凹凸棒土添加脲醛树脂胶的甲醛释放规律研究

自国家新出台人造板及其制品甲醛释放限量标准和强制执行以来,人造板产品始终存在的甲醛释放问题,成为目前人们关注的焦点.采用干燥器测甲醛法对提纯凹土添加脲醛胶和面粉添加脲醛胶的甲醛短期释放和长期释放规律进行对比研究,结果表明甲醛单日释放量都有逐渐降低的趋势,第5天之后趋于稳定,但前者总比后者释放量低;长期放置提纯凹土添加脲醛胶也起到了降低甲醛释放量并廷缓甲醛释放的效果,但超过1个月后效果有限.     

室内甲醛污染和分析

甲醛是室内环境的主要污染物.采用乙酰丙酮分光光度测定方法,通过对一幢五层新建的办公楼随机抽取六间房,每隔3个月分别进行了检测,结果表明室内温度变化对甲醛的释放有较大影响,甲醛的释放随时间逐渐减少.列举了脲醛树脂、酚酞树脂、胺基甲醛树脂等是造成室内甲醛污染的主要来源,甲醛对人体的危害极大,不同国际组织、国家对室内甲醛浓度制定了不同的最高限值.从控制污染源、控制室内条件、处理方法等方面将有效防治室内甲醛污染.     

相对湿度、温度对胶合板甲醛释放的影响

测试了不同相对湿度、温度条件下密闭环境舱中胶合板释放的甲醛浓度,研究了相对湿度和温度对胶合板甲醛释放的影响规律.结果发现:开始3h内密闭舱内甲醛浓度迅速增加,之后7~8h甲醛浓度趋于平衡;相对湿度升高20%,密闭舱内甲醛平衡浓度增加了1.1~1.3倍;温度升高5℃,甲醛平衡浓度增加了1.3~2.5倍;利用变装载度法,求解了胶合板甲醛初始可释放浓度C_m,0、扩散系数D_m和界面气固分配系数K,探讨了相对湿度、温度对各释放参数的影响,构建了相对湿度与温度影响参数模型,模型预测了不同环境条件下的胶合板甲醛释放参数,预测值与实验结果吻合良好.     

居室内甲醛含量的监测

本文研究了新装修的居室内甲醛含量随时间的变化规律，共进行了八次监测，每次监测时间间隔为15天。监测结果表明，使用常用的装饰材料装修居室，居室内甲醛的浓度呈先升后降的变化，在最初约90天内甲醛浓度超过国家标准。     

细木工板中甲醛释放的影响因素及模糊评价

利用环境测试舱模拟不同试验条件,采用跟踪监测环境测试舱内空气中污染物浓度的方法,对细木工板中甲醛释放特征进行研究,并结合试验数据及相关标准引入模糊综合评价方法对细木工板中甲醛释放进行评价,建立室内甲醛散发模型。结果表明:细木工板中甲醛释放在很短的时间内迅速上升达到峰值,然后相对缓慢的逐渐衰退,最后趋于稳定,总体呈现非简单线性相关性;模糊评价模型可以有效反映出细木工板的结构、温度、相对湿度和空气交换率对细木工板中甲醛释放浓度的影响,从而实现综合分析量化;改善通风条件,增加空气交换率,是有效的降低细木工板中甲醛释放对室内空气的不良影响的方法;同时得出室内无净化装置时室内甲醛散发模型d Cidt=(1-Fb)u C0+SCVmu C1。     

板材甲醛释放实验及其释放参数的测定

采用1m3的小型环境模拟舱,测试了不同温度和装载度条件下胶合板、密度板、细木工板和复合地板中甲醛释放规律.研究发现:甲醛浓度在初始阶段(0~3h)均迅速增大,随后速度慢慢减小,最后浓度趋于恒定值;温度升高会促进板材内甲醛释放,温度每升高5 ℃,甲醛释放量会增加10%~30%;而装载度增大则会减少单位体积板材内甲醛的释放量.利用不同装载度条件下板材在密闭环境舱散发过程和平衡状态浓度,求解了影响板材释放特性的关键释放参数:可散发初始浓度Cm,0、扩散系数Dm和分配系数K;模拟计算的浓度结果与实验测试数据吻合良好,为研究板材甲醛释放规律提供了一种有效手段.     

板材甲醛释放实验及其释放参数的测定

采用1m3的小型环境模拟舱,测试了不同温度和装载度条件下胶合板、密度板、细木工板和复合地板中甲醛释放规律.研究发现:甲醛浓度在初始阶段(0~3h)均迅速增大,随后速度慢慢减小,最后浓度趋于恒定值;温度升高会促进板材内甲醛释放,温度每升高5℃,甲醛释放量会增加10%~30%;而装载度增大则会减少单位体积板材内甲醛的释放量.利用不同装载度条件下板材在密闭环境舱散发过程和平衡状态浓度,求解了影响板材释放特性的关键释放参数:可散发初始浓度Cm,0、扩散系数Dm和分配系数K;模拟计算的浓度结果与实验测试数据吻合良好,为研究板材甲醛释放规律提供了一种有效手段.     

