Air pollutant source characterization using the revised regional haze tracking metric and a photochemical grid model and implications for regional haze planning

The 2017 revisions to the Regional Haze Rule clarify that visibility progress at Class I national parks and wilderness areas should be tracked on days with the highest anthropogenic contributions to haze (impairment). We compare the natural and anthropogenic contributions to haze in the western United States in 2011 estimated using the Environmental Protection Agency (EPA) recommended method and using model projections from the Comprehensive Air Quality Model with Extensions (CAMx) and the Particulate Source Apportionment Tool (PSAT). We do so because these two methods will be used by states to demonstrate visibility progress by 2028. If the two methods assume different natural and anthropogenic contributions, the projected benefits of reducing U.S. anthropogenic emissions will differ. The EPA method assumes that episodic elevated carbonaceous aerosols greater than an annual 95th percentile threshold are natural events. For western U.S. IMPROVE monitoring sites reviewed in this paper, CAMx-PSAT confirms these episodes are impacted by carbon from wildfire or prescribed fire events. The EPA method assumes that most of the ammonium sulfate is anthropogenic in origin. At most western sites CAMx-PSAT apportions more of the ammonium sulfate on the most impaired days to global boundary conditions and anthropogenic Canadian, Mexican, and offshore shipping emissions than to U.S. anthropogenic sources. For ammonium nitrate and coarse mass, CAMx-PSAT apportions greater contributions to U.S. anthropogenic sources than the EPA method assigns to total anthropogenic contributions. We conclude that for western IMPROVE sites, the EPA method is effective in selecting days that are likely to be impacted by anthropogenic emissions and that CAMx-PSAT is an effective approach to estimate U.S. source contributions. Improved inventories, particularly international and natural emissions, and further evaluation of global and regional model performance and PSAT attribution methods are recommended to increase confidence in modeled source characterization.

Implications: The western states intend to use the CAMx model to project visibility progress by 2028. Modeled visibility response to changes in U.S. anthropogenic emissions may be less than estimated using the EPA assumptions based on total U.S. and international anthropogenic contributions to visibility impairment. Additional model improvements are needed to better account for contributions to haze from natural and international emissions in current and future modeling years. These improvements will allow more direct comparison of model and EPA estimates of natural and anthropogenic contributions to haze and future visibility progress.     

Evaluation of multisectional and two-section particulate matter photochemical grid models in the Western United States

Version 4.10s of the comprehensive air-quality model with extensions (CAMx) photochemical grid model has been developed, which includes two options for representing particulate matter (PM) size distribution: (1) a two-section representation that consists of fine (PM2.5) and coarse (PM2.5-10) modes that has no interactions between the sections and assumes all of the secondary PM is fine; and (2) a multisectional representation that divides the PM size distribution into N sections (e.g., N = 10) and simulates the mass transfer between sections because of coagulation, accumulation, evaporation, and other processes. The model was applied to Southern California using the two-section and multisection representation of PM size distribution, and we found that allowing secondary PM to grow into the coarse mode had a substantial effect on PM concentration estimates. CAMx was then applied to the Western United States for the 1996 annual period with a 36-km grid resolution using both the two-section and multisection PM representation. The Community Multiscale Air Quality (CMAQ) and Regional Modeling for Aerosol and Deposition (REMSAD) models were also applied to the 1996 annual period. Similar model performance was exhibited by the four models across the Interagency Monitoring of Protected Visual Environments (IMPROVE) and Clean Air Status and Trends Network monitoring networks. All four of the models exhibited fairly low annual bias for secondary PM sulfate and nitrate but with a winter overestimation and summer underestimation bias. The CAMx multisectional model estimated that coarse mode secondary sulfate and nitrate typically contribute <10% of the total sulfate and nitrate when averaged across the more rural IMPROVE monitoring network.     

Analysis of photochemical pollution in summer and winter using a photochemical box model in the center of Tokyo, Japan.

In order to give an effective and rapid analysis of the photochemical pollution and information for emission control strategies, a photochemical box model (PBM) was applied to one moderate summer episode, 11 July 1996, and one typical winter episode, 3 December 1996, in the center of Tokyo, Japan. The box model gave a good prediction of the photochemical pollution with minimal investment. As expected, the peak ozone in summer is higher than in winter. The NOx concentrations in winter are higher than those in summer. In summer, NO and NO2 have one peak in the morning. In winter, NO and NO2 show two peaks during the day. Three model runs including no reactions, a zero ozone boundary condition and dark reactions were conducted to understand the photochemical processes. The effects of emission reduction on the formation of the photochemical pollution in the center of Tokyo have been studied. The results show that the reduction of NMHC emission can decrease the ozone, however, the reduction of NOx emission can increase the ozone. It can be concluded that if the NOx emission are reduced, the reduction of NMHC should be more emphasized in order to decrease the ozone concentration in the center of Tokyo, Japan, especially the reduction of the NMHC from stationary source emission.     

