Remote Sensing for Air Pollution Meteorology

Beran, D. W.; Hall, Freeman F.

doi:10.1175/1520-0477(1974)055<1097:rsfapm>2.0.co;2

Cited by 24 publications

(5 citation statements)

References 18 publications

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“…Such hypothetical city usually contains industrial region and line sources of emission in the form of highways. Beran and Hall (1974) also made the following important remark:…”

Section: Identification Of Problemsmentioning

confidence: 99%

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Emission Monitoring Mobile Experiment (EMME): an overview and first results of the St. Petersburg megacity campaign-2019

Makarova¹,

Alberti²,

Ionov³

et al. 2020

Preprint

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Abstract. Global climate change is one of the most important scientific, societal and economic contemporary challenges. Fundamental understanding of the major processes driving climate change is the key problem which is to be solved not only on a global but also on regional scales. The accuracy of regional climate modelling depends on a number of factors. One of these factors is the adequate and comprehensive information on the anthropogenic impact which is highest in industrial regions and areas with dense population – modern megacities. Megacities are not only heat islands, but also significant sources of emissions of various substances into the atmosphere, including greenhouse and reactive gases. In 2019, the mobile experiment EMME (Emission Monitoring Mobile Experiment) was conducted within the St. Petersburg agglomeration (Russia) aiming to estimate the emission intensity of greenhouse (CO2, CH4) and reactive (CO, NOx) gases for St. Petersburg which is the largest Northern megacity. St. Petersburg State University (Russia), Karlsruhe Institute of Technology (Germany) and the University of Bremen (Germany) jointly ran this experiment. The core instruments of the campaign were two portable FTIR spectrometers Bruker EM27/SUN which were used for ground-based remote sensing measurements of the total column amount of CO2, CH4 and CO at upwind and downwind locations on the opposite sides of the city. The NO2 tropospheric column amount was observed along a circular highway around the city by continuous mobile measurements of scattered solar visible radiation with OceanOptics HR4000 spectrometer using the DOAS technique. Simultaneously, air samples were collected in air bags for subsequent laboratory analysis. The air samples were taken at the locations of FTIR observations at the ground level and also at altitudes of about hundred meters when airbags were lifted by a kite (in case of suitable landscape and favourable wind conditions). The entire campaign consisted of 11 mostly cloudless days of measurements in March–April 2019. Planning of measurements for each day included the determination of optimal location for FTIR spectrometers based on weather forecasts combined with the numerical modelling of the pollution transport in the megacity area. The real-time corrections of the FTIR operation sites were performed depending on the actual evolution of the megacity NOx plume as detected by the mobile DOAS observations. The data processing activities included the following steps: (1) the generation of calibrated spectra from raw interferograms; (2) the retrievals of the CO2, CH4, and CO column averaged abundances using the software tools provided by the COCCON (Collaborative Carbon Column Observing Network); (3) the retrieval of tropospheric NO2 amount from DOAS measurements; (4) the laboratory analysis of air samples; (5) the numerical modelling of the plume movement based on the actual meteorological information. The estimates of the St. Petersburg emission intensities for the considered greenhouse and reactive gases were obtained by coupling a box model and the results of the EMME observational campaign using the mass balance approach. The CO2 emission flux for St. Petersburg as an area source was estimated as 89 ± 28 kt km-2 yr-1 which is two times higher than the corresponding value in the EDGAR database. The experiment revealed the CH4 emission flux of 135 ± 68 t km-2 yr-1 which is about one order of magnitude greater than the value reported by the official inventories of St. Petersburg emissions (~17 t km-2 yr-1 for 2017). At the same time, for the urban territory of St. Petersburg, both the EMME experiment and the official inventories for 2017 give similar results for the CO anthropogenic flux (251 ± 104 t km-2 yr-1 vs. 280 t km-2 yr-1) and for the NOx anthropogenic flux (66 ± 28 t km-2 yr-1 vs. 47 t km-2 yr-1).

