Wyniki wyszukiwania - Biblioteka Nauki

1

Characteristics of aerosol optical depth and Ångström parameters over Mohal in the Kullu Valley of Northwest Himalayan Region, India

100%

Sharma N. L. , Kuniyal J. C. , Singh M. , Sharma M. , Guleria R. P.

Acta Geophysica

|

2011

|

tom Vol. 59, no. 2

334-360

EN

The measurements using a ground based multi wavelength radiometer (MWR) at Mohal (31°54’N, 77°07’E, 1154 m AMSL) in the Kullu valley of Northwestern Himalayan region show that the spectral aerosol optical depth (AOD) and turbidity coefficient, ß, are high in summer, moderate in monsoon season, low in winter and lowest in autumn, while wavelength exponent, α, has an opposite trend. Average annual value of AOD at 500 nm is 0.24±0.01, 0.43±0.02, and 0.28±0.02; that of β is 0.14±0.01, 0.22±0.02, and 0.17±0.03; and that of α is 1.06±0.09, 1.16±0.10, and 0.86±0.13, respectively, for clear, hazy and partially clear sky days. The considerably greater value of β on hazy days indicates more coarse particles in mountain haze. The fractional asymmetry factor (AF) is more negative in summer and autumn months. The AOD and β have significantly positive correlation with temperature and wind speed, suggesting high AODs and turbidity on hot and windy days.

2

Prediction of aerosol optical depth over Pakistan using novel hybrid machine learning model

88%

Zaheer K. , Saeed S. , Tariq S.

|

2023

|

tom Vol. 71, no. 4

2009--2029

EN

Impact of aerosols on health includes both long-term chronic irritation and inflammation of the respiratory tract. Aerosol optical depth (AOD), a crucial optical parameter that assesses the extinction effect of atmospheric aerosols, is frequently used to estimate the extent of air pollution on large scales. So, the better prediction of AOD is crucial for understanding the health impacts of aerosols. The accurate prediction of AOD is difficult due to its nonlinear relationships with other climatic variables, uncertainties, and time series variable characteristics. In this paper, a machine learning (ML) model such as support vector regression (SVR), novel hybrid SVR-GWO model (SVR integrated with gray wolf optimizer (GWO)), and statistical model multi-linear regression (MLR) are used to predict AOD. Also, for SVR-GWO model, SVR hyper-parameters are optimized using meta-heuristic GWO algorithm. Satellite-based data of Pakistan is used for the prediction of AOD on monthly bases. In addition, preprocessing techniques of forward feature selection (FFS) is utilized to select the optimal input features for the SVR-GWO, SVR and MLR models to predict AOD. The performance of the novel hybrid SVR-GWO, SVR, and MLR model is analyzed using RMSE, MAE, RRMSE, R2 and Taylor diagram, and it is found that hybrid SVR-GWO model (RMSE = 0.07, MAE = 0.06, RRMSE = 0.22 and R2=0.60) is better than ordinary SVR model (RMSE = 0.10, MAE = 0.07, RRMSE = 0.29 and R2=0.18) and MLR model (RMSE = 0.11, MAE = 0.07, RRMSE = 0.32 and R2=0.03). Keynotes: (a) The study demonstrates the potential of ML models such as SVR-GWO for accurate prediction of AOD, which can aid in better understanding of the health impacts of aerosols. (b) The use of preprocessing techniques like FFS and optimization algorithms like GWO can significantly improve the performance of the ML (SVR-GWO) model in predicting AOD. (c) The findings of this study can be useful for policymakers and healthcare professionals in identifying regions and populations at risk of aerosol-induced respiratory health issues and designing effective interventions to mitigate them.

3

Comparison of aerosol optical depth products from multi-satellites over densely populated cities of Pakistan

63%

Butt F. M. , Shahzad M. I. , Khalid S. , Iqbal N. , Rasheed A. , Raza G.

International Letters of Natural Sciences

|

2018

|

tom 69

EN

Air pollution in Pakistan is causing damage to health, environment and quality of life. Air pollution in Pakistan is not effectively monitored due to heavy cost involved in setting up ground stations. However, Satellite remote sensing can effectively monitor the air pollution in terms of Aerosol Optical Depth (AOD) at regional as well as global level. However, algorithms used to derive AOD from different sensors have some inherited differences which can pose challenges in monitoring regional AOD at high temporal resolution using more than one sensor. Therefore, this study focuses on comparison of four major satellite based AOD products namely Moderate Resolution Imaging SpectroRadiometer (MODIS), Multi-angle Imaging SpectroRadiometer (MISR), Ozone Monitoring Instrument multiwavelength (OMI) aerosol product and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) with the ground based AErosol RObotic NETwork (AERONET) AOD which is only available from Lahore and Karachi in Pakistan. The correlation of various AOD products with AERONET AOD is estimated statistically through coefficient of determination (R²), Root Mean Square Error (RMSE), slope and intercept. It is noticed that MODIS is relatively accurate and reliable for monitoring air quality on operational bases over the land cover area of Lahore (R² = 0.78; RMSE = 0.18), whereas MISR over the coastal areas of Karachi (R² = 0.82; RMSE = 0.20). The results of the study will help the stakeholders in planning additional ground stations for operational monitoring of air quality at regional level.

4

Investigating the spatial and temporal variation of aerosols and cloud parameters over South Asia using remote sensing

63%

Malik M. , Tariq S. , ul Haq Z.

Acta Geophysica

|

2024

|

tom Vol. 72, no. 2

1413--1438

EN

The most important component in determining the spatiotemporal distribution of aerosol at local and regional levels is aerosol optical depth (AOD). In this study, data has been obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite to examine spatiotemporal variations in AOD and their effects on the Angstrom Exponent (AE), and clouds parameters, namely cloud fraction, cloud optical thickness, cloud top pressure, cloud top temperature, ice cloud water path, liquid cloud water path, ice cloud effective radius, and liquid cloud effective radius over South Asia from July 2002 to July 2021. The highest values of AOD (0.53-0.7) were observed in the Indo-Gangetic basin (IGB) region over South Asia. The value of AOD of 0.7 is observed in the IGB region during summer. The 0.2 AOD value is observed in winter. The highest mean AOD (0.63 ± 0.09) observed in Bangladesh is due to the noteworthy increase in agricultural activities. The negative correlation between AOD and AE was noticed in Karachi (-0.24), Lahore (-0.04), Rawalpindi (-0.08), Mumbai (-0.03), Kathmandu (-0.49), Colombo (-0.19), and in Kabul (-0.51). A positive correlation is observed in Delhi (0.21), Bangalore (0.09), and Dhaka (0.17).