Projecting Climate Change Effects on Forest Net Primary Productivity in Subtropical Louisiana,USA

This study projected responses of forest net primary productivity (NPP) to three climate change scenarios at a resolution of 5 km × 5 km across the state of Louisiana, USA. In addition, we assessed uncertainties associated with the NPP projection at the grid and state levels. Climate data of the scenarios were derived from Community Climate System Model outputs. Changes in annual NPP between 2000 and 2050 were projected with the forest ecosystem model PnET-II. Results showed that forest productivity would increase under climate change scenarios A1B and A2, but with scenario B1, it would peak during 2011–2020 and then decline. The projected average NPP under B1 over the years from 2000 to 2050 was significantly different from those under A1B and A2. Forest NPP appeared to be primarily a function of temperature, not precipitation. Uncertainties of the NPP projection were due to large spatial resolution of the climate variables. Overall, this study suggested that in order to project effects of climate change on forest ecosystem at regional level, modeling uncertainties could be reduced by increasing the spatial resolution of the climate projections.     

人口长期均衡发展及其评价监测模型的构建与应用

在借鉴已有理论的基础上,界定了人口长期均衡发展的内涵,提出人口长期均衡包含人口内部均衡和人口外部均衡两个方面,人口内部均衡包括人口数量、人口质量和人口结构三个维度,人口外部均衡包括人口与经济、人口与社会、人口与资源、人口与环境四个维度;在此基础上构建了人口长期均衡发展的指标体系和评价模型,指标体系设定为三级,第一级包括人口内部均衡和人口外部均衡两个方面,第二级由人口数量、人口质量、人口结构等七个部分构成;第三级指标包括反映内部均衡的7个指标以及反映外部均衡的13个指标.并使用层次分析法确定了指标的权重;采用2000-2007年31个省级单位的数据对人口长期均衡发展评价模型进行了验证,并对人口总体均衡水平以及影响人口均衡发展的关键要素进行了实证分析.结果表明.北京、上海和浙江分列前三,人口均衡发展程度最高.从影响均衡程度的关键因素看.人口质量和人口结构是制约人口自身均衡的主要因素,人口与社会的均衡状况对人口与外部系统均衡制约最大,其次是人口与经济的均衡状况.     

Quantification of nitrate leaching from German forest ecosystems by use of a process oriented biogeochemical model

Simulations with the process oriented Forest-DNDC model showed reasonable to good agreement with observations of soil water contents of different soil layers, annual amounts of seepage water and approximated rates of nitrate leaching at 79 sites across Germany. Following site evaluation, Forest-DNDC was coupled to a GIS to assess nitrate leaching from German forest ecosystems for the year 2000. At national scale leaching rates varied in a range of 0–>80 kg NO₃–N ha⁻¹ yr⁻¹ (mean 5.5 kg NO₃–N ha⁻¹ yr⁻¹). A comparison of regional simulations with the results of a nitrate inventory study for Bavaria showed that measured and simulated percentages for different nitrate leaching classes (0–5 kg N ha⁻¹ yr⁻¹:66% vs. 74%, 5–15 kg N ha⁻¹ yr⁻¹:20% vs. 20%, >15 kg N ha⁻¹ yr⁻¹:14% vs. 6%) were in good agreement. Mean nitrate concentrations in seepage water ranged between 0 and 23 mg NO₃–N l⁻¹.     

Comparison of the daily global solar radiation from different data sources in Northwest China climate

The solar radiation data are of high importance to the solar energy systems. Conventional methods to obtain the solar radiation data are from weather stations, solar radiation models, commercial software databases, and field measurements. In the present study, a new daily global solar radiation model is proposed, by combining the quadratic function of sunshine fraction and sine function of the day of the year. The solar radiation model calculated data are then compared with China Meteorological Data Sharing System (CMDSS) data, TRNSYS data, and field-measured data in Northwest China climate. It is found that the newly proposed solar radiation model has better performance than the other nine solar radiation models in the literature. The solar radiation model calculated data fit well with the CMDSS annually average data. The TRNSYS data are a bit larger than the CMDSS annually average data in summer half year and a little smaller than those in winter half year. The solar radiation model and the CMDSS annually average data have the best correlation, whereas the TRNSYS data and the field-measured data have the worst correlation. The solar radiation model calculated data have the best correlation with the other three data sources.     

