A BAYESIAN HIERARCHICAL MODEL FOR CONSTRAINED DISTRIBUTED LAG FUNCTIONS: ESTIMATING THE TIME COURSE OF HOSPITALIZATION ASSOCIATED WITH AIR POLLUTION EXPOSURE

Numerous time series studies have provided strong evidence of an association between increased levels of ambient air pollution and increased levels of hospital admissions, typically at 0, 1, or 2 days after an air pollution episode. An important research aim is to extend existing statistical models so that a more detailed understanding of the time course of hospitalization after exposure to air pollution can be obtained. Information about this time course, combined with prior knowledge about biological mechanisms, could provide the basis for hypotheses concerning the mechanism by which air pollution causes disease. Previous studies have identified two important methodological questions: (1) How can we estimate the shape of the distributed lag between increased air pollution exposure and increased mortality or morbidity? and (2) How should we estimate the cumulative population health risk from short-term exposure to air pollution? Distributed lag models are appropriate tools for estimating air pollution health effects that may be spread over several days. However, estimation for distributed lag models in air pollution and health applications is hampered by the substantial noise in the data and the inherently weak signal that is the target of investigation. We introduce an hierarchical Bayesian distributed lag model that incorporates prior information about the time course of pollution effects and combines information across multiple locations. The model has a connection to penalized spline smoothing using a special type of penalty matrix. We apply the model to estimating the distributed lag between exposure to particulate matter air pollution and hospitalization for cardiovascular and respiratory disease using data from a large United States air pollution and hospitalization database of Medicare enrollees in 94 counties covering the years 1999-2002.

木本植物对大气污染指示与监测作用研究

本文研究了木本植物的不同部位即叶片、枝条和树皮以及植物的生理指标如气孔阻力对大气S02、TSP和重金属污染的指示和监测作用，并用树木年轮指示大气污染的历史和程度。结果认为： 承德市大气污染自1703年城市化以来开始出现，但达到严重污染水平则出现在本世纪50年代以来尤其是最近10-20年城市化与工业化的加剧，主要污染物以S02为主，从避暑山庄修建前的<0.1μg m-3达到目前的30μg m-3，重金属污染Fe自1927-45大庙铁矿开采后出现，Mn、Ni、Pb等出现在工业化以来的最近40-50年中，上述污染物含量在木质部年轮中明显升高，如S增加了10倍以上，Pb增加了560% (P<0.00l)。 不同城市功能区树皮pH和气孔阻力不同，主要与大气中的S02和TSP有关，据此可监测大气S02和TSP污染。前者以榆树、加拿大杨、垂柳和国槐最佳，相关系数分别可达-0.8384 (P<0.0l)，-0.7447、-0.6904和-0.6552 (P<0.05);后者则以白腊和旱柳下表皮最好，相关系数达0.9968和0.9951 (P<0.00l)。在扫描电镜下发现气孔受大气TSP影响出现不同程度的堵塞现象，主要有2种途径，小型颗粒物(<5μm)进入气孔腔，大型颗粒物(>30μm)可将气孔封盖。 植物不同器官部位污染物含量以树皮为最高，其次是枝条或叶，因而适宜的指示或监测部位是叶或枝条。主分量分析认为：承德市大气污染物以S为主，重金属Fe、Zn、Mn也有一定的贡献，Pb仅出现在繁忙道路区。不同季节污染物含量变化以休眠期最高，生长初期次之，生长旺盛期最低，如S和Pb分别从0.75 mg g-1和0.7 mg g-1上升到1.5 mg g-1和2.0 mg g-1(P<0.001)。植物不同季节污染物含量的变化反应了大气污染物季节变化特点，因而可以指示或监测大气污染尤其是S02污染。其中刺槐多部位复相关模型监测效果最佳，复相关系数可达0.987;某些植物单一部位的监测作用也较好，叶以珍珠梅最佳，相关系数为0.8695 (P<0.001)，枝以油松、珍珠梅、垂柳为好(r≥0.8，P <0.001)，树皮以刺槐为佳，r=0.8615 (P<0.0l)。植物不同部位的污染物含量还可用来评价大气环境质量，其中复合污染指数可以 评价总的大气环境质量，S污染指数和重金属污染指数可以评价S02、重金属和TSP污染，与直接利用污染物浓度法基本一致。油松不同部位对于大气S02的指示作用可表现为年轮对大气污染历史的指示或监测，针叶对现状S02污染的预测，并利用针叶对于S02的监测结果，绘制了大气S02污染分布图。 总之，本文利用古松年轮和现状城市植物的枝条、叶和树皮中的污染物含量以及树皮酸度等不同方面的指标，对承德市大气污染的历史和现状进行了指示与监测，即承德市大气污染从过去到现在均以S02为主，植物不同部位可以非常有效地进行大气S02污染的监测与评价，其中多部位的复相关模型预测效果极佳。另外，由植物监测而绘制的大气S02分布图，较准确地揭示了承德市大气S02现状分布规律。

