Following the widespread assumption that a majority of ubiquitous marine microplastic particles originate from land-based sources, recent studies identify rivers as important pathways for microplastic particles (MPP) to the oceans. Yet a detailed understanding of the underlying processes and dominant sources is difficult to obtain with the existing accurate but extremely time-consuming methods available for the identification of MPP.Thus in the presented study, a novel approach applying short-wave infrared imaging spectroscopy for the quick and semi-automated identification of MPP is applied in combination with a multitemporal survey concept. Volume-reduced surface water samples were taken from transects at ten points along a major watercourse running through the South of Berlin, Germany, on six dates. After laboratory treatment, the samples were filtered onto glass fiber filters, scanned with an imaging spectrometer and analyzed by image processing.The presented method allows to count MPP, classify the plastic types and determine particle sizes. At the present stage of development particles larger than 450 μm in diameter can be identified and a visual validation showed that the results are reliable after a subsequent visual final check of certain typical error types. Therefore, the method has the potential to accelerate microplastic identification by complementing FTIR and Raman microspectroscopy. Technical advancements (e.g. new lens) will allow lower detection limits and a higher grade of automatization in the near future.The resulting microplastic concentrations in the water samples are discussed in a spatio-temporal context with respect to the influence (i) of urban areas, (ii) of effluents of three major Berlin wastewater treatment plants discharging into the canal and (iii) of precipitation events. Microplastic concentrations were higher downstream of the urban area and after precipitation. An increase in microplastic concentrations was discernible for the wastewater treatment plant located furthest upstream though not for the other two.

In order to understand the impacts of regional emission changes and local tourism on sulfur and nitrogen wet deposition in Jiuzhaigou National Nature Reserve of southwestern China, wet deposition was monitored at a background site (Rize) and a tourist-affected site (PE: park entrance) in the reserve during 2015–2016. The observation data were compared between Rize and PE and between 2010–2011 and 2015–2016 monitoring campaigns. Also, the observation data were used in the Positive Matrix Factorization (PMF) model to identify the major sources of sulfur and nitrogen wet deposition. The results show that although local tourism emissions had considerable contributions to NH₄⁺, NO₂⁻, NO₃⁻, and SO₄²⁻ concentrations in wet deposition (p < 0.05), most of the annual Volume Weighted Mean (VWM) concentrations of these four ions were likely from emissions outside Jiuzhaigou. Annual wet deposition fluxes of the four ions were also affected more by precipitation and regional emissions than by local emissions. Although annual precipitation was higher at Rize (818 mm) during 2015–2016 than at another background site near Long Lake (LL: 752 mm) during 2010–2011, the annual concentrations and fluxes of SO₄²⁻ and NO₃⁻ wet deposition decreased by 77% and 74% for SO₄²⁻ and by 12% and 19% for NO₃⁻, respectively, most likely due to regional emission reductions. Similar large reductions in SO₄²⁻ and NO₃⁻ concentrations have been also found in some other sites in southwestern China. In contrast, the annual concentration and flux of NH₄⁺ wet deposition at Rize during 2015–2016 were 1.4 and 1.2 times of that measured at LL during 2010–2011, respectively. The results of source apportionment analysis and tour bus emission estimates suggest that elevated NH₄⁺ wet deposition was possibly related to NH₃ emissions from local tour buses, but additional studies on NH₃ emissions from tour buses in the reserve are needed to confirm this.

