An official website of the United States government
Abstract Using data from the Environmental Protection Agency’s Chemical Speciation Network, we have characterized trends in PM2.5transition metals in urban areas across the United States for the period 2001–2016. The metals included in this analysis—Cr, Cu, Fe, Mn, Ni, V, and Zn—were selected based upon their abundance in PM2.5, known sources, and links to toxicity. Ten cities were included to provide broad geographic coverage, diverse source influences, and climatology: Atlanta (ATL), Baltimore (BAL), Chicago (CHI), Dallas (DAL), Denver (DEN), Los Angeles (LA), New York City (NYC), Phoenix (PHX), Seattle (SEA), and St. Louis (STL). The concentrations of V and Zn decreased in all ten cities, though the V decreases were more substantial. Cr concentrations increased in cities in the East and Midwest, with a pronounced spike in concentrations in 2013. The National Emissions Inventory was used to link sources with the observed trends; however, the causes of the broad Cr concentration increases and 2013 spike are not clear. Analysis of PM2.5metal concentrations in port versus non-port cities showed different trends for Ni, suggesting an important but decreasing influence of marine emissions. The concentrations of most PM2.5metals decreased in LA, STL, BAL, and SEA while concentrations of four of the seven metals (Cr, Fe, Mn, Ni) increased in DAL over the same time. Comparisons of the individual metals to overall trends in PM2.5suggest decoupled sources and processes affecting each. These metals may have an enhanced toxicity compared to other chemical species present in PM, so the results have implications for strategies to measure exposures to PM and the resulting human health effects.
more »
« less
Inter-continental variability in the relationship of oxidative potential and cytotoxicity with PM2.5 mass
Abstract Most fine ambient particulate matter (PM2.5)-based epidemiological models use globalized concentration-response (CR) functions assuming that the toxicity of PM2.5is solely mass-dependent without considering its chemical composition. Although oxidative potential (OP) has emerged as an alternate metric of PM2.5toxicity, the association between PM2.5mass and OP on a large spatial extent has not been investigated. In this study, we evaluate this relationship using 385 PM2.5samples collected from 14 different sites across 4 different continents and using 5 different OP (and cytotoxicity) endpoints. Our results show that the relationship between PM2.5mass vs. OP (and cytotoxicity) is largely non-linear due to significant differences in the intrinsic toxicity, resulting from a spatially heterogeneous chemical composition of PM2.5. These results emphasize the need to develop localized CR functions incorporating other measures of PM2.5properties (e.g., OP) to better predict the PM2.5-attributed health burdens.
more »
« less
- PAR ID:
- 10552362
- Publisher / Repository:
- Nature
- Date Published:
- Journal Name:
- Nature Communications
- Volume:
- 15
- Issue:
- 1
- ISSN:
- 2041-1723
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract India is largely devoid of high‐quality and reliable on‐the‐ground measurements of fine particulate matter (PM2.5). Ground‐level PM2.5concentrations are estimated from publicly available satellite Aerosol Optical Depth (AOD) products combined with other information. Prior research has largely overlooked the possibility of gaining additional accuracy and insights into the sources of PM using satellite retrievals of tropospheric trace gas columns. We evaluate the information content of tropospheric trace gas columns for PM2.5estimates over India within a modeling testbed using an Automated Machine Learning (AutoML) approach, which selects from a menu of different machine learning tools based on the data set. We then quantify the relative information content of tropospheric trace gas columns, AOD, meteorological fields, and emissions for estimating PM2.5over four Indian sub‐regions on daily and monthly time scales. Our findings suggest that, regardless of the specific machine learning model assumptions, incorporating trace gas modeled columns improves PM2.5estimates. We use the ranking scores produced from the AutoML algorithm and Spearman’s rank correlation to infer or link the possible relative importance of primary versus secondary sources of PM2.5as a first step toward estimating particle composition. Our comparison of AutoML‐derived models to selected baseline machine learning models demonstrates that AutoML is at least as good as user‐chosen models. The idealized pseudo‐observations (chemical‐transport model simulations) used in this work lay the groundwork for applying satellite retrievals of tropospheric trace gases to estimate fine particle concentrations in India and serve to illustrate the promise of AutoML applications in atmospheric and environmental research.more » « less
