High resolution satellite observations give new view of UK air quality

Pope, Richard J.; Graham, Ailish M.; Chipperfield, Martyn; Veefkind, J. P.

doi:10.1002/wea.3441

Cited by 6 publications

(4 citation statements)

References 19 publications

(26 reference statements)

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“…GCAS operates as a satellite analog in NASA airborne missions and its high-spatial-resolution observations now provide data for TROPOMI validation . Launched in October 2017, TROPOMI has been shown to generate detailed NO 2 column maps, revealing hotspots undetectable by past space-based sensors due to its order of magnitude-improved spatial resolution. ,− Here, we use the high-spatial-resolution, but limited duration, GCAS dataset to evaluate TROPOMI-derived annual differences in census tract-scale NO 2 columns, both along the GCAS flight track and across the HMA. The comparison provides an evaluation of the suitability of TROPOMI observations to resolve key horizontal NO 2 gradients for assessing air pollution disparities.…”

Section: Resultsmentioning

confidence: 99%

Observing Nitrogen Dioxide Air Pollution Inequality Using High-Spatial-Resolution Remote Sensing Measurements in Houston, Texas

Demetillo

Navarro

Knowles

et al. 2020

Environ. Sci. Technol.

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Houston, Texas is a major U.S. urban and industrial area where poor air quality is unevenly distributed and a disproportionate share is located in low-income, non-white, and Hispanic neighborhoods. We have traditionally lacked city-wide observations to fully describe these spatial heterogeneities in Houston and in cities globally, especially for reactive gases like nitrogen dioxide (NO 2 ). Here, we analyze novel high-spatial-resolution (250 m × 500 m) NO 2 vertical columns measured by the NASA GCAS airborne spectrometer as part of the September-2013 NASA DISCOVER-AQ mission and discuss differences in population-weighted NO 2 at the census-tract level. Based on the average of 35 repeated flight circuits, we find 37 ± 6% higher NO 2 for non-whites and Hispanics living in low-income tracts (LIN) compared to whites living in highincome tracts (HIW) and report NO 2 disparities separately by race ethnicity (11−32%) and poverty status (15−28%). We observe substantial time-of-day and day-to-day variability in LIN-HIW NO 2 differences (and in other metrics) driven by the greater prevalence of NO x (NO + NO 2 ) emission sources in low-income, non-white, and Hispanic neighborhoods. We evaluate measurements from the recently launched satellite sensor TROPOMI (3.5 km × 7 km at nadir), averaged to 0.01°× 0.01°using physics-based oversampling, and demonstrate that TROPOMI resolves similar relative, but not absolute, tract-level differences compared to GCAS. We utilize the high-resolution FIVE and NEI NO x inventories, plus one year of TROPOMI weekday−weekend variability, to attribute tract-level NO 2 disparities to industrial sources and heavy-duty diesel trucking. We show that GCAS and TROPOMI spatial patterns correspond to the surface patterns measured using aircraft profiling and surface monitors. We discuss opportunities for satellite remote sensing to inform decision making in cities generally.

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Section: Resultsmentioning

confidence: 99%

Observing Nitrogen Dioxide Air Pollution Inequality Using High-Spatial-Resolution Remote Sensing Measurements in Houston, Texas

Demetillo

Navarro

Knowles

et al. 2020

Environ. Sci. Technol.

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“…It was seen in the past decade that the air quality of Uttarakhand has been degrading to quite an extent and proper measures need to be taken so that the state located in the lapse of Himalayas does not fall prey to industrialization and severely polluted air. Thus proper monitoring of air quality should be done with the help of satellite data and machine learning methods [4][5][6]. Time series analysis of the satellite derived data can be done to find the seasonality, trend, etc of the data and we can predict some future data, trends and seasonality on the air quality parameters (Time series analysis is a way of collecting and analyzing data points over some time.…”

Section: Introductionmentioning

confidence: 99%

“…For that, we need to do the AD Fuller test on the series and if the value of p comes below 0.05 we can conclude that our series is stationary and hence we can proceed further with differentdifferent tests. (all the time series of air quality parameters are shown in Figs 2,3,4,5,6)…”

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confidence: 99%

Air Quality Assessment of Uttarakhand (India) Using Satellite Data and Machine Learning Techniques

Chandra

Verma

Anand

et al. 2022

CJAST

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Degrading Air Quality is a major concern for all species on this planet. Over the years, it is seen that air quality is constantly degrading mainly because of the reasons such as industrialisation, deforestation, and green-house effect. Main parameters to be considered for the Air Quality are the Carbon Monoxide (CO), Nitrogen Dioxide (NO2), Sulphur Dioxide (SO2), Ozone (O3) and Aerosols. A study of these parameters changing over time is necessary so to keep a check on the degrading air quality. In this study, the data of Carbon Monoxide (CO), Nitrogen Dioxide (NO2), Sulphur Dioxide (SO2), Ozone (O3) and Aerosols are taken for the past 5 years i.e. 2018 to 2022 and their time series is extracted thereafter a test on stationarity is done so as to know whether these series are stationary or not. Two machine learning models namely Holt winter’s Smoothing and FbProphet is applied to predict the value adjacent to the original value and a error metric is comparison is done to find out which model is best suited for forecasting these Air Quality parameters.

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“…Certain regions, such as East Asia, the Middle East and the USA, feature heavily in satellite air quality studies, but difficulties with retrievals over the UK (see Section 3.1) mean it is often neglected, and UK sources are generally omitted from larger studies. Of the handful of UK studies in the literature, the majority focus on regional or city scale emission trends over time [23,[25][26][27][28][29]. This is not sufficient for air quality regulation, because emissions need to be attributable to individual sites/sources to inform the assessment of compliance.…”

Section: Introductionmentioning

confidence: 99%

Satellite Data Applications for Site-Specific Air Quality Regulation in the UK: Pilot Study and Prospects

et al. 2021

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Atmospheric composition data from satellite platforms offers great potential for improving current understanding of anthropogenic emissions. Whilst this data has been used extensively in research, its use by governments to regulate and assess site-specific legislation compliance is minimal. Here, we outline the regulatory context for air quality regulation in the UK, and present a pilot study highlighting the potential of current instruments. The pilot study demonstrates the capabilities and limitations of the TROPOspheric Monitoring Instrument (TROPOMI) for detecting and isolating emissions of NO2 from regulated UK point sources. This study successfully isolated NO2 emissions from a cluster of three closely situated regulated sites in the north east of England, despite their proximity to large urban sources. This is the first time these sites have been resolved from satellite-based observations, and serves as a clear demonstration of the potential of current and future Earth observation data products for site-specific monitoring and investigation within the UK.

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High resolution satellite observations give new view of UK air quality

Cited by 6 publications

References 19 publications

Observing Nitrogen Dioxide Air Pollution Inequality Using High-Spatial-Resolution Remote Sensing Measurements in Houston, Texas

Observing Nitrogen Dioxide Air Pollution Inequality Using High-Spatial-Resolution Remote Sensing Measurements in Houston, Texas

Air Quality Assessment of Uttarakhand (India) Using Satellite Data and Machine Learning Techniques

Satellite Data Applications for Site-Specific Air Quality Regulation in the UK: Pilot Study and Prospects

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