Spatiotemporal relationship between particle air pollution and respiratory emergency hospital admissions in Brisbane, Australia

Estimation of cardiovascular and respiratory diseases attributed to PM10 using AirQ model in Urmia during 2011-2017

Background: Quantification of the attributed effects of air pollution determines the impact of air pollutants on the community and shows the critical condition of air quality. This study aimed to quantify and estimate the cardiovascular and respiratory diseases attributed to PM10 in Urmia during 2011-2016.

Methods: In this descriptive-analytic study, at first, hourly data of pollutant PM10 concentrations were received from air pollutants station located in the Department of Environmental Protection. The data were evaluated using AirQ2.2.3 software after primary and secondary processes and filtering.

Results: The results showed that the mean annual concentration of PM10 during 2011-2016 was 88.66, 92.45, 81.22, 78.38, 113.78, and 92.67 μg /m3, respectively. The number of hospitalized cases due to respiratory diseases attributed to PM10 in this period was 486, 525, 459, 453, 684, and 552, respectively, and the number of cases due to cardiovascular diseases was 188, 203, 177, 175, 263, and 213, respectively.

Conclusion: Considering the attributed health effects of PM10, the necessary measures should be taken to identify the causative agents and to understand the mechanisms of these processes and correct them.

Modifiers of the effect of short-term variation in PM2.5 on mortality in Beijing, China

INTRODUCTION

Epidemiologic studies have reported associations between short-term exposure to particulate matter <2.5 μm in aerodynamic diameter (PM_2.5) and mortality, but the role of modifiers remains unclear with studies reporting inconsistent results. We evaluated the impact of individual (age, gender and education) and township (geographic area, socioeconomic status, background air pollution and road density) level factors on the relationship between short-term variation in PM_2.5 with cause-specific mortality in Beijing (population: 21.7 million in 2016), China.

METHODS

Daily PM_2.5 concentrations in each township (n = 327; township population: 2000-359,400; township area: 1-392 km²) within Beijing were estimated by kriging with external drift using measurements from 35 air quality monitoring stations and geographic variables. Time-stratified case-crossover analysis with township-level mortality data from Oct. 1st, 2012 to Dec. 31st, 2013 was then used to examine associations between PM_2.5 exposure estimates and cause-specific mortality, stratified by the potential effect modifiers.

RESULTS

A 10-μg/m³ increase in PM_2.5 concentration was associated with a 0.17% [95% confidence interval (CI): 0.05%-0.29%] and 0.27% (95%CI:0.01%-0.52%) increase in non-accidental and stroke mortality with no lag, a 0.81% (95%CI:0.39%-1.23%) and 0.96% (95%CI:0.35%-1.57%) increase in respiratory disease (RD) and chronic obstructive pulmonary disease (COPD) mortality at a lag of two-day moving average. For individual-level effect modifiers, the elderly showed higher effects for all the specific causes of mortality; those with lower education level showed higher effects for non-accidental, cardiovascular disease and stroke mortality; females showed higher effects for non-accidental and cause-specific cardiovascular diseases. For township-level effect modifiers, effect estimates tended to be larger for suburban areas, areas of lower road density, lower PM_2.5 and lower socioeconomic status.

CONCLUSIONS

Short-term exposure to township-level ambient PM_2.5 was associated with increased mortality in Beijing, with indications of effect modification by both individual and township-level factors.

The influence of spatial patterning on modeling PM2.5 constituents in Eastern Massachusetts

