Wheat straw burning and its associated impacts on Beijing air quality

Based on MODIS images, large-scale flow field charts and environmental monitoring data, we thor- oughly analyzed the spatial distribution of wheat straw burning in North China, with focus on its envi- ronmental impacts on the air quality of Beijing and pollution transport paths. And we anatomized changes of air quality in Beijing under the impacts of pollution generated by wheat straw burning around. The results indicate that: (1) The North China Plain, a winter-wheat growing area, is the main source of pollutants induced by wheat straw burning in Beijing. The direction of south-west is the dominant heavy pollution transport path. (2) Impacts of wheat straw burning on air quality are mainly manifested by significantly increasing CO concentration. (3) Precursors of O3 generated by wheat straw burning, combining with favorable meteorological conditions, can induce increasing O3 concentration greatly. NO concentration will be greatly increased due to decreasing O3 concentration at night. (4) Atmospheric particles, especially the fine ones, from wheat straw burning exert considerable influ- ence on Beijing air quality. (5) Different contributions of wheat straw burning to pollutants are identified. Ratios of PM10/SO2, CO/SO2, etc., can be applied to indicate pollution extent of wheat straw burning. High ratios of PM10/SO2 and CO/SO2 show that the air quality was heavily impacted by wheat straw burning and these ratios can be employed as indicators of contribution of wheat straw burning to the degradation of Beijing air quality. (6) Randomness of wheat straw burning activities renders random outbreak of air pollution of this type. Regional and extensive wheat straw burning activities can cause serious air pollution event.     

Dependence of air pollution on meteorological conditions based on magnetic susceptibility measurements: a case study from Warsaw

This study reports systematic analysis of air pollution concentration by particulate matter (PM) in relation to annual changes of metrological conditions. The concentration of PM collected on filters was evaluated by magnetic properties. Long series of filters collected during 1977, 1980, 1981 and 1985 in Warsaw have been used for measurement. Such long time series allowed a better determination of correlation between seasonal susceptibility changes and meteorological conditions and to verify the repeatability of annual behavior. The magnetic susceptibility normalized by mass (??) has been used as a proxy of pollution. Monthly variations of ?? (??_m) demonstrated slow and small seasonal changes disturbed by quick, high peaks. The filters with high susceptibilities contain a mixture of magnetic single and multidomain grains which is characteristic for anthropogenic pollution, with the frequency-dependent susceptibility ??_fd being less than 4%. The low susceptibility samples are characterized by admixture of small amount of pseudo-single domain (PSD) and possible small amount of superparamagnetic (SP) grains. Detailed analysis of reduced mean monthly susceptibility ??_R revealed that temperature and absolute humidity correlate positively in cold period of year and negatively in warm months. The linear correlation coefficients range from 0.7 to 0.9 and from 0.3 to 0.4 for negative and positive linear correlation, respectively. The dispersion of pollutants in a city depends on the way of ventilation. The big cities like Warsaw are urban heat islands which act the most effective way for the warm period and for the cold time. The correlation of ??_R with absolute humidity is similar to that for temperature but for different reasons. The presence of big amount of water vapor particles in the atmosphere can favor the adhesion of smaller ferromagnetic grains to their surface which can accelerate the process of falling down of such agglomerates. Such a process can take place during wintertime. We do not observe clear correlation between ??_R and precipitation.     

High resolution mapping of anthropogenic pollution in the Giant Mountains National Park using soil magnetometry

Soil magnetometry was used for detailed mapping of immission load over the territory of the Giant Mountains National Park. This project is a continuation of our previous study, which suggested that low-field magnetic susceptibility of topsoils in this region is controlled by atmospherically deposited anthropogenic ferromagnetic particles. In the present study, we have compiled a map of topsoil magnetic susceptibility on the basis of measurements on more than 460 sites. Elevated values of magnetic susceptibility can be attributed to local sources of pollution, located within and at the margins of the Park. We have identified a group of heavy metals of anthropogenic origin, present in the topsoils, and found a positive correlation between the concentration of Pb and magnetic susceptibility. Our results prove that magnetic mapping is a sensitive, fast and robust method, which can be advantageously applied to regions with relatively low degree of pollution, such as the Giant Mountains National Park.     

