Identification of degradation characteristics – exemplified by Gd–DTPA in a large experimental column

Surface water from rivers or lakes, infiltrating through porous media, is increasingly used as a source of public water supply. During bank filtration the water chemistry is substantially changed by various processes such as biodegradation, sorption and mixing with ambient groundwater, but the details of this natural attenuation are not yet entirely understood. In order to gain a better understanding, the degradation of an organic substance (DTPA) is examined here using various model approaches for steady state conditions. Models are fitted on data from an experimental study under near-field conditions. The results show advantages and disadvantages of the different model approaches and provide a guideline for the modelling of other organic substances under different but similar conditions.     

Phosphorus Load Reduction Goals for Feitsui Reservoir Watershed, Taiwan

The present paper describes an effort for developing the total maximum daily load (TMDL) for phosphorus and a load reduction strategy for the Feitsui Reservoir in Northern Taiwan. BASINS model was employed to estimate watershed pollutant loads from nonpoint sources (NPS) in the Feitsui Reservoir watershed. The BASINS model was calibrated using field data collected during a 2-year sampling period and then used to compute watershed pollutant loadings into the Feitsui Reservoir. The simulated results indicate that the average annual total phosphorus (TP) loading into the reservoir is 18,910 kg/year, which consists of non-point source loading of 16,003 kg/year, and point source loading of 2,907 kg/year. The Vollenweider mass balance model was used next to determine the degree of eutrophication under current pollutant loading and the load reduction needed to keep the reservoir from being eutrophic. It was estimated that Feitsui Reservoir can becoming of the oligotrophic state if the average annual TP loading is reduced by 37% or more. The results provide the basis on which an integrated control action plan for both point and nonpoint sources of pollution in the watershed can be developed.     

Dynamic Modeling of the Ratio Volatile Fatty Acids/Bicarbonate Alkalinity in a UASB Reactor for Potato Processing Wastewater Treatment

In this work, dynamic mathematical model for the prediction of the operational parameter volatile fatty acids/bicarbonate alkalinity (VFA/ALK) in a UASB reactor was developed. The dynamic modeling technique was applied successfully to a two-year data record from an industrial wastewater treatment plant of a potato processing industry. The technique used included regression analysis by residuals. Seventeen parameters were examined including the following: wastewater's flow rate, reactor's temperature and pH, total and soluble influent COD, wastewater's temperature and pH, total and soluble effluent COD, volatile fatty acids, alkalinity, biogas production rate and each parameter with a time lag of up to 10 days. Finally, after all parameters and all time lag trials the best fitted model was developed. The model's adequacy was checked by χ² test for a data record of the same UASB reactor but at a different time period and proved to be satisfactory. Additionally, the model's ability to predict and to control the plant's operation via VFA/ALK was examined. Through this model, in contrary to steady state models, various aspects of the process can be enlighten, such as the fact that the hydrolysis of starch requires at least a resident time of seven days.     

Using a neural network approach and time series data from an international monitoring station in the Yellow Sea for modeling marine ecosystems

The international marine ecological safety monitoring demonstration station in the Yellow Sea was developed as a collaborative project between China and Russia. It is a nonprofit technical workstation designed as a facility for marine scientific research for public welfare. By undertaking long-term monitoring of the marine environment and automatic data collection, this station will provide valuable information for marine ecological protection and disaster prevention and reduction. The results of some initial research by scientists at the research station into predictive modeling of marine ecological environments and early warning are described in this paper. Marine ecological processes are influenced by many factors including hydrological and meteorological conditions, biological factors, and human activities. Consequently, it is very difficult to incorporate all these influences and their interactions in a deterministic or analysis model. A prediction model integrating a time series prediction approach with neural network nonlinear modeling is proposed for marine ecological parameters. The model explores the natural fluctuations in marine ecological parameters by learning from the latest observed data automatically, and then predicting future values of the parameter. The model is updated in a “rolling” fashion with new observed data from the monitoring station. Prediction experiments results showed that the neural network prediction model based on time series data is effective for marine ecological prediction and can be used for the development of early warning systems.     

