Blueberry anthocyanin (BA) have strong health benefits as an active natural antioxidant and perfluorooctanoic acid (PFOA) can result in oxidative stress in animals. In our study, the protective effects of BA against stress induced by PFOA was investigated in the planarian Dugesia japonica using oxidative stress biomarkers, ATP contents, ATPase activity, DNA methylation and mRNA expression. PFOA exposure could resulted in malondialdehyde production. At the same time, treatment with BA decreased the production of malondialdehyde in BA-exposed and co-treatment planarians. PFOA caused activities increase in glutathione peroxidase (GPx), glutathione S-transferase (GST) and activities decrease in glutathione reductase (GR). PFOA exposure decreased the GSH and ATP contents. Additionally, it increased the GSSG contents and ATPase activity. BA administration increased the activities of GPx, GST and GR in BA and co-treatment planarians. Meanwhile BA maintained the contents of ATP, ATPase activity, GSH and GSSG by alleviating PFOA toxicity. Moreover, PFOA and BA increased the contents of 5-methylcytosine and decreased 5-hydroxymethylcytosine in all group. In addition, PFOA and BA treated planarians significantly altered the expression of genes associated with above biochemical parameters. The results showed that the mRNA expression of gpx, Djgst, gr, Djnak and dnmt1 were significantly elevated in all groups. Alterations in the mRNA expression levels indicated a stress response to PFOA exposure and anthocyanin protection. These alterations regulated biomarkers of oxidative stress, energy metabolism and DNA methylation levels in planarians. These results indicate that BA attenuated PFOA-induced oxidative stress, energy metabolism, DNA methylation and gene expression disorders.

Absolute principal component score/multiple linear regression (APCS/MLR) and positive matrix factorization (PMF) were applied to a dataset consisting of 10 heavy metals in 300 surface soils samples. Robust geostatistics were used to delineate and compare the factors derived from these two receptor models. Both APCS/MLR and PMF afforded three similar source factors with comparable contributions, but APCS/MLR had some negative and unidentified contributions; thus, PMF, with its optimal non-negativity results, was adopted for source apportionment. Experimental variograms for each factor from two receptor models were built using classical Matheron's and three robust estimators. The best association of experimental variograms fitted to theoretical models differed between the corresponding APCS and PMF-factors. However, kriged interpolation indicated that the corresponding APCS and PMF-factor showed similar spatial variability. Based on PMF and robust geostatistics, three sources of 10 heavy metals in Guangrao were determined. As, Co, Cr, Cu, Mn, Ni, Zn, and partially Hg, Pb, Cd originated from natural source. The factor grouping these heavy metals showed consistent distribution with parent material map. 43.1% of Hg and 13.2% of Pb were related to atmosphere deposition of human inputs, with high values of their association patterns being located around urban areas. 29.6% concentration of Cd was associated with agricultural practice, and the hotspot coincided with the spatial distribution of vegetable-producing soils. Overall, natural source, atmosphere deposition of human emissions, and agricultural practices, explained 81.1%, 7.3%, and 11.6% of the total of 10 heavy metals concentrations, respectively. Receptor models coupled with robust geostatistics could successfully estimate the source apportionment of heavy metals in soils.

Chemical compositions of fog and rain water were measured between July 2017 and September 2018 at Sejila Mountain, southeast Tibet, where fog events frequently occurred in original fir forests at altitude 3950 m. Fog water samples were collected using a Caltech Active Strand Cloud Collector (CASCC), and rain samples were collected using a precipitation gauge. Differences were observed between fog water and rain composition for most analyzed ions. Ion abundance in fog water was Ca²⁺ > Cl⁻ > Na⁺ > SO₄²⁻ > Mg²⁺ > NH₄⁺ >K⁺ > NO₃⁻ whereas an order of Ca²⁺ > Na⁺ > Cl⁻ > Mg²⁺ > SO₄²⁻ > NO₃⁻ > K⁺ > NH₄⁺ was observed for rain water. All ion concentrations were higher in fog water than in rain water. Additionally, Ca²⁺ was the dominant cation in both fog and rain samples, accounting for more than half of all measured cations. NH₄⁺ and SO₄²⁻ concentrations were notable for being higher in fog than rain water when compared with other ions. For trace elements, Al, As, Mn and Se were the most abundant elements in fog water; only Al and As were detected in rain water. Seventy-two hour back-trajectory analysis showed that air masses during fog and/or rain events mainly came from the south of Sejila Mountain. Spearman correlation analysis and source contribution calculations indicated that both marine and terrestrial sources contributed to the observed ion concentrations. Considering the higher concentrations of NH₄⁺ and higher ratio of NH₄⁺/NO₃⁻ measured in fog than in rain, we suggest that quantification of fog nitrogen deposition and its ecological effect in this area should be given more attention.

