Field verification of sound attenuation modeling and air emission testing in support of missile motor disposal activities

The U.S. Department of Defense-approved activities conducted at the Utah Test and Training Range (UTTR) include both operational readiness test firing of intercontinental ballistic missile (ICBM) motors, as well as the destruction of obsolete or otherwise unusable ICBM motors through open burn/open detonation (OB/OD). Within the Utah Division of Air Quality, these activities have been identified as having the potential to generate unacceptable noise levels, as well as significant amounts of volatile organic compounds (VOCs). Hill Air Force Base, UT, has completed a series of field tests at the UTTR in which sound-monitoring surveillance of OB/OD activities was conducted to validate the Sound Intensity Prediction System (SIPS) model. Using results generated by the SIPS model to support the decision to detonate, the UTTR successfully disposed of missile motors having an aggregate net explosive weight (NEW) of 81,374 lb without generating adverse noise levels within populated areas. In conjunction with collecting noise-monitoring data, air emissions were collected to support the development of air emission factors for both static missile motor firings and OB/OD activities. Through the installation of 15 ground-based air samplers, the generation of combustion-fixed gases, VOCs, and chlorides was monitored during the 81,374-lb NEW detonation event. Comparison of field measurements to predictions generated from the US Navy energetic combustion pollutant formation model, POLU4WN, indicated that, as the detonation fire ball expanded, organic compounds, as well as CO, continued to oxidize as the combustion gases mixed with ambient air. VOC analysis of air samplers confirmed the presence of chloromethane, vinyl chloride, benzene, toluene, and 2-methyl-1-propene. Qualitative chloride analysis indicated that gaseous HCl was generated at low concentrations, if at all.     

民用型煤燃烧的排放特征及推广型煤的减排效果

为探究煤质对型煤燃烧排放特征的影响,选取了呼和浩特市某城区推广使用的6种方型型煤进行了污染物排放水平测试,并结合煤质数据分析了影响型煤燃烧排放特征的主要因素。结果表明,挥发分含量是影响型煤燃烧产生的PM_2.5和NO_x排放浓度的主要因素;VOCs的排放浓度与挥发分和固定碳的总含量相关;计算得到型煤燃烧后的主要污染物(CO、SO₂、NO_x、TSP、PM_2.5、VOCs)的排放因子,分别为(36.0±17.3)、(1.89±0.47)、(1.18±0.62)、(0.47±0.69)、(0.19±0.15)和(0.27±0.18)g·kg⁻¹;设置排放情景,计算得到在该区域推广型煤后主要污染物的减排率分别为61.9%、22.2%、20.6%、89.3%、91.6%和89.1%。在推广民用型煤的过程中,还应加强对煤质的管理,同时推广型煤的配套炉具,减少人为操作造成的污染物排放。     

Use of noise attenuation modeling in managing missile motor detonation activities

The Sound Intensity Prediction System (SIPS) and Blast Operation Overpressure Model (BOOM) are semiempirical sound models that are employed by the Utah Test and Training Range (UTTR) to predict whether noise levels from the detonation of large missile motors will exceed regulatory thresholds. Field validation of SIPS confirmed that the model was effective in limiting the number of detonations of large missile motors that could potentially result in a regulatory noise exceedance. Although the SIPS accurately predicted the impact of weather on detonation noise propagation, regulators have required that the more conservative BOOM model be employed in conjunction with SIPS in evaluating peak noise levels in populated areas. By simultaneously considering the output of both models, in 2001, UTTR detonated 104 missile motors having net explosive weights (NEW) that ranged between 14,960 and 38,938 lb without a recorded public noise complaint. Based on the encouraging results, the U.S. Department of Defense is considering expanding the application of these noise models to support the detonation of missile motors having a NEW of 81,000 lb. Recent modeling results suggest that, under appropriate weather conditions, missile motors containing up to 96,000 lb NEW can be detonated at the UTTR without exceeding the regulatory noise limit of 134 decibels (dB).     

