Spatially dense air pollutant sampling: Implications of spatial variability on the representativeness of stationary air pollutant monitors

Li, Hugh Z.; Gu, Peishi; Ye, Qing; Zimmerman, Naomi; Robinson, Elmer; Subramanian, R.; Apte, Joshua S.; Robinson, Allen L.; Presto, Albert A.

doi:10.1016/j.aeaoa.2019.100012

Cited by 59 publications

(71 citation statements)

References 64 publications

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“…In addition, the different frequency of visits to each neighborhood may cause some uncertainties in the spatial patterns of model inputs. However, several other studies from the same campaign that conducted a greater number of visits to the neighborhoods have identified similar sources and spatial variations of source-specific pollutant level in Pittsburgh (Gu et al 2018;Li et al 2019;Robinson et al 2018). This indicates that the spatial patterns of our model inputs should be relatively robust.…”

Section: Limitationsmentioning

confidence: 64%

“…Second, as shown in Figure S5, the 10 subdivided areas had large subneighborhood variabilities in vehicle and restaurant density, two main sources for ambient particles we identified. The other 10 neighborhoods were relatively homogenous in vehicle and restaurant density and were therefore expected to be more homogeneous in terms of pollutant concentrations (Li et al 2019).…”

Section: Land-use Regression Modelsmentioning

confidence: 99%

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Moving beyond Fine Particle Mass: High-Spatial Resolution Exposure to Source-Resolved Atmospheric Particle Number and Chemical Mixing State

et al. 2020

Environ Health Perspect

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BACKGROUND: Most epidemiological studies address health effects of atmospheric particulate matter (PM) using mass-based measurements as exposure surrogates. However, this approach ignores many critical physiochemical properties of individual atmospheric particles. These properties control the deposition of particles in the human lung and likely their toxicity; in addition, they likely have larger spatial variability than PM mass. OBJECTIVES: This study was designed to quantify the spatial variability in number, size, source, and chemical mixing state of individual particles in a populous urban area. We quantified the population exposure to these detailed particle properties and compared them to mass-based exposures. METHODS: We performed mobile sampling using an advanced single-particle mass spectrometer to measure the spatial variability of number concentration of source-resolved 50-1,000 nm particles and particle mixing state in Pittsburgh, Pennsylvania. We built land-use regression (LUR) models to estimate their spatial patterns and coupled them with demographic data to estimate population exposure. RESULTS: Particle number concentration had a much larger spatial variability than mass concentration within the city. Freshly emitted particles from traffic and cooking drive the variability in particle number, but mass concentrations are dominated by aged background particles composed of secondary materials. In addition, people exposed to elevated number concentrations of atmospheric particles are also exposed to more externally mixed particles. CONCLUSIONS: Our advanced measurement technique provides a new exposure picture that resolves the large intra-city spatial heterogeneity in traffic and cooking particle number concentrations in the populous urban area. Our results provide a complementary and more detailed perspective compared with bulk measurements of composition. In addition, given the influence of particle mixing state on properties such as particle deposition in the lung, the large spatial gradients of chemical mixing state may significantly influence the health effects of fine PM.

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Section: Limitationsmentioning

confidence: 64%

Section: Land-use Regression Modelsmentioning

confidence: 99%

Moving beyond Fine Particle Mass: High-Spatial Resolution Exposure to Source-Resolved Atmospheric Particle Number and Chemical Mixing State

et al. 2020

Environ Health Perspect

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“…The variation of the derived parameters for SNBO ( figure S5) indicated issues with the proxy, which was confirmed by inspection of the frequency distribution of the NO2 concentrations at the proxy site and at the SNBO regulatory site during June and July, partly explaining the lower success of the management framework for these months. NO2 concentrations can vary considerably at the sub-kilometre scale and the success of the management framework strongly depends on the representativeness of the land use surrounding the reference sites for the low-cost sensor site that is calibrated (Li et al, 2019;van Zoest et al, 2019;Weissert et al, 2019). LAXH is the regulatory site at the Los Angeles airport and its proxy site (CMPT) is in central Los Angeles and may therefore not be a representative site for the local emissions at LAXH.…”

Section: Sensors At Reference Sites Using Sensor Ozone Data and Proxmentioning

confidence: 99%

Hierarchical network design for nitrogen dioxide measurement in urban environments

