The role of interdecadal climate oscillations in driving Arctic atmospheric river trends.

Atmospheric rivers (ARs), intrusions of warm and moist air, can effectively drive weather extremes over the Arctic and trigger subsequent impact on sea ice and climate. What controls the observed multi-decadal Arctic AR trends remains unclear. Here, using multiple sources of observations and model experiments, we find that, contrary to the uniform positive trend in climate simulations, the observed Arctic AR frequency increases by twice as much over the Atlantic sector compared to the Pacific sector in 1981-2021. This discrepancy can be reconciled by the observed positive-to-negative phase shift of Interdecadal Pacific Oscillation (IPO) and the negative-to-positive phase shift of Atlantic Multidecadal Oscillation (AMO), which increase and reduce Arctic ARs over the Atlantic and Pacific sectors, respectively. Removing the influence of the IPO and AMO can reduce the projection uncertainties in near-future Arctic AR trends by about 24%, which is important for constraining projection of Arctic warming and the timing of an ice-free Arctic.

Positive feedback to regional climate enhances African wildfires.

Regional climate strongly regulates the occurrence of wildfires partly because drying of fuel load increases fires. The large amounts of aerosols released by wildfires can also strongly affect regional climate. Here we show positive feedback (a seasonal burned area enhancement of 7-17%) due to wildfire aerosol forcing in Africa found in the simulations using the interactive REgion-Specific ecosystem feedback Fire (RESFire) model in the Community Earth System Model (CESM). The positive feedback results partly from the transport of fire aerosols from burning (dry) to wet regions, reducing precipitation and drying fuel load to enhance fires toward the non-burning (wet) region. This internally self-enhanced burning is an important mechanism for the regulation of African ecosystems and for understanding African fire behaviors in a changing climate. A similar mechanism may also help sustain wildfires in other tropical regions.

Colorimetric and antioxidant films based on biodegradable polymers and black nightshade (Solanum nigrum L.) extract for visually monitoring Cyclina sinensis freshness.

In this study, colorimetric and antioxidant films were developed by using cassava starch (CS), κ-carrageenan (KC) and black nightshade fruit anthocyanins (BNA), and their physical and functional properties were investigated. We found BNA presented significant color changes in different pH solutions. And incorporation of BNA significantly increased the tensile strength, water vapor permeability, UV-vis light barrier property, pH-sensitivity and antioxidant activity of CS-KC film. Results of structural characterization indicated that H-bonds were formed between CS, KC and BNA in films and the film compactness was significantly improved by BNA incorporation. Results of the rheological property assay showed the films had high apparent viscosity with an obvious shear-thinning behavior. When applied to monitoring the quality change of Cyclina sinensis, CS-KC-BNA films exhibited significant color changes with the degradation process of C. sinensis qualities. Our results suggested CS-KC-BNA films could be utilized in smart packaging in food industry.

Antioxidant and ammonia-sensitive films based on starch, κ-carrageenan and Oxalis triangularis extract as visual indicator of beef meat spoilage.

In this study, we first investigated the rheological property of sweet potato starch (SPS), κ-carrageenan (KC) and Oxalis triangularis extract (OTE) blends and found that the blends exhibited high apparent viscosity with an apparent shear thinning behavior. And then films based on SPS, KC and OTE were developed and their structural and functional properties were studied. The physico-chemical test results showed that OTE exhibited different colors in solutions with different pH values and the incorporation with OTE and KC could significantly increase the thickness, water vapor permeability, light barrier ability, tensile strength and elongation at break as well as the pH- and ammonia-sensitive properties of the SPS film. The structural property test results showed that some intermolecular interactions between OTE and SPS/KC occurred in SPS-KC-OTE films. Finally, the functional properties of SPS-KC-OTE films were examined and SPS-KC-OTE films showed significant DPPH radical scavenging activity as well as a visible color change in response to changes in beef meat freshness. Our results suggested that the SPS-KC-OTE films could be used as an active and intelligent food packaging material in food industry.

Effect of the increase rate of blood lipid concentration during pregnancy on the adverse pregnancy outcomes: a cohort study of 1051 singleton pregnancy.

