天然气钻井井口安全距离研究分析

分析天然气钻井井场可能发生的事故类型及事故的破坏程度,选择适合的事故后果模型,对天然气井井喷失控后可能发生的蒸气云爆炸及硫化氢扩散的后果进行量化分析,根据超压-冲量准则、热剂量准则和硫化氢扩散行为规律,计算出爆炸波、爆炸火球及硫化氢扩散的危害范围。笔者建立了天然气钻井井口安全距离的计算模型,并提出一种确定安全距离的方法。通过计算给出不同无阻流量、不同硫化氢体积含量的20种条件下的天然气钻井井口安全距离,并应用该模型对某含硫气井井口安全距离进行了计算。实例表明,该方法具备实用性,值得在天然气井选址规划中推广和使用。     

“Vybuchovy trojuhelnik”: A software tool for evaluation of explosibility of coal mine atmosphere

Rescue operations during mine fires or methane explosions are highly dangerous for rescue workers. The knowledge of the composition of the coal mine atmosphere and the calculations of its explosibility may help to increase the safety of the rescuers. In the Czech Republic, a system called “Mine Gas Laboratory” (DPL) has been used for these purposes. The DPL allows measurement of the composition of the mine atmosphere and transmits the data necessary for evaluation to the surface. Up to now the explosibility evaluation of the coal mine atmosphere has depended either on the rescuers’ experience or on software code calculation. The code called “Vybuchovy trojuhelnik” (explosion triangle) is a graphical computing system intended for fast assessment of explosibility of fuel–air mixture. This article introduces the code and describes two simple methods of explosibility evaluation. The first method is “explosion triangle analysis”—a graphical method based on empirical graphs transformed into equations. The second method uses thermodynamic calculation based on chemical balance dynamics and Gibbs and Helmholtz energy. According to the requirements of the Czech Bureau of Mining (CBU) and Central Mine Rescue Service (HBZS), the code solves the problems of explosion triangle for both standard and non-standard coal mine atmosphere compositions. Unfortunately, the atmosphere composition must be introduced manually due to the unknown format of the data transmitted from the old DPL model. On 1 September 2005, a project started to develop a new system for on-line monitoring and atmosphere explosibility evaluation. The system should be able to measure CO₂, O₂, CH₄, H₂ and CO concentrations as well as the wind speed, temperature and humidity. The “Vybuchovy trojuhelnik” code will be used as a basis for explosibility evaluation, and an on-line connection with the new model of DPL will be established.     

苏南地区CO2本底浓度及源汇特征

基于2015~2018年苏州张家港站CO₂在线观测数据，采用时序检查、选取稳定性数据、异常值剔除等质量控制方法获得可靠数据，并通过平均移动过滤（MAF）本底筛分法获得本底数据，讨论苏南地区CO₂变化特征.结果发现：CO₂本底浓度日变化为单峰结构，谷值和峰值分别出现在下午15：00和凌晨5：00前后；季节变化为双峰结构，峰值分别出现在12月和4月；日、季节变化的分布特征均与陆地生态系统、气象条件和人类活动有关.此外，2015~2018年CO₂浓度呈逐年上升趋势，抬升浓度占比逐年增加，吸收浓度占比波动较小，表明人类活动对CO₂浓度的影响正在逐年增加；而陆地生态系统对CO₂吸收汇的作用则相对稳定.源汇分析显示，CO₂抬升浓度随季节小幅波动；吸收浓度则夏半年较低，冬半年较高；抬升浓度日变化为单峰结构，谷值和峰值分别出现在15：00和8：00前后，早晨正值上班高峰，机动车排放可能为早晨峰值的主要因素；吸收浓度日间低、夜间高，这主要与植物光合作用及对流输送有关.分析CO₂浓度与风的关系发现，所有季节静风情况下，CO₂浓度偏高均最为明显，大部分方向CO₂浓度高低与风速大小有明显的负相关，其中S~WNW方向偏高最为明显，这可能是因为SW~NW方向主要为内陆城市群，且测站周边建筑区主要位于W~N方向，弱风有利于本地排放累积的结果.此外，WNW方向风速较大时浓度仍偏高明显，可能与测站W~N方向为建筑区及内陆城市群有关；而测站偏东方向主要为农田和林区，受人类活动影响较小，且海上气流较为洁净，故偏东风较弱时浓度也不高；说明了CO₂浓度除了与风速大小有关外，与周边下垫面类型及较远距离环境特征（城市群或海洋）也有一定的关系.     

