Ilan Levy

On the feasibility of measuring urban air pollution by wireless distributed sensor networks

Accurate evaluation of air pollution on human-wellbeing requires high-resolution measurements. Standard air quality monitoring stations provide accurate pollution levels but due to their sparse distribution they cannot capture the highly resolved spatial variations within cities. Similarly, dedicated field campaigns can use tens of measurement devices and obtain highly dense spatial coverage but normally deployment has been limited to short periods of no more than few weeks. Nowadays, advances in communication and sensory technologies enable the deployment of dense grids of wireless distributed air monitoring nodes, yet their sensor ability to capture the spatiotemporal pollutant variability at the sub-neighborhood scale has never been thoroughly tested. This study reports ambient measurements of gaseous air pollutants by a network of six wireless multi-sensor miniature nodes that have been deployed in three urban sites, about 150 m apart. We demonstrate the networks capability to capture spatiotemporal concentration variations at an exceptional fine resolution but highlight the need for a frequent in-situ calibration to maintain the consistency of some sensors. Accordingly, a procedure for a field calibration is proposed and shown to improve the systems performance. Overall, our results support the compatibility of wireless distributed sensor networks for measuring urban air pollution at a sub-neighborhood spatial resolution, which suits the requirement for highly spatiotemporal resolved measurements at the breathing-height when assessing exposure to urban air pollution.

Evaluating Multipollutant Exposure and Urban Air Quality: Pollutant Interrelationships, Neighborhood Variability, and Nitrogen Dioxide as a Proxy Pollutant

Background: Although urban air pollution is a complex mix containing multiple constituents, studies of the health effects of long-term exposure often focus on a single pollutant as a proxy for the entire mixture. A better understanding of the component pollutant concentrations and interrelationships would be useful in epidemiological studies that exploit spatial differences in exposure by clarifying the extent to which measures of individual pollutants, particularly nitrogen dioxide (NO2), represent spatial patterns in the multipollutant mixture. Objectives: We examined air pollutant concentrations and interrelationships at the intraurban scale to obtain insight into the nature of the urban mixture of air pollutants. Methods: Mobile measurements of 23 air pollutants were taken systematically at high resolution in Montreal, Quebec, Canada, over 34 days in the winter, summer, and autumn of 2009. Results: We observed variability in pollution levels and in the statistical correlations between different pollutants according to season and neighborhood. Nitrogen oxide species (nitric oxide, NO2, nitrogen oxides, and total oxidized nitrogen species) had the highest overall spatial correlations with the suite of pollutants measured. Ultrafine particles and hydrocarbon-like organic aerosol concentration, a derived measure used as a specific indicator of traffic particles, also had very high correlations. Conclusions: Our findings indicate that the multipollutant mix varies considerably throughout the city, both in time and in space, and thus, no single pollutant would be a perfect proxy measure for the entire mix under all circumstances. However, based on overall average spatial correlations with the suite of pollutants measured, nitrogen oxide species appeared to be the best available indicators of spatial variation in exposure to the outdoor urban air pollutant mixture. Citation: Levy I, Mihele C, Lu G, Narayan J, Brook JR. 2014. Evaluating multipollutant exposure and urban air quality: pollutant interrelationships, neighborhood variability, and nitrogen dioxide as a proxy pollutant. Environ Health Perspect 122:65–72; http://dx.doi.org/10.1289/ehp.1306518

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

Land use regression (LUR) models rely on air pollutant measurements for their development, and are therefore limited to recent periods where such measurements are available. Here we propose an approach to overcome this gap and calculate LUR models several decades before measurements were available. We first developed a LUR model for NOx using annual averages of NOx at all available air quality monitoring sites in Israel between 1991 and 2011 with time as one of the independent variables. We then reconstructed historical spatial data (e.g., road network) from historical topographic maps to apply the models prediction to each year from 1961 to 2011. The models predictions were then validated against independent estimates about the national annual NOx emissions from on-road vehicles in a top-down approach. The models cross validated R2 was 0.74, and the correlation between the models annual averages and the national annual NOx emissions between 1965 and 2011 was 0.75. Information about the road network and population are persistent predictors in many LUR models. The use of available historical data about these predictors to resolve the spatial variability of air pollutants together with complementary national estimates on the change in pollution levels over time enable historical reconstruction of exposures.

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.

Robustness of Land-Use Regression Models Developed from Mobile Air Pollutant Measurements

Land-use regression (LUR) models are useful for resolving fine scale spatial variations in average air pollutant concentrations across urban areas. With the rise of mobile air pollution campaigns, characterized by short-term monitoring and large spatial extents, it is important to investigate the effects of sampling protocols on the resulting LUR. In this study a mobile lab was used to repeatedly visit a large number of locations (∼1800), defined by road segments, to derive average concentrations across the city of Montreal, Canada. We hypothesize that the robustness of the LUR from these data depends upon how many independent, random times each location is visited (Nvis) and the number of locations (Nloc) used in model development and that these parameters can be optimized. By performing multiple LURs on random sets of locations, we assessed the robustness of the LUR through consistency in adjusted R2 (i.e., coefficient of variation, CV) and in regression coefficients among different models. As Nloc increased, R2adj became less variable; for Nloc = 100 vs Nloc = 300 the CV in R2adj for ultrafine particles decreased from 0.088 to 0.029 and from 0.115 to 0.076 for NO2. The CV in the R2adj also decreased as Nvis increased from 6 to 16; from 0.090 to 0.014 for UFP. As Nloc and Nvis increase, the variability in the coefficient sizes across the different model realizations were also seen to decrease.

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 NOx 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.

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.

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 (NOx) – 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 NOx 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.

Ecological bias in environmental health studies: the problem of aggregation of multiple data sources

Ecological bias may result from interactions between variables that are characterized by different spatial and temporal scales. Such an ecological bias, also known as aggregation bias or cross-level-bias, may occur as a result of using coarse environmental information about stressors together with fine (i.e., individual) information on health outcomes. This study examines the assumption that distinct within-area variability of spatial patterns of the risk metrics and confounders may result from artifacts of the aggregation of the underlying data layers, and that this may affect the statistical relationships between them. In particular, we demonstrate the importance of carefully linking information layers with distinct spatial resolutions and show that environmental epidemiology studies are prone to exposure misclassification as a result of statistically linking distinctly averaged spatial data (e.g., exposure metrics, confounders, health indices). Since area-level confounders and exposure metrics, as any other spatial phenomena, have characteristic spatiotemporal scales, it is naively expected that the highest spatial variability of both the SES ranking (confounder) and the NOx concentrations (risk metric) will be obtained when using the finest spatial resolution. However, the highest statistical relationship among the data layers was not obtained at the finest scale. In general, our results suggest that assessments of air quality impacts on health require data at comparable spatial resolutions, since use of data layers of distinct spatial resolutions may alter (mostly weaken) the estimated relationships between environmental stressors and health outcomes.