Effect of PM2.5 chemical constituents on atmospheric visibility impairment

PM_2.5 sampling was conducted at a curbside location in Delhi city for summer and winter seasons, to evaluate the effect of PM_2.5 and its chemical components on the visibility impairment. The PM_2.5 concentrations were observed to be higher than the National Ambient Air Quality Standards (NAAQS), indicating poor air quality. The chemical constituents of PM_2.5 (the water-soluble ionic species SO₄^2-, NO₃^?, Cl^?, and NH₄⁺, and carbonaceous species: organic carbon, elemental carbon) were analyzed to study their impact on visibility impairment by reconstructing the light extinction coefficient, b_ext. The visibility was found to be negatively correlated with PM_2.5 and its components. The reconstructed b_ext showed that organic matter was the largest contributor to b_ext in both the seasons which may be attributed to combustion sources. In summer season, it was followed by elemental carbon and ammonium sulfate; however, in winter, major contributions were from ammonium nitrate and elemental carbon. Higher elemental carbon in both seasons may be attributed to traffic sources, while lower concentrations of nitrate during summer, may be attributed to volatility because of higher atmospheric temperatures.

Implications: The chemical constituents of PM_2.5 that majorly effect the visibility impairment are organic matter and elemental carbon, both of which are products of combustion processes. Secondary formations that lead to ammonium sulfate and ammonium nitrate production also impair the visibility.     

Source Profiles of Particulate Matter Emissions from a Pilot-Scale Boiler Burning North American Coal Blends

ABSTRACT

Recent awareness of suspected adverse health effects from ambient particulate matter (PM) emission has prompted publication of new standards for fine PM with aerodynamic diameter less than 2.5 μm (PM_2.5). However, scientific data on fine PM emissions from various point sources and their characteristics are very limited. Source apportionment methods are applied to identify contributions of individual regional sources to tropospheric particulate concentrations. The existing industrial database developed using traditional source measurement techniques provides total emission rates only, with no details on chemical nature or size characteristics of particulates. This database is inadequate, in current form, to address source-receptor relationships.

A source dilution system was developed for sampling and characterization of total PM, PM_2.5, and PM₁₀ (i.e., PM with aerodynamic diameter less than 10 μm) from residual oil and coal combustion. This new system has automatic control capabilities for key parameters, such as relative humidity (RH), temperature, and sample dilution. During optimization of the prototype equipment, three North American coal blends were burned using a 0.7-megawatt thermal (MW_t) pulverized coal-fired, pilot-scale boiler. Characteristic emission profiles, including PM_2.5 and total PM soluble acids, and elemental and carbon concentrations for three coal blends are presented.     

Chemical characterization of particles in winter-night smog in Tokyo

Continuous measurement of PM₁₀, PM_2.5 and carbon (organic, elemental composition) concentrations, and samples of PM₁₀ and PM_2.5 collected on a polycarbonate membrane filter (Nuclepore^®, pore size: 0.8 μm), were carried out during a period from December 1998 to January 1999 at Shinjuku in Tokyo in order to investigate the chemical characterization of particles in winter-night smog within a large area of the Japan Kanto Plain including the Tokyo Metropolitan area. These were measured using an ambient particulate monitor (tapered element oscillating microbalance—TEOM) and a carbon particulate monitor. Elemental compositions in the filter samples of PM₁₀ and PM_2.5 were determined by means of particle-induced X-ray emission (PIXE) analysis. Ionic species (anion: F⁻, Cl⁻, NO₃⁻, SO₄²⁻ and C₂O₄²⁻; cation: Na⁺, NH₄⁺, K⁺, Ca²⁺ and Mg²⁺) in the filter samples were analyzed by ion chromatography. The temporal variation patterns of PM_2.5 were similar to those of PM₁₀ and carbon. PM_2.5 made up 90% of the PM₁₀ at a high concentration, and 70% at a low concentration. Concentrations of 22 elements in both the PM₁₀ and PM_2.5 samples were consistently determined by PIXE, and Na, Mg, Al, Si, S, Cl, K, Ca, Fe, Zn and Pb were found to be the major components. Among these S and Cl were the most dominant elements of the PM_2.5 and PM₁₀ at high concentrations. Ionic species were mainly composed of Cl⁻, NO₃⁻, SO₄²⁻ and NH₄⁺. The component proportion of carbon, the other elements (total amount of measured elements other than S and Cl) and secondary-formed particles of PM_2.5 was similar to that of PM₁₀. The major component was carbon particles at a low concentration and secondary-formed particles at a high concentration. The proportion of NH₄NO₃ and NH₄Cl plus HCl in secondary-formed particles at a high concentration, in particular, was as high as 90%.     