结晶紫褪色分光光度法测定SO_2的研究

在碱性介质中，ＳＯ２能使结晶紫褪色且褪色的程度随ＳＯ２量的增加而呈线性关系，据此建立了分光光度法测定ＳＯ２的新方法，确定了显色测定的最佳条件。方法的最大吸收波长在６００ｎｍ处，表观摩尔吸光系数为７３×１０３Ｌ／ｍｏｌ·ｃｍ。ＳＯ２量在０～１００μｇ／２５ｍｌ范围内符合比尔定律     

Trends in snowpack chemistry and comparison to National Atmospheric Deposition Program results for the Rocky Mountains, US, 1993–2004

Seasonal snowpack chemistry data from the Rocky Mountain region of the US was examined to identify long-term trends in concentration and chemical deposition in snow and in snow-water equivalent. For the period 1993–2004, comparisons of trends were made between 54 Rocky Mountain Snowpack sites and 16 National Atmospheric Deposition Program wetfall sites located nearby in the region. The region was divided into three subregions: Northern, Central, and Southern. A non-parametric correlation method known as the Regional Kendall Test was used. This technique collectively computed the slope, direction, and probability of trend for several sites at once in each of the Northern, Central, and Southern Rockies subregions. Seasonal Kendall tests were used to evaluate trends at individual sites.Significant trends occurred during the period in wetfall and snowpack concentrations and deposition, and in precipitation. For the comparison, trends in concentrations of ammonium, nitrate, and sulfate for the two networks were in fair agreement. In several cases, increases in ammonium and nitrate concentrations, and decreases in sulfate concentrations for both wetfall and snowpack were consistent in the three subregions. However, deposition patterns between wetfall and snowpack more often were opposite, particularly for ammonium and nitrate. Decreases in ammonium and nitrate deposition in wetfall in the central and southern rockies subregions mostly were moderately significant (p<0.11) in constrast to highly significant increases in snowpack (p<0.02). These opposite trends likely are explained by different rates of declining precipitation during the recent drought (1999–2004) and increasing concentration. Furthermore, dry deposition was an important factor in total deposition of nitrogen in the region. Sulfate deposition decreased with moderate to high significance in all three subregions in both wetfall and snowpack. Precipitation trends consistently were downward and significant for wetfall, snowpack, and snow-telemetry data for the central and southern rockies subregions (p<0.03), while no trends were noted for the Northern Rockies subregion.     

中国西北寒旱区农牧民生活碳排放评估

围绕碳排放权开展的气候谈判愈来愈关注贫困人口与弱势群体的生存权与发展权,一方面由于受制于其薄弱的社会经济水平,贫困人口与弱势群体在气候变化实践中表现出更高的脆弱性,另一方面则因气候变化减缓与适应行动而造成的贫困人口生活成本的增加以及生活水平的下降。然而,对这些地区和人口的排放权判断主要基于国家层面和地区层面宏观数据的分析,其结果掩盖了国家和地区内部不同社会经济水平下的人口排放差异,不能准确揭示贫困人口和脆弱群体的低碳排放事实。本文基于国际碳排放评估对人口生活排放的需求,结合IPCC参考方法,利用投入产出分析模型构建了人口生活碳排放评价指标体系,并用于对甘肃、青海和宁夏干旱-高寒地区农牧民生活碳排放的样本调查和分析。评估表明,中国西北干旱—高寒区人口生活碳排放仅为1.85tCO2/人,其中用于满足基本生活需要的碳排放量达到87.25%。研究发现,生活在更冷(海拔更高)区域内的人口生活排放量更高;随着家庭收入的增长,人口生活排放量也随之上升;家庭成员数量越多,家庭的人均碳排放量就会越低。     

