水环境数学模型的研究进展

在作者多年研究工作和查阅大量文献资料的基础上,本文综述了环境数学模型国内外的研究现状、发展历史及未来趋势。重点介绍了这方面研究的最新进展。该文还介绍了本领域所涉及的理论和实际应用问题,提出了今后值得进行研究的若干前沿课题,给出了60余篇关于水环境数学模型研究的主要参考文献目录。     

Model comparison for risk assessment: a case study of contaminated groundwater

Many environmental multimedia risk assessment models have been developed and widely used along with increasing sophistication of the risk assessment method. Despite of the considerable improvement, uncertainty remains a primary threat to the credibility of and users' confidence in the model-based risk assessments. In particular, it has been indicated that scenario and model uncertainty may affect significantly the assessment outcome. Furthermore, the uncertainty resulting from choosing different models has been shown more important than that caused by parameter uncertainty. Based on the relationship between exposure pathways and estimated risk results, this study develops a screening procedure to compare the relative suitability between potential multimedia models, which would facilitate the reduction of uncertainty due to model selection. MEPAS, MMSOILS, and CalTOX models, combined with Monte Carlo simulation, are applied to a realistic groundwater-contaminated site to demonstrate the process. It is also shown that the identification of important parameters and exposure pathways, and implicitly, the subsequent design of uncertainty reduction and risk management measures, would be better-formed.     

An enhanced PM2.5 air quality forecast model based on nonlinear regression and back-trajectory concentrations

An enhanced PM_2.5 air quality forecast model based on nonlinear regression (NLR) and back-trajectory concentrations has been developed for use in the Louisville, Kentucky metropolitan area. The PM_2.5 air quality forecast model is designed for use in the warm season, from May through September, when PM_2.5 air quality is more likely to be critical for human health. The enhanced PM_2.5 model consists of a basic NLR model, developed for use with an automated air quality forecast system, and an additional parameter based on upwind PM_2.5 concentration, called PM24. The PM24 parameter is designed to be determined manually, by synthesizing backward air trajectory and regional air quality information to compute 24-h back-trajectory concentrations. The PM24 parameter may be used by air quality forecasters to adjust the forecast provided by the automated forecast system. In this study of the 2007 and 2008 forecast seasons, the enhanced model performed well using forecasted meteorological data and PM24 as input. The enhanced PM_2.5 model was compared with three alternative models, including the basic NLR model, the basic NLR model with a persistence parameter added, and the NLR model with persistence and PM24. The two models that included PM24 were of comparable accuracy. The two models incorporating back-trajectory concentrations had lower mean absolute errors and higher rates of detecting unhealthy PM2.5 concentrations compared to the other models.     

Modeling exposure to particulate matter

Exposure assessment, a component of risk assessment, links sources of pollution with health effects. Exposure models are scientific tools used to gain insights into the processes affecting exposure assessment. The purpose of this paper is to review the process and methodology of estimating inhalation exposure to particulate matter (PM) using various types of models. Three types of models are discussed in the paper. Indirect type of models are physical models that employ inventories of outdoor and indoor sources and their emission rates to identify major sources contributing to exposure to PM, and use fate and transport and indoor air quality models to estimate PM concentrations at receptor sites. PM concentrations and time spent by a subject at each receptor site are input variables to the conventional exposure model that estimates the desired exposure levels. Direct type models use measured exposure or exposure concentrations in conjunction with information obtained from questionnaires to formulate exposure regression models. Stochastic models use exposure measurements, estimates can also be used, to formulate exposure population distributions and investigate associated uncertainty and variability. Since models developed using databases from western countries are not necessarily applicable in developing countries, the difference in requirements among western and developing countries is highlighted in the paper. Employment of exposure modeling methods in developing countries requires development of local information. Such information includes local outdoor and indoor source inventories, local or regional meteorological conditions, adjustment of indoor models to reflect local building construction conditions, and use of questionnaires to obtain local time budget and activity patterns of the subject population.     

