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重庆市北碚大气中PM2.5、NOx、SO2和O3浓度变化特征研究 被引量:29
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作者 徐鹏 郝庆菊 +5 位作者 吉东生 张军科 刘子锐 胡波 王跃思 江长胜 《环境科学学报》 CAS CSCD 北大核心 2016年第5期1539-1547,共9页
重庆是我国西南工业重镇,但长期受大气污染困扰.利用全自动在线环境监测仪器,于2012年1月—2014年2月,对重庆市北碚区大气中的典型污染物PM2.5、NO_x、SO_2和O_3进行了观测研究.结果表明:重庆北碚大气首要污染物为PM2.5,2012和2013年平... 重庆是我国西南工业重镇,但长期受大气污染困扰.利用全自动在线环境监测仪器,于2012年1月—2014年2月,对重庆市北碚区大气中的典型污染物PM2.5、NO_x、SO_2和O_3进行了观测研究.结果表明:重庆北碚大气首要污染物为PM2.5,2012和2013年平均浓度分别为(67.5±31.9)和(66.6±37.5)μg·m^(-3),是国家环境空气质量一级标准35μg·m^(-3)的1.9倍,两年超过国家二级标准的天数分别为119和126 d,年超标率均大于1/3;两年NO_x,SO_2及O_3的年平均浓度分别为(57.1±24.6)和(55.1±36.6),(43.1±24.0)和(35.0±21.9)及(31.1±24.9)和(48.5±37.4)μg·m^(-3).大气污染物浓度具有明显的季节变化特征,PM2.5和NO_x冬季污染最为严重,两年冬季平均值分别比两年年平均值高33.6%、59.6%和43.2%、8.5%;O_3表现为夏高冬低;SO_2春季最高且污染最轻.大气污染物日变化显示PM2.5和NO_x浓度呈双峰日变化形式,有早晚两个峰值,与城市交通高峰相对应.SO_2和O_3浓度呈单峰日变化,前者峰值出现在午前10∶00—12∶00大气对流层被打破之后,而后者峰值出现在午后16∶00局地光化学最强之时.消减各种污染源的颗粒物直接排放,消减气态污染物SO_2和NO_x的工业排放,消减机动车NO_x和VOCs等的排放,才有可能使重庆北碚的大气污染状况得到改善. 展开更多
关键词 重庆 pm2.5 NOx SO2 O3
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Characteristics and Seasonal Variations of PM2.5, PM10, and TSP Aerosol in Beijing 被引量:20
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作者 WEN-JIE ZHANG YE-LE SUN +1 位作者 GUO-SHUN ZHUANG DONG-QUN XU 《Biomedical and Environmental Sciences》 SCIE CAS CSCD 2006年第6期461-468,共8页
Objective To investigate the seasonal characteristics and the sources of elements and ions with different sizes in the aerosols in Beijing. Methods Samples of particulate matters (PM2,5), PM10, and total suspended p... Objective To investigate the seasonal characteristics and the sources of elements and ions with different sizes in the aerosols in Beijing. Methods Samples of particulate matters (PM2,5), PM10, and total suspended particle (TSP) aerosols were collected simultaneously in Beijing from July 2001 to April 2003. The aerosol was chemically characterized by measuring 23 elements and 18 water-soluble ions by inductively coupled plasma-atomic emission spectroscopy (ICP-AES) and ion chromatography (IC), respectively. Results The samples were divided into four categories: spring non-dust, spring dust, summer dust, and winter dust. TSP, PM10, and PM2.5 were most abundant in the spring dust, and the least in summer dust. The average mass ratios of PM〉10, PM2,5-10, and PM2.5 to TSP confirmed that in the spring dust both the large coarse (PM〉10) and fine particles (PM2.5) contributed significantly in summer PM2.5, PM2,5-10, and PM〉10 contributed similar fractions to TSP, and in winter much PM2.5. The seasonal variation characteristics of the elements and ions were used to divide them into four groups: crustal, pollutant, mixed, and secondary. The highest levels of crustal elements, such as AI, Fe, and Ca, were found in the dust season, the highest levels of pollutant elements and ions, such as As, F, and Cl^-, were observed in winter, and the highest levels of secondary ions (SO4^2-, NO3^-, and NH4^+) were seen both in summer and in winter. The mixed group (Eu, Ni, and Cu) showed the characteristics of both crustal and pollutant elements. The mineral aerosol from outside Beijiug contributed more than that from the local part in all the reasons but summer, estimated using a newly developed element tracer technique. 展开更多