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“…Such hypothetical city usually contains industrial region and line sources of emission in the form of highways. Beran and Hall (1974) also made the following important remark:…”

Section: Identification Of Problemsmentioning

confidence: 99%

“…When studying the application of the remote sensing instruments to the problem of the air pollution meteorology, Beran and Hall (1974) noted:…”

Section: Identification Of Problemsmentioning

confidence: 99%

Emission Monitoring Mobile Experiment (EMME): an overview and first results of the St. Petersburg megacity campaign-2019

Makarova¹,

Alberti²,

Ionov³

et al. 2020

Preprint

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“…In many real applications, one only has the chance to observe a phenomenon and its dynamics once it happens. For examples, recording seismic waves [9], satellite data of weather development [15], remote sensing of air pollution [4], etc. Either the event does not happen often or when it happens next time, the environment and setup is very different.…”

Section: Introductionmentioning

confidence: 99%

How much can one learn a partial differential equation from its solution?

He¹,

Zhao²,

Zhong³

2022

Preprint

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In this work we study the problem about learning a partial differential equation (PDE) from its solution data. PDEs of various types are used as examples to illustrate how much the solution data can reveal the PDE operator depending on the underlying operator and initial data. A data driven and data adaptive approach based on local regression and global consistency is proposed for stable PDE identification. Numerical experiments are provided to verify our analysis and demonstrate the performance of the proposed algorithms.

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“…Analysis of the residuals of PM estimates predicted from AOT against meteorological parameters is able to reveal their influence on the accuracy of prediction. Such knowledge is very critical to comprehend the surface conditions in remotely sensing the air pollution situation on the ground level reliably from space and avoid incorrect estimates [12].…”

Section: Introductionmentioning

confidence: 99%

Meteorological Influence on Predicting Air Pollution from MODIS-Derived Aerosol Optical Thickness: A Case Study in Nanjing, China

Gao

Zha

2010

Remote Sensing

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Abstract:Whether the aerosol optical thickness (AOT) products derived from MODIS data can be used as a reliable proxy of air pollutants measured near the surface depends on meteorological influence. This study attempts to assess the influence of four meteorological parameters (air pressure, temperature, relative humidity, and wind velocity) on predicting air pollution from MODIS AOT data for the city of Nanjing, China. It is found that PM 10 (particulate matter with a diameter <10 μm) is linearly predictable from AOT at R 2 = 0.438. Seasonally, the prediction accuracy is much higher in summer (R 2 = 0.749, n = 24) and autumn (R 2 = 0.634, n = 45), but much lower in spring and winter (R 2 < 0.3). Scatterplots of the four meteorological parameters versus the residuals of PM 10estimates from AOT reveal no definite relationship between them. Thus, the seasonality of any parameters cannot account for the variation in the prediction accuracy. Stepwise regression analysis reveals that air pressure (R 2 = 0.268) is the most important factor affecting the residuals of PM 10 (ΔPM 10 ) estimates in summer. In winter, the most important parameters are air pressure and air temperature (R 2 = 0.278). This accuracy rises to 0.510, similar to 0.409 for the summer season, if all four parameters are used in modeling ΔPM 10 . However, no such models can be established for spring and autumn. It is concluded that meteorological parameters exert a mixed influence on the predictability of air pollution from AOT in the study area where air pollutants are dominated by suspended particulates. OPEN ACCESSRemote Sensing 2010, 2 2137

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Remote Sensing for Air Pollution Meteorology

Cited by 24 publications

References 18 publications

Emission Monitoring Mobile Experiment (EMME): an overview and first results of the St. Petersburg megacity campaign-2019

Emission Monitoring Mobile Experiment (EMME): an overview and first results of the St. Petersburg megacity campaign-2019

How much can one learn a partial differential equation from its solution?

Meteorological Influence on Predicting Air Pollution from MODIS-Derived Aerosol Optical Thickness: A Case Study in Nanjing, China

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