A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Spatial Interpolation Approaches <s> The effect of air pollution on lung function in children and youths ages 6-24 years was examined, after controlling for age, height, race, sex, body mass, cigarette smoking, and respiratory symptoms. Forced vital capacity (FVC), forced expiratory volume at 1 sec (FEV1), and peak expiratory flow all showed statistically significant (P less than 0.05) negative correlations with annual concentrations of total suspended particulates (TSP), nitrogen dioxide, and ozone. The ozone and NO2 relationships were highly significant. The TSP relationship was less significant. No relationship was found with sulfur dioxide. The relationships held whether or not children with respiratory conditions, or smokers were included. Demographic and geographic variables had little or no impact on the pollution relationships, which also held when only persons still residing in their state of birth were considered. Essentially identical relationships were found using pollution averaged over 2 years. The relationships held across most NO2 concentrations, but were only apparent at high particulate and ozone concentrations. <s> BIB001 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Spatial Interpolation Approaches <s> A cohort of approximately 100 student nurses in Los Angeles was recruited for a diary study of the acute effects of air pollution. Smoking histories and presence of asthma and other allergies were determined by questionnaire. Diaries were completed daily and collected weekly for as long as 3 yr. Air pollution was measured at a monitoring location within 2.5 miles of the school. Incidence and duration of a symptom were modeled separately. Pack-years of cigarettes were predictive of the number of episodes of coughing (p < 0.0001) and of bringing up phlegm (p < 0.0001). Current smoking, rather than cumulative smoking, was a better predictor of the duration of a phlegm episode (p < 0.0001). Controlling for personal smoking, a smoking roommate increased the risk of an episode of phlegm (odds ratio [OR] = 1.41, p < 0.001), but not of cough. Excluding asthmatics (who may be medicated), increased the odds ratio for passive smoking to 1.76 (p < 0.0001). In logistic regression models controlling for temperature and... <s> BIB002 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Spatial Interpolation Approaches <s> Abstract The relationship between pulmonary function and quarterly average levels of total suspended particulates (TSP) was examined for adults who resided in 49 of the locations where the First National Health and Nutrition Examination Survey (NHANES I) was conducted. Statistically significant relationships were observed between TSP levels and forced vital capacity (FVC) and forced expiratory volume at one second (FEV1.0). These relationships remained strong across several specifications and sample changes, e.g., exclusion of cities with two highest and two lowest TSP levels, restriction of sample to whites only. Anthropometric measurements and socioeconomic characteristics of the subjects were included in the analysis, and we restricted the sample to “never” smokers. The results indicate a 1 standard deviation increase (about 34 μg/m3) in TSP from the sample mean of 87 μg/m3 was associated with an average decrease in FVC of 2.25%. The results of this analysis also suggest that there is a threshold level... <s> BIB003 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Spatial Interpolation Approaches <s> Methods are needed for retrospective estimation of long-term ozone exposures in epidemiologic studies. The overall objective of this study was to evaluate whether data from available U.S. ozone monitoring sites are useful for estimating lifetime ozone exposures of young adults (for example, college students). Several aspects of this question were evaluated. First, we applied and (compared several spatial interpolation methods to a set of long-term average ozone data from all U.S. monitoring sites in operation from 1981 through 1990. Interpolation methods included simple and weighted averages, linear regression, and, in an exploratory way, kriging. The comparison of methods was carried out for five different metrics of ozone concentration: the daily one-hour maximum (MAX1) and eight-hour maximum (MAX8), the average ozone concentrations between 10 a.m. and 6 p.m. (MID8) and between 10 a.m. and 10 p.m. (MID12), and the sum of all hourly ozone concentrations greater than or equal to 60 parts per billion (ppb) (SUM06). We also tested whether interpolations were improved by modeling the influence of covariates such as population density, elevation, and weather on ozone concentrations. We analyzed the reliability of a set of newly developed questions about past activity levels among a group of 52 freshmen students at Yale University. This was done by analyzing the agreement between answers to the same questionnaire administered two times, one month apart (test and retest), to the same students. Finally, we combined the interpolation models with residential history information obtained by questionnaire to derive long-term ozone exposure estimates for a group of 200 Yale freshmen. Results of our study showed that the density of available monitoring sites appears to be adequate for estimating spatial