Agricultural soils account for more than 50% of nitrogen leaching (L-N) to groundwater in China. When excess levels of nitrogen accumulate in groundwater, it poses a risk of adverse health effects. Despite this recognition, estimation of L-N from cropland soils in a broad spatial scale is still quite uncertain in China. The uncertainty of L-N primarily stems from the shape of nitrogen leaching response to fertilizer additions (N-rate) and the role of environmental conditions. On the basis of 453 site-years at 51 sites across China, we explored the nonlinearity and variability of the response of L-N to N-rate and developed an empirical statistical model to determine how environmental factors regulate the rate of N leaching (LR). The result shows that L-N-N-rate relationship is convex for most crop types, and varies by local hydro-climates and soil organic carbon. Variability of air temperature explains a half (similar to 52%) of the spatial variation of LR. The results of model calibration and validation indicate that incorporating this empirical knowledge into a predictive model could accurately capture the variation in leaching and produce a reasonable upscaling from site to country. The fertilizer-induced L-N in 2008 for China's cropland were 0.88 +/- 0.23 TgN (1 sigma), significantly lower than the linear or uniform model, as assumed by Food and Agriculture Organization and MITERRA-EUROPE models. These results also imply that future policy to reduce N leaching from cropland needs to consider environmental variability rather than solely attempt to reduce N-rate. (C) 2016 Elsevier Ltd. All rights reserved.
Atmospheric polycyclic aromatic hydrocarbons (PAHs) and their derivatives are of great concern due to their adverse health effects. However, source identification and apportionment of these compounds, particularly their nitrated and hydroxylated derivatives (i.e., NPAHs and OHPAHs), in fine particulate matter (PM2.5) in Hong Kong are still lacking. In this study, we conducted a 1-year observation at an urban site in Hong Kong. PM2.5-bound PAHs and their derivatives were measured, with median concentrations of 4590, 44.4 and 31.6 pg m(-3) for Sigma(21)PAHs, Sigma(13)NPAHs, and Sigma(12)OHPAHs, respectively. Higher levels were observed on regional pollution days than on long regional transport (LRT) or local emission days. Based on positive matrix factorization analysis, four sources were determined: marine vessels, vehicle emissions, biomass burning, and a mixed source of coal combustion and NPAHs secondary formation. Coal combustion and biomass burning were the major sources of PAHs, contributing over 85% of PAHs on regional and LRT days. Biomass burning was the predominant source of OHPAHs throughout the year, while NPAHs mainly originated from secondary formation and fuel combustion. For benzo[a]pyrene (BaP)-based PM2.5 toxicity, the mixed source of coal combustion and NPAHs secondary formation was the major contributor, followed by biomass burning and vehicle emissions. (C) 2016 Elsevier Ltd. All rights reserved.
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.