首钢烧结厂PM10治理技术与经济分析

通过对首钢烧结厂的污染结点、污染物特性、污染物控制技术措施和技术规范进行分析,提出了烧结厂PM10控制方案,对烧结厂采用高效除尘设备的PM10削减量及改造费用进行了估算,确定了烧结厂PM10治理技术方案排序,为钢铁企业烧结厂治理颗粒污染物的同时选择最为经济实用的除尘器类型提供理论依据。     

首钢烧结厂PM_(10)治理技术与经济分析

通过对首钢烧结厂的污染结点、污染物特性、污染物控制技术措施和技术规范进行分析,提出了烧结厂PM10控制方案,对烧结厂采用高效除尘设备的PM10削减量及改造费用进行了估算,确定了烧结厂PM10治理技术方案排序,为钢铁企业烧结厂治理颗粒污染物的同时选择最为经济实用的除尘器类型提供理论依据.     

区域大气环境中PM2.5/PM10空间分布研究

提出了一种利用移动监测技术研究区域大气环境中PM_2.5/PM₁₀空间分布的方法,并在2004年12月进行了宁波市全市域PM_2.5/PM₁₀空间分布的研究。数据显示：相同路径所代表的地区PM_2.5和PM₁₀具有很好的相关性,多数路径上PM_2.5与PM₁₀数据的相关系数平方在0.95以上,而不同路径上PM_2.5与PM₁₀的比值不同。文中给出了宁波市PM_2.5/PM₁₀污染的空间分布图,直观地显示出PM_2.5/PM₁₀污染的空间分布情况,突出了污染的重点点位和地区。     

隧道实验测定南京市机动车PM10排放因子

选取南京城市隧道进行机动车PM10平均排放因子的测试研究.采用质量平衡模型和多元线性回归方法计算了4种车型PM10的综合排放因子.结果表明:隧道内机动车PM10平均排放因子为0.347±0.100 g/(km·辆);大型车的PM10排放因子远高于其他车型的排放因子,其次是中型车和摩托车,小型车最小,其综合排放因子分别为1.440 g/(km·辆)、0.850 g/(km·辆)、0.790 g/(km·辆)和0.320 g/(km·辆);在车速相似的情况下,本隧道实验所测机动车的PM10排放因子与国内隧道实验结果相仿,却远大于国外隧道实验结果.     

贵州贫困农村室内PM10污染水平及影响因素分析

为调查贵州贫困农村室内空气污染水平,对织金和六枝农户室内可吸入颗粒物(PM10)进行了系统采样,分析了PM10污染水平,探讨了其影响因素。结果表明:采暖期PM10的平均值为640.5μg/m3,最高质量浓度达到7 739.9μg/m3;非采暖期PM10的平均值为459.9μg/m3,最高质量浓度达到3 515.1μg/m3,与《室内空气质量标准》(GB/T 18883—2002)相比,研究区PM10的超标率为71%～92%,超标倍数最高达50.60倍。经调查可知,贵州贫困农村室内PM10污染水平与燃料类型、炉灶类型、室内通风情况等密切相关,以拌泥煤、纯煤、蜂窝煤和简易敞口灶为主,且室内通风较差。     

应用地质累积指数评价抚顺市PM10中元素的污染

2006-2007年采暖季、风沙季和非采暖季分别在抚顺市的6个采样点采集PM10样品,用等离子体原子发射光谱(ICP-AES)法测定样品中Ti、Al、Mn、Mg、Ca、Na、K、Cu、Zn、As、Pb、Cr、Ni、Co、Cd、Fe、V等17种元素的含量,并用地质累积指数对其污染状况进行初步评价。结果表明:(1)从PM10中元素在不同采样点的含量看,抚顺市PM10中Ti、Mn、Mg、Cu、Zn、Pb、Cr、Ni、Co这9种元素在各采样点间的差别较大;Al、Ca、Na、K、As、Cd、Fe、V这8种元素差别较小。(2)从PM10中元素在不同采样季的含量看,抚顺市PM10中Mn、Mg含量的季间差别较大,其余15种元素季间差别较小。(3)Zn、Cd污染较重;Ti、Al、Mg、Ca、Na、K、As、Fe和V污染较轻;其他6种元素在6个采样点和3个采样季污染程度差别较大。(4)水库采样点各元素污染级别均不是最高;新华采样点PM10中Cu、Zn、Pb、Cr、Ni、Co、Cd污染级别均较高。(5)3个采样季PM10中Cd、Zn污染均较重,属于重度或严重污染;在采暖季PM10中Cu、Pb、Cr的地质累积指数较风沙季、非采暖季大;在非采暖季PM10中Mn、Co受到的污染比采暖季和风沙季稍严重。     

