Towards a control model for the highly cybernetic farming ecosystems

A dynamic model based on the linear systems theory is developed in designing a highly cybernetic farming strategy to efficiently manage residuals generated in farm ecosystems. A linear cybemetic model would be used to describe the dynamic behavior of resource flow in the farm ecosystem in which the state variables are resource quantities, and the control variables are residual quantities. The controlled process is defined as the controlled management strategy change. Cybemetic mechanism shows the application of residuals as control measures have determinate effects on the controlled process as along as the farming system is observable and controllable in the control sense. To illustrate the model algorithm the idea is applied to simulate the dynamic response of residual phosphorus concentrations in an integrated pig/corn farming system located in the south Taiwan region. General results show that the residual phosphorous concentration is influenced by farming activities which are controlled by a system of gross input and net output parameters. This paper demonstrates using input-output analysis technique that residuals generated in the farming system is the most important cybemetic variable,     

Environmental and economic impacts of decision-making at an arable farm: an integrative modeling approach

This study examines the dependency between physical and anthropogenic systems in arable farming. The dynamic simulation model, which has its methodological origins in the modeling traditions of environmental systems analysis and microsimulation, reproduces the mutual links between the physical flows (e.g. energy, materials, emissions, and products), the farmer as a decision-making agent, and structural conditions influencing the farm. In running the model, the intention is to answer the question: What are the impacts on profitability and the environment (i.e. greenhouse gas effects, eutrophication, acidification, and energy use) of variations in prices, subsidies, the farmer's environmental values, and the farmer's skill in making production allocation choices? The results of the model simulations indicate, for example, that in terms of economic performance, a farmer can choose between two relatively sustainable strategies--either to specialize in organic production (thereby benefiting from higher subsidies and output prices), or to focus on conventional cultivation and use of pesticides and fertilizers (thereby benefiting from large yields). Regarding environmental impacts, there was no clear-cut divide between organic and conventional farming due to difficulties in allocating the use of manure. This finding is essentially related to the choice of system boundary, which is thoroughly discussed in the paper.     

Sensitivity analysis for models of greenhouse gas emissions at farm level. Case study of N(2)O emissions simulated by the CERES-EGC model

Modelling complex systems such as farms often requires quantification of a large number of input factors. Sensitivity analyses are useful to reduce the number of input factors that are required to be measured or estimated accurately. Three methods of sensitivity analysis (the Morris method, the rank regression and correlation method and the Extended Fourier Amplitude Sensitivity Test method) were compared in the case of the CERES-EGC model applied to crops of a dairy farm. The qualitative Morris method provided a screening of the input factors. The two other quantitative methods were used to investigate more thoroughly the effects of input factors on output variables. Despite differences in terms of concepts and assumptions, the three methods provided similar results. Among the 44 factors under study, N₂O emissions were mainly sensitive to the fraction of N₂O emitted during denitrification, the maximum rate of nitrification, the soil bulk density and the cropland area.     

Evaluation of a photochemical grid model using estimates of concentration probability density functions

A method for evaluating photochemical grid models based on the estimation of the probability density function (PDF) of the variations in ozone concentrations is described and demonstrated. It is assumed that the ozone concentration PDF is determined from 100 Monte Carlo uncertainty runs based on uncertainties in photochemical grid model input variables. The exercise involves the Urban Airshed Model with Variable grid (UAM-V) as applied to the July 1995 Ozone Transport Assessment Group episode in the eastern US. The focus is on the distribution of model residuals (observed concentration minus model-predicted ensemble mean concentration) for 66 ozone monitors on the OTAG domain. It is concluded that the distribution of observed model residuals is within the 95% range of expected uncertainty, suggesting that the model is performing adequately.     

