National Air Pollution Research Articles

A Regression Approach for Estimating Multiday Adverse Health Effects of PM10 When Daily PM10 Data Are Unavailable

The authors propose a regression-based approach for obtaining multiday estimates of the adverse health effects of ambient particulate matter less than 10 microm in diameter (PM(10)) when daily PM(10) time-series data are unavailable. This situation is common in the United States, because most US cities take PM(10) measurements every 6 days. Current evidence suggests that adverse effects of PM(10) are not concentrated on a single day but rather are spread out over multiple days, so the unavailability of daily PM(10) data presents a problem for the estimation of these effects. The proposed model estimates weights that are used to construct a linear combination of single-lag PM(10) effect estimates obtained from the available PM(10) data. It is shown that this new approach provides estimates of the effect of PM(10) on mortality that have less bias and mean squared error than currently available methods. Application of this method to the US cities contained in the National Morbidity, Mortality, and Air Pollution Study database produces an estimated national average effect of PM(10) on nonaccidental mortality in persons over age 65 years, corresponding to a 0.32% increase per 10-microg/m(3) increment in PM(10). The estimated effects for cardiorespiratory mortality and other mortality are 0.34% and 0.22%, respectively.

Effect Modification by Community Characteristics on the Short-term Effects of Ozone Exposure and Mortality in 98 US Communities

Previous research provided evidence of an association between short-term exposure to ozone and mortality risk and of heterogeneity in the risk across communities. The authors investigated whether this heterogeneity can be explained by community-specific characteristics: race, income, education, urbanization, transportation use, particulate matter and ozone levels, number of ozone monitors, weather, and use of air conditioning. Their study included data on 98 US urban communities for 1987 to 2000 from the National Morbidity, Mortality, and Air Pollution Study; US Census; and American Housing Survey. On average across the communities, a 10-ppb increase in the previous week's ozone level was associated with a 0.52% (95% posterior interval: 0.28, 0.77) increase in mortality. The authors found that community-level characteristics modify the relation between ozone and mortality. Higher effect estimates were associated with higher unemployment, fraction of the Black/African-American population, and public transportation use and with lower temperatures or prevalence of central air conditioning. These differences may relate to underlying health status, differences in exposure, or other factors. Results show that some segments of the population may face higher health burdens of ozone pollution.

Simultaneous Measurements of PM10and PM1using a single TEOM#

A time sharing, sequential scanning inlet system has been developed for a tapered element oscillating microbalance (TEOM) particulate matter (PM) mass measurement instrument, allowing quasi simultaneous measurement of ambient PM10 and PM1. Compared to running two separate instruments this approach eliminates systematic biases between the PM10 and PM1 data due to slightly different response functions of separate instruments. Furthermore, this approach reduces the costs of the measurements considerably. The system has been successfully tested during the winter 2005/2006 in Zurich, Switzerland and 24-hour average data have been compared to ambient concentrations measured with reference high volume sampler systems of the Swiss national air pollution monitoring network (NABEL) showing an excellent correlation. We demonstrate the use of our instrument for characterizing PM by showing the significant differences between PM10 and PM1 in their respective diurnal variation patterns as well as the increase in their concentrations during events of air stagnation.

Does the Effect of PM 10 on Mortality Depend on PM Nickel and Vanadium Content? A Reanalysis of the NMMAPS Data

