Testing for arsenic pollution is commonly performed with chemical test kits of unsatisfying accuracy. Bacterial biosensors are an interesting alternative as they are easily produced, simple, and highly accurate devices. Here, we describe the development of a set of bacterial biosensors based on a nonpathogenic laboratory strain of Escherichia coli, the natural resistance mechanism of E. coli against arsenite and arsenate, and three reporter proteins: bacterial luciferase, beta-galactosidase and Green Fluorescent Protein (GFP). The biosensors were genetically optimized to reduce background expression in the absence of arsenic. In calibration experiments with the biosensors and arsenite-amended potable water, arsenite concentrations at 4 microg of As/L (0.05 microM) were routinely and accurately measured. The currently most quantitative system expressed the bacterial luciferase as reporter protein, responding proportional with a concentration range between 8 and 80 microg of As/L. Sensor cells could be stored as frozen batches, resuspended in plain media, and exposed to the aqueous test sample, and light emission was measured after 30-min incubation. Field testing for arsenite was achieved with a system that contained beta-galactosidase, producing a visible blue color at arsenite concentrations above 8 microg/L. For this sensor, a protocol was developed in which the sensor cells were dried on a paper strip and placed in the aqueous test solution for 30 min after which time color development was allowed to take place. The GFP sensor showed good potential for continuous rather than end point measurements. In all cases, growth of the biosensors and production of the strip test was achieved by very simple means with common growth media, and quality control of the sensors was performed by isolating the respective plasmids with the genetic constructs according to simple standard genetic technologies. Therefore, the biosensor cells and protocols may offer a realistic alternative for measuring arsenic contamination in potable water.

It is known from field and modeling studies that snowfall events and the presence of snow covering soil and vegetation affect the environmental distribution of chemicals.Therefore, it is essential to understand how chemicals interact with snow and ice and how they are transported to cold regions. The latter is especially important in the context of today's chemicals politics aiming to reduce trans-boundary pollution and to protect remote regions against chemical contamination. Persistent organic pollutants and other semi-volatile organic compounds (SOCs) that were measured in snow and ice samples include polychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons (PAHs) and polybrominated diphenyl ethers (PBDEs) as well as current-use pesticides such as dacthal, chlorpyrifos and endosulfan and historic-use pesticides including DDT, hexachlorobenzene (HCB) and α-hexachlorocyclohexane (α-HCH).Multi-media fate and transport models are a useful tool to investigate mechanisms and pathways of long-range transport (LRT) and environmental distribution of chemicals.They can thus be applied to investigate the fate of chemicals in snow and ice and the LRT of chemicals to cold environments. But so far, none of the existing global multi-media box models includes snow or ice compartments. In this work, we present a description of snow and ice implemented in the global multi-media model CliMoChem (Climate Zone Model for Chemicals). Processes describing the removal of chemicals from air to snow and surface ice include deposition of chemicals by wet and dry particle deposition, vapor scavenging by snow and dry gaseous deposition. Deposited chemicals are removed from snow and surface ice by re-volatilization, melt water runoff from surface ice or snow to soil and water, transfer from surface ice to deep ice and degradation in snow or surface ice. Investigated compounds include HCB, PCB28, PCB153, PCB180, PBDE47, PBDE209, α-HCH and dacthal.For snow, the relative importance of each deposition pathway depends on the chemical's affinity for snowflakes and particles in air whereas the chemical's affinity for bulk snow and melt water as well as the average air temperature are the main parameters influencing the removal pathways from snow. Investigated chemicals are mainly Abstract deposited to ice by vapor scavenging by snow or by particle deposition; transfer to deep

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