Influence of particulate matter on the air quality situation in a mediterranean island

The objective of this study was to describe the ambient levels of particulate matter (PM) and its influence to air quality situation on the dry Mediterranean island of Cyprus. From October 2002 to August 2003 PM₁₀ and PM_2.5 samples were collected at 31 different sampling sites in Cyprus. In addition, continuous measurements of PM₁₀ were carried out from 2003 to 2007 at a traffic and a rural site. It can be recognised that at all traffic and at some residential and urban background sites, the actual EU limit values have been exceeded. Special events e.g. long-range transport of Sahara dust storms were recorded over urban as well as rural areas in the order of 6–8 events per year, with a major frequency in summer and spring periods. The comparison of the PM₁₀ concentrations in Cyprus cities with values of other European cities demonstrates the PM₁₀ problem in Cyprus, especially in the dry summer season, when no rain is cleaning the air and the dry surfaces. This underlines the necessity of PM abatement strategies.     

Integrated assessment modeling of atmospheric pollutants in the Southern Appalachian Mountains: Part II. Fine particulate matter and visibility

As part of the Southern Appalachian Mountains Initiative, a comprehensive air quality modeling system was developed to evaluate potential emission control strategies to reduce atmospheric pollutant levels at the Class I areas located in the Southern Appalachian Mountains. Six multiday episodes between 1991 and 1995 were simulated, and the skill of the modeling system was evaluated. Two papers comprise various parts of this study. Part I details the ozone model performance and the methodology that was used to scale discrete episodic pollutant levels to seasonal and annual averages. This paper (part II) addresses issues involved with modeling particulate matter (PM) and its relationship to visibility. For most of the episodes, the fractional error was approximately 50% or less for the major constituents of the fine PM (i.e., sulfate [SO4] and organics) in the region. The mean normalized errors and fractional errors are generally larger for the NO3 and soil components, but these components are relatively small. Variations in modeling bias with pollutant levels were also examined. The model showed a systematic overestimation for low levels and an underestimation for high levels for most PM species. For ammonium, the model showed better performance at lower SO4 concentrations when the measured SO4 was assumed to be completely neutralized (ammonium sulfate) and better performance at higher SO4 concentrations when the partially neutralized (ammonium bisulfate) assumption was made. The contributions of various components of PM to reductions in visibility were also calculated; SO4 was found to be the major contributor.     

Inflammatory effects of particulate matter air pollution

Environmental Science and Pollution Research - Air pollution is an important cause of non-communicable diseases globally with particulate matter (PM) as one of the main air pollutants. PM is...     

Characterization of visibility impacts related to fine particulate matter in Canada

Canada has recently established standards for the management of particulate matter (PM) air quality. National networks currently measure PM mass concentrations and chemical speciation. Methods used in the U.S. IMPROVE network are applied to the 1994--2000 Canadian fine PM data to obtain a regional reconstruction of the visibility based on particle composition. Nationally, the greatest light extinction occurs in the Windsor-Quebec City corridor. Variations in the dominant chemical species responsible for the reduction in visibility are presented for regions across the country. In most regions, sulfate and nitrate contribute most greatly to reduced visibility. The visibility implications of achieving the Canada-Wide Standard (CWS) across the country are evaluated, with the greatest improvement in visibility associated with achieving the CWS in southern Ontario. Elsewhere in the country, achieving the CWS will actually result in deteriorating air quality. Improving current estimates of visibility requires higher spatially and temporally resolved measurements of organic and elemental carbon fractions and particulate nitrate.     

Airborne particulate matter from livestock production systems: A review of an air pollution problem

Livestock housing is an important source of emissions of particulate matter (PM). High concentrations of PM can threaten the environment, as well as the health and welfare of humans and animals. Particulate matter in livestock houses is mainly coarse, primary in origin, and organic; it can adsorb and contain gases, odorous compounds, and micro-organisms, which can enhance its biological effect. Levels of PM in livestock houses are high, influenced by kind of housing and feeding, animal type, and environmental factors. Improved knowledge on particle morphology, primarily size, composition, levels, and the factors influencing these can be useful to identify and quantify sources of PM more accurately, to evaluate their effects, and to propose adequate abatement strategies in livestock houses. This paper reviews the state-of-the-art of PM in and from livestock production systems. Future research to characterize and control PM in livestock houses is discussed.     

