Evaluation of the European population intake fractions for European and Finnish anthropogenic primary fine particulate matter emissions

The intake fraction (iF) has been defined as the integrated incremental intake of a pollutant released from a source category or region summed over all exposed individuals. In this study we evaluated the iFs in the population of Europe for emissions of anthropogenic primary fine particulate matter (PM_2.5) from sources in Europe, with a more detailed analysis of the iF from Finnish sources. Parameters for calculating the iFs include the emission strengths, the predicted atmospheric concentrations, European population data, and the average breathing rate per person. Emissions for the whole of Europe and Finland were based on the inventories of the European Monitoring and Evaluation Programme (EMEP) and the Finnish Regional Emission Scenario (FRES) model, respectively. The atmospheric dispersion of primary PM_2.5 was computed using the regional-scale dispersion model SILAM. The iFs from Finnish sources were also computed separately for six emission source categories. The iFs corresponding to the primary PM_2.5 emissions from the European countries for the whole population of Europe were generally highest for the densely populated Western European countries, second highest for the Eastern and Southern European countries, and lowest for the Northern European and Baltic countries. For the entire European population, the iF values varied from the lowest value of 0.31 per million for emissions from Cyprus, to the highest value of 4.42 per million for emissions from Belgium. These results depend on the regional distribution of the population and the prevailing long-term meteorological conditions. Regarding Finnish primary PM_2.5 emissions, the iF was highest for traffic emissions (0.68 per million) and lowest for major power plant emissions (0.50 per million). The results provide new information that can be used to find the most cost-efficient emission abatement strategies and policies.     

Spatial patterns of mobile source particulate matter emissions-to-exposure relationships across the United States

Assessing the public health benefits from air pollution control measures is assisted by understanding the relationship between mobile source emissions and subsequent fine particulate matter (PM_2.5) exposure. Since this relationship varies by location, we characterized its magnitude and geographic distribution using the intake fraction (iF) concept. We considered emissions of primary PM_2.5 as well as particle precursors SO₂ and NO_x from each of 3080 counties in the US. We modeled the relationship between these emissions and total US population exposure to PM_2.5, making use of a source–receptor matrix developed for health risk assessment. For primary PM_2.5, we found a median iF of 1.2 per million, with a range of 0.12–25. Half of the total exposure was reached by a median distance of 150 km from the county where mobile source emissions originated, though this spatial extent varied across counties from within the county borders to 1800 km away. For secondary ammonium sulfate from SO₂ emissions, the median iF was 0.41 per million (range: 0.050–10), versus 0.068 per million for secondary ammonium nitrate from NO_x emissions (range: 0.00092–1.3). The median distance to half of the total exposure was greater for secondary PM_2.5 (450 km for sulfate, 390 km for nitrate). Regression analyses using exhaustive population predictors explained much of the variation in primary PM_2.5 iF (R²=0.83) as well as secondary sulfate and nitrate iF (R²=0.74 and 0.60), with greater near-source contribution for primary than for secondary PM_2.5. We conclude that long-range dispersion models with coarse geographic resolution are appropriate for risk assessments of secondary PM_2.5 or primary PM_2.5 emitted from mobile sources in rural areas, but that more resolved dispersion models are warranted for primary PM_2.5 in urban areas due to the substantial contribution of near-source populations.     

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

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

Developing intake fraction estimates with limited data: Comparison of methods in Mexico City