海南省大气污染源排放清单及环境影响研究

基于海南省2016年工业环境统计数据,通过自下而上的方法建立海南省2016年工业大气污染源排放清单,并利用中国多尺度排放清单模型(MEIC)排放清单进行背景源补充,使用CALPUFF模型进行大气污染模拟。污染物排放清单结果显示,2016年海南省SO_2、NO_x、CO、PM_(2.5)、PM(10)、黑碳(BC)、有机碳(OC)、挥发性有机物(VOCs)和NH3的排放量分别为1.50×10~4、5.10×10~4、4.56×10~5、2.34×10~4、2.10×10~4、3.50×10~3、1.20×10~4、4.96×10~4、6.50×10~4 t,其中SO_2主要排放源为化石燃料固定燃烧源(分担率66.67%),NO_x主要排放源为交通源(分担率51.18%),CO、PM_(10)、PM_(2.5)主要排放源为生活源(分担率分别59.01%、81.28%和87.62%),VOCs主要排放源为工业溶剂使用源(分担率75.91%),NH_3主要排放源为农业源(分担率93.54%)。排放量较大的区域集中在儋州市、澄迈县一带。SO_2、NO_x年均最大浓度均出现在海口市,PM_(10)、PM_(2.5)年均最大浓度均出现在定安县。交通源对全省污染物浓度贡献突出,工业源虽然对颗粒物浓度贡献率较低,但仍需加强PM_(2.5)治理。     

大气污染物扩散模式的应用研究综述

应用大气污染物扩散模式可以模拟不同尺度、气象、地形条件下工业污染物在大气中的输送与扩散特征,为大气监测、城市环境规划和空气质量预报等工作提供科学依据.归纳了目前广泛应用于模拟工业污染物扩散的模式,着重介绍了近年来国内外对这些模式的主要应用研究进展,比较了各模式在应用上的优缺点,并对大气污染物扩散模式的应用研究前景进行了讨论.     

An analysis of numerical models of air pollutant exposure and vegetation response

A number of empirical (statistical, regression oriented) and mechanistic (process oriented) models are presently available to examine the relationship between air pollution stress and plant response. These models have their strengths and weaknesses. In all these models, a major concern is the numerical definition of the pollutant exposure kinetics (dose). At present there are no numerical definitions of dose which make satisfactory biological sense. A key issue is the existence of a biological time clock where plants respond differently to the pollutant stress at different stages of their growth. On the other hand, policy makers and regulatory personnel prefer a simple approach which would facilitate implementation and administration of ambient air quality standards. Long-term air pollutant averaging techniques create artifacts due to the non-normal distribution of ambient concentrations. A more appropriate approach may be the use of 'median' and 'percentiles' computed from short duration pollutant concentrations. Such an approach would be free of the influence of the non-normal distribution, but would require the development of appropriate exposure-response models. Any transfer of results from unit level models to regional level leads to 'scaling error'. There is no general agreement among researchers on how to deal with the scale problem. While this situation persists, any policy formulated on regional impact assessment must acknowledge the uncertainty.     

Potential assessment of the "support vector machine" method in forecasting ambient air pollutant trends

Monitoring and forecasting of air quality parameters are popular and important topics of atmospheric and environmental research today due to the health impact caused by exposing to air pollutants existing in urban air. The accurate models for air pollutant prediction are needed because such models would allow forecasting and diagnosing potential compliance or non-compliance in both short- and long-term aspects. Artificial neural networks (ANN) are regarded as reliable and cost-effective method to achieve such tasks and have produced some promising results to date. Although ANN has addressed more attentions to environmental researchers, its inherent drawbacks, e.g., local minima, over-fitting training, poor generalization performance, determination of the appropriate network architecture, etc., impede the practical application of ANN. Support vector machine (SVM), a novel type of learning machine based on statistical learning theory, can be used for regression and time series prediction and have been reported to perform well by some promising results. The work presented in this paper aims to examine the feasibility of applying SVM to predict air pollutant levels in advancing time series based on the monitored air pollutant database in Hong Kong downtown area. At the same time, the functional characteristics of SVM are investigated in the study. The experimental comparisons between the SVM model and the classical radial basis function (RBF) network demonstrate that the SVM is superior to the conventional RBF network in predicting air quality parameters with different time series and of better generalization performance than the RBF model.     

Quantification of variability and uncertainty in air pollutant emission inventories: method and case study for utility NOx emissions

The quality of stationary source emission factors is typically described using data quality ratings, which provide no quantification of the precision of the emission factor for an average source, nor of the variability from one source to another within a category. Variability refers to actual differences caused by differences in feedstock composition, design, maintenance, and operation. Uncertainty refers to lack of knowledge regarding the true emissions. A general methodology for the quantification of variability and uncertainty in emission factors, activity factors, and emission inventories (EIs) is described, featuring the use of bootstrap simulation and related techniques. The methodology is demonstrated via a case study for a selected example of NOx emissions from coal-fired power plants. A prototype software tool was developed to implement the methodology. The range of interunit variability in selected activity and emission factors was shown to be as much as a factor of 4, and the range of uncertainty in mean emissions is shown to depend on the interunit variability and sample size. The uncertainty in the total inventory of -16 to +19% was attributed primarily to one technology group, suggesting priorities for collecting data and improving the inventory. The implications for decision-making are discussed.     

