The construction of artificial reservoirs with various regulation types on river is currently an important form of comprehensive utilization of water energy and water resources in river basins. The type of regulation is important in controlling the residence time, which in turn affects the photosynthesis-respiration balance in the water. This process has a significant impact on carbon dioxide (CO₂) emissions from reservoirs. In this study, seasonal observations were carried out from September 2020 to July 2021 at five artificial reservoirs in the Qiantang River Basin, eastern China, to reveal the characteristics of CO₂ emission from the water–air interface of reservoirs with different regulating types. The results showed that the annual average CO₂ emission flux of the studied reservoirs varied significantly, ranging from 4.2 to 155.3 mmol m⁻² day⁻¹ with an average of 48.4 mmol m⁻² day⁻¹, which also had a significant negative correlation with the hydraulic retention time. While downstream of the dam, the annual average CO₂ emission flux was quite high with a range of 105.8 to 543.0 mmol m⁻² day⁻¹, averaging 381.6 mmol m⁻² day⁻¹. This is mainly due to the release of water with high-concentration CO₂ from the bottom of the reservoir. Additionally, using related data of reservoirs around the world, a CO₂ emission model with hydraulic retention time, air temperature, and reservoir age as the primary parameters was developed, which was conducive to evaluate reservoir CO₂ emissions on a larger scale and provided theoretical support for effective reservoir management.

γ-Fe₂O₃/La/Bi₂WO₆ heterojunction composites have been successfully synthesized by simple and convenient hydrothermal method. The photocatalytic activity of the prepared samples was evaluated by the degradation of tetracycline hydrochloride (TCH) solution under simulated visible light irradiation. The results indicated that the removal rate of γ-Fe₂O₃/La/Bi₂WO₆ samples was higher than that of pure Bi₂WO₆ and La/Bi₂WO₆, among which 5%γ-Fe₂O₃/La/Bi₂WO₆ had the highest photocatalytic activity. The removal rate of TCH solution can reach 92.42% in 220 min. The as-prepared samples were characterized by scanning electron microscope (SEM), Fourier transform infrared spectra (FTIR), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Brunauer–Emmett–Teller (BET), and UV–vis diffuse reflectance spectrum (DRS). The material morphology of γ-Fe₂O₃/La/Bi₂WO₆ was nano-flake. γ-Fe₂O₃/La-doped Bi₂WO₆ did not damage the crystal phase and structure of Bi₂WO₆. Through getting insight into the mechanism, γ-Fe₂O₃/La doping increased the specific surface area of Bi₂WO₆ and inhibited the recombination of photogenerated electron pairs, thus enhancing the photocatalytic activity. h⁺ and ·O₂⁻ were the main active substances in the photocatalytic degradation of TCH solution by γ-Fe₂O₃/La/Bi₂WO₆ photocatalysts. In order to better explain the photocatalytic degradation process of TCH solution by γ-Fe₂O₃/La/Bi₂WO₆ photocatalyst, a possible removal mechanism was proposed for the first time. Moreover, γ-Fe₂O₃/La/Bi₂WO₆ sample is magnetic and easy to recover. At the same time, the high stability of γ-Fe₂O₃/La/Bi₂WO₆ sample can be reused.

Transgenic Bacillus thuringiensis (Bt) rice can provide economic and environmental benefits under the current increasing demand for food and socioeconomic pressures for sustainability. However, information about the ecological adaptation of Bt rice under nontarget environmental stress is still lacking. This study compared the adaptability of one Bt rice and its nontransgenic counterpart to soil copper (Cu) pollution in terms of agronomic and physiological traits. With Cu addition, grain yield and biomass of both cultivars were significantly decreased. Within the same Cu treatment, Bt rice exhibited higher biomass and close plant height, chlorophyll content, grain yield, and grain quality compared with non-Bt rice, except for the grain yield with a 35 mg kg⁻¹ Cu addition with respect to which Bt rice was significantly lower by 22%. The Cu content in Bt rice was generally lower, whereas the antioxidant enzyme activity and lipid peroxidation were stronger than the non-Bt. These results demonstrated that Bt rice exhibited close adaptability but higher Cu tolerance compared with the non-Bt under Cu stress.

