Abstract  This study proposes a methodology to determine the origin of industrial emissions in order to attribute responsibility to the industries that pollute nearby towns. The methodology has been applied to the industrial area on the northeastern coast of Venezuela. This area is close to six densely populated towns. The study also gives the estimated PM10 and SO2 levels in the towns adjacent to 11 industries, through modeling the dispersion of air pollutants from stationary sources. The model used has been the Lagrangian particle model LADISMO. The results are discussed by comparing the estimated values by the model with the limits proposed by the World Health Organization and United States Environmental Protection Agency.

Abstract  An intensive field study was conducted in Sumatra, Indonesia, during a peat fire episode to investigate the physical and chemical characteristics of particulate emissions in peat smoke and to provide necessary data for source-receptor analyses. Ambient air sampling was carried out at three different sites located at varying distances from the peat fires to determine changes in mass and number concentrations of PM2.5 and its chemical composition (carbonaceous and nitrogenous materials, polycyclic aromatic hydrocarbons, water-soluble inorganic and organic ions, and total and water-soluble metals). The three sites represent a rural site directly affected by the local peat combustion, a semirural site, and an urban site situated downwind of the peat fires. The mass concentration of PM2.5 and the number concentration of airborne particles were as high as 1600 mu g/m(3) and 1.7 x 10(5) cm(-3), respectively, in the vicinity of peat fires. The major components of PM2.5 in peat smoke haze were carbonaceous particles, particularly organic carbon, NO3-, and SO42-, while the less abundant constituents included ions such as NH4+, NO2-, Na+, K+, organic acids, and metals such as Al, Fe, and Ti. Source apportionment by chemical mass balance receptor modeling indicates that peat smoke can travel long distances and significantly affect the air quality at locations downwind.

Abstract  Benzene (Bz) is well known for its haema and genotoxicity and the carcinogenic effect associated with long time exposure. in urban environment, traffic is an important source for ambient air Bz concentrations. In order to quantify emission-to-intake relationships, intake fraction (iF) was defined as the integrated incremental intake of Bz released from a source (or source category) and summed over all exposed individuals during a given exposure time, per unit of emitted pollutant (Bennet et al., 2000). iF takes into account the dispersion of pollutants, locations and activity of population, and human breathing rates. The calculated iF for Bz is directly applicable to any other inert substance emitted by the traffic, e.g. CO, NOX, so the calculations also provide a ready-to-use too] for health effects studies concerning other pollutants and emission scenarios. This study calculates the spatial distribution of average benzene iF for Helsinki Metropolitan Area (HMA) using the EXPAND model (Kousa et al., 2002). The spatial Bz concentration distributions were obtained by using dispersion models: CAR-FMI (Karppinen et al., 2000) and OSPM (Berkowicz, 2000). A constant breathing rate of 1 m(3) /day was considered. The EXPAND results for 2000 are shown in Figure 1.

Abstract  To evaluate the association between growth in height and growth in lung function, and to identify the potential temporal relationships between airway hyperresponsiveness (AHR), respiratory symptoms, and lung function growth during adolescence and young adulthood, we analyzed data collected from the Belmont cohort. Among the 718 schoolchildren initially studied at 1982 (aged 8-10 yr), 557 were studied between two times and six times at 2-yr intervals until 1992. Baseline lung function, AHR by histamine inhalation test, and recent wheeze by questionnaires, were measured at each visit. We found that between 17 and 19 yr of age, when growth in height had stopped, growth in FEV(1) was approximately 200 ml/yr in boys and 100 ml/yr in girls. Peak growth velocity of height occurred at age 13 both in boys and in girls, whereas peak growth velocity of FEV(1) occurred at the same age only in girls and 1 yr later in boys. Having AHR and recent wheeze at the previous study time were both associated with lower subsequent growth in FEV(1), but not with subsequent growth in FVC. We conclude that lung function continues to grow after the cessation of height growth and that growth in FEV(1) is reduced in subjects with AHR and/or recent wheeze.

