Multi-year hourly PM2.5 carbon measurements in New York: Diurnal,day of week and seasonal patterns

Multi-year hourly measurements of PM_2.5 elemental carbon (EC) and organic carbon (OC) from a site in the South Bronx, New York were used to examine diurnal, day of week and seasonal patterns. The hourly carbon measurements also provided temporally resolved information on sporadic EC spikes observed predominantly in winter. Furthermore, hourly EC and OC data were used to provide information on secondary organic aerosol formation. Average monthly EC concentrations ranged from 0.5 to 1.4 μg m^?3 with peak hourly values of several μg m^?3 typically observed from November to March. Mean EC concentrations were lower on weekends (approximately 27% lower on Saturday and 38% lower on Sunday) than on weekdays (Monday to Friday). The weekday/weekend difference was more pronounced during summer months and less noticeable during winter. Throughout the year EC exhibited a similar diurnal pattern to NO_x showing a pronounced peak during the morning commute period (7–10 AM EST). These patterns suggest that EC was impacted by local mobile emissions and in addition by emissions from space heating sources during winter months. Although EC was highly correlated with black carbon (BC) there was a pronounced seasonal BC/EC gradient with summer BC concentrations approximately a factor of 2 higher than EC. Average monthly OC concentrations ranged from 1.0 to 4.1 μg m^?3 with maximum hourly concentrations of 7–11 μg m^?3 predominantly in summer or winter months. OC concentrations generally correlated with PM_2.5 total mass and aerosol sulfate and with NO_x during winter months. OC showed no particular day of week pattern. The OC diurnal pattern was typically different than EC except in winter when OC tracked EC and NO_x indicating local primary emissions contributed significantly to OC during winter at the urban location. On average secondary organic aerosol was estimated to account for 40–50% of OC during winter and up to 63–73% during summer months.     

Deposition velocity of PM2.5 sulfate in the summer above a deciduous forest in central Japan

In order to increase knowledge of aerosol dry deposition for the regional assessment of acid deposition and transboundary air pollution in East Asia, an experimental study on PM2.5 sulfate deposition was implemented in the early summer of 2009. The experimental field was located in a deciduous forest at the foot of Mt. Asama, central Japan. Aerosol fluxes were obtained using the aerodynamic gradient method. Three aerosol samplers were placed on an experimental tower at 21, 24 and 27 m above the ground surface, and collected PM2.5 on filters for chemical analysis. Vertical concentration differences between 21 m and 27 m of PM2.5 sulfate were detected significantly when the concentration exceeded 1 μg m^?3. Mean deposition velocity was estimated to be 0.9 ± 1.0 cm s^?1 in the daytime and 0.3 ± 0.3 cm s^?1 in the nighttime. In the case that a height-dependent correction in the roughness sub-layer was taken into account, the deposition velocities increased more, especially in daytime. Higher deposition velocities in the daytime were associated with larger friction velocities and unstable conditions. The deposition velocities observed in this study were in agreement with other experimental results found in the literature. On the other hand, they were higher than those calculated by theoretical models. Two empirical parameterizations (Wesely, M.L., Cook, D.R., Hart, R.L., 1985. Measurement and parameterization of particulate sulfur dry deposition over grass. Journal of Geophysical Research 90, 2131–2143; Ruijgrok, W., Tieben, H., Eisinga, P., 1997. The dry deposition of particles to a forest canopy: a comparison of model and experimental results. Atmospheric Environment 31, 399–415) were validated by the observations. The general trend of higher daytime and lower nighttime deposition velocities was similar among the observation and the two parameterizations. The large variability found in the measurement was not reproduced by the parameterizations, because it is attributable to random error from the differences between the samplers. The observations were in accordance with the parameterization of Ruijgrok et al. (1997) for a forest, although much larger than that of Wesely et al. (1985) for grasslands. This indicates the large difference in aerosol deposition velocities between forests and grasslands.     