关键词 pm2 5 PM10 TSP Seasonal variation Sources
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基于室内PM_(2.5)控制和节能的通风策略探讨 被引量:1
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作者 吕晓慧 张泠 +2 位作者 徐秀 王喜良 吴静 《环境工程学报》 CAS CSCD 北大核心 2017年第7期4169-4175,共7页
细颗粒物(PM_(2.5))随空调新风进入室内,和室内产生的PM_(2.5)粒子一起作用,导致人体暴露在室内细颗粒物环境中。为保证室内空气品质,最大限度节约空调系统运行能耗,建立了室内PM_(2.5)浓度与CO_2体积分数双组分模型,提出了适用于某会... 细颗粒物(PM_(2.5))随空调新风进入室内,和室内产生的PM_(2.5)粒子一起作用,导致人体暴露在室内细颗粒物环境中。为保证室内空气品质,最大限度节约空调系统运行能耗,建立了室内PM_(2.5)浓度与CO_2体积分数双组分模型,提出了适用于某会议室不同室内外PM_(2.5)源、不同人数以及不同天气状况下的最佳通风策略,利用Simulink对炎热天气室内有无PM_(2.5)散发源、温和天气室内有无PM_(2.5)散发源4种工况下的不同通风方式进行仿真对比。模拟结果表明:炎热天气存在最小新风量,该值由室内人数决定,过滤送风对控制室内PM_(2.5)浓度效果最好;温和天气存在最大新风量,且该值与过滤器效率成正比;在所研究的情况下,温和天气节能潜力比炎热天气大。 展开更多
关键词 室内空气品质 新风 pm2.5 CO2 能耗
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长沙市城区典型交通路口PM_(10)和PM_(2.5)污染特征研究 被引量:4
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作者 廖岳华 邹辉 +4 位作者 肖辰畅 罗岳平 刘礼 陈阳 周湘婷 《四川环境》 2014年第4期36-41,共6页
利用车栽环境空气质量监测系统对长沙市城区典型交通路口的近地面空气质量进行了实时监测.结果表明,在监测时段(14:00 ~ 20:00)内,该监测点环境空气中PM10的小时质量浓度范围在0.097 ~0.222mg/m3之间,平均值0.163mg/m3;PM2.5的小... 利用车栽环境空气质量监测系统对长沙市城区典型交通路口的近地面空气质量进行了实时监测.结果表明,在监测时段(14:00 ~ 20:00)内,该监测点环境空气中PM10的小时质量浓度范围在0.097 ~0.222mg/m3之间,平均值0.163mg/m3;PM2.5的小时质量浓度范围在0.050 ~0.158mg/m3之间,平均值0.103mg/m3.PM2.5/PM10比值在48.1%~76.6%之间,平均值62.4%.PM10与PM25质量浓度在星期一相对较低,星期二有所升高,星期三至周末总体上保持基本稳定.在监测时段PM10与PM2.5小时质量浓度呈现先降后升的变化规律,即14:00 ~ 15:00,PM10与PM2.5质量浓度相对较高,16:00左右降至最低,从17:00开始逐渐升高,20:00达到峰值.PM10和PM2.5的质量浓度变化与车流量和车速密切相关,温度、相对湿度和风速等气象因素对PM10和PM2.5质量浓度的变化影响也较显著. 展开更多
关键词 PM10 pm2.5 污染特征 交通路口 长沙市
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Characterization of PM_(2.5)/PM_(2.5-10) and source tracking in the juncture belt between urban and rural areas of Beijing 被引量:11
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作者 WANG HaiLin ZHOU YuMin ZHUANG YaHui WANG XiaoKe HAO ZhengPing 《Chinese Science Bulletin》 SCIE EI CAS 2009年第14期2506-2515,共10页
Coarse (PM2.5-10) and fine (PM2.5) atmospheric particulate samples were collected in summer and winter during 2005―2007 in the juncture belt between urban and rural areas of Beijing. Elements, ions, organic/elemental... Coarse (PM2.5-10) and fine (PM2.5) atmospheric particulate samples were collected in summer and winter during 2005―2007 in the juncture belt between urban and rural areas of Beijing. Elements, ions, organic/elemental carbon (OC/EC) and polynuclear aromatic hydrocarbons (PAHs) were determined to obtain some latest information about the particulate pollution in the juncture belt of Beijing. Particulate matter levels at this site were high as compared with the levels at other sampling sites in Beijing. Pollution elements, secondary ions and PAHs were enriched in fine particles rather than in coarse particles. An obvious