Complex Atmospheric Environment Research Articles

Oxygen Impurity-Tuned Structure and Adhesion Properties of the Cu/SiO2 Interface.

The properties of the Cu/SiO2 interface usually deteriorate in the complex atmospheric environment, which may limit its performance and application in the engineering. Using the reactive molecular dynamics method, we investigate how the mechanical behaviors of the Cu/SiO2 interface change as it interacts with oxygen impurities. The interfacial oxidation degree could be enhanced as O2 penetrates into the interface area. This makes the interfacial structure disordered and is not conducive to the survival of Cu-O-Si bondings, which reduces the tensile and shear strengths of the interface. To improve the abrupt bonding property change at the interface and modify the interfacial adhesion properties, O impurities are introduced at the Cu interstitial sites near the interface. By doing so, the interface strength can be significantly enhanced due to the production of typical O-Cu-O bondings while the regular interfacial structure is retained. Meanwhile, the interfacial oxidation also changes the tensile failure site and shearing sliding mode of the interface, i.e., from inside the oxide to between oxide and Cu. The findings of this work may not only advance the understanding of interaction mechanism between oxygen impurities and the Cu/SiO2 interface but also provide new insights into optimizing the bonding properties of the metal/oxide interface.

On the potential use of highly oxygenated organic molecules (HOMs) as indicators for ozone formation sensitivity

Abstract. Ozone (O3), an important and ubiquitous trace gas, protects lives from harmful solar ultraviolet (UV) radiation in the stratosphere but is toxic to living organisms in the troposphere. Additionally, tropospheric O3 is a key oxidant and a source of other oxidants (e.g., OH and NO3 radicals) for various volatile organic compounds (VOCs). Recently, highly oxygenated organic molecules (HOMs) were identified as a new compound group formed from the oxidation of many VOCs, making up a significant source of secondary organic aerosol (SOA). The pathways forming HOMs from VOCs involve autoxidation of peroxy radicals (RO2), formed ubiquitously in many VOC oxidation reactions. The main sink for RO2 is bimolecular reactions with other radicals, such as HO2, NO, or other RO2, and this largely determines the structure of the end products. Organic nitrates form solely from RO2 + NO reactions, while accretion products (“dimers”) form solely from RO2 + RO2 reactions. The RO2 + NO reaction also converts NO into NO2, making it a net source for O3 through NO2 photolysis. There is a highly nonlinear relationship between O3, NOx, and VOCs. Understanding the O3 formation sensitivity to changes in VOCs and NOx is crucial for making optimal mitigation policies to control O3 concentrations. However, determining the specific O3 formation regimes (either VOC-limited or NOx-limited) remains challenging in diverse environmental conditions. In this work we assessed whether HOM measurements can function as a real-time indicator for the O3 formation sensitivity based on the hypothesis that HOM compositions can describe the relative importance of NO as a terminator for RO2. Given the fast formation and short lifetimes of low-volatility HOMs (timescale of minutes), they describe the instantaneous chemical regime of the atmosphere. In this work, we conducted a series of monoterpene oxidation experiments in our chamber while varying the concentrations of NOx and VOCs under different NO2 photolysis rates. We also measured the relative concentrations of HOMs of different types (dimers, nitrate-containing monomers, and non-nitrate monomers) and used ratios between these to estimate the O3 formation sensitivity. We find that for this simple system, the O3 sensitivity could be described very well based on the HOM measurements. Future work will focus on determining to what extent this approach can be applied in more complex atmospheric environments. Ambient measurements of HOMs have become increasingly common during the last decade, and therefore we expect that there are already a large number of groups with available data for testing this approach.

Exploration of actual sky polarization patterns: From influencing factor analyses to polarized light-aided navigation

