车辆限行前后奥运场馆附近空气中苯系物污染特征与来源

在"好运北京"体育赛事车辆限行前后对北京奥运场馆,即四环路健翔桥附近空气中的6种苯系物进行了连续监测。监测结果显示,被测空气的苯系物中甲苯所占比例最大,为32.1%~42.5%,限行后总苯系物平均质量浓度降低了近47%。车辆限行前后,苯系物呈现类似的日变化规律,在交通早、晚高峰时出现峰值。限行前苯系物处于累积状态,日最高值出现在18点,限行后苯系物处于扩散状态,日最高值出现在8点。苯系物来源解析表明,整个交通管制期间苯/甲苯浓度的比值为0.49~0.55,且各苯系物具有良好的相关性,证明被测空气中苯系物具有良好的同源性,均来自汽车尾气。限制车流量可有效降低空气中苯系物浓度。     

吹扫捕集/气相色谱法测定空气中挥发性有机物

研究并建立了用吹扫捕集气相色谱法测定空气中挥发性有机物的分析方法。以空气中苯系物为目标化合物,以静态法配制标准气体,分别作了高、中、低三种浓度的标准曲线,其相关系数在0 98以上。该法对空气中苯系物的方法检出限为0 14～0 16μg m3,相对标准偏差在2 1%～6 3%之间,回收率在92%～120%。     

上海市交通干道空气中苯系物冬季污染特征初探

2004年冬季对上海市交通干道附近空气中苯系物的浓度水平进行了监测,并采用气相色谱法进行分析。结果表明,采样期间大气中苯、甲苯、乙苯、二甲苯的浓度分别为1.77~27.7μg/m3、7.29~195μg/m3、3.11~40.2μg/m3、4.49~82.4μg/m3。每日6:30~9:30和15:30~19:00两个时段苯系物的浓度要高于中午时间的浓度,与国内其他城市相比,上海市甲苯的浓度要高,浓度水平要远远高于国外城市的测定结果。苯系物的浓度受风速和风向影响较大。提出了制订空气中苯系物的排污清单和加强机动车尾气中苯系物控制的建议。     

新乘用车内空气中的挥发性有机物污染特征及健康风险

为调查新生产乘用车内空气中挥发性有机物的含量、时间变化趋势以及致癌风险,2017—2020年在环境舱中采集了48辆新车在常温、高温和通风3种模式下的车内空气样品。结果显示,新车空气中苯系物(苯、甲苯、乙苯、对二甲苯、间二甲苯、邻二甲苯和苯乙烯之和)的质量浓度中值在常温、高温和通风模式下分别为101、335、55.8μg/m³;醛类物质(甲醛、乙醛和丙烯醛之和)的质量浓度中值分别为103、446、30.0μg/m³。常温模式下的乙醛及高温模式下的甲醛和乙醛超出国家标准限值的比例较高,超标比例分别为77.1%、81.3%和89.6%。2017—2020年国内生产的新乘用车,车内空气中的苯系物和醛类组分在3种模式下不同年份间基本无显著性差异。致癌风险评估结果显示,车内空气中苯的致癌风险可接受,甲醛的致癌风险在部分车型中较高。     

地下车库空气中苯系物浓度的时间分布特征与污染评价

采用气相色谱法定量分析了地下车库空气中苯系物(苯、甲苯、二甲苯)的浓度并于不同时间段采集地下车库的空气样品,研究了地下车库内苯系物的浓度随时间的变化特征及污染状况。研究结果表明:①在实验色谱条件下,用外标法测定苯系物的准确度较高,回收率为90%~110%;②苯系物浓度随时间的变化呈现明显的周期性,苯系物单体浓度与总量浓度随时间的分布特征相似,表现为早晨的浓度大于中午及傍晚;③苯系物总量和单体浓度远远高于室外;④地下车库苯系物浓度低于中国室内空气质量标准限值,但检测结果表明地下车库存在苯系物的污染,长期累积对人体健康构成潜在威胁。     