Geostatistical exposure methods for air pollution have inherent uncertainties, resulting in varying levels of exposure misclassification. In this study, we propose that areas representing clusters of PM_2.5 elements are potential predictor variables to be included in spatial models for particle composition. The inclusion of these clusters may minimize the exposure misclassification. We evaluated the influence of spatial patterning on modeling of 10 components of ambient PM_2.5, which included Al, Cu, Fe, K, Ni, Pb, S, Ti, V, and Zn. This study was performed in three stages. First, we applied a hybrid approach (combination of Empirical Bayesian Kriging and land use regression) to estimate spatial variability for each one of the 10 components of ambient PM_2.5. In this stage, we accounted for numerous predictors representing land use, transportation, demographic, and geographical characteristics. In the second stage, we applied the same hybrid approach adding clusters of each PM_2.5 component to the set of predictor variables. The clusters here were estimated by a multivariate clustering approach based on k means. Finally, in the last stage, we compared the estimates obtained from the model without clusters (first stage) and the model with clusters (second stage). Overall, our findings suggest significant influence of spatial clusters on modeling some PM_2.5 components. We observed that the clusters may affect the error of the prediction values and especially the proportion of explained variance for most of the PM_2.5 constituents evaluated in this study. The model with cluster presented a better performance for all PM_2.5 components, except for Pb, which the R² value decreased 8.51% when we included the clusters in the analysis; and for V, which the R² value did not change with the clusters. Models for Cu and Fe explained the highest concentration variance. The R² value for the model without cluster was 0.55 for both pollutants. When we accounted for clusters, R² value increased 13 and 7% for Cu (R² = 0.62) and Fe (R² = 0.59), respectively. The models for K and S presented the lowest performance for both models with and without cluster (although the model with cluster improved substantially the R² values). Better knowledge of the influence of spatial patterns on air pollution modeling should be of interest to policy makers to devise future strategies to improve human exposure assessment to air particulates while controlling for spatial patterns of ambient PM_2.5 elemental concentration.

Multivariate spatial patterns of ambient PM2.5 elemental concentrations in Eastern Massachusetts

Understanding the factors that affect spatial differences in PM_2.5 composition is crucial for implementing emissions control and health policies. Although previous studies have explored modeling of spatial patterns as a tool to improve human exposure assessment, little work has employed a multivariate clustering approach to identify spatial patterns in particle composition. In this study, we used this approach to assess the spatial patterns of ambient PM_2.5 elemental concentrations in Eastern Massachusetts in the United States. To distinguish one cluster of sites from another, we considered air pollution sources and geodemographic variables. We evaluated spatial patterns for 11 elemental components of ambient PM_2.5, which included S, K, Ca, Fe, Zn, Cu, Ti, Al, Pb, V, and Ni. The analyses for S, Ca, Cu, Ti, Al, and Pb resulted in: 2 clusters for Fe, Zn, V, and Ni; 3 clusters; and for 12 clusters for K. Overall, our findings suggest substantial variation of clusters among PM_2.5 components. In addition, land use, population density, and daily traffic were used as variables to more effectively characterize clusters of sites. We used R² values to estimate the effectiveness of each variable in characterizing clusters. Larger R² values indicate better the discrimination among the sites. For example, population density had the highest R² value when the analysis was performed for S, Ca, Zn, Ti, Al, Pb, and V; land use presented the highest R² value for Cu, V, and Ni; and, traffic showed the highest R² value for PM_2.5 mass concentration. This study improves the ability to model both the between- and within-area variability of source emissions and pollution regime, using concentrations of PM_2.5 components.

Exposure to outdoor air pollution and its human health outcomes: A scoping review

Despite considerable air pollution prevention and control measures that have been put into practice in recent years, outdoor air pollution remains one of the most important risk factors for health outcomes. To identify the potential research gaps, we conducted a scoping review focused on health outcomes affected by outdoor air pollution across the broad research area. Of the 5759 potentially relevant studies, 799 were included in the final analysis. The included studies showed an increasing publication trend from 1992 to 2008, and most of the studies were conducted in Asia, Europe, and North America. Among the eight categorized health outcomes, asthma (category: respiratory diseases) and mortality (category: health records) were the most common ones. Adverse health outcomes involving respiratory diseases among children accounted for the largest group. Out of the total included studies, 95.2% reported at least one statistically positive result, and only 0.4% showed ambiguous results. Based on our study, we suggest that the time frame of the included studies, their disease definitions, and the measurement of personal exposure to outdoor air pollution should be taken into consideration in any future research. The main limitation of this study is its potential language bias, since only English publications were included. In conclusion, this scoping review provides researchers and policy decision makers with evidence taken from multiple disciplines to show the increasing prevalence of outdoor air pollution and its adverse effects on health outcomes.

Air Quality in Association With Rural Coal Mining and Combustion in New South Wales Australia

PURPOSE

Rural areas may face under-recognized threats to air quality. We tested 2 hypotheses that 1) rural areas in New South Wales, Australia, would have better air quality than metropolitan Sydney, and that 2) the rural Upper Hunter region characterized by coal mining and coal combustion would have worse air quality than other rural areas of the state.

