An air quality forecasting system in Beijing - Application to the study of dust storm events in China in May 2008

An air pollution forecast system, ARIA Regional, was implemented in 2007-2008 at the Beijing Municipality Environmental Monitoring Center, providing daily forecast of main pollutant concentrations. The chemistry-transport model CHIMERE was coupled with the dust emission model MB95 for restituting dust storm events in springtime so as to improve forecast results. Dust storm events were sporadic but could be extremely intense and then control air quality indexes close to the source areas but also far in the Beijing area. A dust episode having occurred at the end of May 2008 was analyzed in this article, and its impact of particulate matter on the Chinese air pollution index (API) was evaluated. Following our estimation, about 23 Tg of dust were emitted from source areas in Mongolia and in the Inner Mongolia of China, transporting towards southeast. This episode of dust storm influenced a large part of North China and East China, and also South Korea. The model result was then evaluated using satellite observations and in situ data. The simulated daily concentrations of total suspended particulate at 6:00 UTC had a similar spatial pattern with respect to OMI satellite aerosol index. Temporal evolution of dust plume was evaluated by comparing dust aerosol optical depth (AOD) calculated from the simulations with AOD derived from MODIS satellite products. Finally, the comparison of reported Chinese API in Beijing with API calculated from the simulation including dust emissions had showed the significant improvement of the model results taking into account mineral dust correctly.     

Synthesis of TiO2 nanoparticles in different thermal conditions and modeling its photocatalytic activity with artificial neural network

Titanium dioxide (TiO₂) nanoparticles were prepared by sol gel route. The preparation parameters were optimized in the removal of 4-nitrophenol (4-NP). All catalysts were analyzed by X-ray diffraction (XRD) and scanning electron microscopy (SEM). An artificial neural network model (ANN) was developed to predict the photocatalytic removal of 4-NP in the presence of TiO₂ nanoparticles prepared under desired conditions. The comparison between the predicted results by designed ANN model and the experimental data proved that modeling of the removal process of 4-NP using artificial neural network was a precise method to predict the extent of 4-NP removal under different conditions.     

重金属铜在砂质壤土土柱中运移的数值模拟

通过室内土柱出流实验,分析了重金属离子铜在饱和砂质壤土中的运移行为,发现高浓度的入流液有利于铜离子的运移。同时借助于软件包CXTFIT,用确定性平衡模型和非平衡两区模型对铜离子的出流动态进行了拟合,并预测了土柱不同深度处铜离子浓度随时间的变化过程。结果表明,室内饱和均质条件下,应用确定性平衡模型拟合参数模拟精度要高于非平衡两区模型,可以不用考虑不动水体对铜离子运移的影响。     

基于空间分析荞麦地流域地下水健康风险评价

以云南荞麦地流域为研究区域,对该区域2017年24个地下水样品中5种重金属指标（Cr、Pb、Co、Mn、Hg）和1种无机毒理指标（氟化物）进行测定和分析,并基于半方差模型等地学统计方法,对地下水健康风险评价进行空间分析.结果表明：地下水通过饮水途径所引起的成人和儿童的健康风险分别为9.27×10^-10~4.59×10^-5a^-1和1.10×10^-8~5.46×10^-4a^-1.地下水健康风险评价值以Cr为主导,成人与儿童存在较高的一致性.地下水健康风险存在很强的空间规律性,在南北方向上呈条带状分布.受位于中游的药山镇影响,研究区南部下游地区地下水健康风险显著增加.     

TIMBER MANAGEMENT INFLUENCES ON AQUATIC ECOSYSTEMS AND RECOMMENDATIONS FOR FUTURE RESEARCH1

ABSTRACT: Recent watershed research indicates that many timber management practices have profound effects on the water quality of small headwater streams. These streams often support fisheries of high value. Current knowledge seems to indicate that a definite potential exists for a symbiotic relationship between timber and fisheries management. Maximum development of both resources is attainable only if further research efforts recognize the mutually benefiting aspects of these heretofore separate disciplines. Future research should carefully examine the complex interrelationships between small headwater aquatic ecosystems and the riparian forest environment.     

MEAN AREAL PRECIPITATION FOR DAILY HYDROLOGIC MODELING IN MOUNTAINOUS REGIONS1

ABSTRACT: A procedure using detrended kriging has been developed to calculate daily values of mean areal precipitation (MAP) for input to hydrologic models. The important features of this procedure that overcome weaknesses in existing MAP procedures are: (1) specific precipitation-elevation relationships are determined for each time period as opposed to using relationships based on climatological averages, (2) spatial variability is incorporated by estimating precipitation for each grid cell over a watershed, (3) the spatial correlation structure of precipitation is explicitly modeled, and (4) station weights for precipitation estimates are determined objectively and optimally. Detailed cross-validation testing of the procedure was done for the Reynolds Creek research watershed in southwestern Idaho. The procedure is suitable for use in operational streamflow forecasting.     

