Ozone Air Quality over North America: Part II—An Analysis of Trend Detection and Attribution Techniques

ABSTRACT

Assessment of regulatory programs aimed at improving ambient O₃ air quality is of considerable interest to the scientific community and to policymakers. Trend detection, the identification of statistically significant long-term changes, and attribution, linking change to specific clima-tological and anthropogenic forcings, are instrumental to this assessment. Detection and attribution are difficult because changes in pollutant concentrations of interest to policymakers may be much smaller than natural variations due to weather and climate. In addition, there are considerable differences in reported trends seemingly based on similar statistical methods and databases. Differences arise from the variety of techniques used to reduce nontrend variation in time series, including mitigating the effects of meteorology and the variety of metrics used to track changes. In this paper, we review the trend assessment techniques being used in the air pollution field and discuss their strengths and limitations in discerning and attributing changes in O₃ to emission control policies.     

Data fusion modeling for groundwater systems

Engineering projects involving hydrogeology are faced with uncertainties because the earth is heterogeneous, and typical data sets are fragmented and disparate. In theory, predictions provided by computer simulations using calibrated models constrained by geological boundaries provide answers to support management decisions, and geostatistical methods quantify safety margins. In practice, current methods are limited by the data types and models that can be included, computational demands, or simplifying assumptions. Data Fusion Modeling (DFM) removes many of the limitations and is capable of providing data integration and model calibration with quantified uncertainty for a variety of hydrological, geological, and geophysical data types and models. The benefits of DFM for waste management, water supply, and geotechnical applications are savings in time and cost through the ability to produce visual models that fill in missing data and predictive numerical models to aid management optimization. DFM has the ability to update field-scale models in real time using PC or workstation systems and is ideally suited for parallel processing implementation. DFM is a spatial state estimation and system identification methodology that uses three sources of information: measured data, physical laws, and statistical models for uncertainty in spatial heterogeneities. What is new in DFM is the solution of the causality problem in the data assimilation Kalman filter methods to achieve computational practicality. The Kalman filter is generalized by introducing information filter methods due to Bierman coupled with a Markov random field representation for spatial variation. A Bayesian penalty function is implemented with Gauss–Newton methods. This leads to a computational problem similar to numerical simulation of the partial differential equations (PDEs) of groundwater. In fact, extensions of PDE solver ideas to break down computations over space form the computational heart of DFM. State estimates and uncertainties can be computed for heterogeneous hydraulic conductivity fields in multiple geological layers from the usually sparse hydraulic conductivity data and the often more plentiful head data. Further, a system identification theory has been derived based on statistical likelihood principles. A maximum likelihood theory is provided to estimate statistical parameters such as Markov model parameters that determine the geostatistical variogram. Field-scale application of DFM at the DOE Savannah River Site is presented and compared with manual calibration. DFM calibration runs converge in less than 1 h on a Pentium Pro PC for a 3D model with more than 15,000 nodes. Run time is approximately linear with the number of nodes. Furthermore, conditional simulation is used to quantify the statistical variability in model predictions such as contaminant breakthrough curves.     

A Case History of the Science and Management Collaboration in Understanding Hypoxia Events in Long Bay, South Carolina, USA

Communication of knowledge between the scientific and management communities is a difficult process complicated by the distinctive nature of professional career goals of scientists and decision-makers. This article provides a case history highlighting a collaboration between the science and management communities that resulted from a response to a 2004 hypoxia, or low dissolved oxygen, event in Long Bay, off Myrtle Beach, South Carolina. A working group of scientists and decision-makers was established at the time of the event and has continued to interact to develop a firm understanding of the drivers responsible for hypoxia formation in Long Bay. Several factors were found to be important to ensure that these collaborative efforts were productive: (1) genuine interest in collaboratively working across disciplines to examine a problem; (2) commitment by agency leadership, decision-makers, and researchers to create successful communication mechanisms; (3) respect for each others’ perspectives and an understanding how science and management are performed and that they are not mutually exclusive; (4) networking among researchers and decision-makers to ensure appropriate team members are involved in the process; (5) use of decision-maker input in the formulation of research and monitoring projects; and (6) commitment of resources for facilitation to ensure that researchers and decision-makers are communicating effectively.     

