A novel method for spatial allocation of volatile chemical products emissions: A case study of the Pearl River Delta

Abstract

The spatial allocation of volatile chemical products (VCPs) emissions is often inaccurate, which hampers the evaluation and improvement of models and the study of urban secondary pollution and exposure risk. This work developed a 1 km grid spatial allocation factors dataset based on the actual emission characteristics of each VCPs sector. For the industrial VCPs sector, we used industrial output per unit firm as the spatial allocation factors. For the non-industrial VCPs sector, we used specific datasets (e.g., GDP (Gross Domestic Product) for personal care products use, etc.) as the spatial allocation factors. Our work accounted for the high emission characteristics of industrial point sources and the dependence conditions of non-industrial VCPs use. Using this methodology, we developed a 1 km gridded emission inventory (denoted by Mo-E) of VCPs in the Pearl River Delta (PRD, Excluding Hong Kong Special Administrative Region (SAR) and Macau SAR) region in 2017. The results show that the non-methane volatile organic compounds (NMVOCs) emissions of VCPs in the PRD region in 2017 were 1046.7 Kt, with coatings (567.3 Kt) and personal care products (225.5 Kt) being the key sectors for emission control. Different cities in the PRD region have different VCPs control strategies. The industrial VCPs sector was the dominant VCPs emitter in high-emitting cities (64.9%–72.2%), while the non-industrial VCPs sector was the main contributor in low-emitting cities (43.3%–58.4%). More VCPs emissions were allocated to medium-population-density areas (37.6% higher than before) and within 3 km of the urban-rural fringe (41.4% higher than before), compared with the spatial characteristics of VCPs emissions from Multi-resolution Emission Inventory for China (MEIC), due to the relocation of industrial VCPs enterprises and the architectural activities during urban expansion. Meanwhile, the uniform distribution pattern caused by population density allocation in these areas was corrected, and the point-source high-emission feature was tracked. The VCPs emissions in high-population-density areas were overestimated (31.4%), with personal care products (57.9%) as the main emission sources in these areas. We compared the Mo-E inventory with an inventory (Mo-M) derived using MEIC's spatial allocation factors, and found a maximum difference in emissions per unit grid of +14,602 t and a minimum of −880 t. Finally, we discuss the uncertainties in the emissions from each VCP sector (±30–80%) and the spatial emission patterns, which resulted from the spatial allocation dataset (±10.4–20%) and the spatial analysis methods (−6.9-+7.8%).