Back-extrapolating a land use regression model for estimating past exposures to traffic-related air pollution

Decomposing PM2.5 concentrations in urban environments into meaningful factors 2. Extracting the contribution of traffic-related exhaust emissions

Vehicle-emitted fine particulate matter (PM2.5) has been associated with significant health outcomes and environmental risks. This study estimates the contribution of traffic-related exhaust emissions (TREE) to observed PM2.5 using a novel factorization framework. Specifically, co-measured nitrogen oxides (NOx) concentrations served as a marker of vehicle-tailpipe emissions and were integrated into the optimization of a Non-negative Matrix Factorization (NMF) analysis to guide the factor extraction. The novel TREE-NMF approach was applied to long-term (2012-2019) PM2.5 observations from air quality monitoring (AQM) stations in two urban areas. The extracted TREE factor was evaluated against co-measured black carbon (BC) and PM2.5 species to which the TREE-NMF optimization was blind. The contribution of the TREE factor to the observed PM2.5 concentrations at an AQM station from the first location showed close agreement (R2=0.79) with monitored BC data. In the second location, a comparison of the extracted TREE factor with measurements at a nearby Surface PARTiculate mAtter Network (SPARTAN) station revealed moderate correlations with PM2.5 species commonly associated with fuel combustion, and a good linear regression fit with measured equivalent BC concentrations. The estimated concentrations of the TREE factor at the second location accounted for 7-11 % of the observed PM2.5 in the AQM stations. Moreover, analysis of specific days known to be characterized by little traffic emissions suggested that approximately 60-78 % of the traffic-related PM2.5 concentrations could be attributed to particulate traffic-exhaust emissions. The methodology applied in this study holds great potential in areas with limited monitoring of PM2.5 speciation, in particular BC, and its results could be valuable for both future environmental health research, regional radiative forcing estimates, and promulgation of tailored regulations for traffic-related air pollution abatement.

Beyond lung cancer: air pollution and bladder, breast and prostate cancer incidence

BACKGROUND

The carcinogenicity of air pollution and its impact on the risk of lung cancer is well known; however, there are still knowledge gaps and mixed results for other sites of cancer.

METHODS

The current study aimed to evaluate the associations between ambient air pollution [fine particulate matter (PM2.5) and nitrogen oxides (NOx)] and cancer incidence. Exposure assessment was based on historical addresses of >900 000 participants. Cancer incidence included primary cancer cases diagnosed from 2007 to 2015 (n = 30 979). Cox regression was used to evaluate the associations between ambient air pollution and cancer incidence [hazard ratio (HR), 95% CI].

RESULTS

In the single-pollutant models, an increase of one interquartile range (IQR) (2.11 µg/m3) of PM2.5 was associated with an increased risk of all cancer sites (HR = 1.51, 95% CI: 1.47-1.54), lung cancer (HR = 1.73, 95% CI: 1.60-1.87), bladder cancer (HR = 1.50, 95% CI: 1.37-1.65), breast cancer (HR = 1.50, 95% CI: 1.42-1.58) and prostate cancer (HR = 1.41, 95% CI: 1.31-1.52). In the single-pollutant and the co-pollutant models, the estimates for PM2.5 were stronger compared with NOx for all the investigated cancer sites.

CONCLUSIONS

Our findings confirm the carcinogenicity of ambient air pollution on lung cancer and provide additional evidence for bladder, breast and prostate cancers. Further studies are needed to confirm our observation regarding prostate cancer. However, the need for more research should not be a barrier to implementing policies to limit the population's exposure to air pollution.

Residential greenness and lower breast and prostate cancer incidence: Evidence from a retrospective cohort study of 977,644 participants from Israel

BACKGROUND

There is limited evidence on the associations between residential greenness and cancer incidence in longitudinal studies.

OBJECTIVES

The aim of the study was to evaluate the associations between weighted mean residential greenness exposure and cancer incidence.

METHODS

This is a registry based retrospective cohort study of 977,644 participants. The residential greenness exposure was estimated for every participant, as the weighted mean residential greenness exposure. This was based on the mean Normalized Difference Vegetation Index (NDVI) in the residential small geographic area and the duration of the residence in this area. Cancer incidence cases included consecutive newly diagnosed cases of primary cancer. Analyses were conducted for all cancer sites, lung cancer, bladder cancer, breast cancer, prostate cancer and melanoma-skin cancer. Cox regression models were used to evaluate the crude and adjusted associations (hazards ratios (HR) and its 95 % confidence intervals (CIs)) between tertiles of residential greenness and cancer incidence. Further adjusted models to nitrogen oxides (NOx) were estimated.

RESULTS

After adjustment to covariates, exposure to the highest tertile of residential greenness, compared to the lowest, were associated with lower risk for all cancer sites (HR = 0.88, 95 % CI: 0.86-0.90), breast cancer (HR = 0.85, 95 % CI: 0.80-0.89) and prostate cancer (HR = 0.85, 95 % CI: 0.79-0.91). In addition, lower risk were observed for the middle tertile of exposure and all cancer sites (HR = 0.88, 95 % CI: 0.86-0.90), breast cancer (HR = 0.88, 95 % CI: 0.84-0.92) and prostate cancer (HR = 0.83, 95 % CI: 0.79-0.89). There was no evidence for mediation by air pollution (NOx).

DISCUSSION

Residential greenness demonstrated beneficial associations with lower risk for all cancers, breast and prostate cancers. If our observations will be replicated, it may present a useful avenue for public-health intervention to reduce cancer burden through the provision of greenness exposure.

Traffic-related air pollution and dementia incidence in the Adult Changes in Thought Study

BACKGROUND

While epidemiologic evidence links higher levels of exposure to fine particulate matter (PM2.5) to decreased cognitive function, fewer studies have investigated links with traffic-related air pollution (TRAP), and none have examined ultrafine particles (UFP, ≤100 nm) and late-life dementia incidence.

OBJECTIVE

To evaluate associations between TRAP exposures (UFP, black carbon [BC], and nitrogen dioxide [NO2]) and late-life dementia incidence.

