A new framework for evaluating dust emission model development using dichotomous satellite observations of dust emission.

Dust models are essential for understanding the impact of mineral dust on Earth's systems, human health, and global economies, but dust emission modelling has large uncertainties. Satellite observations of dust emission point sources (DPS) provide a valuable dichotomous inventory of regional dust emissions. We develop a framework for evaluating dust emission model performance using existing DPS data before routine calibration of dust models. To illustrate this framework's utility and arising insights, we evaluated the albedo-based dust emission model (AEM) with its areal (MODIS 500 m) estimates of soil surface wind friction velocity (us∗) and common, poorly constrained grain-scale entrainment threshold (u∗ts) adjusted by a function of soil moisture (H). The AEM simulations are reduced to its frequency of occurrence, P(us∗>u∗tsH). The spatio-temporal variability in observed dust emission frequency is described by the collation of nine existing DPS datasets. Observed dust emission occurs rarely, even in North Africa and the Middle East, where DPS frequency averages 1.8 %, (~7 days y-1), indicating extreme, large wind speed events. The AEM coincided with observed dust emission ~71.4 %, but simulated dust emission ~27.4 % when no dust emission was observed, while dust emission occurrence was over-estimated by up to 2 orders of magnitude. For estimates to match observations, results showed that grain-scale u∗ts needed restricted sediment supply and compatibility with areal us∗. Failure to predict dust emission during observed events, was due to us∗ being too small because reanalysis winds (ERA5-Land) were averaged across 11 km pixels, and inconsistent with us∗ across 0.5 km pixels representing local maxima. Assumed infinite sediment supply caused the AEM to simulate dust emission whenever P(us∗>u∗tsH), producing false positives when wind speeds were large. The dust emission model scales of existing parameterisations need harmonising and a new parameterisation for u∗ts is required to restrict sediment supply over space and time.

Satellites reveal Earth's seasonally shifting dust emission sources.

Establishing mineral dust impacts on Earth's systems requires numerical models of the dust cycle. Differences between dust optical depth (DOD) measurements and modelling the cycle of dust emission, atmospheric transport, and deposition of dust indicate large model uncertainty due partially to unrealistic model assumptions about dust emission frequency. Calibrating dust cycle models to DOD measurements typically in North Africa, are routinely used to reduce dust model magnitude. This calibration forces modelled dust emissions to match atmospheric DOD but may hide the correct magnitude and frequency of dust emission events at source, compensating biases in other modelled processes of the dust cycle. Therefore, it is essential to improve physically based dust emission modules. Here we use a global collation of satellite observations from previous studies of dust emission point source (DPS) dichotomous frequency data. We show that these DPS data have little-to-no relation with MODIS DOD frequency. We calibrate the albedo-based dust emission model using the frequency distribution of those DPS data. The global dust emission uncertainty constrained by DPS data (±3.8 kg m-2 y-1) provides a benchmark for dust emission model development. Our calibrated model results reveal much less global dust emission (29.1 ± 14.9 Tg y-1) than previous estimates, and show seasonally shifting dust emission predominance within and between hemispheres, as opposed to a persistent North African dust emission primacy widely interpreted from DOD measurements. Earth's largest dust emissions, proceed seasonally from East Asian deserts in boreal spring, to Middle Eastern and North African deserts in boreal summer and then Australian shrublands in boreal autumn-winter. This new analysis of dust emissions, from global sources of varying geochemical properties, have far-reaching implications for current and future dust-climate effects. For more reliable coupled representation of dust-climate projections, our findings suggest the need to re-evaluate dust cycle modelling and benefit from the albedo-based parameterisation.

Civil war hinders crop production and threatens food security in Syria.

Assessing the impact of violent conflict on Syrian agriculture is challenging given data limitations and attributability issues. Using satellite data at 30 m spatial resolution, we found that the extent of productive cropland showed greater interannual variability and spatial heterogeneity after the start of the civil war in 2011. Using changes in satellite-based night-time light as a proxy for war impact intensity, we also found that cropland close to severely impacted urban settlements faced greater disruption. Fixed-effects models revealed the relationship between productive cropland and precipitation for the pre-war period, whereas a counterfactual scenario constructed for the period 2012-2019 showed substantial variation at the regional level. While the ongoing conflict promoted cropland cultivation in safer zones, cropland reduction took place in the country's northwest and southeast regions. Our study demonstrated the combined utility of daytime and night-time satellite data to assess food insecurity in extreme environments and can help guide distribution of food and aid in Syria.

Drought and land use/land cover impact on dust sources in Southern Great Plains and Chihuahuan Desert of the U.S.: Inferring anthropogenic effect.

