基于BP神经网络的天津市PM2.5浓度预测研究

根据天津市2013年12月1日-2013年12月24日气象监测数据,先进行温度、湿度、风力因素对PM2.5浓度影响的相关性分析及定性分析,绘制温度、湿度和PM2.5浓度的二维趋势分布图及气体扩散风向浓度分布图;再运用BP神经网络模型对天津市2013年12月25日-2014年1月9日PM2.5浓度进行仿真预测,最终得到精确预测值.结果表明:温度及风速因素与PM2.5的浓度成反比,湿度因素与PM2.5的浓度成正比,而且通过BP神经网络模型对于“离散样本”、“气象参数不确定性”的实际天气情况可以得到较高的预测精度.     

ELPI 测定环境空气 PM2．5的现场比对研究

采用ELPI自动监测方法与手工采样－重量法对环境空气PM2．5监测进行现场比对研究,按照两种方法数据的相对偏差来评价ELPI方法的数据质量。实验结果表明,两种方法数据的相对偏差范围为－12．9％～17．4％,加权平均值为3．0％;相对偏差的绝对值范围为1．1％～17．4％,平均值为9．3％。 ELPI法的数据质量总体较好,方法的准确度尚可。只是随着空气PM2．5浓度测定值的升高,ELPI法与重量法相对偏差的绝对值显著减小,可见,在低浓度下,ELPI法的数据质量相对较差,此时方法的准确度有待进一步验证、提高。     

佛山市南海城区环境空气中铅污染现状分析研究

为了解佛山市南海城区环境空气中铅污染现状,在南海城区中心设置空气质量监测点,采用重量法和电感耦合等离子体质谱法分别对空气颗粒物及不同粒径颗粒物的铅含量进行定量分析,测定结果为:TSP、PM10、PM2.5平均浓度分别是150 ug/m3、99 ug/m3、67 ug/m3,其中铅平均浓度分别是214 ng/m3、175 ng/m3、156 ng/m3.结论为:南海城区环境空气中的铅浓度达到国家环境空气质量二级标准,铅污染水平处于珠江三角洲的中游;大气中的铅主要分布在PM2.5中,机动车尾气是其主要来源.     

Elemental composition of total dry particulate matter deposited in the selected industrial areas within Lagos state

The total particulate matter (PM) deposited within 17 selected industrial areas in Lagos state during the dry season (December 2015 to January 2016) was studied. Deposition gauges measuring 0.2 meters (m) in diameter by 0.15 m in depth were placed at the sampling locations for a period of one month to collect the total deposited PM. The PM was then characterized using energy‐dispersive X‐ray florescence (EDXRF). The sources of the heavy metals were evaluated using enrichment factor (EF) analysis. Factor analysis (FA) was then used to determine the correlations between the identified heavy metals. Twenty‐three elements—sodium (Na), silicon (Si), phosphorous (P), sulfur (S), chlorine (Cl), potassium (K), calcium (Ca), titanium (Ti), vanadium (V), chromium (Cr), manganese (Mn), iron (Fe), nickel (Ni), copper (Cu), zinc (Zn), germanium (Ge), arsenic (As), strontium (Sr), zirconium (Zr), lead (Pb), tungsten (W), polonium (Po), and actinium (Ac)—were characterized in the PM collected at the sampling sites. The iron elemental ratio ranged from 0.0003 to 3.8848. The EF ranged from 0.0015 to 1697.47, including at the control location. The FA, using principal component analysis techniques, revealed seven factor loadings with 90.03% cumulative, which suggests that the sources are anthropogenic, such as from industrial activities, vehicular emissions, and the combustion of fuel.     

城市道路二次扬尘中PM_(10)浓度量化模型实验研究

机动车行驶条件、路况与道路二次扬尘中PM10浓度有密切的关系。通过对它们设计正交试验和多元回归统计分析,得到PM10浓度计量模型:ρ=0.0314W0.8248v0.537q0.7021M0.357,该方程具有99%的置信度,且拟合的较好。经回归系数显著性检验,自变量对因变量的影响均极为显著,其显著性大小依次为:道路路面粉尘负荷、机动车车型、车速和车流量。最后采用实测值与模型计算值间的均方差对模型进行验证,结果表明该模型具有较高的可信度。     

广州市PM_2.5和PM_1.0质量浓度变化特征

文章报道了2005年干季和2006年湿季广州市大气细粒子PM2.5和PM1.0质量浓度的实时监测情况。监测结果表明:干季监测点PM2.5日均质量浓度在11.8～164.0μg/m3之间,总平均值为81.7μg/m3;湿季日均质量浓度在19.9～121.2μg/m3之间,总平均值为57.7μg/m3。干季PM1.0日均质量浓度变化范围为14.9～129.1μg/m3,总平均值为59.4μg/m3;湿季日均质量浓度在11.9～86.7μg/m3之间,总平均值为52.9μg/m3。对比发现,PM1.0总平均质量浓度在干、湿季相差很小,且与湿季PM2.5总平均质量浓度也相差不大,显示PM1.0具有相对固定成因来源且基本不受季节变化影响,而且湿季PM2.5的组成主要由PM1.0大气细粒子构成。干季PM2.5和PM1.0质量浓度日变化特征呈明显夜间高、白天低的特点,质量浓度的最大值都出现在晚上21:00左右;湿季由于雨水频繁,没有明显的日变化特征。气象分析表明,干季大气细粒子质量浓度主要受冷空气影响,而湿季主要受降雨影响。     

Mutagenic properties of PM2.5 air pollution in the Padana Plain (Italy) before and in the course of XX Winter Olympic Games of "Torino 2006"

PM2.5 is one of the most important aspects of environmental health. This air pollutant is breathable and it is implicated in several chronic adverse health effects such as the decrease of respiratory functionality and cancer. Several in vitro bioassays are able to predict the mutagenic/carcinogenic activity of the environmental pollutants and mixtures of them. In this study PM2.5 air pollution was daily monitored in three cities located in the Northern part of Italy and the mutagenic properties of the PM2.5 organic extracts were also assessed. Samplings lasted 14 months and cover the period of the Winter Olympic Games of "Torino 2006". In this work, the levels of PM2.5, its mutagenic properties (detected with Salmonella typhimurium assay), the role of the Olympic Games as environmental factor and some meteorological data are discussed. The mean concentration of PM2.5 measured in Torino was 45.4 (+/-30.6) microg/m(3), in Pavia 37.6 (+/-25.6) microg/m(3), in Verona 43.1 (+/-28.5) microg/m(3). Findings of the monthly pool bioassay were in Torino 107 (+/-104) net revertans/m(3), in Pavia 108 (+/-89) net revertans/m(3), in Verona 128 (+/-109) net revertans/m(3). The Olympic Games period data show that PM2.5 pollution and its load of mutagenic potential are different and partially independent phenomena. The Olympic Games had not a great impact on the PM2.5 pollution. The exclusive PM2.5 gravimetric analysis shows a potential human risk if compared with the latest international guide values but it does not describe exhaustively the human health risk associated to the presence of this particular air pollutant. Moreover, the chemical and biological activity qualification of the PM organic extracts as a whole, can instead improve the knowledge.     

环境空气自动监测故障与维修特例

介绍了空气自动监测系统氮氧化物分析仪工作原理 ,对光电倍增管室温度控制系统故障产生的原因及解决方法进行了详细阐述     

Monitoring of Criteria Air Pollutants in Bursa, Turkey

The concentrations of criteria air pollutants such as CO, NO_x (NO + NO₂), SO₂ and PM were measured in the period of May 2001 and April 2003 in the city of Bursa, Turkey. The average concentrations for this period were 1115±1600 μg/m³, 29±50 μg/m³, 51±24 μg/m³, 79±65 μg/m³, 40±35 μg/m³, 98±220 μg/m³, for CO, NO, NO₂, NO_x, SO₂ and PM, respectively. Temporal changes in concentrations were analyzed using meteorological factors. Correlations among pollutant concentrations and meteorological parameters showed weak relations nearly in all data. Lower concentrations were observed in the summer months while higher concentrations were measured in the winter months. The increase in winter concentrations was probably due to residential heating. Pollutants were associated with each other in order to have information about their origin. NO_x/SO₂ ratio was also examined to bring out the source origin contributing on air pollution (i.e., traffic or stationary).     

北京清华园采暖前与采暖期PM10中含碳组分的理化特征

采用美国rp公司生产的Series5400大气颗粒物碳质组分监测仪对清华园PM10中的碳质组分进行了连续在线监测(2002年9月~11月)。结合PM2.5中碳质组分浓度、PM10的浓度和气象数据,分析了碳质组分的污染特征。结果表明,采样期间清华园大气PM10中有机碳(OC)、元素碳(EC)的日平均质量浓度分别在4.07~65.81μg/m3、0.96~26.14μg/m3之间变化,平均值分别为20.8±12.1和7.0±5.1μg/m3。OC在总碳(TC)中占有很大比例,OC/TC平均值为75.84%;TC在PM10中的含量平均为25.0%。本文对9~10月份(秋季)和11月份(初冬)OC、EC的相关性分别进行了分析,结果表明OC、EC之间具有良好的相关性,9月份和10月份相关性系数(R2)为0.83;11月份为0.90。二次生成的OC(OCsec)浓度估算结果表明,9、10月份OCsec在OC中的比例(60.7%)比11月份(38.5%)大。碳质组分主要集中在细颗粒物中,PM10中的OC有70.3%存在于细颗粒物PM2.5中,TC则有58.6%存在于PM2.5中。