METHODS

We ascertained dementia incidence in the Seattle-based Adult Changes in Thought (ACT) prospective cohort study (beginning in 1994) and assessed ten-year average TRAP exposures for each participant based on prediction models derived from an extensive mobile monitoring campaign. We applied Cox proportional hazards models to investigate TRAP exposure and dementia incidence using age as the time axis and further adjusting for sex, self-reported race, calendar year, education, socioeconomic status, PM2.5, and APOE genotype. We ran sensitivity analyses where we did not adjust for PM2.5 and other sensitivity and secondary analyses where we adjusted for multiple pollutants, applied alternative exposure models (including total and size-specific UFP), modified the adjustment covariates, used calendar year as the time axis, assessed different exposure periods, dementia subtypes, and others.

RESULTS

We identified 1,041 incident all-cause dementia cases in 4,283 participants over 37,102 person-years of follow-up. We did not find evidence of a greater hazard of late-life dementia incidence with elevated levels of long-term TRAP exposures. The estimated hazard ratio of all-cause dementia was 0.98 (95 % CI: 0.92-1.05) for every 2000 pt/cm3 increment in UFP, 0.95 (0.89-1.01) for every 100 ng/m3 increment in BC, and 0.96 (0.91-1.02) for every 2 ppb increment in NO2. These findings were consistent across sensitivity and secondary analyses.

DISCUSSION

We did not find evidence of a greater hazard of late-life dementia risk with elevated long-term TRAP exposures in this population-based prospective cohort study.

Residential Greenness and Long-term Mortality Among Patients Who Underwent Coronary Artery Bypass Graft Surgery

BACKGROUND

Studies have reported inverse associations between exposure to residential greenness and mortality. Greenness has also been associated with better surgical recovery. However, studies have had small sample sizes and have been restricted to clinical settings. We investigated the association between exposure to residential greenness and all-cause mortality among a cohort of cardiac patients who underwent coronary artery bypass graft (CABG) surgery.

METHODS

We studied this cohort of 3,128 CABG patients between 2004 and 2009 at seven cardiothoracic departments in Israel and followed patients until death or 1st May 2021. We collected covariate information at the time of surgery and calculated the patient-level average normalized difference vegetation index (NDVI) over the entire follow-up in a 300 m buffer from the home address. We used Cox proportional hazards regression models to estimate associations between greenness and death, adjusting for age, sex, origin, socioeconomic status, type of hospital admission, peripherality, air pollution, and distance from the sea.

RESULTS

Mean age at surgery was 63.8 ± 10.6 for men and 69.5 ± 10.0 for women. During an average of 12.1 years of follow-up (37,912 person-years), 1,442 (46%) patients died. A fully adjusted Cox proportional hazards model estimated a 7% lower risk of mortality (HR: 0.93, 95% CI = [0.85, 1.00]) per 1 interquartile range width increase (0.04) in NDVI. Results were robust to the use of different buffer sizes (100 m-1,250 m from the home) and to the use of average NDVI exposure during the first versus the last 2 years of follow-up.

CONCLUSIONS

Residential greenness was associated with lower risk of mortality in CABG patients.

Development of Spatio-Temporal Land Use Regression Models for Fine Particulate Matter and Wood-Burning Tracers in Temuco, Chile

Biomass burning is common in much of the world, and in some areas, residential wood-burning has increased. However, air pollution resulting from biomass burning is an important public health problem. A sampling campaign was carried out between May 2017 and July 2018 in over 64 sites in four sessions, to develop a spatio-temporal land use regression (LUR) model for fine particulate matter (PM) and wood-burning tracers levoglucosan and soluble potassium (Ksol) in a city heavily impacted by wood-burning. The mean (sd) was 46.5 (37.4) μg m-3 for PM2.5, 0.607 (0.538) μg m-3 for levoglucosan, and 0.635 (0.489) μg m-3 for Ksol. LUR models for PM2.5, levoglucosan, and Ksol had a satisfactory performance (LOSOCV R2), explaining 88.8%, 87.4%, and 87.3% of the total variance, respectively. All models included sociodemographic predictors consistent with the pattern of use of wood-burning in homes. The models were applied to predict concentrations surfaces and to estimate exposures for an epidemiological study.

Long-term exposure to nitrogen dioxide air pollution and breast cancer risk: A nested case-control within the French E3N cohort study

Nitrogen dioxide (NO₂) is an important air pollutant due to its adverse effects on human health. Yet, current evidence on the association between NO₂ and the risk of breast cancer lacks consistency. In this study, we investigated the association between long-term exposure to NO₂ and breast cancer risk in the French E3N cohort study. Association of breast cancer risk with NO₂ exposure was assessed in a nested case-control study within the French E3N cohort including 5222 breast cancer cases identified over the 1990-2011 follow-up period and 5222 matched controls. Annual mean concentrations of NO₂ at participants' residential addresses for each year from recruitment 1990 through 2011, were estimated using a land use regression (LUR) model. Multivariable conditional logistic regression models were used to compute odds ratios (ORs) and their 95% confidence intervals (CIs). Additional analyses were performed using NO₂ concentrations estimated by CHIMERE, a chemistry transport model. Overall, the mean NO₂ exposure was associated with an increased risk of breast cancer. In all women, for each interquartile range (IQR) increase in NO₂ levels (LUR: 17.8 μg/m³), the OR of the model adjusted for confounders was 1.09 (95% CI: 1.01-1.18). The corresponding OR in the fully adjusted model (additionally adjusted for established breast cancer risk factors) was 1.07 (95% CI: 0.98-1.15). By menopausal status, results for postmenopausal women were comparable to those for all women, while no association was observed among premenopausal women. By hormone receptor status, the OR of estrogen receptor positive breast cancer = 1.07 (95% CI: 0.97-1.19) in the fully adjusted model. Additional analyses using the CHIMERE model showed slight differences in ORs estimates. The results of this study indicate an increased risk of breast cancer associated with long-term exposure to NO₂ air pollution. Observing comparable effects of NO₂ exposure estimated by two different models, reinforces these findings.