Abstract  Temporal dynamics of particulate matter (PM) concentration are affected by a variety of complex physical and chemical interactions among ambient pollutants and various exogenous factors (e.g. meteorological variables). Consequently, the dynamics of PM concentration can be considered either as a stochastic process or as a deterministic process. Many studies have applied stochastic and chaotic approaches independently to study the dynamics of PM concentration. However, none of them has compared these two complementary approaches for verification and possible confirmation of the outcomes. The present study makes an attempt to address this issue, through application of the dynamic factor analysis (DFA) (a stochastic method) and the correlation dimension (CD) method (a chaotic method) to study the temporal dynamics of ambient pollutants. More specifically, these two methods are employed to identify the number of variables dominantly governing the dynamics of PM concentration, with analysis of PM10, PM2.5, and ten other variables observed at the Hsing-Chuang station in Taipei (Taiwan). The results from the two methods are found to be consistent, with the DFA method suggesting eight common trends among the observed time series and the CD method suggesting eight variables dominantly governing the dynamics of both PM10 and PM2.5. This study provides an excellent example for the utility of both stochastic and chaotic approaches in modeling atmospheric and environmental systems, as these approaches not only shed light in their own ways but also complement each other in capturing the salient characteristics of such systems, especially from the perspective of simplified modeling. (C) 2012 Elsevier Ltd. All rights reserved.

Abstract  Limited data are available on the emission rates of speciated volatile and semivolatile organic compounds, as well as the physical and chemical characteristics of fine particulate matter (PM) from mobile, in-use diesel engines operated on the road. A design for the sampling of these fractions and the first data from in-use diesel sources are presented in this paper. Emission rates for carbonyls, 1,3-butadiene, benzene, toluene, xylene, PM, and elemental and organic carbon (EC and OC) are reported for a vehicle driven while following the California Air Resources Board (ARB) four-mode heavy heavy-duty diesel truck (HHDDT) cycle and while transiting through a major transportation corridor. Results show that distance specific emission rates are substantially greater in congested traffic as compared with highway cruise conditions. Specifically, emissions of toxic compounds are 3-15 times greater, and PM is 7 times greater under these conditions. The dependence of these species on driving mode suggests that health and source apportionment studies will need to account for driving patterns in addition to emission factors. Comparison of the PM/NOx ratios obtained for the above tests provides insight into the presence and importance of "off-cycle" emissions during on-road driving. Measurements from a stationary source (operated and tested at constant engine speed) equipped with an engine similar to that in the HHDDT yielded a greater understanding of the relative dependence of emissions on load versus engine transients. These data are indicative of the type of investigations made possible by the development of this novel laboratory.

Abstract  Brick manufacturing is the fastest-growing industrial sector in Bangladesh and among the top three sectors, along with vehicle exhaust and resuspended road dust, contributing to the air pollution and health problems in Dhaka. The brick manufacturing in the Greater Dhaka region, from similar to 1,000 brick kilns spread across six districts, is confined to the winter season (October to March) as current technologies do not allow production during the monsoon. The total emissions are estimated at 23,300 t of PM2.5, 15,500 t of sulfur dioxide (SO2), 302,000 t of carbon monoxide (CO), 6,000 t of black carbon, and 1.8 million tons of CO2 emissions from these clusters, to produce 3.5 billion bricks per year, using energy-inefficient fixed chimney bull trench kiln technology and predominantly using coal and agricultural waste as fuel. The associated health impacts largely fall on the densely populated districts of Dhaka Metropolitan Area (DMA), Gazipur, and Narayanganj. Using the Atmospheric Transport Modeling System dispersion model, the impact of brick kiln emissions was estimated over DMA-ranging from 7 to 99 mu g/m(3) (5th and 95th percentile concentration per model grid) at an average of 38 mu g/m(3); and spatial contributions from the surrounding clusters-with 27 % originating from Narayanganj (to the south with the highest kiln density), 30 % from Gazipur (to the north with equally large cluster spread along the river and canals), and 23 % from Savar. The modeling results are validated using evidence from receptor modeling studies conducted in DMA. An introduction of emerging vertical shaft combustion technology can provide faster benefits for public health in DMA and reduce climate precursor emissions by selecting the most influential clusters discussed in this paper.

