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Investigation of spectral (collocation or Galerkin) methods for the solution approximation of different classes of optimal control problems have had been increased in recent years. In this research work, we proposed and analyzed an applicable Legendre spectral collocation approach, based on an efficient space of fractional basis functions, to compute numerically the solution of fractional optimal control problems. We first introduce a fractional Lagrange interpolation and then approximate the unknown control and state by this new set of fractional interpolation functions. The problem is discretized in terms of shifted Legendre–Gauss collocation points and two approaches are suggested to calculate the exact fractional differentiation matrix. Also, we prove that the approximate solutions of the obtained discretized problem converge to the optimal solution of the main problem by assuming some mild conditions. The method is implemented for three numerical test problems to show the claimed efficiency and capability.

Unmanned aerial vehicles (UAVs) equipped with a miniaturized sensor package were developed for aerial observations, which realizes aerial observations affordable to scientists in atmospheric science and achieves aerial measurements in high spatial resolution. UAVs are deployed to a variety of aerial detecting tasks in different scientific scenarios including chemical industry parks (CIPs) with hazardous gases emissions, and some places difficult for humans to reach. In this study, UAV sensing technology was deployed to detect air pollutants in a suburb, a CIP and a natural gas plant, respectively. The effects of atmospheric conditions such as the atmospheric boundary layer height, long-distance transport and atmospheric stability on the spatiotemporal variations of the air pollutants vertical profiles were investigated by the UAV. The UAV with the sensor package was deployed to capture the methane (CH4) leakages in a natural gas plant. The spatiotemporal variations of CH4 in both vertical and horizontal directions studied by UAV were employed to calculate accurate CH4 emissions, which is crucial to reducing the emissions of greenhouse gases. The low-cost UAV sensing technology for air pollutants was developed by Dr. Xiaobing Pang, who was funded by the Newton Fellowship in 2009 and worked in the University of York. This article is part of the theme issue 'Celebrating the 15th anniversary of the Royal Society Newton International Fellowship'. [ABSTRACT FROM AUTHOR]

In the domain of bionic walking control for biped robots, optimizing the parameters of the central pattern generator (CPG) presents a formidable challenge due to its high-dimensional and nonlinear characteristics. The traditional particle swarm optimization (PSO) algorithm often converges to local optima, particularly when addressing CPG parameter optimization issues. To address these challenges, one improved particle swarm optimization algorithm aimed at enhancing the stability of the walking control of biped robots was proposed in this paper. The improved PSO algorithm incorporates a spiral function to generate better particles, alongside optimized inertia weight factors and learning factors. Evaluation results between the proposed algorithm and comparative PSO algorithms were provided, focusing on fitness, computational dimensions, convergence rates, and other metrics. The biped robot walking validation simulations, based on CPG control, were implemented through the integration of the V-REP (V4.1.0) and MATLAB (R2022b) platforms. Results demonstrate that compared with the traditional PSO algorithm and chaotic PSO algorithms, the performance of the proposed algorithm is improved by about 45% (two-dimensional model) and 54% (four-dimensional model), particularly excelling in high-dimensional computations. The novel algorithm exhibits a reduced complexity and improved optimization efficiency, thereby offering an effective strategy to enhance the walking stability of biped robots. [ABSTRACT FROM AUTHOR]

In order to improve the walking stability of a biped robot in multiple scenarios and reduce the complexity of the Central Pattern Generator (CPG) model, a new CPG walking controller based on multivariate linear mapping was proposed. At first, in order to establish a dynamics model, the lower limb mechanical structure of the biped robot was designed. According to the Lagrange and angular momentum conservation method, the hybrid dynamic model of the biped robot was established. The initial value of the robot's passive walking was found by means of Poincaré mapping and cell mapping methods. Then, a multivariate linear mapping model was established to form a new lightweight CPG model based on a Hopf oscillator. According to the parameter distribution of the new CPG model, a preliminary parameter-tuning idea was proposed. At last, the joint simulation of MATLAB and V-REP shows that the biped robot based on the new CPG control has a stable periodic gait in flat and uphill scenes. The proposed method could improve the stability and versatility of bipedal walking in various environments and can provide general CPG generation and a tuning method reference for robotics scholars. [ABSTRACT FROM AUTHOR]