An integrated fuzzy-stochastic modeling approach for assessing health-impact risk from air pollution

High concentrations of air pollutants in the ambient environment can result in breathing problems with human communities. Effective assessment of health-impact risk from air pollution is important for supporting decisions of the related detection, prevention, and correction efforts. However, the quality of information available for environmental/health risk assessment is often not satisfactory enough to be presented as deterministic numbers. Stochastic method is one of the methods for tackling those uncertainties, by which uncertain information can be presented as probability distributions. However, if the uncertainties can not be presented as probabilities, they can then be handled through fuzzy membership functions. In this study, an integrated fuzzy-stochastic modeling (IFSM) approach is developed for assessing air pollution impacts towards asthma susceptibility. This development is based on Monte Carlo simulation for the fate of SO₂ in the ambient environment, examination of SO₂ concentrations based on the simulation results, quantification of evaluation criteria using fuzzy membership functions, and risk assessment based on the combined fuzzy-stochastic information. The IFSM entails (a) simulation for the fate of pollutants in ambient environment, with the consideration of source/medium uncertainties, (b) formulation of fuzzy air quality management criteria under uncertain human-exposure pathways, exposure dynamics, and SPG-response variations, and (c) integrated risk assessment under complexities of the combined fuzzy/stochastic inputs of contamination level and health effect (i.e., asthma susceptibility). The developed IFSM is applied to a study of regional air quality management. Reasonable results have been generated, which are useful for evaluating health risks from air pollution. They also provide support for regional environmental management and urban planning.     

Particulate matter pollution from a small coke-burning factory: soil magnetic screening and its relation with a simple atmospheric dispersion model

Coal combustion processes lead to release of gases and particulate matter (PM) into the atmosphere that are often harmful to human health. These airborne pollutants seem to be dispersed and deposited in soils mainly according to the prevailing atmospheric conditions. Several trace elements can be found attached to PM as well as Fe-rich magnetic particles that can produce magnetic enhancement in the uppermost soil horizons. In the present work, we use a simple Gaussian Dispersion Model (GDM) for modelling the distribution of fine PM emission coming from a small coal (coke) burning factory in order to evaluate the relationship between such modelled data (PM distribution) and measured data (soil magnetic properties and trace metal contents). Our results show a strong spatial variation of concentration-dependent magnetic parameters based on a uniform magnetomineralogy in the overall study area. In addition, these results were analysed using multivariate statistics for 13 magnetic and chemical variables and the GDM results for two different atmospheric stability classes, and hence the in-situ magnetic susceptibility, anhysteretic and saturation remanent magnetization showed positive and statistically significant correlation with the GDM results (R = 0.70). Therefore, these results demonstrate the usefulness of magnetic properties in monitoring the PM distribution in soils or other environmental PM collectors.     

Polycyclic aromatic hydrocarbons from rural household biomass burning in a typical Chinese village

Biomass energy sources are still popular in the rural areas of developing countries for cooking and space heating. Since the incomplete combustion of agricultural residues in home-made ranges might lead to both outdoor and indoor air pollution and cause potential health threat to the rural population, we monitored the ambient levels of 16 US EPA priority polycyclic aromatic hydrocarbons (PAHs) at a typical rural site. Ambient particulate PAH samples (PM2.5 and PM10) were taken during both cooking and non-cooking periods. Source emission monitoring was also conducted for both improved and tra- ditional cooking stoves used in the area. Ambient PAHs had a significant increase during the cooking periods and varied from 72.1 to 554.4 ng/m3. The highest total PAH levels were found during the supper cooking time, in which five- and six-ring species accounted for a large proportion. The observed PAH levels during cooking periods at this rural site were even higher than those in urban areas. A good correlation was found between the benzo[a]pyrene level and the total PAH concentration (r=0.98), making benzo[a]pyrene a potential molecular marker for PAH pollution in the rural areas, where bio- mass burning is typical. The profiles of the particulate PAHs in both ambient air and source emissions showed a high proportion of high molecular-mass PAHs. In addition, emission factors of 16 PAHs from an improved household stove were found to be significantly lower than those from traditional stoves used in China and in other Asian countries.     

