Optimal Policy Mechanism Design for Cross-Sector and Multi-Stage Pollution Control with a Bilevel Model: Application to SO<Subscript>2</Subscript> Emission in China

In this paper, we construct a multi-stage coordinated programming model under tax system to control SO₂ emission. The model is based on an explicitly formulated SO₂ abatement cost function created under Chinese condition. Analysis of the effectiveness and impact on the economy of the model is carried out with consideration of game theory. By solving the model, theoretical results show that the volume-based multi-stage SO₂ tax system has two properties: effectiveness and equal-rate. Based on these theoretical results, empirical study is also performed using Chinese historical data. Compared with yearly single-stage programming model, the tax rate generated by the coordinated multi-stage programming model is time-invariant and rather moderate in scale. The total abatement cost among planning years in our model is 21.03 % less than the actual number and 6.68 % less than that in the single-stage situation. The tax payment suggested by our model is 10.62 % less than by the single-stage model. In general, a coordinated multi-stage programming model helps reduce the overall costs of environmental protection while achieving the same emission control target with less burden added to the economy.     

Urban Air Quality Assessment Model: UAQAM

The Urban Air Quality Assessment Model (UAQAM) calculates urban concentrations caused by city emissions themselves, the so-called city background concentration. Three versions of the model for describing the dispersion were studied: Box, Gifford Hanna (GH) and a combined form of these two (Box–GH model). Regional background emissions contributing to the urban background concentration were accounted for by using measurements and calculations from a dispersion model on the continental scale. The results of the three UAQAM versions for a number of European cities were compared to measurements of SO₂ and NO _X . The Box–GH model shows better results when compared to the Box model and slightly better results in comparison to the GH model. The Box–GH model has been taken as a starting point for the assessment of urban air quality with UAQAM.     

基于复合LSTM模型的PM2.5浓度预测

在对淄博市19个空气质量监测站点监测数据进行分析后,提出了一种基于机器学习的复合模型——灰色关联度分析(GRA)-改进的完备总体经验模态分解(ICEEMD)-长短期记忆网络(LSTM)模型。通过分析淄博市2019年大气污染物和气象数据,选用LSTM模型预测PM_2.5浓度。由于传统单一模块机器学习模型具有训练时间较长和预测精度较低的问题,提出了复合LSTM模型。该模型由3部分组成：GRA,用于PM_2.5浓度影响因素变量筛选;ICEEMD,用于PM_2.5分解、分量筛选和原始大气污染物及气象数据处理;LSTM,用于PM_2.5浓度预测。预测结果表明：淄博市中部丘陵地带PM_2.5浓度高于南部山区和北部平原,东部高于西部;淄博市逐月PM_2.5浓度呈“U”形分布,1月最高,8月最低;淄博市PM_2.5浓度受PM₁₀和CO影响较大,受湿度和温度影响较小。对比单一LSTM模型和GRA-LSTM模型,GRA-ICEEMD-LSTM模型...     

Modeling Solute Transport in Volcanic Ash Soils with Cation Exchange and Anion Retardation

Groundwater under agricultural lands is often contaminated by nitrate. In southern Japan, aquifers are covered by volcanic ash soils that can leach nitrate to the groundwater. In this study, column experiments using two volcanic ash soils (Kuroboku and Akahoya) were carried out. A mixed solution of KNO₃ and K₂SO₄ was used in the leaching experiments. Based on the experiments, a reactive transport model was developed using the Constrained Interpolation Profile method for ion transport and used to calculate chemical equilibrium for the cation exchange reactions. Anion adsorption was modeled by retardation in the numerical model. The developed simulation model results were compared to results obtained by the reactive transport model PHAST. The developed model was shown to quite well reproduce general characteristics of the experimental results. Also, the developed model results agreed well with results from PHAST. Slight discrepancy between observed and calculated breakthrough curves was probably caused by ignoring the kinetic reaction in the model calculations.     

