Temporal trends of spatial correlation within the PM10 time series of the Air...Florencia Parravicini
We analyse the temporal variations which can be observed within time series of variogram parameters (nugget, sill and range) of daily air quality data (PM10) over a ten years time frame.
Merging remote and in-situ land degradation indicators in soil erosion contro...Stankovic G
Tetiana Ilienko, Olexander Tarariko, Olexandr Syrotenko, Tetyana Kuchma, Institute of agroecology and environmental management NAAS. Global Symposium on Soil Erosion (GSER19), 15 - 17 May 2019 at FAO HQ.
Temporal trends of spatial correlation within the PM10 time series of the Air...Florencia Parravicini
We analyse the temporal variations which can be observed within time series of variogram parameters (nugget, sill and range) of daily air quality data (PM10) over a ten years time frame.
Merging remote and in-situ land degradation indicators in soil erosion contro...Stankovic G
Tetiana Ilienko, Olexander Tarariko, Olexandr Syrotenko, Tetyana Kuchma, Institute of agroecology and environmental management NAAS. Global Symposium on Soil Erosion (GSER19), 15 - 17 May 2019 at FAO HQ.
Uncertainty in simulating biomass yield and carbon-water fluxes from Euro-Mediterranean grasslands under Climate Changes_Renata Sándor
LiveM_Macsur_Bilbao_2014
Simulation of atmospheric mercury dispersion and deposition in Tehran cityMohammadaminVahidi
In this study, dispersion and deposition of atmospheric mercury (Hg) in Tehran city was simulated using WRF-SMOKE-CMAQ models. The Weather Research and Forecasting (WRF) model was used to simulate the meteorological parameters. For validation of WRF results; the simulated wind speeds and temperatures were compared with the parameters measured at a meteorological station in Tehran city for 11 days (8 days in fall and 3 days in winter) in 2010 - 2011. The correlation coefficient (r) for temperature and wind speed were 0.94 and 0.49, respectively indicating there was good agreement between measured and modeled results. An atmospheric mercury emission inventory was developed using the United Nations Environment Programme (UNEP), the United States Environmental Protection Agency AP-42 (US-EPA AP-42) and related papers. Sparse Matrix Operator Kernel Emissions (SMOKE) was used to allocate the atmospheric mercury emissions to the modeling domain and the Community Multiscale Air Quality (CMAQ) model was used to simulate the concentration and deposition of atmospheric mercury. To validate the results of the CMAQ model, the simulated atmospheric particulate mercury (PHg) concentrations for 11 days were compared with the measured results at two different stations (Bagh Ferdows and Bahman Square) where it was measured by the Tehran Air Quality Control Company (AQCC). Comparison between the results from the modeled and measurements of PHg in fall was better than winter. Concentrations and dry depositions of the various forms of atmospheric mercury were higher in areas closer to mercury stationary emission sources.
2017 - Plausible Bioindicators of Biological Nitrogen Removal Process in WWTPsWALEBUBLÉ
Reference:
Zornoza, A., Alonso, J.L. and Serrano, S. (2017) Plausible Bioindicators of Biological Nitrogen Removal Process in WWTPs. In: Abstracts of the 7th congress of European microbiologists FEMS 2017, Valencia, Spain, 9-13 July 2017.
Peatland Diversity and Carbon Dynamics - BES 2011mgwhitfield
Peatlands are carbon cycling hotspots. We characterised plant and microbial diversity, carbon stocks and greenhouse-gas fluxes in a UK blanket peat. We aim to test whether measures of functional diversity (i.e. plant functional types, and microbial molecular diversity) can be used to explain and upscale variance in ecosystem carbon dynamics.
AERMOD Tiering Approach Case Study for 1-Hour NO2BREEZE Software
This study reviews 1-hour NO2 concentrations predicted by AERMOD for a hypothetical source at four locations throughout the United States with hourly varying background ozone concentrations.
