This article provides an overview of the main findings of a survey on the effects of ambient temperature and humidity on vehicular emissions of criteria pollutants and greenhouse gases. The present study is focused on the emissions of Carbon Monoxide (CO), Nitrogen Oxides (NOX), Sulfur Oxides (SOX), Particulate Matters (PM), Carbon Dioxide (CO2), Nitrous Oxide (N2O), and Methane (CH4) from gasoline engine passenger cars. In this analytical research, the International Vehicle Emissions (IVE) model was operated, using long-term meteorological data to determine the effects of various ambient temperature and humidity levels on exhaust pipe pollutants and greenhouse gas emissions. The results of present study indicated that as the ambient temperature increases from -7.5 °C to 20 °C, CO, NOX, and CH4 emissions decrease by 35.8%, 6.46%, and 21.44%, respectively, while SOX, PM, CO2, and N2O emissions remain constant. In contrast, increasing the ambient temperature from 20 °C to 37.5 °C increases the emissions of all the investigated pollutants and greenhouse gases. On the other hand, the findings showed that as the ambient humidity increases from 8% to 98% CO and CH4 emissions increase by 7.3% and 2.13%, respectively; while NOX emissions decrease by 16.84%. However, humidity changes did not have noticeable impact on the emissions of SOX, PM, CO2, and N2O. This study concluded that changes in meteorological parameters over a certain period of time, not only affect global warming, but also the emissions of criteria pollutants.

Cold plasma has emerged as a powerful energy-efficient and environmentally friendly advanced oxidation technique in recent decades as a non-thermal approach in a wide range of applications. It is a form of plasma that is created at low temperatures and can be used for various applications, including water treatment This study aims to determine the influence of Cold plasma treatment on endotoxin reduction in dialysis water. A lab-scale unit was built to implement the experiments and synthetic water (feed solution) was prepared with a known level of endotoxin (0.48 EU/mL). The test for Limulus amebocyte lysate was used to assess concentrations of endotoxin in treated water. The experimental results showed reduce of endotoxins in the cold plasma treatment. This type of treatment reduced the concentration of endotoxin to 0.17 ± 0.09 EU/ml. The results of the study indicated that this could be an innovation in cold plasma jet fields, with numerous applications in dialysis fluid preparation.

Biomass extracted from different plant parts can play a role as a cheap, efficient and eco-friendly adsorbent. In this research, Lantana Camara L. Stem biomass (LSB), a low-cost and useless material, was introduced as efficient biomass for divalent zinc biosorption from aqueous environments. For achieving optimal conditions in the zinc biosorption process, the experimental design was applied by the response surface methodology (RSM) based on a Box-Behnken design (BBD) model. Based on the comparison between the measured and predicted amounts, the values of R2,Radj2, and Rpred2 in the Zn(II) biosorption model were 0.9960, 0.9887 and 0.9441. The Zn(II) uptake in the experiments, BBD model-based (p-value of Lack-of-Fit term = 0.228 > 0.05), varied from 15.19% to 81.11%. The maximum analyte uptake at a LSB-to-Zn(II) ratio of 8:1, synthetic solution pH of 6.5 and residence time of 75 min was predicted at 97.12%. The maximum Z.R.% based on the validation test performed based on the optimal predicted conditions was also obtained at 94.65%, which is 2.5% different from the model's data amount, confirming the acceptable accuracy of the quadratic model. The LSB, in optimized conditions, as a low-cost biosorbent, can be a suitable candidate with acceptable potential for heavy metals biosorption from aquatic solutions.