百色市右江区大气PM10中水溶性无机离子的化学特征与来源

2010年10月至2011年9月采集百色市右江区大气PM10样品,分析PM10及其水溶性无机离子的化学特征与来源。结果表明:(1)百色市右江区大气PM10为13.89～319.44μg/m3,年均117.48μg/m3,年均值超过《环境空气质量标准》(GB 3095-2012)二级标准(100μg/m3)。百色市右江区大气可吸入颗粒物的污染主要出现在春冬季节。(2)水溶性无机离子浓度年均值依次为SO24->NO3->Cl->NH4+>K+>Na+>Mg2+>F-,SO24-、NO3-和Cl-浓度最高,分别占水溶性无机离子的57.7%、14.9%和14.5%。(3)百色市右江区大气PM10呈较强的酸性,高浓度的SO42-可能是导致百色市右江区大气PM10呈较强酸性的主要原因。(4)PM10的季节变化受气温和风速的影响极显著;气象因素对SO42-、NO3-、F-的影响不显著。(5)主因子分析表明,PM10中水溶性无机离子可能来自3个方面,Cl-和NO3-主要来自于当地低烟卤煤燃烧排放的烟气;Mg2+、K+和Na+主要来自于自然源;F-、SO24-和NH4+主要来自于混合源。     

声场中PM2.5颗粒碰撞运动模型

建立了声场中PM2.5颗粒碰撞运动模型。模拟结果表明,颗粒碰撞前速度与水平面的夹角θ是影响颗粒运动轨迹的一个重要因素,它的改变将决定颗粒在声场中是否碰撞、碰撞的位置以及碰撞后颗粒如何运动;颗粒碰撞前的速度大小将决定颗粒碰撞后是沿声波方向运动还是逆声波方向运动或是停留在原地振动;声场频率的不同改变了颗粒在发生碰撞时的运动趋势及颗粒的在碰撞时的运动趋势,同时,声场频率的改变将影响碰撞后颗粒的振幅;声场声强的改变不但影响了颗粒运动的振幅,而且影响了颗粒碰撞后运动趋势。     

立窑水泥生产线除尘技术适用性分析

通过对立窑水泥生产线各排尘点情况和不同粉尘治理技术的分析,有针对性地提出了与之相适应的除尘技术方案.     

基于LUR模型的PM2.5浓度空间分布监测及分析

为有效解决传统监测技术无法获取城市内部高分辨率PM_2.5浓度空间分布情况的问题,基于土地利用回归(land use regression,LUR)模型,以关中平原城市群为例模拟其PM_2.5空间分布状况,通过获取研究范围内54个监测站点的PM_2.5浓度数据,结合土地利用类型、气象、地形、植被指数、人口密度、交通和污染源等因素,分别建立春、夏、秋、冬及年均5个LUR模型。结果表明：LUR模型调整后各季节及年平均值的R²分别达到0.831 (春)、0.817 (夏)、0.874 (秋)、0.857 (冬)、0.900 (全年平均),5种模型拟合度均较好;采取交叉互验的方法进行了精度检验,显示5种模型的平均精度均达到80.4%,说明LUR模型在模拟关中平原城市群PM_2.5浓度空间分布时适用性良好。模拟结果显示,研究区各季节的PM_2.5浓度在空间分布上大致相同,呈现出东部高、西部低的明显特征,且空间分布状况受地形因素的影响较大。但在浓度均值的季节变化上则具有夏季低、冬季高的明显差异。本研究结果可为关中平原城市群PM_2.5污染防治提供科学依据,亦可为城市内部PM_2.5浓度空间分布数据的获取提供新思路。     

The measurement of roadway PM10 emission rates using atmospheric tracer ratio techniques

In this work, stationary and mobile point source tracer release techniques have been used to determine PM₁₀ emission rates from four-lane commercial/residential paved roads under sanded and unsanded conditions, and from unpaved roads relative to site-specific vehicular and ambient parameters. Measured street (4 + lanes; ? 10,000 vehicles per day) emission factors for unsanded and sanded roads were 40 and 20% lower, respectively, than the EPA approved reference value. The sanded road emission factor was approximately 40% higher than that for the unsanded road. These results indicate a consistent relationship between PM₁₀ and relative humidity under unsanded conditions. There is some evidence to suggest that street sweeping has a measurable effect on PM,, emission reduction during periods of low relative humidity (i.e. ? 30%). Within the constraints imposed by the variable experimental conditions, the emission factors determined for unpaved roads agreed reasonably well with the unpaved road empirical formula. Limited correlations were observed with ambient meteorological parameters. The capability of the “upwind-dowiawind” concentration modeling method to predict accurate emission was tested using a Gaussian dispersion model (SIMFLUX). Predictions agreed well with the experimentally determined emission factors.     

