株洲市工业区大气中SO2转化速率的研究

在湖南省株洲市工业区的下风向布点,同时测定大气中ＳＯ２的浓度、ＴＳＰ和＜０．２５μｍ细粒子中ＳＯ２－４的浓度．结果表明,大气中ＳＯ２浓度随着离污染源距离的增加迅速下降,和ＳＯ２相比,颗粒物中ＳＯ２－４浓度随距离的增加而降低的程度要慢得多．同步测定的ＳＯ２浓度和细粒子ＳＯ２－４浓度有显著的相关关系．根据风向、风速和新产生的细粒子中的ＳＯ２－４浓度及大气中ＳＯ２的浓度计算了株洲教育学院处ＳＯ２的转化速率,春季湿度大,ＳＯ２转化速率也大,为４．８％／ｈ,秋季湿度小,ＳＯ２的转化速率变低,为１．５％／ｈ．春季中路铺ＳＯ２转化速率为３．１％／ｈ．     

Atmospheric Change and Biodiversity

Strategies to conserve biodiversity need to include the monitoring, modelling, adaptation and regulation of the composition of the atmosphere. Atmospheric issues include climate variability and extremes; climate change; stratospheric ozone depletion; acid deposition; photochemical pollution; suspended particulate matter; and hazardous air pollutants. Coarse filter and fine filter approaches have been used to understand the complexity of the interactions between the atmosphere and biodiversity. In the first approach, climate-based models, using GIS technology, helped create future biodiversity scenarios under a 2 × CO₂ atmosphere. In the second approach, the SI/MAB forest biodiversity monitoring protocols helped calibrate the climate-forest biodiversity baseline and, as global diagnostics, helped identify where the biodiversity was in equilibrium with the present climate. Forest climate monitoring, an enhancing protocol, was used in a co-location approach to define the thermal buffering capacity of forest ecosystems and their ability to reduce and ameliorate global climate variability, extremes and change.     

北京市大气颗粒物PM2.5,PM10及降雪中的汞

研究了北京大气颗粒物PM25,PM10及降雪中的汞.结果表明,北京大气颗粒物PM25中汞的浓度为024—179ng·m-3,PM10中汞的浓度为038—302ng·m-3,冬季PM25和PM10中汞的浓度明显高于夏季;北京大气可吸入颗粒物中的汞均以细粒子(≤25μm)为主,冬季细粒子中汞的浓度高是细粒子多且其汞含量高共同作用的结果,而夏季则是细粒子中汞的含量高.降雪中汞的浓度在106—162ng·l-1之间,降雪中可溶性汞为总汞的一半左右.     

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     

基于多项污染物综合相对重要性的主成分子集合环境测点优选法

根据对多项污染物环境测点的优选,提出了单项污染物测值的相对重要性和多项污染物测值的综合相对重要性新概念,并给出了综合相对重要性的计算公式．根据测点的综合相对重要性,再用主成分子集合选择法优选测点该方法与基于交叉相关性的主成分子集合优选法相比,不仅减少了计算量,而且避免了当各单项污染物选出的测点不一致时,难以确定公共优化点的困难．该方法用于成都市大气污染物的12个测点优选结果符合实际情况．     

吉林省城市大气降水pH值时空分异及成因

通过对吉林省８个代表城市１９９２－１９９７年大气降水观测数据的统计分析,对大气降水ＰＨ值的时空分异进行了探讨。结果表明：吉林省中西部城市大气降水基本呈中性;东部的图们,珲春两市大气降水酸化亚得,多年平均降水ＰＨ分别为５．１２和５．３８;全省碱性降水多集中在吉林市。     

Trends of air pollution in the Fichtelgebirge Mountains, Bavaria

We analyzed 13 years of hourly measurements of SO₂, NO_x, and O₃, at forest ecosystem research sites in SE Germany. A quasi-continuous data record was obtained by combining data sets from two locations. Before interpreting trends in the combined data set, we analyzed if the change of location introduced a systematic bias. We employed autocorrelation functions, Hurst statistics, complexity analysis, and recurrence quantification and found that the partial data sets exhibited no indication of the presence of any bias. For SO₂, we also compared the data from the forest sites with data obtained in nearby cities and also found no indications for any systematic effects. Applying nonparametric trend statistics we found a significant decrease of the SO₂. Most of the observed decrease is due to the reductions of SO₂ emissions in eastern Germany, but reductions in western Germany and the Czech Republic also played important roles. For O₃, we observed a significant increase, the causes of which are unclear from our data alone. No trend was identified for NO_x.     

北京市大气细颗粒物对Balb/c 3T3细胞周期的影响

研究了北京市大气细颗粒物有机提取物 (EOC)对Balb c 3T3细胞周期及调节因子的影响 .实验发现 116 μg mL细颗粒物EOC即有明显的细胞毒性 ,抑制细胞生长 ,并存在剂量 反应关系 ,IC50 为 4 0 2 2 μg mL ;流式细胞仪分析显示 136 6 μg mLPM2 5EOC可造成细胞周期改变 ,G0 G1期、G2 M期细胞分布增加 ,分别为 16 2 4 % (P <0 0 1)与 3 17% (P <0 0 5 ) ,S期细胞分布减少 2 0 4 % (P <0 0 1) ,并伴有P5 3含量的增加 ;4 0 9 8、4 78 1μg mL的PM2 5EOC可诱导Balb c3T3细胞凋亡 .提示PM2 5有机提取物可能通过P5 3途径引起Balb c 3T3细胞周期阻滞 ,并诱导细胞凋亡 .     

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.