Oxygenated volatile organic compounds (OVOCs) are important products of the photo-oxidation of hydrocarbons. They influence the oxidizing capacity and the ozone-forming potential of the atmosphere. In the summer of 2008, 2 months of emission restrictions were enforced in Beijing to improve air quality during the Olympic Games. Observational evidence reported in related studies that these control measures were efficient in reducing the concentrations of primary anthropogenic pollutants (CO, NOx and non-methane hydrocarbons, i.e., NMHCs) by 30-40%. In this study, the influence of the emission restrictions on ambient levels of OVOCs was explored using a neural network analysis with consideration of meteorological conditions. Statistically significant reductions in formaldehyde (HCHO), acetaldehyde (CH3CHO), methyl ethyl ketone (MEK) and methanol were found to be 12.9, 15.8, 17.1 and 19.6%, respectively, when the restrictions were in place. The effect of emission controls on acetone was not detected in neural network simulations, probably due to pollution transport from surrounding areas outside Beijing. Although the ambient levels of most NMHCs were reduced by similar to 35% during the full control period, the emission ratios of reactive alkenes and aromatics closely related to automobile sources did not present much difference (< 30%). A zero-dimensional box model based on the Master Chemical Mechanism version 3.2 (MCM3.2) was applied to evaluate how OVOC production responds to the reduced precursors during the emissions control period. On average, secondary HCHO was produced from the oxidation of anthropogenic alkenes (54%), isoprene (30%) and aromatics (15%). The importance of biogenic sources for the total HCHO formation was almost on par with that of anthropogenic alkenes during the daytime. Anthropogenic alkenes and alkanes dominated the photochemical production of other OVOCs such as acetaldehyde, acetone and MEK. The relative changes of modeled HCHO, CH3CHO, methyl vinyl ketone and methacrolein (MVK + MACR) before and during the pollution controlled period were comparable to the estimated reductions in the neural network, reflecting that current mechanisms can largely explain secondary production of those species under urban conditions. However, it is worth noting that the box model overestimated the measured concentrations of aldehydes by a factor of 1.4-1.7 without consideration of loss of aldehydes on aerosols, and simulated MEK was in good agreement with the measurements when primary sources were taken into consideration. These results suggest that the understanding of the OVOCs budget in the box model remains incomplete, and that there is still considerable uncertainty in particular missing sinks (unknown chemical and physical processes) for aldehydes and absence of direct emissions for ketones
A nonadditive hole-transporting material (HTM) of a triphenylamine derivative of N,N'-di(3-methylphenyl)-N,N'-diphenyl-4,4'-diaminobiphenyl (TPD) is used for the organic-inorganic hybrid perovskite solar cells. The power conversion efficiency (PCE) can be significantly enhanced by inserting a thin layer of 1,4,5,8,9,11-hexaazatriphenylenehexacarbonitrile (HAT-CN) without adding an ion additive because the hole-transporting properties improve. The short-circuit current density (J(sc)) increases from 8.5 to 13.1 mA/cm(2), the open-circuit voltage (V-oc) increases from 0.84 to 0.92 V, and the fill-factor (FF) increases from 0.45 to 0.59, which corresponds to the increase in PCE from 3.2% to 7.1%. Moreover, the PCE decreases by only 10% after approximately 1000 h without encapsulation, which suggests an alternative method to improve the stability of perovskite solar cells.
An indirect simulation-optimization model framework with enhanced computational efficiency and risk-based decision-making capability was developed to determine optimal total maximum daily load (TMDL) allocation under uncertainty. To convert the traditional direct simulation-optimization model into our indirect equivalent model framework, we proposed a two-step strategy: (1) application of interval regression equations derived by a Bayesian recursive regression tree (BRRT v2) algorithm, which approximates the original hydrodynamic and water-quality simulation models and accurately quantifies the inherent nonlinear relationship between nutrient load reductions and the credible interval of algal biomass with a given confidence interval; and (2) incorporation of the calibrated interval regression equations into an uncertain optimization framework, which is further converted to our indirect equivalent framework by the enhanced-interval linear programming (EILP) method and provides approximate-optimal solutions at various risk levels. The proposed strategy was applied to the Swift Creek Reservoir's nutrient TMDL allocation (Chesterfield County, VA) to identify the minimum nutrient load allocations required from eight sub-watersheds to ensure compliance with user-specified chlorophyll criteria. Our results indicated that the BRRT-EILP model could identify critical sub-watersheds faster than the traditional one and requires lower reduction of nutrient loadings compared to traditional stochastic simulation and trial-and-error (TAE) approaches. This suggests that our proposed framework performs better in optimal TMDL development compared to the traditional simulation-optimization models and provides extreme and non-extreme tradeoff analysis under uncertainty for risk-based decision making.
