On the use of data from commercial NOx analyzers for air pollution studies

Dickerson, Russell R.; Anderson, Daniel C.; Ren, Xinrong

doi:10.1016/j.atmosenv.2019.116873

Cited by 42 publications

(27 citation statements)

References 50 publications

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“…Among our pollutant concentration data sets, the Street View data set included only measurements that were taken during the daytime and on weekdays and may not, therefore, have fully captured long-term annual averages. The LUR data set incorporates in situ data, although concentrations are ultimately estimations of the sum of all oxidized atmospheric odd-nitrogen species (NO y ) estimations and not observations of actual NO 2 concentrations (Dickerson et al 2019). Future work can make use of more spatially refined estimates of PM 2:5 (Di et al 2019).…”

Section: Discussionmentioning

confidence: 99%

Assessing the Distribution of Air Pollution Health Risks within Cities: A Neighborhood-Scale Analysis Leveraging High-Resolution Data Sets in the Bay Area, California

Southerland

Anenberg

Harris

et al. 2021

Environ Health Perspect

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BACKGROUND: Air pollution-attributable disease burdens reported at global, country, state, or county levels mask potential smaller-scale geographic heterogeneity driven by variation in pollution levels and disease rates. Capturing within-city variation in air pollution health impacts is now possible with high-resolution pollutant concentrations. OBJECTIVES: We quantified neighborhood-level variation in air pollution health risks, comparing results from highly spatially resolved pollutant and disease rate data sets available for the Bay Area, California. METHODS: We estimated mortality and morbidity attributable to nitrogen dioxide (NO 2 ), black carbon (BC), and fine particulate matter [PM ≤2:5 lm in aerodynamic diameter (PM 2:5 )] using epidemiologically derived health impact functions. We compared geographic distributions of pollutionattributable risk estimates using concentrations from a) mobile monitoring of NO 2 and BC; and b) models predicting annual NO 2 , BC and PM 2:5 concentrations from land-use variables and satellite observations. We also compared results using county vs. census block group (CBG) disease rates. RESULTS: Estimated pollution-attributable deaths per 100,000 people at the 100-m grid-cell level ranged across the Bay Area by a factor of 38, 4, and 5 for NO 2 [mean = 30 (95% CI: 9, 50)], BC [mean = 2 (95% CI: 1, 2)], and PM 2:5 , [mean = 49 (95% CI: 33, 64)]. Applying concentrations from mobile monitoring and land-use regression (LUR) models in Oakland neighborhoods yielded similar spatial patterns of estimated grid-cell-level NO 2 -attributable mortality rates. Mobile monitoring concentrations captured more heterogeneity [mobile monitoring mean = 64 (95% CI: 19, 107) deaths per 100,000 people; LUR mean = 101 (95% CI: 30, 167)]. Using CBG-level disease rates instead of county-level disease rates resulted in 15% larger attributable mortality rates for both NO 2 and PM 2:5 , with more spatial heterogeneity at the grid-cell-level [NO 2 CBG mean = 41 deaths per 100,000 people (95% CI: 12, 68); NO 2 county mean = 38 (95% CI: 11, 64); PM 2:5 CBG mean = 59 (95% CI: 40, 77); and PM 2:5 county mean = 55 (95% CI: 37, 71)]. DISCUSSION: Air pollutant-attributable health burdens varied substantially between neighborhoods, driven by spatial variation in pollutant concentrations and disease rates.

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Section: Discussionmentioning

confidence: 99%

Assessing the Distribution of Air Pollution Health Risks within Cities: A Neighborhood-Scale Analysis Leveraging High-Resolution Data Sets in the Bay Area, California

Southerland

Anenberg

Harris

et al. 2021

Environ Health Perspect

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“…While comparing modeled concentration to observation is the most common and practical way of evaluating emissions inventory, this comparison also should be done with caution because it can give misleading guidance on pollution control policy [74]. Potential sources of uncertainty, including the measuring interference of commercial instrument [74], satellite retrieval error [75], model resolution, changed NO:NO 2 ratios due to diesel vehicle emission control equipment [73], and modeled boundary layer should be carefully considered in the interpretation of NO x emissions inventory. This is an important issue in urban chemistry, because the magnitude and trend of NO x emissions are still controversial in scientific community [76][77][78][79][80].…”

Section: Discussion On Uncertaintiesmentioning

confidence: 99%

Fine-Scale Columnar and Surface NOx Concentrations over South Korea: Comparison of Surface Monitors, TROPOMI, CMAQ and CAPSS Inventory

Kim

Lee

et al. 2020

Atmosphere

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Fine-scale nitrogen oxide (NOx) concentrations over South Korea are examined using surface observations, satellite data and high-resolution model simulations based on the latest emission inventory. While accurate information on NOx emissions in South Korea is crucial to understanding regional air quality in the region, consensus on the validation of NOx emissions is lacking. We investigate the spatial and temporal variation in fine-scale NOx emission sources over South Korea. Surface observations and newly available fine-scale satellite data (TROPOspheric Monitoring Instrument; TROPOMI; 3.5 × 7 km2) are compared with the community multiscale air quality (CMAQ) model based on the clean air policy support system (CAPSS) 2016 emission inventory. The results show that the TROPOMI NO2 column densities agree well with the CMAQ simulations based on CAPSS emissions (e.g., R = 0.96 for June 2018). The surface observations, satellite data and model are consistent in terms of their spatial distribution, the overestimation over the Seoul Metropolitan Area and major point sources; however, the model tends to underestimate the surface concentrations during the cold season.

