Many cities across the world face the challenge of severe fine particulate matter (PM₂.₅) pollution. Among the many factors that affect PM₂.₅ pollution, there is an increasing interest in the impacts of urban structure. However, quantifying these impacts in China has been difficult due to differences of study area and scale in existing research, as well as limited sample sizes. Here, we conducted a continental study focusing on 301 prefectural cities in mainland China to investigate the effects of urban structure, including urban size and urban compactness, on PM₂.₅ concentrations. Based on PM₂.₅ raster and land cover data, we used quantile regression and a general multilinear model to estimate the effects and relative contributions of urban size and urban compactness on urban PM₂.₅ pollution, with explicit consideration for pollution level, urban size and geographical location. We found: (1) nationwide, the larger and more compact that cities were, the heavier the PM₂.₅ pollution tended to be. Additionally, this relationship became stronger with increasing levels of pollution. (2) In general, urban size played a more important role than urban form, and there were no significant interactive effects between the two metrics on urban PM₂.₅ concentrations at the national scale. (3) The impacts of urban size and form varied by city size and geographical location. The impacts of urban size were only significant for small or medium-large cities but not for large cities. Among large cities, only urban form had a significantly positive effect on urban PM₂.₅ concentrations. The further north and west that cities were, the more dependent PM₂.₅ pollution was on urban form, whereas the further south and east that cities were, the greater the impact of urban size. These results provide insights into how urban design and planning can be used to alleviate air pollution.

The prevalence and transmission of antibiotic resistance genes (ARGs) and opportunistic pathogens in water environments can pose great threat to public health. However, the dissemination of ARGs and opportunistic pathogens from water environments to humans has been poorly explored. Here, we employed 16S rRNA gene sequencing and high-throughput quantitative PCR techniques to explore the seasonal distribution of ARGs and opportunistic pathogens in the Yellow River water (source water) and tap water, as well as their relationships with healthy humans at Lanzhou, China. Physiochemical analysis was applied to detect water quality parameters and heavy metal contents. The absolute abundance and diversity of ARGs in the Yellow River and tap water demonstrated distinct seasonal patterns. In winter, the Yellow river water had the highest ARG abundance and diversity, while tap water owned the lowest. Mobile genetic elements (MGEs) were the predominant driver of ARG profiles in both the Yellow river and tap water. Null model analysis showed that ARG assembly in the Yellow River was more influenced by stochastic processes than tap water and this was independent of seasons. Total organic carbon and arsenic contents exhibited positive correlations with many ARGs. Opportunistic pathogens Aeromonas and Pseudomonas may be potential hosts for ARGs. Approximately 80% of detected ARGs were shared between water samples and the human gut. These persistent ARGs could not be entirely eliminated through drinking water treatment processes. Thus, it is crucial to protect sources of tap water from anthropogenic pollution and improve water treatment technologies to reduce the dissemination of ARGs and ensure drinking-water biosafety for human health.

Air pollution has become a major issue in China, especially for traffic-related pollutants such as nitrogen dioxide (NO₂). Current studies in China at the national scale were less focused on NO₂ exposure and consequent health effects than fine particulate exposure, mainly due to a lack of high-quality exposure models for accurate NO₂ predictions over a long period. We developed an advanced modeling framework that incorporated multisource, high-quality predictor data (e.g., satellite observations [Ozone Monitoring Instrument NO₂, TROPOspheric Monitoring Instrument NO₂, and Multi-Angle Implementation of Atmospheric Correction aerosol optical depth], chemical transport model simulations, high-resolution geographical variables) and three independent machine learning algorithms into an ensemble model. The model contains three stages: (1) filling missing satellite data; (2) building an ensemble model and predicting daily NO₂ concentrations from 2013 to 2019 across China at 1×1 km² resolution; (3) downscaling the predictions to finer resolution (100 m) at the urban scale. Our model achieves a high performance in terms of cross-validation to assess the agreement of the overall (R² = 0.72) and the spatial (R² = 0.85) variations of the NO₂ predictions over the observations. The model performance remains moderately good when the predictions are extrapolated to the previous years without any monitoring data (CV R² > 0.68) or regions far away from monitors (CV R² > 0.63). We identified a clear decreasing trend of NO₂ exposure from 2013 to 2019 across the country with the largest reduction in suburban and rural areas. Our downscaled model further improved the prediction ability by 4%–14% in some megacities and captured substantial NO₂ variations within 1-km grids in the urban areas, especially near major roads. Our model provides flexibility at both temporal and spatial scales and can be applied to exposure assessment and epidemiological studies with various study domains (e.g., national or citywide) and settings (e.g., long-term and short-term).