临汾市树叶磁性的时空变化特征及其对大气重金属污染的指示

粉煤灰经工厂废气排放进入大气,对人类健康和生态系统都造成了无法弥补的破坏.本文选取具有高空间分辨率优势的树叶作为收集粉煤灰的载体,对临汾市大气中可吸入颗粒物进行磁学参数和重金属含量监测.结果表明,磁化率最大值出现在工厂污染源附近,磁化率空间分布呈现随污染源距离增加而降低的趋势.工业区收集到的磁性颗粒以低矫顽力、粗粒度的磁铁矿为主.夏季磁性矿物来源单一,主要为人为影响.冬季大气中悬浮的磁性颗粒有部分来自于西北风/北风的自然尘降.同一采样点磁化率随时间变化特征表明,树叶的磁学性质可以灵敏和有效地反映较短时期内大气污染的现状.统计分析表明磁化率和重金属元素(铁,铬,镍,铜,铅,钴)之间存在显著相关性.污染负荷指数用于评估研究区域内重金属各元素综合污染的程度.结果显示,在废弃的旧工业区附近无大气污染指示,但在运营中的工厂集中的地区,大气均受到严重污染.污染负荷指数与表征磁性矿物含量的磁化率呈相关性(r2=0.66),因此树叶的磁性参数可以作为大气重金属污染的替代指标.     

Time series analysis and forecasting for air pollution in small urban area: an SARIMA and factor analysis approach

Despite the existing public and government measures for monitoring and control of air quality in Bulgaria, in many regions, including typical and most numerous small towns, air quality is not satisfactory. In this paper, factor analysis and Box–Jenkins methodology are applied to examine concentrations of primary air pollutants such as NO, NO₂, NO_x, PM10, SO₂ and ground level O₃ in the town of Blagoevgrad, Bulgaria within a 1 year period from 1st September 2011 to 31st August 2012, based on hourly measurements. By using factor analysis with PCA and Promax rotation, a high multicollinearity between the six pollutants has been detected. The pollutants were grouped in three factors and the degree of contribution of the factors to the overall pollution was determined. This was interpreted as the presence of common sources of pollution. The main part of the study involves the performance of time series analysis and the development of univariate stochastic seasonal autoregressive integrated moving average (ARIMA) models with recording on a hourly basis as seasonality. The study also incorporates the Yeo–Johnson power transformation for variance stabilizing of the data and model selection by using Bayersian information criterion. The obtained SARIMA models demonstrated very good fitting performance with regard to the observed air pollutants and short-term predictions for 72 h ahead, in particular in the case of ozone and particulate matter PM10. The presented statistical approaches allow the building of non-complex models, effective for short-term air pollution forecasting and useful for advance warning purposes in urban areas.     

Statistical mapping of PM10 concentrations over Western Europe using secondary information from dispersion modeling and MODIS satellite observations

This paper illustrates the use of statistical techniques to standardize ground based measurements of particulate matter (PM10). Concentrations are interpolated over Western Europe using uncertain secondary information from a chemical transport model and of aerosol optical thickness from MODIS satellite observations. A consistent overview of PM10 concentrations over Europe based solely on ground based measurements is complicated by differences between countries. Different monitoring methods are used and calibrations are applied. There also is an inherent limitation to the spatial representativeness of ground based measurements. Validation showed that adding secondary information from either the chemical transport model or the satellite observations improved the PM10 mapping. The URMSE decreased from 5.14 to 4.26 and 4.58, respectively. A combination of both sources of secondary information gave the most accurate and precise predictions, with an URMSE of 3.62. This means that both external sources contain additional information on the spatial distribution of PM10 concentrations and should therefore be preferred.     