Vertical observations of atmospheric pollutants play crucial roles in a comprehensive understanding of the distribution characteristics and transport of atmospheric pollutants. A hexacopter uncrewed aerial vehicle equipped with miniature monitors was employed to measure the vertical distribution of atmospheric pollutants within a height of 1000 m at a rural site in Xi'an, China, in 2021. The concentrations of carbon monoxide (CO) and particulate matter (PM) showed generally decreasing trends with increasing height. The ozone (O3) concentration showed a general increasing trend with height followed by a gradual decreasing trend. Vertical decrements of PM2.5 and CO from 0 to 1000 m were significantly (p < 0.05) lower on observation days during summer (14.0 ± 8.1 μg m−3 and 8.7 ± 6.6 ppb, respectively), compared with those in winter (78.3 ± 14.1 μg m−3 and 34.8 ± 17.3 ppb, respectively). The horizontal transport of PM and CO mostly occurred in the morning and at night during winter observations at an altitude of 400–500 m. During the winter haze, the PM and CO profile concentrations below 500 m increased substantially with the decrease in the height of the thermal inversion layer. Vertical O3 transportation was observed in the afternoon and evening during summer, and a ∼37.7% (11.6 ppb) increase in ground-level O3 was observed in relation to vertical transport from the upper atmosphere. The results provide insights into the vertical distribution and transport of atmospheric pollutants in rural areas near cities. [ABSTRACT FROM AUTHOR]

Concurrent pollution of fine particulate matter (PM2.5) and ozone has been increasingly reported in China recently. Here, we further confirm widespread co‐occurring summertime PM2.5‐ozone extremes in southern China. Annual‐average frequency of co‐occurrence is above 50% from 2015 to 2022, especially in Pearl River Delta region (72 ± 12%). The spatial extent (city numbers) and temporal persistence (co‐occurrence days) for cities with co‐occurrence frequency >50% increase at a rate of two cities/year and 14 days/year, respectively. We further identify typical synoptic conditions (e.g., typhoon periphery circulation, West Pacific subtropical high) conducive to widespread co‐occurrence. Through combining multi‐source data, Random Forest model well predicts PM2.5‐ozone co‐occurrence and identifies common precursors (e.g., volatile organic compounds) as important variables. Finally, we postulate co‐occurrence is linked to synoptic conditions and secondary generation of PM2.5‐ozone from shared precursors. Our results suggest high potentials for co‐occurring PM2.5‐ozone extremes in southern China and control strategies on common precursors to mitigate concurrent pollution. Plain Language Summary: In recent years, fine particulate matter (PM2.5) and ozone pollution have been increasingly reported to co‐occur in many regions of China. In this study, we further report widespread co‐occurrence of PM2.5‐ozone extremes in the summer of southern China and explain the possible underlying reasons. The annual‐average co‐occurrence is observed with a frequency above 50% from 2015 to 2022, especially in the Pearl River Delta (PRD) region (72 ± 12%). The spatial range and temporal duration for the cities with co‐occurrence frequency above 50% increase annually in southern China. Based on a case study of consecutive co‐occurrence episodes, we find typical weather conditions (e.g., typhoon periphery, West Pacific subtropical high) are along with the widespread co‐occurrence. Then, we combine multi‐source data in the PRD region and apply Random Forest model to predict the co‐occurrence of PM2.5‐ozone extremes and identify common precursors (e.g., volatile organic compounds) as important factors. Finally, we infer the co‐occurring extremes are associated with typical weather conditions and shared precursors of PM2.5 and ozone. Our results not only imply high potentials for co‐occurring PM2.5‐ozone extremes in southern China but also suggest control strategies on shared precursors to tackle concurrent pollution. Key Points: There is a high potential (frequency >50%) for co‐occurring extremes of summertime PM2.5 and ozone in southern China from 2015 to 2022Typhoon periphery circulation is conducive to the co‐occurrence of summertime PM2.5 and ozone extremes in southern ChinaRandom Forest model well predicts co‐occurring extremes and reveals shared precursors as important variables in the Pearl River Delta region [ABSTRACT FROM AUTHOR]

