Elemental Characterization of Ambient Particulate Matter for a Globally Distributed Monitoring Network: Methodology and Implications.

Global ground-level measurements of elements in ambient particulate matter (PM) can provide valuable information to understand the distribution of dust and trace elements, assess health impacts, and investigate emission sources. We use X-ray fluorescence spectroscopy to characterize the elemental composition of PM samples collected from 27 globally distributed sites in the Surface PARTiculate mAtter Network (SPARTAN) over 2019-2023. Consistent protocols are applied to collect all samples and analyze them at one central laboratory, which facilitates comparison across different sites. Multiple quality assurance measures are performed, including applying reference materials that resemble typical PM samples, acceptance testing, and routine quality control. Method detection limits and uncertainties are estimated. Concentrations of dust and trace element oxides (TEO) are determined from the elemental dataset. In addition to sites in arid regions, a moderately high mean dust concentration (6 µg/m3) in PM2.5 is also found in Dhaka (Bangladesh) along with a high average TEO level (6 µg/m3). High carcinogenic risk (>1 cancer case per 100000 adults) from airborne arsenic is observed in Dhaka (Bangladesh), Kanpur (India), and Hanoi (Vietnam). Industries of informal lead-acid battery and e-waste recycling as well as coal-fired brick kilns likely contribute to the elevated trace element concentrations found in Dhaka.

Fine Particulate Matter and Parkinson Disease Risk Among Medicare Beneficiaries.

BACKGROUND AND OBJECTIVES: Numerous studies suggest that environmental exposures play a critical role in Parkinson disease (PD) pathogenesis, and large, population-based studies have the potential to advance substantially the identification of novel PD risk factors. We sought to study the nationwide geographic relationship between PD and air pollution, specifically PM2.5 (particulate matter with a diameter <2.5 micrometers), using population-based US Medicare data. METHODS: We conducted a population-based geographic study of Medicare beneficiaries aged 66-90 years geocoded to US counties and zip+4. We used integrated nested Laplace approximation to create age, sex, race, smoking, and health care utilization-adjusted relative risk (RR) at the county level for geographic analyses with PM2.5 as the primary exposure of interest. We also performed an individual-level analysis using logistic regression with cases and controls with zip+4 centroid PM2.5. We adjusted a priori for the same covariates and verified no confounding by indicators of socioeconomic status or neurologist density. RESULTS: Among 21,639,190 Medicare beneficiaries, 89,390 had incident PD in 2009. There was a nationwide association between average annual PM2.5 and PD risk whereby the RR of PD was 56% (95% CI 47%-66%) greater for those exposed to the median level of PM2.5 compared with those with the lowest level of PM2.5. This association was linear up to 13 µg/m3 corresponding to a 4.2% (95% CI 3.7%-4.8%) greater risk of PD for each additional µg/m3 of PM2.5 (p trend < 0.0001). We identified a region with high PD risk in the Mississippi-Ohio River Valley, where the risk of PD was 19% greater compared with the rest of the nation. The strongest association between PM2.5 and PD was found in a region with low PD risk in the Rocky Mountains. PM2.5 was also associated with PD in the Mississippi-Ohio River Valley where the association was relatively weaker, due to a possible ceiling effect at average annual PM2.5 levels of ∼13 µg/m3. DISCUSSION: State-of-the-art geographic analytic techniques revealed an association between PM2.5 and PD that varied in strength by region. A deeper investigation into the specific subfractions of PM2.5 may provide additional insight into regional variability in the PM2.5-PD association.

Data-Driven Placement of PM2.5 Air Quality Sensors in the United States: An Approach to Target Urban Environmental Injustice.

