Environmental Research Communications - IOPscience

Environmental Research Communications - IOPscience
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Environmental Research Communications
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The following article is
Open access
Political elites’ partisan beliefs about climate change
Alexander C Furnas
et al
2026
Environ. Res. Commun.
041007
View article
, Political elites’ partisan beliefs about climate change
PDF
, Political elites’ partisan beliefs about climate change
Addressing climate change requires political elites to share a basic set of facts about climate science, yet political elites in the United States are divided in their views about climate change. We document this using the first large-scale survey of over 3,500 U.S. political elites—including elected officials, staffers, regulators, lobbyists, and policy professionals—to assess the partisan divide in beliefs about climate change held by political elites. We show near-unanimous agreement among the Democratic elite on the scientific consensus that global warming is occurring, primarily caused by humans, and widely recognized by scientists. In contrast, substantial minorities of Republican elites reject these scientific facts, with fewer than half affirming anthropogenic climate change and nearly one-third endorsing a climate-related conspiracy theory. Comparing elites to the general public, we find that political elites are more aligned with climate science, but partisan gaps among elites are as wide as those observed in mass opinion. Regression analyses show partisan identity explains far more variation in elite climate beliefs than ideology, trust in science, or broader conspiratorial predispositions. These findings suggest that partisan polarization among elites reflects not only strategic electoral behavior but also privately held attitudes.
The following article is
Open access
The relationship between data centers and the climate is a systems challenge: a spatial analysis of United States data centers
Abbey Kollar and Caitlin Grady 2025
Environ. Res. Commun.
111005
View article
, The relationship between data centers and the climate is a systems challenge: a spatial analysis of United States data centers
PDF
, The relationship between data centers and the climate is a systems challenge: a spatial analysis of United States data centers
Data centers are a rapidly growing infrastructure that support digital and artificial intelligence needs. However, their expansion has raised concerns about impact based on electricity and water usage. Beyond quantifying the impact of the data center on the environment, this study presents an energy-climate operational risk analysis of over 2,400 data centers in the United States, focusing on location-specific water and heat risks and the dependency of emissions on local energy mixes. Findings highlight that current framing around the sustainability and reliability of data center operations could benefit from systems approaches for a more holistic understanding. We find that many data centers are in regions with significant water stress and are exposed to heat waves. These operational risks, along with the emissions variability due to regional grid differences, underscore the need for sustainable and reliable future planning of data centers.
The following article is
Open access
Energy use for GWh-scale lithium-ion battery production
Simon Davidsson Kurland 2020
Environ. Res. Commun.
012001
View article
, Energy use for GWh-scale lithium-ion battery production
PDF
, Energy use for GWh-scale lithium-ion battery production
Estimates of energy use for lithium-ion (Li-ion) battery cell manufacturing show substantial variation, contributing to disagreements regarding the environmental benefits of large-scale deployment of electric mobility and other battery applications. Here, energy usage is estimated for two large-scale battery cell factories using publicly available data. It is concluded that these facilities use around 50–65 kWh (180–230 MJ) of electricity per kWh of battery capacity, not including other steps of the supply chain, such as mining and processing of materials. These estimates are lower than previous studies using data on pilot-scale or under-utilized facilities but are similar to recent estimates based on fully utilized, large-scale factories. The environmental impact of battery manufacturing varies with the amounts and form of energy used; especially as renewable sources replace electricity from fossil fuels. As additional large-scale battery factories are taken into use, more data should become available, and the reliance on outdated, unrepresentative, and often incomparable, estimates of energy usage in the emerging Li-ion battery industry should be avoided.
The following article is
Open access
Post-disaster impacts on food security and nutritional demands of vulnerable groups
Bulent Basyigit
et al
2025
Environ. Res. Commun.
092004
View article
, Post-disaster impacts on food security and nutritional demands of vulnerable groups
PDF
, Post-disaster impacts on food security and nutritional demands of vulnerable groups
This review examines the impact of disasters on food security and nutritional needs, with a focus on vulnerable populations. It highlights the nutritional challenges encountered by vulnerable groups, such as lactating women, infants, and the elderly. Disasters trigger acute stress responses that disrupt metabolic rates, inflammation, and tissue repair, creating a mismatch between emergency food supplies and physiological needs. Research highlights both immediate food insecurity and long-term health risks, such as increased obesity rates among evacuees. Older adults require sufficient protein intake to prevent muscle loss, yet evacuation shelter meals often lack essential nutrients. Dietary modifications, such as soft foods, can help address these needs, while emergency diets must also provide adequate vitamins and minerals to mitigate stress, immune dysfunction, and prolonged inactivity. Intervention programs, such as targeted food assistance, have positively impacted food security and nutritional outcomes. Sustainable approaches—like resilience frameworks and local food production initiatives—enhance long-term food security, particularly in resource-limited settings. Additionally, broader perspectives on food sovereignty and health equity emphasize the importance of addressing systemic inequities in food access. Strengthening local control over food resources and governance can improve resilience and food security during disaster recovery. The relationship between displacement, housing instability, and food insecurity further underscores the necessity of coordinated, multi-sectoral interventions. A comprehensive strategy should integrate both short-term emergency food provisions and long-term nutritional planning. This approach must incorporate quantitative data (e.g., household surveys, statistical models) and qualitative insights on community resilience to develop targeted, effective interventions. By adopting an integrated recovery framework, food security efforts can better meet the needs of vulnerable populations, fostering sustainable, long-term resilience in disaster-prone areas.
The following article is
Open access
Stacked ensemble model for accurate crop yield prediction using machine learning techniques
Ramesh V and Kumaresan P 2025
Environ. Res. Commun.
035006
View article
, Stacked ensemble model for accurate crop yield prediction using machine learning techniques
PDF
, Stacked ensemble model for accurate crop yield prediction using machine learning techniques
Predicting crop yields remains a crucial challenge in agriculture, as these forecasts influence decision-making at global, regional, and individual crop levels. Historically, such predictions have utilized diverse data sources, including agricultural, land, climatic, atmospheric, and other pertinent information. Over the past several years, the application of machine learning techniques has emerged as a valuable analytical approach for estimating agricultural productivity, thereby informing decisions regarding crop selection and management strategies throughout the entire growing cycle. Various kinds of machine learning models have been utilized in research to forecast crop yields. Our work proposes a stacked ensemble model designed for the purpose of predicting crop yield. The proposed model employs a stacked ensemble learning approach, with a Decision Tree Regressor functioning as the meta-model to amalgamate predictions from six distinct base learner models: Linear Regression (LR), Elastic Net, XGBoost Regressor, K-Neighbors Regressor (KNR), AdaBoost Regressor, and Random Forest Regressor (RFR). The proposed stacked ensemble model achieves superior crop yield prediction performance, evidenced by a notable enhancement in accuracy and a significant decrease in RMSE, surpassing the predictive capabilities of traditional machine learning models. The ensemble model’s performance was assessed using several metrics, including a Mean Absolute Error of 7.20 tons/hectare, Mean Square Error of 15570.32 tons
/hectare
, Root Mean Square Error of 124.78 tons/hectare, and Coefficient of Determination (R
Score) of 0.98. The performance results demonstrate that stacked ensemble model outperforms other conventional machine learning approaches, achieving a high R-squared score of 98%.
