Air Quality Forecast through Integrated Data Assimilation and Machine Learning

As of now, street transport foundation neglecting to adapt up to the exponential increment in vehicular populace. To registering the quickest driving courses and mishaps within the sight of fluctuating traffic conditions is a basic issue in current route frameworks. To forestall this issue is to examine the vehicle office dataset with AI strategy for finding the best street choice without mishap estimating by forecast consequences of best exactness counts. The examination of dataset by administered AI technique(SMLT) to catch a few data resembles, variable distinguishing proof, uni-variate investigation, bi-variate and multi-variate investigation, missing worth medicines and dissect the information approval, information cleaning/planning and information perception will be done on the whole given dataset. Moreover, to think about and examine the presentation of different AI calculations from the given vehicle office dataset with assessment of GUI based UI air quality forecast by given properties.

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Comparing Air Quality Forecast and a Reanalysis: Improvements Due to Chemical Data Assimilation and Better NWP Forcing

Air Pollution Modeling and its Application XXI - NATO Science for Peace and Security Series C: Environmental Security ◽

10.1007/978-94-007-1359-8_68 ◽

2011 ◽

pp. 403-406

Author(s):

Julius Vira ◽

Mikhail Sofiev

Keyword(s):

Air Quality ◽

Data Assimilation ◽

Chemical Data ◽

Air Quality Forecast ◽

Quality Forecast

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Development of the Global to Mesoscale Air Quality Forecast and Analysis System (GMAF) and Its Application to PM2.5 Forecast in Korea

Atmosphere ◽

10.3390/atmos12030411 ◽

2021 ◽

Vol 12 (3) ◽

pp. 411

Author(s):

SeogYeon Cho ◽

HyeonYeong Park ◽

JeongSeok Son ◽

LimSeok Chang

Keyword(s):

Kalman Filter ◽

Air Quality ◽

Data Assimilation ◽

Bias Correction ◽

Bias Adjustment ◽

Air Quality Forecast ◽

Correction Technique ◽

Analysis System ◽

Mesoscale Air Quality ◽

Quality Forecast

This paper presents the development of the global to mesoscale air quality forecast and analysis system (GMAF) and its application to particulate matter under 2.5 μm (PM2.5) forecast in Korea. The GMAF combined a mesoscale model with a global data assimilation system by the grid nudging based four-dimensional data assimilation (FDDA). The grid nudging based FDDA developed for weather forecast and analysis was extended to air quality forecast and analysis for the first time as an alternative to data assimilation of surface monitoring data. The below cloud scavenging module and the secondary organic formation module of the community multiscale air quality model (CMAQ) were modified and subsequently verified by comparing with the PM speciation observation from the PM supersite. The observation data collected from the criteria air pollutant monitoring networks in Korea were used to evaluate forecast performance of GMAF for the year of 2016. The GMAF showed good performance in forecasting the daily mean PM2.5 concentrations at Seoul; the correlation coefficient between the observed and forecasted PM2.5 concentrations was 0.78; the normalized mean error was 25%; the probability of detection for the events exceeding the national PM2.5 standard was 0.81 whereas the false alarm rate was only 0.38. Both the hybrid bias correction technique and the Kalman filter bias adjustment technique were implemented into the GMAF as postprocessors. For the continuous and the categorical performance metrics examined, the Kalman filter bias adjustment technique performed better than the hybrid bias correction technique.

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