A Process-Based Ammonia Emission Model for Confinement Animal Feeding Operations—Model Development

A process-based modeling approach was used to develop a comprehensive and predictive ammonia emission model for estimating ammonia emission rates from animal feeding operations. The ammonia emission model consists of farm emission model (FEM) and animal allocation processor (AAP) and can be used to calculate ammonia emission rates both from an individual AFO and from a group of AFOs and also allows predictions of different time scale resolutions. The Farm Emission Model (FEM) covers five animal species, including dairy, beef cattle, swine, layers, broilers, and turkeys. For each species, the FEM reflects different farm practices with regards to animal feeding, animal housing, manure collection and storage, and land application. The overall structure and selected model components of FEM are described in this paper. Some computer simulation results for a finishing swine farm are presented. The predicted ammonia emission rates are variable during the day and over the period of the year

An Improved Process Based Ammonia Emission Model for Agricultural Sources—Emission Estimates

Ammonia is an important atmospheric pollutant that combines with sulfuric acid and nitric acid to form aerosol sulfates and nitrate, respectively. These aerosol species are major components of fine particulate matter (PM) and contribute significantly to visibility impairment. Estimates of ammonia emission factors are both highly variable and uncertain. Emissions factors vary depending on meteorological conditions and seasonal and regional differences in farming practices. Previous ammonia emissions inventories have not adequately characterized seasonal and geographical variations in emissions factors. Recent chemical transport modeling suggests that daily and hourly variability in ammonia emissions is required to model accurately the formation of ammonium nitrate and ammonium sulfates. In a companion paper, the development of a process-based model for predicting or estimating ammonia emission rates and factors from individual or a group of animal feeding operations at local, regional and national levels was presented. This paper discusses the data requirements and implementation of the process-based ammonia emission model. Preliminary emission estimates developed from the process-based ammonia emission model are also presented. Detailed description of databases used as input values for the process-developed model and recommendations for future improvement on the farm-based data regarding the animal feeding and manure management practices are documented. Where available, comparisons of the new ammonia emission estimates with existing ammonia emission inventories for livestock farms at a local, regional and national level are presented. The work presented here is sponsored and funded by the Lake Michigan Air Directors Consortium (LADCO)

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

A Process-Based Ammonia Emission Model for Confinement Animal Feeding Operations—Model Development

A process-based modeling approach was used to develop a comprehensive and predictive ammonia emission model for estimating ammonia emission rates from animal feeding operations. The ammonia emission model consists of farm emission model (FEM) and animal allocation processor (AAP) and can be used to calculate ammonia emission rates both from an individual AFO and from a group of AFOs and also allows predictions of different time scale resolutions. The Farm Emission Model (FEM) covers five animal species, including dairy, beef cattle, swine, layers, broilers, and turkeys. For each species, the FEM reflects different farm practices with regards to animal feeding, animal housing, manure collection and storage, and land application. The overall structure and selected model components of FEM are described in this paper. Some computer simulation results for a finishing swine farm are presented. The predicted ammonia emission rates are variable during the day and over the period of the year.This paper was presented at 14th International Emission Inventory Conference, 11–14 April 2005, Las Vegas, NV.</p

An Improved Process Based Ammonia Emission Model for Agricultural Sources—Emission Estimates

Ammonia is an important atmospheric pollutant that combines with sulfuric acid and nitric acid to form aerosol sulfates and nitrate, respectively. These aerosol species are major components of fine particulate matter (PM) and contribute significantly to visibility impairment. Estimates of ammonia emission factors are both highly variable and uncertain. Emissions factors vary depending on meteorological conditions and seasonal and regional differences in farming practices. Previous ammonia emissions inventories have not adequately characterized seasonal and geographical variations in emissions factors. Recent chemical transport modeling suggests that daily and hourly variability in ammonia emissions is required to model accurately the formation of ammonium nitrate and ammonium sulfates. In a companion paper, the development of a process-based model for predicting or estimating ammonia emission rates and factors from individual or a group of animal feeding operations at local, regional and national levels was presented. This paper discusses the data requirements and implementation of the process-based ammonia emission model. Preliminary emission estimates developed from the process-based ammonia emission model are also presented. Detailed description of databases used as input values for the process-developed model and recommendations for future improvement on the farm-based data regarding the animal feeding and manure management practices are documented. Where available, comparisons of the new ammonia emission estimates with existing ammonia emission inventories for livestock farms at a local, regional and national level are presented. The work presented here is sponsored and funded by the Lake Michigan Air Directors Consortium (LADCO).This paper was presented at 14th International Emission Inventory Conference, 11–14 April 2005, Las Vegas, NV.</p

Toll/IL-1 signaling is critical for house dust mite-specific Th1 and Th2 responses

Rationale: One of the immunopathological features of allergic inflammation is the infiltration of helper T type 2 (Th2) cells to the site of disease. Activation of innate pattern recognition receptors such as Toll-like receptors (TLRs) plays a critical role in helper T type 1 cell differentiation, yet their contribution to the generation of Th2 responses to clinically relevant aeroallergens remains poorly defined. Objectives: To determine the requirement for TLR2, TLR4, and the Toll/ IL-1 receptor domain adaptor protein MyD88 in a murine model of allergic asthma. Methods: Wild-type and factor-deficient (⁻/⁻) mice were sensitized intranasally to the common allergen house dust mite (HDM) and challenged 2 weeks later on four consecutive days. Measurements of allergic airway inflammation, T-cell cytokine production, and airway hyperreactivity were performed 24 hours later. Measurements and Main Results: Mice deficient in MyD88 were protected from the cardinal features of allergic asthma, including granulocytic inflammation, Th2 cytokine production and airway hyperreactivity. Although HDM activated NF-kB in TLR2- or TLR4-expressing HEκ cells, only in TLR4⁻/⁻ mice was the magnitude of allergic airway inflammation and hyperreactivity attenuated. The diminished Th2 response present in MyD88⁻/⁻ and TLR4⁻/⁻ mice was associated with fewer OX40 ligand–expressing myeloid dendritic cells in the draininglymph nodes during allergic sensitization. Finally, HDM-specific IL-17 production and airway neutrophilia were attenuated in MyD88⁻/⁻ but not TLR4⁻/⁻ mice. Conclusions: Together, these data suggest that Th2-andTh17-mediated inflammation generated on inhalational HDM exposure is differentially regulated by the presence of microbial products and the activation of distinct MyD88-dependent pattern recognition receptors