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In developing countries, like Pakistan, the pursuit of urbanization and economic development disrupts the delicate ecosystem, resulting in additional biogeochemical emissions of heavy metals into the human habitat and posing significant health risks. The levels of these trace elements in humans remain unknown in areas at higher risk of pollution in Pakistan. In this investigation, selected trace metals including Copper (Cu), Chromium (Cr), Lead (Pb) Cadmium (Cd), Cobalt (Co), Nickel (Ni), and Arsenic (As) were examined in human hair, urine, and nail samples of different age groups from three major cities (Muzaffargarh, Multan, and Vehari) in Punjab province, Pakistan. The results revealed that the mean concentrations (ppm) of Cr (1.1) and Cu (9.1) in hair was highest in Muzaffargarh. In urine samples, the mean concentrations (μg/L) of Co (93), As (79), Cu (69), Cr (56), Ni (49), Cd (45), and Pb (35) were highest in the Multan region, while As (34) and Cr (26) were highest in Vehari. The mean concentrations (ppm) of Ni (9.2), Cr (5.6), and Pb (2.8), in nail samples were highest in Vehari; however, Multan had the highest Cu (28) concentration (ppm). In urine samples, the concentrations of all the studied metals were within permissible limits except for As (34 µg/L) and Cr (26 µg/L) in Vehari. However, in nail samples, the concentrations of Ni in Multan (8.1 ppm), Muzaffargarh (9 ppm), Vehari (9.2 ppm), and Cd (3.69 ppm) in Muzaffargarh exceeded permissible limits. Overall, the concentrations of metals in urine, nail, and hair samples were higher in adults (39–45 age group). Cr, Cu, and Ni revealed significantly higher concentrations of metals in hair and water in Multan, whereas As in water was significantly (p < 0.001) correlated with urinary As in Multan, indicating that the exposure source was region-specific. [ABSTRACT FROM AUTHOR]

The primary component of air pollution and variation in global warming is deemed with carbon dioxide emissions. Livestock has an important contribution to farm production and is a backbone for every economy's economic progress. The current analysis main motive was to examine the influence of CO2 emission to livestock production in Pakistan by taking annual data that varies from 1972 to 2019. The data stationarity was rectified via unit root tests. Further, in order to investigate the association of variables via short- and long-run assessment, the autoregressive distributed lag (ARDL) model was used. Outcomes across long-run expose that the variables milk, mutton, fats, skins and blood production have positive and constructive association with CO2 emission have coefficients (6.631603), (0.385370), (10.54420), (3.546603) and (2.580877) correspondingly with probability values (0.3994), (0.9478), (0.1145), (0.5173) and (0.3550), respectively, while poultry meat, eggs, hair, hides, bones, beef and wool production have adverse interaction with carbon dioxide emission. Furthermore, outcomes via short-run also expose that milk, mutton, fats, skins, blood production exposed a constructive interaction with CO2 emission, while the variables poultry meat, eggs, hair, hides, bones, beef and wool production have an adverse interaction with CO2 emission in Pakistan. The Pakistani government needs possible conservative policies for demonising CO2 emissions in order to increase both livestock output and food production. [ABSTRACT FROM AUTHOR]

Human activities such as deforestation and cultivation contribute to the accumulation of carbon dioxide in the environment. Methane is emitted by energy exploration, coal mining, natural gas spills, waste, and waste dumps. Methane generated by such greenhouse gases has significantly contributed to the climate change and global warming. However, the most significant contributor to climate change and global warming is the use of fossil fuels. These fuels contribute to ozone depletion and global warming by emitting greenhouse gases into the atmosphere. The current study key objective was to determine the CO2 emission effect to key fruit production in Pakistan by taking time series annual data varies from 1970 to 2019. A non-linear autoregressive distributed lag model (NARDL) was employed to check the variables linkages. The consequences of short- and long-run estimates expose that the positive and adverse shocks of citrus fruit create expressively to upsurge the CO2 emission. Similarly, the positive and negative shocks of mango fruit expose an adverse interaction to CO2 emission. The positive shock of apple fruit has constructive but negative shock expose and adverse linkage to CO2 emission. Further, banana fruit also exposes a negative but constructive via positive shock linkage to CO2 emission. Moving towards the apricot fruit production that exposed a constructive linkage via positive and negative shocks to CO2 emission. The almond fruit productivity via positive and negative shocks exposed an adverse relation to carbon dioxide emission. Grape fruit via positive shock shows a construct, but negative shock exposed an adverse association to carbon emission in Pakistan. Finally the guava fruit production exposed a construct linkage to CO2 emission via positive and negative shocks. Since agricultural activities and CO2 emissions are essential to resolving the pollution problem, a series of practicable steps must be taken by the Pakistani authorities to tackle this issue. [ABSTRACT FROM AUTHOR]

