Fusion Method Combining Ground-Level Observations with Chemical Transport Model Predictions Using an Ensemble Deep Learning Framework: Application in China to Estimate Spatiotemporally-Resolved PM2.5 Exposure Fields in 2014–2017

2019 ◽  
Vol 53 (13) ◽  
pp. 7306-7315 ◽  
Author(s):  
Baolei Lyu ◽  
Yongtao Hu ◽  
Wenxian Zhang ◽  
Yunsong Du ◽  
Bin Luo ◽  
...  
2012 ◽  
Vol 12 (2) ◽  
pp. 5939-6018
Author(s):  
C. A. Stroud ◽  
M. D. Moran ◽  
P. A. Makar ◽  
S. Gong ◽  
W. Gong ◽  
...  

Abstract. Observations from the 2007 Border Air Quality and Meteorology Study (BAQS-Met 2007) in southern Ontario (ON), Canada, were used to evaluate Environment Canada's regional chemical transport model predictions of primary organic aerosol (POA). Environment Canada's operational numerical weather prediction model and the 2006 Canadian and 2005 US national emissions inventories were used as input to the chemical transport model (named AURAMS). Particle-component-based factor analysis was applied to aerosol mass spectrometer measurements made at one urban site (Windsor, ON) and two rural sites (Harrow and Bear Creek, ON) to derive hydrocarbon-like organic aerosol (HOA) factors. Co-located carbon monoxide (CO), PM2.5 black carbon (BC), and PM1 SO4 measurements were also used for evaluation and interpretation, permitting a detailed diagnostic model evaluation. At the urban site, good agreement was observed for the comparison of daytime campaign PM1 POA and HOA mean values: 1.1 μg m−3 vs. 1.2 μg m−3, respectively. However, a POA overprediction was evident on calm nights due to an overly-stable model surface layer. Biases in model POA predictions trended from positive to negative with increasing HOA values. This trend has several possible explanations, including (1) underweighting of urban locations in particulate matter (PM) spatial surrogate fields, (2) overly-coarse model grid spacing for resolving urban-scale sources, and (3) lack of a model particle POA evaporation process during dilution of vehicular POA tail-pipe emissions to urban scales. Furthermore, a trend in POA bias was observed at the urban site as a function of the BC/HOA ratio, suggesting a possible association of POA underprediction for diesel combustion sources. For several time periods, POA overprediction was also observed for sulphate-rich plumes, suggesting that our model POA fractions for the PM2.5 chemical speciation profiles may be too high for these point sources. At the rural Harrow site, significant underpredictions in PM1 POA concentration were found compared to observed HOA concentration and were associated, based on back-trajectory analysis, with (1) transport from the Detroit/Windsor urban complex, (2) longer-range transport from the US Midwest, and (3) biomass burning. Daytime CO concentrations were significantly overpredicted at Windsor but were unbiased at Harrow. Collectively, these biases provide support for a hypothesis that combines a current underweighting of PM spatial surrogate fields for urban locations with insufficient model vertical mixing for sources close to the urban measurement sites. The magnitude of the area POA emissions sources in the US and Canadian inventories (e.g., food cooking, road and soil dust, waste disposal burning) suggests that more effort should be placed at reducing uncertainties in these sectors, especially spatial and temporal surrogates.


2019 ◽  
Vol 6 (8) ◽  
pp. 456-461 ◽  
Author(s):  
Jianlin Hu ◽  
Bart Ostro ◽  
Hongliang Zhang ◽  
Qi Ying ◽  
Michael J. Kleeman

2011 ◽  
Vol 11 (20) ◽  
pp. 10331-10347 ◽  
Author(s):  
C. Fountoukis ◽  
P. N. Racherla ◽  
H. A. C. Denier van der Gon ◽  
P. Polymeneas ◽  
P. E. Charalampidis ◽  
...  

Abstract. PMCAMx-2008, a detailed three-dimensional chemical transport model (CTM), was applied to Europe to simulate the mass concentration and chemical composition of particulate matter (PM) during May 2008. The model includes a state-of-the-art organic aerosol module which is based on the volatility basis set framework treating both primary and secondary organic components as semivolatile and photochemically reactive. The model performance is evaluated against high time resolution aerosol mass spectrometer (AMS) ground and airborne measurements. Overall, organic aerosol is predicted to account for 32% of total PM1 at ground level during May 2008, followed by sulfate (30%), crustal material and sea-salt (14%), ammonium (13%), nitrate (7%), and elemental carbon (4%). The model predicts that fresh primary OA (POA) is a small contributor to organic PM concentrations in Europe during late spring, and that oxygenated species (oxidized primary and biogenic secondary) dominate the ambient OA. The Mediterranean region is the only area in Europe where sulfate concentrations are predicted to be much higher than the OA, while organic matter is predicted to be the dominant PM1 species in central and northern Europe. The comparison of the model predictions with the ground measurements in four measurement stations is encouraging. The model reproduces more than 94% of the daily averaged data and more than 87% of the hourly data within a factor of 2 for PM1 OA. The model tends to predict relatively flat diurnal profiles for PM1 OA in many areas, both rural and urban in agreement with the available measurements. The model performance against the high time resolution airborne measurements at multiple altitudes and locations is as good as its performance against the ground level hourly measurements. There is no evidence of missing sources of OA aloft over Europe during this period.


