Sentence examples for sparse monitoring from inspiring English sources

If this information is not available, only the new integral investigation method presented can yield adequate results for the quantification of contaminant mass fluxes under sparse monitoring conditions.

Beyond the short span of sparse monitoring time series, geological archives provide a valuable long-term context for future risk assessment.

The results show that even at a heterogeneous site with a sparse monitoring network point scale investigation methods can provide reliable information on field scale natural attenuation rates, if a dependable flow model or tracer test data is available.

When few pumping tests with sparse monitoring intervals are available, the incorporation of accurate or simplified geological information into geostatistical models reveals more details in heterogeneity and yields more robust validation results.

To compensate for sparse monitoring data, the pre-1999 model also included predicted PM10 (PM with aerodynamic diameter < 10 μm) and extinction coefficients (km−1).

The methodology we used helped to assess the health impacts of air pollution in a town with a sparse monitoring network but where dispersion modeling was available.

Some investigations relied on simple traffic density data or sparse monitors, evaluated relatively brief periods of exposure, or focused on nitrogen oxides rather than carcinogenic particulate pollution.

We used statistical simulation to compare the impact on epidemiological time-series analysis of additive measurement error in sparse monitor data as opposed to geographically and temporally complete model data.

Some previous studies estimated exposure to air pollution, a complex mixture, using relatively sparse monitors (Bonner et al. 2005), simple traffic density data (Lewis-Michl et al. 1996), or reconstructed exposure to nitrogen oxides rather than carcinogenic particulate pollution (Crouse et al. 2010; Raaschou-Nielsen et al. 2011).

This method builds a knowledge-data-integrated sparse (KDIS) monitoring model by integrating process data with fundamental process knowledge.

However, power flow based tracing requires PMU observability of the power flows at the potential sources to function correctly, which limits their practical use in power systems with sparse PMU monitoring or for oscillations where the sources are unmonitored.