基于OAT-GIS技术的地下水污染风险评估指标权重敏感性分析

权重赋值准确性是地下水污染风险评估结论可靠性的重要基础,分析并量化指标权重敏感性是验证权重赋值准确性的难点。以我国37个危险废物填埋场为研究对象,基于构建的填埋场地下水污染风险排序指标体系,利用多准则决策分析模型（MCDA）中的层次分析（AHP）模块对各指标进行权重赋值;利用计算所得的权重最小变化量,基于OAT（one-factor-at-a-time）方法,应用MCDA的权重敏感性分析模块对14项指标权重进行依次改变,得出14项指标的权重敏感性大小;分析指标权重赋值过程中的不确定性,并基于地理信息系统（GIS）平台,开展可视化的空间数据统计与分析。结果表明：我国37个危险废物填埋场地下水污染风险评估的14项指标中,使用年限、渗滤液量和地形坡度3项指标权重改变后,填埋场地下水污染风险排序变化最大,即这3项指标权重敏感性最高;而包气带渗透性系数指标权重改变导致的风险排序变化最小,说明其权重敏感性最低。该方法可以有效地分析、验证指标权重赋值的准确性,识别权重赋值的不确定性,帮助相关决策者有针对性地进行风险管理。     

武烈河流域水质污染特征及污染源解析

以武烈河流域2014年1—12月23项指标的监测数据为基础,采用单因子评价法和水质综合指数评价法,对水质污染特征进行综合评价;运用主成分分析法,确定流域主要污染因子及区域水质空间变化特征,并进一步对流域污染来源进行解析。结果表明：武烈河流域上游支流水质为Ⅱ类,干流段监测断面S1与S3水质为Ⅲ类,S2与S4水质为Ⅳ类,主要污染指标为BOD₅与TP;流域水质由2个主成分组成,COD_Cr、COD_Mn为第一主成分,NH₃-N、DO与BOD₅为第二主成分;流域主要污染源包括生活污水、工业废水、农业化肥和农药的投入和畜禽养殖废水等。     

2008年黑龙江省环境状况及发展趋势分析

黑龙江省生态环境状况良好,野生动植物栖息和生长的环境逐步改善。主要污染物化学需氧量和二氧化硫排放量继续呈下降趋势。城市空气质量良好,处于全国中上游水平,城市噪声污染较轻。松花江水质稳中趋好,为轻度污染,部分支流水质明显改善,出境水质达标的频率大幅增加。     

基于轨迹模型的和田绿洲沙尘移动轨迹及气象特征分析

以塔克拉玛干沙漠南缘的和田绿洲为研究区域,使用HYSPLIT后向轨迹模型和NCEP的GDAS全球气象要素数据,将和田绿洲西北部的墨玉县城作为模拟受点(79.72°E,37.26°N),对2004—2018年墨玉县发生的2 891次沙尘天气进行36 h的后向轨迹模拟,并通过k-means聚类分析法,定量分析沙尘天气期间气流的传输路径及气象特征。结果表明：2004—2018年,研究区沙尘天气的发生频率变化幅度不大,没有明显的增减趋势;年内沙尘天气主要集中在春季和夏季（3—7月）,占全年沙尘天气总数的70.23%,5、6月是强沙尘暴最活跃期。在沙尘天气期间气流的移动高度和轨迹相差较大,按照气流的起源地和到达墨玉县的方向将沙尘暴分为NW-W簇、N-N簇、NE-E簇和E-ES簇4簇轨迹类型,其发生天数占比分别为18.9%、12.3%、60.1%和7.8%。由东向西的沙尘天气发生天数占比最高（60.1%）,但主要以浮尘天气为主;由西北向东南方向的沙尘发生频率不高,但移动速度最快,强沙尘暴和扬沙主要来自西北和西部方向;由北向南的沙尘暴速度最慢（1.36 m/s）,空气湿度最低（26.4%）,但气温最高（292.58 K）。定量印证了墨玉县沙尘暴不同传输路径,可为绿洲区沙尘暴研究提供参考。

     

天津市近岸海域水质变化趋势分析及水质目标研究

掌握海域水质变化趋势、制定科学合理的水质目标,有助于精准实施重点海域排污总量控制,制定有效的污染物管控政策。利用广义加性模型（GAM）,基于2007—2018年天津市近岸海域营养盐浓度及降水量数据,建立水质变化趋势分析模型和水质目标确定方法,在评估天津市近岸海域12个监测站位无机氮和活性磷酸盐浓度变化趋势的基础上,提出天津市近岸海域水质控制目标,并分析水质目标的合理性和可达性。结果表明：2013—2018年与2007—2012年相比,天津市近岸海域无机氮浓度总体呈下降趋势,下降比例为13.19%,95%的置信区间为−30.37%～3.96%;活性磷酸盐浓度总体呈上升趋势,上升比例为7.01%,95%的置信区间为−11.43%～25.45%,尚未恢复到2007—2012年的平均水平;提出2025年天津市近岸海域无机氮、活性磷酸盐二者综合优良水质比例达到75%的控制目标;将天津市近岸海域划分成7个区域,建议据此实施海域水质分区管理,进一步加强农业面源污染防治,强化流域上下游协同治理和省际水污染联防联治,持续改善天津市近岸海域水质。

