Validation of two air quality models for Indian mining conditions

All major mining activity particularly opencast mining contributes to the problem of suspended particulate matter (SPM)directly or indirectly. Therefore, assessment and prediction are required to prevent and minimize the deterioration of SPM due tovarious opencast mining operations. Determination of emission rate of SPM for these activities and validation of air quality models are the first and foremost concern. In view of the above, the study was taken up for determination of emission rate for SPMto calculate emission rate of various opencast mining activitiesand validation of commonly used two air quality models for Indianmining conditions. To achieve the objectives, eight coal and three iron ore mining sites were selected to generate site specific emission data by considering type of mining, method of working, geographical location, accessibility and above all resource availability. The study covers various mining activitiesand locations including drilling, overburden loading and unloading, coal/mineral loading and unloading, coal handling orscreening plant, exposed overburden dump, stock yard, workshop, exposed pit surface, transport road and haul road. Validation of the study was carried out through Fugitive Dust Model (FDM) and Point, Area and Line sources model (PAL2) by assigning the measured emission rate for each mining activity, meteorologicaldata and other details of the respective mine as an input to the models. Both the models were run separately for the same set ofinput data for each mine to get the predicted SPM concentrationat three receptor locations for each mine. The receptor locationswere selected such a way that at the same places the actual filedmeasurement were carried out for SPM concentration. Statisticalanalysis was carried out to assess the performance of the modelsbased on a set measured and predicted SPM concentration data. The value of coefficient of correlation for PAL2 and FDM was calculated to be 0.990-0.994 and 0.966-0.997, respectively, which shows a fairly good agreement between measured and predicted values of SPM concentration. The average index of agreement values for PAL2 and FDM was found to be 0.665 and0.752, respectively, which represents that the prediction by PAL2 and FDM models are accurate by 66.5 and 75.2%, respectively. These indicate that FDM model is more suited for Indian mining conditions.     

Water quality assessment using integrated modeling and monitoring in Narva Bay, Gulf of Finland

A coupled three-dimensional hydrodynamic–ecological model was used for the assessment of water quality in Narva Bay during one biologically active season. Narva Bay is located in the south-eastern Gulf of Finland. Narva River with a catchment’s area covering part of Russia and Estonia discharges water and nutrients to Narva Bay. The ecological model includes phytoplankton carbon, nitrogen and phosphorus, chlorophyll a, zooplankton, detritus carbon, nitrogen and phosphorus, inorganic nitrogen, inorganic phosphorus and dissolved oxygen as state variables. Both the hydrodynamic and ecosystem models were validated using a limited number of measurements. The hydrodynamic model validation included comparison of time series of currents and temperature and salinity profiles. The ecological model results were compared with the monitoring data of phytoplankton biomass, total nitrogen and phosphorus and dissolved oxygen. The comparison of hydrodynamic parameters, phytoplankton biomass, surface layer total phosphorus and dissolved oxygen and near-bottom layer total nitrogen was reasonable. Time series of spatially mean values and standard deviations of selected parameters were calculated for the whole Narva Bay. Combining model results and monitoring data, the characteristic concentrations of phytoplankton biomass, total nitrogen and phosphorus and near-bottom dissolved oxygen were estimated. Phytoplankton biomass and total phosphorus showed seasonal variations, of 0.6–1.1 and 0.022–0.032 mg/l, respectively, during spring bloom, 0.1–0.3 and 0.015–0.025 mg/l in summer and 0.2–0.6 and 0.017–0.035 mg/l during autumn bloom. Total nitrogen and near-bottom oxygen concentrations were rather steady, being 0.25–0.35 and 2–6 mg/l, respectively. The total nitrogen and phosphorus concentrations show that according to the classification of Estonian coastal waters, Narva Bay water belongs to a good water quality class.     

某蓄水工程路基挡墙的病害分析

对某蓄水工程路基挡墙的安全性进行验证，发现抗滑移和抗倾覆均满足要求，但墙身应力超标较多，墙身不满足抗拉要求，墙身产生裂缝，强度急剧下降，抗倾覆安全系数也会降低。该路基挡墙经必要的设计修正后，取得了良好的经济效益。     

Development and validation of HPLC methods for analysis of chlorantraniliprole insecticide in technical and commercial formulations

Effective, selective, precise and accurate liquid chromatographic analytical methods for the analysis of a novel chlorantraniliprole insecticide in technical and formulation (coragen, 20% SC) have been optimized and validated. Eight methods were designed based on different mobile phases, temperature and two HPLC columns. The mobile phase consists of two mixtures (acetonitrile:water, 70:30 and methanol:water, 70:30) with 25 or 40ºC. HPLC analysis of chlorantraniliprole was carried out at a wavelength of 260 nm, with a flow rate of 0.8 mL/min. The calibration curves showed a good linear relationship (R² ? 0.99) in the injected quantities ranged from 0.0125 to 1.00 μg. Limit of detection (LOD) was found to be 3.94 to 14.56 ng and from 5.95 to 12.93 ng using the analytical methods I to IV by MicroPack CN-10 and V-VIII by ZORBAX Eclips Plus C18 columns, respectively, based on SDslope values. ZORBAX Eclips Plus C18 column with method VI was the best one (R² = 1.00 and RSD = 0.30), short retention time (4.936 min), high theoretical plates per column (65457.15) compared to others and LOD = 6.49 ng. The accuracy of the best method was demonstrated by recovery rates of 83.04% to 98.50% for grape samples supplemented with 5, 10 and 50 mg chlorantraniliprole/kg.     

