An evaluation of benthic macroinvertebrate biomass methodology

This study shows that biological assessments of water quality status using biomass estimates of wet, dry, and ash-free dry weights and counts of individual organisms from a small, headwater stream in southwestern Ohio provide essentially similar results concerning the impact of a sewage treatment plant discharge. Of the indices of biotic status for the stream segment employed for data evaluation; Diversity Index (D), Community Diversity Index (d), Trophic Condition Index (TCI), and Empirical Biotic Index (EBI), the latter two provided evaluations most consistent with benchmark water chemistry and physics information concerning the trophic status of the stream. In addition, the percent composition of macro-invertebrate taxa by pollutional category; clean water, facultative, and pollution tolerant, as ascribed using TCI and EBI ranges for individual taxa collected in combination of Ekman grab, rock-filled basket sampler and drift net samples, proves adequate for interpretation of biotic status.     

An economic model of ecolabeling

Firms would like to capitalize on consumers' willingness to pay more for goods that use environmentally friendly production techniques, but have difficulty credibly conveying their environmental friendliness. One possible remedy is “ecolabeling,” where a third party certifies firms' products. To model this phenomenon, I analyzed a market characterized by asymmetric information in which firms decide whether to seek an ecolabel. Market equilibrium is discussed in three settings: where both technologies and outputs are fixed (so the only choice is whether to seek certification), where technologies are fixed but outputs are not, and where firms can choose both technology and output.     

Aerosol Research and Inhalation Epidemiological Study (ARIES): air quality and daily mortality statistical modeling--interim results

The Aerosol Research and Inhalation Epidemiological Study (ARIES) is an EPRI-sponsored project to collect air quality and meteorological data at a single site in northwestern Atlanta, GA. Seventy high-resolution air quality indicators (AQIs) are used to examine statistical relationships between air quality and health outcome end points. Contemporaneous mortality data are collected for Fulton and DeKalb counties in Georgia. Currently, 12 months of air quality and weather data are available for analysis, from August 1998 through July 1999. The interim mortality analysis used Poisson regression in generalized additive models (GAMs). The estimated log-linear association of mortality with various AQIs was adjusted for smoothed functions of time and meteorological data. The analysis considered daily deaths due to all nonaccidental causes, deaths to persons 65 years or older, and deaths in each of the two constituent counties. The fine particle effect associated with the four mortality subgroups, using only today (lag 0), yesterday (lag 1), 2-day average (average of today and yesterday), and first difference (today minus yesterday) measurements of the air quality relative to today's number of deaths was positive for lag 0, lag 1, and 2-day average and positive only for decedents at least 65 years of age using first difference. The t values ranged from 0.81 to 1.15 for lag 0, 1.04 to 1.53 for lag 1, 1.10 to 1.66 for 2-day average, and -0.32 to 0.33 for first difference with 346 or 347 days of data. No statistically significant estimate of the linear coefficient was found for the other 14 air quality variables in our interim analysis for the four mortality subgroups. We discuss diagnostics to support these models. These interim analyses did not include an evaluation of sensitivity to a larger set of lag structures, nonlinear model specifications, multipollutant analyses, alternative weather model and smoothing model specifications, air pollution imputation schemes, or cause-specific mortality indicators, nor did they include a full reporting of model selection or goodness-of-fit indicators. No conclusion can be drawn at this time about whether the findings from subsequent studies have sufficiently greater power to detect effects comparable to those found in other U.S. cities including at least 2 or 3 years of data.     

The occurrence of microplastic contamination in littoral sediments of the Persian Gulf,Iran

Environmental Science and Pollution Research - Microplastics (MPs; <5 mm) in aquatic environments are an emerging contaminant of concern due to their possible ecological and...     

Lessons from the 2006 Louisiana health and population survey

The 2005 hurricane season caused extensive damage and induced a mass migration of approximately 1.1 million people from southern Louisiana in the United States. Current and accurate estimates of population size and demographics and an assessment of the critical needs for public services were required to guide recovery efforts. Since forecasts using pre-hurricane data may produce inaccurate estimates of the post-hurricane population, a household survey in 18 hurricane-affected parishes was conducted to provide timely and credible information on the size of these populations, their demographics and their condition. This paper describes the methods used, the challenges encountered, and the key factors for successful implementation. This post-disaster survey was unique because it identified the needs of the people in the affected parishes and quantified the number of people with these needs. Consequently, this survey established new population and health indicator baselines that otherwise would have not been available to guide the relief and recovery efforts in southern Louisiana.     

