Ammonia (NH3) released to the atmosphere leads to a cascade of impacts on the environment, yet estimation of NH3 volatilization from cropland soils (V-NH3) in a broad spatial scale is still quite uncertain in China. This mainly stems from nonlinear relationships between V-NH3 and relevant factors. On the basis of 495 site-years of measurements at 78 sites across Chinese croplands, we developed a nonlinear Bayesian tree regression model to determine how environmental factors modulate the local derivative of V-NH3 to nitrogen application rates (N-rate) (VR, %). The V-NH3 -N-rate relationship was nonlinear. The VR of upland soils and paddy soils depended primarily on local water input and N-rate, respectively. Our model demonstrated good reproductions of V-NH3 compared to previous models, i.e., more than 91% of the observed VR variance at sites in China and 79% of those at validation sites outside China. The observed spatial pattern of V-NH3 in China agreed well with satellite-based estimates of NH3 column concentrations. The average VRs in China derived from our model were 14.8 +/- 2.9% and 11.8 +/- 2.0% for upland soils and paddy soils, respectively. The estimated annual NH3 emission in China (3.96 +/- 0.76 TgNH(3).yr(-1)) was 40% greater than that based on the IPCC Tier 1 guideline.
To enhance the effectiveness of watershed load reduction decision making, the Risk Explicit Interval Linear Programming (REILP) approach was developed in previous studies to address decision risks and system returns. However, REILP lacks the capability to analyze the tradeoff between risks in the objective function and constraints. Therefore, a refined REILP model is proposed in this study to further enhance the decision support capability of the REILP approach for optimal watershed load reduction. By introducing a tradeoff factor (alpha) into the total risk function, the refined REILP can lead to different compromises between risks associated with the objective functions and the constraints. The proposed model was illustrated using a case study that deals with uncertainty-based optimal load reduction decision making for Lake Qionghai Watershed, China. A risk tradeoff curve with different values of alpha was presented to decision makers as a more flexible platform to support decision formulation. The results of the standard and refined REILP model were compared under 11 aspiration levels. The results demonstrate that, by applying the refined REILP, it is possible to obtain solutions that preserve the same constraint risk as that in the standard REILP but with lower objective risk, which can provide more effective guidance for decision makers.
To enhance the effectiveness of watershed load reduction decision making, the Risk Explicit Interval Linear Programming (REILP) approach was developed in previous studies to address decision risks and system returns. However, REILP lacks the capability to analyze the tradeoff between risks in the objective function and constraints. Therefore, a refined REILP model is proposed in this study to further enhance the decision support capability of the REILP approach for optimal watershed load reduction. By introducing a tradeoff factor ($\alpha$) into the total risk function, the refined REILP can lead to different compromises between risks associated with the objective functions and the constraints. The proposed model was illustrated using a case study that deals with uncertainty-based optimal load reduction decision making for Lake Qionghai Watershed, China. A risk tradeoff curve with different values of $\alpha$ was presented to decision makers as a more flexible platform to support decision formulation. The results of the standard and refined REILP model were compared under 11 aspiration levels. The results demonstrate that, by applying the refined REILP, it is possible to obtain solutions that preserve the same constraint risk as that in the standard REILP but with lower objective risk, which can provide more effective guidance for decision makers.
Recent studies have identified biomarkers of chronological age based on DNA methylation levels. Since active smoking contributes to a wide spectrum of aging-related diseases in adults, this study intended to examine whether active smoking exposure could accelerate the DNA methylation age in forms of age acceleration (AA, residuals of the DNA methylation age estimate regressed on chronological age). We obtained the DNA methylation profiles in whole blood samples by Illumina Infinium Human Methylation450 Beadchip array in two independent subsamples of the ESTHER study and calculated their DNA methylation ages by two recently proposed algorithms. None of the self-reported smoking indicators (smoking status, cumulative exposure and smoking cessation time) or serum cotinine levels was significantly associated with AA. On the contrary, we successfully confirmed that 66 out of 150 smoking-related CpG sites were associated with AA, even after correction for multiple testing (FDR <0.05). We further built a smoking index (SI) based on these loci and demonstrated a monotonic dose-response relationship of this index with AA. In conclusion, DNA methylation-based biological indicators for current and past smoking exposure, but not self-reported smoking information or serum cotinine levels, were found to be related to DNA methylation defined AA. Further research should address potential mechanisms underlying the observed patterns, such as potential reflections of susceptibility to environmental hazards in both smoking related methylation changes and methylation defined AA.
In this study, interval-parameter programming, two-stage stochastic programming (TSP), and conditional value-at-risk (CVaR) were incorporated into a general optimization framework, leading to an interval-parameter CVaR-based two-stage programming (ICTP) method. The ICTP method had several advantages: (i) its objective function simultaneously took expected cost and risk cost into consideration, and also used discrete random variables and discrete intervals to reflect uncertain properties; (ii) it quantitatively evaluated the right tail of distributions of random variables which could better calculate the risk of violated environmental standards; (iii) it was useful for helping decision makers to analyze the trade-offs between cost and risk; and (iv) it was effective to penalize the second-stage costs, as well as to capture the notion of risk in stochastic programming. The developed model was applied to sulfur dioxide abatement in an air quality management system. The results indicated that the ICTP method could be used for generating a series of air quality management schemes under different risk-aversion levels, for identifying desired air quality management strategies for decision makers, and for considering a proper balance between system economy and environmental quality.
Inverted planar heterojunction perovskite solar cells with poly (3,4-ethylenedioxythiophene): poly (styrenesulfonate) sulfonic acid (PEDOT:PSS) as the hole transport layer (HTL) have attracted significant attention during recent years. However, these devices suffer from a serious stability issue due to the acidic and hygroscopic characteristics of PEDOT: PSS. In this work, we demonstrate a room-temperature and solution-processed CuI film which is used as the HTL for inverted perovskite solar cells. As a result, an impressive PCE of 16.8% is achieved by the device based on the CuI HTL. Moreover, the unsealed CuI-based device displays enhanced air stability compared to the PEDOT: PSS-based device. In addition, the fabrication of the CuI HTL is a simple and time-saving procedure without any post-treatment, thus making it a promising candidate as the HTL in inverted perovskite solar cells and a potential target for efficient flexible and tandem solar cells.
Luo G, Zhang W, Zhang J, Cong J. Scaling Up Physical Design, in Proceedings of the 2016 on International Symposium on Physical Design - ISPD '16. New York, New York, USA: ACM Press; 2016:131–137. 访问链接
We describe the results from online measurements of nitrated phenols using a time-of-flight chemical ionization mass spectrometer (ToF-CIMS) with acetate as reagent ion in an oil and gas production region in January and February of 2014. Strong diurnal profiles were observed for nitrated phenols, with concentration maxima at night. Based on known markers (CH4, NOx, CO2), primary emissions of nitrated phenols were not important in this study. A box model was used to simulate secondary formation of phenol, nitrophenol (NP), and dinitrophenols (DNP). The box model results indicate that oxidation of aromatics in the gas phase can explain the observed concentrations of NP and DNP in this study. Photolysis was the most efficient loss pathway for NP in the gas phase. We show that aqueous-phase reactions and heterogeneous reactions were minor sources of nitrated phenols in our study. This study demonstrates that the emergence of new ToF-CIMS (including PTR-TOF) techniques allows for the measurement of intermediate oxygenates at low levels and these measurements improve our understanding on the evolution of primary VOCs in the atmosphere.