The following article is
Open access
Low level jet controlled dynamical and thermodynamical regimes of the diurnal cycle of rainfall over the western ghats
Verma Utkarsh
et al
2026
Environ. Res. Commun.
045026
View article
, Low level jet controlled dynamical and thermodynamical regimes of the diurnal cycle of rainfall over the western ghats
PDF
, Low level jet controlled dynamical and thermodynamical regimes of the diurnal cycle of rainfall over the western ghats
Summer monsoon rainfall over the Western Ghats (WG) of India exhibits pronounced variability across multiple timescales, including the diurnal scale, which plays a key role in shaping regional precipitation. Although recent observational studies have improved understanding of the WG diurnal cycle, the physical mechanisms governing variability in storm structures and convective processes remain unclear. Using high-resolution satellite and reanalysis data, this study shows that diurnal rainfall variability over the WG is primarily governed by two physical regimes: a dynamical regime (DR) and a thermodynamical regime (TR). We demonstrate that contrasts in low-level jet strength and land–sea breeze intensity regulate the diurnal amplitude, phase, and storm depth. The DR (TR) is associated with a strong (weak) Somali jet and weak (strong) land–sea breeze within the boundary layer, with land–sea breeze intensity nearly four times stronger during TR. Consequently, diurnal rainfall is more vigorous and spatially extensive during TR. Shallow storms dominate during DR, indicating warm-rain processes, whereas TR features deeper convection involving both warm and cold microphysics, with storm-top heights reaching 5 km in DR and 8 km in TR. Favourable vertical profiles of equivalent potential temperature, geopotential height, and vertical velocity during TR, together with stronger afternoon CAPE, lower midday CINE, and increasing total column water vapour, support deep convection. In contrast, DR exhibits localized intense rainfall mainly over the northern WG with a latitudinal shift in diurnal peak timing. Although daily mean rainfall is higher during DR, the diurnal component contributes about 10% and 50% of the daily mean rainfall during DR and TR, respectively. These results provide a framework for regime-dependent diurnal rainfall variability over the WG and have implications for improving weather and climate models.
The following article is
Open access
What climate and environmental benefits of regenerative agriculture practices? an evidence review
Emily Rehberger
et al
2023
Environ. Res. Commun.
052001
View article
, What climate and environmental benefits of regenerative agriculture practices? an evidence review
PDF
, What climate and environmental benefits of regenerative agriculture practices? an evidence review
Regenerative agriculture aims to increase soil organic carbon (SOC) levels, soil health and biodiversity. Regenerative agriculture is often juxtaposed against ‘conventional’ agriculture which contributes to land degradation, biodiversity loss, and greenhouse gas emissions. Although definitions of regenerative agriculture may vary, common practices include no or reduced till, cover cropping, crop rotation, reduced use or disuse of external inputs such as agrichemicals, use of farm-derived organic inputs, increased use of perennials and agroforestry, integrated crop-livestock systems, and managed grazing. While the claims associated with some of these practices are supported by more evidence than others, some studies suggest that these practices can be effective in increasing soil organic carbon levels, which can have positive effects both agriculturally and environmentally. Studies across these different regenerative agriculture practices indicate that the increase in soil organic carbon, in comparison with conventional practices, varies widely (ranging from a nonsignificant difference to as high as 3 Mg C/ha/y). Case studies from a range of regenerative agriculture systems suggest that these practices can work effectively in unison to increase SOC, but regenerative agriculture studies must also consider the importance of maintaining yield, or risk the potential of offsetting mitigation through the conversion of more land for agriculture. The carbon sequestration benefit of regenerative practices could be maximized by targeting soils that have been intensively managed and have a high carbon storage potential. The anticipated benefits of regenerative agriculture could be tested by furthering research on increasing the storage of stable carbon, rather than labile carbon, in soils to ensure its permanence.
The following article is
Open access
Strategies for crisis and risk management in sustainable construction: communication and green culture in Pakistan
Sheikh Kamran Abid
et al
2025
Environ. Res. Commun.
035012
View article
, Strategies for crisis and risk management in sustainable construction: communication and green culture in Pakistan
PDF
, Strategies for crisis and risk management in sustainable construction: communication and green culture in Pakistan
The Pakistani construction industry faces significant challenges that hinder project success, including poor risk management, communication gaps, a lack of organizational cohesion, and inadequate green human resource management practices. This study investigates the impact of these critical factors on project outcomes within Pakistan’s unique economic and regulatory landscape. Using semi-structured interviews with project managers across various construction firms, we conducted a thematic analysis to explore how proactive risk management, effective communication strategies, a supportive green organizational culture, and green strategic human resource management practices contribute to project durability of the building and timely completion. The durability of a building entails the utilization of long-lasting resources, environmentally friendly building processes, and ideas that maintain the structure so that it stays operational and practical for its planned duration. Findings reveal that comprehensive crisis and risk management minimizes disruptions and budget overruns, while robust communication management reduces misunderstandings, fostering collaboration and efficiency. Furthermore, a positive green organizational culture enhances teamwork and motivation and targeted green human resource strategies support skill alignment and improve workforce performance. This study provides actionable insights for construction firms in Pakistan aiming to optimize green management strategies to enhance project success rates, suggesting that an integrated approach to risk, communication, green culture, and green human resource practices is essential to meet the demands of this growing sector.
The following article is
Open access
How to estimate carbon footprint when training deep learning models? A guide and review
Lucía Bouza
et al
2023
Environ. Res. Commun.
115014
View article
, How to estimate carbon footprint when training deep learning models? A guide and review
PDF
, How to estimate carbon footprint when training deep learning models? A guide and review
Machine learning and deep learning models have become essential in the recent fast development of artificial intelligence in many sectors of the society. It is now widely acknowledge that the development of these models has an environmental cost that has been analyzed in many studies. Several online and software tools have been developed to track energy consumption while training machine learning models. In this paper, we propose a comprehensive introduction and comparison of these tools for AI practitioners wishing to start estimating the environmental impact of their work. We review the specific vocabulary, the technical requirements for each tool. We compare the energy consumption estimated by each tool on two deep neural networks for image processing and on different types of servers. From these experiments, we provide some advice for better choosing the right tool and infrastructure.
The following article is
Open access
Groundwater recharge suitability mapping in Iligan City, Philippines using WATERS: a consistency-controlled AHP-GIS decision-support tool
Rylle Adriane Galvez
et al
2026
Environ. Res. Commun.
