Resources Environment and Sustainability最新文献

Anaerobic digestion (AD) is an effective method for treating organic waste. However, the performance of anaerobic reactors needs to be improved and comprehensively analyzed. This study proposed a ternary co-feedstock strategy for thermophilic anaerobic co-digestion of tomato plant residue, cattle manure, and food waste. The effects of changing the feedstock composition, feedstock-to-inoculum ratio, and total solids (TS) content were investigated. Among the tested mixtures, a ternary mixture of 60% tomato plant residue, 20% cattle manure, and 20% food waste with a feedstock-to-inoculum ratio of 0.7 gave the highest cumulative biogas (3.230 L), methane yield (2.575 L), methane content (79.71%), co-digestion performance index (1.65) and volatile solids (VS) removal rate (60.59%). Changing the feedstocks compositions caused changes in the specific biogas yield and specific methane yield. In tests with TS contents of 5%, 10%, 15%, and 20%, a higher TS content increased biogas and methane production because of increases in the amount of the biodegradable fraction in the reactor. However, there was a risk of volatile fatty acid overloading and a decrease in the VS removal rate because of inefficient decomposition of the high total solids content by microbes in the inoculum.

Extreme precipitation events can cause geomorphic adjustments in the riverscapes. As a result of the increase in severity and frequency of these events in climate change scenarios, riverscapes may become more sensitive to the geoforms sculpting processes. Consequently, communities in the river valleys may be adversely affected, worsening socio-environmental problems arising from these places’ occupation. This work investigates the relationship between events classified as heavy rainfall and persistent rainfall and geomorphic adjustments in the riverscape. A section of the Itapocu River in southern Brazil between latitudes 26°25’S and 26°32’S was used as a case study. Extreme events recorded in pluviometers’ historical series located around the study area were selected and characterized. These events occurred between 01/30/2019 and 04/15/2019; 11/15/2020 and 12/30/2020; 02/23/2021 and 03/10/2021. The geomorphic adjustments were identified by selecting Sentinel-2 orbital imagery. The satellite images were applied in the river valley geoforms vectorization and correlation among transverse and longitudinal sections to the river valley. The analysis of fluvial sensitivity went through the adjustments’ significance evaluation by the Wilcoxon Signed Rank Test. The results indicated that the river presented adjustments related to extreme precipitation events. These adjustments were identified as channel migration, channel expansion, channel narrowing, river bar expansion, and river bar narrowing. However, only channel migration, channel expansion, and channel narrowing were significant. In 2019, 82.03% of the river was significantly affected by the events, while in 2020 and 2021, events impacted 57.67% and 64.72% of the river, respectively.

Commodity plastics such as polyethylene (PE), polypropylene (PP) and polyethylene terephthalate (PET) are some of the common plastic wastes found in municipal wastes. Lack of technology, resources, coherent programs, policies, mismanagement and incomplete processing of these plastics, results in these plastics mostly ending up in landfills. Adopting a qualitative research approach, the paper reviewed PE, PP and PET recycling waste management processes from selected developed and developing countries. It was found that chemical recycling in terms of catalytic cracking followed by pyrolysis technologies are more suitable for producing higher liquid oils whiles gasification improves fuel gas yield. Given that mechanical recycling requires less capital and resources, it is a more suitable waste management option for plastic products in developing countries. In cases where there are complex separation issues, incineration for energy recovery is appropriate but it should be used with end-of-pipe treatment solutions to mitigate against the environmental impact of incineration. The paper further highlights various opportunities and challenges involved with PE, PP and PET plastic waste treatment pathway, the appropriate yields obtained for each technique and where adaptation can be made.

While economics has long recognized concern for others (e.g. altruism and bequest motives), explicit inclusion of social network metrics in non market valuation models is relatively recent. Social network effects on willingness to pay can propagate through the entire network and bias willingness to pay (WTP) estimates. However, social networks are complex systems of relationships between individuals, and measuring them can be difficult. We explored the potential for egocentric social network (ESN) measures to help explain variations in preference for the status quo. Using simulated random networks, we demonstrate that respondents more likely to choose an alternative to the status quo are part of more dense ESNs. A strong influence of an attitude toward the impact of economic development on the environmental goods and services is consistent with network structure and preference for environmental improvements being jointly determined.

