Neural network forecasting of air pollutants hourly concentrations using optimised temporal averages of meteorological variables and pollutant concentrations

The new method for the forecasting hourly concentrations of air pollutants is presented in the paper. The method was developed for a site in urban residential area in city of Zagreb, Croatia, for four air pollutants (NO₂, O₃, CO and PM₁₀). Meteorological variables and concentrations of the respective pollutant were taken as predictors. A novel approach, based on families of univariate regression models, was employed in selecting the averaging intervals for input variables. For each variable and each averaging period between 1 and 97 h, a separate model was built. By inspecting values of the coefficient of correlation between measured and modelled concentrations, optimal averaging periods for each variable were selected. A new dataset for building the forecasting model was then calculated as temporal moving averages (running means) of former variables. A multi-layer perceptron type of neural networks is used as the forecasting model. Index of agreement, calculated for the entire dataset including the data for model building, ranged from 0.91 to 0.97 for the respective pollutants. As suggested by the analysis of the relative importance of the input variables, different agreements for different pollutants are likely due to different sources and production mechanisms of investigated pollutants. A comparison of the new method with more traditional method, which takes hourly averages of the forecast hour as input variables, showed similar or better performance. The model was developed for the purpose of public-health-oriented air quality forecasting, aiming to use a numerical weather forecast model for the prediction of the part of input data yet unknown at the forecasting time. It is to expect that longer term averages used as inputs in the proposed method will contribute to smaller input errors and the greater accuracy of the model.     

Susceptibility indexing method for irrigation water management planning: applications to K. Menderes river basin, Turkey

A susceptibility indexing method was developed based on vulnerability and quality indices. The contamination susceptibility index (SI) at a given location was calculated by taking the product of the vulnerability index (VI) and the quality index (QI): SI = VI × QI. This method incorporates both hydrogeological and hydrochemical data for a comprehensive index mapping. The DRASTIC index methodology was used for the hydrogeological data evaluations. The quality index calculation procedure based on a water quality classification scheme was introduced to evaluate hydrochemical data. The suggested susceptibility indexing method was applied to the Küçük Menderes river basin located in western Turkey. The susceptibility index map shows both hydrogeological and hydrochemical data related to the contamination problem including areas that should be taken into consideration during water management planning. The index map indicates that the most susceptible groundwater is located along the river channel between Kiraz and Tire towns, in the Selçuk area and along the Fertek stream channel to the north of Torbal? town. The results indicate that the incorporation of both hydrogeological and hydrochemical datasets enables more realistic evaluations than those of an individual dataset to estimate the groundwater contamination susceptibility of an aquifer. The numerical procedure applied could be extended further by including other parameters such as retardation, potential contaminant sources, etc. that affect the water quality in a given basin.     

The influence of use-related,environmental, and managerial factors on soil loss from recreational trails

Recreational uses of unsurfaced trails inevitably result in their degradation, with the type and extent of resource impact influenced by factors such as soil texture, topography, climate, trail design and maintenance, and type and amount of use. Of particular concern, the loss of soil through erosion is generally considered a significant and irreversible form of trail impact. This research investigated the influence of several use-related, environmental, and managerial factors on soil loss on recreational trails and roads at Big South Fork National River and Recreation Area, a unit of the U.S. National Park Service. Regression modeling revealed that trail position, trail slope alignment angle, grade, water drainage, and type of use are significant determinants of soil loss. The introduction of individual and groups of variables into a series of regression models provides improved understanding and insights regarding the relative influence of these variables, informing the selection of more effective trail management actions. Study results suggest that trail erosion can be minimized by avoiding “fall-line” alignments, steep grades, and valley-bottom alignments near streams, installing and maintaining adequate densities of tread drainage features, applying gravel to harden treads, and reducing horse and all-terrain vehicle use or restricting them to more resistant routes.     

Changes in negative air ions concentration under different light intensities and development of a model to relate light intensity to directional change

The purpose of this research was to determine changes in negative air ion (NAI) concentrations produced by plants grown under different light intensities. NAI concentrations were observed and analyzed in an outdoor green space with five plant species (Aloe arborescens, Clivia miniata, Chlorophytum comosum, Opuntia brunnescens, Crassula portulacea) and A. arborescens was then grown under controlled-light conditions in an enclosed space for the subsequent light intensity experiment.     