In order to estimate the health benefits of reducing mobile source emissions, analysts typically use detailed atmospheric models to estimate the change in population exposure that results from a given change in emissions. However, this may not be feasible in settings where data are limited or policy decisions are needed in the short term. Intake fraction (iF), defined as the fraction of emissions of a pollutant or its precursor that is inhaled by the population, is a metric that can be used to compare exposure assessment methods in a health benefits analysis context. To clarify the utility of rapid-assessment methods, we calculate particulate matter iFs for the Mexico City Metropolitan Area using five methods, some more resource intensive than others. First, we create two simple box models to describe dispersion of primary fine particulate matter (PM_2.5) in the Mexico City basin. Second, we extrapolate iFs for primary PM_2.5, ammonium sulfate, and ammonium nitrate from US values using a regression model. Third, we calculate iFs by assuming a linear relationship between emissions and population-weighted concentrations of primary PM_2.5, ammonium nitrate, and ammonium sulfate (a particle composition method). Finally, we estimate PM iFs from detailed atmospheric dispersion and chemistry models run for only a short period of time. Intake fractions vary by up to a factor of five, from 23 to 120 per million for primary PM_2.5. Estimates of 60, 7, and 0.7 per million for primary PM, secondary ammonium sulfate, and secondary ammonium nitrate, respectively, represent credible central estimates, with an approximate factor of two uncertainty surrounding each estimate. Our results emphasize that multiple rapid-assessment methods can provide meaningful estimates of iFs in resource-limited environments, and that formal uncertainty analysis, with special attention to model biases and uncertainty, would be important for health benefits analyses.     

Intake-to-delivered-energy ratios for central station and distributed electricity generation in California

In previous work, we showed that the intake fraction (iF) for nonreactive primary air pollutants was 20 times higher in central tendency for small-scale, urban-sited distributed electricity generation (DG) sources than for large-scale, central station (CS) power plants in California [Heath, G.A., Granvold, P.W., Hoats, A.S., Nazaroff, W.W., 2006. Intake fraction assessment of the air pollutant exposure implications of a shift toward distributed electricity generation. Atmospheric Environment 40, 7164–7177]. The present paper builds on that study, exploring pollutant- and technology-specific aspects of population inhalation exposure from electricity generation. We compare California's existing CS-based system to one that is more reliant on DG units sited in urban areas. We use Gaussian plume modeling and a GIS-based exposure analysis to assess 25 existing CSs and 11 DG sources hypothetically located in the downtowns of California's most populous cities. We consider population intake of three pollutants—PM_2.5, NO_x and formaldehyde—directly emitted by five DG technologies—natural gas (NG)-fired turbines, NG internal combustion engines (ICE), NG microturbines, diesel ICEs, and fuel cells with on-site NG reformers. We also consider intake of these pollutants from existing CS facilities, most of which use large NG turbines, as well as from hypothetical facilities located at these same sites but meeting California's best-available control technology standards. After systematically exploring the sensitivity of iF to pollutant decay rate, the iFs for each of the three pollutants for all DG and CS cases are estimated. To efficiently compare the pollutant- and technology-specific exposure potential on an appropriate common basis, a new metric is introduced and evaluated: the intake-to-delivered-energy ratio (IDER). The IDER expresses the mass of pollutant inhaled by an exposed population owing to emissions from an electricity generation unit per quantity of electric energy delivered to the place of use. We find that the central tendency of IDER is much greater for almost every DG technology evaluated than for existing CS facilities in California.     

Intake fraction of nonreactive motor vehicle exhaust in Hong Kong

The intake fraction (iF) of nonreactive constituents of exhaust from mobile vehicles in the urban area of Hong Kong is investigated using available monitoring data for carbon monoxide (CO) as a tracer of opportunity. Correcting for regional transport of carbon monoxide into Hong Kong, the annual-average iF for nonreactive motor vehicle emissions within the city is estimated to be around 270 per million. This estimated iF is much higher than values previously reported for vehicle emissions in US urban areas, Helsinki and Beijing, and somewhat lower than those reported for a densely populated street canyon in downtown Manhattan, New York City, or for emissions into indoor environments. The reported differences in intake fractions in various cities mainly result from the differences in local population densities. Our analysis highlights the importance of accounting for the influence of upwind transport of pollutants when using ambient data to estimate iF for an urban area. For vehicle exhaust in Hong Kong, it is found that the in/near vehicle microenvironment contributes similarly to the indoor home environment when accounting for the overall iF for children and adults.     

