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

ABSTRACT:

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 f

SUBMITTER: Lin C 

PROVIDER: S-EPMC9808603 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature