Commit 3614de17 authored by Samuël Weber's avatar Samuël Weber
Browse files

update text and ref to article

parent fb99ed42
......@@ -6,33 +6,14 @@ from app_main import app
layout = dbc.Container(
dbc.Row(
dbc.Col(
id="main",
className="h-100 contact-container",
children=dbc.Col(
[
html.H1("Question and contact"),
dcc.Markdown(
"""
## GetOP, stand OP!
The purpose of this website is data vizualisation of the GetOPstandOP project.
However, if you are interested in re-using part of this database or if you
have other questions, please contact us:
- Gaëlle Uzu: ```gaelle.uzu@ird.fr```
- Jean-Luc Jaffrezo: ```jean-luc.jaffrezoz@univ-grenoble-alpes.fr```
## The web app
This app is written in python thanks to [dash](https://dash.plot.ly/), based on
plotly. Many thanks to all the dash developers!
The app in developped and maintained by
- Samuël Weber: ```samuel.weber@univ-grenoble-alpes.fr```
Also, the git repository is available here: [git repo](https://gricad-gitlab.univ-grenoble-alpes.fr/pmall/app_op).
"""
)
open("./apps/texts/contact.mdwn", "r").read(),
dangerously_allow_html=True
)
]
)
)
......
Question and contact
--------------------
### GetOP, stand OP!
The purpose of this website is data vizualisation of the GetOPstandOP project.
However, if you are interested in re-using part of this database or if you
have other questions, please contact us:
- Gaëlle Uzu: ```gaelle.uzu@ird.fr```
- Jean-Luc Jaffrezo: ```jean-luc.jaffrezoz@univ-grenoble-alpes.fr```
### The web app
This app is written in python thanks to [dash](https://dash.plot.ly/), based on
plotly. Many thanks to all the dash developers!
The app in developped and maintained by
- Samuël Weber: ```samuel.weber@univ-grenoble-alpes.fr```
Also, the git repository is available here: [git repo](https://gricad-gitlab.univ-grenoble-alpes.fr/pmall/app_op).
......@@ -17,8 +17,9 @@ take into account for local specificities nor for variation over year of the
sources contributions.
Finally, thanks to the recent development of the scientific community, and notably
[Weber et al. 2021](https://doi.org/10.5194/acp-2021-77) for the France area, we can
attribute an oxidative potential (OP) of a set of sources. A simple multiplication end up
[Weber et al. (2021)](https://acp.copernicus.org/articles/21/11353/2021/acp-21-11353-2021.html) for the
France area, we can attribute an oxidative potential (OP) of a set of sources. A simple
multiplication end up
with the sources contribution to the oxidative potential of PM<sub>10</sub>.
### Pitfall
......
## How to use it?
This dashboard let you interactively plot different informative results obtain in this research program.
This dashboard lets you interactively plot different informative results obtain in this research program.
For the context and discussion of the results, we invite you to read the
manuscript of [Weber et al. 2021](https://doi.org/10.5194/acp-2021-77) that present this work.
research article of [Weber et al. 2021](https://acp.copernicus.org/articles/21/11353/2021/acp-21-11353-2021.html) that presents this work.
Different kind of plot are available and listed on the left. For most of them,
Different kind of plots are available and listed on the left. For most of them,
you can choose which variables you would like to plot from the dropdown lists at the top panel.
All plots are interactive. You can zoom in, hover data to get more info, and updated automatically.
All plots are interactive. You can zoom in, hover data to get more info, and it updates automatically.
Some of them may take some time to render, so be patient (few seconds).
### Raw data
This part present the raw signal of the input variables selected on the top panel.
Notably, you can select the PMF factor for each site and the OP measurements.
This part presents the raw signal of the input variables selected on the top panel.
Notably, you can select the PMF factor for each sites and the OP measurements.
* **Timeserie**: Line plot of the concentration, the `x` axis being the date;
* **Monthly**: Aggregated monthly boxplot or barplot;
......@@ -20,24 +20,24 @@ Notably, you can select the PMF factor for each site and the OP measurements.
