Daily Average Concentrations Of PM2 Research Articles

Ambient Air Particulate Matter in the Yangtze River Delta Region, China: Spatial, Annual, and Seasonal Variations and Health Risks

This study provides the spatial, annual, and seasonal variations and the related human health risk assessment of PM10 in the ambient air of Yangtze River Delta Region (YRDR). Daily average PM10 concentrations of the 25 cities in this region in the previous 5 years (2005–2009) ranged from 52.8 to 112.1 μg/m3. PM10 levels showed a decreasing trend for the major cities in the YRDR during this period. PM10 mass was highest in winter (110.5 μg/m3), followed by spring (95.9 μg/m3) and summer (85.5 μg/m3), with lowest concentration in autumn (78.2 μg/m3). For the potential health risk analysis, the residents were divided into four age categories, namely, infants, children (1 year), children (8–10 years), and adults. The analysis took into account age-specific breathing rates, body weights for different age categories. Risk analyses showed that the potential health risks due to respiratory disease were highest for all age categories in Xuzhou, followed by Yancheng, Hangzhou, and Nanjing, and the citizens in Zhoushan had the lowest health risks. The new information about the relationship between health risks and air pollution in YRDR may have implications for local environmental and social policies.

Neural predictors and wavelet transformation for forecasting the PM10 pollution

PurposeThe aim of this paper is to develop the accurate computer method of predicting the average PM10 pollution for the next day on the basis of some measured atmospheric parameters, like temperature, humidity, wind, etc. This method should be universal and applicable for any place under consideration.Design/methodology/approachThe paper presents the new approach to the accurate forecasting of the daily average concentration of PM10. It is based on the application of the ensemble of neural networks and wavelet transformation of the time series, representing PM10 pollution.FindingsOn the basis of numerical experiments, the paper finds that application of many neural predictors cooperating with each other can significantly improve the quality of results. The paper shows that the developed forecasting system checked on the data of PM10 pollution in Warsaw generated good overall accuracy of prediction in terms of root mean squared error, mean absolute error and mean absolute percentage error.Originality/valueThe main novelty of the proposed approach is the application of the wavelet transformation and many neural networks organized in the form of ensemble. The individual neural predictors are integrated into one forecasting system using different forms of integrations, including the blind source separation method and neural‐based integration.

Evaluation of a multiple regression model for the forecasting of the concentrations of NOx and PM10 in Athens and Helsinki

Forecasting models based on stepwise multiple linear regression (MLR) have been developed for Athens and Helsinki. The predictor variables were the hourly concentrations of pollutants (NO, NO2, NOx, CO, O3, PM2.5 and PM10) and the meteorological variables (ambient temperature, wind speed/direction, and relative humidity) and in case of Helsinki also Monin-Obukhov length and mixing height of the present day. The variables to be forecasted are the maximum hourly concentrations of PM10 and NOx, and the daily average PM10 concentrations of the next day. The meteorological pre-processing model MPP-FMI was used for computing the Monin-Obukhov length and the mixing height. The limitations of such statistical models include the persistence of both the meteorological and air quality situation; the model cannot account for rapid changes (on a temporal scale of hours or less than a day) that are commonly associated, e.g., with meteorological fronts, or episodes of a long-range transport origin. We have selected the input data for the model from one urban background and one urban traffic station both in Athens and Helsinki, in 2005. We have used various statistical evaluation parameters to analyze the performance of the models, and inter-compared the performance of the predictions for both cities. Forecasts from the MLR model were also compared to those from an Artificial Neural Network model (ANN) to investigate, if there are substantial gains that might justify the additional computational effort. The best predictor variables for both cities were the concentrations of NOx and PM10 during the evening hours as well as wind speed, and the Monin-Obukhov length. In Athens, the index of agreement (IA) for NOx ranged from 0.77 to 0.84 and from 0.69 to 0.72, in the warm and cold periods of the year. In Helsinki, the corresponding values of IA ranged from 0.32 to 0.82 and from 0.67 to 0.86 for the warm and cold periods. In case of Helsinki the model accuracy was expectedly better on the average, when Monin-Obukhov length and mixing height were included as predictor variables. The models provide better forecasts of the daily average concentration, compared with the maximum hourly concentration for PM10. The results derived by the ANN model where only slightly better than the ones derived by the MLR methodology. The results therefore suggest that the MLR methodology is a useful and fairly accurate tool for regulatory purposes.

