Atmospheric peroxyacetyl nitrate (PAN) and ozone (O₃) are two typical indicators for photochemical pollution that have adverse effects on the ecosystem and human health. Observation networks for these pollutants have been expanding in developed regions of China, such as North China Plain (NCP) and Pearl River Delta (PRD), but are sparse in Yangtze River Delta (YRD), meaning their concentration and influencing factors remain poorly understood. Here, we performed a one-year measurement of atmospheric PAN, O₃, particulate matter with aerodynamic diameter smaller than 2.5 μm (PM₂.₅), nitrogen oxides (NOₓ), carbon monoxide (CO), and meteorological parameters from December 2016 to November 2017 in Shanghai. Overall, high hourly maximum PAN and O₃ were found to be 7.0 and 185 ppbv in summer, 6.2 and 146 ppbv in autumn, 5.8 and 137 ppbv in spring, and 6.0 and 76.7 ppbv in winter, respectively. Continental air masses probably carried atmospheric pollutants to the sampling site, while frequent maritime winds brought in less polluted air masses. Furthermore, positive correlations (R: 0.72–0.85) between PAN and O₃ were found in summer, indicating a predominant role of photochemistry in their formation. Unlike in summer, weak or no correlations between PAN and O₃ were featured during the other seasons, especially in winter, due to their different loss pathways. Unexpectedly, positive correlations between PAN and PM₂.₅ were found in all seasons. During summer, moderate correlation could be attributed to the strong photochemistry acting as a common driver in the formation of secondary aerosols and PAN. During winter, high PM₂.₅ might promote PAN production through HONO production, hence resulting in a good positive correlation. Additionally, the loss of PAN by thermal decomposition (TPAN) only accounted for a small fraction (ca. 1%) of the total (PAN + TPAN) during a typical winter episode, while it significantly reached 14.4 ppbv (71.1% of the total) in summer.

Based on the wind erosion equation and the use of moderate resolution imaging spectroradiometer (MODIS) satellite remote sensing data combined with parameter normalization processing, an optimized high spatial-temporal resolution soil fugitive dust (SFD) emission inventory compiling method was proposed in this study. The "2 + 26" cities in northern China, where heavy pollution frequently occurs, were used as a case study. Using the optimized method, we estimated that the PM₅₀, PM₁₀, and PM₂.₅ emissions from SFD of "2 + 26" cities in 2018 were 2,014,927, 1,007,463, and 151,120 tons, respectively. The dust emissions and emission factors of each city presented significant differences and were generally of a greater level in high-latitude areas (such as cities in Hebei Province) than in low-latitude areas (such as cities in Henan and Shandong Province). Moreover, with an increase in latitude, vegetation cover factors generally exhibit an upward trend, while temperature and rainfall exhibit a downward trend. The dust emissions in the different months showed significant differences. The total dust emission reached the highest level in "late winter–early spring" season (February to April), and the monthly emission accounted for 15–17% of the annual emissions. While in the "summer–autumn" season (July to November), it is the lowest level of the whole year, monthly emissions accounted for 3–5% of the annual emissions. The emission inventory method proposed in this study can provide a reference for dust emission assessment and further pollution prevention and control work.

The residual layer (RL) stores a large amount of pollutants, but its effect on near-surface pollution is unknown. In this study, a two-year continuous observation was performed in Beijing using a ceilometer. The generalized boundary layer includes the mixing layer and RL. The results showed that there is no significant seasonal difference in the generalized boundary layer height (GBLH). The average GBLHs in spring, summer, autumn and winter are 1155, 1139, 1036 and 1195 m, respectively. The diurnal variation characteristics of spring, summer and autumn are similar, and the RL disappears when the mixing layer height reaches its peak in the afternoon. In winter, the development of the mixing layer is weak, and there is a 33.8% chance that the RL cannot be breached, thus making the mixing layer height at noon much lower than the GBLH. The concentrations of PM₂.₅ in the mixing layer and RL are 89 and 52 μg m⁻³, respectively, and the probability that the PM₂.₅ concentration in the RL was higher than that near the ground was 38.9%. RL transport represents an important beginning of the pollution event during the winter mornings and afternoons in Beijing. This study is helpful to better understand the structure of the RL and its influence on air pollution.

The occurrence of veterinary antibiotics and hydro-chemical parameters in eleven natural springs in a livestock production area is evaluated, jointly with the characterization of their DOM fingerprint by Orbitrap HRMS. Tetracycline and sulfonamide antibiotics were ubiquitous in all sites, and they were detected at low ng L⁻¹ concentrations, except for doxycycline, that was present at μg L⁻¹ in one location. DOM analysis revealed that most molecular formulas were CHO compounds (49 %–68 %), with a remarkable percentage containing nitrogen and sulphur (16 %–23 % and 11 %–24 %, respectively). Major DOM components were phenolic and highly unsaturated compounds (~90 %), typical for soil-derived organic matter, while approximately 11 % were unsaturated aliphatic, suggesting that springs may be susceptible to anthropogenic contamination sources. Comparing the DOM fingerprint among sites, the spring showing the most different profile was the one with surface water interaction and characterized by having lower CHO and higher CHOS formulas and aliphatic compounds. Correlations between antibiotics and DOM showed that tetracyclines positively correlate with unsaturated oxygen-rich substances, while sulfonamides relate with aliphatic and unsaturated oxygen-poor compounds. This indicates that the fate of different antibiotics will be controlled by the type of DOM present in groundwater.

