Atmospheric Turbidity Research Articles

Integration of geographic features and bathymetric inversion in the Yangtze River's Nantong Channel using gradient boosting machine algorithm with ZY-1E satellite and multibeam data

This study investigates the integration of geographic features from ZY-1E satellite data with advanced machine learning techniques to enhance water depth inversion in the Yangtze River's Nantong Channel. Utilizing the Gradient Boosting Machine (GBM) and its geospatially enhanced version, GBM-Lon./Lat., significant improvements in modeling precision were observed, as reflected by lower RMSE and higher R² values compared to traditional depth inversion methods. The research underscores the benefits of incorporating geospatial data, which allows for a more nuanced understanding of the hydrological dynamics and facilitates more accurate predictions in the turbid waters of the channel. Challenges such as atmospheric effects, water turbidity, and data acquisition issues under variable weather conditions were identified. The study proposes further optimization of these models to handle diverse environmental conditions and enhance the accuracy of bathymetric mapping. The integration of machine learning with remote sensing not only supports navigational safety and efficient waterway management but also contributes significantly to environmental monitoring and sustainable riverine infrastructure development.

The Variation in Atmospheric Turbidity over a Tropical Site in Nigeria and Its Relation to Climate Drivers

Atmospheric turbidity exhibits substantial spatial–temporal variability due to factors such as aerosol emissions, seasonal changes, meteorology, and air mass transport. Investigating atmospheric turbidity is crucial for climatology, meteorology, and atmospheric pollution. This study investigates the variation in atmospheric turbidity over a tropical location in Nigeria, utilizing the Ångström exponent (α), the turbidity coefficient (β), the Linke turbidity factor (TL), the Ångström turbidity coefficient (βEST), the Unsworth–Monteith turbidity coefficient (KAUM), and the Schüepp turbidity coefficient (SCH). These parameters were estimated from a six-month uninterrupted aerosol optical depth dataset (January–June 2016) and a one-year dataset (January–December 2016) of solar radiation and meteorological data. An inverse correlation (R = −0.77) was obtained between α and β, which indicates different turbidity regimes based on particle size. TL and βEST exhibit pronounced seasonality, with higher turbidity during the dry season (TL = 9.62 and βEST = 0.60) compared to the rainy season (TL = 0.48 and βEST = 0.20) from May to October. Backward trajectories and wind patterns reveal that high-turbidity months align with north-easterly air flows from the Sahara Desert, transporting dust aerosols, while low-turbidity months coincide with humid maritime air masses originating from the Gulf of Guinea. Meteorological drivers like relative humidity and water vapor pressure are linked to turbidity levels, with an inverse exponential relationship observed between normalized turbidity coefficients and normalized water vapor pressure. This analysis provides insights into how air mass origin, wind patterns, and local climate factors impact atmospheric haze, particle characteristics, and solar attenuation variability in a tropical location across seasons. The findings can contribute to environmental studies and assist in modelling interactions between climate, weather, and atmospheric optical properties in the region.

Empirical and numerical-based predictive analysis of a single-axis PV system under semi-arid climate conditions of Pakistan

Power generation from fossil fuels is the biggest challenge in the next half of the century. Alternative power generation techniques such as solar photovoltaic (PV) show potential to act as a future fuel with a challenge to efficiently convert the harvested solar energy into electrical power. This investigation conclusively focused on setting a 2.160-kW solar PV system capable of working at a higher efficiency by developing a mechanical structure that optimizes power production and minimizes energy losses. In addition to that, solar PV system efficiencies at various tracking positions, performance coefficients during rainy and sunny days, and system degradation rates have also been investigated. The PVsyst v6.8 simulation tool was used to obtain the simulated results, which were compared with the actual experimental results. The parameters considered for the investigations include ambient temperature, irradiance, solar PV module surface temperature, solar PV voltage and current, wind velocity, and atmospheric turbidity. The solar PV system was evaluated based on two modes, namely, M1 (no tracking/fixed type) and M2 (manual tracking by changing the position of the solar PV system every hour). The predictive results obtained using PVsyst v6.8 concluded that total energy production from the installed system was 3,242 kWh/yr and 3,984 kWh/yr for M1 and M2, respectively. The performance ratio (PR), obtained from simulation, was 72% and 78% for M1 and M2, respectively, which was consistent with the experimental results, i.e., 70% and 72% for M1 and M2, respectively. Similarly, the power conversion efficiencies under standard temperature and conditions for both modes, simulated and experimental, were found to be 16.50% and 12.75%, respectively. The estimated degradation rate was observed in the range of −0.6% to −5.0%.

