Measurements of Ultrafine Particles and Other Vehicular Pollutants inside a Mobile Exposure System on Los Angeles Freeways

Abstract

A mobile exposure and air pollution measurement system was developed and used for on-freeway ultrafine particle health effects studies. A nine-passenger van was modified with a high-efficiency particulate air (HEPA) filtration system that can deliver filtered or unfiltered air to an exposure chamber inside the van. State-of-the-art instruments were used to measure concentration and size distribution of fine and ultrafine particles and the concentration of carbon monoxide (CO), black carbon (BC), particle-bound polycyclic aromatic hydrocarbons (PAHs), fine particulate matter (PM_2.5) mass, and oxides of nitrogen (NO_x) inside the exposure chamber. This paper presents the construction and technical details of the van and air pollutant concentrations collected in 32 2-hr runs on two major Los Angeles freeways, Interstate 405 (I-405; mostly gasoline traffic) and Interstate 710 (I-710; large proportion of heavy-duty diesel traffic). More than 97% of particles were removed when the flow through the filter box was switched from bypass mode to filter mode while the vehicle was driving on both freeways. The filtration system thus provides a great particulate matter exposure contrast while keeping gas-phase pollutant concentrations the same. Under bypass mode, average total particle number concentration observed inside the exposure chamber was around 8.4 × 10⁴ and 1.3 × 10⁵ particles cm^-3 on the I-405 and the I-710 freeways, respectively. Bimodal size distributions were consistent and similar for both freeways with the first mode around 16–20 nm and the second mode around 50–55 nm. BC and particle-bound PAH concentrations were more than two times greater on the I-710 than on the I-405 freeway. Very weak correlations were observed between total particle number concentrations and other vehicular pollutants on the freeways.     

A Statistical Assessment of Saturation and Mobile Sampling Strategies to Estimate Long-Term Average Concentrations across Urban Areast

Abstract

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 (NO_x), sulfur dioxide (SO₂), coarse particulate matter (PM₁₀), fine particulate matter (PM_2.5)_, 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 NO_x. Larger errors, up to 50%, are expected for NO, SO₂, PM_2.5, 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.     

Compromising economic cost and air pollutant emissions of municipal solid waste management strategies by fuzzy multiobjective optimization model

Municipal solid waste management (MSWM) is an important environmental challenge and subject in urban planning. A sustainable MSWM strategy should consider not only economic efficiency but also life-cycle assessment of environmental impact. This study employs the fuzzy multiobjective linear programming (FMOLP) technique to find the optimal compromise between economic optimization and pollutant emission reduction for the MSWM strategy. Taichung City in Taiwan is evaluated as a case study. The results indicate that the optimal compromise MSWM strategy can reduce significant amounts of pollutant emissions and still achieve positive net profits. Minimization of the sulfur oxide (SO_x) and nitrogen oxide (NO_x) emissions are the two major priorities in achieving this optimal compromise strategy when recyclables recovery rate is lower; however, minimization of the carbon monoxide (CO) and particulate matter (PM) emissions become priority factors when recovery rate is higher.

Implications: This paper applied the multiobjective optimization model to find the optimal compromise municipal solid waste management (MSWM) strategy, which minimizes both life-cycle operating cost and air pollutant emissions, and also to analyze the correlation between recyclables recovery rates and optimal compromise strategies. It is different from past studies, which only consider economic optimization or environmental impacts of the MSWM system. The result shows that optimal compromise MSWM strategy can achieve a net profit and reduce air pollution emission. In addition, scenario investigation of recyclables recovery rates indicates that resource recycling is beneficial for both economic optimization and air pollutant emission minimization.     

Neural Network and Multiple Regression Models for PM10 Prediction in Athens: A Comparative Assessment

Abstract

Particulate atmospheric pollution in urban areas is considered to have significant impact on human health. Therefore, the ability to make accurate predictions of particulate ambient concentrations is important to improve public awareness and air quality management. This study examines the possibility of using neural network methods as tools for daily average particulate matter with aerodynamic diameter <10 µm (PM₁₀) concentration forecasting, providing an alternative to statistical models widely used up to this day. Based on a data inventory, in a fixed central site in Athens, Greece, ranging over a two-year period, and using mainly meteorological variables as inputs, neural network models and multiple linear regression models were developed and evaluated. Comparison statistics used indicate that the neural network approach has an edge over regression models, expressed both in terms of prediction error (root mean square error values lower by 8.2–9.4%) and of episodic prediction ability (false alarm rate values lower by 7–13%). The results demonstrate that artificial neural networks (ANNs), if properly trained and formed, can provide adequate solutions to particulate pollution prognostic demands.     

