Analysis of Sampling Strategies for Estimating Annual Average Indoor NO2 Concentrations in Residences with Gas Ranges

Abstract

Range gas consumption in households tends to follow an annual cycle resembling a sinusoid, with peak consumption during the winter. When outdoor NO2 concentrations have a constant or small impact, the resulting indoor NO2 concentrations also tend to resemble an annual sinusoid. Optimal monitoring strategies can be designed to take advantage of this knowledge to obtain a better estimate of the true annual average gas consumption or indoor NO2 concentration. Gas consumption data, together with measured outdoor concentrations, house volumes, sampled emission rates, air exchange rates, and NO2 decay rates, are used to model weekly indoor NO2 concentrations throughout the year. Based on the modeling results, various monitoring strategies are evaluated for their accuracy in estimating the annual mean. Analysis of the results indicates that greater accuracy is attained using samples equally spaced throughout the year. In addition, the expected error for various monitoring strategies and various numbers of equally spaced samples is quantified, and their ability to classify homes into correct concentration categories is assessed.     

The Use of Health Risk Assessment to Estimate Desirable Sampling Detection Limits

Abstract

The inclusion of non-detected chemicals in a health risk assessment may lead, in some cases, to estimated risks that exceed regulatory thresholds, because one must use the detection limit or half of the detection limit. This study presents a methodology which will allow one to estimate appropriate detection limits by conducting a health risk assessment prior to the source sampling program. The advantages and shortcomings of various levels of detail in the risk assessment to determine those detection limits are discussed. The application of the methodology is demonstrated with a case study of the potential health effects of power plant stack emissions.     

Significance of Regional Source Contributions to Urban PM-10 Concentrations

Based on data collected in St. Louis, Philadelphia and other eastern U.S. cities, we conclude that a significant fraction of the PM-10 concentration is "background." In these urban areas the background fraction ranges from 35 to 80 percent of the daily, monthly or annual concentration. The bulk of the background appears to be of regional origin. The average chemical makeup of eastern U.S. PM-10 is sulfate as SO₄, 21-34 percent; crustal material, 14-39 percent; "unknown" (carbonaceous matter, ammonium, nitrate and water), 36-51 percent, all of which is difficult to apportion to specific sources. Dispersion modeling using a local source inventory can account only for a small portion of the total PM-10 mass. Emission roll-back of local sources may have a limited effect on reducing total concentrations of PM-10.     

A Note on Comparison of Statistical and Analytical Results for Calculating Oxides of Nitrogen Concentrations

A question often asked about analytical diffusion models is “How do the analytical results for calculating pollution concentrations compare with those obtained by statistical techniques?” Miller and Holzworth (1967) have developed a simple analytical diffusion model that yields relative pollution concentrations, x/Q, as a function of afternoon mixing depth, mean transport wind speed, and city size. This analytical model may also be used in reverse to determine apparent afternoon source strengths from observed afternoon pollution concentrations. The apparent source strengths may then be used with the model on independent data to estimate pollution concentrations. It is the purpose of this note to compare results of calculating average afternoon concentrations of oxides of nitrogen (NO_x) by use of the Miller-Holzworth model with those obtained from statistical regression equations.     

Assessment of Soil Sealing Management Responses,Strategies, and Targets Toward Ecologically Sustainable Urban Land Use Management

Soil sealing has negative impacts on ecosystem services since urban green and soil get lost. Although there is political commitment to stop further sealing, no reversal of this trend can be observed in Europe. This paper raises the questions (1) which strategies can be regarded as being efficient toward ecologically sustainable management of urban soil sealing and (2) who has competences and should take responsibility to steer soil sealing? The analyses are conducted in Germany. The assessment of strategies is carried out using indicators as part of a content analysis. Legal-planning, informal-planning, economic-fiscal, co-operative, and informational strategies are analyzed. Results show that there is a sufficient basis of strategies to secure urban ecosystem services by protecting urban green and reducing urban gray where microclimate regulation is a main target. However, soil sealing management lacks a spatial strategically overview as well as the consideration of services provided by fertile soils.     

Contributions of Pesticide use to Urban Background Concentrations of Arsenic in Denver,Colorado, U.S.A.

