With the acceleration of industrialization, haze pollution has become a severe environmental pollution problem, and green technology innovation is one feasible way to alleviate it. Based on the PM₂.₅ concentration data of 30 provinces in mainland China from 2011 to 2017, we use a spatial panel model to investigate the spatial characteristics of haze pollution and examine the impact of green technology innovation on it. Results show that haze pollution has spatial correlation and a time lag. Its spatial correlation is associated with geographical distance as well as the compound influence of distance and economic development. Green technology innovation and foreign investment have inhibitory and negative spillover effects on haze pollution. Industrial structure and energy consumption structure play a partial intermediary role between green technology innovation and haze pollution, and the former has a significant negative spillover, while the latter has a positive effect. To reduce haze pollution, China should improve the level of green technology innovation, use foreign investment wisely, and enhance policy support and guidance. It should also promote the rationalization of industrial structure, optimize energy structure, and implement energy substitution. Finally, it is crucial that it should strengthen regional collaborative governance and build a multi-agent governance system.

Residential greenness exposure has been linked to a number of physical and mental disorders. Nevertheless, evidence on the association between greenness and geriatric depression was limited and focused on developed countries. This study was aimed to investigate whether the relationship between residential greenness exposure and geriatric depression exists among the elderly with long-term care insurance (LTCI) in Shanghai, China. In 2018, a total of 1066 LTCI elderly from a cross-sectional survey completed a questionnaire in Shanghai. Residential greenness indicators, including normalized difference vegetation index (NDVI) and soil-adjusted vegetation index (SAVI), were calculated from the Landsat 8 imagery data in different buffers (100-m, 300-m, and 500-m). Mediation analysis by perceived social support was conducted to explore potential mechanisms underlying the associations. In the fully adjusted model, one IQR increase of NDVI and SAVI in the 300-m buffer size was associated with an 11.9% (PR: 0.881, 95% CI: 0.795, 0.977) and 14.7% (PR: 0.853, 95% CI: 0.766, 0.949) lower prevalence of geriatric depression, respectively. Stronger association was observed in the elderly with lower education level, living in non-central area, and lower family monthly income. Perceived social support significantly mediated 40.4% of the total effect for NDVI 300-m buffer and 40.3% for SAVI 300-m buffer to the greenness-depression association, respectively. Our results indicate the importance of residential greenness exposure to geriatric depression, especially for the elderly with lower education level, living in non-central area, and lower family monthly income. Perceived social support might mediate the association. Well-designed longitudinal studies are warranted to confirm our findings and investigate the underlying mechanisms.

It is important that a pollution remediation system be able to cater for a variety of pollutant species present in the water to be treated. The aim of this study was to utilise a series of commercial zeolites (H-MOR, H-β, and H-ZSM5) for the concomitant adsorption and photodegradation of Cu²⁺ and tetracycline (TC) molecules. The adsorbent cum photocatalyst was characterised by SEM and FTIR. FTIR confirmed the key functional groups (Si-O-Si and Al-O-Si) in the series of zeolites, and H-β zeolite was demonstrated to be the most effective adsorbent cum photocatalyst for both adsorption and photodegradation of Cu²⁺ and TC molecules. These results were further corroborated from the pseudo-first-order rate constant values. Among the investigated zeolites, H-ZSM5 displayed the least adsorption and photodegradation performance for Cu²⁺ and TC molecules. The photolysis reaction confirms the significant role of zeolites in the photodegradation test, as low performance was recorded in the absence of the zeolites.

