南昌市一次降水自动监测结果的综合分析

利用降水自动监测仪和空气自动监测系统的同步监测结果,分析了一次34 5mm连续降水中全过程的pH、EC和同期SO2、PM10、NO2的变化情况。降水中pH值为7 23～4 08、EC为488 8～7 6μS cm,pH、EC在降水初期变化较大。pH与EC、SO2呈现负相关趋势,降水过程中PM10、NO2基本保持较低水平,SO2变化较大。     

Fecundity as a Basis for Risk Assessment of Nonindigenous Freshwater Molluscs

Abstract:  The most efficient way to reduce future damages from nonindigenous species is to prevent the introduction of harmful species. Although ecologists have long sought to predict the identity of such species, recent methodological advances promise success where previous attempts failed. We applied recently developed risk assessment approaches to nonindigenous freshwater molluscs at two geographic scales: the Laurentian Great Lakes basin and the 48 contiguous states of the United States. We used data on natural history and biogeography to discriminate between established freshwater molluscs that are benign and those that constitute nuisances (i.e., cause environmental and/or economic damage). Two statistical techniques, logistic regression and categorical tree analysis, showed that nuisance status was positively associated with fecundity. Other aspects of natural history and biogeography did not significantly affect likelihood of becoming a nuisance. We then used the derived statistical models to predict the chance that 15 mollusc species not yet in natural ecosystems would cause damage if they become established. We also tested whether time since establishment is related to the likelihood that nonindigenous mollusc species in the Great Lakes and United States would cause negative impacts. No significant relationship was evident at the U.S. scale, but recently established molluscs within the Great Lakes were more likely to cause negative impacts. This may reflect changing environmental conditions, changing patterns of trade, or may be an indication of "invasional meltdown." Our quantitative analyses could be extended to other taxa and ecosystems and offer a number of improvements over the qualitative risk assessments currently used by U.S. (and other) government agencies.     

达州市城区环境空气质量变化趋势及CMAQ模型预报分析

利用Spearman秩相关系数法、污染日历图、浓度分析法和CMAQ预测模型研究了达州市城区2015-2019年空气质量状况.结果表明:2015-2019年,达州市城区O3浓度变化趋势为显著上升(P＜0.05),季度变化明显,8月易发生因O3超标导致的轻度污染状况;CO年均值变化趋势为显著降低(P＜0.05);NO2年均...     

天津市路面雨水径流重金属污染特征

对天津市3条典型主要交通干线雨水径流中的Cr、Cd、Fe、Cu、Hg 5种重金属进行了取样监测,重点探讨了路面径流重金属的平均质量浓度、冲洗特征、重金属之间以及重金属与悬浮物之间的相关关系,并用聚类分析方法对降雨特征对重金属污染的影响进行了研究。结果表明,天津市路面雨水径流重金属污染严重,Fe、Cr和Cd质量浓度较高,污染贡献率大,为径流主要重金属污染物;重金属污染水平高于国内外城市研究结果,反映出天津市在道路路面环境维护和管理上与其他城市的差距。路面径流的重金属具有明显的径流初期冲洗效应,在径流的初始阶段,重金属质量浓度达到最大值,随着降雨和径流的延续,污染物质量浓度呈波浪性锯齿状下降,至降雨结束,重金属质量浓度最终趋于稳定。径流中不同种类重金属具有同源性,且与径流中的悬浮固体具有明显的相互依存关系,重金属主要以悬浮固体吸附的形式或重金属颗粒形式存在。通过聚类分析可知,重金属污染受降雨强度和前期晴天积累影响显著,其次是降雨量和降雨历时。     

不同土地利用方式土壤表层氮、磷流失特征研究

利用室内人工模拟降雨,选择徐州沛沿河流域3种典型土地利用类型,研究了不同土地利用下土壤表层N、P随降雨径流的迁移过程.研究结果表明,在相同降雨条件下,3种土地产流量大小顺序为稻田地〉林地〉果园,产泥沙量大小顺序为果园〉稻田地〉林地.降雨径流水相中TN、TP浓度呈现随降雨时间的持续显著下降,然后逐渐稳定的趋势,氮磷的流失...     

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.     