便携式顶空/气相色谱/质谱联用法快速测定水中苯系物

建立了以便携式顶空/气相色谱/质谱联用技术快速测定水中苯系物的方法,该方法适用于水中苯系物的应急监测。     

实验室苯系物测定关键问题的研讨

苯系物是实验室常测项目之一,本文探讨了苯系物测定中影响较大的3个关键问题,避免因溶剂选择不当、校准曲线检测结果偏差大、三点校正浓度选取不正确等原因,影响最后测定结果,为今后实验室苯系物测定提供了一种既简单又准确可靠的方法总结。     

顶空气相色谱法测定土壤或底泥中挥发性苯系物

采用顶空气相色谱法测定了土壤或底泥中的挥发性苯系物,测定了苯系物在10%NaCl水溶液60℃时的分配常数,给出了加入土壤导致液相体积改变的校正方法。用10% NaCl水溶液作为提取剂在 60℃加热平衡60min, 对5个苯系物的平均回收率达到73.6%, 检出限为4.2~5.5ng/g。     

固定污染源中苯系物的便携式气质联用检测方法研究

建立了适用于固定污染源气体中苯系物的日常检测和监督性检测的便携式气质联用分析方法。利用Mars-400便携式GC-MS具有定量环和吸附管同时并存的功能,无需更换仪器部件,根据预测浓度,选择相应的进样模式(10mg/m3为浓度切换点),以保留时间和特征离子定性,总离子峰面积定量,现场对固定污染源中高低浓度苯系物进行直接分析。在吸附管和定量环两种模式下,选择苯系物的质量浓度范围分别为0.1~10 mg/m3、5~100 mg/m3,两者的线性相关系数均≥0.993,相对标准偏差(n=7)为5%~14%,方法回收率在84%~112%之间。     

攀枝花市环境空气中挥发性有机物污染特征及对策研究

采用SUMMA罐采样,空气预浓缩与气相色谱/质谱联用技术,建立了39种常见挥发性有机物的分析方法。选取攀枝花市不同功能区的5个测点,采集了4个季度的环境空气样品220个,定性检出挥发性有机物54种,其中烃类占24%,卤代烃类占52%,含氧化合物占22%,其它化合物占2%。苯系物的检出率最高。定量的挥发性有机物最大浓度和平均浓度最高的项目均为苯,平均浓度4.59g/m~3,最大浓度29.8g/m~3。苯系物时间分布呈现出旱季高,雨季低的特点;日变化特征为早晨最高,整体呈下降趋势。     

最优化集成方法在城市臭氧数值预报中的应用研究

基于多模式(NAQPMS、CMAQ、CAMx、WRF-Chem)空气质量数值预报业务系统的滚动预报结果,结合站点观测资料,评估了最优化集成方法在城市臭氧数值预报中的可行性和预报效果。一年的评估结果表明:当训练期为15 d时,最优化集成方法能够得到相对较好的结果。总体而言,最优化集成方法对城市臭氧浓度变化趋势和浓度水平的预报效果明显优于单个模式,且在大部分城市优于多模式的最优预报,其预报值和观测的相关系数提高0.11以上,均方根误差降低约10μg/m~3;该方法对城市臭氧污染等级的预报能力也明显优于单个模式,特别是轻、中度污染。此外,在模拟偏差较大的城市,最优化集成方法对预报效果的改进更为显著;在模拟偏差较小的城市,该方法仍可进一步提升预报效果。     

LaSVM-based big data learning system for dynamic prediction of air pollution in Tehran

