The rapid pace of economic growth and urbanization in China affects both large and small cities of the country, causing an increase of pollutant concentrations in the air. The South Gobi is one of the main deserts and semidesert regions of the country; therefore, the study of air pollution near the potential source of natural aerosols is of great importance. Data obtained in the period from 1 January 2016 to 31 December 2019 was used to analyze spatial-temporal characteristics of atmospheric pollutants (PM₂.₅, PM₁₀, SO₂, NO₂, and CO) in eight cities. Total mean concentrations of PM₂.₅ and PM₁₀ were 36.1 ± 21.1 μg/m³ and 98.6 ± 108.7 μg/m³. The occurrence rates of concentrations exceeding the Chinese National Ambient Air Quality Standard (CNAAQS) grade 1 and grade 2 were 40.1% and 5.4% for PM₂.₅ and 82.9% and 11.64% for PM₁₀ in the region. Total concentrations of SO₂, NO₂, and CO did not exceed the CNAAQS standard and were 20.8 ± 23.6 μg/m³, 22.6 ± 11.9 μg/m³, and 0.72 ± 0.39 mg/m³, respectively. The PM₂.₅ to PM₁₀ ratio increased from 0.35 in spring to 0.46 in winter suggesting the predominance of coarse aerosol fractions in the atmosphere. Based on data on aerosol optical depth (AOD) and Ångström exponent (AE) ratio obtained from Moderate Resolution Imaging Spectroradiometer (MODIS), the predominant aerosol types in the region are Clean Сontinental and Mixed. Maximum concentrations of pollutants and the highest AOD values in the region air are observed in spring and winter. Results set forth in this article will be an important basis for further regional studies on air quality and distribution of sources.

Animal-Vehicle Collision (AVC) is a predominant problem in both urban and rural roads and highways. Detecting animals on the road is challenging due to factors like the fast movement of both animals and vehicles, highly cluttered environmental settings, noisy images, and occluded animals. Deep learning has been widely used for animal applications. However, they require large training data; henceforth, the dimensionality increases, leading to a complex model. In this paper, we present an animal detection system for mitigating AVC. The proposed system integrates sparse representation and deep features optimized with FixResNeXt. The deep features extracted from candidate parts of the animals are represented in a sparse form using a feature-efficient learning algorithm called Sparse Network of Winnows (SNoW). The experimental results prove that the proposed system is invariant to the viewpoint, partial occlusion, and illumination. On the benchmark datasets, the proposed system has achieved an average accuracy of 98.5%.

To mitigate environmental problems and to achieve sustainability, China is striving to transition to low-carbon urban economies. Among several significant steps, the country has made remarkable success in controlling the emissions from transportation, buildings, and energy by shutting down or relocating several polluting industries. This study contributes to the issue of sustainable growth debate using time series data from China for the period 1998–2017 and empirically examines the effects of green investment and renewable energy consumption on production-based carbon emissions for China. The strength of this study is that it tested some new variables such as production-based carbon emissions and green investment. Using autoregressive distributed lag model (ARDL) cointegration technique, we found that production-based emission and its determinants move together in the long run. The study found that green investment and renewable energy consumption are both helpful in controlling production-based carbon emissions, while trade openness increases production-based carbon emissions. Hence, green investment and renewable energy consumption contribute to the achievement of sustainable growth. Moreover, based on a robustness check, human capital, financial development, and environment-specific technological innovation are found to be helpful in curbing production-based carbon emissions. Our study recommends financial technology (fin-tech), green investment, and public-private partnership investment in renewable energy to mitigate the effect of production-based carbon emissions.

The current research outlines the course of eutrophication processes emerging when some critical physical and chemical factors interact altogether. For this purpose, investigations were carried out, where nitrogen [N as (NH₄)₂HPO₄ and KNO₃] and phosphorus [P as (NH₄)₂HPO₄] were added to three different water sources (double distilled water, DDW; tap water, TW; and lacustrine water, LW) and the solutions were incubated at two distinct temperatures (17 and 23 °C). Treatments were kept in 1 dm³ glass jars and the incubation time lasted 7 weeks. The eutrophication process emerged only at 23 °C and was stronger for the lacustrine water (LW). In the case of DDW treatments, this process was observed at N/P = 5.1 and even at 60.0, whereas for the TW, no algal blooming was detected (N/P ratio 17.7–640.0). The lacustrine water (LW) outlined patterns with strong eutrophication at N/P = 4.40, but also at ratios 20.9–71.1. Algal blooming significantly intensified according to LW > TW > DDW but was reversely dependent on the P/N ratios, which followed the range DDW (P/N, 1.6–3.78) > TW (P/N, 0.050–0.100) > LW (P/N, 0.016–0.023). At P = constant (P = 0.10 mg dm⁻³) and the N inputs varying from 0.010 to 2.0 mg dm⁻³, it appeared that the higher the N concentrations, the more intensive the eutrophication process. For N/P ratios, phosphorus regulated for most of the intensity of the process, whereas in the case of P/N, the role of N and P was interchangeable. The main finding of the research is that nitrogen revealed in many cases to be a powerful eutrophication-regulating factor than did phosphorus.