A Method for Rapid Calculation of Merchant Vessel Combustion Emission

A simple method for calculating long term (1 year average) combustion emissions from cargo vessels is presented. The method uses emission factors for the five EPA criteria pollutants based on the amount and type of cargo carried by these vessels. Separate emission factors are derived for the underway and docking operations based on current fuel consumption to transport or load/unload cargo. Fuel consumption is then converted to emission factors in the form of tons of pollutant per million ton miles of cargo transported or tons of pollutant per million tons of cargo loaded/unloaded. The emission calculations can be performed on a desk calculator using data published in a federal document. The annual emissions of the Port of Houston are estimated as a sample calculation. The report also Includes a method of determining the spatial distributions of the emissions and a listing of stack parameters.     

Human population intake fractions and environmental fate factors of toxic pollutants in life cycle impact assessment

The present paper outlines an update of the fate and exposure part of the fate, exposure and effects model USES-LCA. The new fate and exposure module of USES-LCA was applied to calculate human population intake fractions and fate factors of the freshwater, marine and terrestrial environment for 3393 substances, including neutral organics, dissociating organics and inorganics, emitted to 7 different emission compartments. The human population intake fraction is on average 10(-5)-10(-8) for organics and 10(-3)-10(-4) for inorganics, depending on the emission compartment considered. Chemical-specific human population intake fractions can be 1-2.7 orders of magnitude higher or lower compared to the typical estimates. For inorganics, the human population intake fractions highly depend on the assumption that exposure via food products can be modelled with constant bioconcentration factors. The environmental fate factor is on average 10(-11)-10(-18) days m(-3) for organics and 10(-10)-10(-12) days m(-3) for inorganics, depending on the receiving environment and the emission compartment considered. Chemical-specific environmental fate factors can be 1-8 orders of magnitude higher or lower compared to the typical estimates. The largest differences between the new and old version of USES-LCA are found for emissions to air and soil. This is caused by a significant change in the structure of the air and soil compartments in the new version of USES-LCA, i.e. the distinction between rural and urban air, including rain-no rain conditions and including soil depth dependent intermedia transport.     

Human intake fractions of pesticides via greenhouse tomato consumption: comparing model estimates with measurements for Captan

Human intake due to pesticide residues in food commodities can be much higher than those related to water consumption and air inhalation, stressing the importance to correctly estimate pesticide uptake into plants and predict subsequent intake by humans. We calculated the human intake fraction of captan via tomato consumption taking into account the time between pesticide application and harvest, the time between harvest and consumption, the absorption of spray deposit on plant surfaces, transfer properties through the cuticle, degradation inside the plant and loss due to food processing. Human population intake fractions due to ingestion were calculated for complete, washed and peeled tomatoes. The calculated intake fractions were compared with measurements derived from an experimental setup in a Mediterranean greenhouse. The fraction of captan applied in the greenhouse as plant treatment that eventually is ingested by the human population is on average 10(-2)-10(-5), depending on the time between pesticide application and ingestion of tomatoes and the processing step considered. Model and experimentally derived intake fractions deviated less than a factor of 2 for complete and washed tomatoes and a factor of 3 for peeled tomatoes. Intake fractions due to air inhalation and consumption of drinking water are expected to be significantly lower (5-9 orders of magnitude) than those induced by the intake of tomatoes in this case study.     

Predicting particulate (PM10) personal exposure distributions using a random component superposition statistical model

