Spatiotemporal Variations and Uncertainty in Crop Residue Burning Emissions over North China Plain: Implication for Atmospheric CO2 Simulation
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Methods for Estimating Crop Residue-Burning Emissions
2.2.1. Statistical-Based Method
2.2.2. Burned Area (BA)-Based Method
2.2.3. Fire Radiative Power (FRP)-Based Method
2.3. Data Description
2.4. Method for Quantifying Uncertainties in Crop Residue Burning Emissions
2.5. Model and Numerical Simulation
3. Results
3.1. Spatial Distribution of Crop Residue Burning Emissions
3.2. Temporal Variations of the Emissions and Driving Force
3.3. Comparisons with Other Studies
3.4. Uncertainty Analysis
4. Implication for the Impact of Crop Residue Burning Emission
5. Conclusions and Discussions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Huang, X.; Song, Y.; Li, M.; Li, J.; Zhu, T. Harvest season, high polluted season in East China. Environ. Res. Lett. 2012, 7, 044033. [Google Scholar] [CrossRef]
- Ding, A.J.; Fu, C.B.; Yang, X.Q.; Sun, J.N.; Petäjä, T.; Kerminen, V.-M.; Wang, T.; Xie, Y.; Herrmann, E.; Zheng, L.F.; et al. Intense atmospheric pollution modifies weather: A case of mixed biomass burning with fossil fuel combustion pollution in eastern China. Atmos. Chem. Phys. 2013, 13, 10545–10554. [Google Scholar] [CrossRef] [Green Version]
- Cheng, Z.; Wang, S.; Fu, X.; Watson, J.G.; Jiang, J.; Fu, Q.; Chen, C.; Xu, B.; Yu, J.; Chow, J.C.; et al. Impact of biomass burning on haze pollution in the Yangtze River delta, China: A case study in summer 2011. Atmos. Chem. Phys. 2014, 14, 4573–4585. [Google Scholar] [CrossRef] [Green Version]
- Zhu, Y.; Yang, L.; Chen, J.; Wang, X.; Xue, L.; Sui, X.; Wen, L.; Xu, C.; Yao, L.; Zhang, J.; et al. Characteristics of ambient volatile organic compounds and the influence of biomass burning at a rural site in Northern China during summer 2013. Atmos. Environ. 2016, 124, 156–165. [Google Scholar] [CrossRef]
- Zhang, L.; Liu, Y.; Hao, L. Contributions of open crop straw burning emissions to PM2.5 concentrations in China. Environ. Res. Lett. 2016, 11, 014014. [Google Scholar] [CrossRef]
- Long, X.; Tie, X.; Cao, J.; Huang, R.; Feng, T.; Li, N.; Zhao, S.; Tian, J.; Li, G.; Zhang, Q. Impact of crop field burning and mountains on heavy haze in the North China Plain: A case study. Atmos. Chem. Phys. 2016, 16, 9675–9691. [Google Scholar] [CrossRef] [Green Version]
- Friedlingstein, P.; O’Sullivan, M.; Jones, M.W.; Friedlingstein, P.; O’sullivan, M.; Jones, M.W.; Andrew, R.M.; Hauck, J.; Olsen, A.; Peters, G.P.; et al. Global Carbon Budget 2020. Earth Syst. Sci. Data 2020, 12, 3269–3340. [Google Scholar] [CrossRef]
- Chen, J.; Li, C.; Ristovski, Z.; Milic, A.; Gu, Y.; Islam, M.S.; Wang, S.; Hao, J.; Zhang, H.; He, C.; et al. A review of biomass burning: Emissions and impacts on air quality, health and climate in China. Sci. Total Environ. 2017, 579, 1000–1034. [Google Scholar] [CrossRef] [Green Version]
- Mehmood, K.; Chang, S.; Yu, S.; Wang, L.; Li, P.; Li, Z.; Liu, W.; Rosenfeld, D.; Seinfeld, J.H. Spatial and temporal distributions of air pollutant emissions from open crop straw and biomass burnings in China from 2002 to 2016. Environ. Chem. Lett. 2018, 16, 301–309. [Google Scholar] [CrossRef]
- Zhang, T.; Wooster, M.; Green, D.; Main, B. New field-based agricultural biomass burning trace gas, PM2.5, and black carbon emission ratios and factors measured in situ at crop residue fires in Eastern China. Atmos. Environ. 2015, 121, 22–34. [Google Scholar] [CrossRef] [Green Version]
- Sun, J.; Peng, H.; Chen, J.; Wang, X.; Wei, M.; Li, W.; Yang, L.; Zhang, Q.; Wang, W.; Mellouki, A. An estimation of CO2 emission via agricultural crop residue open field burning in China from 1996 to 2013. J. Clean. Prod. 2016, 112, 2625–2631. [Google Scholar] [CrossRef]