Radiative parameters specific to Braşov urban area

The paper objective is to present the input data needed for the renewable systems design, namely the recording and the analysis of the local meteorological and climatological parameters such as the solar radiation, the wind speed, the clearness index and the atmospheric turbidity factor. The diagrams presented are obtained on the basis of the meteorological data recorded during three years, 2006-2008. The used meteorological data, for this study, were recorded with a local weather station positioned on the building roof of the Transilvania University of Braşov. The paper ends with an analysis of some very important diagrams concerning the solar and wind power for Braşov area, and there are worded conclusions derived from the comparative analysis of these values.

Detailed Assessment of Multiple‐Scattering Effects on Night‐Sky Brightness Modeling in Turbid Environments: The Impact of Truncation and Convergence Errors

AbstractDetailed modeling of the night‐sky brightness (NSB) is needed to pinpoint environmental impacts caused by artificial light at night. This becomes progressively more intricate with longer distances to emission sources or under high atmospheric turbidity. In such cases, the multiple‐scattering processes that affect light propagation become increasingly critical. To detect and quantify the potential errors caused by popular approximations like single or double scattering in current models, a rigorous multiple‐scattering model is used here. It integrates an advanced method of resolution based on the successive orders of scattering that can provide analytical “exact” solutions. With this tool, it is found that simplistic approaches lead to numerical predictions violating physics principles, resulting in incorrect outcomes that can have profound implications in a large variety of research areas. To avoid this, it is proposed to force the convergence of the modeled radiance to its true value through a carefully controlled process, by which the contribution from the remaining higher‐scattering terms to the modeled radiative property is kept below the specified error tolerance. For green light, it is found that, for a turbid atmosphere with an aerosol optical depth larger than 0.3 and an asymmetry parameter exceeding 0.7, four scattering orders are needed to compute the zenith radiance 60‐km away from the light source within an experimental error tolerance of 10%.

Impact of Meteorological Parameters on Atmospheric Turbidity over Lumbini, Nepal

The main objective of this research is to study of impact of meteorological parameters on atmospheric turbidity index on Lumbini (27.49o N, 83.28o E, 110 m asl), Nepal for a period of one year (2018). The ground based data of spectral aerosol optical depth (AOD) are derived from Aerosol Robotic Network (AERONET) of NASA. Satellite based data of meteorological parameter (maximum temperature, minimum temperature, dew point, relative humidity, water contend and rainfall) are downloaded from NASA website. Daily, monthly and seasonal variation of atmospheric turbidity is analyzed. The annual average of Angstrom exponential ( α), Angstrom coefficient of turbidity ( β) and Linke turbidity factor (LT) are found 1.0 ± 0.2, 0.36 ± 0.20 and 8.3 ± 2.5 respectively. Correlation coefficient of meteorological parameters with atmospheric turbidity is analyzed. No significant effect of maximum temperature, minimum temperature, dew point and water contend on atmospheric turbidity. There is positive effect of rainfall on α, but negative effects of relative humidity and rainfall on β and LT. Atmospheric turbidity index is an important for the air pollution. Result of this research work is beneficial for the further identification, impact and analysis of atmospheric turbidity index on meteorological parameters at different places with same geographical condition.

Impact of Meteorological Parameters on Solar Radiation over Langtang National Park, Nepal

The main objective of this research is to study the impact of meteorological parameters on atmospheric turbidity index on Langtang National Park (28.2112° N, 85.5663° E, 3862 m asl) for a period of one year (2018). Daily data of Aerosol optical depth (AOD) and meteorological parameters are obtained from AERONET and NASA website respectively. The maximum and minimum values of direct solar radiation (IB) during the study period are found 1286 ± 24 W/m2 in January and 1123 ± 3 W/m2 in August respectively. The maximum and minimum value of diffuse solar radiation (ID) is found 119 ± 21 W/m2 in April and 59 ± 2 W/m2 in August respectively. The annual average of direct solar radiation and diffuse solar radiation are found 1202 ± 63 W/m2 and 90 ± 20 W/m2 respectively. There is a negative effect of maximum temperature, minimum temperature, dew point, relative humidity, water contend and rainfall on IB, but no significant effect of those parameters on ID. The result of this research work is beneficial for the further identification, impact and analysis of solar radiation at different places with same geographical and meteorological condition. The study of solar radiation is important for energy harvesting and agriculture.

