Search Results (37,190)

Increased dust emissions from dryland areas and their effects on human health, ecosystem viability, and environmental change are a global concern in the face of the growing climate crisis. Dust plume emissions from the West African landmass, Sahara, and Sahel areas comprise a major fraction of the global aerosol budget. Dust plume intensity is closely related to regional winds (e.g., Harmattan, Sahara Air Layer), the Intertropical Convergence Zone, monsoonal seasonality, marine currents, and physiography. To study terrigenous material emitted from the continent over the last ~260 kyr (late Quaternary), we used X-ray fluorescence spectroscopy (XRF) to analyze a ~755 cm long marine sediment core from the eastern equatorial Atlantic Ocean, resulting in nearly 1400 discrete measurements. Spectral analysis results suggest that concentrations of elements (Rb, Sr, Si, Al) preserved in the sediments are correlated to different types of orbital climate forcing. Chemical weathering intensity indicated by the Rb/Sr ratio was sensitive to seasonal insolation variations controlled by precession cycles (23–18 kyr), which presumably reflects the relationship between monsoonal rainfall and sensible heating of the continent. Spectral analysis of silicate mineral grain size (Si/Al) showed significant 40 kyr cycles that were paced by obliquity. Based on these data, we infer that winter tradewind activity accelerated in response to the intertropical insolation gradient induced by high obliquity. High Rb/Sr ratios during the last glacial maximum and penultimate glacial maximum may have been due to a predominance of mechanical weathering over chemical weathering under dry/cool climates or the dissolution of Sr-bearing carbonates by corrosive glacial bottom waters. Full article

The benefits of nature-based solutions to address the climate and biodiversity challenges have become widely acknowledged. In numerous ways, nature-based solutions align with the Sustainable Development Goals. Serbia, like many other countries, faces many negative impacts of climate change crises. In order to meet sustainable development goals linked to water pollution and biodiversity, we investigated the potential of two plants, namely, Iris pseudocorus L. and Juncus effusus L., and tested for zinc reduction, previously found as an emerging contaminant of urban waters in Serbia. We focused on the investigation of native Serbian plants, that are reported as highly valuable and endangered. Results confirmed that both plant species have high accumulation capacity for Zn uptake, whereas growth and resistance were higher for Juncus effusus L. plants. While the concentration of zinc in Iris plants ranged from 45.85 mg/L to 193.05 mg/L, the concentration found in Juncus plants ranged from 36.2 mg/L to 264.59 mg/L for leaves and 53.20 mg/L for roots. This study contributes to the support for achieving the Sustainable Development Goals in Serbia within biodiversity conservation and sustainable water management, by providing information of plant species that can be included in future sustainable nature-based solutions projects, like bioretention systems and constructed wetlands. Full article

Invasive species are one of the five main causes of biodiversity loss, along with habitat destruction, overexploitation, pollution, and climate change. Numbers and species of invasive organisms represent one of the first barriers to overcome in ecological conservation programs since they are difficult to control and eradicate. Due to the lack of records of invasive exotic species in Panama, this study was necessary for identifying and registering the documented groups of invasive species of the Chordates and Arthropod groups in Panama. This exhaustive search for invasive species was carried out in different bibliographic databases, electronic portals, and scientific journals which addressed the topic at a global level. The results show that approximately 141 invasive exotic species of the Arthropoda and Chordata phyla have been reported in Panama. Of the 141 species, 50 species belonged to the Arthropoda phylum and 91 species belonged to the Chordate phylum. Panamanian economic activity could facilitate the introduction of alien species into the country. This study provides the first list of invasive exotic chordate and arthropod species reported for the Republic of Panama. Full article