- Huang, X.; Li, M.; Li, J.; Song, Y. A high-resolution emission inventory of crop burning in fields in China based on MODIS Thermal Anomalies/Fire products. Atmos. Environ. 2012, 50, 9–15. [Google Scholar] [CrossRef]
- McCarty, J.; Ellicott, E.A.; Romanenkov, V.; Rukhovitch, D.; Koroleva, P. Multi-year black carbon emissions from cropland burning in the Russian Federation. Atmos. Environ. 2012, 63, 223–238. [Google Scholar] [CrossRef]
- Liu, M.; Song, Y.; Yao, H.; Kang, Y.; Li, M.; Huang, X.; Hu, M. Estimating emissions from agricultural fires in the North China Plain based on MODIS fire radiative power. Atmos. Environ. 2015, 112, 326–334. [Google Scholar] [CrossRef]
- Qiu, X.; Duan, L.; Chai, F.; Wang, S.; Yu, Q.; Wang, S. Deriving High-Resolution Emission Inventory of Open Biomass Burning in China based on Satellite Observations. Environ. Sci. Technol. 2016, 50, 11779–11786. [Google Scholar] [CrossRef]
- Yin, L.; Du, P.; Zhang, M.; Liu, M.; Xu, T.; Song, Y. Estimation of emissions from biomass burning in China (2003–2017) based on MODIS fire radiative energy data. Biogeosciences 2019, 16, 1629–1640. [Google Scholar] [CrossRef] [Green Version]
- Li, J.; Li, Y.; Bo, Y.; Xie, S. High-resolution historical emission inventories of crop residue burning in fields in China for the period 1990–2013. Atmos. Environ. 2016, 138, 152–161. [Google Scholar] [CrossRef]
- Streets, D.G.; Yarber, K.F.; Woo, J.-H.; Carmichael, G.R. Biomass burning in Asia: Annual and seasonal estimates and atmospheric emissions. Glob. Biogeochem. Cycles 2003, 17, 1099. [Google Scholar] [CrossRef] [Green Version]
- Hoelzemann, J.J.; Schultz, M.; Brasseur, G.P.; Granier, C.; Simon, M. Global Wildland Fire Emission Model (GWEM): Evaluating the use of global area burnt satellite data. J. Geophys. Res. Space Phys. 2004, 109, 14. [Google Scholar] [CrossRef]
- Roy, B.A.; Pouliot, G.A.; Mobley, J.D.; Pace, T.G.; Pierce, T.E.; Soja, A.J.; Szykman, J.J.; Al-Saadi, J. Development of Fire Emissions Inventory Using Satellite Data. In Air Pollution Modeling and Its Application XIX; Springer: Berlin/Heidelberg, Germany, 2008; pp. 217–225. [Google Scholar]
- Shi, Y.; Yamaguchi, Y. A high-resolution and multi-year emissions inventory for biomass burning in Southeast Asia during 2001–2010. Atmos. Environ. 2014, 98, 8–16. [Google Scholar] [CrossRef]
- Shi, Y.; Matsunaga, T.; Saito, M.; Yamaguchi, Y.; Chen, X. Comparison of global inventories of CO2 emissions from biomass burning during 2002–2011 derived from multiple satellite products. Environ. Pollut. 2015, 206, 479–487. [Google Scholar] [CrossRef] [Green Version]
- Shi, Y.; Matsunaga, T. Temporal comparison of global inventories of CO2 emissions from biomass burning during 2002–2011 derived from remotely sensed data. Environ. Sci. Pollut. Res. 2017, 24, 16905–16916. [Google Scholar] [CrossRef]
- Wu, J.; Kong, S.; Wu, F.; Cheng, Y.; Zheng, S.; Qin, S.; Liu, X.; Yan, Q.; Zheng, H.; Zheng, M.; et al. The moving of high emission for biomass burning in China: View from multi-year emission estimation and human-driven forces. Environ. Int. 2020, 142, 105812. [Google Scholar] [CrossRef]
- Ni, H.; Han, Y.; Cao, J.; Chen, L.-W.; Tian, J.; Wang, X.; Chow, J.C.; Watson, J.; Wang, Q.; Wang, P.; et al. Emission characteristics of carbonaceous particles and trace gases from open burning of crop residues in China. Atmos. Environ. 2015, 123, 399–406. [Google Scholar] [CrossRef]
- Xu, Y.; Huang, Z.; Jia, G.; Fan, M.; Cheng, L.; Chen, L.; Shao, M.; Zheng, J. Regional discrepancies in spatiotemporal variations and driving forces of open crop residue burning emissions in China. Sci. Total Environ. 2019, 671, 536–547. [Google Scholar] [CrossRef] [PubMed]
- Vadrevu, K.P.; Giglio, L.; Justice, C. Satellite based analysis of fire–carbon monoxide relationships from forest and agricultural residue burning (2003–2011). Atmos. Environ. 2013, 64, 179–191. [Google Scholar] [CrossRef]