Soil investigations near a former smelter have revealed that historic use of arsenical pesticides has contributed significantly to anthropogenic background concentrations of arsenic on certain residential properties in Denver, Colorado, U.S.A. Remedial investigation data, based on samples collected in relatively undisturbed locations, had previously indicated that the "upper limit" of background arsenic concentrations was 28 mg/kg in the site vicinity. This value compares reasonably well with more regional data, which indicate increasing arsenic concentrations moving from rural to urban land use. Soil sampling during cleanup, however, revealed the presence of arsenic concentrations of a few hundred to more than 1000 mg/kg on a large number of residential lawns due to historic applications of a crabgrass killer, which was missed by the earlier investigation samples because of the sampling bias toward undisturbed land. Data from over 20,000 soil samples now show that several different populations comprise urban background levels of arsenic and that these populations may be stratified by land use and have spatial patterns that should be considered during any background study. A variety of forensic techniques, including spatial analysis, arsenic speciation, and calculation of metals ratios were necessary to separate the smelter impacts from pesticide impacts.     

Assessment of Uncertainty in Long-Term Mass Balances for Acidification Assessments: A MAGIC Model Exercise

Long-term (1860–2010) catchment mass balance calculations rely on models and assumptions which are sources of uncertainty in acidification assessments. In this article, we report on an application of MAGIC to model acidification at the four Swedish IM forested catchments that have been subject to differing degrees of acidification stress. Uncertainties in the modeled mass balances were mainly associated with the deposition scenario and assumptions about sulfate adsorption and soil mass. Estimated base cation (BC) release rates (weathering) varied in a relatively narrow range of 47–62 or 42–47 meq m⁻² year⁻¹, depending on assumptions made about soil cation exchange capacity and base saturation. By varying aluminum solubility or introducing a dynamic weathering feedback that allowed BC release to increase at more acidic pHs, a systematic effect on predicted changes in acid neutralizing capacity (ΔANC ca. 10–41 μeq l⁻¹) and pH (ca. ΔpH = 0.1–0.6) at all sites was observed. More robust projections of future changes in pH and ANC are dependent on reducing uncertainties in BC release rates, the timing, and extent of natural acidification through BC uptake by plants, temporal changes in soil element pools, and fluxes of Al between compartments.     

A Comparative Validation of the RAM and PTMTP Models for Short-Term SO2 Concentrations in Two Urban Areas

A comprehensive and comparative model validation of two EPA models for short-term SO₂ concentrations was performed. The two models tested were RAM (Urban version) and PTMTP (Terrain version). Both are multiple source, multiple receptor gaussian plume models, recommended in the EPA Guideline On Air Quality Models. ¹ The principal difference between the two models is in their use of empirical dispersion coefficients. It was because of the potential for markedly different predicted maximum SO₂ concentrations, and the absence of any testing data on the RAM model, that the validation analysis was undertaken. The current study utilized a full year of air quality data from monitoring sites in two Indiana cities, Michigan City and Indianapolis. Cumulative frequency distributions for each site and model were prepared and comparisons made. The results indicate that the RAM (Urban) model was highly inaccurate in predicting maximum short-term SO₂ concentrations. The PTMTP model, although conservative in its estimates, produces results which more closely resemble the distribution of observed SO₂ concentrations. The body of information presented in this paper is directed to environmental scientists responsible for air quality modeling, and to those persons who set policy on the use of models in air quality studies.     

Application of the Urban Airshed Model to Forecasting Next-Day Peak Ozone Concentrations in Atlanta,Georgia

ABSTRACT

Twenty-four to forty-eight-hour ozone air quality forecasts are increasingly being used in metropolitan areas to inform the public about potentially harmful air quality conditions. The forecasts are also behind “ozone action day” programs in which the public and private sectors are encouraged or mandated to alter activities that contribute to the formation of ground-level ozone. Presented here is a low-cost application of the Urban Airshed Model (UAM), an Eulerian 3-dimensional photochemical-transport grid model for generating next-day peak ozone concentration forecasts. During the summer of 1997, next-day peak ozone concentrations in Atlanta, GA, were predicted both by a team of eight forecasters and by the Urban Airshed Model in Forecast Mode (UAM-FM). Results are presented that compare the accuracy of the team and the UAM-FM. The results for the summer of 1997 indicate that the UAM-FM may be a better predictor of peak ozone concentrations when concentrations are high (> 0.095 ppmv), and the team may be a better predictor of ozone concentrations when concentrations are low (< 0.095 ppmv). The UAM-FM is also discussed in the context of other forecasting tools, primarily linear regression models and a no-skill, persistence-based technique.     

