Using continuous PM2.5 monitoring data to report an air quality index

As stated in 40 CFR 58, Appendix G (2000), statistical linear regression models can be applied to relate PM2.5 continuous monitoring (CM) measurements with federal reference method (FRM) measurements, collocated or otherwise, for the purpose of reporting the air quality index (AQI). The CM measurements can then be transformed via the model to remove any bias relative to FRM measurements. The resulting FRM-like modeled measurements may be used to provide more timely reporting of a metropolitan statistical area's (MSA's) AQI. Of considerable importance is the quality of the model used to relate the CM and FRM measurements. The use of a poor model could result in misleading AQI reporting in the form of incorrectly claiming either good or bad air quality. This paper describes a measure of adequacy for deciding whether a statistical linear regression model that relates FRM and continuous PM2.5 measurements is sufficient for use in AQI reporting. The approach is the U.S. Environmental Protection Agency's (EPA's) data quality objectives (DQO) process, a seven-step strategic planning approach to determine the most appropriate data type, quality, quantity, and synthesis for a given activity. The chosen measure of model adequacy is r2, the square of the correlation coefficient between FRM measurements and their modeled counterparts. The paper concludes by developing regression models that meet this desired level of adequacy for the MSAs of Greensboro/Winston-Salem/High Point, NC; and Davenport/Moline/Rock Island, IA/IL. In both cases, a log transformation of the data appeared most appropriate. For the data from the Greensboro/Winston-Salem/High Point MSA, a simple linear regression model of the FRM and CM measurements had an r2 of 0.96, based on 227 paired observations. For the data from the Davenport/Moline/Rock Island MSA, due to seasonal differences between CM and FRM measurements, the simple linear regression model had to be expanded to include a temperature dependency, resulting in an r2 of 0.86, based on 214 paired observations.     

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

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

Analysis of short-term ozone and PM2.5 measurements: Characteristics and relationships for air sensor messaging

Air quality sensors are becoming increasingly available to the general public, providing individuals and communities with information on fine-scale, local air quality in increments as short as 1 min. Current health studies do not support linking 1-min exposures to adverse health effects; therefore, the potential health implications of such ambient exposures are unclear. The U.S. Environmental Protection Agency (EPA) establishes the National Ambient Air Quality Standards (NAAQS) and Air Quality Index (AQI) on the best science available, which typically uses longer averaging periods (e.g., 8 hr; 24 hr). Another consideration for interpreting sensor data is the variable relationship between pollutant concentrations measured by sensors, which are short-term (1 min to 1 hr), and the longer term averages used in the NAAQS and AQI. In addition, sensors often do not meet federal performance or quality assurance requirements, which introduces uncertainty in the accuracy and interpretation of these readings. This article describes a statistical analysis of data from regulatory monitors and new real-time technology from Village Green benches to inform the interpretation and communication of short-term air sensor data. We investigate the characteristics of this novel data set and the temporal relationships of short-term concentrations to 8-hr average (ozone) and 24-hr average (PM_2.5) concentrations to examine how sensor readings may relate to the NAAQS and AQI categories, and ultimately to inform breakpoints for sensor messages. We consider the empirical distributions of the maximum 8-hr averages (ozone) and 24-hr averages (PM_2.5) given the corresponding short-term concentrations, and provide a probabilistic assessment. The result is a robust, empirical comparison that includes events of interest for air quality exceedances and public health communication. Concentration breakpoints are developed for short-term sensor readings such that, to the extent possible, the related air quality messages that are conveyed to the public are consistent with messages related to the NAAQS and AQI.

Implications: Real-time sensors have the potential to provide important information about fine-scale current air quality and local air quality events. The statistical analysis of short-term regulatory and sensor data, coupled with policy considerations and known health effects experienced over longer averaging times, supports interpretation of such short-term data and efforts to communicate local air quality.     