In the United States, citizens and policymakers heavily rely upon Environmental Protection Agency mandated regulatory networks to monitor air pollution; increasingly they also depend on low-cost sensor networks to supplement spatial gaps in regulatory monitor networks coverage. Although these regulatory and low-cost networks in tandem provide enhanced spatiotemporal coverage in urban areas, low-cost sensors are located often in higher income, predominantly White areas. Such disparity in coverage may exacerbate existing inequalities and impact the ability of different communities to respond to the threat of air pollution. Here we present a study using cost-constrained multiresolution dynamic mode decomposition (mrDMDcc) to identify the optimal and equitable placement of fine particulate matter (PM2.5) sensors in four U.S. cities with histories of racial or income segregation: St. Louis, Houston, Boston, and Buffalo. This novel approach incorporates the variation of PM2.5 on timescales ranging from 1 day to over a decade to capture air pollution variability. We also introduce a cost function into the sensor placement optimization that represents the balance between our objectives of capturing PM2.5 extremes and increasing pollution monitoring in low-income and nonwhite areas. We find that the mrDMDcc algorithm places a greater number of sensors in historically low-income and nonwhite neighborhoods with known environmental pollution problems compared to networks using PM2.5 information alone. Our work provides a roadmap for the creation of equitable sensor networks in U.S. cities and offers a guide for democratizing air pollution data through increasing spatial coverage of low-cost sensors in less privileged communities.

Environmental manganese exposure and cognitive control in a South African population.

OBJECTIVE: To characterize the association between environmental (residential air) manganese (Mn) exposure and cognitive performance, focusing on cognitive control, in a Black African population. METHODS: We administered the Go-No-Go, Digit Span, and Matrix Reasoning tests to population-based samples age ≥40 from a high Mn (smelter) exposed community, Meyerton (N = 629), and a demographically comparable low (background levels) non-exposed community, Ethembalethu, (N = 96) in Gauteng province, South Africa. We investigated the associations between community and performance on the cognitive tests, using linear regression. We adjusted a priori for age and sex, and examined the effect of adjustment for education, nonverbal IQ, smoking, and alcohol consumption. We measured airborne PM2.5-Mn to confirm community exposure differences. RESULTS: Compared to Ethembalethu residents, Meyerton residents' test scores were lower (poorer) for all tests: 0.55 (95 % confidence interval [CI] 0.08, 1.03) lower scores for Matrix Reasoning, 0.34 (95 % CI -0.07, 0.75) lower for Digit Span, and 0.15 (95 % CI 0.09, 0.21) lower for Go-No-Go (high frequency discriminability index [probability]). The latter represented the most marked difference in terms of z-scores (0.50, 95 % CI 0.30, 0.71 standard deviations lower). The mean of the z-score of each of the three tests was also lower (0.34, 95 % CI 0.18, 0.50 standard deviations lower). These associations were similar in men and women, but attenuated with adjustment for education. Differences for Matrix Reasoning and Digit Span between the two communities were observed only among those who had lived in Meyerton ≥10 years, whereas for Go-No-Go, differences were also apparent among those who had lived in Meyerton <10 years. Mean PM2.5-Mn at a long-term fixed site in Meyerton was 203 ng/m3 and 10 ng/m3 in Ethembalethu. CONCLUSION: Residence in a community near a high Mn emission source is associated with cognitive dysfunction, including aspects of cognitive control as assessed by the Go-No-Go test.

A Global-Scale Mineral Dust Equation.

A robust method to estimate mineral dust mass in ambient particulate matter (PM) is essential, as the dust fraction cannot be directly measured but is needed to understand dust impacts on the environment and human health. In this study, a global-scale dust equation is developed that builds on the widely used Interagency Monitoring of Protected Visual Environments (IMPROVE) network's "soil" formula that is based on five measured elements (Al, Si, Ca, Fe, and Ti). We incorporate K, Mg, and Na into the equation using the mineral-to-aluminum (MAL) mass ratio of (K2O + MgO + Na2O)/Al2O3 and apply a correction factor (CF) to account for other missing compounds. We obtain region-specific MAL ratios and CFs by investigating the variation in dust composition across desert regions. To calculate reference dust mass for equation evaluation, we use total-mineral-mass (summing all oxides of crustal elements) and residual-mass (subtracting non-dust species from total PM) approaches. For desert dust in source regions, the normalized mean bias (NMB) of the global equation (within ±1%) is significantly smaller than the NMB of the IMPROVE equation (-6% to 10%). For PM2.5 with high dust content measured by the IMPROVE network, the global equation estimates dust mass well (NMB within ±5%) at most sites. For desert dust transported to non-source regions, the global equation still performs well (NMB within ±2%). The global equation can also represent paved road, unpaved road, and agricultural soil dust (NMB within ±5%). This global equation provides a promising approach for calculating dust mass based on elemental analysis of dust.