Abstract  This paper offers a brief review of the need for cost-benefit analysis (CBA) and the available policy instruments for air pollution. To prioritize different possible actions, one needs to know which source of pollution causes how much damage. This requires an impact pathway analysis, that is, an analysis of the chain emission --> dispersion --> dose-response function --> monetary valuation. The methodology for this is described and illustrated with the results of the ExternE (External Costs of Energy) project series of the European Commission. Two examples of an application to CBA are shown: one where a proposed reduction of emission limits is justified, and one where it is not. It is advisable to subject any proposed regulation to a CBA, including an analysis of the uncertainties. Even if the uncertainties are large and a policy decision may have to take other considerations into account, a well-documented CBA clarifies the issues and provides a basis for rational discussion. One of the main sources of uncertainty lies in the monetary valuation of premature mortality, the dominant contribution to the damage cost of air pollution. As an alternative, an innovative policy tool is described, the Life Quality Index (LQI), a compound indicator comprising societal wealth and life expectancy. It is applied to the Canada-wide standards for particulate matter and ozone. Regardless of monetary valuation, a 50% reduction of PM10 concentrations in Europe and North America has been shown to yield a population-average life expectancy increase on the order of 4 to 5 mo.

Abstract  Objective Air pollution PM is associated with cardiovascular morbidity and mortality. In Appalachia, PM from mining may represent a health burden to this sensitive population that leads the nation in cardiovascular disease, among others. Cardiovascular consequences following inhalation of PMMTM are unclear, but must be identified to establish causal effects. Methods PM was collected within 1mile of an active MTM site in southern WV. The PM was extracted and was primarily <10m in diameter (PM10), consisting largely of sulfur (38%) and silica (24%). Adult male rats were IT with 300g PMMTM. Twenty-four hours following exposure, rats were prepared for intravital microscopy, or isolated arteriole experiments. Results PMMTM exposure blunted endothelium-dependent dilation in mesenteric and coronary arterioles by 26%, and 25%, respectively, as well as endothelium-independent dilation. In vivo, PMMTM exposure inhibited endothelium-dependent arteriolar dilation (60% reduction). -adrenergic receptor blockade inhibited PVNS-induced vasoconstriction in exposed animals compared with sham. Conclusions These data suggest that PMMTM exposure impairs microvascular function in disparate microvascular beds, through alterations in NO-mediated dilation and sympathetic nerve influences. Microvascular dysfunction may contribute to cardiovascular disease in regions with MTM sites.

Abstract  The World Health Organization estimates that 4.6 million people die each year from causes directly attributable to air pollution. Air pollution damages people, environment, animal and component of natural life. It has high risk priority for World. The recently studies focus the air pollution and other risks for humanity. They propose different solutions to prevent air pollution. In this paper, a methodology based on Process Capability Indices (PCI) has been presented to control air pollution. Traditional and fuzzy process capability indices have been used for this aim. Sulfur dioxide, SO2, and particulate matter (PM), also known as particle pollution are measured and analyzed for air quality. The measurements values Of SO2 and PM10 have been collected for one year in Istanbul, Turkey. The air pollution of Istanbul is analyzed by the aid of this method.

Abstract  BACKGROUND: Although the dispersion model approach has been used in some epidemiologic studies to examine health effects of traffic-specific air pollution, no study has evaluated the model predictions vigorously.

METHODS: We evaluated total and traffic-specific particulate matter < 10 and < 2.5 microm in aero-dynamic diameter (PM(10), PM(2.5)), nitrogren dioxide, and nitrogen oxide concentrations predicted by Gaussian dispersion models against fixed-site measurements at different locations, including traffic-impacted, urban-background, and alpine settings between and across cities. The model predictions were then used to estimate individual subjects' historical and cumulative exposures with a temporal trend model.