Seasonal and diurnal variations of ambient PM2.5 concentration in urban and rural environments in Beijing

Three years of measurement of PM_2.5 with 5-min time resolution was conducted from 2005 to 2007 in urban and rural environments in Beijing to study the seasonal and diurnal variations in PM_2.5 concentration. Pronounced seasonal variation was observed in the urban area, with the highest concentrations typically observed in the winter and the lowest concentrations generally found in the summer. In the rural area, the maximum in PM_2.5 concentration usually appeared during the spring, followed by a second maximum in the summer, while the minimum generally occurred in the winter. Significant diurnal variations in PM_2.5 concentration were observed in both urban and rural areas. In the urban area, the PM_2.5 concentration displays a bimodal pattern, with peaks between 7:00 and 8:00 a.m. and between 7:00 and 11:00 p.m. The minimum generally appears around noon. The morning peak is attributed to enhanced anthropogenic activity during rush hours. The decreases of boundary layer height and wind speed in the afternoon companying with increased source activity during the afternoon rush hour result in the highest PM_2.5 concentration during evening hours. In the rural area, the PM_2.5 concentration shows a unimodal pattern with a significant peak between 5:00 and 11:00 p.m.The seasonal and diurnal variations in PM_2.5 concentration in the urban area are mostly dominated by the seasonal and diurnal variability of boundary layer and source emissions. The year-to-year variability of rainfall also has an important influence on the seasonal variation of PM_2.5 in the urban area. The seasonal and diurnal wind patterns are more important factors for PM_2.5 variation in the rural area. Southerly winds carry pollutants emitted in southern urban areas northward and significantly enhance the PM_2.5 concentration level in the rural area.     

Temporal characteristics from continuous measurements of PM2.5 and speciation at the Taipei Aerosol Supersite from 2002 to 2008

This study uses monitoring data collected at the Taipei Aerosol Supersite from March 2002 to February 2008 to analyze characteristics such as seasonal fluctuations, diurnal variations, and photochemical-related variations of PM_2.5 chemical compositions. The results indicate that the average of PM_2.5 mass concentration in Taipei during this period is 30.3 ± 16.0 μg m^?3. The highest average concentration of PM_2.5 components is that of sulfate, which accounts for 21.1% of the PM_2.5 mass, followed by organic carbon (OC) at 15.9%, nitrate at 5.8%, and elemental carbon (EC) at 5.4%. Concentrations of EC, OC, and nitrate have distinctive but similar seasonal fluctuations, which is highest in spring and lowest in fall. Sulfate concentration has less seasonal fluctuations, and the highest value appears during the fall. Similarly, concentrations of EC, OC, and nitrate have notable diurnal variations; however, the diurnal variation of sulfate concentration is not very apparent. These observation data show that EC, OC, and nitrate in PM_2.5 in the Taipei metropolis come mainly from local emissions, while sulfate comes mainly from the regional transport of pollutants. This is likely because Taiwan is located on the lee zone of the Asian prevailing winds from fall to spring; its air quality is frequently affected by the transport of air pollutants from Mainland China. In addition, the extent of increase in aerosols is much higher than that of CO, indicating the formation of secondary aerosol when photochemical activity is strong. Based on six years of observation data, this study explores three potential scenarios to set up Taiwan's PM_2.5 air quality standard (AQS). The analysis indicates that the optimum standard for 24-h air quality of PM_2.5 should be around 50 μg m^?3.     

PM2.5–10, PM2.5 and associated water-soluble inorganic species at a coastal urban site in the metropolitan region of Rio de Janeiro

