scholarly journals Impacts of Biomass Burning Emission Inventories and Atmospheric Reanalyses on Simulated PM10 over Indochina

Atmosphere ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 160
Author(s):  
Kyohei Takami ◽  
Hikari Shimadera ◽  
Katsushige Uranishi ◽  
Akira Kondo

Biomass burning (BB) is a major source of atmospheric particles over Indochina during the dry season. Moreover, Indochina has convoluted meteorological scales, and regional meteorological conditions dominate the transport patterns of pollutants. This study focused on the impacts of BB emission inventories and atmospheric reanalyses on simulated PM10 over Indochina in 2014 using the Community Multiscale Air Quality (CMAQ) model. Meteorological fields to input to CMAQ were produced by using the Weather Research and Forecasting (WRF) model simulation with the United States National Centers for Environmental Prediction Final (NCEP FNL) Operational Global Analysis or European Centre for Medium Range Weather Forecasts Interim Reanalysis (ERA-interim). The Fire INventory from NCAR (FINN) v1.5 or the Global Fire Emissions Database including small fires (GFED v4.1s) was selected for BB emissions for the air quality simulation. The simulation case with NCEP FNL and FINN v1.5 (FNL + FINN) performed best throughout 2014, including the season when BB activities were intensified. The normalized percentage difference for maximum daily mean PM10 concentrations at Chiang Mai for FNL + FINN and the two simulation cases applying GFED v4.1s for BB emissions (−53% to −27%) was much larger than that between the FNL + FINN and ERA + FINN cases (10%). BB emission inventories more strongly impacted PM10 simulation than atmospheric reanalyses in highly polluted areas by BB over Indochina in 2014.

2016 ◽  
Vol 59 (3) ◽  
Author(s):  
Mohammad Ali Sharifi ◽  
Majid Azadi ◽  
Ali Sam Khaniani

<p>In this work, the effect of assimilation of synoptic, radiosonde and ground-based GPS precipitable water vapor (PWV) data has been investigated on the short-term prediction of precipitation, vertical relative humidity and PWV fields over north of Iran. We selected two rainfall events (i.e. February 1, 2014, and September 17, 2014) caused by synoptic systems affecting the southern coasts of the Caspian Sea. These systems are often associated with a shallow and cold high pressure located over Russia that extends towards the southern Caspian Sea. The three dimensional variational (3DVAR) data assimilation system of the weather research and forecasting (WRF) model is used in two rainfall cases. In each case, three numerical experiments, namely CTRL, CONVDA and GPSCONVDA, are performed. The CTRL experiment uses the global analysis as the initial and boundary conditions of the model. In the second experiment, surface and radiosonde observations are inserted into the model. Finally, the GPSCONVDA experiment uses the GPS PWV data in the assimilation process in addition to the conventional observations. It is found that in CONVDA experiment, the mean absolute error (MAE) of the accumulated precipitation is reduced about 5 and 13 percent in 24h model simulation of February and September cases, respectively, when compared to CTRL. Also, the results in both cases suggest that the assimilation of GPS data has the greatest impact on model PWV simulations, with maximum root mean squares error (RMSE) reduction of 0.7 mm. In the GPSCONVDA experiment, comparison of the vertical profiles of 12h simulated relative humidity with the corresponding radiosonde observations shows a slight improvement in the lower levels.</p>


Author(s):  
Ernesto Pino-Cortés ◽  
Samuel Carrasco ◽  
Luis A. Díaz-Robles ◽  
Francisco Cubillos ◽  
Fidel Vallejo ◽  
...  

Wildfires generate large amounts of atmospheric pollutants yearly. The development of an emissions inventory for this activity is a challenge today, mainly to perform modeling of air quality. There are free available databases with historical information about this source. The main goal of this study was to process the results of biomass burning emissions for the year 2014 from the Global Fire Assimilation System (GFAS). The pollutants studied were the black carbon, the organic carbon, fine and coarse particulate matter, respectively. The inputs were pre-formatted to enter to the simulation software of the emission inventory. In this case, the Sparse Matrix Operator Kernel Emissions (SMOKE) was used and the values obtained in various cities were analyzed. As a result, the spatial distribution of the forest fire emissions in the Southern Hemisphere was achieved, with the polar stereographic projection. The highest emissions were located in the African continent, followed by the northern region of Australia. Future air quality modeling at a local level could apply the results and the methodology of this study. The biomass burning emissions could add a better performance of the results and more knowledge on the effect of this source.