Workplace and Environmental Air Contaminant Concentrations Measured by Open Path Fourier Transform Infrared Spectroscopy: A Statistical Process Control Technique to Detect Changes from Normal Operating Conditions

Open path Fourier transform infrared (OP-FTIR) spectroscopy is a new air monitoring technique that can be used to measure concentrations of air contaminants in real or near-real time. OP-FTIR spectroscopy has been used to monitor workplace gas and vapor exposures, emissions from hazardous waste sites, and to track emissions along fence lines. This paper discusses a statistical process control technique that can be used with air monitoring data collected with an OP-FTIR spectrometer to detect departures from normal operating conditions in the workplace or along a fence line. Time series data, produced by plotting consecutive air sample concentrations in time, were analyzed. Autocorrelation in the time series data was removed by fitting dynamic models. Control charts were used with the residuals of the model fit data to determine if departures from defined normal operating conditions could be rapidly detected. Shewhart and exponentially weighted moving average (EWMA) control charts were evaluated for use with data collected under different room air flow and mixing conditions.

Under rapidly changing conditions the Shewhart control chart was able to detect a leak in a simulated process area. The EWMA control chart was found to be more sensitive to drifts and slowly changing concentrations in air monitoring data. The time series and statistical process control techniques were also applied to data obtained during a field study at a chemical plant. A production area of an acrylonitrile, 1,3-butadiene, and styrene (ABS) polymer process was monitored in near-real time. Decision logics based on the time series and statistical process control technique introduced suggest several applications in workplace and environmental monitoring. These applications might include signaling of an alarm or warning, increasing levels of worker respiratory protection, or evacuation of a community, when gas and vapor concentrations are determined to be out-of-control.     

Dependence of Home Outdoor Particulate Mass and Number Concentrations on Residential and Traffic Features in Urban Areas

Abstract

The associations between residential outdoor and ambient particle mass, fine particle absorbance, particle number (PN) concentrations, and residential and traffic determinants were investigated in four European urban areas (Helsinki, Athens, Amsterdam, and Birmingham). A total of 152 nonsmoking participants with respiratory diseases, not exposed to occupational pollution, were included in the study, which comprised a 7-day intensive exposure monitoring period of both indoor and home outdoor particle mass and number concentrations. The same pollutants were also continuously measured at ambient fixed sites centrally located to the studied areas (fixed ambient sites). Relationships between concentrations measured directly outside the homes (residential outdoor) and at the fixed ambient sites were pollutant-specific, with substantial variations among the urban areas. Differences were more pronounced for coarse particles due to resuspension of road dust and PN, which is strongly related to traffic emissions. Less significant outdoor-to-fixed variation for particle mass was observed for Amsterdam and Birmingham, predominantly due to regional secondary aerosol. On the contrary, a strong spatial variation was observed for Athens and to a lesser extent for Helsinki. This was attributed to the overwhelming and time-varied inputs from traffic and other local sources. The location of the residence and traffic volume and distance to street and traffic light were important determinants of residential outdoor particle concentrations. On average, particle mass levels in suburban areas were less than 30% of those measured for residences located in the city center. Residences located less than 10 m from a street experienced 133% higher PN concentrations than residences located further away. Overall, the findings of this multi-city study, indicated that (1) spatial variation was larger for PN than for fine particulate matter (PM) mass and varied between the cities, (2) vehicular emissions in the residential street and location in the center of the city were significant predictors of spatial variation, and (3) the impact of traffic and location in the city was much larger for PN than for fine particle mass.     

The Effect of NMOC and Ozone Aloft on Modeled Urban Ozone Production and Control Strategies

Carbon bond (CB-III) fractions for non-methane organic carbon compounds (NMOC) measured in the background alrmass adverted into several urban areas in the eastern and southern United States are reported. These, together with ozone measured aloft, were used In an Empirical Kinetic Modeling Approach (EKMA) to model urban ozone production and urban ozone control strategies.

Over a range of zero to double the mean of the measured NMOC concentrations aloft (0 to 70 ppbC) and zero to the highest ozone levels recorded aloft (0 to 65 ppb), it was found that urban ozone production and control strategies were relatively insensitive to NMOC from aloft. However, urban ozone production was sensitive to ozone from aloft, while ozone control strategies were insensitive to ozone from aloft.     

