Haze Weather Conditions Research Articles

Raman Lidar for Synchronous Water Vapor, Liquid Water and Ice Water Profilings

Water is the only atmospheric parameter with three-phase state. An ultraviolet Raman lidar was developed for synchronous measurements for water vapor, liquid water and ice water in Xi’an University of Technology, Xi’an, China (34.233°N, 108.911°E). An accurate retrieval method on the basis of interference degree is proposed for synchronous three-phase water mixing ratio profiles. Preliminary measurements are carried out in the Laser Radar Center of Remote Sensing of Atmosphere (LRCRSA). Several representative examples are obtained and validated the performance of Raman system. Combined with atmospheric temperature profiles, the synchronous water vapor, liquid water and ice water profiling are retrieved and revealed the variation characteristics in three-phase water. The effective detection can reach up to a height of 5 km under cloudy weather, and synchronized growth in water vapor and liquid water content was obtained in cloud layers. Continuous observations are also made under haze weather condition, and the temporal and spatial evolution trend of three-phase water in clouds at 2 km altitude are successfully realized.

Visibility restoration of single image captured in dust and haze weather conditions

Inclement weather existence of fog, haze, and dust generally degrades the visibility of outdoor images. Bad visibility may cause failure in computer vision applications. Existing dehazing methods cannot work well on dust haze images. Such images appear yellowish due to the absorption of blue light by dust particles. In order to solve this problem, we propose an optical compensation method (OCM), which uses histogram matching to change the RGB color channel. In this method, take the red channel as a reference curve and then fit the color curves of the blue and green channels close to the red curve, thereby dust haze images are transformed into haze images. Furthermore, we develop a novel single image dehazing method based on Gaussian adaptive transmission (GAT). GAT uses a Gaussian function with a linear coefficient constraint to optimize the transmission, which can prevent halo artifacts near edges of depth discontinuity and obtaining a more accurate estimate of the transmission, especially in bright areas (e.g., sky). The experimental results show that OCM can eliminate the color cast of dust haze images, and GAT can enhance the visibility of dust haze images effectively.

The analysis on air pollution characteristics at the bus stops in Jinan City

The concentration of pollutants is higher near the bus stops because of the frequent start-stop emissions of bus vehicles, which brings great risks to the travel crowd. At the stop of University of Jinan, in Jinan City, PM2.5 concentration and PM10 concentration generally showed an upward trend, and the maximum concentration was mainly concentrated at 20:00-22:00, during the observation period; the concentration of PM2.5 concentration had two peak value, around 11:00 and after 20:00, respectively, during the non-smog weather; the concentration of PM2.5 concentration and PM10 concentration has been rising gradually from 6:00 to 22:00, and the peak value around 11:00 is not very obvious, during the smog weather, but the late peak of PM2.5 concentration and PM10 concentration around 17:00 is especially obvious. The peak of PM2.5 concentration and PM10 concentration of bus stop appeared due to the frequent start and stop of the bus. The content of heavy metals in atmospheric particulates is obviously higher, especially the concentrations of Cd, Cr, Pb and Hg are 59.56, 4.31, 8.68 and 4.19 times of soil background values, respectively, which are obviously enriched. the heavy metal concentrations in falldust are very close to the heavy metal concentrations of atmosphere particulate matter, the result indicated that the heavy metal emitted by automobile exhaust gas has no obvious influence on atmospheric heavy metal content in a short time, the disturbance of ground dustfall has a great influence on the concentrations of heavy metals in the atmosphere. The exposure level of PM2.5 and PM10 during haze weather conditions is far greater than exposure level of PM2.5 and PM10 during non-haze weather conditions.