Assessment of the nested grid model estimates for driving regional visibility models in the southwestern United States

The Nested Grid Model (NGM) is a primitive-equation meteorological model that is routinely exercised over North America for forecasting purposes by the National Meteorological Center. While prognostic meteorological models are being increasingly used to drive air quality models, their use in conducting annual simulations requires significant resources. NGM estimates of wind fields and other meteorological variables provide an attractive alternative since they are typically archived and readily available for an entire year. Preliminary evaluation of NGM winds during the summer of 1992 for application to the region surrounding the Grand Canyon National Park showed serious shortcomings. The NGM winds along the borders between California, Arizona and Mexico tend to be northwesterly with a speed of about 6 m/sec, while the observed flow is predominantly southerly at about 2-5 m/sec. The mesoscale effect of a thermal low pressure area over the highly heated Southern California and western Arizona deserts does not appear to be represented by the NGM because of its coarse resolution and the use of sparse observations in that region. Tracer simulations and statistical evaluation against special high resolution observations of winds in the southwest United States clearly demonstrate the northwest bias in NGM winds and its adverse effect on predictions of an air quality model. The "enhanced" NGM winds, in which selected wind observations are incorporated in the NGM winds using a diagnostic meteorological model provide additional confirmation on the primary cause of the northwest bias. This study has demonstrated that in situations where limited resources prevent the use of prognostic meteorological models, previously archived coarse resolution wind fields in which additional observations are incorporated to correct known biases provide an attractive option.     

A simple semi-empirical photochemical model for the simulation of ozone concentration in the Seoul metropolitan area in Korea

O₃ concentrations were simulated over the Seoul metropolitan area in Korea using a simple semi-empirical reaction (SEGRS) model which consists of generic reaction set (GRS), photochemical reaction set, and the diagnostic wind field generation model. The aggregated VOC emission strength was empirically scaled by the comparison of the simulated slope of (O₃–2NO–NO₂) concentration as a function of cumulative actinic light flux against measurements on high surface ozone concentration days with the relatively weak easterly geostrophic winds at the 850 hPa level in summer when the effect of horizontal advection was fairly small. The results indicated that the spatial distribution patterns and temporal variations of spatially averaged ground-level ozone concentrations were quite well simulated compared with those of observations with the modified volatile organic compound (VOC) emission strength. The diurnal trend of the surface ozone concentration and the maximum concentration compared observations were also quite reasonably simulated. However, the maximum ozone concentration occurring time at Seoul lagged about 2 h and the ozone concentration in the suburban area was slightly overestimated in the afternoon due to the influx of high ozone concentration from the urban area. It was found that the SEGRS model could be effectively used to simulate or predict the ground-level ozone concentration reasonably well without heavy computational cost provided the emission of ozone precursors are given.     

Evaluation of urban photochemical models for regulatory use

There has been an increasing recognition of the need to evaluate, systematically and quantitatively, the performance of air quality models used for regulatory purposes. However, previous evaluations of urban photochemical models have not tested the ability of the model to predict the change in ozone that will result from a given change in emissions, a necessary feature for a model to be used for regulatory purposes. Instead, the evaluations have tested the models' ability to reproduce ozone production for a variety of meteorological conditions. This paper presents an evaluation of the Urban Airshed Model based on Denver, Colorado data that includes both changes in daily maximum ozone due to changes in meteorology, and changes in daily maximum ozone due to changes in emissions. It is shown that the emissions change testing produces an assessment of the Urban Airshed Model that is fairly independent of the one based on meteorological change. Important inferences about model performance do not carry over from one case to the other. Thus, an emissions change testing should be included in assessing whether an urban photochemical model is good enough for regulatory use.     