Depression and anxiety in a manganese-exposed community.

OBJECTIVE: To characterize the association between residential environmental manganese (Mn) exposure and depression and anxiety, given prior associations among occupationally-exposed workers. METHODS: We administered the Beck Depression Inventory (BDI) and the State-Trait Anxiety Inventory (STAI) to 697 study participants in their preferred languages. These participants represented a population-based sample of residents aged ≥40 from two predominantly Black African communities in Gauteng province, South Africa: 605 in Meyerton, adjacent to a large Mn smelter, and 92 in Ethembalethu, a comparable non-exposed community. We investigated the associations between community (Meyerton vs. Ethembalethu) and severity of depression and anxiety, using linear regression, adjusting for age and sex. To document community-level differences in Mn exposure, we measured airborne PM2.5-Mn. RESULTS: Meyerton residents had BDI scores 5.63 points (95 % CI 3.07, 8.20) higher than Ethembalethu residents, with all questions contributing to this significant difference. STAI-state scores were marginally higher in Meyerton than Ethembalethu residents [2.12 (95 % CI -0.17, 4.41)], whereas STAI-trait scores were more similar between the communities [1.26 (95 % CI -0.82, 3.35)]. Mean PM2.5-Mn concentration was 203 ng/m3 at a long-term fixed site in Meyerton and 10 ng/m3 in Ethembalethu. CONCLUSION: Residence near Mn emission sources may be associated with greater depression symptomatology, and possibly current, but not lifetime, anxiety.

Severity of parkinsonism associated with environmental manganese exposure.

BACKGROUND: Exposure to occupational manganese (Mn) is associated with neurotoxic brain injury, manifesting primarily as parkinsonism. The association between environmental Mn exposure and parkinsonism is unclear. To characterize the association between environmental Mn exposure and parkinsonism, we performed population-based sampling of residents older than 40 in Meyerton, South Africa (N = 621) in residential settlements adjacent to a large Mn smelter and in a comparable non-exposed settlement in Ethembalethu, South Africa (N = 95) in 2016-2020. METHODS: A movement disorders specialist examined all participants using the Unified Parkinson Disease Rating Scale motor subsection part 3 (UPDRS3). Participants also completed an accelerometry-based kinematic test and a grooved pegboard test. We compared performance on the UPDRS3, grooved pegboard, and the accelerometry-based kinematic test between the settlements using linear regression, adjusting for covariates. We also measured airborne PM2.5-Mn in the study settlements. RESULTS: Mean PM2.5-Mn concentration at a long-term fixed site in Meyerton was 203 ng/m3 in 2016-2017 - approximately double that measured at two other neighborhoods in Meyerton. The mean Mn concentration in Ethembalethu was ~ 20 times lower than that of the long-term Meyerton site. UPDRS3 scores were 6.6 (CI 5.2, 7.9) points higher in Meyerton than Ethembalethu residents. Mean angular velocity for finger-tapping on the accelerometry-based kinematic test was slower in Meyerton than Ethembalethu residents [dominant hand 74.9 (CI 48.7, 101.2) and non-dominant hand 82.6 (CI 55.2, 110.1) degrees/second slower]. Similarly, Meyerton residents took longer to complete the grooved pegboard, especially for the non-dominant hand (6.9, CI -2.6, 16.3 s longer). CONCLUSIONS: Environmental airborne Mn exposures at levels substantially lower than current occupational exposure thresholds in the United States may be associated with clinical parkinsonism.

Guidance to Reduce the Cardiovascular Burden of Ambient Air Pollutants: A Policy Statement From the American Heart Association.