seasonal variation of the chemical composition of PM was observed. Source apportionment results showed that secondary components were the largest mass contributor of PM2.5, accounting for 28%; whereas soil-related sources were the largest contributor of PM2.5-10, explaining about 49% of the total mass. The abnormal levels of soil heavy metals at the electronic waste disassembly site in the upwind villages suggested the potential impact of such activities to the environment. 展开更多
关键词 农村地区 北京 城市 pm2.5 大气颗粒物 土壤重金属 追踪 表征
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数据处理中缺失数据填充方法的研究 被引量:9
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作者 胡玄子 陈小雪 +2 位作者 钱叶亮 姜正龙 赵彤洲 《湖北工业大学学报》 2013年第5期82-84,共3页
针对数据处理中常见的缺失数据现象,研究了若干种数据填充方法.分别对拉格朗日插值法、回归分析法、灰色预测法和BP神经网络方法进行了缺失数据计算和算法对比研究.以武汉市2013年3-4月的PM2.5数据作为实验研究对象,用上述四种方法进行... 针对数据处理中常见的缺失数据现象,研究了若干种数据填充方法.分别对拉格朗日插值法、回归分析法、灰色预测法和BP神经网络方法进行了缺失数据计算和算法对比研究.以武汉市2013年3-4月的PM2.5数据作为实验研究对象,用上述四种方法进行了缺失数据验证及估计.对比计算结果发现,对于此类数据,利用拉格朗日插值法进行缺失数据填充效果优于其他三种方法. 展开更多
关键词 缺失数据 pm2 5 数据填充
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Preliminary Analysis of Influencing Factors of Low Visibility on a Hazy Day in Winter in Binzhou City 被引量:2
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作者 Zou Meiling Zhang Chenxi Li Xueping 《Meteorological and Environmental Research》 CAS 2018年第2期16-19,共4页
Based on the monitoring data of visibility,particulate matter( PM2. 5 and PM10) and atmospheric pollutants( SO2,NO2,CO,and O3),and meteorological factors( temperature,humidity,and wind speed) at the six automati... Based on the monitoring data of visibility,particulate matter( PM2. 5 and PM10) and atmospheric pollutants( SO2,NO2,CO,and O3),and meteorological factors( temperature,humidity,and wind speed) at the six automatic air monitoring stations in Binzhou City from December 2016 to February 2017,the correlations between visibility and influencing factors were analyzed to study the main influencing factors of atmospheric visibility. The results showed that the daily average concentration of particulate matter negatively correlated with atmospheric visibility,and the correlation between PM2. 5 concentration and atmospheric visibility was more obvious than that of PM10 concentration. Among atmospheric pollutants,the daily average concentration of CO,NO2 and SO2 also negatively correlated with atmospheric visibility,while there was a positive correlation between visibility and the daily average concentration of O3. Daily average temperature and wind speed positively correlated with visibility,while relative humidity negatively correlated with visibility. Wind speed,relative humidity and PM2. 5 had strong correlation with visibility,and the linear correlation coefficient R2 was 0. 501 6,0. 446 6,and 0. 205 8 respectively,so wind speed,relative humidity,and PM2. 5 were the main factors influencing the decrease of atmospheric visibility on a hazy day in winter. 展开更多
关键词 VISIBILITY pm2 5 Atmospheric pollutants Correlation
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