Improvement of air quality forecasts with satellite and ground based particulate matter observations

Hirtl, Marcus; Mantovani, Simone; Krüger, B. C.; Triebnig, Gerhard; Flandorfer, Claudia; Bottoni, M.; Cavicchi, Mario

doi:10.1016/j.atmosenv.2013.11.027

Cited by 14 publications

(8 citation statements)

References 16 publications

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“…A decrease in MB and an increase in the correlation coefficients after assimilation of the surface PM observations have been reported by Pagowski et al and Wu et al [17,42]. Hirtl et al [2] assimilated both types of measurements-surface PM observations and satellite AOD retrievals-simultaneously and indicated that air quality forecasts with WRF-Chem were successfully improved after data assimilation. Schwartz et al [16] showed that assimilation of AOD alone improved the surface bias over the United States more than the assimilation of PM2.5 alone after the first hour, but combined AOD and surface PM2.5 DA produced the lowest bias for all times.…”

Section: Data Assimilationmentioning

confidence: 71%

“…This is especially important for correctly predicting high pollution concentrations, as it allows prevention of the harmful effects of pollutants on human health [1]. However, while chemical transport models perform relatively well in average air quality conditions, they often fail when it comes to high air pollution concentrations [2]. Therefore, much effort is needed to improve forecasts of severe air quality episodes.…”

Section: Introductionmentioning

confidence: 99%

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Can Data Assimilation of Surface PM2.5 and Satellite AOD Improve WRF-Chem Forecasting? A Case Study for Two Scenarios of Particulate Air Pollution Episodes in Poland

2019

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Based on the Weather Research and Forecasting model with Chemistry (WRF-Chem) model and Gridpoint Statistical Interpolation (GSI) assimilation tool, a forecasting system was used for two selected episodes (winter and summer) over Eastern Europe. During the winter episode, very high particular matter (PM2.5, diameter less than 2.5 µm) concentrations, related to low air temperatures and increased emission from residential heating, were measured at many stations in Poland. During the summer episode, elevated aerosol optical depth (AOD), likely related to the transport of pollution from biomass fires, was observed in Southern Poland. Our aim is to verify if there is a relevant positive impact of surface and satellite data assimilation (DA) on modeled PM2.5 concentrations, and to assess whether there are significant differences in the DA’s impact on concentrations between the two seasons. The results show a significant difference in the impact of surface and satellite DA on the model results between the summer and winter episode, which to a large degree is related to the availability of the satellite data. For example, the application of satellite DA raises the factor of two statistic from 0.18 to 0.78 for the summer episode, whereas this statistic remains unchanged (0.71) for the winter. The study suggests that severe winter air pollution episodes in Poland and Eastern Europe in general, often related to the dense cover of low clouds, will benefit from the assimilation of surface observations rather than satellite data, which can be very sparse in such meteorological situations. In contrast, the assimilation of satellite data can have a greater positive impact on the model results during summer than the assimilation of surface data for the same period.

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Section: Data Assimilationmentioning

confidence: 71%

Section: Introductionmentioning

confidence: 99%

Can Data Assimilation of Surface PM2.5 and Satellite AOD Improve WRF-Chem Forecasting? A Case Study for Two Scenarios of Particulate Air Pollution Episodes in Poland

2019

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“…SVMs are widely accepted in the machine learning community. They have been used for a broad range of applications because of their ability to generalize (e.g., Hirtl et al, 2014). One of its important characteristics is that when a data set is not linearly separable, the SVM method uses a kernel representation to project the data onto a high dimensional feature space where the linear separation is possible.…”

Section: Machine Learning Methods -Support Vector Regressionmentioning

confidence: 99%

“…If sufficient amount of training data is available, this model would be flexible to different retrieval scenarios and more accurate than the deterministic algorithms. Machine learning methods fulfill these requirements and have therefore been widely used in the aerosol science field in the last years (e.g., Hirtl et al, 2014). These studies use global or regional data sets to correct MODIS biases at these scales.…”

Section: Introductionmentioning

confidence: 99%

A Method to Improve MODIS AOD Values: Application to South America

Lanzaco

Olcese

Palancar

et al. 2016

Aerosol Air Qual. Res.