Satellite data and models suggest that oceanic productivity is reduced in response to less nutrient supply under warming. In contrast, anthropogenic aerosols provide nutrients and exert a fertilizing effect, but its contribution to evolution of oceanic productivity is unknown. We simulate the response of oceanic biogeochemistry to anthropogenic aerosols deposition under varying climate from 1850 to 2010. We find a positive response of observed chlorophyll to deposition of anthropogenic aerosols. Our results suggest that anthropogenic aerosols reduce the sensitivity of oceanic productivity to warming from -15.21.8 to -13.31.6PgCyr(-1)degrees C-1 in global stratified oceans during 1948-2007. The reducing percentage over the North Atlantic, North Pacific, and Indian Oceans reaches 40, 24, and 25%, respectively. We hypothesize that inevitable reduction of aerosol emissions in response to higher air quality standards in the future might accelerate the decline of oceanic productivity per unit warming.
Exposure to ozone has been associated with airway inflammation, oxidative stress, and bronchial hyperresponsiveness. The goal of this study was to examine whether these adverse effects of ozone could be prevented or reversed by hydrogen sulfide (H2S) as a reducing agent. The H2S donor sodium (NaHS) (2 mg/kg) or vehicle (PBS) was intraperitoneally injected into mice 1 hour before and after 3-hour ozone (2.5 ppm) or air exposure, and the mice were studied 24 hours later. Preventive and therapeutic treatment with NaHS reduced the ozone-induced increases in the total cells, including neutrophils and macrophages; this treatment also reduced levels of cytokines, including TNF-alpha, chemokine (C-X-C motif) ligand 1, IL-6, and IL-1beta levels in bronchial alveolar lavage fluid; inhibited bronchial hyperresponsiveness; and attenuated ozone-induced increases in total malondialdehyde in bronchoalveolar lavage fluid and decreases in the ratio of reduced glutathione/oxidized glutathione in the lung. Ozone exposure led to decreases in the H2S production rate and in mRNA and protein levels of cystathionine-beta-synthetase and cystathionine-gamma-lyase in the lung. These effects were prevented and reversed by NaHS treatment. Furthermore, NaHS prevented and reversed the phosphorylation of p38 mitogen-activated protein kinase and heat shock protein 27. H2S may have preventive and therapeutic value in the treatment of airway diseases that have an oxidative stress basis.
Understanding the relationships between hydrological regime and climate change is important for water resources management. In this study, the streamflow response to climate change was investigated in the Lake Dianchi watershed, which is one of the most important eutrophic lakes in China. Daily time-series of temperature and precipitation in the future periods (2020, 2050 and 2080s) were projected from HadCM3 model. Statistical downscaling model (SDSM) and the previously calibrated and validated Soil and water assessment tool (SWAT) model were used to quantify the impacts of climate change on streamflow in this watershed. The results showed that SDSM can well capture the statistical relationships between the large scale climate variables and the observed weather at regional scale. The downscaled results showed that annual average maximum and minimum temperature would rise by 4.28 (3.25) and 4.71 A degrees C (3.33 A degrees C) in the 2080s under A2 (B2) scenario. Annual average precipitation would decrease within the range between 20.34 and 74.12 mm under both scenarios in the future. Based on SWAT model simulation, annual average streamflow would decrease in the future by the declination of -7.12 to -21.83 % and -6.34 to -17.09 % under A2 (B2) scenarios in the outlet of this watershed. The frequency of drought and extreme rainfall events would increase in the future, which is not beneficial to protect Lake Dianchi. This study could lead to a better understanding of the streamflow response under climate change and also raised concerns about the sustainability of future water resources in Lake Dianchi watershed.