045001
View article
, Groundwater recharge suitability mapping in Iligan City, Philippines using WATERS: a consistency-controlled AHP-GIS decision-support tool
PDF
, Groundwater recharge suitability mapping in Iligan City, Philippines using WATERS: a consistency-controlled AHP-GIS decision-support tool
Groundwater resources are under increasing pressure due to overexploitation and inadequate planning, particularly in rapidly urbanizing areas. Mapping groundwater recharge suitability provides spatial information that can support groundwater protection planning and the evaluation of areas that may be favorable for future groundwater development. Iligan City, a major urban and industrial center in Northern Mindanao, Philippines, has experienced steady population and economic growth, placing increasing pressure on local water resources. Despite this, spatial information on groundwater recharge suitability within the city remains limited. This study delineates groundwater recharge suitability zones in Iligan City by integrating an Analytic Hierarchy Process (AHP)–based multi-criteria decision analysis with geospatial techniques. Seven parameters—geology, land cover, slope, lineament density, rainfall, drainage density, and soil—were evaluated and weighted using a consistency-controlled AHP procedure implemented through the Weight Assessment Tool for Evaluating Recharge Suitability (WATERS) decision-support tool. The derived weights were incorporated into a GIS-based weighted overlay analysis to generate a groundwater recharge suitability map classified into five relative suitability classes. High and extremely high recharge suitability zones cover 49% and 37% of Iligan City, respectively, whereas the moderate, low, and extremely low classes account for 13.91%, 1.05%, and 0.02% of the area. Overlay analysis using 133 pumping well locations shows that approximately 83% of wells are located within high to extremely high recharge suitability zones. The consistency-controlled AHP–GIS framework integrates real-time consistency assessment and guided revision of expert judgments into groundwater recharge suitability (GRS) mapping and generates spatial information to support evidence-based groundwater planning aligned with Sustainable Development Goal 6.
The following article is
Open access
Effects of tillage depths and legumes integration on soil properties and maize (
Zea Mays L.
) productivity in Northwest Ethiopia
Zenebe Terefe
et al
2026
Environ. Res. Commun.
045035
View article
, Effects of tillage depths and legumes integration on soil properties and maize (Zea Mays L.) productivity in Northwest Ethiopia
PDF
, Effects of tillage depths and legumes integration on soil properties and maize (Zea Mays L.) productivity in Northwest Ethiopia
Intensive tillage and mono-cropping practices are among the major causes of soil degradation, low crop productivity and food insecurity in Sub-Saharan Africa. Information on optimum tillage depths and best maize-legumes intercropping for improving soil properties and crop yield under irrigation system is lacking. Hence, we conducted field experiment over two cropping years to examine the impact of different tillage depths and maize-legumes intercropping on soil properties and maize productivity in northwest Ethiopia. The treatments were combinations of three levels of tillage depths (zero, conventional, and deep tillage) and four levels of maize-legumes intercropping (maize + soybean (
Glycine max
), maize + haricot bean (
Phaseolus vulgaris)
, maize + vetch (
Vicia faba
) and sole maize as a control), arranged in a randomized complete block design with three replications. Both the soil and crop data were subjected to analysis of variance using statistical analysis system software. The results showed that zero tillage depth and maize-soya bean intercropping in combination provided the highest mean soil pH (5.37), organic carbon (2.51%), total N (0.23%), available P (7.27 mg kg
−1
) and CEC (29.18 cmol (+) kg
−1
). Besides, the maximum mean dry biomass (17.52 tons ha
−1
), grain yield (7.73 tons ha
−1
), thousand-grain weight (416) and harvest index (44%) were achieved from deep tillage combined with maize-soya bean intercropping. In conclusion, maize-legumes intercropping under deep tillage can be a promising practice to improve nutrient availability and crop yield. However, further long-term research needs to be conducted in varied combinations of tillage depths and maize-legumes intercropping strategies in different agroecology.
The following article is
Open access
Stratigraphy-driven natural background for potentially toxic elements in a heavily mined Amazonian watershed
Mariana Maha Jana Costa de Figueiredo
et al
2026
Environ. Res. Commun.
045034
View article
, Stratigraphy-driven natural background for potentially toxic elements in a heavily mined Amazonian watershed
PDF
, Stratigraphy-driven natural background for potentially toxic elements in a heavily mined Amazonian watershed
The Verde River watershed (VRW) in southeastern Amazonia is undergoing extensive channel modification and sediment disturbance from illegal alluvial gold mining, yet site-specific geochemical baselines capable of separating natural from mining-derived metal enrichment remain lacking. We integrated morphostratigraphic reconstruction with high-density geochemical analyses of potentially toxic elements (PTE: As, Ba, Cd, Co, Cr, Cu, Hg, Mn, Mo, Ni, Pb, Sn, V, Zn) in stratigraphically constrained alluvial deposits to establish pre-anthropogenic background (natural) values and quantify enrichment in recently reworked sediments. Over the past ∼7600 years, sediment geochemistry has been shaped by shifts in provenance between mineralized mafic and felsic lithologies and by climatic variability, producing naturally elevated Cu (93–455 mg kg
−1
), Cr (124–352 mg kg
−1
), and Ni (40–107 mg kg
−1
) that in some cases exceed Brazilian and international sediment quality guidelines (threshold, probable, and severe effect levels) without anthropogenic input. While these natural concentrations generally indicate low ecological risk, comparisons between lithology-specific backgrounds and residues from illegal mining sites show that Cu, Co, and Ni are the most consistently and significantly enriched PTE, whereas Hg, historically the focus in Amazonian mining studies, remains below moderate contamination factors at most sites. These results highlight that stratigraphy-anchored backgrounds are critical for distinguishing geogenic from anthropogenic signatures in tropical alluvial systems, thereby improving ecological risk assessment and guiding targeted monitoring and remediation in mining-impacted Amazonian watersheds.
The following article is
Open access
Spatial heterogeneity of cloud hydrometeors during extreme monsoon rainfall over India
Darshana Gautam 2026
Environ. Res. Commun.
045033
View article
, Spatial heterogeneity of cloud hydrometeors during extreme monsoon rainfall over India
PDF
, Spatial heterogeneity of cloud hydrometeors during extreme monsoon rainfall over India
Cloud feedback mechanisms remain a major source of uncertainty in weather prediction, particularly over monsoon dominated regions like India where rainfall exhibits strong spatial and temporal variability. This study analyzed Indian Meteorological Department (IMD) daily gridded rainfall, and European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) reanalysis datasets for the monsoon months June-September (JJAS) during 2014–2023, to examine how cloud microphysical properties and processes modulate extreme heavy rainfall across the three monsoon convective zones of India - Western Ghats, Central India, and North-East India. Rainfall categorization, cloud hydrometeor profiles, and cloud microphysical process estimated through Tao’s algorithm were used to investigate the vertical profiles of cloud liquid, ice, and rainwater content, and their linkages with dynamical and thermodynamical conditions. The present work shows pronounced spatial heterogeneity in cloud microphysical properties and processes, with distinct variation in magnitude and vertical distribution, indicating region-specific cloud microphysical characteristics and processes. These differences translate into region-specific microphysical process rates (rain condensation, evaporation, and deposition) which directly influence the formation and intensity of Extreme Heavy rainfall (EH). Deeper convection over North-East region is shown to result in stronger condensation and EH. However, over Western Ghats a higher amount of lower-level liquid water content is found to drive EH. Furthermore, over Central India the high ice water content found in the upper troposphere during EH events is found to form due to aggregation rather than vapor deposition. Such contrasting cloud structures explain the uneven spatial distribution of extreme rainfall across India. This study underscores that spatial heterogeneity in cloud microphysics is fundamental to understanding extreme heavy rainfall variability and strengthening climate resilience in monsoon-affected regions.