METHODS

We analyzed 2017 daily mean values for New South Wales, Australia, for particulate matter (PM2.5 and PM10), sulfur dioxide (SO₂ ), nitric oxide (NO), nitrogen dioxide (NO₂ ), and NOx (sum of NO and NO₂ ). Forty-six air monitoring stations were grouped into 6 rural and urban regional areas. Linear regression models examined pollution levels in association with rural and urban regions and meteorological covariates.

RESULTS

Findings show that daily mean pollutant levels in the rural Upper Hunter were the highest of all regions, and were significantly higher than metropolitan Sydney, with and without control for weather conditions, for every pollutant. For example, daily mean PM2.5 was 8.64 µg/m³ in the rural Upper Hunter, compared to 7.23 µg/m³ in metropolitan Sydney.

CONCLUSIONS

Results highlight the need to consider both urban and rural sources of pollution in air quality studies, and appropriate policy steps to address likely rural air pollution from coal mining.

The association of hospital emergency admissions due to respiratory-cardiovascular diseases and acute myocardial infarction with air pollution in Tehran during 2005-2014

Background: Air pollution is one of the main reasons for disease and emergency hospitalizations. Therefore, air pollution control and hospital preparedness are of paramount importance. This study was conducted to determine the association of air pollutant levels with the rate of hospital emergency admissions due to respiratory and cardiovascular diseases and acute myocardial infarction in Tehran during the last decade.

Methods: This was a cross sectional study. At first, information on hourly concentration of air pollutants was gathered from Tehran Environmental Protection Agency and Air Quality Control Company. Raw data and meteorological parameters were used in Excel format to prepare an input file. The number of emergency hospital admissions due to pollutant exposure was assessed using the AirQ2.2.3 model.

Results: Results of this study revealed that there were 54 352 cases of emergency hospitalizations due to respiratory diseases in a relative risk of 1.0048 [1.0008-1.0112] and 20 990 cases of emergency hospitalizations due to cardiovascular diseases in a relative risk of 1.009[1.006-1.013] during 2005-2014. In addition, 3478 patients were admitted to the emergency department because of acute myocardial infarction with RR of 1.0026 [1.0026-1.0101].

Conclusion: This study demonstrated that a high percentage of hospital emergency admissions was because of respiratory and cardiovascular diseases. Moreover, it was found that acute myocardial infarction could be due to the high level of air pollution and could increase admissions to the emergency department. Therefore, appropriate measures are needed to reduce air pollution and increase hospital preparedness.

Spatial variation in the effects of air pollution on cardiovascular mortality in Beijing, China

Due to lack of data from multiple air quality monitoring stations, studies about spatial association between concentrations of ambient pollutants and mortality in China are rare. To investigate the spatial variation of association between concentrations of particulate matter less than 10 μm in aerodynamic diameter (PM₁₀) and nitrogen dioxide (NO₂) and cardiovascular mortality in Beijing, China, we collected data including daily deaths, concentrations of PM₁₀ and NO₂, and meteorological factors from January 1, 2009, to December 31, 2010, in all 16 districts of Beijing. Generalized additive model (GAM) and generalized additive mixed model (GAMM) were used to examine the citywide and district-specific effects of PM₁₀ and NO₂ on cardiovascular mortality. The citywide effect derived from GAMM was lower than that derived from GAM and the strongest effects were identified for 2-day moving average lag 0-1. For every 10 μg/m³ increases in concentrations of PM₁₀ and NO₂, the corresponding daily cardiovascular mortality increases in 0.31% (95%CI 0.15%, 0.46%) and 1.63% (95%CI 1.11%, 2.13%), respectively. The death risk associated with air pollutants varied across different geographic districts in Beijing. We found spatially varied adverse effects of air pollution on cardiovascular deaths in Beijing. But there was insufficient evidence to show the significant spatial heterogeneity in mortality effects of PM₁₀ and NO₂ in this study.

Individual-level modifiers of the acute effects of air pollution on mortality in Wuhan, China

Background

Ambient air pollution has posed negative effects on human health. Individual-level factors may modify this effect, but previous studies have controversial conclusions, and evidence is lacking especially in developing countries. This study aims to examine the modifying effects of sex, age, and education level of individuals on the associated between daily mortality and air pollutants, including particulate matter < 10 μm in aerodynamic diameter (PM₁₀), sulfur dioxide (SO₂), and nitrogen dioxide (NO₂).