Municipal solid waste is typically managed in developing countries through various disposal methods, such as sanitary landfills or dumpsites. Alternatively, waste to energy (WTE) systems have been recently adopted to provide sustainable waste management and diversify the energy mix. The abundance of remotely sensed datasets and derivatives, along with the rapid development of artificial intelligence, can offer an effective solution for WTE site selection. In this study, an analytical hierarchy process (AHP)-based framework supported by multiple machine learning algorithms (gradient boosted tree (GBT), decision tree (DT), and support vector machines (SVMs)) was established to explore the optimum location for WTE facilities. Various social, legal, environmental, economic, morphological, and land cover parameters were considered under 11 thematic geospatial raster layers. The proposed framework was applied to the 1.5-million-capita city of Sharjah, United Arab Emirates. A novel approach was developed to incorporate Gaussian dispersion modeling for the expected air pollution emissions from a WTE facility. The results showed that the accuracy performance sequence of the algorithms was 94.6, 93.9, and 91.8% for GBT, DT, and SVM, respectively. It was found that the distance from existing landfills had the most critical impact on the optimum location of the WTE facility, followed by the distance from coastline and elevation. The AHP consistency check revealed an acceptable overall criteria consistency index and the ratio of 0.0344 and 0.019, respectively. The results showed that 16.6% of Sharjah was considered extremely highly suitable areas. This research supports decision-makers in developing local guidelines for siting WTE facilities and determining the most suitable locations for such projects.

It is well known that unemployment and environmental degradation are two critical issues across the globe. However, there is an extended dearth of literature that explores the nexus between unemployment and environmental degradation. Kashem and Rahman (Environ. Sci. Pollut. Res. 27(101): 31153–31170, 2020) put forward the Environmental Phillips Curve (EPC) hypothesis, which depicts a negative relationship between unemployment and environmental degradation. This study further explores the validity of the EPC hypothesis in the case of the USA. It also investigates the impact of monetary policy uncertainty (MU), fiscal policy uncertainty (FU), and trade policy uncertainty (TU) on carbon dioxide emissions. To this end, the analysis employs the novel methodology of the dynamic ARDL model. The results document that EPC does not hold in the short run, but it does in the long run. Furthermore, both in the short and long run, MU escalates CO₂ emissions, while FU plunges emissions in both the short and long run. Finally, TU does not alter the level of CO₂ emissions.

Myopia is one of the most common forms of refractive eye disease and considered as a worldwide pandemic experienced by half of the global population by 2050. During the past several decades, myopia has become a leading cause of visual impairment, whereas several factors are believed to be associated with its occurrence and development. In terms of environmental factors, air pollution has gained more attention in recent years, as exposure to ambient air pollution seems to increase peripheral hyperopia defocus, affect the dopamine pathways, and cause retinal ischemia. In this review, we highlight epidemiological evidence and potential biological mechanisms that may link exposure to air pollutants to myopia. A thorough understanding of these mechanisms is a key for establishing and implementing targeting strategies. Regulatory efforts to control air pollution through effective policies and limit individual exposure to preventable risks are required in reducing this global public health burden.

Surface water quality deterioration is commonly associated with environmental changes and human activities. Although some research has been carried out to evaluate the relationship between various influencing factors and water quality, there is still very little scientific understanding on how to accurately define the key factors of water quality deterioration. This study aims to quantify the impact of environmental factors and land use land cover (LULC) changes on water quality in the Ebinur Lake Watershed, Xinjiang, China. A total of 20 water parameters were used to calculate the Environment Water Quality Index (CWQI). Meanwhile, the partial least squares-structural equation model (PLS-SEM) was used to quantify the impact of eleven factors influencing water quality in the watershed. About 33.3% of the monitoring points that located mostly in the downstream region with dominant anthropogenic activities were detected as poor quality. There were no obvious temporal changes in water quality from 2016 to 2019. The PLS-SEM simulation shows that the latent variable “land use/cover types” (path coefficient = − 0.600) and “Environmental factor” (path coefficient = − 0.313) are two major factors affected water quality in the Ebinur Lake Watershed, with a strong explanatory power to water quality change (R² = 0.727). In the latent variable “Environmental factors”, the “NDVI” and “night light brightness value” have a great influence on water quality, with the weights of 0.451 and 0.427, respectively. Correspondingly, the “farmland” and “forest land” within the latent variable of “Land use/cover type” have a considerable impact water quality, with the weights of 0.361 and − 0.340, respectively. In conclusion, the influence of anthropogenic activities on surface water quality of the Ebinur Lake Watershed is greater than that of environmental factors. Compared with the traditional multivariate statistical method, PLS-SEM provides a new insight for quantifying the complex relationship between different influencing factors and water quality.

