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.     

Application of Tabu Search Algorithm With a Coupled AnnAGNPS‐CCHE1D Model to Optimize Agricultural Land Use1

Abstract: A principal contributor to soil erosion and nonpoint source pollution, agricultural activities have a major influence on the environmental quality of a watershed. Impact of agricultural activities on the quality of water resources can be minimized by implementing suitable agriculture land‐use types. Currently, land uses are designed (location, type, and operational schedule) based on field study results, and do not involve a science‐based approach to ensure their efficiency under particular regional, climatic, geological, and economical conditions. At present, there is a real need for new methodologies that can optimize the selection, design, and operation of agricultural land uses at the watershed scale by taking into account environmental, technical, and economical considerations, based on realistic simulations of watershed response. In this respect, the present study proposes a new approach, which integrates computational modeling of watershed processes, fluvial processes in the drainage network, and modern heuristic optimization techniques to design cost effective land‐use plans. The watershed model AnnAGNPS and the channel network model CCHE1D are linked together to simulate the sediment and pollutant transport processes. Based on the computational results, a multi‐objective function is set up to minimize soil losses, nutrient yields, and total associated costs, while the production profits from agriculture are maximized. The selected iterative optimization algorithm uses adaptive Tabu Search heuristic to flip (switching from one alternative to another) land‐change variables. USDA’s Goodwin Creek experimental watershed, located in Northern Mississippi, is used to demonstrate the capabilities of the proposed approach. The results show that the optimized land‐use design with BMPs using an integrated approach at the watershed level can provide efficient and cost‐effective conservation of the environmental quality by taking into account both productivity and profitability.     

Bio-intervention phyto-based material for raw goatskin preservation: a cleaner-sustainable approach

Environmental Science and Pollution Research - The regular practice of using sodium chloride to preserve raw animal skin triggers increasing salinity and total dissolved solids (TDS) in the surface...     

Seasonal variation of gas exchange and pigmentation in branches of three grafted clones of mature ponderosa pine exposed to ozone and acid rain

Gas exchange and pigmentation responses of mature ponderosa pine (Pinus ponderosa Laws.) branches to ozone and acid rain exposure were investigated using three grafted clones growing in a managed seed orchard. Exposure of one-year-old foliage to twice ambient ozone (2 x AMB) resulted in significant decreases in net photosynthesis (Pn), stomatal conductance (gsw) and pigmentation relative to charcoal-filtered (CF) and ambient (AMB) ozone treatments. Ozone effects on gas exchange and pigmentation were most pronounced during late-season and differed significantly among clones. Environmental parameters (e.g. light, vapor pressure deficit, and temperature) accounted for more variation in Pn than did cumulative ozone exposure. Minimal differences in gsw and Pn among ozone treatments occurred during seasonal periods of high temperature and evaporative demand. Negative effects of 2 x AMB ozone on gsw and pigmentation were greatest for the clones having highest and lowest phenotypic vigor under ambient conditions; the clone of moderate phenotypic vigor under ambient conditions was least sensitive to ozone. Application of simulated acid rain of pH 3.0, pH 5.1 or no rain (NR) had little impact on gas exchange or pigmentation.     

Application of geographically weighted regression model to analysis of spatiotemporal varying relationships between groundwater quantity and land use changes (case study: Khanmirza Plain,Iran)

Understanding the spatiotemporal relationships between land use/cover changes (LUCC) and groundwater resources is necessary for effective and efficient land use management. In this paper, geographically weighted regression (GWR) and ordinary least squares (OLS) models have been expanded to analyze varying spatial relationships between groundwater quantity changes and LUCC for three periods: 1987–2000, 2000–2010, and 1987–2010 in the Khanmirza Plain of southwestern Iran. For this purpose, TM images were used to generate LUCC (rainfed, irrigated, meadow, and bare lands). Groundwater quantity variables, including groundwater level changes (GLC) and groundwater withdrawal differences (GWD), were gathered from piezometric and agricultural wells data. The analysis of spatial autocorrelation (Moran’s I and local indicators of spatial association ) demonstrated that GWR has a better ability to model spatially varying data with very minimal clustering of residuals. The results R ² and corrected Akaike’s Information Criterion parameters revealed that the GWR has the lowest similarity in space and time in neighboring situations and it has the high ability to explain more variance in the LUCC as a function of the groundwater quantity changes. All results of the distribution of local R ² values from GWR confirm our assertion that there is a spatiotemporal relationship between types of land use and each of groundwater quantity variables within the region. According to the t test results from GWR, there are significant differences between the GLC and GWD and the land use types in different places of region in each of the three time series. The GWR results can help decision-makers to make appropriate decisions for future planning.     

Mapping air quality zones for coastal urban centers

This study presents a new method that incorporates modern air dispersion models allowing local terrain and land–sea breeze effects to be considered along with political and natural boundaries for more accurate mapping of air quality zones (AQZs) for coastal urban centers. This method uses local coastal wind patterns and key urban air pollution sources in each zone to more accurately calculate air pollutant concentration statistics. The new approach distributes virtual air pollution sources within each small grid cell of an area of interest and analyzes a puff dispersion model for a full year’s worth of 1-hr prognostic weather data. The difference of wind patterns in coastal and inland areas creates significantly different skewness (S) and kurtosis (K) statistics for the annually averaged pollutant concentrations at ground level receptor points for each grid cell. Plotting the S-K data highlights grouping of sources predominantly impacted by coastal winds versus inland winds. The application of the new method is demonstrated through a case study for the nation of Kuwait by developing new AQZs to support local air management programs. The zone boundaries established by the S-K method were validated by comparing MM5 and WRF prognostic meteorological weather data used in the air dispersion modeling, a support vector machine classifier was trained to compare results with the graphical classification method, and final zones were compared with data collected from Earth observation satellites to confirm locations of high-exposure-risk areas. The resulting AQZs are more accurate and support efficient management strategies for air quality compliance targets effected by local coastal microclimates.

Implications: A novel method to determine air quality zones in coastal urban areas is introduced using skewness (S) and kurtosis (K) statistics calculated from grid concentrations results of air dispersion models. The method identifies land–sea breeze effects that can be used to manage local air quality in areas of similar microclimates.     

Dechlorination and decomposition of Aroclor 1242 in real waste transformer oil using a nucleophilic material with a modified domestic microwave oven

This research was done to assess the dechlorination and decomposition of polychlorinated biphenyls (PCBs) in real waste transformer oil through a modified domestic microwave oven (MDMW). The influence of microwave power (200–1000 W), reaction time (30–600 s), polyethylene glycol (PEG) (1.5–7.5 g), iron powder (0.3–1.5 g), NaOH (0.3–1.5 g), and H₂O (0.4–2 ml) were investigated on the decomposition efficiency of PCBs existing in real waste transformer oil with MDMW. Obtained data indicate that PEG and NaOH have the greatest influence on decomposition of PCBs; while, iron did not influence, and H₂O decreased, the decomposition efficiency of PCBs. Experimental data also indicated that with the optimum amount of variables through a central composites design method (PEG = 5.34 g, NaOH = 1.17 g, Fe = 0.6 g, H₂O = 0.8 ml and microwave power 800 W), 78 % of PCBs was degraded at a reaction time of about 6 min. In addition, the PCBs decomposition without using water increased up to 100 % in the reactor with the MDMW at 6 min. Accordingly, results showed that MDMW was a very efficient factor for PCBs decomposition from waste transformer oil. Also, using microwave irradiation, availability and inexpensive materials (PEG, NaOH), and iron suggest this method as a fast, effective, and cheap method for PCB decomposition of waste oils.