Estimation of Organic Compound Emissions from Waste Lagoons

Abstract

With the recent focus on fine particle matter (PM_2.5),new, self-consistent data are needed to characterize emissions from combustion sources. Such data are necessary for health assessment and air quality modeling. To address this need, emissions data for gas-fired combustors are presented here, using dilution sampling as the reference.The dilution method allows for collection of emitted particles under conditions simulating cooling and dilution during entry from the stack into the air. The sampling and analysis of the collected particles in the presence of precursor gases, SO₂, nitrogen oxide, volatile organic compound, and NH₃ is discussed; the results include data from eight gas fired units, including a dual-fuel institutional boiler and a diesel engine powered electricity generator. These data are compared with results in the literature for heavy-duty diesel vehicles and stationary sources using coal or wood as fuels. The results show that the gas-fired combustors have very low PM_2.5 mass emission rates in the range of ～10_-4 lb/million Btu (MMBTU) compared with the diesel backup generator with particle filter, with ～5 × 10_-3 lb/MMBTU. Even higher mass emission rates are found in coal-fired systems, with rates of ～0.07 lb/MMBTU for a bag-filter-controlled pilot unit burning eastern bituminous coal. The characterization of PM_2.5 chemical composition from the gas-fired units indicates that much of the measured primary particle mass in PM_2.5 samples is organic or elemental carbon and, to a much less extent, sulfate. Metal emissions are quite low compared with the diesel engines and the coal- or woodfueled combustors. The metals found in the gas-fired combustor particles are low in concentration, similar in concentration to ambient particles. The interpretation of the particulate carbon emissions is complicated by the fact that an approximately equal amount of particulate carbon (mainly organic carbon) is found on the particle collector and a backup filter. It is likely that measurement artifacts, mostly adsorption of volatile organic compounds on quartz filters, are positively biasing “true” particulate carbon emission results.     

Reduction of atmospheric fine particle level by restricting the idling vehicles around a sensitive area

Atmospheric particles are a major problem that could lead to harmful effects on human health, especially in densely populated urban areas. Chiayi is a typical city with very high population and traffic density, as well as being located at the downwind side of several pollution sources. Multiple contributors for PM_2.5 (particulate matter with an aerodynamic diameter ≥2.5 μm) and ultrafine particles cause complicated air quality problems. This study focused on the inhibition of local emission sources by restricting the idling vehicles around a school area and evaluating the changes in surrounding atmospheric PM conditions. Two stationary sites were monitored, including a background site on the upwind side of the school and a campus site inside the school, to monitor the exposure level, before and after the idling prohibition. In the base condition, the PM_2.5 mass concentrations were found to increase 15% from the background, whereas the nitrate (NO₃^?) content had a significant increase at the campus site. The anthropogenic metal contents in PM_2.5 were higher at the campus site than the background site. Mobile emissions were found to be the most likely contributor to the school hot spot area by chemical mass balance modeling (CMB8.2). On the other hand, the PM_2.5 in the school campus fell to only 2% after idling vehicle control, when the mobile source contribution reduced from 42.8% to 36.7%. The mobile monitoring also showed significant reductions in atmospheric PM_2.5, PM_0.1, polycyclic aromatic hydrocarbons (PAHs), and black carbon (BC) levels by 16.5%, 33.3%, 48.0%, and 11.5%, respectively. Consequently, the restriction of local idling emission was proven to significantly reduce PM and harmful pollutants in the hot spots around the school environment.

Implications: The emission of idling vehicles strongly affects the levels of particles and relative pollutants in near-ground air around a school area. The PM_2.5 mass concentration at a campus site increased from the background site by 15%, whereas NO₃^? and anthropogenic metals also significantly increased. Meanwhile, the PM_2.5 contribution from mobile source in the campus increased 6.6% from the upwind site. An idling prohibition took place and showed impressive results. Reductions of PM_2.5, ionic component, and non-natural metal contents were found after the idling prohibition. The mobile monitoring also pointed out a significant improvement with the spatial analysis of PM_2.5, PM_0.1, PAH, and black carbon concentrations. These findings are very useful to effectively improve the local air quality of a densely city during the rush hour.     

