A high temporal-spatial resolution air pollutant emission inventory for agricultural machinery in China

Agricultural machinery is an important non-road mobile source, which can exhaust multi-pollutants, making primary and secondary contributions to the air pollution. China is a significant agricultural country of the world; however, the agricultural machinery emissions research is at an early stage, and an emission inventory with a high temporal-spatial resolution is still needed. In this study, a comprehensive emission inventory with a high temporal-spatial resolution for agricultural machinery in China was first developed. The results showed that the total emissions in 2014 were 262.69 Gg, 249.25 Gg, 121139 Gg, 2192.05 Gg, 1448.16 Gg and 25.14 Gg for PM10, PM25, THC, NOx, CO and SO2, respectively. Tractors and farm transport vehicles were the top two greatest contributors, accounting for approximately 39.9%-53.6% and 17.4%-24.6%, respectively, of the total emissions of the five pollutants (except THC). The farm transport vehicles contributed the most (81.8%) to the THC emissions. The county-level emissions were further allocated into 1 km x 1 km grids according to source-specific allocation surrogates. The spatial characteristic analysis indicated that high emissions were distributed in northeast, north and central south China. To obtain a high temporal resolution emission inventory, a comprehensive investigation on the agricultural practice timing in different provinces was conducted. Then, the annual emissions in the different provinces were distributed to a spatial resolution of ten-day periods (i.e. the early, mid- and late ten-day periods in each month). It was found that higher emissions in China occurred in late April, mid-June and early October. In addition, the emission uncertainty was also analyzed based on the Monte Carlo simulation. The estimated high temporal-spatial resolution emission inventory could provide important basic information for environmental/climate implications research, emission control policy making, and air quality modeling. (C) 2018 Elsevier Ltd. All rights reserved.