A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Kriging <s> In clinical measurement comparison of a new measurement technique with an established one is often needed to see whether they agree sufficiently for the new to replace the old. Such investigations are often analysed inappropriately, notably by using correlation coefficients. The use of correlation is misleading. An alternative approach, based on graphical techniques and simple calculations, is described, together with the relation between this analysis and the assessment of repeatability. <s> BIB001 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Kriging <s> Geographical information systems could be improved by adding procedures for geostatistical spatial analysis to existing facilities. Most traditional methods of interpolation are based on mathematical as distinct from stochastic models of spatial variation. Spatially distributed data behave more like random variables, however, and regionalized variable theory provides a set of stochastic methods for analysing them. Kriging is the method of interpolation deriving from regionalized variable theory. It depends on expressing spatial variation of the property in terms of the variogram, and it minimizes the prediction errors which are themselves estimated. We describe the procedures and the way we link them using standard operating systems. We illustrate them using examples from case studies, one involving the mapping and control of soil salinity in the Jordan Valley of Israel, the other in semi-arid Botswana where the herbaceous cover was estimated and mapped from aerial photographic survey. <s> BIB002 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Kriging <s> Abstract Ozone concentrations exhibit spatial variability within metropolitan areas, resulting in significantly different personal exposures among individuals. This paper uses the statistical technique, kriging, to explore the 12-h daytime (8am–8pm) ozone spatial variation and to predict mean outdoor ozone levels at home sites within the Toronto metropolitan area. Outdoor ozone measurements taken in the Toronto metropolitan area between June and August 1992 are used in kriging models to predict outdoor ozone concentrations. The performance of the model is evaluated by comparing actual home outdoor measurements with predicted values. Results indicate that kriging predictions are more accurate than using only the closest stationary ambient site measurements for determining home outdoor ozone concentrations within the metropolitan area. The average variogram obtained from pooling data throughout the entire sampling period shows a clear spatial trend in the outdoor ozone variation. Kriging predictions using the parameters from the average variogram perform as well as those using variograms from individual days. An approach for minimizing sample bias can be used to increase the accuracy of the predictions; cross-validation suggests that it is a reasonable procedure. <s> BIB003 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Kriging <s> ABSTRACT Relationships between ambient levels of selected air pollutants and pediatric asthma exacerbation in Atlanta were studied retrospectively. As a part of this study, temporal and spatial distributions of ambient ozone concentrations in the 20-county Atlanta metropolitan area during the summers of 1993, 1994, and 1995 were assessed. A universal kriging procedure was used for spatial interpolation of aerometric monitoring station data. In this paper, the temporal and spatial distributions of ozone are described, and regulatory and epidemiologic implications are discussed. For the study period, the Atlanta ozone nonattainment area based on the 1-h, exceedance-based standard of 0.12 ppm is estimated to expand—from 56% of the Atlanta MSA by area and 71% by population to 88% by area and 96% by population—under the new 8-h, concentration-based standard of 0.08 ppm. Regarding asthma exacerbation, a 4% increase in pediatric asthma rate per 20-ppb increase in ambient ozone concentration was observed (p-value... <s> BIB004 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Kriging <s> 1 Linear Prediction.- 1.1 Introduction.- 1.2 Best linear prediction.- Exercises.- 1.3 Hilbert spaces and prediction.- Exercises.- 1.4 An example of a poor BLP.- Exercises.- 1.5 Best linear unbiased prediction.- Exercises.- 1.6 Some recurring themes.- The Matern model.- BLPs and BLUPs.- Inference for differentiable random fields.- Nested models are not tenable.- 1.7 Summary of practical suggestions.- 2 Properties of Random Fields.- 2.1 Preliminaries.- Stationarity.- Isotropy.- Exercise.- 2.2 The turning bands method.- Exercise.- 2.3 Elementary properties of autocovariance functions.- Exercise.- 2.4 Mean square continuity and differentiability.- Exercises.- 2.5 Spectral methods.- Spectral representation of a random field.- Bochner's Theorem.- Exercises.- 2.6 Two corresponding Hilbert spaces.