This study aims to investigate the impacts of fuel price policies on the concentration of air pollutants in Tehran city. Autoregressive distributed lag (ARDL) estimation models were used to investigate the impacts of gasoline and diesel prices along with the weather and economic variables on the following traffic-related pollutants: carbon monoxide (CO), nitrogen dioxide (NO₂), and particular matter 10 micrometers or less (PM₁₀). In the short term, a 1% increase in gasoline prices leads to a 0.02 and 0.012% decrease in the concentration of CO and PM₁₀, respectively. In addition, in the short term, a 1% increase in diesel prices leads to a 0.008, 0.02, and 0.015 % decrease in the concentration of CO, PM₁₀, and NO₂, respectively. Results demonstrate that a 1% increase in gasoline prices leads to a 0.011 and 0.02 % increase in NO₂ concentration in the short term and long term, respectively. Fuel prices had a greater impact on air pollutant concentration in the long term than in the short term. In the long term, a 1% increase in diesel prices leads to a 0.011, 0.024, and 0.029 % decrease in the concentration of CO, NO₂, and PM₁₀, respectively. Although fuel price increases lead to a significant reduction in PM₁₀ and CO concentrations, other factors related to weather conditions (wind speed, temperature, and rainfall) as well as economic activities have a greater impact on air pollution. Therefore, other policies such as improving fuel quality and technology along with other economic policies can be more effective.

Eco-industrial parks (EIPs) are of increasing importance for implementing industrial ecology strategies and are facing increasing challenges in terms of environmental pollution and resource scarcity. As a complex adaptive system, an EIP involves multiple sectors and faces various disturbances that influence its evolutionary trajectories. This study adopts an agent-based model to simulate the material flows and industrial symbiosis process in the EIP, considering the initiative of each company and the ever-changing environment. The proposed EIP model emphasises the heterogeneity of companies and attempts to reflect multiple and dynamic factors that have received less attention in previous studies. This model contains two types of agents, companies and the external environment. A company agent makes decisions and interacts with other agents following its own behaviour rules, while the external environment agent functions to coordinate the material flows and exert influence on the companies. The model has been verified and validated by simulating a 20-year-period development of an empirical EIP in China. The simulation results are assessed by three indicators: eco-connectance, eco-efficiency, and industrial symbiosis indicator. Results showed that during the growing phase, the eco-connectance increased from 0.02 to 0.1 for the non-disturbance situation. The eco-efficiency and industrial symbiosis indicator also realised 78.5% and 74.8% of their total increments. The outcome of this research provides insights for the design of the strategies to improve the industrial symbiosis performance and is of high potential to facilitate EIPs in promoting eco-transformation and sustainable development.

Microplastics (MPs) released into aquatic environment interact with other pollutants that already exist in water, potentially altering their toxicity, which poses a new problem for aquatic ecosystems. In the present study, we first evaluated the effects of polystyrene MPs (mPS) on copper (Cu) toxicity to the protozoan Euglena gracilis using three methods based on 96-h acute toxicity, orthogonal test and 12-d sub-acute toxicity data. Thereafter, the 12-d sub-acute exposure was employed to investigate protozoan growth, photosynthetic parameters and pigments, soluble protein, total antioxidant capacity and trace metal accumulation in E. gracilis after exposure to either 1.5 mg/L of Cu, 75-nm mPS (1 and 5 mg/L) or a combination therein, with the objective to understand the underlined mechanisms. The results show that the concentration and exposure time are key factors influencing the effects of the mPS on Cu toxicity. A mPS concentration of 5 mg/L caused significantly more dissipation energy, which is used for photosynthesis and thus decreased photosynthetic efficiency, but this effect weakened after 12 d of exposure. Exposure to Cu alone resulted in significantly high Cu accumulation in the cells and inhibited uptake of manganese and zinc. The presence of mPS did not influence the effects of Cu on trace metal accumulation. Our result suggests that application of multiple methods and indices could provide more information for a comprehensive understanding of the effects of mPS on toxicity of other pollutants. In addition, long-term exposure seems necessary for evaluating mPS toxicity.