In order to evaluate the effect of mining activity on the environment of the Qhorveh mining area in the west of Iran, the geological, ecological and environmental data, related to social development and regional economic status, were used. The geological data included seven sub-indices, such as vegetation coverage, land utilization type, and fault activity; ecological data, with two sub-indices, such as degree of ecological environment recovery; and finally, environmental data, with three sub-indices, such as soil and dust pollutions. These were selected based on the literature and expert opinion which were utilized for environmental pollution and geo-ecological (EPGE) risk assessment of the study site. Remote sensing (RS) image, field sampling, digital elevation map, and data retrieved from different government agencies were used to generate layers for the sub-indices in the geographic information system (GIS) environment. In addition, the analytical hierarchy process (AHP) method was used to determine the weight of sub-indices. Five levels consisting of best, good, middle, poor and worst were used to describe the EPGE risk assessment of the Qhorveh mining area. Results showed that worst and poor levels of EPGE risk are in the east and northeast of the study area where the gold and pumice mines are located while best and good levels of EPGE risk are in its center where the stone mines are located. According to the results of this research, the EPGE risk assessment of the Qhorveh mining area is affected by the environmental pollution index with its highest weight (0.3908). It can be concluded that the integration of the RS, GIS and AHP methods proposed in this study improved the evaluation quality of EPGE risk assessment.

Electron transfer capacities (ETC) of humic-like acids (HLA) and their effects on dechlorination are dependent on their redox-active properties. Aging and minerals can affect the chemical compositions and structures of HLA. However, the underlying mechanism and the impacts on the dechlorination capacities of HLA are poorly understood. We investigated how redox properties change in association with the intrinsic chemical natures and exterior minerals of the HLA extracted from landfilled solid wastes. Furthermore, the ETC of the landfill-derived HLA could be strengthened by increasing landfill age and demineralization, thereby facilitating the dechlorination of pentachlorophenol (PCP). The HLA molecules started to polymerize aromatic macromolecules during landfilling, leading to an increase in ETC and dechlorination capacities. Macromolecular HLA were dissociated to smaller molecules and exposed more aromatic and carboxyl groups when separated from minerals, which enhanced the ETC and the dechlorination abilities of the HLA. Microbial-mediated dechlorination was an effective way to degrade PCP, and almost 80% of the PCP was transformed after 40 days of demineralized HLA and Shewanella oneidensis MR-1 incubation. The demineralization and aging further facilitated the microbial-mediated PCP dechlorination. The findings provide a scientific base for improving in-situ bioremediation of chlorinated compound-contaminated soils using freshly synthesized HLA.

The heavy metal concentration of soil samples often exhibits a skewed distribution, especially for soil samples from mining areas with an extremely high concentration of heavy metals. In this study, to model soil contamination in mining areas using reflectance spectroscopy, the skewed distribution was corrected and heavy metal concentration estimated. In total, 46 soil samples from a mining area, along with corresponding field soil spectra, were collected. Laboratory spectra of the soil samples and the field spectra were used to estimate copper (Cu) concentration in the mining area. A logarithmic transformation was used to correct the skewed distribution, and based on the sorption of Cu on spectrally active soil constituents, the spectral bands associated with iron oxides were extracted from the visible and near-infrared (VNIR) region and used in the estimation. A genetic algorithm was adopted for band selection, and partial least squares regression was used to calibrate the estimation model. After transforming the distribution of Cu concentration, the accuracies (R2) of the estimation of Cu concentration using laboratory and field spectra separately were 0.94 and 0.96. The results indicate that Cu concentration in the mining area can be estimated using reflectance spectroscopy following correction of skewed distribution.

Passive air sampling was conducted in Toronto and the Greater Toronto Area from 2016 to 2017 for 6 periods, in order to investigate ambient levels of polycyclic aromatic compounds (PACs) associated with different source types. The selected sampling sites (n = 8) cover geographical areas with varying source emissions including background, traffic, urban, industrial and residential sites. Passive air samples were analyzed for PACs which include PAHs, alkylated PAHs (alk-PAHs), dibenzothiophene and alkylated dibenzothiophenes (DBTs) and results for PAHs were used to calculate inhalation cancer risks using different approaches. The samples were also characterized for PAH derivatives including nitrated PAHs (NPAHs) and oxygenated PAHs (OPAHs). Concentrations of Σalk-PAHs and DBTs, which are known to be enriched in fossil fuels, as well as ΣNPAHs, were highest at a traffic site (MECP) located adjacent to the 18-lane Highway 401 that runs across Toronto. Except for an industrial site (HH/BU), PAC compositions were similar across the sampling sites with Σalk-PAHs being the most abundant class of PACs suggesting traffic emission was a major contributor to PACs in the atmosphere of Toronto. The industrial site exhibited a distinct chemical composition with ΣPAHs dominating over Σalk-PAHs and with elevated levels of fluoranthene, 9-nitroanthracene, and 9,10-anthraquinone, which likely reflects emissions from nearby industrial sources. MECP and HH/BU exhibited higher lifetime excess inhalation cancer risks indicating an association with traffic and industrial sources. The importance of the traffic sector as a source of PACs to ambient air is further supported by strong correlations of the ΣPAHs, Σalk-PAHs, DBTs, and ΣOPAHs with NOx. This study highlights the importance of traffic as an emission source of PACs to urban air and the relevance of PAC classes other than just unsubstituted PAHs that are important but currently not included in air quality guidelines or for assessing inhalation cancer risks.

