Meteorology-driven PM2.5 interannual variability over East Asia.

Atmospheric fine particulate matter (PM2.5) is a human health risk factor, but its ambient concentration depends on both precursor emissions and meteorology. While emission reductions are used to set PM2.5-related health policies, the effect of meteorology is often overlooked. To explore this aspect, we examined PM2.5 interannual variability (IAV) associated with meteorological parameters using the long-term simulation from the Community Earth System Model (CESM1), a global climate-chemistry model, with fixed emissions. The results are subsequently contrasted with the MERRA-2 reanalysis dataset, which inherently considers emission and meteorology effects. Over continental East Asia, the CESM1 domain-average PM2.5 IAV is 6.7 %, mainly attributed to humidity, precipitation, and ventilation variation. The grid-cell PM2.5 IAVs over southern East China are larger, up to 12 % due to the more substantial influence of El Niño-induced meteorological anomalies. Under such climate extreme, sub-regional PM2.5 concentration may occasionally exceed WHO air quality guideline levels despite the compliance of the long-term mean. The simulated PM2.5 IAV over continental East Asia is ~25 % of that derived from the MERRA-2 data, which highlights the influence of both emission and meteorology-driven variations and trends inherent in the latter. Although emission-driven variability is significant to PM2.5 IAV, in remote areas downwind of major source regions in East Asia, North America, and Western Europe, the MERRA-2 data revealed that meteorological variations contributed more to PM2.5 IAV than emission variations. Thus, when setting policies for complying with the WHO PM2.5-related air quality guideline levels, the highest annual PM2.5 associated with climate extremes should be considered instead of that based on average climate conditions.

Prediction of antidepressant responses to non-invasive brain stimulation using frontal electroencephalogram signals: Cross-dataset comparisons and validation.

BACKGROUND: 10-Hz repetitive transcranial magnetic stimulation(rTMS) and intermittent theta-burst stimulation(iTBS) over left prefrontal cortex are FDA-approved, effective options for treatment-resistant depression (TRD). Optimal prediction models for iTBS and rTMS remain elusive. Therefore, our primary objective was to compare prediction accuracy between classification by frontal theta activity alone and machine learning(ML) models by linear and non-linear frontal signals. The second objective was to study an optimal ML model for predicting responses to rTMS and iTBS. METHODS: Two rTMS and iTBS datasets (n = 163) were used: one randomized controlled trial dataset (RCTD; n = 96) and one outpatient dataset (OPD; n = 67). Frontal theta and non-linear EEG features that reflect trend, stability, and complexity were extracted. Pretreatment frontal EEG and ML algorithms, including classical support vector machine(SVM), random forest(RF), XGBoost, and CatBoost, were analyzed. Responses were defined as ≥50 % depression improvement after treatment. Response rates between those with and without pretreatment prediction in another independent outpatient cohort (n = 208) were compared. RESULTS: Prediction accuracy using combined EEG features by SVM was better than frontal theta by logistic regression. The accuracy for OPD patients significantly dropped using the RCTD-trained SVM model. Modern ML models, especially RF (rTMS = 83.3 %, iTBS = 88.9 %, p-value(ACC > NIR) < 0.05 for iTBS), performed significantly above chance and had higher accuracy than SVM using both selected features (p < 0.05, FDR corrected for multiple comparisons) or all EEG features. Response rates among those receiving prediction before treatment were significantly higher than those without prediction (p = 0.035). CONCLUSION: The first study combining linear and non-linear EEG features could accurately predict responses to left PFC iTBS. The bootstraps-based ML model (i.e., RF) had the best predictive accuracy for rTMS and iTBS.

Projecting ozone impact on crop yield in Taiwan under climate warming.