Associations of Exposure to Nitrogen Oxides with Prevalent Asthma and Other Atopic Diseases in Israel

Childhood exposure to nitrogen oxides (NOx) is considered a risk factor for the onset of asthma. However, associations of this exposure with other atopic diseases and factors that modify this association are less clear. We aimed to study associations between exposure to NOx and the prevalence of atopic diseases in Israeli adolescents using a cross-sectional design. The study population comprised all Israeli-born adolescents whose medical status was evaluated for mandatory military recruitment during 1967–2017 (n = 2,523,745), of whom 5.9% had prevalent asthma. We based the exposure assessments on a land-use regression model and estimated associations using multivariable logistic regression models. Across all periods, mean exposure to NOx from birth to adolescence was associated with prevalent asthma at the examination in a dose-response manner, with an odds ratio for the upper quintile of 1.61 (95% CI: 1.56–1.67), in comparison to the lowest quintile. Associations were stronger in males and in lower socioeconomic strata. We found the strongest associations for asthma with comorbid rhinitis, with an almost twofold increase in the odds of upper versus lower quintile of exposure (odds ratio = 1.96, 95% CI: 1.82–2.11). Rhino-conjunctivitis and allergic atopic dermatitis suggested a possible threshold level with NOx. Capsule Summary: Research indicates that half of the global population will suffer from an allergic condition at some point in life. Childhood exposure to nitrogen oxides is a risk factor for the onset of asthma. The association between exposure and allergic diseases other than asthma is unclear. We demonstrate a strong, dose-response relationship between exposure and a group of allergic outcomes, using data comprising 2.5 million subjects over 50 years. The large health benefits from clean air should motivate governments to prioritize mitigation measures.

Characterizing long-term NO2 concentration surfaces across a large metropolitan area through spatiotemporal land use regression modelling of mobile measurements

A spatiotemporal land use regression (LUR) model optimized to predict nitrogen dioxide (NO₂) concentrations obtained from on-road, mobile measurements collected in 2015-16 was independently evaluated using concentrations observed at multiple sites across Toronto, Canada, obtained more than ten years earlier. This spatiotemporal LUR modelling approach improves upon estimates of historical NO₂ concentrations derived from the previously used method of back-extrapolation. The optimal spatiotemporal LUR model (R² = 0.71 for prediction of NO₂ data in 2002 and 2004) uses daily average NO₂ concentrations observed at multiple long-term monitoring sites and hourly average wind speed recorded at a single site, along with spatial predictors based on geographical information system data, to estimate NO₂ levels for time periods outside of those used for model development. While the model tended to underestimate samplers located close to the roadway, it showed great accuracy when estimating samplers located beyond 100 m which are probably more relevant for exposure at residences. This study shows that spatiotemporal LUR models developed from strategic, multi-day (30 days in 3 different months) mobile measurements can enhance LUR model's ability to estimate long-term, intra-urban NO₂ patterns. Furthermore, the mobile sampling strategy enabled this new LUR model to cover a larger domain of Toronto and outlying suburban communities, thereby increasing the potential population for future epidemiological studies.

Long-term Exposure to Low Air Pollutant Concentrations and the Relationship with All-Cause Mortality and Stroke in Older Men

BACKGROUND

Long-term air pollution exposure has been associated with increased risk of mortality and stroke. Less is known about the risk at lower concentrations. The association of long-term exposure to PM2.5, PM2.5 absorbance, NO2, and NOx with all-cause mortality and stroke was investigated in a cohort of men aged ≥ 65 years who lived in metropolitan Perth, Western Australia.

METHODS

Land use regression models were used to estimate long-term exposure to air pollutants at participant's home address (n = 11,627) over 16 years. Different metrics of exposure were assigned: baseline; year before the outcome event; and average exposure across follow-up period. The Mortality Register and Hospital Morbidity Data from the Western Australia Data Linkage System were used to ascertain mortality and stroke cases. Hazard ratios (HRs) and 95% confidence intervals were estimated using Cox proportional hazard models, adjusting for age, smoking, education, and body mass index for all-cause mortality. For fatal and hospitalized stroke, the models included variables controlled for all-cause mortality plus hypertension.

RESULTS

Fifty-four percent of all-participants died, 3% suffered a fatal stroke, and 14% were hospitalized stroke cases. PM2.5 absorbance increased the risk of all-cause mortality with adjusted HR of 1.12 (1.02-1.23) for baseline and average exposures, and 1.14 (1.02-1.24) for past-year exposure. There were no associations between PM2.5 absorbance, NO2, and NOx and stroke outcomes. However, PM2.5 was associated with reduced risks of fatal stroke.

CONCLUSION

Long-term exposure to PM2.5 absorbance was associated with all-cause mortality among older men exposed to low concentrations; and exposure to PM2.5 was associated with reduced risk of fatal stroke.

Estimating historical PM2.5 exposures for three decades (1987-2016) in Japan using measurements of associated air pollutants and land use regression

Accurate estimation of historical PM_2.5 exposures for epidemiological studies is challenging when extensive monitoring data are limited in duration. Here, we develop a national-scale PM_2.5 exposure model for Japan using measurements recorded between 2014 and 2016 to estimate monthly means for 1987 through 2016. Our objective is to obtain accurate PM_2.5 estimates for years prior to implementation of extensive PM_2.5 monitoring, using observations from a limited period. We utilize a neural network to convey the non-linear relationship between the target pollutant and predictors, while incorporating the associated air pollutants. We obtain high R² values of 0.76 and 0.73 through spatial and temporal cross validation, respectively. We evaluate estimation accuracy using an independent data set and achieve an R² of 0.75. Moreover, monthly variations for 2000-2013 are well reproduced with correlation coefficients of greater than 0.78, obtained through a comparison with observations. We estimate monthly means at 1 × 1 km resolution from 1987 through 2016. The estimates show decreases in the area and population weighted means beginning in the 1990s. We successfully estimate monthly mean PM_2.5 concentrations over three decades with outstanding predictive accuracy. Our findings illustrate that the presented approach achieves accurate long-term historical estimations using observations limited in duration.

Preexisting coronary heart disease and susceptibility to long-term effects of traffic-related air pollution: A matched cohort analysis

BACKGROUND

Individuals with coronary heart disease are considered susceptible to traffic-related air pollution exposure. Yet, cohort-based evidence on whether preexisting coronary heart disease modifies the association of traffic-related air pollution with health outcomes is lacking.

