A Comparative Study of Distribution of Heavy Metal Concentrations in the Pomacea insularum Collected from Polluted and Unpolluted Sites of the Freshwater Ecosystem in Malaysia

The snails (Pomacea insularum) were collected from polluted and unpolluted sites at Juru River and the Universiti Putra Malaysia Lake, respectively. Besides the shells, the soft tissues of snails were dissected into 6 different parts: intestine, operculum, foot, pennial sac, lung sac and the remainder. They were analyzed for the concentrations of Cu, Cd, Zn, Pb, Ni and Fe. The present study found 6 interesting points. First, all the metal levels found in the different parts of P. insularum collected from Juru River were significantly (p<0.05) higher than those in the snails of unpolluted lake. These results were also supported by the sediment data in which the Juru River sediment had significantly (p<0.05) higher levels of metal than those in the unpolluted lake. Second, the high levels of Cd and Pb were found in the shell of P. insularum, indicating that it is a potential biomonitoring material for these metals. Third, intestine accumulated elevated levels of Zn, Ni and Fe and thus is a potential biomonitoring organ for the 3 metals. Fourth, high levels of Cu were found in the lung sac and this phenomenon could be related to this metal binding to metallothionein in this organ. Fifth, elevated level of Pb found in the soft tissues of P. insularum indicated that this snail is not suitable for human consumption since its pollution level was higher than the food safety guideline for Pb. Sixth, there was no significant difference (p>0.05) between the different sizes (small sizes: 30.5-33.2 mm; large sizes: 37.7-40.4 mm) of P. insularum. The first 5 points supported the use of different soft tissues of P. insularum as biomonitoring organs of heavy metal pollution in the freshwater ecosystem in Malaysia although further validation is required.     

Investigating snowpack across scale in the northern Great Lakes–St. Lawrence forest region of Central Ontario,Canada

This study investigates scaling issues by evaluating snow processes and quantifying bias in snowpack properties across scale in a northern Great Lakes–St. Lawrence forest. Snow depth and density were measured along transects stratified by land cover over the 2015/2016 and 2016/2017 winters. Daily snow depth was measured using a time‐lapse (TL) camera at each transect. Semivariogram analysis of the transect data was conducted, and no autocorrelation was found, indicating little spatial structure along the transects. Pairwise differences in snow depth and snow water equivalent (SWE) between land covers were calculated and compared across scales. Differences in snowpack between forested sites at the TL points corresponded to differences in canopy cover, but this relationship was not evident at the transect scale, indicating a difference in observed process across scale. TL and transect estimates had substantial bias, but consistency in error was observed, which indicates that scaling coefficients may be derived to improve point scale estimates. TL and transect measurements were upscaled to estimate grid scale means. Upscaled estimates were compared and found to be consistent, indicating that appropriately stratified point scale measurements can be used to approximate a grid scale mean when transect data are not available. These findings are important in remote regions such as the study area, where frequent transect data may be difficult to obtain. TL, transect, and upscaled means were compared with modelled depth and SWE. Model comparisons with TL and transect data indicated that bias was dependent on land cover, measurement scale, and seasonality. Modelled means compared well with upscaled estimates, but model SWE was underestimated during spring melt. These findings highlight the importance of understanding the spatial representativeness of in situ measurements and the processes those measurements represent when validating gridded snow products or assimilating data into models.     

The demographic burden of population loss in US cities, 2000–2010

Journal of Geographical Systems - Although the effects of urban shrinkage on quality of life and the built environment have received a great deal of attention, the characteristics of those...     

Hydrogeophysical Methods for Analyzing Aquifer Storage and Recovery Systems

Hydrogeophysical methods are presented that support the siting and monitoring of aquifer storage and recovery (ASR) systems. These methods are presented as numerical simulations in the context of a proposed ASR experiment in Kuwait, although the techniques are applicable to numerous ASR projects. Bulk geophysical properties are calculated directly from ASR flow and solute transport simulations using standard petrophysical relationships and are used to simulate the dynamic geophysical response to ASR. This strategy provides a quantitative framework for determining site‐specific geophysical methods and data acquisition geometries that can provide the most useful information about the ASR implementation. An axisymmetric, coupled fluid flow and solute transport model simulates injection, storage, and withdrawal of fresh water (salinity ～500 ppm) into the Dammam aquifer, a tertiary carbonate formation with native salinity approximately 6000 ppm. Sensitivity of the flow simulations to the correlation length of aquifer heterogeneity, aquifer dispersivity, and hydraulic permeability of the confining layer are investigated. The geophysical response using electrical resistivity, time‐domain electromagnetic (TEM), and seismic methods is computed at regular intervals during the ASR simulation to investigate the sensitivity of these different techniques to changes in subsurface properties. For the electrical and electromagnetic methods, fluid electric conductivity is derived from the modeled salinity and is combined with an assumed porosity model to compute a bulk electrical resistivity structure. The seismic response is computed from the porosity model and changes in effective stress due to fluid pressure variations during injection/recovery, while changes in fluid properties are introduced through Gassmann fluid substitution.     

