Air Pollution Exposure Estimates Research Articles

Validity of the Emission Weighted Proximity Model in estimating air pollution exposure intensities in large geographic areas

Accurate estimates of air pollution exposure intensities are important to support environmental epidemiology analyses that require data covering large geographic areas over multiple years. The Emission Weighted Proximity Model (EWPM) and the National-Scale Air Toxics Assessment (NATA) are two viable approaches for obtaining estimate exposure intensities. The advantages of the EWPM include its simplicity and significantly lower costs of implementation. However, very limited data are available regarding the validity of the results from the EWPM and how these results would fare when compared with those from the NATA. This study evaluates the validity of the estimated exposure intensities from the EWPM through a correlation analysis with ground monitoring data obtained by the Texas Commission on Environmental Quality (TCEQ). The monitoring data used in the comparison included 27 non-criteria air pollutants at 48 monitoring sites in Texas in 2005. In addition, this study compares the results from the EWPM with those from NATA using the TCEQ data as a gold standard. Analysis results suggest that estimated exposure intensities from the EWPM and the NATA were comparable when the intensities from both approaches are used to categorize environmental exposure intensities into different levels in relative terms. These findings suggest that the EWPM is a valid alternative approach to the NATA in situations where epidemiological analysis requires both environmental data and health outcome data that cover a large geographic area over multiple years.

Dynamic assessment of exposure to air pollution using mobile phone data.

BackgroundExposure to air pollution can have major health impacts, such as respiratory and cardiovascular diseases. Traditionally, only the air pollution concentration at the home location is taken into account in health impact assessments and epidemiological studies. Neglecting individual travel patterns can lead to a bias in air pollution exposure assessments.MethodsIn this work, we present a novel approach to calculate the daily exposure to air pollution using mobile phone data of approximately 5 million mobile phone users living in Belgium. At present, this data is collected and stored by telecom operators mainly for management of the mobile network. Yet it represents a major source of information in the study of human mobility. We calculate the exposure to NO2 using two approaches: assuming people stay at home the entire day (traditional static approach), and incorporating individual travel patterns using their location inferred from their use of the mobile phone network (dynamic approach).ResultsThe mean exposure to NO2 increases with 1.27 μg/m3 (4.3 %) during the week and with 0.12 μg/m3 (0.4 %) during the weekend when incorporating individual travel patterns. During the week, mostly people living in municipalities surrounding larger cities experience the highest increase in NO2 exposure when incorporating their travel patterns, probably because most of them work in these larger cities with higher NO2 concentrations.ConclusionsIt is relevant for health impact assessments and epidemiological studies to incorporate individual travel patterns in estimating air pollution exposure. Mobile phone data is a promising data source to determine individual travel patterns, because of the advantages (e.g. low costs, large sample size, passive data collection) compared to travel surveys, GPS, and smartphone data (i.e. data captured by applications on smartphones).

Design and Evaluation of a Metropolitan Air Pollution Sensing System

Urban air pollution is believed to be a major contributor to premature deaths and chronic illnesses worldwide. Current systems for urban air pollution monitoring rely on static sites with low spatial resolution, and moreover, lack the means to estimate exposures for (potentially mobile) individuals in order to make medical inferences. This paper describes the design and evaluation of a low-cost participatory sensing system called HazeWatch that uses a combination of portable mobile sensor units, smart-phones, cloud computing, and mobile apps to measure, model, and personalize air pollution information for individuals. Our contributions are three-fold: 1) we architect, prototype, and compare multiple hardware devices and software applications for collecting urban air pollution data with high spatial density in real-time; 2) we develop web-based tools and mobile apps for the visualization and estimation of air pollution exposure customized to individuals; and 3) we conduct field trials to validate our system and demonstrate that it yields much more accurate exposure estimates than current systems. We believe our system can increase user engagement in exposure management, and better inform medical studies linking air pollution with human health.