2008 ◽  
Vol 8 (5) ◽  
pp. 16981-17036 ◽  
Author(s):  
T. Stavrakou ◽  
J.-F. Müller ◽  
I. De Smedt ◽  
M. Van Roozendael ◽  
G. R. van der Werf ◽  
...  

Abstract. A new one-decade dataset of formaldehyde (HCHO) columns retrieved from GOME and SCIAMACHY is compared with HCHO columns simulated by an updated version of the IMAGES global chemical transport model. This model version includes an optimized chemical scheme with respect to HCHO production, where the short-term and final HCHO yields from pyrogenically emitted non-methane volatile organic compounds (NMVOCs) are estimated from the Master Chemical Mechanism (MCM) and an explicit speciation profile of pyrogenic emissions. The model is driven by the Global Fire Emissions Database (GFED) version 1 or 2 for biomass burning, whereas biogenic emissions are provided either by the Global Emissions Inventory Activity (GEIA), or by a newly developed inventory based on the Model of Emissions of Gases and Aerosols from Nature (MEGAN) algorithms driven by meteorological fields from the European Centre for Medium-Range Weather Forecasts (ECMWF). The comparisons focus on tropical ecosystems, North America and China, which experience strong biogenic and biomass burning NMVOC emissions reflected in the enhanced measured HCHO columns. These comparisons aim at testing the ability of the model to reproduce the observed features of the HCHO distribution on the global scale and at providing a first assessment of the performance of the current emission inventories. The high correlation coefficients (r>0.8) between the observed and simulated columns over most regions indicate a very good consistency between the model, the implemented inventories and the HCHO dataset. The use of the MEGAN-ECMWF inventory improves the model/data agreement in almost all regions, but biases persist over parts of Africa and the Northern Australia. Although neither GFED version is consistent with the data over all regions, a better match is achieved over Indonesia and Southern Africa when GFEDv2 is used, but GFEDv1 succeeds better in getting the correct seasonal patterns and intensities of the fire episodes over the Amazon basin, as reflected by the higher correlations calculated in this region.


2015 ◽  
Vol 15 (1) ◽  
pp. 363-373 ◽  
Author(s):  
B. Aouizerats ◽  
G. R. van der Werf ◽  
R. Balasubramanian ◽  
R. Betha

Abstract. Smoke from biomass and peat burning has a notable impact on ambient air quality and climate in the Southeast Asia (SEA) region. We modeled a large fire-induced haze episode in 2006 stemming mostly from Indonesia using the Weather Research and Forecasting model coupled with chemistry (WRF-Chem). We focused on the evolution of the fire plume composition and its interaction with the urbanized area of the city state of Singapore, and on comparisons of modeled and measured aerosol and carbon monoxide (CO) concentrations. Two simulations were run with WRF-Chem using the complex volatility basis set (VBS) scheme to reproduce primary and secondary aerosol evolution and concentration. The first simulation referred to as WRF-FIRE included anthropogenic, biogenic and biomass burning emissions from the Global Fire Emissions Database (GFED3) while the second simulation referred to as WRF-NOFIRE was run without emissions from biomass burning. To test model performance, we used three independent data sets for comparison including airborne measurements of particulate matter (PM) with a diameter of 10 μm or less (PM10) in Singapore, CO measurements in Sumatra, and aerosol optical depth (AOD) column observations from four satellite-based sensors. We found reasonable agreement between the model runs and both ground-based measurements of CO and PM10. The comparison with AOD was less favorable and indicated the model underestimated AOD, although the degree of mismatch varied between different satellite data sets. During our study period, forest and peat fires in Sumatra were the main cause of enhanced aerosol concentrations from regional transport over Singapore. Analysis of the biomass burning plume showed high concentrations of primary organic aerosols (POA) with values up to 600 μg m−3 over the fire locations. The concentration of POA remained quite stable within the plume between the main burning region and Singapore while the secondary organic aerosol (SOA) concentration slightly increased. However, the absolute concentrations of SOA (up to 20 μg m−3) were much lower than those from POA, indicating a minor role of SOA in these biomass burning plumes. Our results show that about 21% of the total mass loading of ambient PM10 during the July–October study period in Singapore was due to biomass and peat burning in Sumatra, but this contribution increased during high burning periods. In total, our model results indicated that during 35 days aerosol concentrations in Singapore were above the threshold of 50 μg m−3 day−1 indicating poor air quality. During 17 days this was due to fires, based on the difference between the simulations with and without fires. Local pollution in combination with recirculation of air masses was probably the main cause of poor air quality during the other 18 days, although fires from Sumatra and probably also from Kalimantan (Indonesian part of the island of Borneo) added to the enhanced PM10 concentrations. The model versus measurement comparisons highlighted that for our study period and region the GFED3 biomass burning aerosol emissions were more in line with observations than found in other studies. This indicates that care should be taken when using AOD to constrain emissions or estimate ground-level air quality. This study also shows the need for relatively high resolution modeling to accurately reproduce the advection of air masses necessary to quantify the impacts and feedbacks on regional air quality.