The following article is
Open access
Assessing the impacts of land use/land cover changes on land surface temperature in Baltimore, Maryland, USA
Chichedo I Duru
et al
2026
Environ. Res. Commun.
045032
View article
, Assessing the impacts of land use/land cover changes on land surface temperature in Baltimore, Maryland, USA
PDF
, Assessing the impacts of land use/land cover changes on land surface temperature in Baltimore, Maryland, USA
Spatial and temporal variations in land use and land cover play a critical role in regulating land surface temperature and urban environmental conditions, with implications for ecological stability and human well-being. This study evaluates the spatiotemporal relationships among land use/land cover (LULC), LST, and vegetation dynamics in Baltimore, USA, over a 20-year period (2004–2024). Multi-temporal Landsat imagery was analyzed using GIS and remote sensing techniques to classify LULC into five categories—dense vegetation, developed land, herbaceous cover, barren land, and water bodies—and to derive LST alongside vegetation-related indices, including the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-up Index (NDBI). Results indicate notable LULC changes, with dense vegetation increasing from 681.61 km
in 2004 to 817.65 km
in 2024, while developed land decreased from 719.42 km
to 597.75 km
over the same period. Despite this apparent greening trend, LST exhibited an increase, with 2024 temperatures ranging from 61.51 °C to 91.7 °C, suggesting that global warming, urban heat retention, and reduced cooling effects may have contributed to the elevated surface temperatures. Correlation analysis revealed an inverse relationship between LST and NDVI, highlighting the cooling potential of vegetation through shading and evapotranspiration, whereas a positive relationship between LST and NDBI highlighted the warming contribution of built-up surfaces. The study underscores the importance of integrating vegetation restoration, urban design strategies, and continuous thermal monitoring into land management and planning frameworks. Such measures are essential for mitigating surface heat accumulation, reducing urban heat island effects, and enhancing environmental resilience in rapidly evolving urban landscapes.
The following article is
Open access
Delivering probabilistic climate hazards assessments
David Huard
et al
2026
Environ. Res. Commun.
045028
View article
, Delivering probabilistic climate hazards assessments
PDF
, Delivering probabilistic climate hazards assessments
Decision-making regarding adaptation to climate hazards frequently requires a quantitative assessment of the likelihood of future hazards. This is typically done using climate model projections conditional on future greenhouse gas (GHG) and aerosol concentrations, as well as land-use changes. This leaves decision makers with the responsibility to select the concentration pathway corresponding to their risk tolerance, with little guidance from the scientific community. Here we propose a climate service estimating the occurrence of future climate hazards based on a weighted mixture distribution calibrated on an ensemble of hundreds of simulations from different global climate models and GHG concentration pathways. Each simulation is weighted according to its performance over the historical period, the equilibrium climate sensitivity of climate models, and the likelihood of future GHG concentrations. A prototype implementation for Canada was evaluated by groups of professionals from engineering firms and governments to collect criticism and suggestions. This feedback underlines the interest for climate products that integrate seamlessly with established risk-based frameworks familiar to decision makers.
The following article is
Open access
Low-cost and labour-efficient innovations in household recycling of organic wastes for soil improvement
Jo Smith
et al
2026
Environ. Res. Commun.
042002
View article
, Low-cost and labour-efficient innovations in household recycling of organic wastes for soil improvement
PDF
, Low-cost and labour-efficient innovations in household recycling of organic wastes for soil improvement
Organic matter plays an important role in the health and productivity of soils, but its depletion is a common problem in households in low-income countries. This is due to lack of and competing uses for organic resources, and limited information on recycling methods. Therefore, here we review low-cost and labour-efficient innovations to improve recycling of organic wastes, stabilising residues so that soil organic matter can be increased with less inputs and enhancing nutrient content to produce a more effective organic fertiliser. Composting, anaerobic digestion and pyrolysis are all processes that stabilise organic matter. Innovations in treatments are needed to improve stabilization and control the release of nutrients so that they are available to crops in the right amounts and at the right time. This can be achieved by maintaining appropriate treatment conditions: for composting, carbon to nitrogen ratio 25–35, carbon to phosphorus ratio ∼50, pH 5.5–8.5 and 50%–60% moisture content; for anaerobic digestion, carbon to nitrogen ratio 20–35, bulk density 0.6–0.8 g cm
−3
, lignin content < 7.5%, pH 6.8–7.4 and moisture content 85%–95%; and for pyrolysis, carbon to nitrogen > 40 and moisture content < 20%. Different methods to achieve these ideal conditions are discussed, including appropriate choice of treatment method, co-composting/co-digestion for ideal nutrient content, enhancing nutrients using collected urine, nitrogen-fixing plants, bioslurry or by inoculating with bacterial communities, absorbing excess nutrients on biochar, adjusting pH using wood ash or biochar, pre-treatment to break down lignin and cellulose, and designs to achieve ideal moisture and temperature. Innovations should also ensure that treatment processes do not overuse or compete with other important household resources, such as finances, water or labour. We draw together findings to identify methods with most potential to improve soils in low-income countries, providing decision tables to guide selection of approaches for different contexts.
The following article is
Open access
Current understanding of ENSO and its impacts on Panama’s climate: a review
Elisa Elizabeth Mendieta
et al
2026
Environ. Res. Commun.
042001
View article
, Current understanding of ENSO and its impacts on Panama’s climate: a review
PDF
, Current understanding of ENSO and its impacts on Panama’s climate: a review
The El Niño–Southern Oscillation (ENSO) is the dominant mode of interannual climate variability, arising from coupled ocean–atmosphere interactions in the tropical Pacific that reorganize sea-surface temperature gradients, convection, and the Walker circulation. These large-scale changes project onto Central America by shifting the Intertropical Convergence Zone (ITCZ) and modulating low-level winds and moisture transport across the narrow Panamanian isthmus. As a result, Panama exhibits marked ENSO-conditioned hydroclimate anomalies: El Niño commonly suppresses rainfall by weakening regional moisture convergence and favoring subsidence, whereas La Niña tends to enhance moisture transport and convection, increasing the likelihood of heavy rainfall and flooding. These climate responses have direct societal and operational consequences, affecting agriculture, water availability, hydropower generation, and the water supply that sustains Panama Canal operations. Drought years have been associated with reduced reservoir levels and operational adjustments, while wet extremes can increase flooding, sedimentation, and maintenance demands. This review synthesizes current understanding of the mechanisms linking ENSO to Panama’s seasonal climate and highlights implications for climate-risk management, forecasting, and resilient planning in a region critical to global maritime trade.
The following article is
Open access
Improving water data availability for First Nations communities: a brief review
Matthew Hamilton
et al
2026
Environ. Res. Commun.
