天然沸石对农田退水中氨氮的去除

利用静态吸附实验研究了沸石颗粒大小、氨氮初始浓度、接触时间及Na+离子浓度等因素对天然白银沸石去除模拟黄灌区农田退水中氨氮(NH4+-N)效果的影响,同时研究了吸附等温线特征,并考察了其吸附机理.研究结果表明,沸石颗粒大小、接触时间及NH4+和Na+初始浓度对NH4+-N交换容量都会产生一定影响;根据复相关系数(R2),NH4+-N的吸附等温线更符合三参数等温线模型;而对于两参数等温线模型,Langmuir模型比Freundlich模型能更好地描述NH4+-N在天然沸石上的离子交换过程;NH4+-N吸附实验数据与Elovich模型拟合最好(R2≥0.9766).研究结果表明了天然白银沸石是一种适合NH4+-N去除的离子交换剂,可用于黄灌区农田退水中NH4+-N的去除.     

微咸水膜下滴灌对白僵土盐碱地水盐特性及油葵生长的影响

为了解滴灌条件下不同微咸水矿化度及覆膜与否对盐碱地土壤水盐特性及油葵生长的影响,在宁夏平罗西大滩盐碱地进行不同灌水矿化度(0.2、1、2、3 g/L)膜下滴灌田间试验.结果表明,地膜覆盖比不覆盖处理土壤含水量高约2%,盐分表聚减缓,油葵株高、产量及其构成均高于不覆膜处理,产量最高增幅达82.5%;不同矿化度微咸水之间比较发现,利用矿化度为1 g/L的微咸水进行灌溉时,下层土壤含水量较高,微咸水滴灌不但没有为上层土壤带去过多盐分,反而由于土壤有一定的渗透性能,灌水将土壤上层盐分逐渐淋洗至下层.膜下滴灌微咸水矿化度为1 g/L时,油葵产量最大.因此在适宜条件、合理措施下,微咸水覆膜滴灌技术可以促进作物生长,确保作物产量,在淡水缺乏而微咸水较为丰富的盐碱地地区应积极推广利用.     

污水灌溉区土壤肥力及酶活性特征研究

通过野外调查和采样分析,研究了石家庄栾城县不同污水灌溉时间（0-52a）和不同层位（0-100cm）耕地土壤肥力及酶活性的分布特征。研究结果表明：与清灌对照点相比,污水灌溉区表层（0-20cm）土壤有机质含量、全氮含量、全磷含量、蔗糖酶活性、脲酶活性和磷酸酶活性、亚表层（20-40cm）蔗糖酶活性、磷酸酶活性和下层（40-60cm）蔗糖酶活性均相对较高,而下层有机质含量、全氮含量和底层（60-100cm）全氮含量均相对较低。4层土壤阳离子交换量和过氧化氢酶活性较对照点均无明显的变化规律。表层土壤全氮含量、蔗糖酶活性和磷酸酶活性、亚表层有机质含量、蔗糖酶活性和磷酸酶活性、下层蔗糖酶活性与污灌时间之间均存在显著相关性沪〈0．05）。上述特征反映了土壤肥力及酶活性对污灌的响应具有明显的层位效应和非同步性,而这些响应是由于污水中的营养物质输入土壤后引发微生物活性提高的“激发效应”所致。     

Health risks of heavy metals in contaminated soils and food crops irrigated with wastewater in Beijing, China

Consumption of food crops contaminated with heavy metals is a major food chain route for human exposure. We studied the health risks of heavy metals in contaminated food crops irrigated with wastewater. Results indicate that there is a substantial buildup of heavy metals in wastewater-irrigated soils, collected from Beijing, China. Heavy metal concentrations in plants grown in wastewater-irrigated soils were significantly higher (P相似文献   

土壤种子库研究的几个热点问题

土壤种子库是指存在于土壤表层凋落物和土壤中全部活性种子的总和。土壤中有活性的种子是植物群落的一部分,是新植株的来源。土壤种子库可以分为瞬时土壤种子库和持久土壤种子库。随着群落生态学的发展,土壤种子库的研究已经成为植物生态学重要的一部分,研究内容主要包括:(1)土壤种子库的组成和分布;(2)土壤种子库的动态;(3)地上植被与土壤种子库的关系;(4)干扰对土壤种子库的作用;(5)土壤种子库在生态恢复中的作用。文章在对目前土壤种子库的研究方法、主要研究内容方面总结的基础上,认为土壤种子库在合适的干扰作用下对退化生态系统的恢复以及植被更新发挥重要的作用,同时需进一步加强对这一过程中种子萌发、幼苗建立限制因素的研究。     

Spatial and Seasonal Response of Municipal Water Use to Weather across the Contiguous U.S.

Weather variability has the potential to influence municipal water use, particularly in dry regions such as the western United States (U.S.). Outdoor water use can account for more than half of annual household water use and may be particularly responsive to weather, but little is known about how the expected magnitude of these responses varies across the U.S. This nationwide study identified the response of municipal water use to monthly weather (i.e., temperature, precipitation, evapotranspiration [ET]) using monthly water deliveries for 229 cities in the contiguous U.S. Using city‐specific multiple regression and region‐specific models with city fixed effects, we investigated what portion of the variability in municipal water use was explained by weather across cities, and also estimated responses to weather across seasons and climate regions. Our findings indicated municipal water use was generally well‐explained by weather, with median adjusted R² ranging from 63% to 95% across climate regions. Weather was more predictive of water use in dry climates compared to wet, and temperature had more explanatory power than precipitation or ET. In response to a 1°C increase in monthly maximum temperature, municipal water use was shown to increase by 3.2% and 3.9% in dry cities in winter and summer, respectively, with smaller changes in wet cities. Quantifying these responses allows urban water managers to plan for weather‐driven variability in water use.     