Bayesian hierarchical modeling of personal exposure to particulate matter

In the US EPA's 1998 Baltimore Epidemiology-Exposure Panel Study, a group of 16 residents of a single building retirement community wore personal monitors recording personal fine particulate air pollution concentrations (PM_2.5) for 27 days, while other monitors recorded concurrent apartment, central indoor, outdoor and ambient site PM_2.5 concentrations. Using the Baltimore panel study data, we develop a Bayesian hierarchical model to characterize the relationship between personal exposure and concentrations of PM_2.5 indoors and outdoors. Personal exposure is expressed as a linear combination of time spent in microenvironments and associated microenvironmental concentrations. The model incorporates all available monitoring data and accounts for missing data and sources of uncertainty such as measurement error and individual differences in exposure. We discuss the implications of using personal versus ambient PM_2.5 measurements in characterization of personal exposure to PM_2.5.     

An evaluation of the protein mass of particulate matter

This research study provides the characterization of mass percent of protein-based particulate matter in total ambient particulate matter collected in a metropolitan area of NC. The project determined the percentages of protein-based ambient bioaerosols for particles in the 2.5–10 μm range and for particles in the range of 2.5 μm or less in 298 samples taken over a six-month period. The analysis of total protein mass was used as an all-inclusive indicator of biologically based aerosols. These organic bioaerosols may have nucleated with inorganic non-biological aerosols, or they may be combined with inert aerosols. The source of these bioaerosols may be any combination of pollen, mold, bacteria, insect debris, fecal matter, or dander, and they may induce irritational, allergic, infectious, and chemical responses in exposed individuals. Ambient samples of PM_2.5 and PM_10−2.5 were analyzed for gravimetric mass and total protein mass. The results for 19 of 24 sample periods indicated that between 1% and 4% of PM_10−2.5 and between 1% and 2% of PM_2.5 mass concentrations were made of ambient protein bioaerosols. (The remaining 5 of 24 sample periods yielded protein results which were below detectable limits.)     

A full year evaluation of the CALIOPE-EU air quality modeling system over Europe for 2004

The CALIOPE-EU high-resolution air quality modeling system, namely WRF-ARW/HERMES-EMEP/CMAQ/BSC-DREAM8b, is developed and applied to Europe (12 km × 12 km, 1 h). The model performances are tested in terms of air quality levels and dynamics reproducibility on a yearly basis. The present work describes a quantitative evaluation of gas phase species (O₃, NO₂ and SO₂) and particulate matter (PM2.5 and PM10) against ground-based measurements from the EMEP (European Monitoring and Evaluation Programme) network for the year 2004. The evaluation is based on statistics. Simulated O₃ achieves satisfactory performances for both daily mean and daily maximum concentrations, especially in summer, with annual mean correlations of 0.66 and 0.69, respectively. Mean normalized errors are comprised within the recommendations proposed by the United States Environmental Protection Agency (US-EPA). The general trends and daily variations of primary pollutants (NO₂ and SO₂) are satisfactory. Daily mean concentrations of NO₂ correlate well with observations (annual correlation r = 0.67) but tend to be underestimated. For SO₂, mean concentrations are well simulated (mean bias = 0.5 μg m^?3) with relatively high annual mean correlation (r = 0.60), although peaks are generally overestimated. The dynamics of PM2.5 and PM10 is well reproduced (0.49 < r < 0.62), but mean concentrations remain systematically underestimated. Deficiencies in particulate matter source characterization are discussed. Also, the spatially distributed statistics and the general patterns for each pollutant over Europe are examined. The model performances are compared with other European studies. While O₃ statistics generally remain lower than those obtained by the other considered studies, statistics for NO₂, SO₂, PM2.5 and PM10 present higher scores than most models.     

