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Nanomaterials (NMs), both natural and synthetic, are produced, transformed, and exported into our environment daily. Natural NMs annual flux to the environment is around 97% of the total and is significantly higher than synthetic NMs. However, synthetic NMs are considered to have a detrimental effect on the environment. The extensive usage of synthetic NMs in different fields, including chemical, engineering, electronics, and medicine, makes them susceptible to be discharged into the atmosphere, various water sources, soil, and landfill waste. As ever-larger quantities of NMs end up in our environment and start interacting with the biota, it is crucial to understand their behavior under various environmental conditions, their exposure pathway, and their health effects on human beings. This review paper comprises a large portion of the latest research on NMs and the environment. The article describes the natural and synthetic NMs, covering both incidental and engineered NMs and their behavior in the natural environment. The review includes a brief discussion on sampling strategies and various analytical tools to study NMs in complex environmental matrices. The interaction of NMs in natural environments and their pathway to human exposure has been summarized. The potential of NMs to impact human health has been elaborated. The nanotoxicological effect of NMs based on their inherent properties concerning to human health is also reviewed. The knowledge gaps and future research needs on NMs are reported. The findings in this paper will be a resource for researchers working on NMs all over the world to understand better the challenges associated with NMs in the natural environment and their human health effects.

Slow sand filtration (SSF) has been widely used as a means of providing potable water due to its efficacy, low cost, and minimal maintenance. Advances in analytical instrumentation have revealed the occurrence of pharmaceutically active compounds (PhACs) in surface water as well as in groundwater. It is unclear if the presence of these compounds in the feed water can interfere with the performances of an SSF unit. The aim of this work was to examine i) the ability of two SSF units to remove six PhACs (caffeine, carbamazepine, 17-β estradiol [E2], estrone [E1], gemfibrozil, and phenazone), and ii) the impact of these PhACs on the removal of bacteria by two SSF units. The presence of PhACs in feed water for SSF can occur in surface waters impacted by wastewater or leakage from sewers and septic tanks, as well as in developing countries where unregulated use and improper disposal are prevalent. Two pilot-scale SSF units were used during the study. Unit B1 was fed with stream water with 1% of primary effluent added, while unit B2 was fed with stream water alone. Although limited removal (

The objective of this study was to investigate the effect of temperature, oxygen, and organic matter on the removal of selected pharmaceutically active compounds (PhACs) during simulated riverbank filtration (RBF). The behavior of six PhACs (caffeine, carbamazepine, 17-β estradiol [E2], estrone [E1], gemfibrozil, and phenazone) was evaluated by small flow-through column experiments. Results from our study showed that RBF can be used to treat many of the PhACs found in environmental waters. Local conditions at the RBF site, however, can affect the removal of PhACs and should be investigated. Biodegradation and sorption represented the predominant mechanisms involved during the removal of the selected PhACs. All selected PhACs showed limited and slower removal during the winter. Phenazone was highly impacted by the level of oxygen; complete depletion of phenazone below the analytical limit occurred only under aerobic conditions (dissolved oxygen8 mg L(-1)). Caffeine and E2 were highly impacted by the presence of humic acid in the feed water. Caffeine and E2 were depleted below the detection limit in the presence of humic acid regardless of the temperature and the level of oxygen. E1 was impacted by the different environmental conditions and depletion below the detection limit occurred only during the summer under aerobic conditions. Carbamazepine (10%) and gemfibrozil (30%) showed limited removal regardless of the different levels of temperature, oxygen and humic acid.

Arsenical herbicides were used extensively for emergent weed control in Hawaiian sugar cane cultivation from 1913 to about 1950. As a result, surface soil arsenic concentrations average 280 mg kg − 1 across more than 60 km 2 of former sugar plantation land in the eastern portion of the Island of Hawaii. This study was conducted to elucidate the relationship between soil properties and arsenic bioaccessibility in the iron-rich volcanic soils. Soils are predominantly Andisols, formed by weathering of basaltic lava and tephra, with pedogenic solid phases consisting of short-range order iron oxyhydroxides, allophane-like aluminosilicates, and metal-humus compounds. These reactive solid phases strongly adsorb oxyanions, such as phosphate and arsenite/arsenate. High arsenic sorption capacity limits desorption and vertical migration within the soil column and prevents contamination of the underlying groundwater aquifer, despite high arsenic loading and precipitation rates. In vitro arsenic bioaccessibility, as measured by the SBRC gastric-phase test, ranges from 2% to 35% and averages 9% of total arsenic. Bioaccessible arsenic is higher in less weathered soils (Udifolists, Typic and Lithic Hydrudands) and lower in more weathered ash-dominant soils (Acrudoxic Hydrudands). Soil weathering indicators, such as reactive iron content, are strong predictors of arsenic bioaccessibility. Based on evidence from soil mineralogy, geochemistry and arsenic speciation, as well as limited soil arsenic bioavailability/bioaccessibility comparisons, risks to human health from direct contact (soil ingestion) are significantly reduced by low arsenic bioaccessibility. Nonetheless, some soils within former sugar cane cultivation areas contain bioaccessible arsenic concentrations exceeding Hawaii Department of Health risk-based action levels, and will require mitigating actions. Even higher levels of soil arsenic contamination have been identified at former pesticide storage and mixing areas, but are generally of localized extent.

In this study, a feed-forward back-propagation neural network (BPNN) was developed and applied to predict pesticide concentrations in groundwater monitoring wells. Pesticide concentration data are challenging to analyze because they tend to be highly censored. Input data to the neural network included the categorical indices of depth to aquifer material, pesticide leaching class, aquifer sensitivity to pesticide contamination, time (month) of sample collection, well depth, depth to water from land surface, and additional travel distance in the saturated zone (i.e., distance from land surface to midpoint of well screen). The output of the neural network was the total pesticide concentration detected in the well. The model prediction results produced good agreements with observed data in terms of correlation coefficient ( R = 0.87) and pesticide detection efficiency ( E = 89%), as well as good match between the observed and predicted “class” groups. The relative importance of input parameters to pesticide occurrence in groundwater was examined in terms of R , E , mean error (ME), root mean square error (RMSE), and pesticide occurrence “class” groups by eliminating some key input parameters to the model. Well depth and time of sample collection were the most sensitive input parameters for predicting the pesticide contamination potential of a well. This infers that wells tapping shallow aquifers are more vulnerable to pesticide contamination than those wells tapping deeper aquifers. Pesticide occurrences during post-application months (June through October) were found to be 2.5 to 3 times higher than pesticide occurrences during other months (November through April). The BPNN was used to rank the input parameters with highest potential to contaminate groundwater, including two original and five ancillary parameters. The two original parameters are depth to aquifer material and pesticide leaching class. When these two parameters were the only input parameters for the BPNN, they were not able to predict contamination potential. However, when they were used with other parameters, the predictive performance efficiency of the BPNN in terms of R , E , ME, RMSE, and pesticide occurrence “class” groups increased. Ancillary data include data collected during the study such as well depth and time of sample collection. The BPNN indicated that the ancillary data had more predictive power than the original data. The BPNN results will help researchers identify parameters to improve maps of aquifer sensitivity to pesticide contamination.