Determination of the emissions from an aircraft auxiliary power unit (APU) during the Alternative Aviation Fuel Experiment (AAFEX)

The emissions from a Garrett-AiResearch (now Honeywell) Model GTCP85–98CK auxiliary power unit (APU) were determined as part of the National Aeronautics and Space Administration's (NASA's) Alternative Aviation Fuel Experiment (AAFEX) using both JP-8 and a coal-derived Fischer Tropsch fuel (FT-2). Measurements were conducted by multiple research organizations for sulfur dioxide (SO₂), total hydrocarbons (THC), carbon monoxide (CO), carbon dioxide (CO₂), nitrogen oxides (NO_x), speciated gas-phase emissions, particulate matter (PM) mass and number, black carbon, and speciated PM. In addition, particle size distribution (PSD), number-based geometric mean particle diameter (GMD), and smoke number were also determined from the data collected. The results of the research showed PM mass emission indices (EIs) in the range of 20 to 700 mg/kg fuel and PM number EIs ranging from 0.5?×?10¹⁵ to 5?×?10¹⁵ particles/kg fuel depending on engine load and fuel type. In addition, significant reductions in both the SO₂ and PM EIs were observed for the use of the FT fuel. These reductions were on the order of ～90% for SO₂ and particle mass EIs and ～60% for the particle number EI, with similar decreases observed for black carbon. Also, the size of the particles generated by JP-8 combustion are noticeably larger than those emitted by the APU burning the FT fuel with the geometric mean diameters ranging from 20 to 50 nm depending on engine load and fuel type. Finally, both particle-bound sulfate and organics were reduced during FT-2 combustion. The PM sulfate was reduced by nearly 100% due to lack of sulfur in the fuel, with the PM organics reduced by a factor of ～5 as compared with JP-8.

Implications: The results of this research show that APUs can be, depending on the level of fuel usage, an important source of air pollutant emissions at major airports in urban areas. Substantial decreases in emissions can also be achieved through the use of Fischer Tropsch (FT) fuel. Based on these results, the use of FT fuel could be a viable future control strategy for both gas- and particle-phase air pollutants.     

Ambient air pollution and term birth weight in Texas from 1998 to 2004

Previous studies have explored the association between air pollution levels and adverse birth outcomes such as lower birth weight. Existing literature suggests an association, although results across studies are not consistent. Additional research is needed to confirm the effect, investigate the exposure window of importance, and distinguish which pollutants cause harm.

We assessed the association between ambient pollutant concentrations and term birth weight for 1,548,904 births in TX from 1998 to 2004. Assignment of prenatal exposure to air pollutants was based on maternal county of residence at the time of delivery. Pollutants examined included particulate matter with aerodynamic diameter ≤10 and ≤2.5 µm (PM₁₀ and PM_2.5), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO), and ozone (O₃). We applied a linear model with birth weight as a continuous variable. The model was adjusted for known risk factors and region. We assessed pollutant effects by trimester to identify biological exposure window of concern, and explored interaction due to race/ethnicity.

An interquartile increase in ambient pollutant concentrations of SO₂ and O₃ was associated with a 4.99-g (95% confidence interval [CI], 1.87–8.11) and 2.72-g (95% CI, 1.11–4.33) decrease in birth weight, respectively. Lower birth weight was associated with exposure to O₃ in the first and second trimester, whereas results were not significant for other pollutants by trimester. A positive association was exhibited for PM_2.5 in the first trimester. Effects estimates for PM₁₀ and PM_2.5 were inconsistent across race/ethnic groups.

Current ambient air pollution levels may be increasing the risk of lower birth weight for some pollutants. These risks may be increased for certain racial/ethnic groups. Additional research including consideration of improved methodology is needed to investigate these findings. Future studies should examine the influence of residual confounding.

Implications: This is one of the most comprehensive studies examining criteria air pollutants and lower birth weight in Texas. Our findings confirm results found previously for adverse effects of the air pollutant SO₂ on lower birth weight. Results from our study suggest that adverse pregnancy outcomes such as lower birth weight can occur even while maintaining air pollution levels below regulatory standards. Future studies should incorporate the assessment of differential pollutant exposure as well as effect estimates by race/ethnicity with individual and community-level social factors in order to enhance our understanding of how physical, social, and host factors influence birth outcomes.