Source apportionment of visual impairment during the California regional PM10/PM2.5 air quality study

Gases and particulate matter predictions from the UCD/CIT air quality model were used in a visibility model to predict source contributions to visual impairment in the San Joaquin Valley (SJV), the southern portion of California's Central Valley, during December 2000 and January 2001. Within the SJV, daytime (0800–1700 PST) light extinction was dominated by scattering associated with airborne particles. Measured daytime particle scattering coefficients were compared to predicted values at approximately 40 locations across the SJV after correction for the increased temperature and decreased relative humidity produced by “smart heaters” placed upstream of nephelometers. Mean fractional bias and mean fractional error were ?0.22 and 0.65, respectively, indicating reasonable agreement between model predictions and measurements. Particulate water, nitrate, organic matter, and ammonium were the major particulate species contributing to light scattering in the SJV. Daytime light extinction in the SJV averaged between December 25, 2000 and January 7, 2001 was mainly associated with animal ammonia sources (28%), diesel engines (18%), catalyst gasoline engines (9%), other anthropogenic sources (9%), and wood smoke (7%) with initial and boundary conditions accounting for 13%. The source apportionment results from this study apply to wintertime conditions when airborne particulate matter concentrations are typically at their annual maximum. Further study would be required to quantify source contributions to light extinction in other seasons.     

Exposure to ultrafine particles and PM2.5 in four Sydney transport modes

Concentrations of ultrafine (<0.1 μm) particles (UFPs) and PM_2.5 (<2.5 μm) were measured whilst commuting along a similar route by train, bus, ferry and automobile in Sydney, Australia. One trip on each transport mode was undertaken during both morning and evening peak hours throughout a working week, for a total of 40 trips. Analyses comprised one-way ANOVA to compare overall (i.e. all trips combined) geometric mean concentrations of both particle fractions measured across transport modes, and assessment of both the correlation between wind speed and individual trip means of UFPs and PM_2.5, and the correlation between the two particle fractions. Overall geometric mean concentrations of UFPs and PM_2.5 ranged from 2.8 (train) to 8.4 (bus) × 10⁴ particles cm^?3 and 22.6 (automobile) to 29.6 (bus) μg m^?3, respectively, and a statistically significant difference (p < 0.001) between modes was found for both particle fractions. Individual trip geometric mean concentrations were between 9.7 × 10³ (train) and 2.2 × 10⁵ (bus) particles cm^?3 and 9.5 (train) to 78.7 (train) μg m^?3. Estimated commuter exposures were variable, and the highest return trip mean PM_2.5 exposure occurred in the ferry mode, whilst the highest UFP exposure occurred during bus trips. The correlation between fractions was generally poor, and in keeping with the duality of particle mass and number emissions in vehicle-dominated urban areas. Wind speed was negatively correlated with, and a generally poor determinant of, UFP and PM_2.5 concentrations, suggesting a more significant role for other factors in determining commuter exposure.     

基于PM2.5全面达标的广东省产业结构调整策略研究

为科学指导广东省进一步开展大气污染综合防治,运用统计分析和空气质量模拟方法,分析广东省实现各城市PM_(2.5)浓度全面达标的污染物减排需求,尤其是产业结构调整的需求。结合各行业的污染物排放强度,识别广东省加强产业结构优化调整工作需要关注的重点行业领域,并提出相应政策建议。结果表明,仅依靠既定的末端治理和能源、交通结构调整措施无法实现2020年广东省各城市PM_(2.5)浓度全面达标的目标,产业结构调整在SO_2、NO_x、挥发性有机物(VOCs)的减排中至少需发挥11%～19%的减排贡献作用,重点应针对区域内的非金属矿物制品、电力热力生产和供应、黑色金属冶炼和压延加工、造纸、纺织印染、化学纤维制造等行业进行调控,主动淘汰落后产能。     

The stable carbon isotope composition of PM2.5 and PM10 in Mexico City Metropolitan Area air