Uncertainty in simulating biomass yield and carbon-water fluxes from Euro-Mediterranean grasslands under Climate Changes_Renata Sándor
LiveM_Macsur_Bilbao_2014
Simulation of atmospheric mercury dispersion and deposition in Tehran cityMohammadaminVahidi
In this study, dispersion and deposition of atmospheric mercury (Hg) in Tehran city was simulated using WRF-SMOKE-CMAQ models. The Weather Research and Forecasting (WRF) model was used to simulate the meteorological parameters. For validation of WRF results; the simulated wind speeds and temperatures were compared with the parameters measured at a meteorological station in Tehran city for 11 days (8 days in fall and 3 days in winter) in 2010 - 2011. The correlation coefficient (r) for temperature and wind speed were 0.94 and 0.49, respectively indicating there was good agreement between measured and modeled results. An atmospheric mercury emission inventory was developed using the United Nations Environment Programme (UNEP), the United States Environmental Protection Agency AP-42 (US-EPA AP-42) and related papers. Sparse Matrix Operator Kernel Emissions (SMOKE) was used to allocate the atmospheric mercury emissions to the modeling domain and the Community Multiscale Air Quality (CMAQ) model was used to simulate the concentration and deposition of atmospheric mercury. To validate the results of the CMAQ model, the simulated atmospheric particulate mercury (PHg) concentrations for 11 days were compared with the measured results at two different stations (Bagh Ferdows and Bahman Square) where it was measured by the Tehran Air Quality Control Company (AQCC). Comparison between the results from the modeled and measurements of PHg in fall was better than winter. Concentrations and dry depositions of the various forms of atmospheric mercury were higher in areas closer to mercury stationary emission sources.
2017 - Plausible Bioindicators of Biological Nitrogen Removal Process in WWTPsWALEBUBLÉ
Reference:
Zornoza, A., Alonso, J.L. and Serrano, S. (2017) Plausible Bioindicators of Biological Nitrogen Removal Process in WWTPs. In: Abstracts of the 7th congress of European microbiologists FEMS 2017, Valencia, Spain, 9-13 July 2017.
Peatland Diversity and Carbon Dynamics - BES 2011mgwhitfield
Peatlands are carbon cycling hotspots. We characterised plant and microbial diversity, carbon stocks and greenhouse-gas fluxes in a UK blanket peat. We aim to test whether measures of functional diversity (i.e. plant functional types, and microbial molecular diversity) can be used to explain and upscale variance in ecosystem carbon dynamics.
AERMOD Tiering Approach Case Study for 1-Hour NO2BREEZE Software
This study reviews 1-hour NO2 concentrations predicted by AERMOD for a hypothetical source at four locations throughout the United States with hourly varying background ozone concentrations.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
Cancer cell metabolism: special Reference to Lactate Pathway
Source apportionment approaches and non-linearities
1. JRC
Comparison of source apportionment approaches
8a Giornata della modellistica in ARIA(NET)
Belis C.A.1, G. Pirovano2, M.G. Villani3, G. Calori 4, N. Pepe4, J. P. Putaud1
1 European Commission, Joint Research Centre, via Fermi 2749, 21027 Ispra (VA), Italy
2 RSE Spa, via Rubattino 54, 20134, Milan, Italy
3 ENEA Laboratory of Atmospheric Pollution, via Fermi 2749, 21027 Ispra (VA), Italy
4 ARIANET s.r.l. via Gilino, 9 - 20128 Milan (MI) – Italy
2. Overview
In this study we
• compare PM10 tagged species (TS) contributions with brute force impacts (BF,
or emission reduction impacts) at different emission reduction levels (ERLs)
• analyse the geographical patterns
• compute interaction terms (of the Stein and Alpert algebraic expression) for the
studied sources
• examine the behaviour of PM10 in various areas (urban, rural, etc.) where
different chemical regimes prevail
The focus is on the SIA formation, with particular reference to ammonium nitrate
(NH4NO3) and ammonium sulfate ((NH4)2SO4).
3. Simulation with tagged species and brute force approaches
• Models: CAMx and FARM
• Tagged species module: PSAT
• Pollutant: PM10
• Area: Po Valley
• Reference year: 2010
• Time window: full year
• Domain: 580 x 400 km2
• Grid step: 5 km x 5 km approx.
