Identifying mineral dust emission sources in the Middle East using remote sensing techniques

This investigation presents a new high-resolution mineral dust climatology for the Middle East, describing the dust emission source of over 27,000 dust emission events during the period 2006 – 2013. The inventory was derived from the dust RGB product of the Spinning Enhanced Visual and InfraRed Imager (SEVIRI) on-board Meteosat’s second-generation satellite (MSG). Mineral dust emissions were derived from visual inspection of the SEVIRI scenes, which have 4-5 km2 spatial and 15-minute temporal resolution. The location of every emission event was recorded in a database, along with time and trajectory of dust movement. This is an improvement on previous studies, which derive dust source areas from the daily observations of Aerosol Optical Depth whose maxima do not necessarily coincide with sources of emissions, producing more accurate information on the location of the important dust sources in the region. Results showed that dust sources are constrained to relatively small areas, with 23% of dust emissions generated from areas covering just 1% of the total land surface of the Middle East. Important sources include the Tigris-Euphrates flood plains of Iraq and Syria, Western and Northern Saudi Arabia and the Sistan Basin in Eastern Iran. The Tigris-Euphrates flood plain was the most active dust region, producing 37% of all dust events. Here, agricultural surfaces, especially those producing rain-fed wheat and barley appear to be particularly sensitive to drought conditions, with greatest dust emission frequency at the peak of the 2007 – 2009 drought – the most severe drought in instrumental history. Normalised Difference Vegetation Index (NDVI) data was acquired from the Moderate Resolution Imaging Spectrodiometer (MODIS) (MOD13A2) 1km database and correlated with dust emission frequency data in the region of greatest dust activity. These dust emission ‘hotspots’ showed a significant correlation between vegetation cover and dust emission frequency, with increased vegetation cover during non-drought years producing a marked decrease in dust emission frequency. The southern areas of the Arabian Peninsula recorded very few dust emission observations, contrasting directly to many previous studies, which do not use such high temporal resolution data. The activation and frequency of dust emissions are characterised by strong seasonality developing in response to specific synoptic conditions. ERA Interim reanalysis data were used to characterise synoptic conditions on identified dusty days, demonstrating a concurrent increase in dust emission frequency with intensifying summer (JJA) Shamal (northerly) winds over the Arabian Peninsula

An assessment of SEVIRI imagery at various temporal resolutions and the effect on accurate dust emission mapping

This paper evaluates the use of the ‘Dust red/green/blue (RGB)’ product derived from Spinning Enhanced Visible and Infrared Imager (SEVIRI) data at 15-min, 30-min, and 60-min temporal resolutions, for monitoring dust emissions in the Middle East. From January 2006 to December 2006, observations of dust emission point sources were recorded at each temporal resolution across the Middle East. Previous work has demonstrated that using SEVIRI data is a major improvement on other remote sensing methods for mapping dust sources in the Sahara, by enabling dust-storm observations through sequential images, back to the point of first emission. However, the highest temporal resolution available (15-min observations) produces 96 images per day, resulting in significantly higher data management requirements than data provided at 30-min and 60-min intervals. To optimize future research workflows, this paper investigates the effect of lowering the temporal resolution on the number and spatial distribution of observed dust emission events in the Middle East. The results show that the number of events observed reduced by 17% for 30-min resolution and 50% for 60-min resolution. These differences change seasonally, with the highest reduction observed in summer (34% and 64% reduction, respectively)

Modelled direct causes of dust emission change (2001?2020) in southwestern USA and implications for management