BackgroundLack of knowledge regarding particulate matter (PM) characteristics associated with toxicity is a crucial research gap. Short-term effects of PM can vary by location, possibly reflecting regional differences in mixtures. A report by Lippmann et al. [Lippmann et al., Environ Health Perspect 114:1662–1669 (2006)] analyzed mortality effect estimates from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) for 1987–1994. They found that average concentrations of nickel or vanadium in PM2.5 (PM with aerodynamic diameter < 2.5 μm) positively modified the lag-1 day association between PM10 and all-cause mortality.ObjectiveWe reestimated the relationship between county-specific lag-1 PM10 (PM with aerodynamic diameter < 10 μm) effects on mortality and county-specific nickel or vanadium PM2.5 average concentrations using 1987–2000 effect estimates. We explored whether such modification is sensitive to outliers.MethodsWe estimated long-term average county-level nickel and vanadium PM2.5 concentrations for 2000–2005 for 72 U.S. counties representing 69 communities. We fitted Bayesian hierarchical regression models to investigate whether county-specific short-term effects of PM10 on mortality are modified by long-term county-specific nickel or vanadium PM2.5 concentrations. We conducted sensitivity analyses by excluding individual communities and considering log-transformed data.ResultsOur results were consistent with those of Lippmann et al. However, we found that when counties included in the NMMAPS New York community were excluded from the sensitivity analysis, the evidence of effect modification of nickel or vanadium on the short-term effects of PM10 mortality was much weaker and no longer statistically significant.ConclusionsOur analysis does not contradict the hypothesis that nickel or vanadium may increase the risk of PM to human health, but it highlights the sensitivity of findings to particularly influential observations.

Ozone modifies associations between temperature and cardiovascular mortality: analysis of the NMMAPS data

Objectives: Both ambient ozone and temperature are associated with human health. However, few data are available on whether ozone modifies temperature effects. This study aims to explore whether ozone modified...

Temperature Modifies Short-Term Effects of Ozone on Total Mortality in 60 Large Eastern US Communities

ISEE-213 Objective: Many studies have indicated that ozone is associated with morbidity and mortality. A few studies have reported that the association is heterogeneous across seasons and geographic regions. However, little information is available on whether both temperature and geographic factors simultaneously modify the ozone effect. Material and Methods: We chose 60 large eastern US communities from the National Morbidity, Mortality, and Air Pollution Study database to examine whether maximum temperature modified the association of ozone with total mortality for each community during April to October, 1987 to 2000. Results: Our results show that temperature consistently modified ozone-mortality associations and that such modification varied across geographic regions. In the northeast region, a 10-ppb increment in ozone was associated with an increase of 2.22% [95% posterior interval (PI): 1.19–3.13%], 3.06% (95% PI: 2.21–3.76%), and 6.22% (95% PI: 4.77–7.56%) in mortality at low, moderate, and high temperature levels, respectively, whereas in the southeast region a 10-ppb increment in ozone was associated with an increase of 1.13% (95% PI: −1.12% to 3.18%), 1.50% (95% PI: 0.22–2.81%), and 1.29% (95% PI: −0.33% to 2.96%) in mortality, respectively. Conclusions: It is evident that temperature positively modified the ozone-mortality association in the northeast region, but such a pattern was not apparent in the southeast region. Temperature and geographic factors should be considered in the assessment of ozone effects.

Ozone Modifies Associations Between Temperature and Cardiovascular Mortality—The Analysis Using the NMMAPS Data

ISEE-343 Objective: Both ambient ozone and temperature are associated with human health. However, no study has examined whether ozone modifies temperature effects, although some studies have examined whether temperature modifies air pollution effects. This study was undertaken to explore whether ozone modified associations between maximum temperature and cardiovascular mortality across the United States. Material and Methods: We obtained the data from the US National Morbidity, Mortality, and Air Pollution Study (NMMAPS) Website. We used 2 time-series Poisson regression models (the response surface model and stratification model) to examine whether ozone modified associations between maximum temperature and cardiovascular mortality (CVM) in 95 large US communities during 1987 to 2000. We used Bayesian meta-analysis to pool estimates in each community. Results: The response surface model was used to examine the joint effects of temperature and ozone on CVM in summer. Results illustrate that ozone synergistically modified the temperature-CVM associations across the different regions. The stratification model quantified the temperature-CVM associations across different levels of ozone. Results show that, in general, the higher the ozone concentration, the stronger the temperature-CVM associations across the communities. A 10°C increase in temperature on the same day was associated with an increase in CVM by 1.17% and 8.31% for the lowest and highest level of ozone concentrations, respectively. Conclusions: Ozone modified temperature effects in the different regions in the United States. It is important to evaluate the modifying role of ozone while estimating temperature-related health impacts and to further investigate the reasons behind the regional variability.

