Effects of increasing seawater carbon dioxide concentrations on chain formation of the diatom Asterionellopsis glacialis

Diatoms can occur as single cells or as chain-forming aggregates. These two strategies affect buoyancy, predator evasion, light absorption and nutrient uptake. Adjacent cells in chains establish connections through various processes that determine strength and flexibility of the bonds, and at distinct cellular locations defining colony structure. Chain length has been found to vary with temperature and nutrient availability as well as being positively correlated with growth rate. However, the potential effect of enhanced carbon dioxide (CO2) concentrations and consequent changes in seawater carbonate chemistry on chain formation is virtually unknown. Here we report on experiments with semi-continuous cultures of the freshly isolated diatom Asterionellopsis glacialis grown under increasing CO2 levels ranging from 320 to 3400 µatm. We show that the number of cells comprising a chain, and therefore chain length, increases with rising CO2 concentrations. We also demonstrate that while cell division rate changes with CO2 concentrations, carbon, nitrogen and phosphorus cellular quotas vary proportionally, evident by unchanged organic matter ratios. Finally, beyond the optimum CO2 concentration for growth, carbon allocation changes from cellular storage to increased exudation of dissolved organic carbon. The observed structural adjustment in colony size could enable growth at high CO2 levels, since longer, spiral-shaped chains are likely to create microclimates with higher pH during the light period. Moreover increased chain length of Asterionellopsis glacialis may influence buoyancy and, consequently, affect competitive fitness as well as sinking rates. This would potentially impact the delicate balance between the microbial loop and export of organic matter, with consequences for atmospheric carbon dioxide.

PeECE III - Pelagic Ecosystem CO2 Enrichment Study, Raunefjord, Bergen, Norway, 2005

The effects of CO2-induced seawater acidification on plankton communities were also addressed in a series of 3 mesocosm experiments, called the Pelagic Ecosystem CO2 Enrichment (PeECE I-III) studies, which were conducted in the Large-Scale Mesocosm Facilities of the University of Bergen, Norway in 2001, 2003 and 2005, respectively. Each experiment consisted of 9 mesocosms, in which CO2 was manipulated to initial concentrations of 190, 350 and 750 µatm in 2001 and 2003, and 350, 700 and 1050 µatm in 2005. The present dataset concerns PeECE III.

Prediction of PM10 concentrations using Fuzzy c-Means and ANN

Salamanca has been considered among the most polluted cities in Mexico. The vehicular park, the industry and the emissions produced by agriculture, as well as orography and climatic characteristics have propitiated the increment in pollutant concentration of Particulate Matter less than 10 μg/m3 in diameter (PM10). In this work, a Multilayer Perceptron Neural Network has been used to make the prediction of an hour ahead of pollutant concentration. A database used to train the Neural Network corresponds to historical time series of meteorological variables (wind speed, wind direction, temperature and relative humidity) and air pollutant concentrations of PM10. Before the prediction, Fuzzy c-Means clustering algorithm have been implemented in order to find relationship among pollutant and meteorological variables. These relationship help us to get additional information that will be used for predicting. Our experiments with the proposed system show the importance of this set of meteorological variables on the prediction of PM10 pollutant concentrations and the neural network efficiency. The performance estimation is determined using the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results shown that the information obtained in the clustering step allows a prediction of an hour ahead, with data from past 2 hours

Modelling Saharan dust transport into the Mediterranean basin with CMAQ

The need for a better quantification of the influence of Saharan dust transport processes on the air quality modelling in the Mediterranean basin led to the formulation of a dust emission module (DEM) integrated into the Air Quality Risk Assessment System for the Iberian Peninsula (SERCA). This paper is focused on the formulation of DEM based on the GOCART aerosol model, along with its integration and execution into the air quality model. It also addresses the testing of the module and its evaluation by contrasting results against satellite products such as MODIS and CALIPSO and ground-level observations of aerosol optical thickness (AOT) and concentration levels of PM10 for different periods in July 2007. DEM was found capable of reproducing the spatial (horizontal and vertical) and temporal profiles of Saharan dust outbreaks into the Mediterranean basin and the Atlantic coast of Africa. Moreover, it was observed that its combination with CMAQ increased the correlation degree between observed and modelled PM10 concentrations at the selected monitoring locations. DEM also enhanced CMAQ capabilities to reproduce observed AOT, although significant underestimations remain. The implementation of CMAQ + DEM succeeded in capturing Saharan dust transport into the Iberian Peninsula, with contributions up to 25 and 14 μg m−3 in 1 h and 24 h average PM10 respectively. The general improvement of total PM10 predictions in Spain are however moderate. The analysis of model performance for the main PM components points out that remaining PM10 underestimation is due to dust local sources missing in the inventories and misrepresentation of organic aerosol processes, which constitutes the main areas for future improvement of CMAQ capabilities to simulate particulate matter within SERCA.

Unsupervised method to classify PM10 pollutant concentrations

In this paper a method based mainly on Data Fusion and Artificial Neural Networks to classify one of the most important pollutants such as Particulate Matter less than 10 micrometer in diameter (PM10) concentrations is proposed. The main objective is to classify in two pollution levels (Non-Contingency and Contingency) the pollutant concentration. Pollutant concentrations and meteorological variables have been considered in order to build a Representative Vector (RV) of pollution. RV is used to train an Artificial Neural Network in order to classify pollutant events determined by meteorological variables. In the experiments, real time series gathered from the Automatic Environmental Monitoring Network (AEMN) in Salamanca Guanajuato Mexico have been used. The method can help to establish a better air quality monitoring methodology that is essential for assessing the effectiveness of imposed pollution controls, strategies, and facilitate the pollutants reduction.

