Accurate mapping of air pollutants is essential for epidemiological studies and environmental risk assessments. Concentrations measured by air quality monitoring stations (AQMS) have primarily been used to assess the exposure of PM₂.₅. However, the low coverage and amount of monitoring stations affect the errors of spatial interpolation or geostatistical estimates. In contrast to other integrated approaches developed for improved air pollution estimates, this study utilizes data from low-cost microsensors densely deployed in Taiwan to improve the popular spatial interpolation approach called inverse distance weighting (IDW). A large dataset from thousands of low-cost sensors could improve spatial interpolation by describing the distribution of PM₂.₅ in detail. Therefore, this study presents a clustering-based method to assess the distribution of PM₂.₅. Then, a smarter IDW is performed based on correlated observations from the selected air quality stations. The publicly available data chosen for this investigation pertained to Taiwan, which has deployed 74 monitoring stations and more than 11,000 low-cost sensors since December 2020. The results of leave-one-out cross-validation indicate that there are fewer PM₂.₅ estimation errors in the developed approach than in estimations that use kriging across almost all of the months and sampled dates of 2019 and 2020, particularly those with higher PM₂.₅ spatial heterogeneities. Spatial heterogeneities could result in more significant estimation errors in mainstream approaches. The root mean square error of the monthly average estimate for PM₂.₅ ranged from 1.17 to 3.86 μg/m³. We also found that the clustering of one month characterizing the pattern of PM₂.₅ distribution could perform well in spatial interpolations based on historical data from monitoring stations. According to the information on the openaq platform, low-cost sensors are in demand in cities and areas. This trend might pave the way for the application of the proposed approach in other areas for superior exposure assessments.

With the rapid development of nanotechnology in agriculture, there is increasing urgency to assess the impacts of nanoparticles (NPs) on the soil environment. This study merged raw high-throughput sequencing (HTS) data sets generated from 365 soil samples to reveal the potential ecological effects of NPs on soil microbial community by means of metadata analysis and machine learning methods. Metadata analysis showed that treatment with nanoparticles did not have a significant impact on the alpha diversity of the microbial community, but significantly altered the beta diversity. Unfortunately, the abundance of several beneficial bacteria, such as Dyella, Methylophilus, Streptomyces, which promote the growth of plants, and improve pathogenic resistance, was reduced under the addition of synthetic nanoparticles. Furthermore, metadata demonstrated that nanoparticles treatment weakened the biosynthesis ability of cofactors, carriers, and vitamins, and enhanced the degradation ability of aromatic compounds, amino acids, etc. This is unfavorable for the performance of soil functions. Besides the soil heterogeneity, machine learning uncovered that a) the exposure time of nanoparticles was the most important factor to reshape the soil microbial community, and b) long-term exposure decreased the diversity of microbial community and the abundance of beneficial bacteria. This study is the first to use a machine learning model and metadata analysis to investigate the relationship between the properties of nanoparticles and the hazards to the soil microbial community from a macro perspective. This guides the rational use of nanoparticles for which the impacts on soil microbiota are minimized.

According to fire accident statistics, fires in buildings are increasing. The flame-retardant performance of insulation materials is considered an important factor for preventing the spread of fire and ensuring evacuation. This study evaluated the flame-retardant performance and combustion characteristics of four types of organic thermal insulation used as core materials in sandwich panels. The flame-retardant performance evaluation based on total heat release and heat release rate revealed that phenolic foam (PF) satisfied the criteria for non-combustible grade insulation. An analysis of the hazardous gases released while combustion of the four insulation materials indicated that a significant amount of CO was released—an average of 19,000 ppm or higher—in the rigid urethan foam (PIR) and spray-type polyurethane foam (SPU). The fractional effective dose (FED) value was derived from the gas analysis results according to ISO 13344. PIR and SPU had an average FED value of 2.0 or higher and were identified as very dangerous in the case of fire accidents. Moreover, the evacuation time in the case of a fire in a warehouse-type building was comprehensively analyzed considering the material, size, and height for the four types of insulation. PIR was the most vulnerable to fire, and for PF, the danger limit was not reached until the end of the simulation.