In 2010, the American Heart Association published a statement concluding that the existing scientific evidence was consistent with a causal relationship between exposure to fine particulate matter and cardiovascular morbidity and mortality, and that fine particulate matter exposure is a modifiable cardiovascular risk factor. Since the publication of that statement, evidence linking air pollution exposure to cardiovascular health has continued to accumulate and the biological processes underlying these effects have become better understood. This increasingly persuasive evidence necessitates policies to reduce harmful exposures and the need to act even as the scientific evidence base continues to evolve. Policy options to mitigate the adverse health impacts of air pollutants must include the reduction of emissions through action on air quality, vehicle emissions, and renewable portfolio standards, taking into account racial, ethnic, and economic inequality in air pollutant exposure. Policy interventions to improve air quality can also be in alignment with policies that benefit community and transportation infrastructure, sustainable food systems, reduction in climate forcing agents, and reduction in wildfires. The health care sector has a leadership role in adopting policies to contribute to improved environmental air quality as well. There is also potentially significant private sector leadership and industry innovation occurring in the absence of and in addition to public policy action, demonstrating the important role of public-private partnerships. In addition to supporting education and research in this area, the American Heart Association has an important leadership role to encourage and support public policies, private sector innovation, and public-private partnerships to reduce the adverse impact of air pollution on current and future cardiovascular health in the United States.

Fast sulfate formation from oxidation of SO2 by NO2 and HONO observed in Beijing haze.

Severe events of wintertime particulate air pollution in Beijing (winter haze) are associated with high relative humidity (RH) and fast production of particulate sulfate from the oxidation of sulfur dioxide (SO2) emitted by coal combustion. There has been considerable debate regarding the mechanism for SO2 oxidation. Here we show evidence from field observations of a haze event that rapid oxidation of SO2 by nitrogen dioxide (NO2) and nitrous acid (HONO) takes place, the latter producing nitrous oxide (N2O). Sulfate shifts to larger particle sizes during the event, indicative of fog/cloud processing. Fog and cloud readily form under winter haze conditions, leading to high liquid water contents with high pH (>5.5) from elevated ammonia. Such conditions enable fast aqueous-phase oxidation of SO2 by NO2, producing HONO which can in turn oxidize SO2 to yield N2O.This mechanism could provide an explanation for sulfate formation under some winter haze conditions.

Health assessment of future PM2.5 exposures from indoor, outdoor, and secondhand tobacco smoke concentrations under alternative policy pathways in Ulaanbaatar, Mongolia.

INTRODUCTION: Winter air pollution in Ulaanbaatar, Mongolia is among the worst in the world. The health impacts of policy decisions affecting air pollution exposures in Ulaanbaatar were modeled and evaluated under business as usual and two more-strict alternative emissions pathways through 2024. Previous studies have relied on either outdoor or indoor concentrations to assesses the health risks of air pollution, but the burden is really a function of total exposure. This study combined projections of indoor and outdoor concentrations of PM2.5 with population time-activity estimates to develop trajectories of total age-specific PM2.5 exposure for the Ulaanbaatar population. Indoor PM2.5 contributions from secondhand tobacco smoke (SHS) were estimated in order to fill out total exposures, and changes in population and background disease were modeled. The health impacts were derived using integrated exposure-response curves from the Global Burden of Disease Study. RESULTS: Annual average population-weighted PM2.5 exposures at baseline (2014) were estimated at 59 µg/m3. These were dominated by exposures occurring indoors, influenced considerably by infiltrated outdoor pollution. Under current control policies, exposures increased slightly to 60 µg/m3 by 2024; under moderate emissions reductions and under a switch to clean technologies, exposures were reduced from baseline levels by 45% and 80%, respectively. The moderate improvement pathway decreased per capita annual disability-adjusted life year (DALY) and death burdens by approximately 40%. A switch to clean fuels decreased per capita annual DALY and death burdens by about 85% by 2024 with the relative SHS contribution increasing substantially. CONCLUSION: This study demonstrates a way to combine estimated changes in total exposure, background disease and population levels, and exposure-response functions to project the health impacts of alternative policy pathways. The resulting burden analysis highlights the need for aggressive action, including the elimination of residential coal burning and the reduction of current smoking rates.

Fine particulate matter components and emergency department visits for cardiovascular and respiratory diseases in the St. Louis, Missouri-Illinois, metropolitan area.

