Detecting Elevated Air Pollution Levels by Monitoring Web Search Queries: Algorithm Development and Validation

BACKGROUND

Real-time air pollution monitoring is a valuable tool for public health and environmental surveillance. In recent years, there has been a dramatic increase in air pollution forecasting and monitoring research using artificial neural networks. Most prior work relied on modeling pollutant concentrations collected from ground-based monitors and meteorological data for long-term forecasting of outdoor ozone (O₃), oxides of nitrogen, and fine particulate matter (PM_2.5). Given that traditional, highly sophisticated air quality monitors are expensive and not universally available, these models cannot adequately serve those not living near pollutant monitoring sites. Furthermore, because prior models were built based on physical measurement data collected from sensors, they may not be suitable for predicting the public health effects of pollution exposure.

OBJECTIVE

This study aimed to develop and validate models to nowcast the observed pollution levels using web search data, which are publicly available in near real time from major search engines.

METHODS

We developed novel machine learning-based models using both traditional supervised classification methods and state-of-the-art deep learning methods to detect elevated air pollution levels at the US city level by using generally available meteorological data and aggregate web-based search volume data derived from Google Trends. We validated the performance of these methods by predicting 3 critical air pollutants (O₃, nitrogen dioxide, and PM_2.5) across 10 major US metropolitan statistical areas in 2017 and 2018. We also explore different variations of the long short-term memory model and propose a novel search term dictionary learner-long short-term memory model to learn sequential patterns across multiple search terms for prediction.

RESULTS

The top-performing model was a deep neural sequence model long short-term memory, using meteorological and web search data, and reached an accuracy of 0.82 (F₁-score 0.51) for O_3, 0.74 (F₁-score 0.41) for nitrogen dioxide, and 0.85 (F₁-score 0.27) for PM_2.5, when used for detecting elevated pollution levels. Compared with using only meteorological data, the proposed method achieved superior accuracy by incorporating web search data.

CONCLUSIONS

The results show that incorporating web search data with meteorological data improves the nowcasting performance for all 3 pollutants and suggest promising novel applications for tracking global physical phenomena using web search data.

Postnatal exposure to ambient air pollutants is associated with the composition of the infant gut microbiota at 6-months of age

Epidemiological studies in adults have shown that exposure to ambient air pollution (AAP) is associated with the composition of the adult gut microbiome, but these relationships have not been examined in infancy. We aimed to determine if 6-month postnatal AAP exposure was associated with the infant gut microbiota at 6 months of age in a cohort of Latino mother-infant dyads from the Southern California Mother's Milk Study (n = 103). We estimated particulate matter (PM_2.5 and PM₁₀) and nitrogen dioxide (NO₂) exposure from birth to 6-months based on residential address histories. We characterized the infant gut microbiota using 16S rRNA amplicon sequencing at 6-months of age. At 6-months, the gut microbiota was dominated by the phyla Bacteroidetes, Firmicutes, Proteobacteria, and Actinobacteria. Our results show that, after adjusting for important confounders, postnatal AAP exposure was associated with the composition of the gut microbiota. As an example, PM₁₀ exposure was positively associated with Dialister, Dorea, Acinetobacter, and Campylobacter while PM_2.5 was positively associated with Actinomyces. Further, exposure to PM₁₀ and PM_2.5 was inversely associated with Alistipes and NO₂ exposure was positively associated with Actinomyces, Enterococcus, Clostridium, and Eubacterium. Several of these taxa have previously been linked with systemic inflammation, including the genera Dialister and Dorea. This study provides the first evidence of significant associations between exposure to AAP and the composition of the infant gut microbiota, which may have important implications for future infant health and development.

Long-Term Exposure to Low-Level NO2 and Mortality among the Elderly Population in the Southeastern United States

BACKGROUND

Mounting evidence has shown that long-term exposure to fine particulate matter [PM ≤2.5μm in aerodynamic diameter (PM2.5)] and ozone (O3) can increase mortality. However, the health effects associated with long-term exposure to nitrogen dioxide (NO2) are less clear, in particular the evidence is scarce for NO2 at low levels that are below the current international guidelines.

METHODS

We constructed a population-based full cohort comprising all Medicare beneficiaries (aged ≥65, N=13,590,387) in the southeastern United States from 2000 to 2016, and we then further defined the below-guideline cohort that included only those who were always exposed to low-level NO2, that is, with annual means below the current World Health Organization guidelines (i.e., ≤21 ppb). We applied previously estimated spatially and temporally resolved NO2 concentrations and assigned annual means to study participants based on their ZIP code of residence. Cox proportional hazards models were used to examine the association between long-term exposure to low-level NO2 and all-cause mortality, adjusting for potential confounders.

