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Intelligent compaction is an emerging technology in the management of pavement layers, more specifically, of unbound geomaterial layers. Different types of intelligent compaction measurement values (ICMVs) are available on the basis of the configuration of the roller, vibration mechanism, and data collection and reduction algorithms. The spatial distribution of the estimated ICMVs is usually displayed as a color-coded map, with the ICMVs categorized into a number of classes with specific color codes. The number of classes, as well as the values of the breaks between classes, significantly affect the perception of compaction quality during the quality management process. In this study, three sets of ICMV data collected as a part of a field investigation were subjected to geostatistical analyses to evaluate different classification scenarios and their impact on the interpretation of the data. The classification techniques were evaluated on the basis of the information theory concept of minimizing the information loss ratio. The effect of the ICMV distribution on the selection of the classification method was also studied. An optimization technique was developed to find the optimal class breaks that minimize the information loss ratio. The optimization algorithm returned the best results, followed by the natural breaks and quantile methods, which are suited to the skewness of the ICMV distribution. The identification of less-stiff areas by using the methods presented will assist highway agencies to improve process control approaches and further evaluate construction quality criteria. Although the concepts discussed can apply to any compacted geomaterial layer, the conclusions apply to the type of compacted soil in this particular test section.

This paper presents the results of the assessment of several popular guidelines and test protocols for the practical use of intelligent compaction at construction sites. In addition to the timely data processing and analysis, other practical challenges that impede the wide use of the intelligent compaction are establishing criteria for high quality compaction and relations between the in situ and roller measurements. Several test strips with varying moisture contents were constructed and evaluated to determine the best strategy to select the target intelligent compaction values. Several in situ test devices (such as nuclear density gauge, lightweight deflectometer and dynamic cone penetrometer) were used to compare the compacted layer properties with roller measurements. Considering the inherent variability of the material properties and relations between the in situ and roller measurements, an alternate approach to building test sections for estimating the target intelligent compaction value was evaluated. The alternative approach is based on quantifying the lift contribution to the intelligent compaction value. The proposed approach seems to be a good compromise between achieving a high quality compacted layer and providing enough flexibility to contractors to use their know-how in expediting the construction process.

The combination of global warming and urbanization is expected to exacerbate the well-established urban heat island effect. Unfortunately, little is known about nighttime urban heat retention and its possible health effects on residents. Here we use infrared satellite imagery to evaluate the distribution of nighttime heat and its decadal changes in a large desert metroplex.

Intelligent compaction (IC) is an emerging technology for quality management of compacted pavement layers. Data collected by a vibration sensor and translated to a stiffness-based parameter is usually called the IC measurement value (ICMV). The ICMVs are coordinated with the global positioning system (GPS) data to assess the geospatial distribution of compacted layer properties. The IC rollers can be equipped with the original equipment manufacturer (OEM) systems that are provided by the vendors. However, with the recent introduction of the after-market or retrofit IC kits, it is possible to collect IC data with most regular vibratory rollers. Even though the use of the IC retrofit kits has been gaining popularity, its performance during the field operation has not been documented extensively. In this study, two dynamic vibratory rollers equipped with the OEM IC systems were employed along a test section. A retrofit kit was also installed on one of the rollers to collect the IC data during the same field operation simultaneously with the OEM system. The geospatial distribution of the ICMVs between the two types of systems for the most part was comparable. However, the magnitudes of the ICMVs among rollers and IC systems were somewhat different. These differences in the ICMVs were attributed to the differences in the vibration sensors and their installation as well as the data reduction algorithms among different systems. The reliability of the results from the OEM systems and retrofit kits seemed to be dependent on the proper installation of the vibration sensors as well as the accurate calibration of the GPS unit prior to the field data collection. A validation system and a standard protocol are needed to harmonize the performance of the IC rollers.

This study presents the evaluation of the compaction process on a lime-stabilized clayey subgrade soil using the intelligent compaction (IC) technology. Two test beds with similar clayey subgrade soil were constructed with and without in-situ lime stabilization. These test beds were evaluated using several in situ nondestructive testing devices and an IC-equipped vibratory roller. The results from the study showed significant spatial variability in the in-situ test results and the roller measurements. In comparison to the untreated subgrade, the lime-stabilized test sections demonstrated a reduction in the variability of the roller measurements. The roller measurements on the stabilized section were influenced by the testing time and the underlying support conditions. The laboratory results and the field measurements using the roller and in-situ devices pointed to the improvement in the soil properties after stabilization. The moduli of the stabilized subgrade soil increased by almost two times. Statistical analyses demonstrated the influence of the underlying support condition, the moisture content and the lift thickness variations on the roller measurements.

