Stochastic modeling of atmospheric pollution: a spatial time-series framework. Part I: methodology

A geostatistical framework for joint spatiotemporal modeling of atmospheric pollution is presented. The spatiotemporal distribution of concentration levels is modeled as a joint realization of a collection of spatially correlated time series. Parametric temporal trend models, associated with long-term pollution variability are established from concentration profiles at monitoring stations. Such parameters, e.g., amplitude of seasonal variation, are then regionalized in space for determining trend models at any unmonitored location. The resulting spatiotemporal residual field, associated with short-term pollution variability, is also modeled as a collection of spatially correlated residual time series. Stochastic conditional simulation is proposed for generating alternative realizations of the concentration spatiotemporal distribution, which identify concentration measurements available at monitoring stations. Simulated realizations also reproduce the histogram of the sample data, and a model of their spatiotemporal correlation. Such alternative concentration fields can be used for risk analysis studies.     

Pragmatic estimation of a spatio-temporal air quality model with irregular monitoring data

Statistical analyses of health effects of air pollution have increasingly used GIS-based covariates for prediction of ambient air quality in “land use” regression models. More recently these spatial regression models have accounted for spatial correlation structure in combining monitoring data with land use covariates. We present a flexible spatio-temporal modeling framework and pragmatic, multi-step estimation procedure that accommodates essentially arbitrary patterns of missing data with respect to an ideally complete space by time matrix of observations on a network of monitoring sites. The methodology incorporates a model for smooth temporal trends with coefficients varying in space according to Partial Least Squares regressions on a large set of geographic covariates and nonstationary modeling of spatio-temporal residuals from these regressions. This work was developed to provide spatial point predictions of PM_2.5 concentrations for the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) using irregular monitoring data derived from the AQS regulatory monitoring network and supplemental short-time scale monitoring campaigns conducted to better predict intra-urban variation in air quality. We demonstrate the interpretation and accuracy of this methodology in modeling data from 2000 through 2006 in six U.S. metropolitan areas and establish a basis for likelihood-based estimation.     

Predictions of U.K. regulated power station contributions to regional air pollution and deposition: a model comparison exercise

Contributions of the emissions from a U.K. regulated fossil-fuel power station to regional air pollution and deposition are estimated using four air quality modeling systems for the year 2003. The modeling systems vary in complexity and emphasis in the way they treat atmospheric and chemical processes, and include the Community Multiscale Air Quality (CMAQ) modeling system in its versions 4.6 and 4.7, a nested modeling system that combines long- and short-range impacts (referred to as TRACK-ADMS [Trajectory Model with Atmospheric Chemical Kinetics-Atmospheric Dispersion Modelling System]), and the Fine Resolution Atmospheric Multi-pollutant Exchange (FRAME) model. An evaluation of the baseline calculations against U.K. monitoring network data is performed. The CMAQ modeling system version 4.6 data set is selected as the reference data set for the model footprint comparison. The annual mean air concentration and total deposition footprints are summarized for each modeling system. The footprints of the power station emissions can account for a significant fraction of the local impacts for some species (e.g., more than 50% for SO2 air concentration and non-sea-salt sulfur deposition close to the source) for 2003. The spatial correlation and the coefficient of variation of the root mean square error (CVRMSE) are calculated between each model footprint and that calculated by the CMAQ modeling system version 4.6. The correlation coefficient quantifies model agreement in terms of spatial patterns, and the CVRMSE measures the magnitude of the difference between model footprints. Possible reasons for the differences between model results are discussed. Finally, implications and recommendations for the regulatory assessment of the impact of major industrial sources using regional air quality modeling systems are discussed in the light of results from this case study.     