An evaluation of indoor and outdoor biological particulate matter

The incidences of allergies, allergic diseases and asthma are increasing world wide. Global climate change is likely to impact plants and animals, as well as microorganisms. The World Health Organization, U.S. Environmental Protection Agency, U.S. Department of Agriculture, U.S. Department of Health and Human Services, and the Intergovernmental Panel on Climate Change cite increased allergic reactions due to climate change as a growing concern. Monitoring of indoor and ambient particulate matter (PM) and the characterization of the content for biological aerosol concentrations has not been extensively performed. Samples from urban and rural North Carolina (NC), and Denver (CO), were collected and analyzed as the goal of this research. A study of PM₁₀ (<10 μm in aerodynamic diameter) and PM_2.5 (<2.5 μm in aerodynamic diameter) fractions of ambient bioaerosols was undertaken for a six month period to evaluate the potential for long-term concentrations. These airborne bioaerosols can induce irritational, allergic, infectious, and chemical responses in exposed individuals. Three separate sites were monitored, samples were collected and analyzed for mass and biological content (endotoxins, (1,3)-β-d-glucan and protein). Concentrations of these bioaerosols were reported as a function of PM size fraction, mass and volume of air sampled. The results indicated that higher concentrations of biologicals were present in PM₁₀ than were present in PM_2.5, except when near-roadway conditions existed. This study provides the characterization of ambient bioaerosol concentrations in a variety of areas and conditions.     

Trends in speciated fine particulate matter and visibility across monitoring networks in the Southeastern United States

Trends in fine particulate matter <2.5 microm in diameter (PM2.5) and visibility in the Southeastern United States were evaluated for sites in the Interagency Monitoring of Protected Visual Environments, Speciated Trends Network, and Southeastern Aerosol Research and Characterization Study networks. These analyses are part of the technical assessment by Visibility Improvement-State and Tribal Association of the Southeast (VISTAS), the regional planning organization for the southeastern states, in support of State Implementation Plans for the regional haze rule. At all of the VISTAS IMPROVE sites, ammonium sulfate and organic carbon (OC) are the largest and second largest contributors, respectively, to light extinction on both the 20% haziest and 20% clearest days. Ammonium nitrate, elemental carbon (EC), soils, and coarse particles make comparatively small contributions to PM2.5 mass and light extinction on most days at the Class I areas. At Southern Appalachian sites, the 20% haziest days occur primarily in the late spring to fall, whereas at coastal sites, the 20% haziest days can occur through out the year. Levels of ammonium sulfate in Class I areas are similar to those in nearby urban areas and are generally higher at the interior sites than the coastal sites. Concentrations of OC, ammonium nitrate, and, sometimes, EC, tend to be higher in the urban areas than in nearby Class I areas, although differences in measurement methods complicate comparisons between networks. Results support regional controls of sulfur dioxide for both regional haze and PM2.5 implementation and suggest that controls of local sources of OC, EC, or nitrogen oxides might also be considered for urban areas that are not attaining the annual National Ambient Air Quality Standard for PM2.5.     

Evaluation of the community multiscale air quality (CMAQ) model version 4.5: Sensitivities impacting model performance; Part II—particulate matter

This paper is Part II in a pair of papers that examines the results of the Community Multiscale Air Quality (CMAQ) model version 4.5 (v4.5) and discusses the potential explanations for the model performance characteristics seen. The focus of this paper is on fine particulate matter (PM_2.5) and its chemical composition. Improvements made to the dry deposition velocity and cloud treatment in CMAQ v4.5 addressing compensating errors in 36-km simulations improved particulate sulfate (SO₄²⁻) predictions. Large overpredictions of particulate nitrate (NO₃⁻) and ammonium (NH₄⁺) in the fall are likely due to a gross overestimation of seasonal ammonia (NH₃) emissions. Carbonaceous aerosol concentrations are substantially underpredicted during the late spring and summer months, most likely due, in part, to a lack of some secondary organic aerosol (SOA) formation pathways in the model. Comparisons of CMAQ PM_2.5 predictions with observed PM_2.5 mass show mixed seasonal performance. Spring and summer show the best overall performance, while performance in the winter and fall is relatively poor, with significant overpredictions of total PM_2.5 mass in those seasons. The model biases in PM_2.5 mass cannot be explained by summing the model biases for the major inorganic ions plus carbon. Errors in the prediction of other unspeciated PM_2.5 (PM_Other) are largely to blame for the errors in total PM_2.5 mass predictions, and efforts are underway to identify the cause of these errors.     