North American observed atmospheric dust has shown large variability over the last two decades, coinciding with regional patterns of vegetation and wind speed changes. Dust emission models provide the potential to explain how these direct causes of vegetation and wind speed changes are related to changing dust emission. However, those dust models which assume land cover types are homogeneous over vegetation classes and fixed over time, are unlikely to adequately represent changing aerodynamic roughness of herbaceous cover, woody cover, and litter. To overcome these model limitations and explain changing (2001–2020) dust emission, we used a new MODIS albedo-based dust emission model calibrated to satellite-observed magnitude and frequency of dust emission point source (DPS) data. We focused our work on four regions of southwestern USA, identified previously as the main dust emission sources. We classified the interplay of controlling factors (wind speed and aerodynamic roughness) which created disturbance regimes with dust emission change consistent with diverse land use and management drivers. Our calibrated model results show that dust emission is increasing or decreasing, in different regions, at different times, for different reasons, consistent with the absence of a secular change of observed atmospheric dust. Our work demonstrates that using this calibrated dust emission model, sensitive to changing vegetation structure and configuration and wind speeds, provides new insights to the contemporary factors controlling dust emission. With this same approach, the prospect is promising for modelling historical and future dust emission responses using prognostic albedo in Earth System Modelling

Reducing resolution dependency of dust emission modeling using Albedo‐Based wind friction

Numerical simulations of dust emission processes are essential for dust cycle modeling and dust‐atmosphere interactions. Models have coarse spatial resolutions which, without tackling sub‐grid scale heterogeneity, bias finely resolved dust emission. Soil surface wind friction velocity ( u s *) drives dust emission non‐linearly with increasing model resolution, due mainly to thresholds of sediment entrainment. Albedo is area‐integrated, scales linearly with resolution, is related to u s * and hence represents its sub‐grid scale heterogeneity. Calibrated albedo‐based global dust emission estimates decreased by only 2 Tg y−1 (10.5%) upscaled from 0.5 to 111 km, largely independent of resolution. Without adjusting wind fields, this scaling uncertainty is within recent estimates of global dust emission model uncertainty (±14.9 Tg y−1). This intrinsic scaling capability of the albedo‐based approach offers considerable potential to reduce resolution dependency of dust cycle modeling and improve the representation of local dust emission in Earth system models and operational air quality forecasting

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

Use of combinations of antipsychotics: McLean Hospital inpatients, 2002

Background The empirical use of combinations of antipsychotic agents appears to be increasing with little research support for the relative efficacy, safety or cost-effectiveness of this practice. Such treatment was evaluated in hospitalized psychiatric patients. Methods Samples of consecutive inpatients treated with > 2 ('polytherapy') vs 1 antipsychotic ('monotherapy') were matched on age, sex, diagnosis and admission clinical ratings, and these groups were compared on total daily chlorpromazine-equivalent doses, days in hospital, and changes in clinical ratings between admission and discharge. Results The study sample included 69 polytherapy and 115 well-matched monotherapy subjects. Despite matching for initial CGI and GAF ratings, polytherapy was associated with high PANSS subscale scores of positive symptoms among affective psychosis, and relatively greater PANSS subscale ratings of excitement-agitation among patients diagnosed with schizophrenia. Estimated clinical improvement during hospitalization was similar among poly- and monotherapy patients, but total daily CPZ-eq doses at discharge averaged twice-greater with polytherapy, and hospitalization lasted 1.5 times longer. Conclusions Antipsychotic polytherapy as well as the types of agents combined may reflect clinician responses to particular symptom patterns. The value of specific combinations of antipsychotic agents and their comparison with monotherapies requires specific, prospective, randomized and well-controlled trials that consider matching on clinical characteristics and truly comparable doses across regimens. Copyright (c) 2005 John Wiley & Sons, Ltd

Elucidating Hidden and Enduring Weaknesses in Dust Emission Modeling

Large-scale classical dust cycle models, developed more than two decades ago, assume for simplicity that the Earth's land surface is devoid of vegetation, reduce dust emission estimates using a vegetation cover complement, and calibrate estimates to observed atmospheric dust optical depth (DOD). Consequently, these models are expected to be valid for use with dust-climate projections in Earth System Models. We reveal little spatial relation between DOD frequency and satellite observed dust emission from point sources (DPS) and a difference of up to 2 orders of magnitude. We compared DPS data to an exemplar traditional dust emission model (TEM) and the albedo-based dust emission model (AEM) which represents aerodynamic roughness over space and time. Both models overestimated dust emission probability but showed strong spatial relations to DPS, suitable for calibration. Relative to the AEM calibrated to the DPS, the TEM overestimated large dust emission over vast vegetated areas and produced considerable false change in dust emission. It is difficult to avoid the conclusion that calibrating dust cycle models to DOD has hidden for more than two decades, these TEM modeling weaknesses. The AEM overcomes these weaknesses without using masks or vegetation cover data. Considerable potential therefore exists for ESMs driven by prognostic albedo, to reveal new insights of aerosol effects on, and responses to, contemporary and environmental change projections