Temporal and spatial relationships in fine particle strong acidity, sulphate, PM1O, and PM2.5 across multiple canadian locations

The Canadian Acid Aerosol Measurement Program (CAAMP) was established in 1992 to gain a better understanding of the atmospheric behaviour of fine particle strong acidity (“acid aerosols”) and to facilitate an assessment of the potential health risks associated with acid aerosols and particles in general. During 1992. 1993 and 1994, annular denuder and filter measurements were taken at four sites in Ontario, two in Quebec, three in the Atlantic Provinces and one in the greater Vancouver area. Mean fine particle sulphate concentrations (SO₄²⁻) were highest in southern Ontario (annual average ranged from 40–70 nmol m⁻³), lowest at a site in the Vancouver area (average = 16 nmol m⁻³) and second lowest in rural Nova Scotia. However, mean fine particle strong acid concentrations (H⁺) were geographically different. The highest mean concentrations were at the east coast sites (annual average of up to 30 nmol m⁻³). Acidities were lower in areas where the fine particle acidity experienced greater neutralization from reaction with ammonia. This included the major urban centres (i.e. Toronto and Montréal) and areas with greater amounts of agricultural activity, as in rural southern Ontario. On average, ambient concentrations of fine and coarse particle mass were larger in the urban areas and also in areas where SO₄²⁻ levels were higher. All the particle components were episodic. However, compared to SO₄²⁻ and fine particles (PM_2.5 or PM_2.1, depending upon inlet design), episodes of H⁺ tended to be less frequent and of shorter duration, particularly in Ontario. Saint John, New Brunswick, had the highest mean annual H⁺ concentration, which was 30 nmol m⁻³. H⁺ episodes (24 h concentration > 100 nmol m⁻³) were also the most frequent at this location. The high levels in Saint John were partially due to local sulphur dioxide sources and heterogeneous chemistry occurring in fog, which, on average, led to a 50% enhancement in sulphate, relative to upwind conditions.There was a substantial amount of intersite correlation in the day to day variations in H⁺, SO₄²⁻ , PM_2.5 and PM₁₀ (fine + coarse particles) concentrations, which is due to the influence of synoptic-scale meteorology and the relatively long atmospheric lifetime of fine particles. Sulphate was the most regionally homogenous species. Pearson correlation coefficients comparing SO₄²⁻ between sites ranged from 0.6 to 0.9, depending on site separation and lag time. In many cases, particle episodes were observed to move across the entire eastern portion of Canada with about a two-day lag between the SO₄²⁻ levels in southern Ontario and in southern Nova Scotia.     

Estimation and validation of PM2.5/PM10 exhaust and non-exhaust emission factors for practical street pollution modelling

In order to carry out efficient traffic and air quality management, validated models and PM emission estimates are needed. This paper compares current available emission factor estimates for PM₁₀ and PM_2.5 from emission databases and different emission models, and validates these against eight high quality street pollution measurements in Denmark, Sweden, Germany, Finland and Austria.The data sets show large variation of the PM concentration and emission factors with season and with location. Consistently at all roads the PM₁₀ and PM_2.5 emission factors are lower in the summer month than the rest of the year. For example, PM₁₀ emission factors are in average 5–45% lower during the month 6–10 compared to the annual average.The range of observed total emission factors (including non-exhaust emissions) for the different sites during summer conditions are 80–130 mg km⁻¹ for PM₁₀, 30–60 mg km⁻¹ for PM_2.5 and 20–50 mg km⁻¹ for the exhaust emissions.We present two different strategies regarding modelling of PM emissions: (1) For Nordic conditions with strong seasonal variations due to studded tyres and the use of sand/salt as anti-skid treatment a time varying emission model is needed. An empirical model accounting for these Nordic conditions was previously developed in Sweden. (2) For other roads with a less pronounced seasonal variation (e.g. in Denmark, Germany, Austria) methods using a constant emission factor maybe appropriate. Two models are presented here.Further, we apply the different emission models to data sets outside the original countries. For example, we apply the “Swedish” model for two streets without studded tyre usage and the “German” model for Nordic data sets. The “Swedish” empirical model performs best for streets with studded tyre use, but was not able to improve the correlation versus measurements in comparison to using constant emission factors for the Danish side. The “German” method performed well for the streets without clear seasonal variation and reproduces the summer conditions for streets with pronounced seasonal variation. However, the seasonal variation of PM emission factors can be important even for countries not using studded tyres, e.g. in areas with cold weather and snow events using sand and de-icing materials. Here a constant emission factor probably will under-estimate the 90-percentiles and therefore a time varying emission model need to be used or developed for such areas.All emission factor models consistently indicate that a large part (about 50–85% depending on the location) of the total PM₁₀ emissions originates from non-exhaust emissions. This implies that reduction measures for the exhaust part of the vehicle emissions will only have a limited effect on ambient PM₁₀ levels.     

Sources and processes affecting concentrations of PM10 and PM2.5 particulate matter in Birmingham (U.K.)