032003
View article
, Improving water data availability for First Nations communities: a brief review
PDF
, Improving water data availability for First Nations communities: a brief review
Water quality is a persistent issue in First Nations communities. Due to factors such as isolated locations, limited local economic capacity, and historical underinvestment in infrastructure, water supply on reserves is often prone to equipment malfunctions, chemical shortages, disease outbreaks, or contamination from nearby industrial activities. Addressing such issues and maintaining water security requires reliable, and ideally extensive, data, but such data can be difficult to obtain or may be of inconsistent quality. This review paper examines existing literature on water issues in First Nations communities. It reviews the existing water data availability for First Nations in Canada, identifies additional datasets that may be useful to water studies in First Nations communities, reviews best practices for data collection in First Nations contexts, discusses the sociological and technological challenges faced in such environments, and finally makes recommendations to improve data availability for future studies. The review shows that data on water quality for First Nations communities is often difficult to access due to Indigenous data sovereignty principles, the remote locations of many communities, and limited training on water quality monitoring. Water data availability can be improved by developing a single coherent database and establishing a collaborative network of First Nations engineers to help integrate projects with First Nations worldviews, and adopting co-design as a standard practice to increase the consistency of projects. This study provides valuable perspectives for collaborative research initiatives focused on Indigenous water studies, particularly those grounded in the respectful integration of Indigenous and Western knowledge.
The following article is
Open access
Rapid review: health and maximum indoor temperature thresholds in high income countries
Emily Loud
et al
2026
Environ. Res. Commun.
032002
View article
, Rapid review: health and maximum indoor temperature thresholds in high income countries
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, Rapid review: health and maximum indoor temperature thresholds in high income countries
There are currently no formal guidelines on maximum indoor temperature thresholds in household, community or workplace settings in the UK, despite the built environment being a major determinant of heat-related mortality and morbidity. This rapid review considered at what temperature threshold(s) hot indoor environments begin to harm human health in high income countries, with a view to informing potential future recommendations and UK policy on this topic. We looked for articles measuring human health and high indoor temperatures in high income countries. Searches of Medline, Embase, Web of Science and Scopus identified 5,642 articles published between 2017–2024, of which 12 articles were included. Countries, populations, settings, and methods of exposure measurement differed within the final set of studies. Health outcomes were varied and included measures such as risk or odds of death, indicators of morbidity, ambulance calls, and self-reported heat related illness, among others. Thresholds at which adverse health outcomes were observed spanned a wide range of temperatures, from 17 °C to 31 °C. While there was a correlation between high indoor temperatures and health outcomes, the body of evidence was not coherent or consistent. There remains a dearth of evidence on safe maximum temperatures in indoor settings. Future research should study directly measured or well-modelled indoor temperature with acute health outcomes for groups at highest risk of heat related morbidity and mortality, to inform adaptation policy in the context of a rapidly warming climate. Until there is extensive scientific data to support a maximum indoor temperature threshold, 26 °C may be the most suitable threshold for a maximum indoor temperature threshold for at-risk groups in keeping with the existing guidance.
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Bioplastics: an examination of variety, degradation, and environmental effects
Judith S Weis
et al
2026
Environ. Res. Commun.
032001
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, Bioplastics: an examination of variety, degradation, and environmental effects
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, Bioplastics: an examination of variety, degradation, and environmental effects
There is considerable confusion about ‘bioplastics.’ They are often touted as ‘greener’ than conventional plastics and may be claimed to be biodegradable and compostable. However, there are a great variety of different products claimed to be bioplastics, and they differ greatly in many characteristics. In this review, we discuss the large diversity of bioplastic products derived from biological materials and compare their various physio-chemical characteristics with conventional (petroleum-derived) plastics. We define a number of terms that are often confused or ambiguous. We examine the biodegradability of different bioplastics in various environments (e.g. higher or lower temperatures, composting, soil, freshwater, seawater) and the byproducts (e.g. chemicals, microplastics) that they may release during degradation. In addition, we review studies that evaluated the toxicity of the products released, including toxicity to cell lines and bacteria, to plants, and to terrestrial and aquatic animals. Many studies have compared toxicity of bioplastics with conventional plastics. The diverse bioplastics differ greatly in toxicity. Furthermore, toxicity test methods employed have been varied—some studies have tested solid pieces of plastic, some have used microplastics generated from the bioplastic, and some have used plastic product leachates. The reviewed studies have used different conventional plastics to compare bioplastics with. Many studies found that the bioplastics were more toxic than the conventional plastics. Therefore, it is impossible to draw any overall conclusions that ‘bioplastics’ as a group are more or less toxic than conventional plastics, but it would be incorrect to conclude that they are in general more ‘environmentally friendly.’
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The overlooked methane emissions from wood waste in the global north
Karim
View accepted manuscript
, The overlooked methane emissions from wood waste in the global north
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, The overlooked methane emissions from wood waste in the global north
Wood-waste management in the Global North represents a substantial yet underappreciated source of methane emissions that remains largely absent from greenhouse-gas inventories. Historical disposal practices, coupled with contemporary landfills and large municipal stockpiles, create conditions conducive to slow but persistent anaerobic decomposition of lignocellulosic material. Legacy deposits from nineteenth- and twentieth-century industrial operations, modern landfill cells, and above-ground stockpiles collectively generate methane over multidecadal timescales, driven by interactions among moisture, compaction, particle size, and limited oxygen diffusion. Emerging evidence indicates that these emissions are climatically significant, with wood-containing waste contributing disproportionately to regional methane fluxes in metropolitan areas. Mechanistic insights from mulch–soil systems and preliminary studies on biochar amendment suggest pathways for mitigation by enhancing aeration, moderating moisture, and supporting methanotrophic activity. Addressing these emissions requires coordinated research to quantify fluxes, refine predictive models, and evaluate operational mitigation strategies alongside updated policy frameworks. Recognizing the climatic significance of wood-waste storage can transform a historically overlooked emission source into a manageable component of regional and national carbon budgets.
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A deep learning-based hybrid approach for solar energy prediction using GAT and transformers
Khan et al
View accepted manuscript
, A deep learning-based hybrid approach for solar energy prediction using GAT and transformers
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, A deep learning-based hybrid approach for solar energy prediction using GAT and transformers
Precise solar energy forecasting plays a important role for enhanced management of smart energy grids and energy distribution. Traditional machine learning models usually struggle to capture complex relationships, patterns and dependencies in historical data, which result in imprecise predictions. To deal with these problems, this work proposes a Hybrid Graph-based Temporal Transformer model dubbed as H-GTT. This model integrates Graph Attention Networks to capture spatial dependencies and Transformers to capture long-term dependencies and trends. H-GTT illustrates domineering prediction accuracy in comparison with the existing hybrid and stand-alone models. The H-GTT model is evaluated using historical solar energy data from the Pavagada Solar Plant, stretched across 13,000 acres in Tumkur, Karnataka, with features including temperature, humidity, wind speed, cloud cover, and solar radiation. The exploratory results show that H-GTT surpasses traditional machine learning and deep learning techniques, adjusting effectively to sudden weather changes. By applying attention mechanisms to identify key governing factors , the model demonstrates its potency for real world applications such as smart grids, energy management, and sustainable city planning.