Methane (CH4) is the main pollutant in oil and gas production. The detection and accounting of CH4 is an important issue in the process of greenhouse gas control and emission reduction in oil and gas industry. In this study, a portable CH4 detector based on tunable diode laser absorption spectroscopy (TDLAS) technology was deployed. The three-dimensional distribution of CH4 in a natural gas purification plant in Sichuan was obtained through vertical unmanned aerial vehicle (UAV) flight observations and ground mobile observations. According to the mass balance method, the emission of CH4 on 30 m above ground level (AGL) and 60 m AGL in this site was about 0.012 kg/s (±42% at 1σ) and 0.034 kg/s (±47% at 1σ), respectively, in one day. The vertical distribution showed that the CH4 concentration reached the maximum (2.75 ± 0.19 ppm) with height of 0 to 100 m AGL. The CH4 concentration from 100 to 300 m AGL showed a downward trend with height. Atmospheric instability at high altitude and high wind speed promoted the diffusion of CH4. The CH4 concentrations of horizontal distribution on 30 m AGL and 60 m AGL were 2.48 ± 0.11 ppm and 2.76 ± 0.34 ppm. In the observation of mobile campaigns, the connecting equipment of natural gas treatment facilities was prone to leakage, such as in valves and flanges. CH4 leakage was also detected at the torch mouth, especially when there was an open flame at the torch mouth. During the mobile movement investigation, the downwind measurement (OTM-33A) was applied to determine the overall CH4 emission rate shortly after patrolling the site. This work plays a vital role in optimizing the operation and maintenance of natural gas production stations pipe network, ensuring human safety and minimizing greenhouse gas emissions. [ABSTRACT FROM AUTHOR]

Currently, traffic-related sources are considered to be one of the major contributors to air pollutants in urban areas. As the number of motor vehicles increases, the impact of traffic-related air pollutants (TRAPs) on human health has also increased in recent years. People are easily exposed to TRAPs in their daily lives. However, long-term exposure to TRAPs can have adverse health effects. Mobile monitoring is more flexible compared to traditional urban monitoring stations and can effectively obtain the spatial variation characteristics of air pollutants. We mounted a sensor package on an electric bicycle and conducted mobile measurements of CO, NO2 and SO2 on a circular road in the center of Shaoxing, a city in the center of the Yangtze Delta, China. The CO, NO2 and SO2 concentrations were observed to be higher in the morning and evening rush hours, and the three pollutants show different seasonal and spatial variation characteristics. CO concentration was higher in urban arterial and crossroads. NO2 concentration was variable, alternating between high and low concentrations. SO2 concentration was relatively stable and aggregated. This study provides important information on the spatial and temporal variations of TRAPs, which helps commuters understand how to effectively reduce pollutant exposure during personal travel. [ABSTRACT FROM AUTHOR]

Increasing carbon dioxide (CO2) concentrations threaten human production and life. Currently the equipment used for CO2 monitoring is heavy and expensive, without a portable CO2 detector that is inexpensive and resistant to interference. Here we designed a portable CO2 detector based on no-dispersive infrared sensors to measure CO2 concentration. The detector, which has a mass of 1 kg, is powered by a lithium battery with dimensions of 200 mm (length) × 150 mm (width) × 100 mm (height). Considering the fact that field observations are susceptible to humidity, a series of experiments were carried out to reduce the humidity interference on sensor responses at a laboratory. The values of humidity and CO2 variation were used in a regression model analysis to determine a quadratic function with an R2 above 0.94. The detector was compared with a reference analyzer in ambient CO2 measurement during a 7-day field campaign in Hangzhou, China. After humidity correction, the data show better correlation with the reference data, with the R2 0.62–0.97 increasing from 0.62–0.97 compared to before the correction and the value deviation decreasing to less than 3%. Cluster analysis of sensors revealed a reduction in average relative deviation of up to 1.4% as the number of sensors increased. [ABSTRACT FROM AUTHOR]