Development of nitrogen dioxide and volatile organic compounds land use regression models to estimate air pollution exposure near an Italian airport

Abstract The aim of this study was to evaluate the small scale spatial variability of nitrogen dioxide (NO2) and selected VOCs (benzene, toluene, acrolein and formaldehyde) concentrations using Land Use Regression models (LURs) in a complex multi sources domain (64 km2), containing a mid-size airport: the Ciampino Airport, located in Ciampino, Rome, Italy. 46 diffusion tube samplers were deployed within a domain centred in the airport over two 2-weekly periods (June 2011–January 2012). GIS-derived predictor variables, with varying buffer size, were evaluated to model spatial variation of NO2, benzene, toluene, formaldehyde and acrolein annual average concentrations. The airport apportionment to air quality was investigated using a Lagrangian dispersion model (SPRAY). A stepwise selection procedure was used to develop the linear regression models. The models were validated using leave one out cross validation (LOOCV) method. In this study, the use of LURs was found to be effective to explain spatial variability of NO2 (adjusted-R2 = 0.72), benzene (adjusted-R2 = 0.53), toluene (adjusted-R2 = 0.50) and acrolein (adjusted-R2 = 0.51), while limited power was achieved with the formaldehyde modeling (adjusted-R2 = 0.24). For all pollutants LURs output showed that the small scale spatial variability was mainly explained by local traffic. The airport contribution to the observed spatial variability was adequately quantified only for acrolein (0.43 (±0.69) μg/m3 in an area of about 6 km2, SW located to the airport runway), while for NO2 and formaldehyde, only a little portion of the spatial variability in a limited portion of the study domain was attributable to airport related emissions.

Ambient Fine Particulate Matter, Nitrogen Dioxide, and Hypertensive Disorders of Pregnancy in New York City.

Previous studies suggested a possible association between fine particulate matter air pollution (PM2.5) and nitrogen dioxide (NO2) and the development of hypertensive disorders of pregnancy, but effect sizes have been small and methodologic weaknesses preclude firm conclusions. We linked birth certificates in New York City in 2008-2010 to hospital discharge diagnoses and estimated air pollution exposure based on maternal address. The New York City Community Air Survey provided refined estimates of PM2.5 and NO2 at the maternal residence. We estimated the association between exposures to PM2.5 and NO2 in the first and second trimester and risk of gestational hypertension, mild preeclampsia, and severe preeclampsia among 268,601 births. In unadjusted analyses, we found evidence of a positive association between both pollutants and gestational hypertension. However, after adjustment for individual covariates, socioeconomic deprivation, and delivery hospital, we did not find evidence of an association between PM2.5 or NO2 in the first or second trimester and any of the outcomes. Our data did not provide clear evidence of an effect of ambient air pollution on hypertensive disorders of pregnancy. Results need to be interpreted with caution considering the quality of the available exposure and health outcome measures and the uncertain impact of adjusting for hospital. Relative to previous studies, which have tended to identify positive associations with PM2.5 and NO2, our large study size, refined air pollution exposure estimates, hospital-based disease ascertainment, and little risk of confounding by socioeconomic deprivation, does not provide evidence for an association.

Trends in exposure to industrial air toxins for different racial and socioeconomic groups: A spatial and temporal examination of environmental inequality in the U.S. from 1995 to 2004

In recent decades there have been dramatic declines in industrial air toxins. However, there has yet to be a national study investigating if the drop has mitigated the unequal exposure to industrial toxins by race and social class. This paper addresses this by developing a unique dataset of air pollution exposure estimates, by aggregating the annual fall-out location of 415 air toxins, from 17,604 facilities, for the years 1995 to 2004 up to census block groups (N=216,159/year). These annual estimates of exposure were matched with census data to calculate trends in exposure for different racial and socioeconomic groups. Results show that exposure to air toxins has decreased for everyone, but African-Americans are consistently more exposed than Whites and Hispanics and socioeconomic status is not as protective for African-Americans. These results by race were further explored using spatially specified multilevel models which examine trends over time and across institutional boundaries.

Physical Activity- and Alcohol-dependent Association Between Air Pollution Exposure and Elevated Liver Enzyme Levels: An Elderly Panel Study.