Comparison of artificial neural network and regression models in the prediction of urban stormwater quality.

Urban stormwater quality is influenced by many interrelated processes. However, the site-specific nature of these complex processes makes stormwater quality difficult to predict using physically based process models. This has resulted in the need for more empirical techniques. In this study, artificial neural networks (ANN) were used to model urban stormwater quality. A total of 5 different constituents were analyzed-chemical oxygen demand, lead, suspended solids, total Kjeldahl nitrogen, and total phosphorus. Input variables were selected using stepwise linear regression models, calibrated on logarithmically transformed data. Artificial neural networks models were then developed and compared with the regression models. The results from the analyses indicate that multiple linear regression models were more applicable for predicting urban stormwater quality than ANN models.     

A statistical approach to estimate O3 uptake of ponderosa pine in a mediterranean climate

In highly polluted sites, stomatal behavior is sluggish with respect to light, vapor pressure deficit, and internal CO2 concentration (Ci) and poorly described by existing models. Statistical models were developed to estimate stomatal conductance (gs) of 40-year-old ponderosa pine at three sites differing in pollutant exposure for the purpose of calculating O3 uptake. Gs was estimated using julian day, hour of day, pre-dawn xylem potential and photosynthetic photon flux density (PPFD). The median difference between estimated and observed field gs did not exceed 10 mmol H2O m(-2) s(-1), and estimated gs within 95% confidence intervals. 03 uptake was calculated from hourly estimated gs, hourly O3 concentration, and a constant to correct for the difference in diffusivity between water vapor and 03. The simulation model TREGRO was also used to calculate the cumulative 03 uptake at all three sites. 03 uptake estimated by the statistical model was higher than that simulated by TREGRO because gas exchange rates were proportionally higher. O3 exposure and uptake were significantly correlated (r2>0.92), because O3 exposure and gs were highly correlated in both statistical and simulation models.     

A mass transfer model for simulating VOC sorption on building materials

The sorption of volatile organic compounds (VOCs) by different building materials can significantly affect VOC concentrations in indoor environments. In this paper, a new model has been developed for simulating VOC sorption and desorption rates of homogeneous building materials with constant diffusion coefficients and material–air partition coefficients. The model analytically solves the VOC sorption rate at the material–air interface. It can be used as a “wall function” in combination with more complex gas-phase models that account for non-uniform mixing to predict sorption process. It can also be used in conjunction with broader indoor air quality studies to simulate VOC exposure in buildings.     

Comparison of land-use regression models between Great Britain and the Netherlands

Land-use regression models have increasingly been applied for air pollution mapping at typically the city level. Though models generally predict spatial variability well, the structure of models differs widely between studies. The observed differences in the models may be due to artefacts of data and methodology or underlying differences in source or dispersion characteristics. If the former, more standardised methods using common data sets could be beneficial. We compared land-use regression models for NO₂ and PM_10, developed with a consistent protocol in Great Britain (GB) and the Netherlands (NL).Models were constructed on the basis of 2001 annual mean concentrations from the national air quality networks. Predictor variables used for modelling related to traffic, population, land use and topography. Four sets of models were developed for each country. First, predictor variables derived from data sets common to both countries were used in a pooled analysis, including an indicator for country and interaction terms between country and the identified predictor variables. Second, the common data sets were used to develop individual baseline models for each country. Third, the country-specific baseline models were applied after calibration in the other country to explore transferability. The fourth model was developed using the best possible predictor variables for each country.A common model for GB and NL explained NO₂ concentrations well (adjusted R² 0.64), with no significant differences in intercept and slopes between the two countries. The country-specific model developed on common variables for NL but not GB improved the prediction.The performance of models based upon common data was only slightly worse than models optimised with local data. Models transferred to the other country performed substantially worse than the country-specific models. In conclusion, care is needed both in transferring models across different study areas, and in developing large inter-regional LUR models.     

