The hilly and mountainous regions of China are characterized by unique features such as small plots of land, steep slopes, fragmented fields, and high soil viscosity, which result in a decline in the efficiency of conventional agricultural machinery, or even render its use impractical. To address this issue, this study developed a micro universal chassis adapted to hilly terrains. First, a four-wheel-drive multifunctional electric micro chassis was designed, considering the terrain characteristics of hilly regions and the agronomic requirements of maizesoybean strip intercropping. Second, the kinematics of the chassis were modeled and analyzed to determine optimal posture control strategies, and a fuzzy RBF neural network-based PID control algorithm was designed to enable dynamic adjustment of the chassis. Then, extensive testing was conducted on the prototype chassis, including straight-line driving tests, steering tests, climbing tests, and passability tests, which demonstrated its excellent operational performance. The straight-line driving tests showed an average lateral deviation of 30 mm and a maximum deviation of 60 mm, while the in-situ steering tests recorded a deviation of 20 mm. Finally, the prototype was applied to field weeding operations, where results indicated that its performance, including travel speed, weeding efficiency, and seedling damage rate, significantly outperformed existing traditional models. The findings suggest that the designed multifunctional micro universal chassis is highly effective for use in hilly and mountainous regions, with superior performance particularly under intercropping systems.

In this study, in situ observations were conducted for six criteria air pollutants (PM2.5, PM10, SO2, NO2, CO, and O-3) at 23 sites in western China for 1 year. Subsequently, the detailed Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) results for the pollutants were determined. The WRF-Chem model provided a clear perspective on the spatiotemporal distribution of air pollutants. High pollutant concentrations were mainly observed over highly populated mega-city regions, such as Sichuan and Guanzhong basins, whereas low concentration levels were observed over the Tibetan Plateau (TP). The TP also showed an increased concentration of O-3. Seasonally, all six pollutants except O-3 exhibited high concentration values during winter and low values during summer. O-3 concentrations exhibited an opposite seasonal variation in low-altitude regions. Unlike other pollutants that exhibited gradually decreasing concentrations with an increase in altitude, O-3 concentrations revealed an increasing trend. Furthermore, NO2 concentrations gradually increased in the upper atmosphere possibly due to lighting and stratospheric transmission. Atmospheric pollution is closely related to emissions and meteorological variations in western China. Meteorological conditions in the summer are conducive to pollutant dispersion and wet scavenging; however, unfavourable weather conditions (high pressure as well as a low planetary boundary layer height and precipitation level) in the winter can further worsen air pollution. Atmospheric pollutants from various emission sectors generally exhibited varying monthly profiles. In six typical cities, pollutants were positively correlated with multiple emission sources except for industrial emissions. Further sensitivity simulations indicated that eliminating residential emissions resulted in the largest decrease (up to 70%) in PM2.5 and PM10 concentrations. The most significant reductions in the concentrations of SO2 and NO2 were achieved by eliminating industrial and transportation emissions, respectively. The outcomes of this study could be helpful for future studies on pollution formation mechanisms as well as environmental and health risk assessments in western China. (C) 2019 Elsevier Ltd. All rights reserved.

To alleviate air pollution in western China, experiencing rapid economic growth following national western development strategies, an accurate and compressive assessment of PM2.5 sources is critical. Here, we firstly investigated the spatiotemporal variation in PM2.5 and analyzed its association with weather conditions and emission changes. Then, WRF-Chem simulations were conducted for an entire year to obtain various emission sectors' contributions to the PM2.5 mass by a hybrid method, which considers both the proportions of various components as well as each sector contributing to these components. The results showed that residential emissions had the largest contribution to PM2.5 because of its dominating contribution for primary components of PM2.5 (BC and POA), which can explain > 70% of PM2.5. Seasonally, the residential contributions to PM2.5 were higher in the non-monsoon period than in the monsoon period because of the higher contribution ratios to primary components. Regionally, as an essential source of the gaseous precursors, the industrial and transportation sectors were the second-largest contributors to PM2.5 in the highly populated urban (HP) and remote background (RM) regions, respectively. Further assessment of emission reduction measures indicated that eliminating 50% of residential emissions induced a 29.4% and 33.1% decrease in the annual PM2.5 mass of the HP and RM regions, respectively, with higher decrease proportions in non-monsoon. By comparison, eliminating 50% of industrial emissions caused a significantly lower decrease in PM2.5 for both HP (10%) and RM (4.6%). Eliminating 50% of transportation emissions led to PM2.5 concentrations to decline by 9.3% in RM, which was greater than the 4.6% reduction caused by eliminating 50% of industrial emissions. Therefore, in addition to focusing on the residential sector, especially in non-monsoon in western China, the transportation sector should be a focus to alleviate PM2.5 pollution on the Tibetan Plateau. The outcome of this study provides valuable information for policy-makers to make strategies to mitigate air pollution in western China.