Improving modeled air pollution concentration maps by residual interpolation

Prenatal and postnatal exposure to NO2 and rapid infant weight gain - A population-based cohort study

BACKGROUND

Childhood overweight and obesity is a global public health problem. Rapid infant weight gain is predictive of childhood overweight. Studies found that exposure to ambient air pollution is associated with childhood overweight, and have linked prenatal exposure to air pollution with rapid infant weight gain.

OBJECTIVES

To examine the association between prenatal and postnatal ambient NO2 exposure, a traffic-related marker, with rapid weight gain in infants.

METHODS

We carried out a population-based historical cohort study using data from the Israeli national network of maternal and child health clinics. The study included 474,136 infants born at term with birthweight ≥2500 g in 2011-2019 in central Israel. Weekly averages of NO2 concentration throughout pregnancy (prenatal) and the first 4 weeks of life (postnatal) were assessed using an optimized dispersion model and were linked to geocoded home addresses. We modelled weight gain velocity throughout infancy using the SuperImposition by Translation and Rotation (SITAR) method, a mixed-effects nonlinear model specialized for modelling growth curves, and defined rapid weight gain as the highest velocity tertile. Distributed-lag models were used to assess critical periods of risk and to measure relative risks for rapid weight gain. Adjustments were made for socioeconomic status, population group, subdistrict, month and year of birth, and the alternate exposure period - prenatal or postnatal.

RESULTS

The cumulative adjusted relative risk for rapid weight gain of NO2 exposure was 1.02 (95% confidence intereval [CI] 1.00, 1.04) for exposure throughout pregnancy and 1.02 (95% CI 1.01, 1.04) for exposure during the first four postnatal weeks per NO2 interquartile range increase (7.3 ppb). An examination of weekly associations revealed that the critical period of risk for the prenatal exposure was from mid-pregnancy to birth.

CONCLUSIONS

Prenatal and postnatal exposures to higher concentrations of traffic-related air pollution are each independently associated with rapid infant weight gain, a risk factor for childhood overweight and obesity.

An improved nonnegative matrix factorization with the imputation method model for pollution source apportionment during rainstorm events

Data scarcity caused by extreme conditions during storms adds difficulties in performing pollution source apportionment. This study integrated nonnegative matrix factorization with the imputation method (NMF-IM) to fill in missing data (NAs) and conduct source apportionment. A total of 367 river samples and 35 runoff samples were taken from the Banqiao and Nanfei River basins located in Hefei, China, during four rainfall events from June to August 2020. Sixteen indicators were quantified and used for source diagnostics using NMF-IM. The results showed that total phosphorus (TP) had higher concentrations and more violent fluctuations than total nitrogen (TN) in river samples taken from rain. NMF-IM was shown to recover the value distribution of NAs approximately. The source profiles and contribution rates calculated by NMF-IM with NAs were close to the original results calculated by NMF without NAs, with root mean square error of less than 2.3% and differences less than 9.5%. Multiple forms of nitrogen and phosphorus indicators benefit reaching reasonable source diagnostics results. At least four indicators were needed to reach the same contribution rates as 16 indicator diagnostics. The two good indicator combination groups are nitrate (NO₃-N), nitrite (NO₂-N), ammonia nitrogen (NH₃-N), and total suspended solids (TSS) and NO₃-N, NO₂-N, phosphorus (PO₄-P), and TSS. The pollution source contributions changed with the Antecedent dry period (ADPs) of rain events. Treated tailwater and untreated sewage were major sources, contributing more than 80% of the total pollution of the rainstorm events with short ADPs. Dust wash became the dominant contributor after 60 min and contributed 36% of the total pollution of rainstorm events with long ADPs. The average source contribution rates for rainfall events in the Banqiao River were treated tailwater (41%) > untreated sewage (27%) > dust wash (19%) > other sources (16%). The pollution source diagnostics results were verified to be reasonable by simulation using tested run-off data and literature results.

Associations Between Prenatal Exposure to Air Pollution and Congenital Hypothyroidism

Adequate thyroid hormone availability is required for normal brain development. Studies have found associations between prenatal exposure to air pollutants and thyroid hormones in pregnant women and newborns. We aimed to examine associations of trimester-specific residential exposure to common air pollutants with congenital hypothyroidism (CHT). All term infants born in Israel during 2009-2015 were eligible for inclusion. We used data on CHT from the national neonatal screening lab of Israel, and exposure data from spatiotemporal air pollution models. We used multivariable logistic regression models to estimate associations of exposures with CHT, adjusting for ethnicity, socioeconomic status, geographical area, conception season, conception year, gestational age, birth weight, and child sex. To assess residual confounding, we used postnatal exposures to the same pollutants as negative controls. The study population included 696,461 neonates. We found a positive association between third-trimester nitrogen oxide exposure and CHT (per interquartile-range change, odds ratio = 1.23, 95% confidence interval: 1.08, 1.41) and a similar association for nitrogen dioxide. There was no evidence of residual confounding or bias by correlation among exposure periods for these associations.