Methods

Time-series analysis was conducted to investigate the acute effects of the air pollution on daily mortality from January 2002 to December 2010 in Wuhan, China. Generalized Additive Models (GAM) were used to examine the association stratified by sex for non-accidental, cardiovascular, and respiratory mortality. For non-accidental mortality, stratified analysis was also conducted by age and educational level.

Results

Outdoor air pollution was associated with daily non-accidental and cardiovascular mortality. An increase of 10 μg/m³ in a 2-day average concentration of PM₁₀, SO₂, and NO₂ was corresponding to the increase in non-accidental mortality of 0.29% (95%CI: 0.06–0.53%), 1.22% (95%CI: 0.77–1.67%) and 1.60% (95%CI: 1.00–2.19%), respectively. The effects of air pollution were faster in females than males. The magnitude of the estimates was higher for females with low education, aged 65–75 years for PM₁₀ and < 65 years for SO₂. To be more specific, we observed that per 10 μg/m³ increase in SO₂ was association with increases in non-accidental mortality of 2.03% (95%CI: 1.38–2.67) for all females and 3.10% (95%CI: 2.05–4.16) for females with low education.

Conclusion

Females and people with low-education are more susceptible to the effect of air pollution, which would provide a sound scientific basis for determination of air pollution standards.

The spatial variation in the effects of air pollution on cardiovascular mortality in Beijing, China

Owing to lack of data from multiple air quality monitoring stations, studies about spatial association between concentrations of ambient pollutants and mortality in China are rare. To investigate the spatial variation of association between concentrations of particulate matter less than 10 μm in aerodynamic diameter (PM₁₀), nitrogen dioxide (NO₂) and carbon monoxide (CO) and cardiovascular mortality in Beijing, China, we collected data including daily deaths, concentrations of PM₁₀, NO₂ and CO, and meteorological factors from 1 January 2009 to 31 December 2010 in all 16 districts of Beijing. Generalized additive model (GAM) and generalized additive mixed model (GAMM) were used to examine the citywide and district-specific effects of PM₁₀, NO₂ and CO on cardiovascular mortality. The citywide effect derived from GAMM was lower than that derived from GAM, and the strongest effects were identified for 2-day moving average lag 0-1. The interquartile increases in concentrations of PM₁₀, NO₂ and CO were associated with 2.46 (95% confidence interval (CI), 1.22-3.72), 4.11 (95%CI, 2.82-5.42) and 2.23 (95%CI, 1.14-3.33) percentage increases in daily cardiovascular mortality by GAMM, respectively. The relative risk of each district compared with reference district was generally statistically significant. The death risk associated with air pollutants varies across different geographic districts in Beijing. The data indicate that the risk is high in suburban areas and rural counties. We found significant and spatially varied adverse effects of air pollution on cardiovascular deaths across the rural and urban areas in Beijing.

Spatiotemporal analysis for the effect of ambient particulate matter on cause-specific respiratory mortality in Beijing, China

This study explored the association between particulate matter with an aerodynamic diameter of less than 10 μm (PM10) and the cause-specific respiratory mortality. We used the ordinary kriging method to estimate the spatial characteristics of ambient PM10 at 1-km × 1-km resolution across Beijing during 2008-2009 and subsequently fit the exposure-response relationship between the estimated PM10 and the mortality due to total respiratory disease, chronic lower respiratory disease, chronic obstructive pulmonary disease (COPD), and pneumonia at the street or township area levels using the generalized additive mixed model (GAMM). We also examined the effects of age, gender, and season in the stratified analysis. The effects of ambient PM10 on the cause-specific respiratory mortality were the strongest at lag0-5 except for pneumonia, and an inter-quantile range increase in PM10 was associated with an 8.04 % (95 % CI 4.00, 12.63) increase in mortality for total respiratory disease, a 6.63 % (95 % CI 1.65, 11.86) increase for chronic lower respiratory disease, and a 5.68 % (95 % CI 0.54, 11.09) increase for COPD, respectively. Higher risks due to the PM10 exposure were observed for females and elderly individuals. Seasonal stratification analysis showed that the effects of PM10 on mortality due to pneumonia were stronger during spring and autumn. While for COPD, the effect of PM10 in winter was statistically significant (15.54 %, 95 % CI 5.64, 26.35) and the greatest among the seasons. The GAMM model evaluated stronger associations between concentration of PM10. There were significant associations between PM10 and mortality due to respiratory disease at the street or township area levels. The GAMM model using high-resolution PM10 could better capture the association between PM10 and respiratory mortality. Gender, age, and season also acted as effect modifiers for the relationship between PM10 and respiratory mortality.

