IPSC-Derived Bronchial Airways-On-Chip for the Assessment of Cytokine Secretion Triggered by Volatile Organic Compounds.

Previous studies have shown that climate change has significant cumulative effects on vegetation growth. However, there remains a gap in understanding the characteristics of cumulative climatic effects on different vegetation types and the underlying driving mechanisms. In this study, using the normalized difference vegetation index data from 1982 to 2015, along with accumulated temperature, precipitation, and solar radiation data, we quantitatively investigated the intra-annual cumulative effects of climatic factors on global vegetation biomes across climatic zones. We also explored the underlying driving mechanisms. The results indicate that precipitation has a longer intra-annual cumulative effect on vegetation, with effects lasting up to 12 months for large percentages of most vegetation biomes. The cumulative effect of solar radiation is mostly concentrated within 0–6 months. Temperature has a shorter cumulative effect, with no significant cumulative effect of temperature on large percentages of tree-type vegetation. Compared to other vegetation types, evergreen broadleaf forests, close shrublands, open shrublands, savannas, and woody savannas exhibit more complex cumulative climatic effects. Each vegetation type shows a weak-to-moderate correlation with accumulated precipitation while exhibiting strong-to-extremely-strong positive correlations with accumulated temperature and accumulated solar radiation. The climate-induced regulations of water, heat, and nutrient, as well as the intrinsic mechanisms of vegetation’s tolerance, resistance, and adaptation to climate change, account for the significant heterogeneity of cumulative climatic effects across vegetation biomes in different climatic zones. This study contributes to enriching the theoretical understanding of the relationship between vegetation growth and climate change. It also offers crucial theoretical support for developing climate change adaptation strategies and improving future “vegetation-climate” models.

Cumulative effects of ambient particulate matter pollution on deaths: A multicity analysis of mortality displacement

Vegetation is one of the most important indicators of climate change, as it can show regional change in the environment. Vegetation health is affected by various factors, including drought, which has cumulative and time-lag effects on vegetation response. However, the cumulative and time-lag effects of drought on different terrestrial vegetation in China are still unclear. To address this issue, this study examined the cumulative and time-lag effects of drought on vegetation from 2001 to 2020 using the Standardized Precipitation Evapotranspiration Index (SPEI) in the Global SPEI database and the Normalized Difference Vegetation Index (NDVI) in MOD13A3. Based on Sen-Median trend analysis and the Mann–Kendall test, the change trend and significance of the NDVI from 2001 to 2020 were explored. The Pearson correlation coefficient was used to analyze the correlation between the SPEI and NDVI at each cumulative scale and time-lag scale and to further analyze the cumulative and time-lag effects of drought on vegetation. The results show the following: (1) The NDVI value increased at a rate of 0.019/10 years, and the increased area of the NDVI accounted for 80.53% of mainland China, with a spatial trend of low values in the west and high values in the east. (2) The average SPEI cumulative time scale most relevant to the NDVI was 7.3 months, and the cumulative effect demonstrated a high correlation at the scale of 9–12 months and revealed different distributions in different areas. The cumulative effect was widely distributed at the 9-month scale, followed by the 12-month scale. The correlation coefficients of cumulative effects between the SPEI and NDVI for cropland, woodland and grassland peaked at 9 months. (3) The average SPEI time-lag scale for the NDVI was 6.9 months, and the time-lag effect had the highest correlation coefficient at the 7-month scale. The strongest time-lag effect for cropland and grassland was seen at 7 months, while the strongest time-lag effect for woodland was seen at 6 months. Woodland had a lower time-lag effect than grassland at different scales. The research results are significant for their use in aiding the scientific response to drought disasters and making decisions for climate change precautions.

Satellite monitoring reveals short-term cumulative and time-lag effect of drought and heat on autumn photosynthetic phenology in subtropical vegetation

A hypothetical example of multiple hydroelectric development is used to demonstrate the applicability of the integrated tabular methodology (ITM) that was recommended for cumulative effects assessment in Volume 1. The example consists of an existing mainstem dam and four proposed small hydroelectric developments in a small river basin containing elk summer and winter range and chinook salmon spawning areas. Single-project impact assessments are used collectively in the methodology to estimate the cumulative effects of the projects on elk and salmon. The steps in cumulative assessment are (1) establishing a conceptual and schematic representation of each cumulative effect, (2) calculating interaction coefficients for each pair of projects, (3) developing interaction and impact matrices, (4) multiplying these matrices, (5) evaluating the contribution of shared project features to cumulative effects, and (6) incorporating the effects of already existing projects. The ITM is most effective when single-project assessments are accomplished in a detailed, quantitative manner. The methodology involves the use of impact response curves (such as fry emergence as a function of fine sediment or habitat suitability as a function of road density) for each impact being assessed. 14 refs., 15 figs., 16 tabs.

