ResearchPad - oceanography:-biological-and-chemical https://www.researchpad.co Default RSS Feed en-us © 2020 Newgen KnowledgeWorks <![CDATA[Stringent Emission Control Policies Can Provide Large Improvements in Air Quality and Public Health in India]]> https://www.researchpad.co/article/Nb1098b2b-f8d1-46e9-80dc-86448468f6a3 Air pollution is a major risk factor for human health in IndiaPopulation aging and growth will increase the disease burden due to exposure to particulate air pollution even under no emission changeStringent emission control reduces mortality rate in 2050 below 2015 levels although total premature mortality increases

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<![CDATA[Prevalence and Characterization of Staphylococcus aureus and Methicillin‐Resistant Staphylococcus aureus on Public Recreational Beaches in Northeast Ohio]]> https://www.researchpad.co/article/Nc2cf7d05-879f-4ce2-8ce7-439c7751833c

Abstract

Staphylococcus aureus can cause severe life‐threatening illnesses such as sepsis and endocarditis. Although S. aureus has been isolated from marine water and intertidal beach sand, only a few studies have been conducted to assess prevalence of S. aureus at freshwater recreational beaches. As such, we aimed to determine prevalence and molecular characteristics of S. aureus in water and sand at 10 freshwater recreational beaches in Northeast Ohio, USA. Samples were analyzed using standard microbiology methods, and resulting isolates were typed by spa typing and multilocus sequence typing. The overall prevalence of S. aureus in sand and water samples was 22.8% (64/280). The prevalence of methicillin‐resistant S. aureus (MRSA) was 8.2% (23/280). The highest prevalence was observed in summer (45.8%; 55/120) compared to fall (4.2%; 5/120) and spring (10.0%; 4/40). The overall prevalence of Panton‐Valentine leukocidin genes among S. aureus isolates was 21.4% (15/70), and 27 different spa types were identified. The results of this study indicate that beach sand and freshwater of Northeast Ohio were contaminated with S. aureus, including MRSA. The high prevalence of S. aureus in summer months and presence of human‐associated strains may indicate the possibility of role of human activity in S. aureus contamination of beach water and sand. While there are several possible routes for S. aureus contamination, S. aureus prevalence was higher in sites with wastewater treatment plants proximal to the beaches.

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<![CDATA[WRF 1960–2014 Winter Season Simulations of Particulate Matter in the Sahel: Implications for Air Quality and Respiratory Health]]> https://www.researchpad.co/article/N4dbdd16f-7dfe-4144-8d3d-a37f1cc7a940

Abstract

We use the Weather Research and Forecast model using the Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) dust module (WRF‐CHEM) to simulate the particulate matter (PM) variations in the Sahel during the winter seasons (January–March) of 1960–2014. Two simulations are undertaken where the direct aerosol feedback is turned off, and only transport is considered and where the direct aerosol feedback is turned on. We find that simulated Sahelian PM10 and PM2.5 concentrations were lower in the 1960s and after 2003 and higher during the period between 1988 and 2002. Higher Sahelian PM10 concentrations are due to stronger winds between the surface and 925 hPa over the Sahara, which transport dust into the Sahel. Negative PM10 concentration anomalies are found over the Bodele Depression and associated with weaker 925 wind anomalies after 1997 through 2014. Further west, positive PM10 concentration anomalies are found across the Adrar Plateau in the Sahara and responsible for dust transport to the Western Sahel. The North Atlantic Oscillation (NAO) is positively correlated to Sahelian dust concentrations especially during the periods of 1960–1970 and 1988–2002. The temporal/spatial patterns of PM10 concentrations have significant respiratory health implications for inhabitants of the Sahel.