The present study aims at determining the geotechnical properties of the tailings and the natural bed at Iran Mineral Processing Company, Sites 1 and 5. It qualitatively studies the subsurface layers of the company’s tailings storage site. After drilling different boreholes and conducting in-situ tests, it has made laboratory analyses in the form of field exploration to determine the geotechnical parameters of the extracted samples. Results from the analyses show the permeability coefficient of the subsurface layer of Site No. 1 and 5 to be very small, in the range of 10-7 cm/sec. Considering the conformity of permeability coefficient, percentage of fine grains (98% to 99%), plasticity index (28.5-29.5), and clay content of different layers of Sites 1 and 5 (68%-80%), based on the compacted clay liner criteria, it can be concluded that by nature, the subsurface layers of the mentioned sites are sealed with no need for any compacted clay liner. The tailings for storage Site 5 are fine-grained (80-88<75mm), basically in ML range according to USCS system, with a permeability coefficient of about 10-6 cm/sec. Therefore, the tailings themselves act as a relatively primary sealing layer against the infiltration of hazardous leachates into the natural bed. The method, used in the process of site selection of tailings storage facilities (TSF), can cut the construction time as well as the expenditures, thus reducing the production costs in the long run.

The present study evaluates the water quality of Dez River, a river 23 km long, via QUAL2Kw model, based on simulation of DO and BOD5 p98arameters, through considering water quality standards during six months in three stations of Kashefieh, Pole-Panjom, and Hamidabad. To determine the model’s validity and compare the observational data, the paper uses the square mean square error (RMES) and the squared mean square error coefficient (CV). The achieved results of the model largely indicate the actual conditions of the river, which represent the ability of QUAL2Kw model to simulate qualitative parameters. The main contamination of Dez River comes from municipal wastewater, either directly imported by river residents or collected by urban canals. It, then, enters the river at a certain point. Based on the simulation and observational results of DO at two stations of 5th and Hamidabad Bridge in all months of sampling, it is below 5 mg/L, regarded a threat to aquatic life. In addition, BOD5 parameter goes beyond 6 mg/L in Hamidabad station, being a threatening factor for aquatic life in this station. Critical conditions of Dez River, low discharge, and high loading of pollutants have increased the concentration of water quality parameters. Given the results of RMSE and CV parameters, the model has had the best conformity for DO parameter, followed by BOD5.

Atmospheric models today represent all significant aerosol components. Atmospheric aerosols play an important role in the air, globally through their action on the Earth's radiation balance and locally through their effects on health in heavily polluted areas, they vary considerably in their properties that affect the way they absorb and disperse radiation, and they can thus have a cooling or warming effect, they impact on the formation and life of clouds is one example. Among the main sectors of activity releasing emissions of PM10 (fine particles with a diameter of less than 10 µm) and a diameter of less than 2.5 µm (PM2.5) is the industrial sector, in particular the extraction industry of building materials. The aerosols emitted by this type of industry are composed mainly of a mixture of dust, sulphates, carbon black and nitrates, is clearly perceptible in many continental regions of the northern hemisphere. Improvements in in situ, satellite and surface measurements are needed. However, the mechanisms by which aerosols interact with the environment are extremely complex and still poorly understood. This study is based on satellite atmospheric models to have spatiotemporal variability of concentrations of fine particles smaller than 10 µm in diameter (PM10) and smaller than 2.5 µm in diameter (PM2.5) at the level of the western Rif part of Morocco, home to a large number of extraction quarries and thus offering a high-resolution particle capture system (PM10 and PM2.5).

Global fire cases in recent years and their vast damages are vivid reasons to study the wildfires more deeply. A 25-year period natural wildfire database and a wide array of environmental variables are used in this study to develop an artificial neural network model with the aim of predicting potential fire spots. This study focuses on non-human reasons of wildfires (natural) to compute global warming effects on wildfires. Among the environmental variables, this study shows the significance of temperature for predicting wildfire cases while other parameters are presented in a next study. The study area of this study includes all natural forest fire cases in United States from 1992 to 2015. The data of eight days including the day fire occurred and 7 previous days are used as input to the model to forecast fire occurrence probability of that day. The climatic inputs are extracted from ECMWF. The inputs of the model are temperature at 2 meter above surface, relative humidity, total pressure, evaporation, volumetric soil water layer, snow melt, Keetch–Byram drought index, total precipitation, wind speed, and NDVI. The results show there is a transient temperature span for each forest type which acts like a threshold to predict fire occurrence. In temperate forests, a 0.1-degree Celsius increase in temperature relative to 7-day average temperature before a fire occurrence results in prediction model output of greater than 0.8 for 4.75% of fire forest cases. In Boreal forests, the model output for temperature increase of less than 1 degree relative to past 7-day average temperature represents no chance of wildfire. But the non-zero fire forest starts at 2 degrees increase of temperature which ends to 2.62% of fire forest cases with model output of larger than 0.8. It is concluded that other variables except temperature are more determinant to predict wildfires in temperate forests rather than in boreal forests.

