Leveraging low-cost sensors to predict nitrogen dioxide for epidemiologic exposure assessment

BACKGROUND

Statistical models of air pollution enable intra-urban characterization of pollutant concentrations, benefiting exposure assessment for environmental epidemiology. The new generation of low-cost sensors facilitate the deployment of dense monitoring networks and can potentially be used to improve intra-urban models of air pollution.

OBJECTIVE

Develop and evaluate a spatiotemporal model for nitrogen dioxide (NO2) in the Puget Sound region of WA, USA for the Adult Changes in Thought Air Pollution (ACT-AP) study and assess the contribution of low-cost sensor data to the model's performance through cross-validation.

METHODS

We developed a spatiotemporal NO2 model for the study region incorporating data from 11 agency locations, 364 supplementary monitoring locations, and 117 low-cost sensor (LCS) locations for the 1996-2020 time period. Model features included long-term time trends and dimension-reduced land use regression. We evaluated the contribution of LCS network data by comparing models fit with and without sensor data using cross-validated (CV) summary performance statistics.

RESULTS

The best performing model had one time trend and geographic covariates summarized into three partial least squares components. The model, fit with LCS data, performed as well as other recent studies (agency cross-validation: CV- root mean square error (RMSE) = 2.5 ppb NO2; CV- coefficient of determination (R 2 ) = 0.85). Predictions of NO2 concentrations developed with LCS were higher at residential locations compared to a model without LCS, especially in recent years. While LCS did not provide a strong performance gain at agency sites (CV-RMSE = 2.8 ppb NO2; CV-R 2  = 0.82 without LCS), at residential locations, the improvement was substantial, with RMSE = 3.8 ppb NO2 andR 2  = 0.08 (without LCS), compared to CV-RMSE = 2.8 ppb NO2 and CV-R 2  = 0.51 (with LCS).

IMPACT

We developed a spatiotemporal model for nitrogen dioxide (NO2) pollution in Washington's Puget Sound region for epidemiologic exposure assessment for the Adult Changes in Thought Air Pollution study. We examined the impact of including low-cost sensor data in the NO2 model and found the additional spatial information the sensors provided predicted NO2 concentrations that were higher than without low-cost sensors, particularly in recent years. We did not observe a clear, substantial improvement in cross-validation performance over a similar model fit without low-cost sensor data; however, the prediction improvement with low-cost sensors at residential locations was substantial. The performance gains from low-cost sensors may have been attenuated due to spatial information provided by other supplementary monitoring data.

Publicly available low-cost sensor measurements for PM2.5 exposure modeling: Guidance for monitor deployment and data selection

High-resolution, high-quality exposure modeling is critical for assessing the health effects of ambient PM_2.5 in epidemiological studies. Using sparse regulatory PM_2.5 measurements as principal model inputs may result in two issues in exposure prediction: (1) they may affect the models' accuracy in predicting PM_2.5 spatial distribution; (2) the internal validation based on these measurements may not reliably reflect the model performance at locations of interest (e.g., a cohort's residential locations). In this study, we used the PM_2.5 measurements from a publicly available commercial low-cost PM_2.5 network, PurpleAir, with an external validation dataset at the residential locations of a representative sample of participants from the Adult Changes in Thought - Air Pollution (ACT-AP) study, to improve the accuracy of exposure prediction at the cohort participant locations. We also proposed a metric based on principal component analysis (PCA) - the PCA distance - to assess the similarity between monitor and cohort locations to guide monitor deployment and data selection. The analysis was based on a spatiotemporal modeling framework with 51 "gold-standard" monitors and 58 PurpleAir monitors for model development, as well as 105 home monitors at the cohort locations for model validation, in the Puget Sound region of Washington State from June 2017 to March 2019. After including calibrated PurpleAir measurements as part of the dependent variable, the external spatiotemporal validation R² and root-mean-square error, RMSE, for two-week concentration averages improved from 0.84 and 2.22 μg/m³ to 0.92 and 1.63 μg/m³, respectively. The external spatial validation R² and RMSE for long-term averages over the modeling period improved from 0.72 and 1.01 μg/m³ to 0.79 and 0.88 μg/m³, respectively. The exposure predictions incorporating PurpleAir measurements demonstrated sharper urban-suburban concentration gradients. The PurpleAir monitors with shorter PCA distances improved the model's prediction accuracy more substantially than the monitors with longer PCA distances, supporting the use of this similarity metric.

Field measurements of PM2.5 infiltration factor and portable air cleaner effectiveness during wildfire episodes in US residences

Wildfires have frequently occurred in the western United States (US) during the summer and fall seasons in recent years. This study measures the PM2.5 infiltration factor in seven residences recruited from five dense communities in Seattle, Washington, during a 2020 wildfire episode and evaluates the impacts of HEPA-based portable air cleaner (PAC) use on reducing indoor PM2.5 levels. All residences with windows closed went through an 18-to-24-h no filtration session, with five of seven following that period with an 18-to-24-h filtration session. Auto-mode PACs, which automatically adjust the fan speed based on the surrounding PM2.5 levels, were used for the filtration session. 10-s resolved indoor PM2.5 levels were measured in each residence's living room, while hourly outdoor levels were collected from the nearest governmental air quality monitoring station to each residence. Additionally, a time-activity diary in minute resolution was collected from each household. With the impacts of indoor sources excluded, indoor PM2.5 mass balance models were developed to estimate the PM2.5 indoor/outdoor (I/O) ratios, PAC effectiveness, and decay-related parameters. Among the seven residences, the mean infiltration factor ranged from 0.33 (standard deviation [SD]: 0.06) to 0.76 (SD: 0.05). The use of auto-mode PAC led to a 48%-78% decrease of indoor PM2.5 levels after adjusting for outdoor PM2.5 levels and indoor sources. The mean (SD) air exchange rates ranged from 0.30 (0.13) h-1 to 1.41 (3.18) h-1 while the PM2.5 deposition rate ranged from 0.10 (0.54) h-1 to 0.49 (0.47) h-1. These findings suggest that staying indoors, a common protective measure during wildfire episodes, is insufficient to prevent people's excess exposure to wildfire smoke, and provides quantitative evidence to support the utilization of auto-mode PACs during wildfire events in the US.