Factors Influencing the Spatio–Temporal Variability of Aerosol Optical Depth over the Arid Region of Northwest China

Aerosol optical depth (AOD) is an important physical variable used to characterize atmospheric turbidity for the management and control of air pollution. This study aims to analyze the factors influencing the spatial and temporal variability in AOD across the arid region of Northwest China (ARNC) using MODIS Aqua C006 aerosol product data from 2008 to 2017. In terms of natural and socioeconomic factors, the correlation coefficient (R) was used to identify the most influential factor in the AOD changes. The results show that AOD values in spring and summer were much higher than those in autumn and winter, especially in spring. In general, AOD had an insignificant decreasing trend, with a small overall changing range. Spatial analysis revealed a significantly decreasing trend, mostly across the Gobi Desert area, which is located in the western region of the ARNC. From the perspective of natural factors, AOD was positively correlated with air temperature (AT), wind speed (WP), land surface temperature (LST), and the digital elevation model (DEM) and negatively correlated with precipitation, relative humidity (RH), and the normalized difference vegetation index (NDVI). The greatest positive correlation, with a maximum R value of 0.8, was found between AOD and wind speed. By contrast, AOD and relative humidity had the strongest negative correlation, with R values of −0.77. In terms of anthropogenic factors, gross domestic product (GDP), secondary industry, and population density were the three major anthropogenic factors that influenced the changes in AOD changes in this region. In general, the effects of anthropogenic factors on AOD are more significant in areas with high urban population densities.

Estimation of the Angström coefficient over Ghardaïa city

Atmospheric turbidity over Ghardaïa city is investigated using the Angström coefficient. Its values are derived using five years (2004-2008) of data. They show a seasonal trend along the year with maximum values around summer months and minimum ones around winter months. The annual average value varied between 0.079 and 0.105. The daily average values were less than 0.1, between 0.1 and 0.2 and exceeded 0.2 for 64.6 %, 33.8 % and 1.6 % of the cloudless days respectively. This result reveals that for 64.6 % of the cloudless days the sky over Ghardaïa city was clean to clear, for 33.8 % was clear to turbid and for 1.6 % was turbid to very turbid.

Atmospheric Turbidity Index on Pokhara

Population growth, industrialization and other natural processes effects on air-pollution levels and climatic change. Atmospheric turbidity factors are an important parameter for assessing the air pollution. The main objective of this research is to study atmospheric turbidity factors on Pokhara (28.180° N, 83.970° E and 800 m asl), Nepal for a period of one year (2017). The daily data of aerosol optical depth (AOD) are derived from Aerosol Robotic Network (AERONET) of NASA. Daily, monthly and seasonal variation of atmospheric turbidity index are analyzed. The annual average of Angstrom exponential (α), Angstrom coefficient of turbidity (β) and Linked turbidity (LT) are found 1.2 ± 0.2, 0.19 ± 0.17 and 6.7 ± 3.4 respectively. The annual average of visibility is found 3.2 ± 2.5 km. Result of this research work is beneficial for the further identification, impact and analysis of atmospheric turbidity at different places with same geographical condition.

A systematic bias in present models of circumsolar radiation

Light scattering by aerosol particles is known to be fairly largest modulator of circumsolar radiation, while its contribution to the direct beam irradiance progressively increases with atmospheric turbidity. Due to their forward-lobed radiation patterns, the large aerosol particles are of special importance in understanding the shaping of diffuse light distribution in the solar aureola. The mechanism of intensive small-angle scattering is the Fraunhofer diffraction which scales up linearly with the projected area (shadow) of each single particle.Theoretically founded models of circumsolar radiation usually rely on Mie scattering – a concept exclusively applicable to spherical particles. However, the particles occurring in nature typically have nonspherical shapes with aspect ratio exceeding a few times that for a sphere (for prolate particles) or smaller than unity (for oblate particles). Here we show that models based on Mie theory systematically underestimate circumsolar irradiance compared to what we can expect from nonspherical particles. The average brightening of solar corona due to nonspherical aerosols is 10–20%, but can be as large as 40–50% for needle-like or disk-like particles. We are introducing a correction factor to improve the accuracy of the present models.