Supplemental Materials: Supplementary information relating to characteristics of excluded births, distribution of air pollutant monitors by pollutant, and correlation coefficients of the air pollutants is available in the publisher's online edition of the Journal of the Air & Waste Management Association.     

Plume analysis from field evaluations of a portable air quality monitoring system

ABSTRACT

Near-road measurements in Rochester, NY with a Portable Air Quality Monitoring System indicate a significant plume control of PM_2.5 black carbon (BC) concentrations. This study evaluates the performance of two portable air quality enclosures deployed at collocated research sites to determine their accuracy and usefulness in field deployments, and specifically in pollution plume analysis. One system deployed collocated sensors for measurement of particulate matter mass concentration (Thermo pDR 1500 against Tapered Element Oscillating Microbalance (TEOM) measurement) and the second system deployed sensors for measurement of black carbon (Magee AE33 aethalometer and Brechtel Tricolor Absorption Photometer) in ambient and near-road locations in Rochester, New York, respectively. While the optical PM_2.5 sensors tended to be biased in their determination of concentration by ~15%, they followed changes and trends in concentration very well. The black carbon sensors in the portable systems agreed very well with each other and with the collocated sensor. As a case study to determine the contribution from statistically significant short-lived excursions of pollutant concentration, Morlet wavelet analysis was performed on data from the portable system sensors. Black carbon was found to be strongly influenced by plume behavior with significant plume excursions representing just over 12% of all data points and contributing on average 1 µg/m³ of black carbon above ambient concentrations.

Implications: This paper first evaluates two air pollutant monitoring enclosures with wide applicability including near-road detection of pollutants. Then, we present a novel method to designate isolate statistically significant excursions in air pollution concentration which can be used to determine the impact of pollutant plumes as observed in PM and black carbon behavior near road.     

Effects of Instrument Precision and Spatial Variability on the Assessment of the Temporal Variation of Ambient Air Pollution in Atlanta,Georgia

Abstract

Data from the U.S. Environmental Protection Agency Air Quality System, the Southeastern Aerosol Research and Characterization database, and the Assessment of Spatial Aerosol Composition in Atlanta database for 1999 through 2002 have been used to characterize error associated with instrument precision and spatial variability on the assessment of the temporal variation of ambient air pollution in Atlanta, GA. These data are being used in time series epidemiologic studies in which associations of acute respiratory and cardiovascular health outcomes and daily ambient air pollutant levels are assessed. Modified semivariograms are used to quantify the effects of instrument precision and spatial variability on the assessment of daily metrics of ambient gaseous pollutants (SO₂, CO, NO_x, and O₃) and fine particulate matter ([PM_2.5] PM_2.5 mass, sulfate, nitrate, ammonium, elemental carbon [EC], and organic carbon [OC]). Variation because of instrument imprecision represented 7–40% of the temporal variation in the daily pollutant measures and was largest for the PM_2.5 EC and OC. Spatial variability was greatest for primary pollutants (SO₂, CO, NO_x, and EC). Population–weighted variation in daily ambient air pollutant levels because of both instrument imprecision and spatial variability ranged from 20% of the temporal variation for O₃ to 70% of the temporal variation for SO₂ and EC. Wind rose plots, corrected for diurnal and seasonal pattern effects, are used to demonstrate the impacts of local sources on monitoring station data. The results presented are being used to quantify the impacts of instrument precision and spatial variability on the assessment of health effects of ambient air pollution in Atlanta and are relevant to the interpretation of results from time series health studies that use data from fixed monitors.     

Characterizing the spatiotemporal variability of PM2.5 in Cusco,Peru using kriging with external drift

Advancing the understanding of spatiotemporal aspects of air pollution in the urban environment is an area where improved methods can be of great benefit to exposure assessment and policy support. This paper explores the potential of a technique known as kriging with external drift (KED) to provide high resolution maps of fine particulate matter for a downtown region of Cusco, Peru. There were three stages in this research. The first was to conduct a pilot level monitoring campaign to investigate ambient, regional, and street-level air pollutant concentrations for particulate matter (PM_2.5, PM₁₀) and carbon monoxide (CO) in the Province of Cusco. The second was to compile observations within a geographic information system (GIS) in order to characterize the proximal effect of the local transportation network, elevation, and land use classifications on PM_2.5. Third, regression, ordinary kriging and kriging with external drift were used to model PM_2.5 for three select time periods during a 24-h day. Statistical evaluations indicate kriging with external drift resulted in the strongest models explaining 64% of variability seen with morning particle concentrations, 25% for afternoon particles, and 53% in evening particles. These models capture spatial and temporal variability for air pollution in Cusco. These variations seem to be influenced, to varying degrees, by elevation, meteorological conditions, spatial location, and transportation characteristics. In conclusion, combining GIS, meteorological data and geostatistics proved to be a complementary suite of tools for incorporating spatiotemporal analysis into the air quality assessment.     