BACKGROUND: Given that fine particulate matter (≤ 2.5 µm; PM2.5) is a mixture of multiple components, it has been of high interest to identify its specific health-relevant physical and/or chemical features. OBJECTIVES: We conducted a time-series study of PM2.5 and cardiorespiratory emergency department (ED) visits in the St. Louis, Missouri-Illinois metropolitan area, using 2 years of daily PM2.5 and PM2.5 component measurements (including ions, carbon, particle-phase organic compounds, and elements) made at the St. Louis-Midwest Supersite, a monitoring site of the U.S. Environmental Protection Agency Supersites ambient air monitoring research program. METHODS: Using Poisson generalized linear models, we assessed short-term associations between daily cardiorespiratory ED visit counts and daily levels of 24 selected pollutants. Associations were estimated for interquartile range changes in each pollutant. To allow comparison of relationships among multiple pollutants and outcomes with potentially different lag structures, we used 3-day unconstrained distributed lag models controlling for time trends and meteorology. RESULTS: Considering results of our primary models, as well as sensitivity analyses and models assessing co-pollutant confounding, we observed robust associations of cardiovascular disease visits with 17α(H),21ß(H)-hopane and congestive heart failure visits with elemental carbon. We also observed a robust association of respiratory disease visits with ozone. For asthma/wheeze, associations were strongest with ozone and nitrogen dioxide; observed associations of asthma/wheeze with PM2.5 and its components were attenuated in two-pollutant models with these gases. Differential measurement error due to differential patterns of spatiotemporal variability may have influenced patterns of observed associations across pollutants. CONCLUSIONS: Our findings add to the growing field examining the health effects of PM2.5 components. Combustion-related components of the pollutant mix showed particularly strong associations with cardiorespiratory ED visit outcomes.

Impact of ambient fine particulate matter carbon measurement methods on observed associations with acute cardiorespiratory morbidity.

Elemental carbon (EC) and organic carbon (OC) represent a substantial portion of particulate matter <2.5 µm in diameter (PM2.5), and have been associated with adverse health effects. EC and OC are commonly measured using the National Institute of Occupational Safety and Health (NIOSH) method or the Interagency Monitoring of Protected Visual Environments (IMPROVE) method. Measurement method differences could have an impact on observed epidemiologic associations. Daily speciated PM2.5 data were obtained from the St Louis-Midwest Supersite, and St Louis emergency department (ED) visit data were obtained from the Missouri Hospital Association for the period June 2001 to April 2003. We assessed acute associations between cardiorespiratory ED visits and EC and OC from NIOSH and IMPROVE methods using Poisson generalized linear models controlling for temporal trends and meteorology. Associations were generally similar for EC and OC from the different measurement methods. The most notable difference between methods was observed for congestive heart failure and EC (for example, warm season rate ratios (95% confidence intervals) per interquartile range change in EC concentration were: NIOSH=1.06 (0.99-1.13), IMPROVE=1.01 (0.96-1.07)). Overall, carbon measurement method had little impact on acute associations between EC, OC, and ED visits. Some specific differences were observed, however, which may be related to particle composition.

How fragmentation and corridors affect wind dynamics and seed dispersal in open habitats.

Determining how widespread human-induced changes such as habitat loss, landscape fragmentation, and climate instability affect populations, communities, and ecosystems is one of the most pressing environmental challenges. Critical to this challenge is understanding how these changes are affecting the movement abilities and dispersal trajectories of organisms and what role conservation planning can play in promoting movement among remaining fragments of suitable habitat. Whereas evidence is mounting for how conservation strategies such as corridors impact animal movement, virtually nothing is known for species dispersed by wind, which are often mistakenly assumed to not be limited by dispersal. Here, we combine mechanistic dispersal models, wind measurements, and seed releases in a large-scale experimental landscape to show that habitat corridors affect wind dynamics and seed dispersal by redirecting and bellowing airflow and by increasing the likelihood of seed uplift. Wind direction interacts with landscape orientation to determine when corridors provide connectivity. Our results predict positive impacts of connectivity and patch shape on species richness of wind-dispersed plants, which we empirically illustrate using 12 y of data from our experimental landscapes. We conclude that habitat fragmentation and corridors strongly impact the movement of wind-dispersed species, which has community-level consequences.

Application of an ensemble-trained source apportionment approach at a site impacted by multiple point sources.

