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

2018 ◽  
Vol 183 ◽  
pp. 1110-1121 ◽  
Author(s):  
Jianlei Lang ◽  
Jingjing Tian ◽  
Ying Zhou ◽  
Kanghong Li ◽  
Dongsheng Chen ◽  
...  
2019 ◽  
Vol 84 ◽  
pp. 80-96 ◽  
Author(s):  
Xingke Gu ◽  
Shasha Yin ◽  
Xuan Lu ◽  
Huan Zhang ◽  
Lingling Wang ◽  
...  

2017 ◽  
Author(s):  
Lei Zhang ◽  
Tianliang Zhao ◽  
Sunling Gong ◽  
Shaofei Kong ◽  
Lili Tang ◽  
...  

Abstract. Air pollutant emissions play a determinant role in deteriorating air quality. However, an uncertainty in emission inventories is still the key problem for modeling air pollution. In this study, an updated emission inventory of coal-fired power plants (UEIPP) based on online monitoring data in Jiangsu province of East China for the year of 2012 was implemented in the widely used Multi-resolution Emission Inventory for China (MEIC). By employing the Weather Research and Forecasting Model with Chemistry (WRF-Chem), two simulations were executed to assess the atmospheric environmental change by using the original MEIC emission inventory and the MEIC inventory with the UEIPP. A synthetic analysis shows that (1) compared to the power emissions of MEIC, PM2.5, PM10, SO2 and NOx were lower, and CO, black carbon (BC), organic carbon (OC) and NMVOCs were higher in the UEIPP, reflecting a large discrepancy in the power emissions over East China; (2) In accordance with the changes of UEIPP, the modeled concentrations were reduced for SO2 and NO2, and increased for most areas of primary OC, BC and CO, whose concentrations in atmosphere are highly dependent on emission changes. (3) Interestingly, when the UEIPP was used, the atmospheric oxidizing capacity significantly reinforced, reflecting by increased oxidizing agents, e.g. O3 and OH, thus directly strengthened the chemical production from SO2 and NOx to sulfate and nitrate, which offset the reduction of primary PM2.5 emissions especially in the haze days. This study indicated the importance of updating air pollutant emission inventories in simulating the complex atmospheric environment changes with the implications on air quality and environmental changes.


2020 ◽  
Vol 14 (4) ◽  
pp. 422-445
Author(s):  
Seong-woo Choi ◽  
Eugene Cho ◽  
Young-min Kim ◽  
Chul Yoo ◽  
Taekyu Kim ◽  
...  

2021 ◽  
Vol 21 ◽  
pp. 210003
Author(s):  
Xianbao Shen ◽  
Lei Kong ◽  
Yue Shi ◽  
Xinyue Cao ◽  
Xin Li ◽  
...  

2017 ◽  
Author(s):  
Monica Crippa ◽  
Greet Janssens-Maenhout ◽  
Diego Guizzardi ◽  
Rita Van Dingenen ◽  
Frank Dentener

Abstract. In this work we couple the HTAPv2.2 global air pollutant emission inventory with the global source receptor model TM5-FASST to evaluate the relative contribution of the major anthropogenic emission sources (power generation, industry, ground transport, residential, agriculture and international shipping) to air quality and human health in 2010. We focus on particulate matter (PM) concentrations because of the relative importance of PM2.5 emissions in populated areas and the proven cumulative negative effects on human health. We estimate that in 2010 regional annual averaged anthropogenic PM2.5 concentrations varied between ca. 1 and 40 μg/m3 depending on the region, with the highest concentrations observed in China and India, and lower concentrations in Europe and North America. The relative contribution of anthropogenic emission source sectors to PM2.5 concentrations varies between the regions. European PM pollution is mainly influenced by the agricultural and residential sectors, while the major contributing sectors to PM pollution in Asia and the emerging economies are the power generation, industrial and residential sectors. We also evaluate the emission sectors and emission regions in which pollution reduction measures would lead to the largest improvement on the overall air quality. We show that in order to improve air quality, regional policies should be implemented (e.g. in Europe) due to the transboundary features of PM pollution. In addition, we investigate emission inventory uncertainties and their propagation to PM2.5 concentrations, in order to identify the most effective strategies to be implemented at sector and regional level to improve emission inventories knowledge and air quality. We show that the uncertainty of PM concentrations depends not only on the uncertainty of local emission inventories but also on that of the surrounding regions. Finally, we propagate emission inventories uncertainty to PM concentrations and health impacts.


Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 440
Author(s):  
Yi Ai ◽  
Yunshan Ge ◽  
Zheng Ran ◽  
Xueyao Li ◽  
Zhibing Xu ◽  
...  