- An application to mean square differentiability.- Exercises.- 2.7 Examples of spectral densities on 112.- Rational spectral densities.- Principal irregular term.- Gaussian model.- Triangular autocovariance functions.- Matern class.- Exercises.- 2.8 Abelian and Tauberian theorems.- Exercises.- 2.9 Random fields with nonintegrable spectral densities.- Intrinsic random functions.- Semivariograms.- Generalized random fields.- Exercises.- 2.10 Isotropic autocovariance functions.- Characterization.- Lower bound on isotropic autocorrelation functions.- Inversion formula.- Smoothness properties.- Matern class.- Spherical model.- Exercises.- 2.11 Tensor product autocovariances.- Exercises.- 3 Asymptotic Properties of Linear Predictors.- 3.1 Introduction.- 3.2 Finite sample results.- Exercise.- 3.3 The role of asymptotics.- 3.4 Behavior of prediction errors in the frequency domain.- Some examples.- Relationship to filtering theory.- Exercises.- 3.5 Prediction with the wrong spectral density.- Examples of interpolation.- An example with a triangular autocovariance function.- More criticism of Gaussian autocovariance functions.- Examples of extrapolation.- Pseudo-BLPs with spectral densities misspecified at high frequencies.- Exercises.- 3.6 Theoretical comparison of extrapolation and ointerpolation.- An interpolation problem.- An extrapolation problem.- Asymptotics for BLPs.- Inefficiency of pseudo-BLPs with misspecified high frequency behavior.- Presumed mses for pseudo-BLPs with misspecified high frequency behavior.- Pseudo-BLPs with correctly specified high frequency behavior.- Exercises.- 3.7 Measurement errors.- Some asymptotic theory.- Exercises.- 3.8 Observations on an infinite lattice.- Characterizing the BLP.- Bound on fraction of mse of BLP attributable to a set of frequencies.- Asymptotic optimality of pseudo-BLPs.- Rates of convergence to optimality.- Pseudo-BLPs with a misspecified mean function.- Exercises.- 4 Equivalence of Gaussian Measures and Prediction.- 4.1 Introduction.- 4.2 Equivalence and orthogonality of Gaussian measures.- Conditions for orthogonality.- Gaussian measures are equivalent or orthogonal.- Determining equivalence or orthogonality for periodic random fields.- Determining equivalence or orthogonality for nonperiodic random fields.- Measurement errors and equivalence and orthogonality.- Proof of Theorem 1.- Exercises.- 4.3 Applications of equivalence of Gaussian measures to linear prediction.- Asymptotically optimal pseudo-BLPs.- Observations not part of a sequence.- A theorem of Blackwell and Dubins.- Weaker conditions for asymptotic optimality of pseudo-BLPs.- Rates of convergence to asymptotic optimality.- Asymptotic optimality of BLUPs.- Exercises.- 4.4 Jeffreys's law.- A Bayesian version.- Exercises.- 5 Integration of Random Fields.- 5.1 Introduction.- 5.2 Asymptotic properties of simple average.- Results for sufficiently smooth random fields.- Results for sufficiently rough random fields.- Exercises.- 5.3 Observations on an infinite lattice.- Asymptotic mse of BLP.- Asymptotic optimality of simple average.- Exercises.- 5.4 Improving on the sample mean.- Approximating $$\int_0^1 {\exp } (ivt)dt$$.- Approximating $$\int_{{{[0,1]}^d}} {\exp (i{\omega ^T}x)} dx$$ in more than one dimension.- Asymptotic properties of modified predictors.- Are centered systematic samples good designs?.- Exercises.- 5.5 Numerical results.- Exercises.- 6 Predicting With Estimated Parameters.- 6.1 Introduction.- 6.2 Microergodicity and equivalence and orthogonality of Gaussian measures.- Observations with measurement error.- Exercises.- 6.3 Is statistical inference for differentiable processes possible?.- An example where it is possible.- Exercises.- 6.4 Likelihood Methods.- Restricted maximum likelihood estimation.- Gaussian assumption.- Computational issues.- Some asymptotic theory.- Exercises.- 6.5 Matern model.- Exercise.- 6.6 A numerical study of the Fisher information matrix under the Matern model.- No measurement error and?unknown.- No measurement error and?known.- Observations with measurement error.- Conclusions.- Exercises.- 6.7 Maximum likelihood estimation for a periodic version of the Matern model.- Discrete Fourier transforms.- Periodic case.- Asymptotic results.- Exercises.- 6.8 Predicting with estimated parameters.- Jeffreys's law revisited.- Numerical results.- Some issues regarding asymptotic optimality.- Exercises.- 6.9 An instructive example of plug-in prediction.- Behavior of plug-in predictions.- Cross-validation.- Application of Matern model.- Conclusions.- Exercises.- 6.10 Bayesian approach.- Application to simulated data.- Exercises.- A Multivariate Normal Distributions.- B Symbols.