狭长交叉山谷中污染物浓度预测分析

为更准确地预测狭长交叉山谷中大气污染物浓度,利用高斯山谷扩散模式和美国Earth Technology Incorporat-ed公司研究的中尺度空气质量扩散模型CALPUFF对某山谷工业园区所在的狭长山谷中大气污染物浓度进行了计算。计算中,CALPUFF模型考虑了山谷中地表地形的变化,融入了诊断风场模块CALMET。...     

基于改进型灰色神经网络组合模型的空气质量预测

基于空气质量数据不足及波动较大的情况,将灰色GM（1,1）模型与人工神经网络模型组合并改进,建立改进型灰色神经网络组合模型。利用天津市2001—2008年PM10、SO2和NO2年均值作为原始数据预测2009—2010年PM10、SO2和NO2的浓度以进行模型精度检验,最后利用该模型预测2011—2015年天津市空气质量状况。结果表明,与灰色GM（1,1）模型、传统灰色神经网络组合模型相比,所建立的改进型灰色神经网络组合模型相对模拟误差小,预测结果更为可靠,可以用于空气质量预测。     

Assessment of emissions data for the Toronto region using aircraft-based measurements and an air quality model

An aircraft-based measurement campaign was conducted during the summer of 1996 in the vicinity of Toronto, Canada. The objective of the campaign was to assess the errors in a particular emission inventory used by three-dimensional air quality models. Measurements of NO₂ and hydrocarbons were made both upwind and downwind of Toronto, on days with strong synoptic-scale flow from a west to northerly direction. The chemical composition of the background airmass on these days was typical of unpolluted continental air. Measurements have been compared with the output from an on-line air quality model (MC2-AQ) run at 5 km resolution and suggest that emissions of NO_x from Toronto are well described in the emission database, though evidence that NO_x emissions are underestimated for suburban regions surrounding Toronto was found. In general, no significant underestimation of hydrocarbon emissions was found, though emissions of the model propane species, which includes acetylene and benzene, was underestimated by at least a factor of two.     

火电厂大气污染物排放预测模型

根据大气污染物排放浓度变化特点,将无偏GM(1,1)模型与神经网络模型组合,并以矩阵型输入方式替代传统的数列型数据输入方式,得到改进型灰色神经网络模型,称为UGMN模型。接着,采用烟囱入口烟气自动监控系统(CEMS)数据,将模型运用于贵州省某电厂白天及夜间两段时间段内大气污染物排放浓度的模拟与预测。研究结果表明UGMN模型预测精度较好,可以应用于火电厂大气污染物排放浓度预测。     

Neural network forecasting of air pollutants hourly concentrations using optimised temporal averages of meteorological variables and pollutant concentrations

The new method for the forecasting hourly concentrations of air pollutants is presented in the paper. The method was developed for a site in urban residential area in city of Zagreb, Croatia, for four air pollutants (NO₂, O₃, CO and PM₁₀). Meteorological variables and concentrations of the respective pollutant were taken as predictors. A novel approach, based on families of univariate regression models, was employed in selecting the averaging intervals for input variables. For each variable and each averaging period between 1 and 97 h, a separate model was built. By inspecting values of the coefficient of correlation between measured and modelled concentrations, optimal averaging periods for each variable were selected. A new dataset for building the forecasting model was then calculated as temporal moving averages (running means) of former variables. A multi-layer perceptron type of neural networks is used as the forecasting model. Index of agreement, calculated for the entire dataset including the data for model building, ranged from 0.91 to 0.97 for the respective pollutants. As suggested by the analysis of the relative importance of the input variables, different agreements for different pollutants are likely due to different sources and production mechanisms of investigated pollutants. A comparison of the new method with more traditional method, which takes hourly averages of the forecast hour as input variables, showed similar or better performance. The model was developed for the purpose of public-health-oriented air quality forecasting, aiming to use a numerical weather forecast model for the prediction of the part of input data yet unknown at the forecasting time. It is to expect that longer term averages used as inputs in the proposed method will contribute to smaller input errors and the greater accuracy of the model.     

