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.

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.

Protests are frequently used to raise public awareness of environmental challenges and increase support for pro-environmental behavior and policy. In this paper we examine the influence of protests on environmental attitudes, focusing on Extinction Rebellion's April 2019 campaign of civil disobedience in the UK. Using individual-level survey data collected around the time of the protest, we exploit its exogeneity to the timing of survey response, to compare attitudes towards sustainable lifestyles, perceptions of own environmental impact, support for pro-environmental policy and behavior, and views about the severity and imminence of environmental crises, before and after the protest. There is evidence that the protest is related to lower probability of opposing pro-environmental behavior and policy, and lower willingness to pay a premium for environmentally friendly consumption. We do not find evidence that the protest alienated the public from sustainable lifestyles, influenced perceptions of personal environmental impact, or views about the imminence and severity of environmental crises. Results suggest the need for systematic study of the impact of environmental protests on the general public's environmental attitudes and behaviors.

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.

Cool pavements represent one of several strategies that can mitigate the effects of urban overheating by increasing albedo. By definition, this means increasing reflected and potentially re-absorbed short-wave radiation but also decreased surface and air temperatures and longwave upwelling, thus reducing radiant temperatures. So far, real-world studies have been inconclusive as to net effects from cool pavements. A project by GAF installed reflective pavements in Pacoima, California, in summer of 2022. This study set out to perform detailed, high spatiotemporal resolution, multi-platform observations to quantify micrometeorological benefits of the cool pavements and address concerns regarding glare, chemistry/air quality, and pedestrian thermal comfort. Results indicated large variability, as expected, but that the dominant effects were beneficial both in direct side-by-side, real-time comparisons (RT) between test and reference areas, as well as in difference-of-difference (DofD) to quantify local changes in test areas. During a heatwave in September 2022, maximum air-temperature differences (averaged over individual street segments) reached up to −1.9 °C RT in the afternoon. During non-heatwave, hot summer days, the largest street-segment-averaged afternoon air-temperature differences reached up to −1.4 °C RT or −2.8 °C DofD, and surface temperature up to −9.2 °C RT or −12.2 °C DofD. Whereas above values represent maximum effects, more typical street-segment averages also showed statistically significant benefits. In the afternoon, the mean of air-temperature differences was −0.2 °C RT and −1.2 °C DofD. The mean of surface-temperature differences was −2.6 °C RT and −4.9 °C DofD. Indicators of pedestrian thermal comfort also showed variability but predominantly a cooling effect. The mean of differences in mean radiant temperature was between −0.9 and −1.3 °C RT, and for physiological equivalent temperature, between −0.2 °C and −0.6 °C RT and −1.7 °C DofD. In terms of predicted mean vote, the mean of differences was −0.09 RT and −0.32 DofD.

Waste separation at source has been proved to be an effective way to reduce the amount of municipal solid waste (MSW) which has become a major challenge to China's ecological environment. However, waste source separation requires effort from each individual citizen. As the important drivers of change and potential influencers of the future world, younger Chinese's waste separation behaviour is crucial to the long-term successful implementation of China's MSW separation policy. To explore the waste separation behaviour of younger Chinese and identify the factors that may influence their behaviour so as to better encourage younger generation of Chinese to practice waste sorting in their daily lives, a questionnaire survey of 579 primary and middle school (PMS) students aged between 6 and18 years old (y/o) was carried out in Yingtan City, Jiangxi Province, China. Binary logistic regression was adopted to explore the factors that might influence the respondents' waste separation behaviour. The results indicate that more than half PMS students in Yingtan have participated in waste separation, and junior year students perform better in waste separation practice than their seniors. Students are found to have basic knowledge of MSW classification, but they are more familiar with recyclable waste and hazardous waste than non-recyclable waste. The analysis also highlights positive relationships between PMS students' attitude to waste separation, their willingness to do it, their environmental education and their waste separation behaviour. The level of convenience of waste sorting facilities and influences from friends and families are also positively related to the students' waste separation practice, but families have the strongest influence. The perception of a mandatory waste separation policy would demotivate students in terms of waste separation practice, while giving rewards is considered to be the most effective approach to encouraging waste separation. Finally, management strategies for improving PMS students' waste separation behaviour are discussed and several recommendations for improvement are made.