Modeling the celestial distribution of skylight polarization patterns by incorporating the influence of both the sun and the moon through an analytical model.

The orientation of many polarization-sensitive animals and the hypothetical sky-polarimetric Viking navigation both rely on the polarization pattern of skylight. For 40 years, scientists have attempted to construct various models to simulate this pattern. However, existing theoretical models have only analyzed the polarization pattern of skylight that is influenced separately by the sun or the moon and have built their modeling frameworks based on the position of one light source. This approach fails to account for the combined influence of the sun and the moon on the distribution of skylight polarization patterns at certain times. In fact, ignoring the influence of the moon during the dawn and dusk periods in clear weather conditions may lead to significant errors in the simulation results compared to the measured data. In this paper, we present an analytical model that considers various factors, including skylight intensity, horizon correction factor, atmospheric turbidity condition, and combined influence of both the sun and moon on the distribution of polarized skylight. We believe our model demonstrates enhanced agreement with measured data and will further our understanding of how animals use the celestial polarization pattern for navigation, particularly when both the sun and the moon appear in the sky. Moreover, the findings of this study may facilitate the advancement of bio-inspired navigation systems.

Case Study of the Aerosol Optical Properties in the Atmosphere over Wuhan, China

The research on regional aerosol optical properties is of great significance for exploring climate regulation mechanisms and controlling atmospheric pollution. Based on the solar radiation observation platform, a three-month optical observation of atmospheric aerosols was conducted in Wuhan, China. The daily and monthly variation characteristics of aerosol optical depth (AOD550), Angstrom parameter (α440–870), and turbidity coefficient (β) were revealed, and the interrelations between the three optical parameters were fitted. Then, the potential relationships between atmospheric particulate matter (PM2.5, PM10) with AOD550 and β were discussed. The results show that the average values of AOD550, α440–870, and β in this case study are 0.42, 1.32, and 0.20, respectively. The frequency distribution patterns of the three optical parameters are all unimodal. AOD550 has a good linear correlation system with β, and the Pearson correlation coefficient reaches 0.94, while its correlation with α440–870 is not significant. The daily variation in AOD550 and β both show an increasing trend, and their monthly increases are more than 50%. However, the daily variation in α440–870 is relatively stable, and the fitted line is a nearly horizontal line with no significant monthly variation. The fluctuation of particulate matter concentration affects the aerosol optical properties to some extent, among which β has a prominent effect on the response to the change in PM2.5 concentration with a linear correlation coefficient of 0.861. As the concentration of particulate matter increases, the proportion of fine particulate matter in the atmosphere increases monthly, and the ratio of PM10 to PM2.5 concentrations decreases from 1.8:1 to 1.2:1. Atmospheric pollution conditions are frequent during this observation period, mainly at mildly turbid levels. Atmospheric turbidity shows an increasing trend month by month, and the concentration of particulate matter increases rapidly. The response of atmospheric aerosol optical properties to the changes in fine particulate matter concentration is significant, and controlling the particulate matter content in the atmosphere is an effective means to mitigate aerosol pollution.

Algorithm for the Reconstruction of the Ground Surface Reflectance in the Visible and Near IR Ranges from MODIS Satellite Data with Allowance for the Influence of Ground Surface Inhomogeneity on the Adjacency Effect and of Multiple Radiation Reflection

An atmospheric correction algorithm is proposed for the reconstruction of the ground surface reflectance from the data of satellite measurements. A distinctive feature of the algorithm is that it takes into account the influence of the ground surface inhomogeneity on the adjacency effect and additional illumination of the ground surface by reflected radiation. These factors are important for the reconstruction of the reflectance of ground surface fragments with sharp reflectance changes and high atmospheric turbidity. The algorithm is based on Monte Carlo programs developed by the authors. To reduce the computing time, we have proposed some original criteria and approaches. To estimate the capabilities of the developed algorithm, its results have been validated by comparing with the results of the MOD09 algorithm for four MODIS bands and measurements for the Portugal surface fragment with coordinates 38.829 N, 8.791 W. Good agreement of the results obtained by the proposed algorithm with the surface measurements and the data obtained by the MOD09 algorithm demonstrates the efficiency of the proposed algorithm in the reconstruction of the ground surface reflectance.