Long-term assessment of the wet precipitation chemistry in Austria (1984-1999)

The aim of the present study was to determine the long-time trends in concentrations and depositions of major ions in wet precipitation samples collected at 11 sampling sites from the Austrian precipitation chemistry network in the period 1984-1999. The analytical results were treated by the use of least square linear regression method. It is shown that a serious decrease of sulfate (between 30% and 60% for the period) and hydrogen ion (between 60% and 102% for the period) concentrations and depositions is achieved at almost all sampling sites and in most of these cases the linear trend proves to be statistically significant. Nitrogen containing ions and base cations do not reveal a distinct trend of changing and in the majority of the sites the linear models are not adequate. In principle, an overall slight concentration and deposition decrease for these major ions is observed (up to 30% for the period of observation) but some substantial exceptions are also found (site Haunsberg or site Lobau). The changes in chloride concentration and deposition, too, do not indicate significant linear trend and, in general, are decreasing for the period of monitoring. In order to give some explanation of the exceptional behaviour of some of the major ions in several sites, an additional comparison with Austrian emission data (sulfur dioxide, nitrogen oxides, ammonium) and with data from five EMEP sites from neighbouring countries is performed. A significant West-East trend of acidity increase is found as well as a good correlation with the emission trends. Therefore, both transboundary and specific local factors could be substantial factors in the wet precipitation chemistry in the region.     

Empirical Evaluation of Ambient Ozone Interpolation Procedures to Support Exposure Models

ABSTRACT

An intercomparison study has been performed with six empirical ozone interpolation procedures to predict hourly concentrations in ambient air between monitoring stations. The objective of the study is to use monitoring network data to empirically identify an improved procedure to estimate ozone concentrations at subject exposure points. Four of the procedures in the study are currently used in human exposure models (nearest monitors daily mean and maximum, regression estimate used in the U.S. Environmental Protection Agency's (EPA) pNEM, and inverse distance weighting), and two are being evaluated for this purpose (kriging in space and kriging in space and time). The study focused on spatial estimation during June 1-June 5, 1996, with relatively high observed ozone levels over Houston, Texas. The study evaluated these procedures at three types of locations with monitors of varying proximity. Results from the empirical evaluation indicate that kriging in space and time provides excellent estimates of ozone concentrations within a monitoring network, while the more often used techniques failed to capture observed pollutant concentrations. Improved estimation of pollutant concentrations within the region, and thus at subject locations, should result in improved exposure modeling.     

Adherence of Sulfur Dioxide Concentrations in the Vicinity of a Steam Plant to Plume Dispersion Models

Air monitoring data for a calendar year at one of the TVA power plants has been used to evaluate the appropriateness of the Sutton, the Bosanquet and Pearson, and the USPHS-TVA atmospheric dispersion models to predict ground level concentrations of sulfur dioxide from emission and meterological data. Aerometric data included one half hourly average sulfur dioxide concentrations, recorded by four Thomas autometers, and the necessary meterological parameters for the solving of atmospheric dispersion models. Based on these meterological parameters and observed plume rise data, over 4000 one half hourly average maximum and minimum expected ground line sulfur dioxide concentrations were predicted for each of the above dispersion models by the use of computer techniques. The plant is a line source; however, an empirical correction was applied to emission data to reduce them to emissions for an equivalent point source. The predicted sulfur dioxide levels for each of the dispersion models were compared to the measured levels throughout the year. Three different sets of diffusion coefficients were applied to the Sutton model and successful predictions, according to a criterion utilizing an acceptable range of concentration, varied from 66 to 93%. The Bosanquet and Pearson model produced successful predictions 90% of the time, while the USPHS-TVA model was successful 94% of the time.Unsuccessful predictions were primarily overestimates.     

Designing ambient particulate matter monitoring program for source apportionment study by receptor modeling

Particulate matter (PM) receptor modeling requires specific intensive input data that is always a challenge to produce cost effectively. A well-designed monitoring program is important to collect such PM ambient data in urban areas with diverse and densely distributed sources. This paper presents a general framework for designing such a monitoring program while emphasizing appropriate quality assurance and quality control elements that are particularly applicable where limited resources are available. Topics for discussion include selection of monitoring sites, sampling and analytical techniques, and the uncertainty estimation for ambient concentration input data. The design framework is illustrated by a case study of a monitoring program for PM source apportionment in the Bangkok Metropolitan Region in which 24-h fine and coarse PM samples were collected using two collocated dichotomous samplers. Comparison between black carbon measurements by Smoke Stain reflectometry and Thermal Optical Transmittance method is highlighted.     