Modelling UK Black Smoke and Sulphur Dioxide Concentrations, 1955–2001 to Estimate Lifecourse air Pollution Exposures

ISEE-590 Objective: The UK has a long-running set of national air pollution data with over 3100 monitoring stations in existence at various points from 1955. This project aims to construct a detailed time series database of modeled concentrations of Black Smoke (BS) and SO2 for UK that can be applied to existing British cohort studies. This offers a unique opportunity to investigate health effects of lifecourse air pollution exposures. Materials and Methods: Different methods for modeling concentration surfaces are in development including Kriging, regression mapping, and dispersion modeling. The initial steps reported here are Ordinary Kriging of monitored annual BS concentrations for sample years (1962, 1971, 1981, and 1991), including data from stations with concentrations available for ≥75% of days in the year. Supplementary datasets for possible inclusion in regression and dispersion modeling have also been identified. Results: Kriging models were developed using 75% of the data with remaining data reserved for validation. Models worked well for 1962 and 1971 (r2=0.6 and 0.6, RMSE 49.3 and 18.8) but were less good for later years (r2=0.5 and 0.4, RMSE 7.8 and 6.6) when monitoring station numbers fell from around 1000 to less than 200. Highest concentrations were consistently observed in northern England (Liverpool, Manchester, and Tyneside), with lowest values in Scotland and the South-West. BS concentrations declined markedly over time with 90th percentile values falling from 270 μg/m3 in 1962 to 25 μg/m3 in 1991. Additional historical datasets identified for use in other modeling techniques included population density, land use, emissions inventories, gas networks, smoke control areas (relating to domestic fuel use), and the railway network. Conclusions: Kriging provides reasonable baseline maps against which more sophisticated modeling techniques can be compared. A range of datasets including air pollution concentrations are available to aid modeling of historical exposures to air pollution in UK.

Dominici et al. Respond to "Heterogeneity of Particulate Matter Health Risks"

Tolbert (1) highlights issues that we also found to be complex as we interpreted the results of our analyses (2). We analyzed data from a 14-year interval over which particulate matter levels dropped substantially across the United States. We applied methods developed as part of the National Morbidity, Mortality, and Air Pollution Study for evaluating change in the short-term effect of particulate matter 10 lm in aerodynamic diameter over a period of increasingly stringent regulation that might have changed the chemical composition and toxicity of particulate matter. The decline in particulate matter concentrations over a span with uniformly collected particulate matter measurements and mortality provided an opportunity to assess whether the health of the public had benefited from the decline. Our work can be categorized as accountability assessment, that is, evaluating whether regulations have had an impact on harmful exposures and the occurrence of health effects (3). There is an increasing call for such evaluations, given the cost of some environmental regulations, including the National Ambient Air Quality Standard for particulate matter. For two of the ‘‘criteria pollutants’’ with specific biomarkers—lead (blood lead level) and carbon monoxide (level of carboxyhemoglobin in blood)—declines in levels in population samples were readily linked to specific events, including the removal of lead from gasoline (4). For airborne particulate matter, a heterogeneous mixture with many sources, accountability assessment is far more challenging, as noted by Tolbert (1). In meeting the National Ambient Air Quality Standard for particulate matter, strategies in nonattainment areas typically focus on the controllable sources delivering the greatest emission load, and not necessarily the most toxic particles since we are still uncertain as to the toxicity-determining characteristics of particulate matter. Of necessity, our analyses were descriptive, seeking to identify whether there was an indication of a change in the risk of health events associated with particulate matter, beyond that associated with a decline in overall particulate matter mass concentration. As noted by Tolbert (1), the evidence for a decline in toxicity is suggestive, but far from conclusive. Tolbert lists alternative explanations for the findings. One possible explanation is the play of chance, given the imprecision with which possible effect modification was estimated. The sample size, of course, is fixed, because we used most of the data available. Our estimates could be interpreted as at least bounding the magnitude of any change in risk. The imprecision associated with the temporal changes in the short-term effect of particulate matter 10 lm in aerodynamic diameter, even when estimated from 14 years of data for 100 cities, shows one challenge of accountability assessment, particularly given the graded implementation over time of measures affecting particulate matter concentrations and characteristics.