This paper presents a new statistical model designed to extend our understanding from prior personal exposure field measurements of urban populations to other cities where ambient monitoring data, but no personal exposure measurements, exist. The model partitions personal exposure into two distinct components: ambient concentration and nonambient concentration. It is assumed the ambient and nonambient concentration components are uncorrelated and add together; therefore, the model is called a random component superposition (RCS) model. The 24-hr ambient outdoor concentration is multiplied by a dimensionless "attenuation factor" between 0 and 1 to account for deposition of particles as the ambient air infiltrates indoors. The RCS model is applied to field PM10 measurement data from three large-scale personal exposure field studies: THEES (Total Human Environmental Exposure Study) in Phillipsburg, NJ; PTEAM (Particle Total Exposure Assessment Methodology) in Riverside, CA; and the Ethyl Corporation study in Toronto, Canada. Because indoor sources and activities (smoking, cooking, cleaning, the personal cloud, etc.) may be similar in similar populations, it was hypothesized that the statistical distribution of nonambient personal exposure is invariant across cities. Using a fixed 24-hr attenuation factor as a first approximation derived from regression analysis for the respondents, the distributions of nonambient PM10 personal exposures were obtained for each city. Although the mean ambient PM10 concentrations in the three cities varied from 27.9 micrograms/m3 in Toronto to 60.9 micrograms/m3 in Phillipsburg to 94.1 micrograms/m3 in Riverside, the mean nonambient components of personal exposures were found to be closer: 52.6 micrograms/m3 in Toronto; 52.4 micrograms/m3 in Phillipsburg; and 59.2 micrograms/m3 in Riverside. The three frequency distributions of the nonambient components of exposure also were similar in shape, giving support to the hypothesis that nonambient concentrations are similar across different cities and populations. These results indicate that, if the ambient concentrations were completely controlled and set to zero in all three cities, the median of the remaining personal exposures to PM10 would range from 32.0 micrograms/m3 (Toronto) to 34.4 micrograms/m3 (Phillipsburg) to 48.8 micrograms/m3 (Riverside). The highest-exposed 30% of the population in the three cities would still be exposed to 24-hr average PM10 concentrations of 47-74 micrograms/m3; the highest 20% would be exposed to concentrations of 56-92 micrograms/m3; the highest 10% to concentrations of 88-131 micrograms/m3; and the highest 5% to 133-175 micrograms/m3, due only to indoor sources and activities. The distribution for the difference between personal exposures and indoor concentrations, or the "personal cloud," also was similar in the three cities, with a mean of 30-35 micrograms/m3, suggesting that the personal cloud accounts for more than half of the nonambient component of PM10 personal exposure in the three cities. Using only the ambient measurements in Toronto, the nonambient data from THEES in Phillipsburg was used to predict the entire personal exposure distribution in Toronto. The PM10 exposure distribution predicted by the model showed reasonable agreement with the PM10 personal exposure distribution measured in Toronto. These initial results suggest that the RCS model may be a powerful tool for predicting personal exposure distributions and statistics in other cities where only ambient particle data are available.     

Temporal evolution of DNAPL source and contaminant flux distribution: impacts of source mass depletion

We investigated, using model simulations, the changes occurring in the distribution of dense non-aqueous phase liquid (DNAPL) mass (Sn) within the source zone during depletion through dissolution, and the resulting changes in the contaminant flux distribution (J) at the source control plane (CP). Two numerical codes (ISCO3D and T2VOC) were used to simulate selected scenarios of DNAPL dissolution and transport in three-dimensional, heterogeneous, spatially correlated, random permeability fields with emplaced sources. Data from the model simulations were interpreted based on population statistics (mean, standard deviation, coefficient of variation) and spatial statistics (centroid, second moments, variograms). The mean and standard deviation of the Sn and J distributions decreased with source mass depletion by dissolution. The decrease in mean and standard deviation was proportional for the J distribution resulting in a constant coefficient of variation (CV), while for the Sn distribution, the mean decreased faster than the standard deviation. The spatial distributions exhibited similar behavior as the population distribution, i.e., the CP flux distribution was more stable (defined by temporally constant second moments and range of variograms) than the Sn distribution. These observations appeared to be independent of the heterogeneity of the permeability (k) field (variance of the log permeability field=1 and 2.45), correlation structure (positive vs. negative correlation between the k and Sn domains) and the DNAPL dissolution model (equilibrium vs. rate-limited), for the cases studied. Analysis of data from a flux monitoring field study (Hill Air Force Base, Utah) at a DNAPL source CP before and after source remediation also revealed temporal invariance of the contaminant flux distribution. These modeling and field observations suggest that the temporal evolution of the contaminant flux distribution can be estimated if the initial distribution is known. However, the findings are preliminary and broader implications to sampling strategies for remediation performance assessment need to be evaluated in additional modeling and experimental studies.     

