Precipitation Modeling by Polyhedral RCMARS and Comparison with MARS and CMARS

Climate change is becoming an ever important issue due to the possibility that it may result in extreme weather events such as floods or droughts. Consequently, precipitation forecasting has similarly gained in significance as it is a useful tool in meeting the increasing need for the efficient management of water resources as well as in preventing disasters before they happen. In the literature, there are various statistical and computational methods used for this purpose, including linear and nonlinear regression, kriging, time series models, neural networks, and multivariate adaptive regression splines (MARS). Among them, MARS stands out as the better performing precipitation modeling method. In this article, we used a recently developed method called robust conic mars (RCMARS), based on MARS (also on CMARS), to forecast precipitation owing to its ability to model complex uncertain data. In CMARS, which was developed as a powerful alternative to MARS, the model complexity is penalized in the form of Tikhonov regularization and studied as a conic quadratic programming. In RCMARS, on the other hand, CMARS is refined further by including the existence of uncertainty in the future scenarios and robustifying it with a robust optimization technique. To evaluate the performance of the RCMARS method, it was applied to build a precipitation model constructed as an early warning system for the continental Central Anatolia Region of Turkey, where drought has been a recurrent phenomenon for the last few decades. Furthermore, the performance of the RCMARS precipitation model was also compared to that of MARS and CMARS. The results indicated that RCMARS builds more accurate, precise, and stable precipitation models compared to those of MARS and CMARS. In addition to these advantageous features of the RCMARS precipitation model, it also provided a good fit to the data. As a result, we propose its use in precipitation forecasting for the region studied.     

Application of the probability-based Maryland Biological Stream Survey to the state’s assessment of water quality standards

The Clean Water Act presents a daunting task for states by requiring them to assess and restore all their waters. Traditional monitoring has led to two beliefs: (1) ad hoc sampling (i.e., non-random) is adequate if enough sites are sampled and (2) more intensive sampling (e.g., collecting more organisms) at each site is always better. We analyzed the 1,500 Maryland Biological Stream Survey (MBSS) random sites sampled in 2000–2004 to describe the variability of Index of Biotic Integrity (IBI) scores at the site, reach, and watershed scales. Average variability for fish and benthic IBI scores increased with increasing spatial scale, demonstrating that single site IBI scores are not representative at watershed scales and therefore at best 25% of a state’s stream length can be representatively sampled with non-random designs. We evaluated the effects on total taxa captured and IBI precision of sampling for twice as many benthic macroinvertebrates at 73 MBSS sites with replicate samples. When sampling costs were fixed, the precision of the IBI decreased as the number of sites had to be reduced by 15%. Only 1% more taxa were found overall when the 73 sites where combined. We concluded that (1) comprehensive assessment of a state’s waters should be done using probability-based sampling that allows the condition across all reaches to be inferred statistically and (2) additional site sampling effort should not be incorporated into state biomonitoring when it will reduce the number of sites sampled to the point where overall assessment precision is lower.     

A New Mathematical Approach in Environmental and Life Sciences: Gene–Environment Networks and Their Dynamics

An important research area in life sciences is devoted to modeling, prediction, and dynamics of gene-expression patterns. As clearly understood in these days, this enterprise cannot become satisfactory without acknowledging the role of the environment. To a representation of past, present, and most likely future states, we also encounter measurement errors and uncertainties. This paper surveys and improves recent advances in understanding the foundations and interdisciplinary implications of the newly introduced gene–environment networks, and it integrates the important theme of carbon dioxide emission reduction into the networks and dynamics. We also introduce some operational and managerial issues of practical working and decision making, expressed in terms of sliding windows, quadrants (modules) of parametric effects, and navigating (controlling) between such effects and directing them. Given data from DNA microarray experiments and environmental records, we extract nonlinear ordinary differential equations that contain parameters that have to be determined. For this, we employ modern (Chebychevian) approximation and (generalized semi-infinite) optimization. After this is provided, time- discretized dynamical systems are studied. A combinatorial algorithm with polyhedra sequences allows to detect the region of parametric stability. Finally, we analyze the topological landscape of gene–environment networks with its structural (in)stability. By embedding as a module and investigating CO₂ emission control and figuring out game theoretical aspects, we conclude. This pioneering work is theoretically elaborated, practically devoted to health care, medicine, education, living conditions, and environmental protection, and it invites the readers to future research.      

