曝气器工业化性能测试研究

对7种曝气器产品分别进行了单盘耐用性生产试验、单盘清水充氧性能试验以及对在大生产试验期间的服务面积、供气量、阻力变化、停气后阻力变化、曝气强度等方面进行了测试对比工作。试验表明,中微孔曝气器的各种性能指标明显优于大孔曝气器,可显著提高混合液中氧的吸收率。     

Prediction of the adsorption capability onto activated carbon of a large data set of chemicals by local lazy regression method

Accurate quantitative structure–property relationship (QSPR) models based on a large data set containing a total of 3483 organic compounds were developed to predict chemicals’ adsorption capability onto activated carbon in gas phrase. Both global multiple linear regression (MLR) method and local lazy regression (LLR) method were used to develop QSPR models. The results proved that LLR has prediction accuracy 10% higher than that of MLR model. By applying LLR method we can predict the test set (787 compounds) with Q²_ext of 0.900 and root mean square error (RMSE) of 0.129. The accurate model based on this large data set could be useful to predict adsorption property of new compounds since such model covers a highly diverse structural space.     

Biodegradation of chemicals in a standardized test and in environmental conditions

The estimation of biodegradation rates is an important source of uncertainty in chemical risk assessment. The existing OECD tests for ready biodegradability have been developed to devise screening methods to determine whether a chemical is potentially easily biodegradable, rather than to predict the actual rate, of biodegradation in the environment. However, risk assessment needs degradation rates. In practice these rates are often estimated (default values) from ready biodegradability tests. These tests have many compromising arbitrary features compared to the situation in the real environment. One important difference is the concentration of the chemical. In wastewater treatment or in the environment many chemicals are present at ng l(-1) to microg l(-1) levels whereas in the tests the concentrations exceed 10-400 mg carbon per litre. These different concentrations of the chemical will lead to different growth kinetics and hence different biodegradation rates. At high concentrations the chemical, if it is degradable, can serve as a primary substrate and competent microorganisms will grow exponentially, resulting in a sigmoid biodegradation curve. At low environmental concentrations the chemical does not serve as a primary substrate, and therefore does not support significant growth of the degraders, and the substrate has a linear biodegradation rate. In this study the biodegradation rates of two reference chemicals, aniline and 4-chloroaniline, were compared in a standard method and in more realistic conditions at low concentrations, using 14C-labelled substances and different sources of inocula. Biomass evolution during the tests was monitored by adenosine triphosphate measurement and also on the basis of the residual 14C-activity in the particulate matter. The results partly support the thesis that low concentrations lead to different biodegradation kinetics compared to the concentrations used in the standard tests. Furthermore the biodegradation rates of the chemicals studied, particularly of 4-chloroaniline, in Finnish natural waters appeared to be lower than those reported in some other countries.     

Air pollution modeling for an industrial complex and model performance evaluation

Jamshedpur, the steel city of India situated in the eastern part of India is affected by increasing air pollution levels as a result of concentrated industrial activities. The impact of NOx emissions resulting from various air pollution sources, viz. industries, vehicles and domestic, was estimated using Industrial Source Complex Short-Term gaussian dispersion model. The contribution of NOx concentration from industrial, vehicular and domestic sources was found to be 53, 40 and 7%. Further statistical analysis was carried out to evaluate the model performance by comparing measured and predicted NOx concentrations. The model performance was found good with an accuracy of about 68%.     

Priority setting for industrial chemicals of health and environmental relevance

Decision-making for the reduction of health and far-reaching ecological hazards caused by environmental chemicals must be based on a scientifically founded risk assessment. An Advisory Committee on Existing Chemicals of Environmental Relevance found an approach for dealing with industrial chemicals which are already on the market. Selection was carried out on the basis of criteria applied first as an aggregate and then individually in combination.     