2020 ◽  
Vol 20 (15) ◽  
pp. 9441-9458
Author(s):  
Johannes G. M. Barten ◽  
Laurens N. Ganzeveld ◽  
Auke J. Visser ◽  
Rodrigo Jiménez ◽  
Maarten C. Krol

Abstract. In Colombia, industrialization and a shift towards intensified agriculture have led to increased emissions of air pollutants. However, the baseline state of air quality in Colombia is relatively unknown. In this study we aim to assess the baseline state of air quality in Colombia with a focus on the spatial and temporal variability in emissions and atmospheric burden of nitrogen oxides (NOx = NO + NO2) and evaluate surface NOx, ozone (O3) and carbon monoxide (CO) mixing ratios. We quantify the magnitude and spatial distribution of the four major NOx sources (lightning, anthropogenic activities, soil biogenic emissions and biomass burning) by integrating global NOx emission inventories into the mesoscale meteorology and atmospheric chemistry model, namely Weather Research and Forecasting (WRF) coupled with Chemistry (collectively WRF-Chem), at a similar resolution (∼25 km) to the Emission Database for Global Atmospheric Research (EDGAR) anthropogenic emission inventory and the Ozone Monitoring Instrument (OMI) remote sensing observations. The model indicates the largest contribution by lightning emissions (1258 Gg N yr−1), even after already significantly reducing the emissions, followed by anthropogenic (933 Gg N yr−1), soil biogenic (187 Gg N yr−1) and biomass burning emissions (104 Gg N yr−1). The comparison with OMI remote sensing observations indicated a mean bias of tropospheric NO2 columns over the whole domain (WRF-Chem minus OMI) of 0.02 (90 % CI: [−0.43, 0.70]) ×1015 molecules cm−2, which is <5 % of the mean column. However, the simulated NO2 columns are overestimated and underestimated in regions where lightning and biomass burning emissions dominate, respectively. WRF-Chem was unable to capture NOx and CO urban pollutant mixing ratios, neither in timing nor in magnitude. Yet, WRF-Chem was able to simulate the urban diurnal cycle of O3 satisfactorily but with a systematic overestimation of 10 parts per billion (ppb) due to the equally large underestimation of NO mixing ratios and, consequently, titration. This indicates that these city environments are in the NOx-saturated regime with frequent O3 titration. We conducted sensitivity experiments with an online meteorology–chemistry single-column model (SCM) to evaluate how WRF-Chem subgrid-scale-enhanced emissions could explain an improved representation of the observed O3, CO and NOx diurnal cycles. Interestingly, the SCM simulation, showing especially a shallower nocturnal inversion layer, results in a better representation of the observed diurnal cycle of urban pollutant mixing ratios without an enhancement in emissions. This stresses that, besides application of higher-resolution emission inventories and model experiments, the diurnal cycle in boundary layer dynamics (and advection) should be critically evaluated in models such as WRF-Chem to assess urban air quality. Overall, we present a concise method to quantify air quality in regions with limited surface measurements by integrating in situ and remote sensing observations. This study identifies four distinctly different source regions and shows their interannual and seasonal variability during the last 1.5 decades. It serves as a base to assess scenarios of future air quality in Colombia or similar regions with contrasting emission regimes, complex terrain and a limited air quality monitoring network.