Ozone is a primary air pollutant that impairs photosynthesis and reduces crop yields, an effect that received little attention in Taiwan, especially under the context of climate warming. This study predicted the impact of surface O3 on cash crop yields, specifically in wheat, potatoes, and tomatoes, under 2 °C and 4 °C climate warming scenarios in Taiwan via high-resolution simulations. The simulated O3 concentration (daytime mean) over Taiwan's croplands during the growing seasons was around 35-52 ppb, and it increased by 0.9 and 2.1 ppb under 2 °C and 4 °C warming for wheat and potatoes. In contrast, more minor changes of around 0.4 ppb were found for tomatoes. The O3 concentrations were converted to AOT40 (Accumulated Ozone exposure over a threshold of 40 ppb) and POD3 (Phytotoxic Ozone Dose above a threshold of 3 nmol O3 m-2) metrics to estimate changes in relative yield (RY). The mean RYPOD3 (RYAOT40) reductions over irrigated cropland for wheat, tomatoes, and potatoes under current climate and O3-stress conditions are 27.5 % (19.1 %), 14.7 % (3.8 %), and 8.2 % (1.6 %), respectively. Under 2 °C warming, the additional reductions would be 2.7 % (1.8 %) for wheat, 4.1 % (0.3 %) for tomatoes, and 2.4 % (0.4 %) for potatoes; the values under 4 °C warming become 4.7 % (4.1 %) for wheat, 8.1 % (0.6 %) for tomatoes, and 5.2 % (0.8 %) for potatoes. The contribution of RYPOD3 reduction was separated into O3-induced and climate-induced effects. The former dominated the additional yield reduction under a 2 °C warming climate, yet, the latter prevailed under 4 °C warming. Further analysis indicated that the temperature rise enhances ozone uptake flux; still, the amplified water vapor deficit and more incoming solar radiation can offset it and weakens the overall meteorological effect, especially from 2 °C to 4 °C warming conditions. Such effects demonstrated a nonlinear effect related to the co-dependence of the ozone uptake flux, which requires attention in agriculture policymaking.

CoDDA: A Flexible Copula-based Distribution Driven Analysis Framework for Large-Scale Multivariate Data.

CoDDA (Copula-based Distribution Driven Analysis) is a flexible framework for large-scale multivariate datasets. A common strategy to deal with large-scale scientific simulation data is to partition the simulation domain and create statistical data summaries. Instead of storing the high-resolution raw data from the simulation, storing the compact statistical data summaries results in reduced storage overhead and alleviated I/O bottleneck. Such summaries, often represented in the form of statistical probability distributions, can serve various post-hoc analysis and visualization tasks. However, for multivariate simulation data using standard multivariate distributions for creating data summaries is not feasible. They are either storage inefficient or are computationally expensive to be estimated in simulation time (in situ) for large number of variables. In this work, using copula functions, we propose a flexible multivariate distribution-based data modeling and analysis framework that offers significant data reduction and can be used in an in situ environment. The framework also facilitates in storing the associated spatial information along with the multivariate distributions in an efficient representation. Using the proposed multivariate data summaries, we perform various multivariate post-hoc analyses like query-driven visualization and sampling-based visualization. We evaluate our proposed method on multiple real-world multivariate scientific datasets. To demonstrate the efficacy of our framework in an in situ environment, we apply it on a large-scale flow simulation.

In Situ Distribution Guided Analysis and Visualization of Transonic Jet Engine Simulations.

Study of flow instability in turbine engine compressors is crucial to understand the inception and evolution of engine stall. Aerodynamics experts have been working on detecting the early signs of stall in order to devise novel stall suppression technologies. A state-of-the-art Navier-Stokes based, time-accurate computational fluid dynamics simulator, TURBO, has been developed in NASA to enhance the understanding of flow phenomena undergoing rotating stall. Despite the proven high modeling accuracy of TURBO, the excessive simulation data prohibits post-hoc analysis in both storage and I/O time. To address these issues and allow the expert to perform scalable stall analysis, we have designed an in situ distribution guided stall analysis technique. Our method summarizes statistics of important properties of the simulation data in situ using a probabilistic data modeling scheme. This data summarization enables statistical anomaly detection for flow instability in post analysis, which reveals the spatiotemporal trends of rotating stall for the expert to conceive new hypotheses. Furthermore, the verification of the hypotheses and exploratory visualization using the summarized data are realized using probabilistic visualization techniques such as uncertain isocontouring. Positive feedback from the domain scientist has indicated the efficacy of our system in exploratory stall analysis.

A new species of the lizardfish genus Synodus (Aulopiformes: Synodontidae) from the western Pacific Ocean.

Synodus pacificus sp. nov., a new species of lizardfish, is described based on specimens collected in the western Pacific Ocean. It belongs to a species group with needle-like posterior processes of the pelvic girdle and differs from its congeners by having the following combination of characters: 4 upside-down omega-shaped marks on the lateral body; black patches on the nape and opercular region; cross bars on the ventral surface of the head; bars on the dorsal and caudal fins; orbital large (6.7-8.1% SL); interorbital space narrow (2.1-2.8% SL); snout short (6.3-7.2% SL); anterior palatine teeth not longer than posterior teeth; pectoral fin extending slightly beyond a line from base of pelvic fin to origin of dorsal fin; 7 peritoneal spots; peritoneal membrane pale; nasal flap long and pointed; dorsal-fin rays 11-13 (mainly11); anal-fin rays 9-11 (mainly 10 or 11); pectoral-fin rays 11-14 (mainly 12); total vertebrae 53 or 54; pored lateral-line scales 52-54; transverse scale rows above the lateral line 3.5, below 4.5 or 5; gill rakers 31-37; and 17-29 teeth on free end of tongue.