Objectives:The deleterious effects of air pollution on various health outcomes have been demonstrated. However, few studies have examined the effects of air pollution on liver enzyme levels.Methods:Blood samples were drawn up to three times between 2008 and 2010 from 545 elderly individuals who regularly visited a community welfare center in Seoul, Korea. Data regarding ambient air pollutants (particulate matter ≤2.5 μm [PM2.5], nitrogen dioxide [NO2], ozone [O3], carbon monoxide, and sulfur dioxide) from monitoring stations were used to estimate air pollution exposure. The effects of the air pollutants on the concentrations of three liver enzymes (aspartate aminotransferase [AST], alanine aminotransferase [ALT], and γ-glutamyltranspeptidase [γ-GTP)]) were evaluated using generalized additive and linear mixed models.Results:Interquartile range increases in the concentrations of the pollutants showed significant associations of PM2.5 with AST (3.0% increase, p=0.0052), ALT (3.2% increase, p=0.0313), and γ-GTP (5.0% increase, p=0.0051) levels; NO2 with AST (3.5% increase, p=0.0060) and ALT (3.8% increase, p=0.0179) levels; and O3 with γ-GTP (5.3% increase, p=0.0324) levels. Significant modification of these effects by exercise and alcohol consumption was found (p for interaction <0.05). The effects of air pollutants were greater in non-exercisers and heavy drinkers.Conclusions:Short-term exposure to air pollutants such as PM2.5, NO2, and O3 is associated with increased liver enzyme levels in the elderly. These adverse effects can be reduced by exercising regularly and abstinence from alcohol.

Air Pollution and Lung Function in Dutch Children: A Comparison of Exposure Estimates and Associations Based on Land Use Regression and Dispersion Exposure Modeling Approaches.

BackgroundThere is limited knowledge about the extent to which estimates of air pollution effects on health are affected by the choice for a specific exposure model.ObjectivesWe aimed to evaluate the correlation between long-term air pollution exposure estimates using two commonly used exposure modeling techniques [dispersion and land use regression (LUR) models] and, in addition, to compare the estimates of the association between long-term exposure to air pollution and lung function in children using these exposure modeling techniques.MethodsWe used data of 1,058 participants of a Dutch birth cohort study with measured forced expiratory volume in 1 sec (FEV1), forced vital capacity (FVC), and peak expiratory flow (PEF) measurements at 8 years of age. For each child, annual average outdoor air pollution exposure [nitrogen dioxide (NO2), mass concentration of particulate matter with diameters ≤ 2.5 and ≤ 10 μm (PM2.5, PM10), and PM2.5 soot] was estimated for the current addresses of the participants by a dispersion and a LUR model. Associations between exposures to air pollution and lung function parameters were estimated using linear regression analysis with confounder adjustment.ResultsCorrelations between LUR- and dispersion-modeled pollution concentrations were high for NO2, PM2.5, and PM2.5 soot (R = 0.86–0.90) but low for PM10 (R = 0.57). Associations with lung function were similar for air pollutant exposures estimated using LUR and dispersion modeling, except for associations of PM2.5 with FEV1 and FVC, which were stronger but less precise for exposures based on LUR compared with dispersion model.ConclusionsPredictions from LUR and dispersion models correlated very well for PM2.5, NO2, and PM2.5 soot but not for PM10. Health effect estimates did not depend on the type of model used to estimate exposure in a population of Dutch children.CitationWang M, Gehring U, Hoek G, Keuken M, Jonkers S, Beelen R, Eeftens M, Postma DS, Brunekreef B. 2015. Air pollution and lung function in Dutch children: a comparison of exposure estimates and associations based on land use regression and dispersion exposure modeling approaches. Environ Health Perspect 123:847–851; http://dx.doi.org/10.1289/ehp.1408541

Significance of Personal Exposure Assessment to Air Pollution in the Urban Areas of Egypt

Air pollution, both indoor and outdoor, has been found to be related to serious adverse health effects. Accurate estimation of air pollution exposure has become very important to suggest proper air pollution control policies and to further assess the effectiveness of these policies. In many instances, personal exposures have been found to be greater than concentrations measured at fixed site monitoring stations. As people spend most of their time indoors particularly during harsh weather conditions, it is necessary to consider indoor air quality in exposure assessment studies. The current paper focuses on the importance of personal exposure assessment based on spatial and temporal activity patters both indoors and outdoors.

Interaction effects of long-term air pollution exposure and variants in the GSTP1, GSTT1 and GSTCD genes on risk of acute myocardial infarction and hypertension: a case-control study.