Industrial odor sources and air pollutant concentrations in Globeville,a Denver,Colorado neighborhood

An odor of unknown origin described as a “tar” or “asphalt” smell has become unbearable for many of Globeville, CO, residents over the past few years. Residents report during odor events burning eyes and throat, headaches, skin irritation, and problems sleeping. This study was undertaken to identify the potential sources of the odor and the concentrations of air pollutants making up the odor by conducting meteorological correlations and sampling for a panel of volatile organic compounds (VOCs), sulfur gases, and polycyclic aromatic hydrocarbons (PAHs) in the neighborhood and near suspected sources. Wind speed and direction data collected every 1 min in the neighborhood indicate that when the odor is noticed, the community is directly downwind of a wood preservation facility and an asphalt roofing facility. Air samples collected during high-intensity odor events have shown concentrations of methylene chloride, hexane, toluene, naphthalene, dibenz[a,h]anthracene, benzo[g,h,i]perylene, and indeno[1,2,3-cd]pyrene, each at least two times higher than background concentrations. Naphthalene and the other PAHs are known pollutants emitted from wood treatment processes, and are known to have a coal tar odor. Naphthalene was present in a sample collected directly adjacent to the Koppers facility and was not present in any background samples. Single-compound odor and health thresholds, however, were never surpassed. Given the technical and regulatory challenges of sampling odors and controlling emissions, it is recommended that Globeville residents and neighboring industry pursue a “good neighbor policy” to solve the odor issue. Specific offending industrial processes could be identified for which there exist cost-effective control technologies that would reduce exposure to odors and air toxics in Globeville.

Implications: Meteorological correlations and samples of volatile organic compounds (VOCs), sulfur gases, and polycyclic aromatic hydrocarbons (PAHs) in the Globeville, CO, neighborhood and near suspected sources during odor events indicate potential industrial sources of a transient and noxious odor. Legislative approaches have proven unfruitful and no health or odor thresholds were typically violated. New approaches are warranted to address odor mixture effects in neighborhoods near industrial facilities.     

Measurements of peroxyacyl nitrates (PANS) in Mexico City: implications for megacity air quality impacts on regional scales

Peroxyacyl nitrates (PANs) were measured using gas chromatography with electron capture detection (GC/ECD) in north central Mexico City during February–March of 1997. Peroxyacetyl nitrate (PAN) was observed to exceed 30 ppb during five days of the study, with peroxypropionyl nitrate (PPN) and peroxybutryl nitrate (PBN) reaching 6 and 1 ppb maximum, respectively. Levels of total PANs typically exceeded 10 ppb during the period of measurement and showed a very strong diurnal variation with PANs maximum during the early afternoon and falling to less than 0.1 ppb during the evening hours. These levels of PANs are the highest reported values in North America (and the world) for an urban center, since levels of approximately 30 ppb were reported during the late 1970s in the Los Angeles area (South Coast Air Basin, Tuazon et al., 1978). Hydrocarbon measurements indicate that the levels of olefins, specifically butenes are significant in Mexico City. A time series taken of source indicator hydrocarbons taken before and during a Mexican National Holiday with reduced automobile traffic clearly show that mobile sources of butenes are as important as liquefied petroleum gas. Observations of 10–40 ppb C methyl-t-butyl ether (MTBE) are consistent with MTBE/gasoline fuel usage as a source of isobutene and formaldehyde. Both these reactive species can lead to increased oxidant and PAN formation. The strong diurnal profiles of PANs are consistent with regional clearing of the Mexico City air basin on a daily basis. Estimates are given using a simple box model calculation for a number of key primary and secondary pollutant emissions from this megacity on an annual basis. These calculations indicate that megacities can be important sources of both primary and secondary pollutants, and that PANs produced in megacity environments are likely to contribute strongly to regional scale ozone and aerosol productions during long range transport.     

