The Impacts of Different Meteorology Data Sets on Nitrogen Fate and Transport in the SWAT Watershed Model

In this study, we investigated how different meteorology data sets impacts nitrogen fate and transport responses in the Soil and Water Assessment Tool (SWAT) model. We used two meteorology data sets: National Climatic Data Center (observed) and Mesoscale Model 5/Weather Research and Forecasting (simulated). The SWAT model was applied to two 10-digit hydrologic unit code watersheds in the Coastal Plain and Piedmont zones of North Carolina. Nitrogen cycling and loading response to these meteorological data were investigated by exploring 19 SWAT nitrogen outputs relating to landscape delivery, biogeochemical assimilation, and atmospheric deposition. The largest difference in model output using both meteorology data sets was for large loads/fluxes. Landscape delivery outputs (e.g., NO^? ₃ watershed discharge, groundwater NO^? ₃ flux, soil NO^? ₃ percolation) showed the largest difference across all values. Use of the two weather data sources resulted in a nearly twofold difference in NO^? ₃ watershed discharge and groundwater NO^? ₃ flux. Differences for many nitrogen outputs were greater than those for sub-basin flow. Nitrogen outputs showed the greatest difference for agricultural land covers and there was no flow-related pattern in output differences across sub-basins or over time (years). In general, nitrogen parameter models that had a greater number of nitrate concentration, flow, and temperature terms (equation variables) in each transport model showed the greatest difference between both meteorology applications.     

Digital sustainability: basic conditions for sustainable digital artifacts and their ecosystems

The modern age has heralded a shift from the industrial society, in which natural resources are crucial input factors for the economy, towards a knowledge society. To date, sustainability literature has treated knowledge—and in particular digital artifacts—mainly as a means to the end of achieving sustainable development. In this conceptual paper, we argue that digital artifacts themselves ought also to be considered as resources, which also need to be sustainable. While over-consumption is a problem facing natural resources, with sustainable digital artifacts, underproduction, and underuse are the biggest challenges. In our view, the sustainability of digital artifacts improves their potential impact on sustainable development. A theoretical foundation for digital artifacts and their ecosystem allows us to present the relevant research on digital information, knowledge management, digital goods, and innovation literature. Based on these insights, we propose ten basic conditions for sustainable digital artifacts and their ecosystem to ensure that they provide the greatest possible benefit for sustainable development. We then apply those characteristics to four exemplary cases: Linux kernel development, Bitcoin cryptocurrency, the Wikipedia project, and the Linking Open Drug Data repositories. The paper concludes with a research agenda identifying topics for sustainability scholars and information systems academics, as well as practitioners. A number of suggestions for future studies on digital sustainability are also put forward.     

Extreme value modeling for the analysis and prediction of time series of extreme tropospheric ozone levels: a case study

The occurrence of high concentrations of tropospheric ozone is considered as one of the most important issues of air management programs. The prediction of dangerous ozone levels for the public health and the environment, along with the assessment of air quality control programs aimed at reducing their severity, is of considerable interest to the scientific community and to policy makers. The chemical mechanisms of tropospheric ozone formation are complex, and highly variable meteorological conditions contribute additionally to difficulties in accurate study and prediction of high levels of ozone. Statistical methods offer an effective approach to understand the problem and eventually improve the ability to predict maximum levels of ozone. In this paper an extreme value model is developed to study data sets that consist of periodically collected maxima of tropospheric ozone concentrations and meteorological variables. The methods are applied to daily tropospheric ozone maxima in Guadalajara City, Mexico, for the period January 1997 to December 2006. The model adjusts the daily rate of change in ozone for concurrent impacts of seasonality and present and past meteorological conditions, which include surface temperature, wind speed, wind direction, relative humidity, and ozone. The results indicate that trend, annual effects, and key meteorological variables along with some interactions explain the variation in daily ozone maxima. Prediction performance assessments yield reasonably good results.     

Spatial Variation of Soil Salinity in the Mexicali Valley, Mexico: Application of a Practical Method for Agricultural Monitoring

The degradation of irrigated lands through the process of soil salinization, or the buildup of salts in the soil, has hampered recent increases in agricultural productivity and threatens the sustainability of large-scale cultivation in critical agricultural regions of the world. Rapid detection of soil salinity on a regional basis has been identified as key for effective mitigation of such land degradation. The ability to detect regional patterns of soil salinity at an accuracy sufficient for regional-scale resource management is demonstrated using Landsat 5 Thematic Mapper (TM) imagery. A case study of the Mexicali Valley of Baja California, Mexico was selected due to the region's agricultural significance and concern for future soil salinity increases. Surface soil salinity was mapped using georeferenced field measurements of electrical conductivity (EC), collected concurrently with Landsat 5 TM imagery. Correlations between EC measurements and common indices derived from the satellite imagery were used to produce a model of soil salinity through regression analysis. Landsat band 7, TNDVI, PCA 1, Tasseled Cap 3 and Tasseled Cap 5 were found to offer the most promising correlations with surface soil salinity. Generally low levels of soil salinity were detected, however, distinct areas of elevated surface salinity were detected at levels potentially impacting sensitive crops cultivated within the region. The difficulty detecting low levels of salinity and the mid-range spatial resolution of Landsat 5 TM imagery restrict the applicability of this methodology to the study of broad regional patterns of degradation most appropriate for use by regional resource managers.     