Farm, land, and soil nitrogen budgets for agriculture in Europe calculated with CAPRI

We calculated farm, land, and soil N-budgets for countries in Europe and the EU27 as a whole using the agro-economic model CAPRI. For EU27, N-surplus is 55 kg N ha⁻¹ yr⁻¹ in a soil budget and 65 kg N₂O–N ha⁻¹ yr⁻¹ and 67 kg N ha⁻¹ yr⁻¹ in land and farm budgets, respectively. NUE is 31% for the farm budget, 60% for the land budget and 63% for the soil budget. NS values are mainly related to the excretion (farm budget) and application (soil and land budget) of manure per hectare of total agricultural land. On the other hand, NUE is best explained by the specialization of the agricultural system toward animal production (farm NUE) or the share of imported feedstuff (soil NUE). Total N input, intensive farming, and the specialization to animal production are found to be the main drivers for a high NS and low NUE.     

Resilience building of rural livelihoods in PES programmes: A case study in China’s Loess Hills

In spite of positive expectations for environmental protection, payments for ecosystem services (PES) can bring about unintended disturbances to rural livelihoods. Based on resilience thinking, this article investigates livelihood resilience building at farm level through the interaction between farm adaptation and disturbances induced by China’s Grain for Green project (GGP). Cluster analysis was conducted to investigate the complexity and diversity of farm adaptation; the crafting of composite indexes was designed to value resilience through disturbance, sensitivity, and adaptability; regression analyses linked the resilience indexes and farm adaptation with access to resources. The results show three adaptation typologies (i.e. reclamation of retired lands, contractive farming, and expansive farming) with distinct land use structures and resilience scores, and highlight the need to improving farmers’ access and endowment of tangible (e.g. farming facilities) and intangible resources (e.g. skill training) for resilience-building practices in light of the GGP. The findings imply that policy interventions combining environmental restrictions with widening resource access to support alternative livelihoods can offset the unintended effects and amplify the success of PES programmes.     

Identification of transport processes in column experiments using a frequency domain approach

When a solute transport process is viewed as a dynamic system with input and output, a system identification technique can be used to study it from input-output data. According to the design of excitation signals in the system identification approach, the commonly used rectangular pulse as input signal for column experiments is not optimum because it does not simultaneously meet the requirements for exciting the studied transport process, i.e., possessing frequency components with high enough amplitude and wide enough passband. In this article, stepped sine signals were proposed to replace the rectangular pulse because their amplitude and passband can be independently chosen. The stepped sine signals of concentration were generated by a High Performance Liquid Chromatography (HPLC) and used as the input for the column experiments to identify parameters of the convection-dispersion equation (CDE) and mobile-immobile model (MIM). In order to test the effect of noise on the identification of transport process, numerical experiments were carried out to identify the CDE under white noise when the input was designed as stepped sine functions and rectangular pulse. The results of the numerical experiments showed that the input signal of a sine function was more robust and accurate in process identification than that of a rectangular pulse.     

Incinerator Conference Covers Emissions Control Aspects

The success of the application of computer modeling to decision-making will depend on the degree to which the scientifically valid “cause-and-effect” features of the air pollution system are represented. For this reason, dynamic simulation models are to be preferred to statistical and empirical models. A digital simulation model based on a stoichiometrically logical chemical mechanism and trajectory estimating routines was constructed, using Los Angeles source, meteorological and geographic input. The basic physical concept underlying the simulation model is the process of evolution of photochemical pollution in a parcel of air as it moves in a dynamic urban emission/meteorological environment along a given urban wind trajectory. Both the photochemical evolution and the trajectory are numerically integrated by a standard linear multistep predictor-corrector method. Concentrations of photochemical reactants and products (i.e., primary and secondary contaminants) are determined by this numerical integration, which also includes appropriate terms for relevant effects. In five preliminary validation runs, simulated NO₂, NO, and O₃ values were within 20% or 0.05 ppm of those observed at air monitoring stations located near the termini of the runs. The trajectories were plotted on the basis of hourly meteorological data for 22 stations. Six control strategy exercises were conducted to illustrate the application of the model to problem-solving situations.     

