Lead forms in urban turfgrass and forest soils as related to organic matter content and pH

Identification of reference streams and human disturbance gradients are crucial steps in assessing the effects of human disturbances on stream health. We describe a process for identifying reference stream reaches and assessing disturbance gradients using readily available, geo-referenced stream and human disturbance databases. We demonstrate the utility of this process by applying it to wadeable streams in Michigan, USA, and use it to identify which human disturbances have the greatest impact on streams. Approximately 38% of cold-water and 16% of warm-water streams in Michigan were identified as being in least-disturbed condition. Conversely, approximately 3% of cold-water and 4% of warm-water streams were moderately to severely disturbed by landscape human disturbances. Anthropogenic disturbances that had the greatest impact on moderately to severely disturbed streams were nutrient loading and percent urban land use within network watersheds. Our process for assessing stream health represents a significant advantage over other routinely used methods. It uses inter-confluence stream reaches as an assessment unit, permits the evaluation of stream health across large regions, and yields an overall disturbance index that is a weighted sum of multiple disturbance factors. The robustness of our approach is linked to the scale of disturbances that affect a stream; it will be less robust for identifying less degraded or reference streams with localized human disturbances. With improved availability of high-resolution disturbance datasets, this approach will provide a more complete picture of reference stream reaches and factors contributing to degradation of stream health.     

Susceptibility mapping of groundwater salinity using machine learning models

Environmental Science and Pollution Research - Increasing groundwater salinity has recently raised severe environmental and health concerns around the world. Advancement of the novel methods for...     

Determination of tolerable and toxic salt concentrations in drinking water in rat,pig, and rabbit

In some geographical areas human and animal populations are compelled to use salty water. In this study, tolerable and toxic salt concentrations were evaluated in rat, pig, and rabbit. Ninety Wistar rats weighing approximately 250 g were allocated to nine groups for study. For treatment, distilled water, 0.5, 1, 1.2, 1.4, 1.6, 1.8 or 2% salt (NaCl) concentrations were consumed by test groups, while the control group consumed Zanjan potable water. All animals were observed twice daily for mortality or evidence of morbidity. Serum sodium levels and plasma osmolarity were measured at the onset of experiment and 120 h later. Similar experimental conditions were employed in pig and rabbit. Rat tolerated 0.5, 1, and 1.2% NaCl concentrations in drinking water but higher concentrations were toxic and life threatening, accompanied by significantly increased serum sodium levels and plasma osmolarity. This study showed that pig was the most sensitive and rabbit the most resistant species to different NaCl concentrations. Although NaCl concentrations lower than 1.4% was considered as tolerable in rat, the animal health was affected. NaCl concentrations of 1.4% and higher may jeopardize animal health and were considered as toxic amounts.     

Impacts of land use and land cover change on the interactions among multiple soil-dependent ecosystem services (case study: Jiroft plain,Iran)

The spatial and temporal distribution pattern is an outstanding feature of the relationship among ecosystem services (ESs) that explains links between human activities and disturbed chemical composition of ecosystems. This study investigated the spatiotemporal variation of land use/cover changes (LUCC) and quantifies the change in four essential ecosystem services with an emphasis on soil (nutrient delivery ratio, carbon storage, crop production, and water yield) and their relationships in the Jiroft plain, Iran, during 1996–2016 through analytical tools including Land Change Modeler, and the Integrated Valuation of Ecosystem Services and Tradeoff. During the 20-year concentrate period, there was a considerable overall gain in cropland (5396 km²) and urban (1787 km²), loss of unused land (5692 km²), water (2088 km²), and forest (1083 km²). As a result of LUCC, while crop production and nutrient delivery ratio showed a rising trend, overall carbon storage and water yield decreased. The spatiotemporal trade-off between carbon storage and crop production, the temporal trade-off between crop production and water yield, and synergy between water yield and crop production were widespread in Jiroft plain. These results showed that the interaction among ESs mutates over time and can be changed under planning and policies. This study will enrich the research of the geographical distribution of ESs interaction in dryland ecosystems to provide practical ecosystem management under local conditions.

     

Government management and overexploitation of groundwater resources: absence of local community initiatives in Ardabil plain-Iran

Although mismanagement of groundwater resources has resulted in their destruction over centuries, climate change is speeding up this process more than ever. On the one hand, urgent action by government bodies is needed to address the challenge. On the other hand, a vast body of literature proves that bottom-up collective action, although requiring a longer time period, is a better solution to manage such resources. This research aims to address this dilemma. The groundwater resource in Ardabil plain in Iran, which has long been managed solely through government intervention, has been chosen as a case study to explore the opportunities and limitations of managing a critically endangered area from a common pool resource perspective. Our study suggests that managing Ardabil groundwater resources should be conducted on two scales. The bottom-up institutional agreements should take place at village scale while the government should stay in charge of the overall organization at plain scale.     

Thallium(I) and thallium(III) induce apoptosis in isolated rat hepatocytes by alterations in mitochondrial function and generation of ROS

Environmental metal toxins, generated through diverse anthropogenic activities, constitute one of the major contaminants that have led to global dispersion of these toxic metals in the ecosystem. Thallium is one of these widely dispersed metals that produce severe adverse effects on human and biological systems. The influence of thallium(I) and thallium(III) on the early events that trigger apoptosis signaling were examined in freshly isolated rat hepatocytes. In addition, the role of oxidative stress, and mitochondria in the induction of apoptosis were also investigated. Incubation of thallium(I) and thallium(III) with isolated rat hepatocytes generated reactive oxygen species (ROS), collapse of mitochondrial membrane potential, activation of caspases cascade, and appearance of apoptosis phenotype. Mitochondrial permeability transition (MPT) pore sealing agents (cyclosporine A and carnitine) and ATP generators (L-glutamine, fructose, and xylitol) inhibited the activation of caspase-3 and apoptosis, indicating that both the cations activated apoptosis signaling via mitochondrial pathway. Pretreatment of hepatocytes with antioxidants (α-tocopherol or deferoxamine) also blocked caspase-3 activation induced by these cations, suggesting that oxidative stress may be directly involved in a mitochondrial MPT pore opening and activation of caspases cascade. These findings contribute to a better understanding of the mechanisms that mediate thallium-induced apoptosis in isolated rat hepatocytes.     

