High-precision spatio-temporal variations and future perspectives of multiple air pollutant emissions from Chinese biomass-fired industrial boilers

Sci Total Environ. 2024 Jan 10:907:167982. doi: 10.1016/j.scitotenv.2023.167982. Epub 2023 Oct 20.

Abstract

Biomass-fired industrial boilers (BFIBs) are one of the neglected and important anthropogenic sources of air pollutants. A comprehensive boiler-based emission inventory of multiple air pollutants from BFIBs in China in 2020 was first developed based on the activity level database and updated emission factors. Results showed that national emissions of air pollutants from BFIBs in 2020 were estimated to be 11.5 kt of PM, 10.8 kt of PM10, 7.4 kt of PM2.5, 40.5 kt of SO2, 79.8 kt of NOx, 4.2 kt of organic carbon (OC), 1.0 kt of elemental carbon (EC), 31.7 kt of nonmethane volatile organic compounds (NMVOCs), 15.9 kt of NH3, and 116.5 t of five trace metals (Hg, Cr, Cd, Pb, and As), respectively. Air pollutant emissions exhibited significant spatio-temporal heterogeneity. Monthly air pollutant emissions varied by geographical division due to the combined effects of industrial production and winter heating demand. These emissions were mainly concentrated in the eastern coastal region, with Guangdong, Guangxi, Fujian, Jiangsu, and Zhejiang being the five provinces having the highest emissions. In addition, scenario predictions indicate that as the pollution and carbon reduction strategy is implemented, air pollutant emissions from BFIBs in China could become well controlled, with PM, NOx, SO2, and Hg emissions in 2050 projected to be 3.0-8.3 kt, 36.5-75.7 kt, 16.2-32.8 kt, and 0.52-0.87 t, respectively. Our results can provide a highly spatio-temporal resolution inventory of multiple air pollutant emissions from BFIBs for air quality modelling and support the formulation of air pollution control policies for biomass fuel utilization in the context of the pollution and carbon reduction strategy.

Keywords: Air pollutants; Biomass-fired industrial boiler (BFIB); Emission inventory; Scenario projection; Spatio-temporal variation; Trace metals.