Long-term quantification of springtime aerosols over Saudi Arabia using multi-satellite remotely sensed data.

A comprehensive analysis of aerosol characteristics over Saudi Arabia from 2005 to 2022, utilizing high-resolution satellite-based observations and reanalysis datasets, examining the distribution of aerosols and their subtypes across the three dimensions (temporal, spatial, and altitude based) for March, April, and May. This study focuses on the analysis of parameters such as aerosol optical depth (AOD), angstrom exponent (AE), absorption aerosol optical depth (AAOD), and Ultraviolet Aerosol Index (UVAI), revealing significant spatial disparities, with elevated aerosol concentrations in the central and eastern regions and comparatively lower concentrations along the western coastal areas. In this study, the spatial patterns and temporal trends are analyzed through trajectory modeling. The study also investigates the composition of aerosols in various Saudi cities. Aerosols prevailing in a dozen Saudi Arabian cities were systematically categorized into six sub-types, considering their particle size and UV-absorbing properties. Notably, two major aerosol sub-types, absorbing coarse (AC) aerosols (UVAI > 0.25, AE < 0.70) treated as mineral dust and absorbing mixed (AM) aerosols (0.70 < AE < 1.25) along with neutral fine (NF) particles (- 0.5 < UVAI < 0.25, AE > 1.25) treated as urban, predominate across the Kingdom of Saudi Arabia.

Non-invasive detection of mental fatigue in construction equipment operators through geometric measurements of facial features.

INTRODUCTION: Prolonged operation of construction equipment could lead to mental fatigue, which can increase the chances of human error-related accidents as well as operators' ill-health. The objective detection of operators' mental fatigue is crucial for reducing accident risk and ensuring operator health. Electroencephalography, photoplethysmography, electrodermal activity, and eye-tracking technology have been used to mitigate this issue. These technologies are invasive and wearable sensors that can cause irritation and discomfort. Geometric measurements of facial features can serve as a noninvasive alternative approach. Its application in detecting mental fatigue of construction equipment operators has not been reported in the literature. Although the application of facial features has been widespread in other domains, such as drivers and other occupation scenarios, their ecological validity for construction excavator operators remains a knowledge gap. METHOD: This study proposed employing geometric measurements of facial features to detect mental fatigue in construction equipment operators' facial features. In this study, seventeen operators performed excavation operations. Mental fatigue was labeled subjectively and objectively using NASA-TLX scores and EDA values. Based on geometric measurements, facial features (eyebrow, mouth outer, mouth corners, head motion, eye area, and face area) were extracted. RESULTS: The results showed that there was significant difference in the measured metrics for high fatigue compared to low fatigue. Specifically, the most noteworthy variation was for the eye and face area metrics, with mean differences of 45.88% and 26.9%, respectively. CONCLUSIONS: The findings showed that geometrical measurements of facial features are a useful, noninvasive approach for detecting the mental fatigue of construction equipment operators.

Insights into the source contributions to the elevated fine particulate matter in Nigeria using a source-oriented chemical transport model.

In 2021, Nigeria was ranked by the World Health Organization (WHO) as one of the top countries with highly deteriorating air quality in the world. To date, no study has elucidated the sources of elevated fine particulate matter (PM2.5) concentrations over the entire Nigeria. In this study, the Community Multiscale Air Quality (CMAQ) model was applied to quantify the contributions of seven emissions sectors to PM2.5 and its components in Nigeria in 2021. Residential, industry, and agriculture were the major sources of primary PM (PPM) during the four seasons, elemental carbon (EC) and primary organic carbon (POC) were dominated by residential and industry, while residential, industry, transportation, and agriculture were the important sources of secondary inorganic aerosols (SIA) and its components in most regions. PM2.5 was up to 150 µg/m3 in the north in all the seasons, while it reached ∼80 µg/m3 in the south in January. Residential contributed most to PM2.5 (∼80 µg/m3), followed by industry (∼40 µg/m3), transportation (∼20 µg/m3), and agriculture (∼15 µg/m3). The large variation in the sources of PM2.5 and its components across Nigeria suggests that emissions control strategies should be separately designed for different regions. The results imply that urgent control of PM2.5 pollution in Nigeria is highly necessitated.