A new method to determine the aerosol optical properties from multiple-wavelength O<sub>4</sub> absorptions by MAX-DOAS observation

Abstract. Ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) observation was carried out from November 2016 to February 2017 in Beijing, China, to measure the O4 absorptions in UV and visible bands and further to illustrate its relationship with aerosol optical properties (AOPs) under different weather types. According to relative humidity, visibility, and PM2.5, we classified the observation periods into clear, light-haze, haze, heavy-haze, fog, and rainy weather conditions. There are obvious differences for measured AOPs under different weather conditions, especially scattering coefficient (σsca) and absorption coefficient (σsca). It was also found that both the O4 differential slant column densities (DSCDs) at the UV and visible bands varied in the order of clear days > light-haze days > haze days > heavy-haze days > fog days. The correlation coefficients (R2) between O4 DSCDs at 360.8 and 477.1 nm mainly varied in the order of clear days > light-haze days > haze days > heavy-haze days. Based on the statistics of O4 DSCDs at an elevation angle 1∘ with the corresponding linear regression between UV and visible bands of segmental periods, the relationships between O4 DSCDs and AOPs were established. It should mainly be clear or light-haze days when the correlation slope is greater than 1.0, with a correlation coefficient (R2) greater than 0.9, and O4 DSCDs mainly greater than 2.5×1043 molec. cm−2. Meanwhile, σsca and σabs are less than 45 and 12 Mm−1, respectively. For haze or heavy-haze days, the correlation slope is less than 0.6, with an R2 less than 0.8, and O4 DSCDs mainly less than 1.3×1043 molec. cm−2, under which σsca and σabs are mainly located at 200–900 and 20–60 Mm−1. Additionally, the determination method was well validated based on another MAX-DOAS measurement at Gucheng from 19 to 27 November 2016. For more precise and accurate inversion of AOPs, more detailed look-up tables for O4 multiple-wavelength absorptions need to be developed. Since the ground surface AOPs were determined using MAX-DOAS observation at a 1∘ elevation in this study, we hope to highlight the potential of retrieved vertical spatially resolved AOPs being expected when multiple elevation angles of MAX-DOAS observation are used together.

Size distribution and concentration of aerosol particles in Yinchuan area, China

ABSTRACTThe size distribution and concentration of atmospheric aerosol particles are important parameters that characterize the physical and optical properties of aerosols. Understanding the spectral distribution of atmospheric particles is fundamental for studying particulate pollution. Based on observational data acquired by an aerodynamic particle sizer APS-3321 and a laser dust meter LD-5 from September 2017 to July 2018, the temporal evolution of particle size distribution and concentration of aerosols were studied at the North Minzu University in Yinchuan (38°29’52"N 106°06’28"E), China. By cluster analysis of particle surface area distribution in different weathers, seven clusters were obtained and discussed. The experimental results showed that the particle number and mass concentrations were different in the four seasons of the year. Compared with the unimodal distribution of the particle number concentration and the doublet mass concentration distribution in sunny weather conditions, there were great differences in the particle spectrum distribution in haze, snowfall, and dusty weather conditions. The particle surface area distributions of the seven clusters were clearly different from each other and influenced by meteorological conditions. Additionally, the PM2.5 value has a relationship with the meteorological elements. The research is helpful to reveal the formation mechanism and source of aerosols in Yinchuan, China.

Haze Removal Algorithm for Optical Remote Sensing Image Based on Multi-Scale Model and Histogram Characteristic

Optical imaging remote sensing technology is an important technical means to obtain information of ground objects, but it is restricted by bad weather such as clouds, rain, and haze. In haze weather condition, optical images often have poor contrast and blurred details, which has a great impact on subsequent applications and interpretation. If the acquired images are processed, not only their quality can be improved, but also their visual effect and utilization value can be improved, so as to reduce the impact brought by haze. In general, the haze is removed from the view of image processing. In this paper, through in-depth analysis and study of existing algorithms and characteristics of optical remote sensing images, a new idea is proposed to solve this problem from the view of combination of image content and auxiliary information. Furthermore, a new haze removal algorithm is proposed based on the Retinex multi-scale model and the histogram characteristics of remote sensing images. Because the new method combines multi-scale model (MSM) and histogram characteristics (HC), it is referred to as MSMHC algorithm in this paper. The advantage of the new method is that the content and type of image are considered in the whole process, and then two processing schemes are set for haze removal. In the test experiments, one hundred groups of image data were used to carry out comparative experiments. At the same time, single-scale Retinex (SSR) algorithm, multi-scale Retinex (MSR) algorithm, dark channel priori (DCP) method, brightness preserving dynamic fuzzy histogram equalization (BPDFHE) algorithm, histogram equalization (HE) method, and homomorphic filter (HF) algorithm were used for comparative experiments with the MSMHC method. Five parameters, including standard deviation (SD), information entropy (IE), peak signal to noise ratio (PSNR), structural similarity (SSIM), and image contrast (IC), were used to quantitatively evaluate the test results. The experimental results and the parameter values showed that the MSMHC algorithm could not only effectively remove haze from remote sensing images, obtain high contrast and high definition images, but also have better generalization ability.

