Weekday-Weekend Pollutant Studies of the Los Angeles Basin

Pollutant data from the Los Angeles Basin were analyzed for weekday-weekend differences for the smog months of June through September 1972 and 1973. The pollutants investigated were oxidant, NO, NO₂, total hydrocarbons (HC), CO, and particulates. In order to maintain the diurnal variation, the concentration percentiles were calculated for each weekday and weekend hour.     

Variance Design and Air Pollution Control

Confronted with shortages of low sulfur content residual fuel oil and, consequently, faced with the threat of social and economic upheaval, several air pollution control authorities in the Northeastern states were forced to relax hard-won air quality standards during the winter of 1972. The authorities did so by granting variances to their sulfur content standards for residual fuel oil. This paper examines the institutional characteristics of these variance policies from an economic incentive standpoint. After setting up desirable structural criteria for institutional design of such crisis policies, the authors examine the experience of the winter of 1972 and arrive at policy guidelines which recommend themselves for consideration in future periods of fuel oil shortages.     

Interregional Exchanges of Air Pollution: Model Types and Applications

Regional scale air quality simulation models covering spatial scales of thousands of kilometers are finding increasing applications in studies of acid deposition and other air pollution problems. The purpose of this paper is to familiarize the nonexpert with the characteristics of the major types of interregional air quality models currently in use: Eulerian grid, statistical trajectory, and Lagrangian trajectory. The basic features, advantages, and disadvantages of each of these modeling approaches are summarized, as are the important limitations and problems associated with interregional modeling in general. Typical applications are illustrated using examples from the use of a representative Lagrangian trajectory model, ENAMAP, over the eastern North American area.     

Use of Energy Scenarios in Addressing the CO2 Question

A reference scenario for CO₂ emissions was developed using a model of world energy supply and demand. In the reference scenario, world GNP and world energy demand increase at average rates of 2.1 percent per year and 1.5 percent per year, respectively during the period 1975-2100. The corresponding annual CO₂ emissions rise to a maximum of 16 gigatons of carbon around 2050 and then decline as a result of a transition to nonfossil fuel energy systems. A modified scenario for high CO₂ emissions was obtained by assuming an abundant supply of low cost coal, thus eliminating the transition. A low case was developed in which the low cost of alternative energy (i.e., solar, nuclear) induces an earlier shift away from fossil fuels.

Annual emissions of the three scenarios were used as input to a global carbon cycle model and the CO₂ buildup in the atmosphere during the period 1980-2100 was determined by the model. All three scenarios showed continuous rises in atmospheric CO₂ concentration. The reference scenario reached 775 ppm by 2100. The high CO₂ case resulted in concentrations of over 1040 ppm, and for the low case the 2100 concentration was just under 700 ppm. If the climate theory is correct, even 700 ppm is sufficient to give significant climate warming, but by experiencing the change gradually over a century, adaptation may not be painful. An early transition to nonfossil fuel supplies makes the problem less severe but does not eliminate it.     

Nonparametric functional data estimation applied to ozone data: prediction and extreme value analysis

The study of extreme values and prediction of ozone data is an important topic of research when dealing with environmental problems. Classical extreme value theory is usually used in air-pollution studies. It consists in fitting a parametric generalised extreme value (GEV) distribution to a data set of extreme values, and using the estimated distribution to compute return levels and other quantities of interest. Here, we propose to estimate these values using nonparametric functional data methods. Functional data analysis is a relatively new statistical methodology that generally deals with data consisting of curves or multi-dimensional variables. In this paper, we use this technique, jointly with nonparametric curve estimation, to provide alternatives to the usual parametric statistical tools. The nonparametric estimators are applied to real samples of maximum ozone values obtained from several monitoring stations belonging to the Automatic Urban and Rural Network (AURN) in the UK. The results show that nonparametric estimators work satisfactorily, outperforming the behaviour of classical parametric estimators. Functional data analysis is also used to predict stratospheric ozone concentrations. We show an application, using the data set of mean monthly ozone concentrations in Arosa, Switzerland, and the results are compared with those obtained by classical time series (ARIMA) analysis.     

