Estimating the Frequency Distributions of Particulate Matter and Their Metal Elements in a Temple

Abstract

Air quality inside Asian temples is typically poor because of the burning of incense. This study measured and analyzed concentrations of fine (PM_2.5) and coarse (PM_2.5–10) particulate matter and their metal elements inside a temple in central Taiwan. Experimental results showed that the concentrations of metals Cd, Ni, Pb, and Cr inside the temple were higher than those at rural, suburban, urban, and industrial areas in other studies. Three theoretical parent distributions (lognormal, Weibull, and gamma) were used to fit the measured data. The lognormal distribution was the most appropriate distribution for representing frequency distributions of PM₁₀, PM_2.5, and their metal elements.

Furthermore, the central limit theorem, H-statistic-based scheme, and parametric and nonparametric bootstrap methods were used to estimate confidence intervals for mean pollutant concentrations. The estimated upper confidence limits (UCLs) of means between different methods were very consistent, because the sample coefficient of variation (CV) was <1. When the sample CV was >1, the UCL based on H-statistical method tended to overestimate the UCLs when compared with other methods. Confidence intervals for pollutant concentrations at different percentiles were evaluated using parametric and nonparametric bootstrap methods. The probabilities of pollutants exceeding a critical concentration were also calculated.     

Atmospheric Factors Influencing Daily Sulfate Concentrations in Chicago Air

Abstract

Confidence interval construction for central tendency is a problem of practical consequence for those who must analyze air contaminant data. Determination of compliance with relevant ambient air quality criteria and assessment of associated health risks depend upon quantifying the uncertainty of estimated mean pollutant concentrations. The bootstrap is a resampling technique that has been steadily gaining popularity and acceptance during the past several years. A potentially powerful application of the bootstrap is the construction of confidence intervals for any parameter of any underlying distribution. Properties of bootstrap confidence intervals were determined for samples generated from lognormal, gamma, and Weibull distributions. Bootstrap t intervals, while having smaller coverage errors than Student's t or other bootstrap methods, under-cover for small samples from skewed distributions. Therefore, we caution against using the bootstrap to construct confidence intervals for the mean without first considering the effects of sample size and skew. When sample sizes are small, one might consider using the median as an estimate of central tendency. Confidence intervals for the median are easy to construct and do not under-cover. Data collected by the Northeast States for Coordinated Air Use Management (NESCAUM) are used to illustrate application of the methods discussed.     

Fine (PM2.5), coarse (PM2.5-10), and metallic elements of suspended particulates for incense burning at Tzu Yun Yen temple in central Taiwan

Ambient suspended particulate concentrations were measured at Tzu Yun Yen temple (120 degrees, 34('), 10(") E; 24 degrees, 16('), 12(") N) in this study. This is representative of incense burning and semi-open sampling sites. The Universal-sampler collected fine and coarse particle material was used to measure suspended particulate concentrations, and sampling periods were from 16/08/2001 to 2/1/2002 at Tzu Yun Yen temple. In addition, metallic element concentrations, compositions of PM(2.5) and PM(2.5-10) for incense burning at Tzu Yun Yen temple were also analyzed in this study. The PM(2.5)/PM(10) ratios ranged between 31% and 87% and averaged 70+/-11% during incense the burning period, respectively. The median metallic element concentration order for these elements is Fe>Zn>Cr>Cd>Pb>Mn>Ni>Cu in fine particles (PM(2.5)) at the Tzu Yun Yen temple sampling site. The median metallic element concentration order for these elements is Fe>Zn>Cr>Pb>Cd>Ni>Mn>Cu in coarse particle (PM(2.5-10)) at the Tzu Yun Yen temple sampling site. Fine particulates (PM(2.5)) are the main portion of PM(10) at Tzu Yun Yen temple in this study. From the point of view of PM(10), these data reflect that the elements Fe, Zn, and Cr were the major elements distributed at Tzu Yun Yen temple in this study.     

Simplified Methods for Statistical Interpretation of Monitoring Data

Statistical techniques are useful for interpretation of monitoring data for pollutants, process variables, etc. Simplified nomographical methods are presented for relating numbers of samples to confidence intervals for their mean values, and for determining the proportion of the population exceeding a specified concentration and confidence intervals for the proportion. A chart also is given for design of a sampling program for quality control. Illustrative frequency distribution data are given for hourly-averaged methane concentrations in air over three-week periods. They show trimodal lognormal distributions. These charts are applicable to lognormal distributions, as well as to normal distributions. They are convenient for many common problems.     