不同雨强条件下太湖流域典型蔬菜地土壤磷素的径流特征

以太湖流域典型区域无锡市近郊区鸿声镇的蔬菜地为研究对象,采用人工模拟降雨的方法,通过野外径流小区试验,研究了不同雨强对菜地土壤磷素径流流失的影响.结果表明,初始产流时间随雨强的增大呈幂函数减小(R²=0.99),径流量在雨强较小时,缓慢上升,但随着雨强的增大急剧上升,在雨强0.83、1.17和1.67 mm·min^-1时,总磷(TP)和颗粒态磷(PP)都表现为初始流失浓度较高,随降雨历时延长略有下降,最终趋于稳定,而在大雨强2.50 mm·min^-1时,TP和PP呈现波浪式起伏,没有明显的变化趋势;在整个降雨-径流过程中,溶解态磷(DP)变化比较平缓,占TP的比例为20%～32%,而PP占TP的比例为68%～80%,其变化规律与TP相一致,由此可见,PP是土壤磷素流失的主要形态;通过对比不同雨强下不同形态磷素的流失率,发现TP的流失率,大雨强2.50 mm·min^-1是小雨强0.83 mm·min^-1的20倍,而DP的流失率,却是33倍,这表明随着雨强的增加,加速土壤PP流失的同时,也大大促进了DP的流失,主要原因是降雨前表施磷肥,使得磷肥中大量的无机态磷溶解释放到水环境中,增加了DP的流失,从而会加重受纳水体富营养化的程度.     

基于深度学习的大数据空气污染预报

为了更好地反映环境污染变化趋势,为环境管理决策提供及时、全面的环境质量信息,预防严重污染事件发生,开展城市空气质量预报研究是十分必要的.本文针对环境大数据时代下的城市空气质量预报,提出了一种基于深度学习的新方法.该方法通过模拟人类大脑的神经连接结构,将数据在原空间的特征表示转换到具有语义特征的新特征空间,自动地学习得到层次化的特征表示,从而提高预报性能.得益于这种方式,新方法与传统方法相比,不仅可以利用空气质量监测、气象监测及预报等环境大数据,充分考虑污染物的时空变化、空间分布,得到语义性的污染物变化规律,还可以基于其他空气污染预测方法的结果(如数值预报模式),自动分析其适用范围、优势劣势.因此,新方法通过模拟人脑思考过程实现更充分的大数据集成,一定程度上克服了现有方法的缺陷,应用上更加具有灵活性和可操作性.最后,通过实验证明新方法可以提高空气污染预报性能.     

Development of the Spatial Rainfall Generator (SRGEN) for the Agricultural Policy/Environmental Extender Model

Accurate spatial representation of climatic patterns is often a challenge in modeling biophysical processes at the watershed scale, especially where the representation of a spatial gradient in rainfall is not sufficiently captured by the number of weather stations. The spatial rainfall generator (SRGEN) is developed as an extension of the “weather generator” (WXGEN), a component of the Agricultural Policy/Environmental eXtender (APEX) model. SRGEN generates spatially distributed daily rainfall using monthly weather statistics available at multiple locations in a watershed. The spatial rainfall generator as incorporated in APEX is tested on the Cowhouse watershed (1,178 km²) in central Texas. The watershed presented a significant spatial rainfall gradient of 2.9 mm/km in the lateral (north‐south) directions based on four rainfall gages. A comparative analysis between SRGEN and WXGEN indicates that SRGEN performs well (PBIAS = 2.40%). Good results were obtained from APEX for streamflow (NSE = 0.99, PBIAS = 8.34%) and NO₃‐N and soluble P loads (PBIAS ≈ 6.00% for each, respectively). However, APEX underpredicted sediment yield and organic N and P loads (PBIAS: 24.75‐27.90%) with SRGEN, although its uncertainty in output was lower than WXGEN results (PBIAS: ?13.02 to ?46.13%). The overall improvement achieved in rainfall generation by SRGEN is demonstrated to be effective in the improving model performance on flow and water quality output.     

Development and Operational Testing of a Super‐Ensemble Artificial Intelligence Flood‐Forecast Model for a Pacific Northwest River

Coastal catchments in British Columbia, Canada, experience a complex mixture of rainfall‐ and snowmelt‐driven contributions to flood events. Few operational flood‐forecast models are available in the region. Here, we integrated a number of proven technologies in a novel way to produce a super‐ensemble forecast system for the Englishman River, a flood‐prone stream on Vancouver Island. This three‐day‐ahead modeling system utilizes up to 42 numerical weather prediction model outputs from the North American Ensemble Forecast System, combined with six artificial neural network‐based streamflow models representing various slightly different system conceptualizations, all of which were trained exclusively on historical high‐flow data. As such, the system combines relatively low model development times and costs with the generation of fully probabilistic forecasts reflecting uncertainty in the simulation of both atmospheric and terrestrial hydrologic dynamics. Results from operational testing by British Columbia's flood forecasting agency during the 2013‐2014 storm season suggest that the prediction system is operationally useful and robust.