RESULTS: Modeled PM(10) and NO(2) predicted at least 55% and 72% of the variability of the measured PM(10) and NO(2), respectively. Traffic-specific pollution estimates correlated with the NO(x) measurements (R(2) >or=0.77) for background sites but not for traffic sites. Regional background PM(10) accounted for most PM(10) mass in all cities. Whereas traffic PM(10) accounted for < 20% of the total PM(10), it varied significantly within cities. The modeling error for PM(10) was similar within and between cities. Traffic NO(x) accounted for the majority of NO(x) mass in urban areas, whereas background NO(x) accounted for the majority of NO(x) in rural areas. The within-city NO(2) modeling error was larger than that between cities.

CONCLUSIONS: The dispersion model predicted well the total PM(10), NO(x), and NO(2) and traffic-specific pollution at background sites. However, the model underpredicted traffic NO(x) and NO(2) at traffic sites and needs refinement to reflect local conditions. The dispersion model predictions for PM(10) are suitable for examining individual exposures and health effects within and between cities.

Abstract  This report describes a study to investigate the relationships among daily variations in air pollution in Ho Chi Minh City (HCMC), Vietnam, hospital admissions for acute lower respiratory infections in children under age 5, and poverty. The study was part of HEI's Public Health and Air Pollution in Asia (PAPA) program and is the first study of air quality and health to be performed in Vietnam. The team of investigators, led by Drs. Le Truong Giang, Long Ngo, and Sumi Mehta, collected daily pollutant data for PM10, sulfur dioxide, nitrogen dioxide, and ozone at multiple locations throughout the city and obtained admissions data from the two pediatric hospitals in HCMC. They then performed statistical analysis on the data using Poisson time-series and case–crossover methods.

Abstract  Over the past decade, an increasing interest has evolved by the public in the day-to-day air quality conditions to which they are exposed. Driven by the increasing awareness of the health aspects of air pollution exposure, especially by most sensitive sub-populations such as children and the elderly, short-term air pollution forecasts are being provided more and more by local authorities. The Air Quality Index (AQI) is a number used by governmental agencies to characterize the quality of the air at a given location. AQI is used for local and regional air quality management in many metropolitan cities of the world. The main objective of the present study is to forecast short-term daily AQI through previous day's AQI and meteorological variables using principal component regression (PCR) technique. This study has been made for four different seasons namely summer, monsoon, post monsoon and winter. AQI was estimated for the period of seven years from 2000-2006 at ITO (a busiest traffic intersection) for criteria pollutants such as respirable suspended particulate matter (RSPM), sulfur dioxide (SO2), nitrogen dioxide (NO2) and suspended particulate matter (SPM) using a method of US Environmental Protection Agency (USEPA), in which sub-index and breakpoint pollutant concentration depends on Indian National Ambient Air Quality Standard (NAAQS). The Principal components have been computed using covariance of input data matrix. Only those components, having eigenvalues >= 1, were used to predict the AQI using principal component regression technique. The performance of PCR model, used for forecasting of AQI, was better in winter than the other seasons as studied through statistical error analysis. The values of normalized mean square error (NMSE) were found as 0.0058, 0.0082, 0.0241 and 0.0418 for winter, summer, post monsoon and monsoon respectively. The other statistical parameters are also supporting the same result. (C) Author(s) 2011. This work is distributed under the Creative Commons Attribution 3.0 License.

Abstract  Is wind direction an adequate marker of air mass history? This review looks at the evolution of methods for assessing the effect of the origin and pathway of air masses on composition change and trends. The composition of air masses and how they evolve and the changing contribution of sources and receptors are key elements in atmospheric science. Source receptor relationships of atmospheric composition can be investigated with back trajectory techniques, tracing forward from a defined geographical origin to arrive at measurement sites where the composition may have altered during transport.