Predicting hourly air pollutant levels using artificial neural networks coupled with uncertainty analysis by Monte Carlo simulations

Recent progress in developing artificial neural network (ANN) metamodels has paved the way for reliable use of these models in the prediction of air pollutant concentrations in urban atmosphere. However, improvement of prediction performance, proper selection of input parameters and model architecture, and quantification of model uncertainties remain key challenges to their practical use. This study has three main objectives: to select an ensemble of input parameters for ANN metamodels consisting of meteorological variables that are predictable by conventional weather forecast models and variables that properly describe the complex nature of pollutant source conditions in a major city, to optimize the ANN models to achieve the most accurate hourly prediction for a case study (city of Tehran), and to examine a methodology to analyze uncertainties based on ANN and Monte Carlo simulations (MCS). In the current study, the ANNs were constructed to predict criteria pollutants of nitrogen oxides (NO_x), nitrogen dioxide (NO₂), nitrogen monoxide (NO), ozone (O₃), carbon monoxide (CO), and particulate matter with aerodynamic diameter of less than 10 μm (PM₁₀) in Tehran based on the data collected at a monitoring station in the densely populated central area of the city. The best combination of input variables was comprehensively investigated taking into account the predictability of meteorological input variables and the study of model performance, correlation coefficients, and spectral analysis. Among numerous meteorological variables, wind speed, air temperature, relative humidity and wind direction were chosen as input variables for the ANN models. The complex nature of pollutant source conditions was reflected through the use of hour of the day and month of the year as input variables and the development of different models for each day of the week. After that, ANN models were constructed and validated, and a methodology of computing prediction intervals (PI) and probability of exceeding air quality thresholds was developed by combining ANNs and MCSs based on Latin Hypercube Sampling (LHS). The results showed that proper ANN models can be used as reliable metamodels for the prediction of hourly air pollutants in urban environments. High correlations were obtained with R ² of more than 0.82 between modeled and observed hourly pollutant levels for CO, NO_x, NO₂, NO, and PM₁₀. However, predicted O₃ levels were less accurate. The combined use of ANNs and MCSs seems very promising in analyzing air pollution prediction uncertainties. Replacing deterministic predictions with probabilistic PIs can enhance the reliability of ANN models and provide a means of quantifying prediction uncertainties.     

Concentrations of mobile source air pollutants in urban microenvironments

Human exposures to criteria and hazardous air pollutants (HAPs) in urban areas vary greatly due to temporal-spatial variations in emissions, changing meteorology, varying proximity to sources, as well as due to building, vehicle, and other environmental characteristics that influence the amounts of ambient pollutants that penetrate or infiltrate into these microenvironments. Consequently, the exposure estimates derived from central-site ambient measurements are uncertain and tend to underestimate actual exposures. The Exposure Classification Project (ECP) was conducted to measure pollutant concentrations for common urban microenvironments (MEs) for use in evaluating the results of regulatory human exposure models. Nearly 500 sets of measurements were made in three Los Angeles County communities during fall 2008, winter 2009, and summer 2009. MEs included in-vehicle, near-road, outdoor, and indoor locations accessible to the general public. Contemporaneous 1- to 15-min average personal breathing zone concentrations of carbon monoxide (CO), carbon dioxide (CO₂), volatile organic compounds (VOCs), nitric oxide (NO), nitrogen oxides (NO_x), particulate matter (<2.5 μm diameter; PM_2.5) mass, ultrafine particle (UFP; <100 nm diameter) number, black carbon (BC), speciated HAPs (e.g., benzene, toluene, ethylbenzene, xylenes [BTEX], 1,3-butadiene), and ozone (O₃) were measured continuously. In-vehicle and inside/outside measurements were made in various passenger vehicle types and in public buildings to estimate penetration or infiltration factors. A large fraction of the observed pollutant concentrations for on-road MEs, especially near diesel trucks, was unrelated to ambient measurements at nearby monitors. Comparisons of ME concentrations estimated using the median ME/ambient ratio versus regression slopes and intercepts indicate that the regression approach may be more accurate for on-road MEs. Ranges in the ME/ambient ratios among ME categories were generally greater than differences among the three communities for the same ME category, suggesting that the ME proximity factors may be more broadly applicable to urban MEs.

