The use of plastics as a biofilter medium is an environment-friendly and effective technology for reducing pollutants in liquid waste. The main objective is to analyze the ability of biofilters with plastic media to remove pollutants in wastewater by looking at several parameters. Various types of data were developed and analyzed to answer specific goals set through the search engines EBSCO, Scopus, and ProQuest by examining several parameters, including wastewater source, research scale, research period, temperature, media type, media thickness, and pollutant removal. The obtained data were processed to determine the distribution of the descriptions. Data related to biofiltration using plastic media was obtained from 152 articles, with only 14 articles in the search category. These findings show that all types of plastic media are effective for biofilm attachment and bacterial growth, resulting in a very large removal of pollutants present in liquid waste. Biofilters with plastic media are also known to be able to remove contaminants such as Chemical Oxygen Demand, biological oxygen demand, total organic carbon, nitrogen, phosphorus, ammonia nitrogen, hydrogen sulfide, toluene, ammonia, diethanolami, phenol, total suspended solids, and Escherichia coli. Synthetic wastewater (35.71%) was the most common wastewater source. Research related to biofiltration using plastic as the medium is mostly carried out on a laboratory scale with a total of 64.30% and using units of the day as an indicator of changes in a total of 71.42%, with an average experimental temperature of 29.1 °C.

The study was conducted in Lake Rinconada, a glacial lake affected by artisanal and small-scale gold mining activities in the southern Andes in Peru. The objectives of the study were to investigate the spatial distribution of heavy metals (As, Cu, Hg, Pb and Zn) in water and sediments and to assess the degree of metal pollution and ecological risk using the geoaccumulation and potential ecological risk indexes. The concentrations of As and Hg in sediments from Lake Rinconada exceeded the Canadian sediment quality regulations, whereas the concentrations of As, Hg and Pb in water and sediments from the mining-affected tributary, Lunar de Oro River exceeded the Peruvian and Canadian guidelines for water and sediments quality respectively. According to the geoaccumulation and potential ecological risk indexes, Lake Rinconada is extremely polluted by As and Hg, and the pollution is mostly concentrated in the northern part of the lake, where the mining-affected Lunar de Oro River flows into the lake. Concentrations of Pb are also high in the northern part of the lake, suggesting that the nearby gold mining town is a source of pollution. The results of this study allows to report that Lake Rinconada is completely deteriorated.

During the COVID-19 pandemic large numbers of single-use, surgical style face masks were lost or discarded in public spaces, primarily in on public streets and car parking settings. Many of these masks were blown onto the road surfaces where they were subjected to degradation through the tire impact of passing vehicle traffic. As series of field observations as well as experimental simulations show that the three-ply polypropylene mask fabric is subjected to shear forces when compressed between the tire and the road surface. The mechanical action breaks the bonds between the fibers (both spunbonded and meltblown) leading to a continual shedding of microfibers. Wind disperses these into the environment along road sides, while surface water action moves them into stormwater drains and from there into the waterways. As the decay is rapid, municipal agencies only have a short window of time to remove stray face masks from the urban environment if micro-fiber pollution is to be reduced.

The presence of volatile organic compounds in the indoor environment and their unwanted effects on human health are inevitable. That's why different methods have been proposed to remove them from air. The present study examines using photocatalytic reaction system along with TiO2 particles coated on stainless steel webnet to study direct conversion of toluene using a new design. The study was carried out using UV radiation in a dynamic concentrator system. SEM and XRD analyses were performed to characterize prepared catalysts. Here, the aim was to employ photocatalytic oxidation (PCO) to optimize removal efficiency and elimination capacity using response surface methodology (RSM). To this end, initial concentration and flow rate were selected as independent variables. High removal efficiency and elimination capacity were realized using optimal settings. The findings indicated that PCO process with a new design other than RSM was an option to treat air pollution containing volatile organic compounds.

The pollution generated by metallic trace elements discharged by mines into the environment can become a very worrying source of contamination for soil, water and plants. The characterization of the chemical properties of metals in mine tailings and soils is of crucial importance to assess the risk of their potential mobility and therefore their bioavailability. In this paper, the bioavailability of metallic trace elements  in agricultural soils in the vicinity of the Draa Lasfar mine in the northwest of Marrakech city (Morocco) was studied by determining the contents of Cd, Cu, Pb and Zn in soils and in two plants: wheat (main food for the human population) and couch grass (main food for livestock). The results showed that these metals move from agricultural land to plants. They also showed that couch grass seems to strongly absorb and accumulate metallic trace elements present in the soil; it removes considerable amounts of metallic trace elements from the soil with its deeply penetrating root system.

The present study aims to provide baseline information on the temporal characteristics of PM2.5 and PM10 concentration time series variations, mainly on the cross-correlation between PM2.5 and PM10, using the improved mathematical and nonlinear methods. Firstly, the fractal theory such as fractal dimension is used to detect the pollution level in PM2.5 and PM10 time series. Secondly, the Multifractal Detrending Moving-Average Analysis (MFDMA) is used to analyze the multifractal characteristics of PM2.5 and PM10 concentrations. Thirdly, Multifractal Detrending Moving-Average cross-correlation Analysis (MFXDMA) is used to study the cross-correlation between PM2.5 and PM10 concentrations measured from January 1 to December 31, 2020, along the Boulevard de la Marina, one of the major roads in Cotonou. The results have indicated that: (1) PM10 and PM2.5 concentration time series are characterized by a fractal dimension, which can permit to detect the pollution levels and to analyze the differences in emissions sources; (2) there is a significant multifractal structure in the PM2.5 and PM10 concentration data and their fluctuations are long-range correlated, however, the multifractal properties and self-memory characteristics change with the months; (3) generally, the multifractal degree and the complexity of PM10 are much stronger than those of PM2.5. However, they present a similar multifractality degree in some months of the year; (4) except, in February, the cross-correlation between PM2.5 and PM10 time series in the months of the year presents multifractal characteristics with positive persistence; (5) the cross-correlation multifractal features show monthly variation. This paper provides the inter-relationship between air PM2.5 and PM10 time series which may help taking steps in controlling the air quality and management of the Cotonou port area environment.