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,在获得最优模型参数后,使用2018年6月—2019年6月数据进行模型预测性能测试试验。结果表明,相较于传统的LSTM预测模型,W-LSTM组合预测模型具有更好的预测性能,预测结果的平均绝对百分误差为9.21%。在此基础上,使用最优预测模型对京津冀城市群2019年7月—2020年12月二氧化氮日均浓度进行了预测,并描绘了时空分布图用以表征其时空变化特征。     

基于LSTM-GCN的PM2.5浓度预测模型

应用机器学习算法开展空气质量预测已成为当前研究热点之一,空气质量监测数据具有显著的时空特征,即具有时间维度时序特征和空间维度传输演化特征。面向空气质量监测数据,联合LSTM提取的时间特征和GCN提取的空间特征,提出预测PM_2.5浓度的LSTM-GCN组合模型。以北京市35个空气质量监测站2018—2020年监测数据进行仿真实验,并将LSTM-GCN模型与LSTM模型、GCN模型以及时空地理加权回归模型(GTWR)进行对比,结果显示:LSTM-GCN模型相较于LSTM模型均方根误差(RMSE)、平均绝对误差(MAE)分别降低了11.68%、7.34%;相较于GCN模型RMSE、MAE分别降低了40.22%、36.37%;相较于GTWR模型RMSE、MAE分别降低了17.52%、23.69%,表明所提出LSTM-GCN模型在准确率上有所提升。用LSTM-GCN模型预测2021年1—7月PM_2.5浓度,结果显示预测效果较好。     

Modelling the Competitive Sorption Process of Multiple Solutes During their Transport in Porous Media

A general mathematical model to solve the advection–dispersion transport equation for multiple solutes was developed, where the dual porosity mobile–immobile mass transfer, the two-site non-equilibrium model and first-order transformation reactions were included. The two-site model was expressed with an equilibrium sorption term and a kinetic term. One of three kinetic models could be used: the non-linear, the bilinear and the pore diffusion model. The traditional Freundlich or Langmuir isotherms were employed to simulate no-interaction between the solutes, but with the extended Freundlich or extended Langmuir isotherms, a competitive sorption could be simulated. The transport equation was solved with the Moving Concentration Slope method. The mathematical model was tested and further simplified by using real data from soil column experiments, with 1,2-cis-dichloroethene and trichloroethene as model contaminants and silica gel and real soil samples as porous medium.     

江苏省级区域空气质量数值预报模式效果评估

采用中国科学院大气物理研究所开发的嵌套网格空气质量模式系统(NAQPMS),搭建江苏省级区域空气质量数值预报模式系统,并测试了该系统对2013年夏季江苏省PM2.5质量浓度未来24 h预报以及7 d潜势预测的效果。结果表明,该系统成功应用于江苏省的空气质量预报;所有地市的24 h预报效果均在合理范围内(平均分数偏差小于±60%且平均分数误差小于75%);7 d潜势预测效果比24 h预报效果略差,整体能准确把握PM2.5质量浓度的变化趋势。     

A Three-Dimensional Hydro-Environmental Model of Dublin Bay

In this study, the impact of Escherichia coli emissions from a sewage treatment plant on the bathing water quality of Dublin Bay (Ireland) is assessed using a three-dimensional hydro-environmental model. Before being discharged, the effluent from the plant is mixed with cooling water from a thermal?Celectrical power generation plant, creating a warm buoyant sewage plume that can be 7?C9°C higher and is less saline than the ambient water in the bay. The ability of the three-dimensional model in representing such a stratified condition is assessed based on a comparison of its results with two-dimensional modelling results. Hydrodynamic simulations of water levels and flow velocities in Dublin Bay were obtained using the TELEMAC-3D model in one case and the depth-averaged TELEMAC-2D model in the other. The results of each model were separately used as inputs to the water quality model SUBIEF-3D to simulate the transport and fate of E. coli in the bay and to generate maps of E. coli concentrations over the bay. In addition, the necessity for three-dimensional modelling in simulating the effects of wind direction on the dispersion of E. coli was demonstrated by comparing the results of three-dimensional and two-dimensional model simulations with a number of different wind directions. The comparison showed that the three-dimensional model performed better than the depth-averaged model in simulating the hydrodynamics and resulted in better simulation of the water quality processes in the bay. In particular, the three-dimensional model had reasonably simulated the timing of the delivery of E. coli to the bay. Moreover, the effect of wind on the movement of the buoyant plume of pollution and on the E. coli distribution was found to be more pronounced with the three-dimensional hydrodynamics. The results demonstrate the need for three-dimensional simulations in situations of density differences or significant wind influences.     

