Investigating the Aerosol Optical Depth and Angstrom Exponent and Their Relationships with Meteorological Parameters Over Lahore in Pakistan

Seasonal correlation of aerosols with soil moisture, evapotranspiration, and vegetation over Pakistan using remote sensing

Aerosols have a severe impact on the Earth's climate, human health, and ecosystem. To understand the impacts of aerosols on climate, human health, and the ecosystem we must need to understand the variability of aerosols and their optical properties. Therefore, we used Aqua-MODIS retrieved aerosol optical depth (AOD) (550 nm) and Angstrom exponent (AE) (440/870) data to analyze the Spatio-temporal seasonal variability of aerosols and their relationship with different meteorological parameters over Pakistan from 2002 to 2021. High (>0.5) AOD values were observed during the summer season and low (<0.8) in the spring season. AE values were observed to be high (>1) in the northern regions of Pakistan indicating the dominance of fine mode particles during the winter season. Moreover, AOD showed a positive correlation with Relative Humidity (RH), Evapotranspiration, Wind speed (WS), and Temperature. On the other hand, it showed a negative correlation with Soil moisture (SM), Normalized difference vegetation index (NDVI), and precipitation over Pakistan. Therefore, considering the outcomes of this study will help policymakers to understand the spatiotemporal variability of aerosols and their seasonal correlation with different meteorological parameters.

Analysis of aerosol optical depth and relation to covariates during pre-monsoon season (2002-2019) over Pakistan using ARIMAX model and cross-wavelet analysis

The pre-monsoon season heavily influences the precipitation amount in Pakistan. When hydrometeorological parameters interact with aerosols from multiple sources, a radiative climatic response is observed. In this study, aerosol optical depth (AOD) space-time dynamics were analyzed in relation to meteorological factors and surface parameters during the pre-monsoon season in the years 2002-2019 over Pakistan. Level-3 (L3) monthly datasets from Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-Angle Imaging Spectroradiometer (MISR) were used. Tropical Rainfall Measuring Mission (TRMM) derived monthly precipitation, Atmospheric Infrared Sounder (AIRS) derived air temperature, after moist relative humidity (RH) from Modern-Era Retrospective analysis for Research and Applications, Version-2 (MERRA-2), near-surface wind speed, and soil moisture data derived from Global Land Data Assimilation System (GLDAS) were also used on a monthly time scale. For AOD trend analysis, Mann-Kendall (MK) trend test was applied. Moreover, Autoregressive Integrated Moving Average with Explanatory variable (ARIMAX) technique was applied to observe the actual and predicted AOD trend, as well as test the multicollinearity of AOD with covariates. The periodicities of AOD were analyzed using continuous wavelet transformation (CWT) and the cross relationships of AOD with prevailing covariates on a time-frequency scale were analyzed by wavelet coherence analysis. A high variation of aerosols was observed in the spatiotemporal domain. The MK test showed a decreasing trend in AOD which was most significant in Baluchistan and Punjab, and the overall trend differs between MODIS and MISR datasets. ARIMAX model shows the correlation of AOD with varying meteorological and soil parameters. Wavelet analysis provides the abundance of periodicities in the 2-8 months periodic cycles. The coherency nature of the AOD time series along with other covariates manifests leading and lagging effects in the periodicities. Through this, a notable difference was concluded in space-time patterns between MODIS and MISR datasets. These findings may prove useful for short-term and long-term studies including oscillating features of AOD and covariates.

A study on optical properties, classification, and transport of aerosols during the smog period over South Asia using remote sensing

Over the past few years, South Asian region has experienced frequent and thick smog events because of rapid population growth and enhanced anthropogenic activities, particularly in the Indo-Gangetic Plain (IGP). Therefore, the present study investigates aerosol properties such as aerosol optical depth (AOD) (500 nm), Angstrom exponent (AE) (440-870 nm), single scattering albedo (SSA), fine-mode fraction (FMF), absorption aerosol optical depth (AAOD), and absorption aerosol exponent (AAE) over selected AERONET sites namely Bhola (2012-2021), Dhaka (2012-2021), Jaipur (2011-2021), Kanpur (2011-2021), Karachi (2011-2021), Lahore (2011-2021), and Pokhara (2011-2021) in the IGP during the smog period (October, November, and December). Additionally, different aerosol types were categorized using AERONET direct sun (AOD, AE) and inversion products (VSD, SSA, RI, FMF, and ASY). The monthly mean AOD, AE, and FMF varied from ⁓0.33 to 1.07, ⁓0.3 to 1.4, and 0.6-0.9 µm over all selected AERONET sites during the smog period. Moreover, the outcomes revealed the dominance of biomass-burning and urban/ industrial aerosols over Lahore, Karachi, Dhaka, and Bhola during the smog period. Contrary to this, dust and mixed aerosols were abundant over Jaipur and Karachi, respectively. Furthermore, HYSPLIT cluster analysis is used to trace the transmission paths and potential sources of aerosols over selected sites.