patterns of long-term average ambient ozone concentrations. A simple regression-based interpolation on the three nearest sites produced consistently good results. Including covariates in the interpolation models did not substantially improve the estimates. The largest estimation errors occurred for areas where ozone concentrations were highest. The newly developed activity history questions exhibited fair to moderate reliability, The results of this work imply that reasonably precise estimates of long-term ambient ozone concentrations for use in large-scale epidemiologic studies can be achieved by interpolating ozone concentrations between available U.S. monitoring sites. This study did not address the issues of whether and how retrospective data on factors that modify exposure or dose (e.g., indoor/outdoor penetration of ozone and time outdoors) can be used to derive estimates of long-term personal ozone exposures and contribute to the assessment of received dose. <s> BIB004 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Spatial Interpolation Approaches <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> BIB005 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Spatial Interpolation Approaches <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> BIB006 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Spatial Interpolation Approaches <s> BACKGROUND ::: Fine particulate air pollution has been linked to cardiovascular disease, but previous studies have assessed only mortality and differences in exposure between cities. We examined the association of long-term exposure to particulate matter of less than 2.5 microm in aerodynamic diameter (PM2.5) with cardiovascular events. ::: ::: ::: METHODS ::: We studied 65,893 postmenopausal women without previous cardiovascular disease in 36 U.S. metropolitan areas from 1994 to 1998, with a median follow-up of 6 years. We assessed the women's exposure to air pollutants using the monitor located nearest to each woman's residence. Hazard ratios were estimated for the first cardiovascular event, adjusting for age, race or ethnic group, smoking status, educational level, household income, body-mass index, and presence or absence of diabetes, hypertension, or hypercholesterolemia. ::: ::: ::: RESULTS ::: A total of 1816 women had one or more fatal or nonfatal cardiovascular events, as confirmed by a review of medical records, including death from coronary heart disease or cerebrovascular disease, coronary revascularization, myocardial infarction, and stroke. In 2000, levels of PM2.5 exposure varied from 3.4 to 28.3 microg per cubic meter (mean, 13.5). Each increase of 10 microg per cubic meter was associated with a 24% increase in the risk of a cardiovascular event (hazard ratio, 1.24; 95% confidence interval [CI], 1.09 to 1.41) and a 76% increase in the risk of death from cardiovascular disease (hazard ratio, 1.76; 95% CI, 1.25 to 2.47). For cardiovascular events, the between-city effect appeared to be smaller than the within-city effect. The risk of cerebrovascular events was also associated with increased levels of PM2.5 (hazard ratio, 1.35; 95% CI, 1.08 to 1.68). ::: ::: ::: CONCLUSIONS ::: Long-term exposure to fine particulate air pollution is associated with the incidence of cardiovascular disease and death among postmenopausal women. Exposure differences within cities are associated with the risk of cardiovascular disease. <s> BIB007 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Spatial Interpolation Approaches <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> BIB008 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Spatial Interpolation Approaches <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> BIB009 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Spatial Interpolation Approaches <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> BIB010 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Spatial Interpolation Approaches <s> Air pollution in urban environments has serious health and quality of life implications. A wide variety of anthropogenic air pollution sources increase the levels of background air pollutant concentrations, leading to the deterioration of the ambient air quality. Principal sources of urban air pollution are vehicular traffic, industrial activity and in general fossil fuel combustion, introducing a mixture of chemical components, particulate matter and biological material into the atmosphere. The deterioration of urban air quality is considered worldwide one of the primary environmental issues and current scientific evidence associate the exposure to ambient air pollution with a wide spectrum of health effects like cardiopulmonary diseases, respiratory related hospital admissions and premature mortality (Analitis et al. 2006; Ito et al., 2005; Samet et al., 2000). Direct measurements of sensitive population groups’ exposure to air pollution are scarce and therefore methods of accurate point and areal air quality estimations are prerequisite. This fact highlights the importance of generating accurate fields of air pollution for quantifying present and future health related risks. In the field of air pollution modeling, two different approaches have been adopted by the scientific community, differentiated by their applied fundamental principles. The first approach