低压供气式射流器工业化性能研究

简要介绍了低压供气式射流器的工作原理和作用,对其在氧化沟污水处理工艺中的氧利用率和动力效率等因素做了测试和分析,比较了不同水深对充氧性能的影响。结果表明,低压供气式射流曝气器氧利用率达23.17%,动力效率达2.43 kg O2/(kW.h),优于其他形式的曝气设备,可以有效降低能耗。     

Ring test of a method for assessing the phototransformation of chemicals in water

Seven types of apparatus have been used to measure the quantum yields for direct phototransformation in water of 4 chemicals (pentachlorophenol; 2,4-dichlorophenol; 3,4-dichloroaniline; 4-nitrophenol) under polychromatic (12 laboratories) and monochromatic (3 laboratories) light, according to a common test protocol. The results with both types of light were in good agreement. The reproducibility and accuracy of quantum yields obtained with the ECETOC/University of Clermont-Ferrand apparatus with polychromatic light (6 laboratories) were similar to or better than those obtained with monochromatic light. A disadvantage of the method with polychromatic light is that the calculation is more complicated, but this can be overcome by the use of a desk-top computer. On the other hand experiments with polychromatic light permit the determination of quantum yields of substances of low photoreactivity.     

Development of a low-rise industrial source dispersion model

In Japan, with amendment of the Air Pollution Control Law in May 1996, various substances, including benzene and trichloroethylene, were newly designated as hazardous air pollutants, and environmental standards were established. In this situation, it is necessary to develop a dispersion model that is applicable to environmental impact assessment of industrial areas with a complex of factory buildings. To overcome this problem, modification of the ISC downdraught model was undertaken based on datasets from wind tunnel experiments by the Ministry of International Trade and Industries and Japan Environmental Management Association for Industry. This new model is called the METI-LIS model, and comparison shows that the performance of the model is better than that of the original ISC model.     

Evaluation of a novel high throughput screening tool for relative emissions of industrial chemicals used in chemical products

Tens of thousands of chemicals are currently marketed worldwide, but only a small number of these compounds has been measured in effluents or the environment to date. The need for screening methodologies to select candidates for environmental monitoring is therefore significant. To meet this need, the Swedish Chemicals Agency developed the Exposure Index (EI), a model for ranking emissions to a number of environmental matrices based on chemical quantity used and use pattern. Here we evaluate the EI. Data on measured concentrations of organic chemicals in sewage treatment plants, one of the recipients considered in the EI model, were compiled from the literature, and the correlation between predicted emission levels and observed concentrations was assessed by linear regression analysis. The adequacy of the parameters employed in the EI was further explored by calibration of the model to measured concentrations. The EI was found to be of limited use for ranking contaminant levels in STPs; the r² values for the regressions between predicted and observed values ranged from 0.02 (p = 0.243) to 0.14 (p = 0.007) depending on the dataset. The calibrated version of the model produced only slightly better predictions although it was fitted to the experimental data. However, the model is a valuable first step in developing a high throughput screening tool for organic contaminants, and there is potential for improving the EI algorithm.     

Field calibration of surface: a model of agricultural chemicals in surface waters

Agricultural chemicals sporadically occur at detectable levels in the surface waters of intensively farmed watersheds. HSPF, a previously released model of agricultural chemicals in surface water, had been used to predict concentrations which were much higher (10 X) than those actually observed during monitoring studies. A new model, SURFACE, is described here which is much simpler than HSPF and gives better predictions of surface water concentrations. SURFACE uses PRZM, an EPA model, to calculate edge-of-field runoff losses and simple hydraulic routing algorithms to determine concentrations at the bottom of large river basins. In water systems sampled during 1985 and 1986, SURFACE predictions of annualized mean concentrations for alachlor, atrazine, cyanazine and metolachlor were within 0.09 ppb half of the time.     

Structure-activity relationship assessment of four perfluorinated chemicals using a prolonged zebrafish early life stage test

Perfluorinated compounds (PFCs) are a group of anthropogenic chemicals containing diverse functional groups and chain lengths. They are known to be persistent and bioaccumulative explaining their worldwide environmental presence. The toxicological information on these chemicals is still incomplete and insufficient to assess their environmental impact and structure-activity relationship. In the present study, the developmental effects of PFOS (perfluorooctane sulfonate, C8), PFOA (perfluorooctanoic acid, C8), PFBS (perfluorobutane sulfonate, C4) and PFBA (perfluorobutanoic acid, C4) were evaluated in zebrafish embryos (Danio rerio). The different chain lengths and functional groups of the selected chemicals made it possible to determine the structure-activity relationship of these compounds. PFCs with longer chain lengths (C8) tend to be more toxic than PFCs with shorter chain lengths (C4). Comparison based on the functional groups of compounds with the same chain length indicates that PFCs with a sulfonate group have a larger toxic potential than the ones with a carboxyl group. Furthermore, exposure to the different PFCs resulted in some general effects, such as deformations of the tail and an uninflated swim bladder, as well as in more specific effects which might be related to the structure of the tested chemicals. Oedemas and effects on length could only be detected in 8-carbon PFCs while malformations of the head were a more specific action of the sulfonated PFCs. Effects on hatching rate and success were found in PFOA exposed embryos and heart rates were affected after exposure to PFOS, PFOA and PFBS.     