Environmental monitoring model for a drainage basin obtained through spectral analysis of time series

The quality of results of an environmental monitoring plan is limited to the weakest component, which could be the analytical approach or sampling method. Considering both the possibilities and the fragility that sampling methods offer, this environmental monitoring study focused on the uncertainties caused by the time component. Four time series of nutrient concentration at two sampling points (PB1 and PB2) in the Ribeir?o Garcia basin in Blumenau, Brazil, which were significantly correlated to the spatial component, were considered with a 2-hour resolution to develop efficient sampling models. These models were based on the time at which there was the highest tendency toward adverse environmental effects. Fourier spectral analysis was used to evaluated the time series and resulted in two sampling models: (1) the SMCP (sampling model for critical period) that operated with 100% efficiency for registering the highest concentration of nutrients and was valid for 83% of the studied parameters; and (2) the SMGCP (sampling model for global critical period) that operated with 83 and 50% efficiency for PB1 and PB2, respectively.     

Model performance—Plume dispersion over elevated terrain

A study was conducted to evaluate the ability of a steady-state Gaussian model (MVM) to simulate plume dispersion over high terrain. Modeling results are compared with measured data from a network of monitors around the Tennessee Valley Authority (TVA) Widows Creek Steam Plant. Approximately 2.5 years of monitoring and plant emissions data were used in the study.The MVM modeling results are also compared with results using the RTDM and COMPLEX 1 models. The MVM model and an updated version compared well with measured data. RTDM and the COMPLEX 1 models overpredicted both the 1- and 3-h average concentration by a factor of two-three.     

Sulfur deposition simulations over China, Japan, and Korea: a model intercomparison study for abating sulfur emission

In response to increasing trends in sulfur deposition in Northeast Asia, three countries in the region (China, Japan, and Korea) agreed to devise abatement strategies. The concepts of critical loads and source?Creceptor (S?CR) relationships provide guidance for formulating such strategies. Based on the Long-range Transboundary Air Pollutants in Northeast Asia (LTP) project, this study analyzes sulfur deposition data in order to optimize acidic loads over the three countries. The three groups involved in this study carried out a full year (2002) of sulfur deposition modeling over the geographic region spanning the three countries, using three air quality models: MM5-CMAQ, MM5-RAQM, and RAMS-CADM, employed by Chinese, Japanese, and Korean modeling groups, respectively. Each model employed its own meteorological numerical model and model parameters. Only the emission rates for SO₂ and NO_x obtained from the LTP project were the common parameter used in the three models. Three models revealed some bias from dry to wet deposition, particularly the latter because of the bias in annual precipitation. This finding points to the need for further sensitivity tests of the wet removal rates in association with underlying cloud?Cprecipitation physics and parameterizations. Despite this bias, the annual total (dry plus wet) sulfur deposition predicted by the models were surprisingly very similar. The ensemble average annual total deposition was 7,203.6?±?370 kt S with a minimal mean fractional error (MFE) of 8.95?±?5.24?% and a pattern correlation (PC) of 0.89?C0.93 between the models. This exercise revealed that despite rather poor error scores in comparison with observations, these consistent total deposition values across the three models, based on LTP group's input data assumptions, suggest a plausible S?CR relationship that can be applied to the next task of designing cost-effective emission abatement strategies.     

Anatomy of an urban waterbody: a case study of Boston's Muddy River

The objective of this study was to characterize and understand the water quality of Boston's Muddy River prior to restoration, to help guide those activities and evaluate their success. We use a combination of monitoring, data analysis and mathematical modeling. The seasonal pattern of temperature, pollutant signatures (identified using a principal component analysis), correlations with precipitation and spatial patterns all point to a significant wastewater input at one of the outfalls and suggest significant receiving water impact. However, a quantitative analysis using a mathematical model (QUAL2K) suggests this source is not significant. Rather, internal loading from algae, sediment bed and waterfowl dominate the spatial pattern of water quality. These results suggest significant improvement can be expected from planned sediment dredging. The paper provides a case study of water quality assessment in the context of urban river restoration, and it illustrates the utility of combining monitoring and data analysis with modeling.     

