Nonpoint Pollution Control: Inducing First-best Outcomes through the Use of Threats

In this paper we develop a simple economic model to analyze the use of a policy that combines a voluntary approach to controlling nonpoint-source pollution with a background threat of an ambient tax if the voluntary approach is unsuccessful in meeting a pre-specified environmental goal. We first consider the case where the policy is applied to a single farmer, and then extend the analysis to the case where the policy is applied to a group of farmers. We show that in either case such a policy can induce cost-minimizing abatement without the need for farm-specific information. In this sense, the combined policy approach is not only more effective in protecting environmental quality than a pure voluntary approach (which does not ensure that water quality goals are met) but also less costly than a pure ambient tax approach (since it entails lower information costs). However, when the policy is applied to a group of farmers, we show that there is a potential tradeoff in the design of the policy. In this context, lowering the cutoff level of pollution used for determining total tax payments increases the likely effectiveness of the combined approach but also increases the potential for free riding. By setting the cutoff level equal to the target level of pollution, the regulator can eliminate free riding and ensure that cost-minimizing abatement is the unique Nash equilibrium under which the target is met voluntarily. However, this cutoff level also ensures that zero voluntary abatement is a Nash equilibrium. In addition, with this cutoff level the equilibrium under which the target is met voluntarily will not strictly dominate the equilibrium under which it is not. We show that all results still hold if the background threat instead takes the form of reducing government subsidies if a pre-specified environmental goal is not met.

A QUANTITATIVE INVESTIGATION OF SOURCE AND ELEMENTAL COMPOSITION OF INDOOR AND OUTDOOR FINE PARTICULATE AIR POLLUTION IN HOUSTON (TEXAS)

An investigation was undertaken to determine the chemical characterization of inhalable particulate matter in the Houston area, with special emphasis on source identification and apportionment of outdoor and indoor atmospheric aerosols using multivariate statistical analyses.^ Fine (<2.5 (mu)m) particle aerosol samples were collected by means of dichotomous samplers at two fixed site (Clear Lake and Sunnyside) ambient monitoring stations and one mobile monitoring van in the Houston area during June-October 1981 as part of the Houston Asthma Study. The mobile van allowed particulate sampling to take place both inside and outside of twelve homes.^ The samples collected for 12-h sampling on a 7 AM-7 PM and 7 PM-7 AM (CDT) schedule were analyzed for mass, trace elements, and two anions. Mass was determined gravimetrically. An energy-dispersive X-ray fluorescence (XRF) spectrometer was used for determination of elemental composition. Ion chromatography (IC) was used to determine sulfate and nitrate.^ Average chemical compositions of fine aerosol at each site were presented. Sulfate was found to be the largest single component in the fine fraction mass, comprising approximately 30% of the fine mass outdoors and 12% indoors, respectively.^ Principal components analysis (PCA) was applied to identify sources of aerosols and to assess the role of meteorological factors on the variation in particulate samples. The results suggested that meteorological parameters were not associated with sources of aerosol samples collected at these Houston sites.^ Source factor contributions to fine mass were calculated using a combination of PCA and stepwise multivariate regression analysis. It was found that much of the total fine mass was apparently contributed by sulfate-related aerosols. The average contributions to the fine mass coming from the sulfate-related aerosols were 56% of the Houston outdoor ambient fine particulate matter and 26% of the indoor fine particulate matter.^ Characterization of indoor aerosol in residential environments was compared with the results for outdoor aerosols. It was suggested that much of the indoor aerosol may be due to outdoor sources, but there may be important contributions from common indoor sources in the home environment such as smoking and gas cooking. ^

Spatial analysis of cardiovascular disease mortality and air pollution in Harris County, Texas

