Mangroves play a vital role in protecting the coastal community from the climate change effect and in the restoration of the coastal ecosystem. This research has been designed to determine the spatial and seasonal changes of potentially toxic elements’ (PTEs) concentration in sediments and their potential source contribution among the different human-driven processes in Sundarbans, Bangladesh. Different pollution evaluation indices, random forest (RF) model, conditional inference tree (CIT), self-organizing map (SOM), geographical information system (GIS), and principal component analysis (PCA) were used for the interpretation of sources and risk assessment of PTEs. The mean concentration of PTEs both in winter and monsoon seasons has fallen below the threshold effect level but exceeded the rare effect level of marine sediments quality standards. Results showed that the PTEs were significantly enriched (EF > 1.00 < 70.00) in sediments, whereas the Cd enrichment (7.00% samples) was very alarming (EF = 60–70). Except for Zn and Cd, other PTEs were enriched in 30–60% samples. The highest geoaccumulation and contamination factors for Cd were observed in 46–72% of samples. The ecological risk (ER) factors showed similar results where Cd showed strong to very strong factors (ER = 110–2218) in 80% of samples. The CIT explained the natural/geogenic and anthropogenic sources of pollution, where the higher CIT values for Cd indicated industrial, aquaculture, and coal-based thermal powerplant. The RF model provided that shrimp firms, power plants, industry, and seaport were recognized as the influential sources for Zn, Pb, Cr, Cd, and As in sediments. Though Pb and As were found as the most significant pollutants, Cd was identified as a severe threat to ecology and public health. Based on CIT, RF, SOM and PCA the order of PTEs in mangroves sediment were:industrial/urban > aquaculture/shrimpfirm > powerplant > seaportoperation > tourism > geogenic/natural. The present study will help the policymakers for effective and sustainable management of the mangrove ecosystem.

To accurately visualize the spatial distribution of heavy metal pollution and provide basic information on soil remediation in dairy farm, Geographic Information System (GIS) is used for optimization of sample collection and data analysis. Based on GIS technology, dairy manure, 10 cm-depth surface soil, 50 cm-depth sub soil, and surface water samples were collected from dairy farm in Dulbert Mongolian Autonomous County, Daqing City, Heilongjiang Province in China. The spatial distribution and assessment of heavy metals were performed by using GIS inverse distance weighted interpolation and pollution index method. The single factor pollution index value of As element in the soil was found to indicate the class of extreme contamination, whereas Ni in both surface water inside and outside the farm, and Sb in the cow drinking water were assigned to the level of moderate contamination. The comprehensive pollution index implied serious contamination for soil samples, slight contamination for water samples and safety for manure samples, respectively. Comprehensive score for heavy metal elements followed the orders of As>Zn>Cr>Ni>Cu>Pb>Cd>Hg. The horizontal pollution that mainly occurred in the middle and east regions was increased from north to south, and west to east district. Historically, the dairy farm belonged to heavily polluted saline-alkali soil, where the heavy metals might enter the food chain through transportation from soil to water, the cows, and eventually to the milk and human body. Visualizing spatial distribution of heavy metal contamination by using GIS technology will be of significance to provide useful information for soil remediation of dairy farm.

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.

Useful information on the potential radon risk in existing buildings can be obtained by combining data from sources such as potential risk maps, the ‘Sistema de Información sobre Ocupación del Suelo de España’ (SIOSE) [information system on land occupancy in Spain], cadastral data on built property and population surveys. The present study proposes a method for identifying urban land, premises and individuals potentially subject to radon risk. The procedure draws from geographic information systems (GIS) pooled at the municipal scale and data on buildings possibly affected. The method quantifies the magnitude of the problem in the form of indicators on the buildings, number of premises and gross floor area that may be affected in each risk category. The findings are classified by type of use: residential, educational or office. That information may guide health/prevention policies by targeting areas to be measured based on risk category, or protection policies geared to the construction industry by estimating the number of buildings in need of treatment or remediation. Application of the methodology to Greater Madrid showed that 47% of the municipalities have houses located in high radon risk areas. Using cadastral data to zoom in on those at highest risk yielded information on the floor area of the vulnerable (basement, ground and first storey) premises, which could then be compared to the total. In small towns, the area affected differed only scantly from the total, given the substantial proportion of low-rise buildings in such municipalities.

Groundwater pollution is a critical concern in karst areas. This study used the PLEIK (P: protective cover; L: land use; E: epikarst development; I: infiltration conditions; K: karst development) method to assess the vulnerability of groundwater pollution in Guangxi Province, which is the largest karst area in China. The pollution sources and attenuation consist of groundwater pollution hazards. The attributions for the vulnerability and hazard were measured using the geodetector method from geographical information system in Luzhai County in Guangxi. The results confirmed that the vulnerability of groundwater pollution was higher in karst areas than in non-karst areas. In Guangxi, 36.35% of the groundwater samples were polluted. A total of 49.73% of the areas in Luzhai County contained hazardous levels of pollution. The risk assessment map, which interacted with the vulnerability and hazards, was 58.2% similar to the groundwater pollution distribution. The influence of the hazard on groundwater pollution was 2.6 times that of the vulnerability. It is crucial to control pollution sources to prevent groundwater pollution.

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.