IntroductionExperimental and epidemiological studies have reported associations between air pollution exposure, in particular related to vehicle exhaust, and cardiovascular disease. A potential pathophysiological pathway is pollution-induced pulmonary oxidative stress, with secondary systemic inflammation. Genetic polymorphisms in genes implicated in oxidative stress, such as GSTP1, GSTT1 and GSTCD, may contribute to determining individual susceptibility to air pollution as a promoter of coronary vulnerability.AimsWe aimed to investigate effects of long-term traffic-related air pollution exposure, as well as variants in GSTP1, GSTT1 and GSTCD, on risk of acute myocardial infarction (AMI) and hypertension. In addition, we studied whether air pollution effects were modified by the investigated genetic variants.MethodsGenotype data at 7 single nucleotide polymorphisms (SNPs) in the GSTP1 gene, and one in each of the GSTT1 and GSTCD genes, as well as air pollution exposure estimates, were available for 119 AMI cases and 1310 randomly selected population controls. Population control individuals with systolic blood pressure ≥140 mmHg, diastolic blood pressure ≥90 mmHg or on daily antihypertensive medication were defined as hypertensive (n = 468). Individual air pollution exposure levels were modeled as annual means of NO2 (marker of vehicle exhaust pollutants) using central monitoring data and dispersion models, linking to participants' home addresses.ResultsAir pollution was significantly associated with risk of AMI: OR 1.78 (95%CI 1.04–3.03) per 10 µg/m3 of long-term NO2 exposure. Three GSTP1 SNPs were significantly associated with hypertension. The effect of air pollution on risk of AMI varied by genotype strata, although the suggested interaction was not significant. We saw no obvious interaction between genetic variants in the GST genes and air pollution exposure for hypertension.ConclusionAir pollution exposure entails an increased risk of AMI, and this risk differed over genotype strata for variants in the GSTP1, GSTT1 and GSTCD genes, albeit not statistically-significantly.

Long-term exposure to traffic-related air pollution and cardiovascular health in a Greek cohort study

Our objective is to evaluate the association of exposure to traffic-related air pollution with the incidence of fatal and non-fatal ischemic heart disease (IHD), stroke and total cardiovascular disease (CVD) events in a Greek cohort. We used data from the European Prospective Investigation on Nutrition and Cancer (EPIC) for 2752 subjects followed from 1997 to 2011, whose residence was in 10 municipalities of the Greater Athens area. Air pollution exposure estimation was based on a spatio-temporal land use regression model linking geo-coded residential addresses to long-term average NO2 and PM10 concentrations. We conducted Cox proportional hazards regression analysis, adjusting for potential confounders. Hazard ratios (HR) above 1 (not all statistically significant) were associated with higher PM10 exposure for all outcomes. Weaker associations were found with NO2 exposure. Specifically, the estimated HR for a CVD event associated with 10 μg/m(3) increase in long-term exposure to PM10 was 1.50 (1.05-2.16, p-value: 0.027). The relationship was more evident for subjects ≤50 years old at recruitment. Associations of PM10 and NO2 exposure with IHD events were found only among women with HRs respectively of 2.24 (0.89-5.64, p-value: 0.086) and 1.54 (1.01-2.37, p-value: 0.046) associated with 10 μg/m(3) increase in the corresponding pollutant. In conclusion, the present study suggests that long-term exposure to traffic-related air pollution has an impact on CVD and IHD morbidity, particularly among women and younger subjects.

Indoor exposure and adverse birth outcomes related to fetal growth, miscarriage and prematurity-a systematic review.

The purpose of this review was to summarize existing epidemiological evidence of the association between quantitative estimates of indoor air pollution and all-day personal exposure with adverse birth outcomes including fetal growth, prematurity and miscarriage. We carried out a systematic literature search of MEDLINE and EMBASE databases with the aim of summarizing and evaluating the results of peer-reviewed epidemiological studies undertaken in “westernized” countries that have assessed indoor air pollution and all-day personal exposure with specific quantitative methods. This comprehensive literature search identified 16 independent studies which were deemed relevant for further review and two additional studies were added through searching the reference lists of all included studies. Two reviewers independently and critically appraised all eligible articles using the Critical Appraisal Skills Programme (CASP) tool. Of the 18 selected studies, 14 adopted a prospective cohort design, three were case-controls and one was a retrospective cohort study. In terms of pollutants of interest, seven studies assessed exposure to electro-magnetic fields, four studies assessed exposure to polycyclic aromatic hydrocarbons, four studies assessed PM2.5 exposure and three studies assessed benzene, phthalates and noise exposure respectively. Furthermore, 12 studies examined infant growth as the main birth outcome of interest, six examined spontaneous abortion and three studies assessed gestational age at birth and preterm delivery. This survey demonstrates that there is insufficient research on the possible association of indoor exposure and early life effects and that further research is needed.