福建三明地区被污染土壤的磁学性质及其环境意义

对福建三明某钢铁厂和火电厂附近的污染表土样品进行了多参数的岩石磁学测试分析,包括χ T曲线、磁滞回线、等温剩磁获得曲线等. 三明地区污染表土中的磁性矿物有磁铁矿、赤铁矿和磁黄铁矿. 样品中磁性矿物的平均粒度较粗,为较大的准单畴,甚至多畴,粒度明显大于成土作用所产生的磁性颗粒. 粗粒的磁铁矿颗粒是污染物的主要磁性组分. 虽然磁化率测量可以作为一种简单、快速而且廉价的检测污染土壤的方法,但同时辅以必要的岩石磁学测量将有利于提取更多的污染信息. 对于低磁化率的污染土壤,亚铁磁性硫化物的存在可以作为土壤可能被污染的证据之一.     

北京市朝阳区大气降尘磁学特征及对空气污染物浓度的指示

应用环境磁学方法研究了2008年奥运会前后北京市朝阳区大气降尘及对照点表土样品的磁学特征.结果表明,所有样品中载磁矿物主要为低矫顽力的亚铁磁性矿物(主要为磁铁矿).但大气降尘样品的磁性颗粒粒度较表土偏粗;磁性矿物含量高于表土样品,大气降尘除来自于自然源外,更多为来自于人为因素的结果.大气降尘磁化率与空气污染物浓度随时间...     

Magnetic properties of soils from sites with different geological and environmental settings

Measurements of magnetic susceptibility of soils, reflecting magnetic enhancement of topsoils due to atmospherically deposited magnetic particles of industrial origin, are used recently in studies dealing with outlining polluted areas, as well as with approximate determination of soil contamination with heavy metals. One of the natural limitations of this method is magnetic enhancement of soils caused by weathering magnetically rich parent rock material. In this study we compare magnetic properties of soils from regions with different geological and environmental settings. Four areas in the Czech Republic and Austria were investigated, representing both magnetically rich and poor geology, as well as point-like and diffuse pollution sources. Topsoil and subsoil samples were investigated and the effect of geology and pollution was examined. Magnetic data including mass and volume magnetic susceptibility, frequency-dependent susceptibility, and main magnetic characteristics such as coercivity (Hc and Hcr) and magnetization (Ms and Mrs) parameters are compared with heavy metal contents. The aim of the paper is to assess the applicability of soil magnetometry under different geological-environmental conditions in terms of magnetic discrimination of dominant lithogenic/anthropogenic contributions to soil magnetic signature. Our results suggest that lithology represents the primary effect on soil magnetic properties. However, in case of significant atmospheric deposition of anthropogenic particles, this contribution can be clearly recognized, independent of the type of pollution source (point-like or diffuse), and discriminated from the lithogenic one. Different soil types apparently play no role. Possible effects of climate were not investigated in this study.     

Integrated approach of heavy metal pollution indices and complexity quantification using chemometric models in the Sirsa Basin,Nalagarh valley,Himachal Pradesh,India

Chemometric techniques and pollution assessment indices were applied to determine the source and intensity of pollution in the Sirsa River, Himachal Pradesh,India. Results show EC, Cr, Fe, Mn, and Ni were above the permissible limit as per the Bureau of Indian Standards.The heavy metal pollution index(HPI) and contamination index(Cd) provided contrasting outcome and poor correlation was observed. A heavy metal evaluation index(HEI)method was developed using a multiple of the mean and correlation coefficient values to provide an alternative pollution classification. The criteria of HEI adopted for reclassification of HPI and Cdproduced comparable results; 40 % samples were labeled as low contamination,50 % as medium contamination, and 10 % as high contamination for all indices. Principal component analysis along with cluster analysis was used to identify the main factors responsible for degradation of water quality,namely discharge of industrial effluent, river bed mining,agricultural runoff, and minor natural or geogenic input.The methods and chemometric study proposed here can be used as effective tools to gather information about water quality and water resource management.     