Noise is associated with poor reproductive health. A number of animal studies have suggested the possible effects of exposure to high noise levels on fertility; to date, a little such research has been performed on humans.We examined an association between daytime and nocturnal noise exposures over four years (2002–2005) and subsequent male infertility.We used the National Health Insurance Service-National Sample Cohort (2002–2013), a population-wide health insurance claims dataset. A total of 206,492 males of reproductive age (20–59 years) with no history of congenital malformations were followed up for an 8-year period (2006–2013). Male infertility was defined as per ICD-10 code N46. Data on noise exposure was obtained from the National Noise Information System. Exposure levels of daytime and night time noise were extrapolated using geographic information systems and collated with the subjects' administrative district code, and individual exposure levels assigned.During the study period, 3293 (1.6%) had a diagnosis of infertility. Although there was no association of infertility with 1-dB increments in noise exposure, a non-linear dose-response relationship was observed between infertility and quartiles of daytime and night time noise after adjustment for confounding variables (i.e., age, income, residential area, exercise, smoking, alcohol drinking, blood sugar, body mass index, medical histories, and particulate pollution). Based on WHO criteria, adjusted odds for infertility were significantly increased (OR = 1.14; 95% CI, 1.05–1.23) in males exposed to night time noise ≥ 55 dB.We found a significant association between exposure to environmental noise for four years and the subsequent incidence of male infertility, suggesting long-term exposure to noise has a role in pathogenesis of male infertility.

Current and historic atmospheric nitrogen (N) deposition has impacted aquatic ecosystems in the Greater Yellowstone Area (GYA). Understanding the spatial variation in total atmospheric deposition (wet + dry) of N is needed to estimate air pollution deposition critical loads for sensitive aquatic ecosystems. This is particularly important for areas that have an increasing contribution of ammonia dry deposition to total N (TN), such as the GYA. High resolution geostatistical models and maps of TN deposition (wet + dry) were developed using a variety of techniques including ordinary kriging in a geographic information system, to evaluate spatial variability and identify areas of elevated loading of pollutants for the GYA. TN deposition estimates in the GYA range from <1.4 to 7.5 kg N ha−1 yr−1 and show greater variability than wet inorganic N deposition. Critical loads of TN deposition (CLTNdep) for nutrient enrichment in aquatic ecosystems range from less than 1.5 ± 1.0 kg N ha−1 yr−1 to over 4.0 ± 1.0 kg N ha−1 yr−1 and variability is controlled by differences in basin characteristics. The lowest CLTNdep estimates occurred in high elevation basins within GYA Wilderness boundaries. TN deposition maps were used to identify critical load exceedances for aquatic ecosystems. Estimated CLTNdep exceedances for the GYA range from 17% to 48% depending on the surface water nitrate (NO3−) threshold. Based on a NO3− threshold of 1.0 μmol L−1, TN deposition exceeds CLTNdep in approximately 30% of the GYA. These predictive models and maps can be used to help identify and protect sensitive ecosystems that may be impacted by excess atmospheric N deposition.

Heavy metal soil contamination is associated with potential toxicity to humans or ecotoxicity. Scholars have increasingly used a combination of geographical information science (GIS) with geostatistical and multivariate statistical analysis techniques to examine the spatial distribution of heavy metals in soils at a regional scale. A review of such studies showed that most soil sampling programs were based on grid patterns and composite sampling methodologies. Many programs intended to characterize various soil types and land use types. The most often used sampling depth intervals were 0–0.10 m, or 0–0.20 m, below surface; and the sampling densities used ranged from 0.0004 to 6.1 samples per km², with a median of 0.4 samples per km². The most widely used spatial interpolators were inverse distance weighted interpolation and ordinary kriging; and the most often used multivariate statistical analysis techniques were principal component analysis and cluster analysis. The review also identified several determining and correlating factors in heavy metal distribution in soils, including soil type, soil pH, soil organic matter, land use type, Fe, Al, and heavy metal concentrations. The major natural and anthropogenic sources of heavy metals were found to derive from lithogenic origin, roadway and transportation, atmospheric deposition, wastewater and runoff from industrial and mining facilities, fertilizer application, livestock manure, and sewage sludge. This review argues that the full potential of integrated GIS and multivariate statistical analysis for assessing heavy metal distribution in soils on a regional scale has not yet been fully realized. It is proposed that future research be conducted to map multivariate results in GIS to pinpoint specific anthropogenic sources, to analyze temporal trends in addition to spatial patterns, to optimize modeling parameters, and to expand the use of different multivariate analysis tools beyond principal component analysis (PCA) and cluster analysis (CA).

To reveal the emission patterns of brominated flame retardants (BFRs) in the Beijiang River, South China, concentrations of polybrominated diphenyl ethers (PBDEs) and phenolic BFRs (2,4,6-tribromophenol (TBP), pentabromophenol (PeBP), tetrabromobisphenol A (TBBPA)), and bisphenol A (BPA) in water and sediments were simultaneously measured, and the geographic information system (GIS) were applied to analyse their emission patterns. Results showed that PBDEs, TBP, PeBP, TBBPA and BPA were ubiquitous in the water and sediment samples collected from the Beijiang River. However, most of the concentrations were very low or below the detection limits (DL). In water, Σ20PBDEs (sum of all 20 PBDEs congeners) levels ranged from < DL to 232 pg L−1, with the predominant congeners containing low bromine contents. The levels of TBP, PeBP, TBBPA and BPA in water were lower than 810 pg L−1. In sediments, Σ20PBDEs varied from 260 to 5640 pg g−1 dry weight (d.w.), with the predominant congeners containing high bromine contents. The levels of TBP, PeBP, TBBPA and BPA were lower than 600 pg g−1 d.w.. Risk assessments indicated that the water and sediments at the sampling locations imposed no estrogenic risk (E2EQ < 1.0 ng E2 L−1), and the eco-toxicity assessment at three trophic levels also showed no risk at all sampling sites in water (RQTotal < 1.0), but with a potential eco-toxicity at some sampling points in sediments (1.0<RQTotal < 10.0).