- References. <s> BIB005 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Kriging <s> The environmental decision-making process is related with the interpretation of data both in spatial and temporal dimensions. This paper presents a methodology that integrates the time-space framework of air quality data to infer the temporal pattern and spatial variability that could be interpreted for environmental decision purposes. Variograms that accommodate time and space lags were used for the analysis and proved to be effective. Its environmental meaning, in particular its relationship with traffic patterns is discussed. Data from air quality monitoring stations located in the central part of Lisbon were used in this study. It describes a strategy to identify the type of vehicles responsible for certain pollutant levels, particularly for nitrogen oxides, and discusses the application of new air quality European legislation to the city of Lisbon, Portugal. <s> BIB006 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Kriging <s> The authors address two research questions: (1) Are populations with lower socioeconomic status, compared with people of higher socioeconomic status, more likely to be exposed to higher levels of particulate air pollution in Hamilton, Ontario, Canada? (2) How sensitive is the association between levels of particulate air pollution and socioeconomic status to specification of exposure estimates or statistical models? Total suspended particulate (TSP) data from the twenty-three monitoring stations in Hamilton (1985 - 94) were interpolated with a universal kriging procedure to develop an estimate of likely pollution values across the city based on annual geometric means and extreme events. Comparing the highest with the lowest exposure zones, the interpolated surfaces showed more than a twofold increase in TSP concentrations and more than a twentyfold difference in the probability of exposure to extreme events. Exposure estimates were related to socioeconomic and demographic data from census tract areas by using ordinary least squares and simultaneous autoregressive (SAR) models. Control for spatial autocorrelation in the SAR models allowed for tests of how robust specific socioeconomic variables were for predicting pollution exposure. Dwelling values were significantly and negatively associated with pollution exposure, a result robust to the method of statistical analysis. Low income and unemployment were also significant predictors of exposure, although results varied depending on the method of analysis. Relatively minor changes in the statistical models altered the significant variables. This result emphasizes the value of geographical information systems (GIS) and spatial statistical techniques in modelling exposure. The result also shows the importance of taking spatial autocorrelation into account in future justice - health studies. <s> BIB007 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Kriging <s> Associations have been found between long-term exposure to ambient air pollution and cardiovascular morbidity and mortality. The contribution of air pollution to atherosclerosis that underlies many cardiovascular diseases has not been investigated. Animal data suggest that ambient particulate matter (PM) may contribute to atherogenesis. We used data on 798 participants from two clinical trials to investigate the association between atherosclerosis and long-term exposure to ambient PM up to 2.5 μm in aerodynamic diameter (PM2.5). Baseline data included assessment of the carotid intima-media thickness (CIMT), a measure of subclinical atherosclerosis. We geocoded subjects’ residential areas to assign annual mean concentrations of ambient PM2.5. Exposure values were assigned from a PM2.5 surface derived from a geostatistical model. Individually assigned annual mean PM2.5 concentrations ranged from 5.2 to 26.9 μg/m3 (mean, 20.3). For a cross-sectional exposure contrast of 10 μg/m3 PM2.5, CIMT increased by 5.9% (95% confidence interval, 1–11%). Adjustment for age reduced the coefficients, but further adjustment for covariates indicated robust estimates in the range of 3.9–4.3% (p-values, 0.05–0.1). Among older subjects (≥60 years of age), women, never smokers, and those reporting lipid-lowering treatment at baseline, the associations of PM2.5 and CIMT were larger with the strongest associations in women ≥60 years of age (15.7%, 5.7–26.6%). These results represent the first epidemiologic evidence of an association between atherosclerosis and ambient air pollution. Given the leading role of cardiovascular disease as a cause of death and the large populations exposed to ambient PM2.5, these findings may be important and need further confirmation. <s> BIB008 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Kriging <s> We recognized that many health outcomes are associated with air pollution, but in this project launched by the US EPA, the intent was to assess the role of exposure to ambient air pollutants as risk factors only for respiratory effects in children. The NHANES-III database is a valuable resource for assessing children’s respiratory health and certain risk factors, but lacks monitoring