Fine particulate matter (PM₂.₅) has attracted extensive attention because of its baneful influence on human health and the environment. However, the sparse distribution of PM₂.₅ measuring stations limits its application to public utility and scientific research, which can be remedied by satellite observations. Therefore, we developed a Geo-intelligent long short-term network (Geoi-LSTM) to estimate hourly ground-level PM₂.₅ concentrations in 2017 in Wuhan Urban Agglomeration (WUA). We conducted contrast experiments to verify the effectiveness of our model and explored the optimal modeling strategy. It turned out that Geoi-LSTM with TOA reflectance, meteorological conditions, and NDVI as inputs performs best. The station-based cross-validation R², root mean squared error and mean absolute error are 0.82, 15.44 μg/m³, 10.63 μg/m³, respectively. Based on model results, we revealed spatiotemporal characteristics of PM₂.₅ in WUA. Generally speaking, during the day, PM₂.₅ concentration remained stable at a relatively high level in the morning and decreased continuously in the afternoon. While during the year, PM₂.₅ concentrations were highest in winter, lowest in summer, and in-between in spring and autumn. Combined with meteorological conditions, we further analyzed the whole process of a PM₂.₅ pollution event. Finally, we discussed the loss in removing clouds-covered pixels and compared our model with several popular models. Overall, our results can reflect hourly PM₂.₅ concentrations seamlessly and accurately with a spatial resolution of 5 km, which benefits PM₂.₅ exposure evaluations and policy regulations.

Groundwater contamination originating from anthropogenic industrial activities is a global concern, adversely impacting health of living organisms and affecting natural ecosystems. Monitoring contamination in a complex groundwater system is often limited by sparse data and poor hydrogeological delineation, so that numerous indicators (organic, inorganic, isotopic) are frequently used simultaneously to reduce uncertainty. We suggest that selected Technology-Critical Elements (TCEs), which are usually found in very low concentrations in the groundwater environment, might serve as contamination indicators that can be monitored through aquifer systems. Here, we demonstrate the use of selected TCEs (in particular, Y, Rh, Tl, Ga, and Ge) as indicators for monitoring anthropogenic groundwater contamination in two different groundwater systems, near the Dead Sea, Israel. Using these TCEs, we show that the sources of local groundwater contamination are phosphogypsum ponds located adjacent to fertilizer plants in two industrial areas. In addition, we monitored the spatial distribution of the contaminant plume to determine the extent of well and spring contamination in the region. Results show significant contamination of the groundwater beneath both fertilizer plants, leading to contamination of a series of wells and two natural springs. The water in these springs contains elevated concentrations of toxic metals; U and Tl levels, among others, are above the maximum concentration limits for drinking water.

Daunting amounts of microplastics are present in surface waters worldwide. A main category of microplastics is synthetic microfibers, which originate from textiles. These microplastics are generated and released in laundering and are discharged by wastewater treatment plants or enter surface waters from other sources. The polymers that constitute many common synthetic microfibers are mostly denser than water, and eventually settle out in aquatic environments. The interaction of these microfibers with submerged aquatic vegetation has not been thoroughly investigated but is potentially an important aquatic sink in surface waters. In the Laurentian Great Lakes, prolific growth of macrophytic Cladophora creates submerged biomass with a large amount of surface area and the potential to collect and concentrate microplastics. To determine the number of synthetic microfibers in Great Lakes Cladophora, samples were collected from Lakes Erie and Michigan at multiple depths in the spring and summer of 2018. After rinsing and processing the algae, associated synthetic microfibers were quantified. The average loads of synthetic microfibers determined from the Lake Erie and Lake Michigan samples were 32,000 per kg (dry weight (dw)) and 34,000 per kg (dw), respectively, 2–4 orders of magnitude greater than loads previously reported in water and sediment. To further explore this sequestration of microplastics, fresh and aged Cladophora were mixed with aqueous mixtures of microfibers or microplastic in the laboratory to simulate pollution events. Microscopic analyses indicated that fresh Cladophora algae readily interacted with microplastics via adsorptive forces and physical entanglement. These interactions mostly cease upon algal senescence, with an expected release of microplastics in benthic sediments. Collectively, these findings suggest that synthetic microfibers are widespread in Cladophora algae and the affinity between microplastics and Cladophora may offer insights for removing microplastic pollution.Macroalgae in the Laurentian Great Lakes contain high loads of synthetic microfibers, both entangled and adsorbed, which likely account for an important fraction of microplastics in these surface waters.