Short-term climate fluctuations can have a significant impact on the stability of food resource prices, thus threatening food security, even in cases where the crop production system shows good adaptation to climate change and/or increasing average yields over time. This paper illustrates, in detail, a statistical approach aimed at verifying whether the variation of the crop production system vulnerability to climate fluctuation exhibits a trend over time. These methods were applied to the case study of wheat grown in the Abruzzo region (Italy). The results show that, although the wheat crop yield still shows ongoing growth, the correlation between climate fluctuations and yield oscillations exhibits a systematic increase over the past sixty years. Such an increase in climate-related production fluctuations may represent a disturbing element for market equilibria and be potentially harmful for the various economic subjects involved at various scales, such as producers, distributors, investors/financial traders, and final consumers. The statistical approach illustrated provides a framework for monitoring climate impacts and also provides the basis for building up statistical forecasting models to support informed decision making in agricultural management and financial planning. Full article

As a result of global efforts to combat the rise in global climate change and carbon dioxide emissions, there has been a substantial increase in renewable energy investment for both residential and utility power generation. Solar power facilities are estimated to be among the major contributors to global decarbonization in terms of capacity by 2050. Consequently, the majority of economically significant countries are progressively implementing utility-scale photovoltaic (U-PV) systems. Nevertheless, a major obstacle to the expansion of U-PV generation is the identification and assessment of direct current (DC) faults in the extensive array of PV panels. In order to address this obstacle, it is imperative to provide an evaluation method that can accurately and cost-effectively identify and locate potential DC faults in PV arrays. Therefore, many studies attempted to utilize thermal cameras, voltage and current sensors, power databases, and other detecting elements; however, some of these technologies provide extra hurdles in terms of the quantity and expense of the utilized hardware equipment. This work presents a sophisticated system that aims to diagnose and locate various types of PV faults, such as line-to-ground, line-to-line, inter-string, open-circuit, and partial shading events, within a PV array strings down to a module level. This study primarily depends on three crucial indicators: precise calculation of the PV array output power and current, optimal placement of a limited number of voltage sensors, and execution of specifically specified tests. The estimation of PV array power, along with selectively placed voltage sensors, minimizes the time and equipment required for fault detection and diagnosis. The feasibility of the proposed method is investigated with real field data and the PSCAD simulation platform during all possible weather conditions and array faults. The results demonstrate that the proposed approach can accurately diagnose and localize faults with only N_S/2 voltage sensors, where N_S is the number of PV array parallel strings. Full article

A transition to clean fuels and technology for cooking is increasingly recognised as a cornerstone of sustainable development. However, sufficient, appropriate, affordable finance to support the transition is lacking. Grounded in primary data collection via expert interviews, this study’s research objective was to critically assess development finance institutions’ (DFIs) delivery of climate and development finance to address cooking poverty. Interview findings underscore DFIs’ important role in the transition, including to create the ecosystem conditions conducive to sustained investment. However, as a group they are not demonstrating the risk appetite and financial solutions that clean cooking markets need. Nor are they operating with the agility and flexibility required for rapid scale-up. Consequently, DFIs are not optimally fulfilling their mandates to create additionality and mobilise private capital in these markets. Interviewees call for DFIs to reconsider their approach, and we rely on these findings to posit a theory of change for clean cooking finance. Full article

The Arabian Peninsula, with its rugged mountains, wadis, alluvial plains, sand dune deserts, and diverse coastlines, spans over 3 million km². The Peninsula is situated at the crossroads of Africa and Asia and is a meeting point for diverse biogeographic realms, including the Palearctic, Afrotropical, and Indomalayan regions. This convergence of biogeographic zones has resulted in a remarkably diverse flora and fauna, which is adapted to the harsh and varied climates found throughout the Peninsula. Each of the countries of the Arabian Peninsula are biologically diverse and unique in their own right, but Yemen, Saudi Arabia, and Oman are the most diverse in terms of their landforms and biological diversity. The mountainous regions support a cooler and more moderate climate compared to the surrounding lowlands, thus forming unique ecosystems that function as refugia for plant and animal species, and have a high endemism of plant species. The desert ecosystems support a variety of lifeforms that are specially adapted to an extreme arid climate. Due to its long history of human habitation and subsistence agriculture, particularly in the mountainous areas, the Arabian Peninsula possesses unique crop varieties adapted to extreme arid climates, making them important genetic resources for the future in the face of climate change. The Arabian Peninsula, though rich and diverse in its biological diversity, has been greatly affected by human activities, especially in the last 50 years, including urbanization, habitat destruction, overgrazing, and climate change, which pose significant threats to the biodiversity of the region. This review presents the biogeography and background of conservation efforts made in the countries in the Arabian Peninsula and gives the progress made in botanical research and conservation practices throughout the Peninsula. Full article