公交车燃用B5生物柴油的非常规污染物排放特性及对区域大气污染物减排的作用

为推行区域大气污染联合防治并推广使用B5生物柴油,基于重型底盘测功机,采用C-WTVC循环,对比研究满足国Ⅳ、国Ⅴ排放标准的柴油公交车分别燃用国Ⅴ柴油、京Ⅵ柴油和B5生物柴油时的非常规污染物排放特性。结果表明：相比京Ⅵ柴油在采用B5生物柴油后,在不同耐久里程下的所有样车整体上的1,3-C4H6排放因子平均降低20.53%;C6H6排放因子平均降低7.67%;C7H8排放因子平均降低11.22%;HCHO排放因子平均降低14.92%;SO2排放因子平均降低6.09%。实验证明城市公交车在燃用B5生物柴油后的非常规污染物排放均降低。城市公交车推广使用B5生物柴油可有效净化大气质量,对区域大气污染物减排有着重要意义。     

Combining neural network models to predict spatial patterns of airborne pollutant accumulation in soils around an industrial point emission source

Neural networks (NNs) have the ability to model a wide range of complex nonlinearities. A major disadvantage of NNs, however, is their instability, especially under conditions of sparse, noisy, and limited data sets. In this paper, different combining network methods are used to benefit from the existence of local minima and from the instabilities of NNs. A nonlinear k-fold cross-validation method is used to test the performance of the various networks and also to develop and select a set of networks that exhibits a low correlation of errors. The various NN models are applied to estimate the spatial patterns of atmospherically transported and deposited lead (Pb) in soils around an historical industrial air emission point source. It is shown that the resulting ensemble networks consistently give superior predictions compared with the individual networks because, for the ensemble networks, R2 values were found to be higher than 0.9 while, for the contributing individual networks, values for R2 ranged between 0.35 and 0.85. It is concluded that combining networks can be adopted as an important component in the application of artificial NN techniques in applied air quality studies.     

Near-road air pollutant concentrations of CO and PM2.5: A comparison of MOBILE6.2/CALINE4 and generalized additive models

The contribution of vehicular traffic to air pollutant concentrations is often difficult to establish. This paper utilizes both time-series and simulation models to estimate vehicle contributions to pollutant levels near roadways. The time-series model used generalized additive models (GAMs) and fitted pollutant observations to traffic counts and meteorological variables. A one year period (2004) was analyzed on a seasonal basis using hourly measurements of carbon monoxide (CO) and particulate matter less than 2.5 μm in diameter (PM_2.5) monitored near a major highway in Detroit, Michigan, along with hourly traffic counts and local meteorological data. Traffic counts showed statistically significant and approximately linear relationships with CO concentrations in fall, and piecewise linear relationships in spring, summer and winter. The same period was simulated using emission and dispersion models (Motor Vehicle Emissions Factor Model/MOBILE6.2; California Line Source Dispersion Model/CALINE4). CO emissions derived from the GAM were similar, on average, to those estimated by MOBILE6.2. The same analyses for PM_2.5 showed that GAM emission estimates were much higher (by 4–5 times) than the dispersion model results, and that the traffic-PM_2.5 relationship varied seasonally. This analysis suggests that the simulation model performed reasonably well for CO, but it significantly underestimated PM_2.5 concentrations, a likely result of underestimating PM_2.5 emission factors. Comparisons between statistical and simulation models can help identify model deficiencies and improve estimates of vehicle emissions and near-road air quality.     

Derivation of motor vehicle tailpipe particle emission factors suitable for modelling urban fleet emissions and air quality assessments

Background, aim and scope  

Urban motor vehicle fleets are a major source of particulate matter pollution, especially of ultrafine particles (diameters < 0.1 μm), and exposure to particulate matter has known serious health effects. A considerable body of literature is available on vehicle particle emission factors derived using a wide range of different measurement methods for different particle sizes, conducted in different parts of the world. Therefore, the choice as to which are the most suitable particle emission factors to use in transport modelling and health impact assessments presented as a very difficult task. The aim of this study was to derive a comprehensive set of tailpipe particle emission factors for different vehicle and road type combinations, covering the full size range of particles emitted, which are suitable for modelling urban fleet emissions.     