AIM

Using data of four Israeli cohorts, we compared associations of traffic-related air pollution with mortality and cancer between coronary heart disease patients and matched controls from the general population.

METHODS

Subjects hospitalized with acute coronary syndrome from two patient cohorts (inception years: 1992-1993 and 2006-2014) were age- and sex-matched to coronary heart disease-free participants of two cycles of the Israeli National Health and Nutrition Surveys (inception years: 1999-2001 and 2005-2006). Ambient concentrations of nitrogen oxides at the residential place served as a proxy for traffic-related air pollution exposure across all cohorts, based on a high-resolution national land use regression model (50 m). Data on all-cause mortality (last update: 2018) and cancer incidence (last update: 2016) were retrieved from national registries. Cox-derived stratum-specific hazard ratios with 95% confidence intervals were calculated, adjusted for harmonized covariates across cohorts, including age, sex, ethnicity, neighborhood socioeconomic status, smoking, diabetes, hypertension, prior stroke and prior malignancy (the latter only in the mortality analysis). Effect-modification was examined by testing nitrogen oxides-by-coronary heart disease interaction term in the entire matched cohort.

RESULTS

The cohort (mean (standard deviation) age 61.5 (14) years; 44% women) included 2393 matched pairs, among them 2040 were cancer-free at baseline. During a median (25th-75th percentiles) follow-up of 13 (10-19) and 11 (7-17) years, 1458 deaths and 536 new cancer cases were identified, respectively. In multivariable-adjusted models, a 10-parts per billion nitrogen oxides increment was positively associated with all-cause mortality among coronary heart disease patients (hazard ratio = 1.13, 95% confidence interval 1.05-1.22), but not among controls (hazard ratio = 1.00, 0.93-1.08) (pinteraction = 0.003). A similar pattern was seen for all-cancer incidence (hazard ratioCHD = 1.19 (1.03-1.37), hazard ratioCHD-Free = 0.93 (0.84-1.04) (pinteraction = 0.01)). Associations were robust to multiple sensitivity analyses.

CONCLUSIONS

Coronary heart disease patients might be at increased risk for traffic-related air pollution-associated mortality and cancer, irrespective of their age and sex. Patients and clinicians should be more aware of the adverse health effects on coronary heart disease patients of chronic exposure to vehicle emissions.

Cancer and mortality in relation to traffic-related air pollution among coronary patients: Using an ensemble of exposure estimates to identify high-risk individuals

BACKGROUND

Moderate correlations were previously observed between individual estimates of traffic-related air pollution (TRAP) produced by different exposure modeling approaches. This induces exposure misclassification for a substantial fraction of subjects.

AIM

We used an ensemble of well-established modeling approaches to increase certainty of exposure classification and reevaluated the association with cancers previously linked to TRAP (lung, breast and prostate), other cancers, and all-cause mortality in a cohort of coronary patients.

METHODS

Patients undergoing percutaneous coronary interventions in a major Israeli medical center from 2004 to 2014 (n = 10,627) were followed for cancer (through 2015) and mortality (through 2017) via national registries. Residential exposure to nitrogen oxides (NO_x) -a proxy for TRAP- was estimated by optimized dispersion model (ODM) and land use regression (LUR) (r_Pearson = 0.50). Mutually exclusive groups of subjects classified as exposed by none of the methods (high-certainty low-exposed), ODM alone, LUR alone, or both methods (high-certainty high-exposed) were created. Associations were examined using Cox regression models.

RESULTS

During follow-up, 741 incident cancer cases were diagnosed and 3051 deaths occurred. Using a ≥25 ppb cutoff, compared with high-certainty low exposed, the multivariable-adjusted hazard ratios (95% confidence intervals) for lung, breast and prostate cancer were 1.56 (1.13-2.15) in high-certainty exposed, 1.27 (0.86-1.86) in LUR-exposed alone, and 1.13 (0.77-1.65) in ODM-exposed alone. The association of the former category was strengthened using more extreme NO_x cutoffs. A similar pattern, albeit less strong, was observed for mortality, whereas no association was shown for cancers not previously linked to TRAP.

CONCLUSIONS

Use of an ensemble of TRAP exposure estimates may improve classification, resulting in a stronger association with outcomes.

Spatial PM2.5, NO2, O3 and BC models for Western Europe - Evaluation of spatiotemporal stability

BACKGROUND

In order to investigate associations between air pollution and adverse health effects consistent fine spatial air pollution surfaces are needed across large areas to provide cohorts with comparable exposures. The aim of this paper is to develop and evaluate fine spatial scale land use regression models for four major health relevant air pollutants (PM_2.5, NO₂, BC, O₃) across Europe.

METHODS

We developed West-European land use regression models (LUR) for 2010 estimating annual mean PM_2.5, NO₂, BC and O₃ concentrations (including cold and warm season estimates for O₃). The models were based on AirBase routine monitoring data (PM_2.5, NO₂ and O₃) and ESCAPE monitoring data (BC), and incorporated satellite observations, dispersion model estimates, land use and traffic data. Kriging was performed on the residual spatial variation from the LUR models and added to the exposure estimates. One model was developed using all sites (100%). Robustness of the models was evaluated by performing a five-fold hold-out validation and for PM_2.5 and NO₂ additionally with independent comparison at ESCAPE measurements. To evaluate the stability of each model's spatial structure over time, separate models were developed for different years (NO₂ and O₃: 2000 and 2005; PM_2.5: 2013).

RESULTS

The PM_2.5, BC, NO₂, O₃ annual, O₃ warm season and O₃ cold season models explained respectively 72%, 54%, 59%, 65%, 69% and 83% of spatial variation in the measured concentrations. Kriging proved an efficient technique to explain a part of residual spatial variation for the pollutants with a strong regional component explaining respectively 10%, 24% and 16% of the R² in the PM_2.5, O₃ warm and O₃ cold models. Explained variance at fully independent sites vs the internal hold-out validation was slightly lower for PM_2.5 (65% vs 66%) and lower for NO₂ (49% vs 57%). Predictions from the 2010 model correlated highly with models developed in other years at the overall European scale.

