Impact of land use changes on water quality in headwaters of the Three Gorges Reservoir

The assessment of spatial and temporal variation of water quality influenced by land use is necessary to manage the environment sustainably in basin scales. Understanding the correlations between land use and different formats of nonpoint source nutrients pollutants is a priority in order to assess pollutants loading and predicting the impact on surface water quality. Forest, upland, paddy field, and pasture are the dominant land use in the study area, and their land use pattern status has direct connection with nonpoint source (NPS) pollutant loading. In this study, two land use scenarios (1995 and 2010) were used to evaluate the impact of land use changes on NPS pollutants loading in basins upstream of Three Gorges Reservoir (TGR), using a calibrated and validated version of the soil and water assessment tool (SWAT) model. The Pengxi River is one of the largest tributaries of the Yangtze River upstream of the TGR, and the study area included the basins of the Dong and Puli Rivers, two major tributaries of the Pengxi River. The results indicated that the calibrated SWAT model could successfully reproduce the loading of NPS pollutants in the basins of the Dong and Puli Rivers. During the 16-year study period, the land use changed markedly with obvious increase of water body and construction. Average distance was used to measure relative distribution patterns of land use types to basin outlets. Forest was mainly distributed in upstream areas whereas other land use types, in particular, water bodies and construction areas were mainly distributed in downstream areas. The precipitation showed a non-significant influence on NPS pollutants loading; to the contrary, interaction between precipitation and land use were significant sources of variation. The different types of land use change were sensitive to NPS pollutants as well as land use pattern. The influence of background value of soil nutrient on NPS pollutants loading was evaluated in upland and paddy field. It was found that total nitrogen (TN) and total phosphorous (TP) in upland were more sensitive to NPS pollutants loading than in paddy fields. The results of this study have implications for management of the TGR to reduce the loading of NPS pollutants into downstream water bodies.     

Alginate beads containing water treatment residuals for arsenic removal from water—formation and adsorption studies

Water treatment residuals (WTRs) produced in large quantities during deironing and demanganization of infiltration water, due to high content of iron and manganese oxides, exhibit excellent sorptive properties toward arsenate and arsenite. Nonetheless, since they consist of microparticles, their practical use as an adsorbent is limited by difficulties with separation from treated solutions. The aim of this study was entrapment of chemically pretreated WTR into calcium alginate polymer and examination of sorptive properties of the obtained composite sorbent toward As(III) and As(V). Different products were formed varying in WTR content as well as in density of alginate matrix. In order to determine the key parameters of the adsorption process, both equilibrium and kinetic studies were conducted. The best properties were exhibited by a sorbent containing 5 % residuals, formed in alginate solution with a concentration of 1 %. In slightly acidic conditions (pH 4.5), its maximum sorption capacity was 3.4 and 2.9 mg g^?1 for As(III) and As(V), respectively. At neutral pH, the adsorption effectiveness decreased to 3.3 mg As g^?1 for arsenites and to 0.7 mg As g^?1 for arsenates. The presence of carboxylic groups in polymer chains impeded in neutral conditions the diffusion of anions into sorbent beads; therefore, the main rate-limiting step of the adsorption, mainly in the case of arsenates, was intraparticle diffusion. The optimal condition for simultaneous removal of arsenates and arsenites from water by means of the obtained composite sorbent is slightly acidic pH, ensuring similar adsorption effectiveness for both arsenic species.     

Projected changes in heat wave characteristics in the eastern Mediterranean and the Middle East

According to observed twentieth century temperature trends and twenty-first century climate model projections, the region that encompasses the eastern Mediterranean and the Middle East (EMME) is identified as a climate change hot spot. We extend previous studies by a comprehensive climatology of heat waves in the EMME based on regional climate model simulations for the recent past and the end of the twenty-first century. A percentile-based definition of heat waves is used to account for local climatic conditions. Spatial patterns of several heat wave properties are assessed and associated with atmospheric circulation regimes over specific locations. To cover a range of possible future climates, we use three SRES emission scenarios. According to our results, all indices that characterize heat wave severity will strongly increase compared with the control period of 1961–1990. The northern part of the EMME could be exposed to increased heat wave amplitudes by 6–10 °C, and the southern part may experience 2–3 months more combined hot days and tropical nights. Heat wave peak temperatures will be higher due to the overall mean warming as well as stronger summer anticyclonic conditions. The projected changes will affect human health and the environment in multiple ways and call for impact studies to support the development of adaptation strategies.     