Numerical Modeling of Methane Leakage from a Faulty Natural Gas Well into Fractured Tight Formations

Horizontal drilling and hydraulic fracturing have enabled hydrocarbon recovery from unconventional reservoirs, but led to natural gas contamination of shallow groundwaters. We describe and apply numerical models of gas‐phase migration associated with leaking natural gas wells. Three leakage scenarios are simulated: (1) high‐pressure natural gas pulse released into a fractured aquifer; (2) continuous slow leakage into a tilted fractured formation; and (3) continuous slow leakage into an unfractured aquifer with fluvial channels, to facilitate a generalized evaluation of natural gas transport from faulty natural gas wells. High‐pressure pulses of gas leakage into sparsely fractured media are needed to produce the extensive and rapid lateral spreading of free gas previously observed in field studies. Transport in fractures explains how methane can travel vastly different distances and directions laterally away from a leaking well, which leads to variable levels of methane contamination in nearby groundwater wells. Lower rates of methane leakage (≤1 Mcf/day) produce shorter length scales of gas transport than determined by the high‐pressure scenario or field studies, unless aquifers have low vertical permeabilities (≤1 millidarcy) and fractures and bedding planes have sufficient tilt (～10°) to allow a lateral buoyancy component. Similarly, in fractured rock aquifers or where permeability is controlled by channelized fluvial deposits, lateral flow is not sufficiently developed to explain fast‐developing gas contamination (0‐3 months) or large length scales (～1 km) documented in field studies. Thus, current efforts to evaluate the frequency, mechanism, and impacts of natural gas leakage from faulty natural gas wells likely underestimate contributions from small‐volume, low‐pressure leakage events.     

Rainfall–runoff model parameter estimation and uncertainty evaluation on small plots

Four seasonal rainfall simulations in 2009 and 2010 were applied to a field containing 36 plots (0.75 × 2 m each), resulting in 144 runoff events. In all simulations, a constant rate of rainfall was applied then halted 60 min after initiation of runoff, with plot‐scale monitoring of runoff every 5 min during that period. Runoff was simulated with the Kinematic Runoff and Erosion/Simulator of Transport with Infiltration and Runoff (KINEROS2/STWIR) field‐scale model, whose hydrodynamics are based on the kinematic wave equation. Because of the non‐linear nature of the model and a highly parameterized model with respect to the available data, several approaches were investigated to upscale nine runoff‐related parameters from a series of small monitored plots to the field scale. Inverse modeling was performed using the model‐independent Parameter ESTimation (PEST) algorithm to individually calibrate the nine KINEROS2/STWIR parameters on 36 plots. The parameters were averaged, and bootstrapping was used to assess uncertainty of the parameters via estimation of confidence intervals (CI). A Monte Carlo simulation using the bootstrap results showed reasonable field‐scale representation of flow rates. Median values of calibrated parameters were within the 95% CI obtained with bootstrapping. The simulated results for the median values associated with the 90% CI flow rates produced similar trends as those exhibited with the observed data, suggesting that median values of the calibrated parameters from the PEST inverse modeling could be used to represent the field scale. Copyright © 2013 John Wiley & Sons, Ltd.     

Global Climate Change: Why U.S. Insurers Care

The number of natural catastrophes in this decade is four times greater than in the 1960s; economic losses are eight times greater; and insured losses are 15 times greater. The insurance industry's financial interest is inter-dependent with climate and weather. Natural events drive the demand for insurance coverage and can threaten the viability of an insurer if it is over-exposed in high risk areas. Early in the 1990s, the industry began to recognize that historical data were potentially misleading with respect to future natural catastrophe exposure. The U.S. insurance industry is pursuing a variety of new approaches including: the use of catastrophe computer models to integrate the natural knowledge about extreme events taken from the sciences into the actuarial sciences. The evaluation of building codes and building code enforcement in every community in the country enhanced its support for hazard mitigation.     

Design and evaluation of a geovisual analytics system for uncovering patterns in spatio-temporal event data

ABSTRACT

It remains difficult to develop a clear understanding of geo-located events and their relationships to one another, particularly when it comes to identifying patterns of events in less-structured textual sources, such as news feeds and social media streams. Here we present a geovisualization tool that can leverage computational methods, such as T-pattern analysis, for extracting patterns of interest from event data streams. Our system, STempo, includes coordinated-view geovisualization components designed to support visual exploration and analysis of event data, and patterns extracted from those data, in terms of time, geography, and content. Through a user evaluation, we explore the usability and utility of STempo for understanding patterns of recent political, social, economic, and military events in Syria.     

Hydrogen-isotopic variability in lipids from Santa Barbara Basin sediments

We conducted an extensive survey of hydrogen-isotopic compositions (D/H ratios) of diverse sedimentary lipids from the Santa Barbara Basin (SBB), offshore southern California. The main goal of this survey was to assess the diversity of D/H ratios in lipids from marine sediments, in order to provide a more detailed understanding of relevant biological and geochemical factors impacting lipid isotopic variability. A total of 1182 individual δD values are reported from two stations in SBB, one located in the suboxic basin depocenter and the other on the fully oxic flank of the basin. Sediments collected from the basin depocenter span a depth of ∼2.5 m and reach the methanogenic zone. Lipids that were analyzed include n-alkanes, n-alkanols and alkenols, short- and long-chain fatty acids, linear isoprenoids, steroids, and hopanoids, and exhibit several systematic patterns. First, there are no significant differences in δD values between the two sampling locations, nor with increasing depth for most lipids, indicating that degradation does not influence sedimentary lipid δD values. Second, relatively large differences in δD values among differing molecular structures are observed in all samples. n-Alkyl lipids of probable marine origin have typical δD values between −150 and −200‰, those from terrestrial leaf waxes and aquatic plants range from −80 to −170‰, while petroleum n-alkanes are typically −90 to −150‰. Third, lipids inferred to derive from bacteria (branched fatty acids and hopanols) living at the sediment surface or in the water column tend to be D-enriched relative to similar algal products by 30‰ or more. At the same time, several other lipids have δD values that decrease strongly with depth, presumably as a result of in situ production by anaerobic bacteria. This dichotomy in isotopic compositions of bacterial lipids is inconsistent with a nearly constant D/H fractionation during lipid biosynthesis, and likely reflects significant variations associated with metabolism.