• Meteorology: WRF 14 layers
• Emissions: EMEP (Europe), ISPRA (Italy), INEMAR (regional) processed with SMOKE
4. Simulations with Brute Force approach
Simulation set
Reduced sources
CAMx 100% CAMx 50% CAMx 20% FARM 50% FARM20%
No reduction Base case CAMx Base case FARM
AGRICULTURE (AGR) x x x x x
INDUSTRY (IND) x x x x x
ROAD TRANSPORT (TRA) x x x x x
RESIDENTIAL (RES) x x
AGR-IND x x x x x
AGR-TRA x x x x
IND-TRA x x x x x
RES-IND x x
RES-TRA x
RES-AGR x
AGR-IND-TRA x x x x
RES-IND-TRA x x
Simulations in blue are not discussed in this presentation
8. Non-linear responses of PM10 concentrations to emission reductions (mainly
agriculture) highlighted by:
• Dependence on the emission reduction level (BF)
• BF impacts not proportional to TS contributions
Non-linear responses of PM10 concentrations to emission reductions (mainly
agriculture) investigated, based on:
• Stein and Alpert (1993) decomposition
• The “free ammonia” / “total nitrate” ratio (GR), Ansari and Pandis (1998)
Non linearity
9. Stein and Alpert decomposition (theoretical example)
The interaction terms ( መ
𝐶) of the factor decomposition measure the
consistency between the impacts obtained with single source
reductions compared to those of multiple source reductions and
therefore can be used as indicators of non-linearity.
Clappier et al., 2017
Stein and Alpert decomposition
2 sources
∆𝐶𝑨𝑩= ∆𝐶𝑨 + ∆𝐶𝑩 + መ
𝐶𝑨𝑩
Stein and Alpert decomposition
3 sources
∆𝐶𝑨𝑩𝑪= ∆𝐶𝑨 + ∆𝐶𝑩 + ∆𝐶𝑪 + መ
𝐶𝑨𝑩 + መ
𝐶𝐴𝐶 + መ
𝐶𝑩𝑪 + መ
𝐶𝐴𝐵𝐶
Stein and Alpert, 1993
10. The gas ratio (GR, Ansari and Pandis,1998) was used to define the chemical regime in
each of the simulations :
GR= ([NH3] + [NH4
+] – 2[SO4
2-]) / ([HNO3] + [NO3
-])
where concentrations are nmol.m-³ or in nmol.mol-1 of air (ppb).
The GR value defines three different chemical regimes:
(a) GR>1, in which NH4NO3 formation is limited by the availability of HNO3,
(b) 0<GR<1, in which NH4NO3 formation is limited by the availability of NH3, and
(c) GR<0, in which NH4NO3 formation is inhibited by H2SO4the gas ratio (GR)
“free ammonia” / “total nitrate” gas ratio
11. GR vs interaction terms in a rural site (Cremona province)
negligible interaction terms
interaction terms > 0
interaction terms < 0
C: CAMx and F: FARM.
10, 5 and 2 indicate the 100%, 50% and 20% ERLs, respectively.
A: agriculture, I: industry and T: transport
Legend
12. GR vs interaction terms in an urban site (Milan)
negligible interaction terms
interaction terms > 0
interaction terms < 0
C: CAMx and F: FARM.
10, 5 and 2 indicate the 100%, 50% and 20% ERLs, respectively.
A: agriculture, I: industry and T: transport
Legend
13. • The differences in PM10 attributed to AGR applying the TS and the BF approaches at 100%
ERL reach a factor 2.
• There is less but still considerable dispersion around the identity line between BF impacts and
TS contributions of AGR at 50% and 20% ERLs (indicating spatial inconsistencies).
• The association between AGR with non-linear responses is due to the key role of NH3 (mainly
emitted by this source) in the formation of secondary inorganic aerosol.
• TS and BF lead to comparable PM10 apportionments for the other studied sources IND, TRA
(and RES, not shown).
• The higher comparability between CAMx and PSAT is because are performed with the same
model, while FARM and PSAT result from two different models.
Conclusions 1
14. • The analysis of the interaction terms vs free ammonia / total nitrate ratios (GR) provides
evidence about the relationships between changes in the SIA formation chemical regime and
the non-linear response of PM10 concentrations to SIA precursor emissions reductions.
• As linearity is more important for daily than yearly averages, the differences will be even more
important for daily or hourly values.
• FAIRMODE recommendation: use of TS to infer the response to emission changes should be
limited to linear compounds
Conclusions 2