Few studies have taken a conventional quantitative environmental justice approach to assessing the inequitable implications of climate change at a fine scale, such as across neighborhoods within an urban area. In this paper, we test the “environmental justice hypothesis” for climate change-related variables in the bi-national context of El Paso, Texas and Ciudad Juarez, Chihuahua using a matched set of social indicators from the 2000 US and Mexican censuses and biophysical data related to heat, ozone and flooding. T-test results demonstrated that social marginality and climate change-related hazard exposure were generally higher in Juarez as compared to El Paso. Using spatial regression models, we found patterns of environmental injustice in the sister cities related to these climate change-related hazards. Lower social class neighborhoods generally faced increased risks from extreme heat in both cities, and from floods and peak ozone in El Paso. In El Paso, children also faced significant and disproportionate exposure to peak ozone, while female-headed households were significantly more burdened by flooding and peak ozone in Juarez. Despite the limitations of this cross-sectional study, we can expect injustices to heighten as neighborhoods at-risk now become increasingly exposed under climate change scenarios. In sum, this analysis provides a model for investigating inequities associated with future small area impacts of climate change.

There are significant human impacts associated with climate change. This paper introduces a model for identifying small area risks associated with children’s vulnerability to climate change-related hazard exposures, which is transferable to other regions and adaptable to varied population and exposure scenarios. The cross-national El Paso-Ciudad Juarez (US-Mexico) metropolis serves as the study area for model implementation, which involves mapping social vulnerability, hazard exposure, and cumulative climate change-related risks. This study addresses two limitations of extant fine-scale climate change vulnerability mapping studies. First, rather than focusing on one exposure variable, it assesses the combined risks of multiple exposures (extreme heat, peak ozone, and floods) and, thus, offers a model for mapping neighborhood-level cumulative climate change exposure risks. Second, it provides a model for small area spatial analyses of climate change vulnerability within low-/middle-income countries and in contexts where climate change risks (and appropriate responses) are cross-national in scope.

This article examines spatial relationships between environmental hazards (i.e., pork feed lots, brick kilns, final assembly plants and a rail line) and markers of social marginality in Ciudad Juarez, Mexico. Juarez represents an opportunity for researchers to test for patterns of injustice in a recently urbanizing metropolis of the Global South. We use spatial-econometric modeling to predict the four unique hazard variables and a composite hazard variable using socio-demographic variables at the neighborhood level. Lower class and higher percentages of children and migrants were statistically significant predictors of composite hazard density. These results align with previous studies in the North. However, disaggregating these results by hazard type reveals important and counterintuitive differences in groups at risk based on the market-orientation of the hazard (i.e., domestic vs. transnational) and its location within the urban structure.

Due to the progressive increase in development of desert land in Egypt, the demand for efficient and accurate land cover change information is increasing. In this study, we apply the methodology of post-classification change detection to map and monitor land cover change patterns related to agricultural development and urban expansion in the desert fringes of the Eastern Nile Delta region. Using a hybrid classification approach, we employ multitemporal Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+) images from 1984, 1990 and 2003 to produce three land cover/land-use maps. Post-classification comparison of these maps was used to obtain 'from-to' statistics and change detection maps. The change detection results show that agricultural development increased by 14% through the study period. The average annual rate of land reclamation during 1990-2003 (4511 ha a-1) was comparable to that during 1984-1990 (4644 ha a-1), reflecting a systematic national plan for desert reclamation that went into effect. We find that the increase in urbanization (by ca 21 300 ha) during 1990-2003 was predominantly due to encroachment into traditionally cultivated land at the fringes of urban centres. Our results accurately quantify the land cover changes and delineate their spatial patterns, demonstrating the utility of Landsat data in analysing landscape dynamics over time. Such information is critical for making efficient and sustainable policies for resource management.

