Pregnant women are widely exposed to organophosphate (OP) pesticides, which are potentially neurotoxicant for the developing fetus. We aimed to identify principal demographic and dietary predictors of OP pesticide exposure among 450 pregnant women participating in the New York University Children’s Health and Environment Study (enrolled 2016–19). Urinary concentrations of six dialkyl phosphate (DAP) metabolites (3 dimethyl (DM) metabolites and 3 diethyl (DE) metabolites) of OP pesticides were determined at three time points across pregnancy. At mid-gestation, the Diet History Questionnaire II was used to assess women’s dietary intake over the past year. Demographic characteristics were obtained using questionnaires and/or electronic health records. We used linear mixed models to evaluate the associations of demographic and food groups with DAP metabolite levels, and partial-linear single-index (PLSI) models to analyze the contribution proportions of food groups to DAP metabolite concentrations and the dose-response relationships between them. We observed that pregnant women in NYC had lower levels of OP pesticide metabolites than pregnant populations in Europe, Asia, and other regions in the U.S. Having lower pre-pregnancy body mass index and being Asian, employed, and single were associated with higher DAP metabolite concentrations. Fruit and grain intakes were associated with higher ∑DM, ∑DE, and ∑DAP levels. ∑DE concentrations increased 9.0% (95% confidence interval (CI) = 1.2%, 17.4%) per two-fold increase in dairy consumption, whereas ∑DE concentrations decreased 1.8% (95%CI = −3.1%, −0.4%) per two-fold increase in seafood consumption. The PLSI model indicated that among the food mixture, fruit and grains were the main food groups contributed to higher levels of ∑DAP, while meat contributed to lower levels of ∑DAP. The contribution proportions of fruit, grains, and meat were 18.7%, 17.9%, and 39.3%, respectively. Our results suggest that fruit, grains, and meat are major dietary components associated with OP pesticide exposure in urban pregnant women.

Nitrous oxide (N₂O) is a devastating greenhouse gas and acts as an ozone-depleting agent. Earthworms are a potential source of soil N₂O emissions. Application of biochar can mitigate earthworm-induced N₂O emissions. However, the underlying interactive mechanism between earthworms and biochar in soil N₂O emissions is still unclear. A 35-day laboratory experiment was conducted to examine the soil N₂O emission dynamics for four different treatments, earthworm presence with biochar application (EC), earthworm presence without biochar application (E), earthworm absence with biochar application (C) and earthworm absence without biochar application, and the control. Results indicated a negative impact of biochar on earthworm activity, displaying a significantly (p ≤ 0.05) lower survival rate and biomass of earthworms in treatment EC than E. Compared with the control, earthworm presence significantly (p ≤ 0.05) increased cumulative N₂O emissions, while application of biochar in the presence of earthworms significantly (p ≤ 0.05) decreased cumulative N₂O emissions (485 and 690 μg kg⁻¹ for treatments EC and E, respectively). Treatments E and EC significantly (p ≤ 0.05) increased soil microbial biomass carbon (MBC), ammonium (NH₄⁺-N), nitrate (NO₃⁻N), and dissolved organic carbon (DOC) content and soil pH as compared with the control. The gene copy number of 16 S rRNA, AOA, AOB, nirS, and nosZ increased for all treatments when compared with the control; however, a significant (p ≤ 0.05) difference among the studied genes was only observed for the nosZ gene (2.05 and 2.56 × 10⁶ gene copies g⁻¹ soil for treatments E and EC, respectively). Earthworm-induced soil N₂O emissions were significantly (p ≤ 0.05) reduced by biochar addition. The possible underlying mechanisms may include: (1) short-term negative impacts on earthworm activity; (2) a change of functional gene abundance in earthworm casts; and (3) an increase in soil pH due to addition of biochar.

High-arsenic wastewater derived from the metallurgical industry of nonferrous minerals is one of the most dangerous arsenic (As) sources that usually follow the emission of massive hazardous arsenic-bearing wastes. Considering the properties of red mud (RM), we propose an alternative and environmentally friendly method for the efficient remediation of high-arsenic wastewater using RM through formation of AlAsO₄@silicate precipitate, aiming at ''zero-emission of hazardous solid waste''. The results show nearly 100% of arsenic could be stepwisely removed from high-arsenic wastewater and reduce the arsenic concentration from 6100 mg/L to 40 μg/L using RM at room temperature. The highest arsenic removal capacity of RM reaches 101.5 mg/g at a RM-to-wastewater ratio of 40 g/L due to the superior arsenic adsorption and the co-precipitation of arsenate and Al³⁺ to form insoluble aluminum arsenate. The silicate shell of arsenic-loaded RM created at an alkaline condition acts as an arsenic stabilizer, resulting in a leached arsenic concentration of 1.2 mg/L in TCLP tests. RM acts as a highly effective arsenic remover and stabilizer for the disposal of high-arsenic wastewater. It shows great potential for the remediation of wastewater containing heavy metals with varying concentrations to produce clean water available for industrial purpose.