RESULTS

About 71.1% of the Medicare beneficiaries in the southeastern United States were always exposed to low-level NO2 over the study period. We observed an association between long-term exposure to low-level NO2 and all-cause mortality, with a hazard ratio (HR)= 1.042 (95% CI: 1.040, 1.045) in single-pollutant models and a HR= 1.047 (95% CI: 1.045, 1.049) in multipollutant models (adjusting for PM2.5 and O3), per 10-ppb increase in annual NO2 concentrations. The penalized spline indicates a linear exposure-response relationship across the entire NO2 exposure range. Medicare enrollees who were White, female, and residing in urban areas were more vulnerable to long-term NO2 exposure.

CONCLUSION

Using a large and representative cohort, we provide epidemiological evidence that long-term exposure to NO2, even below the national and global ambient air quality guidelines, was approximately linearly associated with a higher risk of mortality among older adults, independent of PM2.5 and O3 exposure. Improving air quality by reducing NO2 emissions, therefore, may yield significant health benefits. https://doi.org/10.1289/EHP9044.

Long-term exposure to nitrogen dioxide and mortality: A systematic review and meta-analysis

BACKGROUND

Ambient air pollution is among the greatest environmental risks to human health. However, little is known about the health effects of nitrogen dioxide (NO₂), a traffic-related air pollutant. Herein, we aimed to conduct a meta-analysis to investigate the long-term effects of NO₂ on mortality.

METHODS

We conducted a systematic search for studies that were published up to February 2020 and performed a meta-analysis of all available epidemiologic studies evaluating the associations between long-term exposure to NO₂ with all-cause, cardiovascular, and respiratory mortality. Overall pooled effect estimates as well as subgroup-specific pooled estimates (e.g. location, exposure assessment method, exposure metric, study population, age at recruitment, and key confounder adjustment) and 95% confidence intervals were calculated using random-effects models. Risk of bias assessment was accessed by following WHO global air quality guidelines. Publication bias was accessed by visually inspecting funnel plot and Egger's liner regression was used to test of asymmetry.

RESULTS

Our search initially retrieved 1349 unique studies, of which 34 studies met the inclusion criteria. The pooled hazard ratio (HR) for all-cause mortality was 1.06 (95%CI: 1.04-1.08, n = 28 studies, I² = 98.6%) per 10 ppb increase in annual NO₂ concentrations. The pooled HRs for cardiovascular and respiratory mortality per 10 ppb increment were 1.11 (95%CI: 1.07-1.16, n = 20 studies, I² = 99.2%) and 1.05 (95%CI: 1.02-1.08, n = 17 studies, I² = 94.6%), respectively. The sensitivity analysis pooling estimates from multi-pollutant models suggest an independent effect of NO₂ on mortality. Funnel plots indicate that there is no evidence for publication bias in our study.

CONCLUSION

We provide robust epidemiological evidence that long-term exposure to NO₂, a proxy for traffic-sourced air pollutants, is associated with a higher risk of all-cause, cardiovascular, and respiratory mortality that might be independent of other common air pollutants.

Urban Air Pollution May Enhance COVID-19 Case-Fatality and Mortality Rates in the United States

BACKGROUND

The novel human coronavirus disease 2019 (COVID-19) pandemic has claimed more than 240,000 lives worldwide, causing tremendous public health, social, and economic damages. While the risk factors of COVID-19 are still under investigation, environmental factors, such as urban air pollution, may play an important role in increasing population susceptibility to COVID-19 pathogenesis.

METHODS

We conducted a cross-sectional nationwide study using zero-inflated negative binomial models to estimate the association between long-term (2010-2016) county-level exposures to NO2, PM2.5 and O3 and county-level COVID-19 case-fatality and mortality rates in the US. We used both single and multipollutant models and controlled for spatial trends and a comprehensive set of potential confounders, including state-level test positive rate, county-level healthcare capacity, phase-of-epidemic, population mobility, sociodemographic, socioeconomic status, behavior risk factors, and meteorological factors.

RESULTS

1,027,799 COVID-19 cases and 58,489 deaths were reported in 3,122 US counties from January 22, 2020 to April 29, 2020, with an overall observed case-fatality rate of 5.8%. Spatial variations were observed for both COVID-19 death outcomes and long-term ambient air pollutant levels. County-level average NO2 concentrations were positively associated with both COVID-19 case-fatality rate and mortality rate in single-, bi-, and tri-pollutant models (p-values<0.05). Per inter-quartile range (IQR) increase in NO2 (4.6 ppb), COVID-19 case-fatality rate and mortality rate were associated with an increase of 7.1% (95% CI 1.2% to 13.4%) and 11.2% (95% CI 3.4% to 19.5%), respectively. We did not observe significant associations between long-term exposures to PM2.5 or O3 and COVID-19 death outcomes (p-values>0.05), although per IQR increase in PM2.5 (3.4 ug/m3) was marginally associated with 10.8% (95% CI: -1.1% to 24.1%) increase in COVID-19 mortality rate.