- Whitburn, S.; Van Damme, M.; Kaiser, J.; van der Werf, G.; Turquety, S.; Hurtmans, D.; Clarisse, L.; Clerbaux, C.; Coheur, P.-F. Ammonia emissions in tropical biomass burning regions: Comparison between satellite-derived emissions and bottom-up fire inventories. Atmos. Environ. 2015, 121, 42–54. [Google Scholar] [CrossRef]
- Seiler, W.; Crutzen, P.J. Estimates of gross and net fluxes of carbon between the biosphere and the atmosphere from biomass burning. Clim. Chang. 1980, 2, 207–247. [Google Scholar] [CrossRef]
- Song, Y.; Liu, B.; Miao, W.; Chang, D.; Zhang, Y. Spatiotemporal variation in nonagricultural open fire emissions in China from 2000 to 2007. Glob. Biogeochem. Cycles 2009, 23, 2008. [Google Scholar] [CrossRef]
- Wooster, M.J.; Roberts, G.; Perry, G.; Kaufman, Y.J. Retrieval of biomass combustion rates and totals from fire radiative power observations: FRP derivation and calibration relationships between biomass consumption and fire radiative energy release. J. Geophys. Res. Space Phys. 2005, 110, 24311. [Google Scholar] [CrossRef]
- Andela, N.; Kaiser, J.W.; van der Werf, G.R.; Wooster, M.J. New fire diurnal cycle characterizations to improve fire radiative energy assessments made from MODIS observations. Atmos. Chem. Phys. 2015, 15, 8831–8846. [Google Scholar] [CrossRef] [Green Version]
- Li, F.; Zhang, X.; Roy, D.P.; Kondragunta, S. Estimation of biomass-burning emissions by fusing the fire radiative power retrievals from polar-orbiting and geostationary satellites across the conterminous United States. Atmos. Environ. 2019, 211, 274–287. [Google Scholar] [CrossRef]
- Vermote, E.; Ellicott, E.; Dubovik, O.; Lapyonok, T.; Chin, M.; Giglio, L.; Roberts, G.J. An approach to estimate global biomass burning emissions of organic and black carbon from MODIS fire radiative power. J. Geophys. Res. Space Phys. 2009, 114, 18205. [Google Scholar] [CrossRef]
- Shi, Y.; Zang, S.; Matsunaga, T.; Yamaguchi, Y. A multi-year and high-resolution inventory of biomass burning emissions in tropical continents from 2001–2017 based on satellite observations. J. Clean. Prod. 2020, 270, 122511. [Google Scholar] [CrossRef]
- Ichoku, C.; Ellison, L. Global top-down smoke-aerosol emissions estimation using satellite fire radiative power measure-ments. Atmos. Chem. Phys. 2014, 14, 6643–6667. [Google Scholar] [CrossRef] [Green Version]
- Freeborn, P.H.; Wooster, M.; Hao, W.M.; Ryan, C.A.; Nordgren, B.L.; Baker, S.P.; Ichoku, C. Relationships between energy release, fuel mass loss, and trace gas and aerosol emissions during laboratory biomass fires. J. Geophys. Res. Space Phys. 2008, 113, 01301. [Google Scholar] [CrossRef]
- Ellicott, E.; Vermote, E.; Giglio, L.; Roberts, G. Estimating biomass consumed from fire using MODIS FRE. Geophys. Res. Lett. 2009, 36, L13401. [Google Scholar] [CrossRef] [Green Version]
- Wang, S.X.; Zhang, C.Y. Spatial and temporal distribution of air pollutant emissions from open burning of crop residues in China. Sci. Pap. Online 2008, 3, 329–333. (In Chinese) [Google Scholar]
- Liu, J.; Zheng, C.; Zheng, L.; Lei, Y. Ground Water Sustainability: Methodology and Application to the North China Plain. Ground Water 2008, 46, 897–909. [Google Scholar] [CrossRef]
- Li, X.; Wang, S.; Duan, L.; Hao, J.; Li, C.; Chen, Y.; Yang, L. Particulate and Trace Gas Emissions from Open Burning of Wheat Straw and Corn Stover in China. Environ. Sci. Technol. 2007, 41, 6052–6058. [Google Scholar] [CrossRef]
- He, M.; Zheng, J.; Yin, S.; Zhang, Y. Trends, temporal and spatial characteristics, and uncertainties in biomass burning emissions in the Pearl River Delta, China. Atmos. Environ. 2011, 45, 4051–4059. [Google Scholar] [CrossRef]
- Giglio, L. MODIS Collection 6 Active Fire Product User’s Guide, Revision B. 2018. Available online: https://cdn.earthdata.nasa.gov/conduit/upload/10575/MODIS_C6_Fire_User_Guide_B.pdf (accessed on 13 July 2021).