Vertical structures of PM10 and PM 2.5 and their dynamical character in low atmosphere in Beijing urban areas

The vertical structures and their dynamical character of PM2.5 and PM10 over Beijing urban areas are revealed using the 1 min mean continuous mass concentration data of PM2.5 and PM10 at 8, 100, and 320 m heights of the meteorological observation tower of 325 m at Institute of Atmospheric Physics, Chinese Academy of Sciences (IAP CAS tower hereafter) on 10―26 August, 2003, as well as the daily mean mass concentration data of PM2.5 and PM10 and the continuous data of CO and NO2 at 8, 100 (low layer), 200 (middle layer), and 320 m (high layer) heights, in combination with the same period meteorological field observation data of the meteorological tower. The vertical distributions of aerosols observed on IAP CAS tower in Beijing can be roughly divided into two patterns: gradually and rapidly decreasing patterns, I.e. The vertical distribution of aerosols in calm weather or on pollution day belongs to the gradually decreasing pattern, while one on clean day or weak cold air day belongs to the rapidly decreasing pattern. The vertical distributive characters of aerosols were closely related with the dynamical/thermal structure and turbulence character of the atmosphere boundary layer. On the clean day, the low layer PM2.5 and PM10 concentrations were close to those at 8 m height, while the concentrations rapidly decreased at the high layer, and their values were only one half of those at 8 m, especially, the concentration of PM2.5 dropped even more. On the clean day, there existed stronger turbulence below 150 m, aerosols were well mixed, but blocked by the more stronger inversion layer aloft, and meanwhile, at various heights, especially in the high layer, the horizontal wind speed was larger, resulting in the rapid decrease of aerosol concentration, I.e. Resulting in the obvious vertical difference of aerosol concentrations between the low and high layers. On the pollution day, the concentrations of PM2.5 and PM10 at the low, middle, and high layers dropped successively by, on average, about 10% for each layer in comparison with those at 8 m height. On pollution days, in company with the low wind speed, there existed two shallow inversion layers in the boundary layer, but aerosols might be, to some extent, mixed below the inversion layer, therefore, on the pollution day the concentrations of PM2.5 and PM10 dropped with height slowly; and the observational results also show that the concentrations at 320 m height were obviously high under SW and SE winds, but at other heights, the concentrations were not correlated with wind directions. The computational results of footprint analysis suggest that this was due to the fact that the 320 m height was impacted by the pollutants transfer of southerly flow from the southern peripheral heavier polluted areas, such as Baoding, and Shijiazhuang of Hebei Province, Tianjin, and Shandong Province, etc., while the low layer was only affected by Beijing's local pollution source. The computational results of power spectra and periods preliminarily reveal that under the condition of calm weather, the periods of PM10 concentration at various heights of the tower were on the order of minutes, while in cases of larger wind speed, the concentrations of PM2.5 and PM10 at 320 m height not only had the short periods of minute-order, but also the longer periods of hour order. Consistent with the conclusion previously drawn by Ding et al., that air pollutants at different heights and at different sites in Beijing had the character of "in-phase" variation, was also observed for the diurnal variation and mean diurnal variation of PM2.5 and PM10 at various heights of the tower in this experiment, again confirming the "in-phase" temporal/spatial distributive character of air pollutants in the urban canopy of Beijing. The gentle double-peak character of the mean diurnal variation of PM2.5 and PM10 was closely related with the evident/similar diurnal variation of turbulent momentum fluxes, sensible heat fluxes, and turbulent kinetic energy at various heights in the urban canopy. Besides, under the condition of calm weather, the concentration of PM2.5 and PM10 declined with height slowly, it was 90% of 8 m concentration at the low layer, a little lesser than 90% at the middle layer, and 80% at the high layer, respectively. Under the condition of weak cold air weather, the concentration remarkably dropped with height, it was 70% of 8 m concentration at the low layer, and 20%―30% at the middle and high layers, especially the concentration of PM2.5 was even lower.     