On the correlation of air and pollutant exchange for street canyons in combined wind-buoyancy-driven flow

The ventilation and pollutant transport in a two-dimensional (2D) street canyon of building-height-to-street-width (aspect) ratio h/b = 1 under different unstable stratifications were examined. To characterize the combined wind-buoyancy-driven flow and pollutant transport at different Richardson number Ri, a computational fluid dynamics (CFD) model based on the Reynolds-averaged Navier–Stokes (RANS) equations with the Renormalization Group (RNG) k ? ε turbulence model was adopted. Unlike the isothermal condition, a secondary recirculation is initiated at the ground-level windward corner of the street canyon once the unstable stratification is switched on (Ri < 0). It traps the ground-level pollutant leading to elevated pollutant concentration there. As Ri further decreases, the enlarging secondary recirculation enables direct pollutant removal from its core to the shear layer that offsets the ground-level pollutant accumulation. The ventilation and pollutant removal performance under different unstable stratifications are compared by the air (ACH) and pollutant (PCH) exchange rates, and pollutant retention time (τ). Both the mean and turbulent components of ACH are found to increase with decreasing Ri, suggesting that unstable stratification promotes ventilation in street canyons. Moreover, the CFD results agree well with our theoretical model that ACH² varies linearly with Ri. Turbulent transport originally dominates the pollutant removal under isothermal condition. However, progressive domination of pollutant removal by mean wind can be observed with decreasing stability (decreasing Ri from 0 to ?10.6). The critical value is estimated to be Ri = ?8, below which mean wind is the major pollutant removal carrier. Reduction in τ is also observed with decreasing Ri. Hence, in unstable stratification, pollutant resides shorter time in the street canyon compared with its isothermal counterpart, and the ventilation and pollutant removal are more favorable.     

Three-dimensional modeling of air flow and pollutant dispersion in an urban street canyon with thermal effects

Effects of excess ground and building temperatures on airflow and dispersion of pollutants in an urban street canyon with an aspect ratio of 0.8 and a length-to-width ratio of 3 were investigated numerically. Three-dimensional governing equations of mass, momentum, energy, and species were modeled using the RNG k-epsilon turbulence model and Boussinesq approximation, which were solved using the finite volume method. Vehicle emissions were estimated from the measured traffic flow rates and modeled as banded line sources, with a street length and bandwidths equal to typical vehicle widths. Both measurements and simulations reveal that pollutant concentrations typically follow the traffic flow rate; they decline as the height increases and are higher on the leeward side than on the windward side. Three-dimensional simulations reveal that the vortex line, joining the centers of cross-sectional vortexes of the street canyon, meanders between street buildings and shifts toward the windward side when heating strength is increased. Thermal boundary layers are very thin. Entrainment of outside air increases, and pollutant concentration decreases with increasing heating condition. Also, traffic-produced turbulence enhances the turbulent kinetic energy and the mixing of temperature and admixtures in the canyon. Factors affecting the inaccuracy of the simulations are addressed.     

山东省工业废水中石油类污染物排放特征及减排潜力分析

基于环境和经济统计数据,分析了山东省重点工业行业废水中石油类污染物的排放特征,并通过情景分析对2020年该省重点行业的石油类污染物减排潜力进行了测算。结果表明,2006—2012年山东省石油类污染物排放的行业结构特征明显,包括石化行业在内的7个重点行业的排放量占该省排放总量的85%以上;从年际变化来看,石油和天然气开采业和石油加工、炼焦及核燃料加工业的排放量所占比例出现明显的下降,由2006年的约48%下降至2012年的20%左右;而煤炭开采和洗选业的排放量所占比例明显上升,由2006年的0.18%迅速增加到2012年的23.69%,成为山东省工业废水石油类污染物排放的主要行业;在设计的高标准方案下,2020年石油类污染物的排放量预计为485.31t,比2012年降低了8%,其中石化行业的石油类污染物减排潜力最大,与基准情景相比,2020年在高标准方案下石油类污染物的削减量约达1 006t,约占减排总量的55%。     

Prescribed burns and wildfires in Colorado: impacts of mitigation measures on indoor air particulate matter