Benthic ecosystem functioning in the severely contaminated Mar Piccolo of Taranto (Ionian Sea,Italy): focus on heterotrophic pathways

The benthic ecosystem functioning is a rarely applied holistic approach that integrates the main chemical and biological features of the benthic domain with the key processes responsible for the flux of energy and C through the system. For the first time, such conceptual model, with an emphasis on the heterotrophic pathways, has been applied to the sediments at four stations within one of the most polluted coastal areas in Italy: the Mar Piccolo of Taranto. The functioning of the benthic ecosystem was different according to the investigated site. Nearby the military arsenal, i.e., the main source of organic contaminants and heavy metals, the system seemed inhibited at all the investigated structural and functional levels. Slow microbial processes of C reworking together with very limited densities of benthic fauna suggested a modest transfer of C both into a solid microbial loop and to the higher trophic levels. On the other hand, the ingression of marine water through the “Navigabile” channel seemed to stimulate the organic matter degradation and, consequently, the proliferation of meiofauna and macrofauna. In the innermost part of the basin, the system functioning, to some extent, is less impacted by contaminants and more influenced by mussel farms. The organic matter produced by these bivalves fueled faster C reworking by benthic prokaryotes and enhanced the proliferation of filter feeders.     

Cadmium (Cd) distribution and contamination in Chinese paddy soils on national scale

Rice is a staple food by an increasing number of people in China. As more issues have arisen in China due to rice contaminated by cadmium (Cd), Cd contamination in arable soils has become a severe problem. In China, many studies have examined Cd contamination in arable soils on a national scale, but little studies have focused on the distribution of Cd in paddy fields. This study explored the spatial pattern of Cd in paddy soils in China, made a preliminary evaluation of the potential risk, and identified the most critically contaminated regions based on the domestic rough rice trade flow. The results showed that Cd concentrations in paddy soils in China ranged from 0.01 to 5.50 mg/kg, with a median value of 0.23 mg/kg. On average, the highest Cd concentrations were in Hunan (0.73 mg/kg), Guangxi (0.70 mg/kg), and Sichuan (0.46 mg/kg) provinces. Cd concentrations in paddy soils in central and western regions were higher than those in eastern regions, especially the southeastern coastal regions. Of the administrative regions, Cd standard exceedance rate was 33.2 %, and the heavy pollution rate was 8.6 %. Regarding to Cd of paddy soil, soil environmental quality was better in Northeast China Plain than in Yangtze River Basin and southeastern coastal region. Mining activities were the main anthropogenic pollution source of Cd in Chinese paddy soil. Based on rice trade, more of the Chinese population would be exposed to Cd through intake of rice produced in Hunan province. Certain regions that output rice, especially Hunan province, should be given priority in the management and control of Cd contamination in paddy soil.     

Integrated spatial health assessment of yellow perch (<Emphasis Type="Italic">Perca flavescens</Emphasis>) populations from the St. Lawrence River,Quebec, Canada) part A: physiological parameters and pathogen assessment

A multi-disciplinary approach was used to evaluate the health of yellow perch (Perca flavescens) in the St. Lawrence River (Quebec, Canada), which is experiencing a severe population decline in the downstream portion of the river. Physiological parameters, liver alterations, trace metal concentrations, parasite prevalence and abundance, stable isotope composition, and the presence/absence of the viral hemorragic septicemia virus (VHSV) were evaluated in perch collected at six sites along the river: Lake St. François, Lake St. Louis (north and south), Beauregard Island, and Lake St. Pierre (north and south). Trace metal concentrations in surface water were higher in Lake St. Louis and downstream of a major urban wastewater treatment plant discharge, indicating that this effluent was a significant source of Cu, As, Ag, Zn, and Cd. Levels of Pb in surface water exceeded thresholds for the protection of aquatic life in Lake St. Louis and were negatively correlated with body condition index in this lake. In Lake St. Pierre, Cu, Ag, and Cd bioaccumulated significantly in perch liver and lower body condition index and greater liver damage were observed compared to upstream sites. Parasite analyses indicated a higher abundance of metacercariae of the trematodes Apophallus brevis and Diplostomum spp. in Lake St. Louis, and VHSV was not detected in the liver of yellow perch for all studied sites. Overall, results suggested that the global health of yellow perch from Lake St. Pierre is lower compared to upstream studied sites, which could contribute to the documented population collapse at this site.     