The concentrations of PM_2.5−10, PM_2.5 and associated water-soluble inorganic species (WSIS) were determined in a coastal site of the metropolitan region of Rio de Janeiro, Southeastern Brazil, from October 1998 to September 1999 (n=50). Samples were dissolved in water and analyzed for major inorganic ions. The mean (± standard deviation; median) concentrations of PM_2.5−10 and PM_2.5 were, respectively, 26 (± 16; 21) μg m⁻³ and 17 (± 13; 14) μg m⁻³. Their mean concentrations were 1.7–1.8 times higher in dry season (May–October) than in rainy season (November–April). The WSIS comprised, respectively, 34% and 28% of the PM_2.5−10 and PM_2.5 masses. Chloride, Na⁺ and Mg²⁺ were the predominant ions in PM_2.5−10, indicating a significant influence of sea-salt aerosols. In PM_2.5, SO₄²⁻ (∼97% nss-SO₄²⁻) and NH₄⁺ were the most abundant ions and their equivalent concentration ratio (SO₄²⁻/NH₄⁺ ∼1.0) suggests that they were present as (NH₄)₂SO₄ particles. The mean concentration of (NH₄)₂SO₄ was 3.4 μg m⁻³. The mean equivalent PM_2.5 NO₃⁻ concentration was eight times smaller than those of SO₄²⁻ and NH₄⁺. The PM_2.5 NO₃⁻ concentration in dry season was three times higher than in rainy season, probably due to reaction of NaCl (sea salt) with HNO₃ as a result of higher levels of NO_y during the dry season and/or reduced volatilization of NH₄NO₃ due to lower wintertime temperature. Chloride depletion was observed in both size ranges, although more pronouncely in PM_2.5.     

A side-by-side comparison of filter-based PM2.5 measurements at a suburban site: A closure study

Assessing the effects of air quality on public health and the environment requires reliable measurement of PM_2.5 mass and its chemical components. This study seeks to evaluate PM_2.5 measurements that are part of a newly established national network by comparing them with more versatile sampling systems. Experiments were carried out during 2002 at a suburban site in Maryland, United States, where two samplers from the US Environmental Protection Agency (US EPA) Speciation Trends Network: Met One Speciation Air Sampling System—STN_S and Thermo Scientific Reference Ambient Air Sampler—STN_R, two Desert Research Institute Sequential Filter Samplers—DRI_F, and a continuous TEOM monitor (Thermo Scientific Tapered Element Oscillating Microbalance, 1400a) sampled air in parallel. These monitors differ not only in sampling configuration but also in protocol-specific laboratory analysis procedures. Measurements of PM_2.5 mass and major contributing species (i.e., sulfate, ammonium, organic carbon, and total carbon) were well correlated among the different methods with r-values >0.8. Despite the good correlations, daily concentrations of PM_2.5 mass and major contributing species were significantly different at the 95% confidence level from 5% to 100% of the time. Larger values of PM_2.5 mass and individual species were generally reported from STN_R and STN_S. These differences can only be partially accounted for by known random errors. Variations in flow design, face velocity, and sampling artifacts possibly influenced the measurement of PM_2.5 speciation and mass closure. Statistical tests indicate that the current uncertainty estimates used in the STN and DRI network may underestimate the actual uncertainty.     

Characterization and sources assignation of PM2.5 organic aerosol in a rural area of Spain

The results from a year-long study of the organic composition of PM2.5 aerosol collected in a rural area influenced by a highway of Spain are reported. The lack of prior information related to the organic composition of PM2.5 aerosol in Spain, concretely in rural areas, led definition of the goals of this study. As a result, this work has been able to characterize the main organic components of atmospheric aerosols, including several compounds of SOA, and has conducted a multivariate analysis in order to assign sources of particulate matter. A total of 89 samples were taken between April 2004 and April 2005 using a high-volume sampler. Features and abundance of n-alkanes, polycyclic aromatic hydrocarbons (PAHs), alcohols and acids were separately determined using gas chromatography/mass spectrometry and high performance liquid chromatography analysis. The Σn-alkane and ΣPAHs ranged from 3 to 81 ng m^?3 and 0.1 to 6 ng m^?3 respectively, with higher concentrations during colder months. Ambient concentrations of Σalcohols and Σacids ranged from 21 to 184 ng m^?3 and 39 to 733 ng m^?3, respectively. Also, several components of secondary organic aerosol have been quantified, confirming the biogenic contribution to ambient aerosol. In addition, factor analysis was used to reveal origin of organic compounds associated to particulate matter. Eight factors were extracted accounting more than 83% of the variability in the original data. These factors were assigned to a typical high pollution episode by anthropogenic particles, crustal material, plant waxes, fossil fuel combustion, temperature, microbiological emissions, SOA and dispersion of pollutants by wind action. Finally, a cluster analysis was used to compare the organic composition between the four seasons.     