data to estimate subjects’ exposures to ambient air pollutants. Since the 1970s, EPA has regularly monitored levels of several ambient air pollutants across the country and these data may be used to estimate NHANES subject’s exposure to ambient air pollutants. The first stage of the project eventually evolved into assessing different estimation methods before adopting the estimates to evaluate respiratory health. Specifically, this paper describes an effort using EPA’s AIRS monitoring data to estimate ozone and PM10 levels at census block groups. We limited those block groups to counties visited by NHANES-III to make the project more manageable and apply four different interpolation methods to the monitoring data to derive air concentration levels. Then we examine method-specific differences in concentration levels and determine conditions under which different methods produce significantly different concentration values. We find that different interpolation methods do not produce dramatically different estimations in most parts of the US where monitor density was relatively low. However, in areas where monitor density was relatively high (i.e., California), we find substantial differences in exposure estimates across the interpolation methods. Our results offer some insights into terms of using the EPA monitoring data for the chosen spatial interpolation methods. Journal of Exposure Analysis and Environmental Epidemiology (2004) 14, 404–415. doi:10.1038/sj.jea.7500338 <s> BIB009 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Kriging <s> Scientists and policymakers have shown growing interest in the health effects of chronic air pollution exposure. In this study, we use geostatistical techniques in combination with small-area data to address a central research question: "Does chronic exposure to particulate air pollution significantly associate with mortality when the effects of other social, demographic, and lifestyle confounders are taken into account?" Our analysis relies on age-standardized mortality ratios for census tracts (CTs) of Hamilton (average population of 3419 persons), social and demographic data from the 1991 Census of Canada, smoking variables extracted from secondary surveys, and total suspended particulate (TSP) data from 23 monitoring stations operated by the Ministry of the Environment. Air pollution data are interpolated with a geostatistical procedure known as "kriging". This method translates fixed-site pollution monitoring observations into a continuous surface, which was overlaid onto the population-weighted centroids of the CTs. Our results show substantively large and statistically significant health effects for women and men. Evaluated over the inter-quartile range of the data, we found the relative risk of premature mortality for TSP exposure to be 1.19 (95% CI: 1.13-1.26) for women and 1.30 (95% CI: 1.24-1.37) for men. We also tested associations with cardio-respiratory and cancer mortality. We found positive, significant associations between particulate exposure and these causes of death in most models. Inclusion of socioeconomic, demographic, and lifestyle reduced but did not eliminate the health effects of exposure to particulate air pollution. Overall our results suggest that intra-urban variations in particulate air pollution significantly associate with premature, all-cause, cardio-respiratory, and cancer mortality in small areas of Hamilton. <s> BIB010 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Kriging <s> STUDY OBJECTIVE ::: Studies in Europe and North America have reported that living in a disadvantaged neighbourhood is associated with an increased incidence of coronary heart disease. The aim of this study was to test the hypotheses that exposure to traffic and air pollution might account for some of the socioeconomic differences in mortality rates in a city where residents are covered by universal health insurance. ::: ::: ::: DESIGN ::: Cohort mortality study. Individual postal codes used to derive: (1) socioeconomic status from census data; (2) mean air pollution levels from interpolation between governmental monitoring stations; (3) proximity to traffic from the geographical information system. Analysis conducted with Cox proportional hazards models. ::: ::: ::: SETTING ::: Hamilton Census Metropolitan Area, Ontario, Canada, on the western tip of Lake Ontario (population about 480,000). ::: ::: ::: PARTICIPANTS ::: 5228 people, aged 40 years or more, identified from register of lung function laboratory at an academic respirology clinic between 1985 and 1999. ::: ::: ::: MAIN RESULTS ::: Circulatory disease (cardiovascular and stroke) mortality rates were related to measures of neighbourhood deprivation. Circulatory disease mortality rates were also associated with indices of long term ambient pollution at the subjects' residences (relative risk 1.06, 1.00 to 1.13) and with proximity to traffic (relative risk 