Parts-per-trillion LC-MS(Q) analysis of herbicides and transformation products in surface water

The goal of this study was to develop a robust method of analyzing surface water samples for S-triazine herbicides, chloroacetanilide herbicides, and their transformation products (TPs) using solid-phase extraction (SPE) followed by liquid chromatograph-mass spectrometry (LC-MS) with electrospray ionization (ESI) by in-source collision-induced dissociation (ISCID) capability of an orthogonal electrospray ionization probe on a single quadrupole LC-MS system. The method developed here met the goals of the study and yielded estimated method detection limits (EMDLs) averaging 0.3 ± 0.1 ng L(-1) for S-triazines and their TPs and 0.7 ± 0.4 ng L(-1) for chloroacetanilides and TPs. Spiked filtered river water yielded SPE recoveries ranging from 94.2 % ± 4.8 % for S-triazines and TPs after eliminating three compounds with less that 65 % recovery from analysis and 95.9 % ± 19 % for chloroacetanilides and their TPs. The method was field-tested with filtered water samples collected from four sites over a four-month period. Detectible values of S-triazines and TPs ranged from 0.3 to 1540 ng L(-1) with a mean of 79.3 and a median of 19.4 ng L(-1). Detectible values for chloroacetanilides and TPs ranged from 0.31 to 3780 ng L(-1) with a mean of 252 and a median of 25.6 ng L(-1). An additional goal was to determine if the method was useful for microbial degradation studies using native bacterial communities. The bacteria transformed atrazine (2-chloro-4-ethylamino-6-isopropylamino-S-triazine) solely into 2-hydroxy atrazine (2-hydroxy-4-ethylamino-6-isopropylamino-S-triazine) with concentrations of 78.4, 63.3 and 32.5 ng L(-1) after 12 days of incubation compared with 6.3 and 7.1 ng L(-1) for control dark and control sunlight respectively.     

Looking beyond the average agricultural impacts in defining adaptation needs in Europe

The effects of climate change on agriculture are often characterised by changes in the average productivity of crops; however, these indicators provide limited information regarding the risks associated with fluctuations in productivity resulting from future changes in climate variability that may also affect agriculture. In this context, this study evaluates the combined effects of the risks associated with anomalies reflected by changes in the mean crop yield and the variability of productivity in European agro-climatic regions under future climate change scenarios. The objective of this study is to evaluate adaptation needs and to identify regional effects that should be addressed with greater urgency in the light of the risks and opportunities that are identified. The results show differential effects on regional agriculture and highlight the importance of considering both regional average impacts and the variability in crop productivity in setting priorities for the adaptation and maintenance of rural incomes and agricultural insurance programmes.     

Integrated hydrologic–economic decision support system for groundwater use confronting climate change uncertainties in the Tunuyán River basin,Argentina

This study presents an integrated hydrologic–economic model as decision support system for groundwater use and incorporates uncertainties of climate change. The model was developed with the Vensim software (Ventana Systems) for system dynamic simulations. The software permitted the integration of economic variables along with hydrologic variables, in a unified format with the aim of evaluating the economic impacts of climate change on arid environments. To test the model, we applied it in one of the upper Tunuyán River sub-basin, located in the Mendoza Province (Argentina), where irrigation comes from groundwater. The model defines the best mix of crops and the total land use required to maximize the total river sub-basin monetary income, considering as a limit the amount of water that does not exceed the natural annual aquifer recharge. To estimate the impacts of climatic changes, four scenarios were compared: the business as usual (with the number of existing wells) in a dry year with a temperature increase of 4 °C; the business as usual in a wet year with an increase in temperature of 1.1 °C; an efficient use of wells in a dry year and a temperature increase of 4 °C and an efficient use of wells in a wet year with a temperature increase of 1.1 °C. Outputs calculated by the model were: land use per crop, total sub-basin net benefit, total sub-basin water extraction, water extraction limit depending on river discharge and total number of wells required to irrigate the entire area. Preliminary results showed that the number of existing wells exceeded the optimized number of wells required to sustainably irrigate the entire river sub-basin. Results indicated that in an average river discharge year, if wells were efficiently used, further rural development would be possible, until the limit of 350 million m³ of water extraction per year was reached (650 million m³ for a wet year and 180 million m³ for a dry year). The unified format and the low cost of the software license make the model a useful tool for Water Resources Management Institutions, particularly in developing countries.     

Agroforest’s growing role in reducing carbon losses from Jambi (Sumatra), Indonesia

This paper examines the size and intensity of changes among five land categories during the two time intervals in a region of Indonesia that is pioneering negotiations concerning reducing emissions from deforestation and forest degradation (REDD). Maps at 1973, 1993, and 2005 indicate that land-cover change is accelerating, while carbon loss is decelerating in Jambi Province, Sumatra. Land dynamics have shifted from Forest loss during 1973–1993 to Agroforest loss during 1993–2005. Forest losses account for most reductions in aboveground carbon during the both time intervals, but Agroforest plays an increasingly important role in carbon reductions during the more recent interval. These results provide motivation for future REDD policies to count carbon changes associated with all influential land categories, such as Agroforests.