Calibration and verification of a dissolved oxygen management model for a highly polluted river with extreme flow variations in Pakistan

A dissolved oxygen (DO) model is calibrated and verified for a highly polluted River Ravi with large flow variations. The model calibration is done under medium flow conditions (431.5 m³/s), whereas the model verification is done using the data collected during low flow conditions (52.6 m³/s). Biokinetic rate coefficients for carbonaceous biochemical oxygen demand (CBOD) and nitrogenous biochemical oxygen demand (NBOD) (i.e, K _cr and K _n ) are determined through the measured CBOD and ammonia river profiles. The calculated values of K _cr and K _n are 0.36 day^?1 and 0.34 day^?1, respectively. The close agreement between the DO model results and the field values shows that the verified model can be used to develop DO management strategies for the River Ravi. The biokinetic coefficients are known to vary with degree of treatment (DOT) and therefore need to be adjusted for a rational water quality management model. The effect of this variation on level of treatment has been evaluated by using the verified model to attain a DO standard of 4 mg/L in the river using the biokinetic rate coefficients as determined during the model calibration and verification process. The required DOT in this case is found to be 96 %, whereas the DOT is 86 % if adjusted biokinetic rate coefficients are used to reflect the effect of wastewater treatment. The cost of wastewater treatment is known to increase exponentially as the removal efficiency increases; therefore, the use of appropriate biokinetic coefficients to manage the water quality in rivers is important.     

Development of Dissolved Oxygen Model for a Highly Variable Flow River: A Case Study of Ravi River in Pakistan

A framework for dissolved oxygen (DO) modeling of the Ravi River has been developed based on a combination of laboratory measurements and field and monitoring data. Both the classical Streeter-Phelps (CSP) and the modified Streeter-Phelps (MSP) models are used for DO simulations. The MSP model considers the carbonaceous biochemical oxygen demand (CBOD) and nitrogenous biochemical oxygen demand (NBOD) separately, whereas the CSP model is evaluated considering only the CBOD and NBOD is incorporated in the overall BOD utilization rate. CBOD, NBOD and BOD rates have been determined through long-term BOD analysis of five main wastewater outfalls and two surface drains discharging into the Ravi River over a 98 km stretch. Analysis by Thomas Method manifests strong coefficient of determination “R²” between 0.72 and 0.98 for all the three types of BOD rates. Sensitivity analyses have also been carried out to find out a suitable reaeration rate formula for highly variable flows in the Ravi River. The CSP model results based on classical approach of considering only CBOD show significant difference between the model predictions and field measurements suggesting that NBOD needs to be incorporated for the model development. The dissolved oxygen values calculated using the MSP model and the CSP model based on overall BOD rate are in close agreement with field measurements and are thus suitable to model DO levels in the Ravi River.     

Three-dimensional Modelling of Radionuclides Dispersion in a Marine Environment with Application to the Fukushima Dai-ichi Case

In this work, we present the implementation, verification and validation of a three-dimensional model able to reproduce the propagation of \(^{137}C_{s}\) radionuclide in coastal waters and its interaction with suspended sediments, in the framework of the open-source TELEMAC-MASCARET modelling system. The validation of the model was realized by comparing numerical results with field measurements of radionuclides concentration in the Japan Sea nearby the Fukushima Dai-ichi nuclear power plant (NPP). The developed model uses as external forcing the data available immediately after or during the accident, as, e.g. weather conditions (wind, pressure, temperature) and/or the harmonic components of tides. In contrast with previous models implemented in the study area, the model presented here is limited to the coastal area near Fukushima and refined in the coastal area close to the NPP. Numerical results show that the model is able to reproduce the propagation and diffusion of the released \(^{137}C_{s}\) in the vicinity of the Fukushima Dai-ichi NPP. Consequently, we show that the numerical results obtained with a small-scale model with a simple forcing are consistent, at a coastal scale, with models which employed a general circulation model based on data assimilation techniques or variation method for hydrodynamics. Therefore, this model could be employed in an emergency situation, when the dissolved radioactivity is considered.