Performance Limits of FSO Based SAC-OCDMA System Under Weather Conditions

AbstractIn this paper, the Spectral Amplitude Coding Optical Code Division Multiple Access (SAC-OCDMA) is investigated in Free Space Optics (FSO) using Zero Cross Correlation (ZCC) codes to evaluate its performance limits in terms of link range. The system is analyzed under clear, haze, moderate fog and dense fog weather conditions. This system has been evaluated numerically and by simulation analysis by maintaining the transmitted power at 10 dBm, for safety reason, according to International Telecommunication Union (ITU) and minimum acceptable BER of10{10}–9. The simulation results shown that the present system can transmit 622 Mbps/1 Gbps up to maximum link range of 1450 m/1100 m and 61 m/54 m under clear and dense fog conditions, respectively.

Performance of 120 Gbps single channel coherent DP-16-QAM in terrestrial FSO link under different weather conditions

The performance of a 120 Gbps single-carrier coherent transceiver system is evaluated in a terrestrial Free Space Optical Link (FSOL) under the haze, rain, and fog weather conditions. In-phase and Quadrature (IQ) based Dual Polarization 16 Quadrature Amplitude Modulation (DP-16-QAM) is implemented in OptiSystem v.13 simulation platform with homodyne detection and Digital Signal Processing (DSP) at the receiver. The IQ based optical modulator and the DSP unit can be fabricated in the form of Integrated Circuits (ICs) help to keep the system cost low. The system performance is evaluated in terms of Optical Signal to Noise Ratio (OSNR) requirements at an acceptable Bit Error Rate (BER ≤ 2 × 10−3, i.e., below the FEC limit) under low turbulence environment. Simulation results show that the system maintains good OSNR performance under severe weather conditions. This transceiver system shall be useful in short-reach, low-cost, high-capacity, robust communication link with dynamic environmental conditions.

Image dehazing based on dark channel prior and brightness enhancement for agricultural monitoring

Obtaining clear and true images is a basic requirement for agricultural monitoring. However, under the influence of fog, haze and other adverse weather conditions, captured images are usually blurred and distorted, resulting in the difficulty of target extraction. Traditional image dehazing methods based on image enhancement technology can cause the loss of image information and image distortion. In order to address the above-mentioned problems caused by traditional image dehazing methods, an improved image dehazing method based on dark channel prior (DCP) was proposed. By enhancing the brightness of the hazed image and processing the sky area, the dim and un-natural problems caused by traditional image dehazing algorithms were resolved. Ten different test groups were selected from different weather conditions to verify the effectiveness of the proposed algorithm, and the algorithm was compared with the commonly-used histogram equalization algorithm and the DCP method. Three image evaluation indicators including mean square error (MSE), peak signal to noise ratio (PSNR), and entropy were used to evaluate the dehazing performance. Results showed that the PSNR and entropy with the proposed method increased by 21.81% and 5.71%, and MSE decreased by 40.07% compared with the original DCP method. It performed much better than the histogram equalization dehazing method with an increase of PSNR by 38.95% and entropy by 2.04% and a decrease of MSE by 84.78%. The results from this study can provide a reference for agricultural field monitoring. Keywords: agricultural monitoring, image dehazing, monitoring image, dark channel prior (DCP), brightness promoting DOI: 10.25165/j.ijabe.20181102.3357 Citation: Wang X Y, Yang C H, Zhang J, Song H B. Image dehazing based on dark channel prior and brightness enhancement for agricultural monitoring. Int J Agric & Biol Eng, 2018; 11(2): 170–176.