Evaluation of PM10 forecasting based on the artificial neural network model and intake fraction in an urban area: A case study in Taiyuan City,China

Primary fine particulate matters with a diameter of less than 10 µm (PM₁₀) are important air emissions causing human health damage. PM₁₀ concentration forecast is important and necessary to perform in order to assess the impact of air on the health of living beings. To better understand the PM₁₀ pollution health risk in Taiyuan City, China, this paper forecasted the temporal and spatial distribution of PM₁₀ yearly average concentration, using Back Propagation Artificial Neural Network (BPANN) model with various air quality parameters. The predicted results of the models were consistent with the observations with a correlation coefficient of 0.72. The PM₁₀ yearly average concentrations combined with the population data from 2002 to 2008 were given into the Intake Fraction (IF) model to calculate the IFs, which are defined as the integrated incremental intake of a pollutant released from a source category or a region over all exposed individuals. The results in this study are only for main stationary sources of the research area, and the traffic sources have not been included. The computed IFs results are therefore under-estimations. The IFs of PM₁₀ from Taiyuan with a mean of 8.5 per million were relatively high compared with other IFs of the United States, Northern Europe and other cities in China. The results of this study indicate that the artificial neural network is an effective method for PM₁₀ pollution modeling, and the Intake Fraction model provides a rapid population risk estimate for pollutant emission reduction strategies and policies.

Implications The PM₁₀ (particulate matter with an aerodynamic diameter ≤10 μm) yearly average concentration of Taiyuan, with a mean of 0.176 mg/m³, was higher than the 65 μg/m³ recommended by the U.S. Environmental Protection Agency (EPA). The spatial distribution of PM₁₀ yearly average concentrations showed that wind direction and wind speed played an important role, whereas temperature and humidity had a lower effect than expected. Intake fraction estimates of Taiyuan were relatively high compared with those observed in other cities. Population density was the major factor influencing PM₁₀ spatial distribution. The results indicated that the artificial neural network was an effective method for PM₁₀ pollution modeling.     

Exposure of chronic obstructive pulmonary disease patients to particulate matter: relationships between personal and ambient air concentrations

Mot time-series studies of particulate air pollution and acute health outcomes assess exposure of the study population using fixed-site outdoor measurements. To address the issue of exposure misclassification, we evaluate the relationship between ambient particle concentrations and personal exposures of a population expected to be at risk of particle health effects. Sampling was conducted within the Vancouver metropolitan area during April-September 1998. Sixteen subjects (non-smoking, ages 54-86) with physician-diagnosed chronic obstructive pulmonary disease (COPD) wore personal PM2.5 monitors for seven 24-hr periods, randomly spaced approximately 1.5 weeks apart. Time-activity logs and dwelling characteristics data were also obtained for each subject. Daily 24-hr ambient PM10 and PM2.5 concentrations were measured at five fixed sites spaced throughout the study region. SO4(2-), which is found almost exclusively in the fine particle fraction and which does not have major indoor sources, was measured in all PM2.5 samples as an indicator of accumulation mode particulate matter of ambient origin. The mean personal and ambient PM2.5 concentrations were 18 micrograms/m3 and 11 micrograms/m3, respectively. In analyses relating personal and ambient measurements, ambient concentrations were expressed either as an average of the values obtained from five ambient monitoring sites for each day of personal sampling, or as the concentration obtained at the ambient site closest to each subject's home. The mean personal to ambient concentration ratio of all samples was 1.75 (range = 0.24 to 10.60) for PM2.5, and 0.75 (range = 0.09 to 1.42) for SO4(2-). Regression analyses were conducted for each subject separately and on pooled data. The median correlation (Pearson's r) between personal and average ambient PM2.5 concentrations was 0.48 (range = -0.68 to 0.83). Using SO4(2-) as the exposure metric, the median r between personal and average ambient concentrations was 0.96 (range = 0.66 to 1.0). Use of the closest ambient site did not improve the median correlation of the group for either PM2.5 or SO4(2-). All pooled analyses resulted in lower correlation coefficients than the median correlation coefficient of individual regressions. Personal SO4(2-) was more highly correlated with all ambient measures than PM2.5. Inclusion of time-activity and dwelling characteristics data did not result in a useful predictive regression model for PM2.5 personal exposure, but improved the model fit from simply regressing against ambient concentration (R2 = 0.27). The model for SO4(2-) was predictive (R2 = 0.82), as personal exposures were largely explained by ambient levels. These results indicate a relatively low correlation between personal exposure and ambient PM2.5 that is not improved by assigning exposure to the closest ambient monitor. The correlation between personal exposure and ambient concentration is high, however, when using SO4(2-), an indicator of accumulation mode particulate matter of ambient origin.     

