Coal quality parameters, such as carbon content and ash yield, significantly influence the economic and metallurgical value of coal. Accurate determination of these parameters is crucial for optimizing industrial processes as they affect energy efficiency, processing costs, and pollution emissions. The standard ultimate and proximate analyses are time-consuming and resource-intensive. To offer an efficient alternative to the exhaustive standard methods, the present study attempted the use of mid-infrared Fourier Transform Infrared spectroscopy (FTIR) combined with advanced machine learning models to quickly and accurately predict coal carbon content and ash yield. Eighteen coal samples from the Johilla coalfield in Madhya Pradesh, India, was analyzed using FTIR spectroscopy across the range of 4000 to 350 cm−1. Machine learning algorithms like partial least squares regression (PLSR), random forest regression (RFR), support vector regression (SVR), and a multimodel estimation (MME) approach utilizing the average of the three models were employed to predict carbon content and ash yield. The MME model delivered enhanced precision and robustness compared to the individual models, with an R2 of 0.962, RMSE (

The ash yield resulting from the alteration of inorganic elements during the processes of combustion and gasification of coal stands as a crucial quality indicator for coal. Ash yield, along with calorific value, determines the commercial rating, ranking, and industrial usage of coal. Traditional methods of determining the ash yield in coal as per proximate analysis protocols are tedious and time-consuming as they involve the combustion of coal samples. A novel approach that uses mid-infrared Fourier Transform Infrared spectroscopy (FTIR) (optical technique) data in the range of 1450-350 cm-1 to identify spectrally sensitive zones (fourteen selective absorption bands) and to predict the ash yield in coal samples is presented. Multiple algorithms, including piecewise linear regression (PLR), artificial neural networks (ANN), partial least squares regression (PLSR), support vector regression (SVR), and random forest (RF), were utilized to predict the ash yield in coal. The present study suggests a multi-model estimation (MME) approach, using the average of the best three models (PLR, PLSR, and ANN) to achieve greater accuracy and robustness. This method outperforms individual models with a coefficient of determination - R-squared (R2) of 0.883, Root Mean Square Error (RMSE) of 3.059 wt%, RMSE in percentage (RMSE%) of 30.080, Mean Bias Error in percentage (MBE%) of 3.694, and Mean Absolute Error (MAE) of 2.249 wt%. The two-tailed t-test and F-test for mean and variance (99% Confidence Interval, CI) show no significant difference between the proximate analysis-derived ash yield and the multi-model estimated ash yield using FTIR data. FTIR spectroscopy data can accurately predict the ash yield in coal and perform well for coal samples from Johilla Coalfield, Umaria, Madhya Pradesh. The present model using FTIR analysis is a potential industrial tool for the quick determination of ash yield in coal and can be further improved by including data from other basins worldwide.

The level of phosphorus must be carefully monitored for proper and effective utilization of coal and coal ash. The phosphorus content needs to be assessed to optimize combustion efficiency and maintenance costs of power plants, ensure quality, and minimize the environmental impact of coal and coal ash. The detection of low levels of phosphorus in coal and coal ash is a significant challenge due to its complex chemical composition and low concentration levels. Effective monitoring requires accurate and sensitive equipment for the detection of phosphorus in coal and coal ash. X-ray fluorescence (XRF) is a commonly used analytical technique for the determination of phosphorus content in coal and coal ash samples but proves challenging due to their comparatively weak fluorescence intensity. Fourier Transform Infrared spectroscopy (FTIR) emerges as a promising alternative that is simple, rapid, and cost-effective. However, research in this area has been limited. Until now, only a limited number of research studies have outlined the estimation of major elements in coal, predominantly relying on FTIR spectroscopy. In this article, we explore the potential of FTIR spectroscopy combined with machine learning models (piecewise linear regression—PLR, partial least square regression—PLSR, random forest—RF, and support vector regression—SVR) for quantifying the phosphorus content in coal and coal ash. For model development, the methodology employs the mid-infrared absorption peak intensity levels of phosphorus-specific functional groups and anionic groups of phosphate minerals at various working concentration ranges of coal and coal ash. This paper proposes a multi-model estimation (using PLR, PLSR, and RF) approach based on FTIR spectral data to detect and rapidly estimate low levels of phosphorus in coal and its ash (R$$^2$$ 2 of 0.836, RMSE of 0.735 ppm, RMSE (%) of 34.801, MBE of − 0.077 ppm, MBE (%) of 5.499, and MAE of 0.528 ppm in coal samples and R$$^2$$ 2 of 0.803, RMSE of 0.676 ppm, RMSE (%) of 38.050, MBE of − 0.118 ppm, MBE (%) of 4.501, and MAE of 0.474 ppm in coal ash samples). Our findings suggest that FTIR combined with the multi-model approach combining PLR, PLSR, and RF regression models is a reliable tool for rapid and near-real-time measurement of phosphorus in coal and coal ash and can be suitably modified to model phosphorus content in other natural samples such as soil, shale, etc.