Potential impacts of electric vehicles on air quality in Taiwan.

The prospective impacts of electric vehicle (EV) penetration on the air quality in Taiwan were evaluated using an air quality model with the assumption of an ambitious replacement of current light-duty vehicles under different power generation scenarios. With full EV penetration (i.e., the replacement of all light-duty vehicles), CO, VOCs, NOx and PM2.5 emissions in Taiwan from a fleet of 20.6 million vehicles would be reduced by 1500, 165, 33.9 and 7.2Ggyr(-1), respectively, while electric sector NOx and SO2 emissions would be increased by up to 20.3 and 12.9Ggyr(-1), respectively, if the electricity to power EVs were provided by thermal power plants. The net impacts of these emission changes would be to reduce the annual mean surface concentrations of CO, VOCs, NOx and PM2.5 by about 260, 11.3, 3.3ppb and 2.1µgm(-3), respectively, but to increase SO2 by 0.1ppb. Larger reductions tend to occur at time and place of higher ambient concentrations and during high pollution events. Greater benefits would clearly be attained if clean energy sources were fully encouraged. EV penetration would also reduce the mean peak-time surface O3 concentrations by up to 7ppb across Taiwan with the exception of the center of metropolitan Taipei where the concentration increased by <2ppb. Furthermore, full EV penetration would reduce annual days of O3 pollution episodes by ~40% and PM2.5 pollution episodes by 6-10%. Our findings offer important insights into the air quality impacts of EV and can provide useful information for potential mitigation actions.

Surface fractal dimension, water adsorption efficiency, and cloud nucleation activity of insoluble aerosol.

Surface porosity affects the ability of a substance to adsorb gases. The surface fractal dimension D is a measure that indicates the amount that a surface fills a space, and can thereby be used to characterize the surface porosity. Here we propose a new method for determining D, based on measuring both the water vapour adsorption isotherm of a given substance, and its ability to act as a cloud condensation nucleus when introduced to humidified air in aerosol form. We show that our method agrees well with previous methods based on measurement of nitrogen adsorption. Besides proving the usefulness of the new method for general surface characterization of materials, our results show that the surface fractal dimension is an important determinant in cloud drop formation on water insoluble particles. We suggest that a closure can be obtained between experimental critical supersaturation for cloud drop activation and that calculated based on water adsorption data, if the latter is corrected using the surface fractal dimension of the insoluble cloud nucleus.

Visualization and Analysis of Rotating Stall for Transonic Jet Engine Simulation.

Identification of early signs of rotating stall is essential for the study of turbine engine stability. With recent advancements of high performance computing, high-resolution unsteady flow fields allow in depth exploration of rotating stall and its possible causes. Performing stall analysis, however, involves Significant effort to process large amounts of simulation data, especially when investigating abnormalities across many time steps. In order to assist scientists during the exploration process, we present a visual analytics framework to identify suspected spatiotemporal regions through a comparative visualization so that scientists are able to focus on relevant data in more detail. To achieve this, we propose efficient stall analysis algorithms derived from domain knowledge and convey the analysis results through juxtaposed interactive plots. Using our integrated visualization system, scientists can visually investigate the detected regions for potential stall initiation and further explore these regions to enhance the understanding of this phenomenon. Positive feedback from scientists demonstrate the efficacy of our system in analyzing rotating stall.

A trophic model of fringing coral reefs in Nanwan Bay, southern Taiwan suggests overfishing.

Several coral reefs of Nanwan Bay, Taiwan have recently undergone shifts to macroalgal or sea anemone dominance. Thus, a mass-balance trophic model was constructed to analyze the structure and functioning of the food web. The fringing reef model was comprised of 18 compartments, with the highest trophic level of 3.45 for piscivorous fish. Comparative analyses with other reef models demonstrated that Nanwan Bay was similar to reefs with high fishery catches. While coral biomass was not lower, fish biomass was lower than those of reefs with high catches. Consequently, the sums of consumption and respiratory flows and total system throughput were also decreased. The Nanwan Bay model potentially suggests an overfished status in which the mean trophic level of the catch, matter cycling, and trophic transfer efficiency are extremely reduced.