During the 2016 Hangzhou G20 Summit, the chemical composition of submicron particles (PM1) was measured by a High-Resolution Time-of-Flight Aerosol Mass Spectrometer (HR-ToF-AMS) along with a suite of collocated instruments. The campaign was undertaken between August 5 and September 23, 2016. The impacts of emission controls and meteorological conditions on PM1 chemical composition, diurnal cycles, organic aerosol (OA) source apportionment, size distribution and elemental ratios were characterized in detail. Excluding rainy days, the mean PM1 mass concentration during G20 was 30.3 μg/m³, similar to that observed before G20 (28.6 μg/m³), but much lower than that after G20 (42.7 μg/m³). The aerosol chemistry during the three periods was substantially different. Before G20, high PM1 loading mostly occurred at daytime, with OA accounting for 60.1% of PM1, followed by sulfate (15.6%) and ammonium (9.1%). During G20, the OA fraction decreased from 60.1% to 44.6%, whereas secondary inorganic aerosol (SIA) increased from 31.8% to 49.5%. After G20, SIA dominated high PM1 loading, especially at nighttime. Further analysis showed that the nighttime regional transport might play an unfavorable role in the slight increase of secondary PM1 during G20, while the strict emissions controls were implemented. The OA (O/C = 0.58) during G20 was more aged, 48.7% and 13.7% higher than that before and after G20 respectively. Our study highlighted that the emission controls during G20 were of great success in lowering locally produced aerosol and pollutants, despite of co-existence of nighttime regional transport containing aerosol high in low-volatile organics and sulfate. It was implied that not only are emissions controls on both local and regional scale important, but that the transport of pollutants needs to be sufficiently well accounted for, to ensure the successful implementation of air pollution mitigation campaigns in China.

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.

Recent decade has witnessed accelerating expansion of chemical industry and increasing conflicts between the local citizens, governmental authorities and project developers, especially in some coastal and port cities in China. Development and transformation of chemical industrial parks has been adopted as a national initiative recently. However, there is a paucity of research examining public perspectives on chemical industrial parks and their risks. Aiming to understand public perception, attitude, and response and the factors underlying the support/acceptance of chemical industry park, this paper investigated 418 residents neighboring to two chemical industrial parks, Dalian in Bohai Rim through face-to-face questionnaire survey. The results showed the knowledge of the respondents on the chemical industrial parks development was very limited. The respondents had complex perceptions on the environmental impacts, risks control, social-economic benefits, and problem awareness. The current levels of information disclosure and public participation were very low. The central governmental official (44.3%) was the most trustworthy group by the respondents. Only 5.5% and 23.2% of the respondents supported the construction of a new CIP nearby and far away their homes, whilst 13% thought new CIP project as acceptable. The spearman correlation analysis results showed a strong NIMBY effect (Not In My Backyard). Factor analysis results demonstrated five latent factors: knowledge, benefit, information, trust, and participation. Multiple linear regression analysis indicated how socio-demographic differences and five latent factors might impact on the support/acceptance of the chemical industrial parks. Education level, trust, information, and participation were significant predictors of public support/acceptance level. This study contributes to our limited knowledge and understanding of public sentiments to the chemical industry parks in China.

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.

Heavy metals in urban soils may impose a threat to public health and may negatively affect urban tree viability. Vegetation spectroscopy techniques applied to bio-indicators bring new opportunities to characterize heavy metal contamination, without being constrained by laborious soil sampling and lab-based sample processing. Here we used Tilia tomentosa trees, sampled across three European cities, as bio-indicators i) to investigate the impacts of elevated concentrations of cadmium (Cd) and lead (Pb) on leaf mass per area (LMA), total chlorophyll content (Chl), chlorophyll a to b ratio (Chla:Chlb) and the maximal PSII photochemical efficiency (Fv/Fm); and ii) to evaluate the feasibility of detecting Cd and Pb contamination using leaf reflectance spectra. For the latter, we used a partial-least-squares discriminant analysis (PLS-DA) to train spectral-based models for the classification of Cd and/or Pb contamination. We show that elevated soil Pb concentrations induced a significant decrease in the LMA and Chla:Chlb, with no decrease in Chl. We did not observe pronounced reductions of Fv/Fm due to Cd and Pb contamination. Elevated Cd and Pb concentrations induced contrasting spectral changes in the red-edge (690–740 nm) region, which might be associated with the proportional changes in leaf pigments. PLS-DA models allowed for the classifications of Cd and Pb contamination, with a classification accuracy of 86% (Kappa = 0.48) and 83% (Kappa = 0.66), respectively. PLS-DA models also allowed for the detection of a collective elevation of soil Cd and Pb, with an accuracy of 66% (Kappa = 0.49). This study demonstrates the potential of using reflectance spectroscopy for biomonitoring of heavy metal contamination in urban soils.

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.

The oxidative potential (OP) and chemical characteristics of fine particles collected from urban, roadside, rural, and industrial sites in Korea during spring, summer, fall, and winter seasons and an urban site in the Philippines during dry and wet seasons were examined. Significant differences in the OP of fine particles among sites and seasons were found. The industrial site yielded the highest OP activity (both mass and volume-normalized OP) among the sites, suggesting the strongest reactive oxygen species (ROS)-generating capability of industry source-dominant PM₂.₅. Seasonal data show that OP activities increased during the spring and summer possibly due to increased heavy metals caused by dust events and secondary organic aerosols formed by strong photochemical activity, respectively. The strength of the OP association with the chemical components highlights the influence of organic carbon and transition metals on the OP of ambient fine particles. The two OP assays (dithiothreitol (DTT) and electron spin resonance (ESR)) having different ROS-generating mechanisms were found to have different sensitivities to the chemical components facilitating a complementary analysis of the OP of ambient fine particles. Multiple linear regression model equations (OP as a function of chemical components) which were dependent on the sites were derived. A comparison of the daily OP and hazard index (HI) (the ratio of the measured mass concentration to the reference mass concentration of fine particles) suggests that the HI may not be sufficient to accurately estimate the health effects of fine particles, and a direct or indirect measurement of toxicity such as OP should be required in addition to the concentration level.

Pesticides, in particular insecticides, can be very beneficial but have also been found to have harmful side effects on non-target insects. Butterflies play an important role in ecosystems, are well monitored and are recognised as good indicators of environmental health. The amount of information already known about butterfly ecology and the increased availability of genomes make them a very valuable model for the study of non-target effects of pesticide usage. The effects of pesticides are not simply linear, but complex through their interactions with a large variety of biotic and abiotic factors. Furthermore, these effects manifest themselves at a variety of levels, from the molecular to metapopulation level. Research should therefore aim to dissect these complex effects at a number of levels, but as we discuss in this review, this is seldom if ever done in butterflies. We suggest that in order dissect the complex effects of pesticides on butterflies we need to integrate detailed molecular studies, including characterising sequence variability of relevant target genes, with more classical evolutionary ecology; from direct toxicity tests on individual larvae in the laboratory to field studies that consider the potentiation of pesticides by ecologically relevant environmental biotic and abiotic stressors. Such integration would better inform population-level responses across broad geographical scales and provide more in-depth information about the non-target impacts of pesticides.

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.