Dataset Information

Aerosol and precipitation chemistry in the southwestern United States: spatiotemporal trends and interrelationships.

ABSTRACT: This study characterizes the spatial and temporal patterns of aerosol and precipitation composition at six sites across the United States Southwest between 1995 and 2010. Precipitation accumulation occurs mostly during the wintertime (December-February) and during the monsoon season (July-September). Rain and snow pH levels are usually between 5-6, with crustal-derived species playing a major role in acid neutralization. These species (Ca2+, Mg2+, K+, Na+) exhibit their highest concentrations between March and June in both PM2.5 and precipitation due mostly to dust. Crustal-derived species concentrations in precipitation exhibit positive relationships with [Formula: see text], [Formula: see text], and Cl-, suggesting that acidic gases likely react with and partition to either crustal particles or hydrometeors enriched with crustal constituents. Concentrations of particulate [Formula: see text] show a statistically significant correlation with rain [Formula: see text] unlike snow [Formula: see text], which may be related to some combination of the vertical distribution of [Formula: see text] (and precursors) and the varying degree to which [Formula: see text]-enriched particles act as cloud condensation nuclei versus ice nuclei in the region. The coarse : fine aerosol mass ratio was correlated with crustal species concentrations in snow unlike rain, suggestive of a preferential role of coarse particles (mainly dust) as ice nuclei in the region. Precipitation [Formula: see text] : [Formula: see text] ratios exhibit the following features with potential explanations discussed: (i) they are higher in precipitation as compared to PM2.5; (ii) they exhibit the opposite annual cycle compared to particulate [Formula: see text] : [Formula: see text] ratios; and (iii) they are higher in snow relative to rain during the wintertime. Long-term trend analysis for the monsoon season shows that the [Formula: see text] : [Formula: see text] ratio in rain increased at the majority of sites due mostly to air pollution regulations of [Formula: see text] precursors.

SUBMITTER: Sorooshian A

PROVIDER: S-EPMC3890361 | biostudies-literature | 2013 Aug

REPOSITORIES: biostudies-literature

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Aerosol and precipitation chemistry in the southwestern United States: spatiotemporal trends and interrelationships.

Sorooshian A A Shingler T T Harpold A A Feagles C W CW Meixner T T Brooks P D PD

Atmospheric chemistry and physics 20130801 15

This study characterizes the spatial and temporal patterns of aerosol and precipitation composition at six sites across the United States Southwest between 1995 and 2010. Precipitation accumulation occurs mostly during the wintertime (December-February) and during the monsoon season (July-September). Rain and snow pH levels are usually between 5-6, with crustal-derived species playing a major role in acid neutralization. These species (Ca2+, Mg2+, K+, Na+</s ...[more]

PMID: 24432030

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Project description:This study examines co-located aerosol and precipitation chemistry data between 2010 and 2016 at Pinnacles National Monument ~65?km east of the coastline in central California. Positive matrix factorization analysis of the aerosol composition data revealed seven distinct pollutant sources: aged sea salt (25.7% of PM2.5), biomass burning (24.2% of PM2.5), fresh sea salt (15.0% of PM2.5), secondary sulfate (11.7% of PM2.5), dust (10.0% of PM2.5), vehicle emissions (8.2% of PM2.5), and secondary nitrate (5.2% of PM2.5). The influence of meteorology and transport on monthly patterns of PM2.5 composition is discussed. Only secondary sulfate exhibited a statistically significant change (a reduction) over time among the PM2.5 source factors. In contrast, PMcoarse exhibited a significant increase most likely due to dust influence. Monthly profiles of precipitation chemistry are summarized showing that the most abundant species in each month was either SO42-, NO3-, or Cl-. Intercomparisons between the precipitation and aerosol data revealed several features: (i) precipitation pH was inversely related to factors associated with more acidic aerosol constituents such as secondary sulfate and aged sea salt, in addition to being reduced by uptake of HNO3 in the liquid phase; (ii) two aerosol source factors (dust and aged sea salt) and PMcoarse exhibited a positive association with Ca2+ in precipitation, suggestive of directly emitted aerosol types with larger sizes promoting precipitation; and (iii) sulfate levels in both the aerosol and precipitation samples analyzed were significantly correlated with dust and aged sea salt PMF factors, pointing to the partitioning of secondary sulfate to dust and sea salt particles. The results of this work have implications for the region's air quality and hydrological cycle, in addition to demonstrating that the use of co-located aerosol and precipitation chemistry data can provide insights relevant to aerosol-precipitation interactions.

Project description:BackgroundFace mask wearing has been identified as an effective strategy to prevent the transmission of SARS-CoV-2, yet mask mandates were never imposed nationally in the United States. This decision resulted in a patchwork of local policies and varying compliance, potentially generating heterogeneities in the local trajectories of COVID-19 in the United States. Although numerous studies have investigated the patterns and predictors of masking behavior nationally, most suffer from survey biases and none have been able to characterize mask wearing at fine spatial scales across the United States through different phases of the pandemic.ObjectiveUrgently needed is a debiased spatiotemporal characterization of mask-wearing behavior in the United States. This information is critical to further assess the effectiveness of masking, evaluate the drivers of transmission at different time points during the pandemic, and guide future public health decisions through, for example, forecasting disease surges.MethodsWe analyzed spatiotemporal masking patterns in over 8 million behavioral survey responses from across the United States, starting in September 2020 through May 2021. We adjusted for sample size and representation using binomial regression models and survey raking, respectively, to produce county-level monthly estimates of masking behavior. We additionally debiased self-reported masking estimates using bias measures derived by comparing vaccination data from the same survey to official records at the county level. Lastly, we evaluated whether individuals' perceptions of their social environment can serve as a less biased form of behavioral surveillance than self-reported data.ResultsWe found that county-level masking behavior was spatially heterogeneous along an urban-rural gradient, with mask wearing peaking in winter 2021 and declining sharply through May 2021. Our results identified regions where targeted public health efforts could have been most effective and suggest that individuals' frequency of mask wearing may be influenced by national guidance and disease prevalence. We validated our bias correction approach by comparing debiased self-reported mask-wearing estimates with community-reported estimates, after addressing issues of a small sample size and representation. Self-reported behavior estimates were especially prone to social desirability and nonresponse biases, and our findings demonstrated that these biases can be reduced if individuals are asked to report on community rather than self behaviors.ConclusionsOur work highlights the importance of characterizing public health behaviors at fine spatiotemporal scales to capture heterogeneities that may drive outbreak trajectories. Our findings also emphasize the need for a standardized approach to incorporating behavioral big data into public health response efforts. Even large surveys are prone to bias; thus, we advocate for a social sensing approach to behavioral surveillance to enable more accurate estimates of health behaviors. Finally, we invite the public health and behavioral research communities to use our publicly available estimates to consider how bias-corrected behavioral estimates may improve our understanding of protective behaviors during crises and their impact on disease dynamics.

Dataset Information

Aerosol and precipitation chemistry in the southwestern United States: spatiotemporal trends and interrelationships.

Publications

Aerosol and precipitation chemistry in the southwestern United States: spatiotemporal trends and interrelationships.

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