A Simple Model of Pollutant Concentrations in a Street Canyon

A single compartment model has been constructed for predicting hourly concentrations of pollutant concentrations arising from vehicular emissions within a typical street canyon. The model takes account of traffic densities and composition to estimate pollutant emissions within the model compartment. Meteorological data on wind speed and direction are used to define the exchanges of pollutants between the compartment and the surrounding air. A parameter is also included to describe the exchange in calm conditions. The pollutant concentrations are then estimated from a steady state mass balance equation for the compartment, assuming conservation of pollutants. The model was applied to the prediction of carbon monoxide concentrations in Hope Street, Glasgow. Model parameters were fitted using field measurements, together with concurrent meteorological data and traffic flows estimated from traffic census data for Hope Street. The model accounted well for the observed variations in carbon monoxide. It was found that the model parameters varied seasonally, perhaps due to differences in atmospheric stability which have not so far been included in the model formulation.     

A method for assessing generalised nitrogen leaching estimates for agricultural land

The purpose of this study was to develop a method for assessing generalised N leaching estimates from large areas of agricultural land. The system developed was based on calculating a number of N leaching estimates for different typical cropping situations. The estimates were normalised with respect to varying weather conditions and crop production. The different cropping situations were described by setting up a matrix consisting of crucial factors influencing leaching such as soils, crops and climate. Nitrogen leaching was then estimated for a number of combinations of these factors. Calculations were made for three different regions where all the major crops were cultivated on soils with seven different textures and four different organic-N classes and two fertilisation regimes. The three regions are representative of climates and agricultural practices in some of the major agricultural areas in Sweden. The model used was the SOILN model. Leaching of nitrogen from the root zone showed large variations. The range was from 1 to 50 kg ha⁻¹ for different soils and crops when only fertiliser N was applied. Leaching varied both due to different climates and differences in cultivation practices between the regions. Leaching decreased in a south-north gradient. Leaching increased as a result of greater mineralisation when the organic matter content in the soils was increased, leaching was less from soils with a high clay content and was very small for the heavy clay soil. This revised version was published online in July 2006 with corrections to the Cover Date.     

Soil Water Content Estimated by Support Vector Machine for the Assessment of Shallow Landslides Triggering: the Role of Antecedent Meteorological Conditions

Soil water content is a key parameter for representing water dynamics in soils. Its prediction is fundamental for different practical applications, such as identifying shallow landslides triggering. Support vector machine (SVM) is a machine learning technique, which can be used to predict the temporal trend of a quantity since training from past data. SVM was applied to a test slope of Oltrepò Pavese (northern Italy), where meteorological parameters coupled with soil water content at different depths (0.2, 0.4, 0.6, 1.0, 1.2, 1.4 m) were measured. Two SVM models were developed for water content assessment: (i) model 1, considering rainfall amount, air temperature, air humidity, net solar radiation, and wind speed; (ii) model 2, considering the same predictors of model 1 together with antecedent condition parameters (cumulated rainfall of 7, 30, and 60 days; mean air temperature of 7, 30, and 60 days). SVM model 2 showed significantly higher satisfactory results than model 1, for both training and test phases and for all the considered soil levels. SVM models trends were implemented in a methodology of slope safety factor assessment. For a real event occurred in the tested slope, the triggering time was correctly predicted using data estimated by SVM model based on antecedent meteorological conditions. This confirms the necessity of including these predictors for building a SVM technique able to estimate correctly soil moisture dynamics in time. The results of this paper show a promising potential application of the SVM methodologies for modeling soil moisture required in slope stability analysis.     

基于小波分析优化PM 2.5浓度预测模型

采用多元线性回归方法(MLR)和BP神经网络方法(BPNN),按1 h、3 h、6 h、12 h、24 h、48 h预测时长对贵港市2015—2018年PM_2.5浓度建模并检验对比模型准确率。结果表明,基于MLR与BPNN都能对PM_2.5浓度作预测,预测效果随着预测时长的增加而下降,MLR、BPNN模型预测结果平均绝对误差(MAE)分别为4.01μg/m³~15.48μg/m³、3.89μg/m³~15.63μg/m³。采用小波分析方法对污染物数据优化并再次建模,结果表明,小波-多元线性回归(W-MLR)模型与小波-神经网络(W-BPNN)模型均得到优化,3 h~24 h预测时长优化效果尤为显著,W-MLR、W-BPNN模型预测结果分别使MAE降低1.6%~13.5%、0.8%~9.8%,且后者预测效果优于前者。     

24 h diffusive sampling of toxic VOCs in air onto Carbopack X solid adsorbent followed by thermal desorption/GC/MS analysis-laboratory studies

