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 waste-to-energy (WTE) incinerator plant located in the Turin area (Italy) started to recover energy from the combustion of municipal solid waste in 2013. A health surveillance program was implemented to evaluate the potential health effects on the population living near the plant. This program included a longitudinal biomonitoring to evaluate temporal changes of some environmental pollutants, including polycyclic aromatic hydrocarbons (PAHs), in residents living in areas near the Turin incinerator (exposed group, E) compared to those observed in subjects living far from the plant (not exposed group, NE). Ten monohydroxy-PAHs (OH-PAHs), consisting in the principal metabolites of naphthalene, fluorine, phenanthrene, and pyrene, were analyzed in urines collected from the E and NE subjects after one (T₁) and three years (T₂) of plant activity and compared with those determined in the same cohort established before the plant start-up (T₀). Spearman correlation analysis was undertaken to explore possible associations between OH-PAHs and personal characteristics, lifestyle variables, and dietary habits. A linear mixed model (LMM) approach was applied to determine temporal trends of OH-PAHs observed in the E and NE subjects and to evaluate possible differences in trend between the two groups. Temporal trends of OH-PAHs determined by LMM analysis demonstrated that, at all times, the E group had concentrations lower than those assessed in the NE group, all other conditions being equal. Moreover, no increase in OH-PAH concentrations was observed at T₁ and T₂ either in E or in NE group. Significant positive correlations were found between all OH-PAHs and smoking habits. Regarding variables associated to outdoor PAH exposure, residence near high traffic roads and daily time in traffic road was positively correlated with 1-hydroxynaphthalene and 1-hydroxypyrene, respectively. In conclusion, no impact of the WTE plant on exposure to PAHs was observed on the population living near the plant.

Wastewater-based epidemiology (WBE) has emerged as a valuable approach for forecasting disease outbreaks in developed countries with a centralized sewage infrastructure. On the other hand, due to the absence of well-defined and systematic sewage networks, WBE is challenging to implement in developing countries like Bangladesh where most people live in rural areas. Identification of appropriate locations for rural Hotspot Based Sampling (HBS) and urban Drain Based Sampling (DBS) are critical to enable WBE based monitoring system. We investigated the best sampling locations from both urban and rural areas in Bangladesh after evaluating the sanitation infrastructure for forecasting COVID-19 prevalence. A total of 168 wastewater samples were collected from 14 districts of Bangladesh during each of the two peak pandemic seasons. RT-qPCR commercial kits were used to target ORF1ab and N genes. The presence of SARS-CoV-2 genetic materials was found in 98% (165/168) and 95% (160/168) wastewater samples in the first and second round sampling, respectively. Although wastewater effluents from both the marketplace and isolation center drains were found with the highest amount of genetic materials according to the mixed model, quantifiable SARS-CoV-2 RNAs were also identified in the other four sampling sites. Hence, wastewater samples of the marketplace in rural areas and isolation centers in urban areas can be considered the appropriate sampling sites to detect contagion hotspots. This is the first complete study to detect SARS-CoV-2 genetic components in wastewater samples collected from rural and urban areas for monitoring the COVID-19 pandemic. The results based on the study revealed a correlation between viral copy numbers in wastewater samples and SARS-CoV-2 positive cases reported by the Directorate General of Health Services (DGHS) as part of the national surveillance program for COVID-19 prevention. The findings of this study will help in setting strategies and guidelines for the selection of appropriate sampling sites, which will facilitate in development of comprehensive wastewater-based epidemiological systems for surveillance of rural and urban areas of low-income countries with inadequate sewage infrastructure.