Image dehazing based on dark channel prior and brightness enhancement for agricultural monitoring

Obtaining clear and true images is a basic requirement for agricultural monitoring. However, under the influence of fog, haze and other adverse weather conditions, captured images are usually blurred and distorted, resulting in the difficulty of target extraction. Traditional image dehazing methods based on image enhancement technology can cause the loss of image information and image distortion. In order to address the above-mentioned problems caused by traditional image dehazing methods, an improved image dehazing method based on dark channel prior (DCP) was proposed. By enhancing the brightness of the hazed image and processing the sky area, the dim and un-natural problems caused by traditional image dehazing algorithms were resolved. Ten different test groups were selected from different weather conditions to verify the effectiveness of the proposed algorithm, and the algorithm was compared with the commonly-used histogram equalization algorithm and the DCP method. Three image evaluation indicators including mean square error (MSE), peak signal to noise ratio (PSNR), and entropy were used to evaluate the dehazing performance. Results showed that the PSNR and entropy with the proposed method increased by 21.81% and 5.71%, and MSE decreased by 40.07% compared with the original DCP method. It performed much better than the histogram equalization dehazing method with an increase of PSNR by 38.95% and entropy by 2.04% and a decrease of MSE by 84.78%. The results from this study can provide a reference for agricultural field monitoring. Keywords: agricultural monitoring, image dehazing, monitoring image, dark channel prior (DCP), brightness promoting DOI: 10.25165/j.ijabe.20181102.3357 Citation: Wang X Y, Yang C H, Zhang J, Song H B. Image dehazing based on dark channel prior and brightness enhancement for agricultural monitoring. Int J Agric & Biol Eng, 2018; 11(2): 170–176.

Relay-assisted WDM-FSO System: A Better Solution for Communication under Rain and Haze Weather Conditions

Among various conventional wireless communication systems, Free Space Optics (FSO) may be distinguished as well, which provides a good level security, high data rates, an enormous bandwidth and negligible susceptibility to electromagnetic interference. The main problem arises with unfavorable weather conditions, which affect the system’s parameters and require an aversion to other paths. The weather conditions discussed are rain and haze. Elimination of their adverse effects is essential for establishing a better communication system. The routing path is a major issue, as its decreased parameters lead to the diversion of the transmitted signal or to its total loss. In this article, increased system path length and proficiency levels are demonstrated by using relays, which are assisted by Wavelength Division Multiplexing (WDM).

Variations in the water vapor distribution and the associated effects on fog and haze events over Xi'an based on Raman lidar data and back trajectories.

A combination of more than two years of water vapor lidar data with back trajectory analysis using the hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model was used to study the long-range transport of air masses and the water vapor distribution characteristics and variations over Xi'an, China (34.233° N, 108.911° E), which is a typical city in Northwest China. High-quality profiles of the water vapor density were derived from a multifunction Raman lidar system built in Xi'an, and more than 2000 sets of profiles with >400 nighttime observations from October 2013 to July 2016 were collected and used for statistical and quantitative analyses. The vertical variations in the water vapor content were discussed. A mutation height of the water vapor exists at 2-4km with a high occurrence rate of ∼60% during the autumn and winter seasons. This height reflects a distinct stratification in the water vapor content. Additionally, the atmospheric water vapor content was mainly concentrated in the lower troposphere, and the proportion of the water vapor content at 0.5-5km accounted for 80%-90% of the total water vapor below 10km. Obvious seasonal variations were observed, including large water vapor content during the spring and summer and small content during the autumn and winter. Combined with back trajectory analysis, the results showed that markedly different water vapor transport pathways contribute to seasonal variations in the water vapor content. South and southeast airflows dominated during the summer, with 30% of the 84 trajectories originating from these areas; however, the air masses during the winter originated from the north and local regions (64.3%) and from the northwest (27%). In addition, we discussed variations in the water vapor during fog and haze weather conditions during the winter. A considerable enhancement in the mean water vapor density at 0.5-3km exhibited a clear positive correlation (correlation coefficient >0.8) with the PM2.5 and PM10 concentrations. The results indicate that local airflow trajectories mainly affect water vapor transport below the boundary layer, and that these flows are closely related to the formation of fog and haze events in the Xi'an area.

