Prediction of algal blooming using EFDC model: Case study in the Daoxiang Lake

Algal blooming has become one of the key fields of study on eutrophication of water body recently. The mechanism of algal blooming is still not understood well. However, it is obvious to understand that algal blooming has close relationship with chlorophyll-a. Therefore, if the trends of chlorophyll-a concentration can be simulated accurately, it will be helpful for the prediction of algal bloom events. In this study, a model named Environmental Fluid Dynamics Code (EFDC), which was developed by U.S. Environmental Protection Agency, was described and used to simulate the eutrophication process in the Daoxiang Lake, Beijing. To run the eutrophication model for the Lake, a field sampling was conducted in March-October of 2008 with interval of 10-20 days. Meanwhile, the algal bloom assessment criteria were investigated and the indicator of chlorophyll-a concentration was selected as input for the prediction of algal bloom in the Daoxiang Lake. After model calibration and validation, traditional statistics has been done between modeled results and observed values. The modeled results show that the simulated chlorophyll-a concentration basically agrees with the observed concentration except the later period of station 2# and the average algal bloom prediction accuracy is 63.43%. It was verified that the EFDC model can be used for chlorophyll-a concentration simulation and algal blooming prediction in the Daoxiang Lake.     

Generation and Quantification of Hazardous Dusts from Coal Mining in the Indian Context

The increasing trend of opencast coal mining in India tends to release huge amounts of dust. But there is no well-defined method of estimating dust emission due to different coal mining activities. This paper examines the sources of dust emission due to coal mining activities, and focuses on the quantification of dust emission with the development and use of emission factors. Because of their site-specific nature, emission factors developed for one site may not give the correct results for another site. In the present investigation, prediction equations are utilized for the development of emission factors. For the applications of this concept, one large opencast coal project of Bharat Coking Coal Ltd. (BCCL) was investigated, and the total amount of dust emitted due to different mining activities was calculated by the utilization of emission factor data, which was estimated to be 9368.2 kg/day. This paper also focuses on the significance of this study in the field of environmental protection and likely impacts of such study. The paper concludes that once the amount of dust generation is estimated, the impact on air quality can be assessed appropriately and a proper air-pollution control strategy can be developed.     

CFB锅炉多点排放二氧化硫污染预测

循环流化床锅炉（CFB）多点排放时,直接测量分析对环境的影响较为困难。为了确定其中SO2对环境的影响,通过对鼢在CFB锅炉生产过程中的生成、转化和大气中的扩散分析,建立了多点排放的SO2污染预测正态分布模型。模型结果表明,多点排放时大气SO2的浓度分布仍满足正态分布特征,并较好地反映了最大落地浓度及位置,这对于依据区域大气SO2总量控制,确保和提高设备与工艺系统的清洁生产能力提供了可行依据。     

离心风机辐射噪声的降噪量预测

本文运用LMS Testlab软件对某风机现场工作时的噪声进行了测试分析,得出风机工作时的噪声频谱特征;运用LMS TestLab Sound Diagnosis模块的声诊断技术对采集到的风机实时信号进行了降噪量预测,为后期的噪声治理提供依据。避免了噪声治理的盲目性,更经济合理。     

Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines

Rockburst possibility prediction is an important activity in many underground openings design and construction as well as mining production. Due to the complex features of rockburst hazard assessment systems, such as multivariables, strong coupling and strong interference, this study employs support vector machines (SVMs) for the determination of classification of long-term rockburst for underground openings. SVMs is firmly based on the theory of statistical learning algorithms, uses classification technique by introducing radial basis function (RBF) kernel function. The inputs of models are buried depth H, rocks’ maximum tangential stress σ_θ, rocks’ uniaxial compressive strength σ_c, rocks’ uniaxial tensile strength σ_t, stress coefficient σ_θ/σ_c, rock brittleness coefficient σ_c/σ_t and elastic energy index W_et. In order to improve predictive accuracy and generalization ability, the heuristic algorithms of genetic algorithm (GA) and particle swarm optimization algorithm (PSO) are adopted to automatically determine the optimal hyper-parameters for SVMs. The performance of hybrid models (GA + SVMs = GA-SVMs) and (PSO + SVMs = PSO-SVMs) have been compared with the grid search method of support vector machines (GSM-SVMs) model and the experimental values. It also gives variance of predicted data. A rockburst dataset, which consists of 132 samples, was employed to evaluate the current method for predicting rockburst grade, and the good results of overall success rate were obtained. The results indicated that the heuristic algorithms of GA and PSO can speed up SVMs parameter optimization search, the proposed method is robust model and might hold a high potential to become a useful tool in rockburst prediction research.     

Performance evaluation of NOAA-EPA developmental aerosol forecasts

To aid air quality model development and assess air quality forecasts, the Meteorological Development Laboratory (MDL) provided categorical verification metrics for developmental aerosol predictions. The National Air Quality Forecasting Capability (NAQFC) generated 48 h (of) gridded hourly developmental predictions for the lower 48 states (CONUS) domain in 12 km horizontal spacing. The NAQFC uses the North American Mesoscale (NAM) model with EPA’s Community Multiscale Air Quality (CMAQ) model to produce predictions of ground level aerosol concentrations. We used bilinear interpolation to calculate predicted daily maximum values at the location of the observation sites. We compared these interpolated predicted values to the observed daily maximum to produce 2 × 2 contingency tables, with a threshold of 40 μg/m³ during the months of March–August, 2007. The model showed some degree of skill in predicting aerosol exceedances. These results are preliminary as the NAQFC model for aerosol prediction is in the developmental stage. A more comprehensive performance evaluation will be accomplished in 2008, when more data become available. Our verification metrics included categorical analyses for Fraction Correct (FC) or percent correct (FC × 100), Threat Score (TS) or Critical Success Index (CSI), Probability of Detection (POD), and the False Alarm Rate (FAR), Mean Absolute Error (MAE) and mean algebraic error or bias, where bias is forecast minus observation. Graphic products included weekly statistics for the CONUS displayed in the form of bar charts, scatterplots, and graphs. In addition, we split the CONUS into six geographic regions and provided regional statistics on a monthly basis. MDL produced spatial maps of daily 1-h maximum predicted aerosol values overlaid with the corresponding point observations. MDL also provided spatial maps of the daily maximum of the 24-h running average. We derived the 24-h running average from the 1-h average predicted aerosol values and observations.