Agriculture is of great importance in all societies, serving as the fundamental basis for producing food and ensuring the survival of human populations. The process of agricultural production and the associated logistical elements face numerous difficulties, which are further intensified by the worldwide water scarcity resulting from climate change. Nevertheless, the existing body of literature has not sufficiently addressed the consequences of water scarcity on agri-food supply chains. To bridge this research gap and contribute to mitigating the global water crisis induced by climate change, this study proposes a hybrid model that combines optimized neural networks based on meta-heuristic algorithms and mathematical optimization for a sustainable agricultural supply chain. The proposed model integrates particle swarm optimization (PSO) for feature selection and a hybrid convolutional neural network (CNN)-gated recurrent unit (GRU) with a genetic algorithm (GA) optimized structure to predict water consumption. Leveraging the model’s results, a multi-objective sustainable agriculture supply chain model is developed to optimize supply chain profitability while simultaneously addressing environmental pollutants, production waste, food waste, water usage, and manufacturing costs and time. To evaluate the effectiveness of the proposed approach, a real case study in Iran is employed, providing both theoretical and practical insights into the design of agriculture supply chain optimization that incorporates sustainability factors and effectively tackles the growing challenge of water scarcity. The findings of this study hold implications for managers and policymakers in countries where the importance of sustainability is growing. By integrating advanced optimization techniques and predictive models, this research offers a novel framework for enhancing the sustainability of agricultural supply chains and addressing the pressing issues of water scarcity induced by climate change.

This paper explores the awareness, knowledge, attitudes and behaviour of members of the Irish fishing community towards environmental topics such as; microplastics, plastic pollution and recycling. We conducted a mixed method survey consisting of 26 questions (2021) involving members of the Irish fishing community (fishers, aquaculturists etc.). Respondents were generally aware of microplastics and the threats they can pose to different environmental matrices. They noticed litter frequently when engaged in their fishing activities (0% never noticed litter) and in large quantities (35% of respondents noticed over 10+ items) but they were likely (likely 40% and highly likely 35%) to remove it from the environment. Durability was the main reason for the selection of most fishing plastics used by respondents (ranked first in 4 of 5 plastic items) while recyclability played a lesser role. Respondents also viewed plastics as cheap and convenient with these terms accounting for 48% of positive connotations related to the word ‘plastic’, however, in general associated plastic with negative phrases. Barriers to the recycling of used fishing plastics were most frequently identified as being due to a lack of knowledge on how to or a lack of facilities. This study provides novel insight into a previously unstudied cohort in Irish society towards plastics and recycling and can serve as guidance for further work on this group.

Reliable and detailed information on livestock distribution is essential for studies of food security, environmental change, and even sustainable development. However, insufficient accuracy and inadequate validation currently remain in high-resolution livestock distribution datasets primarily resulting from using spatially-continuous models and deficient data. This study presents, for the first time to our knowledge, a spatially detailed dataset on intensive (point) and extensive (30”$\times$ 30” grid) livestock production in China (HIEL-China) in 2017 based on an improved model and multi-scale data. Technical validation shows high accuracy in spatial distribution and farm-size simulation. Based on the more reliable depiction of livestock farms, we addressed the obvious underestimation of livestock density in previous datasets, and found different structures of livestock production systems in urban, peri-urban and rural areas. This study accordingly contributes to an essential data basis for livestock-associated analyses targeting at sustainable development of food systems, especially for the largest contributor to global livestock production.

Energy audits are an important part of reducing energy usage, costs, and carbon emissions, but there have been discrepancies in the quality of audits depending upon the auditor, which can negatively affect the impacts and credibility of the energy assessment. In this paper, historical energy auditing data from a U.S. Department of Energy sponsored research program was gathered and analyzed with a machine-learning algorithm to predict demand savings from a compressed air system assessment recommendation in industrial manufacturing facilities. Different energy auditors calculate savings for repairing leaks in compressed air systems in various ways, so the energy demand savings have been calculated differently throughout the historical assessment recommendations. Machine learning models are utilized in order to enhance the accuracy of the existing practice and reduce variations resulting from the abovementioned discrepancies. A large set of historical assessment recommendation data was used to train five unique machine learning models. Four base learner models and one metalearner model were devised and compared. Results showed that the distributed random forest model best predicted compressed air energy demand savings against the new scenarios within an error of 17%. This indicates that the distributed random forest model can more accurately quantify savings from repairing leaks in compressed air systems. In addition, the results from this study provide insight into the important factors contributing to leaks in the compressed air systems and why it is crucial to repair those leaks regularly to save money and energy while decreasing emissions.