2011 ◽  
Vol 11 (8) ◽  
pp. 23349-23419
Author(s):  
S. P. Urbanski ◽  
W. M. Hao ◽  
B. Nordgren

Abstract. We present the Wildland Fire Emission Inventory (WFEI), a high resolution model for non-agricultural open biomass burning (hereafter referred to as wildland fires) in the contiguous United States (CONUS). WFEI was used to estimate emissions of CO and PM2.5 for the western United States from 2003–2008. The estimated annual CO emitted ranged from 436 Gg yr−1 in 2004 to 3107 Gg yr−1 in 2007. The extremes in estimated annual PM2.5 emitted were 65 Gg yr−1 in 2004 and 454 Gg yr−1 in 2007. Annual wildland fire emissions were significant compared to other emission sources in the western United States as estimated in a national emission inventory. In the peak fire year of 2007, fire emissions were ~20 % of total CO emissions and ~39 % of total PM2.5 emissions. During the months with the greatest fire activity, wildland fires accounted for the majority of CO and PM2.5 emitted across the study region. The uncertainty in the inventory estimates of CO and PM2.5 emissions (ECO and EPM2.5, respectively) have been quantified across spatial and temporal scales relevant to regional and global modeling applications. The uncertainty in annual, domain wide emissions was 28 % to 51 % for CO and 40 % to 65 % for PM2.5. Sensitivity of the uncertainty in ECO and EPM2.5 to the emission model components depended on scale. At scales relevant to regional modeling applications (Δx = 10 km, Δt = 1 day) WFEI estimates 50 % of total ECO with an uncertainty <133 % and half of total EPM2.5 with an uncertainty <146 %. The uncertainty in ECO and EPM2.5 is significantly reduced at the scale of global modeling applications (Δx = 100 km, Δt = 30 day). Fifty percent of total emissions are estimated with an uncertainty <50 % for CO and <64 % for PM2.5. Uncertainty in the burned area drives the emission uncertainties at regional scales. At global scales the uncertainty in ECO is most sensitive to uncertainties in the fuel load consumed while the uncertainty in the emission factor for PM2.5 drives the EPM2.5 uncertainty. Our uncertainty analysis indicates that the large scale aggregate uncertainties (e.g. annual, CONUS) that are typically reported for biomass burning emission inventories may not be appropriate for evaluating and interpreting results of modeling applications that employ the emission estimates. When feasible, biomass burning emission inventories should be evaluated and reported across the scales for which they are intended to be used.


2020 ◽  
Vol 20 (4) ◽  
pp. 2073-2097 ◽  
Author(s):  
Therese S. Carter ◽  
Colette L. Heald ◽  
Jose L. Jimenez ◽  
Pedro Campuzano-Jost ◽  
Yutaka Kondo ◽  
...  