The sources and distribution of carbon in ambient suspended particles (PM_2.5 and PM₁₀) of Mexico City Metropolitan Area (MCMA) air were traced using stable carbon isotopes (¹³C/¹²C). Tested potential sources included rural and agricultural soils, gasoline and diesel, liquefied-petroleum gas, volcanic ash, and street dust. The complete combustion of LP gas, diesel and gasoline yielded the lightest δ¹³C values (?27 to ?29‰ vs. PDB), while street dust (PM₁₀) represented the isotopically heaviest endmember (?17‰). The δ¹³C values of rural soils from four geographically separated sites were similar (?20.7 ± 1.5‰). δ¹³C values of particles and soot from diesel and gasoline vehicle emissions and agricultural soils varied between ?23 and ?26‰. Ambient PM samples collected in November of 2000, and March and December of 2001 at three representative receptor sites of industrial, commercial and residential activities had a δ¹³C value centered around ?25.1‰ in both fractions, resulting from common carbon sources. The predominant carbon sources to MCMA atmospheric particles were hydrocarbon combustion (diesel and/or gasoline) and particles of geological origin. The significantly depleted δ¹³C values from the industrial site reflect the input of diesel combustion by mobile and point source emissions. Based on stable carbon isotope mass balance, the carbon contribution of geological sources at the commercial and residential sites was approximately 73% for the PM₁₀ fraction and 54% for PM_2.5. Although not measured in this study, biomass-burning emissions from nearby forests are an important carbon source characterized by isotopically lighter values (?29‰), and can become a significant contributor (67%) of particulate carbon to MCMA air under the prevalence of southwesterly winds. Alternative sources of these ¹³C-depleted particles, such as cooking fires and municipal waste incineration, need to be assessed. Results show that stable carbon isotope measurements are useful for distinguishing between some carbon sources in suspended particles to MCMA air, and that wind direction has an impact on the distribution of carbon sources in this basin.     

A consistent method for modelling PM10 and PM2.5 concentrations across the United Kingdom in 2004 for air quality assessment

A modelling method has been developed to map PM₁₀ and PM_2.5 concentrations across the UK at background and roadside locations. Separate models have been calibrated using gravimetric measurements and Tapered Element Oscillating Microbalance instruments (TEOM) using source apportionments appropriate to the size fractions and sampling methods. Maps have been prepared for a base year of 2004 and predictions have been calculated for 2010 and 2020 on the basis of current policies. Comparisons of the modelling results with air quality regulations suggest that exceedences of the EU Daughter Directive stage 1 24-h limit value for PM₁₀ at the roadside in 2004 will be largely eliminated by 2020. The concentration cap of 25 μg m⁻³ for PM_2.5 proposed within the CAFÉ Directive is expected to be met at all locations. Projections for 2010 and 2020 suggest that the proposed exposure reduction (ER) target is likely to be considerably more stringent and require additional measures beyond current policies. Thus the model results suggest that the balance between the stringency of the concentration cap and the ER target in the proposed directive is appropriate. Measures to achieve greater reductions should therefore have the maximum public health benefit and air quality policy is not driven by the need to reduce concentrations at isolated ‘hotspots’.     

Source apportionment of wintertime secondary organic aerosol during the California regional PM10/PM2.5 air quality study

The UCD/CIT air quality model with the Caltech Atmospheric Chemistry Mechanism (CACM) was used to predict source contributions to secondary organic aerosol (SOA) formation in the San Joaquin Valley (SJV) from December 15, 2000 to January 7, 2001. The predicted 24-day average SOA concentration had a maximum value of 4.26 μg m^?3 50 km southwest of Fresno. Predicted SOA concentrations at Fresno, Angiola, and Bakersfield were 2.46 μg m^?3, 1.68 μg m^?3, and 2.28 μg m^?3, respectively, accounting for 6%, 37%, and 4% of the total predicted organic aerosol. The average SOA concentration across the entire SJV was 1.35 μg m^?3, which accounts for approximately 20% of the total predicted organic aerosol. Averaged over the entire SJV, the major SOA sources were solvent use (28% of SOA), catalyst gasoline engines (25% of SOA), wood smoke (16% of SOA), non-catalyst gasoline engines (13% of SOA), and other anthropogenic sources (11% of SOA). Diesel engines were predicted to only account for approximately 2% of the total SOA formation in the SJV because they emit a small amount of volatile organic compounds relative to other sources. In terms of SOA precursors within the SJV, long-chain alkanes were predicted to be the largest SOA contributor, followed by aromatic compounds. The current study identifies the major known contributors to the SOA burden during a winter pollution episode in the SJV, with further enhancements possible as additional formation pathways are discovered.     