Uncertainties in predicted ozone concentrations due to input uncertainties for the UAM-V photochemical grid model applied to the July 1995 OTAG domain

The photochemical grid model, UAM-V, has been used by regulatory agencies to make decisions concerning emissions controls, based on studies of the July 1995 ozone episode in the eastern US. The current research concerns the effect of the uncertainties in UAM-V input variables (emissions, initial and boundary conditions, meteorological variables, and chemical reactions) on the uncertainties in UAM-V ozone predictions. Uncertainties of 128 input variables have been estimated and most range from about 20% to a factor of two. 100 Monte Carlo runs, each with new resampled values of each of the 128 input variables, have been made for given sets of median emissions assumptions. Emphasis is on the maximum hourly-averaged ozone concentration during the 12–14 July 1995 period. The distribution function of the 100 Monte Carlo predicted domain-wide maximum ozone concentrations is consistently close to log-normal with a 95% uncertainty range extending over plus and minus a factor of about 1.6 from the median. Uncertainties in ozone predictions are found to be most strongly correlated with uncertainties in the NO₂ photolysis rate. Also important are wind speed and direction, relative humidity, cloud cover, and biogenic VOC emissions. Differences in median predicted maximum ozone concentrations for three alternate emissions control assumptions were investigated, with the result that (1) the suggested year-2007 emissions changes would likely be effective in reducing concentrations from those for the year-1995 actual emissions, that (2) an additional 50% NO_x emissions reductions would likely be effective in further reducing concentrations, and that (3) an additional 50% VOC emission reductions may not be effective in further reducing concentrations.     

Bootstrap estimates of factor model variability

Receptor models for air pollutants in an airshed are constructed from a set of measured concentrations and used for apportioning sources in a quantitative way. Such a dataset even if large is but a sample from the population of pollutants occurring over time in the airshed. Thus the ensuing receptor model and its parameters constitute but one realization of a range of possible experiments. The problem studied in this paper is how large the inherent ranges of variation of the model parameters are, i.e. how reliable the model is. Factor analysis models, where data are autoscaled prior to analysis, were selected for study.Pseudo-repetitions of the experiment are carried out by bootstrapping of the original data and statistics on the model parameters are compiled and reported. The validity of the results of such numerical experiments are based on the assumption that the original data are really representative since by bootstrapping, new artificial data are generated from this set. A number of screening tests for acceptance of data and models are applied to avoid bias and errors in the results. Means and standard deviations of communalities and factor loadings are computed and compared to the original parameters. Regression analysis is used in a search for functional relationships between standard deviations and model parameters.The general conclusion is that well modelled variables carry little model uncertainty and that the largest standard deviations amounting to about 10% are found for loadings in the mid-range.     

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.     

Verification of a source-oriented externally mixed air quality model during a severe photochemical smog episode

The CIT/UCD three-dimensional source-oriented externally mixed air quality model is tested during a severe photochemical smog episode (Los Angeles, 7–9 September 1993) using two different chemical mechanisms that describe the formation of ozone and secondary reaction products. The first chemical mechanism is the secondary organic aerosol mechanism (SOAM) that is based on SAPRC90 with extensions to describe the formation of condensable organic products. The second chemical mechanism is the caltech atmospheric chemistry mechanism (CACM) that is based on SAPRC99 with more detailed treatment of organic oxidation products.The predicted ozone concentrations from the CIT/UCD/SOAM and the CIT/UCD/CACM models agree well with the observations made at most monitoring sites with a mean normalized error of approximately 0.4–0.5. Good agreement is generally found between the predicted and measured NO_x concentrations except during morning rush hours of 6–10 am when NO_x concentrations are under-predicted at most locations. Total VOC concentrations predicted by the two chemical mechanisms agree reasonably well with the observations at three of the four sites where measurements were made. Gas-phase concentrations of phenolic compounds and benzaldehyde predicted by the UCD/CIT/CACM model are higher than the measured concentrations whereas the predicted concentrations of other aromatic compounds approximately agree with the measured values.The fine airborne particulate matter mass concentrations (PM_2.5) predicted by the UCD/CIT/SOAM and UCD/CIT/CACM models are slightly greater than the observed values during evening hours and lower than observed values during morning rush hours. The evening over-predictions are driven by an excess of nitrate, ammonium ion and sulfate. The UCD/CIT/CACM model predicts higher nighttime concentrations of gaseous precursors leading to the formation of particulate nitrate than the UCD/CIT/SOAM model. Elemental carbon and total organic mass are under-predicted by both models during morning rush hour periods. When this latter finding is combined with the NO_x under-predictions that occur at the same time, it suggests a systematic bias in the diesel engine emissions inventory. The mass of particulate total organic carbon is under-predicted by both the UCD/CIT/SOAM and UCD/CIT/CACM models during afternoon hours. Elemental carbon concentrations generally agree with the observations at this time. Both the UCD/CIT/SOAM and UCD/CIT/CACM models predict low concentrations of secondary organic aerosol (SOA) (<3.5 μg m⁻³) indicating that both models could be missing SOA formation pathways. The representation of the aerosol as an internal mixture vs. a source-oriented external mixture did not significantly affect the predicted concentrations during the current study.     