Atmospheric extinction coefficient retrieval and validation for the single-band Mie-scattering Scheimpflug lidar technique

An 808 nm single-band Mie scattering Scheimpflug lidar system is developed in Dalian, Northern China, for real-time, large-area atmospheric aerosol/particle remote sensing. Atmospheric measurement has been performed in urban area during a typical haze weather condition, and time-range distribution of atmospheric backscattering signal is recorded from March 18th to 22nd, 2017, by employing the Scheimpflug lidar system. Atmospheric extinction coefficient is then retrieved according to the Klett-inversion algorithm, while the boundary value is obtained by the slope-method in the far end where the atmosphere is homogeneous in a subinterval region. The correlation between the extinction coefficients retrieved from the Scheimpflug lidar technique and the PM10/PM2.5 concentrations measured by a conventional air pollution monitoring station is also studied. The good agreement between the measurement results, i.e., a correlation coefficient of 0.85, successfully demonstrates the feasibility and great potential of the Scheimpflug lidar technique for atmospheric studies and applications.

Characterization and parameterization of aerosol cloud condensation nuclei activation under different pollution conditions.

To better understand the cloud condensation nuclei (CCN) activation capacity of aerosol particles in different pollution conditions, a long-term field experiment was carried out at a regional GAW (Global Atmosphere Watch) station in the Yangtze River Delta area of China. The homogeneity of aerosol particles was the highest in clean weather, with the highest active fraction of all the weather types. For pollution with the same visibility, the residual aerosol particles in higher relative humidity weather conditions were more externally mixed and heterogeneous, with a lower hygroscopic capacity. The hygroscopic capacity (κ) of organic aerosols can be classified into 0.1 and 0.2 in different weather types. The particles at ~150 nm were easily activated in haze weather conditions. For CCN predictions, the bulk chemical composition method was closer to observations at low supersaturations (≤0.1%), whereas when the supersaturation was ≥0.2%, the size-resolved chemical composition method was more accurate. As for the mixing state of the aerosol particles, in haze, heavy haze, and severe haze weather conditions CCN predictions based on the internal mixing assumption were robust, whereas for other weather conditions, predictions based on the external mixing assumption were more accurate.

Haze removal for UAV reconnaissance images using layered scattering model

During the unmanned aerial vehicles (UAV) reconnaissance missions in the middle-low troposphere, the reconnaissance images are blurred and degraded due to the scattering process of aerosol under fog, haze and other weather conditions, which reduce the image contrast and color fidelity. Considering the characteristics of UAV itself, this paper proposes a new algorithm for dehazing UAV reconnaissance images based on layered scattering model. The algorithm starts with the atmosphere scattering model, using the imaging distance, squint angle and other metadata acquired by the UAV. Based on the original model, a layered scattering model for dehazing is proposed. Considering the relationship between wave-length and extinction coefficient, the airlight intensity and extinction coefficient are calculated in the model. Finally, the restored images are obtained. In addition, a classification method based on Bayesian classification is used for classification of haze concentration of the image, avoiding the trouble of manual working. Then we evaluate the haze removal results according to both the subjective and objective criteria. The experimental results show that compared with the origin image, the comprehensive index of the image restored by our method increases by 282.84%, which proves that our method can obtain excellent dehazing effect.

Single image haze removal based on haze physical characteristics and adaptive sky region detection

Outdoor images are often degraded by haze and other inclement weather conditions, which affect both consumer photographs and computer vision applications severely. Therefore, researchers have proposed plenty of restoration approaches to deal with this problem. However, it is hard to tackle the color distortion problem in restored images with ignoring the differences between fog and haze. Meanwhile, the atmospheric light is also an important variable that influences the global illumination of images. In this paper, we analyze the physical meaning of atmospheric light first, and estimate atmospheric light by a novel method of obtaining the sky region in images, which is based on our newly proposed sky region prior. Then after exploring physical characteristics of fog and haze, we explain why images taken in haze appear yellowish, and eliminate this phenomenon by our adaptive channel equalization method. Quantitative comparisons with seven state-of-art algorithms on a variety of real-world haze images demonstrate that our algorithm can remove haze effectively and keep color fidelity better.

