High-density samples are usually a prerequisite for obtaining high-precision source apportionment results in large-scale areas. In-situ field portable X-ray fluorescence spectrometry (FPXRF) is a fast and cheap way to increase the sample size of soil heavy metals (HMs). Moreover, categorical land-use types may be closely associated with source contributions. However, the above information has rarely been incorporated into the source apportionment. In this study, robust geographically weighted regression (RGWR) was first used to correct the spatially varying effect of the related soil factors (e.g., soil water and soil organic matter) on in-situ FPXRF in an urban-rural fringe of Wuhan City, China, and the correction accuracy of RGWR was compared with those of the traditionally non-spatial multiple linear regression (MLR) and basic GWR. Then, the effect of land-use types on HM concentrations was partitioned using analysis of variance (ANOVA). Last, based on the robust spatial receptor model (i.e., robust absolute principal component scores/RGWR [RAPCS/RGWR]), this study proposed RAPCS/RGWR with categorical land-use types and RGWR-corrected in-situ FPXRF data (RAPCS/RGWR_LU&FPXRF), and its performance was compared with those of RAPCS/RGWR with none or one kind of auxiliary data. Results showed that (i) the performances of the correction models for in-situ FPXRF data were in the order of RGWR > GWR > MLR, and the relative improvement of RGWR over MLR ranged from 52.6% to 70.71% for each HM; (ii) categorical land-use types significantly affected the concentrations of soil Zn, Cu, As, and Pb; (iii) the highest estimation accuracy for source contributions was obtained by RAPCS/RGWR_LU&FPXRF, and the lowest estimation accuracy was obtained by basic RAPCS/RGWR. It is concluded that land-use types and RGWR-corrected in-situ FPXRF data are closely associated with the source contribution, and RAPCS/RGWR_LU&FPXRF is a cost-effective source apportionment method for soil HMs in large-scale areas.

Noise pollution is a pervasive factor that increasingly threatens natural resources and human health worldwide. In particular, large-scale changes in road networks have driven shifts in the acoustic environment of rural landscapes during the past few decades. Using sampling plots from the Spanish Landscape Monitoring System (SISPARES), 16 km² each, we modelled the spatio-temporal changes in road traffic noise pollution in Ecoregion 1 of Spain (approximately 66,000 km²). We selected a study period that was characterised by significant changes in the size of the road network and the vehicle fleet (i.e. between 1995 and 2014) and used standard and validated acoustic computation methods for environmental noise modelling (i.e. European Directive, 2002/49/EC) within sampling plots. We then applied a multiple linear regression to expand noise modelling throughout the whole of Ecoregion 1. Our results showed that the noise level increased by 1.7 dB(A) in average per decade in approximately 65% of the territory, decreased by 1.3 dB(A) per decade in about 33%, and remained unchanged in 2%. This suggests that road traffic noise pollution levels may not grow homogeneously in large geographical areas, maybe due to the concentration of large fast traffic flows on modern motorways connecting towns. Our research exemplifies how landscape monitoring systems such as cost-effective approaches may play an important role when assessing spatio-temporal patterns and the impact of anthropogenic noise pollution at large geographical scales, and even more so in a global context of constricted resources and limited availability of historical data on traffic and environmental noise monitoring.

The objective of this study was to validate the hypothesis that increased serum concentrations of perfluoroalkyl substances (PFASs) cause kidney damage. A causal interpretative study was designed using the US 2003–2018 National Health and Nutrition Examination Survey (NHANES) datasets.Three statistical models, including multivariable linear regression, generalized additive model, and regression discontinuity model (RDM), were applied to the US 2003–2018 NHANES datasets to evaluate the causal relationship between the four PFAS agents and estimated glomerular filtration rate (eGFR). Directed acyclic graphs were plotted for a more valid causal inference.In the RDM, when the natural logarithm of each PFAS agent increases by 1 ng/mL after each cut-off value, eGFR decreased 4.63 mL/min/1.73 m² for perfluorooctanoic acid, 3.42 mL/min/1.73 m² for perfluorooctane sulfonic acid, 2.37 mL/min/1.73 m² for perfluorohexane sulfonic acid, and 2.87 mL/min/1.73 m² for perfluorononanoic acid. The possibility of reverse causation that increased serum PFAS concentration is the consequence of reduced eGFR, not the cause, was low, and an additional adjustment of potential confounders was not needed.This study contributes to the understanding of PFAS-induced kidney damage. Further longitudinal epidemiological and toxicological studies are recommended.