A sorption kinetics model for arsenic adsorption to magnetite nanoparticles

Introduction  

Arsenic is a well known water contaminant that causes toxicological and carcinogenic effects. In this work magnetite nanoparticles were examined as possible arsenic sorbents. The objective of this work was to develop a sorption kinetics model, which could be used to predict the amount of arsenic adsorbed by magnetite nanoparticles in the presence of naturally occurring species using a first-order rate equation, modified to include adsorption, described by a Langmuir isotherm.     

Is Daily Mortality Associated Specifically with Fine Particles? Data Reconstruction and Replication of Analyses

ABSTRACT

In 1996, Schwartz, Dockery, and Neas¹ reported that daily mortality was more strongly associated with concentrations of PM_2.5 than with concentrations of larger particles (coarse mass [CM]) in six U.S. cities (“original paper'V'original analyses”). Because of the public policy implications of the findings and the uniqueness of the concentration data, we undertook a reanalysis of these results. This paper presents results of the reconstruction of these data and replication of the original analyses using the reconstructed data. The original investigators provided particulate air pollution data for this paper. Daily weather and daily counts of total and cause-specific deaths were reconstructed from original public records. The reconstructed particulate air pollution and weather data were consistent with the summaries presented in the original paper. Daily counts of deaths in the reconstructed data set were lower than in the original paper because of restrictions on residence and place of death. The reconstruction process identified an administrative change in county codes that led to higher numbers of deaths in St. Louis. Despite these differences in daily counts of deaths, the estimated effects of par-ticulate air pollution from the reconstructed dataset, using analytic methods as described in the original paper, produced combined effect estimates essentially equivalent to the originally published results. For example, the estimated association of a 10 |j.g/m³ increase in 2-day mean particulate air pollution on total mortality was 1.3% (95% confidence interval [CI] 0.9-1.7%, t = 6.53) for PM₂₅ based on the reconstructed dataset, compared to the originally reported association of 1.5% (95% CI 1.1-1.9%, t = 7.41). For coarse particles, the estimated association from the reconstructed dataset was 0.4% (95% CI -0.2-0.9%, t = 1.43) compared to the originally reported association of 0.4% (95% CI -0.1-1.0%, t = 1.48). These results from the reconstructed data suggest that the original results reported by Schwartz, Dockery, and Neas¹ were essentially replicated.     

The Management of Severe Malnutrition in Burundi: An NGO's Perspective of the Practical Constraints to Effective Emergency and Medium-term Programmes

The NGO, Action contre la Faim (ACF), has been operating in Burundi since 1994, where the situation has dramatically hampered humanitarian programmes. These include poor security resulting in an inability to maintain 24-hour care for the severely malnourished and poor access to the beneficiaries, all within a politically and economically unstable context. However, ACF has been able to capitalise on lessons learnt and reflect on ways to move forward, which have included the improvement of their capacities in the treatment of severe malnutrition and the integration within broader disciplines and national structures.
The protocols for the treatment of severe malnutrition currently used in Burundi have been developed as a result of the research of ACF over the past five years. Other aspects of the nutrition programme remain to be further developed. These include, for example, an improved capacity to care for severely malnourished adolescents and adults; and also a better knowledge of the beneficiaries to allow for stronger links between the provision of treatment and support for their longer-term food security.     

Impact of environmental and socio-economic stressors leading to unequal distribution of COVID-19 incidences in the state of Louisiana

Louisiana (LA) ranks fifth in the United States in cancer mortality rate. LA's infamous “cancer alley” is a well evidenced region near the southeast part of the Mississippi river surrounding the petrochemical hub of the state. LA has also experienced a high COVID-19 death rate and incidences compared to other states during the recent pandemic. In this study we analyzed publicly available datasets related to health and socio-economic parameters in LA to determine the factors triggering high incidences and deaths caused by COVID-19. Correlation analysis was performed to find the impact of different parameters on the outcome of COVID-19. Our analysis showed higher COVID-19 incidences in the parishes which are in and around the “cancer alley” with a correlation of r = 0.9. Interestingly, results also indicated a strong correlation (r = 0.9) between the death rates caused by asbestos toxicity to COVID-19 caused death rate. Furthermore, we found that office-administration related employment has a positive correlation to COVID-19 incidences in the “cancer alley.” However, we also found both white and black races are equally affected by the COVID-19 pandemic in the “cancer alley” region. In conclusion, our analysis strongly suggests that inhabiting “cancer alley” could significantly enhance the chances of getting affected by SARS-CoV-2 virus compared to other regions in LA.