Previous studies have indicated that outdoor light at night (LAN) is associated with a higher prevalence of overweight or obesity in adults. However, the association of LAN levels with overweight or obesity in children is still unknown. This study utilized data from the Seven Northeastern Cities study, which included 47,990 school-aged children and adolescents (ages 6–18 years). Outdoor LAN levels were measured using satellite imaging data. Weight and height were used to calculate age-sex-specific body mass index (BMI) Z-scores based on the World Health Organization (WHO) growth standards. Overweight status and obesity were defined using the Chinese standard. Information regarding socioeconomic status, sleep-related characteristics, and obesogenic factors were obtained using a questionnaire. A generalized linear mixed model examined the associations of outdoor LAN levels (in quartiles) with the outcomes of interest. Compared to children in the lowest quartile of outdoor LAN levels, children exposed to higher outdoor LAN levels had larger BMI Z-scores and higher odds of being overweight (including obesity) or obese, with the largest estimates in the third quartile [BMI Z-score: β = 0.26, 95% CI: 0.18–0.33; overweight (including obesity): OR = 1.40, 95% CI: 1.25–1.56; obesity: OR = 1.46, 95% CI: 1.29–1.65]. There was a significant sex difference (Pᵢₙₜₑᵣₐcₜᵢₒₙ<0.001) in the association of outdoor LAN levels with BMI Z-scores, and the association was stronger in males. Results remained robust following multiple sensitivity analyses and the adjustment of sleep-related characteristics, obesogenic factors, and environmental exposures. Our findings suggest that higher outdoor LAN levels are associated with larger BMI Z-scores and greater odds of overweight (including obesity) and obesity in school-aged children and adolescents. Further, the association between outdoor LAN levels and BMI Z-scores is stronger in males. Future studies with exposure assessments that consider both outdoor and indoor LAN exposures are needed.

The contributions of contaminant sources are difficult to resolve in the sediment record using concentration gradients and flux reconstruction alone. In this study, we demonstrate that source partitioning using lead isotopes provide complementary and unique information to concentration gradients to evaluate point-source releases, transport, and recovery of metal mining pollution in the environment. We analyzed eight sediment cores, collected within 24 km of two gold mines, for Pb stable isotopes, Pb concentration, and sediment chronology. Stable Pb isotope ratios (²⁰⁶Pb/²⁰⁷Pb, ²⁰⁸Pb/²⁰⁴Pb) of mining ore were different from those of background (pre-disturbance) sediment, allowing the use of a quantitative mixing model. As previously reported for some Arctic lakes, Pb isotope ratios indicated negligible aerosol inputs to sediment from regional or long-range pollution sources, possibly related to low annual precipitation. Maximum recorded Pb flux at each site reached up to 63 mg m⁻² yr⁻¹ in the period corresponding to early years of mining when pollution mitigation measures were at a minimum (1950s–1960s). The maximum contribution of mining-derived Pb to these fluxes declined with distance from the mines from 92 ± 8% to 8 ± 4% at the farthest site. Mining-derived Pb was still present at the sediment surface within 9 km of Giant Mine more than ten years after mine closure (5–26 km, 95% confidence interval) and model estimates suggest it could be present for another ∼50–100 years. These results highlight the persistence of Pb pollution in freshwater sediment and the usefulness of Pb stable isotopes to quantify spatial and temporal trends of contamination from mining pollution, particularly as concentrations approach background.

Understanding how mercury (Hg) accumulates in the aquatic food web requires information on the factors driving methylmercury (MeHg) contamination. This paper employs data on MeHg in muscle tissue of three black bass species (Largemouth Bass, Spotted Bass, and Smallmouth Bass) sampled from 21 reservoirs in California. During a two-year period, reservoirs were sampled for total Hg in sediment, total Hg and MeHg in water, chlorophyll a, organic carbon, sulfate, dissolved oxygen, pH, conductivity, and temperature. These data, combined with land-use statistics and reservoir morphometry, were used to investigate relationships to size-normalized black bass MeHg concentrations. Significant correlations to black bass MeHg were observed for total Hg in sediment, total Hg and MeHg in surface water, and forested area. A multivariate statistical model predicted Largemouth Bass MeHg as a function of total Hg in sediment, MeHg in surface water, specific conductivity, total Hg in soils, and forested area. Comparison to historical reservoir sediment data suggested there has been no significant decline in sediment total Hg at five northern California reservoirs during the past 20 years. Overall, total Hg in sediment was indicated as the most influential factor associated with black bass MeHg contamination. The results of this study improve understanding of how MeHg varies in California reservoirs and the factors that correlate with fish MeHg contamination.