Abstract. Fires and the aerosols that they emit impact air quality, health, and climate, but the abundance and properties of carbonaceous aerosol (both black carbon and organic carbon) from biomass burning (BB) remain uncertain and poorly constrained. We aim to explore the uncertainties associated with fire emissions and their air quality and radiative impacts from underlying dry matter consumed and emissions factors. To investigate this, we compare model simulations from a global chemical transport model, GEOS-Chem, driven by a variety of fire emission inventories with surface and airborne observations of black carbon (BC) and organic aerosol (OA) concentrations and satellite-derived aerosol optical depth (AOD). We focus on two fire-detection-based and/or burned-area-based (FD-BA) inventories using burned area and active fire counts, respectively, i.e., the Global Fire Emissions Database version 4 (GFED4s) with small fires and the Fire INventory from NCAR version 1.5 (FINN1.5), and two fire radiative power (FRP)-based approaches, i.e., the Quick Fire Emission Dataset version 2.4 (QFED2.4) and the Global Fire Assimilation System version 1.2 (GFAS1.2). We show that, across the inventories, emissions of BB aerosol (BBA) differ by a factor of 4 to 7 over North America and that dry matter differences, not emissions factors, drive this spread. We find that simulations driven by QFED2.4 generally overestimate BC and, to a lesser extent, OA concentrations observations from two fire-influenced aircraft campaigns in North America (ARCTAS and DC3) and from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network, while simulations driven by FINN1.5 substantially underestimate concentrations. The GFED4s and GFAS1.2-driven simulations provide the best agreement with OA and BC mass concentrations at the surface (IMPROVE), BC observed aloft (DC3 and ARCTAS), and AOD observed by MODIS over North America. We also show that a sensitivity simulation including an enhanced source of secondary organic aerosol (SOA) from fires, based on the NOAA Fire Lab 2016 experiments, produces substantial additional OA; however, the spread in the primary emissions estimates implies that this magnitude of SOA can be neither confirmed nor ruled out when comparing the simulations against the observations explored here. Given the substantial uncertainty in fire emissions, as represented by these four emission inventories, we find a sizeable range in 2012 annual BBA PM2.5 population-weighted exposure over Canada and the contiguous US (0.5 to 1.6 µg m−3). We also show that the range in the estimated global direct radiative effect of carbonaceous aerosol from fires (−0.11 to −0.048 W m−2) is large and comparable to the direct radiative forcing of OA (−0.09 W m−2) estimated in the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC). Our analysis suggests that fire emissions uncertainty challenges our ability to accurately characterize the impact of smoke on air quality and climate.


2015 ◽  
Vol 15 (23) ◽  
pp. 13433-13451 ◽  
Author(s):  
Y. Yin ◽  
F. Chevallier ◽  
P. Ciais ◽  
G. Broquet ◽  
A. Fortems-Cheiney ◽  
...  

Abstract. Negative trends of carbon monoxide (CO) concentrations are observed in the recent decade by both surface measurements and satellite retrievals over many regions of the globe, but they are not well explained by current emission inventories. Here, we analyse the observed CO concentration decline with an atmospheric inversion that simultaneously optimizes the two main CO sources (surface emissions and atmospheric hydrocarbon oxidations) and the main CO sink (atmospheric hydroxyl radical OH oxidation). Satellite CO column retrievals from Measurements of Pollution in the Troposphere (MOPITT), version 6, and surface observations of methane and methyl chloroform mole fractions are assimilated jointly for the period covering 2002–2011. Compared to the model simulation prescribed with prior emission inventories, trends in the optimized CO concentrations show better agreement with that of independent surface in situ measurements. At the global scale, the atmospheric inversion primarily interprets the CO concentration decline as a decrease in the CO emissions (−2.3 % yr−1), more than twice the negative trend estimated by the prior emission inventories (−1.0 % yr−1). The spatial distribution of the inferred decrease in CO emissions indicates contributions from western Europe (−4.0 % yr−1), the United States (−4.6 % yr−1) and East Asia (−1.2 % yr−1), where anthropogenic fuel combustion generally dominates the overall CO emissions, and also from Australia (−5.3 % yr−1), the Indo-China Peninsula (−5.6 % yr−1), Indonesia (−6.7 % y−1), and South America (−3 % yr−1), where CO emissions are mostly due to biomass burning. In contradiction with the bottom-up inventories that report an increase of 2 % yr−1 over China during the study period, a significant emission decrease of 1.1 % yr−1 is inferred by the inversion. A large decrease in CO emission factors due to technology improvements would outweigh the increase in carbon fuel combustions and may explain this decrease. Independent satellite formaldehyde (CH2O) column retrievals confirm the absence of large-scale trends in the atmospheric source of CO. However, it should be noted that the CH2O retrievals are not assimilated and OH concentrations are optimized at a very large scale in this study.


2017 ◽  
Vol 51 (20) ◽  
pp. 11731-11741 ◽  
Author(s):  
Aaron S. Kaulfus ◽  
Udaysankar Nair ◽  
Daniel Jaffe ◽  
Sundar A. Christopher ◽  
Scott Goodrick

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