The distinction between the use of wind sector analysis, trajectory models and dispersion models to interpret composition measurements is explained and the advantages and disadvantages of each are illustrated with examples. Historical uses of wind roses, back trajectories and dispersion models are explained as well as the methods for grouping and clustering air masses. The interface of these methods to the corresponding chemistry measured at the receptor sites is explored. The review does not detail the meteorological derivation of trajectories or the complexity of the models but focus on their application and the statistical analyses used to compare them with in situ composition measurements. A newly developed methodology for analysing atmospheric observatory composition data according to air mass pathways calculated with the NAME dispersion model is given as a detailed case study. The steps in this methodology are explained with relevance to the Weyboume Atmospheric Observatory in the UK. (C) 2011 Elsevier BM. All rights reserved.

Abstract  Typical classification schemes for large data sets of single-particle mass spectra involve statistical or neural network analysis. In this work, a new approach is evaluated in which particle spectra are pre-selected on the basis of an above threshold signal intensity at a specified m/z (mass to charge ratio). This provides a simple way to identify candidate particles that may contain the specific chemical component associated with that m/z. Once selected, the candidate particle spectra are then classified by the fast adaptive resonance algorithm, ART 2-a, to confirm the presence of the targeted component in the particle and to study the intra-particle associations with other chemical components. This approach is used to characterize metals in a 75,000 particle data set obtained in Baltimore, Maryland. Particles containing a specific metal are identified and then used to determine the size distribution, number concentration, time/wind dependencies and intra-particle correlations with other metals. Four representative elements are considered in this study: vanadium, iron, arsenic and lead. Number concentrations of ambient particles containing these elements can exceed 10,000 particles cm(-3) at the measurement site. Vanadium, a primary marker for fuel oil combustion, is observed from all wind directions during this time period. Iron and lead are observed from the east-northeast. Most particles from this direction that contain iron also contain lead and most particles that contain lead also contain iron, suggesting a common emission source for the two. Arsenic and lead are observed from the south-southeast. Particles from this direction contain either arsenic or lead but rarely both, suggesting different sources for each element. (C) 2004 Elsevier Ltd. All rights reserved.

Abstract  A simple air quality index (AQI) was introduced for the Helsinki Metropolitan Area in 1993, in order to inform the public about the current air quality in an easily understood way. The pollutants included in the AQI are CO (1 and 8 h), NO2 (1 and 24 h), SO2 (1 and 24 h), O-3 (1 h) and PM10 (24 h). The AQI is linked to the new air quality guidelines in Finland. The AQI is based on acute health effects, but longterm effects on nature and materials are also taken into consideration. Subindices are calculated hourly for all pollutants and for a given hour the highest subindex becomes the AQI. AQI values are currently calculated for the center of Helsinki and for typical suburban areas.

Abstract  Exposure to environmental pollutants can have short- and long-term effects on lung health. Sources of air pollution include gases (eg, carbon monoxide, ozone) and particulate matter (eg, soot, dust). In the United States, the Environmental Protection Agency regulates air pollution. Elevated ozone concentrations are associated with increases in lung-related hospitalizations and mortality. Elevated particulate matter pollution increases the risk of cardiopulmonary and lung cancer mortality. Occupations with high exposures to pollutants (eg, heavy construction work, truck driving, auto mechanics) pose higher risk of chronic obstructive lung disease. Some industrial settings (eg, agriculture, sawmills, meat packing plants) also are associated with higher risks from pollutants. The Environmental Protection Agency issues an air quality index for cities and regions in the United States. The upper levels on the index are associated with increases in asthma-related emergency department visits and hospitalizations. Damp and moldy housing might make asthma symptoms worse; individuals from lower socioeconomic groups who live in lower quality housing are particularly at risk. Other household exposures that can have negative effects on lung health include radon, nanoparticles, and biomass fuels.