Four receptor models and a chemical transport model were used to quantify PM2.5 source impacts at the St. Louis Supersite (STL-SS) between June 2001 and May 2003. The receptor models used two semi-independent data sets, with the first including ions and trace elements and the second including 1-in-6 day particle-bound organics. Since each source apportionment (SA) technique has limitations, this work compares results from the five different SA approaches to better understand the biases and limitations of each. The source impacts calculated by these models were then integrated into a constrained, ensemble-trained SA approach. The ensemble method offers several improvements over the five individual SA techniques at the STL-SS. Primarily, the ensemble method calculates source impacts on days when individual models either do not converge to a solution or do not have adequate input data to develop source impact estimates. When compared with a chemical mass balance approach using measurement-based source profiles, the ensemble method improves fit statistics, reducing chi-squared values and improving PM2.5 mass reconstruction. Compared to other receptor models, the ensemble method also calculates zero or negative impacts from major emissions sources (e.g., secondary organic carbon (SOC) and diesel vehicles) for fewer days. One limitation of this analysis was that a composite metals profile was used in the ensemble analysis. Although STL-SS is impacted by multiple metals processing point sources, several of the initial SA methods could not resolve individual metals processing impacts. The results of this analysis also reveal some of the subjectivities associated with applying specific SA models at the STL-SS. For instance, Positive Matrix Factorization results are very sensitive to both the fitting species and number of factors selected by the user. Conversely, Chemical Mass Balance results are sensitive to the source profiles used to represent local metals processing emissions. Additionally, the different SA approaches predict different impacts for the same source on a given day, with correlation coefficients ranging from 0.034 to 0.65 for gasoline vehicles, -0.54-0.48 for diesel vehicles, -0.29-0.81 for dust, -0.34-0.89 for biomass burning, 0.38-0.49 for metals processing, and -0.25-0.51 for SOC. These issues emphasize the value of using several different SA techniques at a given receptor site, either by comparing source impacts predicted by different models or by using an ensemble-based technique.

Source region identification using kernel smoothing.

As described in this paper, nonparametric wind regression is a source-to-receptor source apportionment model that can be used to identify and quantify the impact of possible source regions of pollutants as defined by wind direction sectors. It is described in detail with an example of its application to SO2 data from East St. Louis, IL. The model uses nonparametric kernel smoothing methods to apportion the observed average concentration of a pollutant to sectors defined by ranges of wind direction and speed. Formulas are given for the uncertainty of all of the important components of the model, and these are found to give nearly the same uncertainties as blocked bootstrap estimates of uncertainty. The model was applied to data for the first quarter (January, February, and March) of 2003, 2004, and 2005. The results for East St. Louis show that almost 50% of the average SO2 concentration can be apportioned to two 30 degrees wide wind sectors containing a zinc smelter and a brewery; a nearby steel mill did not appearto have a significant impact on SO2 during this period.

Seasonal variations of elemental carbon in urban aerosols as measured by two common thermal-optical carbon methods.

Two commonly employed laboratory-based elemental carbon (EC) and organic carbon (OC) thermal/optical methods for the analysis of ambient particulate matter were used to analyze 709 twenty-four hour integrated PM(2.5) samples along with 76 field blanks from the St. Louis-Midwest Supersite in East St. Louis, Illinois. The two laboratory ECOC methods were the Aerosol Characterization Experiment-Asia (ACE-Asia) method based on National Institute of Occupational Safety and Health (NIOSH 5040) method and the Interagency Monitoring of Protected Visual Environments (IMPROVE) protocol. As in previous intercomparisons between these two methods, there was excellent agreement for total carbon (i.e. sum of EC and OC), but significant differences were observed in the split between the measured EC and OC. The 709 daily PM(2.5) samples spanned a time series of two years that allowed an assessment of seasonal relationships between the EC reported by the two methods. Seasonal average ACE-Asia and IMPROVE EC concentration values were highest in the fall and lowest in the spring. Differences between the seasonal average IMPROVE and ACE-Asia EC concentration values were about 40% greater in summer compared to winter. While IMPROVE EC values were always larger than ACE-Asia EC, the EC difference between these methods exhibited a strong seasonal variation with largest differences occurring in the spring and especially summer with the smallest differences in the fall and winter. Seasonal average EC differences (IMPROVE-ACE-Asia) were anti-correlated with molecular markers for biomass burning and mobile source emissions that had wintertime maximum concentrations. The EC difference between methods did have a moderate positive correlation with indicators of secondary organic aerosol and sulfate suggesting that oxidized organic aerosol associated with atmospheric processing or other secondary components of ambient aerosol could be associated with the seasonal differences between these EC measurements.

