Integrated System and Predictive Model to Assess Air Quality and Rapidly Pinpoint Emerging Sources

Natural and anthropogenic sources of pollution, according to several studies, contribute to in excess of 6 million premature deaths per year worldwide, with more than 200,000 in the US alone. The high number of deaths caused by air pollution shows the importance of making people aware of air quality at their location. Five of the components of greatest concern are listed on the National Ambient Air Quality Standard as part of the Clean Air Act by EPA: CO, NO2, O3, SO2 and PM2.5. In addition to contributing to climate change, these gases and particulate are harmful for humans and attack the respiratory system, penetrating deep into the respiratory system and damages lung tissue and the bronchial system causing pulmonary edema. Satellites are not able, on their own, to detect how high above the ground air pollution is. These systems provide an integrated measurement through the column of troposphere being measured, not readings at ground level. It is difficult to model the vertical distribution of gases and dust. Satellites are therefore often not very accurate in giving estimations for the concentrations of pollutants in the actual air humans breathe. AQ models and existing networks lack local resolution since air quality values are highly dependent on local topography and wind direction. The air quality can vary significantly over a few hundred meters in urban areas and around roads and industrial areas. Hyper-local monitoring would improve accuracy of exposure assessments and improve models. Therefore, we offer this proposal for a truly low-cost, reliable air quality system with integrated data from remote monitors and local sensor nodes.