海岸地区基于AERMOD的热力内边界层对大气污染物扩散影响的模拟研究

海岸地区热力内边界层(TIBL)对大气污染物扩散具有重要影响。选取杭州湾地区某区域为模拟区,采用一个TIBL高度的简单计算模式模拟模拟区的TIBL高度,将其耦合到空气质量模式AERMOD中,并对AERMOD的相关模块和参数进行了相应的修改,再分别利用原AERMOD和改进后的AERMOD,模拟了不同污染源情景下的大气污染物地面浓度分布。结果表明,在多数情况下,由于TIBL对于大气污染物扩散空间的限制,大气污染物的地面最大浓度有所升高,地面浓度的高值区范围也有所增加,具体影响特征取决于污染源与TIBL的相对高度以及污染源距离海岸的相对位置。     

基于指标规范值的室内空气客观评价的概率神经网络模型

为了建立简单、普适、通用的概率神经网络的室内空气评价模型,在适当设定室内空气各项指标的参照值及指标值的规范变换式基础上,使室内空气同级标准不同指标的规范值差异尽可能小,从而用规范值表示的各指标都可用同一个规范指标"等效"替代。因此,概率神经网络隐层各类模式的基函数中心矢量的各指标分量值与同级标准所有15项指标规范值的均值等同。将基于指标规范值的概率神经网络模型用于室内空气的评价实例进行检验,验证了该模型的普适性、通用性和简便性。     

Seasonal change of persistent organic pollutant concentrations in air at Niigata area,Japan

The concentrations of persistent organic pollutants (POPs), such as HCB, alpha-, beta-, gamma- and delta-HCH, trans- and cis-chlordane (t-CHL, c-CHL), DDE, DDD and DDT, in ambient air have been measured at five sampling points in Niigata area, Japan (Niigata, Maki, Tsubame, Jouzo and Yahiko) during the period from September 1999 to November 2001. HCB, alpha-HCH, t-CHL and c-CHL showed higher concentrations than the other chemicals in all locations. All the POPs except t-CHL and c-CHL collected at urban sites of the Niigata Plain was almost the same in their concentration levels. Higher concentrations of t-CHL and c-CHL in residential areas should be attributed to the past usage of the chemical as a termiticide. At Yahiko (remote site), most of the POPs showed lower concentrations than those measured at the other sampling sites, although alpha-HCH and gamma-HCH were comparable with the concentrations found at the other sampling sites. All POPs except alpha-HCH and gamma-HCH tend to decrease 41-80% in their concentrations from 2000 to 2001. The lower POPs concentrations in winter and the higher POPs concentrations in summer at every sampling point can be partly explained by temperature differences. Applying the equation of the logarithm of the POP partial pressure in air versus reciprocal temperature (lnPa=m/T+b) to our data, linear relations were observed. HCB gave a poor linearity and the smallest slope, while beta-HCH, t-CHL and c-CHL gave good linearities and large slopes in the equation. The results suggest that HCB level is influenced by not only the emission from terrestrial sources but the global-scale background pollution. A peculiar observation is that beta-HCH concentration measured in our study showed large temperature dependence, indicating there could be a source of contamination in the surrounding areas.     

北京市燃煤的空气质量影响及其控制研究

建立了2005年北京市燃煤污染源排放清单,利用MM5-CMAQ模型计算了各区县各行业燃煤对北京市空气质量的影响。研究表明,2005年1月北京市燃煤源对各监测站点SO_2浓度的贡献在70%以上,对PM_(10)和NO_x浓度的贡献约为20%～40%和10%～30%;7月本地燃煤源对SO_2浓度的贡献在40%～50%左右。1月采暖锅炉对空气质量影响最大,占50%～70%;7月电厂的影响最大。依据北京市奥运空气质量保障方案以及十一五期间能源规划,建立了2010年燃煤污染源大气排放的规划情景,并模拟了各规划措施对大气质量的改善效果。通过实施电厂脱硫脱硝除尘、炼焦工业停产、钢铁行业和水泥行业搬迁减产、供热锅炉改造、平房用煤改造等措施,与2005年相比,SO_2平均浓度下降30%左右,NO_x和PM_(10)浓度的下降幅度15%。     