Transport of atmospheric fine particulate matter: part 2--findings from recent field programs on the intraurban variability in fine particulate matter.

Air quality field data, collected as part of the fine particulate matter Supersites Program and other field measurements programs, have been used to assess the degree of intraurban variability for various physical and chemical properties of ambient fine particulate matter. Spatial patterns vary from nearly homogeneous to quite heterogeneous, depending on the city, parameter of interest, and the approach or method used to define spatial variability. Secondary formation, which is often regional in nature, drives fine particulate matter mass and the relevant chemical components toward high intraurban spatial homogeneity. Those particulate matter components that are dominated by primary emissions within the urban area, such as black carbon and several trace elements, tend to exhibit greater spatial heterogeneity. A variety of study designs and data analysis approaches have been used to characterize intraurban variability. High temporal correlation does not imply spatial homogeneity. For example, there can be high temporal correlation but with spatial heterogeneity manifested as smooth spatial gradients, often emanating from areas of high emissions such as the urban core or industrial zones.

Transport of atmospheric fine particulate matter: part 1--findings from recent field programs on the extent of regional transport within North America.

Air quality field data, collected as part of the fine particulate matter Supersites program and other field measurements programs, have been used to assess the role of aerosol transport, over length scales of approximately 100-1000 km, on fine particulate matter concentrations. Assessment of data from New York, NY; Baltimore, MD; Pittsburgh, PA; Atlanta, GA; Houston, TX; St. Louis, MO; and Fresno, CA, indicates that in virtually all of the regions, transport of aerosol over distances of 100-1000 km has a significant impact on urban particulate matter concentrations and a dominant role in determining rural particulate matter concentrations, though the nature of the regional contributions differs from region to region. This assessment is generally consistent with previous conceptual models of fine particulate matter formation and accumulation in these regions. The nature of the transported aerosol is largely sulfate in Eastern and Midwestern cities and nitrate in the Central Valley of California. In addition to physical transport of aerosol over distances of 100-1000 km, regional transport of aerosol precursors may lead to conditions conducive to large-scale nucleation events. Regional nucleation events have been reported in the East, Midwest, and in California. The events occurred in the morning soon after surface layers coupled with layers aloft, and the events generate ultrafine particles. In some cases, these nucleation events have been correlated with availability of sulfur dioxide and, therefore, may be sulfate formation events.

Sources of speciated atmospheric mercury at a residential neighborhood impacted by industrial sources.

Speciated measurements of atmospheric mercury plumes were obtained at an industrially impacted residential area of East St. Louis, IL. These plumes were found to result in extremely high mercury concentrations at ground level that were composed of a wide distribution of mercury species. Ground level concentrations as high as 235 ng m(-3) for elemental mercury (Hg0) and 38 300 pg m(-3) for reactive mercury species (reactive gaseous (RGM) plus particulate (PHg) mercury) were measured. The highest mercury concentrations observed during the study were associated with plumes that contained high concentrations of all mercury species (Hg0, RGM, and PHg) and originated from a source located southwest of the sampling site. Variations in proportions of Hg0/RGM/PHg among plumes, with Hg0 dominating some plumes and RGM and/or PHg dominating others, were attributed to differences in emissions from different sources. Correlations between mercury plumes and elevated NO(x) were not observed; however, a correlation between elevated SO2 and mercury plumes was observed during some but not all plume events. Despite the presence of six coal-fired power plants within 60 km of the study site, wind direction data along with Hg/SO2 and Hg/NO(x) ratios suggest that high-concentration mercury plumes impacting the St. Louis-Midwest Particle Matter Supersite are attributable to local point sources within 5 km of the site.