Set Of Monitoring Stations Research Articles

Real-time contamination zoning in water distribution networks for contamination emergencies: a case study

Contamination of urban water distribution systems (WDS) is a critical issue due to the number of victims that might be impacted in a short period of time. Any effective rapid emergency response plan for reducing the number of sick people or deaths among those drinking the polluted water requires a reliable forecast of the water contamination zoning map (CZM). The water CZM is a visual representation of the spread of contamination at the time of contamination detection. This study presents a novel methodology based on the rough set theory (RST) for real-time forecasting of the CZM caused by simultaneous multi-point contamination injection in WDS. Our proposed methodology consists of (i) a Monte Carlo simulation model to capture the uncertainties in a multi-point deliberate contamination, (ii) a numerical simulation model for simulating pipe flow, and (iii) a rough set-based technique for real-time CZM for a WDS equipped with a set of monitoring stations. The proposed methodology can be used for any type of random contamination of WDSs as well as emergencies in deliberate contamination of water distribution networks.

Improved coastal hydrodynamic model offers insight into surface and groundwater flow and restoration objectives in Biscayne Bay, Florida, USA

Biscayne Bay, a marine lagoon along the southeast Florida coast, has been affected by altered freshwater input due to water management construction features and operations. The Comprehensive Everglades Restoration Plan aims to improve the quantity, quality, timing, and distribution of freshwater flow through central Florida to the southern coasts and to restore nearshore estuarine conditions in Biscayne Bay. The Biscayne Bay Simulation Model (BBSM) is used to evaluate the effects of proposed changes to freshwater flow on salinity in the bay. However, a recent version of the model has left out the groundwater component due to uncertainty in its quantity, a critical shortcoming considering the porous substrate connecting the bay to inland freshwater sources and historic evidence of fresh groundwater discharge. As a result, the BBSM had been unable to reproduce mean salinity and salinity variability along the shoreline. In the present effort, salinity data from an expanded set of monitoring stations were used to estimate the quantity of the fresh groundwater discharge component. Adjustments in friction in the shallow nearshore regions were also made to increase salinity variability to mimic natural conditions. The result, referred to as BBSM version 4, has improved salinity behavior in the critical nearshore region while maintaining previous salinity performance mid-bay. A substantial coastal freshwater component was required to simulate nearshore salinity during portions of the year. This is an interesting finding that suggests groundwater and other freshwater sources along the coast, including convection driven precipitation events, may have been underestimated and require further investigation.

Multivariate Time Series Model for Pollutant Transport Using Environmental Monitoring Data

SYNOPTIC ABSTRACTAn important objective of environmental monitoring study is to understand and characterize the effects of local transport of air pollutants through the diurnal or semidiurnal pattern of pollutant concentrations. Due to the complexity of spatial and temporal dynamics of pollutant data, this problem has not received serious attention in statistics. The problem involves developing a statistical model for a hypothesis test for no-transport and estimating the incoming direction and speed of the transport that crosses a set of monitoring stations at a given time period. This paper discusses a nonlinear time series model that can best be described as a complex-valued random coefficient regression, with the design matrix depending nonlinearly on the direction and speed parameters. Using observed pollutant data from a set of local monitoring stations, maximum likelihood estimation for the transport parameters and a hypothesis test for no-transport are considered under stochastic correlated signal assumption. Data used in this study are based on hourly PM2.5 records from three monitoring stations in southeast Texas for 2002. Three known and one unknown PM2.5 transport events are considered to test the proposed model. 95% confidence bands of the estimated directions are given. The proposed model has potential to be a useful tool in air pollution source attribution studies.

Estimation of urban air pollution

The problem of real-time estimation of air pollutant concentrations in an urban atmosphere based on concentration measurements made intermittently at a set of monitoring stations is considered. A square-root distributed parameter filter for a general class of dynamic urban air pollution models is developed. The filter is tested by application to a hypothetical urban area, and the effect of the number of monitoring stations on the estimated concentrations is studied.