Data from an in situ monitoring network and five ozone sondes are analysed during August of 2012, and a high tropospheric ozone episode is observed around the 8th of AUG. The Community Multi-scale Air Quality (CMAQ) model and its process analysis tool were used to study factors and mechanisms for high ozone mixing ratio at different levels of ozone vertical profiles. A sensitive scenario without chemical initial and boundary conditions (ICBCs) from MOZART4-GEOS5 was applied to study the impact of stratosphere-troposphere exchange (STE) on vertical ozone. The simulation results indicated that the first high ozone peak near the tropopause was dominated by STE. Results from process analysis showed that: in the urban area, the second peak at approximately 2 km above ground height was mainly caused by local photochemical production. The third peak (near surface) was mainly caused by the upwind transportation from the suburban/rural areas; in the suburban/rural areas, local photochemical production of ozone dominated the high ozone mixing ratio from the surface to approximately 3 km height. Furthermore, the capability of indicators to distinguish O3-precursor sensitivity along the vertical O3 profiles was investigated. Two sensitive scenarios, which had cut 30% anthropogenic NOX or VOC emissions, showed that O3-precursor indicators, specifically the ratios of O3/NOy, H2O2/HNO3 or H2O2/NOZ, could partly distinguish the O3-precursor sensitivity between VOCs-sensitive and NOx-sensitive along the vertical profiles. In urban area, the O3-precursor relationship transferred from VOCs-sensitive within the boundary layer to NOx-sensitive at approximately 1–3 km above ground height, further confirming the dominant roles of transportation and photochemical production in high O3 peaks at the near-ground layer and 2 km above ground height, respectively.

There is considerable interest in applying omics techniques, which have proven extremely valuable for laboratory-based toxicology studies, towards field-scale ecotoxicology and environmental monitoring. Concerns that confounding factors in natural ecosystems may exacerbate variability in omics datasets must be addressed to validate the transition from laboratory to field. This study explores how temporal variability related to seasonal and climatic trends influence qualitative and quantitative metabolomics outcomes, in fish from reference and metal(loid)-polluted wetlands in Australia. Female mosquitofish (Gambusia holbrooki) were sampled on two separate occasions, from a rehabilitated tailings wetland at the site of historic antimony (Sb) processing and a reference wetland with comparable water quality. The first sampling coincided with greater monthly rainfall and colder water temperature, whereas the second sampling was drier and water was warmer. Despite temporal changes and associated differences in metal(loid) concentrations, site differences in metabolite profiles were qualitatively very similar between sampling events. However, quantitative differences were observed, with a greater number of significantly altered metabolites identified during the second sampling event, which coincided with greater metal(loid) concentrations in both water and fish. The majority of identified metabolites were elevated in fish from the contaminated wetland, but with notable decreases in several metabolites that are known to play a role in various aspects of metal(loid) binding, detoxification and excretion. Specifically, decreased aspartate, histidine, myo-inositol, taurine and choline were observed in fish from the contaminated wetland, and may therefore represent a metabolite suite that is broadly indicative of metal toxicity. Quantitative differences between sampling events are suggestive of a dose-response relationship observable at the cellular level which, if harnessed, may be useful for assigning levels of concern based on the degree of change in a multi-parameter set of metabolite biomarkers.

Understanding the impact on human health during peak episodes in air pollution is invaluable for policymakers. Particles less than PM₂.₅ can penetrate the respiratory system, causing cardiopulmonary and other systemic diseases. Statistical regression models are usually used to assess air pollution impacts on human health. However, when there are databases missing, linear statistical regression may not process well and alternative data processing should be considered. Nonlinear Artificial Neural Networks (ANN) are not employed to research environmental health pollution even though another advantage in using ANN is that the output data can be expressed as the number of hospital admissions. This research applied ANN to assess the impact of air pollution on human health. Three well-known ANN were tested: Multilayer Perceptron (MLP), Extreme Learning Machines (ELM) and Echo State Networks (ESN), to assess the influence of PM₂.₅, temperature, and relative humidity on hospital admissions due to respiratory diseases. Daily PM₂.₅ levels were monitored, and hospital admissions for respiratory illness were obtained, from the Brazilian hospital information system for all ages during two sampling campaigns (2008–2011 and 2014–2015) in Curitiba, Brazil. During these periods, the daily number of hospital admissions ranged from 2 to 55, PM₂.₅ concentrations varied from 0.98 to 54.2 μg m⁻³, temperature ranged from 8 to 26 °C, and relative humidity ranged from 45 to 100%. Of the ANN used in this study, MLP gave the best results showing a significant influence of PM₂.₅, temperature and humidity on hospital attendance after one day of exposure. The Anova Friedman's test showed statistical difference between the appliance of each ANN model (p < .001) for 1 lag day between PM₂.₅ exposure and hospital admission. ANN could be a more sensitive method than statistical regression models for assessing the effects of air pollution on respiratory health, and especially useful when there is limited data available.