Comparison of toxicological impacts of integrated and chemical pest management in Mediterranean greenhouses

The goal of this paper is to assess the relative impacts of pest-control methods in greenhouses, based on current LCA tools. As a case study, the relative impacts of two tomato production methods, chemical pest management (CPM) and integrated pest management (IPM), are assessed. The amount of the active ingredients applied, the fate of the ingredients in the various greenhouse and environmental compartments, the human exposure routes via the various compartments and the inherent toxicity of the ingredients were taken into account in the relative impact calculations. To assess the importance of model selection in the assessment, pesticide-specific fate and exposure factors for humans and aquatic and terrestrial ecosystems, used to aggregate pesticide emissions, were calculated with two different models: (1) the USES-LCA model, adapted in order to calculate the pesticide transfer from greenhouse air and soil to fruits, and (2) the empirical model critical surface time (CST). Impact scores have in general shown a higher level of potential contamination in greenhouses treated with CPM compared to IPM (a factor of 1.4 to 2.3). Relative impacts have been shown highly dependent on the selection of specific pesticides and crop stage development at the moment of pesticide application. This means that both CPM and IPM could be improved by a careful selection of pesticides. In order to improve the relative impact calculations, future research in pesticide transfer to food will be necessary.     

Comparing universal kriging and land-use regression for predicting concentrations of gaseous oxides of nitrogen (NOx) for the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air)

BACKGROUND: Epidemiological studies that assess the health effects of long-term exposure to ambient air pollution are used to inform public policy. These studies rely on exposure models that use data collected from pollution monitoring sites to predict exposures at subject locations. Land use regression (LUR) and universal kriging (UK) have been suggested as potential prediction methods. We evaluate these approaches on a dataset including measurements from three seasons in Los Angeles, CA. METHODS: The measurements of gaseous oxides of nitrogen (NOx) used in this study are from a "snapshot" sampling campaign that is part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). The measurements in Los Angeles were collected during three two-week periods in the summer, autumn, and winter, each with about 150 sites. The design included clusters of monitors on either side of busy roads to capture near-field gradients of traffic-related pollution. LUR and UK prediction models were created using geographic information system (GIS)-based covariates. Selection of covariates was based on 10-fold cross-validated (CV) R(2) and root mean square error (RMSE). Since UK requires specialized software, a computationally simpler two-step procedure was also employed to approximate fitting the UK model using readily available regression and GIS software. RESULTS: UK models consistently performed as well as or better than the analogous LUR models. The best CV R(2) values for season-specific UK models predicting log(NOx) were 0.75, 0.72, and 0.74 (CV RMSE 0.20, 0.17, and 0.15) for summer, autumn, and winter, respectively. The best CV R(2) values for season-specific LUR models predicting log(NOx) were 0.74, 0.60, and 0.67 (CV RMSE 0.20, 0.20, and 0.17). The two-stage approximation to UK also performed better than LUR and nearly as well as the full UK model with CV R(2) values 0.75, 0.70, and 0.70 (CV RMSE 0.20, 0.17, and 0.17) for summer, autumn, and winter, respectively. CONCLUSION: High quality LUR and UK prediction models for NOx in Los Angeles were developed for the three seasons based on data collected for MESA Air. In our study, UK consistently outperformed LUR. Similarly, the 2-step approach was more effective than the LUR models, with performance equal to or slightly worse than UK.     

The use of wind fields in a land use regression model to predict air pollution concentrations for health exposure studies