RASTER-BASED HYDROLOGIC MODELING OF SPATIALLY-VARIED SURFACE RUNOFF1

ABSTRACT: The proliferation of watershed databases in raster Geographic Information System (GIS) format and the availability of radar-estimated rainfall data foster rapid developments in raster-based surface runoff simulations. The two-dimensional physically-based rainfall-runoff model CASC2D simulates spatially-varied surface runoff while fully utilizing raster GIS and radar-rainfall data. The model uses the Green and Ampt infiltration method, and the diffusive wave formulation for overland and channel flow routing enables overbank flow storage and routing. CASC2D offers unique color capabilities to display the spatio-temporal variability of rainfall, cumulative infiltrated depth, and surface water depth as thunderstorms unfold. The model has been calibrated and independently verified to provide accurate simulations of catchment response to moving rainstorms on watersheds with spatially-varied infiltration. The model can accurately simulate surface runoff from flashfloods caused by intense thunderstorms moving across partial areas of a watershed.     

Forecasting air quality time series using deep learning

This paper presents one of the first applications of deep learning (DL) techniques to predict air pollution time series. Air quality management relies extensively on time series data captured at air monitoring stations as the basis of identifying population exposure to airborne pollutants and determining compliance with local ambient air standards. In this paper, 8 hr averaged surface ozone (O₃) concentrations were predicted using deep learning consisting of a recurrent neural network (RNN) with long short-term memory (LSTM). Hourly air quality and meteorological data were used to train and forecast values up to 72 hours with low error rates. The LSTM was able to forecast the duration of continuous O₃ exceedances as well. Prior to training the network, the dataset was reviewed for missing data and outliers. Missing data were imputed using a novel technique that averaged gaps less than eight time steps with incremental steps based on first-order differences of neighboring time periods. Data were then used to train decision trees to evaluate input feature importance over different time prediction horizons. The number of features used to train the LSTM model was reduced from 25 features to 5 features, resulting in improved accuracy as measured by Mean Absolute Error (MAE). Parameter sensitivity analysis identified look-back nodes associated with the RNN proved to be a significant source of error if not aligned with the prediction horizon. Overall, MAE's less than 2 were calculated for predictions out to 72 hours.

Implications: Novel deep learning techniques were used to train an 8-hour averaged ozone forecast model. Missing data and outliers within the captured data set were replaced using a new imputation method that generated calculated values closer to the expected value based on the time and season. Decision trees were used to identify input variables with the greatest importance. The methods presented in this paper allow air managers to forecast long range air pollution concentration while only monitoring key parameters and without transforming the data set in its entirety, thus allowing real time inputs and continuous prediction.     

Land suitability assessment for wind power plant site selection using ANP-DEMATEL in a GIS environment: case study of Ardabil province,Iran

Wind energy is a renewable energy resource that has increased in usage in most countries. Site selection for the establishment of large wind turbines, called wind farms, like any other engineering project, requires basic information and careful planning. This study assessed the possibility of establishing wind farms in Ardabil province in northwestern Iran by using a combination of analytic network process (ANP) and decision making trial and evaluation laboratory (DEMATEL) methods in a geographical information system (GIS) environment. DEMATEL was used to determine the criteria relationships. The weights of the criteria were determined using ANP and the overlaying process was done on GIS. Using 13 information layers in three main criteria including environmental, technical and economical, the land suitability map was produced and reclassified into 5 equally scored divisions from least suitable to most suitable areas. The results showed that about 6.68 % of the area of Ardabil province is most suitable for establishment of wind turbines. Sensitivity analysis shows that significant portions of these most suitable zones coincide with suitable divisions of the input layers. The efficiency and accuracy of the hybrid model (ANP-DEMATEL) was evaluated and the results were compared to the ANP model. The sensitivity analysis, map classification, and factor weights for the two methods showed satisfactory results for the ANP-DEMATEL model in wind power plant site selection.     

Integration of multi-disciplinary geospatial data for delineating agroecosystem uniform management zones

The aim of this study was the development of analytical methods for the simultaneous determination of 25 selected pharmaceuticals, metabolites, and pesticides, belonging to the various chemical classes, in river sediments and their corresponding surface and ground water with the purpose of monitoring the contamination levels. The methods were based on the solid-phase extraction as the sample preparation method for water samples, and the ultrasonic solvent extraction for the sediment samples, followed by the liquid chromatography–tandem mass spectrometry. High recoveries were achieved for extraction from both water and sediment samples for the majority of analytes. Low limits of detection were achieved for all investigated compounds in the water sample (1–5 ng L^?1) as well as in the sediment (1–3 ng g^?1). Applicability of the developed methods was demonstrated by determination of pharmaceutical and pesticide residues in 30 surface water, 44 groundwater, and 5 sediment samples from the Danube River Basin in Serbia. Sixty percent of target compounds were detected in environmental samples. The most frequently detected analytes in river sediments were the pesticides dimethoate and atrazine, while carbamazepine and metamizole metabolites 4-AAA and 4-FAA were the most frequently found in water samples.