Review of surface particulate monitoring of dust events using geostationary satellite remote sensing

Sowden, Miles; Mueller, Ute; Blake, David

doi:10.1016/j.atmosenv.2018.04.020

Cited by 45 publications

(19 citation statements)

References 126 publications

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“…On the other hand, there are certain issues with the satellite remote sensing observations as well, such as limited coverage caused by cloud contamination and biases caused by assumptions in retrieval algorithms (J. Zhang and Reid, 2009). Studies based on the aerosol optical depth (AOD) data provided by satellite remote sensing may have difficulties to accurately distinguish different types of aerosols (natural and human‐induced) (Sowden et al., 2018). Because of these limitations, some of the previous results on the impacts of the ENSO cycle on dust activities appeared to be contradictory (e.g., Abish & Mohanakumar, 2013; Kim et al., 2016).…”

Section: Introductionmentioning

confidence: 99%

Global Impact of ENSO on Dust Activities with Emphasis on the Key Region from the Arabian Peninsula to Central Asia

et al. 2021

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The El Niño‐Southern Oscillation (ENSO) has been found to be closely related to dust aerosol activities in many parts of the world. However, previous studies have rarely explored the ENSO’s impacts in the global context. In this study, the responses of the interannual variability of global atmospheric dust content to the ENSO cycle (including the eastern Pacific and central Pacific types) are examined using the MERRA‐2 atmospheric aerosol reanalysis data. The ENSO‐related tropical eastern Pacific sea surface temperature (SST) anomalies in the antecedent winter (November to January) have the most significant effect on the airborne dust content in a region from the Arabian Peninsula to Central Asia during the following spring and summer (April to July), which is identified as the key region (KR) of the ENSO’s impacts. The positive (negative) tropical eastern Pacific SST anomalies in the preceding winter during the El Niño (La Niña) years correspond to significantly reduced (increased) dust column content in the following spring and summer over the KR, which was confirmed using singular value decomposition. With the above (below)‐normal winter SST anomalies in the tropical eastern Pacific, the Walker Circulation weakens (strengthens), and the divergence (convergence) over the western tropical Pacific and the convergence (divergence) over the southern tropical Indian Ocean in the lower troposphere are strengthened, which facilitate (restrain) the southward diffusion of airborne dust in the KR. Meanwhile, the increased (decreased) precipitation and soil moisture and the corresponding vegetation conditions help limit (promote) the dust emission in the KR during the El Niño (La Niña) years.

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Section: Introductionmentioning

confidence: 99%

Global Impact of ENSO on Dust Activities with Emphasis on the Key Region from the Arabian Peninsula to Central Asia

et al. 2021

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“…[13][14][15][16] In the sense that semiconductor gas sensors can be manufactured on a microchip scale and deployed with high density, a network of microair quality sensors is expected to provide a spatially resolved map of air pollution. [17][18][19] One of highly promising candidates for this application is metal-oxide semiconductor (MOS) sensor arrays. MOS sensor arrays have been demonstrated to detect a variety of gas analytes with sensitivities of parts per billions (ppb) and developed into integrated systems for various purposes.…”

Section: Introductionmentioning

confidence: 99%

On‐Chip Chemiresistive Sensor Array for On‐Road NO_x Monitoring with Quantification

et al. 2020

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The adverse effects of air pollution on respiratory health make air quality monitoring with high spatial and temporal resolutions essential especially in cities. Despite considerable interest and efforts, the application of various types of sensors is considered immature owing to insufficient sensitivity and cross-interference under ambient conditions. Here, a fully integrated chemiresistive sensor array (CSA) with parts-per-trillion sensitivity is demonstrated with its application for on-road NO x monitoring. An analytical model is suggested to describe the kinetics of the sensor responses and quantify molecular binding affinities. Finally, the full characterization of the system is connected to implement on-road measurements on NO x vapor with quantification as its ultimate field application. The obtained results suggest that the CSA shows potential as an essential unit to realize an air-quality monitoring network with high spatial and temporal resolutions.

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“…Many statistical models have been used for the ground PM estimation of AOD and other predictors, such as linear regression models (Kim et al, 2019), random forest models (Stafoggia et al, 2019), neural network models (Sowden et al, 2018), and generalized additive models . However, with the introduction of new machine learning models, the traditional regression model reflects the inability to balance time, space, and random precision.…”

Section: Introductionmentioning

confidence: 99%

PM_2.5 ∕ PM₁₀ ratio prediction based on a long short-term memory neural network in Wuhan, China

Wang

et al. 2020

Geosci. Model Dev.

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Abstract. Air pollution is a serious problem in China that urgently needs to be addressed. Air pollution has a great impact on the lives of citizens and on urban development. The particulate matter (PM) value is usually used to indicate the degree of air pollution. In addition to that of PM2.5 and PM10, the use of the PM2.5 ∕ PM10 ratio as an indicator and assessor of air pollution has also become more widespread. This ratio reflects the air pollution conditions and pollution sources. In this paper, a better composite prediction system aimed at improving the accuracy and spatiotemporal applicability of PM2.5 ∕ PM10 was proposed. First, the aerosol optical depth (AOD) in 2017 in Wuhan was obtained based on Moderate Resolution Imaging Spectroradiometer (MODIS) images, with a 1 km spatial resolution, by using the dense dark vegetation (DDV) method. Second, the AOD was corrected by calculating the planetary boundary layer height (PBLH) and relative humidity (RH). Third, the coefficient of determination of the optimal subset selection was used to select the factor with the highest correlation with PM2.5 ∕ PM10 from meteorological factors and gaseous pollutants. Then, PM2.5 ∕ PM10 predictions based on time, space, and random patterns were obtained by using nine factors (the corrected AOD, meteorological data, and gaseous pollutant data) with the long short-term memory (LSTM) neural network method, which is a dynamic model that remembers historical information and applies it to the current output. Finally, the LSTM model prediction results were compared and analyzed with the results of other intelligent models. The results showed that the LSTM model had significant advantages in the average, maximum, and minimum accuracy and the stability of PM2.5 ∕ PM10 prediction.

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Review of surface particulate monitoring of dust events using geostationary satellite remote sensing

Cited by 45 publications

References 126 publications

Global Impact of ENSO on Dust Activities with Emphasis on the Key Region from the Arabian Peninsula to Central Asia

Global Impact of ENSO on Dust Activities with Emphasis on the Key Region from the Arabian Peninsula to Central Asia

On‐Chip Chemiresistive Sensor Array for On‐Road NO_x Monitoring with Quantification

PM_2.5 ∕ PM₁₀ ratio prediction based on a long short-term memory neural network in Wuhan, China

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Review of surface particulate monitoring of dust events using geostationary satellite remote sensing

Cited by 45 publications

References 126 publications

Global Impact of ENSO on Dust Activities with Emphasis on the Key Region from the Arabian Peninsula to Central Asia

Global Impact of ENSO on Dust Activities with Emphasis on the Key Region from the Arabian Peninsula to Central Asia

On‐Chip Chemiresistive Sensor Array for On‐Road NOx Monitoring with Quantification

PM2.5 ∕ PM10 ratio prediction based on a long short-term memory neural network in Wuhan, China

Contact Info

Product

Resources

About

On‐Chip Chemiresistive Sensor Array for On‐Road NO_x Monitoring with Quantification

PM_2.5 ∕ PM₁₀ ratio prediction based on a long short-term memory neural network in Wuhan, China