移动通信GSM网络与3G网络共站址下的环境影响评价

阐述了移动通信GSM网络与3G网络共站址的情况下对环境的影响，并提出了影响预测模式，计算了某通信公司移动通信典型基站对周围环境电磁辐射的影响，确定了对敏感目标的保护距离，并进行了实测以对保护距离进行验证。     

Assessment of Long-Term Radiological Effects on Plants and Animals from a Deep Geological Repository: No Discernible Impact Detected

This study investigates whether non-human biota are protected against harmful effects of ionizing radiation after a possible future release of radioactive matter from a planned repository for spent nuclear fuel. Radiation dose rates to a broad spectrum of organisms were calculated based on data from sampled organisms and modeled activity concentrations. Calculations were performed with the ERICA Tool, a software program which applies a screening dose-rate value of 10 microgray per hour (μGy h^?1) for all types of organisms. Dose rates below this value are thought to result in minimal risk to the individual or population. All calculated dose rates were below the screening value and below the lowest relevant band of “derived consideration levels” proposed by the International Commission on Radiological Protection. This provides a sound basis for arguing that no individuals or populations of examined species would be harmfully affected by a possible radioactive release from the repository.     

Application of Artificial Neural Networks to Modeling and Prediction of Ambient Ozone Concentrations

ABSTRACT

The deterministic modeling of ambient O₃ concentrations is difficult because of the complexity of the atmospheric system in terms of the number of chemical species; the availability of accurate, time-resolved emissions data; and the required rate constants. However, other complex systems have been successfully approximated using artificial neural networks (ANNs). In this paper, ANNs are used to model and predict ambient O₃ concentrations based on a limited number of measured hydrocarbon species, NO_x compounds, temperature, and radiant energy. In order to examine the utility of these approaches, data from the Coastal Oxidant Assessment for Southeast Texas (COAST) program in Houston, TX, have been used. In this study, 53 hydrocarbon compounds, along with O₃, nitrogen oxides, and meteorological data were continuously measured during summer 1993. Steady-state ANN models were developed to examine the ability of these models to predict current O₃ concentrations from measured VOC and NO concentrations. To predict the future concentrations of O₃, dynamic models were also explored and were used for extraction of chemical information such as reactivity estimations for the VOC species.

The steady-state model produced an approximation of O₃ data and demonstrated the functional relationship between O₃ and VOC-NO_x concentrations. The dynamic models were able to the adequately predict the O₃ concentration and behavior of VOC-NO_x-O₃ system a number of hourly intervals into the future. For 3 hr into the future, O₃ concentration could be predicted with a root-mean squared error (RMSE) of 8.21 ppb. Extending the models further in time led to an RMSE of 11.46 ppb for 5-hr-ahead values. This prediction capability could be useful in determining when control actions are needed to maintain measured concentrations within acceptable value ranges.     

A statistical assessment of saturation and mobile sampling strategies to estimate long-term average concentrations across urban areas

The objectives of this study were: (1) to quantify the errors associated with saturation air quality monitoring in estimating the long-term (i.e., annual and 5 yr) mean at a given site from four 2-week measurements, once per season; and (2) to develop a sampling strategy to guide the deployment of mobile air quality facilities for characterizing intraurban gradients of air pollutants, that is, to determine how often a given location should be visited to obtain relatively accurate estimates of the mean air pollutant concentrations. Computer simulations were conducted by randomly sampling ambient monitoring data collected in six Canadian cities at a variety of settings (e.g., population-based sites, near-roadway sites). The 5-yr (1998-2002) dataset consisted of hourly measurements of nitric oxide (NO), nitrogen dioxide (NO2), oxides of nitrogen (NOx), sulfur dioxide (SO2), coarse particulate matter (PM10), fine particulate matter (PM2.5), and CO. The strategy of randomly selecting one 2-week measurement per season to determine the annual or long-term average concentration yields estimates within 30% of the true value 95% of the time for NO2, PM10 and NOx. Larger errors, up to 50%, are expected for NO, SO2, PM2.5, and CO. Combining concentrations from 85 random 1-hr visits per season provides annual and 5-yr average estimates within 30% of the true value with good confidence. Overall, the magnitude of error in the estimates was strongly correlated with the variability of the pollutant. A better estimation can be expected for pollutants known to be less temporally variable and/or over geographic areas where concentrations are less variable. By using multiple sites located in different settings, the relationships determined for estimation error versus number of measurement periods used to determine long-term average are expected to realistically portray the true distribution. Thus, the results should be a good indication of the potential errors one could expect in a variety of different cities, particularly in more northern latitudes.