The polarized light scattered by the sunlight and atmosphere is a general natural property of the atmospheric space of Earth. This study is devoted to an exploration of actual sky polarization pattern-oriented navigation. For the polarized light-aided VINS (abbreviated ‘visual-inertial navigation system’), one of our major concerns is to prove the heading (orientation) reference stays stable under the actual complex atmospheric environments. The influencing factors that contribute to the variation of the polarization pattern symmetry have been taken into account. The analytic AOP (abbreviated ‘angle of polarization’) and DOP (abbreviated ‘degree of polarization’) maps and quantitative analyses that concern spectrum character, cloudage, and aerosol optical depth relevant to light scattering effects are simultaneously presented. Given the globally referenced heading, one intuitive way of modeling multi-sensor fusion is to use a graph structure. Following the pipeline design of VINS Fusion, a polarization imaging factor constraint (expressed by the polarization measurement residual-encoded edges) is added in the state estimator and the cost function to be minimized during optimization is formulated. Via campus road experiments on the built robot platform, our proposal is compared against VINS Fusion under different weather conditions, revealing that over the entire closed path, our polarized light-aided VINS achieves high rate of both locally and globally consistent estimates. To our best knowledge, this is the first work to illuminate the insights into a practical, polarization imager-integrated navigating application in which the localization results can be optically explained, evaluated, and optimized.

Impact of street canyon morphology on heat and fluid flow: An experimental water tunnel study using simultaneous PIV-LIF technique

Urban areas are known for their complex atmospheric environments, with the building morphology having a significant impact on local climate patterns, air quality, and overall urban microclimate. Understanding the heat transport and fluid flow in complex urban environments is crucial for improving urban climate resilience, which remains an open frontier in the field of urban studies. To gain a more profound insight into the physical processes occurring in urban areas, particularly within street canyons, we conducted an experimental investigation in a large-scale water tunnel. This study involved the simultaneous examination of heat and flow fields, carried out at high spatial and temporal resolutions, utilizing Laser-induced Fluorescence (LIF) for heat analysis and Particle Image Velocimetry (PIV) for flow analysis. Our results of heat and flow in different street canyons indicate that the flow is significantly influenced by a combination of factors, including canyon configuration, the presence of buoyant force, and the magnitude of the approaching flow. The ventilation rate and heat flux from the street canyon, which are key factors shaping the urban microclimate, are found dominated significantly by the street canyon morphology. For instance, changing the aspect ratio of a street canyon results in a significant change of air ventilation rate, ranging from as low as 0.02 to as high as 1.5 under the same flow conditions. Additionally, canyons with high air ventilation rates exhibit significant heat flux removal at the canyon roof level, which is accurately described by the local Richardson number.

Theoretical analysis of the optical rotational Doppler effect under atmospheric turbulence by mode decomposition

The optical rotational Doppler effect associated with orbital angular momentum provides a new means for rotational velocity detection. In this paper, we investigate the influence of atmospheric turbulence on the rotational Doppler effect. First, we deduce the generalized formula of the rotational Doppler shift in atmospheric turbulence by mode decomposition. It is found that the rotational Doppler signal frequency spectrum will be broadened, and the bandwidth is related to the turbulence intensity. In addition, as the propagation distance increases, the bandwidth also increases. And when m−2/3 and 2z ≤ 2 km, the rotational Doppler signal frequency spectrum width d and the spiral spectrum width d 0 satisfy the relationship d = 2d 0–1. Finally, we analyze the influence of mode crosstalk on the rotational Doppler effect, and the results show that it destroys the symmetrical distribution of the rotational Doppler spectrum about 2l⋅Ω/2π. This theoretical model enables us to better understand the generation of the rotational Doppler frequency and may help us better analyze the influence of the complex atmospheric environment on the rotational Doppler frequency.

Significant formation of sulfate aerosols contributed by the heterogeneous drivers of dust surface

Abstract. The importance of dust heterogeneous oxidation in the removal of atmospheric SO2 and formation of sulfate aerosols is not adequately understood. In this study, the Fe-, Ti-, and Al-bearing components, Na+, Cl−, K+, and Ca2+ of the dust surface, were discovered to be closely associated with the heterogeneous formation of sulfate. Regression models were then developed to make a reliable prediction of the heterogeneous reactivity based on the particle chemical compositions. Further, the recognized gas-phase, aqueous-phase, and heterogeneous oxidation routes were quantitatively assessed and kinetically compared by combining the laboratory work with a modelling study. In the presence of 55 µg m−3 airborne dust, heterogeneous oxidation accounts for approximately 28.6 % of the secondary sulfate aerosols during nighttime, while the proportion decreases to 13.1 % in the presence of solar irradiation. On the dust surface, heterogeneous drivers (e.g. transition metal constituents, water-soluble ions) are more efficient than surface-adsorbed oxidants (e.g. H2O2, NO2, O3) in the conversion of SO2, particularly during nighttime. Dust heterogeneous oxidation offers an opportunity to explain the missing sulfate source during severe haze pollution events, and its contribution proportion in the complex atmospheric environments could be even higher than the current calculation results. Overall, the dust surface drivers are responsible for the significant formation of sulfate aerosols and have profound impacts on the atmospheric sulfur cycling.

