Use of Predictive Weather Uncertainties in an Irrigation Scheduling Tool Part II: An Application of Metrics and Adjoints

We apply predictive weather metrics and land model sensitivities to improve the Colorado State University Water Irrigation Scheduler for Efficient Application (WISE). WISE is an irrigation decision aid that integrates environmental and user information for optimizing water use. Rainfall forecasts and verification performance metrics are used to estimate predictive rainfall probabilities that are used as input data within the irrigation decision aid. These input data errors are also used within a land model sensitivity study to diagnose important prognostic water movement behaviors for irrigation tool development purposes simultaneously performing the analysis in space and time. Thus, important questions such as “how long can a crop water application be delayed while maintaining crop yield production?” are addressed by evaluating crop growth stage interactions as a function of soil depth (i.e., space), rainfall events (i.e., time), and their probabilistic uncertainties. Editor’s note : This paper is part of the featured series on Optimizing Ogallala Aquifer Water Use to Sustain Food Systems. See the February 2019 issue for the introduction and background to the series.     

Arsenic exposure in Hungary, Romania and Slovakia

Inorganic arsenic is a potent human carcinogen and toxicant which people are exposed to mainly via drinking water and food. The objective of the present study was to assess current exposure to arsenic via drinking water in three European countries. For this purpose, 520 individuals from four Hungarian, two Slovakian and two Romanian countries were investigated by measuring inorganic arsenic and methylated arsenic metabolites in urine by high performance liquid chromatography with hydride generation and inductively coupled plasma mass spectrometry. Arsenic in drinking water was determined by atomic absorption spectrometry. Significantly higher concentrations of arsenic were found in both the water and the urine samples from the Hungarian counties (median: 11 and 15 microg dm(-3), respectively; p < 0.001) than from the Slovakian (median: 0.94 and 4.5 microg dm(-3), respectively) and Romanian (median: 0.70 and 2.1 microg dm(-3), respectively) counties. A significant correlation was seen between arsenic in water and arsenic in urine (R(2)= 0.46). At low water arsenic concentrations, the relative amount of dimethylarsinic acid (DMA) in urine was increased, indicating exposure via food. Also, high body mass index was associated with higher concentrations of arsenic in urine (p= 0.03), mostly in the form of DMA. Smokers had significantly higher urinary arsenic concentrations than non-smokers (p= 0.03). In conclusion, elevated arsenic exposure via drinking water was prevalent in some of the counties. Exposure to arsenic from food, mainly as DMA, and cigarette smoke, mainly as inorganic arsenic, are major determinants of arsenic exposure at very low concentrations of arsenic in drinking water.     

Price and volatility transmission between primary and scrap metal markets

The relationship between primary and scrap prices has been hypothesized for the most part as unidirectional, characterized by spillovers from primary to scrap prices. The purpose of this study is to evaluate empirically the dynamic interactions between primary and scrap metal prices through multivariate time series methods. In addition, the study expands the investigation at the level of volatility transmission, which has not been previously examined. The metal prices utilized are for copper, lead, and zinc for the period 1984–2001. The paper demonstrates differing long run and short run links. Scrap prices do not improve the long run interpretation of primary prices, but information flows from the scrap to the primary markets exist in the short run. Additionally, the copper and lead markets exhibit bidirectional information flows in terms of volatility transmission. The analysis provides valuable insight into the interactions of the primary and scrap metal sectors which can be used to improve forecasting and planning.     

The influence of work‐family culture and workplace relationships on work interference with family: a multilevel model

This research tested a multilevel model examining the influence of work‐family culture and supportive workplace relationships on work interference with family. Web‐based survey data were provided by 792 information technology employees from 10 organizations. Random coefficient modeling was used to test a path model examining the relationships between work‐family culture, leader‐member exchange (LMX), coworker support, and work interference with family. The direct effects of LMX and coworker support on work interference with family were significant. The indirect effect of work‐family culture on work interference with family was also significant. Results demonstrate the value of work‐family culture in understanding supportive supervisory and coworker relationships and work interference with family and highlight the need to employ multilevel models to understand these relationships. Copyright © 2007 John Wiley & Sons, Ltd.     

Influence of fish size and sex on mercury/PCB concentration: importance for fish consumption advisories

Fish advisories for polychlorinated biphenyls (total-PCBs) and mercury are often given on a length-specific basis and fish sex is usually not considered. The relationship between concentration and length is well established for mercury, however its suitability for total-PCBs and the influence of sex over the large scale covered by most monitoring programs is not well known. Here we use what is perhaps the largest consistent sport fish contaminant dataset to evaluate the relationship between total-PCB/mercury and length and sex. We evaluated seven of the most commonly consumed fishes from the Canadian Great Lakes and two Ontario (Canada) inland lakes. For mercury, the relationship between concentration and length was significant (p<0.05) in most fish species and locations. For total-PCB, this relationship was also generally significant in Chinook salmon and lake trout, which are the species with the most advisories for this compound. In contrast, significant relationships were found less often for whitefish, northern pike, smallmouth bass, walleye, and especially yellow perch. However, mercury usually drives fish consumption advisories for these species. Overall, our results support the protocol of issuing contaminant advice on a length-specific basis in fish monitoring programs with reasonable confidence when at least a moderate number of samples over the natural size range of a species are included in the analysis. Sex differences were common for walleye (males>females, p<0.05) but not other species, suggesting that an equal number of male and female walleye should be used in deriving fish advisories for walleye.     

Evaluating factors influencing groundwater vulnerability to nitrate pollution: developing the potential of GIS

The 1991 EU Nitrate Directive was designed to reduce water pollution from agriculturally derived nitrates. England and Wales implemented this Directive by controlling agricultural activities within their most vulnerable areas termed Nitrate Vulnerable Zones. These were designated by identifying drinking water catchments (surface and groundwater), at risk from nitrate pollution. However, this method contravened the Nitrate Directive because it only protected drinking water and not all waters. In this paper, a GIS was used to identify all areas of groundwater vulnerable to nitrate pollution. This was achieved by constructing a model containing data on four characteristics: the quality of the water leaving the root zone of a piece of land; soil information; presence of low permeability superficial (drift) material; and aquifer properties. These were combined in a GIS and the various combinations converted into a measure of vulnerability using expert knowledge. Several model variants were produced using different estimates of the quality of the water leaving the root zone and contrasting methods of weighting the input data. When the final models were assessed all produced similar spatial patterns and, when verified by comparison with trend data derived from monitored nitrate concentrations, all the models were statistically significant predictors of groundwater nitrate concentrations. The best predictive model contained a model of nitrate leaching but no land use information, implying that changes in land use will not affect designations based upon this model. The relationship between nitrate levels and borehole intake depths was investigated since there was concern that the observed contrasts in nitrate levels between vulnerability categories might be reflecting differences in borehole intake depths and not actual vulnerability. However, this was not found to be statistically important. Our preferred model provides the basis for developing a new set of groundwater Nitrate Vulnerable Zones that should help England and Wales to comply with the EU Nitrate Directive.