Spatiotemporal analysis of particulate air pollution and ischemic heart disease mortality in Beijing, China

BACKGROUND

Few studies have used spatially resolved ambient particulate matter with an aerodynamic diameter of <10 μm (PM10) to examine the impact of PM10 on ischemic heart disease (IHD) mortality in China. The aim of our study is to evaluate the short-term effects of PM10 concentrations on IHD mortality by means of spatiotemporal analysis approach.

METHODS

We collected daily data on air pollution, weather conditions and IHD mortality in Beijing, China during 2008 and 2009. Ordinary kriging (OK) was used to interpolate daily PM10 concentrations at the centroid of 287 township-level areas based on 27 monitoring sites covering the whole city. A generalized additive mixed model was used to estimate quantitatively the impact of spatially resolved PM10 on the IHD mortality. The co-effects of the seasons, gender and age were studied in a stratified analysis. Generalized additive model was used to evaluate the effects of averaged PM10 concentration as well.

RESULTS

The averaged spatially resolved PM10 concentration at 287 township-level areas was 120.3 ± 78.1 μg/m3. Ambient PM10 concentration was associated with IHD mortality in spatiotemporal analysis and the strongest effects were identified for the 2-day average. A 10 μg/m3 increase in PM10 was associated with an increase of 0.33% (95% confidence intervals: 0.13%, 0.52%) in daily IHD mortality. The effect estimates using spatially resolved PM10 were larger than that using averaged PM10. The seasonal stratification analysis showed that PM10 had the statistically stronger effects on IHD mortality in summer than that in the other seasons. Males and older people demonstrated the larger response to PM10 exposure.

CONCLUSIONS

Our results suggest that short-term exposure to particulate air pollution is associated with increased IHD mortality. Spatial variation should be considered for assessing the impacts of particulate air pollution on mortality.

Association between air quality and quality of life

Air quality-or its converse, air pollution-is a significant risk factor for human health. Recent studies have reported association between air pollution and human health. There are numerous diseases that may be caused by air pollution such as respiratory infection, lung cancer, cardiovascular disease, chronic obstructive pulmonary disease, and asthma. In this study, the relationship between air quality and quality of life was examined by using canonical correlation analysis. Data of this study was collected from 27 countries. WHO statistics were used as the main source of quality of life data set (Y variables set). European Environment Agency statistics and (for outdoor air-PM10) WHO statistics were used as the main source of air quality data set (X variables set). It is found that there are significant positive correlation between air quality and quality of life.

Ambient air quality and asthma cases in Niğde, Turkey

Urban air quality is one of the key factors affecting human health. Turkey has transformed itself into an urban society over the last 30 years. At the same time, air pollution has become a serious impairment to health in many urban areas in the country. This is due to many reasons. In this study, a nonparametric evaluation was conducted of health effects that are triggered by urban air pollution. Niğde, the city which is the administrative centre of Nigde province was chosen of the effects of air pollution since, like many central Turkish cities, it is situated on a valley where atmospheric inversion occurs. In this paper, the relationship between ambient urban air quality, namely PM10 and sulphur dioxide (SO2), and human health, specifically asthma, during the winter season is examined. Air pollution data and asthma cases from 2006 to 2010 are covered in this study. The results of our study indicate that total asthma cases reported in Nigde between 2008 and 2010 were highly dependent on ambient SO2 concentration. More asthma cases were recorded when 30 μg m(-3) or higher SO2 was present in the ambient air than those recorded under cleaner ambient air conditions. Moreover, it was determined that in Nigde in 2010, asthma cases reported in males aged between 45 and 64 were closely correlated with ambient SO2 (α=0.05).

