Origin of major ions in monthly rainfall events at the Bamenda Highlands, North West Cameroon

Rainwater characteristics can reveal emissions from various anthropogenic and natural sources into the atmosphere. The physico-chemical characteristics of 44 monthly rainfall events （collected between January and December 2012） from 4 weather stations （Bamenda, Ndop plain, Ndawara and Kumbo） in the Bamenda Highlands （BH） were investigated. The purpose was to determine the sources of chemical species, their seasonal inputs and suitability of the rainwater for drinking. The mean pH of 5 indicated the slightly acidic nature of the rainwater. Average total dissolved solids （TDS） were low （6.7 mg/L）, characteristic of unpolluted atmospheric moisture/air. Major ion concentrations （mg/L） were low and in the order K＋ 〉 Ca2＋ 〉 Mg2~ 〉 Na＋ for cations and NO3 〉〉 HCO3 〉 SO] 〉 CI- 〉 PO3- 〉 F- for anions. The average rainwater in the area was mixed Ca-Mg-SO4-CI water type. The CI-/Na＋ ratio （1.04） was comparable to that of seawater （1.16）, an indication that N a＋ and CI originated mainly from marine （Atlantic Ocean） aerosols. High enrichments of Ca2＋, Mg2＋ and SO2- to Na＋ ratios relative to seawater ratios （constituting 44% of the total ions） demonstrated their terrigenous origin, mainly from Saharan and Sahelian arid dusts. The K＋/Na＋ ratio （2.24）, which was similar to tropical vegetation ash （2.38）, and NO3 was essentially from biomass burning. Light （〈 100 mm） pre-monsoon and post-monsoon convective rains were enriched in major ions than the heavy （〉 100 mm） monsoon rains, indicating a high contribution of major ions during the low convective showers. Despite the acidic nature, the TDS and major ion concentrations classified the rainwater as potable based on the WHO guidelines.     

The distribution and composition of hydrocarbons in sediments from the Fladen Ground, North Sea, an area of oil production

The distribution and composition of hydrocarbons in sediment from the Fladen Ground oilfield in the northern North Sea have been investigated. The total PAH concentrations (2- to 6-ring parent and alkylated PAHs, including the 16 US EPA PAHs) in sediments were relatively low (<100 microg kg(-1) dry weight). The PAH, the Forties crude and diesel oil equivalent concentrations were generally higher in sediment of fine grain size and higher organic carbon concentration. PAH distributions and concentration ratios indicated a predominantly pyrolytic input, being dominated by the heavier, more persistent, 5- and 6-ring compounds, and with a high proportion of parent PAHs. The n-alkane profiles of a number of the sediments contained small, high boiling point, UCMs, indicative of weathered oil arising from a limited petrogenic input. The geochemical biomarker profiles of the sediments that contained UCMs showed a small bisnorhopane peak and a high proportion of norhopane relative to hopane, indicating that there was contamination from both Middle Eastern and North Sea oils. Therefore contamination was not directly as a result of oil exploration activity in the area. The most likely source of petrogenic contamination was from general shipping activity.     

Evaluation of the ADAPT model for simulating nitrogen dynamics in a tile-drained agricultural watershed in central Illinois

Assessing the accuracy of agronomic and water quality simulation models in different soils, land-use systems, and environments provides a basis for using and improving these models. We evaluated the performance of the ADAPT model for simulating riverine nitrate-nitrogen (NO3-N) export from a 1500-km2 watershed in central Illinois, where approximately 85% of the land is used for maize-soybean production and tile drainage is common. Soil chemical properties, crop nitrogen (N) uptake coefficient, dry matter ratio, and a denitrification reduction coefficient were used as calibration parameters to optimize the fit between measured and simulated NO3-N load from the watershed for the 1989 to 1993 period. The applicability of the calibrated parameter values was tested by using these values for simulating the 1994 to 1997 period on the same watershed. Willmott's index of agreement ranged from 0.91 to 0.97 for daily, weekly, monthly, and annual comparisons of riverine nitrate N loads. Simulation accuracy generally decreased as the time interval decreased. Willmott's index for simulated crop yields ranged from 0.91 to 0.99; however, observed crop yields were used as input to the model. The partial N budget results suggested that 52 to 72 kg N ha(-1) yr(-1) accumulated in the soil, but simulated biological N fixation associated with soybeans was considerably greater than literature values for the region. Improvement of the N fixation algorithms and incorporation of mechanisms that describe soybean yield in response to environmental conditions appear to be needed to improve the performance of the model.     