Peroxyacetyl nitrate (PAN) is an important reservoir of atmospheric nitrogen, modulating reactive nitrogen cycle and ozone (O3) formation. To understand the origins of PAN, a field measurement was conducted at Tung Chung site (TC) in suburban Hong Kong from October to November 2016. The average level of PAN was 0.63 ± 0.05 ppbv, with a maximum of 7.30 ppbv. Higher PAN/O3 ratio (0.043–0.058) was captured on episodes, i.e. when hourly maximum O3 exceeded 80 ppbv, than on non-episodes (0.01), since O3 production was less efficient than PAN when there was an elevation of precursors (i.e. volatile organic compounds (VOCs) and nitrogen oxide (NOx)). Model simulations revealed that oxidations of acetaldehyde (65.3 ± 2.3%), methylglyoxal (MGLY, 12.7 ± 1.2%) and other oxygenated VOCs (OVOCs) (8.0 ± 0.6%), and radical cycling (12.2 ± 0.8%) were the major production pathways of peroxyacetyl (PA) radical, while local PAN formation was controlled by both VOCs and nitrogen dioxide (NO2). Among all VOC species, carbonyls made the highest contribution (59%) to PAN formation, followed by aromatics (26%) and biogenic VOCs (BVOCs) (10%) through direct oxidation/decomposition. Besides, active VOCs (i.e. carbonyls, aromatics, BVOCs and alkenes/alkynes) could stimulate hydroxyl (OH) production, thus indirectly facilitating the PAN formation. Apart from primary emissions, carbonyls were also generated from oxidation of first-generation precursors, i.e., hydrocarbons, of which xylenes contributed the most to PAN production. Furthermore, PAN formation suppressed local O3 formation at a rate of 2.84 ppbv/ppbv, when NO2, OH and hydroperoxy (HO2) levels decreased and nitrogen monoxide (NO) value enhanced. Namely, O3 was reduced by 2.84 ppbv per ppbv PAN formation. Net O3 production rate was weakened (∼36%) due to PAN photochemistry, so as each individual production and loss pathway. The findings advanced our knowledge of atmospheric PAN and its impact on O3 production.

Thiocyanate (SCN⁻)-based autotrophic denitrification (AD) has recently been demonstrated as a promising technology that could be integrated with anaerobic ammonium oxidation (Anammox) to achieve simultaneous removal of nitrogen and SCN⁻. However, there is still a lack of a complete SCN⁻-based AD model, and the potential microbial competition/synergy between AD bacteria and Anammox bacteria under different operating conditions remains unknown, which significantly hinders the possible application of coupling SCN⁻-based AD with Anammox. To this end, a complete SCN⁻-based AD model was firstly developed and reliably calibrated/validated using experimental datasets. The obtained SCN⁻-based AD model was then integrated with the well-established Anammox model and satisfactorily verified with experimental data from a system coupling AD with Anammox. The integrated model was lastly applied to investigate the impacts of influent NH₄⁺-N/NO₂⁻-N ratio and SCN⁻ concentration on the steady-state microbial composition as well as the removal of nitrogen and SCN⁻. The results showed that the NH₄⁺-N/NO₂⁻-N ratio in the presence of a certain SCN⁻ level should be controlled at a proper value so that the maximum synergy between AD bacteria and Anammox bacteria could be achieved while their competition for NO₂⁻ would be minimized. For the simultaneous maximum removal (>95%) of nitrogen and SCN⁻, there existed a negative relationship between the influent SCN⁻ concentration and the optimal NH₄⁺-N/NO₂⁻-N ratio needed.

In this study, data collected from an urban air quality monitoring network are being used for the purpose of evaluating various methodologies used for spatial interpolation in the context of proposing an effective yet simple to apply scheme for PM spatial point estimations. The examined methods are the Inverse Distance Weighting, two linear regression models, the Multiple Linear Regression and the Linear Mixed Model, along with a Feed Forward Neural Network (FFNN) model. These schemes utilize daily PM₁₀ and PM₂.₅ concentrations collected from five and three air quality monitoring sites respectively. In order to obtain the resulted estimations, the leave-one-out cross-validation methodology is used for all methods. The evaluation of their predictive ability is performed by using a combination of difference and correlation statistical measures, scatter plots and statistical tests. The results indicate the usefulness of FFNNs as they are found to be statistically significantly superior for modelling the particulate matter spatial variability. The model performance statistics show that in most cases the error values are considerably lower for the FFNN model. Additionally, the rank and Wilcoxon rank tests reveal that the null hypothesis for equal predictive accuracy is rejected for the majority of monitoring sites and schemes (values lower than the critical t-value). According to the comparison results, the FFNN model is selected for forecasting air quality limit exceedances set by the European Union and World Health Organization air quality standards. For two monitoring sites in which the largest number of exceedances occurred, the probability of detection is high while the probability of false detection is very low, further establishing the neural networks’ predictive ability.