Ensemble and bias-correction techniques for air quality model forecasts of surface O3 and PM2.5 during the TEXAQS-II experiment of 2006

Several air quality forecasting ensembles were created from seven models, running in real-time during the 2006 Texas Air Quality (TEXAQS-II) experiment. These multi-model ensembles incorporated a diverse set of meteorological models, chemical mechanisms, and emission inventories. Evaluation of individual model and ensemble forecasts of surface ozone and particulate matter (PM) was performed using data from 119 EPA AIRNow ozone sites and 38 PM sites during a 50-day period in August and September of 2006. From the original set of models, two new bias-corrected model data sets were built, either by applying a simple running mean average to the past 7 days of data or by a Kalman-Filter approach. From the original and two bias-corrected data sets, three ensembles were created by a simple averaging of the seven models. For further improvements three additional weighted model ensembles were created, where individual model weights were calculated using the singular value decomposition method. All six of the ensembles are compared to the individual models and to each other in terms of root mean square error, correlation, and contingency and probabilistic statistics. In most cases, each of the ensembles show improved skill compared to the best of the individual models. The over all best ensemble technique was found to be the combination of Kalman-Filtering and weighted averaging. PM_2.5 aerosol ensembles demonstrated significant improvement gains, mostly because the original model's skill was very low.     

Testing the capability of the chemistry transport model LOTOS-EUROS to forecast PM10 levels in the Netherlands

Since particulate matter has a direct and adverse impact on public health, a good air quality forecast is important. Several European countries presently use statistical forecasting models, which have their limitations, especially for PM10. An alternative approach is to use a chemistry transport model. Here, the ability of the chemical transport model LOTOS-EUROS to forecast PM10 concentrations in the Netherlands was investigated. LOTOS-EUROS models several PM10 components individually. For sulphate, nitrate and ammonium aerosol the evaluation against observations shows that the modelled annual mean concentrations are within 20% of the measured concentration and that the temporal correlation is reasonably good (R > 0.6). For sea salt the model tended to overestimate the measured concentrations. For elemental carbon the correspondence with black smoke observations was reasonable. However, total PM10 is seriously underestimated, due to unmodelled components (secondary organic aerosols, mineral dust) and missing sources. Therefore, a simple bias correction for four seasons was derived based on the years 2004–2006. The model was compared with the Dutch operational statistical model PROPART and ground-level observations. With bias correction, LOTOS-EUROS performed better than PROPART regarding the timing of events. The major flaw of LOTOS-EUROS was that high values (>50 μg m^?3) were still underestimated. Another advantage of LOTOS-EUROS over the statistical model was the more detailed information in space and time, which facilitates communication of the forecast to the general public.     

Effect of local and long-range transport emissions on the elemental composition of PM10–2.5 and PM2.5 in Beirut

The elemental composition of PM_10−2.5 and PM_2.5 were studied in winter, summer, stormy and non-stormy dates during a period extending from February 2004 till January 2005, in a populated area of Beirut. Results of PIXE analysis and enrichment factor (E.F.) calculation, using Si as a reference of crustal material, showed that crustal elements (E.F.<10) like Si, Ca, K, Ti, Mn and Fe were more abundant in PM_10−2.5 while enriched elements (E.F.>10) like S, Cu, Zn and Pb predominated in PM_2.5. In PM_10−2.5, concentrations of crustal elements increased during stormy episodes, all time high Ca concentrations were due to the abundance of calcite and limestone rocks in Lebanon, and increased Cl levels correlated with marine air masses. In PM_2.5, sulfur concentrations were more prominent in the summer due to the enhancement of photochemical reactions. Sources of sulfur were attributed to local, sea-water and long-range transport from Eastern Europe, with the latter being the most predominate. Anthropogenic elements like Cu and Zn were generated from worn brakes and tires in high traffic density area and spikes of Pb were directly linked to a southerly wind originated from Egypt and/or Israel as determined by the air trajectory HYSPLIT model. In brief, elemental variations depended on the regional variability of the transport pattern and the different removal rates of aerosols.     

The sensitivity of the CHIMERE model to emissions reduction scenarios on air quality in Northern Italy