Rayleigh Lidar Signal Denoising Method Combined with WT, EEMD and LOWESS to Improve Retrieval Accuracy

Lidar is an active remote sensing technology that has many advantages, but the echo lidar signal is extremely susceptible to noise and complex atmospheric environment, which affects the effective detection range and retrieval accuracy. In this paper, a wavelet transform (WT) and locally weighted scatterplot smoothing (LOWESS) based on ensemble empirical mode decomposition (EEMD) for Rayleigh lidar signal denoising was proposed. The WT method was used to remove the noise in the signal with a signal-to-noise ratio (SNR) higher than 16 dB. The EEMD method was applied to decompose the remaining signal into a series of intrinsic modal functions (IMFs), and then detrended fluctuation analysis (DFA) was conducted to determine the threshold for distinguishing whether noise or signal was the main component of the IMFs. Moreover, the LOWESS method was adopted to remove the noise in the IMFs component containing the signal, and thus, finely extract the signal. The simulation results showed that the denoising effect of the proposed WT-EEMD-LOWESS method was superior to EEMD-WT, EEMD-SVD and VMD-WOA. Finally, the use of WT-EEMD-LOWESS on the measured lidar signal led to significant improvement in retrieval accuracy. The maximum error of density and temperature retrievals was decreased from 1.36% and 125.79 K to 1.1% and 13.84 K, respectively.

Impact of Lock-In Time Constant on Remote Monitoring of Trace Gas in the Atmospheric Column Using Laser Heterodyne Radiometer (LHR)

The time constant selected for lock-in amplification (LIA) has a crucial impact on observed line shapes in laser heterodyne spectroscopy, in particular in the case of ground-based remote monitoring of trace gas in the atmospheric column using laser heterodyne radiometer (LHR). Conventional simulation could not allow validation of LHR spectra measured in a real and complex atmospheric environment exhibiting large temporal and spatial variability (humidity, temperature, pressure, etc) that impact significantly the measured LHR spectra profiles. High-precision spectral measurement is thus crucial to avoid any spectral distortion resulting from the measurement. In this paper, the impact of LIA time constant on spectral line shape is investigated for LHR operating in continuous laser tuning mode, based on analysis of laboratory heterodyne spectra, in terms of signal-to-noise ratio (SNR), line width broadening, absorption depth and line shift. With respect to the given frequency scanning speed in continuous mode and to the halfwidth of the absorption feature to scan, a reasonable scanning time ΔTscan, the time needed for scanning laser frequency through the halfwidth ΔνHWHM of the absorption line, equal to or longer than 14 times of the LIA time constant τ is concluded in order to efficiently reduce the noise while without significant shift and distortion of the line shape. Experimental validation was carried out using a laser heterodyne absorption spectroscopy approach in the laboratory. Four different combinations of time constants τ and scanning time ΔTscan were used to record heterodyne spectra of a CH4 absorption line near 1242.00 cm−1 in continuous laser tuning mode. An optimal combination of a scanning time of 137 ms with a time constant of 1 ms was obtained. This optimal combination was used for ground-based measurements of CH4 and N2O in the atmospheric column by LHR. The extracted LHR spectrum is in good agreement with a referenced TCCON (Total Carbon Column Observing Network) FT-IR (Fourier-transform infrared) spectrum.

Machine learning combined with the PMF model reveal the synergistic effects of sources and meteorological factors on PM2.5 pollution

