Soil contamination due to atmospheric deposition of metals originating from smelters is a global environmental problem. A common problem associated with this contamination is the discrimination between anthropic and natural contributions to soil metal concentrations: In this context, we investigated the characteristics of soil contamination in the surrounding area of a world class smelter. We attempted to combine several approaches in order to identify sources of metals in soils and to examine contamination characteristics, such as pollution level, range, and spatial distribution. Soil samples were collected at 100 sites during a field survey and total concentrations of As, Cd, Cr, Cu, Fe, Hg, Ni, Pb, and Zn were analyzed. We conducted a multivariate statistical analysis, and also examined the spatial distribution by 1) identifying the horizontal variation of metals according to particular wind directions and distance from the smelter and 2) drawing a distribution map by means of a GIS tool. As, Cd, Cu, Hg, Pb, and Zn in the soil were found to originate from smelter emissions, and As also originated from other sources such as abandoned mines and waste landfill. Among anthropogenic metals, the horizontal distribution of Cd, Hg, Pb, and Zn according to the downwind direction and distance from the smelter showed a typical feature of atmospheric deposition (regression model: y = y0 + αe−βx). Lithogenic Fe was used as an indicator, and it revealed the continuous input and accumulation of these four elements in the surrounding soils. Our approach was effective in clearly identifying the sources of metals and analyzing their contamination characteristics. We believe this study will provide useful information to future studies on soil pollution by metals around smelters.

Multi-elemental isotopic approach associated with a land-use characteristic sampling strategy may be relevant for conducting biomonitoring studies to determine the spatial extent of atmospheric contamination sources. In this work, we investigated how the combined isotopic signatures in epiphytic lichens of two major metallic pollutants, lead (²⁰⁶Pb/²⁰⁷Pb) and mercury (δ²⁰²Hg, Δ¹⁹⁹Hg), together with the isotopic composition of nitrogen and carbon (δ¹⁵N, δ¹³C), can be used to better constrain atmospheric contamination inputs. To this end, an intensive and integrated sampling strategy based on land-use characteristics (Geographic information system, GIS) over a meso-scale area (Pyrénées-Atlantiques, SW France) was applied to more than 90 sampling stations. To depict potential relationships between such multi-elemental isotopic fingerprint and land-use characteristics, multivariate analysis was carried out. Combined Pb and Hg isotopic signatures resolved spatially the contribution of background atmospheric inputs from long range transport, from local legacy contamination (i.e. Pb) or actual industrial inputs (i.e. Pb and Hg from steel industry). Application of clustering multivariate analysis to all studied isotopes provided a new assessment of the region in accordance with the land-use characteristics and anthropogenic pressures.

Pesticides move to surface water via various pathways including surface runoff, spray drift and subsurface flow. Little is known about the relative contributions of surface runoff and spray drift in agricultural watersheds. This study develops a modeling framework to address the contribution of spray drift to the total loadings of pesticides in receiving water bodies. The modeling framework consists of a GIS module for identifying drift potential, the AgDRIFT model for simulating spray drift, and the Soil and Water Assessment Tool (SWAT) for simulating various hydrological and landscape processes including surface runoff and transport of pesticides. The modeling framework was applied on the Orestimba Creek Watershed, California. Monitoring data collected from daily samples were used for model evaluation. Pesticide mass deposition on the Orestimba Creek ranged from 0.08 to 6.09% of applied mass. Monitoring data suggests that surface runoff was the major pathway for pesticide entering water bodies, accounting for 76% of the annual loading; the rest 24% from spray drift. The results from the modeling framework showed 81 and 19%, respectively, for runoff and spray drift. Spray drift contributed over half of the mass loading during summer months. The slightly lower spray drift contribution as predicted by the modeling framework was mainly due to SWAT's under-prediction of pesticide mass loading during summer and over-prediction of the loading during winter. Although model simulations were associated with various sources of uncertainties, the overall performance of the modeling framework was satisfactory as evaluated by multiple statistics: for simulation of daily flow, the Nash-Sutcliffe Efficiency Coefficient (NSE) ranged from 0.61 to 0.74 and the percent bias (PBIAS) < 28%; for daily pesticide loading, NSE = 0.18 and PBIAS = −1.6%. This modeling framework will be useful for assessing the relative exposure from pesticides related to spray drift and runoff in receiving waters and the design of management practices for mitigating pesticide exposure within a watershed.

This study was aimed at monitoring beach litter using an unmanned aerial vehicle (UAV) in the coastal city of Fuzhou, China. The data analysis shows that the optical images obtained by digital cameras on the UAV can help to identify and monitor beach litter using remote sensing and GIS technologies. The threshold method can effectively segment the UAV image in the beach area. It is useful for quickly monitoring the distribution of beach litter in the area of interest, and hence it can help to provide effective technical support for the investigation and assessment of coastal beach litter.

This study is the first to systematically quantify, categorize, and map marine macro-debris across the main Hawaiian Islands (MHI), including remote areas (e.g., Niihau, Kahoolawe, and northern Molokai). Aerial surveys were conducted over each island to collect high resolution photos, which were processed into orthorectified imagery and visually analyzed in GIS. The technique provided precise measurements of the quantity, location, type, and size of macro-debris (>0.05m²), identifying 20,658 total debris items. Northeastern (windward) shorelines had the highest density of debris. Plastics, including nets, lines, buoys, floats, and foam, comprised 83% of the total count. In addition, the study located six vessels from the 2011 Tōhoku tsunami. These results created a baseline of the location, distribution, and composition of marine macro-debris across the MHI. Resource managers and communities may target high priority areas, particularly along remote coastlines where macro-debris counts were largely undocumented.

