Little is known about the main sources of ambient particulate matter (PM) in the 22 Eastern Mediterranean Region (EMR) countries. We designed this study to systematically review all published and unpublished source apportionment (SA), identification and characterization studies as well as emission inventories in the EMR. Of 440 articles identified, 82 (11 emission inventory ones) met our inclusion criteria for final analyses. Of 22 EMR countries, Iran with 30 articles had the highest number of studies on source specific PM followed by Pakistan (n = 15 articles) and Saudi Arabia (n = 8 papers). By contrast, there were no studies in Afghanistan, Bahrain, Djibouti, Libya, Somalia, Sudan, Syria, Tunisia, United Arab Emirates and Yemen. Approximately 72% of studies (51) were published within a span of 2015–2021.48 studies identified the sources of PM₂.₅ and its constituents. Positive matrix factorization (PMF), principal component analysis (PCA) and chemical mass balance (CMB) were the most common approaches to identify the source contributions of ambient PM. Both secondary aerosols and dust, with 12–51% and 8–80% (33% and 30% for all EMR countries, on average) had the greatest contributions in ambient PM₂.₅. The remaining sources for ambient PM₂.₅, including mixed sources (traffic, industry and residential (TIR)), traffic, industries, biomass burning, and sea salt were in the range of approximately 4–69%, 4–49%, 1–53%, 7–25% and 3–29%, respectively. For PM₁₀, the most dominant source was dust with 7–95% (49% for all EMR countries, on average). The limited number of SA studies in the EMR countries (one study per approximately 9.6 million people) in comparison to Europe and North America (1 study per 4.3 and 2.1 million people respectively) can be augmented by future studies that will provide a better understanding of emission sources in the urban environment.

The study objective is to contemplate the effectiveness of COVID-19 on the air pollution of Indian territory from January 2020 to April 2020. We have executed data from European Space Agency (ESA) and CPCB online portal for air quality data dissemination. The Sentinel – 5 P satellite images elucidate that the Air quality of Indian territory has been improved significantly during COVID-19. Mumbai and Delhi are one of the most populated cities. These two cities have observed a substantial decrease in Nitrogen Dioxide (40–50%) compared to the same period last year. It suggests that the emergence of COVID-19 has been proved to a necessary evil as being advantageous for mitigating air pollution on Indian territory during the lock-down. The study found a significant decline in Nitrogen Dioxide in reputed states of India, i.e., Delhi and Mumbai. Moreover, a faded track of Nitrogen Dioxide can be seen at the Maritime route in the Indian Ocean. An upsurge in the environmental quality of India will also be beneficial for its neighbor countries, i.e., China, Pakistan, Iran, and Afghanistan.

The concentrations of organic carbon (OC), elemental carbon (EC), water soluble inorganic ionic components (WSIC), and major & trace elements of PM10 were studied in Delhi, an urban site of the Indo Gangetic Plain (IGP), India during January 2013 to June 2014. The average mass concentration of PM10 recorded as 249.7 ± 103.9 μg m−3 (average ± standard deviation) with a range of 61.4–584.8 μg m−3. The strong seasonal variation was noticed in the mass concentration of PM10 and its chemical composition with maxima during winter (PM10: 293.9 ± 95.6 μg m−3; OC: 30.5 ± 13.7 μg m−3; EC: 15.2 ± 7.4 μg m−3) and minima during monsoon (PM10: 143.9 ± 36.3 μg m−3; OC: 19.9 ± 16.2 μg m−3; EC: 7.4 ± 5.4 μg m−3). The average concentration of major and trace elements (Na, Mg, Al, P, S, Cl, K, Ca, Si, Cr, Ti, As, Br, Pb, Fe, Zn and Mn) was accounted for ∼18.5% of PM10 mass. Results of Positive Matrix Factorization (PMF) model, HYSPLIT4 trajectory model, PSCF analysis and cluster analysis provide region of sources and its strength and types of sources of PM10 over Delhi. Positive PMF provides that the major source of PM10 are soil dust (22.7%) followed by secondary aerosols (20.5%), vehicle emissions (17.0%), fossil fuel burning (15.5%), biomass burning (12.2%), industrial emissions (7.3%) and sea salts (4.8%) at the observational site of Delhi. The cluster analysis of air mass trajectories calculated by HYSPLIT model indicates that the air mass approaches to the observational site mainly from 4 sides (north-western IGP, Pakistan (10%); north-western IGP, Northwest Asia (45%); eastern IGP (38%); Pakistan and Arabian Sea (6%)) during study. Potential Source Contribution Function (PSCF) analysis also supports the cluster analysis indicating that the concentration of PM10 mass contributed, is mainly from IGP region (Uttar Pradesh, Haryana and Punjab etc.), Afghanistan, Pakistan and surrounding areas.