Owing to the increased availability of high-resolution satellite data and the rapid development of Geographic Information System (GIS) technology, the mapping of active faults and quantification of tectonic activity in inaccessible regions has exceedingly improved. We examined the tectonic activity in the Trans-Yamuna region of the NW Himalaya using geomorphic indices derived from a Digital Elevation Model (DEM). In addition, this study evaluates the sensitivity of four space-borne Digital Elevation Models (DEMs) with respect to TAN DEM-X (TerraSAR-X add-on for Digital Elevation Measurements). The Cartosat DEM, generated with a spatial resolution of 5 meters using state-of-the-art methods, demonstrated a reliable representation of topography. Geomorphic indices such as Asymmetry Factor (AF), Transverse Topography (TT), Hypsometric Integral (HI), Valley Floor width (Vf), Stream-length gradient index (SL), and Normalised steepness index (ksn) were computed for 41 sub-watersheds to determine the degree of tectonic activity. We infer that majority of the region is tectonically active, with upliftment continuing to occur in the north of the Main Boundary Thrust (MBT). Furthermore, the existence of mapped active faults north of the MBT further substantiates the fact that strain release is not only concentrated in the frontal Himalaya, but is distributed over a broader area above the decollement.

The high-altitude Hindu Kush-Himalayan region (HKH, average ~5 km from msl) and the adjacent Indo-Gangetic plains (IG plains, ~0–250 m msl), due to their geographical location and complex topography, are reported to be highly sensitive to climatic changes. Recent studies show that the impacts of climate change and associated changes in water resources (glacial/snow melt water and rainfall) in this region are multifaceted, thereby affecting ecosystems, agriculture, industries, and inhabitants. In this study, 45 years of Microwave Sounding Unit/Advanced Microwave Sounding Unit (MSU/AMSU)-derived mid-tropospheric temperature (TMT, 3–7 km altitude) and lower tropospheric temperature (TLT, 0–3 km altitude) data from the Remote Sensing Systems (RSS Version 4.0) were utilized to analyze the overall changes in tropospheric temperature in terms of annual/monthly trends and anomalies. The current study shows that the mid-tropospheric temperature (0–3 km altitude over the HKH region) has already alarmingly increased (statistically significant) in Tibet, the western Himalayas, and the eastern Himalayas by 1.49 °K, 1.30 °K, and 1.35 °K, respectively, over the last 45 years (1978–2022). As compared to a previous report (TMT trend for 30 years, 1979–2008), the present study of TMT trends for 45 years (1978–2022) exhibits a rise in percent change in the trend component in the high-altitude regions of Tibet, the western Himalayas, and the eastern Himalayas by approximately 310%, 80%, and 170%, respectively. In contrast, the same for adjacent plains (the western and eastern IG plains) shows a negligible or much lower percent change (0% and 40%, respectively) over the last 14 years. Similarly, dust source regions in Africa, Arabia, the Middle East, Iran, and Pakistan show only a 130% change in warming trends over the past 14 years. In the monthly breakup, the ‘November to March’ period usually shows a higher TMT trend (with peaks in December, February, and March) compared to the rest of the months, except in the western Himalayas, where the peak is observed in May, which can be attributed to the peak dust storm activity (March to May). Snow cover over the HKH region, where the growing season is known to be from September to February, is also reported to show the highest snow cover in February (with the peak in January, February, or March), which coincides with the warmest period in terms of anomaly and trend observed in the long-term mid-tropospheric temperature data (1978–2022). Thus, the current study highlights that the statistically significant and positive TMT warming trend (95% CI) and its observed acceleration over the high-altitude region (since 2008) can be attributed to being one of the major factors causing an acceleration in the rate of melting of snow cover and glaciers, particularly in Tibet and the Eastern Himalayas.