- Giglio, L.; Loboda, T.; Roy, D.P.; Quayle, B.; Justice, C.O. An active-fire based burned area mapping algorithm for the MODIS sensor. Remote Sens. Environ. 2009, 113, 408–420. [Google Scholar] [CrossRef]
- Giglio, L.; Boschetti, L.; Roy, D.P.; Humber, M.L.; Justice, C.O. The Collection 6 MODIS burned area mapping algorithm and product. Remote. Sens. Environ. 2018, 217, 72–85. [Google Scholar] [CrossRef]
- Zheng, J.; Zheng, Z.; Yu, Y.; Zhong, L. Temporal, spatial characteristics and uncertainty of biogenic VOC emissions in the Pearl River Delta region, China. Atmos. Environ. 2010, 44, 1960–1969. [Google Scholar] [CrossRef]
- Suntharalingam, P.; Jacob, D.J.; Palmer, P.; Logan, J.A.; Yantosca, R.M.; Xiao, Y.; Evans, M.J.; Streets, D.; Vay, S.L.; Sachse, G.W. Improved quantification of Chinese carbon fluxes using CO2/CO correlations in Asian outflow. J. Geophys. Res. Space Phys. 2004, 109, 109. [Google Scholar] [CrossRef]
- Nassar, R.; Jones, D.B.A.; Suntharalingam, P.; Chen, J.M.; Andres, R.J.; Wecht, K.J.; Yantosca, R.M.; Kulawik, S.S.; Bowman, K.W.; Worden, J.R.; et al. Modeling global atmospheric CO2 with improved emission inventories and CO2 production from the oxidation of other carbon species. Geosci. Model Dev. 2010, 3, 689–716. [Google Scholar] [CrossRef] [Green Version]
- Nassar, R.; Napier-Linton, L.; Gurney, K.R.; Andres, R.J.; Oda, T.; Vogel, F.R.; Deng, F. Improving the temporal and spatial distribution of CO2 emissions from global fossil fuel emission data sets. J. Geophys. Res. Atmos. 2013, 118, 917–933. [Google Scholar] [CrossRef]
- Shim, C.; Nassar, R.; Kim, J. Comparison of Model-simulated Atmospheric Carbon Dioxide with GOSAT Retrievals. Asian J. Atmos. Environ. 2011, 5, 263–277. [Google Scholar] [CrossRef] [Green Version]
- Chen, Z.H.; Zhu, J.; Zeng, N. Improved simulation of regional CO2 surface concentrations using GEOS-Chem and fluxes from VEGAS. Atmos. Chem. Phys. 2013, 13, 7607–7618. [Google Scholar] [CrossRef] [Green Version]
- Fu, Y.; Liao, H.; Tian, X.-J.; Gao, H.; Cai, Z.-N.; Han, R. Sensitivity of the simulated CO2 concentration to inter-annual variations of its sources and sinks over East Asia. Adv. Clim. Chang. Res. 2019, 10, 250–263. [Google Scholar] [CrossRef]
- Feng, L.; Palmer, P.I.; Yang, Y.; Yantosca, R.M.; Kawa, S.R.; Paris, J.-D.; Matsueda, H.; Machida, T. Evaluating a 3-D transport model of atmospheric CO2 using ground-based, aircraft, and space-borne data. Atmos. Chem. Phys. 2011, 11, 2789–2803. [Google Scholar] [CrossRef] [Green Version]
- Nassar, R.; Jones, D.B.A.; Kulawik, S.S.; Worden, J.R.; Bowman, K.W.; Andres, R.J.; Suntharalingam, P.; Chen, J.M.; Brenninkmeijer, C.A.M.; Schuck, T.J.; et al. Inverse modeling of CO2 sources and sinks using satellite observations of CO2 from TES and surface flask measurements. Atmos. Chem. Phys. 2011, 11, 6029–6047. [Google Scholar] [CrossRef] [Green Version]
- Liu, J.; Bowman, K.W.; Schimel, D.S.; Parazoo, N.C.; Jiang, Z.; Lee, M.; Bloom, A.A.; Wunch, D.; Frankenberg, C.; Sun, Y.; et al. Contrasting carbon cycle responses of the tropical continents to the 2015–2016 El Niño. Science 2017, 358, eaam5690. [Google Scholar] [CrossRef] [Green Version]
- Philip, S.; Johnson, M.S.; Potter, C.; Genovesse, V.; Baker, D.F.; Haynes, K.D.; Henze, D.K.; Liu, J.; Poulter, B. Prior biosphere model impact on global terrestrial CO2 fluxes estimated from OCO-2 retrievals. Atmos. Chem. Phys. 2019, 19, 13267–13287. [Google Scholar] [CrossRef] [Green Version]