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<![CDATA[Quantification of Rotavirus Diarrheal Risk Due to Hydroclimatic Extremes Over South Asia: Prospects of Satellite‐Based Observations in Detecting Outbreaks]]> https://www.researchpad.co/article/N1c514245-56ef-4185-b93e-7462d32dd374

Abstract

Rotavirus is the most common cause of diarrheal disease among children under 5. Especially in South Asia, rotavirus remains the leading cause of mortality in children due to diarrhea. As climatic extremes and safe water availability significantly influence diarrheal disease impacts in human populations, hydroclimatic information can be a potential tool for disease preparedness. In this study, we conducted a multivariate temporal and spatial assessment of 34 climate indices calculated from ground and satellite Earth observations to examine the role of temperature and rainfall extremes on the seasonality of rotavirus transmission in Bangladesh. We extracted rainfall data from the Global Precipitation Measurement and temperature data from the Moderate Resolution Imaging Spectroradiometer sensors to validate the analyses and explore the potential of a satellite‐based seasonal forecasting model. Our analyses found that the number of rainy days and nighttime temperature range from 16°C to 21°C are particularly influential on the winter transmission cycle of rotavirus. The lower number of wet days with suitable cold temperatures for an extended time accelerates the onset and intensity of the outbreaks. Temporal analysis over Dhaka also suggested that water logging during monsoon precipitation influences rotavirus outbreaks during a summer transmission cycle. The proposed model shows lag components, which allowed us to forecast the disease outbreaks 1 to 2 months in advance. The satellite data‐driven forecasts also effectively captured the increased vulnerability of dry‐cold regions of the country, compared to the wet‐warm regions.

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<![CDATA[Impact of Deadly Dust Storms (May 2018) on Air Quality, Meteorological, and Atmospheric Parameters Over the Northern Parts of India]]> https://www.researchpad.co/article/N8387ef6b-b75b-4aaa-bb39-241535d00866

Abstract

The northern part of India, adjoining the Himalaya, is considered as one of the global hot spots of pollution because of various natural and anthropogenic factors. Throughout the year, the region is affected by pollution from various sources like dust, biomass burning, industrial and vehicular pollution, and myriad other anthropogenic emissions. These sources affect the air quality and health of millions of people who live in the Indo‐Gangetic Plains. The dust storms that occur during the premonsoon months of March–June every year are one of the principal sources of pollution and originate from the source region of Arabian Peninsula and the Thar desert located in north‐western India. In the year 2018, month of May, three back‐to‐back major dust storms occurred that caused massive damage, loss of human lives, and loss to property and had an impact on air quality and human health. In this paper, we combine observations from ground stations, satellites, and radiosonde networks to assess the impact of dust events in the month of May 2018, on meteorological parameters, aerosol properties, and air quality. We observed widespread changes associated with aerosol loadings, humidity, and vertical advection patterns with displacements of major trace and greenhouse gasses. We also notice drastic changes in suspended particulate matter concentrations, all of which can have significant ramifications in terms of human health and changes in weather pattern.

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<![CDATA[How Can Phytoplankton Pigments Be Best Used to Characterize Surface Ocean Phytoplankton Groups for Ocean Color Remote Sensing Algorithms?]]> https://www.researchpad.co/article/N63e13cf1-730b-4762-810d-15dc971439a3

Abstract

High‐performance liquid chromatography (HPLC) remains one of the most widely applied methods for estimation of phytoplankton community structure from ocean samples, which are used to create and validate satellite retrievals of phytoplankton community structure. HPLC measures the concentrations of phytoplankton pigments, some of which are useful chemotaxonomic markers for phytoplankton groups. Here, consistent suites of HPLC phytoplankton pigments measured on global surface water samples are compiled across spatial scales. The global dataset includes >4,000 samples from every major ocean basin and representing a wide range of ecological regimes. The local dataset is composed of six time series from long‐term observatory sites. These samples are used to quantify the potential and limitations of HPLC for understanding surface ocean phytoplankton groups. Hierarchical cluster and empirical orthogonal function analyses are used to examine the associations between and among groups of phytoplankton pigments and to diagnose the main controls on these associations. These methods identify four major groups of phytoplankton on global scales (cyanobacteria, diatoms/dinoflagellates, haptophytes, and green algae) that can be identified by diagnostic biomarker pigments. On local scales, the same methods identify more and different taxonomic groups of phytoplankton than are detectable in the global dataset. Notably, diatom and dinoflagellate pigments group together on global scales, but dinoflagellate marker pigments always separate from diatoms on local scales. Together, these results confirm that HPLC pigments can be used for satellite algorithm quantification of no more than four phytoplankton groups on global scales, but can provide higher resolution for local‐scale algorithm development and validation.