Comparison of the immediate, residual, and cumulative antibacterial effects of Novaderm R, Novascrub R, Betadine Surgical Scrub, Hibiclens, and liquid soap

Increased frequency and intensity of droughts under climate change will have a significant impact on land surface phenology, however, the drought-phenology interactions that are associated with complex temporal effects are not well understood. This study examined the response of land surface phenology to drought cumulative and time-lag effects by using the standardized precipitation and evapotranspiration index (SPEI) and explored the influence of hydrothermal and plant physiological factors on the phenology-drought relationship. We used the maximum correlation (rmax-cml ) between phenology and cumulative SPEI (1- to 12-month) to determine the cumulative effect. The maximum correlation (rmax-lag ) between phenology and lagged SPEI (1-month) was utilized to analyze the time-lag effect. Overall, the cumulative effect affected 25.36% of the vegetated area at the start of the growing season (SOS), 26.43% of the end of the growing season (EOS), and 26.57% of the length of the growing season (LOS). SOS was negatively affected by long-term SPEI, whereas EOS and LOS had positive correlations with short-term SPEI. The rmax-cml for shrubland was the largest, and the SOS and LOS response time scales of the forest were the shortest. The rmax-cml was larger in arid and semi-arid (AR and SAR) than in humid and semi-humid (HU and SHU). Meanwhile, the response time scales were longer in HU and SHU than in AR and SAR. The time-lag effect had a larger area of impact on land surface phenology than the cumulative effect, the areas were 46.12%, 47.93%, and 50.45% for SOS, EOS, and LOS, respectively, and the lagged time scales were longer. The phenology-SPEI correlation was dominantly driven by hydrological conditions, and the time scales were mainly affected by thermal factors. Moreover, the onset of phenology and the growth/senescence rate of plants influenced the relationship, suggesting that hydrothermal conditions may shift the time of phenology by regulating the growth/senescence rate and may thus modulate the phenology–drought interaction.

Federal agencies in the United States are required to consider the cumulative effects (CEs) of their activities combined with those of others. This requirement has placed a burden on the environmental impact assessment (EIA) process due to the technical complexities involved with cumulative effects assessment (CEA). This article presents a CEA methodology that reduces some of the inherent complexities by focusing on the cumulative influence to a single environmental resource, ambient air quality. An eight‐step method is presented herein as a tool for the assessment of cumulative air quality effects. Procedures for accomplishment of the more difficult steps, such as the determination of what activities to include in the evaluation and how to determine the significance of a cumulative effect, are also included.

With increasing human population, large scale climate changes, and the interaction of multiple stressors, understanding cumulative effects on marine ecosystems is increasingly important. Two major drivers of change in coastal and marine ecosystems are industrial developments with acute impacts on local ecosystems, and global climate change stressors with widespread impacts. We conducted a cumulative effects mapping analysis of the marine waters of British Columbia, Canada, under different scenarios: climate change and planned developments. At the coast-wide scale, climate change drove the largest change in cumulative effects with both widespread impacts and high vulnerability scores. Where the impacts of planned developments occur, planned industrial and pipeline activities had high cumulative effects, but the footprint of these effects was comparatively localized. Nearshore habitats were at greatest risk from planned industrial and pipeline activities; in particular, the impacts of planned pipelines on rocky intertidal habitats were predicted to cause the highest change in cumulative effects. This method of incorporating planned industrial development in cumulative effects mapping allows explicit comparison of different scenarios with the potential to be used in environmental impact assessments at various scales. Its use allows resource managers to consider cumulative effect hotspots when making decisions regarding industrial developments and avoid unacceptable cumulative effects. Management needs to consider both global and local stressors in managing marine ecosystems for the protection of biodiversity and the provisioning of ecosystem services.