Ozone (O₃) is a harmful pollutant when present in the lowermost layer of the atmosphere. Therefore, the European Commission formulated directives to regulate O₃ concentrations in near-surface air. However, almost 50% of the 5068 air quality stations in Europe do not monitor O₃ concentrations. This study aims to provide a hybrid modeling system that fills these gaps in the hourly surface O₃ observations on a site scale with much higher accuracy than existing O₃ models. This hybrid model was developed using estimations from multiple linear regression-based eXtreme Gradient Boosting Machines (MLR-XGBM) and O₃ reanalysis from European regional air quality models (CAMS-EU). The binary classification of extremely high O₃ events and the 1- and 24-h forecasts of hourly O₃ were investigated as secondary aims. In this study thirteen stations in Northern Bavaria, out of which six do not monitor O₃, were chosen as test sites. Considering the computational complexity of machine learning algorithms (MLAs), we also applied two recent MLA interpretation methods, namely SHapley Additive exPlanations (SHAP) and Local interpretable model-agnostic explanations (LIME).With SHAP, we showed an increasing effect of temperature on O₃ concentrations which intensifies for temperatures exceeding 17 °C. According to LIME, O₃ concentration peaks are mainly governed by meteorological factors under dry and warm conditions on a regional scale, whereas local nitrogen oxide concentrations control base O₃ concentrations during cold and wet periods.While recently developed MLAs for the spatial estimation of hourly O₃ concentrations had a station-based root-mean-square error (RMSE) above 27 μg/m³, our proposed model significantly reduced the estimation errors by about 66% with an RMSE of 9.49 μg/m³. We also found that logistic regression (LR) and MLR-XGBM performed best in the site-scale classification and 24-h forecast of O₃ concentrations (with a station-averaged accuracy and RMSE of 0.95 and 19.34 μg/m³, respectively).

Although abundant evidence has suggested that early-life antibiotic exposure was associated with adipogenesis later in life, limited data were available on the effect of intrauterine antibiotic exposure on infant growth and growth speed. Additionally, few studies have investigated the role of the neonatal gut microbiota in the above association. In this study, we examined the association between intrauterine cumulative antibiotic exposure and infant growth and explored the potential role of the neonatal gut microbiota in the association. 295 mother-child pairs from the Shanghai Maternal-Child Pairs Cohort (MCPC) study were included, and meconium samples and infant growth measurements were assessed. Z-scores of length-for-age, weight-for-age (weight-for-age), and body mass index (BMI)-for-age (BMI-for-age) were calculated. Eighteen common antibiotics were measured in meconium. Multivariable linear regression models were applied to test the interrelationships between antibiotic exposure, diversity indicators, and the relative abundance of selected bacterial taxa from phylum to genus levels from least absolute shrinkage and selection operator (LASSO) and infant growth indicators. The detection rates of the 18 antibiotics, except for chlortetracycline, penicillin, and chloramphenicol, were below 10 %. Penicillin was found to be positively associated with infant growth at birth and with growth speed from 2 to 6 months. The Pielou and Simpson indexes were negatively associated with meconium penicillin. Nominally significant associations between penicillin and the relative abundances of several bacterial taxa from the phyla Proteobacteria, Bacteroidetes, and Firmicutes were found. The Pielou and Simpson indexes were also found to be negatively associated with infant growth. Among taxa selected from LASSO regression, the relative abundances of the phyla Actinobacteria and Firmicutes and order Bifidobacteriales were found to be significantly associated with weight and BMI growth speeds from 2 to 6 months. In conclusion, intrauterine antibiotic exposure can affect infant growth. The neonatal gut microbiota might play a role in the abovementioned association.