Performance analysis of free space optical links using multi‐input multi‐output and aperture averaging in presence of turbulence and various weather conditions

The authors study the effect of multi-input multi-output (MIMO) spatial diversity schemes when used in a free space optical (FSO) communication link in the presence of turbulence and varied weather conditions such as very clear air, drizzle, haze, fog etc. The performance is evaluated in terms of the bit error rate (BER) and outage probability P out. We show that MIMO schemes cause a decrease in the BER and P out which, at low signal-to-noise ratios, is more significant in presence of very clear air and clear air as compared with haze and fog weather conditions. The diversity gain at a BER of 10−6 is evaluated and observed to increase as the number of transmit/receive apertures increase. However, it remains almost constant over all the weather conditions for a given MIMO scheme and turbulence strength. The performance of FSO link with MIMO schemes is compared with a FSO link using aperture averaging. Owing to constraint on the receiver aperture diameter, the aperture averaging technique fails to give the same kind of performance as provided by the higher order MIMO schemes.

Source apportionment and seasonal variation of PM2.5 carbonaceous aerosol in the Beijing-Tianjin-Hebei Region of China

The seasonal variation of PM2.5 carbonaceous aerosol was investigated in Beijing and Tangshan cities of China. The characteristics of carbonaceous aerosol (e.g., organic carbon, OC and elemental carbon, EC) under different weather conditions and their source apportionment were also examined. The annual average PM2.5 concentration in the study area reached 95.6-197.3 μg/m(3), showing seasonal and spatial variation. The carbonaceous materials accounted for 17.3-21.2 % of the PM2.5, and they had a much higher content under haze weather condition. It was found that the PM2.5 contained more OC than EC. Principal component analysis (PCA) results indicated that the carbonaceous components came from mixed emission sources of coal combustion, vehicle exhaust, and biomass burning. In Beijing, the vehicle emission made a contribution of 63.0 % to the carbonaceous components of PM2.5 in summer, which is higher than that in Tangshan. While in Tangshan, the coal combustion made a contribution of 30.3 %, which is much higher than that in Beijing.

Road Vehicle Monitoring System Based on Intelligent Visual Internet of Things

In recent years, with the rapid development of video surveillance infrastructure, more and more intelligent surveillance systems have employed computer vision and pattern recognition techniques. In this paper, we present a novel intelligent surveillance system used for the management of road vehicles based on Intelligent Visual Internet of Things (IVIoT). The system has the ability to extract the vehicle visual tags on the urban roads; in other words, it can label any vehicle by means of computer vision and therefore can easily recognize vehicles with visual tags. The nodes designed in the system can be installed not only on the urban roads for providing basic information but also on the mobile sensing vehicles for providing mobility support and improving sensing coverage. Visual tags mentioned in this paper consist of license plate number, vehicle color, and vehicle type and have several additional properties, such as passing spot and passing moment. Moreover, we present a fast and efficient image haze removal method to deal with haze weather condition. The experiment results show that the designed road vehicle monitoring system achieves an average real-time tracking accuracy of 85.80% under different conditions.

Comparative Analysis of Point to Point FSO System Under Clear and Haze Weather Conditions

Free space optics (FSO) is an optical mean of communication where free space acts as a medium between transmitter and receiver and both should be in the line of sight for successful transmission of signal. Free space can be air, outer space, or vacuum. This system is economically beneficial than other systems and deployed in a matter of hours. This system poses great advantages like high bandwidth. There are some factors which can affect the transmission in FSO. Presence of foreign elements like rain, fog, and haze, physical obstruction, scattering and atmospheric turbulence are some of these factors. In this paper, two FSO links are designed and compared using WDM to study under clear and haze weather conditions. The present system supports the transmission up to longer link distances of 350 and 47 km under clear weather and haze conditions respectively.