1.40, 1.08 to 1.81). Subjects in more deprived neighbourhoods had greater exposure to ambient particulate and gaseous pollutants and to traffic. ::: ::: ::: CONCLUSIONS ::: At least some of the observed social gradients in circulatory mortality arise from inequalities in environmental exposure to background and traffic air pollutants. <s> BIB011 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Kriging <s> Ambient monitors are commonly used to estimate exposure for epidemiological studies, and air quality modeling is infrequently applied. However air quality modeling systems have the potential to alleviate some, although not all, of the limitations of monitoring networks. To investigate this application, exposure estimates were generated for a case study high ozone episode in the Northern Georgia Region of the United States based on measurements and concentration estimates from an air quality modeling system. Hourly estimates for 2268 4-km by 4-km gridcells were generated in a domain that includes only eight ozone monitors. Individual and population-based ozone exposures were estimated using multiple approaches, including area-weighted average of modeled estimates, nearest monitor, and spatial interpolation by inverse distance weighting and kriging. Results based on concentration fields from the air quality modeling system revealed spatial heterogeneity that was obscured by approaches based on the monitoring network. With some techniques, such as spatial interpolation, monitoring data alone was insufficient to estimate exposure for certain areas, especially for rural populations. For locations far from ozone monitors, the estimates from the nearest monitor approach tended to overestimate exposure, compared to modeled estimates. Counties in which one or more monitors were present had statistically higher population density and modeled ozone estimates than did counties without monitors (p-value <0.05). This work demonstrates the use of air quality modeling to generate higher spatial and temporal resolution exposure estimates, and compares the advantages of this approach to traditional methods that use monitoring data alone. The air quality modeling method faces its own limitations, such as the need to thoroughly evaluate concentration estimates and the use of ambient levels rather than personal exposure. <s> BIB012 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Kriging <s> BACKGROUND ::: Evidence suggests that air pollution exposure adversely affects pregnancy outcomes. Few studies have examined individual-level intraurban exposure contrasts. ::: ::: ::: OBJECTIVES ::: We evaluated the impacts of air pollution on small for gestational age (SGA) birth weight, low full-term birth weight (LBW), and preterm birth using spatiotemporal exposure metrics. ::: ::: ::: METHODS ::: With linked administrative data, we identified 70,249 singleton births (1999-2002) with complete covariate data (sex, ethnicity, parity, birth month and year, income, education) and maternal residential history in Vancouver, British Columbia, Canada. We estimated residential exposures by month of pregnancy using nearest and inverse-distance weighting (IDW) of study area monitors [carbon monoxide, nitrogen dioxide, nitric oxide, ozone, sulfur dioxide, and particulate matter < 2.5 (PM2.5) or < 10 (PM10) microm in aerodynamic diameter], temporally adjusted land use regression (LUR) models (NO, NO2, PM2.5, black carbon), and proximity to major roads. Using logistic regression, we estimated the risk of mean (entire pregnancy, first and last month of pregnancy, first and last 3 months) air pollution concentrations on SGA (< 10th percentile), term LBW (< 2,500 g), and preterm birth. ::: ::: ::: RESULTS ::: Residence within 50 m of highways was associated with a 22% (95% CI, 0.81-1.87) [corrected] increase in LBW. Exposure to all air pollutants except O3 was associated with SGA, with similar odds ratios (ORs) for LUR and monitoring estimates (e.g., LUR: OR = 1.02; 95% CI, 1.00-1.04; IDW: OR = 1.05; 95% CI, 1.03-1.08 per 10-microg/m3 increase in NO). For preterm births, associations were observed with PM2.5 for births < 37 weeks gestation (and for other pollutants at < 30 weeks). No consistent patterns suggested exposure windows of greater relevance. ::: ::: ::: CONCLUSION ::: Associations between traffic-related air pollution and birth outcomes were observed in a population-based cohort with relatively low ambient air pollution exposure. <s> BIB013 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Kriging <s> Abstract Real-time assessment of the ambient air quality has gained an increased interest in recent years. To give support to this evolution, the statistical air pollution interpolation model RIO is developed. Due to the very low computational cost, this interpolation model is an efficient tool for an environment agency when performing real-time air quality assessment. Beside this, a reliable