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.

In the current study, bacteria from agricultural soil systems that have been polluted with pesticides were isolated, identified, and their ability to tolerate pesticides was examined. Target bacterial species were isolated from Psidium guajava (L) and Abelmoschus esculentus (L) cultivating an agriculture field. From 10 distinct soil samples collected from an agricultural field, 27 bacterial species were extracted, and the capacity of these microorganisms to withstand pesticides was examined. Only three bacterial species (PRB-S1P2, PRB-S1P3, and PRB-S6P1) are capable to grow on Nutrient agar medium with different concentration of pesticides dimethoate, Thiamethoxam and Imidacloprid. Apart from these three, one bacterial species were highly tolerant to all test pesticides. The highest pesticide tolerant bacteria are Pseudomonas nitroreducens was identified through 16s rRNA sequencing and the sequences were submitted to the NCBI with the accession No: ON624333.1. Hence, the bacteria can be subjected to further study of its use in the field of bioremediation.

This paper comprehensively searched all the literature on the subject of 'land pollution' through the core collection of the Web of Science database, and systematically processed the research literature from 1944 to 2021 using CiteSpace software, and carried out bibliometric analysis and visual presentation, which uncovers the LP research dynamics in detail, and draw the following conclusions: First, through the indicator of betweenness centrality, the basic authors and journals of the subject are obtained; from the perspective of publishing institutions and affiliated countries, the United States is an important research center for LP. Second, keywords such as 'land use', 'air pollution', 'impact', 'soil pollution' and 'management' are all high-frequency words. The results of keyword clustering and co-citation information in the literature indicate the natural-social dimensions of LP research, such as the use and quality of air, land, and water, as well as urbanization and environmental policies. However, challenges remain and current LP studies are still characterized by a certain degree of fragmentation, which should be enriched by combining land use changes and should require combining experimental results with socioeconomic analysis to propose joint LP remediation approaches. Finally, local and regional forces may strongly influence the LP process, and the drivers of globalization should be emphasized.

This study evaluates and provides guidance on improving the life cycle environmental performance of dishwashing in the typical U.S. household. Typical user behaviors and recommended best practices for manual dishwashing as well as machine dishwasher use are evaluated. A sensitivity analysis shows the influence of varying grid carbon intensity, water heater type, regional water scarcity, and behaviors such as pre-rinsing and machine loading on overall results. Use-phase behaviors are observed through a small-scale laboratory study. Dishwashing following typical manual and machine practices produces 5,620 and 2,090 kg CO₂e life cycle greenhouse gas (GHG) emissions respectively based on washing 4 loads (8 place settings per load) a week for 10 years. Avoiding typical behaviors like pre-rinsing and selecting heated dry can decrease life cycle GHG emissions for machine dishwashing by 3% and 11%, respectively. The running tap style of manual dishwashing results in the highest life cycle GHG emissions of the alternatives in the lab study. Manual dishwashing has the potential to have the lowest GHG emmisions (1,610 kg) when recommended behaviors are followed, less than the 1,960 kg CO₂e for recommended machine dishwasher use. When life cycle water consumption burdens are evaluated, typical manual and machine dishwashing use 34,200 and 16,300 gallons respectively and these results are contextualized to regions with different water scarcity. A life cycle cost (LCC) analysis finds that machine dishwashing costs more than manual dishwashing over a 10-year lifetime even if best practices are followed. However, when a user's time spent washing is valued, machine dishwashers pay for themselves within a year of use.