The alteration of the Earth’s surface due to urbanization and the formation of urban heat islands is one of the most evident and widely discussed anthropogenic impacts on Earth’s microclimate. The elevated land surface temperature in the urban perimeter compared with the surrounding non-urban area is known as the surface urban heat island (SUHI) effect. India has experienced swift urban growth over the past few decades, and this trend is expected to persist in years to come. The literature published on SUHI in India focuses only on a few specific cities, and there is limited understanding of its geospatial variation across a broader region and its long-term trend. Here, we present one of the first studies exploring the long-term diurnal (daytime, and nighttime), seasonal, and annual characteristics of SUHI in the 20 largest urban centers of India and its neighboring countries. The study highlights a statistically significant (95% confidence interval) rise in nighttime surface temperatures across major cities based on a linear fit over 23 years (2000–2023) of MODIS land surface temperature satellite observations. The nighttime SUHI was found to be more conspicuous, positive, and consistent when compared with daytime satellite observations. The nighttime SUHI for April–May–June representing the pre-monsoon and onset of monsoon months for the top 10 cities, ranged from 0.92 to 2.33 °C; for December–January–February, representing the winter season, it ranged from 1.38 to 2.63 °C. In general, the total change in the nighttime SUHI based on linear fit (2000–2023) for the top ten cities showed warming over the urban region ranging from 2.04 to 3.7 °C. The highest warming trend was observed during the months of May–June–July (3.7 and 3.01 °C) in Ahmedabad and Delhi, cities that have undergone rapid urbanization in the last two to three decades. The study identified strongly positive annual SUHI intensity during nighttime, and weakly negative to moderately positive annual SUHI intensity during daytime, for major cities. Jaipur (India), Lahore (Pakistan), Dhaka (Bangladesh), and Colombo (Sri Lanka) showed a nighttime SUHI intensity of 2.17, 2.33, 0.32, and 0.21 °C, respectively, during the months of April–May–June, and a nighttime SUHI intensity of 2.63, 1.68, 0.94, 0.33 °C, respectively, for the months of December–January–February (2000–2023). It is apparent that the geographical location (inland/coastal) of the city has a high influence on the daytime and nighttime SUHI patterns. The current research is intended to help city planners and policymakers better understand SUHI intensity (day and night/seasonal basis) for developing strategies to mitigate urban heat island effects.

The carbon content of different types of coal determines its utility in industries and thermal power generation. The most popular and widely used is the conventional method (ultimate analysis) to determine coal’s carbon content (C, wt.%), along with H, N, and S. In the present study, the authors attempted to analyze the carbon content (C in %) in coals via data from Fourier-transform infrared (FTIR) spectroscopy, which can be a promising alternative. As a reference, the carbon content in the coal samples, referred to as CCHNS (in wt.%), was determined from the ultimate analysis. The mid-infrared FTIR spectroscopic data were used to investigate the response of functional groups associated with carbon or its compounds, which were used to model and estimate the carbon content in coal samples (referred to as CFTIR, in wt.%). FTIR spectral signatures were utilized in specific zones (between wavenumbers 4000 and 400 cm−1) from a total of 18 coal samples from the Johilla coalfield, Umaria district, Madhya Pradesh, India. These 18 coal samples were used to produce 126 Coal+KBr pellets (at seven known dilution factors for each coal sample), and the spectral response (absorbance) from each pellet was recorded. For model development and validation, the training set and test set were formed using a 17:1 split (K-fold cross validation). The carbon content in the coal samples was modeled using the training set data by applying the piecewise linear regression method employing quasi-Newton (QN) with a breakpoint and least squares loss function. The model was validated using an independent test set. A pairwise comparison of estimates of carbon in the laboratory from the CHNS analyzer (CCHNS) and modeled carbon from FTIR data (CFTIR) exhibited a good correlation, relatively low error, and bias (coefficient of determination (R2) up to 0.93, RMSE of 23.71%, and MBE of −0.52%). Further, the significance tests for the mean and variance using the two-tailed t-test and F-test showed that no significant difference occurred between the pair of observed CCHNS and the model’s estimated CFTIR. For high-ash coals from the Johilla coalfield, the model presented here using mid-infrared FTIR spectroscopy data performs well. Thus, FTIR can potentially serve as an important method for quickly determining the carbon content of high-ash coals from various basins and can potentially be extended to soil and shale samples.