Intercomparison of the parameterized Linke turbidity factor in deriving global horizontal irradiance

This study presents a comparative assessment of nine parameterized methods to estimate Linke turbidity (TL) in the five Köppen–Geiger climate zones. The evaluation is intensified by simulating all TL values into a global horizontal irradiance (GHI) model, called the Hammer model. The results found that atmospheric turbidity on the Korean Peninsula varies per season. The atmosphere tends to be more transparent in fall and winter, with lower turbidity. Meanwhile, the turbidity consistently rises in spring and peaks and summer, indicating a significant decline in atmospheric transparency.All nine TL methods generated varied degrees of accuracy according to their Köppen–Geiger climate zones. In terms of relative root mean square error (rRMSE) and relative mean bias error (rMBE), TLValko1, TLValko2, and TLCapdrou consistently perform poorly. Further, these three methods can only classify the atmospheric turbidity into two types: least and fully turbid. This study recommends the following methods for evaluating atmospheric turbidity in various climate types: TLRemund, TLDogniaux1, TLDogniaux2, TLIneichen, TLMolineaux, and TLGreiner. This study concludes that none of the six methods can be considered “the most superior” due to minor differences in rRMSE and rMBE values.

Trend relationship between mountain normalized difference vegetation index (NDVI) and aerosol optical depth (AOD) across two decades: implication for water quality within the Lesotho Highlands, Drakensberg, South Africa

There are growing concerns on contribution of vegetation dynamics to atmospheric turbidity and quality of regional water towers. The study sought to determine the trends in the MODIS/TERRA-derived normalized difference vegetation index (NDVI) and aerosol optical depth (AOD) for Lesotho Highland over 2000-2020. The predictive relationship between the two variables was also examined using regression analysis. Irrespective of yearly AOD patterns, the AOD showed biphasic patterns peaking between mid-winter to early spring (July-October) (highest) and autumn (Feb-April) (next highest), and lowest in the summer (Nov-January). The monthly NDVI was largest in January-March (summer-early fall) with smaller values in winter and spring. This seasonality can be related to the peak of anthropogenic biomass combustion during the winter and strong winds during the spring and early summer. The AOD relationship with NDVI showed quadratic patterns peaking and plunging with changes in season. About 30-80% (R2 = 0.3-0.8%) changes in annual AOD from 2000 to 2020 were explainable by the dynamics of NDVI indicating that increased NDVI contributes to about a 50% decrease in AOD in the Lesotho Highlands. However, an outlier trend was observed in 2007 (R2 = 13%). Incidences of high AOD in months of high NDVI may be indicative of traveling aerosols, i.e., aerosols from non-local sources/activity. On the other hand, high AOD in months of low NDVI implicates local aerosol sources. Trend relationship studies on vegetation loss and AOD in mountain areas of other regions could improve knowledge of contaminant dynamics and risk implications for downstream populations.

Optical remote sensing of large-scale water pollution in Angola and DR Congo caused by the Catoca mine tailings spill

Billions of tons of hazardous mine waste are stored in thousands of tailings storage facilities around the world. These impoundments represent one of the most important environmental risk factors of industrial mining, since occasional tailings spills or dam failures cause devastating impacts on humans and ecosystems, specifically along river corridors. In this study, we developed a satellite remote sensing methodology to assess the impacts of tailings spills on water quality focusing on the controversial incident that occurred at the Catoca diamond mine in Angola in late July 2021. The spill allegedly caused important river pollution in neighbouring Democratic Republic of the Congo (DR Congo) and led to public health concerns including the loss of human lives – however the mining company denied any responsibility. We processed high resolution imagery acquired by ESA’s Sentinel-2 satellites using the Python package Acolite for atmospheric correction and turbidity retrieval, and applied a river skeletonizing algorithm to automatically extract turbidity values for the entire river system. This allowed tracking the propagation of the pollution front from the source at the Catoca mine through the Tshikapa- and the Kasaï River during more than one month and across 1400 km, until the pollution front finally dissipated after discharging into the Congo River. We further analyzed a 6-year time series of virtual stations in the Tshikapa River located up- and downstream of the effluent discharge to compare the impacts of the tailings spill to seasonal variabilities of water quality. Turbidity values caused by the spill largely exceeded the seasonal variability in the Tshikapa River in recent years. These findings confirm that the Catoca tailings spill has significantly affected water quality of the Tshikapa- and the Kasaï River with total suspended solids concentrations that were several 10-fold above drinking water standards in Lunda Norte Province, Angola, and Kasaï Province, DR Congo, making severe public health impacts for residents and fish kills highly probable. After investigating whether this methodology could be applied to other tailings dam failures that have occurred since the Sentinel-2 mission began in 2015, we recommend to apply it to four other incidents in Mexico, Myanmar, Peru and China, respectively. Overall, this Sentinel-2 workflow provides the opportunity to assess the large-scale impacts of pollution incidents in mining areas around the world in locations where hydrological- and water quality data are scarce and monitoring capacities are limited.

