大气环境影响评价工作分级要求要点

根据大气环境影响评价工作的分级要求,提出了针对性的工作要点,以满足大气环境评价工作的需要.     

Non-native Species <Emphasis Type="Italic">DO</Emphasis> Threaten the Natural Environment!

Sagoff [Journal of Agricultural and Environmental Ethics 18 (2005), 215–236] argues, against growing empirical evidence, that major environmental impacts of non-native species are unproven. However, many such impacts, including extinctions of both island and continental species, have both been demonstrated and judged by the public to be harmful. Although more public attention has been focused on non-native animals than non-native plants, the latter more often cause ecosystem-wide impacts. Increased regulation of introduction of non-native species is, therefore, warranted, and, contra Sagoff’s assertions, invasion biologists have recently developed methods that greatly aid prediction of which introduced species will harm the environment and thus enable more efficient regulation. The fact that introduced species may increase local biodiversity in certain instances has not been shown to result in desired changes in ecosystem function. In other locales, they decrease biodiversity, as they do globally.     

BASIN SCALE WATER MANAGEMENT AND FORECASTING USING ARTIFICIAL NEURAL NETWORKS1

ABSTRACT: Water scarcity in the Sevier River Basin in south‐central Utah has led water managers to seek advanced techniques for identifying optimal forecasting and management measures. To more efficiently use the limited quantity of water in the basin, better methods for control and forecasting are imperative. Basin scale management requires advanced forecasts of the availability of water. Information about long term water availability is important for decision making in terms of how much land to plant and what crops to grow; advanced daily predictions of streamflows and hydraulic characteristics of irrigation canals are of importance for managing water delivery and reservoir releases; and hourly forecasts of flows in tributary streams to account for diurnal fluctuations are vital to more precisely meet the day‐to‐day expectations of downstream farmers. A priori streamflow information and exogenous climate data have been used to predict future streamflows and required reservoir releases at different timescales. Data on snow water equivalent, sea surface temperatures, temperature, total solar radiation, and precipitation are fused by applying artificial neural networks to enhance long term and real time basin scale water management information. This approach has not previously been used in water resources management at the basin‐scale and could be valuable to water users in semi‐arid areas to more efficiently utilize and manage scarce water resources.     

SUMMER LOW FLOWS IN NEW ENGLAND DURING THE 20TH CENTURY1

ABSTRACT: High springtime river flows came earlier by one to two weeks in large parts of northern New England during the 20th Century. In this study it was hypothesized that late spring/early summer recessional flows and late summer/early fall low flows could also be occurring earlier. This could result in a longer period of low flow recession and a decrease in the magnitude of low flows. To test this hypothesis, variations over time in the magnitude and timing of low flows were analyzed. To help understand the relation between low flows and climatic variables in New England, low flows were correlated with air temperatures and precipitation. Analysis of data from 23 rural, unregulated rivers across New England indicated little evidence of consistent changes in the timing or magnitude of late summer/early fall low flows during the 20th Century. The interannual variability in the timing and magnitude of the low flows in northern New England was explained much more by the interannual variability in precipitation than by the interannual variability of air temperatures. The highest correlation between the magnitude of the low flows and air temperatures was with May through November temperatures (r =?0.37, p= 0.0017), while the highest correlation with precipitation was with July through August precipitation (r = 0.67, p > 0.0001).     

高含固污泥厌氧消化中Fe/S及pH对原位抑硫效率影响及其交互作用

以城市污水处理厂高含固污泥为对象,分别进行了连续厌氧消化抑硫试验和消化污泥Fe(Ⅲ)投加抑硫试验,探讨不同Fe/S(摩尔比)对污泥厌氧消化中溶解态硫化物去除效率的影响以及Fe(Ⅲ)与pH的交互作用.结果表明,热水解污泥厌氧消化采用原位抑硫技术,在Fe/S(摩尔比)为7.75时沼气中H_2S含量可由170.4×10~(-6)降至14.09×10~(-6),无需进行后续处理;当pH为7.00~7.50、Fe/S为1~11时,pH为原位抑硫主要显著影响因子,提高消化池pH有利于降低Fe(Ⅲ)投加量;高含固污泥厌氧消化沼气满足H_2S利用标准时,所需最低Fe/S为7.0;当消化池pH低于7.30时,将无法通过调节Fe/S实现H_2S浓度达标排放.     

