Quality-assured measurements of animal building emissions: particulate matter concentrations

Federally funded, multistate field studies were initiated in 2002 to measure emissions of particulate matter (PM) < 10 microm (PM10) and total suspended particulate (TSP), ammonia, hydrogen sulfide, carbon dioxide, methane, nonmethane hydrocarbons, and odor from swine and poultry production buildings in the United States. This paper describes the use of a continuous PM analyzer based on the tapered element oscillating microbalance (TEOM). In these studies, the TEOM was used to measure PM emissions at identical locations in paired barns. Measuring PM concentrations in swine and poultry barns, compared with measuring PM in ambient air, required more frequent maintenance of the TEOM. External screens were used to prevent rapid plugging of the insect screen in the PM10 preseparator inlet. Minute means of mass concentrations exhibited a sinusoidal pattern that followed the variation of relative humidity, indicating that mass concentration measurements were affected by water vapor condensation onto and evaporation of moisture from the TEOM filter. Filter loading increased the humidity effect, most likely because of increased water vapor adsorption capacity of added PM. In a single layer barn study, collocated TEOMs, equipped with TSP and PM10 inlets, corresponded well when placed near the inlets of exhaust fans in a layer barn. Initial data showed that average daily mean concentrations of TSP, PM10, and PM2.5 concentrations at a layer barn were 1440 +/- 182 microg/m3 (n = 2), 553 +/- 79 microg/m3 (n = 4), and 33 +/- 75 microg/m3 (n = 1), respectively. The daily mean TSP concentration (n = 1) of a swine barn sprinkled with soybean oil was 67% lower than an untreated swine barn, which had a daily mean TSP concentration of 1143 +/- 619 microg/m3. The daily mean ambient TSP concentration (n = 1) near the swine barns was 25 +/- 8 microg/m3. Concentrations of PM inside the swine barns were correlated to pig activity.     

Projections of highway vehicle population,energy demand,and CO2 emissions in India to 2040

This paper presents projections of motor vehicles, oil demand, and carbon dioxide (CO₂) emissions for India through the year 2040. The populations of highway vehicles and two‐wheelers are projected under three different scenarios on the basis of economic growth and average household size in India. The results show that by 2040, the number of highway vehicles in India would be 206‐309 million. The oil demand projections for the Indian transportation sector are based on a set of nine scenarios arising out of three vehicle‐growth and three fuel‐economy scenarios. The combined effects of vehicle‐growth and fuel‐economy scenarios, together with the change in annual vehicle usage, result in a projected demand in 2040 by the transportation sector in India of 404‐719 million metric tons (8.5‐15.1 million barrels per day). The corresponding annual CO₂ emissions are projected to be 1.2‐2.2 billion metric tons.     

Advanced regeneration and fixed-bed study of ammonium and potassium removal from anaerobic digested wastewater by natural zeolite

Highly efficient regeneration of natural zeolite was developed in conjunction with the removal of high concentrations of ammonia and potassium from the reverse osmosis effluent of anaerobic-digested wastewater by fixed-bed ion exchange.The elution and uptake behavior of ammonium and potassium in the fixed bed were studied.Both batch desorption tests and on-column regeneration were conducted to develop an optimum regeneration condition compatible with the wastewater requirements.The effectiveness of ammonium elution increased with increasing alkaline concentration.The increase of salt dose significantly enhanced the ammonium maximum in the elution solution.Complete ammonium elution was achieved in 6 bed volumes(BV) when the alkaline and salt concentrations were respectively 0.1 mol/L and 18.6 g/L at a flow rate of 2.5-3.0 BV/hr.Due to the higher affinity of potassium with natural clinoptilolite,complete potassium elution was not achieved in all cases.     

Aquatic biodiversity and the electric utility industry

Results from a 1995 survey of utility company biologists indicate that aquatic biodiversity is an emerging and poorly understood issue. As a result, there is some confusion about what aquatic biodiversity actually is, and how we can best conserve it. Only one fourth (24%) of the respondents said their company has a stated environmental policy that addresses biodiversity. Many respondents indicate that over the years they have not specifically managed for biodiversity, but have been doing that through their efforts to assure balanced indigenous populations. While regulations are still the major driver for biological work, an increasing number of companies are involved in voluntary partnerships in managing water resources. Of these voluntary partnerships, 70% have biodiversity as a goal. Biodiversity is becoming an increasingly common subject of study, and a vast majority (75%) of the respondents suggested it should be a goal for utility resource management. Conservation of aquatic biodiversity is a complex task, and to date most aquatic efforts have been directed toward fish and macroinvertebrates. Ecological research and technological development performed by the utility industry have resulted in a number of successful biopreservation and biorestoration success stories. A common theme to preserving or enhancing aquatic biodiversity is preserving aquatic habitat. Increasingly, ecosystem management is touted as the most likely approach to achieve success in preserving aquatic biodiversity. Several utilities are conducting progressive work in implementing ecosystem management. This paper presents the potential interactions between power plants and biodiversity, an overview of aquatic biodiversity preservation efforts within the electric utility industry, more detail on the results of the survey, and recent initiatives in ecosystem management.     