Soil properties, such as soil pH, soil organic matter (SOM), cation exchange capacity (CEC), are the most important factors affecting cadmium (Cd) accumulation in vegetables. In this study, we conducted big data mining of 31,342 soil and vegetable samples to examine the influence of soil properties (soil pH, SOM, CEC, Zn and Mn content) on the accumulation of Cd in root, solanaceous, and leafy vegetables in Hunan Province, China. Specifically, the Cd accumulation capability was in the following order: leafy vegetables > root vegetables > solanaceous vegetables. The soil property thresholds for safety production in vegetables were determined by establishing nonlinear models between Cd bioaccumulation factor (BCF) and the individual soil property, and were 6.5 (pH), 30.0 g/kg (SOM), 13.0 cmol/kg (CEC), 100–140 mg/kg (Zn), and 300–400 mg/kg (Mn). When soil property values were higher than the thresholds, Cd accumulation in vegetables tended to be stable. Prediction models showed that pH and soil Zn were the leading factors influencing Cd accumulation in root vegetables, explaining 87% of the variance; pH, SOM, soil Zn and Mn explained 68% of the variance in solanaceous vegetables; pH and SOM were the main contributors in leafy vegetables, explaining 65% of the variance. Further, variance partitioning analysis (VPA) revealed that the interaction effect of the corresponding key soil properties contributed mostly to BCF. Meanwhile, partial least squares (PLS) path modeling was employed to analyze the path and the interactive effects of soil properties on Cd BCF. pH and SOM were found to be the biggest two players affecting BCF in PLS-models, and the most substantial interactive influence paths of soil properties on BCF were different among the three types of vegetables.

Preconception and prenatal exposure to phthalates has been associated with an increased risk of preterm birth. However, it is unclear whether there are periods of heightened susceptibility during pregnancy. This prospective cohort study included 386 women undergoing fertility treatment who gave birth to a singleton infant during 2005 through 2018. Eleven phthalate metabolites were measured in spot urine samples collected at each trimester. In approximately 50% of participants, two metabolites of 1,2-cyclohexane dicarboxylic acid diisononyl ester (DINCH), a phthalate substitute, were also measured. The molar sum of four di(2-ethylhexyl) phthalate metabolites (∑DEHP) was calculated. We evaluated the associations of mean maternal biomarker concentrations with risk of preterm birth using modified log-binomial models and utilized multiple informant models to compare trimester-specific associations. We examined the relative biomarker concentration across gestation comparing women with preterm birth to women with term delivery using quadratic mixed model. The risk ratio for preterm birth associated with a one-unit increase in the natural log-transformed urinary concentrations of ∑DEHP (mean during pregnancy) was 1.21 (95% confidence interval (CI): 0.84, 1.72). In multiple informant models, these associations were strongest in the third trimester (RR = 1.51; 95% CI: 1.17, 1.95). Estimated mean ∑DEHP concentrations were higher among women with preterm than term delivery, especially late in gestation. Associations with preterm birth were also observed for each of the four individual DEHP metabolites. Detection of cyclohexane-1,2-dicarboxylic acid monocarboxyisooctyl ester (MCOCH), a metabolite of DINCH, appeared to be positively related to preterm birth. In this prospective cohort of subfertile couples, maternal ∑DEHP metabolite concentrations during pregnancy were associated with an increased risk of preterm birth, particularly during late gestation.

Exposure to green space has been proposed to be beneficially associated with cardiovascular morbidity and mortality. Many studies have explored this topic, but the results remain conflicting. We aimed to evaluate the epidemiological evidence on this topic by performing a systematic review with meta-analysis. We searched PubMed, Web of Science and Embase for studies on the association between green space and cardiovascular disease (CVD) that were published till January 2022. Two authors independently performed study selection, data extraction, quality assessment, and risk of bias assessment. For studies providing detailed numeric data, we also conducted quantitative meta-analyses and calculated the pooled odd ratios (ORs) for associations between the most commonly used exposure estimate (normalized difference vegetative index [NDVI]) and five CVD events: CVD mortality, ischemic heart disease (IHD) mortality, cerebrovascular disease (CBVD) mortality, and stroke incidence/prevalence. Additional analyses were conducted to explore the geographical scale effects of NDVI. Publication bias tests were also conducted. Of the 6787 records identified, 53 studies were eligible for inclusion. These studies covered 18 countries and included data from more than 100 million persons. Meta-analyses showed that a 0.1 increase in NDVI was significantly associated with 2–3% lower odds of CVD mortality (OR: 0.97, 95% CI: 0.96–0.99), IHD mortality (OR: 0.98, 95% CI: 0.96–1.00), CBVD mortality (OR: 0.98, 95% CI: 0.97–1.00), and stroke incidence/prevalence (OR: 0.98, 95% CI: 0.96–0.99). There was no significant difference between the pooled estimates for different buffer sizes. No evidence of publication bias was detected. We provide strong and robust evidence for the beneficial effects of green space exposure on cardiovascular health. More prospective studies and mechanistic studies, especially that conducted in low- and middle-income countries, are merited to strengthen our conclusions.