High-ash Indian coals are primarily used as thermal coal in power plants and industries. Due to the presence of sulfur in thermal coal, flue gas is a major environmental concern. Conventional methods (Ultimate Analysis of Coal) for sulfur content estimation are time-consuming, relatively costly, and destructive. In this study, Fourier-transform infrared (FTIR) spectroscopy has emerged as a promising alternative method for the rapid and nondestructive analysis of the sulfur content in coal. In the present study, the actual sulfur content in the coal samples was determined using Ultimate Analysis (CHNS analyzer). In contrast, mid-infrared FTIR spectroscopic data (4000–400 cm−1) were used to analyze the functional groups related to sulfur or its compounds in the coal samples to predict the sulfur content. A comparison of sulfur estimated using a CHNS analyzer and predicted using mid-infrared spectroscopy (FTIR) data shows that it can accurately predict sulfur content in high-ash Indian coals using the piecewise linear regression method (Quasi-Newton, QN). The proposed FTIR-based sulfur prediction model showed a coefficient of determination (R2) of up to 0.93, where the total no. of samples (Coal + KBr pellets, n) was 126 (using 17:1 split, K-fold cross validation). The root-mean-square error (RMSE, wt.%) is 0.0035, mean bias error (MBE, wt.%) is −0.0003, MBE (%) is 3.31% and mean absolute error (MAE, wt.%) is 0.0020. The two-tailed t-test and F-test for mean and variance indicated no significant difference between the pair of values of observed sulfur (SCHNS, wt.%) using CHNS data and the model predicted sulfur (SFTIR, wt.%) using FTIR data. The prediction model using mid-infrared FTIR spectroscopy data and the Quasi-Newton method with a breakpoint and loss function performs well for coal samples from the Johilla Coalfield, Umaria. Thus, it can be a valuable tool for analyzing sulfur in other ash-rich coals from various basins worldwide.

Snow cover changes have a direct bearing on the regional and global energy and water cycles and the change in the Earth's climate conditions. We studied the relatively long-term (2000–2017) altitudinal spatiotemporal changes in the coverage of snow and glaciers in one of the world's largest mountainous regions, the Hindu Kush Himalayan (HKH) region, including Tibet, using remote sensing data (5 km grid resolution) from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra satellite. This dataset provided a unique opportunity to study zonal and hypsographic changes in the intra-annual (accumulating season and melting season) and interannual variations in snow and glacial cover over the HKH region. The zonal and altitudinal (hypsographic) analyses were carried out for the melting season and accumulating season. The altitude-wise linear trend analysis (Pearson's) of snow cover, shown as a hypsographic curve, clearly indicates a major decline in snow cover (average of 5 % or more at 100 m interval aggregates) between 4000–4500 and 5500–6000 m altitudes, which is consistent with the median trend (Theil–Sen – TS) and the monotonic trend (Mann–Kendall – MK; statistics) analysis. This analysis also revealed the regions and altitudes where major and statistically significant increases (10 % to 30 %) or decreases (−10 % to −30 %) in snow cover are identified. The extrapolation of the altitude-wise linear trend shows that it may take between ∼ 74 and 7900 years, for 3001–6000 and 6000–7000 m altitude zones respectively, for mean snow cover to decline approximately 25 % in the HKH. More detailed analysis based on longer observational records and model simulations is warranted to better understand the underlying factors, processes, and feedbacks that affect the dynamic of snow cover in HKH. These preliminary results suggest a need for continued monitoring of this highly sensitive region to climate variability and change that depends on snow as a major source of freshwater for all human activities.