Evaluating multimedia/multipathway model intake fraction estimates using POP emission and monitoring data

This paper presents a structured evaluation of a novel multimedia chemical fate and multi-pathway human exposure model for Western Europe, IMPACT 2002, using data for PCDD/F congeners. PCDD/F congeners provide an illustration of the potential use of POPs (Persistent Organic Pollutant) data for the evaluation of such models. Based on available emission estimates, model predictions with and without spatial resolution are evaluated at three different stages against monitored data: at environmental contamination levels, food exposure concentration, and in terms of human intake fractions (iF): the fraction of an emission that is taken in by the population. The iF is approximately 3.5.10(-3) for emissions of dioxin in Western Europe. This iF compares well to the traditional non-spatial multi-media/-pathway model predictions of 3.9.10(-3) for the same region and to 2.10(-3) for the USA. Approximately 95% of the intake from Western European emissions occurs within the same region, 5% being transferred out of the region in terms of food contaminants and atmospheric advective transport.     

基于改进支持向量回归机的污水处理厂出水总氮预测模型

在小样本数据的情况下,采用粒子群优化算法(PSO)对传统支持向量回归机(SVR)进行改进,将其应用于北京某大型污水处理厂出水总氮浓度预测上。 预测结果精度对比分析表明,PSO-SVR模型预测结果平均相对误差为1.836%,决定系数为67.76%,均方根误差为0.693 9,各评价指标均优于多元线性回归模型、BP神经网络模型。因此在小样本情况下,利用PSO-SVR模型对污水处理厂出水总氮浓度进行预测是可行有效的,为应用数据驱动模型对污水处理过程进行建模模拟提供了一种新方法尝试。     

SG-Ⅰ型多功能光化学反应装置的研制

SG Ⅰ型多功能光化学反应装置是目前国内外处理容量较大、监测系统较为齐全 ,操作控制自动化程度较高的一种准工业化处理环境污染物的中间装置。它能有效地去除水、植物体及其果实中的环境污染物     

The effect of the increase in urban temperature on the concentration of photochemical oxidants

An atmospheric dispersion model, where the inputs of meteorological field were calculated using a meteorological model, was used to reproduce the observed air pollution conditions for the typical fine day in summer period, especially the concentration of the photochemical oxidants. As well, the effects of an increase in the urban temperature and VOC emissions on the concentration of photochemical oxidants were also considered. The following conclusions were drawn.The observed air pollution levels were well modeled by the atmospheric dispersion model using in this study, although modeled NO levels were slightly lower than the observed levels. An analysis of the temperature data showed that a 1 °C increase in temperature leads to a maximal photochemical oxidant concentration of 5.3 ppb, which is an increase of 11%. Additionally, the effect on the photochemical oxidant concentration due to an increase in the vegetation-derived VOCs was more than double the effect due to an increase in the photochemical reactions. It was found that the temperature and photochemical oxidant concentration were linearly related up to a temperature increase of 3 °C. When the temperature increases up to 3 °C, the concentration of photochemical oxidants increases by 19 ppb. An analysis of the effect of vegetation-derived VOCs on photochemical oxidant concentrations showed that, the concentration of photochemical oxidants was 30 ppb higher in the afternoon by the effect of vegetation-derived VOCs, so even in metropolitan areas with relatively little vegetation, vegetation-derived VOCs have a strong impact on photochemical oxidant concentrations.     

基于因子分析法优选河流水体中氮浓度预测方法的研究

定量的河流水体中氮浓度预测方法有很多种,如何优选出预测精度较高的方法一直是学术界多年来致力于研究的重点。本研究采用因子分析法对预测方法的精度评价指标进行分析,并建立了预测方法精度的评价模型,对回归分析法、神经网络法、灰色系统法和增长率统计法4种水体氮浓度预测方法进行综合评估,优选出精度较高的河流水体氮浓度预测模型——BP神经网络预测模型。结果表明,此评估模型对类似研究具有一定的参考价值,能为选择出合适的河流水体氮浓度预测方法提供依据。