Heavy metal contamination of groundwater is one of the most recent and serious environmental issues. Chromium (VI), also called hexavalent chromium, is one such metal. It poses a critical threat to the environment because of its high toxicity and carcinogenic nature. Chromium compounds are extensively used in industries like metallurgies, tanneries, refectories, electroplating, and steel, which in turn releases chromium (VI) containing effluent into the environment. Being a heavy metal, it is non-biodegradable and thus remains persistent in the ecosystem, contaminating water and soil. Before discharge of chromium (VI)–containing effluent, bringing it to a permissible level as per statutory norms requires a remediation process. The World Health Organization recommended a level of 0.1 mg/L of chromium in water for human consumption. Numerous approaches can be used to address these issues. Among the different approaches available, the traditional method involves chemical precipitation, filtration, solvent extraction, electrocoagulation, catalytic reduction, membrane filtration, biological treatment, and ion exchange. The later advancements incorporate the use of nanotechnology and photo-catalytic reduction. Conventional methods are, however, associated with certain drawbacks such as high sludge formation, which requires licence disposal, high chemical/energy requirements, and low removal efficiency. In the recent past, newer, innovative, and more economical technologies like photocatalysis and nanotechnology have been investigated and found to be more efficient even at low metal ion concentrations. The major purpose of this article is to provide up-to-date information on extensively used techniques, with an emphasis on the significant issues that are experienced while removing hexavalent chromium from water.

This paper introduces the lethal, sublethal, and ecotoxic effects of peppermint and palmarosa essential oils (EOs) and their polymeric nanoparticles (PNs). The physicochemical analyses indicated that peppermint PNs were polydisperse (PDI > 0.4) with sizes of 381 nm and loading efficiency (LE) of 70.3%, whereas palmarosa PNs were monodisperse (PDI < 0.25) with sizes of 191 nm and LE of 89.7%. EOs and their PNs were evaluated on the adults of rice weevil (Sitophilus oryzae L.) and cigarette beetle (Lasioderma serricorne F.) and the larvae of Culex pipiens pipiens Say. On S. oryzae and L. serricorne, PNs increased EOs’ lethal activity, extended repellent effects for 84 h, and also modified behavioral variables during 24 h. Moreover, EOs and PNs generated toxic effects against C. pipiens pipiens. On the other hand, peppermint and palmarosa EOs and their PNs were not toxic to terrestrial non-target organisms, larvae of mealworm (Tenebrio molitor L.), and nymphs of orange-spotted cockroach (Blaptica dubia S.). In addition, PNs were slightly toxic to aquatic non-target organisms, such as brine shrimp (Artemia salina L.). Therefore, these results show that PNs are a novel and eco-friendly formulation to control insect pests.

Forecasts of wastewater inflow are considered as a significant component to support the development of a real-time control (RTC) system for a wastewater pumping network and to achieve optimal operations. This paper aims to investigate patterns of the wastewater inflow behaviour and develop a seasonal autoregressive integrated moving average (SARIMA) forecasting model at low temporal resolution (hourly) for a short-term period of 7 days for a real network in South Australia, the Murray Bridge wastewater network/wastewater treatment plant (WWTP). Historical wastewater inflow data collected for a 32-month period (May 2016 to December 2018) was pre-processed (transformed into an hourly dataset) and then separated into two parts for training (80%) and testing (20%). Results reveal that there is seasonality presence in the wastewater inflow time series data, as it is heavily dependent on time of the day and day of the week. Besides, the SARIMA (1,0,3)(2,1,2)₂₄ was found as the best model to predict wastewater inflow and its forecasting accuracy was determined based on the evaluation criteria including the root mean square error (RMSE = 5.508), the mean absolute value percent error (MAPE = 20.78%) and the coefficient of determination (R² = 0.773). From the results, this model can provide wastewater operators curial information that supports decision making more effectively for their daily tasks on operating their systems in real-time.