involves the numerical simulation of atmospheric dispersion based on the current understanding of physics and chemistry that govern the transport, dispersion and transformation of pollutants in the atmosphere. The modeling process typically requires a set of parameters such as meteorological fields, terrain information along with a comprehensive description of pollution sources. An alternative approach is based on statistical analysis of pollutant concentrations collected from air quality monitoring networks commonly deployed in urban areas. The reasoning of the statistical approach is that physical processes are likely to induce correlations in air quality data collected over space and time. Statistical models generate predictions by exploiting these spatio-temporal patterns, enabling the estimation of pollutant concentrations in unmonitored locations. The chapter’s main objective is to present and review the statistical spatial interpolation methodologies which are commonly employed in the field of air pollution modeling. An additional scope of the chapter is to compare and evaluate the accuracy of the interpolation methods for point estimations, using data from a real urban air quality monitoring network located at the greater area of metropolitan Athens in Greece. <s> BIB011 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Spatial Interpolation Approaches <s> The Internet-of-Things (IoT) is the key enabling technology for transforming current urban environments into so-called Smart Cities. One of the goals behind making cities smarter is to provide a healthy environment that improves the citizens' quality of life and wellbeing. In this work, we introduce a novel data aggregation mechanism tailored to the application of large-scale air pollution monitoring with IoT devices. Our design exploits the intra- and inter-source correlations among air-pollution data using the framework of compressed sensing with side information. The proposed method delivers significant improvements in the data reconstruction quality with respect to the state of the art, even in the presence of noise when measuring and transmitting the data. <s> BIB012 </s> A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods <s> Spatial Interpolation Approaches <s> Large-scale data collection by means of wireless sensor network and Internet-of-Things technology poses various challenges in view of the limitations in transmission, computation, and energy resources of the associated wireless devices. Compressive data gathering based on compressed sensing has been proven a well-suited solution to the problem. Existing designs exploit the spatiotemporal correlations among data collected by a specific sensing modality. However, many applications, such as environmental monitoring, involve collecting heterogeneous data that are intrinsically correlated. In this paper, we propose to leverage the correlation from multiple heterogeneous signals when recovering the data from compressive measurements. To this end, we propose a novel recovery algorithm—built upon belief-propagation principles—that leverages correlated information from multiple heterogeneous signals. To efficiently capture the statistical dependencies among diverse sensor data, the proposed algorithm uses the statistical model of copula functions. Experiments with heterogeneous air-pollution sensor measurements show that the proposed design provides significant performance improvements against the state-of-the-art compressive data gathering and recovery schemes that use classical compressed sensing, compressed sensing with side information, and distributed compressed sensing. <s> BIB013
Interpolation methods in general share the same basic mathematical foundation. They all estimate the value at an unmeasured location as a weighted average of the measurements at surrounding monitoring stations. They differ in their choice of sample weights and the surrounding stations. Four interpolation methods are commonly used in air pollution estimation and assessment: spatial averaging, nearest neighbor, inverse distance weighting and kriging approach. Spatially averaging simply calculates the mean of pollutant measurements from the nearby monitoring stations (located within a predefined grid, a country, or even a city). It assumes equal influence of the measurements at different monitoring stations with various distance to the unmeasured location, which is unrealistic in the air pollution estimation, as it omits any spatial variability (as those caused, for example, by urban street canyon effect) BIB004 BIB001 BIB003  . Nearest neighbor assigns the pollutant measurements of the closet monitoring station to the unmeasured location, regardless of the actual distance between them  BIB007 BIB008 BIB002 . The main disadvantage of this technique is that the measurements from other neighboring points are ignored. Because of these limitations, both methods are no longer commonly used in air pollution estimation recently. However, they usually appear in the comparative studies of spatial interpolation methods BIB009 BIB005 BIB011 BIB006  BIB010 . In addition, some researchers started applying compressed sensing to estimate the pollution level at unmeasured locations by discovering the spatial correlations among multiple heterogeneous air pollution data BIB013 BIB012 .