PM2.5 pollution is a complex process mainly affected by emission sources and meteorological conditions. However, it is hard to accurately assess the effects of emission sources and meteorological conditions on the variation of PM2.5 concentrations in the complex atmospheric environment. In this study, the Random Forest model with Shapley Additive exPlanations (RF-SHAP) and Partial Dependence Plot (RF-PDP) was combined with Positive Matrix Factorization (PMF) to evaluate the impacts of various factors on PM2.5 pollution. The results show that anthropogenic emissions and meteorological conditions contributed about 67% (40.5 μg/m3) and 33% (19.7 μg/m3) to variation in PM2.5 concentrations, respectively. Specifically, secondary nitrate (SN) had the greatest impact among all sources (about 45%). Hence, we further explore the impacts of the primary sources and meteorological conditions on SN formation. Coal combustion and vehicle emissions significantly contribute to the formation of SN by providing a large number of precursor NOX. Additionally, the RF-PDP method was further employed to estimate the synergistic effects of primary sources and meteorological conditions on SN formation. The results help reveal strategies to simultaneously reduce SN by controlling primary emissions under suitable meteorological conditions. This work also suggests that the machine learning model can utilize online datasets well and provide a reliable approach for analyzing the causes of PM2.5 pollution.

Efficient Pyramidal GAN for Versatile Missing Data Reconstruction in Remote Sensing Images

Missing data reconstruction is a classical yet challenging problem in remote sensing image processing due to the complex atmospheric environment and variability of satellite sensors. Most of the contemporary reconstruction methods either handle only one specific task or require supplementary data, while the single-input for multi-task reconstruction has not been explored yet. In this paper we propose a novel Generative Adversarial Network-based unified framework for missing remote sensing image reconstruction, which is capable of various reconstruction tasks given only single source data as input. Specifically, we first propose a Mask Extraction Network (MEN) to obtain a united soft mask, which represents the intrinsic prior under various scenarios and indicates not only location but context information. The versatility of mask extraction enables the multi-task reconstruction of remote sensing images. Besides, we propose a Unified Inpainting Network (UIN) to repair diverse degraded images. Being specifically tailored for remote sensing images, Dilated pyramidal convolutions (DPC) and an Attention Fusion Mechanism (AFM) are introduced to further improve the feature extraction ability and thus exhaustly leveraging the single-input information. Extensive experiments demonstrate the uncompromising performance of the proposed method against state-of-the-art multi-input methods on diverse missing restoration. Moreover, further exploration shows the potential of the proposed method to utilize joint spatio-spectral-temporal information, which is evaluated to outperform existing competitors on remote sense images.

Sequential Monte Carlo sampler applied to source term estimation in complex atmospheric environments

The accurate and rapid reconstruction of a pollution source represents an important but challenging problem. Several strategies have been proposed to tackle this issue among which we find the Bayesian solutions that have the interesting ability to provide a complete characterization of the source parameters through their posterior probability density function. However, these existing techniques have certain limitations such as their computational complexity, the required model assumptions, their difficulty to converge, the sensitive choice of model/algorithm parameters which clearly limit their easy use in practical scenarios. In this paper, to overcome these limitations, we propose a novel Bayesian solution based on a general and flexible population-based Monte Carlo algorithm, namely the sequential Monte Carlo sampler. Owing to its full adaptivity through the learning process, the main advantage of such an algorithm lies in its capability to be used without requiring any specific assumptions on the underlying statistical model and also without requiring from the user any difficult choices of certain parameter values. The performance of the proposed inference strategy is assessed using twin experiments in complex built-up environments.

Microstructure, Mechanical Properties and Corrosion Behavior of the Aluminum Alloy Components Repaired by Cold Spray with Al-Based Powders

Aluminum alloy components of high-speed trains have a great risk of being corroded by various corrosive medium due to extremely complex atmospheric environments. This will bring out huge losses and reduce the safety and stability of trains. In order to solve the problem, cold spray process was used for repairing the damage of the aluminum alloy components with Al-based powders. Microstructure, mechanical properties and corrosion behavior were studied. The results indicated that there were very few pores and cracks in the repaired areas after repairing. The average microhardness of the repaired areas was 54.5 HV ± 3.4 HV, and the tensile strength of the repaired samples was 160.4 MPa. After neutral salt spray tests for 1000 h, the rate of mass loss of the samples repaired by cold spray was lower than that of 6A01 aluminum alloy. The electrochemical test results showed that the repaired areas had a higher open circuit potential than 6A01 aluminum alloy. As a result, the repaired areas such as the anode protected its nearby substrate. The samples repaired by cold spray exhibited better corrosion than 6A01 aluminum alloy. Cold spray process and Al-based powders are applicable for repairing the aluminum alloy components of high-speed trains.