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<![CDATA[Soil Moisture Data Assimilation to Estimate Irrigation Water Use]]> https://www.researchpad.co/article/Ne7f87e80-1a31-43ba-924d-25cb6494665e

Abstract

Knowledge of irrigation is essential to support food security, manage depleting water resources, and comprehensively understand the global water and energy cycles. Despite the importance of understanding irrigation, little consistent information exists on the amount of water that is applied for irrigation. In this study, we develop and evaluate a new method to predict daily to seasonal irrigation magnitude using a particle batch smoother data assimilation approach, where land surface model soil moisture is applied in different configurations to understand how characteristics of remotely sensed soil moisture may impact the performance of the method. The study employs a suite of synthetic data assimilation experiments, allowing for systematic diagnosis of known error sources. Assimilation of daily synthetic soil moisture observations with zero noise produces irrigation estimates with a seasonal bias of 0.66% and a correlation of 0.95 relative to a known truth irrigation. When synthetic observations were subjected to an irregular overpass interval and random noise similar to the Soil Moisture Active Passive satellite (0.04 cm3 cm−3), irrigation estimates produced a median seasonal bias of <1% and a correlation of 0.69. When systematic biases commensurate with those between NLDAS‐2 land surface models and Soil Moisture Active Passive are imposed, irrigation estimates show larger biases. In this application, the particle batch smoother outperformed the particle filter. The presented framework has the potential to provide new information into irrigation magnitude over spatially continuous domains, yet its broad applicability is contingent upon identifying new method(s) of determining irrigation schedule and correcting biases between observed and simulated soil moisture, as these errors markedly degraded performance.

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<![CDATA[Considering the Role of Adaptive Evolution in Models of the Ocean and Climate System]]> https://www.researchpad.co/article/Nfe9152c8-e79c-4817-9ec2-593d8a0ca732

Abstract

Numerical models have been highly successful in simulating global carbon and nutrient cycles in today's ocean, together with observed spatial and temporal patterns of chlorophyll and plankton biomass at the surface. With this success has come some confidence in projecting the century‐scale response to continuing anthropogenic warming. There is also increasing interest in using such models to understand the role of plankton ecosystems in past oceans. However, today's marine environment is the product of billions of years of continual evolution—a process that continues today. In this paper, we address the questions of whether an assumption of species invariance is sufficient, and if not, under what circumstances current model projections might break down. To do this, we first identify the key timescales and questions asked of models. We then review how current marine ecosystem models work and what alternative approaches are available to account for evolution. We argue that for timescales of climate change overlapping with evolutionary timescales, accounting for evolution may to lead to very different projected outcomes regarding the timescales of ecosystem response and associated global biogeochemical cycling. This is particularly the case for past extinction events but may also be true in the future, depending on the eventual degree of anthropogenic disruption. The discipline of building new numerical models that incorporate evolution is also hugely beneficial in itself, as it forces us to question what we know about adaptive evolution, irrespective of its quantitative role in any specific event or environmental changes.

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<![CDATA[Including Stable Carbon Isotopes to Evaluate the Dynamics of Soil Carbon in the Land‐Surface Model ORCHIDEE]]> https://www.researchpad.co/article/N50d35cde-1e20-4abc-8ba9-8eb87f5d9f22