The sensitivity of the CHIMERE model to emission reduction scenarios on particulate matter PM2.5 and ozone (O₃) in Northern Italy is studied. The emissions of NO_x, PM2.5 SO₂, VOC or NH₃ were reduced by 50% for different source sectors for the Lombardy region, together with 5 additional scenarios to estimate the effect of local measures on improving the air quality for the Po valley area. Firstly, we evaluate the model performance by comparing calculated surface aerosol concentrations for the standard case (no emission reductions) with observations for January and June 2005. Calculated monthly mean PM10 concentrations are in general underestimated. For June, modelled PM10 concentrations slightly overestimate the measurements. Calculated monthly mean SO₄, NO₃^?, NH₄⁺ concentrations are in good agreement with the observations for January and June. Secondly, the model sensitivity of emission reduction scenarios on PM2.5 and O₃ calculated concentrations for the Po valley area is evaluated. The most effective scenarios to abate PM2.5 concentration are based on the SNAP2 (non-industrial combustion plants) and SNAP7 (road traffic) sectors, for which the NO_x and PM2.5 emissions are reduced by 50%. The number of days that the 2015 PM2.5 limit value of 25 μg m^?3 in Milan is exceeded by reducing primary PM2.5 and NO_x emissions for SNAP2 and 7 by 50%, does not change in January when compared to the standard case for the Milan area. It appears that 40% of the PM2.5 concentration in the greater Milan area is caused by the emissions surrounding the Lombardy region and from the model boundary conditions.This study also showed that a more effective pollutant reduction (emissions) per ton of pollutant reduced (concentrations) for the greater Milan area is obtained by reducing the primary PM2.5 emissions for SNAP7 by 50%. The most effective scenario on PM2.5 decrease for which precursor emissions are reduced is achieved by reducing SO₂ emissions by 50% for SNAP7.Our study showed that during summer time, the largest reductions in O₃ concentrations are achieved for SNAP7 emission reductions, when volatile organic compounds (VOCs) are reduced by 50%.     

Statistical characterization of atmospheric PM10 and PM2.5 concentrations at a non-impacted suburban site of Istanbul, Turkey

Inhalable particulate matter (PM10) has been monitored at several stations by Istanbul Municipality. On the other hand, information about fine fraction aerosols (PM2.5) in Istanbul atmosphere was not reported. In this study, 86 daily aerosol samples were collected between July 2002 and July 2003. The PM10 annual arithmetic mean value of 47.1 microg m(-3), was lower than the Turkish air quality standard of 60 microg m(-3). On the other hand, this value was found higher than the annual European Union air quality PM(10) standard of 40 microg m(-3). Furthermore, the annual mean concentration of PM2.5 20.8 microg m(-3) was found higher than The United States EPA standard of 15 microg m(-3). The statistics and relationships of fine, coarse, and inhalable particles were studied. Cyclic behavior of the monthly average concentrations of PM10 and PM2.5 data were investigated. Several frequency distribution functions were used to fit the measured data. According to Chi-squared and Kolmogorov-Smirnov tests, the frequency distributions of PM2.5 and PM10 data were found to fit Log-logistic functions.     

Design of a cost-effective weighing facility for PM2.5 quality assurance

The U.S. Environmental Protection Agency (EPA) Quality Assurance (QA) Guidance Document 2.12: Monitoring PM2.5 in Ambient Air Using Designated Reference or Class I Equivalent Methods (Document 2.12) requires conditioning of PM2.5 filters at 20-23 degrees C and 30-40% relative humidity (RH) for 24 hr prior to gravimetric analysis. Variability of temperature and humidity may not exceed +/-2 degrees C and +/-5% RH during the conditioning period. The quality assurance team at EPA Region 2's regional laboratory designed a PM2.5 weighing facility that operates well within these strict performance requirements. The traditional approach to meeting the performance requirements of Document 2.12 for PM2.5 filter analysis is to build a walk-in room, with costs typically exceeding $100,000. The initial one-time capital cost for the laboratory at EPA's Edison, NJ, facility was approximately $24,000. Annual costs [e.g., National Institute of Standards and Technology (NIST) recertifications and nitrogen replacement cylinders used for humidity control] are approximately $500. The average 24-hr variabilities in temperature and RH in the Region 2 weighing chamber are small, +/-0.2 degrees C and +/-0.8% RH, respectively. The mass detection limit for the PM2.5 weighing system of 47-mm stretched Teflon (lab blank) filters is 6.3 microg. This facility demonstrates an effective and economical example for states and other organizations planning PM2.5 weighing facilities.     

