With numerous new chemicals introduced into the environment everyday, identification of their potential hazards to the environment and human health is a considerable challenge. Developing adverse outcome pathway (AOP) framework is promising in helping to achieve this goal as it can bring In Vitro testing into toxicity measurement and understanding. To explore the toxic mechanism underlying environmental chemicals via the AOP approach, an integration of adequate experimental data with systems biology understanding is preferred. Here, we describe a novel method to develop reliable and sensible AOPs that relies on chemical-gene interactions, toxicity pathways, molecular regulations, phenotypes, and outcomes information obtained from comparative toxicogenomics database (CTD) and Ingenuity Pathway Analysis (IPA). Using Benzo(a)pyrene (BaP), a highly studied chemical as a stressor, we identified the pivotal IPA toxicity pathways, the molecular initiating event (MIE), and candidate key events (KEs) to structure AOPs in the liver and lung, respectively. Further, we used the corresponding CTD information of multiple typical AHR-ligands, including 2,3,7,8-tetrachlorodibenzoparadioxin (TCDD), valproic acid, quercetin, and particulate matter, to validate our AOP networks. Our approach is likely to speed up AOP development as providing a time- and cost-efficient way to collect all fragmented bioinformation in published studies. It also facilitates a better understanding of the toxic mechanism of environmental chemicals, and potentially brings new insights into the screening of critical paths in the AOP network.

The treatment of industrial waste and harmful bacteria is an important topic due to the release of toxins from the industrial pollutants that damage the water resources. These harmful sources frighten the life of every organism which was later developed as the carcinogenic and mutagenic agents. Therefore, the current study focuses on the breakdown or degradation of 4-chlorophenol and the antibacterial activity against Escherichia coli (E. coli). As a well-known catalyst, pure titanium-di-oxide (TiO₂) had not shown the photocatalytic activity in the visible light region. Hence, band position of TiO₂ need to be shifted to bring out the absorption in the visible light region. For this purpose, the n-type TiO₂ nanocrystalline material's band gap got varied by adding different ratios of p-type CuO. The result had appeared in the formation of p (CuO) – n (TiO₂) junction synthesized from sol-gel followed by chemical precipitation methods. The optical band gap value was determined by Kubelka-Munk (K-M) plot through UV–Vis diffusive reflectance spectroscopy (DRS). Further, the comprehensive mechanism and the results of photocatalytic and antibacterial activities were discussed in detail. These investigations are made for tuning the TiO₂ catalyst towards improving or eliminating the existing various environmental damages.

There must be some uncertainty in the remediation areas delineated based on limited sample points, and resampling in the high-uncertainty areas is particularly necessary. In situ field portable X-ray fluorescence spectrometry (FPXRF), a rapid and cheap analysis method for soil heavy metals, is strongly affected by many spatially non-stationary soil factors. This study first delineated the high-uncertainty area (threshold-exceeding probabilities (PTE) between 30% and 70%) of soil Pb based on the 1000 realizations produced by sequential Gaussian simulation (SGS) with 93 ICP-MS Pb concentrations measured in a peri-urban agriculture area, China. Next, in situ FPXRF was used to increase sample density in this high-uncertainty area. Then, robust geographically weighted regression (RGWR) was used to correct the in situ FPXRF Pb, and the correction accuracies of RGWR, basic GWR, and traditionally-used ordinary least squares regression (OLSR) were compared. Finally, to explore the best way to combine these corrected in situ FPXRF concentrations in delineating the remediation area, we compared the following spatial simulation methods: basic SGS, sequential Gaussian co-simulation (CoSGS) with the RGWR-corrected in situ FPXRF Pb as auxiliary soft data (CoSGS-CorFPXRF), and SGS with the RGWR-corrected in situ FPXRF Pb as part of hard data (SGS-CorFPXRF). Results showed that (i) RGWR produced higher correction accuracy (RI = 71.5%) than GWR (RI = 59.68%) and OLSR (RI = 25.58%) for the in situ FPXRF Pb; (ii) SGS-CorFPXRF produced less uncertainty (G = 0.97) than CoSGS-CorFPXRF (G = 0.95) and SGS (G = 0.91) in the spatial simulation; (iii) High-uncertainty area (30%<PTE<70%) was reduced from 36.55% to 8.7% of the whole study area. It is concluded that the recommended methods are cost-effective to reduce the uncertainty in delineating the remediation areas of soil heavy metals.