interpolation model can be used to produce analysed maps of historical data records as well. Such maps are essential for correctly checking compliance with population exposure limit values as foreseen by the new EU Air Quality Directive. RIO is an interpolation model that can be classified as a detrended Kriging model. In a first step, the local character of the air pollution sampling values is removed in a detrending procedure. Subsequently, the site-independent data is interpolated by an Ordinary Kriging scheme. Finally, in a re-trending step, a local bias is added to the Kriging interpolation results. As spatially resolved driving force in the detrending process, a land use indicator is developed based on the CORINE land cover data set. The indicator is optimized independently for the three pollutants O 3 , NO 2 and PM 10 . As a result, the RIO model is able to account for the local character of the air pollution phenomenon at locations where no monitoring stations are available. Through a cross-validation procedure the superiority of the RIO model over standard interpolation techniques, such as the Ordinary Kriging is demonstrated. Air quality maps are presented for the three pollutants mentioned and compared to maps based on standard interpolation techniques. <s> BIB014 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Kriging <s> Background:Air pollution studies increasingly estimate individual-level exposures from area-based measurements by using exposure prediction methods such as nearest-monitor and kriging predictions. However, little is known about the properties of these methods for health effects estimation. This simu <s> BIB015 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Kriging <s> Evidence is increasing that long-term exposure to ambient air pollution is associated with deaths from cardiopulmonary diseases. In a 2002 pilot study, we reported clear indications that traffic-related air pollution, especially at the local scale, was related to cardiopulmonary mortality in a randomly selected subcohort of 5000 older adults participating in the ongoing Netherlands Cohort Study (NLCS) on diet and cancer. In the current study, referred to as NLCS-AIR, our objective was to obtain more precise estimates of the effects of traffic-related air pollution by analyzing associations with cause-specific mortality, as well as lung cancer incidence, in the full cohort of approximately 120,000 subjects. Cohort members were 55 to 69 years of age at enrollment in 1986. Follow-up was from 1987 through 1996 for mortality (17,674 deaths) and from late 1986 through 1997 for lung cancer incidence (2234 cases). Information about potential confounding variables and effect modifiers was available from the questionnaire that subjects completed at enrollment and from publicly available data (including neighborhood-scale information such as income distributions). The NLCS was designed for a case-cohort approach, which makes use of all the cases in the full cohort, while data for the random subcohort are used to estimate person-time experience in the study. Full information on confounders was available for the subjects in the random subcohort and for the emerging cases of mortality and lung cancer incidence during the follow-up period, and in NLCS-AIR we used the case-cohort approach to examine the relation between exposure to air pollution and cause-specific mortality and lung cancer. We also specified a standard Cox proportional hazards model within the full cohort, for which information on potential confounding variables was much more limited. Exposure to air pollution was estimated for the subjects' home addresses at baseline in 1986. Concentrations were estimated for black smoke (a simple marker for soot) and nitrogen dioxide (NO2) as indicators of traffic-related air pollution, as well as nitric oxide (NO), sulfur dioxide (SO2), and particulate matter with aerodynamic diameter 65 A-weighted decibels (dB(A); decibels with the sound pressure scale adjusted to conform with the frequency response of the human ear). Examination of sex, smoking status, educational level, and vegetable and fruit intake as possible effect modifiers showed that for overall black smoke concentrations, associations with mortality tended to be stronger in case-cohort subjects with lower levels of education and those with low fruit intake, but differences between strata were not statistically significant. For lung cancer incidence, we found essentially no relation to exposure to NO2, black smoke, PM2.5, SO2, or several traffic indicators. Associations of overall air pollution concentrations and traffic indicator variables with lung cancer incidence were, however, found in subjects who had never smoked, with an RR of 1.47 (95% CI, 1.01-2.16) for a 10-microg/m3 increase in overall black smoke concentration. In the current study, the mortality risks associated with both background air pollution and traffic