Healthy coral reefs provide diverse habitats for marine life, playing a crucial role in marine ecosystems. Coral health is under threat due to global climate change, ocean pollution, and other environmental stressors, leading to coral bleaching. Coral bleaching disrupts the symbiotic relationship between corals and algae, ultimately impacting the entire marine ecosystem. Processing complex underwater images manually is time-consuming and burdensome for marine experts. To rapidly locate and monitor coral health, deep neural networks are employed for identifying coral categories, which can facilitate the automated processing of extensive underwater imaging data. However, these classification networks may overlook critical classification criteria like color and texture. This paper proposes a multi-local perception network (ML-Net) for image classification of healthy and bleached corals. ML-Net focuses on local features of coral targets, leveraging valuable information for image classification. Specifically, the proposed multi-branch local adaptive block extracts image details through parallel convolution kernels. Then, the proposed multi-scale local fusion block integrates features of different scales vertically, enhancing the detailed information within the deep network. Residual structures in the shallow network transmit local information with more texture and color to the deep network. Both horizontal and vertical multi-scale fusion blocks in deep networks are used to capture and retain local details. We evaluated ML-Net using six evaluation metrics on the Bleached and Unbleached Corals Classification dataset. In particular, ML-Net achieves an ACC result of 86.35, which is 4.36 higher than ResNet and 8.5 higher than ConvNext. Experimental results demonstrate the effectiveness of the proposed modules for coral classification in underwater environments. Full article

Accurately predicting changes in the potential distribution of crops resulting from climate change has great significance for adapting to and mitigating the impacts of climate change and ensuring food security. After understanding the spatial and temporal suitability of wheat (Triticum aestivum), rice (Oryza sativa), and maize (Zea mays), as well as the main bioclimatic variables affecting crop growth, we used the MaxEnt model. The accuracy of the MaxEnt was extremely significant, with mean AUC (area under curve) values ranging from 0.876 to 0.916 for all models evaluated. The results showed that for wheat, annual mean temperature (Bio-1) and mean temperature of the coldest quarter (Bio-11) contributed 39.2% and 13.4%, respctively; for rice, precipitation of the warmest quarter (Bio-18) and elevation contributed 34.9% and 19.9%, respectively; and for maize, Bio-1 and precipitation of the driest quarter (Bio-17) contributed 36.3% and 14.3%, respectively. The map drawn indicates that the suitability of wheat, rice, and corn in South Asia may change in the future. Understanding the future distribution of crops can help develop transformative climate change adaptation strategies that consider future crop suitability. The study showed an average significant improvement in high-suitable areas of 8.7%, 30.9%, and 13.1%, for wheat, rice, and maize, respectively; moderate-suitable area increases of 3.9% and 8.6% for wheat and rice, respectively; and a decrease of −8.3% for maize as compared with the current values. The change in the unsuitable areas significantly decreases by −2.5%, −13.5%, and −1.7% for wheat, rice, and maize, respectively, compared to current land suitability. The results of this study are crucial for South Asia as they provide policy-makers with an opportunity to develop appropriate adaptation and mitigation strategies to sustain wheat, rice, and corn production in future climate scenarios. Full article