Implications:Estimates of population exposure to air pollutants extrapolated from ambient measurements at ambient fixed site monitors or exposure surrogates are prone to uncertainty. This study measured concentrations of mobile source air toxics (MSAT) and related criteria pollutants within in-vehicle, outdoor near-road, and indoor urban MEs to provide multipollutant ME measurements that can be used to calibrate regulatory exposure models.     

Evaluation of Human Exposure to Ambient PM10 in the Metropolitan Area of Mexico City Using a GIS-Based Methodology

ABSTRACT

The main goal of this study was to evaluate the magnitude of outdoor exposure to fine particulate matter (PM₁₀) potentially experienced by the population of metropolitan Mexico City. With the use of a geographic information system (GIS), spatially resolved PM₁₀ distributions were generated and linked to the local population. The PM₁₀ concentration exceeded the 24-hr air quality standard of 150 μg/m³ on 16% of the days, and the annual air quality standard of 50 μg/m³ was exceeded by almost twice its value in some places. The basic methodology described in this paper integrates spatial demographic and air quality databases, allowing the evaluation of various air pollution reduction scenarios. Achieving the annual air quality standard would represent a reduction in the annual arithmetic average concentration of 14 μg/m³ for the typical inhabitant. Human exposure to particulate matter (PM) has been associated with mortality and morbidity in Mexico City; reducing the concentration levels of this pollutant would represent a reduction in mortality and morbidity and the associated cost of such impacts. This methodology is critical to assessing the potential benefits of the current initiative to improve air quality implemented by the Environmental Metropolitan Commission of Mexico City.     

Application of Satellite Remote-Sensing Data for Source Analysis of Fine Particulate Matter Transport Events

Abstract

Satellite sensors have provided new datasets for monitoring regional and urban air quality. Satellite sensors provide comprehensive geospatial information on air quality with both qualitative imagery and quantitative data, such as aerosol optical depth. Yet there has been limited application of these new datasets in the study of air pollutant sources relevant to public policy. One promising approach to more directly link satellite sensor data to air quality policy is to integrate satellite sensor data with air quality parameters and models. This paper presents a visualization technique to integrate satellite sensor data, ground-based data, and back trajectory analysis relevant to a new rule concerning the transport of particulate matter across state boundaries. Overlaying satellite aerosol optical depth data and back trajectories in the days leading up to a known fine particulate matter with an aerodynamic diameter of <2.5 μm (PM_2.5) event may indicate whether transport or local sources appear to be most responsible for high PM_2.5 levels in a certain location at a certain time. Events in five cities in the United States are presented as case studies. This type of analysis can be used to help understand the source locations of pollutants during specific events and to support regulatory compliance decisions in cases of long distance transport.     

The Impact of Particulate Emissions Control On the Control of Other MWC Air Emissions

On December 20, 1989, the Environmental Protection Agency (EPA) proposed revised new source performance standards for new municipal waste combustion (MWC) units and guidelines for existing sources. The proposed national regulations require tighter particulate matter control and address pre-combustion, combustion, and post-combustion controls, the latter two depending on capacity and age of the facility.

The air pollutants of concern when municipal solid waste (MSW) is burned will be discussed. Generally, particulate control is an inherent part of the systems used to limit the emissions of these air pollutants. The relationships between MWC air emissions (acid gases, trace organics, and trace heavy metals) control and particulate control will be discussed. Test results to quantify air pollutant emissions from MWC units and their control will be presented and compared with the proposed regulations.     

Characterization of organic aerosol in Big Bend National Park,Texas

The Big Bend Regional Aerosol and Visibility Observational (BRAVO) Study was conducted in Big Bend National Park, Texas, July through October 1999. Daily PM_2.5 organic aerosol samples were collected on pre-fired quartz fiber filters. Daily concentrations were too low for detailed organic analysis by gas chromatography-mass spectrometry (GC-MS) and were grouped based on their air mass trajectories. A total of 12 composites, each containing 3–10 daily samples, were analyzed. Alkane carbon preference indices suggest primary biogenic emissions were small contributors to primary PM_2.5 organic matter (OM) during the first 3 months, while in October air masses advecting from the north and south were more strongly influenced by biogenic sources. A series of trace organic compounds previously shown to serve as particle phase tracers for various carbonaceous aerosol source types were examined. Molecular tracer species were generally at or below detection limits, except for the wood smoke tracer levoglucosan in one composite, so maximum possible source influences were calculated using the detection limit as an upper bound to the tracer concentration. Wood smoke was found not to contribute significantly to PM_2.5 OM, with contributions for most samples at <1% of the total organic particulate matter. Vehicular exhaust also appeared to make only minor contributions, with maximum possible influences calculated to be 1–4% of PM_2.5 OM. Several factors indicate that secondary organic aerosol formation was important throughout the study, and may have significantly altered the molecular composition of the aerosol during transport.     