- Gelaro, R.; McCarty, W.; Suárez, M.J.; Todling, R.; Molod, A.; Takacs, L.; Randles, C.A.; Darmenov, A.; Bosilovich, M.G.; Reichle, R.; et al. The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). J. Clim. 2017, 30, 5419–5454. [Google Scholar] [CrossRef]
- Oda, T.; Maksyutov, S. A very high-resolution (1 km × 1 km) global fossil fuel CO2 emission inventory derived using a point source database and satellite observations of nighttime lights. Atmos. Chem. Phys. 2011, 11, 543–556. [Google Scholar] [CrossRef] [Green Version]
- van der Werf, G.R.; Randerson, J.T.; Giglio, L.; Collatz, G.J.; Mu, M.; Kasibhatla, P.S.; Morton, D.C.; DeFries, R.S.; Jin, Y.; van Leeuwen, T.T. Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009). Atmos. Chem. Phys. 2010, 10, 11707–11735. [Google Scholar] [CrossRef] [Green Version]
- Yevich, R.; Logan, J.A. An assessment of biofuel use and burning of agricultural waste in the developing world. Global Biogeochem. Cycles 2003, 17, 1095. [Google Scholar] [CrossRef]
- Messerschmidt, J.; Parazoo, N.; Wunch, D.; Deutscher, N.M.; Roehl, C.; Warneke, T.; Wennberg, P.O. Evaluation of seasonal atmosphere–biosphere exchange estimations with TCCON measurements. Atmos. Chem. Phys. 2013, 13, 5103–5115. [Google Scholar] [CrossRef] [Green Version]
- Takahashi, T.; Sutherland, S.C.; Wanninkhof, R.; Sweeney, C.; Feely, R.A.; Chipman, D.W.; Hales, B.; Friederich, G.; Chavez, F.; Sabine, C.; et al. Climatological mean and decadal change in surface ocean pCO2, and net sea–air CO2 flux over the global oceans. Deep Sea Res. Part II Top. Stud. Oceanogr. 2009, 56, 554–577. [Google Scholar] [CrossRef]
- Corbett, J.J.; Koehler, H.W. Considering alternative input parameters in an activity-based ship fuel consumption and emissions model: Reply to comment by Øyvind Endresen et al. on “Updated emissions from ocean shipping”. J. Geophys. Res. Space Phys. 2004, 109, 23303. [Google Scholar] [CrossRef]
- Simone, N.W.; Stettler, M.E.J.; Barrett, S.R.H. Rapid estimation of global civil aviation emissions with uncertainty quantifica-tion. Transp. Res. Part D 2013, 25, 33–41. [Google Scholar] [CrossRef]
- Olsen, S.C.; Brasseur, G.P.; Wuebbles, D.J.; Barrett, S.R.; Dang, H.; Eastham, S.D.; Jacobson, M.Z.; Khodayari, A.; Selkirk, H.; Sokolov, A.; et al. Comparison of model estimates of the effects of aviation emissions on atmospheric ozone and methane. Geophys. Res. Lett. 2013, 40, 6004–6009. [Google Scholar] [CrossRef] [Green Version]
- Randerson, J.T.; Chen, Y.; van der Werf, G.; Rogers, B.M.; Morton, D.C. Global burned area and biomass burning emissions from small fires. J. Geophys. Res. Space Phys. 2012, 117, 04012. [Google Scholar] [CrossRef]
- Zhu, C.; Kobayashi, H.; Kanaya, Y.; Saito, M. Size-dependent validation of MODIS MCD64A1 burned area over six vegetation types in boreal Eurasia: Large underestimation in croplands. Sci. Rep. 2017, 7, 4181. [Google Scholar] [CrossRef] [PubMed]
- Tao, S.; Ru, M.Y.; Du, W.; Zhu, X.; Zhong, Q.; Li, B.G.; Shen, G.F.; Pan, X.L.; Meng, W.J.; Chen, Y.L.; et al. Quantifying the rural residential energy transition in China from 1992 to 2012 through a representative national survey. Nat. Energy 2018, 3, 567–573. [Google Scholar] [CrossRef]
- Wiedinmyer, C.; Akagi, S.K.; Yokelson, R.J.; Emmons, L.K.; Al-Saadi, J.A.; Orlando, J.J.; Soja, A.J. The Fire INventory from NCAR (FINN): A high resolution global model to estimate the emissions from open burning. Geosci. Model Dev. 2011, 4, 625–641. [Google Scholar] [CrossRef] [Green Version]