Reduction in toxicity and generation of slag in secondary lead process

This paper focuses on the improvement of a secondary lead recycling processing plant, giving special attention to the generation of lead slag. The study was conducted using two different industrial rotary furnaces that together produce three different slag types, which depend on charge composition and lead-containing raw material obtained from a lead-acid battery recycling process. First, characterization of three slag types from different batches was performed, and such characterization included chemical, mineralogical, and structural analyses. By analyzing these data and the operational conditions of the process, it was possible to identify certain deficiencies in the recycling process and implement modifications in order to improve it. A reduction of up to 25% in the quantity of slag generated could be achieved with certain charges. In addition to this process improvement, it was possible to reduce the toxicity of the slag produced when processing a charge containing the same proportion of paste and grid as the lead-acid battery. This improvement lessens the overall environmental impact of the process. By applying this methodology, it was possible to determine some principles of cleaner production in the lead recycling process. So, waste generation could be reduced via improvements in the process and slag characteristics were modified to decrease its toxicity (as determined by lead content in leaching tests).     

Urban and rural mortality rates during heat waves in Berlin and Brandenburg, Germany

In large cities such as Berlin, human mortality rates increase during intense heat waves. Analysis of relevant data from north-eastern Germany revealed that, during the heat waves that occurred between 1990 and 2006, health risks were higher for older people in both rural and urban areas, but that, during the two main heat waves within that 17-year period of time, the highest mortality rates were from the city of Berlin, and in particular from its most densely built-up districts. Adaptation measures will need to be developed, particularly within urban areas, in order to cope with the expected future intensification of heat waves due to global climate change.     

Transfer of pesticides to the brew during mate drinking process and their relationship with physicochemical properties

In order to evaluate the extraction of pesticide residues that are transferred to the brew during mate drinking process of P.U.1 yerba mate leaves (Ilex paraguariensis), a special device to simulate the way in which mate is drunk in Uruguay was developed. The transfer to the brew of 12 organophosphates, 5 synthethic pyrethroids and one organochlorine pesticide from spiked samples was studied. The relationship between the transfer data thus obtained and physicochemical properties like water solubility (Ws), octanol-water coefficient (Kow) and Henry's constant (H) was evaluated. The extractability of the pesticide residues from yerba mate can be correlated with log Ws and log Kow. These transfer values allowed the calculation of ARLs (acceptable residue level) for the pesticides following Food and Agriculture Organization (FAO), World Health Organizaion (WHO) guidelines. These results can help the future establishment of maximum residue levels (MRLs).     

Potential Impact of Clean Air Act Regulations on Nitrogen Fate and Transport in the Neuse River Basin: a Modeling Investigation Using CMAQ and SWAT

There has been extensive analysis of Clean Air Act Amendment (CAAA) regulation impacts to changes in atmospheric nitrogen deposition; however, few studies have focused on watershed nitrogen transfer particularly regarding long-term predictions. In this study, we investigated impacts of CAAA NOx emissions on the fate and transport of nitrogen for two watersheds in the Neuse River Basin. We applied the Soil and Water Assessment Tool (SWAT) using simulated deposition rates from the Community Multiscale Air Quality (CMAQ) model. Two scenarios were investigated: one that considered CAAA emission controls in CMAQ simulation (with) and a second that did not (without). By 2020, results showed a 70 % drop in nitrogen discharge for the Little River watershed and a 50 % drop for the Nahunta watershed from 1990 levels under the with-CAAA scenario. Denitrification and plant nitrogen uptake played important roles in nitrogen discharge from each watershed. Nitrogen watershed response time to a change in atmospheric nitrogen deposition was 4 years for Nahunta and 2 years for Little River. We attribute these differences in nitrogen response time to contrasts in agricultural land use and diversity of crop types. Soybean, hay, and corn land covers had comparatively longer response times to changes in atmospheric deposition. The studied watersheds demonstrate relatively large nitrogen retention: ≥80 % of all delivered nitrogen.     

Assessing spatial variability of SO2 field as detected by an air quality network using Self-Organizing Maps,cluster, and Principal Component Analysis

In Bilbao (Spain), an air quality network measures sulphur dioxide levels at 4 locations. The objective of this paper is to develop a practical methodology to identify redundant sensors and evaluate a network's capability to correctly follow and represent SO₂ fields in Bilbao, in the frame of a continuous network optimization process.The methodology is developed and tested at this particular location, but it is general enough to be useable at other places as well, since it is not tied neither to the particular geographical characteristics of the place nor to the phenomenology of the air quality over the area.To assess the spatial variability of SO₂ measured at 4 locations in the area, three different techniques have been used: Self-Organizing Maps (SOMs), cluster analysis (CA) and Principal Component Analysis (PCA). The results show that the three techniques yield the same results, but the information obtained via PCA can be helpful not only for that purpose but also to throw light on the major mechanisms involved. This might be used in future network optimization stages. The main advantage of cluster analysis and SOMs is that they provide readily interpretable results. All the calculations have been carried out using the freely available software R.