Sources and toxicities of phenolic polychlorinated biphenyls (OH-PCBs)

Polychlorinated biphenyls (PCBs), a group of 209 congeners that differ in the number and position of chlorines on the biphenyl ring, are anthropogenic chemicals that belong to the persistent organic pollutants (POPs). For many years, PCBs have been a topic of interest because of their biomagnification in the food chain and their environmental persistence. PCBs with fewer chlorine atoms, however, are less persistent and more susceptible to metabolic attack, giving rise to chemicals characterized by the addition of one or more hydroxyl groups to the chlorinated biphenyl skeleton, collectively known as hydroxylated PCBs (OH-PCBs). In animals and plants, this biotransformation of PCBs to OH-PCBs is primarily carried out by cytochrome P-450-dependent monooxygenases. One of the reasons for infrequent detection of lower chlorinated PCBs in serum and other biological matrices is their shorter half-lives, and their metabolic transformation, resulting in OH-PCBs or their conjugates, such as sulfates and glucuronides, or macromolecule adducts. Recent biomonitoring studies have reported the presence of OH-PCBs in human serum. The occurrence of OH-PCBs, the size of this group (there are 837 mono-hydroxyl PCBs alone), and their wide spectra of physical characteristics (pKa’s and log P’s ranging over 5 to 6 orders of magnitude) give rise to a multiplicity of biological effects. Among those are bioactivation to electrophilic metabolites that can form covalent adducts with DNA and other macromolecules, interference with hormonal signaling, inhibition of enzymes that regulate cellular concentrations of active hormones, and interference with the transport of hormones. This new information creates an urgent need for a new perspective on these often overlooked metabolites.     

Authentication of synthetic environmental contaminants and their (bio)transformation products in toxicology: polychlorinated biphenyls as an example

Environmental Science and Pollution Research - Toxicological studies use “specialty chemicals” and, thus, should assess and report both identity and degree of purity (homogeneity) of...     

How to address data privacy concerns when using social media data in conservation science

Social media data are being increasingly used in conservation science to study human–nature interactions. User-generated content, such as images, video, text, and audio, and the associated metadata can be used to assess such interactions. A number of social media platforms provide free access to user-generated social media content. However, similar to any research involving people, scientific investigations based on social media data require compliance with highest standards of data privacy and data protection, even when data are publicly available. Should social media data be misused, the risks to individual users' privacy and well-being can be substantial. We investigated the legal basis for using social media data while ensuring data subjects’ rights through a case study based on the European Union's General Data Protection Regulation. The risks associated with using social media data in research include accidental and purposeful misidentification that has the potential to cause psychological or physical harm to an identified person. To collect, store, protect, share, and manage social media data in a way that prevents potential risks to users involved, one should minimize data, anonymize data, and follow strict data management procedure. Risk-based approaches, such as a data privacy impact assessment, can be used to identify and minimize privacy risks to social media users, to demonstrate accountability and to comply with data protection legislation. We recommend that conservation scientists carefully consider our recommendations in devising their research objectives so as to facilitate responsible use of social media data in conservation science research, for example, in conservation culturomics and investigations of illegal wildlife trade online.     

Digital data sources and methods for conservation culturomics

Ongoing loss of biological diversity is primarily the result of unsustainable human behavior. Thus, the long-term success of biodiversity conservation depends on a thorough understanding of human–nature interactions. Such interactions are ubiquitous but vary greatly in time and space and are difficult to monitor efficiently at large spatial scales. However, the Information Age also provides new opportunities to better understand human–nature interactions because many aspects of daily life are recorded in a variety of digital formats. The emerging field of conservation culturomics aims to take advantage of digital data sources and methods to study human–nature interactions and thus to provide new tools for studying conservation at relevant temporal and spatial scales. Nevertheless, technical challenges associated with the identification, access, and analysis of relevant data hamper the wider adoption of culturomics methods. To help overcome these barriers, we propose a conservation culturomics research framework that addresses data acquisition, analysis, and inherent biases. The main sources of culturomic data include web pages, social media, and other digital platforms from which metrics of content and engagement can be obtained. Obtaining raw data from these platforms is usually desirable but requires careful consideration of how to access, store, and prepare the data for analysis. Methods for data analysis include network approaches to explore connections between topics, time-series analysis for temporal data, and spatial modeling to highlight spatial patterns. Outstanding challenges associated with culturomics research include issues of interdisciplinarity, ethics, data biases, and validation. The practical guidance we offer will help conservation researchers and practitioners identify and obtain the necessary data and carry out appropriate analyses for their specific questions, thus facilitating the wider adoption of culturomics approaches for conservation applications.     

A framework for investigating illegal wildlife trade on social media with machine learning

Article impact statement: Machine learning can be used to automatically monitor and assess illegal wildlife trade on social media platforms.