Size distribution of airborne particulate matter and associated heavy metals in the roadside environment

The size distributions of airborne particulate matter (PM) and associated heavy metals Pb, Cd, Ni, Cr, V, Mn, Cu and Fe in different inhalable fractions (< 0.8 microm, 0.8-1.3 microm, 1.3-2.7 microm, 2.7-6.7 microm and > 6.7 microm) were determined at a traffic-orientated urban site in the city of Thessaloniki, northern Greece. The airborne PM displayed a bimodal distribution with most of the mass (52%) contained in the submicron size range (< 0.8 microm) and an additional minor mode (20%) in the coarse size fraction (> 6.7 microm). Characteristic size distributions of heavy metals allowed identification of three main behavioral types: (a) metals whose mass was resided mainly within the accumulation mode (Pb,Cd), (b) those which were distributed between fine, intermediate and coarse modes (Ni,Cu,Mn), and (c) those which were mainly found within particles larger than 2.7 microm in diameter (Fe). The mean mass median aerodynamic diameter (MMAD) of PM was found at 0.85+/-0.71 microm, while the mean MMADs of heavy metals followed the order Pb (0.96 +/- 0.71 microm) < Cd (1.14 +/- 0.82 microm) < V (1.38 +/- 0.63 microm) < Ni (1.45 +/- 0.88 microm) < Cu (2.04 +/- 0.77 microm) < Mn (2.61 +/- 1.23 microm) < Cr (2.91 +/- 1.40 microm) < Fe (3.82 +/- 0.88 microm). The measured distributions are believed to result from a combination of processes including local anthropogenic and natural sources, such as traffic, industrial emissions and resuspension of road dust.     

Performance and diagnostic evaluation of ozone predictions by the Eta-Community Multiscale Air Quality Forecast System during the 2002 New England Air Quality Study

A real-time air quality forecasting system (Eta-Community Multiscale Air Quality [CMAQ] model suite) has been developed by linking the National Centers for Environmental Estimation Eta model to the U.S. Environmental Protection Agency (EPA) CMAQ model. This work presents results from the application of the Eta-CMAQ modeling system for forecasting ozone (O3) over the Northeastern United States during the 2002 New England Air Quality Study (NEAQS). Spatial and temporal performance of the Eta-CMAQ model for O3 was evaluated by comparison with observations from the EPA Air Quality System (AQS) network. This study also examines the ability of the model to simulate the processes governing the distributions of tropospheric O3 on the basis of the intensive datasets obtained at the four Atmospheric Investigation, Regional Modeling, Analysis, and Estimation (AIRMAP) and Harvard Forest (HF) surface sites. The episode analysis reveals that the model captured the buildup of O3 concentrations over the northeastern domain from August 11 and reproduced the spatial distributions of observed O3 very well for the daytime (8:00 p.m.) of both August 8 and 12 with most of normalized mean bias (NMB) within +/- 20%. The model reproduced 53.3% of the observed hourly O3 within a factor of 1.5 with NMB of 29.7% and normalized mean error of 46.9% at the 342 AQS sites. The comparison of modeled and observed lidar O3 vertical profiles shows that whereas the model reproduced the observed vertical structure, it tended to overestimate at higher altitude. The model reproduced 64-77% of observed NO2 photolysis rate values within a factor of 1.5 at the AIRMAP sites. At the HF site, comparison of modeled and observed O3/nitrogen oxide (NOx) ratios suggests that the site is mainly under strongly NOx-sensitive conditions (>53%). It was found that the modeled lower limits of the O3 production efficiency values (inferred from O3-CO correlation) are close to the observations.     