Apportioning sources of environmental pollutants is key to controlling pollution. In this study, the sources of heavy metals to 234 agricultural soils from the Jianghan Plain (JHP) (∼22454 km2) in central China were discriminated between using Cd and Pb isotope compositions and multivariate statistical analyses. Concentrations of some metals in JHP soils (0.48 ± 0.2, 48.2 ± 15.9, 0.12 ± 0.23, 48.8 ± 16.4, 36.5 ± 9.8, and 96.8 ± 42.2 mg kg−1 for Cd, Cu, Hg, Ni, Pb, and Zn, respectively) were higher than background concentrations in Chinese soil. The Cd isotope compositions for the JHP soils (δ114/110Cd values −0.76‰ to −0.25‰) were similar to Cd isotope compositions found for smelter dust and incinerator fly ash, indicating Cd was supplied to the JHP soils by ore smelting and/or refining processes. The Pb isotope compositions for the JHP soils (206Pb/207Pb 1.182–1.195 and 208Pb/206Pb 2.078–2.124) were between the Pb isotope compositions found for Chinese coal and natural sources, which a binary isotope mixing model indicated contributed 52% and 48%, respectively, of the Pb in JHP soils. Cluster analysis and positive matrix factorization indicated that the sources of heavy metals in JHP soils may consist of smelting and/or refining activities, coal combustion, agricultural activities, and natural sources (including Han River sediment and soil parent materials). The isotope fingerprints and multivariate statistical analyses together indicated that coal combustion and smelting and/or refining activities were the main anthropogenic sources of heavy metals polluting JHP soils.

To constrain sources of anthropogenic nitrogen (N) deposition is critical for effective reduction of reactive N emissions and better evaluation of N deposition effects. This study measured δ¹⁵N signatures of nitrate (NO3⁻), ammonium (NH4⁺) and total dissolved N (TDN) in precipitation at Guiyang, southwestern China and estimated contributions of dominant N sources using a Bayesian isotope mixing model. For NO3⁻, the contribution of non-fossil N oxides (NOx, mainly from biomass burning (24 ± 12%) and microbial N cycle (26 ± 5%)) equals that of fossil NOx, to which vehicle exhausts (31 ± 19%) contributed more than coal combustion (19 ± 9%). For NH4⁺, ammonia (NH3) from volatilization sources (mainly animal wastes (22 ± 12%) and fertilizers (22 ± 10%)) contributed less than NH3 from combustion sources (mainly biomass burning (17 ± 8%), vehicle exhausts (19 ± 11%) and coal combustions (19 ± 12%)). Dissolved organic N (DON) accounted for 41% in precipitation TDN deposition during the study period. Precipitation DON had higher δ¹⁵N values in cooler months (13.1‰) than in warmer months (−7.0‰), indicating the dominance of primary and secondary ON sources, respectively. These results newly underscored the importance of non-fossil NOx, fossil NH3 and organic N in precipitation N inputs of urban environments.

Remote high-elevation lakes represent unique environments for evaluating the bioaccumulation of atmospherically deposited mercury through freshwater food webs, as well as for evaluating the relative importance of mercury loading versus landscape influences on mercury bioaccumulation. The increase in mercury deposition to these systems over the past century, coupled with their limited exposure to direct anthropogenic disturbance make them useful indicators for estimating how changes in mercury emissions may propagate to changes in Hg bioaccumulation and ecological risk. We evaluated mercury concentrations in resident fish from 28 high-elevation, sub-alpine lakes in the Pacific Northwest region of the United States. Fish total mercury (THg) concentrations ranged from 4 to 438 ng/g wet weight, with a geometric mean concentration (±standard error) of 43 ± 2 ng/g ww. Fish THg concentrations were negatively correlated with relative condition factor, indicating that faster growing fish that are in better condition have lower THg concentrations. Across the 28 study lakes, mean THg concentrations of resident salmonid fishes varied as much as 18-fold among lakes. We used a hierarchal statistical approach to evaluate the relative importance of physiological, limnological, and catchment drivers of fish Hg concentrations. Our top statistical model explained 87% of the variability in fish THg concentrations among lakes with four key landscape and limnological variables: catchment conifer density (basal area of conifers within a lake's catchment), lake surface area, aqueous dissolved sulfate, and dissolved organic carbon. Conifer density within a lake's catchment was the most important variable explaining fish THg concentrations across lakes, with THg concentrations differing by more than 400 percent across the forest density spectrum. These results illustrate the importance of landscape characteristics in controlling mercury bioaccumulation in fish.