Secondary structure changes are an inherent part of antimicrobial (AMP) and amyloidogenic peptide activity, especially in close proximity to membranes, and impact the peptides' function and dysfunction roles. The formation, and stability of α-helical components are regarded as essential 'intermediates' for both these functions. To illuminate the conformational transitions leading to amyloid formation we use short cationic AMPs, from an Australian toadlet, Uperoleia mjobergii, (Uperin 3 family, U3) and assess the impact on secondary structural elements in the presence of a membrane mimetic surfactant, sodium dodecyl sulfate (SDS). Specifically, Uperin 3.x, where x=4, 5, 6 wild-type peptides and position seven variants for each, R7A or K7A, were investigated using a combination of experimental and simulation approaches. In water, U3 peptides remain largely unstructured as random coils, with the addition of salts initiating structural transitions leading to assembly towards amyloid. Solution NMR data show that an unstructured U3.5 wt peptide transitions in the presence of SDS to a well-defined α-helical structure that spans nearly the entire sequence. Circular dichroism (CD) and ThT fluorescence studies show that all six U3 peptides aggregate in solution, albeit with vastly varying rates, and a dynamic equilibrium between soluble aggregates rich in either α-helices or β-sheets may exist in solution. However, the addition of SDS leads to a rapid disaggregation for all peptides and stabilisation of predominantly α-helical content in all the U3 peptides. Molecular dynamics (MD) simulations show that the adsorption of U3.5 wt/R7A peptides onto the SDS micelle is driven by Coulombic attraction between peptide cationic residues and the negatively charged sulfate head-groups on SDS. Simulating the interactions of various kinds of β-sheet dimers (of both U3.5 wt and its variant U3.5 R7A) with SDS micelles confirmed β-sheet content decreases in the dimers after their attachment to the SDS micelle. Adsorbed peptides interact favourably with the hydrophobic core of the micelle, promoting intramolecular hydrogen bonds leading to stabilisation of the α-helical structure in peptides, and resulting in a corresponding decrease in intermolecular hydrogen bonds responsible for β-sheets.

Publicly available Digital Elevation Models (DEM) derived from various space-based platforms (Satellite/Space Shuttle Endeavour) have had a tremendous impact on the quantification of landscape characteristics, and the related processes and products. The accuracy of elevation data from six major public domain satellite-derived Digital Elevation Models (a 30 m grid size—ASTER GDEM version 3 (Ast30), SRTM version 3 (Srt30), CartoDEM version V3R1 (Crt30)—and 90 m grid size—SRTM version 4.1 (Srt90), MERIT (MRT90), and TanDEM-X (TDX90)), as well as the improvement in accuracy achieved by applying a correction (linear fit) using Differential Global Positioning System (DGPS) estimates at Ground Control Points (GCPs) is examined in detail. The study area is a hard rock terrain that overall is flat-like with undulating and uneven surfaces (IIT (ISM) Campus and its environs) where the statistical analysis (corrected and uncorrected DEMs), correlation statistics and statistical tests (for elevation and slope), the impact of resampling methods, and the optimum number of GCPs for reduction of error in order to use it in further applications have been presented in detail. As the application of DGPS data at GCPs helps in the substantial reduction of bias by the removal of systematic error, it is recommended that DEMs may be corrected using DGPS before being used in any scientific studies.