Bayesian entropy for spatial sampling design of environmental data

We develop a spatial statistical methodology to design national air pollution monitoring networks with good predictive capabilities while minimizing the cost of monitoring. The underlying complexity of atmospheric processes and the urgent need to give credible assessments of environmental risk create problems requiring new statistical methodologies to meet these challenges. In this work, we present a new method of ranking various subnetworks taking both the environmental cost and the statistical information into account. A Bayesian algorithm is introduced to obtain an optimal subnetwork using an entropy framework. The final network and accuracy of the spatial predictions is heavily dependent on the underlying model of spatial correlation. Usually the simplifying assumption of stationarity, in the sense that the spatial dependency structure does not change location, is made for spatial prediction. However, it is not uncommon to find spatial data that show strong signs of nonstationary behavior. We build upon an existing approach that creates a nonstationary covariance by a mixture of a family of stationary processes, and we propose a Bayesian method of estimating the associated parameters using the technique of Reversible Jump Markov Chain Monte Carlo. We apply these methods for spatial prediction and network design to ambient ozone data from a monitoring network in the eastern US.

Time‐Varying Coefficient Models for the Analysis of Air Pollution and Health Outcome Data

In this article a time-varying coefficient model is developed to examine the relationship between adverse health and short-term (acute) exposure to air pollution. This model allows the relative risk to evolve over time, which may be due to an interaction with temperature, or from a change in the composition of pollutants, such as particulate matter, over time. The model produces a smooth estimate of these time-varying effects, which are not constrained to follow a fixed parametric form set by the investigator. Instead, the shape is estimated from the data using penalized natural cubic splines. Poisson regression models, using both quasi-likelihood and Bayesian techniques, are developed, with estimation performed using an iteratively re-weighted least squares procedure and Markov chain Monte Carlo simulation, respectively. The efficacy of the methods to estimate different types of time-varying effects are assessed via a simulation study, and the models are then applied to data from four cities that were part of the National Morbidity, Mortality, and Air Pollution Study.

Methods for Bias Reduction in Time-Series Studies of Particulate Matter Air Pollution and Mortality

In many cities of the United States, measurements of ambient particulate matter air pollution (PM) are available only every sixth day. Time-series studies conducted in these cities that investigate the relationship between mortality and PM are restricted to using a single day's PM as the measure of PM exposure, rather than using measurements taken over several consecutive days. Studies showed that using a single-day PM as the measure of PM exposure can result in estimates that have a negative bias, sometimes in the order of over half of the value being estimated. In this article two methods are introduced that can be used to obtain estimates that can in some situations reduce the bias to negligible proportions when only every-sixth-day PM concentrations are available. Using one of these methods, the national average PM mortality effect estimates obtained for total mortality and cardiovascular and respiratory mortality, respectively, correspond to 0.27% and 0.39% increases in mortality per 10-μg/m3 increment in PM. The corresponding effect estimates obtained using the single-day lag-1 PM concentration are 0.18% and 0.23%. The estimates obtained using the lag-1 PM concentration were the most widely reported results from the recent multicity National Morbidity, Mortality, and Air Pollution Study (NMMAPS) analyses. The more accurate estimates obtained from the methods introduced in this article will enable more accurate quantification of the increased incidence in mortality due to elevation in PM levels and the benefit of current or more stringent regulatory standards.