Neuroinflammation induced by lead exposure (Pb) is a major cause of neurotoxicity of Pb in the central nervous system (CNS). The NLR family, domain of pyrin containing 3 (NLRP3) involves in various neurological diseases, while the question of whether NLRP3 plays a role in lead-induced neuroinflammation has not yet been reported.Developmental and knockout (KO) NLRP3 mice were used to establish two in vivo models, and BV2 cells were used to establish an in vitro model. Behavioral and electrophysiologic tests were used to assess the neurotoxicity of Pb, and immunofluorescence staining was used to assess neuroinflammation. Real-time PCR and western blot were performed to examine the mRNA and protein levels of inflammatory cytokines and NLRP3 inflammasomes. siRNA technology was used to block NLRP3 expression.Pb exposure led to neural injure and microglial activation in the hippocampus region, while minocycline intervention attenuated Pb-induced neurotoxicity by inhibiting neuroinflammation. Pb increased the expression of NLRP3 and promoted cleavage of caspase-1 in mRNA and protein levels, and minocycline partially reversed the effects of Pb on NLRP3 inflammasomes. Blocking of NLRP3 by KO mice or siRNA attenuated neural alterations induced by Pb, weakened microglial activation in vivo and in vitro as well, without affecting the accumulation of Pb. Pb increased autophagic protein levels and phosphorylation of NF-κB, while suppressing autophagy or NF-κB inhibited Pb's effects on NLRP3.NLRP3 is involved in the regulation of Pb-induced neurotoxicity. These findings expand mechanism research of Pb neurotoxicity and may help establish new prevention strategies for Pb neurotoxicity.

Lakes in arid regions are experiencing mercury pollution via air deposition and surface runoff, posing a threat to ecosystem safety and human health. Furthermore, salinity and organic matter input could influence the mercury cycle and composition of bacterial communities in the sediment. In this study, the effects of salinity and algae biomass as an important organic matter on the genes (merA and hgcA) involved in the mercury cycle under mercury contamination were investigated. Archaeal merA and hgcA were not detected in sediments of lake microcosms, indicating that bacteria rather than archaea played a crucial role in mercury reduction and methylation. The high content of mercury (300 ng g⁻¹) could reduce the abundance of both merA and hgcA. The effects of salinity and algae biomass on mercury cycling genes depended on the gene type and dose. A higher input of algae biomass (250 mg L⁻¹) led to an increase of merA abundance, but a decrease of hgcA abundance. All high inputs of mercury, salinity, and algae biomass decreased the richness and diversity of bacterial communities in sediment. Further analysis indicated that higher mercury (300 ng g⁻¹) led to an increased relative abundance of mercury methylators, such as Ruminococcaceae, Bacteroidaceae, and Veillonellaceae. Under saline conditions (10 and 30 g L⁻¹), the richness of specific bacteria associated with mercury reduction (Halomonadaceae) and methylation (Syntrophomonadaceae) increased compared to the control. The input of algae biomass led to an increase in the specific bacterial communities associated with the mercury cycle and the richness of bacteria involved in the decomposition of organic matter. These results provide insight into mercury cycle-related genes and bacterial communities in the sediments of lakes in arid regions.

Soil moisture has a strong impact on the fluorescence intensity of PAHs, which is undoubtedly posing a challenge for the development of rapid real-time fluorescence detection technology of PAHs in soil. In this work, NIR diffuse reflectance spectroscopy was used to correct the fluorescence spectra of PAHs in order to reduce the effect of the soil moisture. To establish the correction method, eight soil samples with different moisture contents and a given phenanthrene concentration (8 mg/g) were prepared. The fluorescence and NIR diffuse reflectance spectra were collected for of all samples. It was found that the fluorescence spectra of the soil samples that vary with the moisture content together with the NIR diffuse reflectance spectra were considered for the correction of the fluorescence intensity of phenanthrene related to the moisture content. The results showed that the ratio of the fluorescence intensity at 384 nm to the NIR diffuse reflectance spectrum absorbance at 5184 cm⁻¹ can be used as a correction factor to reduce the effect of the soil moisture on the fluorescence intensity of phenanthrene in the soil. The validity of the correction method was verified by the quantitative analysis of PAHs with different concentrations and soil moisture contents. The results showed better linearity between the fluorescence intensity and the concentration of PAHs after the correction (with a correlation coefficient R of 0.99) than before the correction (with R of 0.86). The relative prediction errors for three unknown samples decreased from 19%, 51% and 40% before the correction to 5%, 13% and 0.44% after the correction, respectively, indicating the feasibility of the detection of PAHs in the soil by the combination of fluorescence and NIR diffuse reflectance spectroscopy.