Evidence of the health impact of air pollution in Morocco is scarce. We aimed to test our hypothesis that exposure to air pollutants has a significant impact on the health of Moroccans. For this systematic review, we searched PubMed, ScienceDirect, LILACS, and ProQuest databases, Google Scholar, and forward and backward citations for studies published between the database inception and August 16, 2022. All studies and reports that measure air quality in Morocco and its health impact were included, without language restrictions. This study is registered on PROSPERO under number CRD42020163948. Studies were selected based on inclusion and exclusion criteria rather than their methods. The data was extracted, coded, and prepared for future examination. After that, descriptive and thematic analyses were carried out. Of 1230 records identified, 31 were eligible, all of which had annual air pollutant concentrations in excess of WHO Air Quality Guidelines. The health impact was demonstrated in five studies. The most studied pathologies were asthma, respiratory and cardiac infections in children under 12 years and adults. In addition to heavy metals, the most investigated pollutants were PM10, O3, SO2, and NOx. The significant association between exposure to air pollutants and health in the Moroccan population has been demonstrated, even if it is not causal. Future research should quantify the health impact of pollution in other Moroccan cities.

BTEX is one of the common compounds in the breathing air of gas station workers, which can cause high carcinogenic and health risks. The present study was conducted to assess the carcinogenic and health risks of occupational exposure to BTEX compounds in gasoline fuel distribution stations in Karaj. This descriptive and cross-sectional study was conducted to assess the carcinogenic and health risks caused by exposure to BTEX compounds in 2021 during the summer and winter in six fuel distribution stations in Karaj. Occupational exposure to BTEX was measured according to the NIOSH 1501 method. Cancer and non-cancer risk assessment were performed according to the United States Environmental Protection Agency (USEPA) method. Data were analyzed in SPSS software version 26. The average occupational exposure to benzene, toluene, ethyl benzene, and xylene during a work shift among all participants in summer and winter were 83.33 - 89.33, 202 - 210.66, 126.55 - 136.83, and 168.81 - 174.83 µg.m-3, respectively. The highest concentration of BTEX compounds was observed in Gas station in the center of the city. The mean carcinogenic risk value of benzene and ethylbenzene were 139×10-2 and 27×10-2, respectively. The highest carcinogenic risk value due to exposure to benzene and ethyl benzene was observed in Gas station in the center of the city. The mean non-carcinogenic and health risks of occupational exposure to benzene, toluene, ethyl benzene, and xylene were 173.79, 14.19, 3.61, and 12.87, respectively.  The findings demonstrated the values of carcinogenic and non-carcinogenic risk in the majority of participants were within the definitive and unacceptable risk levels. Therefore, corrective measures are necessary to protect the employees from the non-cancer and cancer risks.

Effect of salinity and oxidation-reduction potential (ORP) in the mobility of metals bound to suspended sediments at estuarine zone is investigated. Saline and freshwater samples as well as suspended sediments from estuarine zone of the Chalus River and the Caspian Sea, have been collected. Two series of four aquaria sets (natural and ORP-augmented conditions) containing turbid water with salinities of 0.25, 0.75, 1.5 and 2.5 psu were arranged. An increasing pattern of exchangeable-phase of all studied metals contents (at higher salinities) was observed under natural and ORP-modified conditions. Furthermore, the exchangeable-phase metal contents under ORP-modified conditions are higher (or equal) when compared with natural conditions. The overall trend of metals mobility potential might be evaluated as: Cd > Pb > Mn > Cu > Zn > Co > Ni. Findings of this research confirm the direct effect of both salinity and ORP parameters in mobility of metals bound to suspended particles in estuarine zones.

Researchers are interested in developing techniques to monitor, manage and predict the risks of gases and particles emitted from cement factories, which have a direct and negative impact on human health. Deep learning (DL) is a critical component of data mining, which further involves statistics and prediction. In this study, we developed a deep learning prediction model called the Deep Pollutant Prediction Model (DPPM). The data used for DPPM are separated into two types: observed data from a pollution monitoring station of the Institute of Mental Health in Ahmedabad City, India coded as (GJ001), to validate the model and simulated data generated using the Gaussian Plume Model for the hypothetical receptor (Laylan District, Kirkuk, Iraq) to predict the pollution that emitted from Kirkuk Cement Plant 5 km apart from the study area. The findings indicated that the DPPM has high efficiency in both Allahabad and Laylan stations, with more closed results for the data in the Laylan station, which is based on the Gaussian equation simulated data. Since the highest loss function value in the Laylan is 0.0221 of the CaO parameter, while it is 4.466 of the AQI parameter for the Allahabad Station, and the smallest loss function value in the Laylan is equal to 0.0041of both Fe2O3 and MgO parameters, it corresponds to 0.038 of Xylene for the Allahabad station. The results of the study proved that data continuity and non-volatility produce excellent outcomes for DPPM.