Diffusive sampling of a mixture of 42 volatile organic compounds (VOCs) in humidified, purified air onto the solid adsorbent Carbopack X was evaluated under controlled laboratory conditions. The evaluation included variations in sample air temperature, relative humidity and ozone concentration. Linearity of samples with loading was examined both for a constant concentration with time varied up to 24 h and for different concentrations over 24 h. Reverse diffusion and its increase with accumulation of sample were determined for all compounds. Tubes were examined for blank levels, change of blanks with storage time, and variability of blanks. Method detection limits were determined based on seven replicate samples. Based on this evaluation, 27 VOCs were selected for quantitative monitoring in the concentration range from approximately 0.1 to 4 ppbv. Comparison results of active and diffusive samples taken over 24 h and under the same simulated ambient conditions at a constant 2 ppbv were interpreted to estimate the effective diffusive sampling rates (ml min(-1)) and their uncertainties and to calculate the corresponding diffusive uptake rates (ng ppmv(-1) min(-1)).     

Estimation of the contributions of long range transported aerosol in East Asia to carbonaceous aerosol and PM concentrations in Seoul, Korea using highly time resolved measurements: a PSCF model approach

The contributions of long range transported aerosol in East Asia to carbonaceous aerosol and particulate matter (PM) concentrations in Seoul, Korea were estimated with potential source contribution function (PSCF) calculations. Carbonaceous aerosol (organic carbon (OC) and elemental carbon (EC)), PM(2.5), and PM(10) concentrations were measured from April 2007 to March 2008 in Seoul, Korea. The PSCF and concentration weighted trajectory (CWT) receptor models were used to identify the spatial source distributions of OC, EC, PM(2.5), and coarse particles. Heavily industrialized areas in Northeast China such as Harbin and Changchun and East China including the Pearl River Delta region, the Yangtze River Delta region, and the Beijing-Tianjin region were identified as high OC, EC and PM(2.5) source areas. The conditional PSCF analysis was introduced so as to distinguish the influence of aerosol transported from heavily polluted source areas on a receptor site from that transported from relatively clean areas. The source contributions estimated using the conditional PSCF analysis account for not only the aerosol concentrations of long range transported aerosols but also the number of transport days effective on the measurement site. Based on the proposed algorithm, the condition of airmass pathways was classified into two types: one condition where airmass passed over the source region (PS) and another condition where airmass did not pass over the source region (NPS). For most of the seasons during the measurement period, 249.5-366.2% higher OC, EC, PM(2.5), and coarse particle concentrations were observed at the measurement site under PS conditions than under NPS conditions. Seasonal variations in the concentrations of OC, EC, PM(2.5), and coarse particles under PS, NPS, and background aerosol conditions were quantified. The contributions of long range transported aerosols on the OC, EC, PM(2.5), and coarse particle concentrations during several Asian dust events were also estimated. We also investigated the performance of the PSCF results obtained from combining highly time resolved measurement data and backward trajectory calculations via comparison with those from data in low resolutions. Reduced tailing effects and the larger coverage over the area of interest were observed in the PSCF results obtained from using the highly time resolved data and trajectories.     

The assessment of spatial distribution of soil salinity risk using neural network

Soil salinity in the Aral Sea Basin is one of the major limiting factors of sustainable crop production. Leaching of the salts before planting season is usually a prerequisite for crop establishment and predetermined water amounts are applied uniformly to fields often without discerning salinity levels. The use of predetermined water amounts for leaching perhaps partly emanate from the inability of conventional soil salinity surveys (based on collection of soil samples, laboratory analyses) to generate timely and high-resolution salinity maps. This paper has an objective to estimate the spatial distribution of soil salinity based on readily or cheaply obtainable environmental parameters (terrain indices, remote sensing data, distance to drains, and long-term groundwater observation data) using a neural network model. The farm-scale (∼15 km²) results were used to upscale soil salinity to a district area (∼300 km²). The use of environmental attributes and soil salinity relationships to upscale the spatial distribution of soil salinity from farm to district scale resulted in the estimation of essentially similar average soil salinity values (estimated 0.94 vs. 1.04 dS m⁻¹). Visual comparison of the maps suggests that the estimated map had soil salinity that was uniform in distribution. The upscaling proved to be satisfactory; depending on critical salinity threshold values, around 70–90% of locations were correctly estimated.     

An expert system for water quality modelling

The RAISON-micro (Regional Analysis by Intelligent System ON a micro-computer) expert system is being used to predict the effects of mine effluents on receiving waters in Ontario. The potential of this system to assist regulatory agencies and mining industries to define more acceptable effluent limits was shown in an initial study. This system has been further developed so that the expert system helps the model user choose the most appropriate model for a particular application from a hierarchy of models. The system currently contains seven models which range from steady state to time dependent models, for both conservative and nonconservative substances in rivers and lakes. The menu driven expert system prompts the model user for information such as the nature of the receiving water system, the type of effluent being considered, and the range of background data available for use as input to the models. The system can also be used to determine the nature of the environmental conditions at the site which are not available in the textual information database, such as the components of river flow. Applications of the water quality expert system are presented for representative mine sites in the Timmins area of Ontario.     