Hubei and Jiangsu Provinces, significant in grain production, play a crucial role in national food security. We studied the carbon footprint of main grain crops (rice, maize, and wheat) from 2005 to 2019 in these provinces to identify trends, contributing factors, and emission efficiencies. This study seeks to inform sustainable agricultural practices and policies in the context of climate change mitigation. Jiangsu Province’s rice and wheat output surpasses Hubei’s due to higher yields per unit area. Rice consistently shows the highest carbon footprint per unit area, followed by wheat, with maize exhibiting the lowest. Carbon footprint per unit yield varies significantly: for rice, it ranges from 0.15 to 0.29 kg CO₂-eq per kg; for wheat, from 0.19 to 0.22 kg CO₂-eq per kg; and for maize, from 0.13 to 0.15 kg CO₂-eq per kg. The distribution of crop production affects these footprints; central regions generally exhibit lower values compared to southwest and southeast areas. Fertilizer and electricity together contribute significantly to carbon emissions, especially in rice production (over 75%), and to a lesser extent in maize and wheat production (approximately 69% and 85%, respectively). Improving fertilizer efficiency, irrigation, and mechanization is crucial for developing low-carbon agriculture in these pivotal grain-producing regions. Full article

Estimating tree height at the national to regional scale is crucial for assessing forest health and forest carbon storage and understanding forest ecosystem processes. It also aids in formulating forest management and restoration policies to mitigate global climate change. Extensive ground-survey data offer a valuable resource for estimating tree height. In tree height estimation modeling, a few comparative studies have examined the effectiveness of global-based versus local-based models, and the spatial heterogeneity of independent variable parameters remains insufficiently explored. This study utilized ~200,000 ground-survey data points covering the entire provincial region to compare the performance of the global-based Ordinary Least Squares (OLS) and Random Forest (RF) model, as well as local-based Geographically Weighted Regression (GWR) model, for predicting the average tree height of Chinese fir forests in Zhejiang Province China. The results showed that the GWR model outperformed both OLS and RF in terms of predictive accuracy, achieving an R-squared (R²) and adjusted R² of 0.81 and MAE and RMSE of 0.93 and 1.28, respectively. The performance indicated that the local-based GWR held advantages over global-based models, especially in revealing the spatial non-stationarity of forests. Visualization of parameter estimates across independent variables revealed spatial non-stationarity in their impact effects. In mountainous areas with dense forest coverage, the parameter estimates for average age were notably higher, whereas in forests proximate to urban areas, the parameters were comparatively lower. This study demonstrates the effectiveness of large ground-survey data and GWR in tree height estimation modeling at a provincial scale. Full article

The impending challenge posed by escalating heatwave events due to projected global warming scenarios of 1.5 and 2.0 °C underscores the critical need for a comprehensive understanding of their impact on human health and socio-economic realms. This study delves into the anticipated implications of elevated global temperatures, specifically the 1.5 and 2.0 °C warming scenarios under the SSP2-4.5 and SSP5-8.5 pathways, on population and GDP exposure to heatwaves in China. We also evaluated the aggregated impacts of climate, population, and GDP and their interactions on future socio-economic exposure across China. We leveraged data sourced from the climatic output of Coupled Model Intercomparison Project Phase 6 (CMIP6) for heatwave analysis and integrated population and GDP projections under divergent Shared Socio-economic Pathways (SSPs), including SSP2-4.5 (low emission) and SSP5-8.5 (high-emission). Results indicate a drastic surge in the number of heatwave days under both warming scenarios, particularly in regions like Xinjiang (XJ), North China (NC), and South China (SC) subregions, with a notable disparity in the elevation of heatwave days among different levels. There is an alarming surge in population exposure, escalating approximately 7.94–8.70 times under the 1.5 °C warming scenario and markedly increasing by 14.48–14.75 times by the 2100s relative to the baseline (1985–2014) under the more extreme 2.0 °C warming level. Likewise, the study unveils a substantial elevation in GDP exposure, ranging from 40.65 to 47.21 times under the 1.5 °C warming level and surging dramatically by 110.85–113.99 times under the 2.0 °C warming level. Further analyses disclose that the climate effect predominantly influences changes in population exposure, constituting 72.55–79.10% of the total change. Meanwhile, the interaction effect notably shapes GDP exposure alterations, contributing 77.70–85.99% to the total change. The comprehensive investigation into alterations in population and GDP exposure under varying warming scenarios, coupled with the quantification of each contributing factor, holds paramount importance in mitigating the detrimental repercussions of heatwaves on both human life and socio-economic landscapes. Full article

