三江平原湿地植物的土壤环境因子分析

以三江平原毛果苔草(Carex lasiocarpa)湿地和小叶章(Deyeuxia angustifolia)湿地为研究对象,选取土壤全氮(TN)、全磷(TP)、有效氮、有效磷、有机质、pH值和土壤含水量为主要环境控制因子,通过主成分分析(PCA)和逐步多元回归分析,来确定环境因子对物种相对密度和地上生物量的贡献值.结果表明,2个样地中土壤全氮、全磷、有效氮和有效磷的贡献率能达50%左右.毛果苔草湿地中主成分1能够解释88%的物种相对密度和84%的地上生物量;小叶章湿地中主成分1对相对密度和地上生物量的解释量则分别为75%和58%.湿地营养状况决定着植物的生产量;土壤含水量的变化影响着植物对营养物质的吸收和利用.     

Geochemistry of tin (Sn) in Chinese coals

Environmental Geochemistry and Health - Based on 1625 data collected from the published literature, the geochemistry of tin (Sn) in Chinese coals, including the abundance, distribution, modes of...     

A performance evaluation of the National Air Quality Forecast Capability for the summer of 2007

This paper provides a performance evaluation of the real-time, CONUS-scale National Air Quality Forecast Capability (NAQFC) that supported, in part, its transition into operational status. This evaluation focuses primarily on discrete forecasts for the maximum 8-h O₃ concentrations covering the 4-month period, June through September, 2007, using measurements obtained from EPA's AIRNow network. Results indicate that the 2007 NAQFC performed as well or better than previous configurations, despite the expansion of the forecast domain into the western half of the nation that is dominated by complex terrain. The mean, domain-wide, season-long correlation was 0.70. When examined over time, the domain-wide correlations exhibit a fairly consistent nature, with values exceeding 0.60 (0.70) over 90% (55%) of the days. The NAQFC systematically over-predicted the 8-h O₃ concentrations, continuing a trend established by earlier NAQFC configurations, though to a lesser degree. The summer-long mean forecast value of 53.2 ppb was 4.2 ppb higher than the observed value, resulting in a domain-wide Normalized Mean Bias (NMB) of 8.7%. Most of the over-prediction is associated with observed concentrations less than 50 ppb. In fact the model tends to under-predict when concentrations exceed 70 ppb. As with the bias, the error associated with the latest configuration was also lower. The summer-long Root Mean Square Error of 13.0 ppb (Normalized Mean Error (NME) = 20.4%) represented marked improvements over earlier forecasts. Examination of the spatial distribution of both the NMB and NME reveals that the NAQFC was generally within 25% for the NME and 25% for the NMB over a majority of the domain. Several areas of poorer performance, where the NMB and NME often exceed 25% and in some cases 50%, were noted. These areas include southern California, where the NAQFC tended to under-predict concentrations (especially on weekends) and the southeast Atlantic and Gulf coasts regions, where the model over-predicted. Subsequent analysis revealed that the incorrect temporal allocation of precursor emissions was likely the source of the under-prediction in southern California, while inaccurate simulation of PBL heights likely contributed to the over-prediction in the coastal regions.     

APPLICATION OF GREY MODEL AND ARTIFICIAL NEURAL NETWORKS TO FLOOD FORECASTING1

The main focus of this study was to compare the Grey model and several artificial neural network (ANN) models for real time flood forecasting, including a comparison of the models for various lead times (ranging from one to six hours). For hydrological applications, the Grey model has the advantage that it can easily be used in forecasting without assuming that forecast storm events exhibit the same stochastic characteristics as the storm events themselves. The major advantage of an ANN in rainfall‐runoff modeling is that there is no requirement for any prior assumptions regarding the processes involved. The Grey model and three ANN models were applied to a 2,509 km² watershed in the Republic of Korea to compare the results for real time flood forecasting with from one to six hours of lead time. The fifth‐order Grey model and the ANN models with the optimal network architectures, represented by ANN1004 (34 input nodes, 21 hidden nodes, and 1 output node), ANN1010 (40 input nodes, 25 hidden nodes, and 1 output node), and ANN1004T (14 input nodes, 21 hidden nodes, and 1 output node), were adopted to evaluate the effects of time lags and differences between area mean and point rainfall. The Grey model and the ANN models, which provided reliable forecasts with one to six hours of lead time, were calibrated and their datasets validated. The results showed that the Grey model and the ANN1010 model achieved the highest level of performance in forecasting runoff for one to six lead hours. The ANN model architectures (ANN1004 and ANN1010) that used point rainfall data performed better than the model that used mean rainfall data (ANN1004T) in the real time forecasting. The selected models thus appear to be a useful tool for flood forecasting in Korea.     

彭州市环境空气质量变化趋势及规律分析

“九五”期间 ,彭州市环境空气监测主要因子有二氧化硫、二氧化氮、总悬浮颗粒物、降尘和降水。市区空气中主要污染物排序依次为降尘、总悬浮颗粒物、二氧化硫、二氧化氮。其季节变化规律一般为第一季度 >第二季度 >第四季度 >第三季度。主要污染物年变化依次为降尘、总悬浮颗粒物、二氧化硫、二氧化氮 ,在“九五”期间除降尘量出现缓慢下降趋势外 ,其余 3项均不明显。     

改性木屑吸附剂对印染废水的脱色性能研究

本文介绍了以廉价易得的杨树木屑为原料，经环氧氯丙烷交联，制备的一种新的生物吸附剂，研究各种反应因素（氢氧化钠浓度、环氧氯丙烷用量、反应温度、木屑粒径等）对反应过程和吸附剂性能的影响，检验了其对品红及孔雀石绿在不同条件下的吸附性能，并探讨了它的吸附动力学曲线和再生性能。     

抗灾减灾效益估计方法探讨

从综合角度阐述减灾效益的概念,进而以减灾效益内容为基础,提出了估计减灾效益的方法。     

中国新克强指数的构建与实证分析

随着中国经济进入新常态,英国《经济学人》杂志构建的克强指数已无法反映中国经济的全貌,如何客观地反映中国经济的真实状况成为一个迫切需要解决的现实问题。为更好地测度中国经济运行的真实状况,本文根据李克强总理提出的"未来会更加关注就业、居民收入和生态环境的持续改善"的愿景,在原有克强指数的基础上,增加年末就业人员数、农村居民家庭人均纯收入、城镇居民家庭人均可支配收入等就业和收入方面的指标,使用中国1995—2015年的相关数据和层次分析法构建了克强指数的修订版本即新克强指数,并将新克强指数与GDP增长率、克强指数进行了比较,发现新克强指数在反映中国经济波动和经济整体运行状态上有更为优良的性质。与GDP增长率相比,新克强指数克服了服务业比重提高带来的熨平效应,进而灵敏地反映中国经济波动情况,并对异常事件更加具有敏感性。相对于克强指数而言,新克强指数破除了因中国在第二产业上存在失衡性发展所带来的经济波动的杠杆效应,降低了经济增长中的虚假成分,从而与中国经济运行的整体关联度更为密切。此外,相对于克强指数而言,新克强指数在反映经济景气方面有着较好的时效性与关联性。采用时差相关分析法对新克强指数与宏观经济指标之间的关系进行分析,发现新克强指数在反映第一产业增加值、第三产业增加值、最终消费支出、资本形成总额、对外贸易、通货膨胀等方面具有优势。研究表明:新克强指数是更适宜测度中国经济的多元评价指标,为测度中国经济运行的真实状况提供了科学的参考依据。