Benzo(a)pyren-7,8-dihydrodiol-9,10-epoxide (BPDE) is an endocrine disrupter and ultimate carcinogenic product of benzo(a)pyrene (BaP). Numerous studies have shown that BPDE causes trophoblast-related diseases, such as preeclampsia, growth restriction or miscarriages. However, the underlying mechanism, especially the mitochondria-related BPDE-induced trophoblast dysfunction remains unknown. In this study, we examined mitochondrial functions in BPDE-induced human trophoblast cell line Swan 71. BPDE decreased cell ability, attenuated cell invasion and HCG secretion, induced cell apoptosis, decreased mitochondrial membrane potential, increased reactive oxygen species (ROS) and MDA, and decreased SOD activity in a dose-dependent manner. In the mechanism, BPDE significantly increased pro-apoptosis protein (P53 and Bak1) and decreased anti-apoptosis protein (Bcl-2). Furthermore, the protein expression levels of mitochondrial fusion genes (Mfn1, Mfn2, and OPA1) were decreased and those of fission genes (Fis1 and Drp1) were increased with increasing concentrations of BPDE and incubation time, resulting in the release of Cyt c and activation of Caspase 3, which irreversibly induced trophoblast cell apoptosis. This study reveals the mechanism of dysfunction of trophoblast cells through cell apoptosis due to the disorder of mitochondrial fission/fusion after exposure to BPDE, providing a further experimental understanding the adverse effects of BaP on trophoblast cells in early pregnancy.

A Community Multiscale Air Quality (CMAQ) model with source-oriented lumped SAPRC-11 (S11L) photochemical mechanism and secondary organic aerosol (SOA) module was applied to determine the contributions of anthropogenic and biogenic sources to SOA concentrations in China. A one-year simulation of 2013 using the Multi-resolution Emission Inventory for China (MEIC) shows that summer SOA are generally higher (10–15 μg m−3) due to large contributions of biogenic (country average 60%) and industrial sources (17%). In winter, SOA formation was mostly due to anthropogenic emissions from industries (40%) and residential sources (38%). Emissions from other countries in southeast China account for approximately 14% of the SOA in both summer and winter, and 46% in spring due to elevated open biomass burning in southeast Asia. The Regional Emission inventory in ASia v2.1 (REAS2) was applied in this study for January and August 2013. Two sets of simulations with the REAS2 inventory were conducted using two different methods to speciate total non-methane carbon into model species. One approach uses total non-methane hydrocarbon (NMHC) emissions and representative speciation profiles from the SPECIATE database. The other approach retains the REAS2 speciated species that can be directly mapped to S11L model species and uses source specific splitting factors to map other REAS2 lumped NMHC species. Biogenic emissions are still the most significant contributor in summer based on these two sets of simulations. However, contributions from the transportation sector to SOA in January are predicted to be much more important based on the two REAS2 emission inventories (∼30–40% vs. ∼5% by MEIC), and contributions from residential sources according to REAS2 was much lower (∼21–24% vs. ∼42%). These discrepancies in source contributions to SOA need to be further investigated as the country seeks for optimal emission control strategies to fight severe air pollution.