Estimation of Trends in Atmospheric Concentrations of Sulfate in the Northeastern United States

Abstract

Daily atmospheric concentrations of sulfate collected at six locations in the northeastern United States are regressed against meteorological factors, ozone, seasonal cycles, and time in order to determine if a significant trend in sulfate can be detected. The data used in this analysis were collected during the Sulfate Regional Experiment (SURE, 1977-1978) and the Eulerian Model Evaluation Field Study (EMEFS, 1988-1989). Ozone, specific humidity, and seasonal terms (reflecting the potential of the atmosphere for oxidation of sulfur dioxide) emerged as important explanatory variables. After accounting for the variability explained by environmental factors, the median estimated change in sulfate concentration from the six locations over the 11-year period is -22% (or -28% if ozone is not used as an explanatory variable). Although there are wide variations among locations, these changes are commensurate with an estimated 25% decline in sulfur emissions in the northeastern U.S. during the same period. These analyses provide insight into methods for detecting reductions in sulfate that may be expected to occur as a result of the Clean Air Act Amendments of 1990. Uncertainties in the estimates, with consideration of serial correlation in the data, imply a minimum detectable reduction of 10% using this modeling procedure with similar data availability.     

Air quality mapping using GIS and economic evaluation of health impact for Mumbai City,India

Mumbai, a highly populated city in India, has been selected for air quality mapping and assessment of health impact using monitored air quality data. Air quality monitoring networks in Mumbai are operated by National Environment Engineering Research Institute (NEERI), Maharashtra Pollution Control Board (MPCB), and Brihanmumbai Municipal Corporation (BMC). A monitoring station represents air quality at a particular location, while we need spatial variation for air quality management. Here, air quality monitored data of NEERI and BMC were spatially interpolated using various inbuilt interpolation techniques of ArcGIS. Inverse distance weighting (IDW), Kriging (spherical and Gaussian), and spline techniques have been applied for spatial interpolation for this study. The interpolated results of air pollutants sulfur dioxide (SO₂), nitrogen dioxide (NO₂) and suspended particulate matter (SPM) were compared with air quality data of MPCB in the same region. Comparison of results showed good agreement for predicted values using IDW and Kriging with observed data. Subsequently, health impact assessment of a ward was carried out based on total population of the ward and air quality monitored data within the ward. Finally, health cost within a ward was estimated on the basis of exposed population. This study helps to estimate the valuation of health damage due to air pollution.

Implications: Operating more air quality monitoring stations for measurement of air quality is highly resource intensive in terms of time and cost. The appropriate spatial interpolation techniques can be used to estimate concentration where air quality monitoring stations are not available. Further, health impact assessment for the population of the city and estimation of economic cost of health damage due to ambient air quality can help to make rational control strategies for environmental management. The total health cost for Mumbai city for the year 2012, with a population of 12.4 million, was estimated as USD8000 million.     

Use of principal component analysis to profile temporal and spatial variations of chlorinated solvent concentration in groundwater

This work aims at evaluating spatial distribution patterns of concentration variations for chlorinated solvents in groundwater, based on principal component analysis and geographic information system (GIS) tools. The study investigates long-time series of chlorinated solvent concentrations in groundwater measured for 18 contaminated industrial sites. The characterization of contaminant plumes and delineation of pollutant sources are essential for choosing appropriate monitoring and remediation strategies, as contaminated groundwaters are characterized by complex patterns of spatial and temporal concentration variability, with wide unpredictable fluctuations over time. The present work describes the results of a new exploratory statistical method called the Variability Index Method (VIM) applied to environmental data to assess the performance of using concentration variations as molecular tracers to reveal aquifer dynamics, industrial impacts, and point sources for contamination plumes. The application of this method provides a useful assessment of controls over contaminant concentration variations as well as support for remediation techniques.     