Determination of broadband atmospheric turbidity from global irradiance or photovoltaic power data using deep neural nets

Photovoltaic technologies provide significant capacity to electric grids, however, resource variability and production uncertainty complicate power balancing and reserve management. A crucial step in predicting solar generation is determining clear-sky irradiance. Clear-sky attenuation can be modeled using broadband atmospheric turbidity factors, but model accuracy is dependent on the measurements used to determine the current and future state of aerosol loading and water vapor content, which requires close proximity measurements, in time and space, to account for turbidity variability. Such measurements, though, are only available in near real-time at a limited, and decreasing, number of sites. This paper proposes a new method for estimating time-varying local turbidity conditions from more readily available pyranometer or PV output data. The method employs a long short-term memory recurrent neural network to distill the turbidity-driven signal from global irradiance (or global irradiance driven) observations, despite an inherent dampening issue. The method is developed to operate in near real-time for solar forecasting applications. Validation examines the ability of the method to (1) reproduce turbidity estimates derived from historical measurements of beam irradiance under clear-sky conditions; and (2) provide input for clear-sky models in the form of persistence forecasts generated from daily mean values.

Nesting Elterman Model and Spatiotemporal Linear Mixed-Effects Model to Predict the Daily Aerosol Optical Depth over the Southern Central Hebei Plain, China

Aerosol optical depth (AOD), an important indicator of atmospheric aerosol load, characterizes the impacts of aerosol on radiation balance and atmospheric turbidity. The nesting Elterman model and a spatiotemporal linear mixed-effects (ST-LME) model, which is referred to as the ST-Elterman retrieval model (ST-ERM), was employed to improve the temporal resolution of AOD prediction. This model produces daily AOD in the Southern Central Hebei Plain (SCHP) region, China. Results show that the ST-ERM can effectively capture the variability of correlations between daily AOD and meteorological variables. After being validated against the daily Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD, the correlation coefficient between daily retrieved AOD from ST-ERM and MAIAC observations in 2017 reached 0.823. The validated Nash–Sutcliffe efficiency (Ef) of daily MAIAC AOD and ST-ERM-retrieved AOD is greater than or equal to 0.50 at 72 of the 95 stations in 2017. The relative error (Er) is less than 14% at all the stations except for Shijiazhuang (17.5%), Fengfeng (17.8%), and Raoyang (30.1%) stations. The ST-ERM significantly outperforms the conventional meteorology–AOD prediction approaches, such as the revised Elterman retrieval model (R-ERM). Thus, the ST-ERM shows great potential for daily AOD estimation in study regions with missingness of data.

Atmospheric Linke Turbidity Index over Deukhuri Valley, Dang

The aim of this project is to study Atmospheric Linke turbidity index over Deukhuri Valley, Dang (27°50’32.2" N, 82°45’41.9" E and 445 m a. s. l.). The atmospheric Linke Turbidity index is calculated from solar radiation measurements of NASA satellite for period of 4 years (2015, 2016, 2017, 2018). The annual average clearness index (KT ), Linke turbidity index (LT ) and visibility are 0.55 ± 0.12, 2.5 ± 1.1 and 10.4 ± 3.8 km respectively. Atmospheric Linke turbidity index is used on agriculture, hydrology, climate change and energy harvesting. In study period, number of good days (visibility > 15 km) and number of bad days (visibility < 5 km) are found to be 188 and 94 respectively. This research work is beneficial for the further identification, impact and analysis of atmospheric turbidity at different places.