The spatial characteristics of ambient particulate matter and daily mortality in the urban area of Beijing, China

Few epidemiological studies have reported the spatial characteristics of the association between particulate matter <10μm in aerodynamic diameter (PM(10)) and mortality in China. This study explored the spatial characteristics of the association between ambient PM(10) and mortality in the urban area of Beijing, China. We collected daily data on air pollution, weather conditions and mortality in the eight urban districts of Beijing from Jan. 1st 2008 to Dec. 31st 2009. A Poisson Generalized Additive Model (GAM) was used to examine the district-specific effects of PM(10) on cause-specific mortality. A Poisson Generalized Additive Mixed Model (GAMM) was used to examine the urban-wide association between PM(10) and cause-specific mortality while controlling for the random effects of districts, compared with GAM which did not control for the random effects of districts. The inter-quartile ranges (IQRs) of annual PM(10) ranged from 83.5 μg/m(3) (Chaoyang district) to 96.0 μg/m(3) (Shijingshan district). A 96.0 μg/m(3) increase of PM(10) was associated with a 7.52% (95%CI: 1.78%-13.56%) increase of cardiovascular mortality in Shijingshan district while an 87.0 μg/m(3) increase of PM(10) was associated with a 7.68% (95%CI: 0.08%-15.86%) increase of respiratory deaths in Dongcheng district. The urban-wide effects derived from GAMM showed that an 88.0 μg/m(3) increase of PM(10) was associated with an increase of 1.30% (95%CI: 0.45%-2.16%), 2.60% (95%CI: 0.14%-5.11%) in non-accidental and respiratory mortality, illustrating, higher results than those from the GAM. In conclusion, there is spatial variation in ambient PM(10) concentration as well as in the effects of PM(10) on cause-specific mortality in the urban area of Beijing. Additionally, GAMM model may be more effective in estimating the spatial association between urban-wide PM(10) and cause-specific mortality.

Characterization of Ambient Air Pollution Measurement Error in a Time-Series Health Study using a Geostatistical Simulation Approach

In recent years, geostatistical modeling has been used to inform air pollution health studies. In this study, distributions of daily ambient concentrations were modeled over space and time for 12 air pollutants. Simulated pollutant fields were produced for a 6-year time period over the 20-county metropolitan Atlanta area using the Stanford Geostatistical Modeling Software (SGeMS). These simulations incorporate the temporal and spatial autocorrelation structure of ambient pollutants, as well as season and day-of-week temporal and spatial trends; these fields were considered to be the true ambient pollutant fields for the purposes of the simulations that followed. Simulated monitor data at the locations of actual monitors were then generated that contain error representative of instrument imprecision. From the simulated monitor data, four exposure metrics were calculated: central monitor and unweighted, population-weighted, and area-weighted averages. For each metric, the amount and type of error relative to the simulated pollutant fields are characterized and the impact of error on an epidemiologic time-series analysis is predicted. The amount of error, as indicated by a lack of spatial autocorrelation, is greater for primary pollutants than for secondary pollutants and is only moderately reduced by averaging across monitors; more error will result in less statistical power in the epidemiologic analysis. The type of error, as indicated by the correlations of error with the monitor data and with the true ambient concentration, varies with exposure metric, with error in the central monitor metric more of the classical type (i.e., independent of the monitor data) and error in the spatial average metrics more of the Berkson type (i.e., independent of the true ambient concentration). Error type will affect the bias in the health risk estimate, with bias toward the null and away from the null predicted depending on the exposure metric; population-weighting yielded the least bias.

Comparison of exposure estimation methods for air pollutants: ambient monitoring data and regional air quality simulation

Air quality modeling could potentially improve exposure estimates for use in epidemiological studies. We investigated this application of air quality modeling by estimating location-specific (point) and spatially-aggregated (county level) exposure concentrations of particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM(2.5)) and ozone (O(3)) for the eastern U.S. in 2002 using the Community Multi-scale Air Quality (CMAQ) modeling system and a traditional approach using ambient monitors. The monitoring approach produced estimates for 370 and 454 counties for PM(2.5) and O(3), respectively. Modeled estimates included 1861 counties, covering 50% more population. The population uncovered by monitors differed from those near monitors (e.g., urbanicity, race, education, age, unemployment, income, modeled pollutant levels). CMAQ overestimated O(3) (annual normalized mean bias=4.30%), while modeled PM(2.5) had an annual normalized mean bias of -2.09%, although bias varied seasonally, from 32% in November to -27% in July. Epidemiology may benefit from air quality modeling, with improved spatial and temporal resolution and the ability to study populations far from monitors that may differ from those near monitors. However, model performance varied by measure of performance, season, and location. Thus, the appropriateness of using such modeled exposures in health studies depends on the pollutant and metric of concern, acceptable level of uncertainty, population of interest, study design, and other factors.