Exploring the effects of drought and land use/land cover (LULC) on aeolian dust emission is important to enhance dust models to account for anthropogenic land surface change. Hitherto, there has been no systematic effort to quantitatively estimate associations between LULC and drought with the number of dust point sources as a surrogate for emission on both local and global levels. Previously, we created a dataset of dust emission point sources in the southwestern United States (U.S.) during the years 2001-2016, including a period of extreme drought. However, that work did not investigate the effects of drought on the detected dust point sources. Here, we used that dataset to test the hypothesis that there is a statistically significant association between drought level and LULC that may contribute to the number of dust point sources in the Southern Great Plains and Chihuahuan Desert regions of the U.S. The spatio-temporal analysis showed that the geographic mean center for all the dust points, as well as the majority of the annual geographic mean centers between 2001 and 2016, is located in the Southern High Plains. Areas suffering from severe to exceptional drought tend to attract the annual geographic mean center of dust points. The chi-square analysis results showed a significant association between land cover type (as defined in the National Land Cover Database) and drought level with the number of dust point sources (χ2 (6) = 45.54, р < 0.001), thus supporting the proposed hypothesis. Results from this study indicate that human activities in dust-prone regions have clear potential to worsen the negative impacts of drought by changing LULC and increasing erodibility in multiple ways. This study paves the way for future efforts that can utilize more data and conduct more robust statistical analysis of the drought-LULC-dust linkage on both regional and global scales.

Blowing dust and highway safety in the southwestern United States: Characteristics of dust emission "hotspots" and management implications.

Despite the widespread media attention of chain-reaction traffic incidents and property damage caused by windblown dust in the U.S. and elsewhere in the world, very few studies have provided in-depth analysis on this issue. Remote sensing and field observations reveal that wind erosion in the southwestern U.S. typically occurs in localized source areas, characterized as "hotspots", while most of the landscape is not eroding. In this study, we identified the spatial and temporal distribution patterns of hotspots that may contribute dust blowing onto highways in the southwestern U.S. We further classified the hotspots for the potential of blowing dust production based upon field observations and wind erosion modeling. Results of land use and land cover show that shrubland, grassland, and cropland accounted for 42%, 31%, and 21% of the overall study area, respectively. However, of the 620 total hotspots identified, 164 (26%), 141 (22%), and 234 (38%) are located on shrubland, grassland, and cropland, respectively. Barren land represented 0.9% of the land area but 8% of the dust hotspots. While a majority of these hotspots are located close to highways, we focused on 55 of them, which are located <1km to adjacent highways and accessible via non-private roads. Field investigations and laboratory analysis showed that soils at these hotspot sites are dominated by sand and silt particles with threshold shear velocities ranging from 0.17-0.78m s-1, largely depending on the land use of the hotspot sites. Dust emission modeling showed that 13 hotspot sites could produce annual emissions >3.79kg m-2, yielding highly hazardous dust emissions to ground transportation with visibility <200m. Results of location, timing, and magnitude of the dust production at the hotspots are critical information for highway authorities to make informed and timely management decisions when wind events strike.

Groundwater-induced alterations in elemental concentration and interactions in semi-arid soils of the Southern High Plains, USA.

This study examined and compared arsenic (As) and other element [copper (Cu), iron (Fe), potassium (K), manganese (Mn), rubidium (Rb), and zinc (Zn)] distribution, as well as their interactions in historically irrigated and non-irrigated semi-arid agricultural soils of the Southern High Plains (SHP) in the USA, using findings from a portable X-ray fluorescence spectrometer. The portable X-ray fluorescence (PXRF) findings revealed that the concentrations of As (5.1 vs. 3.1 mg kg(-1)), Cu (7.2 vs. 6.2), Fe (7398 vs. 5677), K (8638 vs. 7061), Mn (80 vs. 68), Rb (36 vs. 27), and Zn (19.3 vs. 13.8) were higher in the historically irrigated field compared to the non-irrigated and, when examined within depths, were consistently and significantly higher (P<0.001) in the subsurface soils of the non-irrigated field, a trend not evident in the irrigated field. The strengths of the correlation among elements were higher in the non-irrigated field compared to the irrigated (average R of 0.60 vs. 0.54), suggesting a possible external input of these pollutants in the historically irrigated field which could have altered the natural background concentration and association among them. Furthermore, findings from principal component analysis (PCA) indicated that As, Mn, Rb, Fe, Zn, and K were more closely associated in the non-irrigated field and likely of lithogenic origin. The observed differences in elemental concentration and interaction between the irrigated and non-irrigated soils support the hypothesis that water from the local aquifer could be a potential source of pollutant addition to these semi-arid alkaline soils.

Implications of leading crop production practices on environmental quality and human health.

Globally, much weight is currently being placed on agriculture to provide food for the growing population as well as feedstock for the bioenergy industry. Unfortunately, the intensification of agricultural operations to satisfy these growing needs has been associated with a number of environmental and human health risks. A review of publications on the subject was conducted and emphasis was placed on articles focusing on agriculture, environment, and public health as well as their interactions. Supporting information was also gathered from publications of various agricultural and environmental agencies. Agricultural practices with potential negative implications on the environment and human health were identified broadly as: (a) utilization of biosolids and animal manures, (b) use of agricultural chemicals, (c) management of post-harvest residue, (d) irrigation, and (e) tillage operations. Soil, water, and air contamination by nutrients, heavy metals, pathogens, and pesticides, as well as air contamination by particulate matters, noxious gases, and pathogens were among the leading environmental impacts. Some of the human-health impacts identified included neurological and reproductive defects, cardiovascular risks, cancers and other diseases (of kidney, liver, lung, and skin), skin allergies, gastroenteritis, and methemoglobinemia. Continual awareness on the impacts of the reviewed agricultural practices on environmental quality and human health and the implementation of experimentally-backed best management practices in agricultural systems remain indispensable.