Estimating ground‐level PM2.5 using aerosol optical depth determined from satellite remote sensing

We assess the relationship of ground‐level fine particulate matter (PM2.5) concentrations for 2000–2001 measured as part of the Canadian National Air Pollution Surveillance (NAPS) network and the U.S. Air Quality System (AQS), versus remote‐sensed PM2.5 determined from aerosol optical depths (AOD) measured by the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging Spectroradiometer (MISR) satellite instruments. A global chemical transport model (GEOS‐CHEM) is used to simulate the factors affecting the relation between AOD and PM2.5. AERONET AOD is used to evaluate the method (r = 0.71, N = 48, slope = 0.69). We find significant spatial variation of the annual mean ground‐based measurements with PM2.5 determined from MODIS (r = 0.69, N = 199, slope = 0.82) and MISR (r = 0.58, N = 199, slope = 0.57). Excluding California significantly increases the respective slopes and correlations. The relative vertical profile of aerosol extinction is the most important factor affecting the spatial relationship between satellite and surface measurements of PM2.5; neglecting this parameter would reduce the spatial correlation to 0.36. In contrast, temporal variation in AOD is the most influential parameter affecting the temporal relationship between satellite and surface measurements of PM2.5; neglecting daily variation in this parameter would decrease the correlation in eastern North America from 0.5–0.8 to less than 0.2. Other simulated aerosol properties, such as effective radius and extinction efficiency have a minor role temporally, but do influence the spatial correlation. Global mapping of PM2.5 from both MODIS and MISR reveals annual mean concentrations of 40–50 ug/m3 over northern India and China.

Recent Developments of the National Morbidity Mortality Air Pollution Study: 1987–2000

SS1-03 Abstract: Multisite time series studies of particulate matter (PM) and health have provided evidence that daily variation in air pollution levels is associated with daily variation in mortality and morbidity counts. Key areas of research concern: 1) assessing uncertainties in the adjustment for potential confounders and in the selection of the statistical model; 2) quantifying spatial and seasonal variation of PM10 health effects to understand toxicity of various PM components; and 3) developing a computational framework for reproducible research. To address these questions, we: 1) explore statistical properties of semiparametric regressions to adjust for confounding bias; 2) extend the NMMAPS hierarchical semiparametric Poisson regression models to allow for seasonally and spatially varying PM effects; and 3) introduce a R software package (NMMAPS-R add web site), which implements the statistical methods described and serves as a platform for conducting systematic and reproducible research. We apply methods to the National Morbidity, Mortality, and Air Pollution Study (NMMAPS), a national study covering 100 U.S. cities for the years 1987–2000 for PM10 and for the years 1999–2000 for PM2.5. We found strong evidence that lag 1 exposure to PM10 continues to be associated with all-cause and cardiorespiratory mortality with the greatest effect in the eastern United States. Under the NMMAPS basic model, a 10-μg increase in PM10 at lag 1 is associated with a 0.19% and 0.24% increase in all-cause mortality and cardiorespiratory mortality (95% confidence interval [CI]: 0.10 to 0.28 and 0.13 to 0.36, respectively). In addition a 10-μg increase in PM2.5 at lag 1 is associated with 0.29% and 0.38% increases in all-cause mortality and cardiorespiratory mortality (95% CI: 0.01 to 0.57 and −0.01 to 0.82, respectively). These findings provided key epidemiologic evidence for the EPA's review of the U.S. National Ambient Air Quality Standards for particulate matter.

Childrenʼs Exposure to Ambient Air Pollution: The Application of Different Exposure Assessment Methodologies