Perfluoroalkyl substances (PFAS) are known to be endocrine-disrupting compounds, but are nevertheless widely used in consumer and industrial products and have been detected globally in human and wildlife. Data from animal and epidemiological studies suggest that PFAS may affect human fertility. This led us to consider whether maternal or paternal plasma PFAS had effects on in vitro fertilization (IVF) outcomes. The study population consisted of 96 couples who underwent IVF treatment in 2017 due to tubal factor infertility. The concentrations of 10 PFAS in blood samples from both male and female partners were measured. Poisson regression with log link was performed to evaluate the association between the tertiles of PFAS concentrations and numbers of retrieved oocytes, mature oocytes, two-pronuclei (2 PN) zygotes, and good-quality embryos, while multiple linear regression models were used to investigate the correlation between plasma PFAS and semen parameters. Multivariable logistic regression was used to evaluate the association between the tertiles of PFAS concentrations and clinical outcomes. It was found that maternal plasma concentrations of perfluorooctanoic acid (PFOA) were negatively associated with the numbers of retrieved oocytes (pₜᵣₑₙd = 0.023), mature oocytes (pₜᵣₑₙd = 0.015), 2 PN zygotes (pₜᵣₑₙd = 0.014), and good-quality embryos (pₜᵣₑₙd = 0.012). Higher paternal plasma PFOA concentrations were found to be significantly associated with reduced numbers of 2 PN zygotes (pₜᵣₑₙd = 0.047). None of the maternal or paternal PFAS were significantly associated with the probability of implantation, clinical pregnancy, or live birth. To our knowledge, the present study is the first to assess the association between parental exposure to PFAS and IVF outcomes. Our results suggest the potential reproductive effects of PFAS on both men and women, and that exposure to PFAS may negatively affect IVF outcomes. Future studies, particularly with large sample size cohorts, are needed to confirm these findings.

Decision-making related to nitrogen (N) fertilization is a crucial step in agronomic practices because of its direct interactions with agronomic productivity and environmental risk. Here, we hypothesized that soil apparent N balance could be used as an indicator to determine the thresholds of N input through analyzing the responses of the yield and N loss to N balance. Based on the observations from 951 field experiments conducted in rice (Oryza sativa L.) cropping systems of China, we established the relationships between N balance and ammonia (NH₃) volatilization, yield increase ratio, and N application rate, respectively. Dramatical increase of NH₃ volatilizations and stagnant increase of the rice yields were observed when the N surplus exceeded certain levels. Using a piecewise regression method, the seasonal upper limits of N surplus were determined as 44.3 and 90.9 kg N ha⁻¹ under straw-return and straw-removal scenarios, respectively, derived from the responses of NH₃ volatilization, and were determined as 53.0–74.9 and 97.9–112.0 kg N ha⁻¹ under straw-return and straw-removal scenarios, respectively, derived from the maximum-yield consideration. Based on the upper limits of N surplus, the thresholds of N application rate suggested to be applied in single, middle-MLYR, middle-SW, early, and late rice types ranged 179.0–214.9 kg N ha⁻¹ in order to restrict the NH₃ volatilization, and ranged 193.3–249.8 kg N ha⁻¹ in order to achieve the maximum yields. If rice straw was returned to fields, on average, the thresholds of N application rate could be theoretically decreased by 17.5 kg N ha⁻¹. This study provides a robust reference for restricting the N surplus and the synthetic fertilizer N input in rice fields, which will guide yield goals and environmental protection.