Evolving the ensemble of predictors model for forecasting the daily average PM10

The paper develops the methods of accurately forecasting the daily average concentration of PM10. We apply the Support Vector Machine in the regression mode (SVR) as the main workhorse of prediction. Different approaches to the prediction are tried: the direct application of SVR, the combination of SVR and wavelet decomposition, and the Blind Source Separation (BSS) method for improving the final accuracy of prediction. The main novelty of the proposed approach is the application of the ensemble of predictors integrated using the BSS method. The numerical experiments of predicting the daily concentration of the PM10 pollution in Warsaw have shown good overall accuracy of prediction in terms of RMSE, MAE and MAPE errors, as well as correlation and index of agreement measures.

The Impact of Yellow Dusts From China on Air Quality in Northern Taiwan From 1994 to 2008

PP-29-035 Background/Aims: Air quality in Taiwan has been improved in recent years, causing significant changes in the yellow dusts from north-west China affecting Taiwan. The objective of this study is to recognize the effect of yellow dust on Taiwan originating from China on a daily basis. Methods: Between 1994 and 2008, the data are derived from hourly particulate matter (PM10) and CO value taken from the monitoring stations located in the city center, northern and eastern coast, and national park. We determine that a day is affected by dust when PM10 concentration increases from both northern and eastern coast, and the National Park monitoring stations simultaneously, and at the same time, the daily average concentration of PM10 from the city center monitoring station records values >125 μg/m3. We also distinguish the Asian dust events by measuring decreasing CO levels during the dust period, and increasing CO levels during periods of locally accumulating pollution. Results: It is estimated that 53 dust events were influenced by yellow dust from China during 1994 to 2008, with the most occurrences during 2000 to 2007 showing an increasing trend, especially during 2000 where occurrences went up to 9. The most frequent times occurred in the months of March and April, totaling at 46%. On average, the concentration of PM10 in dust impact day is 4 times higher than its non-dust impact day. In addition, CO is not accumulated in dust impact day. Conclusion: Taiwan is largely affected by the dust storms from China, even though it is away from the originate area of dust. Increasing PM concentrations in the ambient air causes human health hazards such as respiratory tract and cardiovascular disease. We should enforce our authority to concern this issue and do better in forecast.

Particulate Matter Concentrations and Emissions of a High-Rise Layer House in Iowa

Particulate matter (PM) associated with animal feeding operations is a concern for the occupants and the surrounding community. Baseline measurements of PM concentration and emission rate are the first step toward assessing the magnitude of concentrations and emissions and evaluating effectiveness of dust control strategies. This study presents the results of PM measurements at a high-rise layer house (approx. 250,000 hens) in central Iowa using tapered element oscillating microbalance (TEOM) equipment. Daily average concentrations of PM10 and PM2.5 over a 17-month measurement period were 393 (257 SD) and 44 (36 SD) g m-3, respectively. Daily average PM10 and PM2.5 emission rates during the same monitoring period were, respectively, 26.1 (15.8 SD) and 3.6 ( 3.7 SD) mg bird-1 d-1, or 8.16 (4.94) and 1.13 (1.16) g AU-1 d-1 (AU = animal unit = 500 kg body weight). PM emission rate was positively related to ventilation rate but was negatively related to relative humidity.

Prediction of PM10 concentrations through multi–gene genetic programming

This study aims to apply a multi–gene genetic programming (MGP) methodology for predicting the daily average of PM10 concentrations on the next day. This methodology is based on the principles of the simple genetic programming (GP) algorithm. The models are also encoded in tree structures (tree expressions) that are modified following an iterative process; the model structure and parameters are optimized, simultaneously. The main differences between these two methodologies are: (i) an individual is composed by several tree structures, called genes, and not a single one; and (ii) the output value is calculated through the linear combination of the outputs of the different genes belonging to the same individual.The case study here considered was to predict the daily average of PM10 concentrations on the next day. The data were collected in an urban site with traffic influences in Oporto Metropolitan Area, Northern Portugal. The air pollutants data (daily average concentrations of SO2, CO, NO, NO2 and PM10) and the meteorological data (daily averages of temperature – T, relative humidity – RH and wind speed – WS) were used as inputs for the models. The studied period was from January 2003 to December 2005.Ten MGP runs were applied and the results showed that RH, NO2 and PM10 concentrations were the most relevant input variables, as they appeared in almost all models. The MGP runs lead to selection of different models, which presented similar results in both training and test periods. Their predictive performances were compared with ones obtained with linear statistical models. MGP models did not present better results than linear models. However, considering that the relationships between air quality and meteorological variables are nonlinear and unknown, MGP was considered as a promising technique for the prediction of the daily average PM10 concentrations.

