The correct analysis of the emission characteristics of tractors under different working conditions is the basis for compiling emission lists and analyzing and controlling regional pollutant emissions. Based on the measured data, the emission characteristics of four pollutants such as CO, HC, NOX, and PM under different operating conditions were analyzed. At the same time, the correlation between single operating condition parameters and pollutant emissions was analyzed, and the impact of different operating conditions such as speed, fuel consumption flow rate, and speed on different pollutant emissions was quantitatively analyzed based on the BP-MIV model. The results showed that the instantaneous emission rates of CO, HC, NOX, and PM pollutants under four cultivation modes were 0.041 6 g/s, 0.013 2 g/s, 0.088 g/s, and 0.011 6 g/s, respectively, which were higher than those of walking and idle modes. The emission factors of CO, HC, and NOX based on fuel consumption for the four pollutants were the lowest under rotary tillage conditions. The correlation between single operating condition parameters and pollutant emissions was analyzed, and it was found that engine speed had the greatest impact on pollutant emission rate. The effects of rotational speed, fuel consumption flow rate and speed on instantaneous emission rate were quantified. Under idle conditions, fuel consumption flow rate had the greatest impact on the four pollutants, while under cultivation conditions, the degree of influence of the three influencing factors on the four pollutants was different. The study of pollutant emissions from agricultural tractors in Xinjiang under different working conditions helps to clarify the pollutant emissions characteristics of agricultural machinery and provide data support for regional joint prevention and control of air pollution.

The establishment of tractor pollutant emission inventory has important research value in analyzing, evaluating and formulating emission control measures for agricultural mechanization production. In this paper, based on the portable emission measurement system (PEMS), the emissions of tractors under actual working conditions were tested, and the emission factors based on fuel consumption were measured. Combined with the estimation method of emission inventory of nonroad mobile sources, the emission inventories of PM, HC, NOX and CO from 66 counties and districts in Xinjiang from 2008 to 2019 were established, and its evolution trend is analyzed. Taking the emission inventory of 66 counties in 2019 as the clustering sample, the Kmeans clustering method was applied to divide 66 counties. The results showed that the total pollutant emission of 66 counties in Xinjiang from 2008 to 2019 increased from 2.72×104 t to 3.52×104 t, with an average annual growth rate of 2.37%, and the variation trend showed three trends: large increase, steady increase and fluctuation decrease. In terms of spatial dimension, 66 counties were divided into mild, moderate and severe emission control areas in 2019, and their pollutant emissions accounted for 39.99%、41.31% and 18.70% of the total, respectively. The average emission was 312.56 t、806.97 t and 2 192.22 t, respectively. The distribution pattern of light and heavy emission control areas is relatively dispersed, while the spatial distribution of moderate emission control areas is relatively concentrated. The different driving mechanisms of different emission regions can be summarized into different levels of economic and social development, the difference of natural environment, the incentive of agricultural production scale and the leading degree of agricultural mechanization. This study provides a reference for the estimation of tractor emission inventory in counties, the division of emission control areas of agricultural mechanization production and the formulation of differentiated pollutant emission control measures.

In general, the identification of working conditions of agricultural machinery had significant research value in refining the working conditions of agricultural machinery and helping to master the trend of regional pollutant discharge. Based on the time series of the tractor running speed, engine speed, and real-time fuel consumption under different running conditions, the research introduced the image recognition method into tractor working condition identification for the first time. At the same time, the research also applied the parameter optimized support vector machine and the convolutional neural network (CNN) to conduct a systematical study related to the tractor working conditions. The related research results indicated that a support vector machine based on parameter optimization could realize the working condition identification of sample points in an ideal way, with the recognition accuracy reaching 99.851 9%. Nevertheless, it cannot realize the continuous identification of agricultural machinery working conditions, nor can it effectively identify the conversion stage of agricultural machinery working conditions. Moreover, in this study, a range of information, including tractor running speed and engine speed, are used to construct the sample image, thereby describing the data expression of agricultural machinery working condition change. The application of the convolutional neural network (CNN) is beneficial to realize the continuous recognition of agricultural machinery working conditions effectively, with the recognition accuracy reaching 93.3%. In short, the research not only provided reference value for the research related to the identification of agricultural machinery working conditions but also provided corresponding technical support for the subsequent research on the regional pollutant emissions produced by agricultural machinery under different working conditions.