Ambient suspended particulate matters and related chemical species study in central Taiwan,Taichung during 1998–2001

Ambient suspended particulate (PM_2.5, PM_2.5–10, TSP) was collected from June 1998 to February 2001 in Taichung, central Taiwan. In addition, the related water-soluble ionic species (Cl⁻, NO₃⁻, SO₄²⁻, Na⁺, NH₄⁺, K⁺, Mg²⁺, Ca²⁺) and metallic species (Fe, Zn, Pb, Ni) were also analyzed in this study. The results showed that the concentrations of particulate mass are higher in the traffic site (CCRT) than the other sampling sites in this study. Also, the fine particle (PM_2.5) concentration is the dominant species of the total suspended particles in Taichung, central Taiwan. The dominant species for PM_2.5 are sulfate and ammonium at all sampling sites during the period of 1998–2001. The results of diurnal variation at THUC sampling site are also discussed in this study. Overall, acidic and secondary aerosol (Cl⁻, NO₃⁻, SO₄²⁻ and NH₄⁺) is a more serious air pollutant issue in southern and central Taiwan than at several sites around the world. Therefore, ambient suspended particulate monitoring in Taichung, central Taiwan will be continuing in our following study to provide more information for the government to formulate environmental strategy.     

Particulate matter emissions from on-road vehicles in a freeway tunnel study

Particulate matter, including coarse particles (PM_2.5–10, aerodynamic diameter of particle between 2.5 and 10 μm) and fine particles (PM_2.5, aerodynamic diameter of particle lower than 2.5 μm) and their compositions, including elemental carbon, organic carbon, and 11 water-soluble ionic species, and elements, were measured in a tunnel study. A comparison of the six-hour average of light-duty vehicle (LDV) flow of the two sampling periods showed that the peak hours over the weekend were higher than those on weekdays. However, the flow of heavy-duty vehicles (HDVs) on the weekdays was significant higher than that during the weekend in this study. EC and OC content were 49% for PM_2.5–10 and 47% for PM_2.5 in the tunnel center. EC content was higher than OC content in PM_2.5–10, but EC was about 2.3 times OC for PM_2.5. Sulfate, nitrate, ammonium were the main species for PM_2.5–10 and PM_2.5. The element contents of Na, Al, Ca, Fe and K were over 0.8 μg m^?3 in PM_2.5–10 and PM_2.5. In addition, the concentrations of S, Ba, Pb, and Zn were higher than 0.1 μg m^?3 for PM_2.5–10 and PM_2.5. The emission factors of PM_2.5–10 and PM_2.5 were 18 ± 6.5 and 39 ± 11 mg km^?1-vehicle, respectively. The emission factors of EC/OC were 3.6/2.7 mg km^?1-vehicle for PM_2.5–10 and 15/4.7 mg km^?1-vehicle for PM_2.5 Furthermore, the emission factors of water-soluble ions were 0.028(Mg²⁺)–0.81(SO₄^2?) and 0.027(NO₂^?)–0.97(SO₄^2?) mg km^?1-vehicle for PM_2.5–10 and PM_2.5, respectively. Elemental emission factors were 0.003(V)–1.6(Fe) and 0.001(Cd)–1.05(Na) mg km^?1-vehicle for PM_2.5–10 and PM_2.5, respectively.     

Determination of sources of fine particles in different ambient atmospheres in South Korea using a chemical mass balance model