SS6-03 Abstract: The Border Air Quality Strategy (BAQS), an international agreement between Canada and the United States, has led to a prioritization of research in Windsor, Ontario. Much of the health and exposure assessment research has focused on the potential impact of exposure to ambient air pollution on the health of preadolescent children. In September 2004, Health Canada issued a cross-sectional survey to all children, aged 7 to 14, enrolled in Windsor schools (n = 20,159) to determine their respiratory health status. Other respiratory risk factor data collected in the survey included parental health, housing conditions, and self-reported measures of exposure to traffic. A response rate of 63.8% (n = 12,694) was obtained. Chronic exposure to a variety of air pollutants is being estimated for these participants using different methods. Ongoing air pollution measurements include intraurban monitoring at approximately 50 locations measuring particulate matter (fine and coarse mode), nitrogen dioxide, sulfur dioxide, polycyclic aromatic hydrocarbons, volatile organic compounds, and acid vapor. These pollutants are being monitored for a 2-week period in 4 different seasons between 2004 and 2007. Air pollution exposures are also being assessed using personal, indoor, and outdoor microenvironmental monitoring at 48 residences during cold and warm seasons throughout 2005 to 2007. This exposure study has a total of 1440 sample days (480 annually), which will be used to assess infiltration factors and activity patterns that influence personal exposure. Both of these air pollution monitoring studies are also colocated at both of the Environment Canada National Air Pollution Surveillance (NAPS) sites in Windsor, which are the typical central monitoring sites used when assigning exposure in health effects research. Chronic exposure will be estimated using historical data obtained from NAPS and compared with results obtained from the BAQS exposure assessment methods. Although the use of personal monitoring is the preferred method for characterizing an individual's exposure to air pollution, this method is not always viable in epidemiologic studies. The different methods of assessing exposure for the same population will allow a comprehensive investigation of the sources of exposure measurement error. This will be assessed by conducting an intermethod reliability analysis. Correlational analyses using the Pearson and Spearman rank correlation coefficients will identify any association between exposure metrics generated using these data sources and, moreover, explore whether agreement is differential by health status, age, and socioeconomic status. Findings from this intermethod reliability study will assist in the application of methods to adjust associations between air pollution and health outcome data for the effects of exposure measurement error.

Ozone Overload: Current Standards May Not Protect Health

Ozone is a common urban pollutant that has been linked to health effects such as reduced lung function, increases in respiratory symptoms, and development of asthma. Now a team of researchers reports that ozone may pose a danger to human health even at levels far below the limits set by current U.S. and international regulations [EHP 114:532–536; Bell et al.]. The team conducted a study of 98 U.S. urban communities between 1987 and 2000 to investigate whether there is a threshold below which ozone does not affect mortality, and report that they were unable to identify such a threshold. More than 100 million Americans live in areas that exceed the EPA’s National Ambient Air Quality Standard (NAAQS) for ozone of 80 parts per billion (ppb) ozone averaged over a peak 8-hour time period. The EPA is currently reviewing scientific evidence to determine whether that NAAQS should be revised in order to meet the 1997 Clean Air Act’s goal of protecting human health with an adequate margin of safety. The researchers embarked on this project to better identify that margin. Data were gathered from the National Morbidity, Mortality, and Air Pollution Study, a project launched in 1996 to address questions about the degree to which particulate matter is responsible for changes in daily mortality rates. They also used ozone data from the EPA and weather data from the National Climatic Data Center. Then they applied a Bayesian hierarchical model to mortality data from the National Center for Health Statistics to evaluate the relationship between ambient ozone levels and mortality rates within each community over a 14-year period. The key finding of their research is strong and consistent evidence that daily increases in ambient ozone exposure were associated with daily increases in premature mortality. This was true even at very low pollution levels, including an idealized scenario in which every community always met current ozone regulations. In that scenario, each daily 10-ppb increase of 8-hour ozone was associated with a 0.30% increase in mortality. “All results indicate that any threshold would exist at very low concentrations, far below current U.S. and international regulations and nearing background levels,” the authors write. They conclude that any reduction in ambient ozone levels, such as through transportation planning in urban areas, could be expected to yield important benefits to public health, even in areas that already meet current regulatory standards.

Applying a moving total mortality count to the cities in the NMMAPS database to estimate the mortality effects of particulate matter air pollution

Objectives: To apply a new method for estimating the association between daily ambient particulate matter air pollution (PM) and daily mortality to data from over 100 United States cities contained...

The association between low level exposures to ambient air pollution and term low birth weight: a retrospective cohort study

