农田NO排放的自动观测

详细介绍农田ＮＯ排放自动观测的方法原理,系统整体构造及电路和气路配置,并讨论了观测结果的可靠性。同地,还就已取得的一些初步研究结果进行了分析,对华东秋,冬季麦田的ＮＯ排放进行自动连续观测的结果表明,秋季麦田的ＮＯ排放具有与温度几乎完全同步的日变化规律。     

人为排放气溶胶引起的辐射强迫研究

利用建立的大气气溶胶辐射强迫模式,对我国历年大气气溶胶(TSP和硫酸盐气溶胶)引起的直接辐射强迫进行了计算,并给出其全国分布。得到了一些有意义的结果:我国大气气溶胶引起的辐射强迫与我国能源,特别是燃煤的消耗量密切相关,随着消耗量的增加,大气气溶胶(TSP和硫酸盐气溶胶粒子)引起的辐射强迫也增加;指出在利用辐射模式讨论大气气溶胶引起的直接辐射强迫时,不能忽视扬尘和沙尘的作用;我国由于大气气溶胶引起的直接辐射强迫主要集中在工业比较发达的城市或地区,四川盆地由于其特殊的地理位置和气候条件,在该地区因大气气溶胶产生的辐射强迫始终比较大。      

Monitoring of the Phytosanitary State of Navarra's Forests, Spain

Between 1988 and 1993 six surveys on the condition of forests in Navarra (North Spain) were carried out. The results, relating to 408 trees in 17 sampling points, showed that the overall percentage of damaged trees increased from 2.86% in 1988 to 18.6% in 1993, which indicates a general large-scale deterioration of forest condition. Among the most common groups of trees, the most severely affected species were Quercus faginea, Quercus robur and Pinus sylvestris. Furthermore, it was observed that defoliation and discoloration processes did not occur randomly between stand trees, but the trees that were damaged one year (1992) were more predisposed to suffer damage in the next year (1993).     

农田NO排放的时间变异性

在采用基于箱法的自动观测技术对苏州地区一个完整的科小麦生育期的麦田的ＮＯ排放进行全天候连续观测的基础上,讨论了ＮＯ排放的季节变化和日变化特征及其温度和植物生长的影响,观测实验发现,苏州地区上麦田的ＮＯ排放具有极其显著的季节变化规律怀,冬季以前２的小麦苗期物返青至淹水种稻之前的春季,ＮＯ排放量分别为０．１５６－０．７５８ｍｇ·ｍ＾－２·ｈ＾－１和１．２２９－１０．８０２·ｍ＾－２·ｈ＾－１,前者是全     

水稻土中胱氨酸分解产生含硫气体的研究

测定在室内培养情况下南京水稻土中挥发性含硫气体的释放．结果表明,该土壤中产生Ｈ２Ｓ、羰基硫（ＣＯＳ）和二甲基硫（ＭＤＳ）气体．当土壤中加入胱氨酸后,检测到甲硫醇（ＣＨ３ＳＨ）、二硫化碳（ＣＳ２）、ＣＯＳ、Ｈ２Ｓ和ＤＭＳ气体．除ＤＭＳ之外,这些气体的释放量随胱氨酸添加量增加而增加．据此推测,水稻土中胱氨酸的分解可能是ＣＨ３ＳＨ、ＣＳ２、ＣＯＳ、Ｈ２Ｓ等４种气体产生释放的来源之一．在厌氧条件（充氮淹水）下检测到的含硫气体低于好氧条件（普通大气淹水）．光照、ｐＨ值、土壤含水量等对含硫气体的释放量均有影响     