合肥市生活垃圾产量多模型预测与分析

针对合肥市生活垃圾产量现状,通过建立时间序列(ARIMA)、多元线性回归(MLR)、灰色系统GM(1,1)和反向传播神经网络(BPNN)模型对历史数据进行验证比较分析。结果表明,ARIMA(0,1,2)模型的MAPE、MAE、RMSE、NRMSE分别为1.879%、2.240、2.781、0.021,其精度最高、效果最好,为合肥市生活垃圾产量的最佳预测模型。用该模型预测合肥市2021—2025年的城市生活垃圾产量,结果显示生活垃圾产生量为218.89万t～290.71万t。     

Simple Model of Dissolved Oxygen Consumption in a Bay within High Organic Loading: An Applied Remediation Tool

San Vicente Bay is a coastal shallow embayment in Central Chile with multiple uses, one of which is receiving wastewater from industrial fisheries, steel mill effluents, and domestic sewage. A simulation model was developed and applied to dissolved oxygen consumption by organic residues released into this embayment. Three compartments were established as function of: depth, circulation and outfall location. The model compartments had different volumes, and their oxygen saturation value was used as baseline. The parameters: (a) BOD₅ of the industrial and urban effluents, (b) oxygen demand by organic sediments, (c) respiration, (d) photosynthesis and (e) re-aeration were included in the model. Iteration results of the model showed severe alterations in Compartment 1, with a decrease of 65% in the oxygen below saturation. Compartment 2 showed a small decline (10%) and compartment 3 did not show apparent changes in oxygen values. Measures recommended for remediation were to decrease the BOD₅ loading by 30% in the affected sector. Iteration of the model for 200 h following recommendations derived from the preceding results produced an increase in saturation of 60% (5 ml O₂ L⁻¹), which suggested an improvement of the environmental conditions.     

基于信号改进深度学习网络的京津冀城市群二氧化氮浓度预测

在中国华北地区,二氧化氮污染仍旧不容忽视,尤其是在机动车辆密集和工业生产相对集中的京津冀城市群.运用小波分解(WD)和长短期记忆(LSTM)神经网络建立了W-LSTM组合模型,用于预测未来京津冀地区二氧化氮日均浓度和分指数.使用2014年1月-2018年5月主要大气污染物数据对组合预测模型进行训练试验,在获得最优模型参...     

Assessment of groundwater quality: a fusion of geochemical and geophysical information via Bayesian neural networks

Deplorable quality of groundwater arising from saltwater intrusion, natural leaching and anthropogenic activities is one of the major concerns for the society. Assessment of groundwater quality is, therefore, a primary objective of scientific research. Here, we propose an artificial neural network-based method set in a Bayesian neural network (BNN) framework and employ it to assess groundwater quality. The approach is based on analyzing 36 water samples and inverting up to 85 Schlumberger vertical electrical sounding data. We constructed a priori model by suitably parameterizing geochemical and geophysical data collected from the western part of India. The posterior model (post-inversion) was estimated using the BNN learning procedure and global hybrid Monte Carlo/Markov Chain Monte Carlo optimization scheme. By suitable parameterization of geochemical and geophysical parameters, we simulated 1,500 training samples, out of which 50 % samples were used for training and remaining 50 % were used for validation and testing. We show that the trained model is able to classify validation and test samples with 85 % and 80 % accuracy respectively. Based on cross-correlation analysis and Gibb’s diagram of geochemical attributes, the groundwater qualities of the study area were classified into following three categories: “Very good”, “Good”, and “Unsuitable”. The BNN model-based results suggest that groundwater quality falls mostly in the range of “Good” to “Very good” except for some places near the Arabian Sea. The new modeling results powered by uncertainty and statistical analyses would provide useful constrain, which could be utilized in monitoring and assessment of the groundwater quality.     