Abstract

Soil organic carbon (SOC) is a crucial component of the terrestrial carbon cycle and its turnover time in models is a key source of uncertainty. Studies have highlighted the utility of δ13C measurements for benchmarking SOC turnover in global models. We used 13C as a tracer within a vertically discretized soil module of a land‐surface model, Organising Carbon and Hydrology In Dynamic Ecosystems‐ Soil Organic Matter (ORCHIDEE‐SOM). Our new module represents some of the processes that have been hypothesized to lead to a 13C enrichment with soil depth as follows: 1) the Suess effect and CO2 fertilization, 2) the relative 13C enrichment of roots compared to leaves, and 3) 13C discrimination associated with microbial activity. We tested if the upgraded soil module was able to reproduce the vertical profile of δ13C within the soil column at two temperate sites and the short‐term change in the isotopic signal of soil after a shift in C3/C4 vegetation. We ran the model over Europe to test its performance at larger scale. The model was able to simulate a shift in the isotopic signal due to short‐term changes in vegetation cover from C3 to C4; however, it was not able to reproduce the overall vertical profile in soil δ13C, which arises as a combination of short and long‐term processes. At the European scale, the model ably reproduced soil CO2 fluxes and total SOC stock. These findings stress the importance of the long‐term history of land cover for simulating vertical profiles of δ13C. This new soil module is an emerging tool for the diagnosis and improvement of global SOC models.

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<![CDATA[Reassessing Southern Ocean Air‐Sea CO 2 Flux Estimates With the Addition of Biogeochemical Float Observations]]> https://www.researchpad.co/article/N52b24823-1e08-4ca3-a65d-a54e81e297ed

Abstract

New estimates of pCO2 from profiling floats deployed by the Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) project have demonstrated the importance of wintertime outgassing south of the Polar Front, challenging the accepted magnitude of Southern Ocean carbon uptake (Gray et al., 2018, https://doi:10.1029/2018GL078013). Here, we put 3.5 years of SOCCOM observations into broader context with the global surface carbon dioxide database (Surface Ocean CO2 Atlas, SOCAT) by using the two interpolation methods currently used to assess the ocean models in the Global Carbon Budget (Le Quéré et al., 2018, https://doi:10.5194/essd-10-2141-2018) to create a ship‐only, a float‐weighted, and a combined estimate of Southern Ocean carbon fluxes (<35°S). In our ship‐only estimate, we calculate a mean uptake of −1.14 ± 0.19 Pg C/yr for 2015–2017, consistent with prior studies. The float‐weighted estimate yields a significantly lower Southern Ocean uptake of −0.35 ± 0.19 Pg C/yr. Subsampling of high‐resolution ocean biogeochemical process models indicates that some of the differences between float and ship‐only estimates of the Southern Ocean carbon flux can be explained by spatial and temporal sampling differences. The combined ship and float estimate minimizes the root‐mean‐square pCO2 difference between the mapped product and both data sets, giving a new Southern Ocean uptake of −0.75 ± 0.22 Pg C/yr, though with uncertainties that overlap the ship‐only estimate. An atmospheric inversion reveals that a shift of this magnitude in the contemporary Southern Ocean carbon flux must be compensated for by ocean or land sinks within the Southern Hemisphere.

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<![CDATA[Anthropogenic Aerosol Indirect Effects in Cirrus Clouds]]> https://www.researchpad.co/article/5c75659bd5eed0c484cbe366

Abstract

We have implemented a parameterization for forming ice in large‐scale cirrus clouds that accounts for the changes in updrafts associated with a spectrum of waves acting within each time step in the model. This allows us to account for the frequency of homogeneous and heterogeneous freezing events that occur within each time step of the model and helps to determine more realistic ice number concentrations as well as changes to ice number concentrations. The model is able to fit observations of ice number at the lowest temperatures in the tropical tropopause but is still somewhat high in tropical latitudes with temperatures between 195°K and 215°K. The climate forcings associated with different representations of heterogeneous ice nuclei (IN or INPs) are primarily negative unless large additions of IN are made, such as when we assumed that all aircraft soot acts as an IN. However, they can be close to zero if it is assumed that all background dust can act as an INP irrespective of how much sulfate is deposited on these particles. Our best estimate for the forcing of anthropogenic aircraft soot in this model is −0.2 ± 0.06 W/m2, while that from anthropogenic fossil/biofuel soot is −0.093 ± 0.033 W/m2. Natural and anthropogenic open biomass burning leads to a net forcing of −0.057 ± 0.05 W/m2.

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