Hourly average concentrations of PM₁₀ and PM_2.5 have been measured simultaneously at a site within Birmingham U.K. between October 1994 and October 1995. Comparison of PM₁₀ and NO_x data with two other sites in the same city shows comparable summer and winter mean concentrations and highly significant inter-site correlations for both hourly and daily mean data. Over a four-month period samples were also collected for chemical analysis of sulphate, nitrate, chloride, ammonium and elemental and organic carbon. Analysis of the data indicates a marked difference between summer and winter periods. In the winter months PM_2.5 comprises about 80% of PM₁₀ and is strongly correlated with NO_x indicating the importance of road traffic as a source. In the summer months, coarse particles (PM₁₀−PM_2.5) account for almost 50% of PM₁₀ and the influence of resuspended surface dusts and soils and of secondary particulate matter is evident. The chemical analysis data are also consistent with three sources dominating the PM₁₀ composition: vehicle exhaust emissions, secondary ammonium salts and resuspended surface dusts. Coarse particles from resuspension showed a positive dependence on windspeed, whilst elemental carbon derived from road traffic exhibited a negative dependence.     

冬季沈阳市典型源排放PM_(10)浓度分布模拟分析

选取沈阳市7个典型的大气污染源2006年12月~2007年2月的PM10排放浓度资料,利用CALPUFF对PM10浓度月平均分布做模拟分析。模拟结果分析表明:冬季月平均PM10浓度分布的范围与风场、地形有直接的关系。地势平坦、风速大时,污染物扩散范围大,污染物浓度小;地势不平、风速小时,污染物扩散范围小,污染物浓度大。1月份是沈阳市冬季月平均大气污染最严重的月份,污染物分布主要集中在市区的北部、东部和南部地区,东部地区大气污染最为严重。     

Determination and analysis of PM10 source apportionment during episodes of air pollution in Central Eastern European urban areas: The case of wintertime 2006

Source apportionment of air pollution due to particulate matter with an aerodynamic diameter <10 μm (PM₁₀) was investigated in Central Eastern European urban areas. A combination of four methods was developed to distinguish long-range transport (LRT) and regional transport (RT) from local pollution (LP) sources as well as to discern the involvement of traffic or residential sources in LP. Sources of PM₁₀ events of pollution were determined in January 2006 in representative Polish cities using monitored air quality and meteorological data, backward air mass trajectories, correlation and principal component analysis (PCA). Daily patterns of PM₁₀ levels show that several peak episodes were registered in Poland; January 21–30th being the most polluted days. Air mass back-trajectory analysis shows that all cities were under the influence of LRT from North-eastern origins (Russia–Belarus–Ukraine), most were also under LRT from Southern origin (Slovakia, Czech Republic), and northern cities were under national RT influence. PCA analysis shows that ion-sums of secondary inorganic aerosols account for LRT pollution while arsenic and chromium represents markers of RT (industrial) and LP (residential) sources of PM₁₀, respectively. Determination of several ratios (REG/UB, REG/TRAF, TRAF/UB) calculated between PM₁₀ levels measured at regional background (REG); urban background (UB) and traffic (TRAF) monitoring sites shows that, with ratios REG/UB ≥ 0.57, PM₁₀ episodes in both Szczecin and Warsaw bore a marked RT origin. The lower REG/UB ≤ 0.35 in the Southern cities of Cracow and Zabrze indicates that LP was the main contributor to the observed episodes. Only PM₁₀ episodes in Southern-western Poland (Jelenia Góra) were clearly of LP origin as characterized, by the lowest REG/UB ratio (<0.2). The high TRAF/UB ratios obtained for all cities (close to 1) indicate that there was a great uniformity of PM levels on an urban scale owing to the meteorologically stagnant conditions. A high correlation between PM₁₀, NO₂ and CO confirms that traffic emission represented a common and an important LP source of urban pollution in most Polish cities during January 2006. On the other hand PM₁₀ which is also highly correlated with SO₂ in 4 cities out of 6, indicates that coal combustion through domestic heating or industrial activities was also an important LP source of PM₁₀. Finally, extremely unfavourable meteorological conditions caused by the influence of a Siberian high-pressure system were found to be associated with the occurrence of severe PM₁₀ episodes of pollution.     

南京市典型地区可吸入颗粒物(PM10)中颗粒的微观形貌特征及其矿物组成

应用扫描电镜技术(SEM/EDX)对南京市两典型地区PM10中颗粒的微观形貌及其矿物组成进行了研究.结果表明,南京市大厂区(典型工业区)PM10中的颗粒多以形态规则矿物颗粒为主,山西路地区(典型商业区)PM10中的颗粒多以形态不规则出现,形态规则颗粒主要是碳酸盐、硫酸盐和铝硅酸盐矿物,形态不规则颗粒主要是烟尘结合体、生物质和原生矿物.