A national satellite-based land-use regression model for air pollution exposure assessment in Australia

Land-use regression (LUR) is a technique that can improve the accuracy of air pollution exposure assessment in epidemiological studies. Most LUR models are developed for single cities, which places limitations on their applicability to other locations. We sought to develop a model to predict nitrogen dioxide (NO2) concentrations with national coverage of Australia by using satellite observations of tropospheric NO2 columns combined with other predictor variables. We used a generalised estimating equation (GEE) model to predict annual and monthly average ambient NO2 concentrations measured by a national monitoring network from 2006 through 2011. The best annual model explained 81% of spatial variation in NO2 (absolute RMS error=1.4 ppb), while the best monthly model explained 76% (absolute RMS error=1.9 ppb). We applied our models to predict NO2 concentrations at the ~350,000 census mesh blocks across the country (a mesh block is the smallest spatial unit in the Australian census). National population-weighted average concentrations ranged from 7.3 ppb (2006) to 6.3 ppb (2011). We found that a simple approach using tropospheric NO2 column data yielded models with slightly better predictive ability than those produced using a more involved approach that required simulation of surface-to-column ratios. The models were capable of capturing within-urban variability in NO2, and offer the ability to estimate ambient NO2 concentrations at monthly and annual time scales across Australia from 2006–2011. We are making our model predictions freely available for research.

Different splitting techniques with application to air pollution models

Splitting techniques are commonly used when large-scale models, which appear in different fields of science and engineering, are treated numerically. Four types of splitting procedures are defined and discussed. The problem of the choice of a splitting procedure is investigated. Several numerical tests, by which the influence of the splitting errors on the accuracy of the results is studied, are given. It is shown that the splitting errors decrease linearly when (1) the splitting procedure is of first order and (2) the splitting errors are dominant. Three examples for splitting procedures used in all large-scale air pollution models are presented. Numerical results obtained by a particular air pollution model, Unified Danish Eulerian Model (UNI-DEM), are given and analysed.

Weibull and generalised exponential overdispersion models with an application to ozone air pollution

We consider the problem of estimating the mean and variance of the time between occurrences of an event of interest (inter-occurrences times) where some forms of dependence between two consecutive time intervals are allowed. Two basic density functions are taken into account. They are the Weibull and the generalised exponential density functions. In order to capture the dependence between two consecutive inter-occurrences times, we assume that either the shape and/or the scale parameters of the two density functions are given by auto-regressive models. The expressions for the mean and variance of the inter-occurrences times are presented. The models are applied to the ozone data from two regions of Mexico City. The estimation of the parameters is performed using a Bayesian point of view via Markov chain Monte Carlo (MCMC) methods.

Highway air pollution dispersion modeling: preliminary evaluation of thirteen models. Final report.

Transportation Department, Office of the Assistant Secretary for Systems Development and Technology, Washington, D.C.

A Parameter Estimation and Identifiability Analysis Methodology Applied to a Street Canyon Air Pollution Model

Mathematical models are increasingly used in environmental science thus increasing the importance of uncertainty and sensitivity analyses. In the present study, an iterative parameter estimation and identifiability analysis methodology is applied to an atmospheric model – the Operational Street Pollution Model (OSPMr). To assess the predictive validity of the model, the data is split into an estimation and a prediction data set using two data splitting approaches and data preparation techniques (clustering and outlier detection) are analysed. The sensitivity analysis, being part of the identifiability analysis, showed that some model parameters were significantly more sensitive than others. The application of the determined optimal parameter values was shown to succesfully equilibrate the model biases among the individual streets and species. It was as well shown that the frequentist approach applied for the uncertainty calculations underestimated the parameter uncertainties. The model parameter uncertainty was qualitatively assessed to be significant, and reduction strategies were identified.