Abstract

To determine the sources of particulate matter less than 2.5?μm (PM_2.5 in different ambient atmospheres (urban, roadside, industrial, and rural sites), the chemical components of PM_2.5 such as ions (Cl^-, NO₃^-, SO₄^2-, NH₄⁺, Na⁺, K⁺, Ca²⁺, and Mg²⁺), carbonaceous species, and elements (Al, As, Ba, Cd, Cu, Fe, Mn, Ni, Pb, Se, V, and Zn) were measured. The average mass concentrations of PM_2.5 at the urban, roadside, industrial, and rural sites were 31.5?±?14.8, 31.6?±?22.3, 31.4?±?16.0, and 25.8?±?12.4?μg/m³, respectively. Except for secondary ammonium sulfate and ammonium nitrate, the model results showed that the traffic source (i.e., the sum of gasoline and diesel vehicle sources) was the most dominant source of PM_2.5 (17.1%) followed by biomass burning (13.8%) at the urban site. The major primary sources of PM_2.5 were consistent with the site characteristics (diesel vehicle source at the roadside site, coal-fired plants at the industrial site, and biomass burning at the rural site). Seasonal data from the urban site suggested that ammonium sulfate and ammonium nitrate were the most dominant sources of PM_2.5 during all seasons. Further, the contribution of road dust source to PM_2.5 increased during spring and fall seasons. We conclude that the determination of the major PM_2.5 sources is useful for establishing efficient control strategies for PM_2.5 in different regions and seasons.     

Chemical characteristics of fine particulate matters measured during severe winter haze events in Ulaanbaatar,Mongolia

In order to investigate the chemical characteristics of atmospheric aerosol measured during a severe winter haze event, 12-hr PM_2.5 (particulate matter with an aerodynamic diameter ≤2.5 μm) samples were collected at an urban site in Ulaanbaatar, Mongolia, from January 9 to February 17, 2008. On average, 12-hr PM_2.5 mass concentration was 105.1 ± 34.9 μg/m³. Low PM_2.5 mass concentrations were measured when low pressure developed over central Mongolia. The 12-hr average organic mass by carbon (OMC) varied from 6.4 to 132.3 μg/m³, with a mean of 54.9 ± 25.4 μg/m³, whereas elemental carbon (EC) concentration ranged from 0.1 to 3.6 μgC/m³, with a mean of 1.5 ± 0.8 μgC/m³. Ammonium sulfate was found to be the most abundant water-soluble ionic component in Ulaanbaatar during the sampling period, with an average concentration of 11.3 ± 5.0 μg/m³. In order to characterize the effect of air mass pathway on fine particulate matter characteristics, 5-day back-trajectory analysis was conducted, using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. The haze level was classified into three categories, based on the 5-day air mass back trajectories, as Stagnant (ST), Continental (CT), and Low Pressure (LP) cases. PM_2.5 mass concentration during the Stagnant condition was approximately 2.5 times higher than that during the Low Pressure condition, mainly due to increased pollutant concentration of OMC and secondary ammonium sulfate.

Implications: Mongolia is experiencing rapid rates of urbanization similar to other Asian countries, resulting in air pollution problems by the growing number of automobiles and industrialization. Ulaanbaatar, capital of Mongolia, is inherently vulnerable to air pollution because of its emission sources, topography, and meteorological characteristics. Very limited measurements on chemical characteristics of particulate matter have been carried out in Ulaanbaatar, Mongolia.     

Chemical characteristics and source apportionment of PM<Subscript>2.5</Subscript> using PCA/APCS,UNMIX, and PMF at an urban site of Delhi,India

The present study investigated the comprehensive chemical composition [organic carbon (OC), elemental carbon (EC), water-soluble inorganic ionic components (WSICs), and major & trace elements] of particulate matter (PM_2.5) and scrutinized their emission sources for urban region of Delhi. The 135 PM_2.5 samples were collected from January 2013 to December 2014 and analyzed for chemical constituents for source apportionment study. The average concentration of PM_2.5 was recorded as 121.9 ± 93.2 μg m^?3 (range 25.1–429.8 μg m^?3), whereas the total concentration of trace elements (Na, Ca, Mg, Al, S, Cl, K, Cr, Si, Ti, As, Br, Pb, Fe, Zn, and Mn) was accounted for ～17% of PM_2.5. Strong seasonal variation was observed in PM_2.5 mass concentration and its chemical composition with maxima during winter and minima during monsoon seasons. The chemical composition of the PM_2.5 was reconstructed using IMPROVE equation, which was observed to be in good agreement with the gravimetric mass. Source apportionment of PM_2.5 was carried out using the following three different receptor models: principal component analysis with absolute principal component scores (PCA/APCS), which identified five major sources; UNMIX which identified four major sources; and positive matrix factorization (PMF), which explored seven major sources. The applied models were able to identify the major sources contributing to the PM_2.5 and re-confirmed that secondary aerosols (SAs), soil/road dust (SD), vehicular emissions (VEs), biomass burning (BB), fossil fuel combustion (FFC), and industrial emission (IE) were dominant contributors to PM_2.5 in Delhi. The influences of local and regional sources were also explored using 5-day backward air mass trajectory analysis, cluster analysis, and potential source contribution function (PSCF). Cluster and PSCF results indicated that local as well as long-transported PM_2.5 from the north-west India and Pakistan were mostly pertinent.     