Long-term study of palladium in road tunnel dust and sewage sludge ash

The present work summarizes data about palladium contents of road tunnel dust from 1994 to 2007 and sewage sludge ash from 1972 to 2006. Since palladium is emitted from automotive catalytic converters as elemental particles, road dust is quiet useful to study traffic-related Pd emissions. Very high Pd values of up to 516 μg Pd kg⁻¹ were found in the road dust samples collected in 2007. Heavy metals of all urban emissions, also dental practice effluent, are enriched in sewage sludge ash and thus this matrix is useful for the documentation of palladium emission caused by the use of Pd alloys in dental medicine. In sewage sludge ash highest Pd contents of maximum 460 μg Pd kg⁻¹ were found in the years 1986-1997. In both matrices correlations of Pd content to Pd demand of industry are discussed.     

Lagrangian sampling of 3-D air quality model results for regional transport contributions to sulfate aerosol concentrations at Baltimore,MD, in summer 2004

We use ensemble-mean Lagrangian sampling of a 3-D Eulerian air quality model, CMAQ, together with ground-based ambient monitors data from several air monitoring networks and satellite (MODIS) observations to provide source apportionment and regional transport vs. local contributions to sulfate aerosol and PM_2.5 concentrations at Baltimore, MD, for summer 2004. The Lagrangian method provides estimates of the chemical and physical evolution of air arriving in the daytime boundary layer at Baltimore. Study results indicate a dominant role for regional transport contributions on those days when sulfate air pollution is highest in Baltimore, with a principal transport pathway from the Ohio River Valley (ORV) through southern Pennsylvania and Maryland, consistent with earlier studies. Thus, reductions in sulfur emissions from the ORV under the EPA's Clean Air Interstate Rule may be expected to improve particulate air quality in Baltimore during summer. The Lagrangian sampling of CMAQ offers an inexpensive and complimentary approach to traditional methods of source apportionment based on multivariate observational data analysis, and air quality model emissions separation. This study serves as a prototype for the method applied to Baltimore. EPA is establishing a system to allow air quality planners to readily produce and access equivalent results for locations of their choice.     

Assessment of five bioaccessibility assays for predicting the efficacy of petroleum hydrocarbon biodegradation in aged contaminated soils

In this study, the bioaccessibility of petroleum hydrocarbons in aged contaminated soils (1.6-67gkg(-1)) was assessed using four non-exhaustive extraction techniques (100% 1-butanol, 100% 1-propanol, 50% 1-propanol in water and hydroxypropyl-β-cyclodextrin) and the persulfate oxidation method. Using linear regression analysis, residual hydrocarbon concentrations following bioaccessibility assessment were compared to residual hydrocarbon concentrations following biodegradation in laboratory-scale microcosms in order to determine whether bioaccessibility assays can predict the endpoint of hydrocarbon biodegradation. The relationship between residual hydrocarbon concentrations following microcosm biodegradation and bioaccessibility assessment was linear (r(2)=0.71-0.97) indicating that bioaccessibility assays have the potential to predict the extent of hydrocarbon biodegradation. However, the slope of best fit varied depending on the hydrocarbon fractional range assessed. For the C(10)-C(14) hydrocarbon fraction, the slope of best fit ranged from 0.12 to 0.27 indicating that the non-exhaustive or persulfate oxidation methods removed 3.5-8 times more hydrocarbons than biodegradation. Conversely, for the higher molecular weight hydrocarbon fractions (C(29)-C(36) and C(37)-C(40)), biodegradation removed up to 3.3 times more hydrocarbons compared to bioaccessibility assays with the resulting slope of best fit ranging from 1.0-1.9 to 2.0-3.3 respectively. For mid-range hydrocarbons (C(15)-C(28)), a slope of approximately one was obtained indicating that C(15)-C(28) hydrocarbon removal by these bioaccessibility assays may approximate the extent of biodegradation. While this study demonstrates the potential of predicting biodegradation endpoints using bioaccessibility assays, limitations of the study include a small data set and that all soils were collected from a single site, presumably resulting from a single contamination source. Further evaluation and validation is required using soils from a range of hydrocarbon contamination sources in order to develop robust assays for predicting bioremediation endpoints in the field.     