Optimal temporal scale for the correlation of AOD and ground measurements of PM2.5 in a real-time air quality estimation system

Aerosol optical depth (AOD), an indirect estimate of particulate matter using satellite observations, has shown great promise in improving estimates of PM_2.5 (particulate matter with aerodynamic diameter less than or equal to 2.5 μm) surface. Currently, few studies have been conducted to explore the optimal way to apply AOD data to improve the model accuracy of PM_2.5 in a real-time air quality system. We believe that two major aspects may be worthy of consideration in that area: 1) an approach that integrates satellite measurements with ground measurements in the estimates of pollutants and 2) identification of an optimal temporal scale to calculate the correlation of AOD and ground measurements. This paper is focused on the second aspect, identifying the optimal temporal scale to correlate AOD with PM_2.5. Five following different temporal scales were chosen to evaluate their impact on the model performance: 1) within the last 3 days, 2) within the last 10 days, 3) within the last 30 days, 4) within the last 90 days, and 5) the time period with the highest correlation in a year. The model performance is evaluated for its accuracy, bias, and errors based on the following selected statistics: the Mean Bias, the Normalized Mean Bias, the Root Mean Square Error, Normalized Mean Error, and the Index of Agreement. This research shows that the model with the temporal scale: within the last 30 days, displays the best model performance in a southern multi-state area centered in Mississippi using 2004 and 2005 data sets.     

驻极体空气过滤器对办公环境PM2.5的净化效果

针对办公环境PM2.5的净化问题,现场测试了以3种不同过滤面积的驻极体空气过滤器为核心过滤元件的空气净化器的过滤性能,并与普通高效微粒空气过滤器(high-efficiency particulate air,HEPA)、初效碳纤维滤层和活性炭滤网等进行了对比.测试点为上海某三楼办公室座位区离地面1.1 m处人体坐姿呼吸平面.采用蜡烛烟雾作为室内微细颗粒污染物的来源.分别测试了40 min内PM2.5的质量浓度衰减值和相应运行功率,并计算了净化器处理风量和洁净空气量.结果表明,过滤面积在0.20~0.54 m2范围内驻极体过滤器的过滤效率随面积增加而提高;过滤面积为0.29 m2的驻极体处理风量最大;以洁净空气量与功率的比值作为指标,可以直观判断出净化效果最好的是初效滤网叠加过滤面积为0.54 m2的驻极体过滤器;该工况下40 min内PM2.5浓度衰减率与HEPA几乎相同且均接近70%,但是洁净空气量大于HEPA.     

Using a chemistry transport model to account for the spatial variability of exposure concentrations in epidemiologic air pollution studies

Environmental epidemiology and more specifically time-series analysis have traditionally used area-averaged pollutant concentrations measured at central monitors as exposure surrogates to associate health outcomes with air pollution. However, spatial aggregation has been shown to contribute to the overall bias in the estimation of the exposure-response functions. This paper presents the benefit of adding features of the spatial variability of exposure by using concentration fields modeled with a chemistry transport model instead of monitor data and accounting for human activity patterns. On the basis of county-level census data for the city of Paris, France, and a Monte Carlo simulation, a simple activity model was developed accounting for the temporal variability between working and evening hours as well as during transit. By combining activity data with modeled concentrations, the downtown, suburban, and rural spatial patterns in exposure to nitrogen dioxide, ozone, and PM2.5 (particulate matter [PM] < or = 10 microm in aerodynamic diameter) were captured and parametrized. Exposures predicted with this model were used in a time-series study of the short-term effect of air pollution on total nonaccidental mortality for the 4-yr period from 2001 to 2004. It was shown that the time series of the exposure surrogates developed here are less correlated across co-pollutants than in the case of the area-averaged monitor data. This led to less biased exposure-response functions when all three co-pollutants were inserted simultaneously in the same regression model. This finding yields insight into pollutant-specific health effects that are otherwise masked by the high correlation among co-pollutants.     

声场中PM2.5颗粒碰撞运动模型

建立了声场中PM2.5颗粒碰撞运动模型。模拟结果表明,颗粒碰撞前速度与水平面的夹角θ是影响颗粒运动轨迹的一个重要因素,它的改变将决定颗粒在声场中是否碰撞、碰撞的位置以及碰撞后颗粒如何运动;颗粒碰撞前的速度大小将决定颗粒碰撞后是沿声波方向运动还是逆声波方向运动或是停留在原地振动;声场频率的不同改变了颗粒在发生碰撞时的运动趋势及颗粒的在碰撞时的运动趋势,同时,声场频率的改变将影响碰撞后颗粒的振幅;声场声强的改变不但影响了颗粒运动的振幅,而且影响了颗粒碰撞后运动趋势。