Application of positive matrix factorization in characterization of PM(10) and PM(2.5) emission sources at urban roadside

The 24-h average coarse (PM₁₀) and fine (PM_2.5) fraction of airborne particulate matter (PM) samples were collected for winter, summer and monsoon seasons during November 2008-April 2009 at an busy roadside in Chennai city, India. Results showed that the 24-h average ambient PM₁₀ and PM_2.5 concentrations were significantly higher in winter and monsoon seasons than in summer season. The 24-h average PM₁₀ concentration of weekdays was significantly higher (12-30%) than weekends of winter and monsoon seasons. On weekends, the PM_2.5 concentration was found to slightly higher (4-15%) in monsoon and summer seasons. The chemical composition of PM₁₀ and PM_2.5 masses showed a high concentration in winter followed by monsoon and summer seasons.The U.S.EPA-PMF (positive matrix factorization) version 3 was applied to identify the source contribution of ambient PM₁₀ and PM_2.5 concentrations at the study area. Results indicated that marine aerosol (40.4% in PM₁₀ and 21.5% in PM_2.5) and secondary PM (22.9% in PM₁₀ and 42.1% in PM_2.5) were found to be the major source contributors at the study site followed by the motor vehicles (16% in PM₁₀ and 6% in PM_2.5), biomass burning (0.7% in PM₁₀ and 14% in PM_2.5), tire and brake wear (4.1% in PM₁₀ and 5.4% in PM_2.5), soil (3.4% in PM₁₀ and 4.3% in PM_2.5) and other sources (12.7% in PM₁₀ and 6.8% in PM_2.5).     

Contrasting seasonal pattern between ground-based PM2.5 and MODIS satellite-based aerosol optical depth (AOD) at an urban site in Hanoi,Vietnam

Environmental Science and Pollution Research - This study investigated the seasonal variations of the ground-based PM2.5 concentration measured at an urban site and Terra MODIS satellite-based...     

Source apportionment of particulate matter (PM2.5) in an urban area using dispersion,receptor and inverse modelling

Air pollution emission inventories are the basis for air quality assessment and management strategies. The quality of the inventories is of great importance since these data are essential for air pollution impact assessments using dispersion models. In this study, the quality of the emission inventory for fine particulates (PM_2.5) is assessed: first, using the calculated source contributions from a receptor model; second, using source apportionment from a dispersion model; and third, by applying a simple inverse modelling technique which utilises multiple linear regression of the dispersion model source contributions together with the observed PM_2.5 concentrations. For the receptor modelling the chemical composition of PM_2.5 filter samples from a measurement campaign performed between January 2004 and April 2005 are analysed. Positive matrix factorisation is applied as the receptor model to detect and quantify the various source contributions. For the same observational period and site, dispersion model calculations using the Air Quality Management system, AirQUIS, are performed. The results identify significant differences between the dispersion and receptor model source apportionment, particularly for wood burning and traffic induced suspension. For wood burning the receptor model calculations are lower, by a factor of 0.54, but for the traffic induced suspension they are higher, by a factor of 7.1. Inverse modelling, based on regression of the dispersion model source contributions and the PM_2.5 concentrations, indicates similar discrepancies in the emissions inventory. In order to assess if the differences found at the one site are generally applicable throughout Oslo, the individual source category emissions are rescaled according to the receptor modelling results. These adjusted PM_2.5 concentrations are compared with measurements at four independent stations to evaluate the updated inventory. Statistical analysis shows improvement in the estimated concentrations for PM_2.5 at all sites. Similarly, inverse modelling is applied at these independent sites and this confirms the validity of the receptor model results.     

Photochemical model performance for PM2.5 sulfate,nitrate, ammonium,and precursor species SO2, HNO3, and NH3 at background monitor locations in the central and eastern United States