Greenhouse gases are considered an immense threat for the environment and humanity on the planet, and also cause the climatic change. The present analysis key aim was to explore the effect of CO2 emission on foreign investment, renewable energy utilization, and population growth in Pakistan. The ARDL bounds testing technique was applied to investigate the variables' interaction via short- and long-run analysis. Furthermore, pairwise Granger causality method was also utilized to check the causal relation amid the study variables. Outcomes expose that CO2 emission has an adverse interaction with renewable energy with probability value (0.5497), while the variable foreign investment and population growth exposed a constructive association with carbon dioxide emission with probability values (0.3548) and (0.4217) consistently. Similarly, the results through long-run analysis expose that CO2 emission has an adverse influence to renewable energy usage with P-value (0.4646). Moreover, the results also uncovered that foreign investment and population growth has positive interaction with CO2 emission and having probability values (0.3577) and (0.5715). Solid steps are required from the Pakistani government regarding the demonization of CO2 emission in order to upsurge the economic progress. [ABSTRACT FROM AUTHOR]

Polycyclic aromatic hydrocarbons (PAHs) are carcinogenic compounds which are emitted through incomplete combustion of organic materials, fossil fuels, consumption of processed meat, smoked food, and from various industrial activities. High molecular mass and mobility make PAHs widespread and lethal for human health. A cellular system in human detoxifies these toxicants through specialized enzymatic machinery called xenobiotic-metabolizing (CYP450) and phase-II (GSTs) enzymes (XMEs). These metabolizing enzymes include cytochromes P450 family (CYP1, CYP2), glutathione s-transferases, and ALDHs. Gene polymorphisms in XMEs encoding genes can compromise their metabolizing capacity to detoxify ingested carcinogens (PAHs etc.) that may lead to prolong and elevated exposure to ingested toxicants and may consequently lead to cancer. Moreover, PAHs can induce cancer through reprograming XMEs' gene functions by altering their epigenetic markers. This review article discusses possible interplay between individual's gene polymorphism in XMEs' genes, their altered epigenetic markers, and exposure to PAHs in cancer susceptibility in Pakistan. [ABSTRACT FROM AUTHOR]

Globalization has contributed to several advances in technology including linking people around the globe and driving us to modern economies. With fast economic growth and industrialization progress, the negative impact of globalization on biodiversity can be easily ignored. Globalization is an undeniable factor in our planetary devastation from pollution to global warming and climate change. The major intention of our recent analysis was to examine the globalization, energy consumption, trade, economic growth, and fuel importation to determine the ecological footprint in Pakistan by taking the annual data variables from 1974–2017. A linear ARDL (autoregressive distributed lag) technique with limited information maximum likelihood and linear Gaussian model estimation were utilized to check the variables association. Outcomes show that in the long run, globalization, energy usage, trade, and GDP growth have consistently productive interactions with the ecological footprint, while an examination of fuel importation uncovers an adversative linkage to impacts on the ecological footprint in Pakistan. Similarly, the findings of short-run interactions also reveal that globalization, energy usage, trade, and GDP growth have constructive linkages; however, an examination of fuel importation also uncovers an adversative linkage to impacts on the ecological footprint. The outcomes of limited information maximum likelihood also expose that the variables of globalization, energy usage, trade, and fuel importation have productive linkages, while an examination the GDP growth uncovers an adversative linkage to the ecological footprint. Furthermore, the outcomes of the linear Gaussian model estimation also uncover that globalization and energy usage demonstrate a constructive linkage, while other variables reveal an adverse linkage to the ecological footprint. Environmental pollution is now an emerging issue which causes the climatic variations associated with greenhouse gases emissions. The Pakistani government must adopt new strategies to ensure that CO2 emissions are reduced in order to stimulate economic growth. [ABSTRACT FROM AUTHOR]

Highlights • Quantification of e-waste inventory and its processing discussed from Pakistan. • Local generation of e-waste in Pakistan is calculated about 38 kt per year. • The study reveals that 50 kt of e-waste is being imported as scrap to Pakistan. • This study recommends need for effective monitoring & control of informal e-waste. Abstract Rapid increase in the quantity of electronic/electrical-waste (e-waste) has become an emerging issue throughout the world. To avoid higher expenditures on safe disposal and recycling, large quantities of e-waste are being exported from developed to developing countries like Pakistan. Emerging issue of e-waste in Pakistan demands its effective management strategy for the country. However, it cannot be achieved until assessment of e-waste quantification and disposal is carried out. The main objective of this study was to quantify the e-waste inventory and it's processing from major cities of Pakistan to evaluate its generation (domestic/import) and recycling practices. This study comprises the information of only those e-waste items (desktop computers, laptops/notebooks, computer monitors and liquid-crystal display units) which form the major portion of e-waste imported to Pakistan. Survey based data collected from three major cities/areas have been extrapolated to develop an e-waste generation inventory for the country. The study reveals that approximately 50 kt of e-waste is being imported as scrap in addition to its local generation of about 38 kt per year. During field visits and data collection surveys, it has been observed that the processing of e-waste in the country is being carried out in crude manner without safety gears. Findings of our study strongly recommend dire need for urgent and effective monitoring as well as control of informal e-waste management in Pakistan. [ABSTRACT FROM AUTHOR]