Classification of aerosols using particle linear depolarization ratio (PLDR) over seven urban locations of Asia.

Active lidar remote sensing has been used to obtain detailed and quantitative information about the properties of aerosols. We have analyzed the spatio-temporal classification of aerosols using the parameters of particle linear depolarization ratio and single scattering albedo from Aerosol Robotic Network (AERONET) over seven megacities of Asia namely; Lahore, Karachi, Kanpur, Pune, Beijing, Osaka, and Bandung. We find that pollution aerosols dominate during the winter season in all the megacities. The concentrations, however, vary concerning the locations, i.e., 70-80% pollution aerosols are present over Lahore, 40-50% over Karachi, 90-95% over Kanpur and Pune, 60-70% and over Beijing and Osaka. Pure Dust (PD), Pollution Dominated Mixture (PDM), and Dust Dominated Mixture (DDM) are found to be dominant during spring and summer seasons.This proposes that dust over Asia normally exists as a mixture with pollution aerosols instead of pure form. We also find that black carbon (BC) dominated pollution aerosols.

Analysis of nighttime aerosols and relation to covariates over a highly polluted sub-Saharan site using Mann-Kendall and wavelet coherence approach.

High emissions of aerosols and trace gases during nighttime can cause serious air quality, climate, and health issues, particularly in extremely polluted cities. In this paper, an effort has been made to examine the variations in aerosols and trace gases over a sub-Saharan city of Ilorin (Nigeria) during nighttime. We have used Aerosol Robotic Network data of aerosol optical depth (AOD) at 500 nm, Angstrom exponent (AE) (440/870), and precipitable water (WVC). Both AE and WVC showed a decreasing trend of -0.0012% and -0.0010% per year, respectively. We also analyzed nighttime data of carbon monoxide (CO), methane (CH4 ), and ozone (O3 ) from Atmospheric Infrared Sounder and aerosol subtypes from CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation). AOD, AE, and WVC average values are found to be 0.64 ± 0.33, 0.74 ± 0.24, and 3.40 ± 0.97, respectively. As a result of northeasterly winds carrying Saharan dust during the dry season, the greatest value of AOD (1.29) was observed in February. Desert dust aerosols (37.63%) were the most prevalent type, followed by mixed aerosols (44.15%). Winds at a height of 1500 m above ground level were likely transporting Saharan dust to Ilorin. CALIPSO images revealed that Ilorin's atmosphere contained dust, polluted continental, clean maritime, and polluted dust on high AOD days. The National Oceanic and Atmospheric Administration's vertical sounding profiles showed that the presence of high AOD values was caused by the inversion layer trapping aerosol pollution. Average nighttime concentrations of CO, O3 , and CH4 were measured to be 127 ± 18, 29.7 ± 2.1, and 1822.6 ± 12.7 ppbv, respectively. The wavelet coherence spectra exhibited significant quasi-biannual and quasi-annual oscillations at statistically significant levels.

Analyzing the impacts of renewable energy, patents, and trade on carbon emissions-evidence from the novel method of MMQR.

Achieving sustainable development necessitates proactive measures to mitigate the economy's negative impact on environmental standards. A new empirical association between renewable energy patent innovation and net international trade on carbon emissions in ASEAN countries from 1990 to 2021 is presented, along with its significance. Using present panel data techniques, this study investigates the connections between these factors. Second-generation cointegration and unit root tests, as well as a novel method of Moments Quantile Regression, are used in the econometric procedure. Compared to standard quantile regression, this method is more resistant to outliers and provides an asymmetric relationship between the variables. The findings show that trade increases carbon emissions in countries with medium to high emissions, that patent innovation contributes to increasing emissions, and that renewable energy mitigates carbon emissions in countries with low to medium emerging economies. Our results are consistent with other specifications, including quantile regression canay (Canay 2011), fully modified, dynamic, and fixed effect regressions, proving the EKC hypothesis. These countries need to prioritize greener products and adopt advanced manufacturing technologies to reduce carbon emissions from consumption. However, as prosperity increases, it also leads to higher consumption-based carbon emissions, worsening ecological damage in the region. Implementing policies like trade synchronization and increasing investment in patent innovations are proposed in this study to lower the current level of carbon emissions.