Atmospheric lead concentration distribution in Northern Taiwan

Atmospheric lead concentrations were measured randomly, approximately once per week, at five traffic sites in northern Taiwan from September 1994 to May 1995. Three types of theoretical distributions, lognormal, Weibull and gamma were selected to fit the frequency distribution of the measured lead concentration. Four goodness-of-fit criteria were used to judge which theoretical distribution is the most appropriate to represent the frequency distributions of atmospheric lead.The results show that atmospheric lead concentrations in total suspended particulates fit the lognormal distribution reasonably well in northern Taiwan. The intervals of fitted theoretical cumulative frequency distributions (CFDs) can successfully contain the measured data when the population mean is estimated with a 95% confidence interval. In addition, atmospheric lead concentration exceeding a critical concentration is also predicted from the fitted theoretical CFDs.     

Worshippers' exposure to particulate matter in two temples in Taiwan

Worshippers in temples may be exposed to high concentrations of pollutants emitted from incense burning. This work assessed the PM2.5 and PM10 exposures of temple worshippers in Taiwan and explored the important exposure determinants such as numbers of passing visitors and joss sticks in censers, worshipping dates, and temple characteristics. Sampling was conducted on the 1st, 2nd, 15th, and 16th of the lunar month in two temples in Taichung, Taiwan. Research staff took samples by wearing one PM2.5 and one PM10 sampler and imitating worshipping activity. Personal environmental monitors connected to personal pumps with 2-L/min flow rates were used for sampling. PM10 samples were also simultaneously taken outside the temples. The results suggested that burning joss sticks in temples is a significant PM exposure source. The geometric mean of personal exposure was 444 microg/m3 PM2.5 [geometric standard deviation (GSD) = 1.8] and 583 microg/m3 PM10 (GSD = 1.4). The latter was approximately 4-6 times that of roadside concentrations. Exposures on the 1st and 15th (with more visitors and more joss sticks) were about 130 microg/m3 PM2.5 and 249 microg/m3 PM10 higher than those on the 2nd and the 16th. Furthermore, each joss stick in the censer contributed about 0.40 microg/m3 of particles to the worshippers' exposure. In the worst case, PM exposure during one temple visit would account for 11% of the personal exposure in one day.     

Estimating national-scale ground-level PM25 concentration in China using geographically weighted regression based on MODIS and MISR AOD

Taking advantage of the continuous spatial coverage, satellite-derived aerosol optical depth (AOD) products have been widely used to assess the spatial and temporal characteristics of fine particulate matter (PM2.5) on the ground and their effects on human health. However, the national-scale ground-level PM2.5 estimation is still very limited because the lack of ground PM2.5 measurements to calibrate the model in China. In this study, a national-scale geographically weighted regression (GWR) model was developed to estimate ground-level PM2.5 concentration based on satellite AODs, newly released national-wide hourly PM2.5 concentrations, and meteorological parameters. The results showed good agreements between satellite-retrieved and ground-observed PM2.5 concentration at 943 stations in China. The overall cross-validation (CV) R ² is 0.76 and root mean squared prediction error (RMSE) is 22.26 μg/m³ for MODIS-derived AOD. The MISR-derived AOD also exhibits comparable performance with a CV R ² and RMSE are 0.81 and 27.46 μg/m³, respectively. Annual PM2.5 concentrations retrieved either by MODIS or MISR AOD indicated that most of the residential community areas exceeded the new annual Chinese PM2.5 National Standard level 2. These results suggest that this approach is useful for estimating large-scale ground-level PM2.5 distributions especially for the regions without PMs monitoring sites.     

