Spatial and temporal variability of benzo[a]pyrene over Poland based on modelling and observations

2020 ◽  
Author(s):  
Jacek W. Kaminski ◽  
Joanna Struzewska ◽  
Pawel Durka ◽  
Grzegorz Jeleniewicz ◽  
Marcin Kawka
Keyword(s):  
Temporal Variability ◽  
Emission Inventory ◽  
Regional Scale ◽  
Meteorological Conditions ◽  
Spatial And Temporal Variability ◽  
Entire Area ◽  
P Concentration ◽  
The World ◽  
National Measurement ◽  
Waste Burning

<p>Benzo[a]pyrene is relatively stable in the atmosphere and can be transported on a regional scale. Benzo[a]pyrene concentrations exceed standard limits in many regions of the world. It is proved that this compound is harmful to the environment and human health.</p><p>According to the CAFÉ Directive (2008/50/EC), the objective is to achieve a concentration of B[a]P below 1ng/m3 in PM10 aerosol. Observed B[a]P concentration in Poland is among the highest in Europe. These exceedances are attributed to the emission from individual heating, where many old installations are still in operation. Major B[a]P emissions are due to low-quality fuels and non-reported municipal waste burning.</p><p>To support the Chief Inspectorate of Environmental Protection in the frame of the annual assessment for 2018 and five-year assessment for the period 2014-2018, the spatial distribution of B[a]P was calculated using the GEM-AQ model (Kaminski et al. 2008). A new national high-resolution bottom-up emission inventory was used for the entire area of Poland. The results at the resolution of 2.5 km were compared with observations from over 100 stations from the National Measurement Network. We will discuss the spatial and seasonal variability od B[a]P concentrations as well as year-to-year changes related to meteorological conditions.</p><p> </p>

scholarly journals Multiscale observations of NH<sub>3</sub> around Toronto, Canada

2021 ◽  
Vol 14 (2) ◽  
pp. 905-921
Author(s):  
Shoma Yamanouchi ◽  
Camille Viatte ◽  
Kimberly Strong ◽  
Erik Lutsch ◽  
Dylan B. A. Jones ◽  
...  
Keyword(s):  
Temporal Variability ◽  
Grid Point ◽  
Regional Scale ◽  
Model Performance ◽  
In Situ Measurements ◽  
Coefficient Of Determination ◽  
Spatial And Temporal Variability ◽  
Predictive Capacity ◽  
Total Column

Abstract. Ammonia (NH3) is a major source of nitrates in the atmosphere and a major source of fine particulate matter. As such, there have been increasing efforts to measure the atmospheric abundance of NH3 and its spatial and temporal variability. In this study, long-term measurements of NH3 derived from multiscale datasets are examined. These NH3 datasets include 16 years of total column measurements using Fourier transform infrared (FTIR) spectroscopy, 3 years of surface in situ measurements, and 10 years of total column measurements from the Infrared Atmospheric Sounding Interferometer (IASI). The datasets were used to quantify NH3 temporal variability over Toronto, Canada. The multiscale datasets were also compared to assess the representativeness of the FTIR measurements. All three time series showed positive trends in NH3 over Toronto: 3.34 ± 0.89 %/yr from 2002 to 2018 in the FTIR columns, 8.88 ± 5.08 %/yr from 2013 to 2017 in the surface in situ data, and 8.38 ± 1.54 %/yr from 2008 to 2018 in the IASI columns. To assess the representative scale of the FTIR NH3 columns, correlations between the datasets were examined. The best correlation between FTIR and IASI was obtained with coincidence criteria of ≤25 km and ≤20 min, with r=0.73 and a slope of 1.14 ± 0.06. Additionally, FTIR column and in situ measurements were standardized and correlated. Comparison of 24 d averages and monthly averages resulted in correlation coefficients of r=0.72 and r=0.75, respectively, although correlation without averaging to reduce high-frequency variability led to a poorer correlation, with r=0.39. The GEOS-Chem model, run at 2∘ × 2.5∘ resolution, was compared to FTIR and IASI to assess model performance and investigate the correlation of observational data and model output, both with local column measurements (FTIR) and measurements on a regional scale (IASI). Comparisons on a regional scale (a domain spanning 35 to 53∘ N and 93.75 to 63.75∘ W) resulted in r=0.57 and thus a coefficient of determination, which is indicative of the predictive capacity of the model, of r2=0.33, but comparing a single model grid point against the FTIR resulted in a poorer correlation, with r2=0.13, indicating that a finer spatial resolution is needed for modeling NH3.

