Herbicide pollution is persistent, which not only has a negative impact on individual organisms, but also may endanger the interspecific relationship between predators and prey. Cladocerans, i.e. zooplankton that plays an important role in the energy flow and material circulation in freshwater ecosystem, usually develop induced defense in response to predation risk. We used atrazine, one of the most used herbicides in the world, and Daphnia pulex, a representative cladocerans, to test the possible interference effect of herbicides on the induced defensive traits of cladocerans in response to predator fish (Rhodeus ocellatus) kairomone, including morphological defense, life history strategies, and the expression of defense-related genes. Atrazine reduced the body size, spine size, growth rate, total offspring, and the relative reproductive output of D. pulex, which further affected the response strength of the morphological and life history defenses, i.e., atrazine significantly reduced the spine size, relative spine size, and fecundity of D. pulex in response to R. ocellatus kairomone. Exposure to atrazine affected the expression of defense-related genes, and we speculated that atrazine affected the signaling process in the induced anti-predation defense of cladocerans. Specially, fish kairomone attenuated the negative effects of high concentrations of atrazine on the life history traits of D. pulex. Our results will help to accurately assess the potential risk of artificial compounds in freshwater ecosystems from the perspective of interspecific relationships, and help to understand the impact of environmental changes on the inducible anti-predator defense of prey in aquatic ecosystems.

A growing number of studies report associations between air pollution and COVID-19 mortality. Most were ecological studies at the county or regional level which disregard important local variability and relied on data from only the first few months of the pandemic. Using COVID-19 deaths identified from death certificates in California, we evaluated whether long-term ambient air pollution was related to weekly COVID-19 mortality at the census tract-level during the first ∼12 months of the pandemic. Weekly COVID-19 mortality for each census tract was calculated based on geocoded death certificate data. Annual average concentrations of ambient particulate matter <2.5 μm (PM₂.₅) and <10 μm (PM₁₀), nitrogen dioxide (NO₂), and ozone (O₃) over 2014–2019 were assessed for all census tracts using inverse distance-squared weighting based on data from the ambient air quality monitoring system. Negative binomial mixed models related weekly census tract COVID-19 mortality counts to a natural cubic spline for calendar week. We included adjustments for potential confounders (census tract demographic and socioeconomic factors), random effects for census tract and county, and an offset for census tract population. Data were analyzed as two study periods: Spring/Summer (March 16-October 18, 2020) and Winter (October 19, 2020–March 7, 2021). Mean (standard deviation) concentrations were 10.3 (2.1) μg/m³ for PM₂.₅, 25.5 (7.1) μg/m³ for PM₁₀, 11.3 (4.0) ppb for NO₂, and 42.8 (6.9) ppb for O₃. For Spring/Summer, adjusted rate ratios per standard deviation increase were 1.13 (95% confidence interval: 1.09, 1.17) for PM₂.₅, 1.16 (1.11, 1.21) for PM₁₀, 1.06 (1.02, 1.10) for NO₂, and 1.09 (1.04, 1.14) for O₃. Associations were replicated in Winter, although they were attenuated for PM₂.₅ and PM₁₀. Study findings support a relation between long-term ambient air pollution exposure and COVID-19 mortality. Communities with historically high pollution levels might be at higher risk of COVID-19 mortality.

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.

The Nanfei River was one of dominant inflowing rivers of the fifth largest freshwater Chaohu Lake in China, which had been subjected to increasing nutrients and contaminants from population expansion, rapid industrialization and agricultural intensification in recent decades. In present study, surface sediment from the Nanfei River was collected to investigate the anthropogenic impact on distribution and bioavailability of heavy metals. Possible Cd sources along the river were constrained by using Cd isotope signatures and labile concentrations of heavy metals in sediment were determined through the DGT technique for risk assessment. Results showed that Cd in river sediment showed greatest enrichment (EF 0.8–9.4), indicating massive pollution from anthropogenic activities. Among the various possible Cd source materials, urban road dust, industrial soil and chicken manure, displayed higher Cd abundance and enrichment that might contribute to Cd accumulation in river sediment. Cadmium isotopic composition in river sediment was ranged from −0.21 ± 0.01‰ to 0.13 ± 0.03‰, whereas yielded relative variation from −0.31 ± 0.02‰ to 0.23 ± 0.01‰ in source materials. Accordingly, Cd sources along the river were constrained, i.e. traffic and industrial activities in the upper and middle reaches whereas agricultural activities in the lower reaches. Furthermore, the evaluation on ecological risk of heavy metals in sediment on basis of SQGs and DGT-labile concentrations demonstrated that Pb and Zn might pose higher risk on aquatic species. The present study confirmed that Cd isotopes were promising source tracer in environmental studies.

Estuarine rivers are the primary medium for transporting pollutants from human activities to the ocean. Polycyclic aromatic hydrocarbons (PAHs) have substantial toxicity and pose a significant risk to ecosystem and human health. However, the influences of urbanization on their distribution, particularly in China where urbanization is occurring rapidly, remain unclear. This study took three coastal economic circles of China as research areas, and investigated PAHs (16 species) in the estuarine river water. 95.9% of the sampling sites demonstrated moderate PAHs pollution and moderate ecological risk. Coal and petroleum combustion was the primary source of PAHs, but the source composition varied among the regions. Air pollution caused by energy emissions, particularly carbon emissions, has a critical and differential effect on PAHs distribution and deposition. With the increasing use of clean energy, PAHs emissions have been gradually reduced, which provides an effective option for PAHs reduction in a rapidly urbanizing coastal region.