Characteristics of Major PM2.5 Components during Winter in Tianjin, China

Fine particles were simultaneously collected at an urban, industrial and coastal site in Tianjin, China from December 28, 2006 to January 31, 2007 to investigate the chemical characteristics of major PM2.5 components during an annually occurring season of high pollution. Water-soluble inorganic ions, organic carbon (OC) and elemental carbon (EC) were analyzed. Daily average concentrations of PM2.5 ranged from 61.5-566.8 μg/m3 with an average of 223.0 μg/m3. The sum of the measured species accounted for about 60% of the PM2.5 mass with organic aerosols and sulfates being the two largest contributors. Total carbon (OC + EC) constituted 23.3%, 25.5% and 19.2%, and secondary ionic species (SO42-, NO3- and NH4+) constituted 27.6%, 19.0% and 25.9% of PM2.5 at the urban, industrial and coastal sites, respectively. The PM2.5 composition in the industrial area was influenced by local industrial emission sources. About 80% of the total carbon existed in the form of OC, and the average OC/EC ratios were 4.5, 5.6 and 4.8 at the urban, industrial and coastal sites, respectively. Secondary organic carbon (SOC), estimated by the minimum OC/EC ratio, accounted for about 37.2%~50.3% of total OC. High Cl- concentrations averaging 9.3 μg/m3 were found, which mostly originated from anthropogenic sources, rather than from sea-salt aerosols.

충남지역 대기 중 미세입자 오염 현황

Concentrations of atmospheric fine particles in Chungnam were measured at 7 sampling sites during 2005. The daily average concentrations of PM 10, PM2.5, and PM1 ranged from 14.9 to <TEX>$136.5{\mu}g/m^3$</TEX>, 8.2 to <TEX>$113.2{\mu}g/m^3$</TEX>, and 5.7 to <TEX>$107.5{\mu}g/m^3$</TEX>, respectively, and the highest levels were observed at Yeongi site. The lowest concentrations for the all size fractions of particulate were observed at Taean located at the west end of the peninsula. The daily average PM10 concentrations were below the current National Standard at all sites, while the daily average PM2.5 concentrations frequently exceeded the US Standard at Cheonan, Dangjin, Boryeong, and Yeongi sites. The frequencies of PM2.5 concentrations exceeding the US standard at Cheonan, Dangjin, Boryeong, and Yeongi were 10.8%, 6.7%, 6.7%, and 26.7%, respectively. In addition, <TEX>$68{\sim}80%$</TEX> of PM10 was in the PM2.5 fraction indicating that fine particles were the major component of atmospheric particles in Chungnam.

Interaction Between Ambient Air Pollution and Typical Asthma Medication Regimens on Exhaled Nitric Oxide

P-111 Abstract: Airway inflammation is considered one of the major mechanisms by which air pollution affects respiratory health. Exhaled nitric oxide (NO), a non-invasive measure of airway inflammation, has only been used as an outcome variable in limited air pollution epidemiological studies. This study is designed to examine the association between ambient air pollution and exhaled NO in patients with moderate asthma, and to examine the modifications of asthma medications on the associations. We compared airway inflammation response to ambient air pollution in 119 asthmatics who were enrolled in a controlled clinical trial of three therapies from the Salmeterol Off Corticosteroids Study (SOCS) within the Asthma Clinical Research Network (ACRN). The three therapies were an inhaled corticosteroid alone, an inhaled long-acting beta-agonist alone, and placebo. The participants were non-smokers aged 12 through 63 years recruited from six university-based ambulatory care centers from February 1997 to January 1999. The ambient pollutant data was derived from the U.S. Environmental Protection Agency (EPA) Aerometric Information Retrieval System (AIRS) which provides average daily measured concentrations of NO2, PM10, O3, and SO2. An increase of 10 ppb of ambient NO2 daily concentrations was associated with an increase in exhaled NO of 0.13 ppb (95% confidence interval (CI) 0.06 to 0.19) in models adjusted for age, sex, race/ethnicity, asthma clinical center, season, week, daily average temperature, and daily average relative humidity. Similarly, an increase in the daily average PM10 concentration of 10 μg/m3 was associated with an increase in exhaled NO of 0.07 ppb (95% CI 0.02 to 0.12). A negative association was observed for a maximum 8- hour average O3 concentration of 10 ppb with exhaled NO (-0.09 ppb, 95% CI −0.13 to −0.05). In general, there was a trend of a more pronounced association between exhaled NO with NO2, PM10 and O3, respectively in the group of asthmatics on the beta-agonist. Ambient air pollution at levels below the current ambient air quality standards may still enhance airway inflammation on patients with moderate asthma even on typical asthma medication regimens. An inhaled long-acting beta-agonist may enhance the effects of air pollution on airway inflammation.