Passive Samplers for NOx Monitoring: A Critical Review

Nitrogen oxide is an important gaseous air pollutant. It plays a major role in atmospheric chemistry, particularly in the formation of secondary air pollutants such as ozone, peroxyacyl nitrate (PAN), nitrate aerosols, and contributes to environmental acidification. A comprehensive assessment of NO₂ levels in the atmosphere is required for developing effective strategies for control of air pollution and improvement of air quality. Consequently, spatial NO₂ monitoring has become an important aspect of air quality assessment. Present NO₂ monitoring networks are mostly confined to urban areas, having a limited number of monitoring sites. Wide spatial NO₂ monitoring in India is constrained because of the limited availability of the monitoring equipment, such as continuous chemiluminesence monitors, high volume and handy samplers, which are expensive and need to be imported. Moreover, they require elaborate infrastructure, scientific personnel, technical support and uninterrupted power supply. In an effort to overcome the above shortcomings, passive samplers have been successfully used for large scale monitoring of NO₂ in the ambient environment.In recent years, passive samplers have been gaining increasing attention because they are simple, lightweight and cheap devices, which operate without any power source. Passive samplers have been found to be efficient, cost effective and free from the need for elaborate calibration and maintenance. Hence, they are ideally suited for developing a wide spatial network for NO₂ monitoring. This paper presents a comprehensive review including, historical development and critical assessment of validation studies along with comparison of both badge and tube type passive samplers. An attempt has been also made to highlight advantages and limitations, as well as discussion on the factors affecting the efficiency of passive samplers under field conditions.     

Nest selection by snow petrels Pagodroma nivea in East Antarctica: Validating predictive habitat selection models at the continental scale

Little is known on the factors controlling distribution and abundance of snow petrels in Antarctica. Studying habitat selection through modeling may provide useful information on the relationships between this species and its environment, especially relevant in a climate change context, where habitat availability may change. Validating the predictive capability of habitat selection models with independent data is a vital step in assessing the performance of such models and their potential for predicting species’ distribution in poorly documented areas.From the results of ground surveys conducted in the Casey region (2002–2003, Wilkes Land, East Antarctica), habitat selection models based on a dataset of 4000 nests were created to predict the nesting distribution of snow petrels as a function of topography and substrate. In this study, the Casey models were tested at Mawson, 3800 km away from Casey. The location and characteristics of approximately 7700 snow petrel nests were collected during ground surveys (Summer 2004–2005). Using GIS, predictive maps of nest distribution were produced for the Mawson region with the models derived from the Casey datasets and predictions were compared to the observed data. Models performance was assessed using classification matrixes and Receiver operating characteristic (ROC) curves. Overall correct classification rates for the Casey models varied from 57% to 90%. However, two geomorphologically different sub-regions (coastal islands and inland mountains) were clearly distinguished in terms of habitat selection by Casey model predictions but also by the specific variations in coefficients of terms in new models, derived from the Mawson data sets. Observed variations in the snow petrel aggregations were found to be related to local habitat availability.We discuss the applicability of various types of models (GLM, CT) and investigate the effect of scale on the prediction of snow petrel habitats. While the Casey models created with data collected at the nest scale did not perform well at Mawson due to regional variations in nest micro-characteristics, the predictive performance of models created with data compiled at a coarser scale (habitat units) was satisfactory. Substrate type was the most robust predictor of nest presence between Casey and Mawson. This study demonstrate that it is possible to predict at the large scale the presence of snow petrel nests based on simple predictors such as topography and substrate, which can be obtained from aerial photography. Such methodologies have valuable applications in the management and conservation of this top predator and associated resources and may be applied to other Antarctic, Sub-Antarctic and lower latitudes species and in a variety of habitats.     

泰勒杆试验的动态本构模型确认应用研究

本文运用模型确认理论,对泰勒杆试验在材料动态本构关系确认性方面的应用进行研究。以Johnson-Cook模型描述的OFHC材料为例,采用试验和数值模拟相比较迭代的优化方法,结合灵敏度分析,提出了一种简捷的确认和确定动态本构关系参数的方法。     

国内环境遥感监测指标验证方法研究进展

随着经济的快速发展,我国环境问题日益突出,环境治理已刻不容缓。卫星遥感技术的发展,为环境监测提供了有力的技术手段,并在环境监测领域发挥越来越重要的作用,在生态环境、水环境、大气环境等三个环境领域建立了环境遥感监测指标。然而,环境遥感监测指标验证工作相对较少,缺少规范化与标准化,未形成健全的验证体系。综述了目前国内具有代表性的环境监测指标验证方法,总结了验证中存在的问题,就其今后的发展方向,进行了有益的探讨。     

Energy at work: A measurement validation and linkage to unit effectiveness

We introduce the notion that the energy of individuals can manifest as a higher‐level, collective construct. To this end, we conducted four independent studies to investigate the viability and importance of the collective energy construct as assessed by a new survey instrument—the productive energy measure (PEM). Study 1 (n = 2208) included exploratory and confirmatory factor analyses to explore the underlying factor structure of PEM. Study 2 (n = 660) cross‐validated the same factor structure in an independent sample. In study 3, we administered the PEM to more than 5000 employees from 145 departments located in five countries. Results from measurement invariance, statistical aggregation, convergent, and discriminant‐validity assessments offered additional support for the construct validity of PEM. In terms of predictive and incremental validity, the PEM was positively associated with three collective attitudes—units' commitment to goals, the organization, and overall satisfaction. In study 4, we explored the relationship between the productive energy of firms and their overall performance. Using data from 92 firms (n = 5939_employees), we found a positive relationship between the PEM (aggregated to the firm level) and the performance of those firms. Copyright © 2011 John Wiley & Sons, Ltd.