Characterizing exposure to chemicals from soil vapor intrusion using a two-compartment model

Though several different models have been developed for sub-surface migration, little attention has been given to the effect of subsurface transport on the indoor environment. Existing methods generally assume that a house is one well-mixed compartment. A two-compartment model was developed to better characterize this exposure pathway; the model treats the house as two well-mixed compartments, one for the basement and one for the remainder of the house. A field study was completed to quantify parameters associated with the two-compartment model, such as soil gas intrusion rates and basement to ground floor air exchange rates. Two residential test houses in Paulsboro, New Jersey were selected for this study. All experiments were completed using sulfur hexafluoride (SF₆) as a tracer gas. Soil gas intrusion rates were found to be highly dependent on the soil gas to basement pressure difference, varying from 0.001 m³ m⁻² h⁻¹ for a pressure drop of –0.2 Pa to 0.011 m³ m⁻² h⁻¹ for a pressure drop of –6.0 Pa. Basement ventilation rates ranged from 0.17 to 0.75 air changes per hour (ACH) for basement to ambient pressure differences ranging from –1.1 to –7.6 Pa (relative to ambient). Application of experimental results in conjunction with the two-compartment model indicate that exposures are highly dependent on gas intrusion rates, basement ventilation rate, and fraction of time spent in the basement. These results can also be significantly different when compared with the simple well-mixed house assumption.     

Simulating industrial emissions using atmospheric dispersion modeling system: model performance and source emission factors

In this paper, the Gaussian Atmospheric Dispersion Modeling System (ADMS4) was coupled with field observations of surface meteorology and concentrations of several air quality indicators (nitrogen oxides (NOx), carbon monoxide (CO), fine particulate matter (PM10) and sulfur dioxide (SO2)) to test the applicability of source emission factors set by the European Environment Agency (EEA) and the United States Environmental Protection Agency (USEPA) at an industrial complex. Best emission factors and data groupings based on receptor location, type of terrain and wind speed, were relied upon to examine model performance using statistical analyses of simulated and observed data. The model performance was deemed satisfactory for several scenarios when receptors were located at downwind sites with index of agreement 'd' values reaching 0.58, fractional bias 'FB' and geometric mean bias 'MG' values approaching 0 and 1, respectively, and normalized mean square error 'NMSE' values as low as 2.17. However, median ratios of predicted to observed concentrations 'Cp/Co' at variable downstream distances were 0.01, 0.36, 0.76 and 0.19 for NOx, CO, PM10 and SO2, respectively, and the fraction of predictions within a factor of two of observations 'FAC2' values were lower than 0.5, indicating that the model could not adequately replicate all observed variations in emittant concentrations. Also, the model was found to be significantly sensitive to the input emission factor bringing into light the deficiency in regulatory compliance modeling which often uses internationally reported emission factors without testing their applicability.     

Monitoring, modelling and environmental exposure assessment of industrial chemicals in the aquatic environment

Monitoring and laboratory data play integral roles alongside fate and exposure models in comprehensive risk assessments. The principle in the European Union Technical Guidance Documents for risk assessment is that measured data may take precedence over model results but only after they are judged to be of adequate reliability and to be representative of the particular environmental compartments to which they are applied. In practice, laboratory and field data are used to provide parameters for the models, while monitoring data are used to validate the models' predictions. Thus, comprehensive risk assessments require the integration of laboratory and monitoring data with the model predictions. However, this interplay is often overlooked. Discrepancies between the results of models and monitoring should be investigated in terms of the representativeness of both. Certainly, in the context of the EU risk assessment of existing chemicals, the specific requirements for monitoring data have not been adequately addressed. The resources required for environmental monitoring, both in terms of manpower and equipment, can be very significant. The design of monitoring programmes to optimise the use of resources and the use of models as a cost-effective alternative are increasing in importance. Generic considerations and criteria for the design of new monitoring programmes to generate representative quality data for the aquatic compartment are outlined and the criteria for the use of existing data are discussed. In particular, there is a need to improve the accessibility to data sets, to standardise the data sets, to promote communication and harmonisation of programmes and to incorporate the flexibility to change monitoring protocols to amend the chemicals under investigation in line with changing needs and priorities.     