Due to critical impacts of air pollution, prediction and monitoring of air quality in urban areas are important tasks. However, because of the dynamic nature and high spatio-temporal variability, prediction of the air pollutant concentrations is a complex spatio-temporal problem. Distribution of pollutant concentration is influenced by various factors such as the historical pollution data and weather conditions. Conventional methods such as the support vector machine (SVM) or artificial neural networks (ANN) show some deficiencies when huge amount of streaming data have to be analyzed for urban air pollution prediction. In order to overcome the limitations of the conventional methods and improve the performance of urban air pollution prediction in Tehran, a spatio-temporal system is designed using a LaSVM-based online algorithm. Pollutant concentration and meteorological data along with geographical parameters are continually fed to the developed online forecasting system. Performance of the system is evaluated by comparing the prediction results of the Air Quality Index (AQI) with those of a traditional SVM algorithm. Results show an outstanding increase of speed by the online algorithm while preserving the accuracy of the SVM classifier. Comparison of the hourly predictions for next coming 24 h, with those of the measured pollution data in Tehran pollution monitoring stations shows an overall accuracy of 0.71, root mean square error of 0.54 and coefficient of determination of 0.81. These results are indicators of the practical usefulness of the online algorithm for real-time spatial and temporal prediction of the urban air quality.     

基于遥感影像的木兰溪水质参数反演

水质监测对水环境评价及污染预防至关重要,但地面监测成本高、监测面积有限等,难以满足实时、大范围监测的要求。为了更好地解决该问题,基于遥感影像的空中监测技术越来越得到研究人员的青睐。以木兰溪为研究区,利用和地面监测数据同步的Landsat-8卫星遥感影像数据,对木兰溪的典型水质参数总磷、总氮、溶解氧、高锰酸盐指数的反演问题进行研究。首先,根据Landsat-8的水体敏感波段,分别选取总磷、总氮、溶解氧、高锰酸盐指数的反演特征波段组合为(b1-b2)/(b2+b3),(b1-b2)/(b3-b4),b2/(b1+b4),b1/b2;其次,利用反演特征波段组合分别构建总磷、总氮、溶解氧、高锰酸盐指数浓度的SVR(Support Vector Regression)反演模型,通过IPSO算法对SVR模型的参数进行优选;然后,将IPSO-SVR反演模型和统计回归反演模型、广义回归神经网络(GRNN)反演模型在验证集上进行评估,以平均绝对误差和均方根误差作为评价指标进行对比分析,结果表明IPSO-SVR反演模型的平均绝对误差和均方根误差最小,说明IPSO-SVR反演模型具有较高的精度和较好的实用性...     

Hybrid Neural Networks and Boosted Regression Tree Models for Predicting Roadside Particulate Matter

This paper examines the application of artificial neural network (ANN) and boosted regression tree (BRT) methods in air quality modelling. The methods were applied to developing air quality models for predicting roadside particle mass concentration (PM₁₀, PM_2.5) and particle number counts (PNC) based on air pollution, traffic and meteorological data from Marylebone Road in London. Elastic net, Lasso and principal components analysis were used as feature selection methods for the ANN models to reduce the number of predictor variables and improve their generalisation. The performance of the ANN with feature selection (ANN hybrid) and the BRT models was evaluated and compared using statistical performance metrics. The performance parameters include root mean square error (RMSE), fraction of prediction within a factor of two of the observation (FAC2), mean bias (MB), mean gross error (MGE), the coefficient of correlation (R) and coefficient of efficiency (CoE) values. The input variables selected by the elastic net produced the best performing ANN models. The ANN hybrid produced models performed only slightly better than the BRT models. The R values of the ANN elastic net and BRT models were 0.96 and 0.95 for PM₁₀, 0.96 and 0.96 for PM_2.5 and 0.89 and 0.87 for PNC, respectively. Their corresponding CoE values were 0.72 and 0.70 for PM₁₀, 0.74 and 0.76 for PM_2.5 and 0.81 and 0.71 for PNC respectively. About 80–99% of all the model predictions are within a factor of two of the observed particle concentrations. The BRT models offer more advantages regarding model interpretation and permit feature selection. Therefore, the study recommends the use of BRT over ANN where the model interpretation is a priority.     

Generalized regression neural network (GRNN)-based approach for colored dissolved organic matter (CDOM) retrieval: case study of Connecticut River at Middle Haddam Station,USA

The prediction of colored dissolved organic matter (CDOM) using artificial neural network approaches has received little attention in the past few decades. In this study, colored dissolved organic matter (CDOM) was modeled using generalized regression neural network (GRNN) and multiple linear regression (MLR) models as a function of Water temperature (TE), pH, specific conductance (SC), and turbidity (TU). Evaluation of the prediction accuracy of the models is based on the root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (CC), and Willmott’s index of agreement (d). The results indicated that GRNN can be applied successfully for prediction of colored dissolved organic matter (CDOM).     