家庭室内装修后甲醛污染现状及其变化规律探讨

探讨家庭室内装修后空气中甲醛污染现状及其变化规律.按照室内装修后时间长短不同及温度的变化进行跟踪检测.经过装修的居室室内空气中的甲醛浓度均高出(甚至远远高出)楼房竣工验收时的浓度.说明甲醛污染主要由装修引起,装修后装饰材料(板材等)仍能释放甲醛,并造成室内甲醛超标,随着时间的推移,室内甲醛浓度逐渐降低.但是,随着夏季气温的升高,冬季装修的居室甲醛浓度会急剧升高,造成室内空气严重的"二次污染".     

长三角对流层甲醛柱浓度时空变化及驱动因素

通过OMI卫星数据分析了2005~2016年长江三角洲对流层甲醛柱浓度的时空变化规律.同时结合2008年和2010年各部门VOCs人为源排放量,利用BP神经网络和RBFN神经网络模型对对流层甲醛柱浓度进行了县域尺度上的回归模拟和各部门排放量贡献度分析.结果表明：长三角城市群对流层甲醛柱浓度在2005~2010年存在着增加趋势,2011~2016年甲醛浓度有下降的趋势.高值区域分布在皖北苏北、上海及其附近,低值区域分布在浙西南一带.人为源排放使得经济发达地区的甲醛柱浓度显著增高.工业源在长三角的分布较为广泛,电力源分布稀疏且VOC排放量远小于工业源排放量,居民源的VOC排放量介于工业源和电力源之间,有明显的南北差异.交通源主要集中在苏南、浙北和上海附近,少部分沿交通线条状分布.机器学习算法可以较好地利用人为源排放数据对甲醛柱浓度进行模拟.神经网络的拟合精度可以达到0.6~0.8,比线性回归的拟合精度超出0.3~0.4.模型变量重要性计算显示各部门中居民源对甲醛柱浓度的贡献程度最高.研究对流层甲醛柱浓度的长期时空变化及其影响因素有利于深入研究臭氧污染,同时也为大气治理和政策制定提供了科学依据.     

人造板甲醛释放影响因素的环境箱试验与模型研究

采用环境箱试验与数学模型模拟人造板中甲醛的释放过程,探讨温度、湿度、封边处理、材质等因素对甲醛释放的影响.环境箱实验表明,随环境温度增加或湿度增加,甲醛初始释放量增高;人造板封边后甲醛释放率明显减小;3种材质的人造板甲醛释放速率依次为中密度板>刨花板>大芯板.经验衰减模型与物质传输模型的对比分析表明,物质传输模型比衰减模型更有利于解释环境箱试验结果.     

广州市氮氧化物的数值模拟及暴露影响评价

主要介绍了大气暴露风险评价ADMER模式的模块组成及其主要功能,并利用该模式对广州地区常规的氮氧化物进行了暴露风险评价研究.利用中尺度气象模式模拟的5km气象场数据和收集整理的年平均污染排放源资料进行了大气污染扩散模拟计算.结果表明,无论是氮氧化物的浓度值还是其时空变化趋势,ADMER模式模拟的结果与实际观测均较一致,相关系数达0.76.氮氧化物的浓度高值出现在冬春季节,夏季的浓度相对较低,这主要是受气象场条件的影响.空间场上,氮氧化物的高值区位于广州地区的西南和中部,与工业大点源以及地面源排放的分布一致,而广州地区东北部氮氧化物的浓度值相对较低.在浓度评估的基础上,对暴露人口也进行了估算.由于广州是广东省的主要人口密集区,所以,定量化暴露人口对于进一步开展污染控制减排策略有一定的指示意义.     

利用逆向轨迹反演模式估算北京地区甲烷源强

利用连续监测的大气甲烷浓度数据和拉格朗日逆向轨迹反演模式估算出北京甲烷源排放强度，并与根据最新调查数据建立的北京地区甲烷源排放清单进行了比较。排放清单结果表明，北京地区甲烷排放总量为296．4Gg/a，其中，最主要的甲烷排放源为城市垃圾和化石燃料，反映了北京作为一特大城市甲烷排放以人为源为主的特点。利用2000年6月至12月连续观测的有湿合层代表性的北京大气甲烷浓度，通过奇异值分解法（Singular Value Decomposition，SVD）反演出模拟区域的甲烷排放源强度和分布。模式计算与排放清单在甲烷源定性分布上对应较好，定量结果也是合理的。但由于可输入的气象数据有限，轨迹在整个模拟区域内覆盖不均匀，反演出的源块位置有偏差，其中偏差最大的为煤矿的甲烷排放。     