Spatio-temporal covariation of urban particle number concentration and ambient noise

Mobile measurements of ambient noise and particle number concentrations were carried out within an urban residential area in Essen, Germany, during summer 2008. A busy major road with a traffic intensity of about 44,000 vehicles per day was situated within the study area. The spatio-temporal distribution of noise and particles was closely coupled to road traffic on the major road. Total particle number concentrations in proximity to the main road were on average between 25,000 cm^?3 and 35,000 cm^?3 while sound levels reached 70–78 dB(A). These estimates were more than double-fold (factor 2.4) in comparison to the urban residential background. At a 50 m distance off the road particle number concentrations were decaying to about 50% of the initial value. The measurements were characterised by close spatial correlation between total particle number concentration and ambient noise with correlation coefficients of up to r = 0.74. However, during one measurement day coupling between both quantities was weak due to higher turbulent mixing within the canopy layer and a change in ambient wind directions. Enhanced dilution of particle emission from road traffic by turbulent mixing and ‘decoupling’ from the influence of road traffic are believed to be responsible.     

Effect of soil ageing on in vivo arsenic bioavailability in two dissimilar soils

Arsenic (As) bioavailability in spiked soils aged for up to 12 months was assessed using in vitro and in vivo methodologies. Ageing (natural attenuation) of spiked soils resulted in a decline in in vivo As bioavailability (swine assay) of over 75% in soil A (Red Ferrosol) but had no significant effect on in vivo As bioavailability even after 12 months of ageing in soil B (Brown Chromosol). Sequential fractionation, however, indicated that there was repartitioning of As within the soil fractions extracted during the time course investigated. In soil A, the As fraction associated with the more weakly bound soil fractions decreased while the residual fraction increased from 12% to 35%. In contrast, little repartitioning of As was observed in soil B indicating that natural attenuation may be only applicable for As in soils containing specific mineralogical properties.     

Application of an in vivo swine model for the determination of arsenic bioavailability in contaminated vegetables

Considerable information is available in the literature regarding the uptake of arsenic (As) from contaminated soil and irrigation water by vegetables. However, few studies have investigated As speciation in these crops while a dearth of information is available on As bioavailability following their consumption. In this study, the concentration and speciation of As in chard, radish, lettuce and mung beans was determined following hydroponic growth of the vegetables using As-contaminated water. In addition, As bioavailability was assessed using an in vivo swine feeding assay. While As concentrations ranged from 3.0 to 84.2mg As kg(-1) (dry weight), only inorganic As (arsenite and arsenate) was detected in the edible portions of the vegetables. When As bioavailability was assessed through monitoring blood plasma As concentrations following swine consumption of As-contaminated vegetables, between 50% and 100% of the administered As dose was absorbed and entered systemic circulation. Arsenic bioavailability decreased in the order mung beans>radish>lettuce=chard.     

Analysing the influence of different street vegetation on traffic-induced particle dispersion using microscale simulations

Urban vegetation can be viewed as compensation to the environmental drawbacks of urbanisation. However, its ecosystem function is not well-known and, for urban planning, vegetation is mainly considered as an element of urban design. This article argues that planning practice needs to re-examine the impact of vegetation cover in the urban fabric given our evaluation of vegetation's effects on air quality, including the dispersion of traffic-induced particles at street level. Using the three-dimensional microclimate model ENVI-met?, we evaluate these effects regarding the height-to-width ratio of streets flanked by buildings and the vertical and horizontal density of street vegetation. Our results reveal vegetation's effect on particle dispersion through its influence on street ventilation. In general, vegetation was found to reduce wind speed, causing inhibition of canyon ventilation and, consequently, an increase in particle concentrations. Vegetation was also found to reduce wind speed at crown-height and to disrupt the flow field in close vicinity to the canopy. With increasing height-to-width ratio of street canyons, wind speed reduction increases and the disturbance of the flow impacts across a canyon's entire width. We also found that the effect is more pronounced in configurations with poor ventilation, such as the low wind speed, perpendicular inflow direction, and in deep canyons cases.