Agricultural activities often lead to nutrient enrichment and habitat modification in rivers, possibly altering macroinvertebrate assemblages and hence ecosystem functioning. For the sake of environmental management and conservation, therefore, assessing the impacts of agricultural activities becomes indispensable, especially when these activities are predicted to be intensified in the future. In this study, the plain river network in the Lake Chaohu Basin was chosen to examine how agricultural activities influence the functioning of rivers by assessing land use, water quality, habitat condition and macroinvertebrate assemblages, followed by calculating the macroinvertebrate-based multimetric index (MMI) to indicate overall ecosystem health of the rivers. We found that agricultural activities lowered the diversity of macroinvertebrates (e.g. total number of taxa and Simpson index) primarily due to elevated ammonium concentrations in water and reduced microhabitat types, thereby impairing the habitat integrity and nutrient cycling of rivers. The macroinvertebrate-based MMI was positively correlated with increasing habitat quality but negatively with increasing nutrient concentrations, suggesting its high reliability for indicating the impacts of agricultural activities, which was further substantiated by classification and regression tree (CART) analysis. We recommend analyzing macroinvertebrate assemblages using both multivariate and multimetric approaches to offer a more comprehensive evaluation of the impacts of agricultural activities on ecosystem health. Some environmental (CODMₙ, NH₄⁺-N and PO₄³⁻-P) and biological parameters (total number of taxa), however, can be used as good proxies for MMI, when time and resources for gathering information to develop MMI are limited.

Economic development, population growth, industrialization, and urbanization dramatically increase urban water quality deterioration, and thereby endanger human life and health. However, there are not many efficient methods and techniques to monitor urban black and odorous water (BOW) pollution. Our research aims at identifying primary indicators of urban BOW through their spectral characteristics and differentiation. This research combined ground in-situ water quality data with ground hyperspectral data collected from main urban BOWs in Guangzhou, China, and integrated factorial data mining and machine learning techniques to investigate how to monitor urban BOW. Eight key water quality parameters at 52 sample sites were used to retrieve three latent dimensions of urban BOW quality by factorial data mining. The synchronically measured hyperspectral bands along with the band combinations were examined by the machine learning technique, Lasso regression, to identify the most correlated bands and band combinations, over which three multiple regression models were fitted against three latent water quality indicators to determine which spectral bands were highly sensitive to three dimensions of urban BOW pollution. The findings revealed that the many sensitive bands were concentrated in higher hyperspectral band ranges, which supported the unique contribution of hyperspectral data for monitoring water quality. In addition, this integrated data mining and machine learning approach overcame the limitations of conventional band selection, which focus on a limited number of band ratios, band differences, and reflectance bands in the lower range of infrared region. The outcome also indicated that the integration of dimensionality reduction with feature selection shows good potential for monitoring urban BOW. This new analysis framework can be used in urban BOW monitoring and provides scientific data for policymakers to monitor it.

Research on the environmental fate and behavior of novel brominated flame retardants (NBFRs) remains limited, especially in the remote alpine regions. In this study, the concentrations and distributions of NBFRs were investigated in soils and mosses collected from two slopes of Shergyla in the southeast of the Tibetan Plateau (TP), to unravel the environmental behaviors of NBFRs in this background area. The total NBFR concentrations (∑₇NBFRs) ranged from 34.2 to 879 pg/g dw in soil and from 72.8 to 2505 pg/g dw in moss. ∑₇NBFRs in soil samples collected in 2019 were significantly higher than those in 2012 (p < 0.05). Decabromodiphenyl ethane (DBDPE) was the predominant NBFR, accounting for 90% of ∑₇NBFRs on average. The ratio of the concentrations in moss and soil showed significantly positive correlations with LogKOA except for DBDPE (p < 0.05), indicating that the role of mosses as accumulators compared to soils are more pronounced for more volatile NBFRs. In addition, the concentrations of NBFRs generally decreased with increasing altitude on the south-facing slope, whereas on the north-facing slope some NBFRs exhibited different trends, suggesting concurrent local and long-range transport sources. Normalization based on total organic carbon/lipid concentrations strengthened the correlation with altitude, implying that the altitude gradient of the mountain slope and forest cover could jointly affect the distribution of NBFRs in the TP. Furthermore, principal components analysis (PCA) with multiple linear regression analysis (MLRA) showed that the average contribution of the mountain cold trapping effect (MCTE) accounted for the major (77%) contribution and forest filter effect (FFE) has only a modest contribution to the deposition of NBFRs in soil.