Titanium or vanadium metals or their alloys are important industrial metals/alloys. Because these resources are in short supply, the investigation of potential titaniferous-vanadiferous deposits needs special attention to bridge the supply-demand gap. The study integrates geological, geochemical, remote sensing, and geophysical data for assessing the potentiality of titaniferous-vanadiferous, magnetite-ilmenite mineralization in and around the Sudamakund and Paharpur areas, Gaya and Jehanabad districts, Bihar, India, and delineation of specific targets for detailed exploration. Field visits for large scale mapping on (1:12,500 scale) were used to conduct a reconnaissance survey for magnetite-ilmenite mineralization in parts of toposheet number 72G/04 in the Gaya and Jehanabad districts of Bihar, as well as the collection of bedrock samples (BRS), pit/trench samples (PTS), petrographic samples (PS), and petrochemical samples (PCS), followed by petrographic and ore microscopic study, and interpretation of chemical results. Signatures of oxidized iron-bearing sulphides (iron-oxides ratio) and other ferrous-iron-bearing minerals surrounded by altered rocks (clay bearing minerals) are visible in remote sensing images. The geological work was followed by ground geophysical gravity and magnetic surveys in selected blocks by the Geophysics Division, eastern region (ER) on a 1:12,500 scale. The magnetite ore is hard, compact, crystalline, and at some places, granular in nature. The analytical value of these magnetite ore bodies indicates average Fe content at 49.53% (range 25.85–60.78%), with a considerable amount of TiO2 (average 15.85%, range 1.47–26.77%), and V (average 144.79 ppm, range 30.00–256.00 ppm, from PTS). The trends of these magnetite ore deposits correspond to the major lineaments (NE-SW and NW-SE). The superimposition of gravity and magnetic contour maps with the geological map (1:12,500 scale) helps explain the observed geophysical anomalies, and the possible subsurface (horizontal and vertical) expansion of magnetite ore deposits in alluvium cover regions warrants further investigation.

Dynamical and physical features of a long range transported dust event originating in China affecting Korea early March 2008 are examined using an integrative multi-sensor and multi-algorithm approach. Aerosol loadings and their size mode were analyzed over both ocean and land surfaces using the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD), employing standard dark target (DT) and deep blue (DB) algorithms, and the Angstrom exponent (AE). Anthropogenic absorbing aerosols and smoke were found to be significant over the Indochina Peninsula, the Philippines and southern China, while a mixture of dust and pollution were predominant over central to northern China, as identified by the AE analysis and the Multi-angle Imaging SpectroRadiometer (MISR) spherecitiy and plume height. Remarkable aerosol absorptions in both the near ultraviolet (UV) and the visible were spread over central, central western and northern China, probably due to aerosol mixtures including desert dust and industrial pollution as well as smoke from biomass burning as evidenced from the Ozone Monitoring Instrument (OMI). Long range transport is validated as dust storm reached up to 4-5 km vertically and a mixed cloud layer was identified over the Yellow Sea as disclosed by the vertical structure of dust aerosols as well as observed aerosols subtypes from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). The real time detection and monitoring of the dust outbreak and its subsequent evolution are available through the infrared optical depth index (IODI), developed from the MTSAT-1R geostationary satellite imager.

We investigate the temporal characteristics of major air pollutants collected from 44 air quality stations over the city of Seoul, Korea, namely, nitrogen dioxide, carbon monoxide, particular matter at 10 microns, and sulfur dioxide (SO2) between 2000 and 2009. The corresponding satellite datasets, namely, aerosol optical depth (AODsat), Ångström exponent, and fine mode fraction, collected from moderate resolution imaging spectroradiometer (MODIS) as well as the Aeronet ground aerosol optical depth (AODaeronet), have been analyzed. Pollutants’ seasonal effect has been inferred from the precipitation and temperature. The four pollutants under study show varying temporal characteristics with different annual mean concentration patterns. The monthly mean of mentioned pollutants all show similar low concentrations during the summer season and high concentrations during the winter season. We found that pollution is strongly linked to temperature and precipitation variability, especially during the fall season. Satellite data analysis provides information on the pollutants origin whether of natural or anthropogenic type. Our results indicate that the anthropogenic aerosol is dominant in the summer season even though the concentration was lower than the other seasons. AODaeronet and Ångström exponent indicated high positive and negative correlation coefficients with PM10, 0.60, and −0.45, respectively. Both small and large sizes of aerosols existed in 2007; however coarse size of aerosols was the primary component in 2002.