Gravity prospecting is a very useful geophysical technique for the investigation of karst subsurface features, in urban areas, which are a common natural hazard in several parts of the world. Gravity data were used to determine the subsurface conditions in central Qatar to map zones vulnerable to sinkhole formation in the vicinity of Dahel Al-Hamam area in the State of Qatar. Gravity modeling was a useful technique to map the spatial extension of the karts topography associated with these sinkholes. Clusters of closely spaced contours of low gravity values observed in the northeastern part of the studied area are indicative of relatively very low negative anomaly which points to missing mass due to karst processes. This was interpreted as either as an air-filled or water-filled sinkhole. The obtained video images of the subsurface features of the studied area confirmed that the existing subsurface caves are water filled. The extension of the sinkhole and associated openings are connected to the Arabian Gulf. To prevent the growth of associated underground cavities, the bibliographical research indicated that filling up the uppermost voids with stable grout mix is one of the appropriate treatment options to help reduce the macro-permeability of the rock and to prohibit the migration of the favorable conditions of cavity formation. The findings have both local and general applications. Locally, the findings delineate the subsurface extension of the karst topography, and it is generally demonstrated that non-invasive surveying methods constitute a useful tool to enhance knowledge on sinkhole phenomenon. 2016, Springer-Verlag Berlin Heidelberg. Scopus

Visible and Near-Infrared (VNIR) through Short Wavelength Infrared (SWIR) (0.4–2.5 μm) AVIRIS data, along with laboratory spectral measurements and analyses of field samples, were used to characterize grain size variations in aeolian gypsum deposits across barchan-transverse, parabolic, and barchan dunes at White Sands, New Mexico, USA. All field samples contained a mineralogy of ∼ 100% gypsum. In order to document grain size variations at White Sands, surficial gypsum samples were collected along three Transects parallel to the prevailing downwind direction. Grain size analyses were carried out on the samples by sieving them into seven size fractions ranging from 45 to 621 μm, which were subjected to spectral measurements. Absorption band depths of the size fractions were determined after applying an automated continuum-removal procedure to each spectrum. Then, the relationship between absorption band depth and gypsum size fraction was established using a linear regression. Three software processing steps were carried out to measure the grain size variations of gypsum in the Dune Area using AVIRIS data. AVIRIS mapping results, field work and laboratory analysis all show that the interdune areas have lower absorption band depth values and consist of finer grained gypsum deposits. In contrast, the dune crest areas have higher absorption band depth values and consist of coarser grained gypsum deposits. Based on laboratory estimates, a representative barchan-transverse dune (Transect 1) has a mean grain size of 1.16 ϕ (449 μm). The error bar results show that the error ranges from − 50 to + 50 μm. Mean grain size for a representative parabolic dune (Transect 2) is 1.51 ϕ (352 μm), and 1.52 ϕ (347 μm) for a representative barchan dune (Transect 3). T-test results confirm that there are differences in the grain size distributions between barchan and parabolic dunes and between interdune and dune crest areas. The t-test results also show that there are no significant differences between modeled and laboratory-measured grain size values. Hyperspectral grain size modeling can help to determine dynamic processes shaping the formation of the dunes such as wind directions, and the relative strengths of winds through time. This has implications for studying such processes on other planetary landforms that have mineralogy with unique absorption bands in VNIR-SWIR hyperspectral data.

Scholars have recognized a climate gap, wherein poor communities face disproportionate impacts of climate change. Others have noted that climate change and economic globalization may mutually affect a region or social group, leading to double exposure. This paper investigates how current and changing patterns of neighborhood demographics are associated with extreme heat in the border city of Juárez, Mexico. Many Juárez neighborhoods are at-risk to triple exposures, in which residents suffer due to the conjoined effects of the global recession, drug war violence, and extreme heat. Due to impacts of the recession on maquiladora employment and the explosion of drug violence (since 2008), over 75% of neighborhoods experienced decreasing population density between 2000 and 2010 and the average neighborhood saw a 40% increase in the proportion of older adults. Neighborhoods with greater drops in population density and increases in the proportion of older residents over the decade are at significantly higher risk to extreme heat, as are neighborhoods with lower population density and lower levels of education. In this context, triple exposures are associated with a climate gap that most endangers lower socioeconomic status and increasingly older aged populations remaining in neighborhoods from which high proportions of residents have departed.