CONCLUSIONS

We developed robust PM_2.5, NO₂, O₃ and BC hybrid LUR models. At the West-European scale models were robust in time, becoming less robust at smaller spatial scales. Models were applied to 100 × 100 m surfaces across Western Europe to allow for exposure assignment for 35 million participants from 18 European cohorts participating in the ELAPSE study.

The importance of spatial resolutions of Community Multiscale Air Quality (CMAQ) models on health impact assessment

We used the Community Multiscale Air Quality (CMAQ) simulation model to predict daily average of fine particulate matter (PM_2.5) concentrations. The primary focus of our study was to investigate the sensitivity of CMAQ prediction accuracy associated with the horizontal grid resolutions and assess its impact on human health studies. To illustrate our point we ran CMAQ model at 4 km and 12 km resolutions over New York State for the year 2011, and systematically assessed the differences between two modeled PM_2.5 concentrations. Model performance was evaluated against PM_2.5 measured values at monitoring stations. The results indicated that simulations at both 4 km and 12 km resolutions reproduced measured PM_2.5 values with fractional error (54.41% for 4 km and 52.28% for 12 km) that are within the recommend performance criteria except for summer seasons and rural areas. Additionally, model results at 12 km compared to 4 km resolution generally performed better and had substantially lower computational burden. In our health impact assessment study, we found that estimated adverse health outcomes associated with PM_2.5 exposure derived from the two CMAQ models were compatible, especially in rural areas. Based on our findings, we conclude that the CMAQ simulation at 12 km resolution with further calibration and/or downscaling is a viable option than 4 km simulation to estimate small-scale within-city variations of air pollution concentrations.

Long-term nitrogen dioxide exposure assessment using back-extrapolation of satellite-based land-use regression models for Australia

Assessing historical exposure to air pollution in epidemiological studies is often problematic because of limited spatial and temporal measurement coverage. Several methods for modelling historical exposures have been described, including land-use regression (LUR). Satellite-based LUR is a recent technique that seeks to improve predictive ability and spatial coverage of traditional LUR models by using satellite observations of pollutants as inputs to LUR. Few studies have explored its validity for assessing historical exposures, reflecting the absence of historical observations from popular satellite platforms like Aura (launched mid-2004). We investigated whether contemporary satellite-based LUR models for Australia, developed longitudinally for 2006-2011, could capture nitrogen dioxide (NO₂) concentrations during 1990-2005 at 89 sites around the country. We assessed three methods to back-extrapolate year-2006 NO₂ predictions: (1) 'do nothing' (i.e., use the year-2006 estimates directly, for prior years); (2) change the independent variable 'year' in our LUR models to match the years of interest (i.e., assume a linear trend prior to year-2006, following national average patterns in 2006-2011), and; (3) adjust year-2006 predictions using selected historical measurements. We evaluated prediction error and bias, and the correlation and absolute agreement of measurements and predictions using R² and mean-square error R² (MSE-R²), respectively. We found that changing the year variable led to best performance; predictions captured between 41% (1991; MSE-R² = 31%) and 80% (2003; MSE-R² = 78%) of spatial variability in NO₂ in a given year, and 76% (MSE-R² = 72%) averaged over 1990-2005. We conclude that simple methods for back-extrapolating prior to year-2006 yield valid historical NO₂ estimates for Australia during 1990-2005. These results suggest that for the time scales considered here, satellite-based LUR has a potential role to play in long-term exposure assessment, even in the absence of historical predictor data.

Longitudinal associations of long-term exposure to ultrafine particles with blood pressure and systemic inflammation in Puerto Rican adults

BACKGROUND

Few longitudinal studies have examined the association between ultrafine particulate matter (UFP, particles < 0.1 μm aerodynamic diameter) exposure and cardiovascular disease (CVD) risk factors. We used data from 791 adults participating in the longitudinal Boston Puerto Rican Health Study (Massachusetts, USA) between 2004 and 2015 to assess whether UFP exposure was associated with blood pressure and high sensitivity C-reactive protein (hsCRP, a biomarker of systemic inflammation).

METHODS

Residential annual average UFP exposure (measured as particle number concentration, PNC) was assigned using a model accounting for spatial and temporal trends. We also adjusted PNC values for participants' inhalation rate to obtain the particle inhalation rate (PIR) as a secondary exposure measure. Multilevel linear models with a random intercept for each participant were used to examine the association of UFP with blood pressure and hsCRP.

RESULTS

Overall, in adjusted models, an inter-quartile range increase in PNC was associated with increased hsCRP (β = 6.8; 95% CI = - 0.3, 14.0%) but not with increased systolic blood pressure (β = 0.96; 95% CI = - 0.33, 2.25 mmHg), pulse pressure (β = 0.70; 95% CI = - 0.27, 1.67 mmHg), or diastolic blood pressure (β = 0.55; 95% CI = - 0.20, 1.30 mmHg). There were generally stronger positive associations among women and never smokers. Among men, there were inverse associations of PNC with systolic blood pressure and pulse pressure. In contrast to the primary findings, an inter-quartile range increase in the PIR was positively associated with systolic blood pressure (β = 1.03; 95% CI = 0.00, 2.06 mmHg) and diastolic blood pressure (β = 1.01; 95% CI = 0.36, 1.66 mmHg), but not with pulse pressure or hsCRP.

CONCLUSIONS

We observed that exposure to PNC was associated with increases in measures of CVD risk markers, especially among certain sub-populations. The exploratory PIR exposure metric should be further developed.

Satellite-Based Estimates of Daily NO2 Exposure in China Using Hybrid Random Forest and Spatiotemporal Kriging Model

A novel model named random-forest-spatiotemporal-kriging (RF-STK) was developed to estimate the daily ambient NO₂ concentrations across China during 2013-2016 based on the satellite retrievals and geographic covariates. The RF-STK model showed good prediction performance, with cross-validation R² = 0.62 (RMSE = 13.3 μg/m³) for daily and R² = 0.73 (RMSE = 6.5 μg/m³) for spatial predictions. The nationwide population-weighted multiyear average of NO₂ was predicted to be 30.9 ± 11.7 μg/m³ (mean ± standard deviation), with a slowly but significantly decreasing trend at a rate of -0.88 ± 0.38 μg/m³/year. Among the main economic zones of China, the Pearl River Delta showed the fastest decreasing rate of -1.37 μg/m³/year, while the Beijing-Tianjin Metro did not show a temporal trend ( P = 0.32). The population-weighted NO₂ was predicted to be the highest in North China (40.3 ± 10.3 μg/m³) and lowest in Southwest China (24.9 ± 9.4 μg/m³). Approximately 25% of the population lived in nonattainment areas with annual-average NO₂ > 40 μg/m³. A piecewise linear function with an abrupt point around 100 people/km² characterized the relationship between the population density and the NO₂, indicating a threshold of aggravated NO₂ pollution due to urbanization. Leveraging the ground-level NO₂ observations, this study fills the gap of statistically modeling nationwide NO₂ in China, and provides essential data for epidemiological research and air quality management.

