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.     

Synergistic Techniques for Better Understanding and Classifying the Environmental Structure of Landscapes

The desire to capture natural regions in the landscape has been a goal of geographic and environmental classification and ecological land classification (ELC) for decades. Since the increased adoption of data-centric, multivariate, computational methods, the search for natural regions has become the search for the best classification that optimally trades off classification complexity for class homogeneity. In this study, three techniques are investigated for their ability to find the best classification of the physical environments of the Mt. Lofty Ranges in South Australia: AutoClass-C (a Bayesian classifier), a Kohonen Self-Organising Map neural network, and a k-means classifier with homogeneity analysis. AutoClass-C is specifically designed to find the classification that optimally trades off classification complexity for class homogeneity. However, AutoClass analysis was not found to be assumption-free because it was very sensitive to the user-specified level of relative error of input data. The AutoClass results suggest that there may be no way of finding the best classification without making critical assumptions as to the level of class heterogeneity acceptable in the classification when using continuous environmental data. Therefore, rather than relying on adjusting abstract parameters to arrive at a classification of suitable complexity, it is better to quantify and visualize the data structure and the relationship between classification complexity and class homogeneity. Individually and when integrated, the Self-Organizing Map and k-means classification with homogeneity analysis techniques also used in this study facilitate this and provide information upon which the decision of the scale of classification can be made. It is argued that instead of searching for the elusive classification of natural regions in the landscape, it is much better to understand and visualize the environmental structure of the landscape and to use this knowledge to select the best ELC at the required scale of analysis.     

垃圾填埋场地下水污染修复技术优选研究

结合国内外地下水污染场地的修复经验,综合考虑当地社会经济条件、地下水脆弱性、污染物特征等因素,采用层次分析法建立了修复技术优化指标体系;在此基础上运用多准则决策分析模型(MCDA)进行地下水污染修复技术优选排序,构建垃圾填埋场地下水污染修复技术筛选模型;并以湖北省某简易垃圾填埋场为例进行应用研究。结果表明:在备选的5种场地地下水修复技术(监测自然衰减技术、渗透反应格栅、多相抽提技术、抽出-处理技术、原位微生物修复技术)中,抽出-处理技术、原位微生物修复技术较为理想。综合考虑该污染场地条件、地下水污染治理要求及附近受体分布情况,得到最佳修复方案为:高污染风险区域采用抽出-处理技术和原位微生物修复技术组合;低污染风险区域采用监测自然衰减技术。