Road Dust Emission Sources and Assessment of Street Washing Effect

Although previous studies report on the effect of street washing on ambient particulate matter levels, there is a lack of studies investigating the results of street washing on the emission strength of road dust. A sampling campaign was conducted in Madrid urban area during July 2009 where road dust samples were collected in two sites, namely Reference site (where the road surface was not washed) and Pelayo site (where street washing was performed daily during night). Following the chemical characterization of the road dust particles the emission sources were resolved by means of Positive Matrix Factorization, PMF (Multilinear Engine scripting) and the mass contribution of each source was calculated for the two sites. Mineral dust, brake wear, tire wear, carbonaceous emissions and construction dust were the main sources of road dust with mineral and construction dust being the major contributors to inhalable road dust load. To evaluate the effectiveness of street washing on the emission sources, the sources mass contributions between the two sites were compared. Although brake wear and tire wear had lower concentrations at the site where street washing was performed, these mass differences were not statistically significant and the temporal variation did not show the expected build-up after dust removal. It was concluded that the washing activities resulted merely in a road dust moistening, without effective removal and that mobilization of particles took place in a few hours between washing and sampling. The results also indicated that it is worth paying attention to the dust dispersed from the construction sites as they affect the emission strength in nearby streets.

Fossil and non-fossil source contributions to atmospheric carbonaceous aerosols during extreme spring grassland fires in Eastern Europe

In early spring the Baltic region is frequently affected by high-pollution events due to biomass burning in that area. Here we present a comprehensive study to investigate the impact of biomass/grass burning (BB) on the evolution and composition of aerosol in Preila, Lithuania, during springtime open fires. Non-refractory submicron particulate matter (NR-PM1) was measured by an Aerodyne aerosol chemical speciation monitor (ACSM) and a source apportionment with the multilinear engine (ME-2) running the positive matrix factorization (PMF) model was applied to the organic aerosol fraction to investigate the impact of biomass/grass burning. Satellite observations over regions of biomass burning activity supported the results and identification of air mass transport to the area of investigation. Sharp increases in biomass burning tracers, such as levoglucosan up to 683 ngm-3 and black carbon (BC) up to 17 μgm-3 were observed during this period. A further separation between fossil and non-fossil primary and secondary contributions was obtained by coupling ACSM PMF results and radiocarbon (14C) measurements of the elemental (EC) and organic (OC) carbon fractions. Non-fossil organic carbon (OCnf/ was the dominant fraction of PM1, with the primary (POCnf/ and secondary (SOCnf/ fractions contributing 26–44% and 13–23% to the total carbon (TC), respectively. 5–8% of the TC had a primary fossil origin (POCf/, whereas the contribution of fossil secondary organic carbon (SOCf/ was 4–13 %. Nonfossil EC (ECnf/ and fossil EC (ECf/ ranged from 13–24 and 7–13 %, respectively. Isotope ratios of stable carbon and nitrogen isotopes were used to distinguish aerosol particles associated with solid and liquid fossil fuel burning.

(Table 2) pH and concentrations of dissolved and particulate chemical elements in the mixing zone of the Yur'eva River water and the Okhotsk Sea water

Data on behavior of iron, manganese, nickel, copper, and zinc in the zone where acidic volcanic waters of the Yur'eva River (Paramushir Island, Kuril Islands) mix with sea water are presented. Distributions of dissolved and particulate forms of these elements indicate that the mixing zone acts as a pH-based geochemical barrier, at which almost all dissolved iron and smaller amounts of other metals are precipitated. When chemogenic particulate matter formed in the mixing zone enters the open ocean, it can sorb trace elements from sea water.

Particulate concentrations and vertical fluxes of sedimentary material and chemical elements, composition of particulate and sinking material in the water column of the Bear Island Trough, Norwegian Sea, July 1995

CTD and nephelometric sounding data are considered along with parameters of the near-bottom currents and particulate fluxes measured by a subsurface mooring station in the northern part of the Bear Island Trough. It is shown that the near-bottom current is characterized by highly variable parameters, while distribution of suspended particulate matter demonstrates surface and bottom maxima. Horizontal and vertical fluxes of sedimentary material in the nepheloid layer are studied.

A conceptual study of possible collecting systems for use in stratospheric sampling /

Because the use of filters to sample particulate matter suspended in the upper atmosphere has been investigated and has yielded rather disappointing results, an examination of other methods of upper atmospheric sampling is desirable, and this is the aim of the present report. The nature of any radioactive material, and its relation to the size and composition of the suspended particles is of particular interest.

New law limits idling of diesel vehicles to reduce emissions and save fuel.

Diesel trucks and buses account for approximately 50 percent of the particulate matter (PM) and oxides of nitrogen (NOx) air pollution from on-road vehicles in Illinois. PM and NOx may contribute to a variety of health effects, including nausea, headaches, increased risk of asthma attacks, lung cancer, and premature death. Children and people with lung and heart conditions, are generally the most sensitive to diesel exhaust. Millions of tons of air pollution are emitted every year in the U.S. by trucks and buses that idle while parked.

Essays on Environmental and Health Economics

Thesis (Ph.D.)--University of Washington, 2016-06