Twenty daily time step–based SWAT simulation models for the Duhok, Adhaim and Dokan dam watersheds, in Iraq, were implemented using five land cover (LC) and digital elevation model (DEM) of different resolutions. The optimal LC and DEM for computing the most accurate streamflow for each watershed were specified. Results indicated that delineation of the flat watersheds is significantly affected by the DEM resolution and there was no evident trend on the computation of watersheds’ total areas, boundaries, number of subbasins and stream networks. Moreover, there is no significant trend between the increase in LC and DEM resolutions and accuracy of the computed streamflow. The most accurate streamflows for the Duhok, Adhaim and Dokan watersheds were computed using LC (DEM) of 30 m, 1000 m and 1000 m.

There is limited information on the effect of characteristics of soil in the arid regions on the transport of pollutants. Al-Qurain landfill site is located about 1.5 km from the Arabian Gulf shoreline in Kuwait. The site was operated as an open dump and was designed without a liner or leachate collection system. This study was performed after 35 years of the site closure to investigate the quality of soil, leachate, and groundwater in its vicinity. A total of 25 test boreholes and 17 observation wells were used for the investigation. Analysis of soil, leachate, and groundwater samples shows high concentrations of organics (COD), nutrients (nitrogen, phosphorus), heavy metals, and minerals. Aging of the landfill has its impact on performance of landfill which was reflected on the characteristics of soil, leachate, and groundwater. The principal pollutants in leachate were COD, ammonia, and heavy metals as landfill age increased. A good indicator of landfill aging was the very low (0.04) BOD/COD ratio and BOD decay rate constant of 0.027/year. Water samples were also heavily polluted and their organic content ranged between 9 and 255 mg/L as BOD and 38 and 15,052 mg/L as COD. Solids levels in the water samples from the observation wells were generally higher than those typically reported in groundwater. The results showed that the mobility of the heavy metal was largely element-dependent. The organic content of the landfill played an important role in such mobility. This suggests segregation of waste disposed in landfills and replacing the open dumps by sanitary landfills to avoid such high long-term pollution that affects the site rehabilitation plans.

Arsenic (As) toxicity is a global phenomenon, and it is continuously threatening human life. Arsenic remains in the Earth’s crust in the forms of rocks and minerals, which can be released into water. In addition, anthropogenic activity also contributes to increase of As concentration in water. Arsenic-contaminated water is used as a raw water for drinking water treatment plants in many parts of the world especially Bangladesh and India. Based on extensive literature study, adsorption is the superior method of arsenic removal from water and Fe is the most researched periodic element in different adsorbent. Oxides and hydroxides of Fe-based adsorbents have been reported to have excellent adsorptive capacity to reduce As concentration to below recommended level. In addition, Fe-based adsorbents were found less expensive and not to have any toxicity after treatment. Most of the available commercial adsorbents were also found to be Fe based. Nanoparticles of Fe-, Ti-, Cu-, and Zr-based adsorbents have been found superior As removal capacity. Mixed element-based adsorbents (Fe-Mn, Fe-Ti, Fe-Cu, Fe-Zr, Fe-Cu-Y, Fe-Mg, etc.) removed As efficiently from water. Oxidation of AsO₃³⁻ to AsO₄³⁻and adsorption of oxidized As on the mixed element-based adsorbent occurred by different adsorbents. Metal organic frameworks have also been confirmed as good performance adsorbents for As but had a limited application due to nano-crystallinity. However, using porous materials having extended surface area as carrier for nano-sized adsorbents could alleviate the separation problem of the used adsorbent after treatment and displayed outstanding removal performances.