Evaluation of observation-fused regional air quality model results for population air pollution exposure estimation

In this study, Community Multiscale Air Quality (CMAQ) model was applied to predict ambient gaseous and particulate concentrations during 2001 to 2010 in 15 hospital referral regions (HRRs) using a 36-km horizontal resolution domain. An inverse distance weighting based method was applied to produce exposure estimates based on observation-fused regional pollutant concentration fields using the differences between observations and predictions at grid cells where air quality monitors were located. Although the raw CMAQ model is capable of producing satisfying results for O3 and PM2.5 based on EPA guidelines, using the observation data fusing technique to correct CMAQ predictions leads to significant improvement of model performance for all gaseous and particulate pollutants. Regional average concentrations were calculated using five different methods: 1) inverse distance weighting of observation data alone, 2) raw CMAQ results, 3) observation-fused CMAQ results, 4) population-averaged raw CMAQ results and 5) population-averaged fused CMAQ results. It shows that while O3 (as well as NOx) monitoring networks in the HRRs are dense enough to provide consistent regional average exposure estimation based on monitoring data alone, PM2.5 observation sites (as well as monitors for CO, SO2, PM10 and PM2.5 components) are usually sparse and the difference between the average concentrations estimated by the inverse distance interpolated observations, raw CMAQ and fused CMAQ results can be significantly different. Population-weighted average should be used to account for spatial variation in pollutant concentration and population density. Using raw CMAQ results or observations alone might lead to significant biases in health outcome analyses.

The Association of Ambient Air Pollution and Physical Inactivity in the United States

BackgroundPhysical inactivity, ambient air pollution and obesity are modifiable risk factors for non-communicable diseases, with the first accounting for 10% of premature deaths worldwide. Although community level interventions may target each simultaneously, research on the relationship between these risk factors is lacking.ObjectivesAfter comparing spatial interpolation methods to determine the best predictor for particulate matter (PM2.5; PM10) and ozone (O3) exposures throughout the U.S., we evaluated the cross-sectional association of ambient air pollution with leisure-time physical inactivity among adults.MethodsIn this cross-sectional study, we assessed leisure-time physical inactivity using individual self-reported survey data from the Centers for Disease Control and Prevention's 2011 Behavioral Risk Factor Surveillance System. These data were combined with county-level U.S. Environmental Protection Agency air pollution exposure estimates using two interpolation methods (Inverse Distance Weighting and Empirical Bayesian Kriging). Finally, we evaluated whether those exposed to higher levels of air pollution were less active by performing logistic regression, adjusting for demographic and behavioral risk factors, and after stratifying by body weight category.ResultsWith Empirical Bayesian Kriging air pollution values, we estimated a statistically significant 16–35% relative increase in the odds of leisure-time physical inactivity per exposure class increase of PM2.5 in the fully adjusted model across the normal weight respondents (p-value<0.0001). Evidence suggested a relationship between the increasing dose of PM2.5 exposure and the increasing odds of physical inactivity.ConclusionsIn a nationally representative, cross-sectional sample, increased community level air pollution is associated with reduced leisure-time physical activity particularly among the normal weight. Although our design precludes a causal inference, these results provide additional evidence that air pollution should be investigated as an environmental determinant of inactivity.