室内空气中颗粒物污染特征研究

为获得室内空气颗粒物污染特征,2009年8月18～24日在某单位工作及生活区选取4个室内点和1个室外点进行颗粒物采样和成分分析.结果表明,室内粗颗粒(PM10)符合<室内空气质量标准>(GB/T 18883-2002),而细粒子(PM2.5)的浓度水平较高,表明室内PM2.5的污染较重;室内与室外PM2.5比值显示,P...     

Comparative evaluation of four suspended particulate matter (SPM) sampling devices and their use for monitoring SPM quality

Environmental Science and Pollution Research - Representative sampling of suspended particulate matter is fundamental for assessing river sediment quality, including the distribution and...     

Source apportionment of airborne particulate matter in Southeast Texas using a source-oriented 3D air quality model

A nested version of the source-oriented externally mixed UCD/CIT model was developed to study the source contributions to airborne particulate matter (PM) during a two-week long air quality episode during the Texas 2000 Air Quality Study (TexAQS 2000). Contributions to primary PM and secondary ammonium sulfate in the Houston–Galveston Bay (HGB) and Beaumont–Port Arthur (BPA) areas were determined.The predicted 24-h elemental carbon (EC), organic compounds (OC), sulfate, ammonium ion and primary PM_2.5 mass are in good agreement with filter-based observations. Predicted concentrations of hourly sulfate, ammonium ion, and primary OC from diesel and gasoline engines and biomass burning organic aerosol (BBOA) at La Porte, Texas agree well with measurements from an Aerodyne Aerosol Mass Spectrometer (AMS).The UCD/CIT model predicts that EC is mainly from diesel engines and majority of the primary OC is from internal combustion engines and industrial sources. Open burning contributes large fractions of EC, OC and primary PM_2.5 mass. Road dust, internal combustion engines and industries are the major sources of primary PM_2.5. Wildfire dominates the contributions to all primary PM components in areas near the fires. The predicted source contributions to primary PM are in general agreement with results from a chemical mass balance (CMB) model. Discrepancy between the two models suggests that further investigations on the industrial PM emissions are necessary.Secondary ammonium sulfate accounts for the majority of the secondary inorganic PM. Over 80% of the secondary sulfate in the 4 km domain is produced in upwind areas. Coal combustion is the largest source of sulfate. Ammonium ion is mainly from agriculture sources and contributions from gasoline vehicles are significant in urban areas.     

Development of an empirical model to estimate real-world fine particulate matter emission factors: the traffic air quality model

The purpose of the study was to quantify the impact of traffic conditions, such as free flow and congestion, on local air quality. The Borman Expressway (I-80/94) in Northwest Indiana is considered a test bed for this research because of the high volume of class 9 truck traffic traveling on it, as well as the existing and continuing installation of the Intelligent Transportation System (ITS) to improve traffic management along the highway stretch. An empirical traffic air quality (TAQ) model was developed to estimate the fine particulate matter (PM2.5) emission factors (grams per kilometer) based solely on the measured traffic parameters, namely, average speed, average acceleration, and class 9 truck density. The TAQ model has shown better predictions that matched the measured emission factor values more than the U.S. Environmental Protection Agency (EPA)-PART5 model. During congestion (defined as flow-speeds < 50 km/hr [30 mi/hr]), the TAQ model, on average, overpredicted the measured values only by a factor of 1.2, in comparison to a fourfold underprediction using the EPA-PART5 model. On the other hand, during free flow (defined as flow-speeds > 80 km/hr [50 mi/hr]), the TAQ model was conservative in that it overpredicted the measured values by 1.5-fold.     