Spatial differences in ambient coarse and fine particles in the Monterrey metropolitan area,Mexico: Implications for source contribution

The ambient air of the Monterrey Metropolitan Area (MMA) in Mexico frequently exhibits high levels of PM₁₀ and PM_2.5. However, no information exists on the chemical composition of coarse particles (PM_c = PM₁₀ – PM_2.5). A monitoring campaign was conducted during the summer of 2015, during which 24-hr average PM₁₀ and PM_2.5 samples were collected using high-volume filter-based instruments to chemically characterize the fine and coarse fractions of the PM. The collected samples were analyzed for anions (Cl^–, NO₃^–, SO₄^2–), cations (Na⁺, NH₄⁺, K⁺), organic carbon (OC), elemental carbon (EC), and 35 trace elements (Al to Pb). During the campaign, the average PM_2.5 concentrations did not showed significance differences among sampling sites, whereas the average PM_c concentrations did. In addition, the PM_c accounted for 75% to 90% of the PM₁₀ across the MMA. The average contribution of the main chemical species to the total mass indicated that geological material including Ca, Fe, Si, and Al (45%) and sulfates (11%) were the principal components of PM_c, whereas sulfates (54%) and organic matter (30%) were the principal components of PM_2.5. The OC-to-EC ratio for PM_c ranged from 4.4 to 13, whereas that for PM_2.5 ranged from 3.97 to 6.08. The estimated contribution of Secondary Organic Aerosol (SOA) to the total mass of organic aerosol in PM_2.5 was estimated to be around 70–80%; for PM_c, the contribution was lower (20–50%). The enrichment factors (EF) for most of the trace elements exhibited high values for PM_2.5 (EF: 10–1000) and low values for PM_c (EF: 1–10). Given the high contribution of crustal elements and the high values of EFs, PM_c is heavily influenced by soil resuspension and PM_2.5 by anthropogenic sources. Finally, the airborne particles found in the eastern region of the MMA were chemically distinguishable from those in its western region.

Implications: Concentration and chemical composition patterns of fine and coarse particles can vary significantly across the MMA. Public policy solutions have to be built based on these observations. There is clear evidence that the spatial variations in the MMA’s coarse fractions are influenced by clearly recognizable primary emission sources, while fine particles exhibit a homogeneous concentration field and a clear spatial pattern of increasing secondary contributions. Important reductions in the coarse fraction can come from primary particles’ emission controls; for fine particles, control of gaseous precursors—particularly sulfur-containing species and organic compounds—should be considered.     

Emission factors of atmospheric and climatic pollutants from crop residues burning

Biomass burning is a common agricultural practice, because it allows elimination of postharvesting residues; nevertheless, it involves an inefficient combustion process that generates atmospheric pollutants emission, which has implications on health and climate change. This work focuses on the estimation of emission factors (EFs) of PM_2.5, PM₁₀, organic carbon (OC), elemental carbon (EC), carbon monoxide (CO), carbon dioxide (CO₂), and methane (CH₄) of residues from burning alfalfa, barley, beans, cotton, maize, rice, sorghum, and wheat in Mexico. Chemical characteristics of the residues were determined to establish their relationship with EFs, as well as with the modified combustion efficiency (MCE). Essays were carried out in an open combustion chamber with isokinetic sampling, following modified EPA 201-A method. EFs did not present statistical differences among different varieties of the same crop, but were statistically different among different crops, showing that generic values of EFs for all the agricultural residues can introduce significant uncertainties when used for climatic and atmospheric pollutant inventories. EFs of PM_2.5 ranged from 1.19 to 11.30 g kg^?1, and of PM₁₀ from 1.77 to 21.56 g kg^?1. EFs of EC correlated with lignin content, whereas EFs of OC correlated inversely with carbon content. EFs of EC and OC in PM_2.5 ranged from 0.15 to 0.41 g kg^?1 and from 0.33 to 5.29 g kg^?1, respectively, and in PM₁₀, from 0.17 to 0.43 g kg^?1 and from 0.54 to 11.06 g kg^?1. CO₂ represented the largest gaseous emissions volume with 1053.35–1850.82 g kg^?1, whereas the lowest was CH₄ with 1.61–5.59 g kg^?1. CO ranged from 28.85 to 155.71 g kg^?1, correlating inversely with carbon content and MCE. EFs were used to calculate emissions from eight agricultural residues burning in the country during 2016, to know the potential mitigation of climatic and atmospheric pollutants, provided this practice was banned.