Per- and polyfluoroalkyl substances (PFASs) are environmentally and biologically persistent anthropogenic chemicals linked to adverse health outcomes. Epidemiological data have revealed association between exposure to specific PFAS and disruption of insulin level in bodies. However, the effect of PFASs on insulin secretion and the responsible molecular mechanism are poorly understood. In the present study, we used perfluorooctane sulfonate (PFOS) as a representative PFAS family member to investigate its effect on the insulin secretion in mouse pancreatic β cells (β-TC-6). Our results showed that exposure to PFOS inhibited silent information regulator 1 (SIRT1) activity, and molecular simulation showed PFOS could fit into the pocket overlapped with the nicotinamide adenine dinucleotide (NAD⁺) binding cavity in SIRT1. PFOS exposure upregulated uncoupling protein 2 (UCP2) expression, and this upregulation was blunted in the presence of Ex-527, a SIRT1 specific inhibitor. The mitochondria membrane potential (ΔΨm), as well as the glucose-induced ATP production and Ca²⁺ influx decreased under PFOS treatment. PFOS continual exposure (48 h) impaired glucose stimulated insulin secretion (GSIS), while the gene expression of insulin was not significantly altered. Importantly, the SIRT1 activator and UCP2 inhibitor could partly reverse the PFOS-induced impairment of GSIS. Taken together, the results suggested that PFOS continual exposure could inhibit SIRT1 activity, and the SIRT1-UCP2 pathway mediated, at least partially, the PFOS induced GSIS impairment.

Salinity stress affects aquatic microalgal growth and their physiological responses have been studied extensively. However, arsenic (As) accumulation and biotransformation by freshwater phytoplankton under a salinity gradient have never been addressed. This study reports a distinctive pattern of As uptake, accumulation, and biotransformation by four axenic freshwater phytoplankton species, i.e., Scenedesmus acutus, Closterium aciculare, Staurastrum paradoxum, and Pediastrum duplex. Phytoplankton cells were incubated in sterilised C medium modified with varying salinity levels (0–5‰) in association with arsenate and phosphate concentrations. The biotransformation of arsenate (i.e., As(V)) to arsenite (As(III)) and to further methylated species decreased with increasing salinity in the culture medium whereas As accumulation increased. Among the four strains, only S. acutus and S. paradoxum converted As(V) to As(III), with no detected methylated species. In contrast, C. aciculare and P. duplex biotransformed As(V) to As(III) and further to methyl arsenic species, such as DMAA. S. acutus and S. paradoxum exhibited higher accumulation tendency than the other two species. S. paradoxum showed the lowest As reduction rate (i.e., As(V) to As(III)) compared to other species, although, without significant variations. The morphological changes were observed in phytoplankton cells in response to increased salinity stress. Moreover, As(V) concentrations in the culture medium significantly decreased by day 7–14. Thus, this study presents a conceptual model of the As biotransformation pattern by axenic freshwater phytoplankton.

In this work, a sand and dust storm vulnerability mapping (SDS-VM) approach is developed to model the vulnerability of urban blocks to SDS using GIS spatial analysis and a range of geographical data. The SDS-VM was carried out in Ahvaz, IRAN, representing one of the most dust-polluted cities in West Asia. Here, vulnerability is defined as a function of three components: exposure, sensitivity, and adaptive capacity of the people in the city blocks to sand and dust storms. These components were formulated into measurable indicators (i.e. GIS layers) including: PM₂.₅, wind speed, distance from dust emission sources, demographic statistics (age, gender, family size, education level), number of building floors, building age, land surface temperature (LST), land use, percentage of literate population, distance from health services, distance from city facilities (city center, shopping centers), distance from infrastructure (public transportation, main roads and highways), distance from parks and green spaces, and green area per capita. The components and the indicators were weighted using analytical hierarchy process (AHP). Different levels of risks for the components and the indicators were defined using ordered weighted averaging (OWA). Urban SDS vulnerability maps at different risk levels were generated through spatial multi-criteria data analysis procedure. Vulnerability maps, with different risk levels, were validated against field-collected data of 781 patients hospitalized for dust-related diseases (i.e. respiratory, cardiovascular, and skin). Results showed that (i) SDS vulnerability map, obtained from the developed methodology, gives an overall accuracy of 79%; (ii); regions 1 and 5 of Ahvaz are recognized with the highest and lowest vulnerabilities to SDS, respectively; and (iii) ORness equal to 0 (very low risk) is the optimum SDS-VM risk level for decision-making to mitigate the harmful impacts of SDS in the deposition areas of Ahvaz city.

Based on the concentrations of ten heavy metals (As, Cd, Cr, Cu, Hg, Mn, Ni, Pb, Zn, Fe) in 144 road dust samples collected from 36 sites across 4 seasons from 2016 to 2017 in Beijing, this study systematically analyzed the levels and main sources of health risks in terms of their temporal and spatial variations. A combination of receptor models (positive matrix factorization and multilinear engine-2), human health risk assessment models, and Monte Carlo simulations were used to apportion the seasonal variation of the health risks associated with these heavy metals. While non-carcinogenic risks were generally acceptable, Cr and Ni induced cautionary carcinogenic risks (CR) to children (confidence levels was approximately 80% and 95%, respectively).. Additionally, fuel combustion posed cautionary CR to children in all seasons, while the level of CR from other sources varied, depending on the seasons. Heavy metal concentrations were the most influential variables for uncertainties, followed by ingestion rate and skin adherence factor. The values and spatial patterns of health risks were influenced by the spatial pattern of risks from each source.