Sensitivity analysis of isoprene and aerosol emission in a suburban plantation using long short-term memory model

Plants emit volatile organic compounds (VOCs), which undergo photochemical reactions to generate secondary organic aerosols and cause ozone (O3) accumulation; thus, they are the focus of urban pollution control strategies. Although some studies on biogenic VOCs and aerosols have been conducted, their environmental effects at the community scale are uncertain, particularly in suburban areas where abundant vegetation and pollutants coexist. Simple data processing methods cannot well address complex environmental conditions, whereas the long short-term memory (LSTM) model can learn relationships between research objects autonomously, thus enabling the analysis of influencing factors. Therefore, we collected 11 indicators of isoprene and submicron aerosol (PM1) concentrations, as well as other pollutants and meteorological factors, for six consecutive months in a suburban plantation in Shanghai. On the basis of correlation and redundancy analyses, LSTM was used to determine the main environmental factors influencing isoprene and PM1 concentrations. The results showed that nitrogen oxides (NOX), O3, photosynthetically active radiation (PAR), and temperature had the highest influence. The reactions between NOX, O3, and isoprene, PM1 are complex, and high levels of both isoprene and NOX in suburban areas may favor O3 production. PAR and temperature control the emission and photooxidation of isoprene and influence their conversion from a gaseous state to a particulate state. Considering the complex atmospheric environment, this study analyzed the sensitivity of isoprene and PM1 to environmental factors by artificial intelligence method. It demonstrated the effectiveness of LSTM for analyzing complex time-series data with unknown mapping relationships, which provided solutions for large-scale studies. And the results provided a scientific basis to further explain the mechanism through which influencing factors affect suburban communities and to control suburban air pollution.

Local dispersion characteristics of dust in large open-air piles under the action of one-way wind.

A large amount of dust particles produced by the wind in an open-air pile is one of the important reasons for air pollution. Studying the law of dust diffusion in local areas is of great significance for the atmospheric particulate control. In this study, a pile of sodium carbonate in a large open-air pile in Weifang, China, is regarded as the research object. The dispersion characteristics of dust particles around the pile under the action of unidirectional wind are studied through wind tunnel test and numerical simulation. The complex atmospheric environment is simplified as unidirectional wind, and the influence of different wind speeds on the dispersion of particles with diverse sizes in the pile is studied. Although a large gap exists between the assumption and the real atmospheric environment, this study provides a reference for the evaluation of the pollution scope of blowing dust and prevention and control of pollution. Results show that a high-concentration range of the dust exists near the pile behind the wind direction and may continue to spread to the height due to the influence of a whirlpool, and the dispersion distance and width can increase with the increase in wind speed. The increase in particle diameter increases the kinetic energy loss of particles for the fluid. Under the same starting speed, the dispersion distance of dust decreases with the increase in particle diameter. With the increase in particle diameter, the dust concentration distribution presents the trend of interior hollowing and high-concentration area fragmenting.

The enhanced mixing states of oxalate with metals in single particles in Guangzhou, China

Recently, internal mixing states of oxalate with metals in single particles have been reported from field studies, yet the role of metals in the formation processes of oxalate remains unclear due to the diversity of chemical components and complex atmospheric environment. In this study, the mixing states of oxalate with five metals, including zinc (Zn), copper (Cu), lead (Pb), vanadium (V) and iron (Fe) were investigated in Guangzhou, China. It was found that 55% of oxalate-containing particles were internally mixed with these metals. The number fraction of oxalate in the metal-containing particles ranged from 5.4–26%, which is much higher than that in the total detected particles (4.0%), indicating significant enrichment of oxalate in the metal-containing particles. Enhanced oxalate production was found in the Fe- and V-containing particles based on distinctly higher relative peak area (RPA) ratios of oxalate to its precursors compared to the total particles, possibly due to enhanced aqueous phase reactions in the Fe- and V-containing particles. However, enrichment of oxalate in the Zn-, Pb-, and Cu-containing particles was possibly associated with complexation of gas phase oxalic acid with the metals, as indicated by the small increase in RPA ratios in these particles. On the other hand, the internal mixing of oxalate with metals was found to provide a way of efficient photolysis of oxalate-metal complexes, which led to a decrease in oxalate after sunrise in the metal-containing particles. In this study, the enhanced mixing states of oxalate with metals have revealed the important role of metals in the production and degradation of oxalate, providing insights for the evaluation of metals in the formation processes of organic aerosol in field studies, which is beneficial to the further study of air pollution in metal emission areas.