Based on the ion chromatography method, the chemical characterization of rainwater (RW) samples collected over Srinagar (a location in central Himalaya) has been done during monsoon 2016 (MON-2016). The rainwater shows near acidic pH values ranging from 5.1 to 6.2 (average, 5.7 ± 0.6) during the study. The average ionic concentrations of 97 ± 10 μeq/1 were reported during MON-2016. Ca²⁺ has significantly high contribution of 24% as compared to NH₄⁺ (18%), Na⁺ (9%), K⁺ (4%), and Mg²⁺ (3%) among cations, whereas Cl⁻, SO₄²⁻, and NO₃⁻ have contribution of ~ 15, 11, and 7%, respectively, among anions during chemical analysis. We have reported SO₄²⁻/NO₃⁻ ratio as 1.49, which shows contribution of 60 and 40% from SO₄²⁻ and NO₃⁻ ions within the predicted limit of RW (H₂SO₄, 60–70%, and HNO₃, 30–40%). Ca²⁺, Mg²⁺, and NH₄⁺ have neutralization factors as 2.51, 0.37, and 2.01, respectively, due to the neutralization of acidic species in RW. The non-sea salt (NSS) contribution to total Ca²⁺, K⁺, and Mg²⁺ indicates the major contribution from crustal origin, whereas the NSS contribution to the total Cl⁻ and SO₄²⁻ was from the anthropogenic source. The principle component analysis (PCA) indicates that the first factor (i.e., natural sources, mainly dust and sea salts) has only ~ 9% variance. In contrast, the second factor (i.e., fossil fuel and biomass burning) has ~ 17% variance, and the third factor has 27% variance may be due to soil, agricultural, and biomass burning origin. The rest of the contributions are from mixed emission sources as well as by the transport of polluted air mass from the Indo-Gangetic Plain (IGP) and Punjab Rajasthan, Pakistan, and Afghanistan. This manuscript helps to understand the impact of crustal and anthropogenic sources in rainwater over the central Himalaya region of Uttarakhand.

Due to the development of the metallurgical and energy industries and the operation of incinerators, more and more environmental pollution is occurring. Toxic elements accumulate in the biosphere and affect the state of the population of the regions of large-scale production or the disposal of industrial waste. The main goal of this study was to compare the toxic elements hair composition in people from different regions of the world. The concentrations of toxic and potentially toxic elements (Al, As, Be, Cd, Hg, Pb, Sn) in 198 people, first-year students of People’s Friendship University of Russia, who arrived from different regions of the world, were measured with inductively coupled plasma mass spectrometry. Students were divided into 6 groups: from South and East Asia, from Latin America, from Arab countries, Central Asia and Afghanistan, from South and Central Africa, from Iran and Azerbaijan, and from Russia, Ukraine, and Moldova. Medians of the concentrations of elements in the hair in the general group were 5.8 μg/g for Al, 30 ng/g for As, 0.6 ng/g for Be, 9.0 ng/g for Cd, 0.11 μg/g for Hg, 0.24 μg/g for Pb, and 0.11 μg/g for Sn. All these values fall within the normal range. Students from Russia, Moldova, and Ukraine showed a significantly higher Sn content (0.28 μg/g) in their hair than subjects from other regions except for Latin America, p<0,05. Except for As, cases of exceeding their recommended concentrations in the hair were identified. However, the proportion of subjects with deviations in each group was not high — not more than 7%. In all regions, a positive correlation was found between Cd, Pb, and Sn, p<0.05, r>0.5 for all. Cases of exceeding the maximum permissible concentrations of various toxic elements in the hair were detected in people from all regions of the Earth included in the study. And although the overall picture of the content of toxic elements in the hair of students from all regions in our study does not look critical, the results of previous studies, as well as information about the total deterioration of the environmental situation throughout the Earth, necessitate further large-scale environmental studies.