Calculation of long-term averaged and episodic oxidant concentrations for the Netherlands

Ground-level ozone and oxidant (sum of O₃ and NO₂) concentrations in The Netherlands are calculated during the growing season (May–September) by means of a Lagrangian long-range transport model. Two air parcels—one representative of the mixed layer, the other representative of the polluted layer above the mixed layer (aged smog layer)—are followed along 96 h back trajectories. Long-term averaged and 98 percentile values of hourly averaged concentrations are estimated on the basis of concentrations calculated for four arrival times per day for all days in the period considered.In a number of sensitivity runs the influence of European anthropogenic NO_x and VOC emissions on the oxidant concentrations in The Netherlands has been investigated. In general, the influence of European emissions on the 98-percentile values is 2–3 times as large as on the averaged concentrations. This indicates that long-term averaged concentrations more strongly depend on the concentrations in the free troposphere whereas the episodic concentrations are determined by photochemical production over Europe. VOC emission reduction is more effective in lowering episodic concentrations than NO_x emission reduction. For long-term averaged concentrations, however, NO_x and VOC emission reduction of 50% or more are nearly equally effective.     

基于地基观测及源清单的2017-2019年德州市秋冬季大气污染防治效果评估

为评估污染减排措施实施效果,基于地基观测及排放清单数据,运用WRF中尺度气象模型和CAMx空气质量模型,对德州市2017-2019年秋冬季大气污染攻坚实施效果进行了评估.结果表明,2017-2018年秋冬季,德州市ρ（PM_2.5）同比下降31.7%,高于京津冀及周边地区平均水平（25.6%）,大气污染攻坚措施成效显著;2018-2019年秋冬季,德州市ρ（PM_2.5）同比增加8.5%,高于京津冀及周边地区平均水平（4.2%）,这与不利气象条件及排放量同比减少有关.观测结果显示,2018-2019秋冬季,德州市PM_2.5中无机组分、一次排放示踪物以及SO₂和CO等气态前体物浓度较上一年度呈下降趋势,ρ（SOA）（SOA为二次有机气溶胶）、ρ（NH₄+）同比有大幅增长,增幅分别为53.8%和19.1%,这与大气中VOCs（挥发性有机物,增加46.5%）及大气氧化性（增加6.4%）的增加密切相关,表明德州市复合型大气污染加剧,PM_2.5防控难度加大.综合气象和减排评估结果可知,2017-2018年秋冬季,气象条件（13.4%）和长效措施（9.4%）是德州市PM_2.5改善的两个主要因素;2018-2019年秋冬季,长效措施减排效果较为有限,减排主要来自预警应急（5.0%）和区域减排（5.2%）,若仅考虑不利气象条件的影响,将导致同比约19.9%的反弹.因此,持续深入推进长效减排措施,降低污染物排放水平,是德州市实现空气质量改善的根本途径.      

Prediction of seasonal extremes of one-hour average urban CO concentrations

The annual and seasonal extremes of pollutant concentrations in urban areas tend to represent samples from a long-term non-stationary series so that purely non-causal, statistical methods for their prediction are largely inapplicable. The paper described a method to determine the seasonal extremes of 1-h average CO concentrations from vehicle patterns and emissions, basic meteorological measurements and historical records of ambient concentrations. The method links the output of a deterministic Gaussian plume line source model (which provides average winter trends on an annual basis) with knowledge of a suitable parametric form of the probability density function (pdf) of the daily peak 1-h CO concentrations. The deterministic model requires only average emission and meteorological data as input, although the approach outlined can be extended to include more complex deterministic models with more detailed dynamic input information. Knowledge of the pdf of ambient concentrations is gained from past data by applying goodness-of-fit tests based upon maximum likelihood estimation and its accuracy is assessed by examining prediction performance for the extremes of interest. Problems of non-stationarity and autocorrelation are minimized by restricting attention to the winter season and to the evening peak concentration. The method is used to predict maxima of 1-h CO concentrations for winter seasons in Canberra, Australia, although it applies to other extremes at other time averages, such as 8-h averages, and to other pollutants where they are dispersed predominantly from mobile sources.     