AimDeclining biodiversity across ecosystems and myriad human pressures necessitate high‐level regional assessments for effective management. Evaluation of biodiversity patterns and stressor accumulation through beta diversity and cumulative effect analyses are two key methods for management prioritization. This study links these concepts to develop a novel cumulative effect metric based on beta diversity responses.LocationFraser River basin, British Columbia, Canada.MethodsMulti‐Site Generalized Dissimilarity Models were used to evaluate nonlinear relationships between fish species compositional differences (ζn, number of shared species across any number of watersheds compared) and human pressure, environmental, and geospatial differences among all watersheds and within low, mid‐, and high elevation clusters. A cumulative effect metric was calculated as the sum of response values generated by the model for each human pressure variable specific to each watershed, when evaluated for ζ2 (equivalent to pairwise beta diversity). This metric was tested against the local contribution of each watershed to beta diversity to determine whether watersheds with unique communities had low cumulative effects and are, therefore, candidates for conservation and conversely, whether watersheds with non‐distinctive communities had high cumulative effects and warrant restoration. Species contributions to beta diversity were also assessed across the basin.ResultsZeta diversity across low elevation watersheds indicated stronger filtering by human pressures than mid‐ and high elevation watersheds, which showed more stochastic community assembly. The relative importance and response to human pressures varied based on the diversity component (i.e., total diversity including compositional nestedness vs. turnover) and order of zeta (number of watersheds compared). Cumulative effects were negatively related to community uniqueness, supporting the use of these metrics for developing management priorities.Main ConclusionsThis assessment contributes to biodiversity conservation efforts by identifying important watersheds, species, and human pressures to manage as well as providing a cumulative effect metric directly based on biodiversity responses.

Drought-related cumulative and time-lag effects on vegetation dynamics across the Yellow River Basin, China

Background: Benzene affects human health through environmental exposure in addition to occupational contact. However, few studies have examined the associations between long-term exposure to low concentrations of ambient benzene and mortality risks in nonoccupational settings.Methods: This prospective cohort study consists of 393,042 participants without stroke, myocardial infarction, or cancer at baseline from the UK Biobank. Annual average concentrations of benzene for each year during follow-up were measured using air dispersion models. The main outcomes were all-cause mortality and mortality from specific causes. Cox proportional-hazards models with time-varying exposure measurements were used to estimate the hazard ratios and 95% confidence intervals (CIs) for mortality risks. Restricted cubic spline models were used to estimate exposure-response relationships.Measurements and Main Results: With each interquartile range increase in the average annual concentration of benzene, the adjusted hazard ratios of mortality risk from all causes, cardiovascular disease, cancer, and respiratory disease were 1.26 (95% CI, 1.24-1.27), 1.24 (95% CI, 1.21-1.28), 1.27 (95% CI, 1.25-1.29), and 1.25 (95% CI, 1.20-1.30), respectively. The monotonically increasing exposure-response curves showed no threshold and plateau within the observed concentration range. Furthermore, the effect of benzene exposure on mortality persisted across different subgroups and was somewhat stronger in younger and White people (P for interaction < 0.05).Conclusions: Long-term exposure to low concentrations of ambient benzene significantly increases mortality risk in the general population. Ambient benzene represents a potential threat to public health, and further investigations are needed to support timely pollution regulation and health protection.

BackgroundAlthough ambient temperature is known to influence hospitalizations for respiratory diseases, none has focused specifically on pulmonary hypertension (PH) hospitalizations.MethodsUsing an 11-year dataset (2013–2023) from Shanghai, China, we performed a time-series analysis to assess the impact of daily temperature on PH-related hospitalizations. A distributed lag non-linear model was applied to examine both the immediate (single-day) and prolonged (cumulative) effects of temperature exposure over a lag period of up to 30 days. Cumulative risk estimates were used to assess overall impact of specific temperature exposures by calculating the relative risk (RR) of PH hospitalizations. We also plotted exposure-response curves. Stratification analyses were conducted by age, sex, and medical insurance status to identify potentially vulnerable subpopulations.ResultsOver the study period, a total of 12,218 hospitalizations for PH were recorded. We observed a non-linear, U-shaped relationship between daily mean temperature and PH hospitalization risk. Specifically, cold extremes, including extreme cold (5th percentile of daily average temperature, 4.6 °C) and moderate cold (25th percentile of daily average temperature, 10.4 °C) were significantly linked to increased risk, while no significant associations were observed for heat extremes. Compared with the reference temperature (18 °C), extreme cold showed the strongest effect, with a single-day lag effect peaking at lag 3 (RR: 1.05, 95% CI: 1.01–1.10) and a cumulative effect over lag 0–30 days (RR: 2.80, 95% CI: 1.19–6.59). The attributable fractions of PH hospitalization were 8% for extreme cold and 26% for moderate cold. Stratified analyses revealed higher susceptibility among females, younger individuals (<65 years), and those without medical insurance.ConclusionThis study provides novel epidemiological evidence that cold exposure significantly increases the risk of PH hospitalization, with particularly pronounced effects observed among females, individuals aged < 65 years, and those without medical insurance.