Estimating smallholder crop yields robustly and timely is crucial for improving agronomic practices, determining yield gaps, guiding investment, and policymaking to ensure food security. However, there is poor estimation of yield for most smallholders due to lack of technology, and field scale data, particularly in Egypt. Automated machine learning (AutoML) can be used to automate the machine learning workflow, including automatic training and optimization of multiple models within a user-specified time frame, but it has less attention so far. Here, we combined extensive field survey yield across wheat cultivated area in Egypt with diverse dataset of remote sensing, soil, and weather to predict field-level wheat yield using 22 Ml models in AutoML. The models showed robust accuracies for yield predictions, recording Willmott degree of agreement, (d > 0.80) with higher accuracy when super learner (stacked ensemble) was used (R² = 0.51, d = 0.82). The trained AutoML was deployed to predict yield using remote sensing (RS) vegetative indices (VIs), demonstrating a good correlation with actual yield (R² = 0.7). This is very important since it is considered a low-cost tool and could be used to explore early yield predictions. Since climate change has negative impacts on agricultural production and food security with some uncertainties, AutoML was deployed to predict wheat yield under recent climate scenarios from the Coupled Model Intercomparison Project Phase 6 (CMIP6). These scenarios included single downscaled General Circulation Model (GCM) as CanESM5 and two shared socioeconomic pathways (SSPs) as SSP2-4.5and SSP5-8.5during the mid-term period (2050). The stacked ensemble model displayed declines in yield of 21% and 5% under SSP5-8.5 and SSP2-4.5 respectively during mid-century, with higher uncertainty under the highest emission scenario (SSP5-8.5). The developed approach could be used as a rapid, accurate and low-cost method to predict yield for stakeholder farms all over the world where ground data is scarce.

Phasing out fossil fuels is inherent to sustainable energy transitions, but implementing energy policies related to phasing out processes involves risks that may affect their public support. Trust in institutions responsible for handling these risks is crucial for public acceptability, as it serves as a heuristic for risk assessment. In the current study, using the Dutch energy scenario, we examine how trust in institutions relates to public support for phasing out natural gas in the Netherlands. We build from previous research by examining this for the two types of trust most commonly distinguished in the literature, namely competence- and integrity-based trust, and for institutions operating at both national and local levels. Results showed that trust depends on the type of trust people evaluate and on the institution's level of operation. Locally, institutions are seen as more honest and transparent, while nationally, they're perceived as more skilled and having more knowledge. Further, integrity-based trust in both local and national institutions better explained public support for phasing out natural gas than competence-based trust. We discuss these results in terms of their implications for energy policy, suggesting policymakers consider trust dynamics and tailor strategies based on trust dimensions and institutional levels to facilitate phasing out processes.

Land-surface phenology is a widely used indicator of how terrestrial ecosystems respond to environmental change. The spatial variability of this plant functional trait has also been advocated as an indicator of the functional composition of ecosystems. However, a global-scale assessment of spatial patterns in the spatial heterogeneity of forest phenology is currently lacking. To address this knowledge gap, we developed an index based on satellite retrievals and used it to quantify phenological diversity across global forest biomes. We show that there is considerable variation in phenological diversity among biomes, with the highest overall levels occurring in arid and temperate regions. An analysis of the drivers of the spatial patterns revealed that temperature-related factors primarily determine the variation in phenological diversity. Notably, temperature seasonality and mean annual temperature emerged as the most significant variables in explaining this global-scale variability. Furthermore, an assessment of temporal changes over an 18-year period revealed strong climate-driven shifts of phenological diversity in boreal and arid regions, suggesting that there may be an ongoing widespread homogenisation of land surface phenology within forest ecosystems. Our findings ultimately contribute to the development of a novel Essential Biodiversity Variable, which may enable scientists and practitioners to quantify the functional composition of ecosystems at unprecedented spatial and temporal scales.