The association between fine particulate matter air pollution (PM2.5) and cardiovascular disease (CVD) mortality was spatially analyzed for Harris County, Texas, at the census tract level. The objective was to assess how increased PM2.5 exposure related to CVD mortality in this area while controlling for race, income, education, and age. An estimated exposure raster was created for Harris County using Kriging to estimate the PM2.5 exposure at the census tract level. The PM2.5 exposure and the CVD mortality rates were analyzed in an Ordinary Least Squares (OLS) regression model and the residuals were subsequently assessed for spatial autocorrelation. Race, median household income, and age were all found to be significant (p<0.05) predictors in the model. This study found that for every one μg/m3 increase in PM2.5 exposure, holding age and education variables constant, an increase of 16.57 CVD deaths per 100,000 would be predicted for increased minimum exposure values and an increase of 14.47 CVD deaths per 100,000 would be predicted for increased maximum exposure values. This finding supports previous studies associating PM2.5 exposure with CVD mortality. This study further identified the areas of greatest PM2.5 exposure in Harris County as being the geographical locations of populations with the highest risk of CVD (i.e., predominantly older, low-income populations with a predominance of African Americans). The magnitude of the effect of PM2.5 exposure on CVD mortality rates in the study region indicates a need for further community-level studies in Harris County, and suggests that reducing excess PM2.5 exposure would reduce CVD mortality.^

PM2.5, PM10, and PM10-associated elements in the air in Bamako, Mali (2012-2013)

Saharan dust incursions and particulates emitted from human activities degrade air quality throughout West Africa, especially in the rapidly expanding urban centers in the region. Particulate matter (PM) that can be inhaled is strongly associated with increased incidence of and mortality from cardiovascular and respiratory diseases and cancer. Air samples collected in the capital of a Saharan-Sahelian country (Bamako, Mali) between September 2012 - July 2013 were found to contain inhalable PM concentrations that exceeded World Health Organization (WHO) and US Environmental Protection Agency (USEPA) PM2.5 and PM10 24-h limits 58 - 98% of days and European Union (EU) PM10 24-h limit 98% of days. Mean concentrations were 1.2-to-4.5 fold greater than existing limits. Inhalable PM was enriched in transition metals, known to produce reactive oxygen species and initiate the inflammatory reaction, and other potentially bioactive and biotoxic metals/metalloids. Eroded mineral dust composed the bulk of inhalable PM, whereas most enriched metals/metalloids were likely emitted from oil combustion, biomass burning, refuse incineration, vehicle traffic, and mining activities. Human exposure to inhalable PM and associated metals/metalloids over 24-h was estimated. The findings indicate that inhalable PM in the Sahara-Sahel region may present a threat to human health, especially in urban areas with greater inhalable PM and transition metal exposure.

An Institutional Analysis of Environmental Pollution Disputes in Taiwan: Cases of 'Self-Relief'

During the late 1980s and early 1990s in Taiwan, people's protests against environmental pollution often took the form of "self-relief," meaning that they attempted to fight polluters using their own resources, without relying on legal or administrative procedures. Why did such an extreme form of disputes become so widespread? What institutional changes did these movements bring about? These questions are analyzed using the analytical framework of "law and economics." Our research shows that "self-relief" functioned to a certain extent as a means of realizing quick compensation for victims, and for reflecting the opinions of local people concerning development projects; in addition, it served to promote the formulation of law and administrative systems. However, as it was based on direct negotiations between the parties concerned, the outcome of each dispute only reflected the transient balance of forces, and the experience gained in negotiations was not accumulated as a social norm.

Management for a Variety of Environmental Pollution and North-South Trade

A simple static model incorporating a variety of environmental pollution is developed. An autarky model shows that a developing country regulates fewer types of pollution by income-induced environmental policy. As income grows, the types of regulated pollution increase and also introduced regulations become tougher.Then the model incorporates international trade between a developed country and a developing country. The model gives a new interpretation for the pollution haven hypothesis. Some types of pollution abated with inefficient technology are emitted more in a developing country but other types necessarily increase in a developed country in order to meet the trade balance.