DISCUSSIONS AND CONCLUSIONS

Long-term exposure to NO2, which largely arises from urban combustion sources such as traffic, may enhance susceptibility to severe COVID-19 outcomes, independent of long-term PM2.5 and O3 exposure. The results support targeted public health actions to protect residents from COVID-19 in heavily polluted regions with historically high NO2 levels. Moreover, continuation of current efforts to lower traffic emissions and ambient air pollution levels may be an important component of reducing population-level risk of COVID-19 deaths.

Air Pollutant Exposure and Stove Use Assessment Methods for the Household Air Pollution Intervention Network (HAPIN) Trial

BACKGROUND

High quality personal exposure data is fundamental to understanding the health implications of household energy interventions, interpreting analyses across assigned study arms, and characterizing exposure-response relationships for household air pollution. This paper describes the exposure data collection for the Household Air Pollution Intervention Network (HAPIN), a multicountry randomized controlled trial of liquefied petroleum gas stoves and fuel among 3,200 households in India, Rwanda, Guatemala, and Peru.

OBJECTIVES

The primary objectives of the exposure assessment are to estimate the exposure contrast achieved following a clean fuel intervention and to provide data for analyses of exposure-response relationships across a range of personal exposures.

METHODS

Exposure measurements are being conducted over the 3-y time frame of the field study. We are measuring fine particulate matter [PM <   2.5 μ m in aerodynamic diameter (PM 2.5 )] with the Enhanced Children's MicroPEM™ (RTI International), carbon monoxide (CO) with the USB-EL-CO (Lascar Electronics), and black carbon with the OT21 transmissometer (Magee Scientific) in pregnant women, adult women, and children < 1   year of age, primarily via multiple 24-h personal assessments (three, six, and three measurements, respectively) over the course of the 18-month follow-up period using lightweight monitors. For children we are using an indirect measurement approach, combining data from area monitors and locator devices worn by the child. For a subsample (up to 10%) of the study population, we are doubling the frequency of measurements in order to estimate the accuracy of subject-specific typical exposure estimates. In addition, we are conducting ambient air monitoring to help characterize potential contributions of PM 2.5 exposure from background concentration. Stove use monitors (Geocene) are being used to assess compliance with the intervention, given that stove stacking (use of traditional stoves in addition to the intervention gas stove) may occur.

CONCLUSIONS

The tools and approaches being used for HAPIN to estimate personal exposures build on previous efforts and take advantage of new technologies. In addition to providing key personal exposure data for this study, we hope the application and learnings from our exposure assessment will help inform future efforts to characterize exposure to household air pollution and for other contexts. https://doi.org/10.1289/EHP6422.

Summary of the workshop on methodologies for environmental public health tracking of air pollution effects

The US Centers for Disease Control and Prevention established the Environmental Public Health Tracking (EPHT) program to support state and local projects that characterize the impact of the environment on health. The projects involve compiling, linking, analyzing, and disseminating environmental and health surveillance information, thereby engaging stakeholders and guiding actions to improve public health. One of the EPHT objectives is to track the public health impact of ambient air pollution with analyses that are timely and relevant to state and local stakeholders. To address methodological issues relevant to this objective, in January 2008, government officials and researchers from the USA, Canada, and Europe gathered in Baltimore, Maryland for a 2-day workshop. Using commissioned papers and presentations on key methodological issues as well as examples of previous air pollution impact assessments, work group discussions produced a set of consensus recommendations for the EPHT program. These recommendations noted the need for data that will encourage local stakeholders to support continued progress in air pollution control. The limitations of using only local data for analyses were also noted. To improve local estimates of air pollution health impacts, methods were recommended that "borrow strength" from other evidence. An incremental approach to implementing such methods was recommended. The importance and difficulty of communicating uncertainties in local health impact assessments was emphasized, as was the need for coordination among different agencies conducting health impact assessments.

The U.S. Environmental Protection Agency Particulate Matter Health Effects Research Centers Program: a midcourse report of status, progress, and plans

In 1998 Congress mandated expanded U.S. Environmental Protection Agency (U.S. EPA) health effects research on ambient air particulate matter (PM) and a National Research Council (NRC) committee to provide research oversight. The U.S. EPA currently supports intramural and extramural PM research, including five academically based PM centers. The PM centers in their first 2.5 years have initiated research directed at critical issues identified by the NRC committee, including collaborative activities, and sponsored scientific workshops in key research areas. Through these activities, there is a better understanding of PM health effects and scientific uncertainties. Future PM centers research will focus on long-term effects associated with chronic PM exposures. This report provides a synopsis of accomplishments to date, short-term goals (during the next 2.5 years) and longer-term goals. It consists of six sections: biological mechanisms, acute effects, chronic effects, dosimetry, exposure assessment, and the specific attributes of a coordinated PM centers program.