Accuracy Assessment of NOAA Gridded Daily Reference Evapotranspiration for the Texas High Plains

The National Oceanic and Atmospheric Administration (NOAA) provides daily reference evapotranspiration (ET_ref) maps for the contiguous United States using climatic data from North American Land Data Assimilation System (NLDAS). This data provides large‐scale spatial representation of ET_ref, which is essential for regional scale water resources management. Data used in the development of NOAA daily ET_ref maps are derived from observations over surfaces that are different from short (grass — ET_os) or tall (alfalfa — ET_rs) reference crops, often in nonagricultural settings, which carries an unknown discrepancy between assumed and actual conditions. In this study, NOAA daily ET_os and ET_rs maps were evaluated for accuracy, using observed data from the Texas High Plains Evapotranspiration (TXHPET) network. Daily ET_os, ET_rs and the climatic data (air temperature, wind speed, and solar radiation) used for calculating ET_ref were extracted from the NOAA maps for TXHPET locations and compared against ground measurements on reference grass surfaces. NOAA ET_ref maps generally overestimated the TXHPET observations (1.4 and 2.2 mm/day ET_os and ET_rs, respectively), which may be attributed to errors in the NLDAS modeled air temperature and wind speed, to which reference ET_ref is most sensitive. Therefore, a bias correction to NLDAS modeled air temperature and wind speed data, or adjustment to the resulting NOAA ET_ref, may be needed to improve the accuracy of NOAA ET_ref maps.     

Identifying and Evaluating a Suitable Index for Agricultural Drought Monitoring in the Texas High Plains

Drought is a complex and highly destructive natural phenomenon that affects portions of the United States almost every year, and severe water deficiencies can often become catastrophic for agricultural production. Evapotranspiration (ET) by crops is an important component in the agricultural water budget; thus, it is advantageous to include ET in agricultural drought monitoring. The main objectives of this study were to (1) conduct a literature review of drought indices with a focus to identify a simple but simultaneously adequate drought index for monitoring agricultural drought in a semiarid region and (2) using the identified drought index method, develop and evaluate time series of that drought index for the Texas High Plains. Based on the literature review, the Standardized Precipitation‐Evapotranspiration Index (SPEI) was found to satisfy identified constraints for assessing agricultural drought. However, the SPEI was revised by replacing reference ET with potential crop ET to better represent actual water demand. Data from the Texas High Plains Evapotranspiration network was used to calculate SPEIs for the major irrigated crops. Trends and magnitudes of crop‐specific, time‐series SPEIs followed crop water demand patterns for summer crops. Such an observation suggests that a modified SPEI is an appropriate index to monitor agricultural drought for summer crops, but it was found to not account for soil water stored during the summer fallow period for winter wheat.     

Fall from equipment injuries in U.S. mining: Identification of specific research areas for future investigation

Introduction

The objective of this study was to evaluate the circumstances leading to fall from equipment injuries in the mining industry.

Method

The 2006 and 2007 Mine Safety and Health Administration annual injury databases were utilized for this study whereby the injury narrative, nature of injury, body part injured, mine type, age at injury, and days lost were evaluated for each injury.

Results

The majority of injuries occurred at surface mining facilities (∼ 60%) with fractures and sprains/strains being the most common injuries occurring to the major joints of the body. Nearly 50% of injuries occurred during ingress/egress, predominately during egress, and approximately 25% of injuries occurred during maintenance tasks. The majority of injuries occurred in relation to large trucks, wheel loaders, dozers, and conveyors/belts. The severity of injury was independent of age and the median days lost was seven days; however, there was a large range in severity.

Impact on industry

From the data obtained in this study, several different research areas have been identified for future work, which include balance and stability control when descending ladders and equipment design for maintenance tasks.     

Ozone Air Quality over North America: Part I—A Review of Reported Trends

ABSTRACT

Ozone and precursor trends can be used to measure the effectiveness of regulatory programs that have been implemented. In this paper, we review trends in the concentrations of O₃, NO_x, and HCs over North America that have been reported in the literature. Although most existing trend studies are confounded by meteorological variability, both the raw data trends and the trends adjusted for meteorology collectively indicate a general decreasing trend in O concentrations in most areas of the United States during 1985-1996. In Canada, mean daily maximum 1-hr O₃ concentrations at urban sites show mixed trends with a majority of sites showing an increase from 1980 to 1993. Mean daily maximum 1-hr O₃ at most regionally representative Canadian sites appears to decrease from 1985 to 1993 or shows no significant change. There are far fewer data and analyses of NO_x and HC trends. Available studies covering various ranges of years indicate decreases in ambient NO_x and HC concentrations in Los Angeles, CA, decreases in HC concentrations in northeastern U.S. cities, and decreases in NO concentrations in Canadian cities. Two key needs are long-term HC and NO_x measurements, particularly at rural sites, and a systematic comparison of trend detection techniques on a reference data set.