- van der Werf, G.R.; Randerson, J.T.; Giglio, L.; van Leeuwen, T.T.; Chen, Y.; Rogers, B.M.; Mu, M.; van Marle, M.J.E.; Morton, D.C.; Collatz, G.J.; et al. Global fire emissions estimates during 1997–2016. Earth Syst. Sci. Data 2017, 9, 697–720. [Google Scholar] [CrossRef] [Green Version]
- Gao, X.; Ma, W.; Ma, C.; Zhang, F.; Wang, Y. Analysis on the current status of utilization of crop straw in China. J. Huazhong Agric. Univ. 2002, 21, 242–247. (In Chinese) [Google Scholar]
- Yan, X.; Ohara, T.; Akimoto, H. Bottom-up estimate of biomass burning in mainland China. Atmos. Environ. 2006, 40, 5262–5273. [Google Scholar] [CrossRef]
- Yang, S.; He, H.; Lu, S.; Chen, D.; Zhu, J. Quantification of crop residue burning in the field and its influence on ambient air quality in Suqian, China. Atmos. Environ. 2008, 42, 1961–1969. [Google Scholar] [CrossRef]
- Kaiser, J.W.; Heil, A.; Andreae, M.O.; Benedetti, A.; Chubarova, N.; Jones, L.; Morcrette, J.J.; Razinger, M.; Schultz, M.G.; Suttie, M.; et al. Biomass burning emissions estimated with a global fire assimilation system based on observed fire radiative power. Biogeosciences 2012, 9, 527–554. [Google Scholar] [CrossRef] [Green Version]
- Zhao, Y.; Nielsen, C.P.; Lei, Y.; McElroy, M.B.; Hao, J. Quantifying the uncertainties of a bottom-up emission inventory of anthropogenic at-mospheric pollutants in China. Atmos. Chem. Phys. 2011, 11, 2295–2308. [Google Scholar] [CrossRef] [Green Version]
- Zhang, Y.; Shao, M.; Lin, Y.; Luan, S.; Mao, N.; Chen, W.; Wang, M. Emission inventory of carbonaceous pollutants from biomass burning in the Pearl River Delta Region, China. Atmos. Environ. 2013, 76, 189–199. [Google Scholar] [CrossRef]
- Wu, J.; Kong, S.; Wu, F.; Cheng, Y.; Zheng, S.; Yan, Q.; Zheng, H.; Yang, G.; Zheng, M.; Liu, D.; et al. Estimating the open biomass burning emissions in central and eastern China from 2003 to 2015 based on satellite observation. Atmos. Chem. Phys. 2018, 18, 11623–11646. [Google Scholar] [CrossRef] [Green Version]
- Shi, Y.; Matsunaga, T.; Yamaguchi, Y. High-Resolution Mapping of Biomass Burning Emissions in Three Tropical Regions. Environ. Sci. Technol. 2015, 49, 10806–10814. [Google Scholar] [CrossRef] [PubMed]
- Freeborn, P.H.; Wooster, M.J.; Roy, D.P.; Cochrane, M.A. Quantification of MODIS fire radiative power (FRP) measurement uncertainty for use in satellite-based active fire characterization and biomass burning estimation. Geophys. Res. Lett. 2014, 41, 1988–1994. [Google Scholar] [CrossRef]
- Monni, S.; Syri, S.; Savolainen, I. Uncertainties in the Finnish greenhouse gas emission inventory. Environ. Sci. Policy 2004, 7, 87–98. [Google Scholar] [CrossRef]
- Ramírez, A.; de Keizer, C.; Van der Sluijs, J.P.; Olivier, J.; Brandes, L. Monte Carlo analysis of uncertainties in the Nether-lands greenhouse gas emission inventory for 1990–2004. Atmos. Environ. 2008, 42, 8263–8272. [Google Scholar] [CrossRef] [Green Version]
- Kou, X.; Zhang, M.; Peng, Z.; Wang, Y. Assessment of the biospheric contribution to surface atmospheric CO2 concentrations over East Asia with a regional chemical transport model. Adv. Atmos. Sci. 2015, 32, 287–300. [Google Scholar] [CrossRef]
- Zeng, N.; Han, P.; Liu, D.; Liu, Z.; Oda, T.; Martin, C.; Liu, Z.; Yao, B.; Sun, W.; Wang, P.; et al. Global to local impacts on atmospheric CO2 caused by COVID-19 lockdown. arXiv 2020, arXiv:2010.13025. Available online: https://arxiv.org/abs/2010.13025v1 (accessed on 23 September 2021).