扩散采样法测定室内空气中的BTEX

建立了扩散吸附管采样,热脱附仪和气相色谱联用测定室内空气中BTEX的监测分析方法,方法的最低检出限为0.0002mg/m3。该方法使用方便、便于携带、无需电源,且与传统的泵采样方法具有较好的可比性,适合于室内空气中BTEX的监测。     

A Wavelet-based Neural Network Model to Predict Ambient Air Pollutants’ Concentration

The present paper proposes a wavelet based recurrent neural network model to forecast one step ahead hourly, daily mean and daily maximum concentrations of ambient CO, NO₂, NO, O₃, SO₂ and PM_2.5 — the most prevalent air pollutants in urban atmosphere. The time series of each air pollutant has been decomposed into different time-scale components using maximum overlap wavelet transform (MODWT). These time-scale components were made to pass through Elman network. The number of nodes in the network was decided on the basis of the strength (power) of the corresponding input signals. The wavelet network model was then used to obtain one-step ahead forecasts for a period extending from January 2009 to June 2010. The model results for out of sample forecast are reasonably good in terms of model performance parameters such as mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), normalized mean absolute error (NMSE), index of agreement (IOA) and standard average error (SAE). The MAPE values for daily maximum concentrations of CO, NO₂, NO, O₃, SO₂ and PM2.5 were found to be 9.5%, 17.37%, 21.20%, 13.79%, 17.77% and 11.94%, respectively, at ITO, Delhi, India. Bearing in mind that the forecasts are for daily maximum concentrations tested over a long validation period, the forecast performance of the model may be considered as reasonably good. The model results demonstrate that a judicious selection of wavelet network design may be employed successfully for air quality forecasting.     

便携式质谱仪应用于交通干线苯系物污染特征分析

介绍了EcoSys-P便携式质谱仪在无车日前后对交通干线环境空气中苯系物现场监测中的情况,同时该方法与经典的气相-质谱联机进行了对比试验,相对偏差≤25%,能够满足交通干线环境空气苯系物的现场监测要求.     

Assessing monthly average solar radiation models: a comparative case study in Turkey

Solar radiation data are required by solar engineers, architects, agriculturists, and hydrologists for many applications such as solar heating, cooking, drying, and interior illumination of buildings. In order to achieve this, numerous empirical models have been developed all over the world to predict solar radiation. The main objective of this study is to examine and compare 147 solar radiation models available in the literature for the prediction of monthly solar radiation at Ankara (Turkey) based on selected statistical measures such as percentage error, mean percentage error, root mean square error, mean bias error, and correlation coefficient. Our results showed that Ball et al. (Agron J 96:391?C397, 2004) model and Chen et al. (Energy Convers Manag 47:2859?C2866, 2006) model performed best in the estimation of solar radiation on a horizontal surface for Ankara.     

三维变分在PM2.5重污染数值模拟中的应用研究

为了探讨三维变分法(3DVAR)对成渝城市群冬季PM2.5重污染模拟的改善效果,采用3DVAR对成渝城市群2017年12月至2018年1月的空气质量数值模拟结果进行资料同化,对比评估嵌套网格空气质量预报模式(NAQPMS)原始数据与同化再分析数据的准确率,并分析成渝重污染特征。研究结果显示,3DVAR在PM2.5、PM10和NO2的同化实验中均取得较好的改善效果,成渝地区检验站点各污染物相关系数(r)的平均提升比例依次为44%、90%和332%,r改善的站点占检验站点总数的比例分别为98%、100%和82%;检验站点均方根误差(RMSE)的平均下降比例分别为15%、37%和31%,RMSE改善的站点占检验站点总数的比例为65%、98%和84%。与原始模拟结果相比,同化结果能够更准确地反映成渝地区冬季重污染期间的PM2.5和PM10空间分布特征。