A new adsorbent from Jerivá coconut (a fruit native to the Atlantic Forest) was developed through thermal and chemical activation with H₃PO₄. The characterization of activated carbon using SEM–EDS revealed an irregular and heterogeneous surface, with a surface area of 750 m² g⁻¹, while FTIR indicated the presence of functional groups. The adsorption kinetics was favorable, reaching equilibrium in 80 min, removing more than 99% of the initial concentration (100 mg L⁻¹) of methylene blue; the Avrami model had a better fit to the data. The sorption isotherms performed at four temperatures showed endothermic behavior, with a maximum adsorption capacity of 254.40 mg g⁻¹, with adjustment to the Sips model. The mechanisms involved in dye adsorption were discussed and elucidated. The adsorbent was tested to remove the color of the real effluent from the textile industry, and the results showed discoloration superior to 93%, meeting international disposal limits. The results confirm the efficiency of the new adsorbent and the possibility of application in the treatment of textile effluents.

Environmental microorganism (EM) offers a highly efficient, harmless, and low-cost solution to environmental pollution. They are used in sanitation, monitoring, and decomposition of environmental pollutants. However, this depends on the proper identification of suitable microorganisms. In order to fasten, lower the cost, and increase consistency and accuracy of identification, we propose the novel pairwise deep learning features (PDLFs) to analyze microorganisms. The PDLFs technique combines the capability of handcrafted and deep learning features. In this technique, we leverage the Shi and Tomasi interest points by extracting deep learning features from patches which are centered at interest points’ locations. Then, to increase the number of potential features that have intermediate spatial characteristics between nearby interest points, we use Delaunay triangulation theorem and straight line geometric theorem to pair the nearby deep learning features. The potential of pairwise features is justified on the classification of EMs using SVMs, Linear discriminant analysis, Logistic regression, XGBoost and Random Forest classifier. The pairwise features obtain outstanding results of 99.17%, 91.34%, 91.32%, 91.48%, and 99.56%, which are the increase of about 5.95%, 62.40%, 62.37%, 61.84%, and 3.23% in accuracy, F1-score, recall, precision, and specificity respectively, compared to non-paired deep learning features.

The aim of this study is to compare sunshine duration-based models and the other meteorological parameter-based models and to develop new forecasting models. The estimation and comparison of global solar radiation has been made by using twenty-four empirical models including three new models for different location named Arbil, Dohuk, and Sulaimania of Northern Iraq. The reason of using these different locations is to test the accuracy of the other meteorological parameter models by comparing the sunshine duration models for different region. Mostly common statistical error values are used to evaluate the performance of the estimation models and to identify the models that will give the closest results to the actual values. According to the results, it was seen that the models based on other meteorological parameters have better predictions than the models based on the sunshine duration. While the R² value of the best models depending on the sunshine duration ranged from 0.97 to 0.99, the R² values of the best models of other meteorological parameters are above 0.99. Furthermore, it is observed that the new proposed models provide better estimates of global solar radiation at different locations than all models used in this study.

Access to healthy drinking water is vital to human health and development. Bottled water consumption has been on the rise in recent years. As several chemical and bacteriological parameters affect bottled water quality, it is difficult to choose the highest-quality bottled water. Numerous studies have proposed the use of multiple-criteria decision-making (MCDM) methods to overcome this problem. Herein, the two-stage fuzzy analytic hierarchy process (FAHP) and technique for order preference by similarity to ideal solution (TOPSIS) method were adopted to rank different brands of bottled water. The FAHP approach allows working at the intervals of judgment rather than absolute values. TOPSIS is a technique for ordering performance based on its similarity to the ideal solution. An expert panel selected and classified the criteria and sub-criteria. A pairwise comparison questionnaire was then developed, and the weights of the criteria and sub-criteria were assigned by water quality experts. The data on the quality of different brands of water were collected from the Iranian bottled water database. The final data analysis and weight determination of each parameter were performed in Excel and R software Programs. Finally, the CCᵢ (value of closeness coefficient) and rank of 71 bottled water brands were calculated, and the best brand was introduced. Among the selected criteria, carcinogenic chemical compounds with the weight of 0.368 were the most important compound in ranking bottled water brands, followed by bacteriologic, pathogenic chemical compounds, chemical compounds important in terms of toxicity, nutritious chemical compounds with a low toxicity level, chemical compounds related to esthetic effects, and chemical compounds without health effects, respectively.