Soil salinity and sodicity are the major environmental issues that lead to the deterioration of soil properties, nutrient cycling, and soil ecosystems around the globe. Nevertheless, the reciprocal effects of salinity and sodicity levels on depth-wise soil organic matter (SOM) and micronutrients remain elusive, particularly in Thailand. For a better understanding of such an issue, soil samples were collected from 38 sites at depths of 0–20, 30–50, 60–80, and 80–120 cm where they were affected by salts with variable levels of salinity and sodicity, having electrical conductivity (EC_e), and exchangeable sodium percentage (ESP) from 0.20–74.70 dS m^–1, and 2.74%–113.23%, respectively. Soil physicochemical properties, including distribution of sand, silt, and clay, pH, soil organic carbon (SOC), and micronutrients (Fe, Zn, Mn, Cu, and B) were determined. The results exhibited that SOC content, ranging from 3.36–14.74 g kg^–1, was higher in topsoil (0–20 cm) compared to the other three soil depths and it correlated negatively with EC_e (0–20 and 80–120 cm) and ESP (80–120 cm), suggesting the declines in SOC amount due to high salinity and sodicity levels. Topsoil Mn concentration (0.06–182.06 mg kg^–1) also tended to be greater than the other soil depths while Fe concentration in that soil depth (0.02–33.99 mg kg^–1) tended to be smaller. The EC_e correlated negatively with the concentrations of Fe, Cu (all soil depths), and Zn (30–50 and 60–80 cm), and positively with Mn concentration (60–80 and 80–120 cm), suggesting that the availability of Fe Cu and Zn is vulnerable to high salinity and sodicity levels. Overall, our findings highlight that high salinity and sodicity levels brought about a reduction in SOC content and low concentrations of micronutrients in soils, irrespective of Mn concentration.

Plastic waste management is a critical concern in municipal solid waste management systems worldwide. Despite the efforts of waste management personnel to segregate waste manually, the existing challenges persist. In municipal waste facilities, individuals responsible for waste segregation face numerous obstacles. Consequently, a significant amount of plastic waste ends up in landfills, exacerbating the plastic waste problem. To overcome these challenges, this research focuses on developing an automated system capable of categorizing plastic waste based on its visual characteristics. The trained model exhibits high precision in identifying various types of plastic waste, including PET, HDPE, PVC, LDPE, PP, and PS. Specifically, the model achieves an Average Precision of 0.917 and an Average Recall of 0.801. Moreover, the model maintains a good balance between precision and recall. In real-time operation, an overhead camera locates the positions of both the waste items and the gripper. By calculating the positional difference between the waste and the gripper, the system achieves a higher level of segregation accuracy, resembling human-like hand-eye coordination. The proposed system offers a solution to the challenges faced in MSW facilities, where the timely segregation of waste is crucial. By automating the plastic waste categorization process, the system can significantly improve waste management practices, leading to a more sustainable approach to plastic waste disposal and recycling.

Indonesia is renowned as an agricultural powerhouse, ranking first globally in oil palm production. This prominence in agriculture leads to the consistent generation of agro-industrial waste, notably Palm Oil Mill Effluent (POME). Effectively addressing these waste concerns is important due to their adverse impacts on aquatic ecosystems and the nation's health and economy. Anthropogenic wastewater with excessive phosphorus content can trigger eutrophication and toxic algal blooms, posing environmental risks and potentially precipitating a future clean water crisis. Thus, a comprehensive approach is necessary to restore the environment and biogeochemical cycles. Treatment efforts involving bioremediation agents aim to recycle organic and inorganic pollutants in the environment. Photosynthetic organisms like plants and microalgae serve as effective bioremediation agents, capable of absorbing excess phosphorus. They can utilize phosphate as an energy source to boost biomass. Integrating these bioremediation agents with bioengineering technology optimizes the treatment efficacy while simultaneously producing valuable biomass for products and bioenergy. This review article explores photosynthetic organisms' multifunctional role as phosphorus bioremediation agents for wastewater treatment, minimizing environmental pollutant impacts, and providing biomass for fertilizers, polymers, bioplastics, and renewable energy. Furthermore, this study unveils opportunities for future technological advancements in this field.

Climate change adaptation research is currently a policy priority. For smallholder farmers, it provides opportunities for resilience building. The research area is growing rapidly and calls to synthesize existing data have been made. Existing work forms a basic picture of the trends in the research area. However, it is limited in scope and methodological approaches used. This work synthesizes climate change adaptation research on smallholder farmers in Southern Africa. It gives an overview of past and current directions of climate change adaptation research using a combination of bibliometric analysis techniques and the Preferred Reporting Items for Systematic Reviews and Meta-Analysis. Results show a steady growth in research, a disproportionate distribution of research and weak research collaboration among Southern African countries. Diverse methodologies are in use but a combination of approaches is rare. Co-occurrence of keywords show recognizable strides in research ranging from adaptation to mitigation linkages to the influence of climate change impacts on adaptation and livelihood outcomes. Strengthened research collaboration between countries in Southern Africa should be advocated for. This would help develop viable, appropriate and localized adaptation solutions. Equitable allocation of funding is pertinent to ensure uniform research activity and adaptation action across the region. A combination of research approaches is needed to push forward adaptation research on smallholder farmers in Southern Africa.