Chronic exposure to traffic-related air pollution and cancer incidence among 10,000 patients undergoing percutaneous coronary interventions: A historical prospective study

Background Exposure to traffic-related air pollution (TRAP) is considered to have a carcinogenic effect. The authors previously reported a nonsignificant association between TRAP and cancer risk in a relatively small cohort of myocardial infarction survivors. This study assessed whether TRAP exposure is associated with subsequent cancer in a large cohort of coronary patients. Methods & results Consecutive patients undergoing percutaneous coronary interventions in a major medical centre in central Israel from 2004 to 2014 were followed for cancer through 2015. Residential levels of nitrogen oxides (NO_x) - a proxy for TRAP - were estimated based on a high-resolution national land use regression model. Cox proportional hazards models were constructed to study relationships with cancer. Among 12,784 candidate patients, 9816 had available exposure data and no history of cancer (mean age, 68 years; 77% men). During a median (25th-75th percentiles) follow-up of 7.0 (3.9-9.3) years, 773 incident cases of cancer (8%) were diagnosed. In a multivariable-adjusted model, a 10-ppb increase in mean NO_x exposure was associated with hazard ratios (HRs) of 1.07 (95% confidence interval [CI] 1.00-1.15) for all-site cancer and 1.16 (95% CI 1.05-1.28) for cancers previously linked to TRAP (lung, breast, prostate, kidney and bladder). A stronger association was observed for breast cancer (HR = 1.43; 95% CI 1.12-1.83). Associations were slightly strengthened after limiting the cohort to patients with more precise exposure assessment. Conclusion Coronary patients exposed to TRAP are at increased risk of several types of cancer, particularly lung, prostate and breast. As these cancers are amenable to prevention strategies, identifying highly exposed patients may provide an opportunity to improve clinical care.

Spatiotemporal prediction of daily ambient ozone levels across China using random forest for human exposure assessment

In China, ozone pollution shows an increasing trend and becomes the primary air pollutant in warm seasons. Leveraging the air quality monitoring network, a random forest model is developed to predict the daily maximum 8-h average ozone concentrations ([O₃]_MDA8) across China in 2015 for human exposure assessment. This model captures the observed spatiotemporal variations of [O₃]_MDA8 by using the data of meteorology, elevation, and recent-year emission inventories (cross-validation R² = 0.69 and RMSE = 26 μg/m³). Compared with chemical transport models that require a plenty of variables and expensive computation, the random forest model shows comparable or higher predictive performance based on only a handful of readily-available variables at much lower computational cost. The nationwide population-weighted [O₃]_MDA8 is predicted to be 84 ± 23 μg/m³ annually, with the highest seasonal mean in the summer (103 ± 8 μg/m³). The summer [O₃]_MDA8 is predicted to be the highest in North China (125 ± 17 μg/m³). Approximately 58% of the population lives in areas with more than 100 nonattainment days ([O₃]_MDA8>100 μg/m³), and 12% of the population are exposed to [O₃]_MDA8>160 μg/m³ (WHO Interim Target 1) for more than 30 days. As the most populous zones in China, the Beijing-Tianjin Metro, Yangtze River Delta, Pearl River Delta, and Sichuan Basin are predicted to be at 154, 141, 124, and 98 nonattainment days, respectively. Effective controls of O₃ pollution are urgently needed for the highly-populated zones, especially the Beijing-Tianjin Metro with seasonal [O₃]_MDA8 of 140 ± 29 μg/m³ in summer. To the best of the authors' knowledge, this study is the first statistical modeling work of ambient O₃ for China at the national level. This timely and extensively validated [O₃]_MDA8 dataset is valuable for refining epidemiological analyses on O₃ pollution in China.

Improving modeled air pollution concentration maps by residual interpolation

Models that are used to map air pollutant concentrations are not free of errors. A possible approach for improving the final concentration map is to interpolate the residuals of the initial model concentration estimates. Due to the possible spatial autocorrelation of the residuals of the initial model estimates, Bayesian inference schemes were suggested for this task, since they can correctly adjust the level of fitting of the residuals to the random measurement errors. However, the complexity of Bayesian methods often discourages their use. Here, we present an alternative and simpler approach, using a leave-one-out cross-validation to determine the optimal level of fitting of the residual correction. We show that the optimal correction level is related to the extent of the spatial autocorrelation of the cross-validated residuals. Namely, when the residuals are not autocorrelated residual correction is unnecessary, and if employed may actually degrade the quality of the final concentration map. Moreover, our approach enables to optimize the residual correction based on different target performance measures, with a possibly different optimal correction depending on the performance measure used. Hence, different target performance measures can be chosen to fit best the specific application of interest. The method is demonstrated using output of three different models used for estimating NO_x and NO₂ concentrations over Israel. We show that our approach can be used as an exploratory step, for assessing the potential benefit of residual correction, and as a simple alternative to Bayesian schemes.