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Finest-resolution socioeconomic vulnerability assessment and key driver identification for hydro-climatic extremes in Maharashtra, India
Dev et al
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, Finest-resolution socioeconomic vulnerability assessment and key driver identification for hydro-climatic extremes in Maharashtra, India
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, Finest-resolution socioeconomic vulnerability assessment and key driver identification for hydro-climatic extremes in Maharashtra, India
Hydro-climatic extremes are intensifying across India, underscoring the need for fine-resolution socioeconomic vulnerability (SEV) assessments that can guide adaptation and disaster risk reduction (DRR). Most existing SEV studies in the Global South rely on coarse spatial units, subjective weighting, and inconsistent indicator frameworks, limiting their utility for local planning. This study presents the first statewide, sub-district (taluka)-scale SEV assessment in India, covering all 357 talukas of Maharashtra. A dual-scenario framework—Sensitive and Adaptive—is introduced to capture complementary dimensions of vulnerability. Composite indicators derived from Principal Component Analysis (PCA) are used to reduce multicollinearity and subjective bias, while the BCC variant of Data Envelopment Analysis (DEA) provides objective, non-parametric vulnerability scoring. Results show that the largest share of the population (22%) falls in the Low vulnerability category, followed by 18% in Medium and 16% in Very Low, while 8% lies in the Very High vulnerability category. A pronounced rural–urban disparity is observed, with nearly 62% of the urban population concentrated in the Very Low vulnerability class, whereas rural populations are more evenly distributed and disproportionately represented in higher vulnerability categories. Indicator contribution analysis identifies main agricultural workers (4.176%), marginal workers (4.161%), and marginal female workers (4.094%) as the highest-contributing indicators to socioeconomic vulnerability. Spatial clustering and local hotspot diagnostics reveal clear regional contrasts, with Central and Eastern Vidarbha exhibiting the highest SEV, while North Konkan shows comparatively lower vulnerability. The proposed framework provides a scalable and transferable blueprint for sub-national vulnerability assessment and supports evidence-based, place-specific climate adaptation and DRR planning.
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Open access
Observed and projected increases in short-duration extreme precipitation under climate change over Greater Sydney
Khadke et al
View accepted manuscript
, Observed and projected increases in short-duration extreme precipitation under climate change over Greater Sydney
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, Observed and projected increases in short-duration extreme precipitation under climate change over Greater Sydney
Emerging observational evidence shows an intensification of short-duration precipitation events (≤1 hour) at a faster rate than longer-duration events, These short-duration extreme precipitation events frequently trigger flash floods and pose significant risks in urban areas to infrastructure and people. Despite this, regional-scale assessments of sub-hourly to hourly precipitation events remain scarce. Using 16 automated weather stations (AWS), we analyze short-duration extreme precipitation (5-, 10-, 20-, 30-, and 60-minute) events across the rapidly urbanizing Greater Sydney region, New South Wales, Australia. Our analysis reveals strong increasing trend in extreme precipitation (99th (p99) and 99.5th percentiles (p99.5)) at 5-10 min compared to hourly scale. At the hourly scale, an ensemble of ten convection-permitting regional climate model simulations (4 km resolution) reproduces the upper tail of the precipitation distribution but slightly overestimate the frequency of extreme events. Future projections under three Shared Socioeconomic Pathway (SSP) scenarios (SSP126, SSP245, SSP370) show a consistent intensification of p99 precipitation, with the largest increases under SSP370 and a pronounced shift in the upper 1% of historical extremes (up to 125%). In contrast, the total number of wet hours decreases, indicating a transition toward shorter and more intense precipitation events. Despite inter-model spread and spatial variability, models project an increase in median trend at p99 extremes across most scenarios (up to 0.15 mm/year). Under the low-emission scenario (SSP126), a reduction in extreme precipitation is projected in the far future, highlighting the potential benefits of mitigation. These findings highlight the need to integrate short duration extreme precipitation events into urban planning and to mitigate escalating flood risks under climate change.
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The impact of the particulate matter management policy on the electric power sector in South Korea: Evidence from coal-fired generators
Kwon
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, The impact of the particulate matter management policy on the electric power sector in South Korea: Evidence from coal-fired generators
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, The impact of the particulate matter management policy on the electric power sector in South Korea: Evidence from coal-fired generators
Given the substantial contribution of coal-fired power generation to environmental pollution in South Korea, evaluating the effectiveness of policies in the electric power sector is essential. The South Korean government has implemented the Particulate Matter Management Policy (PMMP) in the electric power sector to control coal-fired emissions. However, current policy evaluation reports are limited and, when available, are conducted at the plant level, which leads to a biased estimate. This study examines the impact of Particulate Matter Management Policy (PMMP) on the electric power sector in South Korea, with a focus on the coal-fired generators. A Difference-in-Differences (DiD) approach is employed to estimate the effect of PMMP and compiles data from representative power companies on electric generation and emissions from 2014 to 2019. The findings suggest that PMMP is associated with a 50.1% reduction in nitrogen oxides (NOx) emissions and a 25% reduction in total suspended particles (TSP) emissions. Furthermore, the estimates of emission intensity are consistent with reductions of 53% for NOx and 30% for TSP. Based on the estimates, this study calculates the environmental benefits associated with PMMP implementation at USD 7.24 billion per year. These results suggest that the electric power sector is important in advancing environmental sustainability.
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How to estimate carbon footprint when training deep learning models? A guide and review
Lucía Bouza
et al
2023
Environ. Res. Commun.
115014
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, How to estimate carbon footprint when training deep learning models? A guide and review
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, How to estimate carbon footprint when training deep learning models? A guide and review
Machine learning and deep learning models have become essential in the recent fast development of artificial intelligence in many sectors of the society. It is now widely acknowledge that the development of these models has an environmental cost that has been analyzed in many studies. Several online and software tools have been developed to track energy consumption while training machine learning models. In this paper, we propose a comprehensive introduction and comparison of these tools for AI practitioners wishing to start estimating the environmental impact of their work. We review the specific vocabulary, the technical requirements for each tool. We compare the energy consumption estimated by each tool on two deep neural networks for image processing and on different types of servers. From these experiments, we provide some advice for better choosing the right tool and infrastructure.
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Technical potentials and costs for reducing global anthropogenic methane emissions in the 2050 timeframe –results from the GAINS model
Lena Höglund-Isaksson
et al
2020
Environ. Res. Commun.