Regional scale spatial and temporal variability of soil moisture in a prairie region

2016 ◽  
Vol 30 (20) ◽  
pp. 3639-3649 ◽  
Author(s):  
Travis T. Burns ◽  
Aaron A. Berg ◽  
Jaclyn Cockburn ◽  
Erica Tetlock
Keyword(s):  
Soil Moisture ◽  
Temporal Variability ◽  
Regional Scale ◽  
Spatial And Temporal Variability

Computational Statistics in Climatology

1996 ◽  
Author(s):  
Ilya Polyak
Keyword(s):  
Climate Change ◽  
Temporal Variability ◽  
Spatial And Temporal Variability ◽  
Computational Statistics ◽  
Design And Development ◽  
Random Functions ◽  
The World

Scientific descriptions of the climate have traditionally been based on the study of average meteorological values taken from different positions around the world. In recent years however it has become apparent that these averages should be considered with other statistics that ultimately characterize spatial and temporal variability. This book is designed to meet that need. It is based on a course in computational statistics taught by the author that arose from a variety of projects on the design and development of software for the study of climate change, using statistics and methods of random functions.

scholarly journals Climatic Trends in Hail Precipitation in France: Spatial, Altitudinal, and Temporal Variability

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Lucía Hermida ◽  
José Luis Sánchez ◽  
Laura López ◽  
Claude Berthet ◽  
Jean Dessens ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Temporal Variability ◽  
Kendall Test ◽  
Spatial And Temporal Variability ◽  
Positive Trend ◽  
High Concentration ◽  
Entire Area ◽  
Orographic Effects ◽  
June July ◽  
Pearson Correlations

Hail precipitation is characterized by enhanced spatial and temporal variability. Association Nationale d’Etude et de Lutte contre les Fléaux Atmosphériques (ANELFA) installed hailpad networks in the Atlantic and Midi-Pyrénées regions of France. Historical data of hail variables from 1990 to 2010 were used to characterize variability. A total of 443 stations with continuous records were chosen to obtain a first approximation of areas most affected by hail. The Cressman method was selected for this purpose. It was possible to find relationships between spatial distributions of the variables, which are supported by obtained Pearson correlations. Monthly and annual trends were examined using the Mann-Kendall test for each of the total affected hailpads. There were 154 pads with a positive trend; most were located between Tarbes and Saint-Gaudens. We found 177 pads with a negative trend, which were largely south of a pine forest in Landes. The remainder of the study area showed an elevated spatial variability with no pattern, even between relatively close hailpads. A similar pattern was found in Lérida (Spain) and Southeast France. In the entire area, monthly trends were predominantly negative in June, July, and August, whereas May had a positive trend; again, however, there was no spatial pattern. There was a high concentration of hailpads with positive trend near the Pyrenees, probably owing to orographic effects, and if we apply cluster analysis with the Mann-Kendall values, the spatial variability is accentuated for stations at higher altitude.

