The MAPM (Mapping Air Pollution eMissions) method for inferring particulate matter emissions maps at city scale from in situ concentration measurements: description and demonstration of capability

Nathan, Brian; Kremser, Stefanie; Fletcher, S. E. Mikaloff; Bodeker, G. E.; Bird, Leroy; Dale, Ethan; Lin, Dongqi; Olivares, Gustavo; Somervell, Elizabeth

doi:10.5194/acp-21-14089-2021

Cited by 4 publications

(2 citation statements)

References 57 publications

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“…Backward trajectories for the analysis of the surface ozone surge were simulated using the Flexible Lagrangian particle dispersion model (FLEXPART), which works with the WRF model (FLEXPART-WRF, Version 3.3.2; Brioude et al, 2013; https://www.flexpart.eu/wiki/FpLimitedareaWrf, last access: 20 June 2022). The FLEXPART model (Stohl et al, 2005) was originally developed at the Norwegian Institute for Air Research in the Department of Atmospheric and Climate Research, and was further tailored to WRF models so that the model can be widely used to study the influence of mesoscale processes on pollution transport (e.g., Aliaga et al, 2021;Nathan et al, 2021). Based on the WRF simulation results of the innermost domain with a 3-km horizontal resolution, we conducted backward trajectory calculations using FLEXPART-WRF.…”

Section: Wrf Simulations and Flexpart Backward Trajectoriesmentioning

confidence: 99%

Transport of substantial stratospheric ozone to the surface by a dying typhoon and shallow convection

et al. 2022

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Abstract. Stratospheric ozone transported to the troposphere is estimated to account for 5 %–15 % of the tropospheric ozone sources. However, the chances of intruded stratospheric ozone reaching the surface are low. Here, we report an event of a strong surface ozone surge of stratospheric origin in the North China Plain (NCP, 34–40∘ N, 114–121∘ E) during the night of 31 July 2021. The hourly measurements reveal surface ozone concentrations of up to 80–90 ppbv at several cities over the NCP from 23:00 LST (Local Standard time, = UTC +8 h) on 31 July to 06:00 LST on 1 August 2021. The ozone enhancement was 40–50 ppbv higher than the corresponding monthly mean. A high-frequency surface measurement indicates that this ozone surge occurred abruptly, with an increase reaching 40–50 ppbv within 10 min. A concurrent decline in surface carbon monoxide (CO) concentrations suggests that this surface ozone surge might have resulted from the downward transport of a stratospheric ozone-rich and CO-poor air mass. This is further confirmed by the vertical evolutions of humidity and ozone profiles based on radiosonde and satellite data respectively. Such an event of stratospheric impact on surface ozone is rarely documented in view of its magnitude, coverage, and duration. We find that this surface ozone surge was induced by a combined effect of dying Typhoon In-fa and shallow local mesoscale convective systems (MCSs) that facilitated transport of stratospheric ozone to the surface. This finding is based on analysis of meteorological reanalysis and radiosonde data, combined with high-resolution Weather Research and Forecasting (WRF) simulation and backward trajectory analysis using the FLEXible PARTicle (FLEXPART) particle dispersion model. Although Typhoon In-fa on the synoptic scale was at its dissipation stage when it passed through the NCP, it could still bring down a stratospheric dry and ozone-rich air mass. As a result, the stratospheric air mass descended to the middle-to-low troposphere over the NCP before the MCSs formed. With the pre-existing stratospheric air mass, the convective downdrafts of the MCSs facilitated the final descent of stratospheric air mass to the surface. Significant surface ozone enhancement occurred in the convective downdraft regions during the development and propagation of the MCSs. This study underscores the substantial roles of weak convection in transporting stratospheric ozone to the lower troposphere and even to the surface, which has important implications for air quality and climate change.

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Section: Wrf Simulations and Flexpart Backward Trajectoriesmentioning

confidence: 99%

Transport of substantial stratospheric ozone to the surface by a dying typhoon and shallow convection

et al. 2022

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“…In [23], publicly available TROPOMI-S5 satellite data are employed, compared with measurements obtained from ground stations in Poland. The approach proposed by [24] aims to develop a method for deriving particulate matter (PM) emissions maps from in situ PM concentration measurements using an inverse model generating air pollution maps. The study by [25] indicates that mobile sensors can improve the spatiotemporal resolution of the received pollution data but, nevertheless, the quality of the mobile sensors is important in order to be reliable.…”

Section: Introductionmentioning

confidence: 99%

Air Pollution PM10 Forecasting Maps in the Maritime Area of the Bay of Algeciras (Spain)

Rodríguez-García,

Carrasco-García,

Ribeiro

et al. 2024

JMSE

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Predicting the levels of a pollutant in a given area is an open problem, mainly because historical data are typically available at certain locations, where monitoring stations are located, but not at all locations in the area. This work presents an approach based on developing predictions at each of the points where an immission station is available; in this case, based on shallow Artificial Neural Networks, ANNs, and then using a simple geostatistical interpolation algorithm (Inverse Distance Weighted, IDW), a pollutant map is constructed over the entire study area, thus providing predictions at each point in the plane. The ANN models are designed to make 1 h ahead and 4 h ahead predictions, using an autoregressive scheme as inputs (in the case of 4 h ahead as a jumping strategy). The results are then compared using the Friedman and Bonferroni tests to select the best model at each location, and predictions are made with all the best models. In general, to the 1 h ahead prediction models, the optimal models typically have fewer neurons and require minimal historical data. For instance, the best model in Algeciras has an R of almost 0.89 and consists of 1 hidden neuron and 3 to 5 lags, similar to Colegio Los Barrios. In the case of 4h ahead prediction, Colegio Carteya station shows the best model, with an R of almost 0.89 and a MSE of less than 240, including 5 hidden neurons and different lags from the past. The results are sufficiently adequate, especially in the case of predictions 4 h into the future. The aim is to integrate the models into a tool for citizens and administrations to make decisions.

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Lightweight Open Data Assimilation of Pan-European Urban Air Quality

Miasayedava,

Kaugerand,

Tuhtan

2023

IEEE Access

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The MAPM (Mapping Air Pollution eMissions) method for inferring particulate matter emissions maps at city scale from in situ concentration measurements: description and demonstration of capability

Cited by 4 publications

References 57 publications

Transport of substantial stratospheric ozone to the surface by a dying typhoon and shallow convection

Transport of substantial stratospheric ozone to the surface by a dying typhoon and shallow convection

Air Pollution PM10 Forecasting Maps in the Maritime Area of the Bay of Algeciras (Spain)

Lightweight Open Data Assimilation of Pan-European Urban Air Quality

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