     

Radioactive Contamination Control by Atmospheric Dispersion Assessment of Airborne Indicator Contaminants: Numerical Model Validation

In this work, a numerical model is proposed to estimate air concentration of released airborne radioactive contaminants ¹³¹I and ¹³⁷Cs. A Gaussian dispersion model is used to assess the atmospheric dispersion of radioactive contaminants released continuously from a nuclear power plant as a result of an accident. The model uses various input parameters such as source height, release rate, stability class, wind speed, and wind direction. The validation of the model was carried out by comparing its predicted values with published experimental data. The model was extensively tested by simulating several accidental situations. The main conclusion drawn from these tests is that for large downwind distances from the release point, the contaminant concentrations predicted by the model diverge drastically from measured data, while for short distances, the predicted values generally agree quite well with experimental data. The obtained activity concentrations range from 1.57?×?10² to 6.43?×?10³ Bq/m³ for ¹³¹I and from 3.18?×?10^?2 to 9.72?×?10² Bq/m³ for ¹³⁷Cs. The estimated standard deviation coefficients values range of 7.2 to 6847.7 m, and the maximum absolute error predicted by the model for these parameters was less than 5%.     

Optimizing the Monitoring Strategy of Wastewater Treatment Plants by Multiobjective Neural Networks Approach

This paper applies artificial neural network (ANN) to model the observed effluent quality data. The ANN’s structure, involving the number of hidden layer and node and their connection, is determined endogenously by resorting to the compromise of data cost minimization and prediction accuracy maximization. To obtain the best compromise possible, the model introduces an aspiration variable (μ) that represents the level of aspiration achieved in one objective and the conjugate of μ, (1 − μ), represents level of aspiration achieved in the other objective. Because a massive amount of calculation is required, the model applies genetic algorithm (GA) for its computational flexibility and capability to ensure global solution. Feasibility and practicality of the model is tested by a case study with a set of 150 daily observations on 17 operational variables and quality parameters at an industrial wastewater treatment plant (WTP) located in southern Taiwan. Of these 17 variables open to selection, only 6 variables, wastewater flow rate (Q), CN⁻, SS, MLSS, pH and COD are selected by the model to achieve the maximum accuracy of prediction, 0.94, with a total cost of 5,950 NT$. By constraining budget availability, the variables included in the model are reduced in number, causing a concomitant reduction in prediction accuracy, that is, by varying μ (aspiration level of accuracy), a trajectory of cost and accuracy is generated. The calculation results a cost of 3,650 NT$ and 0.54 accuracy for the case with variables including flow rate, SCN⁻ and SS in equalization basin; aeration tank hydraulic retention time (HRT) and percentage of returned sludge (R%) are selected for building the prediction model when the importance of required budget is equal to the accuracy of prediction model. In addition, when required cost for building ANN model is between 3,650 NT$ and 3,900 NT$, the marginal return of budget input is highest in the entire range of calculation.     

Flow and Pollutant Dispersion in Street Canyons using FLUENT and ADMS-Urban

This paper is devoted to the study of flow within a small building arrangement and pollutant dispersion in street canyons starting from the simplest case of dispersion from a simple traffic source. Flow results from the commercial computational fluid dynamics (CFD) code FLUENT are validated against wind tunnel data (CEDVAL). Dispersion results from FLUENT are analysed using the well-validated atmos pheric dispersion model ADMS-Urban. The k − ε turbulence model and the advection-diffusion (AD) method are used for the CFD simulations. Sensitivity of dispersion results to wind direction within street canyons of aspect ratio equal to 1 is investigated. The analysis shows that the CFD model well reproduces the wind tunnel flow measurements and compares adequately with ADMS-Urban dispersion predictions for a simple traffic source by using a slightly modified k − ε model. It is found that a Schmidt number of 0.4 is the most appropriate number for the simulation of a simple traffic source and in street canyons except for the case when the wind direction is perpendicular to the street canyon axis. For this last case a Schmidt number equal to 0.04 gives the best agreement with ADMS-Urban. Overall the modified k − ε turbulence model may be accurate for the simulation of pollutant dispersion in street canyons provided that an appropriate choice for coefficients in the turbulence model and the Schmidt number in the diffusion model are made.     