On March 12th, 2020, the WHO declared COVID-19 as a pandemic. The collective impact of environmental and ecosystem factors, as well as biodiversity, on the spread of COVID-19 and its mortality evolution remain empirically unknown, particularly in regions with a wide ecosystem range. The aim of our study is to assess how those factors impact on the COVID-19 spread and mortality by country. This study compiled a global database merging WHO daily case reports with other publicly available measures from January 21st to May 18th, 2020. We applied spatio-temporal models to identify the influence of biodiversity, temperature, and precipitation and fitted generalized linear mixed models to identify the effects of environmental variables. Additionally, we used count time series to characterize the association between COVID-19 spread and air quality factors. All analyses were adjusted by social demographic, country-income level, and government policy intervention confounders, among 160 countries, globally. Our results reveal a statistically meaningful association between COVID-19 infection and several factors of interest at country and city levels such as the national biodiversity index, air quality, and pollutants elements (PM₁₀, PM₂.₅, and O₃). Particularly, there is a significant relationship of loss of biodiversity, high level of air pollutants, and diminished air quality with COVID-19 infection spread and mortality. Our findings provide an empirical foundation for future studies on the relationship between air quality variables, a country’s biodiversity, and COVID-19 transmission and mortality. The relationships measured in this study can be valuable when governments plan environmental and health policies, as alternative strategy to respond to new COVID-19 outbreaks and prevent future crises.

Water pollution is one of the main challenges and water crises, which has caused the existing water resources to be unusable due to contamination. To understand the determinants of the distribution and abundance of antibiotic resistance genes (ARGs), we examined the distribution of 22 ARGs in relation to habitat type, heavy metal pollution and antibiotics concentration across six lakes and wetlands of Iran. The concentration of 13 heavy metals was determined by inductively coupled plasma atomic emission spectroscopy (ICP-AES) by Thermo Electron Corporation, and five antibiotics by online enrichment and triple-quadrupole LC-MS/MS were investigated. We further performed a global meta-analysis to evaluate the distribution of ARGs across global lakes compared with our studied lakes. While habitat type effect was negligible, we found a strong correlation between waste discharge into the lakes and the abundance of ARGs. The ARGs abundance showed stronger correlation with the concentration of heavy metals, such as Vanadium, than with that of antibiotics. Our meta-analysis also confirmed that overuse of antibiotics and discharge of heavy metals in the studied lakes. These data point to an increase in the distribution of ARGs among bacteria and their increasing resistance to various antibiotics, implying the susceptibility of aquatic environment to industrial pollution.

One of the worst socio-environmental disasters to mark the history of Brazil and the world occurred in November 2015 and involved the mining sector. The collapse of the Fundao dam released approximately 43 million m³ of iron ore tailings, which moved downstream to reach the Doce River. This resulted in the contamination of water, soil, and sediments along the entire course of the river, which also affected its mouth in the Atlantic Ocean. Four years after the disaster, several socio-environmental problems continue to persist in the affected areas. In this context, the reservoirs built along the Doce River deserve special attention as they are artificial environments that are highly vulnerable to changes in water parameters. This study aims to determine water quality indicators of these reservoirs using remote sensing data and image processing methods, including semi-analytical algorithms, to comprehend the progress of eutrophication processes. Operational land imager/Landsat-8 data (from 2013 to 2019) were used to map the suspended particulate matter (SPM), euphotic zone (Zₑᵤ) and chlorophyll-a (Chl-a) before and after the collapse. The results showed significant changes in SPM and Zₑᵤ in the reservoirs after the collapse. Non-conformity of these parameters is observed even now, and they tend to intensify during rainy periods when resuspension processes of sediments occur. Moreover, there has been an increase in the eutrophication of reservoirs as noticed by the significant increase in Chl-a after the disaster, especially in January, July, and August.

The study of emissions and depositions of atmospheric reactive nitrogen species (Nᵣs) in a region is important to uncover the sources and sinks of atmospheric Nᵣs in the region. In this study, atmospheric total Nᵣs depositions including both wet-only and dry deposition were monitored simultaneously across major land-use types in a 105 km² catchment called Jinjing River Catchment (JRC) in subtropical central China from 2015 to 2016. Based on activity data and emission factors for the main Nᵣs emission sources, ammonia (NH₃) and nitrogen oxides (NOₓ) emission inventories for the catchment were also compiled. The estimated total Nᵣs deposition in JRC was 35.9 kg N ha⁻¹ yr⁻¹, with approximately 49.7 % attributed to reduced compounds (NHₓ), and 40.5 % attributed to oxidized (NOy). The total Nᵣs emission rate in JRC was 80.4 kg N ha⁻¹ yr⁻¹, with 61.5 and 18.9 kg N ha⁻¹ yr⁻¹ from NH₃ and NOₓ emissions, respectively. Livestock excretion and fertilization were the two main contributing emission sources for NH₃, while vehicle sources contributed the bulk of NOₓ emissions. The net atmospheric budgets of Nᵣs in paddy field, forest, and tea field were +3.7, −36.1, and +23.8 kg N ha⁻¹ yr⁻¹, respectively. At the catchment scale, the net atmospheric budget of Nᵣs was +47.7 kg N ha⁻¹ yr⁻¹, with +43.7 kg N ha⁻¹ yr⁻¹ of NHₓ and +4.0 kg N ha⁻¹ yr⁻¹ of NOy, indicating that the subtropical catchment was net sources of atmospheric Nᵣs. Considering that excessive atmospheric Nᵣ emissions and deposition may cause adverse effects on the environment, effects should be conducted to mitigate the Nᵣs emissions from agriculture and transportation, and increasing the area of forest is good for reducing the net positive budget of atmospheric Nᵣs in the subtropical catchments in China.