Political economy of low sulfurization and air pollution control policy in Japan : SOx emission reduction by fuel conversion

In the early stages of the development of Japan’s environmental policy, sulfur oxide (SOx) emissions, which seriously damage health, was the most important air pollution problem. In the second half of the 1960s and the first half of the 1970s, the measures against SOx emissions progressed quickly, and these emissions were reduced drastically. The most important factor of the reduction was the conversion to a low-sulfur fuel for large-scale fuel users, such as the electric power industry. However, industries started conversion to low-sulfur fuel not due to environmental concerns, but simply to reduce costs. Furthermore, the interaction among the various interests of the electric power industry, oil refineries, the central government, local governments, and citizens over the energy and environmental policies led to the measures against SOx emissions by fuel conversion.

Aplicación de técnicas de inteligencia artificial a la predicción de contaminantes atmosféricos

Abstract Air pollution is a big threat and a phenomenon that has a specific impact on human health, in addition, changes that occur in the chemical composition of the atmosphere can change the weather and cause acid rain or ozone destruction. Those are phenomena of global importance. The World Health Organization (WHO) considerates air pollution as one of the most important global priorities. Salamanca, Gto., Mexico has been ranked as one of the most polluted cities in this country. The industry of the area led to a major economic development and rapid population growth in the second half of the twentieth century. The impact in the air quality is important and significant efforts have been made to measure the concentrations of pollutants. The main pollution sources are locally based plants in the chemical and power generation sectors. The registered concerning pollutants are Sulphur Dioxide (SO2) and particles on the order of ∼10 micrometers or less (PM10). The prediction in the concentration of those pollutants can be a powerful tool in order to take preventive measures such as the reduction of emissions and alerting the affected population. In this PhD thesis we propose a model to predict concentrations of pollutants SO2 and PM10 for each monitoring booth in the Atmospheric Monitoring Network Salamanca (REDMAS - for its spanish acronym). The proposed models consider the use of meteorological variables as factors influencing the concentration of pollutants. The information used along this work is the current real data from REDMAS. In the proposed model, Artificial Neural Networks (ANN) combined with clustering algorithms are used. The type of ANN used is the Multilayer Perceptron with a hidden layer, using separate structures for the prediction of each pollutant. The meteorological variables used for prediction were: Wind Direction (WD), wind speed (WS), Temperature (T) and relative humidity (RH). Clustering algorithms, K-means and Fuzzy C-means, are used to find relationships between air pollutants and weather variables under consideration, which are added as input of the RNA. Those relationships provide information to the ANN in order to obtain the prediction of the pollutants. The results of the model proposed in this work are compared with the results of a multivariate linear regression and multilayer perceptron neural network. The evaluation of the prediction is calculated with the mean absolute error, the root mean square error, the correlation coefficient and the index of agreement. The results show the importance of meteorological variables in the prediction of the concentration of the pollutants SO2 and PM10 in the city of Salamanca, Gto., Mexico. The results show that the proposed model perform better than multivariate linear regression and multilayer perceptron neural network. The models implemented for each monitoring booth have the ability to make predictions of air quality that can be used in a system of real-time forecasting and human health impact analysis. Among the main results of the development of this thesis we can cite: A model based on artificial neural network combined with clustering algorithms for prediction with a hour ahead of the concentration of each pollutant (SO2 and PM10) is proposed. A different model was designed for each pollutant and for each of the three monitoring booths of the REDMAS. A model to predict the average of pollutant concentration in the next 24 hours of