Biological methods offer eco-friendly and cost-effective alternatives for the synthesis of silver nanoparticles (AgNPs). The present study highlights a green process where AgNPs were synthesized and optimized by using silver nitrate (AgNO₃) and the aqueous extract of Piper betle (Pbet) leaf as the reducing and capping agent. The stable and optimized process for the synthesis of Pbet-AgNPs was exposure of reaction mixture into the sunlight for 40 min, pH 9.0, and 2 mM AgNO₃ using 1:4 diluted Pbet leaf aqueous extract. The optimized Pbet-AgNPs were characterized by UV–visible spectroscopy, high-resolution field emission scanning electron microscopy (FE-SEM), X-ray diffractometry (XRD), and Fourier-transform infrared spectroscopy (FTIR). The prepared Pbet-AgNPs were spherical in shape with size in the range of 6–14 nm. These nanoparticles were stable for 6 months in aqueous solution at room temperature under dark conditions. The biogenic synthesized Pbet-AgNPs are found to have significant antifungal activity against plant pathogenic fungi, Alternaria brassicae and Fusarium solani. Synthesized Pbet-AgNPs potentially reduced the fungal growth in a dose-dependent manner. Microscopic observation of treated mycelium showed that Pbet-AgNPs could disrupt the mycelium cell wall and induce cellular permeability. Protein leakage assay supports these findings. Overall, this study revealed the efficacy of green synthesized AgNPs to control the plant fungal pathogens. Pbet leaves are a rich source of phenolic biomolecule(s). It was hypothesized that these biomolecule(s) mediated metal reduction reactions. In this context, the present work investigates the phytobiomolecule(s) of the aqueous extract of Pbet leaves using high-resolution liquid chromatography–mass spectroscopy (HR-LCMS) method. The analysis revealed that eugenol, chavicol, and hydroxychavicol were present in the Pbet aqueous extract.

The rapid development of industrial sector has increased the heavy metal pollution issue recently, as the need of various metals is increasing for manufacturing purpose. These metals are the natural components that can be found in soil, but contamination happens when the concentration of these metals are high in soil due to anthropogenic activities. Several remediation techniques such as physical method, thermal desorption, chemical, and electrokinetic remediation are used to remediate the soil contaminated by heavy metals recently. As these remediation technologies have limitation on cost, effectiveness, and environmental friendly remediation issue, phytoremediation is then attracting the attention from various researchers due to its advantages of efficient, cost-effective, and eco-friendly remediation method. The mechanisms of phytoremediation are phytoextraction, phytostabilization, phytovolatilization, phytodegradation, phytodesalination, rhizofiltration, rhizodegradation, and phytoevaporation. However, these mechanisms were affected by several factors such as the plant species, properties of medium, bioavailability of metal, and the addition of chelating agent. The type of plant utilized for phytoremediation (metallophytes) is categorized as metal indicators, metal excluders, and metal hyperaccumulators. This review article comprehensively discusses the source and effect of heavy metal on human health as well as phytoremediation techniques and mechanism during the heavy metal removal.

Storage of greywater is controversial for environmental and health reasons. Artificial greywater was assessed after 2 and 7 days of storage time. Two different greywater pollutant strengths were statistically compared at each storage time. A negative significant (p < 0.05) correlation was evident with increasing storage time for the 5-day biochemical oxygen demand for more than 2 days. However, the concentrations of 5-day biochemical oxygen and chemical oxygen demands reduced significantly at 2 days of storage when compared with freshly prepared greywater. Biodegradability (5-day biochemical oxygen demand/chemical oxygen demand ratio) decreased significantly after storage to between 0.14 and 0.39. The nitrification process was improved significantly with increasing storage time concerning low strength greywater with a significant increase in the removal of ammonia-nitrogen and a non-significant decrease in the removal of nitrate-nitrogen. The correlation was significantly positive between ammonia-nitrogen and 5-day biochemical oxygen demand for stored greywater, while it was significantly negative between total suspended solids and both 5-day biochemical oxygen demand and dissolved oxygen. Significant reductions in colour, total suspended solids and turbidity were correlated positively with storage time. Precipitation of dissolved metals was suspected to occur in storing greywater by binding the inorganic components with the sediment and collide surfaces through adsorption, allowing a significant drop in concentrations of dissolved and undissolved metals with increasing storage time through sedimentation. Synthetic greywater of low mineral pollution had significantly higher removals for almost all concentrations compared with those for high concentrations. More advanced technologies for high trace element removal are required.