We analyzed the spatial distribution of an antifouling biocide, 4,5-dichloro-2-n-octyl-4-isothiazolin-3-one (Sea-Nine 211) in the surface water and sediments of Hiroshima Bay, Japan to determine the extent of contamination by this biocide. A quantitative estimate of the environmental concentration distribution (ECD) and species sensitivity distributions (SSDs) for marine organisms were derived by using a Bayesian statistical model to carry out a probabilistic ecological risk analysis, such as calculation of the expected potentially affected fraction (EPAF). The spatial distribution analysis supported the notion that Sea-Nine 211 is used mainly for treatment of ship hulls in Japan. The calculated EPAF suggests that approximately up to a maximum of 0.45% of marine species are influenced by the toxicity of Sea-Nine 211 in Hiroshima Bay. In addition, estimation of the ecological risk with a conventional risk quotient method indicated that the risk was a cause for concern in Hiroshima Bay.

We monitored 108 children ≤5 years on a 6-month basis for up to 5 years in a major urban setting. Samples (n ∼ 7000) included blood, urine, handwipes (interior, and after exterior playing), 6-day duplicate diet, drinking water, interior house and day care dust-fall accumulation using petri dishes, exterior dust-fall accumulation, exterior dust sweepings, paint, soil and urban air. The geometric mean blood Pb (PbB) was 2.1 μg/dL and blood Mn (MnB) was 10.0 μg/L. Following a path modelling approach, mixed model analyses for a fully adjusted model showed the strongest associations for PbB were with interior house dust and soil; for MnB there were no significant associations with any predictors. Predictor variables only explained 9% of the variance for Pb and 0.7% for Mn. Relationships between environmental measures and PbB in children are not straightforward; soil and dust sweepings contribute only about 1/5th of the amounts to PbB found in other studies.

To identify different NO₃ ⁻ sources in surface water and to estimate their proportional contribution to the nitrate mixture in surface water, a dual isotope and a Bayesian isotope mixing model have been applied for six different surface waters affected by agriculture, greenhouses in an agricultural area, and households. Annual mean δ¹⁵N–NO₃ ⁻ were between 8.0 and 19.4‰, while annual mean δ¹⁸O–NO₃ ⁻ were given by 4.5–30.7‰. SIAR was used to estimate the proportional contribution of five potential NO₃ ⁻ sources (NO₃ ⁻ in precipitation, NO₃ ⁻ fertilizer, NH₄ ⁺ in fertilizer and rain, soil N, and manure and sewage). SIAR showed that “manure and sewage” contributed highest, “soil N”, “NO₃ ⁻ fertilizer” and “NH₄ ⁺ in fertilizer and rain” contributed middle, and “NO₃ ⁻ in precipitation” contributed least. The SIAR output can be considered as a “fingerprint” for the NO₃ ⁻ source contributions. However, the wide range of isotope values observed in surface water and of the NO₃ ⁻ sources limit its applicability.

Poor air quality is becoming a critical environmental concern in different countries over the last several years. Most of the air pollutants have serious consequences on human health and wellbeing. In this context, efficient forecasting of air pollutants is currently crucial to predict future events with a view to taking corrective actions and framing effective environmental policies. Although deep learning (DL) as well as statistical forecasting models are investigated in the literature, they have rarely used in air pollutant-specific optimal model building for long-term forecasting. In this paper, our aim is to develop the pollutant-specific optimal forecasting models for the phases spanning from preprocessing to model building by investigating a set of predictive techniques. In this regard, this paper presents a methodology for long-term forecasting of some important air pollutants. More specifically, a total of eight best performing models such as stacked LSTM, LSTM auto-encoder, Bi-LSTM, convLSTM, Holt-Winters, auto-regressive (AR), SARIMA, and Prophet are investigated for developing pollutant-specific optimal forecasting models. The study is carried out based on the real-world data obtained from government-run air quality monitoring units in Kolkata over a period of 4 years. The models such as Holt-Winters, Bi-LSTM, and ConvLSTM achieve high forecasting accuracy with respect to MAE and RMSE values for majority of the pollutants.