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.

As a strategic emerging industry, environmental protection industry is important in the development of national economy. However, few empirical studies on the performance of environmental protection industry are carried out. In order to explore development performance and influencing factors of environmental protection industry in China, the selection of development data of China’s 31 provincial regions under the environmental protection industry was conducted based on industrial efficiency, economic contribution, industrial scale, and development speed. Development performance of environmental protection industry was qualitatively analysed and influencing factors were explored by using the method of factor analysis. Results show that the factor score of the eastern region is positive in both industry scale and pollution treatment. Factor score of industry scale, industry contribution and pollution treatment in the central region is positive. Development of environmental protection industry in the western region lags far behind other areas, and only factor score of industry contribution are positive and significantly higher than other areas. Factor score of industrial scale, industrial contribution, and pollution treatment in the northeast region is lower than that in the eastern and central region. Suggestions and countermeasures are proposed to promote the development of the environmental protection industry.

Water pollution by heavy metals due to discharge of industrial and anthropogenic waste leads to serious environmental and health problems as most of these heavy metals are carcinogenic in nature. In the present study chromium biosorption capacity of live and dead biomass of bacterial strain HGB1 isolated from Ganga River in Haridwar, which was examined as Bacillus subtilis, following 16S rDNA sequence analysis, was examined for different physical parameters such as pH, time of incubation and temperature. Experimental results indicate that the Bacillius subtilis has maximum tolerance capacity up to 1000 mg.L-1 with highest metal uptake of 95.64%, 97.25% and 97.11% at pH 3, 60 minutes, 2.5 mg/mL biomass respectively in case of dead biomass. In case of living biomass, highest metal uptake was 81.64%, 96.79 % and 95.89% at pH 7, 72hr and 32°C respectively. The surface chemical functional groups of Bacillus subtilis identified by FTIR were amino, carboxyl, hydroxyl and carbonyl groups. The morphological changes were examined by SEM analysis.

The construction industry is among the pillars of China’s economic development. However, it causes high carbon emission and high energy consumption, which should be considered when drafting energy saving and emission reduction policies. The sustainable development of this industry lies in the effective estimation of carbon emission efficiency and implementation of energy-saving and emission reduction measures in accordance with local practical situations. First, investigations on the emission efficiency of the construction industry and relevant emission reduction policies in foreign developed countries were reviewed in this study. Second, the carbon emission efficiency of the construction industry in 13 prefecture-level cities in Hunan Province (China) from 2011 to 2017 were estimated using the SBM (Slack Based Measure) model involving unexpected outputs. Finally, specific suggestions on reducing the carbon emission of the construction industry were proposed. CO2 emissions in the construction industry increase annually as a response to economic development and urbanization. The carbon emission efficiency of Hunan Province’s construction industry maintained a stable growth rate in 2011-2017. The industry achieved an annual growth rate of 2.7% in 2017 from that in 2011. The carbon emission efficiency of Changsha City, Zhangjiajie City, and Yiyang City were relatively higher than those of other cities in the province. Such findings serve as a reference to the quantification of potential of Hunan Province in reducing the carbon emission, the formulation of specific carbon emission reduction goal, the augmentation of the means to evaluate energy saving and emission reduction, and the development of a low-carbon construction industry.

The concentration of chlorophyll-α in the Gorontalo Bay is necessary to be observed since it could describe the condition of water richness. The semi-enclosed Gorontalo Bay morphology causes the status of water fertility to be largely determined by the input of inorganic or organic materials originating from the mainland. This study aimed to figure out the concentration and horizontal distribution pattern of the chlorophyll-α then further to decide the relationship between the concentration of chlorophyll-α and the nutrients in the Gorontalo Bay. There were fifteen sub-sampling sites selected based on coastal and ecological characteristics. Results showed that the distribution pattern of chlorophyll-α in the Gorontalo Bay in June and July 2017 was dissimilar and its concentration ranged from 0.984 to 3.744 mg.m-3. In addition, there was a positive and substantial relationship between chlorophyll-α and phosphate (p<0.01). Nonetheless, there was no significant correlation between chlorophyll-α and nitrate (p>0.01) and ammonia (p>0.01).

