Air Pollution Research Needs: Herbaceous and Ornamental Plants and Agriculturally Generated Pollutants

Effects of various air pollutants on economically important crops and ornamentals have been studied since before the turn of the century.

Summaries of this research on the effects of air pollutants, that have appeared in criteria documents developed by the Environmental Protection Agency, should be reviewed with respect to differences in plant susceptibility found in various regions of the country. These susceptibility differences are associated with variations in both environmental conditions and distribution of pollutants. Research efforts on air pollution injury to vegetation have often been poorly coordinated leaving many gaps in our knowledge. A better assessment of the impact of air pollution on vegetation is required to attain realistic controls for pollutants affecting agriculture. Research areas of major concern include: baseline information on effects of pollutants on agricultural productivity; dose-response information to support predictive mathematical models for acute and chronic studies of growth, yield, and quality effects; effects of pollutants interacting with other pollutants and with insects and plant diseases; mechanisms of pollutant action; genetic changes related to pollutant effects; effects of environmental stresses on plant response to pollutants; evaluation of plants including soil microbes as pollutant sinks; development of techniques to minimize pollutant effects; and, the effects of agricultural chemicals as air pollutants. There is a need for studies that consider the whole plant in its natural environment. Conceptual models interrelating pollutant effects and their interactions and ultimately mathematical models will be needed to develop an intelligent approach to land management. The effects of agriculturally produced pollutants on plants and other receptors must be identified and quantified.     

Application of genetic algorithms for the design of ozone control strategies

Designing air quality management strategies is complicated by the difficulty in simultaneously considering large amounts of relevant data, sophisticated air quality models, competing design objectives, and unquantifiable issues. For many problems, mathematical optimization can be used to simplify the design process by identifying cost-effective solutions. Optimization applications for controlling nonlinearly reactive pollutants such as tropospheric ozone, however, have been lacking because of the difficulty in representing nonlinear chemistry in mathematical programming models. We discuss the use of genetic algorithms (GAs) as an alternative optimization approach for developing ozone control strategies. A GA formulation is described and demonstrated for an urban-scale ozone control problem in which controls are considered for thousands of pollutant sources simultaneously. A simple air quality model is integrated into the GA to represent ozone transport and chemistry. Variations of the GA formulation for multiobjective and chance-constrained optimization are also presented. The paper concludes with a discussion of the practically of using more sophisticated, regulatory-scale air quality models with the GA. We anticipate that such an approach will be practical in the near term for supporting regulatory decision-making.     

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.     

Coupling air quality and land use modeling within an optimization framework

Land use regulations and air quality standards can be effective tools to control air pollution. Atmospheric transport/chemistry simulation models could be used to develop suitable regulations and standards; however, these models are not as efficient as air quality management models developed from embedding governing equations for atmospheric transport/chemistry into an optimization framework. Formulations of two steady-state air quality management models are presented to facilitate the development or evaluation of land use strategies to protect regional air quality from pollution generated from distributed point or nonpoint sources. Both models are linear programs constructed with equations that describe steady-state atmospheric pollutant fate and transport. The first model determines feasible pollutant loading patterns for multiple land use activities to accommodate the greatest regional population. The second model ascertains patterns of expanded land use which have a minimum impact on air quality. The primary goal of this paper is to explain how air pollution and land use modeling may be coupled to create an effective management tool to aid scientists and engineers with decisions affecting air quality and land use. The secondary goal is to show the types of air quality and regulatory information which could be obtained from these models. This latter goal is attained with general conclusions as consequence of applying ‘duality theory.’     

Coupling of the Weather Research and Forecasting Model with AERMOD for pollutant dispersion modeling. A case study for PM10 dispersion over Pune,India

The prediction of spatial variation of the concentration of a pollutant governed by various sources and sinks is a complex problem. Gaussian air pollutant dispersion models such as AERMOD of the United States Environmental Protection Agency (USEPA) can be used for this purpose. AERMOD requires steady and horizontally homogeneous hourly surface and upper air meteorological observations. However, observations with such frequency are not easily available for most locations in India. To overcome this limitation, the planetary boundary layer and surface layer parameters required by AERMOD were computed using the Weather Research and Forecasting (WRF) Model (version 2.1.1) developed by the National Center for Atmospheric Research (NCAR). We have developed a preprocessor for offline coupling of WRF with AERMOD. Using this system, the dispersion of respirable particulate matter (RSPM/PM10) over Pune, India has been simulated. Data from the emissions inventory development and field-monitoring campaign (13–17 April 2005) conducted under the Pune Air Quality Management Program of the Ministry of Environment and Forests (MoEF), India and USEPA, have been used to drive and validate AERMOD. Comparison between the simulated and observed temperature and wind fields shows that WRF is capable of generating reliable meteorological inputs for AERMOD. The comparison of observed and simulated concentrations of PM10 shows that the model generally underestimates the concentrations over the city. However, data from this single case study would not be sufficient to conclude on suitability of regionally averaged meteorological parameters for driving Gaussian models like AERMOD and additional simulations with different WRF parameterizations along with an improved pollutant source data will be required for enhancing the reliability of the WRF–AERMOD modeling system.     