The levels, sources and reactivity of volatile organic compounds in a typical urban area of Northeast China

Air concentrations of volatile organic compounds (VOCs) were continually measured at a monitoring site in Shenyang from 20 August to 16 September 2017. The average concentrations of alkanes, alkenes, aromatics and carbonyls were 28.54, 6.30, 5.59 and 9.78 ppbv, respectively. Seven sources were identified by the Positive Matrix Factorization model based on the measurement data of VOCs and CO. Vehicle exhaust contributed the most (36.15%) to the total propene-equivalent concentration of the measured VOCs, followed by combustion emission (16.92%), vegetation emission and secondary formation (14.33%), solvent usage (10.59%), petrochemical industry emission (9.89%), petrol evaporation (6.28%), and liquefied petroleum gas (LPG) usage (5.84%). Vehicle exhaust, solvent usage and combustion emission were found to be the top three VOC sources for O₃ formation potential, accounting for 34.52%, 16.55% and 11.94%, respectively. The diurnal variation of the total VOCs from each source could be well explained by their emission characteristics, e.g., the two peaks of VOC concentrations from LPG usage were in line with the cooking times for breakfast and lunch. Wind rose plots of the VOCs from each source could reveal the possible distribution of the sources around the monitoring site. The O₃ pollution episodes during the measurement period were found to be coincident with the elevation of VOCs, which was mainly due to the air parcel from the southeast direction where petrochemical industry emission was found to be dominant, suggesting that the petrochemical industry emission from the southeast was probably a significant cause of O₃ pollution in Shenyang.     

CMAQ模式及其修正预报在珠三角区域的应用检验

为检验CAMQ空气质量数值预报模式对区域性空气质量的预报准确度,通过对珠江三角洲地区16个监测站点数据进行聚类分析,对划分的评价区域进行预报误差分析。结果表明,CMAQ模式输出的污染物浓度水平存在明显偏低的现象,且可吸入颗粒物的浓度偏离最大,这与污染源清单削减程度有关。污染物浓度时变规律分析表明,CMAQ模式能较好地模拟可吸入颗粒物、二氧化氮和臭氧小时浓度的日变化特征,但对二氧化硫的模拟能力较弱,反映污染源时间分配因子存在不适应性。为提高预报的初始浓度值,采用预报日前一天的监测数据作为修正项,并考虑CMAQ模式预报的浓度变化趋势,从而进行修正预报。误差统计表明,修正预报的准确度显著提高,反映了引入实际监测数据对空气质量数值预报模式进行修正的研究意义和可行性。     

Uncertainties in source emission rate estimates using dispersion models

The source emission rates during the Prairie Grass dispersion experiments were carefully observed and were adjusted by the experimentalists so that they were about twice as high during unstable conditions as during stable conditions. The question was asked whether observed concentrations and meteorological conditions could be used in dispersion models in order to predict source emission rates and verify this factor of two difference. Three types of simple dispersion models were applied to this problem, with the result that for the model based on Monin-Obukhov similarity theory, the uncertainties in predictions of source emission rates for individual runs were at best about ±10–20% when observed crosswind integrated concentrations from the 50m arc were used. Consequently this model could discern the factor of two difference in average source emission rates for the two sets of field trials which consisted of about 20 runs each. However, some models, such as the Gaussian plume model, exhibit uncertainties of about ±70% to a factor of two in predictions for individual runs, and hence could not discern the difference in average source emission rates when concentration observations at downwind distances of 100–800 m are used. It is found that the use of observed cross-wind integrated concentrations produces more accurate conclusions that the use of observed point concentrations, for the uncertainties in predictions of source emission rates are about a factor of two larger when the observed point concentrations are used.     

木家具关键挥发性有机化合物散发传质特性

木家具中挥发性有机化合物(Volatile Organic Compounds,VOCs)的散发是一个污染环境的复杂传质过程.为把握污染物散发全周期特性,首先建立了一套描述散发行为的显性完全解析模型,适用于模拟对人体最不利的无换气情况.然后基于对模型的分析开发了一种简便快捷的实验方法,能够利用木家具在密闭舱中散发的逐时浓度求取目标VOCs的3个重要传质参数:可散发浓度C0、扩散系数D和分配系数K.实验部分测算了从10个厂家依多种原材料定制的5类20件常见家具,参考出现频率、健康影响、可散发量确定了19类关键污染物,发现总体C0、D、K服从正态分布.此外,结合常见散发参数范围下的数值实验和参数回归分析,提出一组反映散发机理、预测类比不同时空尺度下散发数据的准则关联式,并分析得出空气交换率和承载率对散发影响较大、空气流速对散发影响较小.     

基于粗糙集与灰色SVM的中国CO2排放量预测

从我国CO2排放量的不确定性、不完整性、小样本等特征出发,以灰色系统(GM)模型和支持向量机(SVM)模型为基础,建立基于粗糙集的组合预测模型.利用该模型以我国1990~2011年CO2排放量的数据以及同期的人口数量、GDP和能源消耗总量数据为基础对我国同期CO2排放量进行预测来验证其有效性,最后对我国2012~2017的CO2排放量进行预测.结果表明,灰色系统理论与支持向量机模型仅能够反应我国CO2排放的长期变化趋势,在预测精度上存在一定缺陷而基于粗糙集与灰色SVM的组合预测模型在预测精度上明显优于以上两种方法,能够对我国未来CO2排放量进行准确有效的预测分析.