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.

Assessment of variability in PM2.5 and its impact on human health in a West African country.

PM2.5 has become a global challenge threatening human health, climate, and the environment. PM2.5 is ranked as the most common cause of premature mortality and morbidity. Therefore, the current study endeavors to probe the spatiodynamic characteristics of PM2.5 in the Republic of Niger and its impacts on human health from 1998 to 2019. Based on remotely sensed satellite datasets, the study found that the concentration of PM2.5 continued to rise in Niger from 68.85 µg/m3 in 1998 to 70.47 µg/m3 in 2019. During the study period, the annual average PM2.5 concentration is far above the WHO guidelines and the interim target-1 (35 µg/m3). The overall annual growth rate of PM2.5 concentration in Niger is 0.02 µg/m3/year. The health risk (HR) due to PM2.5 exposure is also escalated in Niger, particularly, in Southern Niger. The extent of the extremely high-risk areas corresponding to 1 × 104-9.4 × 105 µg.persons/m3 is increased from 0.9% (2000) to 2.8% (2019). Niamey, southern Dakoro, Mayahi, Tessaoua, Mirriah, Magaria, Matameye, Aguié, Madarounfa, Groumdji, Madaoua, Bouza, Keita, eastern Tahoua, eastern Illéla, Bkomnni, southern Dogon-Doutchi, Gaya, eastern Boboye, central Kollo, and western Tillabéry are experienced high HR due to long-term exposure to PM2.5. These findings indicate that PM2.5 causes a serious health risk across Niger. There is an immediate need to carry out its regional control. Therefore, policymakers and the Nigerien government should make conscious efforts to identify the priority target areas with radically innovative appropriate mitigation interventions.

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.

Evaluating the impact of digitalization, renewable energy use, and technological innovation on load capacity factor in G8 nations.

Ecosystems are in danger due to human-caused air, water, and soil pollution, so it is important to find the underlying causes of this issue and develop practical solutions. This study adds to environmental research gap by suggesting the load capability factor (LCF) and using it to look at the factors affectting environmental health. The load capacity factor simplifies monitoring environmental health by illustrating the distinction between ecological footprint and biocapacity. We examine the interplay between mobile phone users (Digitalization DIG), technological advancements (TEC), renewable energy use, economic growth, and financial development. This study assesses G8 economies' data from 1990 to 2018, using a Cross-Section Improved Autoregressive Distributed Lag CS-ARDL estimator and a cointegration test. The data shows that green energy, TEC innovation, and DIG are all beneficial for natural health. Based on the results of this study, the G8 governments should focus on environmental policies that promote economic growth, increase the use of renewable energy sources, guide technological progress in key areas, and encourage the development of digital information and communications technologies that are better for the environment.

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.

Estimating environmental efficiency of the selected Asian countries: does convergence exist?