Evaluation of time-resolved PM2.5 data in urban/suburban areas of New Jersey

Time-resolved data is needed for public notification of unhealthful air quality and to develop an understanding of atmospheric chemistry, including insights important to control strategies. In this research, continuous fine particulate matter (PM2.5) mass concentrations were measured with tapered element oscillating microbalances (TEOMs) across New Jersey from July 1997 to June 1998. Data features indicating the influence of local sources and long-distance transport are examined, as well as differences between 1-hr maxima and 24-hr average concentrations that might be relevant to acute health effects. Continuous mass concentrations were not significantly different from filter-collected gravimetric mass concentrations with 95% confidence intervals during any season. Annual mean PM2.5 concentrations from July 1997 to June 1998 were 17.3, 16.4, 14.1, and 15.3 micrograms/m3 at Newark, Elizabeth, New Brunswick, and Camden, NJ, respectively. Monthly averaged 24- and 1-hr daily maximum PM2.5 concentrations suggest the existence of a high PM2.5 (May-October) and a low PM2.5 (November-April) season. PM2.5 magnitudes and temporal trends were very similar across the state during high PM2.5 events. In fact, the between-site coefficients of determination (R2) for daily PM2.5 measurements were 84-98% for June and July. Additionally, during the most pronounced PM2.5 episode, PM2.5 concentrations closely tracked the daily maximum 1-hr O3 concentrations. These observations suggest the importance of transport and atmospheric chemistry (i.e., secondary formation) to PM2.5 episodes in New Jersey. The influence of local sources was observed in diurnal concentration profiles and annual average between-site differences. Urban wintertime data illustrate that high 1-hr maximum PM2.5 concentrations can occur on low 24-hr PM2.5 days.     

Comparisons of statistical characteristic of air pollutants in Taiwan by frequency distribution

The lognormal, Weibull, and type V Pearson distributions were selected to fit the concentration frequency distributions of particulate matter with an aerodynamic diameter of < or = 10 microm (PM10) and SO2 in the Taiwan area. Air quality data from three stations, Hsin-Chu, Shalu, and Gain-Jin, were fitted with three distributions and compared with the measured data. The parameters of unimodal and bimodal fitted distributions were obtained by the methods of maximum likelihood and nonlinear least squares, respectively. Moreover, the root mean square error (RMSE), index of agreement (d), and Kolmogorov-Smirnov (K-S) test were used as criteria to judge the goodness-of-fit of these three distributions. These results show that the frequency distributions of PM10 concentration at the Hsin-Chu and Shalu stations are unimodal, but the distribution at Gain-Jin is bimodal. The distribution type of PM10 concentration varied greatly in different areas and could be influenced by local meteorological conditions. For SO2 concentration distribution, the distributions were all unimodal. The results also show that the lognormal distribution is the more appropriate to represent the PM10 distribution, while the Weibull and lognormal distributions are more suitable to represent the SO2 distribution. Moreover, the days exceeding the air quality standard (AQS) (PM10 > 125 microg/ m3) for the Hsin-Chu, Shalu, and Gain-Jin stations in the coming year are successfully predicted by the theoretic distributions.     

Estimating the emission source reduction of PM10 in central Taiwan

Three theoretical parent frequency distributions; lognormal, Weibull and gamma were used to fit the complete set of PM10 data in central Taiwan. The gamma distribution is the best one to represent the performance of high PM10 concentrations. However, the parent distribution sometimes diverges in predicting the high PM10 concentrations. Therefore, two predicting methods, Method I: two-parameter exponential distribution and Method II: asymptotic distribution of extreme value, were used to fit the high PM10 concentration distributions more correctly. The results fitted by the two-parameter exponential distribution are better matched with the actual high PM10 data than that by the parent distributions. Both of the predicting methods can successfully predict the return period and exceedances over a critical concentration in the future year. Moreover, the estimated emission source reductions of PM10 required to meet the air quality standard by Method I and Method II are very close. The estimated emission source reductions of PM10 range from 34% to 48% in central Taiwan.     

Size distribution and diurnal characteristics of particle-bound metals in source and receptor sites of the Los Angeles Basin

Measurement of daily size-fractionated ambient PM₁₀ mass, metals, inorganic ions (nitrate and sulfate) and elemental and organic carbon were conducted at source (Downey) and receptor (Riverside) sites within the Los Angeles Basin. In addition to 24-h concentration measurements, the diurnal patterns of the trace element and metal content of fine (0–2.5 μm) and coarse (2.5–10 μm) PM were studied by determining coarse and fine PM metal concentrations during four time intervals of the day.The main source of crustal metals (e.g., Al, Si, K, Ca, Fe and Ti) can be attributed to the re-suspension of dust at both source and receptor sites. All the crustals are predominantly present in supermicron particles. At Downey, potentially toxic metals (e.g., Pb, Sn, Ni, Cr, V, and Ba) are predominantly partitioned (70–85%, by mass) in the submicron particles. Pb, Sn and Ba have been traced to vehicular emissions from nearby freeways, whereas Ni and Cr have been attributed to emissions from powerplants and oil refineries upwind in Long Beach. Riverside, adjacent to Southern California deserts, exhibits coarser distributions for almost all particle-bound metals as compared to Downey. Fine PM metal concentrations in Riverside seem to be a combination of few local emissions and those transported from urban Los Angeles. The majority of metals associated with fine particles are in much lower concentrations at Riverside compared to Downey. Diurnal patterns of metals are different in coarse and fine PM modes in each location. Coarse PM metal concentration trends are governed by variations in the wind speeds in each location, whereas the diurnal trends in the fine PM metal concentrations are found to be a function both of the prevailing meteorological conditions and their upwind sources.     

