Most countries aim to regulate and protect the state of our environment under a system of environmental standards to control the level of certain pollutants present in various media of concern. Many such standards are often set without due consideration of uncertainty and variation and based on poorly defined principles. A sound statisticallybased approach to setting environmental standards can be based on the statistically verifiable ideal standard (SVIS) of Barnett and O'Hagan (1997). The SVIS is developed and practical implications of its use considered in terms of applications to pollution situations in air, water and soil, working in co-operation with relevant bodies. Developments include a non-parametric binomial approach and quantile testing for several simple parent pollutant distributions; properties of these approaches are examined in detail. A best linear unbiased quantile estimator (BLUQE) is examined, and 5% and 1 % critical values for the 0.95 and 0.99 BLUQE tabulated for use in an approximate significance testing procedure. This work is extended to a BLUQE for ranked set sampling, demonstrating impressive efficiency gains. Assessment of the SVIS using composite sample data is also investigated, with major improvements in test perrormance over the use of the commonly accepted 'divide-by-n' rule for critical value calculation. Following Barnett and O'Hagan (1997), the problem of setting directly equivalent compatible standards at different stages of the pollutant cause-effect chain is investigated. A statistically verifiable ideal guard point standard with two levels is also developed to avoid benefit of the doubt in testing procedures for standards, and its use demonstrated for both normal and gamma parent pollutant distributions. A reference point standard is proposed for a spatially dependent pollutant variable, with a krigingbased testing procedure. Finally, a 'hotspot' identification procedure is also developed, using outlier methods and composite sampling. The work concludes with suggestions for further related research.