Predicting Carbon Monoxide Concentrations in the Air of Pardis City,Iran, Using an Artificial Neural Network

To date, several methods have been proposed to explain the complex process of air pollution prediction. One of these methods uses neural networks. Artificial neural networks (ANN) are a branch of artificial intelligence, and because of their nonlinear mathematical structures and ability to provide acceptable forecasts, they have gained popularity among researchers. The goal of our study as documented in this article was to compare the abilities of two different ANNs, the multilayer perceptron (MLP) and radial basis function (RBF) neural networks, to predict carbon monoxide (CO) concentrations in the air of Pardis City, Iran. For the study, we used data collected hourly on temperature, wind speed, and humidity as inputs to train the networks. The MLP neural network had two hidden layers that contained 13 neurons in the first layer and 25 neurons in the second layer and reached a mean bias error (MBE) of 0.06. The coefficient of determination (R²), index of agreement (IA), and the Nash–Scutcliffe efficiency (E) between the observed and predicted data using the MLP neural network were 0.96, 0.9057, and 0.957, respectively. The RBF neural network with a hidden layer containing 130 neurons reached an MBE of 0.04. The R², IA, and E between the observed and predicted data using the RBF neural network were 0.981, 0.954, and 0.979, respectively. The results provided by the RBF neural network had greater acceptable accuracy than was the case with the MLP neural network. Finally, the results of a sensitivity analysis using the MLP neural network indicated that temperature is the primary factor in the prediction of CO concentrations and that wind speed and humidity are factors of second and third importance when forecasting CO levels.     

Variability and Trends in Runoff Efficiency in the Conterminous United States

Variability and trends in water‐year runoff efficiency (RE) — computed as the ratio of water‐year runoff (streamflow per unit area) to water‐year precipitation — in the conterminous United States (CONUS) are examined for the 1951 through 2012 period. Changes in RE are analyzed using runoff and precipitation data aggregated to United States Geological Survey 8‐digit hydrologic cataloging units (HUs). Results indicate increases in RE for some regions in the north‐central CONUS and large decreases in RE for the south‐central CONUS. The increases in RE in the north‐central CONUS are explained by trends in climate, whereas the large decreases in RE in the south‐central CONUS likely are related to groundwater withdrawals from the Ogallala aquifer to support irrigated agriculture.     

Balancing Conservation and Economic Sustainability: The Future of the Amazon Timber Industry

Logging has been a much maligned feature of frontier development in the Amazon. Most discussions ignore the fact that logging can be part of a renewable, environmentally benign, and broadly equitable economic activity in these remote places. We estimate there to be some 4.5 ± 1.35 billion m³ of commercial timber volume in the Brazilian Amazon today, of which 1.2 billion m³ is currently profitable to harvest, with a total potential stumpage value of $15.4 billion. A successful forest sector in the Brazilian Amazon will integrate timber harvesting on private lands and on unprotected and unsettled government lands with timber concessions on public lands. If a legal, productive, timber industry can be established outside of protected areas, it will deliver environmental benefits in synergy with those provided by the region’s network of protected areas, the latter of which we estimate to have an opportunity cost from lost timber revenues of $2.3 billion over 30 years. Indeed, on all land accessible to harvesting, the timber industry could produce an average of more than 16 million m³ per year over a 30-year harvest cycle—entirely outside of current protected areas—providing $4.8 billion in returns to landowners and generating $1.8 billion in sawnwood sales tax revenue. This level of harvest could be profitably complemented with an additional 10% from logging concessions on National Forests. This advance, however, should be realized only through widespread adoption of reduced impact logging techniques.     