Nonlinear analysis and prediction of coarse particulate matter concentration in ambient air

This study attempts to characterize and predict coarse particulate matter (PM10) concentration in ambient air using the concepts of nonlinear dynamical theory. PM10 data observed daily from 1999 to 2002 at a site in Mumbai, India, was used to study the applicability of the chaos theory. First, the autocorrelation function and Fourier power spectrum were used to analyze the behavior of the time-series. The dynamics of the time-series was additionally studied through correlation integral analysis and phase space reconstruction. The nonlinear predictions were then obtained using local polynomial approximation based on the reconstructed phase space. The results were then compared with the autoregressive model. The results of nonlinear analysis indicated the presence of chaotic character in the PM10 time-series. It was also observed that the nonlinear local approximation outperforms the autoregressive model, because the observed relative error of prediction for the autoregressive model was greater than the local approximation model. The invariant measures of nonlinear dynamics computed for the predicted time-series using the two models also supported the same findings.     

Application of air pollution dispersion modeling for source-contribution assessment and model performance evaluation at integrated industrial estate-Pantnagar

Source-contribution assessment of ambient NO₂ concentration was performed at Pantnagar, India through simulation of two urban mathematical dispersive models namely Gaussian Finite Line Source Model (GFLSM) and Industrial Source Complex Model (ISCST-3) and model performances were evaluated. Principal approaches were development of comprehensive emission inventory, monitoring of traffic density and regional air quality and conclusively simulation of urban dispersive models. Initially, 18 industries were found responsible for emission of 39.11 kg/h of NO₂ through 43 elevated stacks. Further, vehicular emission potential in terms of NO₂ was computed as 7.1 kg/h. Air quality monitoring delineates an annual average NO₂ concentration of 32.6 μg/m³. Finally, GFLSM and ISCST-3 were simulated in conjunction with developed emission inventories and existing meteorological conditions. Models simulation indicated that contribution of NO₂ from industrial and vehicular source was in a range of 45-70% and 9-39%, respectively. Further, statistical analysis revealed satisfactory model performance with an aggregate accuracy of 61.9%.     

Designing ambient particulate matter monitoring program for source apportionment study by receptor modeling

Particulate matter (PM) receptor modeling requires specific intensive input data that is always a challenge to produce cost effectively. A well-designed monitoring program is important to collect such PM ambient data in urban areas with diverse and densely distributed sources. This paper presents a general framework for designing such a monitoring program while emphasizing appropriate quality assurance and quality control elements that are particularly applicable where limited resources are available. Topics for discussion include selection of monitoring sites, sampling and analytical techniques, and the uncertainty estimation for ambient concentration input data. The design framework is illustrated by a case study of a monitoring program for PM source apportionment in the Bangkok Metropolitan Region in which 24-h fine and coarse PM samples were collected using two collocated dichotomous samplers. Comparison between black carbon measurements by Smoke Stain reflectometry and Thermal Optical Transmittance method is highlighted.     

Resolving local-scale emissions for modeling air quality near roadways

A large body of literature published in recent years suggests increased health risk due to exposure of people to air pollution in close proximity to roadways. As a result, there is a need to more accurately represent the spatial concentration gradients near roadways to develop mitigation strategies. In this paper, we present a practical, readily adaptable methodology, using a "bottom-up" approach to develop a detailed highway vehicle emission inventory that includes emissions for individual road links. This methodology also takes advantage of geographic information system (GIS) software to improve the spatial accuracy of the activity information obtained from a Travel Demand Model. In addition, we present an air quality modeling application of this methodology in New Haven, CT. This application uses a hybrid modeling approach, in which a regional grid-based model is used to characterize average local ambient concentrations, and a Gaussian dispersion model is used to provide texture within the modeling domain because of spatial gradients associated with highway vehicle emissions and other local sources. Modeling results show substantial heterogeneity of pollutant concentrations within the modeling domain and strong spatial gradients associated with roadways, particularly for pollutants dominated by direct emissions.