- Zeng, N.; Mariotti, A.; Wetzel, P. Terrestrial mechanisms of interannual CO2 variability. Glob. Biogeochem. Cycles 2005, 19, 1016. [Google Scholar] [CrossRef]
- Graven, H.D.; Keeling, R.F.; Piper, S.C.; Patra, P.K.; Stephens, B.B.; Wofsy, S.C.; Welp, L.R.; Sweeney, C.; Tans, P.P.; Kelley, J.J.; et al. Enhanced Seasonal Exchange of CO2 by Northern Ecosystems Since 1960. Science 2013, 341, 1085–1089. [Google Scholar] [CrossRef] [Green Version]
- Zhang, T.; Wooster, M.J.; de Jong, M.C.; Xu, W. How Well Does the ‘Small Fire Boost’ Methodology Used within the GFED4.1s Fire Emissions Database Represent the Timing, Location and Magnitude of Agricultural Burning? Remote Sens. 2018, 10, 823. [Google Scholar] [CrossRef] [Green Version]
- Gao, H.; Zhang, J.; Zheng, W.; Liu, C. Comparative study on the emission estimation from the forest fire based on different resolution satellite data. Geogr. Res. 2017, 36, 850–860. [Google Scholar]
- Chen, J.; Zheng, W.; Gao, H.; Shao, J.; Liu, C. Estimation method of straw burned area based on multi-source satellite remote sensing. Trans. Chin. Soc. Agric. Eng. 2015, 31, 207–214, (In Chinese with English abstract). [Google Scholar]
- Zhang, T.; Wooster, M.; Xu, W. Approaches for synergistically exploiting VIIRS I- and M-Band data in regional active fire detection and FRP assessment: A demonstration with respect to agricultural residue burning in Eastern China. Remote Sens. Environ. 2017, 198, 407–424. [Google Scholar] [CrossRef] [Green Version]
- Zhang, T.; de Jong, M.C.; Wooster, M.J.; Xu, W.; Wang, L. Trends in eastern China agricultural fire emissions derived from a combination of geostationary (Himawari) and polar (VIIRS) orbiter fire radiative power products. Atmos. Chem. Phys. 2020, 20, 10687–10705. [Google Scholar] [CrossRef]
Province | CO2 | CO | CH4 | NMHC | SO2 | NH3 | NOx | BC | OC | PM2.5 | PM10 |
---|---|---|---|---|---|---|---|---|---|---|---|
Statistical-based method (2003–2018) | |||||||||||
Hebei | 1813.724 | 81.432 | 4.812 | 10.796 | 0.892 | 0.962 | 4.726 | 0.975 | 5.922 | 9.994 | 8.884 |
Beijing | 38.734 | 1.739 | 0.103 | 0.231 | 0.013 | 0.021 | 0.101 | 0.021 | 0.126 | 0.213 | 0.190 |
Tianjing | 76.256 | 3.424 | 0.202 | 0.454 | 0.025 | 0.040 | 0.199 | 0.041 | 0.249 | 0.420 | 0.373 |
Shandong | 1926.989 | 86.518 | 5.112 | 11.470 | 0.629 | 1.022 | 2.021 | 1.036 | 6.292 | 10.618 | 9.438 |
Henan | 2350.021 | 105.511 | 6.235 | 13.988 | 0.767 | 1.247 | 6.123 | 1.263 | 7.674 | 12.949 | 11.510 |
Jiangsu | 1558.856 | 69.989 | 4.136 | 9.279 | 0.509 | 0.827 | 4.062 | 0.838 | 5.090 | 8.590 | 7.635 |
Anhui | 3440.868 | 154.488 | 9.129 | 20.841 | 1.124 | 1.826 | 8.965 | 1.849 | 11.235 | 18.960 | 16.853 |
Total | 11,205.448 | 503.101 | 29.729 | 67.059 | 3.959 | 5.945 | 26.197 | 6.023 | 36.588 | 61.744 | 54.883 |
BA-based method (2003–2019) | |||||||||||
Hebei | 88.948 | 3.994 | 0.236 | 0.529 | 0.029 | 0.047 | 0.232 | 0.048 | 0.290 | 0.490 | 0.436 |
Beijing | 0.480 | 0.022 | 0.001 | 0.003 | 0.000 | 0.000 | 0.001 | 0.000 | 0.002 | 0.003 | 0.002 |