Measurements of ultrafine particles and other vehicular pollutants inside a mobile exposure system on Los Angeles freeways

A mobile exposure and air pollution measurement system was developed and used for on-freeway ultrafine particle health effects studies. A nine-passenger van was modified with a high-efficiency particulate air (HEPA) filtration system that can deliver filtered or unfiltered air to an exposure chamber inside the van. State-of-the-art instruments were used to measure concentration and size distribution of fine and ultrafine particles and the concentration of carbon monoxide (CO), black carbon (BC), particle-bound polycyclic aromatic hydrocarbons (PAHs), fine particulate matter (PM2.5) mass, and oxides of nitrogen (NOx) inside the exposure chamber. This paper presents the construction and technical details of the van and air pollutant concentrations collected in 32 2-hr runs on two major Los Angeles freeways, Interstate 405 (1-405; mostly gasoline traffic) and Interstate 710 (1-710; large proportion of heavy-duty diesel traffic). More than 97% of particles were removed when the flow through the filter box was switched from bypass mode to filter mode while the vehicle was driving on both freeways. The filtration system thus provides a great particulate matter exposure contrast while keeping gas-phase pollutant concentrations the same. Under bypass mode, average total particle number concentration observed inside the exposure chamber was around 8.4 x 10(4) and 1.3 x 10(5) particles cm(-3) on the I-405 and the I-710 freeways, respectively. Bimodal size distributions were consistent and similar for both freeways with the first mode around 16-20 nm and the second mode around 50-55 nm. BC and particle-bound PAH concentrations were more than two times greater on the I-710 than on the I-405 freeway. Very weak correlations were observed between total particle number concentrations and other vehicular pollutants on the freeways.     

Spatial and temporal variations of PM(2.5) concentration and composition throughout an urban area with high freeway density-the Greater Cincinnati study

The PM(2.5) concentration and its elemental composition were measured in the Cincinnati metropolitan area, which is characterized by intense highway traffic. The spatial and temporal variations were investigated for various chemical elements that contributed to the PM(2.5) fraction during a 1-year-long measurement campaign (December 2001-November 2002). The ambient aerosol monitoring was performed in 11 locations around the city during nine measurement cycles. During each cycle, four Harvard-type impactors were operating in parallel in specific locations to explore various factors affecting the PM(2.5) elemental concentrations. The sampling was performed during business days, thus assuring traffic uniformity. The 24-h PM(2.5) samples were collected on Teflon and quartz filters. Teflon filters were analyzed by X-ray fluorescence (XRF) analysis while quartz filters were analyzed by thermal-optical transmittance (TOT) analysis. In addition to PM(2.5) measurements, particle size-selective sampling was performed in two cycles using micro-orifice uniform deposit impactor; the collected fractionated deposits were analyzed by XRF. It was found that PM(2.5) concentration ranged from 6.70 to 48.3 mug m(-3) and had low spatial variation (median coefficient of variation, CV=11.3%). The elemental concentrations demonstrated high spatial variation, with the median CV ranged from 38.2% for Fe to 68.7% for Ni. For traffic-related trace metals, the highest concentration was detected in the city center site, which was close to a major highway. The particle size selective measurement revealed that mass concentration of the trace metals, such as Zn, Pb, Ni, as well as that of sulfur reach their peak values in the particle size range of 0.32-1.0 mum. Meteorological parameters and traffic intensity were not found to have a significant influence on the PM(2.5) elemental concentrations.     

A comparison of statistical methods for deriving freshwater quality criteria for the protection of aquatic organisms

Species sensitivity distributions (SSDs) are increasingly used in both ecological risk assessment and derivation of water quality criteria. However, there has been debate about the choice of an appropriate approach for derivation of water quality criteria based on SSDs because the various methods can generate different values. The objective of this study was to compare the differences among various methods. Data sets of acute toxicities of 12 substances to aquatic organisms, representing a range of classes with different modes of action, were studied. Nine typical statistical approaches, including parametric and nonparametric methods, were used to construct SSDs for 12 chemicals. Water quality criteria, expressed as hazardous concentration for 5 % of species (HC₅), were derived by use of several approaches. All approaches produced comparable results, and the data generated by the different approaches were significantly correlated. Variability among estimates of HC₅ of all inclusive species decreased with increasing sample size, and variability was similar among the statistical methods applied. Of the statistical methods selected, the bootstrap method represented the best-fitting model for all chemicals, while log-triangle and Weibull were the best models among the parametric methods evaluated. The bootstrap method was the primary choice to derive water quality criteria when data points are sufficient (more than 20). If the available data are few, all other methods should be constructed, and that which best describes the distribution of the data was selected.     