Wheat yield functions for analysis of land-use change in China

CERES-Wheat, a dynamic process crop growth model, is specified and validated for eight sites in the major wheat-growing regions of China. Crop model results are then used to test the Mitscherlich-Baule and the quadratic functional forms for yield response to nitrogen fertilizer, irrigation water, temperature, and precipitation. The resulting functions are designed to be used in a linked biophysical-economic model of land-use and land-cover change in China. While both functions predict yield responses adequately, the Mitscherlich-Baule function is preferable to the quadratic function because its parameters are biologically and physically realistic. Variables explaining a significant proportion of simulated yield variance are nitrogen, irrigation water, and precipitation; temperature was a less significant component of yield variation within the range of observed year-to-year variability at the study sites. Crop model simulations with a generic soil with median characteristics of the eight sites compared to simulations with site-specific soils showed that agricultural soils in China have similar and, in general, low-to-moderate water-holding capacities and organic matter contents. The validated crop model is useful for simulating the range of conditions under which wheat is grown in China, and provides the means to estimate production functions when experimental field data are not available.     

Multilayer perceptron neural network for flow prediction

Artificial neural networks (ANNs) have proven to be a tool for characterizing, modeling and predicting many of the non-linear hydrological processes such as rainfall-runoff, groundwater evaluation or simulation of water quality. After proper training they are able to generate satisfactory predictive results for many of these processes. In this paper they have been used to predict 1 or 2 days ahead the average and maximum daily flow of a river in a small forest headwaters in northwestern Spain. The inputs used were the flow and climate data (precipitation, temperature, relative humidity, solar radiation and wind speed) as recorded in the basin between 2003 and 2008. Climatic data have been utilized in a disaggregated form by considering each one as an input variable in ANN(1), or in an aggregated form by its use in the calculation of evapotranspiration and using this as input variable in ANN(2). Both ANN(1) and ANN(2), after being trained with the data for the period 2003-2007, have provided a good fit between estimated and observed data, with R(2) values exceeding 0.95. Subsequently, its operation has been verified making use of the data for the year 2008. The correlation coefficients obtained between the data estimated by ANNs and those observed were in all cases superior to 0.85, confirming the capacity of ANNs as a model for predicting average and maximum daily flow 1 or 2 days in advance.     

Dechlorination of pentachlorophenol in supercritical carbon dioxide with zero-valent palladium-magnesium bimetallic mixture

A stream of substrate pentachlorophenol [PCP, 5 mg min(-1) in water-methanol (1 + 4, v/v)] was merged with 1.5 ml min(-1) of supercritical carbon dioxide (scCO2) and delivered to a reactor column (25 cm x 1 cm) of zero-valent palladium-magnesium mixture. The resulting dechlorinations, although very efficient, were not quantitative. For continuous operation at 400 degrees C for 6 h, phenol was the principal product, with lesser quantities of methylated products and only traces of chlorinated products (principally monochlorinated species). PCP deoxygenation was not observed and ring methylation was decreased relative to analogous reactions in hydroxylic organic solvent. With time, the reactor column slowly lost dechlorination activity. Reducing the loading of Pd0 on Mg0 from 2% to 1% (w/w) apparently did not change the course of the reaction; however, the dechlorination capacity was decreased correspondingly. None the less, over 6 h or 5 h of continued operation, the dechlorination efficiency was 0.995 for the 2% (w/w) loading of Pd0 on Mg0 and 0.984 for the 1% (w/w) loading.     

AgInput: An Agricultural Nutrient and Pesticide Source Model

Agriculture can be a major nonpoint source (NPS) of nutrient and pesticide contamination in the environment. Available databases do not provide accurate and dynamic data on fertilizer and pesticide application, which limits the ability of complex watershed models to simulate contaminant loads into impaired water bodies. A model for estimating agricultural nutrient and pesticide input for watershed modeling has been developed. Climate, soils, and major agricultural operations are considered within the model, so that it can be adapted to any watershed or subregion within a watershed. The timing of the agricultural operations is a function of the weather data, providing realistic results at daily, monthly, or annual application rates. The model also predicts irrigation demand and biomass production, which can be used to calibrate the model. Model output can be used in any watershed model that considers agricultural land uses. Two case studies were evaluated, using grape vineyards in the Napa River and strawberry production in Newport Bay as examples. The predicted time to maturity corresponded well with actual data. Irrigation and fertilizer needs were very sensitive to weather input. Although the model can generate weather from long-term averages, the simulated results are best when at least observed precipitation and temperature are provided, to capture extreme events. The model has data for 98 crops and 126 pesticides, based on the California Department of Pesticide Regulation database. The databases are easily modifiable by the user to adapt them to local conditions. The output from AgInput is much needed for watershed modeling and for development of total maximum daily loads (TMDLs), based on realistic targets of irrigation, nutrient, and pesticide inputs. The model is available for free download at .