Surface Air Pollutant Concentration Frequency Distributions: Implications for Urban Modeling

With the development of ambient air quality standards (AAQS), the need arises to describe the characteristics of regional surface air-pollutant concentration frequency distributions. In the evaluation of land use plans, numerous agencies will be concerned with evaluating the effectiveness of emission zoning and/or control actions. On a regional basis, one means of performing this assessment lies in determining the changes in the pollutant frequency distributions resulting from control actions.

This study presents new data concerning the surface air-pollutant concentration frequency distributions observed for area sources and continuous point sources, and compares these distributions with those of the pertinent meteorological variables describing the transport and diffusion of the pollutant. The observed surface air pollutant frequency distributions are compared to those corresponding to simple modeling concepts from either an urban area source or a continuous point source. For an urban source and a relatively inert pollutant like CO, we found that the observed frequency distribution for CO surface air concentration parallels the approximately log-normal frequency distribution of the reciprocal of the wind speed. We show that the constant relating these two well-correlated frequency distributions can be determined either experimentally or with a numerical simulation model of air pollution. The usefulness of numerical models in air pollution is discussed.     

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.     

Application of Genetic Algorithms for the Design of Ozone Control Strategies

ABSTRACT

Designing air quality management strategies is complicated by the difficulty in simultaneously considering large amounts of relevant data, sophisticated air quality models, competing design objectives, and unquantifiable issues. For many problems, mathematical optimization can be used to simplify the design process by identifying cost-effective solutions. Optimization applications for controlling nonlinearly reactive pollutants such as tropospheric ozone, however, have been lacking because of the difficulty in representing nonlinear chemistry in mathematical programming models.

We discuss the use of genetic algorithms (GAs) as an alternative optimization approach for developing ozone control strategies. A GA formulation is described and demonstrated for an urban-scale ozone control problem in which controls are considered for thousands of pollutant sources simultaneously. A simple air quality model is integrated into the GA to represent ozone transport and chemistry. Variations of the GA formulation for multiobjective and chance-constrained optimization are also presented. The paper concludes with a discussion of the practicality of using more sophisticated, regulatory-scale air quality models with the GA. We anticipate that such an approach will be practical in the near term for supporting regulatory decision-making.     

Modeling distributions of air pollutant concentrations—III. The hybrid deterministic-statistical distribution approach

A general modeling approach is proposed to predict the distribution of air pollutant concentrations and in particular the upper percentiles. The approach is hybrid in that it combines features of both deterministic and statistical distribution models. These features include causality and the attempted quantification of stochastic variability and uncertainty. The properties of deterministic and statistical distribution models are discussed separately and this clarifies the contribution that can be made by hybrid modeling. In this way the underlying assumptions are clearly presented. The range of successful applications of the hybrid approach is briefly reviewed. These involve relatively inert pollutants from urban/industrial, point source, elevated point source and roadway emissions. Areas of further research are outlined which would enhance the routine use of the approach and extend its application. Sufficient development has been undertaken, however, that the present standard set of air pollutant dispersion models could be easily updated to provide hybrid models capable of predicting frequency distributions of air pollutant levels under stipulated assumptions.     

Derivation of exemption formulas for air quality regulatory applications

The regulatory agencies and the industries have the responsibility for assessing the environmental impact from the release of air pollutants, and for protecting environment and public health. The simple exemption formula is often used as a criterion for the purpose of screening air pollutants. That is, the exemption formula is used for air quality review and to determine whether a facility applying for and described in a new, modified, or revised air quality plan is exempted from further air quality review. The Bureau of Ocean Energy Management’s (BOEM) air quality regulations are used to regulate air emissions and air pollutants released from the oil and gas facilities in the Gulf of Mexico. If a facility is not exempt after completing the air quality review, a refined air quality modeling will be required to regulate the air pollutants. However, at present, the scientific basis for BOEM’s exemption formula is not available to the author. Therefore, the purpose of this paper is to provide the theoretical framework and justification for the use of BOEM’s exemption formula. In this paper, several exemption formulas have been derived from the Gaussian and non-Gaussian dispersion models; the Gaussian dispersion model is a special case of non-Gaussian dispersion model. The dispersion parameters obtained from the tracer experiments in the Gulf of Mexico are used in the dispersion models. In this paper, the dispersion parameters used in the dispersion models are also derived from the Monin-Obukhov similarity theory. In particular, it has been shown that the total amount of emissions from the facility for each air pollutant calculated using BOEM’s exemption formula is conservative.