Studies on the human body burden of dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) in populations around municipal solid waste incinerators (MSWIs) in China are limited. The objective of this study was to assess the potential adverse health effects of an 8-year MSWI on the surrounding population and identify possible exposure pathways. We hypothesized that the MSWI would result in different environmental impacts and population health outcomes between upwind and downwind of its 3 km vicinity. We conducted a 10-year retrospective mortality survey on the population surrounding the MSWI. Then, we selected 50 residents aged 50 years or older on each of the upwind and downwind sides of MSWI to test serum PCDD/Fs. Meanwhile, environmental and food exposures to PCDD/Fs were tested for selected residents. The age-adjusted mortality rates were significantly higher for residents downwind than upwind, but no significant difference was found in the standardized mortality ratio before and after the MSWI operation. The toxic equivalents (TEQ) and major congeners of PCDD/Fs were significantly higher in the sera of the downwind residents than in the upwind. The PCDD/Fs in air, soil, dust, and vegetables on the downwind side were not significantly different from those on the upwind side, but the mean concentrations of PCDD/Fs in downwind hen eggs was significantly higher than those from upwind. In conclusion, downwind residents living within 3 km of the MSWI had higher age-adjusted mortality and serum level of PCDD/Fs than upwind residents. This higher mortality rate among downwind residents was not associated with MSWI. However, the higher levels of PCDD/Fs in downwind hen eggs suggest that the downwind population dioxin exposure was related to their location.

Hourly PM₂.₅ speciation data have been widely used as an input of positive matrix factorization (PMF) model to apportion PM₂.₅ components to specific source-related factors. However, the influence of constant source profile presumption during the observation period is less investigated. In the current work, hourly concentrations of PM₂.₅ water-soluble inorganic ions, bulk organic and elemental carbon, and elements were obtained at an urban site in Nanjing, China from 2017 to 2020. PMF analysis based on observation data during specific pollution (firework combustion, sandstorm, and winter haze) and emission-reduction (COVID-19 pandemic) periods was compared with that using the whole 4-year data set (PMFwₕₒₗₑ). Due to the lack of data variability, event-based PMF solutions did not separate secondary sulfate and nitrate. But they showed better performance in simulating average concentrations and temporal variations of input species, particularly for primary source markers, than the PMFwₕₒₗₑ solution. After removing event data, PMF modeling was conducted for individual months (PMFₘₒₙₜₕ) and the 4-year period (PMF₄₋yₑₐᵣ), respectively. PMFₘₒₙₜₕ solutions reflected varied source profiles and contributions and reproduced monthly variations of input species better than the PMF₄₋yₑₐᵣ solution, but failed to capture seasonal patterns of secondary salts. Additionally, four winter pollution days were selected for hour-by-hour PMF simulations, and three sample sizes (500, 1000, and 2000) were tested using a moving window method. The results showed that using short-term observation data performed better in reflecting immediate changes in primary sources, which will benefit future air quality control when primary PM emissions begin to increase.

Thallium, a highly toxic heavy metal and priority pollutant, has been widely reported to cause neurodevelopmental toxicity in animals. However, accessible epidemiological studies concerning the neurodevelopmental toxicity of early-life thallium exposure in humans are limited. In a prospective birth cohort including 2164 mother-child pairs, we explored the effect of prenatal serum thallium exposure on cognitive development among preschool-aged children born in Ma'anshan, Anhui, China. Serum thallium concentrations were measured in the first trimester, second trimester, third trimester, and cord blood by inductively coupled plasma mass spectrometry (ICP-MS). Child cognitive development was appraised by the Chinese version of the Wechsler Preschool and Primary Scale of Intelligence-Fourth Edition (WPPSI-IV) at 4.5 years old. Multiple informants generalized estimating equations (GEEs) were fit to jointly estimate the association between the four repeated measurements of thallium concentrations and the preschool-aged children's cognitive test scores. After adjusting for potential confounders, the visual spatial index (VSI) was 1.45 points lower in the highest tertile of serum thallium during the first trimester than in the lowest tertile (p for trend = 0.04). Moreover, children in the highest tertile of serum thallium during the third trimester had a significantly lower full-scale intelligence quotient (FSIQ) (β = −1.51, 95% CI: −2.68, −0.35), VSI (β = −1.79, 95% CI: −3.16, −0.42), fluid reasoning index (FRI) (β = −1.41, 95% CI: −2.73, −0.10), and processing speed index (PSI) (β = −1.47, 95% CI: −2.71, −0.24) scores than the children in the lowest tertile. When performing stratified analysis by child sex, the associations of first- and third-trimester thallium concentrations with cognitive test scores were more prominent in boys than in girls. Our findings revealed that maternal serum thallium exposure during the first and third trimesters, but not other periods, had detrimental effects on preschoolers' cognitive development, and these effects showed sex differences.