Statistical Interpretation of Weekday/Weekend Differences of Ambient Particulate Matter, Vehicular Traffic and Meteorological Parameters in an Urban Region of Kolkata, India

An air quality sampling program was designed and implemented to collect data on ambient PM10, TSP, vehicular traffic and meteorological parameters at weekdays and weekends from November 2003 to November 2004 in Kolkata. Educational institutions and most offices and small-scale industries are closed during weekends. Meteorological parameters were measured by the Indian Meteorological Department, Kolkata. Air quality monitoring was performed weekly for 24-h periods at residential (Kasba) and industrial (Cossipore) sites in an urban region of Kolkata and weekday/weekend differences were compared at both the monitoring sites. Significantly higher weekday values of PM10 and vehicular traffic were observed. The daily average PM10 concentrations on weekdays and at weekends were, respectively, 147.7 and 125.5 g·m 3 at the residential site and 212.3 and 186.3 g·m 3 at the industrial site. Corresponding TSP concentrations were 288.4 and 391.9 g·m 3 and 229.6 and 344.9 g/m3. Average traffic flow for weekdays and weekends were 2.3 104 and 1.9 104 vehicles at the residential site and 2.8 104 and 2.4 104 vehicles at the industrial site. There was a strong linear relationship between PM10 and TSP (R2 &gt;0.90) during weekdays at the residential site and at weekends at the industrial site. TSP had moderate (R2 &gt;0.50) linear relationship with PM10 during weekends at the residential site and good (R2 &gt;0.75) linear relationship during weekdays at the industrial site. The Spearman rank correlation coefficient showed that measured particulate pollutants had an inverse correlation with rainfall, temperature and relative humidity at both sites. Average PM10/TSP ratio was moderately correlated (r&gt;0.50) with average vehicular traffic.

Particulate Matter from Stone Crushing Industry: Size Distribution and Health Effects

A cluster of 50 stone crushing units located at Pammal, in suburban Chennai, the capital of Tamil Nadu State, India, is a source of high levels of dust generation in the vicinity of the crushers and in the communities surrounding them. Ambient air quality network consisting of 26 sampling locations were operated to continuously monitor the total and respirable particulate matter concentrations (TSP and PM10 ). The daily average ambient concentrations of TSP and PM10 varied from 342 to 2,470 and 90 to 1,200 μg∕ m3 , respectively, near the source, while the average concentrations varied from 86 to 257 and 39 to 138 μg∕ m3 in ambient air. The average PM2.5 concentration varied from 41 to 388 μg∕ m3 at the source, whereas the concentration varied from 17 to 48 μg∕ m3 in ambient air. Personal samplers were also employed to quantify the TSP and RPM in the work environment and they varied from 22.5 to 80.5 and 13.5 to 53.7 mg∕ m3 , respectively. Both ambient concentrations and occupational exposure levels exceed...