Implications: The emission factors of particles, short-lived climatic pollutants, and atmospheric pollutants from the crop residues burning of eight agricultural wastes crops, determined in this study using a standardized method, provides better knowledge of the emissions of those species in Latin America and other developing countries, and can be used as inputs in air quality models and climatic studies. The EFs will allow the development of more accurate inventories of aerosols and gaseous pollutants, which will lead to the design of effective mitigation strategies and planning processes for sustainable agriculture.     

Major ionic compositions of fine particulate matter in an animal feeding operation facility and its vicinity

Animal feeding operations (AFOs) produce particulate matter (PM) and gaseous pollutants. Investigation of the chemical composition of PM_2.5 inside and in the local vicinity of AFOs can help to understand the impact of the AFO emissions on ambient secondary PM formation. This study was conducted on a commercial egg production farm in North Carolina. Samples of PM_2.5 were collected from five stations, with one located in an egg production house and the other four located in the vicinity of the farm along four wind directions. The major ions of NH₄⁺, Na⁺, K⁺, SO₄^2?, Cl^?, and NO₃^? were analyzed using ion chromatography (IC). In the house, the mostly abundant ions were SO₄^2?, Cl^?, and K⁺. At ambient stations, SO₄^2?, and NH₄⁺ were the two most abundant ions. In the house, NH₄⁺, SO₄^2?, and NO₃^? accounted for only 10% of the PM_2.5 mass; at ambient locations, NH₄⁺, SO₄^2?, and NO₃^? accounted for 36–41% of the PM_2.5 mass. In the house, NH₄⁺ had small seasonal variations indicating that gas-phase NH₃ was not the only major force driving its gas–particle partitioning. At the ambient stations, NH₄⁺ had the highest concentrations in summer. In the house, K⁺, Na⁺, and Cl^? were highly correlated with each other. In ambient locations, SO₄^2? and NH₄⁺ had a strong correlation, whereas in the house, SO₄^2? and NH₄⁺ had a very weak correlation. Ambient temperature and solar radiation were positively correlated with NH₄⁺ and SO₄^2?. This study suggests that secondary PM formation inside the animal house was not an important source of PM_2.5. In the vicinity, NH₃ emissions had greater impact on PM_2.5 formation.

ImplicationsThe chemical composition of PM_2.5 inside and in the local vicinity of AFOs showed the impact of the AFO emissions on ambient secondary PM_2.5 formation, and the fate and transport of air pollutants associated with AFOs. The results may help to manage in-house animal facility air quality, and to develop regional air quality control strategies and policies, especially in animal agriculture-concentrated areas.     

A detailed investigation of ambient aerosol composition and size distribution in an urban atmosphere

This research was executed between March 2009 and March 2010 to monitor particulate matter size distribution and its composition in Istanbul. Particulate matter composition was determined using ion chromatography and inductively coupled plasma optical emission spectrometry. The sampling point is adjacent to a crowded road and the Bosporus Strait. Two prevailing particulate modes are found throughout PM₁₀ by sampling with a nine-stage low-volume cascade impactor. First mode in the fine mode is found to be between 0.43 and 0.65 μm, whereas the other peak was observed between 3.3 and 4.7 μm, referring to the coarse mode. The mean PM₁₀ concentration was determined as 41.2 μg/m³, with a standard deviation of 16.92 μg/m³. PM_0.43 had the highest mean concentration value of 10.67 μg/m³, making up nearly one fourth of the total PM₁₀ mass. For determining the effect of traffic on particulate matter (PM) composition and distribution, four different sampling cycles were applied: entire day, nighttime, rush hour, and rush hour at weekdays. SO ₄ ^?2 and organic carbon/elemental carbon proportions are found to be lower in night samples, representing a decrease in traffic. The long-range transports of dust storms were observed during the sampling periods. Their effects were determined analytically and their route models were run by the HYSPLIT model and validated through satellite photographs taken by the NASA Earth Observatory.     