种植杂交稻对甲烷排放的影响及评价

早、中、晚稻三季24h稻田甲烷监测结果表明,种植杂交稻没有明显增进福田甲烷排放的作用．甲烷释放总量在3个种植季节中,除连作晚稻的杂交稻田比常规稻田高11．6％外,早稻、单季稻杂交稻田分别低于常规稻田6．4％和8．9％．稻田甲烷每周日平均释放量在水稻生长前期（移栽后5～7周）杂交稻高于常规稻,孕穗至收获期杂交稻低于常规稻．温度对甲烷释放影响十分明显．在不同水稻种植季节,稻田甲烷释放模式各不相同．经测定,杂交稻田的土壤产甲烷细菌数量及土壤产甲烷潜力明显高于常规稻田,其中产甲烷细菌数可相差数倍至2个数量级．试验结果还表明,种植水稻明显增加了稻田甲烷排放量,与侵水稻田相比．植稻田甲烷排放量增加了41．4％．     

南京市小型餐饮娱乐服务业废水排放系数计算

运用数理统计学的分层抽样法对南京市2001年-2003年小型餐饮娱乐服务业废水中具有相关性的主要污染因子的排放浓度进行了统计,并计算出排放系数,提出用计算的废水排放系数征收小型餐饮娱乐服务业的排污费,实例表明,在该行业运用这一方法征收排污费是可行的。     

木薯乙醇-汽油混合燃料生命周期排放多目标优化研究

建立了木薯乙醇-汽油混合燃料生命周期排放单目标和多目标优化模型.以生命周期CO,NO_x,PM,HC,SO_x,CO₂排放为优化目标,对木薯乙醇-汽油混合燃料生命周期排放进行了单目标及多目标优化,并进行了灵敏度分析.结果表明:多目标优化后木薯乙醇-汽油混合燃料的混合比例为63%.与原始值相比,多目标优化后生命周期CO排放略有升高,NO_x升高15%,PM升高19%;生命周期HC、SO_x和CO₂分别降低8%、50%和21%.     

电子垃圾拆解回收VOCs排放特征与排放因子

电子垃圾拆解回收过程中的高温处理往往产生大量挥发性有机物(VOCs),对周边环境及人体健康产生不可忽视的影响.本研究选取某电子垃圾集中拆解回收场,对场区加热烤板车间、塑料制粒车间、湿法提取车间和火法冶炼车间废气处理设施排放口的VOCs浓度和组分进行监测,分析了不同生产工艺VOCs排放特征及其总VOCs排放因子.结果表明...     

A new prediction method of industrial atmospheric pollutant emission intensity based on pollutant emission standard quantification

● Established a quantification method of pollutant emission standard. ● Predicted the SO₂ emission intensity of single coking enterprises in China. ● Evaluated the influence of pollutant discharge standard on prediction accuracy. ● Analyzed the SO₂ emissions of Chinese provincial and municipal coking enterprises. Industrial emissions are the main source of atmospheric pollutants in China. Accurate and reasonable prediction of the emission of atmospheric pollutants from single enterprise can determine the exact source of atmospheric pollutants and control atmospheric pollution precisely. Based on China’s coking enterprises in 2020, we proposed a quantitative method for pollutant emission standards and introduced the quantification results of pollutant emission standards (QRPES) into the construction of support vector regression (SVR) and random forest regression (RFR) prediction methods for SO₂ emission of coking enterprises in China. The results show that, affected by the types of coke ovens and regions, China’s current coking enterprises have implemented a total of 21 emission standards, with marked differences. After adding QRPES, it was found that the root mean squared error (RMSE) of SVR and RFR decreased from 0.055 kt/a and 0.059 kt/a to 0.045 kt/a and 0.039 kt/a, and theR² increased from 0.890 and 0.881 to 0.926 and 0.945, respectively. This shows that the QRPES can greatly improve the prediction accuracy, and the SO₂ emissions of each enterprise are highly correlated with the strictness of standards. The predicted result shows that 45% of SO₂ emissions from Chinese coking enterprises are concentrated in Shanxi, Shaanxi and Hebei provinces in central China. The method created in this paper fills in the blank of forecasting method of air pollutant emission intensity of single enterprise and is of great help to the accurate control of air pollutants.