Prediction of the Fate and Transport Processes of Atrazine in a Reservoir

The fate and transport processes of a toxic chemical such as atrazine, an herbicide, in a reservoir are significantly influenced by hydrodynamic regimes of the reservoir. The two-dimensional (2D) laterally-integrated hydrodynamics and mass transport model, CE-QUAL-W2, was enhanced by incorporating a submodel for toxic contaminants and applied to Saylorville Reservoir, Iowa. The submodel describes the physical, chemical, and biological processes and predicts unsteady vertical and longitudinal distributions of a toxic chemical. The simulation results from the enhanced 2D reservoir model were validated by measured temperatures and atrazine concentrations in the reservoir. Although a strong thermal stratification was not identified from both observed and predicted water temperatures, the spatial variation of atrazine concentrations was largely affected by seasonal flow circulation patterns in the reservoir. In particular, the results showed the effect of flow circulation on spatial distribution of atrazine during summer months as the river flow formed an underflow within the reservoir and resulted in greater concentrations near the surface of the reservoir. Atrazine concentrations in the reservoir peaked around the end of May and early June. A good agreement between predicted and observed times and magnitudes of peak concentrations was obtained. The use of time-variable decay rates of atrazine led to more accurate prediction of atrazine concentrations, while the use of a constant half-life (60 days) over the entire period resulted in a 40% overestimation of peak concentrations. The results provide a better understanding of the fate and transport of atrazine in the reservoir and information useful in the development of reservoir operation strategies with respect to timing, amount, and depth of withdrawal.     

Non-conventional gas phase remediation of volatile halogenated compounds by dehydrated bacteria

Traditional biological removal processes are limited by the low solubility of halogenated compounds in aqueous media. A new technology appears very suitable for the remediation of these volatile organic compounds (VOCs). Solid/gas bio-catalysis applied in VOC remediation can transform halogenated compounds directly in the gas phase using dehydrated cells as a bio-catalyst.     

An Interval-Parameter Waste-Load-Allocation Model for River Water Quality Management Under Uncertainty

A simulation-based interval quadratic waste load allocation (IQWLA) model was developed for supporting river water quality management. A multi-segment simulation model was developed to generate water-quality transformation matrices and vectors under steady-state river flow conditions. The established matrices and vectors were then used to establish the water-quality constraints that were included in a water quality management model. Uncertainties associated with water quality parameters, cost functions, and environmental guidelines were described as intervals. The cost functions of wastewater treatment units were expressed in quadratic forms. A water-quality planning problem in the Changsha section of Xiangjiang River in China was used as a study case to demonstrate applicability of the proposed method. The study results demonstrated that IQWLA model could effectively communicate the interval-format uncertainties into optimization process, and generate inexact solutions that contain a spectrum of potential wastewater treatment options. Decision alternatives can be generated by adjusting different combinations of the decision variables within their solution intervals. The results are valuable for supporting local decision makers in generating cost-effective water quality management strategies.     

Health benefits valuation of regulatory intervention for air pollution control in thermal power plants in Delhi,India

This study estimates minimum marginal health benefits (morbidity reduction only) of air pollution control and total health benefits arising from regulatory intervention regarding the adoption of the World Bank emission guidelines (WBEG) for thermal power plants (TPPs) in Delhi. The Industrial Source Complex-Short-Term Version–3 (ISCST3) model has been used to estimate the contribution to air pollution from TPPs. The household health production function (avertive behaviour) has been used to value health benefits of air pollution control. The study revealed that the ambient air pollution due to TPPs is reduced by between 62.17% to 83.45% by adopting the WBEG. Annual marginal benefit due to reduction in exposure to air pollution by 1 μg m^?3 is estimated to be US$0.353 per person. Total annual health benefits for adopting the WBEG for TPPs are estimated at US$235.19 million. This study provides a novel methodology to evaluate health benefits of regulatory intervention.     

河网水质模型研究进展

纵观水质模型的研究、应用及相关科学的发展,本文针对现有的水质模型做了分析评价,简要介绍了河网水质模型的未来研究趋势,并提出了几点建议。     

六塘河泗阳段水环境状况及污染防治对策

结合近年来对江苏泗阳县城区内河（沟）水质监测和调查,分析六塘河泗阳段水环境污染现状和成因,并对控制淮河流域重点控制断面——六塘河石渡段面（泗阳县）水质达标,结合泗阳实际情况,提出相关对策。