Congenital anomalies among live births in a high environmental risk area—A case-control study in Brindisi (southern Italy)

Maternal exposure to ambient pollution has been increasingly linked to the risk of congenital anomalies (CAs) in the fetus and newborns. Recently, a descriptive study in the high environmental risk city of Brindisi (Italy) revealed an increased prevalence of total CAs, especially congenital heart disease (CHD) and ventricular septal defects (VSDs), both at the local level and in comparison with the pool of EUROCAT registries. This paper concerns a population-based case control study to investigate the association between maternal exposure to air pollutants - sulfur dioxide (SO2) and total suspended particulate (TSP) matter - and the risk of CA. Cases were newborns up to 28 days of age, born to mothers resident in Brindisi between 2001 and 2010, and discharged with a diagnosis of CA. Cases and controls were individually matched according to sex, socio-economic status of the census area of residence of the mother, and year of beginning of pregnancy. Up to four controls were extracted for each case. Concentration data from monitoring stations were used to estimate air pollution exposure. Each case and control was assigned pollutant concentration values as mean and 90th percentile of the daily average values during weeks 3-8 of pregnancy. Exposure as both continuous and categorical variables was considered and a conditional logistic regression model was constructed to quantify the odds ratios of exposure to air pollutants and the occurrence of total CAs, CHDs and VSDs. We found exposure to the 90th percentile of SO2 to be associated with CHDs (p for trend =0.01) and VSDs (p for trend <0.05). Findings for TSP were less consistent. In conclusion, in the studied area, maternal exposure to sulfur dioxide increased risk of CHD.

Measurement error in air pollution epidemiology: Guidance for uncertain times.

EnvironmetricsVolume 24, Issue 8 p. 529-530 Discussion Paper Measurement error in air pollution epidemiology: guidance for uncertain times Roger D. Peng, Corresponding Author Roger D. Peng Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, U.S.A.Correspondence to: Roger D. Peng, Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, U.S.A. [email protected]Search for more papers by this author Roger D. Peng, Corresponding Author Roger D. Peng Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, U.S.A.Correspondence to: Roger D. Peng, Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, U.S.A. [email protected]Search for more papers by this author First published: 21 January 2014 https://doi.org/10.1002/env.2242Citations: 1Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article.Citing Literature Volume24, Issue8December 2013Pages 529-530 RelatedInformation

Physical inactivity, ambient air pollution and obesity in the United States

Background: Physical inactivity, ambient air pollution and obesity are modifiable risk factors for non-communicable diseases, with the former accounting for 10% of premature deaths worldwide. Although community level interventions may target each simultaneously, research on the relationship between these risk factors is lacking. Aims: After comparing spatial interpolation methods to determine the best predictor for particulate matter ((PM2.5); (PM10)) and ozone (O3) exposures throughout the U.S., we evaluated the cross-sectional association of ambient air pollution with physical inactivity among adults. Methods: Using Behavioral Risk Factor Surveillance System 2011 data, we assessed physical inactivity and using the U.S. Environmental Protection Agency’s Air Quality System data we modeled and compared county-level air pollution exposure estimates using two interpolation methods: Inverse Distance Weighting (IDW) and Empirical Bayesian Kriging (EBK). We evaluated whether those exposed to higher levels of air pollution were less active using logistic regression, adjusted for demographic and behavioral risk factors and stratified by body weight category. Results: With EBK air pollution values, we estimated a 4-5% increased odds of physical inactivity per ug/m3 increase of (PM2.5) in a fully adjusted model across all weight strata (p-value&lt;0.0001). We identified dose-response relationships between (PM2.5) exposure and the odds of physical inactivity. Additionally, the normal weight stratum was found to have the highest odds ratios by (PM2.5) exposure class followed by the overweight and then obese strata. Conclusions: In a nationally representative, cross-sectional sample, increased community level air pollution is associated with reduced physical activity particularly among the normal weight. Although our design precludes a causal inference, these results provide additional evidence that air pollution should be investigated as an environmental determinant of inactivity.

Remote sensing approaches to estimate air pollution exposure for disease burden and epidemiology

Background: While the vast majority air pollution epidemiology and risk assessment relies upon ambient surface monitoring, large segments of the population live in locations that are not well-chara...

A hybrid model using temporal and spatial regression to estimate air pollution exposure during pregnancy

A hybrid model using temporal and spatial regression to estimate air pollution exposure during pregnancy

Measurement error in epidemiologic studies of air pollution based on land use regression models

Background: Land use regression (LUR) models are increasingly used to estimate air pollution exposure in epidemiological studies. These models use air pollution measurements at a small set of locat...