In silico prediction of Tetrahymena pyriformis toxicity for diverse industrial chemicals with substructure pattern recognition and machine learning methods

There is an increasing need for the rapid safety assessment of chemicals by both industries and regulatory agencies throughout the world. In silico techniques are practical alternatives in the environmental hazard assessment. It is especially true to address the persistence, bioaccumulative and toxicity potentials of organic chemicals. Tetrahymena pyriformis toxicity is often used as a toxic endpoint. In this study, 1571 diverse unique chemicals were collected from the literature and composed of the largest diverse data set for T. pyriformis toxicity. Classification predictive models of T. pyriformis toxicity were developed by substructure pattern recognition and different machine learning methods, including support vector machine (SVM), C4.5 decision tree, k-nearest neighbors and random forest. The results of a 5-fold cross-validation showed that the SVM method performed better than other algorithms. The overall predictive accuracies of the SVM classification model with radial basis functions kernel was 92.2% for the 5-fold cross-validation and 92.6% for the external validation set, respectively. Furthermore, several representative substructure patterns for characterizing T. pyriformis toxicity were also identified via the information gain analysis methods.     

Application of air pollution dispersion modeling for source-contribution assessment and model performance evaluation at integrated industrial estate-Pantnagar

Source-contribution assessment of ambient NO₂ concentration was performed at Pantnagar, India through simulation of two urban mathematical dispersive models namely Gaussian Finite Line Source Model (GFLSM) and Industrial Source Complex Model (ISCST-3) and model performances were evaluated. Principal approaches were development of comprehensive emission inventory, monitoring of traffic density and regional air quality and conclusively simulation of urban dispersive models. Initially, 18 industries were found responsible for emission of 39.11 kg/h of NO₂ through 43 elevated stacks. Further, vehicular emission potential in terms of NO₂ was computed as 7.1 kg/h. Air quality monitoring delineates an annual average NO₂ concentration of 32.6 μg/m³. Finally, GFLSM and ISCST-3 were simulated in conjunction with developed emission inventories and existing meteorological conditions. Models simulation indicated that contribution of NO₂ from industrial and vehicular source was in a range of 45-70% and 9-39%, respectively. Further, statistical analysis revealed satisfactory model performance with an aggregate accuracy of 61.9%.     

Evaluation of a Gaussian and a Lagrangian model against a roadside data set,with emphasis on low wind speed conditions

The evaluation of the high percentiles of concentration distributions is required by most national air quality guidelines, as well as the EU directives. However, it is problematic to compute such high percentiles in stable, low wind speed or calm conditions. This study utilizes the results of a previous measurement campaign near a major road at Elimäki in southern Finland in 1995, a campaign specifically designed for model evaluation purposes. In this study, numerical simulations were performed with a Gaussian finite line source dispersion model CAR-FMI and a Lagrangian dispersion model GRAL, and model predictions were compared with the field measurements. In comparison with corresponding results presented previously in the literature, the agreement of measured and predicted data sets was good for both models considered, as measured using various statistical parameters. For instance, considering all NO_x data (N=587), the so-called index of agreement values varied from 0.76 to 0.87 and from 0.81 to 1.00 for the CAR-FMI and GRAL models, respectively. The CAR-FMI model tends to slightly overestimate the NO_x concentrations (fractional bias FB=+14%), while the GRAL model has a tendency to underestimate NO_x concentrations (FB=−16%). The GRAL model provides special treatment to account for enhanced horizontal dispersion in low wind speed conditions; while such adjustments have not been included in the CAR-FMI model. This type of Lagrangian model therefore predicts lower concentrations, in conditions of low wind speeds and stable stratification, in comparison with a standard Lagrangian model. In low wind speed conditions the meandering of the flow can be quite significant, leading to enhanced horizontal dispersion. We also analyzed the difference between the model predictions and measured data in terms of the wind speed and direction. The performance of the CAR-FMI model deteriorated as the wind direction approached a direction parallel to the road, and for the lowest wind speeds. However, the performance of the GRAL model varied less with wind speed and direction; the model simulated better the cases of low wind speed and those with the wind nearly parallel to the road.