Arsenic risk mapping in Bangladesh: a simulation technique of cokriging estimation from regional count data

Targeting at risk households in Bangladesh exposed to arsenic contamination: a spatial analysis

This study seeks to identify the spatial risk pattern of households (HHs) exposed to arsenic contamination in Bangladesh by adjusting potential socio-economic, demographic factors. Data from Bangladesh Multiple Indicator Cluster Survey 2012-13 are used where hierarchical Bayesian spatial ordered logit model is implemented. The analysis shows that 25% of HH water samples were arsenic contaminated, although the majority (95%) of HHs used improved water sources. Arsenic contamination risk in the HH water was significantly associated with water source type and location, place of residence and districts. The model-based spatial prediction reveals that the north-east and south-west parts of Bangladesh have a high risk of contamination. To ensure the quality of HH water, our findings suggest that chemical test should be promoted considering the spatial risk of arsenic contaminations variations among HHs of Bangladesh. Furthermore, the study findings can effectively contribute in the planning of future interventions and programs.

Spatial uncertainty of joint health risk of multiple trace metals in rice grain in Jiaxing city, China

Trace metals in rice may affect human health if their concentrations reach risk levels in the human body due to long-term ingestion. In this study, concentrations of five trace metals (Cd, Cr, Cu, Pb and Zn) in field-sampled rice grains were measured at 285 sampling sites in Jiaxing city, China. The objective was to map the spatial distribution and uncertainty of the potential human health risk of trace metals in local rice grains at a regional scale. The probability map of multiple trace metals that tend to be hazardous was produced based on the permissible limits in Chinese National Standards. It showed higher probabilities, which exceeded the national standards, for one or more of trace metals in rice grains were mainly located in several small subareas around the center of the study area. We used the Target Hazard Quotient (THQ) index and the Total THQ (TTHQ) index to represent the potential human health risks caused by individual and multiple trace metals in local rice grains. The TTHQ was essentially regarded as an integrative uniform rice quality index in this study. Stochastic simulation was then conducted to simulate the TTHQ for mapping the spatial distribution and uncertainty of the total potential health risk. Probability maps of different risk grades for TTHQ indicated that most places were in unsafe grades. In general, maps of site-specific health risks posed by trace metals in local rice grains and associated uncertainty information are valuable to spatial decision making in agricultural planning, rice uses, and environmental management.

Health risk estimates for groundwater and soil contamination in the Slovak Republic: a convenient tool for identification and mapping of risk areas

We undertook a quantitative estimation of health risks to residents living in the Slovak Republic and exposed to contaminated groundwater (ingestion by adult population) and/or soils (ingestion by adult and child population). Potential risk areas were mapped to give a visual presentation at basic administrative units of the country (municipalities, districts, regions) for easy discussion with policy and decision-makers. The health risk estimates were calculated by US EPA methods, applying threshold values for chronic risk and non-threshold values for cancer risk. The potential health risk was evaluated for As, Ba, Cd, Cu, F, Hg, Mn, NO3 (-), Pb, Sb, Se and Zn for groundwater and As, B, Ba, Be, Cd, Cu, F, Hg, Mn, Mo, Ni, Pb, Sb, Se and Zn for soils. An increased health risk was identified mainly in historical mining areas highly contaminated by geogenic-anthropogenic sources (ore deposit occurrence, mining, metallurgy). Arsenic and antimony were the most significant elements in relation to health risks from groundwater and soil contamination in the Slovak Republic contributing a significant part of total chronic risk levels. Health risk estimation for soil contamination has highlighted the significance of exposure through soil ingestion in children. Increased cancer risks from groundwater and soil contamination by arsenic were noted in several municipalities and districts throughout the country in areas with significantly high arsenic levels in the environment. This approach to health risk estimations and visualization represents a fast, clear and convenient tool for delineation of risk areas at national and local levels.

Hotspot analysis of spatial environmental pollutants using kernel density estimation and geostatistical techniques

Concentrations of four heavy metals (Cr, Cu, Ni, and Zn) were measured at 1,082 sampling sites in Changhua county of central Taiwan. A hazard zone is defined in the study as a place where the content of each heavy metal exceeds the corresponding control standard. This study examines the use of spatial analysis for identifying multiple soil pollution hotspots in the study area. In a preliminary investigation, kernel density estimation (KDE) was a technique used for hotspot analysis of soil pollution from a set of observed occurrences of hazards. In addition, the study estimates the hazardous probability of each heavy metal using geostatistical techniques such as the sequential indicator simulation (SIS) and indicator kriging (IK). Results show that there are multiple hotspots for these four heavy metals and they are strongly correlated to the locations of industrial plants and irrigation systems in the study area. Moreover, the pollution hotspots detected using the KDE are the almost same to those estimated using IK or SIS. Soil pollution hotspots and polluted sampling densities are clearly defined using the KDE approach based on contaminated point data. Furthermore, the risk of hazards is explored by these techniques such as KDE and geostatistical approaches and the hotspot areas are captured without requiring exhaustive sampling anywhere.

The effect of arsenite on denitrification using volatile fatty acids (VFAs) as a carbon source

A lab-scale sequencing batch reactor (SBR) was operated at a solids retention time (SRT) of 20 +/- 2.5 days to develop a consortium of denitrifying bacteria with a mean specific denitrification rate of 0.11 g nitrate (NO(3)(-)-N)/g volatile suspended solids (VSS)/day. This biomass was used in a series of denitrification batch tests which used volatile fatty acids (VFAs) as their external carbon source. The VFAs were generated from an anaerobic digester that had an effluent VFA concentration of 5655 +/- 876 mg/L (expressed as acetic acid (HAc)). The denitrification batch reactors were spiked with NO(3)(-)-N (carbon-to-nitrogen (C:N) ratio of 3.0) and different concentrations of arsenite (a trivalent arsenic salt) in order to quantify the effect of arsenite on the denitrification process. A steady deterioration in the ability of the biomass to denitrify under increasing arsenite concentrations was observed with the mean specific denitrification rate dropping from 0.183 g NO(3)(-)-N/gVSS/day at an arsenite concentration of 5 mg/L, to a value of 0.047 g NO(3)(-)-N/gVSS/day at a concentration of 25 mg/L.