Rainfall prediction, based on meteorological data and models, forecasts the possible rainfall conditions for a period in the future. It is one of the important issues in meteorology and hydrology, and holds significant scientific and social value for enhancing human society's adaptive capacity, reducing the risk of natural disasters, promoting sustainable development, and protecting the environment. This study proposes a rainfall prediction model based on CEEMDAN-VMD-BiLSTM, which couples CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise), VMD (Variational Mode Decomposition), and BiLSTM (Bidirectional Long Short-Term Memory). The model first employs CEEMDAN and VMD, two decomposition algorithms, for a secondary decomposition of the original data, followed by prediction using the BiLSTM network. The study integrates the characteristics of CEEMDAN, which include adaptability, completeness, denoising capability, and high precision, the characteristic of VMD in extracting trend information, and the ability of the BiLSTM model to better capture contextual information in sequence data and solve long-term dependency issues, thereby increasing the accuracy of rainfall prediction. The research selected Zhongwei City in the Ningxia Hui Autonomous Region as the study object and used 20 years of monthly rainfall data from 2001 to 2020 as the research data. The model was compared with standalone BiLSTM models, CEEMDAN-BiLSTM coupled models, and VMD-BiLSTM coupled models. The model was validated using four indicators: RMSE, MARE, MAE, and NSE. The results showed that the maximum relative error of the CEEMDAN-VMD-BiLSTM neural network rainfall prediction coupled model was 7.22%, and the minimum relative error was -7.03%. The prediction qualification rate was 100%. The overall NSE value of the model ranged from 0.63 to 0.97, with most values between 0.86 and 0.97. The excellent rate was about 84.6%, and the good and above rate was 92.3%. In the rainfall prediction for Zhongwei City, the prediction accuracy of this coupled model was better than the other three models. In summary, the CEEMDAN-VMD-BiLSTM rainfall prediction model proposed in this paper combines the advantages of various methods and has shown good predictive effects in experiments, providing an effective prediction method for rainfall.
This study investigated the radioactivity of groundwater and bottom silt from wells in southern Sinai, Egypt. Eight well sites were chosen (Abu Redis, Abu Zenima, and Al-Tor) and composite samples of water and silt were created from each. Southern Sinai well water (Egypt) was safe for drinking based on tested elements (226Ra < 300 Bq/L, 232Th < 100 Bq/L). However, some bottom silt samples showed elevated 226Ra, 232Th, and 222Rn-, potentially posing health risks through inhalation or ingestion. Further investigation is needed on these specific silt samples due to potential internal and external radiation exposure.
The quality of water is significantly impacted by the presence of Cr6+ and Ni2+ ions. This study investigates the effectiveness of a flow-by porous graphite electrode cell in removing these contaminants from simulated industrial wastewater. We explore the impact of various factors on the removal process, demonstrating the method's potential for efficient removal. The initial concentration of nickel and chromium ions (20 to 80 mg/l and 20 to 100 mg/l, respectively), the feed flow rate (0.28 to 1.11 ml/s), current density (0.2 to 2.25 mA/cm2) and pH all influence the removal rate and efficiency. A higher feed flow rate negatively affects the removal efficiency of both Ni2+ and Cr6+ ions. Nickel removal efficiency decreased by 34.9% at 20 ppm and 26% at 80 ppm, representing the highest and lowest reductions in efficiency, respectively. Chromium removal efficiency decreased by 19% at 100 ppm and 6.5% at 50 ppm, indicating the highest and lowest reductions in efficiency, respectively, under the same flow rate change. Under optimal conditions, the removal efficiency for Ni2+ was 99.47% after 15 min of operation at a current density of 1.96 mA/cm2, a flow rate of 0.28 ml/s, and a pH of 8 and the removal efficiency for Cr6+ was 99.97% after 10 min of operation at a current density of 2.25 mA/cm2, a flow rate of 0.28 ml/s, and a pH of 2. The flow-through porous electrode system achieves efficient heavy metal removal with operating costs of 0.24 USD/m3 for nickel and 0.38 USD/m3 for chromium at optimal conditions.
Manganese oxide (MnOx) on the surface of the filter material can be used to effectively remove ammonium (NH4+) and manganese ions (Mn2+) from water, but overgrow oxide film gradually shortens backwashing interval after several years of long-term filtration system operation. Different influent pollutant loading result in different durations for chemical peeling film. A growth kinetics model for MnOx was established by adjusting the different initial concentrations of Mn2+ in the influent, which provided a theoretical basis for determining a specific time point for film peeling and recovered the shortened backwashing intervals in the filter columns. The variation in film thickness demonstrated a linear dependence on time, confirming the high accuracy of the kinetics model for film growth. The pseudo-first-order kinetic model better fits among adsorption and oxidation kinetic models of Mn2+. Hydrogen peroxide (H2O2) was identified as an effective agent in the chemical peeling film process. Hydroxyl radicals, generated by H2O2, destroy coordination bonds, producing extremely low solubility (≡MnO2), which was then removed during the backwashing process.