Research on Adaptive Threshold of Received Signal in Communication System

When the light beam propagates in the atmosphere, the signal will be absorbed and scattered by the gas molecules and water mist in the atmosphere, which will cause the loss of power rate. The complex atmospheric environment will produce a variety of adverse effects on the signal. The interference produced by these effects overlaps with each other, which will seriously affect the strength of the received signal. Therefore, how to effectively suppress the atmospheric turbulence effect in the random atmospheric turbulence channel, ensure the normal transmission of the signal in the atmospheric channel, and reduce the bit error rate of the communication system, is very necessary to improve the communication system. When processing the received signal, it is an important step to detect the transmitted signal by comparing the received signal with the threshold. In this paper, based on the atmospheric turbulence distribution model, the adaptive signal decision threshold is obtained through the estimation of high-order cumulant. Monte Carlo method is used to verify the performance of adaptive threshold detection. The simulation results show that the high-order cumulant estimation of atmospheric turbulence parameters can realize the adaptive change of the decision threshold with the channel condition. It is shown that the adaptive threshold detection can effectively restrain atmospheric turbulence, improve the performance of free space optical and improve the communication quality.

Application of Improved CFD Modeling for Prediction and Mitigation of Traffic-Related Air Pollution Hotspots in a Realistic Urban Street

The correct prediction of air pollutants dispersed in urban areas is of paramount importance to safety, public health and a sustainable environment. Vehicular traffic is one of the main sources of nitrogen oxides (NO x) and particulate matter (PM), strongly related to human morbidity and mortality. In this study, the pollutant level and distribution in a section of one of the main road arteries of Antwerp (Belgium, Europe) are analyzed. The assessment is performed through computational fluid dynamics (CFD), acknowledged as a powerful tool to predict and study dispersion phenomena in complex atmospheric environments. The two main traffic lanes are modeled as emitting sources and the surrounding area is explicitly depicted. A Reynolds-averaged Navier–Stokes (RANS) approach specific for Atmospheric Boundary Layer (ABL) simulations is employed. After a validation on a wind tunnel urban canyon test case, the dispersion within the canopy of two relevant urban pollutants, nitrogen dioxide (NO2) and particulate matter with an aerodynamic diameter smaller than 10 μm (PM10), is studied. An experimental field campaign led to the availability of wind velocity and direction data, as well as PM10 concentrations in some key locations within the urban canyon. To accurately predict the concentration field, a relevant dispersion parameter, the turbulent Schmidt number, Sct, is prescribed as a locally variable quantity. The pollutant distributions in the area of interest – exhibiting strong heterogeneity – are finally demonstrated, considering one of the most frequent and concerning wind directions. Possible local remedial measures are conceptualized, investigated and implemented and their outcomes are directly compared. A major goal is, by realistically reproducing the district of interest, to identify the locations inside this intricate urban canyon where the pollutants are stagnating and to analyze which solution acts as best mitigation measure. It is demonstrated that removal by electrostatic precipitation (ESP), an active measure, and by enhancing the dilution process through wind catchers, a passive measure, are effective for local pollutant removal in a realistic urban canyon. It is also demonstrated that the applied ABL methodology resolves some well known problems in ABL dispersion modeling.

Non-methane hydrocarbon (NMHC) fingerprints of major urban and agricultural emission sources for use in source apportionment studies