Tianjing | 6.385 | 0.287 | 0.017 | 0.038 | 0.002 | 0.003 | 0.017 | 0.003 | 0.021 | 0.035 | 0.031 |
Shandong | 191.934 | 8.617 | 0.509 | 1.142 | 0.063 | 0.102 | 0.500 | 0.103 | 0.627 | 1.058 | 0.940 |
Henan | 579.051 | 25.998 | 1.536 | 3.447 | 0.189 | 0.307 | 1.509 | 0.311 | 1.891 | 3.191 | 2.836 |
Jiangsu | 264.729 | 11.886 | 0.702 | 1.576 | 0.086 | 0.140 | 0.690 | 0.142 | 0.864 | 1.459 | 1.297 |
Anhui | 693.905 | 31.155 | 1.841 | 4.130 | 0.227 | 0.368 | 1.808 | 0.373 | 2.266 | 3.824 | 3.399 |
Total | 1825.432 | 81.959 | 4.842 | 10.865 | 0.596 | 0.967 | 4.757 | 0.980 | 5.961 | 10.060 | 8.941 |
FRP-based method (2003–2019) | |||||||||||
Hebei | 409.139 | 18.369 | 1.085 | 2.435 | 0.134 | 0.217 | 1.066 | 0.220 | 1.336 | 2.254 | 2.004 |
Beijing | 14.576 | 0.654 | 0.039 | 0.087 | 0.005 | 0.008 | 0.038 | 0.008 | 0.048 | 0.080 | 0.071 |
Tianjing | 66.832 | 3.001 | 0.177 | 0.398 | 0.022 | 0.035 | 0.174 | 0.036 | 0.218 | 0.368 | 0.327 |
Shandong | 528.461 | 23.727 | 1.402 | 3.146 | 0.173 | 0.280 | 1.377 | 0.284 | 1.726 | 2.912 | 2.588 |
Henan | 951.747 | 42.731 | 2.525 | 5.665 | 0.311 | 0.505 | 2.480 | 0.511 | 3.108 | 5.244 | 4.662 |
Jiangsu | 383.677 | 17.226 | 1.018 | 2.284 | 0.125 | 0.204 | 1.000 | 0.206 | 1.253 | 2.114 | 1.879 |
Anhui | 756.689 | 33.974 | 2.008 | 4.504 | 0.247 | 0.402 | 1.972 | 0.407 | 2.471 | 4.170 | 3.706 |
Total | 3111.121 | 139.682 | 8.254 | 18.519 | 1.017 | 1.651 | 8.107 | 1.672 | 10.160 | 17.142 | 15.237 |
Sources | Emission | Period | Methods |
---|---|---|---|
Liu et al. [14] | 16.0 | 2006 | FRP-based method |
Huang et al. [12] | 27.5 | 2006 | Statistical-based method |
This study | 9.89 | 2006 | Statistical-based method |
4.24 | 2006 | BA-based method | |
3.47 | 2006 | FRP-based method | |
Yin et al. [16] * | 7.04 | 2003–2017 | FRP-based method |
MCD64A1 [16] * | 0.50 | 2003–2017 | BA-based method |
GFED4 [70] * | 7.64 | 2003–2016 | BA-based method |
GFASv1 [74] * | 6.54 | 2003–2013 | FRP-based method |
FINNv1.5 [69] * | 7.62 | 2003–2016 | BA-based method |
This study | 11.45 | 2003–2017 | Statistical-based method |
2.35 | 2003–2017 | BA-based method | |
3.31 | 2003–2017 | FRP-based method |
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Fu, Y.; Gao, H.; Liao, H.; Tian, X. Spatiotemporal Variations and Uncertainty in Crop Residue Burning Emissions over North China Plain: Implication for Atmospheric CO2 Simulation. Remote Sens. 2021, 13, 3880. https://doi.org/10.3390/rs13193880
Fu Y, Gao H, Liao H, Tian X. Spatiotemporal Variations and Uncertainty in Crop Residue Burning Emissions over North China Plain: Implication for Atmospheric CO2 Simulation. Remote Sensing. 2021; 13(19):3880. https://doi.org/10.3390/rs13193880
Chicago/Turabian StyleFu, Yu, Hao Gao, Hong Liao, and Xiangjun Tian. 2021. "Spatiotemporal Variations and Uncertainty in Crop Residue Burning Emissions over North China Plain: Implication for Atmospheric CO2 Simulation" Remote Sensing 13, no. 19: 3880. https://doi.org/10.3390/rs13193880