基于环境因子和邻近信息的土壤属性空间分布预测

为探索乡镇尺度上土壤属性空间分布预测的最佳方法,以江西省万年县齐埠镇为例,借助四方位搜索法、地统计学和遥感影像分析技术提取环境因子（地形因子和植被覆盖指数）和邻近信息[w（有机质）与w（速效钾）],构建OK法（普通克里金法）、RK1法（仅基于环境因子的回归克里金法）以及RK2法（基于环境因子和邻近信息的回归克里金法）对齐埠镇耕地表层（0~20 cm）土壤w（有机质）、w（速效钾）空间分布进行预测.结果表明:齐埠镇土壤w（有机质）平均值为35.03 g/kg,w（速效钾）平均值为96.73 mg/kg,均为中等空间变异性.对62个样点进行建模,16个测试样点进行独立验证的误差分析表明,RK2法对土壤w（有机质）、w（速效钾）预测结果的均方根误差、平均绝对误差和平均相对误差较OK法分别降低了18.05%、18.01%、21.77%和7.25%、9.49%、9.84%;较RK1法分别降低了22.48%、20.91%、22.02%和9.27%、12.61%、13.52%.研究显示,RK2法明显提高了土壤w（有机质）、w（速效钾）空间分布模拟精度,并且存在改进和提高的空间.      

北京市大气重污染过程中AQI的时空扩散特征及其预测模型

通过对北京市大气重度污染时段AQI与气象因子分析,探讨AQI的时间和空间变化特征,并建立了AQI的空间预测模型。结果表明:1)风速>2.5 m/s,且持续时间长(超过5 h)时,AQI曲线特征呈现倒"V"型。若其在1.7 m/s中部>北部,4 d后则中部>南部>北部。3)各方向上中部和北部的AQI预测模型中幂函数模型精度最高,且两者受西南方向的影响最大。     

兰州市废旧手机产生量预测分析研究

手机普及率日益提高,随之产生的废弃量也日益增多,分别采用时间梯度模型、市场供给A模型、斯坦福(stanford)模型对兰州市2013年-2022年废旧手机的产生量进行了预测.结果显示,不同模型预测结果之间存在较大差异,市场供给A模型和斯坦福(Stanford)模型的预测结果比较接近,均大于时间梯度模型的预测结果,三种模型预测的兰州市2022年废旧手机的产生量分别为40.40万、46.67万和21.04万部.总体上看,兰州市废旧手机的产生量增长趋势较快.     

ABC算法优化SVR的磨损故障预测模型

目的为了提高故障预测的精度,针对支持向量回归SVR(Support vector machine for regression,SVR)参数选择困难的问题,提出一种采用人工蜂群(artificial bee colony,ABC)算法优化支持向量回归(SVR)的故障预测模型(ABC-SVR)。方法该模型先对样本数据进行重构,然后将故障预测误差(适应度)作为优化目标,通过ABC算法寻优找到最优的SVR参数,建立故障预测模型。最后通过实例仿真验证模型的优越性。结果采用ABC算法优化的SVR故障预测模型进行时间序列预测,能够较好地跟踪发动机滑油金属元素浓度的变化过程,并且能够提前2个取样时间预测异常情况的出现。结论 ABC-SVR模型有效解决了SVR参数选择难题,能够更加准确地表现故障变化规律,提高了故障预测精度。     

海南东环铁路（海口段）运行噪声预测研究

本文对海南东环铁路海口城区的南海大道附近的敏感点列车开通的近期（2020年）和远期（2030年）的噪声进行预测：根据噪声暴露声级与等效声级的关系公式,对列车加密后南海大道距离轨道30m处的噪声进行预测,发现当小时列车数达到14车次时,其昼问等效声级将超过70dBA的标准,为城区段控制小时列车次数提供参考。