On 5 July 2011, a massive dust storm struck Phoenix, Arizona (USA), raising concerns for increased cases of valley fever (coccidioidomycosis, or, cocci). A quasi-operational experimental airborne dust forecast system predicted the event and provides model output for continuing analysis in collaboration with public health and air quality communities. An objective of this collaboration was to see if a signal in cases of valley fever in the region could be detected and traced to the storm - an American haboob. To better understand the atmospheric life cycle of cocci spores, the DREAM dust model (also herein, NMME-DREAM) was modified to simulate spore emission, transport and deposition. Inexact knowledge of where cocci-causing fungus grows, the low resolution of cocci surveillance and an overall active period for significant dust events complicate analysis of the effect of the 5 July 2011 storm. In the larger context of monthly to annual disease surveillance, valley fever statistics, when compared against PM10 observation networks and modeled airborne dust concentrations, may reveal a likely cause and effect. Details provided by models and satellites fill time and space voids in conventional approaches to air quality and disease surveillance, leading to land-atmosphere modeling and remote sensing that clearly mark a path to advance valley fever epidemiology, surveillance and risk avoidance. (C) 2014 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

A dust storm of fearful proportions hit Phoenix in the early evening hours of 5 July 2011. This storm, an American haboob, was predicted hours in advance because numerical, land–atmosphere modeling, computing power and remote sensing of dust events have improved greatly over the past decade. High-resolution numerical models are required for accurate simulation of the small scales of the haboob process, with high velocity surface winds produced by strong convection and severe downbursts. Dust productive areas in this region consist mainly of agricultural fields, with soil surfaces disturbed by plowing and tracks of land in the high Sonoran Desert laid barren by ongoing draught. Model simulation of the 5 July 2011 dust storm uses the coupled atmospheric-dust model NMME–DREAM (Non-hydrostatic Mesoscale Model on E grid, Janjic et al., 2001; Dust REgional Atmospheric Model, Nickovic et al., 2001; Pérez et al., 2006) with 4 km horizontal resolution. A mask of the potentially dust productive regions is obtained from the land cover and the normalized difference vegetation index (NDVI) data from the Moderate Resolution Imaging Spectroradiometer (MODIS). The scope of this paper is validation of the dust model performance, and not use of the model as a tool to investigate mechanisms related to the storm. Results demonstrate the potential technical capacity and availability of the relevant data to build an operational system for dust storm forecasting as a part of a warning system. Model results are compared with radar and other satellite-based images and surface meteorological and PM10 observations. The atmospheric model successfully hindcasted the position of the front in space and time, with about 1 h late arrival in Phoenix. The dust model predicted the rapid uptake of dust and high values of dust concentration in the ensuing storm. South of Phoenix, over the closest source regions (~25 km), the model PM10 surface dust concentration reached ~2500 μg m−3, but underestimated the values measured by the PM10 stations within the city. Model results are also validated by the MODIS aerosol optical depth (AOD), employing deep blue (DB) algorithms for aerosol loadings. Model validation included Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), equipped with the lidar instrument, to disclose the vertical structure of dust aerosols as well as aerosol subtypes. Promising results encourage further research and application of high-resolution modeling and satellite-based remote sensing to warn of approaching severe dust events and reduce risks for safety and health.

In this study, the Savitzky-Golay filter was applied to smooth observed unnatural variations in the temporal profiles of the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) time series from the MODerate Resolution Imaging Spectroradiometer (MODIS). We computed two sets of land cover classifications based on the NDVI and EVI time series before and after applying the Savitzky-Golay filter. The resulting classification from the filtered versions of the vegetation indices showed a substantial improvement in accuracy when compared to the classifications from the unfiltered versions. The classification by the EVIsg had the highest K (0.72) for all classes compared to those of the EVI (0.67), NDVI (0.63), and NDVIsg (0.62). Therefore, we conclude that the EVIsg is best suited for land cover classification compared to the other data sets in this study.