BackgroundStudies in areas with relatively high levels of air pollution have found some positive associations between exposures to ambient levels of air pollution and several birth outcomes including low birth weight (LBW). The purpose of this study was to examine the association between LBW among term infants and ambient air pollution, by trimester of exposure, in a region of lower level exposures.MethodsThe relationship between LBW and ambient levels of particulate matter up to 10 um in diameter (PM10), sulfur dioxide (SO2) and ground-level ozone (O3) was evaluated using the Nova Scotia Atlee Perinatal Database and ambient air monitoring data from the Environment Canada National Air Pollution Surveillance Network and the Nova Scotia Department of Environment. The cohort consisted of live singleton births (≥37 weeks of gestation) between January1,1988 and December31,2000. Maternal exposures to air pollution were assigned to women living within 25 km of a monitoring station at the time of birth. Air pollution was evaluated as a continuous and categorical variable (using quartile exposures) for each trimester and relative risks were estimated from logistic regression, adjusted for confounding variables.ResultsThere were 74,284 women with a term, singleton birth during the study period and with exposure data. In the analyses unadjusted for year of birth, first trimester exposures in the highest quartile for SO2 and PM10suggested an increased risk of delivering a LBW infant (relative risk = 1.36, 95% confidence interval = 1.04 to 1.78 for SO2 exposure and relative risk = 1.33, 95% confidence interval = 1.02 to 1.74 for PM10). After adjustment for birth year, the relative risks were attenuated somewhat and not statistically significant. A dose-response relationship for SO2 was noted with increasing levels of exposure. No statistically significant effects were noted for ozone.ConclusionOur results suggest that exposure during the first trimester to relatively low levels of some air pollutants may be associated with a reduction in birth weight in term-born infants. These findings have implications for the development of effective risk management strategies to minimize the public health impacts for pregnant women.

Model Choice in Time Series Studies of Air Pollution and Mortality

SummaryMulticity time series studies of particulate matter and mortality and morbidity have provided evidence that daily variation in air pollution levels is associated with daily variation in mortality counts. These findings served as key epidemiological evidence for the recent review of the US national ambient air quality standards for particulate matter. As a result, methodological issues concerning time series analysis of the relationship between air pollution and health have attracted the attention of the scientific community and critics have raised concerns about the adequacy of current model formulations. Time series data on pollution and mortality are generally analysed by using log-linear, Poisson regression models for overdispersed counts with the daily number of deaths as outcome, the (possibly lagged) daily level of pollution as a linear predictor and smooth functions of weather variables and calendar time used to adjust for time-varying confounders. Investigators around the world have used different approaches to adjust for confounding, making it difficult to compare results across studies. To date, the statistical properties of these different approaches have not been comprehensively compared. To address these issues, we quantify and characterize model uncertainty and model choice in adjusting for seasonal and long-term trends in time series models of air pollution and mortality. First, we conduct a simulation study to compare and describe the properties of statistical methods that are commonly used for confounding adjustment. We generate data under several confounding scenarios and systematically compare the performance of the various methods with respect to the mean-squared error of the estimated air pollution coefficient. We find that the bias in the estimates generally decreases with more aggressive smoothing and that model selection methods which optimize prediction may not be suitable for obtaining an estimate with small bias. Second, we apply and compare the modelling approaches with the National Morbidity, Mortality, and Air Pollution Study database which comprises daily time series of several pollutants, weather variables and mortality counts covering the period 1987–2000 for the largest 100 cities in the USA. When applying these approaches to adjusting for seasonal and long-term trends we find that the Study's estimates for the national average effect of PM10 at lag 1 on mortality vary over approximately a twofold range, with 95% posterior intervals always excluding zero risk.

Effects of Economic and Environmental Reform on the Diffusion of Cleaner Coal Technology in China

Despite the urgent need to address severe air pollution problems caused by China's coal consumption, diffusion of cleaner coal technologies (CCTs) has been slow. This contribution utilizes the fragmented authoritarianism model to explain this slow diffusion by examining the structure of bureaucratic decision making and changes in incentives that have accompanied fiscal decentralization and enterprise deregulation. Case studies on the diffusion of two CCTs — flue gas desulphurization (FGD) and coal washing — highlight challenges of environmental policy design and implementation during the economic reforms of the past decade. Enterprises had little incentive to adopt FGD when central government agencies disagreed on promoting the technology and local agencies did not rigorously enforce national air pollution control policies. For washed coal, production slowed when coal pricing reform and coal industry restructuring were not co-ordinated with environmental policies. Further promotion of CCTs in China requires changes in incentive structures for local governments and enterprises, as well as enhanced policy co-ordination among central government agencies.