025004
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, Technical potentials and costs for reducing global anthropogenic methane emissions in the 2050 timeframe –results from the GAINS model
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, Technical potentials and costs for reducing global anthropogenic methane emissions in the 2050 timeframe –results from the GAINS model
Methane is the second most important greenhouse gas after carbon dioxide contributing to human-made global warming. Keeping to the Paris Agreement of staying well below two degrees warming will require a concerted effort to curb methane emissions in addition to necessary decarbonization of the energy systems. The fastest way to achieve emission reductions in the 2050 timeframe is likely through implementation of various technical options. The focus of this study is to explore the technical abatement and cost pathways for reducing global methane emissions, breaking reductions down to regional and sector levels using the most recent version of IIASA’s Greenhouse gas and Air pollution Interactions and Synergies (GAINS) model. The diverse human activities that contribute to methane emissions make detailed information on potential global impacts of actions at the regional and sectoral levels particularly valuable for policy-makers. With a global annual inventory for 1990–2015 as starting point for projections, we produce a baseline emission scenario to 2050 against which future technical abatement potentials and costs are assessed at a country and sector/technology level. We find it technically feasible in year 2050 to remove 54 percent of global methane emissions below baseline, however, due to locked in capital in the short run, the cumulative removal potential over the period 2020–2050 is estimated at 38 percent below baseline. This leaves 7.7 Pg methane released globally between today and 2050 that will likely be difficult to remove through technical solutions. There are extensive technical opportunities at low costs to control emissions from waste and wastewater handling and from fossil fuel production and use. A considerably more limited technical abatement potential is found for agricultural emissions, in particular from extensive livestock rearing in developing countries. This calls for widespread implementation in the 2050 timeframe of institutional and behavioural options in addition to technical solutions.
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Validation of FABDEM, a global bare-earth elevation model, against UAV-lidar derived elevation in a complex forested mountain catchment
Christopher B Marsh
et al
2023
Environ. Res. Commun.
031009
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, Validation of FABDEM, a global bare-earth elevation model, against UAV-lidar derived elevation in a complex forested mountain catchment
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, Validation of FABDEM, a global bare-earth elevation model, against UAV-lidar derived elevation in a complex forested mountain catchment
Space-based, global-extent digital elevation models (DEMs) are key inputs to many Earth sciences applications. However, many of these applications require the use of a ‘bare-Earth’ DEM versus a digital surface model (DSM), the latter of which may include systematic positive biases due to tree canopies in forested areas. Critical topographic features may be obscured by these biases. Vegetation-free datasets have been created by using statistical relationships and machine learning to train on local-scale datasets (e.g., lidar) to de-bias the global-extent datasets. Recent advances in satellite platforms coupled with increased availability of computational resources and lidar reference products has allowed for a new generation of vegetation- and urban-canopy removals. One of these is the Forest And Buildings removed Copernicus DEM (FABDEM), based on the most recent and most accurate global DSM Copernicus-30. Among the more challenging landscapes to quantify surface elevations are densely forested mountain catchments, where even airborne lidar applications struggle to capture surface returns. The increasing affordability and availability of UAV-based lidar platforms have resulted in new capacity to fly modest spatial extents with unrivalled point densities. These data allow an unprecedented ability to validate global sub-canopy DEMs against representative UAV-based lidar data. In this work, the FABDEM is validated against up-scaled lidar data in a steep and forested mountain catchment considering elevation, slope, and Terrain Position Index (TPI) metrics. Comparisons of FABDEM with SRTM, MERIT, and the Copernicus-30 dataset are made. It was found that the FABDEM had a 24% reduction in elevation RMSE and a 135% reduction in bias compared to the Copernicus-30 dataset. Overall, the FABDEM provides a clear improvement over existing deforested DEM products in complex mountain topography such as the MERIT DEM. This study supports the use of FABDEM in forested mountain catchments as the current best-in-class data product.
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Open access
Applying IPCC 2014 framework for hazard-specific vulnerability assessment under climate change
Jagmohan Sharma and Nijavalli H Ravindranath 2019
Environ. Res. Commun.
051004
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, Applying IPCC 2014 framework for hazard-specific vulnerability assessment under climate change
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, Applying IPCC 2014 framework for hazard-specific vulnerability assessment under climate change
The Intergovernmental Panel on Climate Change (IPCC), Working Group II Report (2014) presents vulnerability as a pre-existing characteristic property of a system. Accordingly, indicators for ‘sensitivity’ and ‘adaptive capacity’, which are internal properties of a system, are employed to assess it. Comparatively, the IPCC 2007 report includes ‘exposure’, an external factor, as the third component of vulnerability. We have compared the construct of vulnerability presented in IPCC 2007 and 2014 reports. It is argued that the results of vulnerability assessment obtained by adopting IPCC 2014 framework are practically more useful for reducing current vulnerability in preparedness to deal with an uncertain future. In the process, we have articulated the novel concepts of ‘selecting hazard-relevant vulnerability indicators’ and ‘assessing hazard-specific vulnerability’. Use of these concepts improves the contextualization of an assessment and thereby the acceptability of assessment results by the stakeholders.
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What climate and environmental benefits of regenerative agriculture practices? an evidence review
Emily Rehberger
et al
2023
Environ. Res. Commun.
052001
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, What climate and environmental benefits of regenerative agriculture practices? an evidence review
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, What climate and environmental benefits of regenerative agriculture practices? an evidence review
Regenerative agriculture aims to increase soil organic carbon (SOC) levels, soil health and biodiversity. Regenerative agriculture is often juxtaposed against ‘conventional’ agriculture which contributes to land degradation, biodiversity loss, and greenhouse gas emissions. Although definitions of regenerative agriculture may vary, common practices include no or reduced till, cover cropping, crop rotation, reduced use or disuse of external inputs such as agrichemicals, use of farm-derived organic inputs, increased use of perennials and agroforestry, integrated crop-livestock systems, and managed grazing. While the claims associated with some of these practices are supported by more evidence than others, some studies suggest that these practices can be effective in increasing soil organic carbon levels, which can have positive effects both agriculturally and environmentally. Studies across these different regenerative agriculture practices indicate that the increase in soil organic carbon, in comparison with conventional practices, varies widely (ranging from a nonsignificant difference to as high as 3 Mg C/ha/y). Case studies from a range of regenerative agriculture systems suggest that these practices can work effectively in unison to increase SOC, but regenerative agriculture studies must also consider the importance of maintaining yield, or risk the potential of offsetting mitigation through the conversion of more land for agriculture. The carbon sequestration benefit of regenerative practices could be maximized by targeting soils that have been intensively managed and have a high carbon storage potential. The anticipated benefits of regenerative agriculture could be tested by furthering research on increasing the storage of stable carbon, rather than labile carbon, in soils to ensure its permanence.
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Coupling coordination analysis of urban social vulnerability and human activity intensity
Fan Qindong
et al
2025
Environ. Res. Commun.