An analysis of asthma hospitalizations, air pollution, and weather conditions in Los Angeles County, California

There is now a large body of literature supporting a linkage between exposure to air pollutants and asthma morbidity. However, the extent and significance of this relationship varies considerably between pollutants, location, scale of analysis, and analysis methods. Our primary goal is to evaluate the relationship between asthma hospitalizations, levels of ambient air pollution, and weather conditions in Los Angeles (LA) County, California, an area with a historical record of heavy air pollution. County-wide measures of carbon monoxide (CO), nitrogen dioxide (NO(2)), ozone (O(3)), particulate matter<10 μm (PM(10)), particulate matter<2.5 μm (PM(2.5)), maximum temperature, and relative humidity were collected for all months from 2001 to 2008. We then related these variables to monthly asthma hospitalization rates using Bayesian regression models with temporal random effects. We evaluated model performance using a goodness of fit criterion and predictive ability. Asthma hospitalization rates in LA County decreased between 2001 and 2008. Traffic-related pollutants, CO and NO(2), were significant and positively correlated with asthma hospitalizations. PM(2.5) also had a positive, significant association with asthma hospitalizations. PM(10), relative humidity, and maximum temperature produced mixed results, whereas O(3) was non-significant in all models. Inclusion of temporal random effects satisfies statistical model assumptions, improves model fit, and yields increased predictive accuracy and precision compared to their non-temporal counterparts. Generally, pollution levels and asthma hospitalizations decreased during the 9 year study period. Our findings also indicate that after accounting for seasonality in the data, asthma hospitalization rate has a significant positive relationship with ambient levels of CO, NO(2), and PM(2.5).

Particulate air pollution and cardiorespiratory hospital admissions in a temperate Australian city: A case-crossover analysis

BACKGROUND

Although ambient air pollution exposure has been linked with poor health in many parts of the world, no previous study has investigated the effect on morbidity in the city of Adelaide, South Australia.

OBJECTIVE

To explore the association between particulate matter (PM) and hospitalisations, including respiratory and cardiovascular admissions in Adelaide, South Australia.

METHODS

For the study period September 2001 to October 2007, daily counts of all-cause, cardiovascular and respiratory hospital admissions were collected, as well as daily air quality data including concentrations of particulates, ozone and nitrogen dioxide. Visibility codes for present weather conditions identified days when airborne dust or smoke was observed. The associations between PM and hospitalisations were estimated using time-stratified case-crossover analyses controlling for covariates including temperature, relative humidity, other pollutants, day of the week and public holidays.

RESULTS

Mean PM(10) concentrations were higher in the warm season, whereas PM(2.5) concentrations were higher in the cool season. Hospital admissions were associated with PM(10) in the cool season and with PM(2.5) in both seasons. No significant effect of PM on all-age respiratory admissions was detected, however cardiovascular admissions were associated with both PM(2.5) and PM(10) in the cool season with the highest effects for PM(2.5) (4.48%, 95% CI: 0.74%, 8.36% increase per 10μg/m(3) increase in PM(2.5)).

CONCLUSION

These findings suggest that despite the city's relatively low levels of air pollution, PM concentrations are associated with increases in morbidity in Adelaide. Further studies are needed to investigate the sources of PM which may be contributing to the higher cool season effects.

Minimizing external indirect health costs due to aerosol population exposure: a case study from Northern Italy

Environmental Agencies require Decision Support Systems, in order to plan Air Quality Policies considering the cost of emission reduction measures and the human health effects (with related social costs). The use of Decision Support Systems is also useful to spread information to general public, explaining the effectiveness of proposed air quality plans. In this paper, a multi-objective approach to control PM10 concentration at a regional level is presented. The problem considers both the internal costs (due to the implementation of emission reduction measures) and the external costs (due to population exposure to high PM10 concentrations). To model PM10 concentrations, a single surrogate model is used for the entire domain, allowing the implementation of a very efficient optimization procedure. The surrogate model is derived through a set of 10 simulations, performed using a Chemistry Transport Model fed with different emission reduction scenarios. The methodology is applied to Northern Italy, a region affected by very high PM10 concentrations that exceed the limit values specified by the EU legislation.