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We present a method to correct aerosol optical depth (AOD) values taken from Collection 6 MODIS observations, which resulted in values closer to those recorded by the ground-based network AERONET. The method is based on machine learning techniques (Artificial Neural Networks and Support Vector Regression), and uses MODIS AOD values and meteorological parameters as inputs.The method showed improved results, compared with the direct MODIS AOD, when applied to nine stations in South America. The percentage of improvement, measured in terms of R 2 , ranged from 2% (Alta Floresta) to 79% (Buenos Aires). This improvement was also quantified considering the percentage of data within the MODIS expected error, being 91% for this method and 57% for direct correlation.The method corrected not only the systematic bias in temporal data series but also the outliers. To highlight this ability, the results for each AERONET station were individually analyzed.Considering the results as a whole, this method showed to be a valuable tool to enhance MODIS AOD retrievals, especially for locations with systematic deviations.

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“…Several researchers used aerosol optical depth (AOD) or aerosol optical thickness (AOT) derived from satellite data for PM estimation (Chu et al 2003, Wang and Christopher 2003, Engel-Cox et al 2004, Badarinath et al 2007, Gupta and Christopher 2008, Lee et al 2011, Pelletier et al 2007, Schaap et al 2009, Hirtl et al 2014, Zha et al 2010, Ma et al 2014. Methods for estimating PM vary from linear regression (LR) or multiple linear regression (MLR) to the non-linear regression methods such as artificial neural network (ANN), support vector regression (SVR) and self organizing map (SOM) (Gupta and Christopher 2009a, 2009b, Yahi et al 2011, Hirtl et al 2014. Recently, modelling systems such as GEOS-Chem or CMAQ are also used for relating AOT to PM (Liu et al 2007a, Liu et al 2007b, Liu et al 2007c.…”

Section: Introductionmentioning

confidence: 99%

Particulate matter concentration mapping from MODIS satellite data: a Vietnamese case study

Nguyen¹,

Bui²,

Pham³

et al. 2015

Environ. Res. Lett.

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Particulate Matter (PM) pollution is one of the most important air quality concerns in Vietnam. In this study, we integrate ground-based measurements, meteorological and satellite data to map temporal PM concentrations at a 10×10 km grid for the entire of Vietnam. We specifically used MODIS Aqua and Terra data and developed statistically-significant regression models to map and extend the ground-based PM concentrations. We validated our models over diverse geographic provinces i.e., North East, Red River Delta, North Central Coast and South Central Coast in Vietnam. Validation suggested good results for satellite-derived PM 2.5 data compared to ground-based PM 2.5 (n=285, r 2 =0.411, RMSE=20.299 μg m −3 and RE=39.789%). Further, validation of satellitederived PM 2.5 on two independent datasets for North East and South Central Coast suggested similar results (n=40, r 2 =0.455, RMSE=21.512 μg m −3 , RE=45.236% and n=45, r 2 =0.444, RMSE=8.551 μg m −3 , RE=46.446% respectively). Also, our satellite-derived PM 2.5 maps were able to replicate seasonal and spatial trends of ground-based measurements in four different regions. Our results highlight the potential use of MODIS datasets for PM estimation at a regional scale in Vietnam. However, model limitation in capturing maximal or minimal PM 2.5 peaks needs further investigations on ground data, atmospheric conditions and physical aspects. OPEN ACCESS RECEIVED

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Improvement of air quality forecasts with satellite and ground based particulate matter observations

Cited by 14 publications

References 16 publications

Can Data Assimilation of Surface PM2.5 and Satellite AOD Improve WRF-Chem Forecasting? A Case Study for Two Scenarios of Particulate Air Pollution Episodes in Poland

Can Data Assimilation of Surface PM2.5 and Satellite AOD Improve WRF-Chem Forecasting? A Case Study for Two Scenarios of Particulate Air Pollution Episodes in Poland

A Method to Improve MODIS AOD Values: Application to South America

Particulate matter concentration mapping from MODIS satellite data: a Vietnamese case study

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