Wildfires and prescribed burns are receiving increasing attention as sources of fine particulate matter (PM2.5). The goal of this research project was to understand the impact of mitigation strategies for residences impacted by scheduled prescribed burns and wildfires. Pairs of residences were solicited to have PM2.5 concentrations monitored inside and outside of their houses during four fires. The effect of using air cleaners on indoor PM2.5 was investigated, as well as the effect of keeping windows closed. Appropriately sized air cleaners were provided to one of each pair of residences; occupants of all of the residences were asked to keep windows shut and minimize opening of exterior doors. Additionally, residents were asked to record all of the activities that may be a source of particulate matter, such as cooking and cleaning. Measurements were made during one prescribed burn and three wildfires during the 2002 fire season. Outdoor 24-hr average PM2.5 concentrations ranging from 6 to 38 microg/m3 were measured during the fires, compared with levels of 2-5 microg/m3 during background measurements when no fires were burning. During the fires, PM2.5 was < 3 microg/m3 inside all of the houses with air cleaners installed. This corresponds with a decrease of 63-88% in homes with the air cleaners operating when compared with homes without air cleaners. In the homes without the air cleaners, measured indoor concentrations were 58-100% of the concentrations measured outdoors.     

A study of the association between daily mortality and ambient air pollutant concentrations in Pittsburgh, Pennsylvania

We have studied the possible association of daily mortality with ambient pollutant concentrations (PM10, CO, O3, SO2, NO2, and fine [PM2.5] and coarse PM) and weather variables (temperature and dew point) in the Pittsburgh, PA, area for two age groups--less than 75, and 75 and over--for the 3-year period of 1989-1991. Correlation functions among pollutant concentrations show important seasonal dependence, and this fact necessitates the use of seasonal models to better identify the link between ambient pollutant concentrations and daily mortality. An analysis of the seasonal model results for the younger-age group reveals significant multicollinearity problems among the highly correlated concentrations of PM10, CO, and NO2 (and O3 in spring and summer), and calls into question the rather consistent results of the single- and multi-pollutant non-seasonal models that show a significant positive association between PM10 and daily mortality. For the older-age group, dew point consistently shows a significant association with daily mortality in all models. Collinearity problems appear in the multi-pollutant seasonal and non-seasonal models such that a significant, positive PM10 coefficient is accompanied by a significant, negative coefficient of another ambient pollutant, and the identity of this other pollutant changes with season. The PM2.5 data set is half that of PM10. Identical-model runs for both data sets reveal instability in the pollutant coefficients, especially for the younger age group. The concern for the instability of the pollutant coefficients due to a small signal-to-noise ratio makes it impossible to ascertain credibly the relative associations of the fine- and coarse-particle modes with daily mortality. In this connection, we call for caution in the interpretation of model results for causal inference when the models use fully or partially estimated PM values to fill large data gaps.     

Long-term trends of primary and secondary pollutant concentrations in Switzerland and their response to emission controls and economic changes

A detrending technique is developed for short-term and yearly variations in order to identify long-term trends in primary and secondary pollutants. In this approach, seasonal and weekly variations are removed by using a mean year; the residual meteorological short-term variation is removed by using a multiple linear regression model. This methodology is employed to detrend ozone (O₃), NO_x, VOC and CO concentrations in Switzerland. We show that primary pollutants (NO_x,VOC and CO) at urban and sub-urban stations show a downward trend over the last decade which correlates well with the reductions in the estimated Swiss emissions. In spite of these large decreases achieved in precursor emissions, summer peak ozone concentrations do not show any statistically significant trend over the last decade. Application of this method to ozone concentrations measured at the Jungfraujoch (3580 m a.s.l.) also shows no trend over the last 10 years. Detrended summer ozone correlates well with European Union gross national product and industrial production growth rates. These results suggest that if substantial reductions in summer peak ozone in Switzerland are desired, emissions reduction strategies must be part of control program involving a much larger region.     

Estimation of vinyl chloride emissions from gasholders and validation of in situ emission reduction methods

Gasholders consist of a floating bell in a tank filled with water. Water provides a seal between the enclosed gas and ambient air. Gasholder emissions come from the contaminated water seal exposed to ambient air and the wet bell wall. The study objectives were to define parameters influencing gasholder emissions, determine the quantities of vinyl chloride (VC) emitted, generate a correlation equation between parameters of influence and mass emissions, and evaluate the efficiency of emission reduction methods. The research project was carried out on a laboratory-scale representation of a gasholder. The classic two-phase resistance model was used successfully to generate a correlation equation, which can be used to calculate the gasholder water seal emissions. A strictly empirical model was generated to estimate the wet wall emissions. Two in situ reduction methods were evaluated with the laboratory installations: floating objects and an oil layer. Both methods showed significant emission reductions, but the oil layer was the most effective. To reduce emissions even further, it is recommended that the water level of the gasholder be set to the lowest achievable level, that a windshield be placed around the water seal perimeter, and that hydrophobic paint be used on the bell wall.