Gut microbial communities are part of the regulatory array of various processes within their hosts, ranging from nutrition to pathogen control. Recent evidence shows that dung beetle's gut microbial communities release substances with antifungal activity. Because of the enormous diversity of gut microorganisms in dung beetles, there is a possibility of discovering novel compounds with antifungal properties. We tested the antifungal activity mediated by gut microbial communities of female dung beetles against nine phytopathogenic fungi strains (Colletotrichum asianum-339, C. asianum-340, C. asianum-1, C. kahawae-390, C. karstii-358, C. siamense-220, Fusarium oxysporum-ATCC338, Nectria pseudotrichia-232, Verticillium zaelandica-22). Our tests included the gut microbial communities of three species of dung beetles: Canthon cyanellus (roller beetle), Digitonthophagus gazella (burrower beetle), and Onthophagus batesi (burrower beetle), and we followed the dual confrontation protocol, i.e., we challenged each fungal strain with the microbial communities of each species of beetles in Petri dishes containing culture medium. Our results showed that gut microbial communities of the three dung beetle species exhibit antifungal activity against at least seven of the nine phytopathogenic fungal strains. The gut microbial communities of Onthophagus batesi significantly decreased the mycelial growth of the nine phytopathogenic fungi strains; the gut microbial communities of Canthon cyanellus and Digitonthophagus gazella significantly reduced the mycelial growth of seven strains. These results provide a basis for investigating novel antifungal substances within gut microbial communities of dung beetles.
Anthropogenic climate change is already causing dangerous and widespread disruptions in global ecological and social systems and affects the lives of billions of people around the world. Even with scaled-up risk management and adaptation, the limits of adaptation will often be reached. Currently, very little is known about the degree to which societies can adapt to climate change, and where and when limits to adaptation will be reached. In this paper, we conceptualize adaptation limits through a novel methodological framework, assess adaptation limits along adaptation pathways, and propose a research strategy for empirical and model-based limits assessments based on biophysical and socio-economic data. Assessing limits is central to national and international adaptation policymaking. More efficient adaptation can also help climate mitigation efforts.
This paper reviews recent advances in the use of graphite carbon nitride (g-C3N4)-based composite photocatalysts for antibiotic and dye degradation and hydrogen production. It also discusses the structure, synthesis, modification, morphology, doping, preparation, and application of a particular subject and evaluates the advantages and disadvantages of different morphologies and preparation processes. The photocatalysts based on g-C3N4-based composites have demonstrated great potential. The g-C3N4 has been modified and tailored into various novel structures and morphologies to improve its efficiency in the photocatalytic degradation of pollutants. The techniques such as doping, metal deposition, heterojunction formation, and structural tuning enhance the rate of light absorption, charge transfer, and charge separation of g-C3N4. This leads to improved photocatalytic performance for antibiotic and dye degradation and hydrogen production.
As emerging methods for carbon removal and controversial proposals around solar radiation modification are gaining traction in climate assessments and policy debates, a better understanding of how the public perceives these approaches is needed. Relying on qualitative data from 44 focus groups (n = 323 respondents), triangulated with a survey conducted in 22 countries (n = over 22 000 participants), we examine the role that climate change beliefs and attitudes towards climate action play in the formation of public perceptions of methods for carbon removal and solar radiation modification. We find that nationally varying degrees of perceived personal harm from climate change and climate worry predict support for these technologies. In addition to different perceptions of the problem, varying perceptions of the solution – i.e. the scope of climate action needed − shape publics’ assessment. Various tensions manifest themselves in publics’ reflections on the potential contribution of these climate technologies to climate action, including “buying time vs. delaying action”, “treating the symptoms vs. tackling the root causes”, and “urgency to act vs. effects only in the distant future”. We find that public perceptions are embedded in three broader narratives about transformation pathways, each reflecting varying notions of responsibility: (i) behavior change-centred pathways, (ii) top-down and industry-centred pathways, and (iii) technology-centred pathways. These results suggest that support for the deployment of the climate technologies studied hinges on them being tied to credible system-wide decarbonization efforts as well as their ability to effectively respond to a variety of perceived climate impacts.
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