The deposition of light absorbing impurities (LAIs) (e.g., black carbon (BC), organic carbon (OC), mineral dust (MD)) on snow is an important attribution to accelerate snowmelt across the northern Xinjiang, China. At present, there is still a lack of understanding of the LAIs concentration, elution and enrichment process in snow cover over Xinjiang. Based on these, continuously sampling during two years carried out to investigate the concentrations, impacts and potential sources of LAIs in snow at Kuwei Station in the southern Altai Mountains. The average concentrations of BC, OC and MD in the surface snow were 2787 ± 2334 ng g⁻¹, 6130 ± 6127 ng g⁻¹, and 70.03 ± 62.59 μg g⁻¹, respectively, which dramatically increased along with snowmelt intensified, reflecting a significant enrichment process of LAIs at the snow surface. Besides, high LAIs concentrations also found in the subsurface and melting layers of the snowpit, reflecting the elution and redistribution of LAIs. With the simulation of the SNow ICe Aerosol Radiative model, BC was the main dominant factor in reducing snow albedo and radiative forcing (RF), its impact was more remarkable in the snowmelt period. The average contribution rates of BC, MD and BC + MD to snow albedo reduction increased by 20.0 ± 1.9%, 13.0 ± 0.2%, and 20.5 ± 2.3% in spring compared with that in winter; meanwhile, the corresponding average RFs increased by 15.8 ± 3.4 W m⁻², 4.7 ± 0.3 W m⁻² and 16.4 ± 3.2 W m⁻², respectively. Changes in the number of snowmelt days caused by BC and MD decreased by 3.0 ± 0.4 d to 8.3 ± 1.3 d. It indicated that surface enrichment of LAIs during snow melting might accelerate snowmelt further. Weather Research and Forecasting Chemistry model showed that the resident emission was the main potential source of BC and OC in snow. This implied that the mitigation of intensive snowmelt needs to mainly reduce resident emission of LAIs in the future.

Controlling ammonia (NH₃) emissions has been proposed as a strategy to mitigate haze pollution. To explore the role of NH₃ in haze pollution in Sichuan Basin, where agricultural activities are intense, hourly in situ data of NH₃, as well as nitric acid and secondary inorganic aerosols (SIAs) were gathered in Chengdu from April 2017 to March 2018. We found that NH₃ had an annual mean concentration of 9.7 ± 3.5 (mean ± standard deviation) μg m⁻³, and exhibited seasonal variations (spring > summer > autumn and winter) due to changes in emission sources and meteorological conditions (particularly temperature). Chengdu's atmosphere is generally NH₃-sufficient, especially in the warm seasons, implying that the formation of SIAs is more sensitive to the availability of nitric acid. However, an NH₃ “sufficient-to-deficient” transition was found to occur during winter pollution periods, and the frequency of NH₃ deficiency increased with the aggravation of pollution. Under NH₃-deficient conditions, the nitrogen oxidation ratio increased linearly with the increase in free NH₃, implying that NH₃ contributes appreciably to the formation of nitrate and thus to high PM₂.₅ loadings. No relationships of NH₃ with fossil fuel combustion–related pollutants were found. The NH₃ emissions from farmland and livestock waste in the suburbs of Chengdu and regional transport from west of Chengdu probably contribute to the occurrence of high PM₂.₅ loading in winter and spring, respectively. These results suggest that to achieve effective mitigation of PM₂.₅ in Chengdu, local and regional emission control of NH₃ and NOx synergistically would be effective.

To explore high-resolution temporal variation characteristics of atmospheric metal elements concentration and more accurate pollution sources apportionment, online monitoring of metal elements in PM₂.₅ with 1-h time resolution was conducted in Beijing from August 22, 2018 to August 21, 2019. Concentration of 18 elements varied between detection limit (ranging from 0.1 to 100 ng/m³) and nearly 25 μg/m³. Si, Fe, Ca, K and Al represented major elements and accounted for 93.47% of total concentration during the study period. Compared with previous studies, airborne metal pollution in Beijing has improved significantly which thanks to strict comprehensive control measures under the Clean Air Action Plan since 2013. Almost all elements present higher concentrations on weekdays than weekends, while concentrations of elements associated with dust sources during holidays are higher than those in working days after the morning peak, and there is almost no concentration difference in the evening peak period. Soil and dust, vehicle non-exhaust emissions, biomass, industrial processes and fuel combustion were apportioned as main sources of atmospheric metal pollution, accounting for 63.6%, 18.4%, 16.8%, 1.0% and 0.18%, respectively. Furthermore, main occurrence season of metal pollution is judged by characteristic radar chart of varied metal elements proposed for the first time in this study, for example, fuel combustion type pollution mainly occurs in winter and spring. Results of 72-h backward trajectory analysis of air masses showed that, except for local emissions, atmospheric metal pollution in Beijing is also affected by regional transport from Inner Mongolia, Hebei, the Bohai Sea and Heilongjiang.