Samples of ambient PM₂.₅ were collected in the Qingdao harbor area between 21 March and May 25, 2016, and analyzed to investigate the compositions and sources of PM₂.₅ and to assess source-specific selected toxic element health risks to workers via a combination of positive matrix factorization (PMF) and health risk (HR) assessment models. The mean concentration of PM₂.₅ in harbor area was 48 μg m⁻³ with organic matter (OM) dominating its mass. Zn and V concentrations were significantly higher than the other selected toxic elements. The hazard index (HI) and cancer risk (Rᵢ) of all selected toxic elements were lower than the United States Environmental Protection Agency (USEPA) limits. There were no non-cancer and cancer risks for workers in harbor area. The contributions from industrial emissions (IE), ship emissions (SE), vehicle emissions (VE), and crustal dust and coal combustion (CDCC) to selected toxic elements were 39.0%, 12.8%, 24.0%, and 23.0%, respectively. The HI values of selected toxic elements from IE, CDCC, SE, and VE were 1.85 × 10⁻¹, 7.08 × 10⁻², 6.36 × 10⁻², and 3.37 × 10⁻², respectively; these are lower than the USEPA limits. The total cancer risk (Rₜ) value from selected toxic elements in CDCC was 2.04 × 10⁻⁷, followed by IE (6.40 × 10⁻⁸), SE (2.26 × 10⁻⁸), and VE (2.18 × 10⁻⁸). CDCC and IE were the likely sources of cancer risk in harbor area. The Bo Sea and coast were identified as the likely source areas for health risks from IE via potential source contribution function (PSCF) analysis based on the results of PMF-HR modelling. Although the source-specific health risks were below the recommended limit values, this work illustrates how toxic species in PM₂.₅ health risks can be associated with sources such that control measures could be undertaken if the risks warranted it.

Microplastics (MPs) are widely found in coastal areas and oceans worldwide. The MPs are environmentally concerning due to their bioavailability and potential impacts on a wide range of marine biota, so assessing their impact on the biota has become an urgent research priority. In the present study, we exposed Crassostrea gigas oysters to irregular MPs of two polymer types (polyethylene (PE) and polyethylene terephthalate (PET)) at concentrations of 10 and 1000 μg L⁻¹ for 21 days. Accumulation of MPs, changes in metabolic enzyme activity, and histological damage were evaluated, and metabolomics analysis was conducted. Results demonstrated that PE and PET MPs were detected in the gills and digestive gland following exposure to both tested concentrations, confirming ingestion of MPs by the organisms. Moreover, both PE and PET MPs inhibited lipid metabolism, while energy metabolism enzyme activities were activated in the oysters. Histopathological damage of exposed oysters was also observed in this study. Integrated biomarker response (IBR) results showed that MPs toxicity increased with increasing MPs concentration, and the toxic effects of PET MPs on oysters was greater than PE MPs. In addition, metabolomics analysis suggested that MPs exposure induced alterations in metabolic profiles in oysters, with changes in energy metabolism and inflammatory responses. This study reports new insights into the consequences of MPs exposure in marine bivalves at environmentally relevant concentrations, providing valuable information for ecological risk assessment of MPs in a realistic conditions.

Plastic mulching and straw incorporation are common agricultural practices in China. Plastic mulching is suspected to be a significant source of microplastics in terrestrial environments. Straw incorporation has many effects on the storage of soil organic carbon (SOC) and greenhouse gas emissions, but these effects have not been studied in the presence of microplastic pollution. In this study, 365-day soil incubation experiments were conducted to assess the effects of maize straw and polyethylene microplastics on SOC fractions and carbon dioxide (CO₂) and nitrous oxide (N₂O) emissions in two different soils (fluvo-aquic and latosol). Against the background of straw incorporation, microplastics reduced the mineralization and decomposition of SOC, resulting in a microbially available SOC content decrease by 18.9%. In addition, microplastics were carbon-rich, but relatively stable and difficult to be used by microorganisms, thus increasing the mineral-associated SOC content by 52.5%. This indicated that microplastics had adverse effects on microbially available SOC and positive effects on mineral-associated SOC. Microplastics also decreased coarse particulate SOC (>250 μm), and increased non-aggregated silt and clay aggregated SOC (<53 μm). Furthermore, microplastics changed microbial community compositions, thereby reducing the CO₂ and N₂O emissions of straw incorporation by 26.5%–33.9% and 35.4%–39.7%, respectively. These results showed that microplastics partially offset the increase of CO₂ and N₂O emissions induced by straw incorporation. Additionally, the inhibitory effect of microplastics on CO₂ emissions in fluvo-aquic soil was lower than that in latosol soil, whereas the inhibitory effect on N₂O emissions had the opposite trend.