Augmenting the Standard Operating Procedures of Health and Air Quality Stakeholders With NASA Resources.

The combination of air quality (AQ) data from satellites and low-cost sensor systems, along with output from AQ models, have the potential to augment high-quality, regulatory-grade data in countries with in situ monitoring networks and provide much needed AQ information in countries without them, including Low and Moderate Income Countries (LMICs). We demonstrate the potential of free and publicly available USA National Aeronautics and Space Administration (NASA) resources, which include capacity building activities, satellite data, and global AQ forecasts, to provide cost-effective, and reliable AQ information to health and AQ professionals around the world. We provide illustrative case studies that highlight how global AQ forecasts along with satellite data may be used to characterize AQ on urban to regional scales, including to quantify pollution concentrations, identify pollution sources, and track the long-range transport of pollution.

Sub-City Scale Hourly Air Quality Forecasting by Combining Models, Satellite Observations, and Ground Measurements.

While multiple information sources exist concerning surface-level air pollution, no individual source simultaneously provides large-scale spatial coverage, fine spatial and temporal resolution, and high accuracy. It is, therefore, necessary to integrate multiple data sources, using the strengths of each source to compensate for the weaknesses of others. In this study, we propose a method incorporating outputs of NASA's GEOS Composition Forecasting model system with satellite information from the TROPOMI instrument and ground measurement data on surface concentrations.

Characterizing the Aging of Alphasense NO Sensors in Long-Term Field Deployments.

Low-cost NO sensors have been widely deployed for atmospheric sampling. While their initial performance has been characterized, few studies have examined their long-term degradation. This study focused on the performance of Alphasense low-cost NO sensors (NO2-B42F and NO2-B43F) over 4 years (2016-2020).

Using a network of lower-cost monitors to identify the influence of modifiable factors driving spatial patterns in fine particulate matter concentrations in an urban environment.

Background: There is substantial interest in using networks of lower-cost air quality sensors to characterize urban population exposure to fine particulate matter mass (PM). However, sensor uncertainty is a concern with these monitors.

Objectives: (1) Quantify the uncertainty of lower-cost PM sensors; (2) Use the high spatiotemporal resolution of a lower-cost sensor network to quantify the contribution of different modifiable and non-modifiable factors to urban PM.

Demonstration of a Low-Cost Multi-Pollutant Network to Quantify Intra-Urban Spatial Variations in Air Pollutant Source Impacts and to Evaluate Environmental Justice.

Air quality monitoring has traditionally been conducted using sparsely distributed, expensive reference monitors. To understand variations in PM on a finely resolved spatiotemporal scale a dense network of over 40 low-cost monitors was deployed throughout and around Pittsburgh, Pennsylvania, USA. Monitor locations covered a wide range of site types with varying traffic and restaurant density, varying influences from local sources, and varying socioeconomic (environmental justice, EJ) characteristics.