Antibiotic-resistant bacteria and antibiotic resistance genes (ARGs) are pollutants of worldwide concern that seriously threaten public health and ecosystems. Machine learning (ML) prediction models have been applied to predict ARGs in beach waters. However, the existing studies were conducted at a single location and had low prediction performance. Moreover, ML models are "black boxes" that do not reveal their predictions' internal nuances and mechanisms. This lack of transparency and trust can result in serious consequences when using these models in high-stakes decisions. In this study, we developed a gradient boosted regression tree based (GBRT) ML model and then described its behavior using six explainable artificial intelligence (XAI) model-agnostic explanation methods. We used hydro-meteorological and qPCR data from the beaches in South Korea and Pakistan and developed ML prediction models for aac (6′-lb-cr), sul 1, and tet X with 10-fold time-blocked cross-validation performances of 4.9, 2.06 and 4.4 root mean squared logarithmic error, respectively. We then analyzed the local and global behavior of the developed ML model using four interpretation methods. The developed ML models showed that water temperature, precipitation and tide are the most important predictors for prediction of ARGs at recreational beaches. We show that the model-agnostic interpretation methods not only explain the behavior of the ML model but also provide insights into the behavior of the ML model under new unseen conditions. Moreover, these post-processing techniques can be a debugging tool for ML-based modeling. [Display omitted] • ARGs were detected by qPCR at recreational beaches. • ML models were developed with high prediction performance to predict ARGs. • Explainable Artificial Intelligence explained black-box models' behavior for ARGs. • Water temperature, precipitation, and tide greatly affected ARGs abundance. • Presented post-processing techniques can be a debugging tool for ML modeling. [ABSTRACT FROM AUTHOR]

Human activities such as deforestation and cultivation contribute to the accumulation of carbon dioxide in the environment. Methane is emitted by energy exploration, coal mining, natural gas spills, waste, and waste dumps. Methane generated by such greenhouse gases has significantly contributed to the climate change and global warming. However, the most significant contributor to climate change and global warming is the use of fossil fuels. These fuels contribute to ozone depletion and global warming by emitting greenhouse gases into the atmosphere. The current study key objective was to determine the CO 2 emission effect to key fruit production in Pakistan by taking time series annual data varies from 1970 to 2019. A non-linear autoregressive distributed lag model (NARDL) was employed to check the variables linkages. The consequences of short- and long-run estimates expose that the positive and adverse shocks of citrus fruit create expressively to upsurge the CO 2 emission. Similarly, the positive and negative shocks of mango fruit expose an adverse interaction to CO 2 emission. The positive shock of apple fruit has constructive but negative shock expose and adverse linkage to CO 2 emission. Further, banana fruit also exposes a negative but constructive via positive shock linkage to CO 2 emission. Moving towards the apricot fruit production that exposed a constructive linkage via positive and negative shocks to CO 2 emission. The almond fruit productivity via positive and negative shocks exposed an adverse relation to carbon dioxide emission. Grape fruit via positive shock shows a construct, but negative shock exposed an adverse association to carbon emission in Pakistan. Finally the guava fruit production exposed a construct linkage to CO 2 emission via positive and negative shocks. Since agricultural activities and CO 2 emissions are essential to resolving the pollution problem, a series of practicable steps must be taken by the Pakistani authorities to tackle this issue., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The aim of our study was to compare the surgical complications and short-term outcome of renal transplants with single and multiple renal artery grafts. We reviewed the records of 105 kidney transplantations performed consecutively at our institution from July 2006 to May 2010. The data of 33 (31.4%) renal transplants with multiple arteries were compared with the 72 transplants with single artery (68.6%), and the incidence of surgical complications, post-transplant hypertension, acute tubular necrosis, acute graft rejection, mean creatinine level, and patient and graft survival was analyzed. We further subdivided the study recipients into three groups: group A (n = 72) with one-renal-artery allografts and one-artery anastomosis, group B (n = 6) with multiple-artery allografts with single-artery anastomosis, and group C (n = 27) with multiple-artery allografts with multiple arterial anasatomosis, and compared their outcome. No significant differences were observed among the recipients of all the three groups regarding early vascular and urological complications, post-transplant hypertension, acute tubular necrosis, acute rejection, creatinine level, and graft and patient survival. The mean cold ischemia time in groups B and C was significantly higher (P <0.05). One patient in group A developed renal vein thrombosis resulting in graft nephrectomy. None of the patients with multiple renal arteries developed either vascular or urological complications. In conclusion, kidney transplantation using grafts with multiple renal arteries is equally safe as using grafts with single renal artery, regarding vascular, urological complications, as well as patient and graft survival.