A methodology is developed to include wind flow effects in land use regression (LUR) models for predicting nitrogen dioxide (NO₂) concentrations for health exposure studies. NO₂ is widely used in health studies as an indicator of traffic-generated air pollution in urban areas. Incorporation of high-resolution interpolated observed wind direction from a network of 38 weather stations in a LUR model improved NO₂ concentration estimates in densely populated, high traffic and industrial/business areas in Toronto-Hamilton urban airshed (THUA) of Ontario, Canada. These small-area variations in air pollution concentrations that are probably more important for health exposure studies may not be detected by sparse continuous air pollution monitoring network or conventional interpolation methods. Observed wind fields were also compared with wind fields generated by Global Environmental Multiscale-High resolution Model Application Project (GEM-HiMAP) to explore the feasibility of using regional weather forecasting model simulated wind fields in LUR models when observed data are either sparse or not available. While GEM-HiMAP predicted wind fields well at large scales, it was unable to resolve wind flow patterns at smaller scales. These results suggest caution and careful evaluation of regional weather forecasting model simulated wind fields before incorporating into human exposure models for health studies. This study has demonstrated that wind fields may be integrated into the land use regression framework. Such integration has a discernable influence on both the overall model prediction and perhaps more importantly for health effects assessment on the relative spatial distribution of traffic pollution throughout the THUA. Methodology developed in this study may be applied in other large urban areas across the world.     

A comparison of indices that describe the relationship between exposure to ozone and reduction in the yield of agricultural crops

The objective of this study is to compare the use of several indices of exposure in describing the relationship between O₃ and reduction in agricultural crop yield. No attempt has been made to determine which exposure-response models best fit the data sets examined. Hourly mean O₃ concentration data, based on two-three measurements per hour, were used to develop indices of exposure from soybean and winter wheat experiments conducted in open-top chambers at the Boyce Thompson Institute, Ithaca, New York NCLAN field site. The comparative efficacy of cumulative indices (i.e. number of occurrences equal to or above specific hourly mean concentrations, sum of all hourly mean concentrations equal to or above a selected level, and the weighted sum of all hourly mean concentrations) and means calculated over an experimental period to describe the relationship between exposure to O₃ and reductions in the yield of agricultural crops was evaluated. None of the exposure indices consistently provided a best fit with the Weibull and linear models tested. The selection of the model appears to be important in determining the indices that best describe the relationship between exposure and response. The focus of selecting a model should be on fitting the data points as well as on adequately describing biological responses. The investigator should be careful to couple the model with data points derived from indices relevant to the length of exposure. While we have used a small number of data sets, our analysis indicates that exposure indices that weight peak concentrations differently than lower concentrations of an exposure regime can be used in the development of exposure-response functions. Because such indices may have merit from a regulatory perspective, we recommend that additional data sets be used in further analyses to explore the biological rationale for various indices of exposure and their use in exposure-response functions.     

Dynamical coupling of PBPK/PD and AUC-based toxicity models for arsenic in tilapia Oreochromis mossambicus from blackfoot disease area in Taiwan

A physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) models were developed for arsenic (As) in tilapia Oreochromis mossambicus from blackfoot disease area in Taiwan. The PBPK/PD model structure consisted of muscle, gill, gut wall, alimentary canal, and liver, which were interconnected by blood circulation. We integrate the target organ concentrations and dynamic response describing uptake, metabolism, and disposition of As and the associated area-under-curve (AUC)-based toxicological dynamics following an acute exposure. The model validations were compared against the field observations from real tilapia farms and previously published uptake/depuration experimental data, indicating that predicted and measured As concentrations in major organs of tilapia were in good agreement. The model was utilized to reasonably simulate and construct a dose-dependent dynamic response between mortality effect and equilibrium target organ concentrations. Model simulations suggest that tilapia gills may serve as a surrogate sensitive biomarker of short-term exposure to As. This integrated As PBPK/PD/AUC model quantitatively estimates target organ concentration and dynamic response in tilapia and is a strong framework for future waterborne metal model development and for refining a biologically-based risk assessment for exposure of aquatic species to waterborne metals under a variety of scenarios.     