Compromising economic cost and air pollutant emissions of municipal solid waste management strategies by fuzzy multiobjective optimization model

Municipal solid waste management (MSWM) is an important environmental challenge and subject in urban planning. A sustainable MSWM strategy should consider not only economic efficiency but also life-cycle assessment of environmental impact. This study employs the fuzzy multiobjective linear programming (FMOLP) technique to find the optimal compromise between economic optimization and pollutant emission reduction for the MSWM strategy. Taichung City in Taiwan is evaluated as a case study. The results indicate that the optimal compromise MSWM strategy can reduce significant amounts of pollutant emissions and still achieve positive net profits. Minimization of the sulfur oxide (SO_x) and nitrogen oxide (NO_x) emissions are the two major priorities in achieving this optimal compromise strategy when recyclables recovery rate is lower; however, minimization of the carbon monoxide (CO) and particulate matter (PM) emissions become priority factors when recovery rate is higher.

Implications: This paper applied the multiobjective optimization model to find the optimal compromise municipal solid waste management (MSWM) strategy, which minimizes both life-cycle operating cost and air pollutant emissions, and also to analyze the correlation between recyclables recovery rates and optimal compromise strategies. It is different from past studies, which only consider economic optimization or environmental impacts of the MSWM system. The result shows that optimal compromise MSWM strategy can achieve a net profit and reduce air pollution emission. In addition, scenario investigation of recyclables recovery rates indicates that resource recycling is beneficial for both economic optimization and air pollutant emission minimization.     

Uncertainty analysis of developed ANN and ANFIS models in prediction of carbon monoxide daily concentration

This study aims to predict daily carbon monoxide (CO) concentration in the atmosphere of Tehran by means of developed artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models. Forward selection (FS) and Gamma test (GT) methods are used for selecting input variables and developing hybrid models with ANN and ANFIS. From 12 input candidates, 7 and 9 variables are selected using FS and GT, respectively. Evaluation of developed hybrid models and its comparison with ANN and ANFIS models fed with all input variables shows that both FS and GT techniques reduce not only the output error, but also computational cost due to less inputs. FS–ANN and FS–ANFIS models are selected as the best models considering R², mean absolute error and also developed discrepancy ratio statistics. It is also shown that these two models are superior in predicting pollution episodes. Finally, uncertainty analysis based on Monte-Carlo simulation is carried out for FS–ANN and FS–ANFIS models which shows that FS–ANN model has less uncertainty; i.e. it is the best model which forecasts satisfactorily the trends in daily CO concentration levels.     

The Sensitivity of Probabilistic Risk Assessment Results to Alternative Model Structures: A Case Study of Municipal Waste Incineration

Abstract

In this analysis, human health risk due to exposure to municipal waste incinerator emissions is assessed as an example of the application of probabilistic techniques (e.g., Monte Carlo or Latin Hypercube simulations). Incinerator risk assessments are characterized by the dominance of indirect exposure, thus this analysis focuses on exposure via the ingestion of locally grown foods. In addition, since exposure to 2, 3, 7, 8-TCDD drives most incinerator risk assessments, this compound is the subject of the illustrative calculations. An important part of probabilistic risk assessment is determining the relative influence of the input parameters on the magnitude of the variance in the output distribution. This constitutes an important step toward prioritizing data needs for additional research. However, under various possible model forms reflecting incompletely understood aspects of contaminant transport, differences may be observed in estimates of risk, variance in risk, and the relative contributions of individual uncertain and variable inputs to the variance. In this analysis, a sequential structural decomposition of the relationships between the input variables is used to partition the variance in the output (i.e., risk) to identify the most influential contributors to overall variance among them. For comparison, the partitioning of variance is repeated, using techniques of multivariate regression. In summary, this study considers the degree to which results of a probabilistic assessment are contingent on critical model assumptions about the representation of deposition velocity. Specifically, this analysis assesses the impact on the results of uncertainty about the best model of the vapor/particle partitioning behavior of semi-volatile airborne pollutants.     