Health studies have shown premature death is statistically associated with exposure to particulate matter <2.5 μm in diameter (PM2.5). The United States Environmental Protection Agency requires all States with PM2.5 non-attainment counties or with sources contributing to visibility impairment at Class I areas to submit an emissions control plan. These emission control plans will likely focus on reducing emissions of sulfur oxides and nitrogen oxides, which form two of the largest chemical components of PM2.5 in the eastern United States: ammonium sulfate and ammonium nitrate. Emission control strategies are simulated using three-dimensional Eulerian photochemical transport models.A monitor study was established using one urban (Detroit) and nine rural locations in the central and eastern United States to simultaneously measure PM2.5 sulfate ion (SO₄²⁻), nitrate ion (NO₃⁻), ammonium ion (NH₄⁺), and precursor species sulfur dioxide (SO₂), nitric acid (HNO₃), and ammonia (NH₃). This monitor study provides a unique opportunity to assess how well the modeling system predicts the spatial and temporal variability of important precursor species and co-located PM2.5 ions, which is not well characterized in the central and eastern United States.The modeling system performs well at estimating the PM2.5 species, but does not perform quite as well for the precursor species. Ammonia is under-predicted in the coldest months, nitric acid tends to be over-predicted in the summer months, and sulfur dioxide appears to be systematically over-predicted. Several indicators of PM2.5 ammonium sulfate and ammonium nitrate formation and chemical composition are estimated with the ambient data and photochemical model output. PM2.5 sulfate ion is usually not fully neutralized to ammonium sulfate in ambient measurements and is usually fully neutralized in model estimates. The model and ambient estimates agree that the ammonia study monitors tend to be nitric acid limited for PM2.5 nitrate formation. Regulatory strategies in this part of the country should focus on reductions in NO_X rather than ammonia to control PM2.5 ammonium nitrate.     

Characterization of PM2.5 aerosols dominated by local pollution and Asian dust observed at an urban site in Korea during aerosol characterization experiments (ACE)--Asia Project

Daily fine particulate matter (PM2.5) samples were collected at Gwangju, Korea, during the Aerosol Characterization Experiments (ACE)-Asia Project to determine the chemical properties of PM2.5 originating from local pollution and Asian dust (AD) storms. During the study period, two significant events occurred on April 10-13 and 24-25, 2001, and a minor event occurred on April 19, 2001. Based on air mass transport pathways identified by back-trajectory calculation, the PM2.5 dataset was classified into three types of aerosol populations: local pollution and two AD aerosol types. The two AD types were transported along different pathways. One originated from Gobi desert area in Mongolia, passing through Hunshandake desert in Northern Inner Mongolia, urban and polluted regions of China (AD1), and the other originated in sandy deserts located in the Northeast Inner Mongolia Plateau and then flowed southward through the Korean peninsula (AD2). During the AD2 event, a smoke plume that originated in North Korea was transported to our study site. Mass balance closures show that crustal materials were the most significant species during both AD events, contributing -48% to the PM2.5 mass; sulfate aerosols (19.1%) and organic matter (OM; 24.6%) were the second greatest contributors during the AD1 and AD2 periods, respectively, indicating that aerosol properties were dependent on the transport pathway. The sulfate concentration constituted only 6.4% (4.5 microg/m3) of the AD2 PM2.5 mass. OM was the major chemical species in the local pollution-dominated PM2.5 aerosols, accounting for 28.7% of the measured PM2.5 mass, followed by sulfate (21.4%), nitrate (15%), ammonium (12.8%), elemental carbon (8.9%), and crustal material (6.5%). Together with substantial enhancement of the crustal elements (Mg, Al, K, Ca, Sc, Ti, Mn, Fe, Sr, Zr, Ba, and Ce), higher concentrations of pollution elements (S, V, Ni, Zn, As, Cd, and Pb) were observed during AD1 and AD2 than during the local pollution period, indicating that, in addition to crustal material, the AD dust storms also had a significant influence on anthropogenic elements.     

PM2.5 characterization for time series studies: Organic molecular marker speciation methods and observations from daily measurements in Denver