035009
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, Coupling coordination analysis of urban social vulnerability and human activity intensity
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, Coupling coordination analysis of urban social vulnerability and human activity intensity
An in-depth exploration of the coupling and coordination relationship between urban social vulnerability and human activity intensity can provide a scientific basis for urban sustainable development, planning optimization, and policy decisions. Based on multi-source remote sensing data and social-economic statistics in 2021, Zhengzhou’s social vulnerability and human activity intensity were quantified. Subsequently, a coupling coordination degree model was applied to reveal the extent of coordination between these two factors. Finally, the geographical detector was used to analyze the impact of driving factors on the degree of coupling coordination. Results indicated that the spatial distribution pattern of social vulnerability and human activity intensity in Zhengzhou exhibits a gradual attenuation trend from the center toward the periphery, indicating a strong correlation between the two factors. The regions with high coupling coordination degrees were primarily concentrated in the central region. The total output value of the primary industry, patch aggregation index, per capita cultivated land area, and human activity intensity were identified as key drivers affecting changes in coupling coordination degree. These factors exhibit evident synergistic enhancement effects, resulting in comprehensive impacts on the spatial distribution of coupling. This study can provide a reference for urban development decision-making.
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Groundwater extraction may drown mega-delta: projections of extraction-induced subsidence and elevation of the Mekong delta for the 21st century
P S J Minderhoud
et al
2020
Environ. Res. Commun.
011005
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, Groundwater extraction may drown mega-delta: projections of extraction-induced subsidence and elevation of the Mekong delta for the 21st century
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, Groundwater extraction may drown mega-delta: projections of extraction-induced subsidence and elevation of the Mekong delta for the 21st century
The low-lying and populous Vietnamese Mekong delta is rapidly losing elevation due to accelerating subsidence rates, primarily caused by increasing groundwater extraction. This strongly increases the delta’s vulnerability to flooding, salinization, coastal erosion and, ultimately, threatens its nearly 18 million inhabitants with permanent inundation. We present projections of extraction-induced subsidence and consequent delta elevation loss for this century following six mitigation and non-mitigation extraction scenarios using a 3D hydrogeological model with a coupled geotechnical module. Our results reveal the long-term physically response of the aquifer system following different groundwater extraction pathways and show the potential of the hydrogeological system to recover. When groundwater extraction is allowed to increase continuously, as it did over the past decades, extraction-induced subsidence has the potential to drown the Mekong delta single-handedly before the end of the century. Our quantifications also disclose the mitigation potential to reduce subsidence by limiting groundwater exploitation and hereby limiting future elevation loss. However, the window to mitigate is rapidly closing as large parts of the lowly elevated delta plain may already fall below sea level in the coming decades. Failure to mitigate groundwater extraction-induced subsidence may result in mass displacement of millions of people and could severely affect regional food security as the food producing capacity of the delta may collapse.
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Increased frequency of and population exposure to extreme heat index days in the United States during the 21st century
Kristina Dahl
et al
2019
Environ. Res. Commun.
075002
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, Increased frequency of and population exposure to extreme heat index days in the United States during the 21st century
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, Increased frequency of and population exposure to extreme heat index days in the United States during the 21st century
The National Weather Service of the United States uses the heat index—a combined measure of temperature and relative humidity—to define risk thresholds warranting the issuance of public heat alerts. We use statistically downscaled climate models to project the frequency of and population exposure to days exceeding these thresholds in the contiguous US for the 21st century with two emissions and three population change scenarios. We also identify how often conditions exceed the range of the current heat index formulation. These ‘no analog’ conditions have historically affected less than 1% of the US by area. By mid-21st century (2036–2065) under both emissions scenarios, the annual numbers of days with heat indices exceeding 37.8 °C (100 °F) and 40.6 °C (105 °F) are projected to double and triple, respectively, compared to a 1971–2000 baseline. In this timeframe, more than 25% of the US by area would experience no analog conditions an average of once or more annually and the mean duration of the longest extreme heat index event in an average year would be approximately double that of the historical baseline. By late century (2070–2099) with a high emissions scenario, there are four-fold and eight-fold increases from late 20th century conditions in the annual numbers of days with heat indices exceeding 37.8 °C and 40.6 °C, respectively; 63% of the country would experience no analog conditions once or more annually; and extreme heat index events exceeding 37.8 °C would nearly triple in length. These changes amount to four- to 20-fold increases in population exposure from 107 million person-days per year with a heat index above 37.8 °C historically to as high as 2 billion by late century. The frequency of and population exposure to these extreme heat index conditions with the high emissions scenario is roughly twice that of the lower emissions scenario by late century.
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Open access
Regional characteristics of flash droughts across the United States
Jordan I Christian
et al
2019
Environ. Res. Commun.
125004
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, Regional characteristics of flash droughts across the United States
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, Regional characteristics of flash droughts across the United States
Rapid intensification toward drought, also known as flash drought, is a subseasonal feature of the climate system whereby the persistence of extreme atmospheric anomalies for several weeks can quickly deplete soil moisture and dramatically increase evaporative stress on the environment. These events can lead to significant impacts on agricultural production during the growing season. This study performs a climatological regional analysis across the United States to explore geographic differences that exist in the rapid onset and development of drought. The Standardized Evaporative Stress Ratio (SESR) is applied to a reanalysis dataset to quantify regional flash drought characteristics across nine climate regions in the United States. May and June had a higher frequency of flash drought events in the western United States, while a climatological peak in flash drought frequency was found in July and August for the eastern United States. For all climate regions, flash drought intensity was found to increase throughout the beginning of the growing season, then decrease in the latter portion of the growing season. Analysis of preceding moisture conditions revealed that antecedent dry conditions increased flash drought risk for all regions. Lastly, less than half of all flash droughts persisted to hydrological drought across the United States.
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Open access
Rice leaf disease detection based on bidirectional feature attention pyramid network with YOLO v5 model
V Senthil Kumar
et al
2023
Environ. Res. Commun.
065014
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, Rice leaf disease detection based on bidirectional feature attention pyramid network with YOLO v5 model
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, Rice leaf disease detection based on bidirectional feature attention pyramid network with YOLO v5 model
To ensure higher quality, capacity, and production of rice, it is vital to diagnose rice leaf disease in its early stage in order to decrease the usage of pesticides in agriculture which in turn avoids environmental damage. Hence, this article presents a Multi-scale YOLO v5 detection network to detect and classify the rice crop disease in its early stage. The experiment is initially started by pre-processing the rice leaf images obtained from the RLD dataset, after which data set labels are created, which are then divided into train and test sets. DenseNet-201 is used as the backbone network and depth-aware instance segmentation is used to segment the different regions of rice leaf. Moreover, the proposed Bidirectional Feature Attention Pyramid Network (Bi-FAPN) is used for extracting the features from the segmented image and also enhances the detection of diseases with different scales. Furthermore, the feature maps are identified in the detection head, where the anchor boxes are then applied to the output feature maps to produce the final output vectors by the YOLO v5 network. The subset of channels or filters is pruned from different layers of deep neural network models through the principled pruning approach without affecting the full framework performance. The experiments are conducted with RLD dataset with different existing networks to verify the generalization ability of the proposed model. The effectiveness of the network is evaluated based on various parameters in terms of average precision, accuracy, average recall, IoU, inference time, and F1 score, which are achieved at 82.8, 94.87, 75.81, 0.71, 0.017, and 92.45 respectively.
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2018-present
Environmental Research Communications
doi: 10.1088/issn.2515-7620
Online ISSN: 2515-7620