Investigating the Sources of Formaldehyde and Corresponding Photochemical Indications at a Suburb Site in Shanghai From MAX‐DOAS Measurements

Zhang, Sanbao; Wang, Shanshan; Zhang, Ruifeng; Guo, Yanlin; Yan, Yuhao; Ding, Z. F.; Zhou, Bin

doi:10.1029/2020jd033351

Cited by 17 publications

(4 citation statements)

References 114 publications

(165 reference statements)

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“…This point was documented in accordance with previous studies (Fan et al., 2021 ; Y. Sun et al., 2021 ; S. Zhang et al., 2021 ).…”

Section: Resultssupporting

confidence: 93%

“…Further details regarding the aerosol and trace gas profile retrievals, including the corresponding aerosol parameters input in SCIATRAN and a priori information settings, are extensively described in S. Zhang et al. ( 2021 ). Examples of aerosol extinction coefficient, NO 2 and HCHO profile retrievals are exhibited in Figure S2 in Supporting Information S1 .…”

Section: Max‐doas Measurements and Ancillary Datasetsmentioning

confidence: 99%

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Impact Assessment of COVID‐19 Lockdown on Vertical Distributions of NO₂ and HCHO From MAX‐DOAS Observations and Machine Learning Models

et al. 2022

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Responses to the COVID‐19 pandemic led to major reductions on air pollutant emissions in modern history. To date, there has been no comprehensive assessment for the impact of lockdowns on the vertical distributions of nitrogen dioxide (NO2) and formaldehyde (HCHO). Based on profiles from 0 to 2 km retrieved by Multi‐AXis‐Differential Optical Absorption Spectroscopy observation and a large volume of real‐time data at a suburb site in Shanghai, China, four types of machine learning models were developed and compared, including multiple linear regression, support vector machine, bagged trees (BT), and artificial neural network. Ultimately BT model was employed to reproduce NO2 and HCHO profiles with the best performance. Predictions with different meteorological and surface pollution scenarios were conducted from 2017 to 2019, for assessing the corresponding impacts on the changes of NO2 and HCHO profiles during COVID‐19 lockdown. The simulations illustrate that the NO2 decreased in 2020 by 43.8%, 45.5%, and 44.6%, relative to 2017, 2018, and 2019, respectively. For HCHO, the lockdown‐induced situation presented the declines of 28.6%, 32.1%, and 10.9%, respectively. In the comparisons of vertical distributions, NO2 maintained decreasing at all altitudes, while HCHO decreased at low altitudes and increased at high altitudes. During COVID‐19 lockdown, the reduction of NO2 and HCHO from the variation of surface pollutants was dominated below 0.5 km, while the relevant meteorological factors played a more significant role above 0.5 km.

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“…This point was documented in accordance with previous studies (Fan et al., 2021 ; Y. Sun et al., 2021 ; S. Zhang et al., 2021 ).…”

Section: Resultssupporting

confidence: 93%

Section: Max‐doas Measurements and Ancillary Datasetsmentioning

confidence: 99%

Impact Assessment of COVID‐19 Lockdown on Vertical Distributions of NO₂ and HCHO From MAX‐DOAS Observations and Machine Learning Models

et al. 2022

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“…37 The model tended to underestimate both HCHO and acetone in cases BS and S1. Comparison with the HCHO concentration from satellite retrieval at a monitoring site near Dianshan Lake, a rural area in Shanghai from Oct 17 to Nov 21, 2019 (3.5 ± 1.4 ppbV) 55 also indicated its underprediction in cases BS and S1 (1.0−1.4 ppbV). One possible reason for this may be the underestimated background contribution in the model.…”

Section: ■ Materials and Methodsmentioning

confidence: 94%

Fate of Oxygenated Volatile Organic Compounds in the Yangtze River Delta Region: Source Contributions and Impacts on the Atmospheric Oxidation Capacity

Xie

et al. 2022

Environ. Sci. Technol.

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The Community Multiscale Air Quality model (CMAQv5.2) was implemented to investigate the sources and sinks of oxygenated volatile organic compounds (OVOCs) during a high O 3 and high PM 2.5 season in the Yangtze River Delta (YRD) region, based on constraints from observations. The model tends to overpredict nonoxygenated VOCs and underpredict OVOCs, which has been improved with adjusted emissions of all VOCs. The OVOCs in the YRD are dominated by ketones, aldehydes, and alcohols. Ketones and aldehydes mainly originate from direct emissions and secondary formation in the northern YRD, and primarily originate from secondary formation in the southern part influenced by biogenic emissions. The concentration of secondary organic aerosols (SOA) produced by OVOCs is 0.5−1.5 μg/m 3 , with 40−80% originated from organic nitrates, 20−70% originated from dicarbonyls, and 0−20% originated from isoprene epoxydiols. The influences of OVOCs on the atmospheric oxidation capacity are dominated by the OH • pathway during the day (∼350%) and by the NO 3• pathway at night (∼150%). Consequently, O 3 is enhanced by 30−70% in the YRD. Aerosols are also enhanced by 50−140%, 20−80%, and ∼20% for SOA, nitrate, and sulfate, respectively.

