Time-series analysis of satellite-derived fine particulate matter pollution and asthma morbidity in Jackson, MS (original) (raw)
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Although it is known that air pollution may lead to increased asthma prevalence, no clear scientific evidence of direct association between air pollution and asthma rate has been reported. In the present study, a Geographical Information System (GIS) approach was developed to determine the association between asthma hospital discharge rate (ADR) and seasonal exposure to specific ambient air pollutants in eastern Texas, USA, during the period 2009 to 2011. Quarterly asthma data were obtained from Texas State Department of Health, National Asthma Survey surveillance of Texas State, USA. Quarterly mean concentrations of fine particular matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3) were determined from the corresponding measured daily data collected by various air quality monitoring stations distributed in different counties in the study area. Using Pearson correlation analysis, quarterly average of air pollutant concentrations was compared to quarterly Asthma discharge rate (ADR). The results revealed that the association between quarterly exposure of air pollution and ADR was not statistically significant in the study area. During the study period, a negative correlation coefficient was observed between the quarterly mean concentration of ozone and NO2 with the quarterly ADR. However, in most of the cases a positive correlation coefficient was observed between the quarterly mean concentration of PM2.5 and the quarterly ADR, indicating a probable association between ambient air pollution exposure and asthma prevalence.
International Journal of Environmental Research and Public Health, 2014
Studies on asthma have shown that air pollution can lead to increased asthma prevalence. The aim of this study is to examine the association between air pollution (fine particulate matter (PM 2.5 ), sulfur dioxide (SO 2 ) and ozone (O 3 )) and human health (asthma emergency department visit rate (AEVR) and asthma discharge rate (ADR)) among residents of New York, USA during the period 2005 to 2007. Annual rates of asthma were calculated from population estimates for 2005, 2006, and 2007 and number of asthma hospital discharge and emergency department visits. Population data for New York were taken from US Bureau of Census, and asthma data were obtained from New York State Department of Health, National Asthma Survey surveillance report. Data on the concentrations of PM 2.5 , SO 2 and ground level ozone were obtained from various air quality monitoring stations distributed in different counties. Annual means of these concentrations were compared to annual variations in asthma prevalence by using Pearson correlation coefficient. We found different associations between the annual mean concentration of PM 2.5 , SO 2 and surface ozone and the annual rates of asthma discharge and asthma emergency visit from 2005 to 2007. A positive correlation coefficient was observed between the annual mean concentration of PM 2.5 , and SO 2 and the annual rates of asthma discharge and asthma emergency department visit from 2005 to 2007. Int. J. Environ. Res. Public Health 2014, 11 4846 However, the correlation coefficient between annual mean concentrations of ground ozone and the annual rates of asthma discharge and asthma emergency visit was found to be negative from 2005 to 2007. Our study suggests that the association between elevated concentrations of PM 2.5 and SO 2 and asthma prevalence among residents of New York State in USA is consistent enough to assume concretely a plausible and significant association.
Aerosol optical depth (AOD) Air quality model Air Quality System (AQS) Asthma Case crossover Community Multi-Scale Air Quality (CMAQ) model Fine particulate matter (PM 2.5 ) Heart failure (HF) Hierarchical Bayesian Model (HBM) Myocardial infarction (MI) a b s t r a c t An enhanced research paradigm is presented to address the spatial and temporal gaps in fine particulate matter (PM 2.5 ) measurements and generate realistic and representative concentration fields for use in epidemiological studies of human exposure to ambient air particulate concentrations. The general approach for research designed to analyze health impacts of exposure to PM 2.5 is to use concentration data from the nearest ground-based air quality monitor(s), which typically have missing data on the temporal and spatial scales due to filter sampling schedules and monitor placement, respectively. To circumvent these data gaps, this research project uses a Hierarchical Bayesian Model (HBM) to generate estimates of PM 2.5 in areas with and without air quality monitors by combining PM 2.5 concentrations measured by monitors, PM 2.5 concentration estimates derived from satellite aerosol optical depth (AOD) data, and Community-Multiscale Air Quality (CMAQ) model predictions of PM 2.5 concentrations. This methodology represents a substantial step forward in the approach for developing representative PM 2.5 concentration datasets to correlate with inpatient hospitalizations and emergency room visits data for asthma and inpatient hospitalizations for myocardial infarction (MI) and heart failure (HF) using case-crossover analysis. There were two key objective of this current study. First was to show that the inputs to the HBM could be expanded to include AOD data in addition to data from PM 2.5 monitors and predictions from CMAQ. The second objective was to determine if inclusion of AOD surfaces in HBM model algorithms results in PM 2.5 air pollutant concentration surfaces which more accurately predict hospital admittance and emergency room visits for MI, asthma, and HF. This study focuses on the New York City, NY metropolitan and surrounding areas during the 2004-2006 time period, in order to compare the health outcome impacts with those from previous studies and focus on any benefits derived from the changes in the HBM model surfaces. Consistent with previous studies, the results show high PM 2.5 exposure is associated with increased risk of asthma, myocardial infarction and heart failure. The estimates derived from concentration surfaces that incorporate AOD had a similar model fit and estimate of risk as compared to those derived from combining monitor and CMAQ data alone. Thus, this study demonstrates that estimates of PM 2.5 concentrations from satellite data can be used to supplement PM 2.5 monitor data in the estimates of risk associated with three common health outcomes. Results from this study were inconclusive regarding the potential benefits derived from adding AOD data to the HBM, as the addition of the satellite data did not significantly increase model performance. However, this study was limited to one metropolitan area over a short two-year time period. The use of next-generation, high temporal and spatial resolution satellite AOD data from geostationary and polar-orbiting satellites is expected to improve predictions in epidemiological studies in areas with fewer pollutant monitors or over wider geographic areas.
