Explore the vast range of titles available.

Coastal ecosystems are increasingly experiencing anthropogenic pressures such as climate warming, CO 2 increase, metal and organic pollution, overfishing, and resource extraction. Some resulting stressors are more direct like pollution and fisheries, and others more indirect like ocean acidification, yet they jointly affect marine biota, communities, and entire ecosystems. While single-stressor effects have been widely investigated, the interactive effects of multiple stressors on ecosystems are less researched. In this study, we review the literature on multiple stressors and their interactive effects in coastal environments across organisms. We classify the interactions into three categories: synergistic, additive, and antagonistic. We found phytoplankton and bivalves to be the most studied taxonomic groups. Climate warming is identified as the most dominant stressor which, in combination, with other stressors such as ocean acidification, eutrophication, and metal pollution exacerbate adverse effects on physiological traits such as growth rate, fitness, basal respiration, and size. Phytoplankton appears to be most sensitive to interactions between warming, metal and nutrient pollution. In warm and nutrient-enriched environments, the presence of metals considerably affects the uptake of nutrients, and increases respiration costs and toxin production in phytoplankton. For bivalves, warming and low pH are the most lethal stressors. The combined effect of heat stress and ocean acidification leads to decreased growth rate, shell size, and acid-base regulation capacity in bivalves. However, for a holistic understanding of how coastal food webs will evolve with ongoing changes, we suggest more research on ecosystem-level responses. This can be achieved by combining in-situ observations from controlled environments (e.g. mesocosm experiments) with modelling approaches.

Ambient air pollution is the most important environmental factor impacting human health. Urban landscapes present unique air quality challenges, which are compounded by climate change adaptation challenges, as air pollutants can also be affected by the urban heat island effect, amplifying the deleterious effects on health. Nature-based solutions have shown potential for alleviating environmental stressors, including air pollution and heat wave abatement. However, such solutions must be designed in order to maximize mitigation and not inadvertently increase pollutant exposure. This study aims to demonstrate potential applications of nature-based solutions in urban environments for climate stressors and air pollution mitigation by analyzing two distinct scenarios with and without green infrastructure. Utilizing low-cost sensors, we examine the relationship between green infrastructure and a series of environmental parameters. While previous studies have investigated green infrastructure and air quality mitigation, our study employs low-cost sensors in tropical urban environments. Through this novel approach, we are able to obtain highly localized data that demonstrates this mitigating relationship. In this study, as a part of the NERC-FAPESP-funded GreenCities project, four low-cost sensors were validated through laboratory testing and then deployed in two locations in São Paulo, Brazil: one large, heavily forested park (CIENTEC) and one small park surrounded by densely built areas (FSP). At each site, one sensor was located in a vegetated area (Park sensor) and one near the roadside (Road sensor). The locations selected allow for a comparison of built versus green and blue areas. Lidar data were used to characterize the profile of each site based on surrounding vegetation and building area. Distance and class of the closest roadways were also measured for each sensor location. These profiles are analyzed against the data obtained through the low-cost sensors, considering both meteorological (temperature, humidity and pressure) and particulate matter (PM1, PM2.5 and PM10) parameters. Particulate matter concentrations were lower for the sensors located within the forest site. At both sites, the road sensors showed higher concentrations during the daytime period. These results further reinforce the capabilities of green–blue–gray infrastructure (GBGI) tools to reduce exposure to air pollution and climate stressors, while also showing the importance of their design to ensure maximum benefits. The findings can inform decision-makers in designing more resilient cities, especially in low-and middle-income settings.

The operation of modern power networks is increasingly exposed to overlapping climate extremes and volatile system conditions, making it essential to adopt scheduling approaches that are resilient as well as economical. In this study, a two-stage stochastic formulation is advanced, where indicators of system adaptability are embedded directly into the optimization process. The objective integrates standard operating expenses—generation, reserve allocation, imports, responsive demand, and fuel resources—with a Conditional Value-at-Risk component that reflects exposure to rare but damaging contingencies, such as extreme heat, severe cold, drought-related hydro scarcity, solar output suppression from wildfire smoke, and supply chain interruptions. Key adaptability dimensions, including storage cycling depth, activation speed of demand response, and resource ramping behavior, are modeled through nonlinear operational constraints. A stylized test system of 30 interconnected areas with a 46 GW demand peak is employed, with more than 2000 climate-informed scenarios compressed to 240 using distribution-preserving reduction techniques. The results indicate that incorporating risk-sensitive policies reduces expected unserved demand by more than 80% during compound disruptions, while the increase in cost remains within 12–15% of baseline planning. Pronounced spatiotemporal differences emerge: evening reserve margins fall below 6% without adaptability provisions, yet risk-adjusted scheduling sustains 10–12% margins. Transmission utilization curves further show that CVaR-based dispatch prevents extreme flows, though modest renewable curtailment arises in outer zones. Moreover, adaptability provisions promote shallower storage cycles, maintain an emergency reserve of 2–3 GWh, and accelerate the mobilization of demand-side response by over 25 min in high-stress cases. These findings confirm that combining stochastic uncertainty modeling with explicit adaptability metrics yields measurable gains in reliability, providing a structured direction for resilient system design under escalating multi-hazard risks.

