Lawrence and Neff compartmentalized the data into three categories according to distance traveled: local (0–10 km), regional (10–1000 km), and global (>1000 km). The deposition rates decreased with distance in a predictably exponential progression. Local emissions had an average deposition rate of 200 g m–2yr–1, followed by a mere 20 and 0.4 g m–2yr–1 for average regional and global depositions, respectively. The local dusts contained more coarse silts and fine sands than the regional or global samples, such that 10–60% of total mass comprised of particles greater than 20 µm diameter, and an average of 30% of the particulate was sand. Regional dusts contained more fine silts and clay, comprising an average of 85% of the total mass, while global emissions contain only fine silt and clay in about a 70:30 ratio. Mineral content fits accordingly with particulate distribution, such that the sand-containing local dust contained more quartz, the regional dust contained equal concentrations of feldspar and phyllosilcate minerals, and the silt-dominant global dust contained the most phyllosilcates. Yet these arbitrary dust categories considerably overlap, making the distinction less decipherable from the data tables. Local dust still consists of 5–30% phyllosilcate and 10–30% feldspar minerals, and global dust still typically consists of 20% quartz and 20–30% feldspar. Likewise, silt-sized particles greater than 10 µm predominate in all dust samples, probably due to their ideal transport size: just enough surface area to get picked up by the wind, but still small enough to be energetically economical.
Lawrence and Neff (2009) compiled observational data on the deposition rates, chemical composition, mineral content, and particulate size distributions of wind-blown dust from fifty-two peer-reviewed, published articles throughout the world. They found that the particulate size distribution of wind-blown dust tends to negatively correlate with the distance traveled from the source; the farther the dust travels, the smaller the dominant particle size. Correspondingly, the mineral distribution also changes according to distance, and all dust tends to carry more trace elements such as rare earth metals than the average upper continental crustal rock (UCC), which gets used as a comparative proxy between standard soil composition and dust. While the results of the compiled data are entirely unsurprising, they do demonstrate some major quandaries with current methods of dust data collection, and highlight the inaccuracies of current global dust models, which overestimate global, and underestimate local and regional sources. .— Elise Wanger
Lawrence, C., Neff, J., 2009. The contemporary physical and chemical flux of aolian dust: A synthesis of direct measurements of dust deposition. Chemical Geology 267, 46–63.
As crustal soil gets physically and chemically eroded into the loose, small-sized particulates that become wind-blown dust, the more mobile elements that can bond with into larger compound molecules and leach into lower soil layers decrease in concentration. Thus a common earth element like sodium (Na) averages only half the concentration in dust as in the UCC, while iron (Fe), calcium (Ca), phosphorus (P), titanium (Ti), nickel (Ni), copper (Cu) and lead (Pb) are an average 0.5–4.8 times higher in dust samples, in ascending order, than the UCC. Such elemental enrichment in dust may significantly contribute to biogeochemical cycling in ecosystems; for example, plants depend on trace levels of titanium and copper to continue growth, often making such elements limiting factors of productivity. The effects of the enriched Pb content—as well as zinc (Zn) and cadmium (Cd) (for which observational data were too sparse in the peer-reviewed literature to be quantitatively evaluated)—cannot be accounted for through means of natural geochemical weathering, and thus most likely derive from mixing with other emission sources such as industrial production, volcanic activity, and biomass burning. These factors may also increase the organic P content, since wind-blown dust often has a higher P concentration than could be explained by weathering processes or the parent soil.
Although still qualitatively valuable, many of the results Lawrence and Neff compiled lack reliability because of inconsistent data collection methods between studies. Lawrence and Neff only included passive sampling from direct observational research. Passive sampling typically consists of measuring the deposits that land on a non-reactive collection pan filled with glass marbles to provide ample surface area and cohesion. Deposits that land on snow, ice, or even soil and can still be distinguishably measured also count as passive sampling, analogous to the collection pan method, and can even reveal seasonal and annual variations through core extractions. However, Lawrence and Neff took core data solely from snow or ice, since sediment cores get more chemically and physically confounded from weathering. Active sampling—collecting particles with an air filter near the ground, usually some poly-fiber material—was entirely excluded because such methods don’t typically entrap wet deposition such as dust particulates in raindrops, and don’t account for the further fractionalization of particulates during the deposition process, when the dust combines and collides with the surface. Lastly, active sampling requires a modeling-derived conversion of atmospheric dust concentrations—which is what the filter traps—into dust deposition rates, which is how much dust actually lands on the ground and stays there in a given time span.
Lawrence and Neff used the compiled data from passive collections in the field to verify the accuracy of global dust models. Dust circulation and deposit models have highly sensitive input parameters calculated using an often inadequate amount of raw data. The limited data inputs are partially an attempt to simplify the model by focusing specifically on the range of dust the model is concerned with, typically long-distance transport (>1000 km), but to a greater extent are more symptomatic of an overall lack of dust collection field data. Furthermore, the observational data that do manage to get collected lack a universal method that allow it to be systematically compared to or combined with previous studies. Most researchers only examine particulates within a restricted diameter of micrometers, as is often necessary given that the frequency of spectroscopy waves will only detect certain particulate sizes. However, since no standardized protocol exists, every study subjectively determines its own diameter range, meaning that every new data collection examines particulates that partially overlap with some previous studies, and not entirely with any. Furthermore, without a standardized manner to arrange collection trays or filters, each study has a slightly distinctive set-up, and no studies try to synchronize seasonally, which can confound the results of compiled data if studies have been conducted over limited time periods (under a year) with incongruous start and end dates.
Dust carries many nutrients, microorganisms, and essential elements that can bolster ecosystem productivity and augment biotic development. Yet dust can also carry heavy metals, pathogens, and contaminants (such as from human activity and volcanic influences) that may harm both ecosystems and human health. To help better understand where our dust travels—and what deleterious or beneficial particles it transmits in the process—Lawrence and Neff advocate for studies to be more comprehensive in particulate diameters and conducted over longer periods (at least one year) using a standardized system of analysis and methodology. Most areas of environmental research derive conclusive, globally applicable statistics by compiling the extensive research gathered over multiple years and continents. Because of the inconsistent practices of dust collection, Lawrence and Neff could not make any such significant conclusions, nor enhance the accuracy of current models (except for pointing out that they may underestimate shorter-distance deposits, and overestimate global ones). Hopefully this initial compilation will help the sedimentary research community implement research practices that could be compared within different regions and eras.