Functions of maize genes encoding pyruvate phosphate dikinase in developing endosperm

Maize opaque2 (o2) mutations are beneficial for endosperm nutritional quality but cause negative pleiotropic effects for reasons that are not fully understood. Direct targets of the bZIP transcriptional regulator encoded by o2 include pdk1 and pdk2 that specify pyruvate phosphate dikinase (PPDK). This enzyme reversibly converts AMP, pyrophosphate, and phosphoenolpyruvate to ATP, orthophosphate, and pyruvate and provides diverse functions in plants. This study addressed PPDK function in maize starchy endosperm where it is highly abundant during grain fill. pdk1 and pdk2 were inactivated individually by transposon insertions, and both genes were simultaneously targeted by endosperm-specific RNAi. pdk2 accounts for the large majority of endosperm PPDK, whereas pdk1 specifies the abundant mesophyll form. The pdk1- mutation is seedling-lethal, indicating that C4 photosynthesis is essential in maize. RNAi expression in transgenic endosperm eliminated detectable PPDK protein and enzyme activity. Transgenic kernels weighed the same on average as nontransgenic siblings, with normal endosperm starch and total N contents, indicating that PPDK is not required for net storage compound synthesis. An opaque phenotype resulted from complete PPDK knockout, including loss of vitreous endosperm character similar to the phenotype conditioned by o2-. Concentrations of multiple glycolytic intermediates were elevated in transgenic endosperm, energy charge was altered, and starch granules were more numerous but smaller on average than normal. The data indicate that PPDK modulates endosperm metabolism, potentially through reversible adjustments to energy charge, and reveal that o2- mutations can affect the opaque phenotype through regulation of PPDK in addition to their previously demonstrated effects on storage protein gene expression

A North American dust emission climatology (2001–2020) calibrated to dust point sources from satellite observations

Measurements of atmospheric dust have long influenced our understanding of dust sources and dust model calibration. However, assessing dust emission magnitude and frequency may reveal different dust source dynamics and is critical for informing land management. Here we use MODIS (500 m) albedo-based daily wind friction estimates to produce a new dust emission climatology of North America (2001–2020), calibrated by the novel use of dust point sources from optical satellite observations (rather than being tuned to dust in the atmosphere). Calibrated dust emission occurred predominantly in the biomes of the Great Plains (GP) and North American Deserts (NAD), in broad agreement with maps of aerosol optical depth and dust deposition but with considerably smaller frequency and magnitude. Combined, these biomes produced 7.2 Tg y-1 with contributions split between biomes (59.8% NAD, 40.2% GP) due to the contrasting conditions. Dust emission is dependent on different wind friction conditions on either side of the Rocky Mountains. In general, across the deserts, aerodynamic roughness was persistently small and dust sources were activated in areas prone to large wind speeds; desert dust emissions were wind speed limited. Across the Great Plains, large winds persist, and dust emission occurred when vegetation cover was reduced; vegetated dust emissions were roughness limited. We found comparable aerodynamic roughness exists across biomes/vegetation classes demonstrating that dust emission areas are not restricted to a single biome, instead they are spread across an ‘envelope’ of conducive wind friction conditions. Wind friction dynamics, describing the interplay between changing vegetation roughness (e.g., due to climate and land management) and changing winds (stilling and its reversal), influence modelled dust emission magnitude and frequency and its current and future climatology. We confirm previous results that in the second half of the 21st century the southern Great Plains is the most vulnerable to increased dust emission and show for the first time that risk is due to increased wind friction (by decreased vegetation roughness and / or increased wind speed). Regardless of how well calibrated models are to atmospheric dust, assuming roughness is static in time and / or homogeneous over space, will not adequately represent current and future dust source dynamics