OBJECTIVE: To investigate the effects of blood lipid concentration and blood lipid growth rate in the first and third trimesters of pregnancy on adverse pregnancy outcomes. METHODS: The study subjects were 1051 singleton pregnant women who established archives in Chengdu Maternity and Child Specialty Hospital. The pregnancy information was continuously recorded until the pregnant woman gave birth, and lipid biochemical indicators were performed every three months. RESULTS: The average age of the pregnant women recruited was 27.82 ± 3.07 years. Compared with the first trimester, the concentration of triglyceride, total cholesterol, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol in the third trimester were increased significantly (all p < .05). The growth rate of triglyceride was closely related to the occurrence of gestational diabetes mellitus and large for gestational, and the growth rate of total cholesterol and low-density lipoprotein cholesterol was closely related to gestational diabetes mellitus and cholestasis of pregnancy. For every unit increase in triglyceride growth rate, the risk of gestational diabetes mellitus and large for gestational age increases by 1.237 times and 1.317 times, respectively. CONCLUSION: Among non-high-risk pregnant women, blood lipid concentration is generally high among pregnant women with complications during pregnancy. It is necessary to combine a single blood lipid test concentration during pregnancy with the blood lipid growth rate to assess the condition of pregnant women and provide better guidance during pregnancy.

Increasing large wildfires over the western United States linked to diminishing sea ice in the Arctic.

The compound nature of large wildfires in combination with complex physical and biophysical processes affecting variations in hydroclimate and fuel conditions makes it difficult to directly connect wildfire changes over fire-prone regions like the western United States (U.S.) with anthropogenic climate change. Here we show that increasing large wildfires during autumn over the western U.S. are fueled by more fire-favorable weather associated with declines in Arctic sea ice during preceding months on both interannual and interdecadal time scales. Our analysis (based on observations, climate model sensitivity experiments, and a multi-model ensemble of climate simulations) demonstrates and explains the Arctic-driven teleconnection through regional circulation changes with the poleward-shifted polar jet stream and enhanced fire-favorable surface weather conditions. The fire weather changes driven by declining Arctic sea ice during the past four decades are of similar magnitude to other leading modes of climate variability such as the El Niño-Southern Oscillation that also influence fire weather in the western U.S.

A multi-analysis approach for estimating regional health impacts from the 2017 Northern California wildfires.

Smoke impacts from large wildfires are mounting, and the projection is for more such events in the future as the one experienced October 2017 in Northern California, and subsequently in 2018 and 2020. Further, the evidence is growing about the health impacts from these events which are also difficult to simulate. Therefore, we simulated air quality conditions using a suite of remotely-sensed data, surface observational data, chemical transport modeling with WRF-CMAQ, one data fusion, and three machine learning methods to arrive at datasets useful to air quality and health impact analyses. To demonstrate these analyses, we estimated the health impacts from smoke impacts during wildfires in October 8-20, 2017, in Northern California, when over 7 million people were exposed to Unhealthy to Very Unhealthy air quality conditions. We investigated using the 5-min available GOES-16 fire detection data to simulate timing of fire activity to allocate emissions hourly for the WRF-CMAQ system. Interestingly, this approach did not necessarily improve overall results, however it was key to simulating the initial 12-hr explosive fire activity and smoke impacts. To improve these results, we applied one data fusion and three machine learning algorithms. We also had a unique opportunity to evaluate results with temporary monitors deployed specifically for wildfires, and performance was markedly different. For example, at the permanent monitoring locations, the WRF-CMAQ simulations had a Pearson correlation of 0.65, and the data fusion approach improved this (Pearson correlation = 0.95), while at the temporary monitor locations across all cases, the best Pearson correlation was 0.5. Overall, WRF-CMAQ simulations were biased high and the geostatistical methods were biased low. Finally, we applied the optimized PM2.5 exposure estimate in an exposure-response function. Estimated mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% CI: 0, 196) with 47% attributable to wildland fire smoke.Implications: Large wildfires in the United States and in particular California are becoming increasingly common. Associated with these large wildfires are air quality and health impact to millions of people from the smoke. We simulated air quality conditions using a suite of remotely-sensed data, surface observational data, chemical transport modeling, one data fusion, and three machine learning methods to arrive at datasets useful to air quality and health impact analyses from the October 2017 Northern California wildfires. Temporary monitors deployed for the wildfires provided an important model evaluation dataset. Total estimated regional mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% confidence interval: 0, 196) with 47% of these deaths attributable to the wildland fire smoke. This illustrates the profound effect that even a 12-day exposure to wildland fire smoke can have on human health.