EVALUATION OF AN INDUCED INFILTRATION MODEL AS APPLIED TO GLACIAL AQUIFER SYSTEMS1

ABSTRACT: Numerical models were used to examine the limitations of the assumptions used in an analytical induced infiltration model. The assumptions tested included negligible streambed effects, negligible areal recharge, two-dimensional ground water flow, fully penetrating rivers and wells, and constant surface water stage. For situations that deviate from the underlying assumptions, the analytical model becomes a less reliable predictor of induced infiltration. The numerical experiments show that streambed effects cannot be neglected if the streambed conductivity is more than one order of magnitude lower than the aquifer hydraulic conductivity. Areal recharge cannot be neglected if the ground water basin receives more than 5 in/yr of areal recharge. Three-dimensional flow effects due to well partial penetration cannot be neglected if the ratio of horizontal hydraulic conductivity to vertical hydraulic conductivity (K_h/K_u) is greater than 10. Surface water elevation fluctuations can significantly influence the induced infiltration rate, depending on the degree of fluctuations and the ground water hydraulic gradient.     

The prediction of safe lifting behavior: an application of the theory of planned behavior

PROBLEM: Injuries resulting from lifting are costly, and create significant pain and discomfort. While engineering controls are the most effective means of reducing risks, most organizations continue to rely on manual lifting techniques. The problem, however, is that the use of safe-lifting techniques is inconsistent and managers have a difficult time motivating use of these techniques. Consequently, it is important to understand the factors driving safe-lifting behaviors. METHODS: This study used a survey to apply the theory of planned behavior (Ajzen., I., 1991. The theory of planned behavior. Organization Behavior and Human Processes, 50, 179-211) to safe-lifting among 136 materials management employees at a heavy manufacturing organization. Structural equation modeling and factor analysis were employed to analyze relationships among constructs. RESULTS: Results revealed that perceived behavioral control and intention were the strongest predictors of safe-lifting behavior. Subjective norms, to a lesser degree, were also important influences on intention. Attitudes did not surface as effective direct predictors of safe-lifting behavior, but did affect behavior and intent via mediating factors (subjective norms and perceived behavioral control). Finally, the theory of planned behavior was supported as an effective model explaining safe-lifting behavior, and had potential application for many other safety-related behaviors. IMPACT ON INDUSTRY: Results from this study emphasize the importance of perceived behavioral control as a factor associated with safety-related behavior.     

A stochastic-advective transport model for NAPL dissolution and degradation in non-uniform flows in porous media

Remediation schemes for contaminated sites are often evaluated to assess their potential for source zone reduction of mass, or treatment of the contaminant between the source and a control plane (CP) to achieve regulatory limits. In this study, we utilize a stochastic stream tube model to explain the behavior of breakthrough curves (BTCs) across a CP. At the local scale, mass dissolution at the source is combined with an advection model with first-order decay for the dissolved plume. Field-scale averaging is then employed to account for spatial variation in mass within the source zone, and variation in the velocity field. Under the assumption of instantaneous mass transfer from the source to the moving liquid, semi-analytical expressions for the BTC and temporal moments are developed, followed by derivation of expressions for effective velocity, dispersion, and degradation coefficients using the method of moments. It is found that degradation strongly influences the behavior of moments and the effective parameters. While increased heterogeneity in the velocity field results in increased dispersion, degradation causes the center of mass of the plume to shift to earlier times, and reduces the dispersion of the BTC by lowering the concentrations in the tail. Modified definitions of effective parameters are presented for degrading solutes to account for the normalization constant (zeroth moment) that keeps changing with time or distance to the CP. It is shown that anomalous dispersion can result for high degradation rates combined with wide variation in velocity fluctuations. Implications of model results on estimating cleanup times and fulfillment of regulatory limits are discussed. Relating mass removal at the source to flux reductions past a control plane is confounded by many factors. Increased heterogeneity in velocity fields causes mass fluxes past a control plane to persist, however, aggressive remediation between the source and CP can reduce these fluxes.     

Pipeline leakage detection and isolation: An integrated approach of statistical and wavelet feature extraction with multi-layer perceptron neural network (MLPNN)

Leakage diagnosis of hydrocarbon pipelines can prevent environmental and financial losses. This work proposes a novel method that not only detects the occurrence of a leakage fault, but also suggests its location and severity. The OLGA software is employed to provide the pipeline inlet pressure and outlet flow rates as the training data for the Fault Detection and Isolation (FDI) system. The FDI system is comprised of a Multi-Layer Perceptron Neural Network (MLPNN) classifier with various feature extraction methods including the statistical techniques, wavelet transform, and a fusion of both methods. Once different leakage scenarios are considered and the preprocessing methods are done, the proposed FDI system is applied to a 20-km pipeline in southern Iran (Goldkari-Binak pipeline) and a promising severity and location detectability (a correct classification rate of 92%) and a low False Alarm Rate (FAR) were achieved.