Particulate matter speciation profiles for light-duty gasoline vehicles in the United States

Representative profiles for particulate matter particles less than or equal to 2.5 µm (PM_2.5) are developed from the Kansas City Light-Duty Vehicle Emissions Study for use in the U.S. Environmental Protection Agency (EPA) vehicle emission model, the Motor Vehicle Emission Simulator (MOVES), and for inclusion in the EPA SPECIATE database for speciation profiles. The profiles are compatible with the inputs of current photochemical air quality models, including the Community Multiscale Air Quality Aerosol Module Version 6 (AE6). The composition of light-duty gasoline PM_2.5 emissions differs significantly between cold start and hot stabilized running emissions, and between older and newer vehicles, reflecting both impacts of aging/deterioration and changes in vehicle technology. Fleet-average PM_2.5 profiles are estimated for cold start and hot stabilized running emission processes. Fleet-average profiles are calculated to include emissions from deteriorated high-emitting vehicles that are expected to continue to contribute disproportionately to the fleet-wide PM_2.5 emissions into the future. The profiles are calculated using a weighted average of the PM_2.5 composition according to the contribution of PM_2.5 emissions from each class of vehicles in the on-road gasoline fleet in the Kansas City Metropolitan Statistical Area. The paper introduces methods to exclude insignificant measurements, correct for organic carbon positive artifact, and control for contamination from the testing infrastructure in developing speciation profiles. The uncertainty of the PM_2.5 species fraction in each profile is quantified using sampling survey analysis methods. The primary use of the profiles is to develop PM_2.5 emissions inventories for the United States, but the profiles may also be used in source apportionment, atmospheric modeling, and exposure assessment, and as a basis for light-duty gasoline emission profiles for countries with limited data.

Implications: PM_2.5 speciation profiles were developed from a large sample of light-duty gasoline vehicles tested in the Kansas City area. Separate PM_2.5 profiles represent cold start and hot stabilized running emission processes to distinguish important differences in chemical composition. Statistical analysis was used to construct profiles that represent PM_2.5 emissions from the U.S. vehicle fleet based on vehicles tested from the 2005 calendar year Kansas City metropolitan area. The profiles have been incorporated into the EPA MOVES emissions model, as well as the EPA SPECIATE database, to improve emission inventories and provide the PM_2.5 chemical characterization needed by CMAQv5.0 for atmospheric chemistry modeling.     

Characterization of Major Chemical Components of Fine Particulate Matter in North Carolina

Abstract

This paper presents measurements of daily sampling of fine particulate matter (PM_2.5) and its major chemical components at three urban and one rural locations in North Carolina during 2002. At both urban and rural sites, the major insoluble component of PM_2.5 is organic matter, and the major soluble components are sulfate (SO₄ ^2?), ammonium (NH₄ ⁺), and nitrate (NO₃ ^?). NH₄ ⁺ is neutralized mainly by SO₄ ^2? rather than by NO₃ ^?, except in winter when SO₄ ^2? concentration is relatively low, whereas NO₃ ^? concentration is high. The equivalent ratio of NH₄ ⁺ to the sum of SO₄ ^2? and NO₃ ^? is <1, suggesting that SO₄ ^2?and NO₃ ^?are not completely neutralized by NH₄ ⁺. At both rural and urban sites, SO₄ ^2?concentration displays a maximum in summer and a minimum in winter, whereas NO₃ ^?displays an opposite seasonal trend. Mass ratio of NO₃ ^? to SO₄ ^2?is consistently <1 at all sites, suggesting that stationary source emissions may play an important role in PM_2.5 formation in those areas. Organic carbon and elemental carbon are well correlated at three urban sites although they are poorly correlated at the agriculture site. Other than the daily samples, hourly samples were measured at one urban site. PM_2.5 mass concen trations display a peak in early morning, and a second peak in late afternoon. Back trajectory analysis shows that air masses with lower PM_2.5 mass content mainly originate from the marine environment or from a continental environment but with a strong subsidence from the upper troposphere. Air masses with high PM_2.5 mass concentrations are largely from continental sources. Our study of fine particulate matter and its chemical composition in North Carolina provides crucial information that may be used to determine the efficacy of the new National Ambient Air Quality Standard (NAAQS) for PM fine. Moreover, the gas-to-particle conversion processes provide improved prediction of long-range transport of pollutants and air quality.     

