Discriminate Analysis for Dust Storm Prediction in the Gobi and Steppe Regions in Mongolia

A discriminate analysis method for probability forecast of dust storms in Mongolia has been developed. The prediction method uses data recorded at 23 meteorological stations in the Gobi and steppe regions of Mongolia, including surface air pressure and geo-potential height at the 500-hPa level on grid points, and weather maps from 1975 to 1990. Weather elements such as air temperature, pressure, geo-potential height etc, which influence the formation of dust storms, are prepared as predictors. To select the most informative/important predictors (variables), we used a mean correlation matrix of variables together with the Mahalonobis distance, and correlation coefficients between dust storms and predictors with an orthogonalization for removing correlated predictors. The most informative predictors for dust storm prediction are intensities of surface cyclones and migratory anticyclones, passage of cold fronts, the horizontal gradients of the surface air pressure in the cold frontal zone, cyclonic circulations from the ground surface up to the 500-hPa level, the geo-potential height at 500-hPa level and its temporal changes. Selected predictors are used in discriminate analysis for formulating dust storm prediction equations. Sandstorm data have been classified into three classes, viz., strong, moderate and weak dust storms, depending on their intensities, durations and areas covered. Predictions of the probabilities of dust storm occurrence use the prediction equations for each class. The prediction is made from 12 hours to 36 hours. Verification of the probability forecasts of dust storms is also shown. The accuracy of forecasts is 72.2–79.9% with the data used for developing equations (dependent variables), in contrast to 67.1–72.0% with unrelated data for deriving equations (independent variables).     

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.     

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。     

SEA中非突发性大气环境健康风险分析

建立了一种基于指示性污染物的非突发性环境健康风险评价模式。根据研究区域的能源类型及气候条件选择对该区域人群健康产生明显影响的指示性污染物,并根据多个城市及地区的历史数据确定指示性污染物与常住人口死亡率之间的定量对应关系,将大气预测模型和GIS系统进行集成开发,再将指示性污染物浓度与区域常住人口死亡率之间的定量对应关系嵌入与GIS集成后的大气预测模型中．．通过将区域气象数据及规划中关于指示性污染物连续排放的源强数据输入上述模型进行模拟预测,得出区域不同级别大气环境健康风险范围分布。