Previous studies have demonstrated that outdoor temperature exposure was an important risk factor for respiratory diseases. However, no study investigates the effect of indoor temperature exposure on respiratory diseases and further assesses cumulative effect. The objective of this study is to study the cumulative effect of indoor temperature exposure on emergency department visits due to infectious (IRD) and non-infectious (NIRD) respiratory diseases among older adults. Subjects were collected from the Longitudinal Health Insurance Database in Taiwan. The cumulative degree hours (CDHs) was used to assess the cumulative effect of indoor temperature exposure. A distributed lag nonlinear model with quasi-Poisson function was used to analyze the association between CDHs and emergency department visits due to IRD and NIRD. For IRD, there was a significant risk at 27, 28, 29, 30, and 31 °C when the CDHs exceeded 69, 40, 14, 5, and 1 during the cooling season (May to October), respectively, and at 19, 20, 21, 22, and 23 °C when the CDHs exceeded 8, 1, 1, 35, and 62 during the heating season (November to April), respectively. For NIRD, there was a significant risk at 19, 20, 21, 22, and 23 °C when the CDHs exceeded 1, 1, 16, 36, and 52 during the heating season, respectively; the CDHs at 1 was only associated with the NIRD at 31 °C during the cooling season. Our data also indicated that the CDHs was lower among men than women. We conclude that the cumulative effects of indoor temperature exposure should be considered to reduce IRD risk in both cooling and heating seasons and NIRD risk in heating season and the cumulative effect on different gender.

This study adapts and applies the evidence‐based approach for causal inference, a medical standard, to the restoration and sustainable management of large‐scale aquatic ecosystems. Despite long‐term investments in restoring aquatic ecosystems, it has proven difficult to adequately synthesize and evaluate program outcomes, and no standard method has been adopted. Complex linkages between restorative actions and ecosystem responses at a landscape scale make evaluations problematic and most programs focus on monitoring and analysis. Herein, we demonstrate a new transdisciplinary approach integrating techniques from evidence‐based medicine, critical thinking, and cumulative effects assessment. Tiered hypotheses about the effects of landscape‐scale restorative actions are identified using an ecosystem conceptual model. The systematic literature review, a health sciences standard since the 1960s, becomes just one of seven lines of evidence assessed collectively, using critical thinking strategies, causal criteria, and cumulative effects categories. As a demonstration, we analyzed data from 166 locations on the Columbia River and estuary representing 12 indicators of habitat and fish response to floodplain restoration actions intended to benefit culturally and economically important, threatened and endangered salmon. Synthesis of the lines of evidence demonstrated that hydrologic reconnection promoted macrodetritis export, prey availability, and juvenile fish access and feeding. Upon evaluation, the evidence was sufficient to infer cross‐boundary, indirect, compounding, and delayed cumulative effects, and suggestive of nonlinear, landscape‐scale, and spatial density effects. Therefore, on the basis of causal inferences regarding food‐web functions, we concluded that the restoration program is having a cumulative beneficial effect on juvenile salmon. The lines of evidence developed are transferable to other ecosystems: modeling of cumulative net ecosystem improvement, physical modeling of ecosystem controlling factors, meta‐analysis of restoration action effectiveness, analysis of data on target species, research on critical ecological uncertainties, evidence‐based review of the literature, and change analysis on the landscape setting. As with medicine, the science of ecological restoration needs scientific approaches to management decisions, particularly because the consequences affect species extinctions and the availability of ecosystem services. This evidence‐based approach will enable restoration in complex coastal, riverine, and tidal‐fluvial ecosystems like the lower Columbia River to be evaluated when data have accumulated without sufficient synthesis.