Nanoplastics (NPs) are becoming emerging pollutants of global concern. Understanding the environmental behavior of NPs is crucial for their environmental and human risk assessment. In this study, the aggregation and stability of polystyrene (PS) NPs were investigated under different hydrochemical conditions such as pH, salt type (NaCl, CaCl₂, Na₂SO₄), ionic strength (IS), and natural organic matter (NOM). The critical coagulation concentrations of PS NPs were determined to be 158.7 mM NaCl, 12.2 mM CaCl₂, and 80.0 mM Na₂SO₄. Ca²⁺ was more effective in destabilizing PS NPs, compared to Na⁺, owing to its stronger charge screening effect. In the presence of monovalent ions, NOM reduced aggregation through steric repulsion, whereas in the case of divalent ions, NOM induced aggregation through cation bridging. Initial and long-term stability studies demonstrated that, in waters with high IS and NOM content, NOM was the most significant factor affecting NPs aggregation. PS NPs would be highly suspended in all freshwaters, and even in wastewater, whereas they would aggregate rapidly and deposit in seawater. Finally, a statistical model was established to evaluate the hydrodynamic diameter of NPs in different waters. The results indicated the stability of PS NPs in natural aquatic environments and their potential for long-term transport.

In this study, we evaluated the effects of geo-climatic parameters and other potential risk factors on the prevalence of chronic toxoplasmosis (CT) in pregnant women. We searched PubMed/MEDLINE, Web of Science, EMBASE, Scopus, and SciELO databases for seroepidemiological studies published between January 1988, and February 2021. We performed meta-analysis and meta-regression by using a random effect model to synthesize data. A total of 360 eligible datasets, including 1,289,605 pregnant women from 94 countries, were included in this study. The highest and lowest prevalence rates were estimated for latitudes of 0–10° (49.4%) and ≥50° (26.8%); and for the longitude of 80–90° (44.2%) and 110–120° (7.8%), respectively. Concerning climatic parameters, the highest and lowest prevalence rates were estimated in regions with the mean relative humidities of >80% (46.6%) and <40% (27.0); annual precipitation between 1000 and 1500 mm (39.2%) and 250–500 mm (26.8%); and mean annual temperature of 20–30 °C (36.5%), and <7 °C (24.9%), respectively. Meta-regression analyses indicated significant increasing trends in prevalence of CT in pregnant women with decrease in geographical latitude (coefficient, = −0.0035), and geographical longitudes (C = −0.0017). While it was positively associated (P < 0.01) with the mean environmental temperature (C = 0.0047), annual precipitation (C = 0.000064), and mean relative humidity (C = 0.002). Our results highlighted various effects of environmental parameters on the prevalence of CT. Therefore, different regions in the world may benefit from different types of interventions, and thus, novel preventive measures in a region should be developed according to local climate, agricultural activities and people culture.

Air quality in the megacity Delhi is affected not only by local emissions but also by pollutants from crop residue burning in the surrounding areas of the city, particularly the rice straw burning in the post monsoon season. As a major burning product, gaseous CO₂, which is rather inert in the polluted atmosphere, provides an alternative solution to characterize the impact of biomass burning from a new perspective that other common tracers such as particulate matters are limited because of their physical and chemical reactiveness. Here, we report conventional ([CO₂], δ¹³C, and δ¹⁸O) and unconventional (Δ¹⁷O) isotope data for CO₂ collected at Connaught Place (CP), a core area in the megacity Delhi, and two surrounding remote regions during a field campaign in October 18–20, 2017. We also measured the isotopic ratios near a rice straw burning site in Taiwan to constrain their end member isotopic compositions. Rice straw burning produces CO₂ with δ¹³C, δ¹⁸O, and Δ¹⁷O values of −29.02 ± 0.65, 19.63 ± 1.16, and 0.05 ± 0.02‰, respectively. The first two isotopic tracers are less distinguishable from those emitted by fossil fuel combustion but the last one is significantly different. We then utilize these end member isotopic ratios, with emphasis on Δ¹⁷O for the reason given above, for partitioning sources that affect the CO₂ level in Delhi. Anthropogenic fraction of CO₂ at CP ranges from 4 to 40%. Further analysis done by employing a three-component (background, rice straw burning, and fuel combustion) mixing model with constraints from the Δ¹⁷O values yields that rice straw burning contributes as much as ∼70% of the total anthropogenic CO₂, which is more than double of the fossil fuel contribution (∼30%), during the study days.

