Multi-scale modeling study of the source contributions to near-surface ozone and sulfur oxides levels over California during the ARCTAS-CARB period

Chronic high surface ozone (O3) levels and the increasing sulfur oxides (SOx = SO2+SO4) ambient concentrations over South Coast (SC) and other areas of California (CA) are affected by both local emissions and long-range transport. In this paper, multi-scale tracer, full-chemistry and adjoint simulations using the STEM atmospheric chemistry model are conducted to assess the contribution of local emission sourcesto SC O3 and to evaluate the impacts of transported sulfur and local emissions on the SC sulfur budgetduring the ARCTAS-CARB experiment period in 2008. Sensitivity simulations quantify contributions of biogenic and fire emissions to SC O3 levels. California biogenic and fire emissions contribute 3–4 ppb to near-surface O3 over SC, with larger contributions to other regions in CA. During a long-range transport event from Asia starting from 22 June, high SOx levels (up to ~0.7 ppb of SO2 and ~1.3 ppb of SO4) is observed above ~6 km, but they did not affect CA surface air quality. The elevated SOx observed at 1–4 km is estimated to enhance surface SOx over SC by ~0.25 ppb (upper limit) on ~24 June. The near-surface SOx levels over SC during the flight week are attributed mostly to local emissions. Two anthropogenic SOx emission inventories (EIs) from the California Air Resources Board (CARB) and the US Environmental Protection Agency (EPA) are compared and applied in 60 km and 12 km chemical transport simulations, and the results are compared withobservations. The CARB EI shows improvements over the National Emission Inventory (NEI) by EPA, but generally underestimates surface SC SOx by about a factor of two. Adjoint sensitivity analysis indicated that SO2 levels at 00:00 UTC (17:00 local time) at six SC surface sites were influenced by previous day maritime emissions over the ocean, the terrestrial emissions over nearby urban areas, and by transported SO2 from the north through both terrestrial and maritime areas. Overall maritime emissions contribute 10–70% of SO2 and 20–60% fine SO4 on-shore and over the most terrestrial areas, with contributions decreasing with in-land distance from the coast. Maritime emissions also modify the photochemical environment, shifting O3 production over coastal SC to more VOC-limited conditions. These suggest an important role for shipping emission controls in reducing fine particle and O3concentrations in SC

Pain Management in Acute Pancreatitis: A Systematic Review and Meta-Analysis of Randomised Controlled Trials

Background: Pain management is an important priority in the treatment of acute pancreatitis (AP). Current evidence and guideline recommendations are inconsistent on the most effective analgesic protocol. This systematic review and meta-analysis of randomised controlled trials (RCTs) aimed to compare the safety and efficacy of analgesics for pain relief in AP. Methods: A literature search was performed to identify all RCTs assessing analgesics in patients with AP. The primary outcome was the number of participants who needed rescue analgesia. Study quality was assessed using Jadad score. Pooled odds ratios (ORs) or weighted mean differences (WMDs) with 95% confidence intervals (CI) were analysed using a random-effects model. Results: Twelve studies comprising 699 patients with AP (83% mild AP) were analysed. The tested analgesics significantly decreased the need for rescue analgesia (3 studies, OR.36, 95% CI 0.21 to 0.60) vs. placebo or conventional treatment. The analgesics also improved the pain score [Visual Analogue Scale (Δ-VAS)] at 24 h (WMD 18.46, 0.84 to 36.07) and by the 3rd to 7th days (WMD 11.57, 0.87 to 22.28). Opioids vs. non-opioids were associated with a decrease in the need for rescue analgesia (6 studies, OR 0.25, 95% CI 0.07 to 0.86, p = 0.03) but without significance in pain score. In subgroup analyses, opioids were similar to non-steroidal anti-inflammatory drugs (NSAIDs) regarding the primary outcome (4 studies, OR 0.56, 95% CI 0.24 to 1.32, p = 0.18). There were no significant differences in other clinical outcomes and rate of adverse events. Other studies, comparing epidural anaesthesia vs. patient-controlled analgesia and opioid (buprenorphine) vs. opioid (pethidine) did not show significant difference in primary outcome. Study quality issues significantly contributed to overall study heterogeneity. Conclusions: NSAIDs and opioids are equally effective in decreasing the need for rescue analgesia in patients with mild AP. The relative paucity of trials and high-quality data in this setting is notable and the optimal analgesic strategy for patients with moderately severe and severe AP still requires to be determined

Ethanol-sensing performance of tin dioxide octahedral nanocrystals with exposed high-energy {111} and {332} facets

National Natural Science Foundation of China [61376073]Tin dioxide octahedral nanocrystals with exposed high-energy {111} and {332} facets were hydrothermally synthesized and characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and selected-area electron diffraction (SAED). Gas sensors were fabricated from the prepared SnO2 nanocrystals and applied to ethanol-sensing tests. Octahedral SnO2 {332} exhibited a maximum response of 2200 under an ethanol concentration of 800 ppm at 250 degrees C with a response time of 1.5 s and a recovery time of 32.5 s, whereas SnO2 {111} exhibited a maximum response of 179 at 360 degrees C with a response time of 9.5 s and a recovery time of 6.7 s. The sensing mechanisms responsible for SnO2 nanocrystals to ethanol vapor are discussed