The potential of Indonesian bays as alginate producers was assessed by determining the stock of wild brown algae and exploring their biomass as alginophytes at the scale of entire bay, using a combination of field observations, remote sensing high resolution data and GIS tools. Ekas Bay in Lombok Island presented a stock of brown macroalgae which varied with season and species: for Padina the biomass reached 97.85±12.63 and 79.54±2.53tons in May/June and November respectively; for Sargassaceae species, it reached 669.70±109.64 and 147.70±77.97tons in May/June and November respectively. The best alginate yields occurred during the May/June period: Padina could produce 9.10±0.06tons DW of alginates. Interestingly, Sargassum/Turbinaria together allow 207.61±0.42tons DW of alginates. This study suggests that wild Sargassaceae represent an interesting stock in terms of biomass, alginate yield and M/G ratio.

The anthropogenic pressure in recent years has driven us to investigate the environmental quality at 22 stations in Parangipettai by collecting seawater samples monthly from 2014 to 2015. The sampling stations were grouped into three different environments, namely, Vellar Estuary (VE), Coleroon Estuary (CE), and Open Sea (OS). Factor analysis showed a total variance of 65.63% and exhibited a strong factor loading for atmospheric temperature (0.914), water temperature (0.917), ammonia (0.767), inorganic phosphate (0.897), total phosphorus (0.783), and phytoplankton (0.829). The index value showed water quality was good in OS (74.18), whereas it was moderate in VE (69.73) and CE (68.47). The visual model developed using Geographical Information System (GIS) displayed a spatial pattern of water temperature and phytoplankton dispersion in a distinct manner. The results obtained through multivariate analysis and GIS-based model are imperative to establish reference for a comparative study with other similar ecosystem for better planning and management of tropical seawaters.

The current study establishes baseline water quality properties for Sydney estuary, Australia for long periods of quiescence, which characterize the region. The study was undertaken in response to numerous requests for such data by researchers, government agencies and contractors.During quiescent periods, the range in Secchi depth transparency, turbidity, salinity and total suspended solid (TSS) values was 0.3–5.3 m, 18.6–0.1 NTU, 26.4–35.3 PSU and 8.3–1.0 mg/L in the upper and lower estuary, respectively. Baseline particulate metal concentrations were high, however TSS metal mass was greater during high rainfall.Tables and GIS-based maps allow baseline physio-chemical values to be extracted from the database for any location in Sydney estuary for quiescent conditions. Strong inter-parameter baseline relationships enable interpolation between water quality data. Baseline physio-chemical values were used to assess the impact of a high-precipitation event to demonstrate the utility of the new database.

Faced with health, environmental, and socio-economic issues related to the heavy use of pesticides, diffuse phytosanitary pollution becomes a major concern shared by all the field actors. These actors, namely the farmers and territorial managers, have expressed the need to implement decision support tools for the territorial management of diffuse pollution resulting from the plant protection practices and their impacts. To meet these steadily increasing requests, a cartographic analysis approach was implemented based on GIS which allows the spatialization of the diffuse pollution impacts related to plant protection practices on the Etang de l’Or catchment area in the South of France. Risk mapping represents a support-decision tool that enables the different field actors to identify and locate vulnerable areas, so as to determine action plans and agri-environmental measures depending on the context of the natural environment. This work shows that mapping is helpful for managing risks related to the use of pesticides in agriculture by employing indicators of pressure (TFI) and risk on the applicator’s health (IRSA) and on the environment (IRTE). These indicators were designed to assess the impact of plant protection practices at various spatial scales (field, farm, etc.). The cartographic analysis of risks related to plant protection practices shows that diffuse pollution is unequally located in the North (known for its abundant garrigues and vineyards) and in the South of the Etang de l’Or catchment area (the Mauguio-Lunel agricultural plain known for its diversified cropping systems). This spatial inequity is essentially related to land use and agricultural production system. Indeed, the agricultural lands cover about 60% of the total catchment area. Consequently, this cartographic analysis helps the territorial actors with the implementation of strategies for managing risks of diffuse pollution related to pesticides use in agriculture, based on environmental and socio-economic issues and the characteristics of the natural environment.

Designing optimization models and meta-heuristic algorithms for minimization of traveling routes of vehicles in solid waste collection has been gaining interest in environmental modeling. The computer models and methods are useful to bring out specific strategies for prevention and precaution of possible disasters that could be foreseen worldwide. This paper proposes a new Spatial Geographic Information System (GIS)-based Genetic Algorithm for optimizing the route of solid waste collection. The proposed algorithm, called SGA, uses a modified version of the original Dijkstra algorithm in GIS to generate optimal solutions for vehicles. Then, a pool of solutions, which are optimal routes of all vehicles, is encoded in Genetic Algorithm. It is iteratively evolved to a better one and finally to the optimal solution. Experiments on the case study at Sfax city in Tunisia are performed to validate the performance of the proposal. It has been shown that the proposed method has better performance than the practical route and the original Dijkstra method.