Influence of hydrogeochemical processes on zero-valent iron reactive barrier performance: a field investigation

Geochemical and mineralogical changes were evaluated at a field Fe0-PRB at the Oak Ridge Y-12 site concerning operation performance during the treatment of U in high NO3- groundwater. In the 5-yr study period, the Fe0 remained reactive as shown in pore water monitoring data, where increases in pH and the removal of certain ionic species persisted. However, coring revealed varying degrees of cementation. After 3.8-yr treatment, porosity reduction of up to 41.7% was obtained from mineralogical analysis on core samples collected at the upgradient gravel-Fe0 interface. Elsewhere, Fe0 filings were loose with some cementation. Fe0 corrosion and pore volume reduction at this site are more severe due to the presence of NO3- at a high level. Tracer tests indicate that hydraulic performance deteriorated: the flow distribution was heterogeneous and under the influence of interfacial cementation a large portion of water was diverted around the Fe0 and transported outside the PRB. Based on the equilibrium reductions of NO3- and SO4(2-) by Fe0 and mineral precipitation, geochemical modeling predicted a maximum of 49% porosity loss for 5 yr of operation. Additionally, modeling showed a spatial distribution of mineral precipitate volumes, with the maximum advancing from the interface toward downgradient with time. This study suggests that water quality monitoring, coupled with hydraulic monitoring and geochemical modeling, can provide a low-cost method for assessing PRB performance.     

Influence of hydrogeochemical processes on zero-valent iron reactive barrier performance: a field investigation

Geochemical and mineralogical changes were evaluated at a field Fe0-PRB at the Oak Ridge Y-12 site concerning operation performance during the treatment of U in high NO3- groundwater. In the 5-year study period, the Fe0 remained reactive as shown in pore-water monitoring data, where increases in pH and the removal of certain ionic species persisted. However, coring revealed varying degrees of cementation. After 3.8-year treatment, porosity reduction of up to 41.7% was obtained from mineralogical analysis on core samples collected at the upgradient gravel-Fe0 interface. Elsewhere, Fe0 filings were loose with some cementation. Fe0 corrosion and pore volume reduction at this site are more severe due to the presence of NO3- at a high level. Tracer tests indicate that hydraulic performance deteriorated: the flow distribution was heterogeneous and under the influence of interfacial cementation a large portion of water was diverted around the Fe0 and transported outside the PRB. Based on the equilibrium reductions of NO3- and SO4(2-) by Fe0 and mineral precipitation, geochemical modeling predicted a maximum of 49% porosity loss for 5 years of operation. Additionally, modeling showed a spatial distribution of mineral precipitate volumes, with the maximum advancing from the interface toward downgradient with time. This study suggests that water quality monitoring, coupled with hydraulic monitoring and geochemical modeling, can provide a low-cost method for assessing PRB performance.     

Development of a speciated, hourly, and gridded air pollutants emission modeling system--a case study on the precursors of photochemical smog in the Seoul metropolitan area, Korea

A speciated, hourly, and gridded air pollutants emission modeling system (SHEMS) was developed and applied in predicting hourly nitrogen dioxide (NO2) and ozone (O3) levels in the Seoul Metropolitan Area (SMA). The primary goal of the SHEMS was to produce a systemized emission inventory for air pollutants including ozone precursors for modeling air quality in urban areas. The SHEMS is principally composed of three parts: (1) a pre-processor to process emission factors, activity levels, and spatial and temporal information using a geographical information system; (2) an emission model for each source type; and (3) a post-processor to produce report and input data for air quality models through database modeling. The source categories in SHEMS are point, area, mobile, natural, and other sources such as fugitive emissions. The emission database produced by SHEMS contains 22 inventoried compounds: sulfur dioxide, NO2, carbon monoxide, and 19 speciated volatile organic compounds. To validate SHEMS, the emission data were tested with the Urban Airshed Model to predict NO2 and O3 concentrations in the SMA during selected episode days in 1994. The results turned out to be reliable in describing temporal variation and spatial distribution of those pollutants.     