Geographic information systems (GISs) based on WebGIS architectures have transformed geospatial data visualization and analysis, offering rapid access to critical information and enhancing decision making across sectors. This study conducted a bibliometric review of 358 publications using the Web of Science database. The analysis utilized tools, such as Bibliometrix (version R 4.3.0) and Biblioshiny (version 1.7.5), to study authors, journals, keywords, and collaborative networks in the field of information systems. This study identified two relevant clusters in the literature: (1) voluntary geographic information (VGI) and crowdsourcing, focusing on web integration for collaborative mapping through contributions from non-professionals and (2) GIS management for decision making, highlighting web-based architectures, open sources, and service-based approaches for storing, processing, monitoring, and sharing geo-referenced information. The journals, authors, and geographical distribution of the most important publications were identified. China, Italy, the United States, Germany, and India have excelled in the application of geospatial technologies in areas such as the environment, risk, sustainable development, and renewable energy. These results demonstrate the impact of web-based GISs on forest conservation, climate change, risk management, urban planning, education, public health, and disaster management. Future research should integrate AI, mobile applications, and geospatial data security in areas aligned with sustainable development goals (SDGs) and other global agendas. Full article

Electric vehicles (EVs) have gained considerable popularity, driven in part by an increased concern for the impact of automobile emissions on climate change. Electric vehicles (EVs) cover more than just conventional cars and trucks. They also include electric motorcycles, such as those produced by Gogoro, which serve as the primary mode of transportation for food and package delivery services in Taiwan. Consequently, the Electric Vehicle Routing Problem (EVRP) has emerged as an important variation of the Capacitated Vehicle Routing Problem (CVRP). In addition to the CVRP’s constraints, the EVRP requires vehicles to visit a charging station before the battery level is insufficient to continue service. EV battery consumption is linearly correlated to their weight. These additional constraints make the EVRP more challenging than the conventional CVRP. This study proposes an improved Harmony Search Algorithm (HSA), with performance validated by testing 24 available benchmark instances in the EVRP. This study also proposes a novel update mechanism in the improvement stage and a strategy to improve the routes with charging stations. The results show that in small and large instances, the proposed HSA improved the number of trips to the charging stations by 24% and 4.5%, respectively. These results were also verified using the Wilcoxon signed-rank significant test. Full article

Turfgrass systems hold significant climate change mitigation value, but their management often negates the beneficial effects due to the intense adoption of external inputs. The research objective in this paper was to assess the nitrogen fertilization rate able to maintain the ideal esthetic characteristics of Zoysia turfgrass, reducing the environmental impacts associated with greenhouse gas (GHG) emissions. A two-year open field experiment was conducted. Nitrogen was added to the soil at six rates (0, 50, 100, 150, 200, and 250 kg ha⁻¹). The GHG emissions were monitored using a portable gas analyzer and the static chamber methodology. Cumulative environmental impacts were calculated from the inclusion of CO₂, CH₄, and, N₂O using the Global Warming Potential (GWP). The quality assessment of the turf was assessed through a visual and instrumental approach. Higher CO₂ and N₂O fluxes were linked to high nitrogen rates, ranging from 83.55 to 87.50 and from 0.046 to 0.047 g N-N₂O ha⁻¹ day⁻¹ for 200 and 250 kg N ha⁻¹, respectively. CH₄ emissions were not correlated to nitrogen rates. Higher GWP impacts were linked to high N rate treatments. A rate of 100 kg N ha⁻¹ is recommended as the best strategy to reduce GHG emissions while maintaining high turf quality. Full article