Ocean Acidification: pH Variability Across Space and Time

EDIS ◽  
2020 ◽  
Vol 2020 (5) ◽  
pp. 4
Author(s):  
Lisa Krimsky ◽  
Joseph Henry ◽  
Joshua Patterson
Keyword(s):  
Carbon Dioxide ◽  
Temporal Variability ◽  
Atmospheric Carbon Dioxide ◽  
Chemical Properties ◽  
Forest Resources ◽  
Marine Resources ◽  
Spatial And Temporal Variability ◽  
The World ◽  
Aquatic Sciences ◽  
Economic Ties

The absorption of atmospheric carbon dioxide by the oceans has changed the chemical properties of seawater and made it more acidic all over the world. Florida, with an extensive coastline and deep cultural and economic ties to marine resources, will be directly affected. This 4-page fact sheet written by Lisa Krimsky, Joseph Henry, and Joshua Patterson and published by the UF/IFAS Program in Fisheries and Aquatic Sciences, School of Forest Resources and Conservation focuses on the spatial and temporal variability in oceanic pH and provide an overview of pH variability in Florida's coastal waters.https://edis.ifas.ufl.edu/fa227

Königshafen Submarine Groundwater Discharge Network (KiSNet)

2020 ◽  
Author(s):  
Ulf Mallast ◽  
Hannelore Waska ◽  
Nils Moosdorf
Keyword(s):  
Temporal Variability ◽  
Submarine Groundwater Discharge ◽  
Chemical Constituents ◽  
Regional Scale ◽  
Groundwater Discharge ◽  
Interdisciplinary Approach ◽  
Biological Indicators ◽  
Spatial And Temporal Variability ◽  
Flux Data ◽  
Submarine Groundwater

&lt;p&gt;Submarine groundwater discharge (SGD) as a pathway for water and chemical constituents between land and ocean is a rather young topic. For a long time it has been neglected by the scientific community and coastal managers. However, it has increasingly attracted attention since the turn of the millennium. Yet, SGD is mostly investigated either by terrestrial or marine disciplines although a broader, interdisciplinary approach would benefit SGD research. Moreover, so far reported SGD flux data at local to regional scale are a) hardly comparable as, to our best knowledge, only a few, mostly isolated studies directly compared available SGD methods in a quantitative fashion and b) flux data contain large uncertainties, either because they were up-scaled from local discrete (point) measurements to regional scales or because they were derived from modelling/ budgeting of regional or even global matter fluxes despite the known high spatial and temporal variability.&amp;#160;&lt;/p&gt;&lt;p&gt;In order to pave the way for a more standardized and interdisciplinary SGD research that would reduce inherited measurement/ extrapolation uncertainties, the K&amp;#246;nigshafen Submarine Groundwater Discharge Network (KiSNet)&amp;#160; seeks to contribute through three concrete aims:&amp;#160;&amp;#160;&lt;/p&gt;&lt;ol&gt;&lt;li&gt;forming an interdisciplinary group of SGD experts to initiate and intensify collaborative ties across disciplines&lt;/li&gt; &lt;li&gt;improving individual methodologies by groundtruthing through interdisciplinary intercomparison, which includes a focus on spatial and temporal variability, and&lt;/li&gt; &lt;li&gt;providing a method catalogue which outlines optimal combinations for qualitative and quantitative SGD investigations that may serve as basis for future standardized SGD research.&lt;/li&gt; &lt;/ol&gt;&lt;p&gt;The network will convene at the bay of K&amp;#246;nigshafen on Sylt, Germany, during two different points in time. Each time, all members of the network will apply qualitative (remote sensing, marine and terrestrial ground-based geophysics, biological indicators and socio-scientific methods) and quantitative (seepage meters, temperature rods, natural tracers, numerical simulation) methods from terrestrial and marine disciplines to investigate SGD synchronously and provide a robust basis to tackle above mentioned aims.&amp;#160;&lt;/p&gt;&lt;p&gt;Here, we will outline exact procedures, methods and anticipated results the network will produce and provide an overview on future actions the network anticipates.&lt;/p&gt;

scholarly journals Relationships between rainfall and throughfall in a secondary forest in southeastern Brazil: an evaluation of different statistical models

RBRH ◽  
2018 ◽  
Vol 23 (0) ◽  
Author(s):  
Raul Sampaio de Lima ◽  
Vandoir Bourscheidt ◽  
Marcel Okamoto Tanaka
Keyword(s):  
Statistical Models ◽  
Temporal Variability ◽  
Linear Models ◽  
Secondary Forest ◽  
Pinus Elliottii ◽  
Meteorological Conditions ◽  
Southeastern Brazil ◽  
Spatial And Temporal Variability ◽  
Rainfall Events ◽  
Best Fit