Determination of PM2.5 sources using time-resolved integrated source and receptor models

Multivariate statistical techniques are applied to particulate matter (PM) and meteorological data to identify the sources responsible for evening PM spikes at Sunland Park, NM (USA). The statistical techniques applied are principal components analysis (PCA), redundancy analysis (RDA), and absolute principal components scores analysis (APCSA), and the data evaluated are 3-h average (6–9 p.m.) PM_2.5 mass and chemical composition and 1-h average PM_2.5 and PM₁₀ mass and environmental data collected in the winter of 2002. Although the interpretation of the data was complicated by the presence of sources which are likely changing in time (e.g. brick kilns), the multivariate analyses indicate that the evening high PM_2.5 is associated with burning-activities occurring to the south of Sunland Park, and these emissions are characterized by elevated Sb, Cl⁻, and elemental carbon; ∼68% of the PM_2.5 mass can be attributed to this source. The PM₁₀ evening peaks, on the other hand, are mainly caused by resuspended dust generated by vehicular movements south of the site and transported by the local terrain-induced drainage flow.     

Characterization and seasonal variations of levoglucosan in fine particulate matter in Xi’an,China

PM_2.5 (particulate matter with an aerodynamic diameter <2.5 μm) samples (n = 58) collected every sixth day in Xi’an, China, from 5 July 2008 to 27 June 2009 are analyzed for levoglucosan (1,6-anhydro-β-d-glucopyranose) to evaluate the impacts of biomass combustion on ambient concentrations. Twenty-four-hour levoglucosan concentrations displayed clear summer minima and winter maxima that ranged from 46 to 1889 ng m^?3, with an average of 428 ± 399 ng m^?3. Besides agricultural burning, biomass/biofuel combustion for household heating with straws and branches appears to be of regional importance during the heating season in northwestern China. Good correlations (0.70 < R < 0.91) were found between levoglucosan relative to water-soluble K⁺, Cl^?, organic carbon (OC), elemental carbon (EC), and glyoxal. The highest levoglucosan/OC ratio of 2.3% was found in winter, followed by autumn (1.5%). Biomass burning contributed to 5.1–43.8% of OC (with an average of 17.6 ± 8.4%).

Implications:?PM_2.5 levoglucosan concentrations and the correlation between levoglucosan relative to other compounds during four seasons in Xi’an showed that the influence of biomass burning is maximum during the residential heating season (winter), although some important influences may be detected in spring (field preparation burnings) and autumn (corn stalks and wheat straw burning, fallen dead leaves burning) at Xi’an and surrounding areas. Household heating with biomass during winter was quite widespread in Guanzhong Plain. Therefore, the control of biomass/biofuel combustion could be an effective method to reduce pollutant emission on a regional scale.     

Determination of Source Contributions to Ambient PM2.5 in Kaohsiung,Taiwan, Using a Receptor Model

ABSTRACT

Ambient particulates of PM_2.5 were sampled at three sites in Kaohsiung, Taiwan, during February and March 1999. In addition, resuspended PM_2.5 collected from traffic tunnels, paved roads, fly ash of a municipal solid waste (MSW) incinerator, and seawater was obtained. All the samples were analyzed for twenty constituents, including water-soluble ions, organic carbon (OC), elemental carbon (EC), and metallic elements. In conjunction with local source profiles and the source profiles in the model library SPECIATE EPA, the receptor model based on chemical mass balance (CMB) was then applied to determine the source contributions to ambient PM_2.5.

The mean concentration of ambient PM_2.5 was 42.6953.68 μj.g/m³ for the sampling period. The abundant species in ambient PM_2.5 in the mass fraction for three sites were OC (12.7-14.2%), SO₄ ^2- (12.8-15.1%), NO₃ ^- (8.110.3%), NH₄+ (6.7-7.5%), and EC (5.3-8.5%). Results of CMB modeling show that major pollution sources for ambient PM_2.5 are traffic exhaust (18-54%), secondary aerosols (30-41% from SO₄ ^2- and NO₃ ^-), and outdoor burning of agriculture wastes (13-17%).