Weissert

Miles²,

Miskell

et al. 2020

Atmospheric Environment

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We present a management and data correction framework for low-cost electrochemical sensors for nitrogen dioxide (NO2) deployed within a hierarchical network of low-cost and regulatorygrade instruments. The framework is founded on the idea that it is possible in a suitably configured network to identify a source of reliable 'proxy' data for each sensor site that has a similar probability distribution of measurement values over a suitable time period. Previous work successfully applied these ideas to a sensor system with a simple linear 2-parameter (slope and offset) response, with parameters estimated by moment matching site and proxy data distributions. However, applying these ideas to electrochemical sensors for NO2 presents significant additional difficulties for which we demonstrate solutions. The three NO2 sensor response parameters (offset, ozone (O3) response slope, and NO2 response slope) are known to vary significantly as a consequence of ambient humidity and temperature variations. Here we demonstrate that these response parameters can be estimated by minimising the Kullback-Leibler divergence between sensor-estimated and proxy NO2 distributions over a 3-day b Present address: Trustpower, 108 Durham St, Tauranga, New Zealand 1 window. We then estimate an additional offset term by using co-location data. This offset term is dependent on climate and spatially correlated and can thus be projected across the network.Co-location data also estimates the time-, space-and concentration-dependent error distribution between sensors and regulatory-grade instruments. Robust O3 measurements are obtained using a semiconducting oxide-based instrument, previously described. We show how the parameter variations can be used to indicate both sensor failure and failure of the proxy assumption. With these procedures, we demonstrate measurement at nine different locations across two regions of Southern California over seven months with average root mean square error ± 7.2 ppb (range over locations 4 -11 ppb) without calibration other than the remote proxy comparison. We apply the procedures to a network of 56 sensors distributed across the Inland Empire and Los Angeles County regions. The results show large variations in NO2 concentration taking place on short time-and distance scales across the region. These spatiotemporal NO2 variations were not captured by the more sparsely distributed regulatory network of air monitoring stations demonstrating the need for reliable data from dense networks of monitors to supplement the existing regulatory networks.

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“…This is likely a result of the quite dense network of low-cost sensors present in Pittsburgh, where the median distance between sensors in the network is about 1km. With this dense network, there is a good chance that the nearest ground monitor will be quite close to the location at which concentrations are to be estimated, and the resulting estimate is therefore likely to be quite good, as PM concentrations tend to be homogenous at this spatial scale have in Pittsburgh (Li et al, 2019). When PM2.5 is instead estimated from satellite data, https://doi.org/10.5194/amt-2020-67 Preprint.…”

Section: Comparison Of Aod-based Surface Pm25 To Measurements From Amentioning

confidence: 99%

Application of Low-Cost Fine Particulate Mass Monitors to Convert Satellite Aerosol Optical Depth Measurements to Surface Concentrations in North America and Africa

Malings¹,

Westervelt²,

Hauryliuk³

et al. 2020

Preprint

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Abstract. Low-cost particulate mass sensors provide opportunities to assess air quality at unprecedented spatial and temporal resolutions. Established traditional monitoring networks have limited spatial resolution and are frequently absent in less-developed countries (e.g. in sub-Saharan Africa). Satellites provide snapshots of regional air pollution, but require ground-truthing. Low-cost monitors can supplement and extend data coverage from these sources worldwide, providing a better overall air quality picture. We demonstrate such a multi-source data integration using two case studies. First, in Pittsburgh, Pennsylvania, both traditional monitoring and dense low-cost sensor networks are present, and are compared with satellite aerosol optical depth (AOD) data from NASA's MODIS system. We assess the performance of linear conversion factors for AOD to surface PM2.5 using both networks, and identify relative benefits provided by the denser low-cost sensor network. In particular, with 10 or more ground monitors in the city, there is a two-fold reduction in worst-case surface PM2.5 estimation mean absolute error compared to using only a single ground monitor. Second, in Rwanda, Malawi, and the Democratic Republic of the Congo, traditional ground-based monitoring is lacking and must be substituted with low-cost sensor data. Here, we assess the ability of regional-scale satellite retrievals and local-scale low-cost sensor measurements to complement each other. In Rwanda, we find that combining local ground monitoring information with satellite data provides a 40 % improvement (in terms of surface PM2.5 estimation accuracy) with respect to using ground monitoring data alone. Overall, we find that combining ground-based low-cost sensor and satellite data can improve and expand spatio-temporal air quality data coverage in both well-monitored and data-sparse regions.

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Spatially dense air pollutant sampling: Implications of spatial variability on the representativeness of stationary air pollutant monitors

Cited by 59 publications

References 64 publications

Moving beyond Fine Particle Mass: High-Spatial Resolution Exposure to Source-Resolved Atmospheric Particle Number and Chemical Mixing State

Moving beyond Fine Particle Mass: High-Spatial Resolution Exposure to Source-Resolved Atmospheric Particle Number and Chemical Mixing State

Hierarchical network design for nitrogen dioxide measurement in urban environments

Application of Low-Cost Fine Particulate Mass Monitors to Convert Satellite Aerosol Optical Depth Measurements to Surface Concentrations in North America and Africa

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