A study of the association between daily mortality and ambient air pollutant concentrations in Pittsburgh, Pennsylvania

We have studied the possible association of daily mortality with ambient pollutant concentrations (PM10, CO, O3, SO2, NO2, and fine [PM2.5] and coarse PM) and weather variables (temperature and dew point) in the Pittsburgh, PA, area for two age groups--less than 75, and 75 and over--for the 3-year period of 1989-1991. Correlation functions among pollutant concentrations show important seasonal dependence, and this fact necessitates the use of seasonal models to better identify the link between ambient pollutant concentrations and daily mortality. An analysis of the seasonal model results for the younger-age group reveals significant multicollinearity problems among the highly correlated concentrations of PM10, CO, and NO2 (and O3 in spring and summer), and calls into question the rather consistent results of the single- and multi-pollutant non-seasonal models that show a significant positive association between PM10 and daily mortality. For the older-age group, dew point consistently shows a significant association with daily mortality in all models. Collinearity problems appear in the multi-pollutant seasonal and non-seasonal models such that a significant, positive PM10 coefficient is accompanied by a significant, negative coefficient of another ambient pollutant, and the identity of this other pollutant changes with season. The PM2.5 data set is half that of PM10. Identical-model runs for both data sets reveal instability in the pollutant coefficients, especially for the younger age group. The concern for the instability of the pollutant coefficients due to a small signal-to-noise ratio makes it impossible to ascertain credibly the relative associations of the fine- and coarse-particle modes with daily mortality. In this connection, we call for caution in the interpretation of model results for causal inference when the models use fully or partially estimated PM values to fill large data gaps.     

Assessing the relationship between personal particulate and gaseous exposures of senior citizens living in Baltimore, MD

We conducted a multi-pollutant exposure study in Baltimore, MD, in which 15 non-smoking older adult subjects (> 64 years old) wore a multi-pollutant sampler for 12 days during the summer of 1998 and the winter of 1999. The sampler measured simultaneous 24-hr integrated personal exposures to PM2.5, PM10, SO4(2-), O3, NO2, SO2, and exhaust-related VOCs. Results of this study showed that longitudinal associations between ambient PM2.5 concentrations and corresponding personal exposures tended to be high in the summer (median Spearman's r = 0.74) and low in the winter (median Spearman's r = 0.25). Indoor ventilation was an important determinant of personal PM2.5 exposures and resulting personal-ambient associations. Associations between personal PM2.5 exposures and corresponding ambient concentrations were strongest for well-ventilated indoor environments and decreased with ventilation. This decrease was attributed to the increasing influence of indoor PM2.5 sources. Evidence for this was provided by SO4(2-) measurements, which can be thought of as a tracer for ambient PM2.5. For SO4(2-), personal-ambient associations were strong even in poorly ventilated indoor environments, suggesting that personal exposures to PM2.5 of ambient origin are strongly associated with corresponding ambient concentrations. The results also indicated that the contribution of indoor PM2.5 sources to personal PM2.5 exposures was lowest when individuals spent the majority of their time in well-ventilated indoor environments. Results also indicate that the potential for confounding by PM2.5 co-pollutants is limited, despite significant correlations among ambient pollutant concentrations. In contrast to ambient concentrations, PM2.5 exposures were not significantly correlated with personal exposures to PM2.5-10, PM2.5 of non-ambient origin, O3, NO2, and SO2. Since a confounder must be associated with the exposure of interest, these results provide evidence that the effects observed in the PM2.5 epidemiologic studies are unlikely to be due to confounding by the PM2.5 co-pollutants measured in this study.     

区域大气环境中PM_(2.5)/PM_(10)空间分布研究

提出了一种利用移动监测技术研究区域大气环境中PM2.5/PM10空间分布的方法,并在2004年12月进行了宁波市全市域PM2.5/PM10空间分布的研究.数据显示:相同路径所代表的地区PM2.5和PM10具有很好的相关性,多数路径上PM2.5与PM10数据的相关系数平方在0.95以上,而不同路径上PM2.5与PM10的比值不同.文中给出了宁波市PM2.5/PM10污染的空间分布图,直观地显示出PM2.5/PM10污染的空间分布情况,突出了污染的重点点位和地区.     