The influence of regional transport on aerosol pollution has been explored in previous studies based on numerical simulation or surface observation. Nevertheless, owing to inhomogeneous vertical distribution of air pollutants, vertical observations should be conducted for a comprehensive understanding of regional transport. Here we obtained the vertical profiles of aerosol and its precursors using ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) at the Nancheng site in suburban Beijing on the southwest transport pathway of the Beijing-Tianjin-Hebei (BTH) region, China, and then estimated the vertical profiles of transport fluxes in the southwest-northeast direction. The maximum net transport fluxes per unit cross-sectional area, calculated as pollutant concentration multiply by wind speed, of aerosol extinction coefficient (AEC), NO₂, SO₂ and HCHO were 0.98 km⁻¹ m s⁻¹, 24, 14 and 8.0 μg m⁻² s⁻¹ from southwest to northeast, which occurred in the 200–300 m, 100–200 m, 500–600 m and 500–600 m layers, respectively, due to much higher pollutant concentrations during southwest transport than during northeast transport in these layers. The average net column transport fluxes were 1200 km⁻¹ m² s⁻¹, 38, 26 and 15 mg m⁻¹ s⁻¹ from southwest to northeast for AEC, NO₂, SO₂ and HCHO, respectively, in which the fluxes in the surface layer (0–100 m) accounted for only 2.3%–4.2%. Evaluation only based on surface observation would underestimate the influence of the transport from southwest cities to Beijing. Northeast or weak southwest transports dominated in clean conditions with PM₂.₅ <75 μg m⁻³ and intense southwest transport dominated in polluted conditions with PM₂.₅ >75 μg m⁻³. Southwest transport through the middle boundary layer was a trigger factor for aerosol pollution events in urban Beijing, because it not only directly bringing air pollutants, but also induced an inverse structure of aerosols, which resulted in stronger atmospheric stability and aggravated air pollution in urban Beijing.

Atmospheric nitrogen (N) deposition is becoming an increasingly important factor affecting the nutrient level of lakes, especially considering the long-term control measures for external N inputs in developed regions. However, few studies have investigated the effects of atmospheric N deposition and the respective ecological significance in eutrophic waters. In this study, bulk and wet deposition rates of all N species and water N concentrations in Lake Taihu were determined based on the long-term (2010–2018) high-resolution (weekly or monthly) systematic observations. The results indicated that the decline in wind speed and change in land-use type likely decreased the N deposition rate. The bulk N deposition rates decreased from 45.77 kg N ha⁻¹ yr⁻¹ in 2012 to 22.06 kg N ha⁻¹ yr⁻¹ in 2018, which could account for decrease of 1.01 mg N L⁻¹ in the lake N concentrations via a rough estimation, and this value was close to the actual variation in N concentration in Lake Taihu. The correlation between N concentrations and atmospheric deposition fluxes was stronger than that between N concentrations and riverine N inputs or lake storage, which further indicated that change in atmospheric N deposition was the key reason for the variation in N concentrations. The direct bulk N deposition into Lake Taihu accounted for 17.5% and 51.4% of the riverine N inputs and lake N inventory, respectively. Moreover, atmospheric N deposition was concentrated in summer, which was dominated by reduced N, and it may be important for the duration of algal blooms. Therefore, external N inputs, including atmospheric N deposition, should be further controlled for an effective mitigation of eutrophication and algal blooms in Lake Taihu.

The nationwide occurrence of endosulfan residues in cotton fields has not yet been investigated. Therefore, in this study, 202 surface soil samples from 27 cities were collected from cotton fields in 8 major cotton-planting provinces of China, covering more than 97% of the national cotton sown area. The results showed that endosulfan residues were detected in cotton fields throughout the country. The main type of residue found was endosulfan sulfate (ES-sulfate), followed by β-endosulfan and α-endosulfan, with average concentrations of 0.475, 0.129, and 0.048 μg/kg, respectively. Significant spatial variations in the endosulfan residues was noted, and the highest concentration of endosulfan residues was observed in the northwest inland cotton-growing area, followed by that in the Yellow River basin and Yangtze River basin cotton-growing areas. The endosulfan residues showed significant positive correlations with soil organic matter and soil clay contents. The α/β endosulfan ratio was determined to be in the range of 0.02–1.20, indicating that endosulfan residues originated from the endosulfan application performed in historical cotton cultivation efforts. Together with the literature data, the concentrations of α-endosulfan and β-endosulfan residues peaked in 2015 and 2017, respectively, and showed an overall decreasing trend from 2002 to 2021. The results of the ecological risk assessment suggested that Folsomia candida was most sensitive to endosulfan residues, with 20.8% of the sites presenting a high risk. However, in general, the soil ecological risk of cotton fields across the country was low. Our study demonstrated that China has achieved promising results in controlling the use and pollution of endosulfan, especially after 2014.