Stochastic Transfer Function Model and Neural Networks to Estimate Soil Moisture1

Abstract: A practical methodology is proposed to estimate the three‐dimensional variability of soil moisture based on a stochastic transfer function model, which is an approximation of the Richard’s equation. Satellite, radar and in situ observations are the major sources of information to develop a model that represents the dynamic water content in the soil. The soil‐moisture observations were collected from 17 stations located in Puerto Rico (PR), and a sequential quadratic programming algorithm was used to estimate the parameters of the transfer function (TF) at each station. Soil texture information, terrain elevation, vegetation index, surface temperature, and accumulated rainfall for every grid cell were input into a self‐organized artificial neural network to identify similarities on terrain spatial variability and to determine the TF that best resembles the properties of a particular grid point. Soil moisture observed at 20 cm depth, soil texture, and cumulative rainfall were also used to train a feedforward artificial neural network to estimate soil moisture at 5, 10, 50, and 100 cm depth. A validation procedure was implemented to measure the horizontal and vertical estimation accuracy of soil moisture. Validation results from spatial and temporal variation of volumetric water content (vwc) showed that the proposed algorithm estimated soil moisture with a root mean squared error (RMSE) of 2.31% vwc, and the vertical profile shows a RMSE of 2.50% vwc. The algorithm estimates soil moisture in an hourly basis at 1 km spatial resolution, and up to 1 m depth, and was successfully applied under PR climate conditions.     

Using penalty functions to evaluate aggregation models for environmental indices

The purpose of indices is to summarize a large volume of information into a single number that is easy to understand and interpret. Environmental indices provide a composite picture of an environmental condition derived from a series of observed measurements and parameters. They are used as communication tools by regulatory agencies to characterize the state of a specific environmental system (air, water, and sediments) and to study the impact of regulatory policies on various environmental management practices. In the development of environmental indices, a few issues and problems have been encountered arising as a result of the abstraction of information and data. These problems are referred to as characteristic properties that include ambiguity, eclipsing, compensation and rigidity. These characteristic properties have long been identified and interpreted in Boolean (e.g., Yes/No) or qualitative (e.g., low, medium, high) terms. In this paper, we propose a new approach to describe the above stated characteristic properties on a continuous scale to evaluate and compare the behavior of various aggregation models. Our approach is based on developing penalty functions for each characteristic property. A water quality index example by Swamee and Tyagi (2000) is used to explain our approach. A detailed case study for a developing microbial risk index is also provided to show how the proposed approach can be extended to complex hierarchical systems. Results show that it is possible to improve aggregation models for index development. Future research directions to improve index development are also discussed.     

Determination of direct and fugitive PM emissions in a Mediterranean harbour by means of classic and novel tracer methods

Remodelling works are frequent in harbour areas, given that they must adapt to rapidly changing market requirements. Depending on their nature (construction, demolition, etc.), these works may be carried out during long periods of time and thus exert a significant impact on the air quality at the harbours and their surroundings. The air quality at the Valencia harbour was studied during an enlargement project. Four sampling stations aimed to cover the major dust-emitting activities in the harbour. In addition, a sampling campaign was carried out to collect dust materials at their emission sources (e.g., loading and unloading of clinker and petroleum coke, as well as the enlargement works). Differences obtained between PM levels at the monitoring sites were correlated with the distance between sampling stations and enlargement works and/or harbour operations, as well as with the preferential wind direction. In all cases, the days with the highest PM_10 levels were linked to wind directions coinciding with the location of the enlargement works or the clinker and petroleum coke stocks. Classic source apportionment methodologies (PCA and CMB) were applied to the data, but their interpretation was complex due to the similar chemical signatures of PM originating from direct and fugitive emissions from stocked materials (e.g., clinker), and the enlargement works. To overcome this difficulty, a novel non-statistical approach was used to obtain quantitative estimations of the contributions from sources (petroleum coke and clinker), based on the correlation between specific PM components (e.g., carbonaceous species) and source tracers (e.g., V). Finally, a qualitative test using phenolphthalein was devised to identify the presence of clinker on the filters, which provided positive results. This novel tracer approach is thus recommended for harbour authorities.     

Microbial biomass governs enzyme activity decay during aging of worm-worked substrates through vermicomposting

Vermicomposting is the biooxidation and stabilization of organic matter involving the joint action of earthworms and microorganisms, thereby turning wastes into a valuable soil amendment called vermicompost. Studies have focused on the changes in the type of substrates available before and after vermicomposting, but little is known on how these changes take place, especially those changes related with maturation of vermicompost. This study investigated the effects of aging on the microbiological properties of fresh vermicompost produced from pig slurry by analyzing the substrate after the earthworms had left it. We incubated 16-wk-old vermicompost and sampled it after 15, 30, 45, and 60 d analyzing microbial biomass and activity (assessed as microbial biomass N and basal respiration respectively) and four enzymatic activities (beta-glucosidase, cellulase, protease, and alkaline phosphatase). Aging of vermicompost resulted in decreases of microbial biomass and activity. Three of the four enzymes analyzed also showed decrease. An initial increase followed by a rapid decrease in alkaline phosphatase was also recorded. High and significant correlations between microbial biomass and beta-glucosidase (r = 0.62, P < 0.001), cellulase (r = 0.56, P < 0.01), and protease (r = 0.82, P < 0.001) were found. Results suggest that there may be two steps involved in the aging dynamics of vermicompost with regards to extracellular enzyme activity; the first step was characterized by a decrease in microbial populations, which resulted in a reduction in the synthesis of new enzymes. The second step was the degradation of the pool of remaining enzymes. This dynamic does not seem to be affected by earthworms because similar decaying patterns of microbial biomass and activity were found in substrate where earthworms were present.     

Analysis of underlying causes of investigated loss of containment incidents in Dutch Seveso plants using the Storybuilder method

In the Netherlands there are around 400 “Seveso” sites that fall under the Dutch Major Hazards Decree (BRZO) 1999. Between 2006 and 2010 the Dutch Labour Inspectorate's Directorate for Major Hazard Control completed investigations of 118 loss of containment incidents involving hazardous substances from this group. On the basis of investigation reports the incidents were entered in a tailor-made tool called Storybuilder developed for the Dutch Ministry of Social Affairs and Employment for identifying the dominant patterns of technical safety barrier failures, barrier task failures and underlying management causes associated with the resulting loss of control events. The model is a bow-tie structure with six lines of defence, three on either side of the central loss of containment event. In the first line of defence, failures in the safety barriers leading to loss of control events were primarily equipment condition failures, pre start-up and safeguarding failures and process deviations such as pressure and flow failures. These deviations, which should have been recovered while still within the safe envelope of operation, were missed primarily because of inadequate indication signals that the deviations have occurred. Through failures of subsequent lines of defence they are developing into serious incidents. Overall, task failures are principally failures to provide adequate technical safety barriers and failures to operate provided barriers appropriately. Underlying management delivery failures were mainly found in equipment specifications and provisions, procedures and competence. The competence delivery system is especially important for identifying equipment condition, equipment isolation for maintenance, pre-start-up status and process deviations. Human errors associated with operating barriers were identified in fifty per cent of cases, were mostly mistakes and feature primarily in failure to prevent deviations and subsequently recover them. Loss of control associated with loss of containment was primarily due to the containment being bypassed (72% of incidents) and less to material strength failures (28%). Transfer pipework, connections in process plant and relief valves are the most frequent release points and the dominant release material is extremely flammable. It is concluded that the analysis of a large number of incidents in Storybuilder can support the quantification of underlying causes and provide evidence of where the weak points exist in major hazard control in the prevention of major accidents.