Atmospheric Chemistry and Physics, 2012
Statistically significant correlations between increase of asthma attacks in children and elevated concentrations of particulate matter of diameter 10 microns and less (PM 10) were determined for metropolitan Phoenix, Arizona. Interpolated concentrations from a five-site network provided spatial distribution of PM 10 that was mapped onto census tracts with population health records. The case-crossover statistical method was applied to determine the relationship between PM 10 concentration and asthma attacks. For children ages 5-17, a significant relationship was discovered between the two, while children ages 0-4 exhibited virtually no relationship. The risk of adverse health effects was expressed as a function of the change from the 25th to 75th percentiles of mean level PM 10 (36 µg m −3). This increase in concentration was associated with a 12.6 % (95 % CI: 5.8 %, 19.4 %) increase in the log odds of asthma attacks among children ages 5-17. Neither gender nor other demographic variables were significant. The results are being used to develop an asthma early warning system for the study area.
Air Quality and Asthma Hospitalization: Evidence of PM2.5 Concentrations in Pennsylvania Counties
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2014
Abstract: Studies on asthma have shown that air pollution can lead to increased asthma prevalence. The aim of this study is to examine the association between air pollution (fine particulate matter (PM2.5), sulfur dioxide (SO2) and ozone (O3)) and human health (asthma emergency department visit rate (AEVR) and asthma discharge rate (ADR)) among residents of New York, USA during the period 2005 to 2007. Annual rates of asthma were calculated from population estimates for 2005, 2006, and 2007 and number of asthma hospital discharge and emergency department visits. Population data for New York were taken from US Bureau of Census, and asthma data were obtained from New York State Department of Health, National Asthma Survey surveillance report. Data on the concentrations of PM2.5, SO2 and ground level ozone were obtained from various air quality monitoring stations distributed in different counties. Annual means of these concentrations were compared to annual variations in asthma preval...
Climatic factors and air pollution are important in predicting asthma exacerbations among children. This study was designed to determine if a relationship exists between asthma exacerbations among elementary school children and the combined effect of daily upper atmosphere observations (temperature, relative humidity, dew point, and mixing ratio) and daily air pollution (particulate matter, sulfur dioxide, nitrogen dioxide, carbon monoxide, and ozone) and, if so, to predict asthma exacerbations among children using a mathematical model. Using an ecological study design, school health records of 168,825 students in elementary schools enrolled in "Health eTools for Schools" within 49 Pennsylvania counties were analyzed. Data representing asthma exacerbations were originally recorded by school nurses as the type of treatment given to a student during a clinic visit on a particular day. Daily upper atmosphere measurements from ground level to the 850-mb pressure level and air pollution measurements were obtained. A generalized estimating equation model was used to predict the occurrence of Ͼ48 asthma exacerbations, the daily mean for 2008 -2010. The greatest occurrence of asthma among school children was in the fall, followed by summer, spring, and winter. Upper atmosphere temperature, dew point, mixing ratio, and six air pollutants as well as their interactions predicted the probability of asthma exacerbations occurring among children. Monitoring of upper atmosphere observation data and air pollutants over time can be a reliable means for predicting increases of asthma exacerbations among elementary school children. Such predictions could help parents and school officials implement effective precautionary measures.