ABSTRACT Throughfall (TF) is influenced by different meteorological conditions, which can result in high spatial and temporal variability, when interacting with vegetation and mutually with each other. This study aimed to evaluate rainfall (RF) influence on TF, as well as to describe the behavior of these variables in an area dominated by Pinus elliottii in southeastern Brazil, by exploring different statistical models proposed in the literature. For this, RF and TF data were recorded in 24 rainfall events by 180 gauges distributed in six 10 x 10 m plots. The results indicate a significant influence of RF volume on response variables [TF volume (TFmm), TF fraction (TF%) and coefficient of variation of TF (CVTF)]. While the linear model presented the best fit for TFmm , the non-linear models had better results for TF% and CVTF as a function of RF, allowing the identification of distinct behaviors for different RF volumes. In general, it was verified that RF is the main source of variability in TF estimates in the study area. However, it should be noted that other variables may be acting simultaneously on TF% and CVTF, in which 45.4 and 38.1% of the variation, respectively, remain unexplained, requiring complementary studies to identify and quantify the influence of other factors.

scholarly journals Multiscale observations of NH<sub>3</sub> around Toronto, Canada

2020 ◽  
Author(s):  
Shoma Yamanouchi ◽  
Camille Viatte ◽  
Kimberly Strong ◽  
Erik Lutsch ◽  
Dylan B. A. Jones ◽  
...  
Keyword(s):  
Temporal Variability ◽  
Grid Point ◽  
Regional Scale ◽  
Model Performance ◽  
In Situ Measurements ◽  
Coefficient Of Determination ◽  
Spatial And Temporal Variability ◽  
Predictive Capacity ◽  
Total Column

Abstract. Ammonia (NH3) is a major source of nitrates in the atmosphere, and a major source of fine particulate matter. As such, there have been increasing efforts to measure the atmospheric abundance of NH3 and its spatial and temporal variability. In this study, long-term measurements of NH3 derived from multiscale datasets are examined. These NH3 datasets include 16 years of total column measurements using Fourier transform infrared (FTIR) spectroscopy, three years of surface in-situ measurements, and 10 years of total column measurements from the Infrared Atmospheric Sounding Interferometer (IASI). The datasets were used to quantify NH3 temporal variability over Toronto, Canada. The multiscale datasets were also compared to assess the observational footprint of the FTIR measurements. All three time series showed positive trends in NH3 over Toronto: 3.34 ± 0.46 %/year from 2002 to 2018 in the FTIR columns, 8.88 ± 2.83 %/year from 2013 to 2017 in the surface in-situ data, and 8.38 ± 0.77 %/year from 2008 to 2018 in the IASI columns. To assess the observational footprint of the FTIR NH3 columns, correlations between the datasets were examined. The best correlation between FTIR and IASI was obtained with coincidence criteria of ≤ 25 km and ≤ 20 minutes, with r = 0.73 and a slope of 1.14 ± 0.06. Additionally, FTIR column and in-situ measurements were standardized and correlated. Comparison of 24-day averages and monthly averages resulted in correlation coefficients of r = 0.72 and r = 0.75, respectively, although correlation without resampling to reduce high-frequency variability led to a poorer correlation, with r = 0.39. The GEOS-Chem model, run at 2° × 2.5° resolution, was compared against FTIR and IASI to assess model performance and investigate correlation of observational data and model output, both with local column measurements (FTIR) and measurements on a regional scale (IASI). Comparisons on a regional scale (a domain spanning 35° N to 53° N, and 93.75° W to 63.75° W) resulted in r = 0.57, and thus a coefficient of determination, which is indicative of the predictive capacity of the model, of r2 = 0.33, but comparing a single model grid point against the FTIR resulted in a poorer correlation, with r2 = 0.13, indicating that a finer spatial resolution is needed for modeling NH3.

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