Diesel-powered agricultural machinery (AM) is a significant contributor to air pollutant emissions, including nitrogen oxides (NOx) and particulate matter (PM). However, the fuel consumption and pollutant emissions from AM remain poorly quantified in many countries due to a lack of accurate activity data and emissions factors. In this study, the fuel consumption and air pollutant emission from AM were estimated using a survey and emission factors from the literature. A case study was conducted using data collected in Anhui, one of the agricultural provinces of China. The annual active hours of AM in Anhui ranged 130 to 175 h. The estimated diesel fuel consumption by AM was 1.45 Tg in 2013, approximately 25% of the total diesel consumption in the province. The air pollutants emitted by AM were 57 Gg of carbon monoxide, 14 Gg of hydrocarbon, 74 Gg of NOx and 5.7 Gg of PM in 2013. The NOx and PM emissions from AM were equivalent to 17% and 22% of total on-road traffic emissions in Anhui. Among nine types of AM considered, rural vehicles are the largest contributors to fuel consumption (31%) and air emissions (33–45%).


2000 ◽  
Vol 8 ◽  
pp. 295-299
Author(s):  
Tomohiro MATSUO ◽  
Hongwei YANG ◽  
Yuzuru MATSUOKA

2015 ◽  
Vol 15 (10) ◽  
pp. 5443-5456 ◽  
Author(s):  
H. Y. Zhao ◽  
Q. Zhang ◽  
D. B. Guan ◽  
S. J. Davis ◽  
Z. Liu ◽  
...  

Abstract. Substantial anthropogenic emissions from China have resulted in serious air pollution, and this has generated considerable academic and public concern. The physical transport of air pollutants in the atmosphere has been extensively investigated; however, understanding the mechanisms how the pollutant was transferred through economic and trade activities remains a challenge. For the first time, we quantified and tracked China's air pollutant emission flows embodied in interprovincial trade, using a multiregional input–output model framework. Trade relative emissions for four key air pollutants (primary fine particle matter, sulfur dioxide, nitrogen oxides and non-methane volatile organic compounds) were assessed for 2007 in each Chinese province. We found that emissions were significantly redistributed among provinces owing to interprovincial trade. Large amounts of emissions were embodied in the imports of eastern regions from northern and central regions, and these were determined by differences in regional economic status and environmental policy. It is suggested that measures should be introduced to reduce air pollution by integrating cross-regional consumers and producers within national agreements to encourage efficiency improvement in the supply chain and optimize consumption structure internationally. The consumption-based air pollutant emission inventory developed in this work can be further used to attribute pollution to various economic activities and final demand types with the aid of air quality models.


2021 ◽  
Author(s):  
Lin Huang ◽  
Song Liu ◽  
Zeyuan Yang ◽  
Jia Xing ◽  
Jia Zhang ◽  
...  

Abstract. The inaccuracy of anthropogenic emission inventory on a high-resolution scale due to insufficient basic data is one of the major reasons for the deviation between air quality model and observation results. A bottom-up approach, as a typical emission inventory estimation approach, requires a lot of human labor and material resources, and a top-down approach focuses on individual pollutants that can be measured directly and relies heavily on traditional numerical modelling. Lately, deep neural network has achieved rapid development due to its high efficiency and non-linear expression ability. In this study, we proposed a novel method to model the dual relationship between emission inventory and pollution concentration for emission inventory estimation. Specifically, we utilized a neural network based comprehensive chemical transport model (NN-CTM) to learn the complex correlation between emission and air pollution. We further updated the emission inventory based on backpropagating the gradient of the loss function measuring the deviation between NN-CTM and observations from surface monitors. We first mimicked the CTM model with neural networks (NN) and achieved a relatively good representation of CTM with similarity reaching 95 %. To reduce the gap between CTM and observations, the NN model would suggest an updated emission of NOx, NH3, SO2, VOC and primary PM2.5 which changes by −1.34 %, −2.65 %, −11.66 %, −19.19 % and 3.51 %, respectively, on average of China. Such ratios of NOx and PM2.5 are even higher (~10 %) particularly in Northwest China where suffers from large uncertainties in original emissions. The updated emission inventory can improve model performance and make it closer to observations. The mean absolute error for NO2, SO2, O3 and PM2.5 concentrations are reduced significantly by about 10 %~20 %, indicating the high feasibility of NN-CTM in terms of significantly improving both the accuracy of emission inventory as well as the performance of air quality model.


Sign in / Sign up

Export Citation Format

Share Document