Implications:?The operation of offshore oil and gas facilities under BOEM’s jurisdiction is required to comply with the BOEM’s regulations. BOEM’s air quality regulations are used to regulate air emissions and air pollutants released from the oil and gas facilities in the Gulf of Mexico. The exemption formulas have been used by BOEM and other regulatory agencies as a screening tool to regulate air emissions emitted from the oil and gas and other industries. Because of the BOEM’s regulatory responsibility, it is important to establish the scientific basis and provide the justification for the exemption formulas. The methodology developed here could also be adopted and used by other regulatory agencies.     

扬水曝气器的水质改善功能及提水、充氧性能研究

扬水曝气器是水源水质改善设备,应用于湖泊水库水源地,抑制藻类生长,控制底泥污染物释放,取得了良好的效果.建立了扬水曝气器上升流速数学模型,用于模拟计算扬水曝气器的提水能力.建立了扬水曝气器曝气室的充氧能力数学模型.在实验室实测了扬水曝气器上升流速及其对水体的充氧过程,实测值与模拟计算值吻合较好,验证了提水、充氧能力数学模型.     

The Characterization of Ozone and Sulfur Dioxide Air Quality Data for Assessing Possible Vegetation Effects

Since the 1960s, much effort has been devoted to collecting and formatting air quality data. This paper discusses 1) the availability of air quality data for assessing potential biological impacts associated with ozone and sulfur dioxide ambient exposures, 2) examples of how air quality data can be characterized for assessing vegetation effects, and 3) the limitations associated with some exposure parameters used for developing relevant vegetation doseresponse yield reduction models. Data are presented showing that some ozone monitoring sites not continuously affected by local urban sources experience consecutive hourly ozone exposures ≥0.10 ppm in the late evening and early morning hours. These sites experience their maximum ozone concentrations either in the spring or summer months. Sites influenced by local rural sources experience their maximum ozone concentrations during the summer months. It is suggested that further research be performed to identify whether the sensitivity of a target organism at the time of exposure, as well as the pollutant concentration and chemical form that enters into the target organism, is as important in defining effects as air pollutant exposure alone.     

Field monitoring design considerations for assessing indoor exposures to combustion pollutants

Laboratory and controlled field studies of indoor air quality (IAQ) have characterized pollutant emission rates from combustion sources and have measured other key indoor air pollution parameters such as air exchange rates and indoor reactivity rates for the houses investigated. In addition, several field studies have attempted to measure, with varying degrees of success, pollutant exposures, indoor pollutant concentrations, and other parameters in large populations. To date, there exists no comprehensive strategy for assessing distributions of exposures to combustion pollutants and distributions of factors that affect such exposures in large populations. This paper outlines important parameters that affect combustion-related indoor air pollution concentrations and exposures, delineates weaknesses in our current understanding of exposures and field sampling methodologies, and mentions important considerations in planning appropriate field sampling strategies.     

A critical assessment of shrinkage-based regression approaches for estimating the adverse health effects of multiple air pollutants

Most investigations of the adverse health effects of multiple air pollutants analyse the time series involved by simultaneously entering the multiple pollutants into a Poisson log-linear model. Concerns have been raised about this type of analysis, and it has been stated that new methodology or models should be developed for investigating the adverse health effects of multiple air pollutants. In this paper, we introduce the use of the lasso for this purpose and compare its statistical properties to those of ridge regression and the Poisson log-linear model. Ridge regression has been used in time series analyses on the adverse health effects of multiple air pollutants but its properties for this purpose have not been investigated. A series of simulation studies was used to compare the performance of the lasso, ridge regression, and the Poisson log-linear model. In these simulations, realistic mortality time series were generated with known air pollution mortality effects permitting the performance of the three models to be compared. Both the lasso and ridge regression produced more accurate estimates of the adverse health effects of the multiple air pollutants than those produced using the Poisson log-linear model. This increase in accuracy came at the expense of increased bias. Ridge regression produced more accurate estimates than the lasso, but the lasso produced more interpretable models. The lasso and ridge regression offer a flexible way of obtaining more accurate estimation of pollutant effects than that provided by the standard Poisson log-linear model.     

Source apportionment of indoor air pollution

An understanding of the relative contributions from important pollutant sources to human exposures is necessary for the design and implementation of effective control strategies. In the past, societal efforts to control air pollution have focused almost exclusively on the outdoor (ambient) environment. As a result, substantial amounts of time and money have been spent to limit airborne discharges from mobile and stationary sources. Yet it is now recognized that exposures to elevated pollutant concentrations often occur as a result of indoor, rather than outdoor, emissions. While the major indoor sources have been identified, their relative impacts on indoor air quality have not been well defined. Application of existing source apportionment models to nonindustrial indoor environments is only just beginning. It is possible that these models might be used to distinguish between indoor and outdoor emissions, as well as to distinguish among indoor sources themselves. However, before the feasibility and suitability of source-apportionment methods for indoor applications can be assessed adequately, it is necessary to take account of model assumptions and associated data requirements. This paper examines the issue of indoor source apportionment and reviews the need for emission characterization studies to support such source-apportionment efforts.     