A dynamic artificial clam (Corbicula fluminea) allows parsimony on-line measurement of waterborne metals

We introduce a novel on-line biomonitoring system based on a valvometric conversion technique for clam Corbicula fluminea, allowing for rapid, continuous, and ecological relevant water quality control. Our model builds upon the basic principles of biological early warning system model in two ways. We first adopted a risk-based methodology to build a dynamic artificial clam for simulating how the bivalve closure rhythm in response to waterborne copper (Cu) and cadmium (Cd). Secondly, we integrated a probabilistic model associated with the time-varying dose-response relationships of valve closing behavior into the mechanisms of a dynamic artificial clam, allowing estimation of the time-varying waterborne Cu/Cd concentrations for on-line providing the outcomes of the toxicity detection technique. Measurements with Cu/Cd were performed and the calculated EC50 values were compared with published data for the valve movement test with C. fluminea. This proposed dynamic artificial clam provides a better quantitative understanding of on-line biomonitoring measurements of waterborne metals and may foster applications in clam farm management strategy and ecotoxicological risk assessment.     

Vulnerability of coastal livelihoods to shrimp farming: Insights from Mozambique

Millions of people around the world depend on shrimp aquaculture for their livelihoods. Yet, the phenomenal growth of shrimp farming has often given rise to considerable environmental and social damage. This article examines the impacts of commercial, export-oriented shrimp aquaculture on local livelihood vulnerability by comparing the exposure, sensitivity, and adaptive capacity of shrimp farm employees with non-farm employees in rural Mozambique. Exposure to stressors was similar between the two groups. Shrimp farm employees had higher assets and higher adaptive capacity than non-farm employees. However, because their income is heavily dependent on a single commodity, shrimp farm employees were highly susceptible to the boom crop nature of intensive shrimp farming. The implications for aquaculture policy and vulnerability research are discussed. The article argues that coastal vulnerability is dynamic, variable, and influenced by multiple processes operating at multiple scales.     

An Evaluation of Emissions and Control Technologies for the Metal Decorating Process

A simple but effective sampling and analytical procedure is described for determining total organics, carbon dioxide, carbon monoxide, and methane emitted from web offset printing presses. Data are reported on a number of controlled and uncontrolled sources and emission levels are related to process variables such as press speed, ink coverage and dryer type. For controlled sources employing either catalytic or thermal incineration units, conversion efficiencies are reported as a function of temperature. Operational curves are presented for each control system studied indicating the dependency of carbon dioxide, NO_xt and organic output to incineration temperatures.     

Forecasting summertime surface-level ozone concentrations in the Lower Fraser Valley of British Columbia: an ensemble neural network approach

Empirical models for predicting daily maximum hourly average ozone concentrations were developed for 10 monitoring stations in the Lower Fraser Valley (LFV) of British Columbia. According to data from 1991 to 1996, ensemble neural network models increased explained variance an average of 7% over multiple linear regression models using the same input variables. Without modification, all models performed poorly on days when the observed peak ozone concentration exceeded 82 parts per billion, the National Ambient Air Quality Objective. When numbers of extreme events in training data were increased using a histogram equalization process, models were able to forecast exceedances with improved accuracy. Modified generalized additive model (GAM) plots and associated measures of input variable importance and interaction were generated for a subset of the trained models and used to investigate relationships between input variables and ozone levels. The neural network models displayed a high degree of interaction among inputs, and it is likely the ability of these model types to account for interactions, rather than the nonlinearity of individual input variables, that explains their improved forecast skill. Inspection of GAM-style plots indicated that the relative importance of input variables in the ensemble neural network models varied with geographic location within the LFV. Four distinct groups of stations were identified, and rankings of inputs within the groups were generally consistent with physical intuition and results of prior studies.     

Prediction of maximum of 24-h average of PM10 concentrations 30 h in advance in Santiago,Chile

We have developed a neural network based model that uses values of PM10 concentrations measured until 6 p.m. on the present day plus measured and forecasted values of meteorological variables as input in order to predict the level reached by the maximum of the 24-h moving average (24MA) of PM10 concentration on the next day. We have adjusted the parameters of the model using 1998 data to predict 1999 conditions and 1999 data to forecast 2000 maximum concentrations. We have found that among the relevant meteorological input variables, the forecasted difference between maximum and minimum temperature is the most important. Due to the fact that local authorities impose restrictions to emissions on days when the maximum of 24MA of PM10 concentration is expected to exceed 240 μg/m³, we have corrected the measured concentrations on these days before evaluating the efficacy of the forecasting model. Percent errors in forecasting the numerical value are of the order of 20%. The performance of the neural network is better than that of a linear model with the same inputs, but the difference being not great is an indication that the right choice of input variables may be more important than the decision to use a linear or a nonlinear model.     