Journal of Exposure Science and Environmental Epidemiology, 2013
Regulatory monitoring data and land-use regression (LUR) models have been widely used for estimating individual exposure to ambient air pollution in epidemiologic studies. However, LUR models lack fine-scale temporal resolution for predicting acute exposure and regulatory monitoring provides daily concentrations, but fails to capture spatial variability within urban areas. This study coupled LUR models with continuous regulatory monitoring to predict daily ambient nitrogen dioxide (NO 2 ) and particulate matter (PM 2.5 ) at 50 homes in Windsor, Ontario. We compared predicted versus measured daily outdoor concentrations for 5 days in winter and 5 days in summer at each home. We also examined the implications of using modeled versus measured daily pollutant concentrations to predict daily lung function among asthmatic children living in those homes. Mixed effect analysis suggested that temporally refined LUR models explained a greater proportion of the spatial and temporal variance in daily household-level outdoor NO 2 measurements compared with daily concentrations based on regulatory monitoring. Temporally refined LUR models captured 40% (summer) and 10% (winter) more of the spatial variance compared with regulatory monitoring data. Ambient PM 2.5 showed little spatial variation; therefore, daily PM 2.5 models were similar to regulatory monitoring data in the proportion of variance explained. Furthermore, effect estimates for forced expiratory volume in 1 s (FEV 1 ) and peak expiratory flow (PEF) based on modeled pollutant concentrations were consistent with effects based on household-level measurements for NO 2 and PM 2.5 . These results suggest that LUR modeling can be combined with continuous regulatory monitoring data to predict daily household-level exposure to ambient air pollution. Temporally refined LUR models provided a modest improvement in estimating daily householdlevel NO 2 compared with regulatory monitoring data alone, suggesting that this approach could potentially improve exposure estimation for spatially heterogeneous pollutants. These findings have important implications for epidemiologic studies -in particular, for research focused on short-term exposure and health effects.
An enhanced research paradigm is presented to address the spatial and temporal gaps in fine particulate matter (PM 2.5) measurements and generate realistic and representative concentration fields for use in epidemiological studies of human exposure to ambient air particulate concentrations. The general approach for research designed to analyze health impacts of exposure to PM 2.5 is to use concentration data from the nearest ground-based air quality monitor(s), which typically have missing data on the temporal and spatial scales due to filter sampling schedules and monitor placement, respectively. To circumvent these data gaps, this research project uses a Hierarchical Bayesian Model (HBM) to generate estimates of PM 2.5 in areas with and without air quality monitors by combining PM 2.5 concentrations measured by monitors, PM 2.5 concentration estimates derived from satellite aerosol optical depth (AOD) data, and Community-Multiscale Air Quality (CMAQ) model predictions of PM 2.5 concentrations. This methodology represents a substantial step forward in the approach for developing representative PM 2.5 concentration datasets to correlate with in-patient hospitalizations and emergency room visits data for asthma and inpatient hospitalizations for myocardial infarction (MI) and heart failure (HF) using case-crossover analysis. There were two key objective of this current study. First was to show that the inputs to the HBM could be expanded to include AOD data in addition to data from PM 2.5 monitors and predictions from CMAQ. The second objective was to determine if inclusion of AOD surfaces in HBM model algorithms results in PM 2.5 air pollutant concentration surfaces which more accurately predict hospital admittance and emergency room visits for MI, asthma, and HF. This study focuses on the New York City, NY metropolitan and surrounding areas during the 2004–2006 time period, in order to compare the health outcome impacts with those from previous studies and focus on any benefits derived from the changes in the HBM model surfaces. Consistent with previous studies, the results show high PM 2.5 exposure is associated with increased risk of asthma, myocardial infarction and heart failure. The estimates derived from concentration surfaces that incorporate AOD had a similar model fit and estimate of risk as compared to those derived from combining monitor and CMAQ data alone. Thus, this study demonstrates that estimates of PM 2.5 concentrations from satellite data can be used to supplement PM 2.5 monitor data in the estimates of risk associated with three common health outcomes. Results from this study were inconclusive regarding the potential benefits derived from adding AOD data to the HBM, as the addition of the satellite data did not significantly increase model performance. However, this study was limited to one metropolitan area over a short two-year time period. The use of next-generation, high temporal and spatial resolution satellite AOD data from geostationary and polar-orbiting satellites is expected to improve predictions in epidemiological studies in areas with fewer pollutant monitors or over wider geographic areas.