Evaluation of air quality by passive and active sampling in an urban city in Turkey: current status and spatial analysis of air pollution exposure

Concentrations of air pollutants, nitrogen dioxide (NO(2)), sulfur dioxide (SO(2)), ozone (O(3)), particulate matter (PM(2.5) and PM(10)), trace metals, and polycyclic aromatic hydrocarbons (PAHs) were measured in 2008 and 2009 in the city of Eski?ehir, central Turkey. Spatial distributions of NO(2), SO(2), and ozone were determined by passive sampling campaigns carried out during two different seasons with fairly large spatial coverage. A basic population exposure assessment was carried out employing Geographical Information System techniques by combining population density maps with pollutant distribution maps of NO(2) and SO(2). It was found that 95 % of the population is exposed to NO(2) levels close to the World Health Organization guideline value. Regarding SO(2), a large proportion of the population (83 %) is exposed to levels above the WHO second interim target value. Concentrations of all the pollutants showed a seasonal pattern increasing in winter period, except for ozone having higher concentrations in summer season. Daily PM(10) and PM(2.5) concentrations exceeded European Union limit values almost every sampling day. Toxic fractions of the measured PAHs were calculated and approximately fourfold increase was observed in winter period. Copper, Pb, Sn, As, Cd, Zn, Sb, and Se were found to be moderately to highly enriched in PM(10) fraction, indicating anthropogenic input to those elements measured. Exposure assessment results indicate the need for action to reduce pollutant emissions especially in the city center. Passive sampling turns out to be a practical and economical tool for air quality assessment with large spatial coverage.     

Effects of wind direction on coarse and fine particulate matter concentrations in southeast Kansas

Field data for coarse particulate matter ([PM] PM10) and fine particulate matter (PM2.5) were collected at selected sites in Southeast Kansas from March 1999 to October 2000, using portable MiniVol particulate samplers. The purpose was to assess the influence on air quality of four industrial facilities that burn hazardous waste in the area located in the communities of Chanute, Independence, Fredonia, and Coffeyville. Both spatial and temporal variation were observed in the data. Variation because of sampling site was found to be statistically significant for PM10 but not for PM2.5. PM10 concentrations were typically slightly higher at sites located within the four study communities than at background sites. Sampling sites were located north and south of the four targeted sources to provide upwind and downwind monitoring pairs. No statistically significant differences were found between upwind and downwind samples for either PM10 or PM2.5, indicating that the targeted sources did not contribute significantly to PM concentrations. Wind direction can frequently contribute to temporal variation in air pollutant concentrations and was investigated in this study. Sampling days were divided into four classifications: predominantly south winds, predominantly north winds, calm/variable winds, and winds from other directions. The effect of wind direction was found to be statistically significant for both PM10 and PM2.5. For both size ranges, PM concentrations were typically highest on days with predominantly south winds; days with calm/variable winds generally produced higher concentrations than did those with predominantly north winds or those with winds from "other" directions. The significant effect of wind direction suggests that regional sources may exert a large influence on PM concentrations in the area.     

Estimating fine particulate matter component concentrations and size distributions using satellite-retrieved fractional aerosol optical depth: part 2--a case study

We use the fractional aerosol optical depth (AOD) values derived from Multiangle Imaging Spectroradiometer (MISR) aerosol component measurements, along with aerosol transport model constraints, to estimate ground-level concentrations of fine particulate matter (PM2.5) mass and its major constituents in the continental United States. Regression models using fractional AODs predict PM2.5 mass and sulfate (SO4) concentrations in both the eastern and western United States, and nitrate (NO3) concentrations in the western United States reasonably well, compared with the available ground-level U.S. Environment Protection Agency (EPA) measurements. These models show substantially improved predictive power when compared with similar models using total-column AOD as a single predictor, especially in the western United States. The relative contributions of the MISR aerosol components in these regression models are used to estimate size distributions of EPA PM2.5 species. This method captures the overall shapes of the size distributions of PM2.5 mass and SO4 particles in the east and west, and NO3 particles in the west. However, the estimated PM2.5 and SO4 mode diameters are smaller than those previously reported by monitoring studies conducted at ground level. This is likely due to the satellite sampling bias caused by the inability to retrieve aerosols through cloud cover, and the impact of particle hygroscopicity on measured particle size distributions at ground level.