buildings, and to accelerate the benign development of energy-saving buildings.