Origin of polluted air masses in the Alps. An overview and first results for MONARPOP

The contribution of ZAMG to MONAROP consists of special weather forecasts to control the SOCs sampling procedure and of the analysis of the specific transport processes for SOCs, which is still in progress.In this paper, air pollutant transport into the Alps is demonstrated by examples of inorganic pollutants: Measurements of NO_x and ozone provide evidence for air pollutant transport by local wind systems (valley and slope winds), especially at low elevated sites of the Alps. In addition, trajectory analyses for the high elevation sites demonstrate the importance of large scale synoptic air pollutant transport. The effects of these transport processes with different spatial and temporal scales are governed by the physical and chemical properties of the particular pollutant.First results for the high alpine MONARPOP stations show that air masses from east Europe influence mostly Sonnblick (Austria), whereas the influence of the Po basin is strongest at Weissfluhjoch (Switzerland).     

Air quality and its possible impacts on the terrestrial ecosystems of the North American Great Plains: an overview

Over the past several decades, numerous studies have been conducted on the impacts of air pollutants (air quality) on terrestrial ecosystems (crops and forests). Although ambient air is always composed of pollutant mixtures, in determining the relative air quality and its ecosystem impacts at a given geographic location and time, a predominant number of studies have shown that at the present time surface-level O(3) is the most important phytotoxic air pollutant. Within the North American Great Plains, the precursors for surface-level O(3) are mainly anthropogenic NO(x) and VOCs (volatile organic compounds). Texas and Alberta are the top regions of such emissions in the United States and Canada, respectively. This appears to be due mainly to the prevalence of natural gas and/or oil industry in the two regions and the consequent urbanization. Nevertheless, the total emissions of NO(x) and VOCs within the North American Great Plains represent only about 25-36% of the corresponding total emissions within the contiguous United States and the whole of Canada. Within the Great Plains many major crop and tree species are known to be sensitive to O(3). This sensitivity assessment, however, is based mainly on our knowledge from univariate (O(3) only) exposure-plant response studies. In the context of global climate change, in almost all similar univariate studies, elevated CO(2) concentrations have produced increases in plant biomass (both crop and tree species). The question remains as to whether this stimulation will offset any adverse effects of elevated surface O(3) concentrations. Future research must address this important issue both for the Great Plains and for all other geographic locations, taking into consideration spatial and temporal variabilities in the ambient concentrations of the two trace gases.     

Passive sampling of ambient, gaseous air pollutants: an assessment from an ecological perspective

During some past two decades there has been a growing interest among air pollution-vegetation effects-scientists to use passive sampling systems for quantifying ambient, gaseous air pollutant concentrations, particularly in remote and wilderness areas. On the positive side, excluding the laboratory analysis costs, passive samplers are inexpensive, easy to use and do not require electricity to operate. Therefore, they are very attractive for use in regional-scale air quality assessments. Passive samplers allow the quantification of cumulative air pollutant exposures, as total or average pollutant concentrations over a sampling duration. Such systems function either by chemical absorption or by physical adsorption of the gaseous pollutant of interest onto the sampling medium. Selection of a passive sampler must be based on its known or tested characteristics of specificity and linearity of response to the chemical constituent being collected. In addition, the effects of wind velocity, radiation, temperature and relative humidity must be addressed in the context of absorbent/adsorbent performance and sampling rate. Because of all these considerations, passive samplers may provide under- or overestimations of the cumulative exposures, compared to the corresponding data from co-located continuous monitors or active samplers, although such statistical variance can be minimized by taking necessary precautions. On the negative side, cumulative exposures cannot identify short-term (相似文献   

Application of Gas-Aerosol Adsorption Data to the Selection of Air Quality Standards

A quantitative approach is presented for selecting air quality standards which take into account pollutant gas-aerosol synergistic effects. These synergistic health effects have been postulated to be due to the adsorption or absorption of the pollutant gas by the aerosol particles. The approach presented in this paper assumes that the synergistic toxic agent is the adsorbed pollutant gas. Therefore, limiting the concentration of the adsorbed pollutant gas limits the magnitude of the synergistic effects. The concentration of the adsorbed pollutant gas is related to the concentrations of the gaseous phase pollutant gas and the atmospheric aerosol using the Langmuir adsorption isotherm. An example is presented of the selection of air quality standards for sulfur dioxide and the atmospheric aerosol using concentration data for these two pollutants along with health effect data.