Environmental degradation has attained much attention from researchers and policymakers at national and global levels. The ever-increasing energy use in production methods is considered one of the fundamental reasons for environmental degradation. The concept of environmental efficiency in the wake of sustainable growth evolved in the last three decades. The present study has been designed to estimate environmental efficiency using the Malmquist-Luenberger productivity index (MLI) using annual data from 43 Asian countries from 1990 to 2019. The MLI is an established econometric approach to estimate cases where input variables are used to get output variables in desirable and undesirable forms. Labor, capital, and energy consumption are input variables, while carbon dioxide (CO2) emissions (undesirable variable) and gross domestic product (undesirable variable) are taken as output variables. The results suggested that, on average, environmental efficiency has decreased by 0.3% over the period in selected Asian countries. Cambodia, Turkey, and Nepal have the highest total factor productivity (TFP) output growth rate on average among 43 Asian countries. These countries are excellent examples of sustainable development that balances environmental protection and efficiency. On the other hand, Kuwait, Mongolia, and Yemen showed the least TFP growth. The study also employed unconditional and convergence tests where the countries' conditional convergence is based on foreign direct investment, population density, inflation, industrialization, and globalization. Some policy implications for Asian countries are also discussed at the end of the study.

Long term spatiotemporal trends and health risk assessment of remotely sensed PM2.5 concentrations in Nigeria.

PM2.5 is an important indicator reflecting air quality variations. Currently, environmental pollution related issues have become more severe that significantly threaten human health. The current study is an attempt to analyze the spatio-dynamic characteristics of PM2.5 in Nigeria based on the directional distribution and trend clustering analysis from 2001 to 2019. The results indicated that PM2.5 concentration increased in most of the Nigerian states, particularly in mid-northern and southern states. The lowest PM2.5 concentration in Nigeria is even beyond the interim target-1 (35 µg/m3) of the WHO. During the study period, the average PM2.5 concentration increased at a growth rate of 0.2 µg/m3/yr from 69 µg/m3 to 81 µg/m3. The growth rate varied from region to region. Kano, Jigawa, Katsina, Bauchi, Yobe, and Zamfara experienced the fastest growth rate of 0.9 µg/m3/yr with 77.9 µg/m3 mean concentration. The median center of the national average PM2.5 moved toward the north indicating the highest PM2.5 concentration in northern states. The Saharan desert dust is the dominant source of PM2.5 in northern areas. Moreover, agricultural practices and deforestation activities along with low rainfall increase desertification and air pollution in these regions. Health risks increased in most of the mid-northern and southern states. The extent of ultra-high health risk (UHR) areas corresponding to the 8×104-7.3×106 µg⋅person/m3 increased from 1.5% to 2.8%. Mainly Kano, Lagos, Oyo, Edo, Osun, Ekiti, southeastern Kwara, Kogi, Enugu, Anambra, Northeastern Imo, Abia, River, Delta, northeastern Bayelsa, Akwa Ibom, Ebonyi, Abuja, Northern Kaduna, Katsina, Jigawa, central Sokoto, northeastern Zamfara, central Borno, central Adamawa, and northwestern Plateau are under UHR areas.

Variations in the aerosol index and its relationship with meteorological parameters over Pakistan using remote sensing.

Particulate pollution has become a major issue in developing countries including Pakistan. Aerosols are causing severe impacts on climate and human health. To understand the effects of aerosols on the environment and human health, we must first understand their optical and physical properties. In this paper, we used ozone monitoring instrument (OMI) retrieved ultraviolet aerosol index (UVAI) to analyze spatial and temporal distribution, annual and seasonal trends of absorbing aerosols, and their relationship with meteorological parameters (e.g., temperature, relative humidity, and wind speed) over Pakistan from October 2004 to December 2021. Significant spatiotemporal changes in UVAI values were found with high values in southern and central regions and low values in northern regions of Pakistan. The mean UVAI over Pakistan showed an increasing trend of 2.89% year-1. Seasonally, UVAI increases at the rate of 3.97% winter-1, 3.24% autumn-1, 0.81% summer-1, and 0.71% spring-1. A strong positive correlation of UVAI with precipitation and temperature (~ 0.6) is observed in the central and southern regions of Pakistan. A negative and positive correlation of -0.3223 and 0.4284 of UVAI with CO2 emissions and primary industry is observed in Pakistan, respectively. We also found potential sources of aerosols over major cities of Pakistan using the Hybrid Single Particle Langrangian Integrated Trajectory (HYSPLIT) model. It determines that the dominant aerosols over Karachi are natural aerosols like sea salt and dust particles and anthropogenic aerosols are dominant over Lahore. Moreover, the natural and anthropogenic factors influencing absorbing aerosols are also discussed herein. Considering the outcomes of this study different methods would be used to reduce the concentration of particulate pollution like afforestation, efficient fuel energy consumption, promotion of public transport networks, etc.