pollutants SO2 and PM10 is proposed, based on artificial neural network combined with clustering algorithms. Model was designed for each booth of the REDMAS and each pollutant separately. Resumen La contaminación atmosférica es una amenaza aguda, constituye un fenómeno que tiene particular incidencia sobre la salud del hombre. Los cambios que se producen en la composición química de la atmósfera pueden cambiar el clima, producir lluvia ácida o destruir el ozono, fenómenos todos ellos de una gran importancia global. La Organización Mundial de la Salud (OMS) considera la contaminación atmosférica como una de las más importantes prioridades mundiales. Salamanca, Gto., México; ha sido catalogada como una de las ciudades más contaminadas en este país. La industria de la zona propició un importante desarrollo económico y un crecimiento acelerado de la población en la segunda mitad del siglo XX. Las afectaciones en el aire son graves y se han hecho importantes esfuerzos por medir las concentraciones de los contaminantes. Las principales fuentes de contaminación son fuentes fijas como industrias químicas y de generación eléctrica. Los contaminantes que se han registrado como preocupantes son el Bióxido de Azufre (SO2) y las Partículas Menores a 10 micrómetros (PM10). La predicción de las concentraciones de estos contaminantes puede ser una potente herramienta que permita tomar medidas preventivas como reducción de emisiones a la atmósfera y alertar a la población afectada. En la presente tesis doctoral se propone un modelo de predicción de concentraci ón de los contaminantes más críticos SO2 y PM10 para cada caseta de monitorización de la Red de Monitorización Atmosférica de Salamanca (REDMAS). Los modelos propuestos plantean el uso de las variables meteorol ógicas como factores que influyen en la concentración de los contaminantes. La información utilizada durante el desarrollo de este trabajo corresponde a datos reales obtenidos de la REDMAS. En el Modelo Propuesto (MP) se aplican Redes Neuronales Artificiales (RNA) combinadas con algoritmos de agrupamiento. La RNA utilizada es el Perceptrón Multicapa con una capa oculta, utilizando estructuras independientes para la predicción de cada contaminante. Las variables meteorológicas disponibles para realizar la predicción fueron: Dirección de Viento (DV), Velocidad de Viento (VV), Temperatura (T) y Humedad Relativa (HR). Los algoritmos de agrupamiento K-means y Fuzzy C-means son utilizados para encontrar relaciones existentes entre los contaminantes atmosféricos en estudio y las variables meteorológicas. Dichas relaciones aportan información a las RNA para obtener la predicción de los contaminantes, la cual es agregada como entrada de las RNA. Los resultados del modelo propuesto en este trabajo son comparados con los resultados de una Regresión Lineal Multivariable (RLM) y un Perceptrón Multicapa (MLP). La evaluación de la predicción se realiza con el Error Medio Absoluto, la Raíz del Error Cuadrático Medio, el coeficiente de correlación y el índice de acuerdo. Los resultados obtenidos muestran la importancia de las variables meteorológicas en la predicción de la concentración de los contaminantes SO2 y PM10 en la ciudad de Salamanca, Gto., México. Los resultados muestran que el MP predice mejor la concentración de los contaminantes SO2 y PM10 que los modelos RLM y MLP. Los modelos implementados para cada caseta de monitorizaci ón tienen la capacidad para realizar predicciones de calidad del aire, estos modelos pueden ser implementados en un sistema que permita realizar la predicción en tiempo real y analizar el impacto en la salud de la población. Entre los principales resultados obtenidos del desarrollo de esta tesis podemos citar: Se propone un modelo basado en una red neuronal artificial combinado con algoritmos de agrupamiento para la predicción con una hora de anticipaci ón de la concentración de cada contaminante (SO2 y PM10). Se diseñó un modelo diferente para cada contaminante y para cada una de las tres casetas de monitorización de la REDMAS. Se propone un modelo de predicción del promedio de la concentración de las próximas 24 horas de los contaminantes SO2 y PM10, basado en una red neuronal artificial combinado con algoritmos de agrupamiento. Se diseñó un modelo para cada caseta de monitorización de la REDMAS y para cada contaminante por separado.

Risk of nitrate pollution in agricultural systems

Se propone una metodología que nos permita evaluar un óptimo manejo de la fertirrigación integrando aspectos agronómicos y medioambientales.