Recently, with the rapid development of China's economy, the pollution of trace metal(loid)s (TMs) in soils has become increasingly severe and attracted widespread attention. Based on 1,402 published papers from 2000 to 2021, this study aimed to analyze the pollution intensity, ecological risk and driving factors for eight TMs (As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) in Chinese soils. Results showed that the average concentrations of eight TMs in Chinese soils all exceeded background values, and the pollution of Cd and Hg was the most serious. Based on Principal component analysis of pollution intensity and ecological risk, the priority control TMs were identified for the heavily polluted provinces. The results of Geo-detector model suggested that Urban development factors contributed most to the TM accumulation in Chinese soils. Further, spatial analysis using bivariate Moran's I indicated that industrial activities contributed most to soil TM accumulation in the middle and lower reaches of the Yangtze River, while soil TM pollution in the southwest and northwest provinces was mainly caused by mining and metal smelting. This study investigated the relationship between soil TM pollution and anthropogenic activities, thus providing a scientific basis for controlling soil TM pollution at a large-scale level.

Estuarine environments faced with contaminations from coastal zones and the inland are vital sinks of antibiotic resistance genes (ARGs). However, little is known about the temporal-spatial pattern of ARGs and its predominant constraints in estuarine environments. Here, we leveraged metagenomics to investigate ARG profiles from 16 China's estuaries across 6 climate zones in dry and wet seasons, and disentangled their relationships with environmental constraints. Our results revealed that ARG abundance, richness, and diversity in dry season were higher than those in wet season, and ARG abundance exhibited an increasing trend with latitude. The prevalence of ARGs was significantly driven by human activities, mobile gene elements, microbial communities, antibiotic residuals, physicochemical properties, and climatic variables. Among which, climatic variables and human activities ranked the most important factors, contributing 44% and 36% of the total variance of observed ARGs, respectively. The most important climatic variable shaping ARGs is temperature, where increasing temperature is associated with decreased ARGs. Our results highlight that the prevalence of ARGs in estuarine environments would be co-driven by anthropogenic activities and climate, and suggest the dynamics of ARGs under future changing climate and socioeconomic development.

Xiangjiang (XJ) is a typical urban inland river that serves as a drinking water source, which may be affected by the currently used organochlorine pesticides (CUOCPs) originating from agricultural activities in the vicinity. On this basis, this study comprehensively explored the occurrence and distribution characteristics of CUOCPs in surface water and sediments under long-term precipitation and subsequent floods. Considering the low concentration of CUOCPs in water, a technique combining high-throughput organic analysis with high-volume solid phase extraction (High-throat/Hi-volume SPE) was introduced for effective analysis of CUCOPs. The results showed that the concentration of CUOCPs in the water and sediments of XJ ranged from 2.33 to 6.40 ng L⁻¹ (average of 3.93 ± 1.15 ng L⁻¹) and from 1.52 to 21.2 ng g⁻¹ (average of 6.60 ± 4.31 ng g⁻¹ dw), respectively. The distribution of CUOCPs in water was consistent throughout XJ, but that in sediments was not uniform, indicating a stronger impact of floods on water than on sediments. Water-sediment partition coefficients were generally >2 L g⁻¹, showing a tendency of CUOCP dominance in sediments. The results of principal component analysis and cluster analysis showed that the occurrence of CUOCPs is significantly affected by exogenous disturbance, which could be flood events; meanwhile, clusters of CUOCPs were found in both water and sediments in the source-limited middle reaches in urban areas. Redundancy analysis (RDA) showed that CUOCP occurrences were not positively correlated with nutrient elements (nitrogen and phosphorus), but related to pH and dissolved oxygen (DO), indicating complex sources.

Urban rivers play a vital role in global methane (CH₄) emissions. Previous studies have mainly focused on CH₄ concentrations in urban rivers with a large amount of organic sediment. However, to date, the CH₄ concentration in gravel-bed urban rivers with very little organic sediment has not been well documented. Here, we collected water samples from an oxic urban river (Xin'an River, China; annual mean dissolved oxygen concentration was 9.91 ± 1.99 mg L⁻¹) with a stony riverbed containing very little organic sediment. Dissolved CH₄ concentrations were measured using a membrane inlet mass spectrometer to investigate whether such rivers potentially act as an important source of atmospheric CH₄ and the corresponding potential drivers. The results showed that CH₄ was supersaturated at all sampling sites in the five sampling months. The mean CH₄ saturation ratio (ratio of river dissolved CH₄ concentration to the corresponding CH₄ concentration that is in equilibrium with the atmosphere) across all sampling sites in the five sampling months was 204 ± 257, suggesting that the Xin'an River had a large CH₄ emission potential. The CH₄ concentration was significantly higher in the downstream river than in the upstream river (p < 0.05), which suggested that human activities along the river greatly impacted the CH₄ level. Statistical analyses and incubation experiments indicated that algae can produce CH₄ under oxic conditions, which may contribute to the significantly higher CH₄ concentration in August 2020 (p < 0.001) when a severe algal bloom occurred. Furthermore, other factors, such as heavy rainfall events, dissolved organic carbon concentration, and water temperature, may also be vital factors affecting CH₄ concentration. Our study enhances the understanding of dissolved CH₄ dynamics in oxic urban rivers with very little organic sediment and further proposes feasible measures to control the CH₄ concentration in urban rivers.