西藏农村能源消费及环境影响研究

西藏农村人口现有220多万,占全区总人口80％以上,农村经济落后,城乡差距很大。西藏农村能源资源主要有水力、地热、太阳能、风力、林木和畜粪等,煤炭、石油、天然气等高热值的优质能源很少。在这种经济发展水平及能源资源基础条件下,农村能源消费中高热值的现代能源比重很低,主要以生物质能如薪柴、牛粪、草皮等为主。这种低水平的能源消费结构已经对西藏脆弱的生态环境造成了巨大的破坏,西藏未来的能源发展战略迫切需要改变农村目前的消费结构,应发展太阳能、风能、农村小水电及输入高热值的化石能源替代目前生物质能的消费,减少因能源消费对西藏生态环境的破坏。     

关于室内装修有害气体烘焙排风稀释技术数值研究

依据实测数据,应用数值计算手法,对烘焙排风稀释技术不同工况下总挥发性有机化合物的排除量、烘焙排风稀释阶段室内空气中总挥发性有机化合物的浓度变化进行计算并讨论烘焙温度、换气次数对去除效果的影响。     

GSM移动通信基站电磁辐射污染状况研究

介绍了GSM移动通信基站系统,指出,在进行GSM移动通信基站环境影响分析时,要注意采样点位的设置和测量要求.现场监测结果表明,GSM移动通信基站对环境电磁辐射水平有一定的影响,特别是对50 m内建筑物影响显著,但绝大多数基站基本符合国家有关电磁辐射环境影响标准的要求,在设备正常运行情况下,不会对周围环境造成电磁辐射污染.     

AERMOD在国内环境影响评价中的实例验证与应用

AERMOD是美国环保局推出的新一代空气质量模式系统,它由AERMET(气象数据预处理器)、AERMAP(地形数据预处理器)和AERMOD(大气扩散模型)3部分组成.结合宁波市北仑区域大气环境影响评价,对该模式系统进行模式验证,并应用于实际预测评价.验证结果表明,在采用适当的模型参数时,该系统预测值与实际监测值具有很好的一致性,SO2、NO2日均最高浓度预测准确率分别达到64.3%和85.7%.最后结合实际预测评价工作,提出AERMOD模式系统在国内环境影响评价工作中的优势及不足.     

《燃煤二氧化硫排放污染防治技术政策》绩效定量评估

基于全国46个城市(其中38个城市位于"两控区")1996~2005年的面板数据,运用difference-in-differ-ences(DID)模型对《燃煤二氧化硫排放污染防治技术政策》的绩效进行了定量评估。结果表明,该政策的实施对于降低工业二氧化硫排放量起到积极作用,使"两控区"城市年工业二氧化硫的排放量平均降低了19.3%。     

哈尔滨市蔬菜生产基地土壤重金属污染状况评价

根据对哈尔滨市所辖8区蔬菜生产基地土壤环境的调查和监测,利用土壤综合污染指数进行评价,哈尔滨市蔬菜生产基地土壤质量状况。     

环境约束下大都市城郊土地利用结构评价——基于信息熵和非期望产出模型分析

土地利用过程中会产生期望和非期望产出,针对以往土地利用结构评价中忽略环境产生的非期望产出而影响土地利用结构效率真实性问题,以大都市城郊的上海青浦区为例,宏观上,运用信息熵模型对该区2005~2014年土地利用结构有序性进行评价;微观上,运用非期望产出模型测算青浦区研究期间土地利用结构效率,为寻求区域土地利用结构效率损失的原因及改善途径提供参考。结果表明:(1)2005~2014年青浦区土地利用结构信息熵值时序上呈现"M"形变化趋势,空间上由中部向东西两翼递减;(2)2005~2014年青浦区土地利用结构效率变化趋势在时间序列上呈现"W"形变化趋势,空间上由中部的中心城区向东西两翼递减格局;(3)将环境的负产出纳入非期望产出模型进行土地利用结构效率评价,能更加准确的反映土地利用结构效率的真实性;(4)影响青浦区土地利用结构效率水平变化的主要因素为纯技术效率的变化,资源过度消耗和环境污染物排放过量是制约青浦区土地利用结构效率提升的主要原因。针对土地利用结构效率损失的原因及区域差异提出效率提升途径。研究结果可为青浦区土地利用结构优化和可持续发展提供科学借鉴。     

A new approach to quantify the degradation kinetics of linuron with UV, ozonation and UV/O3 processes

The degradation of linuron, one of phenylurea herbicides, was investigated for its reaction kinetics by different treatment processes including ultraviolet irradiation (UV), ozonation (O₃), and UV/O₃. The decay rate of linuron by UV/O₃ process was found to be around 3.5 times and 2.5 times faster than sole-UV and ozone-alone, respectively. Experimental results also indicate overall rate constants increased exponentially with pH above 9.0 while the increase of rate constants with pH below 9 is insignificant in O₃ system. All dominant parameters involved in the three processes were determined in the assistant of proposed linear models in this study. The approach was found useful in predicting the process performances through the quantification of quantum yield (rate constant for the formation of free radical HOO⁻ from ozone decomposition at high pH), rate constant of linuron with ozone (k_O3,LNR), rate constant of linuron with hydroxyl radical (k_OH,LNR), and α (the ratio of the production rate of OH and the decay rate of ozone in UV/O₃ system).     

GC/MS法测定润滑油基础油中多环芳烃

对润滑油基础油中多环芳烃的气相色谱/质谱(GC/MS)测定方法进行了研究,探讨了样品的提取,固相萃取柱净化等前处理方法,同时也对仪器测定条件进行了优化.结果表明,在检测润滑油基础油中多环芳烃时,经优化的GC/MS法的加标回收率和精密度均达到较满意的水平.     

Neural network forecasting of air pollutants hourly concentrations using optimised temporal averages of meteorological variables and pollutant concentrations

The new method for the forecasting hourly concentrations of air pollutants is presented in the paper. The method was developed for a site in urban residential area in city of Zagreb, Croatia, for four air pollutants (NO₂, O₃, CO and PM₁₀). Meteorological variables and concentrations of the respective pollutant were taken as predictors. A novel approach, based on families of univariate regression models, was employed in selecting the averaging intervals for input variables. For each variable and each averaging period between 1 and 97 h, a separate model was built. By inspecting values of the coefficient of correlation between measured and modelled concentrations, optimal averaging periods for each variable were selected. A new dataset for building the forecasting model was then calculated as temporal moving averages (running means) of former variables. A multi-layer perceptron type of neural networks is used as the forecasting model. Index of agreement, calculated for the entire dataset including the data for model building, ranged from 0.91 to 0.97 for the respective pollutants. As suggested by the analysis of the relative importance of the input variables, different agreements for different pollutants are likely due to different sources and production mechanisms of investigated pollutants. A comparison of the new method with more traditional method, which takes hourly averages of the forecast hour as input variables, showed similar or better performance. The model was developed for the purpose of public-health-oriented air quality forecasting, aiming to use a numerical weather forecast model for the prediction of the part of input data yet unknown at the forecasting time. It is to expect that longer term averages used as inputs in the proposed method will contribute to smaller input errors and the greater accuracy of the model.