Neural network forecasting of air pollutants hourly concentrations using optimised temporal averages of meteorological variables and pollutant concentrations

The new method for the forecasting hourly concentrations of air pollutants is presented in the paper. The method was developed for a site in urban residential area in city of Zagreb, Croatia, for four air pollutants (NO₂, O₃, CO and PM₁₀). Meteorological variables and concentrations of the respective pollutant were taken as predictors. A novel approach, based on families of univariate regression models, was employed in selecting the averaging intervals for input variables. For each variable and each averaging period between 1 and 97 h, a separate model was built. By inspecting values of the coefficient of correlation between measured and modelled concentrations, optimal averaging periods for each variable were selected. A new dataset for building the forecasting model was then calculated as temporal moving averages (running means) of former variables. A multi-layer perceptron type of neural networks is used as the forecasting model. Index of agreement, calculated for the entire dataset including the data for model building, ranged from 0.91 to 0.97 for the respective pollutants. As suggested by the analysis of the relative importance of the input variables, different agreements for different pollutants are likely due to different sources and production mechanisms of investigated pollutants. A comparison of the new method with more traditional method, which takes hourly averages of the forecast hour as input variables, showed similar or better performance. The model was developed for the purpose of public-health-oriented air quality forecasting, aiming to use a numerical weather forecast model for the prediction of the part of input data yet unknown at the forecasting time. It is to expect that longer term averages used as inputs in the proposed method will contribute to smaller input errors and the greater accuracy of the model.     

A review and test of predictive models for the bioaccumulation of radiostrontium in fish

Empirical relations between the ⁹⁰Sr concentration factor (CF) and the calcium concentration in freshwater aquatic systems have previously been determined in studies based on data obtained prior to the Chernobyl accident. The purpose of the present research is to review and compare these models, and to test them against a database of post-Chernobyl measurements from rivers and lakes in Ukraine, Russia, Belarus and Finland. It was found that two independently developed models, based on pre-Chernobyl empirical data, are in close agreement with each other, and with empirical data. Testing of both models against new data obtained after the Chernobyl accident confirms the models’ predictive ability. An investigation of the influence of fish size on ⁹⁰Sr accumulation showed no significant relationship, though the data set was somewhat limited.     

A new approach to quantify the degradation kinetics of linuron with UV, ozonation and UV/O3 processes

The degradation of linuron, one of phenylurea herbicides, was investigated for its reaction kinetics by different treatment processes including ultraviolet irradiation (UV), ozonation (O₃), and UV/O₃. The decay rate of linuron by UV/O₃ process was found to be around 3.5 times and 2.5 times faster than sole-UV and ozone-alone, respectively. Experimental results also indicate overall rate constants increased exponentially with pH above 9.0 while the increase of rate constants with pH below 9 is insignificant in O₃ system. All dominant parameters involved in the three processes were determined in the assistant of proposed linear models in this study. The approach was found useful in predicting the process performances through the quantification of quantum yield (rate constant for the formation of free radical HOO⁻ from ozone decomposition at high pH), rate constant of linuron with ozone (k_O3,LNR), rate constant of linuron with hydroxyl radical (k_OH,LNR), and α (the ratio of the production rate of OH and the decay rate of ozone in UV/O₃ system).     

A method for ranking components importance in presence of epistemic uncertainties

Importance Measures (IMs) are used to rank the contributions of components or basic events to the system performance, e.g. its reliability or risk. Most times, IMs are calculated without due account of the uncertainties in the model of the behavior of the system. The objective of this work is to investigate how uncertainties can influence IMs and to develop a method for giving them due account in the corresponding ranking of the components or basic events. The uncertainties considered in this work affect the model parameters values and are assumed to be described by probability density functions. The method for ranking the contributors to the system performance measure is applied to the auxiliary feedwater system of a nuclear pressurized water reactor.     

中国工业碳排放达峰的情景预测与减排潜力评估

实现2030年碳排放达峰不仅是中国为应对全球气候变化向国际社会做出的郑重承诺,也是中国未来经济结构转型与可持续发展的必然选择。基于中国实现2030年碳排放达到峰值的宏观目标为背景,本文以中国碳排放的主要行业工业为研究对象,首先运用拓展的STIRPAT模型对工业及其9个细分行业的碳排放达峰进行了情景预测,然后基于公平和效率的双重视角对工业细分行业的减排潜力进行评估。研究表明:(1)仅有低碳情景和抑制排放情景2可以实现中国碳排放2030年达峰,低碳情景是实现中国工业碳排放达峰的最佳发展模式,达峰时间最早(2030年),峰值最低(140.43亿t)。激进排放情景则是最差的发展模式,达峰时间最晚(2036年),峰值也最高(150.09亿t)。(2)工业内部各细分行业碳排放的最优达峰情景差别较大。建材和纺织制造业能够实现提前达峰,可以在这类行业率先实施达峰管理措施,使其带动其他行业陆续达峰。(3)最具减排潜力的行业是石油制造业,其次是电力行业,这些减排潜力较大的行业应该成为国家节能减排的重点对象。(4)基于工业各细分行业在减排公平性和效率性上的差异将工业9个细分行业分为四类。其中,石油、钢铁制造业和电力行业属于"高效高公平行业";化工、建材制造业属于"低效高公平行业";采掘业属于"高效不公平行业";纺织、轻工和机电制造业属于"低效不公平行业"。中国应针对不同类型的行业制定出相应的减排战略,将减排重点放在各行业最具潜力的方面。最后,文章对实现中国工业碳排放达峰管理提出了几点政策建议。