A large anthropogenic source of mercury pollution is mercury-dependent artisanal and small-scale gold mining (ASGM). Thabeikkyin Township is a small-scale gold mining township located in Pyin Oo Lwin District in the Mandalay Region, Myanmar. The villages of Thabeikkyin Township engage in gold ore crushing, screening, refining, and other mining activities for a living. Miners in this area commonly use mercury for gold recovery by heating amalgam at their homes, gold shops, on the street, and near the riverbank. The evaporated mercury is released into the atmosphere during the heating process and then transported and deposited in the surrounding environments, resulting in the mercury pollution of air, water, soil, etc. To assess atmospheric mercury pollution, a preliminary study on the environmental mercury contamination from ASGM was conducted in Thabeikkyin Township. High mercury concentrations were observed in plant samples collected near the refining sites, ranging 0.33–6.51 ug/g, compared with 0.02 ug/g in Wet Thay Village, where no mercury use was reported. Correlation coefficients between Hg and other heavy metals showed that no heavy metal concentration significantly correlated with that of Hg. The results indicated that the atmospheric environment in the ASGM area of Thabeikkyin Township was contaminated with mercury originating from ASGM, which could very likely deteriorate the health of surrounding residents.

Organophosphates are one of the most common pesticides in the world. Among them, one can find malathion that is classified as carcinogenesis, and, as a result, should be appropriately removed since it is highly consumed and possesses a lot of pathogenicity. So far, several processes have been used to remove malathion from aqueous media. The present study investigates its removal by means of Fe3O4 iron oxide nanoparticles. Based on experimental-laboratory studies, using the Response Surface Methodology (RSM), the impact of independent variables such as pH, iron oxide nanoparticle concentration, and contact time on malathion removal efficiency have been investigated. Results show that the pH of the solution is the most important and effective parameter in the process. Optimal conditions of malathion removal based on the appropriate model, obtained from RSM, include 0.4 g/L iron oxide nanoparticles, pH of about 5 (acidic conditions), and contact time of about 1 h with ultraviolet radiation being equal to 82% malathion removal. The process, used in this study, can remove malathion from aqueous solutions according to the so-called conditions, and changing the laboratory conditions can effectively remove it. This process can also be recommended as an economic and scientific method to remove malathion from drinking water.

In this work, Fe3O4-SiO2 nanoparticles were synthesized, characterized, and applied as adsorbent material to remove methyl blue stain from an aqueous solution. The prepared nanoparticles were characterized by X-ray diffraction (XRD), scanning electron microscope (SEM), transmission electron microscope (TEM), and Brunauer–Emmett–Teller (BET) to determine the physical surface properties and correlate them to the adsorption efficiency. In addition, this study investigated the influence of several parameters on the removal percentage and adsorption capacity. Specifically, this study investigated the impact of changing the following parameters: pH (1 – 8), agitation speed (Uspeed; 100 - 350 rpm), initial methyl blue (MB) concentration (1 - 100 mg/L), adsorbent dose (0.05 to 0.15 g), and contact time (10 - 100 min). The characterization study reveals that the prepared material has an excellent surface area (385 ± 5 m2/g) and pore volume (0.31 cm3/g) which enhances the adsorption capacity. In addition, the prepared material showed excellent efficiency where the removal percentage reached 99.0±1% at the optimal operating conditions and the maximum adsorption capacity was 40 mg/g. This study delivers a full elucidation of the adsorption of MB dye by Fe3O4-SiO2 NPs which considers a promising inexpensive adsorbent. It also delivers important insight information about the adsorption process and the influence of each parameter, which fill the lack in this field.