Simulating reactive nitrogen, carbon monoxide, and ozone in California during ARCTAS-CARB 2008 with high wildfire activity

Predictions of O_3, CO, total NO_y and individual NO_y species (NO, NO_2, HNO_3, PAN, alkyl nitrates and aerosol nitrate) from a fine resolution regional air quality modeling system for the South Coast Air Basin (SoCAB) and San Joaquin Valley Air Basin (SJVAB) of California are presented and evaluated for the 2008 ARCTAS-CARB campaign. The measurements of the chemical compounds from the fire plumes during the field campaign allow for the evaluation of the model's ability to simulate fire-influenced air masses as well. In general, the model successfully simulated the broad spatial distribution of chemical compounds in both air basins as well as the variation within the basins. Using inventories that reflect 2008 emissions levels, the model performed well in simulating NO_x (NO + NO_2) in SoCAB. Therefore, the under prediction of O_3 over these areas is more likely caused by uncertainties with the VOC emissions, chemistry, or discrepancies in the meteorology. The model did not capture the relatively high levels of O_3, and some reactive nitrogen species that were measured off shore of the SoCAB, indicating potential missing sources or the transport from on shore to off shore was not successfully captured. In SJVAB, the model had good performance in simulating different chemical compounds in the Fresno and Arvin areas. However, enhanced concentrations of O_3, NO_x, HNO_3 and PAN near dairy farms were significantly underestimated in the model. Negative biases also exist for O_3 and HNO_3 near oil fields, suggesting larger uncertainties associated with these emission sources. While the model simulated the total NO_y mixing ratios reasonably well, the prediction for partitioning between individual compounds showed larger uncertainties in the model simulation. Although the fire emissions inventory was updated to include the latest emissions estimates and speciation profiles, our model shows limited improvement in simulating the enhancement of O_3, CO, and PAN under fire impact as compared to a previous version of the modeling system. Further improvements in simulating fire emissions, especially the timing and the plume injection heights, are desired in order to better simulate the impact of fires

Search Web Images Using Objects, Backgrounds and Conditions

ABSTRACT As the volumes of web images have grown rapidly in the last decade, Content-Based Image Retrieval (CBIR) has attracted substantial interests as an effective tool to manage the images. Most existing CBIR systems focus on the object in the image, while ignoring the conditions (day/night, sunny/rain, etc.) and the backgrounds, both of which are very helpful to meet the user's information need. To overcome this shortcoming, in this paper, we present a novel CBIR system depending on a novel query formulation considering three aspects: Object, Background and Condition. Specifically, we design a user-friendly interface to help the user formulate a query. The interface can allow the user to give the percentage, relative position and size of each object in the background. Moreover, a corresponding effective ranking method is proposed to return the desirable search results. Experimental results demonstrate that our proposed system improves the searching performance and the user experience compared with the existing searching systems

Semi-supervised nonlinear hashing using bootstrap sequential projection learning.

Abstract-In this paper, we study the effective semi-supervised hashing method under the framework of regularized learningbased hashing. A nonlinear hash function is introduced to capture the underlying relationship among data points. Thus, the dimensionality of the matrix for computation is not only independent from the dimensionality of the original data space but also much smaller than the one using linear hash function. To effectively deal with the error accumulated during converting the real-value embeddings into the binary code after relaxation, we propose a semi-supervised nonlinear hashing algorithm using bootstrap sequential projection learning which effectively corrects the errors by taking into account of all the previous learned bits holistically without incurring the extra computational overhead. Experimental results on the six benchmark datasets demonstrate that the presented method outperforms the state-of-the-art hashing algorithms at a large margin. Index Terms-Hashing, semi-supervised hashing, nearest neighbor search

Detecting Multilevel Poverty-Causing Factors of Farmer Households in Fugong County: A Hierarchical Spatial–Temporal Regressive Model

Accurate examination of poverty-causing factors and their mechanisms of poverty-stricken farmer households from a fine scale is conducive to policy implementation and long-term effective poverty reduction. The spatial effects in most previous studies are not fully considered, resulting in less reliability of detection results. Therefore, by fully considering background effects and spatial–temporal effects, this study designs a hierarchical spatial–temporal regressive model (HSTRM) to accurately identify the factors as well as mechanisms that cause poverty more reasonably. The empirical study of Fugong County, Yunnan Province, China, shows that: (1) There has been a certain degree of spatial effects in the study area over the years; therefore, spatial effects should be considered. (2) The poverty degree of farmer households in the study area is affected by individual factors and background factors. Therefore, poverty-causing factors should be observed at different levels. (3) Poverty-causing factors feature different action mechanisms. The influence of the village-level factors on poverty is greater than that of the household level. In addition, the village-level factors have a certain impact on the contribution of household-level factors to poverty. This study offers technical support and policy guidance for sustainable poverty reduction and development of poor farmer households