2012 ◽  
Vol 12 (2) ◽  
pp. 3781-3874 ◽  
Author(s):  
D. Simpson ◽  
A. Benedictow ◽  
H. Berge ◽  
R. Bergström ◽  
L. D. Emberson ◽  
...  

Abstract. The Meteorological Synthesizing Centre-West (MSC-W) of the European Monitoring and Evaluation Programme (EMEP) has been performing model calculations in support of the Convention on Long Range Transboundary Air Pollution (CLRTAP) for more than 30 yr. The EMEP MSC-W chemical transport model is still one of the key tools within European air pollution policy assessments. Traditionally, the EMEP model has covered all of Europe with a resolution of about 50 × 50 km2, and extending vertically from ground level to the tropopause (100 hPa). The model has undergone substantial development in recent years, and is now applied on scales ranging from local (ca. 5 km grid size) to global (with 1 degree resolution). The model is used to simulate photo-oxidants and both inorganic and organic aerosols. In 2008 the EMEP model was released for the first time as public domain code, along with all required input data for model runs for one year. Since then, many changes have been made to the model physics, and input data. The second release of the EMEP MSC-W model became available in mid 2011, and a new release is targeted for early 2012. This publication is intended to document this third release of the EMEP MSC-W model. The model formulations are given, along with details of input data-sets which are used, and brief background on some of the choices made in the formulation are presented. The model code itself is available at www.emep.int, along with the data required to run for a full year over Europe.


2021 ◽  
Author(s):  
Baolei Lyu ◽  
Ran Huang ◽  
Xinlu Wang ◽  
Weiguo Wang ◽  
Yongtao Hu

Abstract. Well-estimated air pollutant concentration fields through data fusion are critically important to compensate the observations that are only sparsely available, especially over non-urban areas. Previous data fusion methods generally used statistical models to relate target observations and supporting data variables at known stations. In this study, we built a new data fusion paradigm by designing a dedicated deep learning framework to learn multi-variable spatial correlations from Chemical Transport Model (CTM) simulations, before using it to estimate PM2.5 reanalysis fields from station observations. The model was composed of two modules, which include an explainable PointConv operation to pre-process isolated observations and a regression grid-to-grid network to reflect correlations among multiple variables. The model was evaluated in two aspects of reproducing PM2.5 CTM simulations and generating reanalysis/fused PM2.5 fields. First, the fusion model was able to well reproduce CTM simulations from sampled station CTM data items with an average R2 = 0.94. Second, the fusion model achieved good performance with R2 = 0.77 and R2 = 0.83 respectively evaluated at the stringent city-level and station-level. The generated reanalysis PM2.5 fields have complete spatial coverage within the modelling domain and at daily time scale. One significant benefit of our fusion framework is that the model training does not rely on observations, which can be used to predict PM2.5 fields in newly-setup observation networks such as those using portable sensors. The fusion model has high computing efficiency (< 1 s/day) in predicting PM2.5 concentrations due to acceleration using GPU. As an alternative to generate chemical/meteorological reanalysis fields, the method can be readily applied for other simulated variables that with measurements available.


2011 ◽  
Vol 11 (5) ◽  
pp. 14183-14220 ◽  
Author(s):  
C. Fountoukis ◽  
P. N. Racherla ◽  
H. A. C. Denier van der Gon ◽  
P. Polymeneas ◽  
P. E. Haralabidis ◽  
...  

Abstract. PMCAMx-2008, a detailed three dimensional chemical transport model (CTM), was applied to Europe to simulate the mass concentration and chemical composition of particulate matter (PM) during May 2008. The model includes a state-of-the-art organic aerosol module which is based on the volatility basis set framework treating both primary and secondary organic components to be semivolatile and photochemically reactive. The model performance is evaluated against high time resolution aerosol mass spectrometer (AMS) ground and airborne measurements. Overall, organic aerosol is predicted to account for 32% of total PM1 at ground level during May 2008, followed by sulfate (30%), crustal material and sea-salt (14%), ammonium (13%), nitrate (7%), and elemental carbon (4%). The model predicts that fresh primary OA (POA) is a small contributor to organic PM concentrations in Europe during late spring, and that oxygenated species (oxidized primary and biogenic secondary) dominate the ambient OA. The Mediterranean region is the only area in Europe where sulfate concentrations are predicted to be much higher than the OA, while organic matter is predicted to be the dominant PM1 species in Central and Northern Europe. The comparison of the model predictions with the ground measurements in four measurement stations is encouraging. The model reproduces more than 94% of the daily averaged data and more than 87% of the hourly data within a factor of 2 for PM1 OA. The model tends to predict relatively flat diurnal profiles for PM1 OA in many areas, both rural and urban, in agreement with the available measurements. The model performance against the high time resolution airborne measurements at multiple altitudes and locations is as good as its performance against the ground level hourly measurements. There is no evidence of missing sources of OA aloft over Europe during this period.


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