Methods, availability, and applications of PM2.5 exposure estimates derived from ground measurements, satellite, and atmospheric models.

Fine particulate matter (PM2.5) is a well-established risk factor for public health. To support both health risk assessment and epidemiological studies, data are needed on spatial and temporal patterns of PM2.5 exposures. This review article surveys publicly available exposure datasets for surface PM2.5 mass concentrations over the contiguous U.S., summarizes their applications and limitations, and provides suggestions on future research needs. The complex landscape of satellite instruments, model capabilities, monitor networks, and data synthesis methods offers opportunities for research development, but would benefit from guidance for new users. Guidance is provided to access publicly available PM2.5 datasets, to explain and compare different approaches for dataset generation, and to identify sources of uncertainties associated with various types of datasets. Three main sources used to create PM2.5 exposure data are ground-based measurements (especially regulatory monitoring), satellite retrievals (especially aerosol optical depth, AOD), and atmospheric chemistry models. We find inconsistencies among several publicly available PM2.5 estimates, highlighting uncertainties in the exposure datasets that are often overlooked in health effects analyses. Major differences among PM2.5 estimates emerge from the choice of data (ground-based, satellite, and/or model), the spatiotemporal resolutions, and the algorithms used to fuse data sources.Implications: Fine particulate matter (PM2.5) has large impacts on human morbidity and mortality. Even though the methods for generating the PM2.5 exposure estimates have been significantly improved in recent years, there is a lack of review articles that document PM2.5 exposure datasets that are publicly available and easily accessible by the health and air quality communities. In this article, we discuss the main methods that generate PM2.5 data, compare several publicly available datasets, and show the applications of various data fusion approaches. Guidance to access and critique these datasets are provided for stakeholders in public health sectors.

Machine Learning-Based Integration of High-Resolution Wildfire Smoke Simulations and Observations for Regional Health Impact Assessment.

Large wildfires are an increasing threat to the western U.S. In the 2017 fire season, extensive wildfires occurred across the Pacific Northwest (PNW). To evaluate public health impacts of wildfire smoke, we integrated numerical simulations and observations for regional fire events during August-September of 2017. A one-way coupled Weather Research and Forecasting and Community Multiscale Air Quality modeling system was used to simulate fire smoke transport and dispersion. To reduce modeling bias in fine particulate matter (PM2.5) and to optimize smoke exposure estimates, we integrated modeling results with the high-resolution Multi-Angle Implementation of Atmospheric Correction satellite aerosol optical depth and the U.S. Environmental Protection Agency AirNow ground-level monitoring PM2.5 concentrations. Three machine learning-based data fusion algorithms were applied: An ordinary multi-linear regression method, a generalized boosting method, and a random forest (RF) method. 10-Fold cross-validation found improved surface PM2.5 estimation after data integration and bias correction, especially with the RF method. Lastly, to assess transient health effects of fire smoke, we applied the optimized high-resolution PM2.5 exposure estimate in a short-term exposure-response function. Total estimated regional mortality attributable to PM2.5 exposure during the smoke episode was 183 (95% confidence interval: 0, 432), with 85% of the PM2.5 pollution and 95% of the consequent multiple-cause mortality contributed by fire emissions. This application demonstrates both the profound health impacts of fire smoke over the PNW and the need for a high-performance fire smoke forecasting and reanalysis system to reduce public health risks of smoke hazards in fire-prone regions.

Impacts of rural worker migration on ambient air quality and health in China: From the perspective of upgrading residential energy consumption.