Air Pollution and Autism Spectrum Disorder in Israel: A Negative Control Analysis

BACKGROUND

Residual confounding is a major concern for causal inference in observational studies on air pollution-autism spectrum disorder (ASD) associations. This study is aimed at assessing confounding in these associations using negative control exposures.

METHODS

This nested case-control study included all children diagnosed with ASD (detected through 31 December 2016) born during 2007-2012 in Israel and residing in the study area (N = 3,843), and matched controls of the same age (N = 38,430). We assigned individual house-level exposure estimates for each child. We estimated associations using logistic regression models, mutually adjusted for all relevant exposure periods (prepregnancy, pregnancy, and postnatal). We assessed residual confounding using postoutcome negative control exposure at age 28-36 months.

RESULTS

In mutually adjusted models, we observed positive associations with ASD for postnatal exposures to NOx (odds ratio per interquartile range, 95% confidence interval: 1.19, 1.02-1.38) and NO2 (1.20, 1.00-1.43), and gestational exposure to PM2.5-10 (1.08, 1.01-1.15). The result for the negative control period was 1.04, 0.99-1.10 for PM2.5, suggesting some residual confounding, but no associations for PM2.5-10 (0.98, 0.81-1.18), NOx (1.02, 0.84-1.25), or NO2 (0.98, 0.81-1.18), suggesting no residual confounding.

CONCLUSIONS

Our results further support a hypothesized causal link with ASD that is specific to postnatal exposures to traffic-related pollution.

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.

A hybrid model for evaluating exposure of the general population in Israel to air pollutants

Exposure to air pollution is associated with a wide range of health effects, including increased respiratory symptoms, cancer, reproductive and birth defects, and premature death. Air quality measurements by standardized measuring equipment, although accurate, can only provide an estimate for part of the population, with decreasing accuracy further away from the monitoring sites. Estimating pollution levels over large geographical domains requires the use of air quality models which ideally incorporate air quality measurements. In order to estimate actual exposure of the population to air pollution (population-weighted concentrations of air pollutants), there is a need to combine data from air quality models with population density data. Here we present the results of exposure estimates for the entire population of Israel using a chemical transport model combined with measurements from the national monitoring network. We evaluated the individual exposure levels for the entire population to several air pollutants based on census tract units. Using this hybrid model, we found that the entire population of Israel is exposed to concentrations of PM₁₀ and PM_2.5 that exceed the target values but are below the environmental values according to the Israeli Clean Air Law. In addition, we found and that over 1.5 million residents are exposed to NO_x at concentrations higher than the target values. This data may help decision makers develop targeted interventions to reduce the concentrations of specific pollutants, based on population-weighted exposure.

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.

Traffic-Related Air Pollution and Autism Spectrum Disorder: A Population-Based Nested Case-Control Study in Israel

Accumulating evidence suggests that perinatal air pollutant exposures are associated with increased risk of autism spectrum disorder (ASD), but evidence for traffic pollutants outside the United States is inconclusive. We assessed the association between nitrogen dioxide, a traffic pollution tracer, and risk of ASD. We conducted a nested case-control study among the entire population of children born during 2005-2009 in the central coastal area of Israel. Cases were identified through the National Insurance Institute of Israel (n = 2,098). Controls were a 20% random sample of the remaining children (n = 54,191). Exposure was based on an optimized dispersion model. We estimated adjusted odds ratios and 95% confidence intervals using logistic regression and a distributed-lag model. In models mutually adjusted for the 2 periods, the odds ratio per 5.85-parts per billion (ppb) increment of nitrogen dioxide exposure during pregnancy (median, 16.8 ppb; range, 7.5-31.2 ppb) was 0.77 (95% confidence interval: 0.59, 1.00), and the odds ratio for exposure during the 9 months after birth was 1.40 (95% confidence interval: 1.09, 1.80). A distributed-lag model revealed reduced risk around week 13 of pregnancy and elevated risk around week 26 after birth. These findings suggest that postnatal exposure to nitrogen dioxide in Israel is associated with increased odds of ASD, and prenatal exposure with lower odds. The latter may relate to selection effects.

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.