Characterization of odor released during handling of swine slurry: Part I. Relationship between odorants and perceived odor concentrations

Odor emission from livestock production systems is a major nuisance in many rural areas. This study aimed at determining the major airborne chemical compounds responsible for the unpleasant odor perceived in swine facilities during slurry handling, and at proposing predictive models of odor concentration (OC) based on the concentrations of specific odorants in the air. A multivariate data analysis strategy involving principal components analysis and multiple linear regressions was implemented to analyze the relationships between concentration of 35 gases (measured by GC/MS or gas detection tubes), and the overall OC perceived by sensory analysis. The study compiled data on the concentration of odor and odorants, measured in the headspace of 24 unstored and stored slurry samples collected from three different types of production units on 8 commercial swine farms. Among all the measured constituents, OC was found to have the highest correlation with the sulfur containing compounds (i.e. hydrogen sulfide, dimethylsulfide, dimethyldisulfide, dimethyltrisulfide). The concentration of hydrogen sulfide accounted for 68% of the variation in OC above the stirred slurry samples. The highest concentrations of volatile organic compounds were observed for phenols and indoles, which made a significant contribution to the overall OC when the slurry was fresh. The contribution of ammonia to the OC was only significant in the absence of hydrogen sulfide. The precision of predictive models of OC based on the concentration of specific odorants in the air was satisfactory (R² between 0.66 and 0.89). Hence, this study suggests that monitoring of specific odor compounds released from agitated swine slurry can be used to predict the concentration of odor perceived close to the source (e.g. at storage units), allowing the assessment of odor nuisance potentials.     

Catalytic Cigarette Filter for Carbon Monoxide Reduction

A comparative study was performed in order to determine the relative accuracy of a gaussian dispersion model. The U.S. EPA’s RAM (Urban) model was chosen to estimate 24-hour average sulfur dioxide concentrations in the Cleveland, Ohio area. Point and area source emissions, along with a background concentration were included in the modeling effort. Projections from the model made at the ambient air stations were compared to measured sulfur dioxide concentrations. A total of 3020 comparisons were performed at 33 monitoring sites. An analysis of the results illustrates that, on a daily basis, the predictions of the model did not reflect actual air quality. The correlation coefficients of the 24-hour comparisons at the monitoring sites varied from a low of —0.121 to a high of 0.541. When the highest and second highest modeled concentrations were evaluated with respect to the highest and second highest measured concentrations, over a period of a year, a more favorable comparison was observed.     

Acid rain and acidification in China: the importance of base cation deposition

Acid deposition has been recognized as a serious environmental problem in China. Most acid deposition studies have focused on sulfur deposition and the pH of precipitation. However, as high concentration of alkaline dust is an important feature of the atmosphere in large parts of China, base cation deposition must be taken into account when discussing possible effects on soils and vegetation from acid deposition. We estimate the deposition of sulfur as well as calcium, i.e. the dominating anion and cation, on a regional scale in China using data both from measurements and modeling. The ratio of sulfur/calcium in deposition is then used as an indicator for identifying areas where deposition acidity exceeds alkalinity, and where soils may be at risk to acidification. The dynamic soil acidification model MAGIC is applied with data from two sites receiving high deposition loads in southwest China. The model predictions indicate that considerable soil acidification has been going on for the last decades due to acid deposition inputs. Effects on the spatial distribution of acidic deposition in China, using different future deposition scenarios, are illustrated. As the size of the anthropogenic fraction of the base cation deposition is unknown, different possible future trends in calcium deposition were used. Soil response, according to the model, using different combinations of sulfur and calcium deposition scenarios is shown. Applying the most strict measures to reduce sulfur emission will almost eliminate the acid deposition problem; however, such a scenario is not economically feasible in the short term. A strict, but possibly realistic, future scenario for sulfur may be enough to keep the situation at the present level, assuming only moderate reductions in calcium deposition. With large decreases in base cation deposition, increased soil acidification can be expected even with considerable sulfur emission reductions.