     

多重水质评价方法在地下水水质评价中的对比研究—以北京市朝阳区为例

目前地下水水质评价方法众多,选择合适的评价方法对地下水的管理与利用具有重要意义。以北京市朝阳区2020年枯水期采集的地下水水样检测结果为基础,利用改进的层次分析法、单因子指数法和综合污染指数法对地下水水质进行评价,并对比3种方法评价结果的准确性以及存在的不合理之处。结果表明：1）3种方法的水质评价结果差异较大,改进的层次分析法的水质评价结果最优,单因子指数法的评价结果最差,综合污染指数法的评价结果与其他方法的一致性最高;2）在进行水质评价时可优先选择综合污染指数法,单因子指数法适用于水质要求较高或超标污染物少的水体,改进的层次分析法适用于有多种污染物超标的水体;3）为进一步确定3种方法对地下水水质评价结果的准确性,将3种方法的评价结果与可靠性分析结果进行对比,发现改进的层次分析法、单因子指数法和综合污染指数法与可靠性分析的水质评价结果相同的占比分别为90.48%、42.86%、47.62%。因此,在对地下水水质进行综合评价时,可使用改进的层次分析法。

     

蒸汽锅炉爆炸危险性分析及预防措施

简要叙述了锅炉爆炸机理及爆炸冲击波的破坏、伤害作用,采用TNT当量法对中国石化胜利油田石化总厂动力蒸汽锅炉爆炸事故后果进行了预测计算,分析了导致锅炉爆炸事故发生的原因,并给出了应对措施。     

Fault-dynamic modelling of a phthalic anhydride reactor

A new methodology, fault-dynamic modelling, has been developed for analysis of potentially hazardous situations in the process industries. Traditional fault-tree analysis is used to determine the combinations of component failure that can lead to a particular process upset condition. Realistic dynamic modelling is then used to calculate the time available for corrective action once the upset has started. The method is applied to a phthalic anhydride reactor. The results of the analysis identify three process upsets that can lead to catastrophic failure in 2–5 min if left uncorrected. Other process upsets lead to safe conditions.     

Estimation of Cumulative Frequency Distribution for Carbon Monoxide Concentration from Wind-Speed Data, in Buenos Aires (Argentina)

In this article we apply and test a methodology to estimate cumulative frequency distribution for air pollutant concentration from wind-speed data. We use the inverse relationship after Simpson et al. (Atmospheric Environment, 19, 75–82, 1985) between the opposing percentile values in the statistical distributions for air pollutant concentrations and wind-speed data. This relationship is valid, irrespective of the statistical distributions of both variables, if an inverse relationship between them is also applicable. The available data are five years of 8-h average carbon monoxide concentration and 8-h mean wind-speed, observed in Buenos Aires (Argentina). The performance of the obtained empirical expressions in estimating cumulative frequency distributions for 8-h CO is statistically evaluated. The results show that it is possible to obtain an acceptable cumulative frequency distribution for 8-h CO concentration at the site if the cumulative frequency distribution for wind-speed is known. Q–Q plots show a good agreement between estimated and observed values. From our data, the mean relative error of the estimations was found to be as much as 8.0%.     

Temporal and Spatial Relationships Between Watershed Land Use and Salt Marsh Disturbance in a Pacific Estuary

Historical and recent remote sensing data can be used to address temporal and spatial relationships between upland land cover and downstream vegetation response at the watershed scale. This is demonstrated for sub-watersheds draining into Elkhorn Slough, California, where salt marsh habitat has diminished because of the formation of sediment fans that support woody riparian vegetation. Multiple regression models were used to examine which land cover variables and physical properties of the watershed most influenced sediment fan size within 23 sub-watersheds (1.4 ha to 200 ha). Model explanatory power increased (adjusted R(2) = 0.94 vs. 0.75) among large sub-watersheds (>10 ha) and historical watershed variables, such as average farmland slope, flowpath slope, and flowpath distance between farmland and marsh, were significant. It was also possible to explain the increase in riparian vegetation by historical watershed variables for the larger sub-watersheds. Sub-watershed area is the overriding physical characteristic influencing the extent of sedimentation in a salt marsh, while percent cover of agricultural land use is the most influential land cover variable. The results also reveal that salt marsh recovery depends on relative cover of different land use classes in the watershed, with greater chances of recovery associated with less intensive agriculture. This research reveals a potential delay between watershed impacts and wetland response that can be best revealed when conducting multi-temporal analyses on larger watersheds.