Self-assembly of iron oxide precursor micelles driven by magnetic stirring time in sol-gel coatings

The purpose of this work is to fabricate self-assembled microstructures by the sol–gel method and study the morphological, structural and compositional dependence of ε-Fe₂O₃ nanoparticles embedded in silica when glycerol (GLY) and cetyl-trimethylammonium bromide (CTAB) are added as steric agents simultaneously. The combined action of a polyalcohol and a surfactant significantly modifies the morphology of the sample giving rise to a different microstructure in each of the studied cases (1, 3 and 7 days of magnetic stirring time). This is due to the fact that the addition of these two compounds leads to a considerable increase in gelation time as GLY can interact with the alkoxide group on the surface of the iron oxide precursor micelle and/or be incorporated into the hydrophilic chains of CTAB. This last effect causes the iron oxide precursor micelles to be interconnected forming aggregates whose size and structure depend on the magnetic stirring time of the sol–gel synthetic route. In this paper, crystalline structure, composition, purity and morphology of the sol–gel coatings densified at 960 °C are examined. Emphasis is placed on the nominal percentage of the different iron oxides found in the samples and on the morphological and structural differences. This work implies the possibility of patterning ε-Fe₂O₃ nanoparticles in coatings and controlling their purity by an easy one-pot sol–gel method.

Sol–gel synthesis of self-assembled iron oxide precursor micelles.

Beyond "Asian": Specific East and Southeast Asian Races or Ethnicities Associated With Jaundice Readmission

OBJECTIVES

Clinical practice guidelines have recognized "Asian" and "East Asian" as risk factors for newborn jaundice and readmission. We sought to identify more detailed and specific, parent-identified races or ethnicities associated with jaundice readmission.

METHODS

We conducted a case control study of 653 newborn infants born (2014-2016) at a West-Coast, urban hospital to examine specific parent-described races or ethnicities that are associated with newborn hospital readmissions for hyperbilirubinemia. Parent-reported race or ethnicity was abstracted from the California Newborn Screening Test.

RESULTS

Our sample included 105 infants readmitted for jaundice (cases) and 548 infants as controls. In the full cohort, 66 infants (10.1%) were Coombs positive, 39 infants (6.0%) were born before 37 weeks' gestational age, and 405 infants (62.0%) were born to first-time mothers. The parents described the 653 infants using 45 unique races and ethnicities. In a multivariable model that controlled for Coombs positivity, gestational age <37 weeks, and primiparity, infants described as "Far East Asian" (odds ratio [OR] = 3.17; 95% confidence interval [CI] = 1.94-5.18) or "Southeast Asian" (OR = 3.17; 95% CI = 1.66-6.08) had increased risk for jaundice readmission. Infants described as Southeast Asian (eg, Laotian, Cambodian, Indonesian, Vietnamese, and Filipino) and Far East Asian (eg, Chinese, Korean, Taiwanese, Japanese, and Mongolian) had an increased risk of readmission. Finally, we did not find an association between South Asian (OR = 0.79; 95% CI = 0.33-1.92) race or ethnicity and risk of jaundice readmission.

CONCLUSIONS

In this study, we help clarify and move beyond the term "Asian" as a risk factor for readmission due to hyperbilirubinemia.

Origin of the magnetic transition at 100 K in ε-Fe2O3 nanoparticles studied by x-ray absorption fine structure spectroscopy

The current study unveils the structural origin of the magnetic transition of the ε-Fe₂O₃ polymorph from an incommensurate magnetic order to a collinear ferrimagnetic state at low temperature. The high crystallinity of the samples and the absence of other iron oxide polymorphs have allowed us to carry out temperature-dependent x-ray absorption fine structure spectroscopy experiments out. The deformation of the structure is followed by the Debye-Waller factor for each selected Fe-O and Fe-Fe sub-shell. For nanoparticle sizes between 7 and 15 nm, the structural distortions between the Fe_te and Fe-D1_oc sites are localized in a temperature range before the magnetic transition starts. On the contrary, the inherent interaction between the other sub-shells (named Fe-O1,2 and Fe-Fe1) provokes cooperative magneto-structural changes in the same temperature range. This means that the Fe_te with Fe-D1_oc polyhedron interaction seems to be uncoupled with temperature dealing with these nanoparticle sizes wherein the structural distortions are likely moderate due to surface effects.

Growth, structure and magnetism of ε-Fe2O3 in nanoparticle form

We present a novel and easy synthetic path to prepare ε-Fe₂O₃ (∼90%) with a small portion of α-Fe₂O₃ nanoparticles embedded in an amorphous silica matrix.