The groundwater vulnerability assessment is known as a useful tool for predicting and prevention of groundwater pollution. This study targets the DRASTIC, evidential belief function (EBF), and logistic regression (LR) models to assess vulnerability in Kabul aquifers, Afghanistan Country. The growth of urban sprawl, groundwater overexploitation, and lack of suitable municipal sewage systems as anthropogenic sources have been the main potential to increase groundwater contaminants such as nitrate in the study area. The vulnerability map has been developed based on various effective factors including altitude, slope (percentage rise), aspect, curvature, land-use type, drainage density, distance from river, annual mean precipitation, net recharge, geology/lithology units, the impact of the vadose zone, aquifer media, depth to water (unsaturated zone), saturated zone, drawdown, and hydraulic conductivity. To identify groundwater pollution, the spatial variation of nitrate concentration data in 2018 was considered indication of groundwater pollution. Based on descriptive statistics, the value of 2.65 mg/l (the median of the pixel values of nitrate map) was selected as a threshold to differentiate the occurrence and non-occurrence of pollution. The groundwater quality data were selected and randomly divided into two datasets for training and validation, including 70% and 30%, respectively. The success-rate and prediction-rate curves were computed based on the receiver operating characteristic (ROC) curve and the area under the curve (AUC) to estimate the efficiency of models. The ROC-AUC of success rates for EBF, LR, and DRASTIC models were estimated to be 67%, 66%, and 52%, respectively. Moreover, the ROC-AUC of the prediction rates of the EBF, LR, and DRASTIC models were obtained 61%, 63%, and 55%, respectively. Based on correlation between mean nitrate concentration and the mean vulnerability indexes in each model, the EBF model is the most compatible with the current developed vulnerability zones as the role of mankind in changing the environment in real conditions in comparison to LR and DRASTIC models.

Since 1970, numerous governments have established strategic petroleum reserves (SPRs) in relation to oil supply interruptions. In this study, important oil reserves, physical oil supply disruption and social welfare losses due to physical distribution of oil supply have been measured. The physical oil supply disruption has been measured in the form of oil supply vulnerability index and oil volatility index of the South Asian economies. Analysis reveals that the accumulation and drawdown of important national crude oil strategic petroleum reserves where the state wants to optimize individual social welfare while individuals hold over stock optimize their earnings levels. The monetary deciding factors utilize the government’s optimum important stockpile policy and simultaneously the amount and economic factors vital for the nongovernment market to actuate the optimum accumulation and nonaccumulation of important fossil fuels stockpile. Additionally, findings show that India is the lowest crude oil insecure country while Afghanistan and Bangladesh are the highest insecure countries in terms of oil supply. India’s topmost mark shows a bigger possibility to alter the fossil fuels producers while Afghanistan, Bangladesh, Bhutan and Nepal have the minimum mark corroborating the group as the utmost producer risk exposed nations.

The paper presents the spatio-temporal variation of chemical compositions (organic carbon (OC), elemental carbon (EC), and water-soluble inorganic ionic components (WSIC)) of particulate matter (PM₁₀) over three locations (Delhi, Varanasi, and Kolkata) of Indo Gangetic Plain (IGP) of India for the year 2011. The observational sites are chosen to represent the characteristics of upper (Delhi), middle (Varanasi), and lower (Kolkata) IGP regions as converse to earlier single-station observation. Average mass concentration of PM₁₀ was observed higher in the middle IGP (Varanasi 206.2 ± 77.4 μg m⁻³) as compared to upper IGP (Delhi 202.3 ± 74.3 μg m⁻³) and lower IGP (Kolkata 171.5 ± 38.5 μg m⁻³). Large variation in OC values from 23.57 μg m⁻³ (Delhi) to 12.74 μg m⁻³ (Kolkata) indicating role of formation of secondary aerosols, whereas EC have not shown much variation with maximum concentration over Delhi (10.07 μg m⁻³) and minimum over Varanasi (7.72 μg m⁻³). As expected, a strong seasonal variation was observed in the mass concentration of PM₁₀ as well as in its chemical composition over the three locations. Principal component analysis (PCA) identifies the contribution of secondary aerosol, biomass burning, fossil fuel combustion, vehicular emission, and sea salt to PM₁₀ mass concentration at the observational sites of IGP, India. Backward trajectory analysis indicated the influence of continental type aerosols being transported from the Bay of Bengal, Pakistan, Afghanistan, Rajasthan, Gujarat, and surrounding areas to IGP region.

In the last few decades, developing countries continued to increase their manufacturing industries’ phenomenal growth rate. Due to the emergence of globalization, these developing countries are getting economic growth at the cost of environmental pollution. In this context, the extent of linkages between globalization and carbon dioxide (CO₂) emissions has been investigated over the time period of 1972–2013 in South Asian countries. The econometric and graphical analyses are found U-shape association between globalization and CO₂ emissions in Nepal, Afghanistan, Bangladesh, and Sri Lanka, and an inverted U-shape relationship is observed in Pakistan and Bhutan. Moreover, results have shown that there exists a bi-directional causality between globalization and CO₂ emissions in Pakistan, Bangladesh, and Nepal. This indicates that globalization is increasing CO₂ emissions and CO₂ emissions impact globalization by economic growth. However, after some threshold level, globalization is responsible for decreasing CO₂ emissions in Pakistan and Bhutan. For the first time, globalization is incorporated in the economic analysis, showing the U-shape and inverted U-shape associations between globalization and CO₂ emissions. This study suggests some strong policy recommendations to consider globalization as cost-effective tool to achieve sustainable economic growth in South Asian countries.