Abstract

Abstract. Air pollutant emissions from open biomass burning (OBB) in the Yangtze River Delta (YRD) were estimated for 2005–2015 using three (traditional bottom-up, fire radiative power (FRP), and constraining) approaches, and the differences among those methods and their sources were analyzed. The species included PM10, PM2.5, organic carbon (OC), elemental carbon (EC), CH4, non-methane volatile organic compounds (NMVOCs), CO, CO2, NOx, SO2 and NH3. The interannual trends in emissions with FRP-based and constraining methods were similar to the fire counts in 2005–2012, while those with the traditional method were not. For most years, emissions of all species estimated with the constraining method were smaller than those with the traditional method except for NMVOCs, while they were larger than those with the FRP-based method except for EC, CH4 and NH3. Such discrepancies result mainly from different masses of crop residue burned in the field (CRBF) estimated in the three methods. Chemistry transport modeling (CTM) was applied using the three OBB inventories. The simulated PM10 concentrations with constrained emissions were closest to the available observations, implying that the constraining method provided the best emission estimates. CO emissions in the three methods were compared with other studies. Similar temporal variations were found for the constrained emissions, FRP-based emissions, GFASv1.0 and GFEDv4.1s, with the largest and the lowest emissions estimated for 2012 and 2006, respectively. The temporal variations in the emissions based on the traditional method, GFEDv3.0, and the method of Xia et al. (2016) were different. The constrained CO emissions in this study were commonly smaller than those based on the traditional bottom-up method and larger than those based on burned area or FRP in other studies. In particular, the constrained emissions were close to GFEDv4.1s that contained emissions from small fires. The contributions of OBB to two particulate pollution events in 2010 and 2012 were analyzed with the brute-force method. Attributed to varied OBB emissions and meteorology, the average contribution of OBB to PM10 concentrations in 8–14 June 2012 was estimated at 37.6 % (56.7 µg m−3), larger than that in 17–24 June 2010 at 21.8 % (24.0 µg m−3). Influences of diurnal curves of OBB emissions and meteorology on air pollution caused by OBB were evaluated by designing simulation scenarios, and the results suggested that air pollution caused by OBB would become heavier if the meteorological conditions were unfavorable and that more attention should be paid to the OBB control at night. Quantified with Monte Carlo simulation, the uncertainty of the traditional bottom-up inventory was smaller than that of the FRP-based one. The percentages of CRBF and emission factors were the main source of uncertainty for the two approaches. Further improvement on CTM for OBB events would help better constrain OBB emissions.

Highlights

  • Influences of diurnal curves of Open biomass burning (OBB) emissions and meteorology on air pollution caused by OBB were evaluated by designing simulation scenarios, and the results suggested that air pollution caused by OBB would become heavier if the meteorological conditions were unfavorable and that more attention should be paid to the OBB control at night

  • As emission factors were assumed unchanged during the period, similar interannual trends were found for all species and CO2 was selected as a representative species for further discussion

  • Similar temporal variability was found for fire counts, which increased by 138.5 % from 2005 to 2012, with the most and the second most counts found at 17 074 and 12 322 for 2012 and 2010, respectively

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Summary

Introduction

Open biomass burning (OBB) is an important source of atmospheric particulate matter (PM) and trace gases including methane (CH4), non-methane volatile organic compounds (NMVOCs), carbon monoxide (CO), carbon dioxide (CO2), oxides of nitrogen (NOx), sulfur dioxide (SO2) and ammonia (NH3) (Andreae and Merlet, 2001; van der Werf et al, 2010; Wiedinmyer et al, 2011; Kaiser et al, 2012; Giglio et al, 2013; Qiu et al, 2016; Zhou et al, 2017a) As they have significant impacts on air quality and climate (Crutzen and Andreae, 1990; Cheng et al, 2014; Hodzic and Duvel, 2018), it is important to understand the amount, temporal variation and spatial pattern of OBB emissions. Uncertainties of the three OBB inventories were analyzed and quantified with Monte Carlo simulation

Traditional bottom-up method
FRP-based method
Constraining method
Temporal and spatial distributions
Configuration of air quality modeling
OBB emissions estimated with the three methods
Evaluation of the three OBB inventories with CMAQ
Comparisons of different methods and studies
Contribution of OBB to particulate pollution and its influencing factors
Uncertainty analysis
Conclusions
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