Wireless Distributed Environmental Sensor Networks for Air Pollution Measurement-The Promise and the Current Reality

The evaluation of the effects of air pollution on public health and human-wellbeing requires reliable data. Standard air quality monitoring stations provide accurate measurements of airborne pollutant levels, but, due to their sparse distribution, they cannot capture accurately the spatial variability of air pollutant concentrations within cities. Dedicated in-depth field campaigns have dense spatial coverage of the measurements but are held for relatively short time periods. Hence, their representativeness is limited. Moreover, the oftentimes integrated measurements represent time-averaged records. Recent advances in communication and sensor technologies enable the deployment of dense grids of Wireless Distributed Environmental Sensor Networks for air quality monitoring, yet their capability to capture urban-scale spatiotemporal pollutant patterns has not been thoroughly examined to date. Here, we summarize our studies on the practicalities of using data streams from sensor nodes for air quality measurement and the required methods to tune the results to different stakeholders and applications. We summarize the results from eight cities across Europe, five sensor technologies-three stationary (with one tested also while moving) and two personal sensor platforms, and eight ambient pollutants. Overall, few sensors showed an exceptional and consistent performance, which can shed light on the fine spatiotemporal urban variability of pollutant concentrations. Stationary sensor nodes were more reliable than personal nodes. In general, the sensor measurements tend to suffer from the interference of various environmental factors and require frequent calibrations. This calls for the development of suitable field calibration procedures, and several such in situ field calibrations are presented.

Development of PM2.5 and NO2 models in a LUR framework incorporating satellite remote sensing and air quality model data in Pearl River Delta region, China

High resolution pollution maps are critical to understand the exposure and health effect of local residents to air pollution. Currently, none of the single technologies used to measure or estimate concentrations of pollutants can provide sufficient resolved exposure data. Land use regression (LUR) models were developed to combine ground-based measurements, satellite remote sensing (SRS) and air quality model (AQM), together with geographic and local source related spatial inputs, to generate high resolution pollution maps for both PM_2.5 and NO₂ in Pearl River Delta (PRD), China. Four sets of LUR models (LUR without SRS or AQM, with SRS only, with AQM only, and with both SRS and AQM), all including local traffic emissions and land use variables, were compared to evaluate the contribution of SRS and AQM data to the performance of LUR models in PRD region. For NO₂, the annual model with SRS estimate performed best, explaining 60.5% of the spatial variation. For PM_2.5, the annual model with traditional predictor variables without SRS or AQM estimates showed the best performance, explaining 88.4% of the spatial variation. Pollution surfaces at 200 m*200 m resolution were generated according to the best performed models.

Association of Long-Term Near-Highway Exposure to Ultrafine Particles with Cardiovascular Diseases, Diabetes and Hypertension

Ultrafine particle (UFP) concentrations are elevated near busy roadways, however, their effects on prevalence of cardiovascular diseases, diabetes, and hypertension are not well understood. To investigate these associations, data on demographics, diseases, medication use, and time of activities were collected by in-home surveys for 704 participants in three pairs of near-highway and urban background neighborhoods in and near Boston (MA, USA). Body mass index (BMI) was measured for a subset of 435 participants. Particle number concentration (PNC, a measure of UFP) was collected by mobile monitoring in each area. Intra-neighborhood spatial-temporal regression models (approximately 20 m resolution) were used to estimate hourly ambient PNC at the residences of participants. We used participant time activity information to adjust annual average residential PNC values and assign individualized time activity adjusted annual average PNC exposures (TAA-PNC). Using multivariate logistic regression models, we found an odds ratio (OR) of 1.35 (95% CI: 0.83, 2.22) of TAA-PNC with stroke and ischemic heart diseases (S/IHD), an OR of 1.14 (95% CI: 0.81, 1.62) with hypertension, and an OR of 0.71 (95% CI: 0.46, 1.10) for diabetes. A subset analysis controlling for BMI produced slightly stronger associations for S/IHD (OR = 1.61, 95% CI: 0.88, 2.92) and hypertension (OR = 1.28, 95% CI: 0.81, 2.02), and no association with diabetes (OR = 1.09, 95% CI = 0.61, 1.96). Further research is needed with larger sample sizes and longitudinal follow-up.

Exposure estimation errors to nitrogen oxides on a population scale due to daytime activity away from home

Accurate estimation of exposure to air pollution is necessary for assessing the impact of air pollution on the public health. Most environmental epidemiology studies assign the home address exposure to the study subjects. Here, we quantify the exposure estimation error at the population scale due to assigning it solely at the residence place. A cohort of most schoolchildren in Israel (~950,000), age 6-18, and a representative cohort of Israeli adults (~380,000), age 24-65, were used. For each subject the home and the work or school addresses were geocoded. Together, these two microenvironments account for the locations at which people are present during most of the weekdays. For each subject, we estimated ambient nitrogen oxide concentrations at the home and work or school addresses using two air quality models: a stationary land use regression model and a dynamic dispersion-like model. On average, accounting for the subjects' work or school address as well as for the daily pollutant variation reduced the estimation error of exposure to ambient NO_x/NO₂ by 5-10ppb, since daytime concentrations at work/school and at home can differ significantly. These results were consistent regardless which air quality model as used and even for subjects that work or study close to their home. Yet, due to their usually short commute, assigning schoolchildren exposure solely at their residential place seems to be a reasonable estimation. In contrast, since adults commute for longer distances, assigning exposure of adults only at the residential place has a lower correlation with the daily weighted exposure, resulting in larger exposure estimation errors. We show that exposure misclassification can result from not accounting for the subjects' time-location trajectories through the spatiotemporally varying pollutant concentrations field.

Combining land use regression models and fixed site monitoring to reconstruct spatiotemporal variability of NO2 concentrations over a wide geographical area

The epidemiological research benefits from an accurate characterization of both spatial and temporal variability of exposure to air pollution. This work aims at proposing a method to combine the high spatial resolution of Land Use Regression (LUR) models with the high temporal resolution of fixed site monitoring data, to model spatiotemporal variability of NO₂ over a wide geographical area in Northern Italy. We developed seasonal LUR models to reconstruct the spatial distribution of a scaling factor that relates local concentrations to those measured at two reference central sites, one for the northern flat area and one for the southern mountain area. We calculated the daily average concentrations at 19 locations spread over the study areas as the product of the local scaling factor and the reference central site concentrations. We evaluated model performance comparing modeled and measured NO₂ data. LUR model's R² ranges from 0.76 to 0.92. The main predictors refers substantially to traffic, industrial land use, buildings volume and altitude a.s.l. The model's performance in reproducing measured concentrations was satisfactory. The temporal variability of concentrations was well captured: Spearman correlation between model and measures was >0.7 for almost all sites. Model's average absolute errors were in the order of 10μgm^-3. The model for the southern area tends to overestimate measured concentrations. Our modeling framework was able to reproduce spatiotemporal differences in NO₂ concentrations. This kind of model is less data-intensive than usual regional atmospheric models and it may be very helpful to assess population exposure within studies in which individual relevant exposure occurs along periods of days or months.