Correlates of mercury in fish from lakes near Clyde Forks, Ontario, Canada

Subsurface soils near Clyde Forks, Ontario, Canada, can have naturally high concentrations of mercury (Hg) from local geological sources. To investigate Hg in local aquatic food webs, Hg was measured in fish dorsal muscle (mainly yellow perch [YP] and pumpkinseed sunfish [PS]) and surface sediments from 10 regional lakes. Water chemistry, along with fork length, weight, and stable isotopes (delta15N, delta13C, delta34S) in fish were also measured. No lake sediments had elevated (>0.3microg/g dw) Hg, and average Hg concentrations in fish were not sufficiently high (<1microg/g dw) to be of concern for fish-eating wildlife. Variance in fish Hg was best explained by dietary carbon source (delta13C), and certain lake variables (e.g., pH for YP). PS with more pelagic feeding habits had higher delta34S and Hg than those with more littoral feeding habits. Potential biological linkages between fish Hg and delta34S, a parameter that may be related to the lake sulphate-reducing bacteria activity, requires further investigation.     

可燃气体(液体蒸气)的爆炸极限与最大允许氧含量的对比研究

通过实验研究了可燃气体(液体蒸气)的爆炸极限规律,从全新的角度分析了各种浓度可燃气体(液体蒸气)的最大允许氧含量的规律,并运用数值分析原理拟合出其规律函数,可从理论上求得各种浓度可燃气体(液体蒸气)的最大允许氧含量值。通过爆炸极限和最大允许氧含量规律的对比研究,分析了两者相辅相成的重要关系,指出两者从不同角度界定了可燃气体(液体蒸气)的爆炸范围,是衡量可燃气体(液体蒸气)爆炸危险性的两个重要参数。     

Stepwise multiple regression method of greenhouse gas emission modeling in the energy sector in Poland

The energy sector in Poland is the source of 81% of greenhouse gas (GHG) emissions. Poland, among other European Union countries, occupies a leading position with regard to coal consumption. Polish energy sector actively participates in efforts to reduce GHG emissions to the atmosphere, through a gradual decrease of the share of coal in the fuel mix and development of renewable energy sources. All evidence which completes the knowledge about issues related to GHG emissions is a valuable source of information. The article presents the results of modeling of GHG emissions which are generated by the energy sector in Poland. For a better understanding of the quantitative relationship between total consumption of primary energy and greenhouse gas emission, multiple stepwise regression model was applied. The modeling results of CO₂ emissions demonstrate a high relationship (0.97) with the hard coal consumption variable. Adjustment coefficient of the model to actual data is high and equal to 95%. The backward step regression model, in the case of CH₄ emission, indicated the presence of hard coal (0.66), peat and fuel wood (0.34), solid waste fuels, as well as other sources (− 0.64) as the most important variables. The adjusted coefficient is suitable and equals R² = 0.90. For N₂O emission modeling the obtained coefficient of determination is low and equal to 43%. A significant variable influencing the amount of N₂O emission is the peat and wood fuel consumption.     

Stepwise multiple regression method of greenhouse gas emission modeling in the energy sector in Poland

The energy sector in Poland is the source of 81% of greenhouse gas (GHG) emissions. Poland, among other European Union countries, occupies a leading position with regard to coal consumption. Polish energy sector actively participates in efforts to reduce GHG emissions to the atmosphere, through a gradual decrease of the share of coal in the fuel mix and development of renewable energy sources. All evidence which completes the knowledge about issues related to GHG emissions is a valuable source of information. The article presents the results of modeling of GHG emissions which are generated by the energy sector in Poland. For a better understanding of the quantitative relationship between total consumption of primary energy and greenhouse gas emission, multiple stepwise regression model was applied. The modeling results of CO₂ emissions demonstrate a high relationship (0.97) with the hard coal consumption variable. Adjustment coefficient of the model to actual data is high and equal to 95%. The backward step regression model, in the case of CH₄ emission, indicated the presence of hard coal (0.66), peat and fuel wood (0.34), solid waste fuels, as well as other sources (-0.64) as the most important variables. The adjusted coefficient is suitable and equals R² = 0.90. For N₂O emission modeling the obtained coefficient of determination is low and equal to 43%. A significant variable influencing the amount of N₂O emission is the peat and wood fuel consumption.