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. We also provide recommendations to data product developers to facilitate and broaden usage of NASA resources by health and AQ stakeholders.

Characterizing the Aging of Alphasense NO2 Sensors in Long-Term Field Deployments.

Low-cost NO2 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 NO2 sensors (NO2-B42F and NO2-B43F) over 4 years (2016-2020). A total of 29 NO2 sensors from 10 batches were collocated 78 times at two sites with reference instruments. Raw signals from "functional" NO2 sensors correlated linearly with reference NO2 concentrations. After long-term deployment, sensor raw signals started to deviate from reference NO2 concentrations due to sensor aging, an accumulated effect after sensor unpacking. Several sensors eventually became "non-functional" as sensor raw signals showed no correlation with reference NO2 concentrations. Sensor aging and non-functionality may be primarily caused by expiration of the ozone (O3) scrubber built into these sensors so that sensors responded to both ambient NO2 and O3. The influence of O3 on sensor response is quantified through the permutation importance method. Most of the sensors are non-functional after approximately 200-400 days of deployment, and no sensor was functional after 400 days of deployment. This result agrees well with the estimated lifetime of the built-in ozone scrubbers considering the ambient ozone concentration in the Pittsburgh area where these sensors were deployed. To ensure reliable data quality in long-term field deployments, we recommend collocating NO2 sensors with reference instruments regularly after 200-400 days of deployment to identify and replace non-functional sensors in a timely manner.

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 (PM2.5). However, sensor uncertainty is a concern with these monitors. OBJECTIVES: (1) Quantify the uncertainty of lower-cost PM2.5 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 PM2.5. METHODS: A network of 64 lower-cost monitors was deployed across Pittsburgh, PA, USA. Measurement and sampling uncertainties were quantified by comparison to local reference monitors. Data were sorted by land-use characteristics, time of day, and wind direction. RESULTS: Careful calibration, temporal averaging, and reference site corrections reduced sensor uncertainty to 1 µg/m3, ~10% of typical long-term average PM2.5 concentrations in Pittsburgh. Episodic and long-term enhancements to urban PM2.5 due to a nearby large metallurgical coke manufacturing facility were 1.6 ± 0.36 µg/m3 and 0.3 ± 0.2 µg/m3, respectively. Daytime land-use regression models identified restaurants as an important local contributor to urban PM2.5. PM2.5 above EPA and WHO daily health standards was observed at several sites across the city. SIGNIFICANCE: With proper management, a large network of lower-cost sensors can identify statistically significant trends and factors in urban exposure.

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 PM2.5 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. Variability between and within site groupings was observed. Concentrations were higher near the source-influenced sites than the Urban or Suburban Residential sites. Gaseous pollutants (NO2 and SO2) were used to differentiate between traffic (higher NO2 concentrations) and industrial (higher SO2 concentrations) sources of PM2.5. Statistical analysis proved these differences to be significant (coefficient of divergence > 0.2). The highest mean PM2.5 concentrations were measured downwind (east) of the two industrial facilities while background level PM2.5 concentrations were measured at similar distances upwind (west) of the point sources. Socioeconomic factors, including the fraction of non-white population and fraction of population living under the poverty line, were not correlated with increases in PM2.5 or NO2 concentration. The analysis conducted here highlights differences in PM2.5 concentration within site groupings that have similar land use thus demonstrating the utility of a dense sensor network. Our network captures temporospatial pollutant patterns that sparse regulatory networks cannot.

Quantifying high-resolution spatial variations and local source impacts of urban ultrafine particle concentrations.

To quantify the fine-scale spatial variations and local source impacts of urban ultrafine particle (UFP) concentrations, we conducted 3-6 weeks of continuous measurements of particle number (a proxy for UFP) and other air pollutant (CO, NO2, and PM2.5) concentrations at 32 sites in Pittsburgh, Pennsylvania during the winters of 2017 and 2018. Sites were selected to span a range of urban land use attributes, including urban background, near local and arterial roads, traffic intersections, urban street canyon, near-highway, near large industrial source, and restaurant density. The spatial variations in urban particle number concentrations varied by about a factor of three. Particle number concentrations are 2-3 times more spatially heterogeneous than PM2.5 mass. The observed order of spatial heterogeneity is UFP > NO2 > CO > PM2.5. On average, particle number concentrations near local roads with a cluster of restaurants and near arterial roads are roughly two times higher than the urban background. Particle number concentrations in the urban street canyon, downwind of a major highway, and near large industrial sources are 2-4 times higher than background concentrations. While traffic is known as an important contributor to particle number concentrations, restaurants and industrial emissions also contribute significantly to spatial variations in Pittsburgh. Particle size distribution measurements using a mobile laboratory show that the local spatial variations in particle number concentrations are dictated by concentrations of particles smaller than 50 nm. A large fraction of urban residents (e.g., ~50%) in Pittsburgh live near local sources and are therefore exposed to 50%-300% higher particle number concentrations than urban background location. These locally emitted particles may have greater health effects than background particles.