Defining the role of renewable energy, economic growth, globalization, energy consumption, and population growth on PM2.5 concentration: evidence from South Asian countries.

Rapid industrialization and economic development in South Asia (SA) caused serious air pollution-related issues. Air pollutants, particularly fine particulate matter (PM2.5), have negative effects on health, instigating widespread concern. The current study is an attempt to analyze the impact of non-renewable energy (NRE), globalization (GLO), GDP, renewable energy (RE), and population (POP) on PM2.5 concentration in SA from 1998 to 2020. In doing so, this study incorporated advanced and robust econometric techniques, i.e., Pesaran (Economet Rev 34(6-10), 1089-1117, 2015), to check the cross-sectional dependency, and the unit root presence checked through Cross-sectional Im, Pesaran, and Shin (CIPS) and Cross-sectionally Augmented Dickey-Fuller (CADF) unit root tests. Moreover, the long and short-run association among the selected variables was analyzed through Westerlund and Edgerton (Econ Lett 97(3), 185-190, 2007), cointegration test, and cross-sectional augmented ARDL (CS-ARDL). The empirical results indicate that the panel was cross-sectionally correlated, stationary at the first difference, and co-integrated in the long run. Moreover, the CS-ARDL model indicates a positive association between GDP and PM2.5 concentration. Similarly, NRE and POP contribute significantly to increasing the PM2.5 concentration in SA. However, RE and GLO play an important role to decrease the PM2.5 concentration in SA.

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.

Investigating the long-term trends in aerosol optical depth and its association with meteorological parameters and enhanced vegetation index over Turkey.

Aerosol optical depth (AOD) provides useful information on particulate matter pollution at both regional and global levels. In this study, the long-term datasets of aerosols, meteorological parameters, and enhanced vegetation index (EVI) were used from September 2002 to December 2021 over Turkey. This study examined the spatiotemporal distribution of aerosols and their association with meteorological parameters (temperature (Temp), relative humidity (RH), wind speed (WS)), and EVI over Turkey from 2002 to 2021. Moreover, this study also performed a comparison of AOD retrieved from Aqua with other satellites (Terra, SeaWiFS, and MISR) and ground-based (AERONET) products. The higher mean seasonal AOD (> 0.3) was observed over Southeastern Anatolia Region due to the dust transport from the Saharan Desert and Arabian Peninsula. Moreover, AOD was positively correlated with Temp and WS in the east of Turkey, while negative correlations were observed in the coastal regions. The correlation between AOD and RH was also observed negative in most parts of Turkey. Furthermore, in the coastal region, the correlation between AOD and EVI was found to be positive, whereas a negative correlation was seen over less vegetative areas. The multi-seasonal AOD averages were calculated as 0.187, 0.183, 0.138, and 0.104 for the spring, summer, autumn, and winter seasons, respectively. The most important result of this study is the regional differences in AOD over Turkey. For new studies, AOD should be observed separately for coastal areas and the eastern part of Turkey.

Financial Institutional and Market Deepening, and Environmental Quality Nexus: A Case Study in G-11 Economies Using CS-ARDL.