环境空气质量自动监测站的管理与维护

随着工业化、城镇化的深入推进,二氧化硫、氮氧化物、烟粉尘和挥发性有机物等各类污染物排放到环境中,致使中国大气受到严重污染,给人体的健康、动植物的生长、发育和繁殖等带来负面的影响。为实时监测环境空气质量,建立环境空气质量自动监测站逐渐成为大气污染防治的主要手段。文中以环境空气质量自动监测站为研究对象,提出环境空气质量自动监测站管理与维护面临的问题,探讨相应的解决措施,以期为环境空气质量自动监测站的管理与维护提供参考依据。     

Critical evaluation of approaches in setting indoor air quality guidelines and reference values

The importance of good indoor air quality for the health of the individual was recognized as long as 150 years ago and that period also saw recommendations, which essentially related to questions of ventilation and carbon dioxide. The first evaluation standards for organic and inorganic substances were laid down in the 1970s, often on an empirical basis. It was in the mid-1980s of the 20th century that a shift occurred towards systematically evaluating the results of indoor air measurements, carrying out representative environmental surveys and deriving guideline values and reference values on the basis of toxicological, epidemiological and statistical criteria. Generally speaking the indoor environment is an area which can only be assessed with difficulty since its occupants are in most cases exposed to mixtures of substances and there can be great local and temporal variations in the substance spectrum. Data are available today for a large number of substances and this makes it possible, with the aid of statistically derived reference values and toxicologically based guideline values, to make useful recommendations regarding good indoor air quality. Nevertheless, it is still difficult to evaluate reactive compounds and reaction products. What is disadvantageous, however, is the fact that different guideline values may be published for one and the same substance, whose justification and area of application are often not transparent. A guideline or reference value can only be regarded as rational when necessary and when a strategy for its verification is available.     

地下水水质评价的逻辑斯谛曲线模型

为检验已订的地下水水质评价标准的合理性，解决各单项水质指标的评估结果的不相容性问题，提高水质等级模型的分辨率，提出了一种新的水质评价模型－逻辑斯谛曲线模型，它的水质等级是连续的实数值。根据LOG模型的参数值可以分析各水质指标值对水质等级的影响程度，据此检验原订水质等级标准的合理性。并给出了基于实码遗传算法的LOG建模的实施方案。实例研究的结果说明，该方案是可行、有效且通用的，在环境质量综合评价中具有广泛的应用价值。     

A simple semi-empirical model for predicting missing carbon monoxide concentrations

Carbon monoxide monitoring using continuous samplers is carried out in most major urban centres in the world and generally forms the basis for air quality assessments. Such assessments become less reliable as the proportion of data missing due to equipment failure and periods of calibration increases. This paper presents a semi-empirical model for the prediction of atmospheric carbon monoxide concentrations near roads for the purpose of interpolating missing data without the need for any traffic or emissions information. The model produces reliable predictions while remaining computationally simple by being site-specifically optimized. The model was developed for, and evaluated at, both a suburban site and an inner city site in Hamilton, New Zealand. Model performance statistics were found to be significantly better than other simple methods of interpolation with little additional computational complexity.     

Using a photochemical air quality model to evaluate emissions and simulate concentrations

The air-quality modelling system was conducted to evaluate emissions inventory and simulate air concentration over Thailand. The coupling model of the Regional Atmospheric Modelling System (RAMS) and the models-3 Community Multi-scale Air Quality (CMAQ) was applied to simulate the concentration distributions of gaseous pollutants (i.e., NO_x, SO₂ and CO) over the Central and Eastern regions of Thailand. CMAQ was run for a summertime episode in a sub-grid scale. Simulated air concentrations were then compared with monitoring data. The evaluating results between modelling simulation and monitoring observation show a good agreement within a factor of 2.0 and relevant trend line, representing the acceptable level of emissions and concentration. This coupling model can be applied to support emission control strategies and clean air acts.     

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.