Abstract. In complex atmospheric emission environments such as urban agglomerates, multiple sources control the ambient chemical composition driving air quality and regional climate. In contrast to pristine sites, where reliance on single or a few chemical tracers is often adequate for resolving pollution plumes and source influences, the comprehensive chemical fingerprinting of sources using non-methane hydrocarbons (NMHCs) and the identification of suitable tracer molecules and emission ratios becomes necessary. Here, we characterise and present chemical fingerprints of some major urban and agricultural emission sources active in South Asia, such as paddy stubble burning, garbage burning, idling vehicular exhaust and evaporative fuel emissions. A total of 121 whole air samples were actively collected from the different emission sources in passivated air sampling steel canisters and then analysed for 49 NMHCs (22 alkanes, 16 aromatics, 10 alkenes and one alkyne) using thermal desorption gas chromatography flame ionisation detection. Several new insights were obtained. Propane was found to be present in paddy stubble fire emissions (8 %), and therefore, for an environment impacted by crop residue fires, the use of propane as a fugitive liquefied petroleum gas (LPG) emission tracer must be done with caution. Propene was found to be ∼ 1.6 times greater (by weight) than ethene in smouldering paddy fires. Compositional differences were observed between evaporative emissions of domestic LPG and commercial LPG, which are used in South Asia. While the domestic LPG vapours had more propane (40 ± 6 %) than n-butane (19 ± 2 %), the converse was true for commercial LPG vapours (7 ± 6 % and 37 ± 4 %, respectively). Isoprene was identified as a new tracer for distinguishing paddy stubble and garbage burning in the absence of isoprene emissions at night from biogenic sources. Analyses of source-specific inter-NMHC molar ratios revealed that toluene/benzene ratios can be used to distinguish among paddy stubble fire emissions in the flaming (0.38 ± 0.11) and smouldering stages (1.40 ± 0.10), garbage burning flaming (0.26 ± 0.07) and smouldering emissions (0.59 ± 0.16), and traffic emissions (3.54 ± 0.21), whereas i-pentane ∕ n-pentane can be used to distinguish biomass burning emissions (0.06–1.46) from the petrol-dominated traffic and fossil fuel emissions (2.83–4.13). i-butane ∕ n-butane ratios were similar (0.20–0.30) for many sources and could be used as a tracer for photochemical ageing. In agreement with previous studies, i-pentane, propane and acetylene were identified as suitable chemical tracers for petrol vehicular and evaporative emissions, LPG evaporative and vehicular emissions and flaming-stage biomass fires, respectively. The secondary pollutant formation potential and human health impact of the sources was also assessed in terms of their hydroxyl radical (OH) reactivity (s−1), ozone formation potential (OFP; gO3/gNMHC) and fractional benzene, toluene, ethylbenzene and xylenes (BTEX) content. Petrol vehicular emissions, paddy stubble fires and garbage fires were found to have a higher pollution potential (at ≥95 % confidence interval) relative to the other sources studied in this work. Thus, many results of this study provide a new foundational framework for quantitative source apportionment studies in complex emission environments.

A general numerical model for rotor aerodynamics based on added mass force

When wind turbines operate in complex atmospheric environments, the flow fields around their blades are complex and unsteady. To capture the lag characteristics of induced velocity when the dynamic environment of a rotor changes, this study further modified the equilibrium wake model using the blade element momentum and acceleration potential theories. In addition, a novel expression for the added mass force mA=128/75ρR3 was derived. Consequently, a numerical aerodynamics model of a rotor with an added mass force was developed. The model was used to calculate the aerodynamic load of a 5 MW wind turbine under steady wind, turbulence, gust, wind direction change, and rapid pitching. The results demonstrated that the numerical model with added mass force could reflect the rotor aerodynamic performance more accurately than its counterpart. The fluctuation amplitudes of the flapwise and edgewise bending moments of the blade root were smaller than those from the equilibrium wake model. The overshoot in loading, which was caused by the added mass force during pitching, could not be ignored. The research results were compared with experimental data from the National Renewable Energy Laboratory. It was found that the proposed model provided a new strategy for calculating the added mass force and predicted rotor aerodynamic loads more accurately than the model without added mass force.

Ultraviolet anti-collision and localization algorithm in UAV formation network

Ultraviolet (UV) Communication is a communication mode used 200nm˜280nm wavelength ultraviolet light as an information carrier. It has the advantages of all-weather operations, non-line-of-sight (NLOS) communication with a wide field and strong anti-interference ability etc. Solar blind characteristics can achieve anti-collision in unmanned aerial vehicles (UAV) formation flying. The distance calculation formula for line-of-sight (LOS) and NLOS is analyzed. One kind of three dimensional (3D) localization algorithm based on wireless UV communication is put forward. UAVs need anti-collision and location methods to ensure flight safety when flying or performing tasks in a complex atmospheric environment. Computer simulation result shows that the ranging precision of UV ranging algorithm can reach 3m, The node localization accuracy rate of localization algorithm can reach more than 80%. Numerical simulation results show that the localization algorithm has a high location accuracy, which can meet the requirements of anti-collision in UAV formation.