Comparison of Mobile and Fixed-Site Black Carbon Measurements for High-Resolution Urban Pollution Mapping

Chambliss, Sarah; Preble, C.; Caubel, Julien J.; Cados, T.; Messier, Kyle P.; Alvarez, Ramón A.; LaFranchi, B. W.; Lunden, Melissa M.; Marshall, Julian; Szpiro, Adam A.; Kirchstetter, Thomas W.; Apte, Joshua S.

doi:10.1021/acs.est.0c01409

Cited by 41 publications

(47 citation statements)

References 47 publications

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“…In some cases-near highways and strong point sources-pollution gradients may vary over finer spatial scales than those captured by census block spatial units (~110 m). However, the integration of multiple road segments provides an increase in total number of visits and total sampling time per spatial unit which reduces sampling error and measurement uncertainty (51). While on-road measurements are not a perfect approximation of concentrations throughout a census block, a previous comparison of on-and near-road measurements in West Oakland showed no evidence of bias in on-road concentrations due to increased proximity to on-road emissions (51).…”

Section: Data Processingmentioning

confidence: 99%

“…However, the integration of multiple road segments provides an increase in total number of visits and total sampling time per spatial unit which reduces sampling error and measurement uncertainty (51). While on-road measurements are not a perfect approximation of concentrations throughout a census block, a previous comparison of on-and near-road measurements in West Oakland showed no evidence of bias in on-road concentrations due to increased proximity to on-road emissions (51). The general spatial representativeness of on-road measurements holds especially in low-traffic residential neighborhoods with mixed wind conditions (34).…”

Section: Data Processingmentioning

confidence: 99%

See 1 more Smart Citation

Local and Regional-Scale Racial and Ethnic Disparities in Air Pollution Determined by Long-Term Mobile Monitoring

Chambliss¹,

Pinon²,

Messier³

et al. 2021

Preprint

Self Cite

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Disparity in air pollution exposure arises from variation at multiple spatial scales: along urban-to-rural gradients, between individual cities within a metropolitan region, within individual neighborhoods, and between city blocks. Here, we improve on existing capabilities to systematically compare urban variation at several scales, from hyperlocal (<100m) to regional (>10km), and to assess consequences for outdoor air pollution experienced by residents of different races and ethnicities, by creating a set of uniquely extensive and high-resolution observations of spatially-variable pollutants: NO, NO2, black carbon (BC), and ultrafine particles (UFP). We conducted full-coverage monitoring of a wide sample of urban and suburban neighborhoods (93 km 2 , 450,000 residents) in four counties of the San Francisco Bay Area using Google Street View cars equipped with the Aclima mobile platform. Comparing scales of variation across the sampled population, greater differences arise from localized pollution gradients for BC and NO (pollutants dominated by primary sources) and from regional gradients for UFP and NO2 (pollutants dominated by secondary contributions). Median concentrations of UFP, NO, and NO2 are, for Hispanic and Black populations, 8%-30% higher than the population average; for white populations, average exposures to these pollutants are 9%-14% lower than the population average. Systematic racial/ethnic disparities are influenced by regional concentration gradients due to sharp contrasts in demographic composition among cities and urban districts, while within-group extremes arise from local peaks. Our results illustrate how detailed and extensive fine-scale pollution observations can add new insights about differences and disparities in air pollution exposures at the population scale.

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Section: Data Processingmentioning

confidence: 99%

Section: Data Processingmentioning

confidence: 99%

Local and Regional-Scale Racial and Ethnic Disparities in Air Pollution Determined by Long-Term Mobile Monitoring

Chambliss¹,

Pinon²,

Messier³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…In some cases-near highways and strong point sources-pollution gradients may vary over finer spatial scales than those captured by census block spatial units (~110 m). However, the integration of multiple road segments provides an increase in total number of visits and total sampling time per spatial unit which reduces sampling error and measurement uncertainty (50). While on-road measurements are not a perfect approximation of concentrations throughout a census block, a previous comparison of on-and near-road measurements in West Oakland showed no evidence of bias in on-road concentrations due to increased proximity to on-road emissions (50).…”