The relationship between particulate air pollution and emergency hospital visits for hypertension in Beijing, China

BACKGROUND: A number of epidemiological studies have examined the adverse effect of air pollution on mortality and morbidity. Also, several studies have investigated the associations between air pollution and specific-cause diseases including arrhythmia, myocardial infarction, and heart failure. However, little is known about the relationship between air pollution and the onset of hypertension. OBJECTIVE: To explore the risk effect of particulate matter air pollution on the emergency hospital visits (EHVs) for hypertension in Beijing, China. METHODS: We gathered data on daily EHVs for hypertension, fine particulate matter less than 2.5 microm in aerodynamic diameter (PM(2.5)), particulate matter less than 10 microm in aerodynamic diameter (PM(10)), sulfur dioxide, and nitrogen dioxide in Beijing, China during 2007. A time-stratified case-crossover design with distributed lag model was used to evaluate associations between ambient air pollutants and hypertension. Daily mean temperature and relative humidity were controlled in all models. RESULTS: There were 1,491 EHVs for hypertension during the study period. In single pollutant models, an increase in 10 microg/m(3) in PM(2.5) and PM(10) was associated with EHVs for hypertension with odds ratios (overall effect of five days) of 1.084 (95% confidence interval (CI): 1.028, 1.139) and 1.060% (95% CI: 1.020, 1.101), respectively. CONCLUSION: Elevated levels of ambient particulate matters are associated with an increase in EHVs for hypertension in Beijing, China.

The short-term effect of air pollution on cardiovascular mortality in Tianjin, China : comparison of time series and case–crossover analyses

Background: Many studies have illustrated that ambient air pollution negatively impacts on health. However, little evidence is available for the effects of air pollution on cardiovascular mortality (CVM) in Tianjin, China. Also, no study has examined which strata length for the time-stratified case–crossover analysis gives estimates that most closely match the estimates from time series analysis. Objectives: The purpose of this study was to estimate the effects of air pollutants on CVM in Tianjin, China, and compare time-stratified case–crossover and time series analyses. Method: A time-stratified case–crossover and generalized additive model (time series) were applied to examine the impact of air pollution on CVM from 2005 to 2007. Four time-stratified case–crossover analyses were used by varying the stratum length (Calendar month, 28, 21 or 14 days). Jackknifing was used to compare the methods. Residual analysis was used to check whether the models fitted well. Results: Both case–crossover and time series analyses show that air pollutants (PM10, SO2 and NO2) were positively associated with CVM. The estimates from the time-stratified case–crossover varied greatly with changing strata length. The estimates from the time series analyses varied slightly with changing degrees of freedom per year for time. The residuals from the time series analyses had less autocorrelation than those from the case–crossover analyses indicating a better fit. Conclusion: Air pollution was associated with an increased risk of CVM in Tianjin, China. Time series analyses performed better than the time-stratified case–crossover analyses in terms of residual checking.

Gaseous air pollution and emergency hospital visits for hypertension in Beijing, China : a time-stratified case-crossover study

Background: A number of epidemiological studies have been conducted to research the adverse effects of air pollution on mortality and morbidity. Hypertension is the most important risk factor for cardiovascular mortality. However, few previous studies have examined the relationship between gaseous air pollution and morbidity for hypertension. ---------- Methods: Daily data on emergency hospital visits (EHVs) for hypertension were collected from the Peking University Third Hospital. Daily data on gaseous air pollutants (sulfur dioxide (SO2) and nitrogen dioxide (NO2)) and particulate matter less than 10 μm in aerodynamic diameter (PM10) were collected from the Beijing Municipal Environmental Monitoring Center. A time-stratified case-crossover design was conducted to evaluate the relationship between urban gaseous air pollution and EHVs for hypertension. Temperature and relative humidity were controlled for. ---------- Results: In the single air pollutant models, a 10 μg/m3 increase in SO2 and NO2 were significantly associated with EHVs for hypertension. The odds ratios (ORs) were 1.037 (95% confidence interval (CI): 1.004-1.071) for SO2 at lag 0 day, and 1.101 (95% CI: 1.038-1.168) for NO2 at lag 3 day. After controlling for PM10, the ORs associated with SO2 and NO2 were 1.025 (95% CI: 0.987-1.065) and 1.114 (95% CI: 1.037-1.195), respectively.---------- Conclusion: Elevated urban gaseous air pollution was associated with increased EHVs for hypertension in Beijing, China.