The effects of bushfire smoke on respiratory health

Bushfire smoke has the potential to affect millions of people and is therefore a major public health problem. The air pollutant that increases most significantly as a result of bushfire smoke is particulate matter (PM). During bushfire smoke episodes, PM concentrations are usually much higher than urban background concentrations, at which effects on respiratory health have been observed. The smoke can cover large areas including major cities and even small increases in the risk of respiratory health effects can cause large public health problems. The association between respiratory morbidity and exposure to bushfire smoke is consistent with the associations found with urban air pollution. Although using different methods, all studies looking at Emergency Department presentations in relation to a bushfire smoke event have found associations and most studies have also found an association with hospital admissions. However, only a few studies have distinguished between the effects of bushfire PM(10) (particles with a median aerodynamic diameter less than 10 µm) and background PM(10). These studies suggest that PM(10) from bushfire smoke is at least as toxic as urban PM(10), but more research is needed.

Ambient air pollutant measurement error: characterization and impacts in a time-series epidemiologic study in Atlanta

In time-series studies of ambient air pollution and health in large urban areas, measurement errors associated with instrument precision and spatial variability vary widely across pollutants. In this paper, we characterize these errors for selected air pollutants and estimate their impacts on epidemiologic results from an ongoing study of air pollution and emergency department visits in Atlanta. Error was modeled for daily measures of 12 air pollutants using collocated monitor data to characterize instrument precision and data from multiple study area monitors to estimate population-weighted spatial variance. Time-series simulations of instrument and spatial error were generated for each pollutant, added to a reference pollutant time-series, and used in a Poisson generalized linear model of air pollution and cardiovascular emergency department visits. Reductions in risk ratio due to instrument precision error were less than 6%. Error due to spatial variability resulted in average risk ratio reductions of less than 16% for secondary pollutants (O(3), PM(2.5) sulfate, nitrate and ammonium) and between 43% and 68% for primary pollutants (NO(x), NO(2), SO(2), CO, PM(2.5) elemental carbon); pollutants of mixed origin (PM(10), PM(2.5), PM(2.5) organic carbon) had intermediate impacts. Quantifying impacts of measurement error on health effect estimates improves interpretation across ambient pollutants.

An examination of exposure measurement error from air pollutant spatial variability in time-series studies

Relatively few studies have evaluated the effects of heterogeneous spatiotemporal pollutant distributions on health risk estimates in time-series analyses that use data from a central monitor to assign exposures. We present a method for examining the effects of exposure measurement error relating to spatiotemporal variability in ambient air pollutant concentrations on air pollution health risk estimates in a daily time-series analysis of emergency department visits in Atlanta, Georgia. We used Poisson generalized linear models to estimate associations between current-day pollutant concentrations and circulatory emergency department visits for the 1998-2004 time period. Data from monitoring sites located in different geographical regions of the study area and at different distances from several urban geographical subpopulations served as alternative measures of exposure. We observed associations for spatially heterogeneous pollutants (CO and NO(2)) using data from several different urban monitoring sites. These associations were not observed when using data from the most rural site, located 38 miles from the city center. In contrast, associations for spatially homogeneous pollutants (O(3) and PM(2.5)) were similar, regardless of the monitoring site location. We found that monitoring site location and the distance of a monitoring site to a population of interest did not meaningfully affect estimated associations for any pollutant when using data from urban sites located within 20 miles from the population center under study. However, for CO and NO(2), these factors were important when using data from rural sites located > or = 30 miles from the population center, most likely owing to exposure measurement error. Overall, our findings lend support to the use of pollutant data from urban central sites to assess population exposures within geographically dispersed study populations in Atlanta and similar cities.

Spatial distribution of PM₁ and PM₁₀ during Saharan dust episodes in Athens, Greece

Abstract. The objective of this study is to present and analyse the spatial distribution of PM1 (particulate matter with diameter less than 1 μm) and PM10 (particulate matter with diameter less than 10 μm) within the greater area of Athens (GAA), Greece, during two extreme Saharan dust episodes in 2006 and 2008. Two portable detectors, based on light scattering method, were used to record the particulate matter concentrations. The samples were collected in the same morning hour of the day which coincided with the peak of vehicles traffic. We analysed the recorded data on normal days and on days with extreme Saharan dust events in order to find out the exceedances of the particulate matter concentrations. Using Kriging method, the spatial patterns of PM1 and PM10 concentrations were constructed for GAA. It is already known that particulate matter represent the main hazard in cardiovascular and respiratory syndromes within the most polluted cities of Europe, which confront high traffic problems, amplified by Saharan dust episodes, which are frequent especially in the Southern Europe, during spring time. The results of the performed analysis showed that during these episodes, PM concentrations over exceed the thresholds set by the European Union, exacerbating the human health in Athens.