Particulate matter less than 2.5 microns in diameter (PM_2.5) has been shown to have a wide range of adverse health effects and consequently is regulated in accordance with the US-EPA's National Ambient Air Quality Standards. PM_2.5 originates from multiple primary sources and is also formed through secondary processes in the atmosphere. It is plausible that some sources form PM_2.5 that is more toxic than PM_2.5 from other sources. Identifying the responsible sources could provide insight into the biological mechanisms causing the observed health effects and provide a more efficient approach to regulation. This is the goal of the Denver Aerosol Sources and Health (DASH) study, a multi-year PM_2.5 source apportionment and health study.The first step in apportioning the PM_2.5 to different sources is to determine the chemical make-up of the PM_2.5. This paper presents the methodology used during the DASH study for organic speciation of PM_2.5. Specifically, methods are covered for solvent extraction of non-polar and semi-polar organic molecular markers using gas chromatography–mass spectrometry (GC–MS). Vast reductions in detection limits were obtained through the use of a programmable temperature vaporization (PTV) inlet along with other method improvements. Results are presented for the first 1.5 years of the DASH study revealing seasonal and source-related patterns in the molecular markers and their long-term correlation structure. Preliminary analysis suggests that point sources are not a significant contributor to the organic molecular markers measured at our receptor site. Several motor vehicle emission markers help identify a gasoline/diesel split in the ambient data. Findings show both similarities and differences when compared with other cities where similar measurements and assessments have been made.     

Optimal temporal scale for the correlation of AOD and ground measurements of PM2.5 in a real-time air quality estimation system

Aerosol optical depth (AOD), an indirect estimate of particulate matter using satellite observations, has shown great promise in improving estimates of PM_2.5 (particulate matter with aerodynamic diameter less than or equal to 2.5 μm) surface. Currently, few studies have been conducted to explore the optimal way to apply AOD data to improve the model accuracy of PM_2.5 in a real-time air quality system. We believe that two major aspects may be worthy of consideration in that area: 1) an approach that integrates satellite measurements with ground measurements in the estimates of pollutants and 2) identification of an optimal temporal scale to calculate the correlation of AOD and ground measurements. This paper is focused on the second aspect, identifying the optimal temporal scale to correlate AOD with PM_2.5. Five following different temporal scales were chosen to evaluate their impact on the model performance: 1) within the last 3 days, 2) within the last 10 days, 3) within the last 30 days, 4) within the last 90 days, and 5) the time period with the highest correlation in a year. The model performance is evaluated for its accuracy, bias, and errors based on the following selected statistics: the Mean Bias, the Normalized Mean Bias, the Root Mean Square Error, Normalized Mean Error, and the Index of Agreement. This research shows that the model with the temporal scale: within the last 30 days, displays the best model performance in a southern multi-state area centered in Mississippi using 2004 and 2005 data sets.     

Spatial variability and population exposure to PM2.5 pollution from woodsmoke in a New South Wales country town

A portable radiance research nephelometer was used to measure the variation in woodsmoke pollution in Armidale (a small town of 22,000 people), New South Wales, Australia, on 14 winter nights in 1996. Winter nights are characterised by inversions that trap the air within the valley and reduce winds to very low speeds (averaging 0.15 m s⁻¹). Pollution varied considerably with location. Mean scattering coefficients (bsp/10 km) for 14 measurement nights ranged from less than 1.0 on the undeveloped fringes of the city to 8.7, the latter representing a 14-night average of 200 μg m⁻³ of PM2.5. Pollution was generally highest in the residential areas on either side of the valley, where the smoke was generated, rather than the low-lying central creeklands. In places, average pollution levels increased 4-fold within 41 m. The correlation between nephelometer and gravimetric pollution measurements ranged 0.95–0.99. The presence of large, sudden but repeatable changes in air pollution, and high correlations between nephelometer and gravimetric measurements, indicate that mobile pollution monitoring devices provide a useful and accurate estimate of spatial variability. Estimated exposure for the town as a whole was 1.02 for the 6 months from April to September, 0.25 in October as heater use declines, and 0.12 in normal summer months. For comparison, published 25th, 50th and 75th percentiles of the distribution of nephelometer coefficients in Sydney were 0.15, 0.24 and 0.37, respectively. Thus annual exposure to PM2.5 pollution in Armidale from woodsmoke is more than double that from all sources in Sydney, a city of 4 million. Overseas estimates of 6% increased mortality for each additional 10 μg m⁻³ of PM2.5 suggest that wood heaters in Armidale may increase mortality in Armidale by about 7%, with estimated cost of about $4270 per woodheater per year. Alternative cheap and environmentally friendly methods of keeping houses warm in winter, such as solar heating, should therefore be developed.