微环境新风量的检测原理及方法研究

新风量是评价室内微环境空气卫生质量的主要卫生指标之一,也是计算室内某种气体单位时间排放量的重要参数。以CO2作为示踪气体,利用于冰升华和人体呼吸产生CO2示踪气体两种测量方法对室内和车内微环境进行了检测,并考虑室内人呼出CO2量的影响,运用箱子模式的各种推导公式（稳态法、解析解法和差分法）对新风量进行了计算,并对结果进行了讨论。结果表明,没有人存在下,用箱子模式的解析解法和差分法计算的新风量值没有明显的统计差异;微环境内有人时必须考虑人释放的影响,这样箱子模式的各种推导公式都可以计算新风量值,且结果准确,准确度高。利用人体呼吸产生CO2示踪气体法,用差分法计算结果不理想,偏差很大;用稳态法计算重现性高,结果可靠。     

Residential Indoor,Outdoor, and Workplace Concentrations of Carbonyl Compounds: Relationships with Personal Exposure Concentrations and Correlation with Sources

Abstract

Personal 48-hr exposures of 15 randomly selected participants as well as microenvironment concentrations in each participant’s residence and workplace were measured for 16 carbonyl compounds during summer–fall 1997 as a part of the Air Pollution Exposure Distributions within Adult Urban Populations in Europe (EXPOLIS) study in Helsinki, Finland. When formaldehyde and acetaldehyde were excluded, geometric mean ambient air concentrations outside each participant’s residence were less than 1 ppb for all target compounds. Geometric mean residential indoor concentrations of carbonyls were systematically higher than geometric mean personal exposures and indoor workplace concentrations. Additionally, residential indoor/outdoor ratios indicated substantial indoor sources for most target compounds. Carbonyls in residential indoor air correlated significantly, suggesting similar mechanisms of entry into indoor environments. Overall, this study demonstrated the important role of non-traffic-related emissions in the personal exposures of participants in Helsinki and that comprehensive apportionment of population risk to air toxics should include exposure concentrations derived from product emissions and chemical formation in indoor air.     

Dietary intake of PCDD/F by small children with different food consumption measured by the duplicate method

The dietary intake of PCDD/F by 42 small children, 21 boys and 21 girls in the age range 14-47 months, with different food consumption behaviour living in urban and rural areas of Germany was measured by the duplicate method with a 7 day sampling period from May to September 1998. The PCDD/F-levels in the food duplicates were in the range 39.2-325 fg I-TEq/g(dry weight) (median: 90.7; arithmetic mean: 96.6) and the doses were in the range 0.681-5.43 with a median of 1.56 and an arithmetic mean (AM) of 1.60 pg I-TEq/(kg(body weight) x d). Children living in an urban industrialized area with food consumption including products from the family owned vegetable gardens or the surrounding area and/or products from domestic animals showed no statistically significant different concentrations in the food duplicates or in the dietary intake of PCDD/F, calculated as I-TEq, than children living in a rural area with similar food consumption behavior or than children consuming exclusively food from the supermarket.     

Adaptive neuro-fuzzy based modelling for prediction of air pollution daily levels in city of Zonguldak

Air pollution is a growing problem arising from domestic heating, high density of vehicle traffic, electricity production, and expanding commercial and industrial activities, all increasing in parallel with urban population. Monitoring and forecasting of air quality parameters in the urban area are important due to health impact. Artificial intelligent techniques are successfully used in modelling of highly complex and non-linear phenomena. In this study, adaptive neuro-fuzzy logic method has been proposed to estimate the impact of meteorological factors on SO2 and total suspended particular matter (TSP) pollution levels over an urban area. The model forecasts satisfactorily the trends in SO2 and TSP concentration levels, with performance between 75-90% and 69-80 %, respectively.     

Numerical simulation of air pollution in urban areas: model performance

The numerical model developed in the first part of this investigation is applied to assess the behavior of sulfur dioxide and sulfate concentration distributions in an urban area using the St Louis Regional Air Pollution Study (RAPS) data. Statistical techniques chosen to determine the accuracy and uncertainty associated with the numerical model results include paired analysis and resampling analysis. The results of the numerical model are also compared with those of RAM, a Gaussian plume model. Finally, the behavior of point and area emission sources in an urban area is assessed to provide an insight into the complex interrelationships between the emissions and meteorological conditions which determine the distribution of ground level concentrations.