In China, rural migrant workers (RMWs) are employed in urban workplaces but receive minimal resources and welfare. Their residential energy use mix (REM) and pollutant emission profiles are different from those of traditional urban (URs) and rural residents (RRs). Their migration towards urban areas plays an important role in shaping the magnitudes and spatial patterns of pollutant emissions, ambient PM2.5 (fine particulate matter with a diameter smaller than 2.5 µm) concentrations, and associated health impacts in both urban and rural areas. Here we evaluate the impacts of RMW migration on REM pollutant emissions, ambient PM2.5, and subsequent premature deaths across China. At the national scale, RMW migration benefits ambient air quality because RMWs tend to transition to a cleaner REM upon arrival at urban areas-though not as clean as urban residents'. In 2010, RMW migration led to a decrease of 1.5 µg/m3 in ambient PM2.5 exposure concentrations (Cex) averaged across China and a subsequent decrease of 12,200 (5700 to 16,300, as 90% confidence interval) in premature deaths from exposure to ambient PM2.5. Despite the overall health benefit, large-scale cross-province migration increased megacities' PM2.5 levels by as much as 10 µg/m3 due to massive RMW inflows. Model simulations show that upgrading within-city RMWs' REMs can effectively offset the RMW-induced PM2.5 increase in megacities, and that policies that properly navigate migration directions may have potential for balancing the economic growth against ambient air quality deterioration. Our study indicates the urgency of considering air pollution impacts into migration-related policy formation in the context of rapid urbanization in China.

Arctic sea ice, Eurasia snow, and extreme winter haze in China.

The East China Plains (ECP) region experienced the worst haze pollution on record for January in 2013. We show that the unprecedented haze event is due to the extremely poor ventilation conditions, which had not been seen in the preceding three decades. Statistical analysis suggests that the extremely poor ventilation conditions are linked to Arctic sea ice loss in the preceding autumn and extensive boreal snowfall in the earlier winter. We identify the regional circulation mode that leads to extremely poor ventilation over the ECP region. Climate model simulations indicate that boreal cryospheric forcing enhances the regional circulation mode of poor ventilation in the ECP region and provides conducive conditions for extreme haze such as that of 2013. Consequently, extreme haze events in winter will likely occur at a higher frequency in China as a result of the changing boreal cryosphere, posing difficult challenges for winter haze mitigation but providing a strong incentive for greenhouse gas emission reduction.

Short communication: isolation and phylogenetic analysis of an avian-origin H3N2 canine influenza virus in dog shelter, China.

A H3N2 canine influenza virus, A/canine/Guangdong/3/2011 (H3N2), was isolated from roaming dogs in rural China. Sequence and phylogenetic analysis of eight gene segments revealed that the A/canine/Guangdong/3/2011 (H3N2) was most similar to a recent H3N2 canine influenza virus isolated in cats from South Korea, which originated from an avian strain. To our knowledge, this is the first report of an avian-origin H3N2 CIV which was isolated from roaming dogs in China. The epidemiologic information provided herein suggests that continued study is required to determine if this virus could be established in the roaming dog population in rural China and pose potential threats to public health.

Targeting matrix metalloproteinases and endothelial cells with a fusion peptide against tumor.

Development of novel therapy for patients with tumor is still a challenge at the present time. We designed a fusion peptide (RK5) with two targets as a novel agent against tumor. The fusion peptide RK5 containing the kringle 5 fragment of human plasminogen and a decapeptide (CTTHWGFTLC) was constructed and expressed in yeast. Matrix metalloproteinase (MMP) activity, proliferation, and migration of endothelial cells were examined in vitro, respectively. Angiogenesis, tumor growth, metastasis, and survival time were evaluated in in vivo models. Administration of RK5 was delivered by both protein and gene approach. The results showed that RK5 inhibited the activity of MMP-9 and exhibited more inhibitory effects on proliferation and migration of endothelial cells than that of kringle 5 fragment and decapeptide individually. RK5 also inhibited angiogenesis, tumor growth, and metastasis and increased survival time of mice bearing tumor. In addition, the effectiveness of RK5 could be achieved by both protein and gene delivery. In conclusion, RK5 has potential to inhibit tumor growth and metastasis and to prolong survival time of animals bearing tumor. Therefore, fusion peptide RK5 with two targets provides a new design for the development of antitumor drugs and has potential for clinical application.