The level of radioactivity in the soil has been increasing unpredictably due to the human uranium mining exploitation of uranium over the past 100 years. Remediation of metals in actual soil confronts many challenges, remaining poorly understood. This study intends to investigate the concentrations and distributions of U, Cd, Zn, Pb, and Cu in soils surrounded by a uranium mill tailing pond. Furthermore, a phosphorus-modified bio-char was prepared in order to determine its role in immobilizing uranium in soil samples. Results show that the contents of U and Pb are much higher than that of the background values, due to the influence of the uranium mill tailing pond. Phosphorus can enhance the immobilization efficiency of U, Cd, Pb, and Cu in soil samples. The concentration of uranium in the leaching supernatant of phosphorus-modified bio-char group is lower than that of control and unmodified bio-char groups due to the fact that the biosorption occurred in the exterior surface of the biomass, which imply that phosphorus-modified bio-char is a potential immobilization material to reduce the leaching rate of metals. These findings can provide references for remediation technology of metals in natural soil.

The toxic impacts of CuO nanoparticles (NPs) on the marine phytoplankton Nannochloropsis oculata were evaluated by measuring a number of biological parameters. Exposure to different concentrations of CuO-NPs (5–200 mg/L) significantly decreased the growth and content of chlorophyll a of N. oculata. The results showed that CuO-NPs were toxic to this microalga with a half maximal effective concentration (EC50) of 116.981 mg/L. Exposure to CuO-NPs increased the hydrogen peroxide (H₂O₂) content and induced the membrane damages. Moreover, the concentration of phenolic compounds was increased, while the levels of carotenoids were markedly decreased in comparison to the control sample. The activity of catalase (CAT), ascorbate peroxidase (APX), polyphenol oxidase (PPO) and lactate dehydrogenase (LDH) enzymes significantly was increased in response to CuO-NPs treatments. These results indicated that CuO-NPs stimulated the antioxidant defense system in N. oculata to protect the cells against the oxidative damages. The Fourier-transform infrared spectroscopy (FTIR) analyses showed that the main functional groups (C=O and C–O–C) interacted with CuO-NPs. The images of scanning electron microscopy (SEM) and transmission electron microscopy (TEM) revealed the cell membrane damage and the change of cell wall structure which may be contributed to the nanotoxicity. These findings may provide additional insights into the mechanisms of cytotoxicity induced by CuO-NPs.

This article studies the price competition and cooperation in a duopoly that is subjected to carbon emissions cap. The study assumes that in a departure from the classical Bertrand game, there is still a market for both firms’ goods regardless of the product price, even though production capacity is limited by carbon emissions regulation. Through the decentralized decision making of both firms under perfect information, the results are unstable. The firm with the lower maximum production capacity under carbon emissions regulation and the firm with the higher maximum production capacity both seek market price cooperation. By designing an internal carbon credits trading mechanism, we can ensure that the production capacity of the firm with the higher maximum production capacity under carbon emissions regulation reaches price equilibrium. Also, the negotiation power of the duopoly would affect the price equilibrium.

Investigations of deleterious effects on non-target species, including earthworms, have been conducted for a number of pesticides, but there is a need for additional assessments of potential adverse effects. In the present study, the acute toxicity of eight pesticides to the earthworm Eisenia andrei was assessed and compared. The exposures were conducted using the filter paper contact toxicity method. Based on the 48-h LC₅₀ values, one pesticide was classified as supertoxic (combined fungicide containing difenoconazole and fludioxonil), four as extremely toxic (combined herbicide containing pethoxamide and terbuthylazine, combined fungicide containing fluopyram and tebuconazole, fungicide containing pyrimethanil, and combined fungicide containing thiram and carboxin), two as very toxic (combined fungicide containing flutriafol and thiabendazole, and herbicide containing fluroxypyr-meptyl), and one as moderately toxic (insecticide containing thiamethoxam). Additionally, effects of pesticides on the multixenobiotic resistance (MXR) activity were measured. Results showed that four pesticides caused significant effects with a recorded inhibition of the activity, which can consequently lead to a higher toxicity due to longer retention of the pesticides in the cells. Finally, for three chosen pesticides, gene expression of cat, sod, and gst was measured, and significant changes were observed. The obtained results show that earthworms could be significantly affected by pesticides commonly used in agriculture.