Modeling the Effects of Meteorological Factors on SO2 and PM10 Concentrations with Statistical Approaches

Air quality has been deteriorated seriously in urban areas as a result of increasing anthropogenic activities. Meteorological conditions affect air pollution levels in the urban atmosphere significantly due to their important role in transport and dilution of the pollutants. This paper aims to investigate usability of some promising statistical methods for examining the impacts of metrological factors on SO₂ and PM10 levels. Data were collected from city centre of Kocaeli in winter periods from 2007 to 2010 as pollutant concentrations increase in winters due to expanding combustion facilities. Results of bivariate correlation analysis showed that humidity and rainfall have remarkable negative correlations with the pollutants. Multiple linear regression models and artificial neural network (ANN) models were used to predict next day's PM10 and SO₂ levels. In regression models calculated R² values were 0.89 and 0.75 for PM10 and SO₂, respectively. Among the various architectures, single layer networks provided better performance in ANN applications. Highest R² values were obtained as 0.89 and 0.69 for PM10 and SO₂, respectively, by using appropriate networks.     

Magnetic signature of indoor air pollution: Household dust study

The combination of magnetic and geochemical methods was used to determine the mineralogy, grain size and domain structure of magnetic particles in indoor dust collected in 195 sites in Warsaw, Poland. Data show an asymmetric distribution of magnetic susceptibility (χ) in the wide range of 20–1514 × 10^?8 m³ kg^?1. Comparison of magnetic parameters shows that the internal dust contains outside pollution characteristic for air and soil. More than 90% of indoor dust samples were characterized by roughly uniform magnetic mineralogy, typical for fine grained magnetite (diameter of 0.2–5 μm), and grain size between pseudo-single-domain and small multi-domain with small contribution of superpara-magnetic particles (～10%). Samples with χ larger than 220 × 10^?8 m³ kg^?1 contain mainly magnetite and an anthropogenic metallic Fe with T _C > 700°C. The indoor dust contains, characteristic for the urban areas, spherical magnetic particles originated from fossil fuel combustion processes and mixture of irregular angular iron-oxides grains containing other elements, including Na, Ca, Al, Si, K, S, Mn, Cl, and Mg.     

Use of variograms for field magnetometry analysis in Upper Silesia Industrial Region

Measurements of topsoil magnetic susceptibility are often used for quick assessment of soil contamination of anthropogenic origin, with heavy metals or other pollutants. However, because of complicated correlations between low-field magnetic susceptibility (shortened to magnetic susceptibility) of topsoil and soil pollution, the outcome of a field magnetometry survey can not be related directly to soil pollution. For each case study, the results should be interpreted on their own taking into account not only the type of pollution but also pedogenic, biogenic and environmental factors. In practice, it is very difficult to measure and consider all these factors. Here we illustrate the merit of geostatistical methods, which are focused on the spatial variability of a phenomenon, in the interpretation of soil magnetometry results. This article presents the analysis of spatial variability of top soil layers magnetic susceptibility-within the Upper Silesia Industrial Region (USIR)-using semivariance analysis. It also explains how to adjust the sampling density of field magnetometry measurements to spatial variability of the soil pollution as well as to the spatial scale of the investigated area. For this purpose, the values of magnetic susceptibility have been measured by using various sampling densities at areas of different size located within USIR. This enabled to determine the main scales of magnetic susceptibility spatial variability of soils within USIR using semivariance. A few distinct scales of variability were found from the site scale to a more regional scale. Variability ranges of 30 km, 12 km, and 5 km refer to the large regional scale, whereas smaller ranges of few hundreds down to a few tens of meters, can be attributed to the local (site) scale. In addition, the precision of the measuring campaigns, performed within USIR with different sampling densities, was compared through the analysis of the spatial variability of the soil magnetic susceptibility signal by using ordinary kriging. jarek97@yahoo.com, piotr.fabijanczyk@is.pw.edu.pl     

Loss of soil and PM10 from agricultural fields associated with high winds on the Columbia Plateau