Research on methane (CH₄) emissions from the oil and gas (O&G) industry informs policies, regulations, and international initiatives that target reductions. However, there has been little integration and synthesis of the literature to document the state of knowledge, identify gaps, and determine key insights that can guide research priorities and mitigation. To address this, we performed a scoping review of 237 English-language peer-reviewed articles on CH₄ emissions from onshore O&G sources, charting data on five research themes: publication trends, geography, measurement levels and methods, emissions sources, and emissions rates. Almost all articles (98%) were published between 2012 and 2022 with an increasing publication rate, indicating a nascent and evolving understanding of the science. Most articles (72%) focused on CH₄ emissions from the U.S. O&G industry and were written by U.S.-based authors (69%), while other major O&G-producing countries like Saudi Arabia, Russia, and China were under-represented. Upstream was the most frequently studied supply chain segment, where U.S.-focused articles accounted for 75% of the research. Nearly half the articles (43%) included in the review reported site-level measurements, limiting the identification of equipment- and component-level emissions sources and root cause. Articles that measured or identified equipment-level sources (18%) noted high emissions from tanks, unlit flares, and compressors. The most common stand-off measurement platforms were vehicles and aircraft, while the use of satellites increased in articles published since 2019. Reported emissions profiles were consistently heavy-tailed and indicate method-based and geographic differences in magnitude and skew. All articles (n = 26) that compared inventory- to measurement-based estimates of emissions found large discrepancies in that inventories under-estimated the latter by a factor of 1.2–10 times. We recommend future research focus on: (i) field-based emissions studies for under-represented regions and source categories, (ii) identifying root causes and linking measurements to mitigation, and (iii) multi-level measurement integration.

Although traffic-related air pollution (TRAP) has been a long-standing problem, few bibliometric- and visual analysis-based literature reviews have been performed. In light of this issue, future research plans and directions in the field of TRAP must be determined. Therefore, this study performed a bibliometric analysis of the TRAP publishing trends, including the countries, institutional collaborations, author collaborations, keywords, and hotspots. The information visualization software CiteSpace was used to analyze the relevant literature collected from the Web of Science (WoS) from 2003 to 2022. The main findings of this study included the following: (1) the main keywords in TRAP research are particulate matter, exposure, health, nitrogen dioxide, and mortality; (2) current research is focused on the impacts of TRAP on humans; and (3) potential hotspots for future TRAP research are source apportionment, asthma, heart rate variability, and mobile monitoring. This article aims to develop a better understanding of current research trends in TRAP and provide directions for future research.

Extreme weather events linked to climate change are becoming more frequent. The online public discourse on and during these events, especially on social media, attracts misinformation that can undermine short-term emergency responses, but can also be aimed at influencing long-term public perceptions of climate change. This contribution reviews existing research on online misinformation with the aim to understand the types, origins, and potential impacts of misinformation during extreme weather events like storms, floods, and wildfires. The screening of 289 publications reveals that there is scarce body of only 13 studies addressing this question. Relevant studies exploring online misinformation during extreme weather events rarely document misinformation immediately relevant for emergency responses and only recently link this to the discussion about climate change. The reviewed research provides however insights to derive a framework that can guide future research into this topic. Specifically, that misinformation in social media during environmental emergencies 1) cuts across domains and merges different areas of public interest, 2) cuts across temporal and geographical scales, and 3) needs to be studied as part of an interconnected online media landscape. Misinformation differs between emergency event types, can undermine the debate about climate change in diverse ways, appeal to completely different audiences and thus will likely require different responses and countermeasures. Structured research with comparable methodologies is urgently needed.