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“…The molybdenum converter analyzer measures NO 2 indirectly by thermal conversion of NO 2 to NO using molybdenum and detection of NO by chemiluminescence that results from the reaction of NO with ozone. Since the reduction process could convert not only NO 2 but also other reactive nitrogen species, this instrument could overestimate NO 2 concentrations (Dunlea et al, 2007;Steinbacher et al, 2007;Lamsal et al, 2008;Dickerson et al, 2019). The magnitude of interference depends on the relative concentration of NO 2 , nitric acid, alkyl nitrates, and peroxy-acetyl nitrate, which vary spatially, diurnally, and seasonally, and is difficult to quantify.…”

Section: In Situ Surface No 2 Measurementsmentioning

confidence: 99%

Assessment of NO<sub>2</sub> observations during DISCOVER-AQ and KORUS-AQ field campaigns

Choi¹,

Lamsal²,

Follette-Cook³

et al. 2019

Preprint

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Abstract. NASA’s Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) campaign in the United States and the joint NASA and National Institute of Environmental Research (NIER) Korea-United States Air Quality Study (KORUS-AQ) in South Korea were two field study programs that provided comprehensive, integrated datasets of airborne and surface observations of atmospheric constituents, including nitrogen dioxide (NO2), with a goal of improving the interpretation of spaceborne remote sensing data. Various types of NO2 measurements were made, including in situ concentrations and column amounts of NO2 using ground- and aircraft-based instruments, while NO2 column amounts were being derived from the Ozone Monitoring Instrument (OMI) on the Aura satellite. This study takes advantage of these unique data sets by first evaluating in situ data taken from two different instruments on the same aircraft platform, comparing coincidently sampled profile-integrated columns from aircraft spirals with remotely sensed column observations from ground-based Pandora spectrometers, intercomparing column observations from the ground (Pandora), aircraft (in situ vertical spirals), and space (OMI), and evaluating NO2 simulations from coarse Global Modeling Initiative (GMI) and high-resolution regional models. We then use these data to interpret observed discrepancies due to differences in sampling and deficiencies in the data reduction process. Finally, we assess satellite retrieval sensitivity to observed and modeled a-priori NO2 profiles. Contemporaneous measurements from two aircraft instruments that likely sample similar air masses generally agree very well but are also found to differ in integrated columns by up to 33.3 %. These show even larger differences with Pandora, reaching up to 53.1 %, potentially due to a combination of strong gradients in NO2 fields that could be missed by aircraft spirals and errors in the Pandora retrievals. OMI NO2 values are about a factor of two lower in these highly polluted environments, due in part to inaccurate retrieval assumptions (e.g., a priori profiles), but mostly to OMI’s areal (> 312 km2) averaging.

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On the use of data from commercial NOx analyzers for air pollution studies

Cited by 42 publications

References 50 publications

Assessing the Distribution of Air Pollution Health Risks within Cities: A Neighborhood-Scale Analysis Leveraging High-Resolution Data Sets in the Bay Area, California

Assessing the Distribution of Air Pollution Health Risks within Cities: A Neighborhood-Scale Analysis Leveraging High-Resolution Data Sets in the Bay Area, California

Fine-Scale Columnar and Surface NOx Concentrations over South Korea: Comparison of Surface Monitors, TROPOMI, CMAQ and CAPSS Inventory

Assessment of NO<sub>2</sub> observations during DISCOVER-AQ and KORUS-AQ field campaigns

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On the use of data from commercial NOx analyzers for air pollution studies

Cited by 42 publications

References 50 publications

Assessing the Distribution of Air Pollution Health Risks within Cities: A Neighborhood-Scale Analysis Leveraging High-Resolution Data Sets in the Bay Area, California

Assessing the Distribution of Air Pollution Health Risks within Cities: A Neighborhood-Scale Analysis Leveraging High-Resolution Data Sets in the Bay Area, California

Fine-Scale Columnar and Surface NOx Concentrations over South Korea: Comparison of Surface Monitors, TROPOMI, CMAQ and CAPSS Inventory

Assessment of NO&lt;sub&gt;2&lt;/sub&gt; observations during DISCOVER-AQ and KORUS-AQ field campaigns

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Assessment of NO<sub>2</sub> observations during DISCOVER-AQ and KORUS-AQ field campaigns