The longevity of polymer-based sealant and jointing products, including elastomers, significantly depends on the level of exposure to sunlight and joint movement. These factors are particularly crucial in the application of polymers in construction due to their susceptibility to degradation under environmental conditions. For instance, diurnal cycles of contraction and dilation, arising from daily temperature fluctuations, impose significant stress on sealants and joints, impacting their durability over time. The elastic nature of polymeric sealants enables them to endure these cyclic mechanical loads. Athough there is considerable information on sealant durability obtained from laboratory accelerated aging, there is limited knowledge about the effect of climatic factors using historical and projected weather data on the durability and expected service life of these products. This study employed the Shephard crack growth model to predict the performance of sealants in a Canadian context; the crack growth and time-to-failure of hypothetical silicone sealants were investigated across 564 locations, for which historical climate data were obtained from 1998 to 2017, including gridded reanalysis data for the period of 1836–2015. The historical climate data were classified into four climate categories, and crack growth was estimated based on historical climatic data within the valid range for the Shephard model, revealing that locations in colder climates with lower levels of precipitation typically exhibit higher cumulative crack growth. The impact of climatic variation and environmental stressors on the longevity of sealants in the context of climate change was also investigated using future projected data.

IntroductionCoastal ecosystems are vital blue carbon sinks that are increasingly threatened by climate change. Their vulnerability and sensitivity are strongly influenced by ecological structures and local conditions.MethodsUsing the Ecopath model, we evaluated the responses of three bays in Fujian, China (Sansha Bay, Fuqing Bay, and Xinghua Bay) under scenarios of increased precipitation, ocean deoxygenation, and warming. The analysis focused on how differences in food web structures shape ecosystem responses to climate pressures and determine their sensitivity and vulnerability.ResultsThe results revealed differentiated response patterns dictated by baseline food web characteristics, including constant, linear, and non-linear threshold collapse, in which the baseline ecological structure of a bay dictates its degree of vulnerability. Fuqing Bay, despite having the lowest total system throughput (2405 t/km²/year), showed the highest resilience and bivalve ecological carrying capacity (22.80 t/km²). In contrast, Xinghua Bay, a high biomass system (Total Biomass: 39.93 t/km²), exhibited the highest sensitivity, with its food web structure collapsing even under low deoxygenation stress (shrimp EE > 1). Under severe warming, bivalve ecological carrying capacity declined linearly by up to 50% across all the bays, with absolute losses being the greatest in the most productive systems.DiscussionOur findings underscore the critical role of baseline ecosystem structure in shaping divergent climate responses and provide a scientific basis for site-specific adaptive management and blue carbon conservation strategies.

Increasing climate variability and change coupled with steady population growth is threatening water resources and livelihoods of communities living in the Wami-Ruvu and Rufiji basins in Tanzania. These basins are host to three large urban centers, namely Dar es Salaam, Dodoma and Morogoro, with a combined total of more than 7 million people. Increased demand for ecosystem services from the available surface water resources and a decreasing supply of clean and safe water are exacerbating the vulnerability of communities in these basins. Several studies have analyzed climate projects in the two basins but little attention has been paid to identify locations that have vulnerable communities in a spatially-explicit form. To address this gap, we worked with stakeholders from national and local government agencies, basin water boards and the Water Resources Integration Development Initiative (WARIDI) project funded by USAID to map the vulnerability of communities to climate variability and change in the two basins. A generalized methodology for mapping social vulnerability to climate change was used to integrate biophysical and socioeconomic indicators of exposure, sensitivity and adaptive capacity and produced climate vulnerability index maps. Our analysis identified vulnerability “hotspots” where communities are at a greater risk from climate stressors. The results from this study were used to identify priority sites and adaptation measures for the implementation of resilience building interventions and to train local government agencies and communities on climate change adaptation measures in the two basins.

醫療機構所建立相關的安全政策與制度，讓醫療人員感受到組織安全的相關作為，即是醫院安全風氣，其可以直接影響醫療人員整體工作表現，及間接地影響病人的照護品質，並進一步影響職業危害的發生狀況。醫療職場常見的職業危害，包含傳染性疾病、過勞高風險、輪班造成的晝夜節律、睡眠型態與飲食習慣改變、肌肉骨骼不適、職場暴力、職場壓力及針扎等；本文即是分享臺灣在針扎防治的推動歷程，如何運用實證研究結果的科學證據，倡議及提供政策制定的重要佐證及說帖，進而提升醫院安全風氣與職業危害事件降低之互相影響與改善的過程；未來應鼓勵透過更多的科學研究投入，針對職場壓力源的了解與改善，提供政府主管部門及醫療機構制定政策之參考，以創造安全風氣高的護理執業環境及提升護理人員對執業環境安全的整體知覺，共創合宜、友善的醫療執業環境。