Evaluating the role of renewable energy and technology innovations in lowering CO2 emission: a wavelet coherence approach

Environmental sustainability is one of the most critical issues that require efficient environmental and economic policies in modern times. Advancements in renewables and green technologies contribute significantly to sustained long-term development without affecting environmental quality. Several studies focus on the association of carbon dioxide emissions (CO₂e) with economic variables. However, they ignored the impact of technological innovations and renewable energy consumption on CO₂e in developed countries. Therefore, this study examines the relationship between CO₂e, energy consumption, gross domestic product (GDP), renewable energy consumption, and technology innovations in G-7 countries by employing cross-sectionally augmented autoregressive distributed (CS-ARDL) lag and wavelet coherence techniques during 1990-2020. The results depict that GDP and renewable energy consumption are inversely related to CO₂e. A 1% increase in CO₂e will decrease GDP and renewable energy consumption by 0.459 and 0.172% in the long run and by 0.471 and 0.183% in the short run in G7 countries. Technology innovations negatively impact CO₂e in the short run while positively influencing it in the long run. Considering the advancements in green technologies in different energy-dependent and manufacturing sectors is crucial for a sustainable environment in the long run. Such initiatives ensure the effective use of energy sources by limiting CO₂e in the atmosphere. Moreover, the dynamic common correlated effects mean group model confirms the reliability and effectiveness of the CS-ARDL. The wavelet coherence approach revealed a causality relation between CO2e and technology innovation in Italy, Japan, the UK, and the USA during the study period.

Exploring the relationships between ground-measured particulate matter and satellite-retrieved aerosol parameters in China

In this study, the PM_2.5 and PM₁₀ concentrations from 367 cities in China were integrated with MODIS-retrieved aerosol optical depth (AOD) and Angstrom exponent (AE) data to explore the relationship between ground-measured surface particle concentrations and remote-sensing aerosol parameters. The impact of meteorological and topographical factors and seasonality were also taken into consideration and the partial least squares (PLS) regression model was adopted to evaluate the effects of surface particulate matter (PM) concentration and meteorological factors on the variation of aerosol parameters. PM concentrations and aerosol parameters all presented strong spatial disparity and seasonal patterns in China. After implementation of stringent clean air actions and policies, both the ground-measured and satellite-retrieved aerosol parameters revealed that the concentrations of suspended particles in China's cities declined dramatically from 2015 to 2018. The PM/AOD ratio showed conspicuous south-north and west-east differences. The ratio was strongly correlated to meteorological and topographic factors, and it tended to be higher in arid and less polluted regions. Moreover, the dominant factors affecting seasonal PM/AOD ratios varied among China's five regions. The correlations of daily PM-AOD were always strong in southwest China and in basin terrain (e.g., Sichuan Basin and Tarim Basin). In contrast, the PM-AOD correlation was found to be negative in some cities on the Tibetan Plateau because local relative humidity makes a greater contribution to AOD variation. Since the climate is arid and the ratio of coarse particles (e.g., PM₁₀) is much higher, PM tended to have a significantly negative correlation with AE in northwestern cities. Whereas in many southern cities, PM was positively correlated with AE because of the area's high relative humidity and aerosol hygroscopic properties.

Long-term spatiotemporal trends in aerosol optical depth and its relationship with enhanced vegetation index and meteorological parameters over South Asia

Satellite-based aerosol optical depth (AOD) is columnar light extinction by aerosol absorption and scattering and has become the most important variable for the assessment of the spatiotemporal distribution of aerosols at a regional and global level. In this paper, we have used AOD observations of multiangle imaging spectroradiometer (MISR) from September 2002 to May 2017, moderate resolution imaging spectroradiometer (MODIS) from September 2002 to December 2020, and sea-viewing wide field-of-view sensor (SeaWiFS) from September 2002 to December 2010 over South Asia. We have observed the association of AOD with enhanced vegetation index (EVI) and meteorological variables (temperature (TEMP), wind speed (WS), and relative humidity (RH)) acquired from Giovanni during the period September 2002-December 2020. The satellite observations of Terra-, MISR-, and SeaWiFS-AOD were also compared with Aqua-AOD. The findings show that AOD in eastern Pakistan is higher than in the western Pakistan due to increase in population density and biomass burning. Mean annual peak AOD (˃ 0.7) has been observed over the IGB region because of the significant increase in economical, industrial, and agricultural activities while AOD of ˃ 0.6 is observed over Bangladesh. The lowest mean annual AOD of ˂ 0.3 is observed over northeastern Afghanistan, western Nepal, and Bhutan whereas the AOD of 0.3 is seen over Sri Lanka. The highest seasonal mean AOD of 0.8 has been seen over Bihar, India, and AOD of ~ 0.7 is observed over Bangladesh while the lowest AOD is observed over Afghanistan, Sri Lanka, Nepal, and Bhutan during the winter season. However, the mean AOD over eastern Pakistan is maximum in both monsoon and post-monsoon season but relatively low in pre-monsoon and winter. The highest positive seasonal AOD anomalies were observed over South Asia in winter season followed by post-monsoon, pre-monsoon, and least being monsoon. The higher mean AOD anomaly value is found to be 0.2 over eastern Pakistan and western India. In northeastern Pakistan and central India, AOD and RH are positively correlated (r ˃ 0.54) while negatively correlated over Afghanistan, southwestern region of Pakistan, eastern India, Nepal, Bhutan, and Bangladesh. AOD is negatively correlated (r =  ~  - 0.3) with EVI over eastern Pakistan and western India. The highest correlation coefficient (r) obtained among Terra and Aqua is 0.97, MISR and Aqua is 0.93, and SeaWiFS and Aqua is 0.58 over South Asia.