Development of a black carbon-inclusive multi-media model: application for PAHs in Stockholm

A multi-media model was developed for predicting the fate of organic chemicals in the Greater Stockholm Area, Sweden, and applied to selected polycyclic aromatic hydrocarbons (PAHs). Although urban models have been previously developed, this model is novel in that it includes sorption to pyrogenically-derived particles, commonly termed "black carbon" (BC), within the model structure. To examine the influence of BC sorption on environmental fate of PAHs, two versions of the model were generated and run: one in which sorption to BC was included and one in which BC sorption was excluded. The inclusion of BC sorption did not cause any significant variations to air levels, but it did cause an average 20-30% increase in sediment concentrations related to increased sediment solids partitioning. The model also predicted reduced advective losses out of the model domain, as well as chemical potential to diffuse from sediments, whilst total chemical inventory increased. In all cases, the lighter PAHs were more affected by BC inclusion than their heavier counterparts. We advocate the addition of sorption to BC in future multi-media fate and exposure models, which as well as influencing fate will also alter (lower) chemical availability and, thus, wildlife exposure to hydrophobic chemicals. A quantification of the latter was derived with the help of the soot-inclusive model version, which estimated a lowering of dissolved water concentrations between five and >200 times for the different PAHs of this study.     

An object-oriented software for fate and exposure assessments

The model system CemoS¹ (Chemical Exposure Model System) was developed for the exposure prediction of hazardous chemicals released to the environment. Eight different models were implemented involving chemicals fate simulation in air, water, soil and plants after continuous or single emissions from point and diffuse sources. Scenario studies are supported by a substance and an environmental data base. All input data are checked on their plausibility. Substance and environmental process estimation functions facilitate generic model calculations. CemoS is implemented in a modular structure using object-oriented programming. e-mail: cemos@aphrodite.mathematik.uni-osnabrueck.de     

The Emissions and Dispersion Modeling System (EDMS): Its Development and Application at Airports and Airbases

A new complex source microcomputer model has been developed for use at civil airports and Air Force bases. This paper describes both the key features of this model and its application in evaluating the air quality impact of new construction projects at three airports: one in the United States and two in Canada.

The single EDMS model replaces the numerous models previously required to assess the air quality impact of pollution sources at airports. EDMS also employs a commercial data base to reduce the time and manpower required to accurately assess and document the air quality impact of airfield operations.

On July 20, 1993, the U.S. Environmental Protection Agency (EPA) issued the final rule (Federal Register, 7/20/93, page 38816) to add new models to the Guideline on Air Quality Models. At that time EDMS was incorporated into the Guideline as an Appendix A model.     

Fate and exposure models in relation to risk assessment

The recognition of potential long-term effects of substances, has led to the development of regulations and legislation which emphasis the need for assessing the risks posed by both new and existing substances for man and the environment. The evaluation of fate and exposure models is put into perspective in relation to the developments over the last five years in the framework of the EU regulations and national legislation within the European Community. The validation of environmental exposure models (multi-media models) is described in the context of quality assurance of models available to a user group, which is different from the quality assurance aspects for a developers group.     

Simulations of stomatal conductance and ozone uptake to Norway spruce saplings in open-top chambers

A simulation model was developed to estimate the stomatal conductance and ozone flux to Norway spruce saplings in open-top chambers. The model was parameterized against needle conductance measurements that were made on 4-6-year-old spruce saplings, grown in open-top chambers, in July-September during three different seasons. The spruce saplings were either maintained well watered or subject to a 7-8 week drought period in July-September each year. The simulated conductance showed a good agreement with the measured conductance for the well-watered as well as the drought stress-treated saplings. The simulations were significantly improved when different vapour pressure deficit (VPD) functions were applied for well-watered and drought-stressed spruce saplings. The cumulated ozone uptake which was calculated from the conductance simulations showed less variation between years, compared to the cumulative ozone exposure index AOT40 (accumulated exposure over a threshold of 40 ppb or nl l(-1)) for the corresponding time periods. Measurements in May 1995 demonstrated the occurrence of long-term 'memory-effects' from the drought stress treatments on the conductance. Memory-effects need to be considered when simulation models for stomatal conductance are to be applied to long-lived forest trees under a multiple stress situation.