Air pollution, temperature and pediatric influenza in Brisbane, Australia

Previous studies have demonstrated the importance of weather variables in influencing the incidence of influenza. However, the role of air pollution is often ignored in identifying the environmental drivers of influenza. This research aims to examine the impacts of air pollutants and temperature on the incidence of pediatric influenza in Brisbane, Australia. Lab-confirmed daily data on influenza counts among children aged 0-14years in Brisbane from 2001 January 1st to 2008 December 31st were retrieved from Queensland Health. Daily data on maximum and minimum temperatures for the same period were supplied by the Australian Bureau of Meteorology. Winter was chosen as the main study season due to it having the highest pediatric influenza incidence. Four Poisson log-linear regression models, with daily pediatric seasonal influenza counts as the outcome, were used to examine the impacts of air pollutants (i.e., ozone (O3), particulate matter≤10μm (PM10) and nitrogen dioxide (NO2)) and temperature (using a moving average of ten days for these variables) on pediatric influenza. The results show that mean temperature (Relative risk (RR): 0.86; 95% Confidence Interval (CI): 0.82-0.89) was negatively associated with pediatric seasonal influenza in Brisbane, and high concentrations of O3 (RR: 1.28; 95% CI: 1.25-1.31) and PM10 (RR: 1.11; 95% CI: 1.10-1.13) were associated with more pediatric influenza cases. There was a significant interaction effect (RR: 0.94; 95% CI: 0.93-0.95) between PM10 and mean temperature on pediatric influenza. Adding the interaction term between mean temperature and PM10 substantially improved the model fit. This study provides evidence that PM10 needs to be taken into account when evaluating the temperature-influenza relationship. O3 was also an important predictor, independent of temperature.

Projecting future air pollution-related mortality under a changing climate: Progress, uncertainties and research needs

Background Climate change may affect mortality associated with air pollutants, especially for fine particulate matter (PM2.5) and ozone (O3). Projection studies of such kind involve complicated modelling approaches with uncertainties. Objectives We conducted a systematic review of researches and methods for projecting future PM2.5-/O3-related mortality to identify the uncertainties and optimal approaches for handling uncertainty. Methods A literature search was conducted in October 2013, using the electronic databases: PubMed, Scopus, ScienceDirect, ProQuest, and Web of Science. The search was limited to peer-reviewed journal articles published in English from January 1980 to September 2013. Discussion Fifteen studies fulfilled the inclusion criteria. Most studies reported that an increase of climate change-induced PM2.5 and O3 may result in an increase in mortality. However, little research has been conducted in developing countries with high emissions and dense populations. Additionally, health effects induced by PM2.5 may dominate compared to those caused by O3, but projection studies of PM2.5-related mortality are fewer than those of O3-related mortality. There is a considerable variation in approaches of scenario-based projection researches, which makes it difficult to compare results. Multiple scenarios, models and downscaling methods have been used to reduce uncertainties. However, few studies have discussed what the main source of uncertainties is and which uncertainty could be most effectively reduced. Conclusions Projecting air pollution-related mortality requires a systematic consideration of assumptions and uncertainties, which will significantly aid policymakers in efforts to manage potential impacts of PM2.5 and O3 on mortality in the context of climate change.

Long-term ambient air pollution exposure and self-reported morbidity in the Australian Longitudinal Study on Women's Health: A cross-sectional study

- Objective We sought to assess the effect of long-term exposure to ambient air pollution on the prevalence of self-reported health outcomes in Australian women. - Design Cross-sectional study - Setting and participants The geocoded residential addresses of 26 991 women across 3 age cohorts in the Australian Longitudinal Study on Women's Health between 2006 and 2011 were linked to nitrogen dioxide (NO2) exposure estimates from a land-use regression model. Annual average NO2 concentrations and residential proximity to roads were used as proxies of exposure to ambient air pollution. - Outcome measures Self-reported disease presence for diabetes mellitus, heart disease, hypertension, stroke, asthma, chronic obstructive pulmonary disease and self-reported symptoms of allergies, breathing difficulties, chest pain and palpitations. - Methods Disease prevalence was modelled by population-averaged Poisson regression models estimated by generalised estimating equations. Associations between symptoms and ambient air pollution were modelled by multilevel mixed logistic regression. Spatial clustering was accounted for at the postcode level. - Results No associations were observed between any of the outcome and exposure variables considered at the 1% significance level after adjusting for known risk factors and confounders. - Conclusions Long-term exposure to ambient air pollution was not associated with self-reported disease prevalence in Australian women. The observed results may have been due to exposure and outcome misclassification, lack of power to detect weak associations or an actual absence of associations with self-reported outcomes at the relatively low annual average air pollution exposure levels across Australia.