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.

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.

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.

Development of a wet-chemical method for the speciation of iron in atmospheric aerosols.

The ability to quantify the chemical and physical forms of transition metals in atmospheric particulate matter (PM) is essential in determining potential human health and ecological effects. A method for the speciation of iron in atmospheric PM has been adapted which involves extraction in a well-defined solution followed by oxidation state specific detection. The method was applied to a suite of environmental aerosols. Ambient atmospheric aerosols in an urban area of St. Louis (the St. Louis-Midwest Supersite) were collected on Teflon substrates and were leached in one of four different solutions: (1) >18.0 Momega water; (2) 140 microM NaCl solution; (3) pH = 7.4 NaHCO3 solution; and (4) pH = 4.3 acetate buffering system. Fe(ll) was determined directly using the Ferrozine method as adapted to liquid waveguide spectrophotometry using a 1 m path-length cell. Fe(lll) was determined similarly after reduction to Fe(ll). It was found that, at low ionic strength, pH exerted a major influence on Fe(ll) solubility with the greatest Fe(ll) concentration consistently found in the pH = 4.3 acetate buffer. Soluble Fe(lll) (as defined by a 0.2 microm filter) varied little with extractant, which implies that most of the Fe(lll) detected was colloidal. To characterize well-defined materials for future reference, NIST standard reference materials were also analyzed for soluble Fe(ll) and Fe(lll). For all SRMs tested, a maximum of 2.4% of the total iron (Urban Dust 1649a) was soluble in pH = 4.3 acetate buffer. For calibration curves covering the ranges of 0.5-20 microg/L Fe(ll), excellent linearity was observed in all leaching solutions with R2 values of > 0.999. Co-located filters were used to test the effect of storage time on iron oxidation state in the ambient particles as a function of time. On two samples, an average Fe(ll) decay rate of 0.89 and 0.57 ng Fe(ll) g(-1) PM day(-1) was determined from the slope of the regression, however this decrease was determined not to be significant over 3 months (95% confidence). As an application of this method to mobile source emissions, size-resolved PM10 samples were collected at the inlet and outlet of the Caldecott Motor Vehicle Tunnel in northern California. These samples indicate that the coarse fraction (PM10-PM2.5) contains almost 50% of the total soluble Fe(ll) in the aerosol.