An in-depth understanding of the impacts of surface roughness on road-deposited sediment (RDS) build-up and wash-off is essential for the estimation of surface runoff loads and design of RDS control measures. In this study, RDS build-up and wash-off dynamic processes were investigated on paired asphalt and concrete road surfaces with 35 days of continuous sampling during different natural rainfall events. Our results showed that RDS build-up loads and grain size composition were affected by surface roughness, while the impact of surface roughness on the length of the dynamic equilibrium period was not notable. Selective wash-off of RDS with different effects according to grain size are more likely to occur on asphalt road surfaces during rainfall-runoff, but the RDS wash-off percentage is not affected by surface roughness during snowmelt-runoff. Both total apparent depression depth and micro-depression structures influence RDS build-up and wash-off dynamics. These results imply that surface roughness has combined effects on RDS build-up and wash-off dynamics during the generation and control of urban diffuse pollution.

Nitrous oxide (N₂O) is a major greenhouse gas, with elevated emission being reported from subtropical forests that receive high nitrogen (N) deposition. After 10 years of monthly addition of ammonium nitrate (NH₄NO₃) or sodium nitrate (NaNO₃) to a Mason pine forest at Tieshanping, near Chongqing city in Southwest China, the simulated N deposition was stopped in October 2014. The results of soil N₂O emissions monitoring in different seasons during the nitrogen application period showed that nitrogen addition significantly increased soil N₂O emission. In general, the N₂O emission fluxes were positively correlated to nitrate (NO₃⁻) concentrations in soil solution, supporting the important role of denitrification in N₂O production, which was also modified by environmental factors such as soil temperature and moisture. After stopping the application of nitrogen, the soil N₂O emissions from the treatment plots were no longer significantly higher than those from the reference plots, implying that a decrease in nitrogen deposition in the future would cause a decrease in N₂O emission. Although the major forms of N deposition, NH₄⁺ and NO₃⁻, had not shown significantly different effects on soil N₂O emission, the reduction in NH₄⁺ deposition may decrease the NO₃⁻ concentrations in soil solution faster than the reduction in NO₃⁻ deposition, and thus be more effective in reducing N₂O emission from N-saturated forest soil in the future.

For human health benefits it is crucial to see if carcinogenic air pollutants like polycyclic aromatic hydrocarbons (PAHs) are reduced accordingly along with the control of the criteria pollutants including fine particles (PM₂.₅). A number of studies documented that enhanced temporary emission control during the 2014 Asia-Pacific Economic Cooperation summit (APEC) in Beijing resulted in substantial drops of observed ambient PM₂.₅, as well as PAHs, in urban areas of Beijing, yet it is not clear whether PM₂.₅-bound PAHs in the rural areas were also lowered during the APEC. Here filter-based PM₂.₅ samples were collected at a rural site in northeast of Beijing, and analyzed for 25 PAHs before (Oct. 27-Nov. 2, 2014), during (Nov. 3–12, 2014) and after (Nov. 13, 2014–Jan. 14, 2015) the APEC. Observed concentrations of PM₂.₅, OC and EC during the APEC dropped by about 30%, however, average PM₂.₅-bound PAHs and their incremental lifetime cancer risk (ILCR), 25.65 ng/m³ and 3.2 × 10⁻⁴, remained almost unchanged when compared to that of 25.48 ng/m³ and 3.5 × 10⁻⁴, respectively, before the APEC. After the APEC with the start of wintertime central heating in urban Beijing on Nov. 15, 2014, average total concentration of PAHs and their ILCR highly elevated and reached 118.25 ng/m³ and 1.5 × 10⁻³, respectively. Source apportioning by positive matrix factorization (PMF) revealed that coal combustion was the largest source that contributed 63.2% (16.1 ng/m³), 78.5% (20.1 ng/m³) and 56.1% (66.3 ng/m³) to the total PAHs before, during and after the APEC, respectively. Uncontrolled residential coal use during the APEC was found to be the reason for unabated levels of PAHs, and the largely aggravated PAHs after the APEC was resulted from increased coal consumption for wintertime residential heating. Our results suggested reducing emission from residential coal combustion is crucial to mitigate carcinogenic PAHs in ambient air, especially in rural areas.