Use of Predictive Weather Uncertainties in an Irrigation Scheduling Tool Part II: An Application of Metrics and Adjoints

We apply predictive weather metrics and land model sensitivities to improve the Colorado State University Water Irrigation Scheduler for Efficient Application (WISE). WISE is an irrigation decision aid that integrates environmental and user information for optimizing water use. Rainfall forecasts and verification performance metrics are used to estimate predictive rainfall probabilities that are used as input data within the irrigation decision aid. These input data errors are also used within a land model sensitivity study to diagnose important prognostic water movement behaviors for irrigation tool development purposes simultaneously performing the analysis in space and time. Thus, important questions such as “how long can a crop water application be delayed while maintaining crop yield production?” are addressed by evaluating crop growth stage interactions as a function of soil depth (i.e., space), rainfall events (i.e., time), and their probabilistic uncertainties. Editor’s note : This paper is part of the featured series on Optimizing Ogallala Aquifer Water Use to Sustain Food Systems. See the February 2019 issue for the introduction and background to the series.     

An Evaluation of Selenium Concentrations in Water, Sediment, Invertebrates, and Fish from the Republican River Basin: 1997–1999

The Republican River Basin of Colorado,Nebraska, and Kansas lies in a valley which contains PierreShale as part of its geological substrata. Selenium is anindigenous constituent in the shale and is readily leached intosurrounding groundwater. The Basin is heavily irrigated throughthe pumping of groundwater, some of which is selenium-contaminated, onto fields in agricultural production. Water,sediment, benthic invertebrates, and/or fish were collected from46 sites in the Basin and were analyzed for selenium to determinethe potential for food-chain bioaccumulation, dietary toxicity,and reproductive effects of selenium in biota. Resultingselenium concentrations were compared to published guidelines orbiological effects thresholds. Water from 38% of the sites (n = 18) contained selenium concentrations exceeding 5 g L^-1, which is reported to be a high hazard for selenium accumulation into the planktonic food chain. An additional 12 sites (26% of the sites) contained selenium in water between 3–5 g L^-1, constituting a moderate hazard. Selenium concentrations in sedimentindicated little to no hazard for selenium accumulation fromsediments into the benthic food chain. Ninety-five percent ofbenthic invertebrates collected exhibited selenium concentrationsexceeding 3 g g^-1, a level reported as potentially lethal to fish and birds that consume them. Seventy-five percent of fish collected in 1997, 90% in 1998, and 64% in 1999 exceeded 4 g g^-1selenium, indicating a high potential for toxicity andreproductive effects. However, examination of weight profilesof various species of collected individual fish suggestedsuccessful recruitment in spite of selenium concentrations thatexceeded published biological effects thresholds for health andreproductive success. This finding suggested that universalapplication of published guidelines for selenium may beinappropriate or at least may need refinement for systems similarto the Republican River Basin. Additional research is needed todetermine the true impact of selenium on fish and wildliferesources in the Basin.     

Comparison of Stream Invertebrate Response Models for Bioassessment Metrics1

Waite, Ian R., Jonathan G. Kennen, Jason T. May, Larry R. Brown, Thomas F. Cuffney, Kimberly A. Jones, and James L. Orlando, 2012. Comparison of Stream Invertebrate Response Models for Bioassessment Metrics. Journal of the American Water Resources Association (JAWRA) 48(3): 570-583. DOI: 10.1111/j.1752-1688.2011.00632.x Abstract: We aggregated invertebrate data from various sources to assemble data for modeling in two ecoregions in Oregon and one in California. Our goal was to compare the performance of models developed using multiple linear regression (MLR) techniques with models developed using three relatively new techniques: classification and regression trees (CART), random forest (RF), and boosted regression trees (BRT). We used tolerance of taxa based on richness (RICHTOL) and ratio of observed to expected taxa (O/E) as response variables and land use/land cover as explanatory variables. Responses were generally linear; therefore, there was little improvement to the MLR models when compared to models using CART and RF. In general, the four modeling techniques (MLR, CART, RF, and BRT) consistently selected the same primary explanatory variables for each region. However, results from the BRT models showed significant improvement over the MLR models for each region; increases in R² from 0.09 to 0.20. The O/E metric that was derived from models specifically calibrated for Oregon consistently had lower R² values than RICHTOL for the two regions tested. Modeled O/E R² values were between 0.06 and 0.10 lower for each of the four modeling methods applied in the Willamette Valley and were between 0.19 and 0.36 points lower for the Blue Mountains. As a result, BRT models may indeed represent a good alternative to MLR for modeling species distribution relative to environmental variables.     

Irrigation Water Allocation Using an Inexact Two‐Stage Quadratic Programming with Fuzzy Input under Climate Change

Agricultural irrigation accounts for nearly 70% of the total water use around the world. Uncertainties and climate change together exacerbate the complexity of optimal allocation of water resources for irrigation. An interval‐fuzzy two‐stage stochastic quadratic programming model is developed for determining the plans for water allocation for irrigation with maximum benefits. The model is shown to be applicable when inputs are expressed as discrete, fuzzy or random. In order to reflect the effect of marginal utility on benefit and cost, the model can also deal with nonlinearities in the objective function. Results from applying the model to a case study in the middle reaches of the Heihe River basin, China, show schemes for water allocation for irrigation of different crops in every month of the crop growth period under various flow levels are effective for achieving high economic benefits. Different climate change scenarios are used to analyze the impact of changing water requirement and water availability on irrigation water allocation. The proposed model can aid the decision maker in formulating desired irrigation water management policies in the wake of uncertainties and changing environment.