exposure variables were much smaller than the estimate previously reported in the pilot study for risk of cardiopulmonary mortality associated with living near a major road (RR, 1.95; 95% CI, 1.09-3.51). The differences are most likely due to the extension of the follow-up period in the current study and to random error in the pilot study related to sampling from the full cohort. Though relative risks were generally small in the current study, long-term average concentrations of black smoke, NO2, and PM2.5 were related to mortality, and associations of black smoke and NO2 exposure with natural-cause and respiratory mortality were statistically significant. Traffic intensity near the home was also related to natural-cause mortality. The highest relative risks associated with background air pollution and traffic variables were for respiratory mortality, though the number of deaths was smaller than for the other mortality categories. <s> BIB016 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Kriging <s> Interpolation methods can estimate individual-level exposures to air pollution from ambient monitors; however, few studies have evaluated how different approaches may affect health risk estimates. We applied multiple methods of estimating exposure for several air pollutants. We investigated how different methods of estimating exposure may influence health effect estimates in a case study of lung function data, forced expiratory volume in 1s (FEV1), and forced vital capacity (FVC), for 2102 cohort subjects in Ulsan, Korea, for 2003-2007. Measurements from 13 monitors for particulate matter <10 μm (PM(10)), ozone, nitrogen dioxide, sulfur dioxide, and carbon monoxide were used to estimate individual-level exposures by averaging across values from all monitors, selecting the value from the nearest monitor, inverse distance weighting, and kriging. We assessed associations between pollutants and lung function in linear regression models, controlling for age, sex, and body mass index. Cross-validation indicated that kriging provided the most accurate estimated exposures. FVC was associated with all air pollutants under all methods of estimating exposure. Only ozone was associated with FEV1. An 11 ppb increase in lag-0-2 8-h maximum ozone was associated with a 6.1% (95% confidence interval 5.0, 7.3%) decrease in FVC and a 0.50% (95% confidence interval 0.03, 0.96%) decrease in FEV1, based on kriged exposures. Central health effect estimates were generally higher using exposures based on averaging across all monitors or kriging. Results based on the nearest monitor approach had the lowest variance. Findings suggest that spatial interpolation methods may provide better estimates than monitoring values alone by reflecting the spatial variability of individual-level exposures and generating estimates for locations without monitors. <s> BIB017 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Kriging <s> BackgroundThere is increasing concern regarding the potential adverse health effects of air pollution, particularly hazardous air pollutants (HAPs). However, quantifying exposure to these pollutants is problematic.ObjectiveOur goal was to explore the utility of kriging, a spatial interpolation method, for exposure assessment in epidemiologic studies of HAPs. We used benzene as an example and compared census tract-level kriged predictions to estimates obtained from the 1999 U.S. EPA National Air Toxics Assessment (NATA), Assessment System for Population Exposure Nationwide (ASPEN) model.MethodsKriged predictions were generated for 649 census tracts in Harris County, Texas using estimates of annual benzene air concentrations from 17 monitoring sites operating in Harris and surrounding counties from 1998 to 2000. Year 1999 ASPEN modeled estimates were also obtained for each census tract. Spearman rank correlation analyses were performed on the modeled and kriged benzene levels. Weighted kappa statistics were computed to assess agreement between discretized kriged and modeled estimates of ambient air levels of benzene.ResultsThere was modest correlation between the predicted and modeled values across census tracts. Overall, 56.2%, 40.7%, 31.5% and 28.2% of census tracts were classified as having 'low', 'medium-low', 'medium-high' and 'high' ambient air levels of benzene, respectively, comparing predicted and modeled benzene levels. The weighted kappa statistic was 0.26 (95% confidence interval (CI) = 0.20, 0.31), indicating poor agreement between the two methods.ConclusionsThere was a lack of concordance between predicted and modeled ambient air levels of benzene. Applying methods of spatial interpolation for assessing exposure to ambient air pollutants in health effect studies is hindered by the placement and number of existing stationary monitors collecting HAP data. Routine monitoring needs to be expanded if we are to use these data to better assess environmental health risks in the future. <s> BIB018
