Fusion of land use regression modeling output and wireless distributed sensor network measurements into a high spatiotemporally-resolved NO2 product.

Land use regression modeling is a common method for assessing exposure to ambient pollutants, yet it suffers from very coarse temporal resolution. Wireless distributed sensor networks (WDSN) is a promising technology that can provide extremely high spatiotemporal pollutant patterns but is known to suffer from several limitations that put into question its data reliability. This study examines the advantages of fusing data from these two methods and obtaining high spatiotemporally-resolved product that can be used for exposure assessment. We demonstrate this approach by estimating nitrogen dioxide (NO2) concentrations at a sub-urban scale, with the study area limited by the deployment of the WDSN nodes. Specifically, hourly-resolved fused-data estimates were obtained by combining a stationary traffic-based land use regression (LUR) model with observations (15 min sampling frequency) made by an array of low-cost sensor nodes, with the sensors' readings mapped over the whole study area. Data fusion was performed by merging the two independent information products using a fuzzy logic approach. The performance of the fused product was examined against reference hourly observations at four air quality monitoring (AQM) stations situated within the study area, with the AQM data not used for the development of any of the underlying information layers. The mean hourly RMSE between the fused data product and the AQM records was 9.3 ppb, smaller than the RMSE of the two base products independently (LUR: 14.87 ppb, WDSN: 10.45 ppb). The normalized Moran's I of the fused product indicates that the data-fusion product reveals more realistic spatial patterns than those of the base products. The fused NO2 concentration product shows considerable spatial variability relative to that evident by interpolation of both the WDSN records and the AQM stations data, with significant non-random patterns in 74% of the study period.

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.

Impacts of Personal Mobility and Diurnal Concentration Variability on Exposure Misclassification to Ambient Pollutants.

Appreciating the uncertainty margins of exposure assessment to air pollution requires good understanding of its variability throughout the daily activities. This study describes a modeling framework for estimating exposure to air pollutants for a representative sample of working Israeli adults ( N ∼ 168 000) for which both the residence and workplace addresses were available. Individual daily trajectories were simulated by accounting for five generic daily activities: at home, at work, while in commute from home to work and back, and during out-of-home leisure activities. The integrated daily exposure to nitrogen dioxide (NO2) was estimated for each individual by tracking the daily trajectory through an NO2 concentration map, obtained using a dynamic and highly resolved dispersion-like model (temporal resolution, half-hourly; spatial resolution, 500 m). Accounting for the subjects' daily mobility was found to affect their exposure more significantly than accounting solely for the diurnal concentration variability, yet a synergistic effect was noted when accounting for both factors simultaneously. Exposure misclassification varies along the day, with the work microenvironment found to contribute the most to it. In particular, regardless of the high concentrations encountered during the commute, their contribution to the integrated daily exposure is small due to the relatively short time spent in this activity by most people.

Node-to-node field calibration of wireless distributed air pollution sensor network.

Low-cost air quality sensors offer high-resolution spatiotemporal measurements that can be used for air resources management and exposure estimation. Yet, such sensors require frequent calibration to provide reliable data, since even after a laboratory calibration they might not report correct values when they are deployed in the field, due to interference with other pollutants, as a result of sensitivity to environmental conditions and due to sensor aging and drift. Field calibration has been suggested as a means for overcoming these limitations, with the common strategy involving periodical collocations of the sensors at an air quality monitoring station. However, the cost and complexity involved in relocating numerous sensor nodes back and forth, and the loss of data during the repeated calibration periods make this strategy inefficient. This work examines an alternative approach, a node-to-node (N2N) calibration, where only one sensor in each chain is directly calibrated against the reference measurements and the rest of the sensors are calibrated sequentially one against the other while they are deployed and collocated in pairs. The calibration can be performed multiple times as a routine procedure. This procedure minimizes the total number of sensor relocations, and enables calibration while simultaneously collecting data at the deployment sites. We studied N2N chain calibration and the propagation of the calibration error analytically, computationally and experimentally. The in-situ N2N calibration is shown to be generic and applicable for different pollutants, sensing technologies, sensor platforms, chain lengths, and sensor order within the chain. In particular, we show that chain calibration of three nodes, each calibrated for a week, propagate calibration errors that are similar to those found in direct field calibration. Hence, N2N calibration is shown to be suitable for calibration of distributed sensor networks.

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 NOx/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.