Search

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.

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]