Kriging is also a weighted combination of measurements at surrounding monitoring stations. Instead of assuming a function of inverse distance, as IDW method does, kriging assigns weights at each concentration by exploiting the spatial correlation among the observed measurements BIB013 BIB015 BIB002 BIB005 . A major advantage of kriging interpolation is that it generates both the estimates and their standard errors at the unmeasured sites. These standard errors quantify the degree of uncertainty in the estimates BIB004 . Two different forms of kriging are commonly used in geostatistics: ordinary and universal. Ordinary kriging assumes a constant unknown mean in the local neighborhood, while universal kriging assumes a general polynomial trend model. Ordinary kriging is the most general and widely used method BIB003 BIB006 BIB014 . Universal kriging is usually used when the existence of the trend is certain and the trend is describable BIB010 BIB008 . Researchers have explored their use in different applications BIB016 BIB011 . Liu explored the daytime O 3 spatial variation using measurements obtained from 19 monitoring stations in Toronto (ON), Canada BIB003 . An ordinary kriging model was used to estimate the outdoor ozone concentration levels. Liu evaluated the performance of the model by comparing actual outdoor measurements from 40 homes in this area with the estimated values. Results showed higher estimation accuracy of the kriging technique than the nearest neighbor method. Ferreira et al. measured the air pollution concentrations over the central part of Lisbon using an ordinary kriging model from nine monitoring stations BIB006 . They also calculated the correlations between traffic patterns and the levels of three pollutants (CO, NO and NO 2 ). Differently, Janssen et al. developed statistical air pollution interpolation model, which applied an ordinary kriging scheme, but with adding land-use characteristics of the surroundings of the monitoring stations BIB014 . Using this model, the concentration levels of the pollutants were estimated on 4 × 4 km 2 grid level in Belgium. A cross-validation procedure demonstrated the superiority of the RIO model over other interpolation techniques. Künzli et al. generated a surface of PM 2.5 covered the entire Los Angeles metropolitan area using a combination of a a universal kriging model with a quadratic drift and a multiquadric radial basis function model BIB008 . The data was obtained from 23 state and local district monitoring stations. They also proved the association between PM 2.5 concentrations and atherosclerosis, which underlaid many cardiovascular diseases. Jerrett et al. utilized a universal kriging procedure to develop an estimate of pollutant values cross the whole city using data from 23 monitoring stations in Hamilton, ON, Canada BIB010 BIB007 . They also found significant associations between cardio-respiratory, cancer mortality and the exposure to these pollutants O 3 , NO 2 , PM  . Whitworth et al. studied the exposure to ambient air levels of benzene in Harris County, TX, USA from 17 monitoring stations located in Harris and surrounding counties BIB018 . They assessed both universal and ordinary kriging and proved the ordinary kriging model more appropriate in their application using a Bland-Altman analysis BIB001 . Moreover, some of the interpolation approaches have been implemented and compared on the same data set. Wong et al. applied all four methods to estimate the concentration levels of O 3 and PM 10 over the whole USA BIB009 . All methods produced similar estimations in the areas with low-density monitors; different methods generated vastly different concentration levels in the areas with high-density monitors, such as California. Wu et al. estimated the concentrations of particulate matter (PM, PM 2.5 and PM 10 ) using monitoring data from 37 PM stations at a zip-code level in southern California, USA  . In their research, IDW and kriging methods demonstrated similar performance in areas with a small number of monitoring stations. Bell estimated the O 3 exposure at the county level in Northern Georgia, USA, using hourly measurements obtained from 15 monitoring sites BIB012 . This research proved a high correlation between the concentrations estimated from kriging and IDW approaches. In addition, the nearest neighbor approach tended to overestimate the concentrations for locations far from the monitoring stations, compared to the area-weighted average. Son et al. compared these four methods on several different air pollutants using measurements obtained from 13 monitors in Ulsan, Korea BIB017 . Their results showed high correlation between estimates from different methods for PM  , O 3 , NO 2 and SO 2 , but relevantly low correlation for CO. This research suggests that importance of the interpolation method depends strongly upon the nature of the local monitor network.