Interval estimation of urban ozone level and selection of influential factors by employing automatic relevance determination model

In this work, we focus on simulating the ground-level ozone (O3) time series and its daily maximum concentration in Hong Kong urban air by employing the multilayer perceptron (MLP) model combined with the automatic relevance determination (ARD) method (for simplicity, we name it as MLP-ARD model). Two air quality monitoring sites in Hong Kong, i.e., Tsuen Wan and Tung Chung, are selected for the numerical experiments. The MLP-ARD model based on Bayesian evidence framework can provide reliable interval estimation of real observation as well as offering efficient strategy to avoid over-fitting. The performance comparisons between MLP-ARD model and traditional artificial neural network (ANN) model based on maximum likelihood indicate that MLP-ARD model is more powerful to capture the wild fluctuation of O3 level especially during O3 episodes than the traditional model. Furthermore, it can assess and rank the input variables for the prediction according to their relative importance to the output variable, i.e., the daily maximum O3 concentration in this study. The preliminary experimental results indicate that nitric oxide (NO) and solar radiation are the most important input variables for O3 prediction at both selected sites. In addition, the previous daily maximum O3 level is also important for Tung Chung site. In this regard, MLP-ARD model is a feasible tool to interpret the real physical and chemical process of urban O3 variation.     

An Evaluation of Emissions and Control Technologies for the Metal Decorating Process

Data are reported on a number of controlled and uncontrolled sources from various metal decorating operations. Emission levels are related to process variables such as ink coverage, coater speed, sheet size, and applied coating film weight. Results are presented depicting variation of organic emission levels from a coating process line with elapsed process time. For controlled sources employing either catalytic or thermal incineration, conversion efficiencies are reported as a function of temperature. Operational curves are presented for each control system studied indicating the dependence of carbon dioxide, NO_x, and organic output on incineration temperatures.     

Economic and demographic factors affecting mangrove loss in the coastal provinces of Thailand, 1979-1996

The following paper analyzes the economic and demographic factors determining the conversion of mangroves in the coastal provinces of Thailand to commercial shrimp farming. Mangrove conversion therefore is determined by the returns to shrimp farmers, (i.e. the price of shrimp), the input costs to farming shrimp (e.g. feed price and wages) and the "accessibility" of mangrove areas. Additional exogenous influences, such as income per capita, population growth, and in-migration (i.e. the number of shrimp farms) also are important. Both a mangrove conversion and a shrimp farm expansion relationship are estimated empirically through a panel analysis across 21 coastal provinces of Thailand between 1979-1996. Results show that the price of shrimp, minimum wage, distance from market, feed price, population growth, income per capita, and shrimp-farm density all have important influences on mangrove loss due to shrimp farming in Thailand.     

Neural Network Model for Predicting Peak Photochemical Pollutant Levels

ABSTRACT

In this paper, an attempt is made for the 24-hr prediction of photochemical pollutant levels using a neural network model. For this purpose, a model is developed that relates peak pollutant concentrations to meteorological and emission variables and indexes. The analysis is based on measurements of O₃ and NO₂ from the city of Athens. The meteorological variables are selected to cover atmospheric processes that determine the fate of the airborne pollutants while special care is taken to ensure the availability of the required input data from routine observations or forecasts. The comparison between model predictions and actual observations shows a good agreement. In addition, a series of sensitivity tests is performed in order to evaluate the sensitivity of the model to the uncertainty in meteorological variables. Model forecasts are generally rather insensitive to small perturbations in most of the input meteorological data, while they are relatively more sensitive in changes in wind speed and direction.