This study presents a new insight into the dynamic relationship between financial institutional deepening (FID), financial deepening, financial market deepening (FMD), foreign direct investment (FDI), economic growth (GDP), population, and carbon dioxide emissions (CO2e) in the G-11 economies by employing a cross-sectionally augmented autoregressive distributed lag (CS-ARDL) approach during 1990-2019. The outcomes from the CS-ARDL and dynamic common correlated effects mean group (DCCEMG) models shows that financial deepening, GDP, FDI, and population degraded environmental quality both in the short run and the long run. Contrary to this, FID and FMD improves environmental quality in these countries. The government should work to maximize financial institutions (access, depth, efficiency) and financial markets (access, depth, efficiency) to reduce the CO2e. A strong positive and in-phase correlation of CO2e with economic growth and population is observed for G-11 countries. These results suggest policy makers should further improve financial institutions by creating opportunities for their populations. Moreover, the governments of G-11 countries should revise their foreign direct investment policies and attention should be given to import efficient means of energy production.

Spatial and temporal variations in PM2.5 and associated health risk assessment in Saudi Arabia using remote sensing.

Air pollutants, especially ambient particulate matter (PM2.5), detrimentally impact human health and cause premature deaths. The dynamic characteristics and associated health risks of PM2.5 are analyzed based on the standard deviational ellipse (SDE) and trend analysis in Saudi Arabia (SAU) from 1998 to 2018 by utilizing recently updated satellite-derived PM2.5 concentrations (V4.GL.03). The outcomes show that the national average PM2.5 concentration increased from 28 µg/m3 to 45 µg/m3 with a growth rate of 2.3 µg/m3/year. The center of median PM2.5 concentrations moved to the southeast over the years studied due to the presence of vast sandy deserts, sand dunes, a busy port, and coastal and industrial areas in this region. The areas of SAU that experienced PM2.5 concentrations above 35 µg/m3 increased from 20% to 70%. The rapid-fast growth (RFG) class acquired from the unsupervised classification has the fastest growth rate of 2.5 µg/m3/yr, occurring in southeastern SAU, namely Ash-Sharqiyah, Ar-Riyad, and Najran. It covered ∼27% of the total area of SAU over the study period. Whereas, the slow growth (SG) class with a less than 0.2 µg/m3/yr growth rate covered 12% of the total area of SAU, distributed in northwestern regions. The extent of extremely-high risk areas corresponding to greater than 1 × 103 µg·person/m3 increased from 4% to 11%, particularly in Makkah, Central Al-Madinah, and western Asir, Jizan, mid-eastern Najran, Al-Quassim, and mid-eastern Ar-Riyad and Ash Sharqiyah.

Can Financial Institutional Deepening and Renewable Energy Consumption Lower CO2 Emissions in G-10 Countries: Fresh Evidence from Advanced Methodologies.

To tackle the challenges associated with global warming and climate change, several countries set their targets to lower carbon emissions in accordance with COP21 (Paris Conference). Even though studies highlighted the different aspects that contribute to environmental degradation, there still exists the scarcity of adequate research that emphasizes the environmental implications of financial institutional deepening, renewable energy consumption (REC), and technology innovations. Therefore, this study investigated the significance of financial institutional deepening, REC, gross domestic product (GDP), imports, exports, and technology innovations to achieve sustainability in G-10 countries, namely The Netherlands, Germany, France, Switzerland, United Kingdom, Sweden, Japan, Belgium, Canada, and Italy from 1990 to 2020. The results obtained from cross-sectionally augmented autoregressive distributed lag (CS-ARDL) and the dynamic common correlated effects mean group (DCCEMG) models reveal that financial institutional deepening and imports positively impact CO2 emissions (CO2e) both in the long and short run. A 1% increase in financial institutional deepening and import will increase CO2e by 0.5403% and 0.2942% in the short run and 0.2980% and 0.1479% in the long run levels, respectively. Contrary to this, REC, GDP, exports, and technology innovations improve environmental quality in these countries. The Dumitrescu & Hurlin causality test shows bidirectional causality between imports and CO2e, GDP and CO2e, exports and CO2e, and financial institutional deepening and CO2e, compared to unidirectional causality from technology innovations to CO2e and from REC to CO2e. Apart from this, the outcomes suggest that policymakers in G-10 countries have to consider their financial markets and firms to revise their current environmental policies.