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“…The MAX-DOAS measurements were performed at the Dianshan Lake (DSL) Environmental Monitoring Station in Shanghai (31.10° N, 120.98° E). − The MAX-DOAS system employed in this research is composed of a scanning telescope, a camera, a spectrometer (Ocean Optics, QE65 Pro), and a computer operating the MAX-DOAS system. The spectrometer, equipped with a charge-coupled device (CCD) detector (1044 horizontal × 64 vertical), measures the wavelength range from 300 to 480 nm with a spectral resolution of 0.5 nm full width half-maximum (fwhm).…”

Section: Methodsmentioning

confidence: 99%

Stacking Machine Learning Models Empowered High Time-Height-Resolved Ozone Profiling from the Ground to the Stratopause Based on MAX-DOAS Observation

Zhang,

Wang,

Zhu

et al. 2024

Environ. Sci. Technol.

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Ozone (O3) profiles are crucial for comprehending the intricate interplay among O3 sources, sinks, and transport. However, conventional O3 monitoring approaches often suffer from limitations such as low spatiotemporal resolution, high cost, and cumbersome procedures. Here, we propose a novel approach that combines multiaxis differential optical absorption spectroscopy (MAX-DOAS) and machine learning (ML) technology. This approach allows the retrieval of O3 profiles with exceptionally high temporal resolution at the minute level and vertical resolution reaching the hundred-meter scale. The ML models are trained using parameters obtained from radiative transfer modeling, MAX-DOAS observations, and a reanalysis data set. To enhance the accuracy of retrieving the aqueous phosphorus from O3, we employ a stacking approach in constructing ML models. The retrieved MAX-DOAS O3 profiles are compared to data from an in situ instrument, lidar, and satellite observation, demonstrating a high level of consistency. The total error of this approach is estimated to be within 25%. On balance, this study is the first ground-based passive remote sensing of high time-height-resolved O3 distribution from ground to the stratopause (0–60 km). It opens up new avenues for enhancing our understanding of the dynamics of O3 in atmospheric environments. Moreover, the cost-effective and portable MAX-DOAS combined with this versatile profiling approach enables the potential for stereoscopic observations of various trace gases across multiple platforms.

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Investigating the Sources of Formaldehyde and Corresponding Photochemical Indications at a Suburb Site in Shanghai From MAX‐DOAS Measurements

Cited by 17 publications

References 114 publications

Impact Assessment of COVID‐19 Lockdown on Vertical Distributions of NO₂ and HCHO From MAX‐DOAS Observations and Machine Learning Models

Impact Assessment of COVID‐19 Lockdown on Vertical Distributions of NO₂ and HCHO From MAX‐DOAS Observations and Machine Learning Models

Fate of Oxygenated Volatile Organic Compounds in the Yangtze River Delta Region: Source Contributions and Impacts on the Atmospheric Oxidation Capacity

Stacking Machine Learning Models Empowered High Time-Height-Resolved Ozone Profiling from the Ground to the Stratopause Based on MAX-DOAS Observation

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Investigating the Sources of Formaldehyde and Corresponding Photochemical Indications at a Suburb Site in Shanghai From MAX‐DOAS Measurements

Cited by 17 publications

References 114 publications

Impact Assessment of COVID‐19 Lockdown on Vertical Distributions of NO2 and HCHO From MAX‐DOAS Observations and Machine Learning Models

Impact Assessment of COVID‐19 Lockdown on Vertical Distributions of NO2 and HCHO From MAX‐DOAS Observations and Machine Learning Models

Fate of Oxygenated Volatile Organic Compounds in the Yangtze River Delta Region: Source Contributions and Impacts on the Atmospheric Oxidation Capacity

Stacking Machine Learning Models Empowered High Time-Height-Resolved Ozone Profiling from the Ground to the Stratopause Based on MAX-DOAS Observation

Contact Info

Product

Resources

About

Impact Assessment of COVID‐19 Lockdown on Vertical Distributions of NO₂ and HCHO From MAX‐DOAS Observations and Machine Learning Models

Impact Assessment of COVID‐19 Lockdown on Vertical Distributions of NO₂ and HCHO From MAX‐DOAS Observations and Machine Learning Models