Characteristics of vertical profiles and sources of PM2.5, PM10 and carbonaceous species in Beijing

In August 2003 during the anticipated month of the 2008 Beijing Summer Olympic Games, we simultaneously collected PM10 and PM2.5 samples at 8, 100, 200 and 325 m heights up a meteorological tower and in an urban and a suburban site in Beijing. The samples were analysed for organic carbon (OC) and elemental carbon (EC) contents. Particulate matter (PM) and carbonaceous species pollution in the Beijing region were serious and widespread with 86% of PM2.5 samples exceeding the daily National Ambient Air Quality Standard of the USA (65 μg m−3) and the overall daily average PM10 concentrations of the three surface sites exceeding the Class II National Air Quality Standard of China (150 μg m−3). The maximum daily PM2.5 and PM10 concentrations reached 178.7 and 368.1 μg m−3, respectively, while those of OC and EC reached 22.2 and 9.1 μg m−3 in PM2.5 and 30.0 and 13.0 μg m−3 in PM10, respectively. PM, especially PM2.5, OC and EC showed complex vertical distributions and distinct layered structures up the meteorological tower with elevated levels extending to the 100, 200 and 300 m heights. Meteorological evidence suggested that there exist fine atmospheric layers over urban Beijing. These layers were featured by strong temperature inversions close to the surface (<50 m) and more stable conditions aloft. They enhanced the accumulation of pollutants and probably caused the complex vertical distributions of PM and carbonaceous species over urban Beijing. The built-up of PM was accompanied by transport of industrial emissions from the southwest direction of the city. Emissions from road traffic and construction activities as well as secondary organic carbon (SOC) are important sources of PM. High OC/EC ratios (range of 1.8–5.1 for PM2.5 and 2.0–4.3 for PM10) were found, especially in the higher levels of the meteorological tower suggesting there were substantial productions of SOC in summer Beijing. SOC is estimated to account for at least 33.8% and 28.1% of OC in PM2.5 and PM10, respectively, with higher percentages at the higher levels of the tower.

Spatial and Temporal Variation of PM10Mass Concentrations within the Greater Area of Athens, Greece

This study presents the results of a yearlong PM10 measurement campaign, conducted at four sampling sites, within the Greater Athens Area, between June 2001 and May 2002. Daily average PM10 concentrations were determined using reference samplers installed in two urban locations (Maroussi and Aristotelous), a mixed urban–industrial location (Elefsina) and a background location (Thrakomacedones). The 24-h and annual PM10 average concentrations were calculated and compared to the corresponding air quality standards, promulgated by the European Union, revealing severe exceedances of the limit values. The mean concentrations for the sampling periods were 73.8, 83.2, 32.9 and 54.9 μg/m3 for Maroussi (MAR), Aristotelous (ARI), Thrakomacedones (THR) and Elefsina (ELE), respectively. The spatial variation of PM10, was investigated resulting at a coefficient of variation of 0.36 within the study area and correlation coefficients ranging from 0.57 to 0.84. In addition, strong associations between PM10 and other primary gaseous pollutants were found. Regression analysis of PM10 against NOx (used as an indicator for road traffic emissions) revealed significant vehicular contributions to the measured PM10 concentrations. Higher PM10 levels were recorded during prevailing winds of the S–SW sectors, while lower levels were observed during strong northerly flows. The overall results allowed a first assessment of the severity of PM10 air pollution in the Athens basin, and of the potential sources responsible for it. The control of traffic-related particle emissions appears to be a principal objective, for the confrontation of the PM10 pollution problem affecting the area.

A Comparison of Two Statistical Models for Analyzing the Association Between Pm10 and Hospital Admissions for Chronic Obstructive Pulmonary Disease

Two statistical regression models-the autoregressive integrated moving average (ARIMA) and the generalized additive model (GAM)-were compared in terms of their ability to assess the association between ambient particulate air pollution concentrations (PM10) and daily hospital admissions for chronic obstructive pulmonary disease (COPD) in Reno-Sparks, Nevada, during the period 1990 to 1994. The study involved 3115 admissions for COPD. The daily average concentration of ambient PM10 was 36.55 μg/m3. After being adjusted for the effects of weather, day of the week, season, and time trend, both the ARIMA and the GAM methods consistently found that PM10 is a statistically significant predictor of daily hospital admissions for COPD. The percentage increase in hospital admissions for COPD for an interquartile increase (26.6 μg/m3) of the 24-h average of PM10 on the 14 prior days is 4.29% (95% CI 1.22∼7.36%) with ARIMA analysis and 5.62% (95% CI 2.16∼9.08%) with GAM analysis. The percentage increase in hospital a...

Indoor/outdoor PM10 and PM2.5 in Bangkok, Thailand.