Population dynamics and the economics of invasive species management: the greenhouse whitefly in California-grown strawberries

In agriculture, relatively few efficacious control measures may be available for an invasive pest. In the case of a new insect pest, insecticide use decisions are affected by regulations associated with its registration, insect population dynamics, and seasonal market price cycles. We assess the costs and benefits of environmental regulations designed to regulate insecticide applications on an invasive species. We construct a bioeconomic model, based on detailed scientific data, of management decisions for a specific invasion: greenhouse whiteflies in California-grown strawberries. The empirical model integrates whitefly population dynamics, the effect of whitefly feeding on strawberry yields, and weekly strawberry price. We use the model to assess the optimality of alternative treatment programs on a simulated greenhouse whitefly population. Our results show that regulations may lead growers to "under-spray" when placed in an economic context, and provide some general lessons about the design of optimal invasive species control policies.     

Incorporating Uncertainty Into the Ranking of SPARROW Model Nutrient Yields From Mississippi/Atchafalaya River Basin Watersheds1

Abstract: Excessive loads of nutrients transported by tributary rivers have been linked to hypoxia in the Gulf of Mexico. Management efforts to reduce the hypoxic zone in the Gulf of Mexico and improve the water quality of rivers and streams could benefit from targeting nutrient reductions toward watersheds with the highest nutrient yields delivered to sensitive downstream waters. One challenge is that most conventional watershed modeling approaches (e.g., mechanistic models) used in these management decisions do not consider uncertainties in the predictions of nutrient yields and their downstream delivery. The increasing use of parameter estimation procedures to statistically estimate model coefficients, however, allows uncertainties in these predictions to be reliably estimated. Here, we use a robust bootstrapping procedure applied to the results of a previous application of the hybrid statistical/mechanistic watershed model SPARROW (Spatially Referenced Regression On Watershed attributes) to develop a statistically reliable method for identifying “high priority” areas for management, based on a probabilistic ranking of delivered nutrient yields from watersheds throughout a basin. The method is designed to be used by managers to prioritize watersheds where additional stream monitoring and evaluations of nutrient‐reduction strategies could be undertaken. Our ranking procedure incorporates information on the confidence intervals of model predictions and the corresponding watershed rankings of the delivered nutrient yields. From this quantified uncertainty, we estimate the probability that individual watersheds are among a collection of watersheds that have the highest delivered nutrient yields. We illustrate the application of the procedure to 818 eight‐digit Hydrologic Unit Code watersheds in the Mississippi/Atchafalaya River basin by identifying 150 watersheds having the highest delivered nutrient yields to the Gulf of Mexico. Highest delivered yields were from watersheds in the Central Mississippi, Ohio, and Lower Mississippi River basins. With 90% confidence, only a few watersheds can be reliably placed into the highest 150 category; however, many more watersheds can be removed from consideration as not belonging to the highest 150 category. Results from this ranking procedure provide robust information on watershed nutrient yields that can benefit management efforts to reduce nutrient loadings to downstream coastal waters, such as the Gulf of Mexico, or to local receiving streams and reservoirs.     

The contemporary cement cycle of the United States

A country-level stock and flow model for cement, an important construction material, was developed based on a material flow analysis framework. Using this model, the contemporary cement cycle of the United States was constructed by analyzing production, import, and export data for different stages of the cement cycle. The United States currently supplies approximately 80% of its cement consumption through domestic production and the rest is imported. The average annual net addition of in-use new cement stock over the period 2000–2004 was approximately 83 million metric tons and amounts to 2.3 tons per capita of concrete. Nonfuel carbon dioxide emissions (42 million metric tons per year) from the calcination phase of cement manufacture account for 62% of the total 68 million tons per year of cement production residues. The end-of-life cement discards are estimated to be 33 million metric tons per year, of which between 30% and 80% is recycled. A significant portion of the infrastructure in the United States is reaching the end of its useful life and will need to be replaced or rehabilitated; this could require far more cement than might be expected from economic forecasts of demand for cement.