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Air pollution accountability research: Moving from a chain to a web.

ABSTRACT: Air pollution accountability studies examine the relationship(s) between an intervention, regulation, or event and the resulting downstream impacts, if any, on emissions, exposure, and/or health. The sequence of events has been schematically described as an accountability chain. Here, we update the existing framework to capture real-life complexities and to highlight important factors that fall outside the linear chain. This new "accountability web" is intended to convey the intricacies associated with conducting an accountability study to various audiences, including researchers, policy makers, and stakeholders. We also identify data considerations for planning and completing a robust accountability study, including those relevant to novel and innovative air pollution and exposure data. Finally, we present a series of recommendations for the accountability research community that can serve as a guide for the next generation of accountability studies.

SUBMITTER: Ebelt S

PROVIDER: S-EPMC10708994 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

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Air pollution accountability research: Moving from a chain to a web.

Ebelt S S Baxter L L Erickson H S HS Henneman L R F LRF Lange S S Luben T J TJ Neidell M M Rule A M AM Russell A G AG Hess J Wendt JW Burns C J CJ LaKind J S JS Goodman J E JE

Global epidemiology 20231115

Air pollution accountability studies examine the relationship(s) between an intervention, regulation, or event and the resulting downstream impacts, if any, on emissions, exposure, and/or health. The sequence of events has been schematically described as an accountability chain. Here, we update the existing framework to capture real-life complexities and to highlight important factors that fall outside the linear chain. This new "accountability web" is intended to convey the intricacies associat ...[more]

PMID: 38074085

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Project description:Air pollution is a matter of concern among the public, especially for those living in urban and industrial areas. Markov chain modeling is often used to model the underlying dynamics of air pollution, which involves describing the transition probability of going from one air pollution state to another. Thus, estimating the transition probability matrix for the data of the air pollution index (API) is an essential process in the modeling. However, one may observe many zero probabilities in the transition probability matrix, especially when faced with a small sample, interpreting the results with respect to the climate condition less realistic. This study proposes a robust empirical Bayes method, which incorporates a method of smoothing the zero frequencies in the count matrix, contributing to an improved estimation of the transition probability matrix. The robustness of the empirical Bayesian estimation is investigated based on Bayes risk. The transition probability matrices estimated based on the robust empirical Bayes method for the hourly API data collected from seven monitoring stations in Malaysia for the period 2012 to 2014 are used for determining the air pollution characteristics such as the mean residence time, the steady-state probability and the mean recurrence time. Furthermore, the proposed method has been evaluated by Monte Carlo simulations. Results suggest that it is quite effective in producing non-zero transition probability estimates, and superior to the maximum likelihood method in terms of minimizing the mean squared error for individual and entire transition probabilities. Therefore, the robust empirical Bayes method proves to be an improved approach to the estimation of the Markov chain. When applied to API data, it could provide important information on air pollution dynamics that may help guiding the development of proper strategies for managing the impact of air quality.Supplementary informationThe online version contains supplementary material available at 10.1007/s40201-020-00607-4.

Project description:BackgroundReal-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 (PM2.5). 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.ObjectiveThis 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.MethodsWe 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 PM2.5) 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.ResultsThe 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 O3, 0.74 (F1-score 0.41) for nitrogen dioxide, and 0.85 (F1-score 0.27) for PM2.5, when used for detecting elevated pollution levels. Compared with using only meteorological data, the proposed method achieved superior accuracy by incorporating web search data.ConclusionsThe 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.

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Air pollution accountability research: Moving from a chain to a web.

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Air pollution accountability research: Moving from a chain to a web.

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