Abstract  Daily commutes may contribute disproportionately to overall daily inhalations of urban air contaminants. Understanding factors that explain variability of exposures during travel, and especially differences across transportation modes, is essential to accurately assess health impacts of traffic emissions and to develop effective mitigating measures. We evaluated exposures and inhaled doses of air pollution and assessed factors that contributed to their variability in different travel modes in Barcelona. Black carbon (BC), ultrafine particles (UFP), carbon monoxide (CO), fine particle mass (PM2.5) and carbon dioxide (CO2) were measured and compared across walk, bike, bus, and car modes for a total of 172 trips made on two different round trip routes. On average, the car mode experienced highest concentrations for all contaminants. In pairwise t-tests between concurrent mode runs, statistically significant differences were found for cars compared to walking and biking. Car-to-walk or car-to-bike concentration ratios ranged from 1.3 for CO2 to 25 for CO and were 2–3 for PM2.5, BC, and UFP. In multivariate analyses, travel mode explained the greatest variability in travel exposures, from 8% for PM2.5 to 70% for CO. Different modal patterns emerged when estimating daily inhaled dose, with active commuters' two to three times greater total inhalation volume during travel producing about equal UFP and BC daily inhaled doses to car commuters and 33–50% higher UFP and BC doses compared to bus commuters. These findings, however, are specific to the bike and pedestrian lanes in this study being immediately adjacent to the roadways measured. Dedicated bike or pedestrian routes away from traffic would lead to lower active travel doses.

Abstract  The authors undertook a cross-sectional study of the potential adverse health effects of air pollution in Bangkok, Thailand. During 1998 and 1999, the authors administered lung function spirometry tests and a Thai version of the American Thoracic Society's Division of Lung Diseases (ATS-DLD) respiratory questionnaire to 78 male traffic police and 60 male nontraffic police in Bangkok, as well as to 68 male general police in Ayutthaya province, a rural area in Thailand. No consistent trend of decreased pulmonary function was observed in traffic police. The authors controlled for age, height, and smoking index, after which mean levels of forced expiratory volume in 1 sec and maximal expiratory flow rate in 25% of vital capacity (V25) were significantly lower in Bangkok police than in Ayutthaya police. The prevalence of respiratory symptoms among Bangkok police was slightly higher than among Ayutthaya police. Multiple regression analysis identified age and workplace as statistically significant factors that contributed to the values of forced expiratory volume in 1 sec and V25. This study provided some evidence of an increase in prevalence of obstructive changes in the peripheral airways among traffic police in Bangkok.

Abstract  Despite governmental efforts to improve the quality of outdoor air, a significant number of children growing up in the US are exposed to airborne pollutants. It is now recognized that infants generally at risk for atrophy when exposed to specific environmental airborne pollutants are more likely to develop asthma. Once asthma is established, airborne pollutants are important triggers in causing exacerbations. Airborne ozone and suspended articles are the two most important criteria pollutants with respect to exposure prevalence and suspected adverse health effects in US children. Pediatricians should be involved both in community advocacy programs to improve air quality and as knowledgeable practitioners in discussing practical air pollution avoidance strategies with patients and their families.

Abstract  The Community Multiscale Air Quality (CMAQ) model is widely used in air quality management and scientific investigation. Numerous studies have been conducted investigating how well CMAQ simulates fine particle mass concentrations, but relatively few studies have addressed how well CMAQ simulates fine particle number distribution. Accurate simulation of particle number concentrations is important because particle number and surface area concentrations may be directly related to human health and visibility. Simulated fine particle number concentrations derived using CMAQ are compared to measurements to identify problems and to improve model performance. Evaluation is done using measured particle number concentrations in Atlanta, Georgia, from 1/1/1999 to 8/31/2000. While homogeneous binary nucleation mechanism used in CMAQ needs to be modified for better prediction of particle number concentrations, there are also other factors that affect the predicted particle level. Assumed particle size of the primary emissions in CMAQ causes number concentrations to be significantly underestimated, while particle density has a small impact. Assuming particle size distributions by three lognormal modes cannot accurately simulate particles with size less than 0.01 mu m, particularly during nucleation events. An additional mode that accounts for particles smaller than 0.01 mu m can improve the accuracy of the number concentration simulations. Though, the use of the Expectation-Maximization (EM) algorithm to estimate size distribution parameters of measured particles suggests that assumed parameters for the lognormal modes in CMAQ are generally reasonable.