Section: Data Processingmentioning

confidence: 99%

“…However, the integration of multiple road segments provides an increase in total number of visits and total sampling time per spatial unit which reduces sampling error and measurement uncertainty (50). While on-road measurements are not a perfect approximation of concentrations throughout a census block, a previous comparison of on-and near-road measurements in West Oakland showed no evidence of bias in on-road concentrations due to increased proximity to on-road emissions (50). The general spatial representativeness of on-road measurements holds especially in low-traffic residential neighborhoods with mixed wind conditions (34).…”

Section: Data Processingmentioning

confidence: 99%

Local and Regional-Scale Racial and Ethnic Disparities in Air Pollution Determined by Long-Term Mobile Monitoring

Chambliss¹,

Pinon²,

Messier³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Disparity in air pollution exposure arises from variation at multiple spatial scales: along urban-to-rural gradients, between individual cities within a metropolitan region, within individual neighborhoods, and between city blocks. Here, we improve on existing capabilities to systematically compare urban variation at several scales, from hyperlocal (<100m) to regional (>10km), and to assess consequences for the outdoor air pollution experienced by residents of different races and ethnicities, by creating a set of uniquely extensive and high-resolution observations of spatially-variable pollutants: NO, NO2, black carbon (BC), and ultrafine particles (UFP). We conducted full coverage monitoring of a wide sample of urban and suburban neighborhoods (93 km2, 450,000 residents) in four counties of the San Francisco Bay Area using Google Street View cars. Comparing scales of variation across the sampled population, greater differences arise from localized pollution gradients for BC and NO (pollutants dominated by primary sources) and from regional gradients for UFP and NO2 (pollutants dominated by secondary contributions). Median concentrations of UFP, NO, and NO2 are, for Hispanic and Black populations, 8%-30% higher than the population average; for white populations, average exposures to the same pollutants are 9%-14% lower than the population average. Systematic racial/ethnic disparities are strongly affected by regional differences in background concentrations due to sharp contrasts in demographic composition among cities and urban districts, while within-group extremes arise from local peaks. Our results illustrate how detailed and extensive fine-scale pollution observations can add new insights about differences and disparities in air pollution exposures at the population scale.

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“…Understanding air pollution exposure is important, as it has been linked to various adverse health conditions (Caplin et al, 2019;Zhang et al, 2018). Mobile monitoring, a technique in which continuous air pollution measurements are collected using instrumentation on a mobile platform, is becoming increasingly important for characterizing exposure because air pollution varies on spatial scales finer than the typical distance between stationary monitors (Apte et al, 2017;Chambliss et al, 2020;Messier et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

SIBaR: A New Method for Background Quantification and Removal from Mobile Air Pollution Measurements

Actkinson¹,

Ensor²,

Griffin³

2021

Preprint

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Abstract. Mobile monitoring is becoming increasingly popular for characterizing air pollution on fine spatial scales. In identifying local source contributions to measured pollutant concentrations, the detection and quantification of background are key steps in many mobile monitoring studies, but the methodology to do so requires further development to improve replicability. Here we discuss a new method for quantifying and removing background in mobile monitoring studies, State Informed Background Removal (SIBaR). The method employs Hidden Markov Models (HMMs), a popular modelling technique that detects regime changes in time series. We discuss the development of SIBaR and assess its performance on an external dataset. We find 86 % agreement between the predictions made by SIBaR and the predetermined allocation of background and non-background data points. We compare five-minute averages of SIBaR-derived background NOx measurements to five-minute averages of NOx measurements taken by a stationary monitor sitting 70 m above ground level near downtown Houston, finding greater disagreement between SIBaR and the stationary monitor than the disagreement between other background detection techniques and the same stationary monitor. We then assess its application to a data set collected in Houston, TX, by mapping the fraction of points designated as background and comparing source contributions to those derived using other published background detection and removal techniques. Results suggest that SIBaR could serve as a framework for improved background quantification and removal in future mobile monitoring studies.

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Comparison of Mobile and Fixed-Site Black Carbon Measurements for High-Resolution Urban Pollution Mapping

Cited by 41 publications

References 47 publications

Local and Regional-Scale Racial and Ethnic Disparities in Air Pollution Determined by Long-Term Mobile Monitoring

Local and Regional-Scale Racial and Ethnic Disparities in Air Pollution Determined by Long-Term Mobile Monitoring

Local and Regional-Scale Racial and Ethnic Disparities in Air Pollution Determined by Long-Term Mobile Monitoring

SIBaR: A New Method for Background Quantification and Removal from Mobile Air Pollution Measurements

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