High-resolution ammonia emissions inventories in Fujian, China, 2009-2015

摘要

A high-resolution NH3 emission inventory was developed based on the corrected emission factors and county-level activity data. To provide model-ready emission input, the NH3 emission inventory was gridded for the modeling domain at 1 x 1 km resolution using source-based spatial surrogates and a GIS system. The best estimate of total NH3 emission for the province was 228.02 kt in 2015 with a percentage uncertainty of 16.3%. Four major contributors were farmland ecosystem, livestock wastes, humans and waste treatment, which contributed 39.4%, 43.1%, 4.9%, and 4.2% of the total emissions, respectively. The averaged NH3 emission density for the whole region was 1.88 t km(-2) yr(-1) and the higher values were found in coastal areas with higher dense populations. The seasonal patterns, with higher emissions in summer, were consistent with the patterns of temperature and planting practices. From 2009 to 2015, annual NH3 emissions increased from 218.49 kt to 228.02 kt. All of these changes are insignificant compared to the estimated overall uncertainties in the analysis, but indicative of changes in the source categories over this period. Between 2009 and 2015, the largest changes occurred in human emissions and waste treatment plants, which were consistent with the process of rapid urbanization. Meanwhile, the decrease of emissions from pigs was slightly higher than the increased emissions from broilers and the increased emissions from meat goats and beef cattle due to the combine effects of increasingly stringent environmental requirements for pig farms and shift away from pork consumption to beef, chicken and mutton. The validity of the estimates was further evaluated using uncertainty analysis, comparison with previous studies, and correlation analysis between emission density and observed ground ammonia. The inventories reflect the changes in economic progress and environmental protection and can provide scientific basis for the establishment of effective PM2.5 control strategies. (C) 2017 Elsevier Ltd. All rights reserved.