This study analyzed the variability and trend in aerosol optical depth (AOD) over North China using the latest MODIS/Terra C6 merged Dark Target/Deep Blue AOD monthly data at 550 nm from 2001 to 2016. The spatial distribution of the annual mean AOD was generally characterized by two prominent high-value centers located in the industrially and economically developed areas of the North China Plain and East China, and the dust aerosol-dominated areas of southern Xinjiang. The seasonally averaged AOD reached its maximum in spring (0.430 ± 0.049), followed by summer (0.356 ± 0.035) and winter (0.282 ± 0.039), with the minimum occurring in autumn (0.219 ± 0.022). There were notable long-term annual trends in AOD in different regions over North China during 2001-2016: a decreasing AOD trend was found in Qinghai Tibet (−0.015 ± 0.010/decade), Northwest China (−0.059 ± 0.013/decade at 99% confidence level), and the North China Plain (−0.007 ± 0.021/decade), but a positive increasing trend was identified in northern Xinjiang (0.01 ± 0.006/decade), southern Xinjiang (0.002 ± 0.013/decade), East China (0.053 ± 0.042/decade), and Northeast China (0.016 ± 0.029/decade). Seasonal patterns in the AOD regional long-term trend were evident. The AODs in spring over all the study regions, except East China, exhibited a decreasing trend, with the maximum trend value observed in Northwest China (−0.099 ± 0.029/decade at 99% confidence level); whereas AODs in autumn, except in Northwest China, showed an increasing trend, with the maximum trend value occurring in East China (0.073 ± 0.038/decade). Geographically, we also examined the annual and seasonal spatial patterns of AOD trends over North China. The annual spatial trends in AOD revealed a dominance of positive trends in most regions over the whole of North China from 2001 to 2016, but especially in East and Northeast China (AOD trend value of about 0.16/decade); whereas a negative trend was observed over northern Inner Mongolia (AOD trend value of about −0.12/decade). In addition, seasonal spatial trend analyses indicated that a continual clear upward trend occurred in East China in the autumn and winter seasons during the study period, with the maximum average increase occurring in winter (about 0.20/decade).

This study analyzed the variability and trends in precipitable water vapor (PWV) in North China from 1979 to 2015. The spatial distribution of annual mean PWV was generally characterized by two high PWV centers in Eastern China and the Tarim Basin and two low PWV centers in Northern Tibet and Qinghai Province and in Inner Mongolia. The levels of seasonal mean PWV were highest in summer, followed by autumn and spring, and lowest in winter. The maximum monthly mean PWV occurred in July and August, while the minimum occurred in December to February. Increasing trends in PWV, with the trend magnitude ranging from 0.1 to 1.2 mm decade−1 over North China, were observed in the radiosonde, ERA-interim, and MERRA-2 PWV data from 1979 to 1999; but a slightly decreasing trend of −0.4 mm decade−1 from radiosonde was found in most regions of North China from 1979 to 2007. A monotonically increasing PWV trend was detected throughout North China between 1979 and 1999, with the maximum trend occurring in summer and the minimum occurring in winter. For the period of 1979–2007, a slightly but less marked decreasing trend was found at most stations in North China in all four seasons.

Despite advances in modern control theory and artificial intelligence technology, current methods for tuning proportional-integral-derivative (PID) controller parameters based on the traditional particle swarm optimization (PSO) algorithm do not meet the requirements for controlling an unmanned surface vessel (USV) propulsion motor. To overcome the disadvantages of the PSO algorithm, such as low precision and easily falling into a local optimum, the beetle antennae search (BAS) algorithm can be introduced into the PSO algorithm by replacing particles with beetles, and effectively prevents the PSO algorithm from easily falling into the local optimum. At the same time, the BAS algorithm will no longer be limited to single objective parameterization. Herein, we propose a PID parameter optimization method based on the hybrid BAS-PSO algorithm for a USV propulsion motor. The PID parameter optimization of propulsion motor effectively becomes a beetle foraging problem with group optimization. Numerical results show that the method can effectively solve the problems of PSO and greatly improve convergence speed. Compared with the genetic algorithm and standard PSO algorithm, the BAS-PSO algorithm is superior for PID parameter tuning and can improve performance of USV propulsion system.