基于BP神经网络的藻类水华预测模型研究

以宁波大学校内池塘2009年3—10月间30周的监测数据为基础,运用BP人工神经网络方法构建预测模型,探求颤藻生物量与总氮、总磷、透明度等6项环境因子之间的关系,选出最佳预测模型,并对模型进行敏感度分析。结果显示:①BP神经网络模型对颤藻生物量预测值与实测值之间拟合程度良好,相关系数达到了0.984,说明BP神经网络模型可以用于水体中藻类水华的短期预测。②通过对构建的BP神经网络模型进行敏感度分析,阐明了宁波大学校内池塘藻类水华的主要驱动因素,并指出控制水体的pH是宁波大学校内池塘藻类水华防治工作的重点。     

Biomonitoring of testate amoebae (protozoa) as toxic metals absorbed in aquatic bryophytes from the Hg-Tl mineralized area (China)

A new method has been developed for the determination of chemical oxygen demand (COD) in pulping effluent using chemometrics-assisted spectrophotometry. Two calibration models were established by inducing UV-visible spectroscopy (model 1) and derivative spectroscopy (model 2), combined with the chemometrics software Smica-P. Correlation coefficients of the two models are 0.9954 (model 1) and 0.9963 (model 2) when COD of samples is in the range of 0 to 405 mg/L. Sensitivities of the two models are 0.0061 (model 1) and 0.0056 (model 2) and method detection limits are 2.02?C2.45 mg/L (model 1) and 2.13?C2.51 mg/L (model 2). Validation experiment showed that the average standard deviation of model 2 was 1.11 and that of model 1 was 1.54. Similarly, average relative error of model 2 (4.25%) was lower than model 1 (5.00%), which indicated that the predictability of model 2 was better than that of model 1. Chemometrics-assisted spectrophotometry method did not need chemical reagents and digestion which were required in the conventional methods, and the testing time of the new method was significantly shorter than the conventional ones. The proposed method can be used to measure COD in pulping effluent as an environmentally friendly approach with satisfactory results.     

北京市公交车噪声测试与分析

公交车已成为当前北京市道路交通噪声的主要束源之一,针对公交车声源模型缺乏而沿用大型车声源模型所致的噪声预测误差问题,在北京市选取了两类常见公交车进行了537辆车的单车通过噪声测试,在无效数据剔除和背景噪声修正后,利用回归分析法获得了北京市公交车声源模型,通过与现有《公路建设项目环境影响评价规范》中大型车声源模型的比较,显示出建立北京市公交车噪声声源模型的必要性。基于《公路建设项目环境影响评价规范》中的道路交通噪声预测方法,提出了符合北京市实际情况的道路交通噪声预测模型。     

Extended Analytical Turbulent Diffusion Model for Particle Dispersion and Deposition in a Horizontal Pipe: Comparison with CFD Simulation

A 2D analytical turbulent diffusion model for particle dispersion and deposition at different heights along the pipe flow and circumferential deposition has been developed. This liquid–solid turbulent diffusion model presented in this paper has emanated from an existing gas–liquid turbulent diffusion model. This model can be used as a handy tool for quick estimation one and two-dimensional deposition fluxes of particles in water distribution networks. A comprehensive 3D numerical investigation has been carried out using multiphase mixture model available in “Fluent 6.2” to verify the above analytical model. Different particles sizes and densities were used for 3D numerical investigations. The deposition was studied as a function of particle diameter, density, and fluid velocity. The deposition of particles, along the periphery of the pipe wall and at different depths, was investigated. Both the models findings matched with qualitative phenomena such as deposition of heavier particles at the bottom of the pipe wall were higher at lower velocities and lower at higher velocities. The lighter particles were found mostly suspended with homogeneous distribution. Smaller particles were also suspended with marginal higher concentration near the bottom of the pipe wall. This marginal higher concentration of the smaller particles was found to be slightly pronounced for lower velocity. These analogies of particles are well discussed with the ratio between free-flight velocity and the gravitational settling velocity. Extended analytical model results were compared with the 3D computational fluid dynamics simulation results. Discrepancies in the model results were discussed.     

A model for species diversity monitoring at community level and its applications

A simple, handly and powerful tool, called the DIMO model based on the Shannon-Wiener index, has been developed for analysis and evaluation of species diversity at plant community (alpha) level. Three axes, i.e. species richness, species evenness and the Shannon-Wiener index, were plotted in a two-dimensional space. Any change in these parameters and their relationship can be easily analysed and demonstrated by the model. A modified Shannon-Wiener index,Q, based on the DIMO model, was constructed to integrate species richness and evenness. The model and theQ index were tested with the real-life data collected from vegetation monitoring by the Swedish National Environmental Monitoring Program (PMK) during a 12-year period. Because of its dynamic features, the DIMO model is especially suitable for biodiversity monitoring at community level and can be applied to other levels as well if one replaces species taxa by other categories.