-
Transition metals in particulate matter (PM) are hypothesized to have enhanced toxicity based on their oxidative potential (OP). The acellular dithiothreitol (DTT) assay is widely used to measure the OP of PM and its chemical components. In our prior study, we showed that the DTT assay (pH 7.4, 0.1 M phosphate buffer, 37 °C) provides favorable thermodynamic conditions for precipitation of multiple metals present in PM. This study utilizes multiple techniques to characterize the precipitation of aqueous metals present at low concentrations in the DTT assay. Metal precipitation was identified using laser particle light scattering analysis, direct chemical measurement of aqueous metal removal, and microscopic imaging. Experiments were run with aqueous metals from individual metal salts and a well-characterized urban PM standard (NIST SRM-1648a, Urban Particulate Matter). Our results demonstrated rapid precipitation of metals in the DTT assay. Metal precipitation was independent of DTT but dependent on metal concentration. Metal removal in the chemically complex urban PM samples exceeded the thermodynamic predictions and removal seen in single metal salt experiments, suggesting co-precipitation and/or adsorption may have occurred. These results have broad implications for other acellular assays that study PM metals using phosphate buffer, and subsequently, the PM toxicity inferred from these assays.more » « less
-
Abstract Burning plastic waste releases massive amounts of atmospheric particulate matter (PM), but its chemical composition and health-related properties are largely unelucidated. Here we characterize chemical composition of PM generated from burning common types of plastics and quantify reactive oxygen/chlorine species and PM oxidative potential (OP). We find that plastic burning PM contains high levels of environmentally persistent free radicals (EPFRs), transition metals, and polycyclic aromatic hydrocarbons. In the aqueous phase, PM generates hydrogen peroxide, •OH radicals, and carbon-centered organic radicals, exhibiting high levels of OP as characterized by dithiothreitol (DTT) and OH assays. Remarkably, plastic burning PM is associated with high concentrations of hypochlorous acid. Kinetic model simulations demonstrate that the PM respiratory deposition leads to •OH formation via complex redox reactions among its constituents and antioxidants in lung lining fluid. Our study highlights significant atmospheric and health implications for unregulated plastic burning, particularly common in many areas of developing countries.more » « less
-
IntroductionGlobally, 3–4 billion people rely on solid fuels for cooking, and 1 billion use kerosene to light their homes. While household air pollution (HAP) emitted from burning these fuels has well-established links to numerous health outcomes, the relationship between active tuberculosis (TB) and HAP exposure remains inconclusive. MethodsWe explore the association between HAP exposure and TB among adult women in Lilongwe’s high-density suburbs using hospital and community-based health data, objectively measured exposure to HAP, and sociodemographic data controlling for individual, household and community-level confounders. Only one other study combines public health, exposure and sociodemographic data to explore the association between HAP and TB. We report results from a case–control study of 377 primary cooks (76 cases; 301 controls) on the association between risk of developing active TB and HAP exposure. We calculate ORs for developing active TB using indicators of HAP exposure including primary fuel used for cooking, cooking location and frequency of kerosene use for lighting, and in a subset of households, by directly measured cooking area and personal exposure to fine particulate matter (PM2.5) and carbon monoxide. ResultsWe are unable to find an association between self-reported cooking with solid fuels and TB in our sample; we do find that increased frequency of kerosene use for lighting is associated with significantly higher odds of TB. Household area PM2.5concentration is the only direct HAP measure associated with significantly higher odds of TB. We find that 16.8% of the relationship between TB and kerosene use is mediated by increases in area PM2.5. ConclusionOur findings suggest that efforts to reduce the risk of active TB within the home environment should include strategies to reduce or eliminate kerosene, commonly used for lighting and cooking in many low-income country settings.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1038/s41467-024-49649-4