Phenanthrene (PHE) is widely distributed, and it can cause genotoxicity in humans by interacting with enzymes in the body. A current challenge for PHE bioremediation is the inhibitory effect of biotoxic intermediates on bacterial growth. Notably, the aerobic biotransformation processes for PHE in the presence of sophorolipids have been poorly studied. Here, a PHE-degrading strain was isolated from sediments and identified as Pseudomonas stutzeri and named LSH-PAH1. It was observed that 1-naphthol (a biotoxic substance that can inhibit strain growth) was produced during the PHE metabolism process of LSH-PAH1. The biodegradation ratio increased from 21.4% to 91.7% within 48 h after the addition of sophorolipids. Unexpectedly, this addition accelerated the metabolic process for 1-naphthol rather than causing its accumulation. The cometabolism of 1-naphthol and sophorolipids alleviated the biotoxic effects for the strain, which was verified by gene expression analysis. We identified a new PHE-degrading strain and provided a mechanism for PHE biodegradation using LSH-PAH1 with the addition of sophorolipids, which provides a reference for practical applications of the bioremediation of PHE and study of the cometabolism of biotoxic intermediates.

In recent years, the cardiovascular toxicity of urban fine particulate matter (PM₂.₅) has sparked significant alarm. Mitochondria produce 90% of ATP and make up 30% of the volume of cardiomyocytes. Thus knowledge of myocardial mitochondrial dysfunction due to PM₂.₅ exposure is essential for further cardiotoxic effects. Here, the mechanism of PM₂.₅-induced cardiac hypertrophy through calcium overload and mitochondrial dysfunction was investigated in vivo and in vitro. Male and female BALB/c mice were given 1.28, 5.5, and 11 mg PM₂.₅/kg bodyweight weekly through oropharyngeal inhalation for four weeks and were assigned to low, medium, and high dose groups, respectively. PM₂.₅-induced myocardial edema and cardiac hypertrophy were detected in the high-dose group. Mitochondria were scattered and ruptured with abnormal ultrastructural morphology. In vitro experiments on human cardiomyocyte AC16 showed that exposure to PM₂.₅ for 24 h caused opened mitochondrial permeability transition pore --leading to excessive calcium production, decreased mitochondrial membrane potential, weakened mitochondrial respiratory metabolism capacity, and decreased ATP production. Nevertheless, the administration of calcium chelator ameliorated the mitochondrial damage in the PM₂.₅-treated group. Our in vivo and in vitro results confirmed that calcium overload under PM₂.₅ exposure triggered mTOR/AKT/GSK-3β activation, leading to mitochondrial bioenergetics dysfunction and cardiac hypertrophy.

The occurrence of excessive ammonium in groundwater threatens human and aquatic ecosystem health across many places worldwide. As the fate of ammonium in groundwater systems is often affected by a complex mixture of transport and biogeochemical transformation processes, identifying the sources of groundwater ammonium is an important prerequisite for planning effective mitigation strategies. Elevated ammonium was found in both a shallow and an underlying deep groundwater system in an alluvial aquifer system beneath an agricultural area in the central Yangtze River Basin, China. In this study we develop and apply a novel, indirect approach, which couples the random forest classification (RFC) of machine learning method and fluorescence excitation-emission matrices with parallel factor analysis (EEM-PARAFAC), to distinguish multiple sources of ammonium in a multi-layer aquifer. EEM-PARAFAC was applied to provide insights into potential ammonium sources as well as the carbon and nitrogen cycling processes affecting ammonium fate. Specifically, RFC was used to unravel the different key factors controlling the high levels of ammonium prevailing in the shallow and deep aquifer sections, respectively. Our results reveal that high concentrations of ammonium in the shallow groundwater system primarily originate from anthropogenic sources, before being modulated by intensive microbially mediated nitrogen transformation processes such as nitrification, denitrification and dissimilatory nitrate reduction to ammonium (DNRA). By contrast, the linkage between high concentrations of ammonium and decomposition of soil organic matter, which ubiquitously contained nitrogen, suggested that mineralization of soil organic nitrogen compounds is the primary mechanism for the enrichment of ammonium in deeper groundwaters.