Phytoplankton dynamics with a special emphasis on harmful algal blooms in the Mar Piccolo of Taranto (Ionian Sea,Italy)

The response of phytoplankton assemblages to the closure of urban sewage outfalls (USOs) was examined for the Mar Piccolo of Taranto (Mediterranean Sea), a productive semi-enclosed coastal marine ecosystem devoted to shellfish farming. Phytoplankton dynamics were investigated in relation to environmental variables, with a particular emphasis on harmful algal blooms (HABs). Recent analyses evidenced a general reduction of the inorganic nutrient loads, except for nitrates and silicates. Also phytoplankton biomass (chlorophyll a) and abundances were characterized by a decrease of the values, except for the inner area of the basin (second inlet). The phytoplankton composition changed, with nano-sized species, indicators of oligotrophic conditions, becoming dominant over micro-sized species. If the closure of the USOs affected phytoplankton dynamics, however, it did not preserve the Mar Piccolo from HABs and anoxia crises. About 25 harmful species have been detected throughout the years, such as the potentially domoic acid producers Pseudo-nitzschia cf. galaxiae and P seudo-nitzschia cf. multistriata, identified for the first time in these waters. The presence of HABs represents a threat for human health and aquaculture. Urgent initiatives are needed to improve the communication with authorities responsible for environmental protection, economic development, and public health for a sustainable mussel culture in the Mar Piccolo.     

Impacts of climate change on the hydrology of two Natura 2000 sites in Northern Greece

Greece is included among the most vulnerable regions of Europe by climate change on account of higher temperature and reduced rainfall in areas already facing water scarcity. With respect to wetland systems, many ephemeral ones are expected to disappear and several permanent to shrink due to climate change. As regards two specific wetlands of Greece, the change in hydroperiod of Cheimaditida and Kerkini lakes due to climate change was studied. Lakes’ water balance was simulated using historical climate data and the emission scenarios Α1Β for the period 2020–2050 and Α1Β and Α2 for the period 2070–2100. Future climate scenarios, based on emission scenarios A1B and A2, were provided in the context of the study of Climate Change Impacts Study Committee. The surface area of Lake Cheimaditida will undergo a substantial decrease, initially by 20 % during the period 2020–2050 and later until 37 % during the period 2070–2100. In Lake Kerkini, the surface area will decrease, initially by 5 % during the period 2020–2050 and later until 14 % during the period 2070–2100. Climate change is anticipated to impact the hydroperiod of the two wetlands, and the sustainable water management is essential to prevent the wetland’s biodiversity loss.     

Polychlorinated biphenyl (PCB) anaerobic degradation in marine sediments: microcosm study and role of autochthonous microbial communities

Polychlorobiphenyl (PCB) biodegradation was followed for 1 year in microcosms containing marine sediments collected from Mar Piccolo (Taranto, Italy) chronically contaminated by this class of hazardous compounds. The microcosms were performed under strictly anaerobic conditions with or without the addition of Dehalococcoides mccartyi, the main microorganism known to degrade PCBs through the anaerobic reductive dechlorination process. Thirty PCB congeners were monitored during the experiments revealing that the biodegradation occurred in all microcosms with a decrease in hepta-, hexa-, and penta-chlorobiphenyls (CBs) and a parallel increase in low chlorinated PCBs (tri-CBs and tetra-CBs). The concentrations of the most representative congeners detected in the original sediment, such as 245-245-CB and 2345-245-CB, and of the mixture 2356-34-CB+234-245-CB, decreased by 32.5, 23.8, and 46.7 %, respectively, after only 70 days of anaerobic incubation without any bioaugmentation treatment. Additionally, the structure and population dynamics of the microbial key players involved in the biodegradative process and of the entire mixed microbial community were accurately defined by Catalyzed Reporter Deposition Fluorescence In Situ Hybridization (CARD-FISH) in both the original sediment and during the operation of the microcosm. The reductive dehalogenase genes of D. mccartyi, specifically involved in PCB dechlorination, were also quantified using real-time PCR (qPCR). Our results demonstrated that the autochthonous microbial community living in the marine sediment, including D. mccartyi (6.32E+06 16S rRNA gene copy numbers g^?1 sediment), was able to efficiently sustain the biodegradation of PCBs when controlled anaerobic conditions were imposed.     

Deep learning architecture for air quality predictions

With the rapid development of urbanization and industrialization, many developing countries are suffering from heavy air pollution. Governments and citizens have expressed increasing concern regarding air pollution because it affects human health and sustainable development worldwide. Current air quality prediction methods mainly use shallow models; however, these methods produce unsatisfactory results, which inspired us to investigate methods of predicting air quality based on deep architecture models. In this paper, a novel spatiotemporal deep learning (STDL)-based air quality prediction method that inherently considers spatial and temporal correlations is proposed. A stacked autoencoder (SAE) model is used to extract inherent air quality features, and it is trained in a greedy layer-wise manner. Compared with traditional time series prediction models, our model can predict the air quality of all stations simultaneously and shows the temporal stability in all seasons. Moreover, a comparison with the spatiotemporal artificial neural network (STANN), auto regression moving average (ARMA), and support vector regression (SVR) models demonstrates that the proposed method of performing air quality predictions has a superior performance.