Extreme urban-rural temperatures in the coastal city of Turku, Finland: Quantification and visualization based on a generalized additive model

Fundamental knowledge on the determinants of air temperatures across spatial and temporal scales is essential in climate change mitigation and adaptation. Spatial-based statistical modelling provides an efficient approach for the analysis and prediction of air temperatures in human-modified environments at high spatial accuracy. The aim of the study was firstly, to analyse the environmental factors affecting extreme air temperature conditions in a coastal high-latitude city and secondly, to explore the applicability of generalized additive model (GAM) in the study of urban-rural temperatures. We utilized air temperature data from 50 permanent temperature logger stations and extensive geospatial environmental data on different scales from Turku, SW Finland. We selected five temperature situations (cases) and altogether 12 urban and natural explanatory variables for the analyses. The results displayed that (i) water bodies and topographical conditions were often more important than urban variables in controlling the spatial variability of extreme air temperatures, (ii) case specificity of the explanatory variables and their scales should be considered in the analyses and (iii) GAM was highly suitable in quantifying and visualizing the relations between urban-rural temperatures and environmental determinants at local scales. The results promote the use of GAMs in spatial-based statistical modelling of air temperature in future.

Long-term exposure to traffic-related air pollution and cancer among survivors of myocardial infarction: A 20-year follow-up study

BACKGROUND

Previous studies suggested a carcinogenic effect of exposure to traffic-related air pollution. Recently, higher rates of cancer incidence were observed among myocardial infarction survivors compared with the general population. We examined the association between chronic exposure to nitrogen oxides, a proxy measure for traffic-related air pollution, and cancer incidence and mortality in a cohort of myocardial infarction patients.

METHODS

Patients aged ≤65 years admitted to hospital in central Israel with a first myocardial infarction in 1992-1993 were followed to 2013 for cancer incidence and cause-specific mortality. Data on sociodemographic and cancer risk factors were obtained, including time-varying information on smoking. Using land use regression models, annual averages of nitrogen oxides during follow-up were estimated individually according to home addresses. Cox proportional hazards models were constructed to study the relationships with cancer outcomes.

RESULTS

During a mean follow-up of 16 (SD 7) years, 262 incident cancers and 105 cancer deaths were identified among 1393 cancer-free patients at baseline (mean age 54 years; 81% men). In adjusted models, a 10 ppb increase in mean nitrogen oxide exposure was associated with a hazard ratio (HR) of 1.06 (95% confidence interval (CI) 0.96-1.18) for cancer incidence and HR of 1.08 (95% CI 0.93-1.26) for cancer mortality. The association with lung, bladder, kidney or prostate cancer (previously linked to air pollution) was stronger (HR 1.16; 95% CI 1.00-1.33).

CONCLUSIONS

Chronic exposure to traffic-related air pollution may constitute an environmental risk factor for cancer post-myocardial infarction. Variation in the strength of association between specific cancers needs to be explored further.

Back-extrapolated and year-specific NO2 land use regression models for Great Britain - Do they yield different exposure assessment?

Robust methods to estimate historic population air pollution exposures are important tools for epidemiological studies evaluating long-term health effects. We developed land use regression (LUR) models for NO2 exposure in Great Britain for 1991 and explored whether the choice of year-specific or back-extrapolated LUR yields 1) similar LUR variables and model performance, and 2) similar national and regional address-level and small-area concentrations. We constructed two LUR models for 1991using NO2 concentrations from the diffusion tube monitoring network, one using 75% of all available measurement sites (that over-represent industrial areas), and the other using 75% of a subset of sites proportionate to population by region to study the effects of monitoring site selection bias. We compared, using the remaining (hold-out) 25% of monitoring sites, the performance of the two 1991 models with back-extrapolation of a previously published 2009 model, developed using NO2 concentrations from automatic chemiluminescence monitoring sites and predictor variables from 2006/2007. The 2009 model was back-extrapolated to 1991 using the same predictors (1990 & 1995) used to develop 1991 models. The 1991 models included industrial land use variables, not present for 2009. The hold-out performance of 1991 models (mean-squared-error-based-R(2): 0.62-0.64) was up to 8% higher and ~1μg/m(3) lower in root mean squared error than the back-extrapolated 2009 model, with best performance from the subset of sites representing population exposures. Year-specific and back-extrapolated exposures for residential addresses (n=1.338,399) and small areas (n=10.518) were very highly linearly correlated for Great Britain (r>0.83). This study suggests that year-specific model for 1991 and back-extrapolation of the 2009 LUR yield similar exposure assessment.

The effect of ego-motion on environmental monitoring

Air pollution has a proven impact on public health. Currently, pollutant levels are obtained by high-priced, sizeable, stationary Air Quality Monitoring (AQM) stations. Recent developments in sensory and communication technologies have made relatively low-cost, micro-sensing units (MSUs) feasible. Their lower power consumption and small size enable mobile sensing, deploying single or multiple units simultaneously. Recent studies have reported on measurements acquired by mobile MSUs, mounted on cars, bicycles and pedestrians. While these modes of transportation inherently present different velocity and acceleration regimes, the effect of the sensors' varying movement characteristics have not been previously accounted for. This research assesses the impact of sensor's motion on its functionality through laboratory measurements and a field campaign. The laboratory setup consists of a wind tunnel to assess the effect of air flow on the measurements of nitrogen dioxide and ozone at different velocities in a controlled environment, while the field campaign is based on three cars mounted with MSUs, measuring pollutants and environmental variables at different traveling speeds. In both experimental designs we can regard the MSUs as a moving object in the environment, i.e. having a distinct ego-motion. The results show that MSU's behavior is highly affected by variation in speed and sensor placement with respect to direction of movement, mainly due to the physical properties of installed sensors. This strongly suggests that any future design of MSU must account for the speed effect from the design stage all the way through deployment and results analysis. This is the first report examining the influence of airflow variations on MSU's ability to accurately measure pollutant levels.