Abstract:Hurricane Ida made landfall on August 29, 2021, in southwestern
Louisiana and devastated the region’s industrial landscape.
Its disruptions to atmospheric composition were detected by the TROPOspheric
Monitoring Instrument (TROPOMI). This study quantifies NO2 spatial changes and estimates top-down NO
x
emissions for two cities impacted by Hurricane Ida, New Orleans
and Baton Rouge. Considering the difference in NO2 lifetime
pre- and post-Ida, top-down derived NO
x
emissions for each impacted city saw decreas… Show more
“…Specifically, the relative cumulative frequency of the magnitude is used to assess the region and reveal where high magnitude values encompassing a pollutant hotspot are found. Following (Lee et al., 2022; Matschullat et al., 2000) the relative cumulative frequency of the magnitude is used to determine the threshold below which data is excluded and the main pollutant burden is revealed. The relative cumulative frequency curve is fitted with a piecewise linear function which produces “turning points” to be used as threshold values.…”
Satellite instruments have the most potential of capturing trace gas variability as they continually observe the atmosphere and its composition over wide regions. Yet the increasingly large data size of satellite products poses a challenge for their use as traditional data processing methods (e.g., averaging) may not be effective to extract the spatiotemporal variability without prior knowledge of an emission source's spatial and temporal behavior, such as location, time, and plume shape. Here, an agile clustering algorithm entitled CLustering of Atmospheric Satellite Products (CLASP) is presented to identify the spatiotemporal variability of trace gases captured in satellite observations. We find the knowledge discovery method for large data sets, clustering, is suited for identifying the variability of trace gases in satellite observations, as such CLASP is rooted in density‐based clustering methods. CLASP detects features from satellite observations and identifies their spatial, magnitude, and temporal axis leading to a better understanding of the spatiotemporal variability of atmospheric trace gases. To test the applicability of CLASP, the algorithm is applied to TROPOspheric Monitoring Instrument NO2 observations illustrating some of its different capabilities. Implementing CLASP for event identification, capturing plume variability, and source detection, CLASP identified wildfires, observed disruptions from COVID‐19 lockdown restrictions, and detected irregular emissions from oil and gas operations.
“…Specifically, the relative cumulative frequency of the magnitude is used to assess the region and reveal where high magnitude values encompassing a pollutant hotspot are found. Following (Lee et al., 2022; Matschullat et al., 2000) the relative cumulative frequency of the magnitude is used to determine the threshold below which data is excluded and the main pollutant burden is revealed. The relative cumulative frequency curve is fitted with a piecewise linear function which produces “turning points” to be used as threshold values.…”
Satellite instruments have the most potential of capturing trace gas variability as they continually observe the atmosphere and its composition over wide regions. Yet the increasingly large data size of satellite products poses a challenge for their use as traditional data processing methods (e.g., averaging) may not be effective to extract the spatiotemporal variability without prior knowledge of an emission source's spatial and temporal behavior, such as location, time, and plume shape. Here, an agile clustering algorithm entitled CLustering of Atmospheric Satellite Products (CLASP) is presented to identify the spatiotemporal variability of trace gases captured in satellite observations. We find the knowledge discovery method for large data sets, clustering, is suited for identifying the variability of trace gases in satellite observations, as such CLASP is rooted in density‐based clustering methods. CLASP detects features from satellite observations and identifies their spatial, magnitude, and temporal axis leading to a better understanding of the spatiotemporal variability of atmospheric trace gases. To test the applicability of CLASP, the algorithm is applied to TROPOspheric Monitoring Instrument NO2 observations illustrating some of its different capabilities. Implementing CLASP for event identification, capturing plume variability, and source detection, CLASP identified wildfires, observed disruptions from COVID‐19 lockdown restrictions, and detected irregular emissions from oil and gas operations.
“…Mass-balance or curve-fitting methods can directly invert NO x emissions using satellite NO 2 measurements, without the aid of chemical model simulations. − The Exponential Modified Gaussian (EMG) model, which accounts for the highly nonlinear small-scale chemistry and horizontal dispersion of the NO 2 plume, can effectively quantify NO x emissions from isolated point sources, such as large industrial facilities and power plants. , Comparing the observed variation in NO 2 patterns under windy and calm conditions can effectively mitigate the effects of nearby interfering sources and achieve greater precision in determining NO 2 lifetimes and NO x emissions . In addition, Beirle et al introduced a flux divergence method according to the steady-state continuity equation to retrieve NO x emissions for Riyadh with full coverage and high spatial resolution.…”
Bottom-up
emission inventories of atmospheric nitrogen
oxides (NO
x
= NO + NO2) are
usually limited
to annual updates and have large uncertainties. The recent launch
of the Geostationary Environmental Monitoring Spectrometer (GEMS)
first provides hourly measurements of trace gas pollutants from space,
enabling new insights into the diurnal variations in anthropogenic
NO
x
emissions. In this study, we present
an improved top-down estimation of NO
x
emission using GEMS NO2 observations and characterize
the hourly NO
x
emissions over cities in
East Asia. We use the Gaussian model and polynomial fitting to calculate
the hourly NO2 lifetimes for several “point-source”
cities and then derive NO
x
emissions using
the flux divergence method. GEMS observations show significant hourly
variations in the NO
x
emissions. Systematic
biases in NO
x
emission estimates are found
between the GEMS-based hourly estimations and previous polar-orbiting
satellites with a single daily overpass. Compared to using empirically
diurnal emission factors, chemical model simulations using GEMS-based
NO
x
emissions substantially reduce the
biases with satellite and surface NO2 measurements (e.g.,
for Wuhan, the biases decreased by 31%). This study highlights the
essential role of geostationary satellite observations in characterizing
the emission and chemistry of atmospheric pollutants and informing
emission control policies.
“…For gases (mainly NO 2 and SO 2 ) emitted from point sources (e.g., megacities or power plants), the combined analysis of satellite observations and wind fields reveals the downwind decay of plumes and has been further used to estimate their lifetimes and emissions (Beirle et al., 2011; de Foy et al., 2015; Fioletov et al., 2016; Goldberg et al., 2019; Lee et al., 2022; Lu et al., 2015). However, similar observation‐based approaches have long been recognized as missing for NMVOCs, which are equally important for urban air pollution (von Schneidemesser et al., 2023).…”
Non‐methane volatile organic compounds (NMVOCs) have a significant impact on air quality in urban areas. Detecting NMVOCs emission with its proxy HCHO on urban scales from space, however, has been limited by the lack of discernible enhancement. Here we show clear urban HCHO plumes from 16 cities over the globe by rotating TROPOspheric Monitoring Instrument HCHO pixels according to wind directions. We fit the downwind structure of the plumes with the exponentially modified Gaussian approach to quantify urban HCHO effective production rates between 7.0 and 88.5 mol s−1. Our results are in line with total NMVOC emissions from the EDGAR inventory (r = 0.76). Our work offers a new measure of total NMVOC emissions from urban areas and highlights the potential of satellite HCHO data to provide new information for monitoring urban air quality.
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