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.

China continues to suffer from severe acid deposition, despite the government implying a series of policies to control air pollution. In this study, rainwater samples were collected from 2011 to 2016 in Sichuan province to measure the pH values and the concentrations of nine inorganic ions (SO₄²⁻, NO₃⁻, NH₄⁺, Cl⁻, Na⁺, Ca²⁺, K⁺, Mg²⁺, and F⁻), and then to investigate their spatiotemporal variations. Besides, the dominant sources for the acidic ions in the precipitation were also revealed by statistical model. The results showed that the rainwater continued to be highly acidic, and the Volume-Weighted Mean (VWM) pH value was calculated to be 5.18 during 2011 and 2016. NH₄⁺, Ca²⁺, NO₃⁻, and SO₄²⁻ were the dominant water-soluble inorganic ions, accounting for 79.2% of the total ions on average. The remarkable decrease in NO₃⁻ and SO₄²⁻ concentrations (from 75.9 to 54.3 μeq L⁻¹ and from 285 to 145 μeq L⁻¹, respectively) resulted in an increase in the pH value of rainwater from 5.24 in 2011 to 5.70 in 2016. The concentrations of SO₄²⁻, NO₃⁻, F⁻, Na⁺, and K⁺ showed remarkably seasonal variation, with the highest value observed in winter, followed by spring and autumn, and the lowest value observed in summer. High VWM concentration of these ions in winter were mainly due to adverse meteorological conditions (e.g., rare rainfall, lower planetary boundary height, and stagnant air) and intensive anthropogenic emissions. SO₄²⁻, NO₃⁻, and F⁻ ions peaked in the southeastern Sichuan province, which is a typical industrial region. NH₄⁺ concentrations decreased from 268 μeq L⁻¹ in the east to 10.4 μeq L⁻¹ in the western Sichuan province, which could be related to the development of agriculture in the eastern Sichuan province. Ca²⁺ peaked in southeastern Sichuan province due to intensive construction activities and severe stone desertification. On the basis of Positive Matrix Factorization (PMF) analysis, four sources of inorganic ions in rainwater were identified, including anthropogenic source, crust, biomass burning, and aging sea salt aerosol. Geographically Weighted Regression (GWR) was used to find the spatial correlations between the socio-economic factors and ions in the rainwater. At the regional scale, the influence of fertilizer consumption and Gross Agricultural Production (GAP) on NH₄⁺ increased from east to west; moreover the influence of Gross Industrial Production (GIP) on SO₄²⁻ and NO₃⁻ also increased.

Spatiotemporal resolved models were developed predicting daily fine particulate matter (PM₂.₅) concentrations across Switzerland from 2003 to 2013. Relatively sparse PM₂.₅ monitoring data was supplemented by imputing PM₂.₅ concentrations at PM₁₀ sites, using PM₂.₅/PM₁₀ ratios at co-located sites. Daily PM₂.₅ concentrations were first estimated at a 1 × 1km resolution across Switzerland, using Multiangle Implementation of Atmospheric Correction (MAIAC) spectral aerosol optical depth (AOD) data in combination with spatiotemporal predictor data in a four stage approach. Mixed effect models (1) were used to predict PM₂.₅ in cells with AOD but without PM₂.₅ measurements (2). A generalized additive mixed model with spatial smoothing was applied to generate grid cell predictions for those grid cells where AOD was missing (3). Finally, local PM₂.₅ predictions were estimated at each monitoring site by regressing the residuals from the 1 × 1km estimate against local spatial and temporal variables using machine learning techniques (4) and adding them to the stage 3 global estimates. The global (1 km) and local (100 m) models explained on average 73% of the total,71% of the spatial and 75% of the temporal variation (all cross validated) globally and on average 89% (total) 95% (spatial) and 88% (temporal) of the variation locally in measured PM₂.₅ concentrations.