Association between Levels of Fine Particulate and Emergency Visits for Pneumonia and other Respiratory Illnesses among Children in Santiago,Chile

ABSTRACT

Recent evidence has implicated the fine fraction of particulate as the major contributor to the increase in mortality and morbidity related to particulate ambient levels. We therefore evaluated the impact of daily variation of ambient PM_2.5 and other pollutants on the number of daily respiratory-related emergency visits (REVs) to a large pediatric hospital of Santiago, Chile. The study was conducted from February 1995 to August 1996. Four monitoring stations from the network of Santiago provided air pollution data. The PM2.5 24-hr average ranged from 10 to 111 μg/m³ during September to April (warm months) and from 10 to 156 μg/m³ during May to August (cold months). Other contaminants (ozone (O₃), nitrogen dioxide (NO₂), and sulfur dioxide (SO₂)) were, in general, low during the study period. The increase in REVs was significantly related to PM₁₀ and PM_2.5 ambient levels, with the relationship between PM_2.5 levels and the number of REVs the stronger. During the cold months, an increase of 45 ìg/m³ in the PM_2.5 24-hr average was related to a 2.7% increase in the number of REVs (95% CI, 1.1–4.4%) with a two-day lag, and to an increase of 6.7% (95% CI, 1.7–12.0%) in the number of visits for pneumonia with a three-day lag. SO₂ and NO₂ were also related to REVs. We conclude that urban air pollutant mixture, particularly fine particulates, adversely affect the respiratory health of children residing in Santiago.     

Air Quality Measurements for the Aerosol Research and Inhalation Epidemiology Study

Abstract

Measurements of pollutant gases, airborne particulate matter mass and composition, and meteorology have been made at a core site near downtown Atlanta, GA, since August 1998 in support of the Aerosol Research and Inhalation Epidemiology Study (ARIES). This site is one of eight in the Southeastern Aerosol Research and Characterization network. The measurement objective is to provide a long-term, multivariate dataset suitable for investigating statistical associations of respiratory and cardiovascular disease with airborne particulate matter composition, meteorology, and copollutant gases through epidemiologic modeling. Measurements are expected to continue through 2010. Ancillary multiyear measurements at additional sites in the Atlanta metropolitan area and in short-term exposure assessments have been used to estimate the exposure/measurement error associated with using data from a central site to approximate human exposures for the entire area. To date, 13-, 25-, and 53-month air quality datasets have been used in epidemiologic analyses.     

PM10 Levels in the Lower Fraser Valley,British Columbia,Canada: An Overview of Spatiotemporal Variations and Meteorological Controls

ABSTRACT

Three years of hourly averaged PM₁₀ (particulate matter less than 10 Lrm in diameter) tapered element oscillating microbalance (TEOM) data from 10 sites in the large coastal valley incorporating Greater Vancouver were used to investigate the spatiotemporal dimensions and air pollution meteorology of particulate pollution. During the period studied, the provincial “acceptable” objective daily concentration of 50 μg m^-3 was exceeded at 7 of the 10 sites. The highest annual, seasonal, and maximum hourly concentrations were recorded at Abbotsford in the central valley. Mean seasonal PM₁₀ concentrations were highest in the wintertime in the western Lower Fraser Valley (LFV) and in the summertime at the central and eastern valley locations. Within the network, interstation correlations of daily average concentrations exceed 0.8 at interstation distances less than 20 km and decrease thereafter. For daily maximum concentrations (hourly), interstation correlations decrease sharply with distance. Meteorological conditions responsible for elevated par-ticulate concentrations in the LFV are associated with (1) short periods (1- to 3-hr duration) of reduced dispersion during summer nights at sites close to primary sources, (2) summer anticyclonic conditions when photochemical pollutant concentrations build up across the entire valley, and (3) occasional wintertime “gap wind” events in the eastern valley.