Winter wheat–summer fallow is the conventional cropping system employed on >1·5 million ha within the Columbia Plateau of eastern Washington and northern Oregon. Wind erosion contributes to poor air quality in the region, yet little is known concerning the magnitude of soil and PM10 (particulate matter of ≤10 µm in aerodynamic diameter) loss from agricultural lands. Therefore, loss of soil and PM10 was assessed from a silt loam in eastern Washington during 2003 and 2004. Field sites were maintained in fallow using conventional tillage practices in 2003 (9 ha field) and 2004 (16 ha field) and instrumented to assess horizontal soil flux and PM10 concentrations at the windward and leeward positions in the field during high‐wind events. Soil flux was measured using creep and airborne sediment collectors while PM10 concentrations were measured using high‐volume PM10 samplers. Aggregate size distribution of parent soil and eroded sediment was characterized by rotary and sonic sieving. Six high‐wind events occurred over the two year period, with soil loss ranging from 43 kg ha^?1 for the 12–22 September 2003 event to 2320 kg ha^?1 for the 27–29 October 2003 event. Suspension‐sized particulates (<100 µm in diameter) comprised ≥90 per cent of the eroded sediment, indicating that direct suspension may be an important process by which the silt loam eroded. The corresponding loss of PM10 for these two events ranged from 5 to 210 kg ha^?1. Loss of PM10 comprised 9–12 per cent of the total soil loss for the six events. This study suggests that the relatively small loss of PM10 from eroding agricultural fields maintained in summer fallow can affect air quality in the Columbia Plateau. Therefore, alternative tillage practices or cropping systems are needed for minimizing PM10 emissions and improving air quality in the region. Copyright © 2006 John Wiley & Sons, Ltd.     

Fuzzy robust process capability indices for risk assessment of air pollution

Air pollution is one of the most important threats for the humanity. It can damage not only human health but also Earth’s ecosystem. Because of the harmful effects of air pollution, it should be controlled very carefully. To do the risk assessment of air pollution in Istanbul, the process capability indices (PCIs) which are very effective statistics to summarize the performance of process are used in this paper. Fuzzy PCIs are used to determine the levels of the air pollutants which are measured in different nine stations in Istanbul. Robust PCIs (RPCIs) are used when air pollutants have correlation. Fuzzy set theory has been applied for both PCIs and RPCIs to have more sensitive results. More flexible PCIs obtained by using fuzzy specification limits and fuzzy standard deviation are used to evaluate the air pollution’s level of Istanbul. Additionally some evaluation criteria have been constructed for fuzzy PCIs to interpret the air pollution.     

Dispersion modelling of air pollution caused by road traffic using a Markov Chain–Monte Carlo model

 Although the strict legislation regarding vehicle emissions in Europe (EURO 4, EURO 5) will lead to a remarkable reduction of emissions in the near future, traffic related air pollution still can be problematic due to a large increase of traffic in certain areas. Many dispersion models for line-sources have been developed to assess the impact of traffic on the air pollution levels near roads, which are in most cases based on the Gaussian equation. Previous studies gave evidence, that such kind of models tend to overestimate concentrations in low wind speed conditions or when the wind direction is almost parallel to the street orientation. This is of particular interest, since such conditions lead generally to the highest observed concentrations in the vicinity of streets. As many air quality directives impose limits on high percentiles of concentrations, it is important to have good estimates of these quantities in environmental assessment studies. The objective of this study is to evaluate a methodology for the computation of especially those high percentiles required by e.g. the EU daughter directive 99/30/EC (for instance the 99.8 percentile for NO₂). The model used in this investigation is a Markov Chain – Monte Carlo model to predict pollutant concentrations, which performs well in low wind conditions as is shown here. While usual Lagrangian models use deterministic time steps for the calculation of the turbulent velocities, the model presented here, uses random time steps from a Monte Carlo simulation and a Markov Chain simulation for the sequence of the turbulent velocities. This results in a physically better approach when modelling the dispersion in low wind speed conditions. When Lagrangian dispersion models are used for regulatory purposes, a meteorological pre-processor is necessary to obtain required input quantities like Monin-Obukhov length and friction velocity from routinely observed data. The model and the meteorological pre-processor applied here, were tested against field data taken near a major motorway south of Vienna. The methodology used is based on input parameters, which are also available in usual environmental assessment studies. Results reveal that the approach examined is useful and leads to reasonable concentration levels near motorways compared to observations. We wish to thank Andreas Schopper (Styrian Government) for providing air quality values, M. Kalina for providing the raw data of the air quality stations near the motorway and J. Kukkonen for providing the road site data set from the Finish Meteorological Institute (FMI). The study was partly funded by the Austrian science fund under the project P14075-TEC.