Twenty-four-hour averaged PM10 and PM2.5 concentrations were obtained by using 4-liter-per-minute-pumps and impactors in microenvironments of a busy shopping district and a university hospital campus. In both areas, most people live directly adjacent to their worksites--minimizing the need to measure commuting exposure as part of total daily exposure. Co-located samplers were set in indoor microenvironments, the near-ambient zone of the households, and at nearby streetside central ambient monitoring stations. Smoking and use of other indoor PM sources were recorded daily via questionnaires. Consistent with previous studies, smoking and the use of charcoal stoves increased indoor particulate matter levels. The sampled air-conditioned hospital area had substantially lower particle concentrations than outdoors. A simple total exposure model was used to estimate the human exposure. The averaged ratios of co-located PM2.5/PM10 concentrations in various microenvironments are reported for each location. A single daily indoor average PM10 concentration for all households measured in a given sampling day is calculated for correlation analysis. Results showed that day-to-day fluctuations of these calculated indoor PM10 levels correlated well with near-ambient data and moderately well with ambient data collected at the nearby central monitoring site. This implies that ambient monitors are able to capture the daily variations of indoor PM levels or even personal exposure and may help explain the robust association of ambient PM levels and health effects found in many epidemiological studies. Absolute PM exposures, however, were substantially underestimated by ambient monitors in the shopping district, probably because of strong local sources.

AIR PARTICULATE POLLUTION AND HOSPITAL ADMISSIONS FOR CHRONIC OBSTRUCTIVE PULMONARY DISEASE IN RENO, NEVADA

This study assessed the association between ambient PM10 pollution and daily hospital admissions for chronic obstructive pulmonary disease (COPD) in Reno-Sparks, Nevada, for the period 1990-1994. All three hospitals in the region were included. There was a total of 3115 admissions for COPD during this period. Daily ambient PM10 values were available from one of seven air monitoring stations in this region. Weather variables including temperature and wind speed were also collected from this station. The daily average concentration of PM10 was 36.55 mu g/m3. The generalized additive model (GAM) was used in the whole analysis. After adjusting for the effects of weather variables, day of week, seasons, and time trend, the results show that PM10 is a statistically significant predictor for daily hospital admissions for COPD. The relative risk (RR) of hospital admissions for COPD for an interquartile increase (26.6 mu g/m3) of the 24-h average level of PM10 is 1.049 (95% CI 1.011-1.087).

Comparison of Two Ambient Beta Gauge PM10 Samplers.

This paper reports on the performance of the Kimoto 180 sampler and the Wedding ambient PM10 beta gauge sampler. Monodisperse ammonium fluorescein test particles were generated in the laboratory and used to determine the penetration curve of the Kimoto 180 cyclonic inlet. It was found that the actual DpaSOof the Kimoto 180 inlet, 3.5 (xm, is much lower than the designated standard value, 10 um. In the field test, the two beta gauge samplers were collocated with an Andersen SA1200 high-volume sampler to compare their measured daily average PM10 concentrations. The low Dpa50 of the Kimoto 180 inlet serves to explain why its daily average PM10 concentrations were much lower than the actual PM10 concentrations found in the field study. In addition, the PM10 concentrations of the Kimoto 180 beta gauge sampler were found to be seriously affected by the water vapor content of the ambient air. In contrast, the daily average PM10 concentrations of the Wedding beta gauge sampler were found to be more accurate, and influences by ambient conditions were insignificant.

Effect of photochemical air pollution on acute respiratory symptoms in children.

In the spring and summer of 1989, an epidemiologic study of the acute effects of photochemical air pollution on the respiratory health of 300 children 7 to 11 yr old was conducted. The children studied were a general population sample of the children living in two rural towns in the Netherlands. For these children, small pulmonary function decrements associated with ambient ozone concentrations were reported previously. In this paper, the association between photochemical ambient air pollution and acute respiratory symptoms is evaluated. Occurrence of acute respiratory symptoms was registered by the parents of the children in a diary on a daily basis. The association of symptom prevalence and incidence with air pollution was evaluated using a logistic regression model that took auto-correlation of the residuals into account. Several photochemical episodes occurred in the study period of 102 d. Hourly maximum ozone concentrations ranged from 14 to 114 ppb. Daily average PM10 concentrations ranged from 11 to 136 micrograms/m3. Levels of acid aerosol were low. No associations of daily symptom prevalence or incidence with same-day or previous day concentration levels of ozone, PM10, fine particle sulfate, or nitrate were observed.