### Source apportionment of PM : PMF (Positive Matrix Factorization)
This part present the results of the PMF studies used as input variable for the
This part presents the results of the PMF studies used as input variable for the
attribution of the OP to the PM sources.
* **Overview per station**: display the species concentration in each selected PMF
* **Overview per station**: displays the species concentration in each selected PMF
profile (in µg/µg or in µg/m⁻³) as well as the contribution time series to PM mass of
each determined factor;
* **Inter-sites variability** : variability of the chemical composition for each factor
at the given sites;
* **Profiles chemical similitude (DeltaTool)**: estimate quantitatively the chemical
* **Profiles chemical similitude (DeltaTool)**: estimates quantitatively the chemical
similitude of each pairs of factor name similarly (i.e. biomass burning, road traffic,
etc), following the approach
proposed by [Pernigotti & Belis, 2018](https://doi.org/10.1016/j.atmosenv.2018.02.046).
* **Per specie variability (BS and DISP)**: give an estimation of the uncertainties of
* **Per specie variability (BS and DISP)**: gives an estimation of the uncertainties of
the profiles species concentration thanks to both the *Bootstrap* and *Displacement*
estimates of the EPA PMF5 software.
* **Species repartition**: display the amount of each species apportioned by each PMF factors.
* **Species repartition**: displays the amount of each species apportioned by each PMF factors.
### Oxidative Potential apportionment
### Oxidative potential apportionment
This part is the results of the multiple linear regression (MLR) of the PMF
factors against the OP<sup>AA</sup> and OP<sup>DTT</sup> measurements, following
......@@ -52,11 +52,11 @@ where G is the contribution of each factor given by the PMF, OP is the observed
* **Obs. vs model**: scatter plot of the observation against the model OP
reconstructed by the MLR ;
* **Intrinsic OP**: display the intrinsic OP (i.e. coefficient of the MLR)
* **Intrinsic OP**: displays the intrinsic OP (i.e. coefficient of the MLR)
for each PMF factor at each site, together with their uncertainties;
* **OP contribution (all)**: give the overall PMF factor contribution to the
* **OP contribution (all)**: gives the overall PMF factor contribution to the
OP (daily and seasonal mean or median), taking into account all the selected
site;
* **OP contribution (timeseries)**: display individual timeseries of the PMF
factor contribution to the OP. You can select to see the daily contribution
or the monthly or season mean and median contribution.
sites;
* **OP contribution (timeseries)**: displays individual timeseries of the PMF
factor contribution to the OP. You can select the daily, monthly or seasonal contribution
as well as mean or median contribution.
Source apportionement of the Oxidative Potential of aerosols
Source apportionement of the oxidative potential of aerosols
------------------------------------------------------------
### About
This application lets you browse the datasets and results obtained within the
framework of the *Get OP stand OP* ANR program. It gathers the measurement of Oxidative Potential (OP) of
aerosols, sampled during several French research program, and attribute the
aerosols, sampled during several French research program, and attributes the
instrinsic OP of the different sources of aerosols, together with their relative
contribution to the observed OP.
contributions to the observed OP.
<a href="/results" class="btn btn-success mx-auto my-3 w-50 d-block">
......@@ -17,40 +17,41 @@ Start exploring the results
This visualisation tool acts also as a supplementary information for a reasearch
manuscript submitted to *Atmospheric chemistry and physics*, [Weber et al.
(2021)](https://doi.org/10.5194/acp-2021-77), entitled
manuscript published to *Atmospheric chemistry and physics*, [Weber et al.
(2021)](https://acp.copernicus.org/articles/21/11353/2021/acp-21-11353-2021.html), entitled
<p class="article-title">
Source apportionment of atmospheric PM<sub>10</sub> Oxidative Potential: synthesis of 15 year-round
Source apportionment of atmospheric PM<sub>10</sub> oxidative potential: synthesis of 15 year-round
urban datasets in France.
</p>
### Introduction
Atmospheric ambient arerosols (or particulate matter, PM) has already been shown to be
linked to diverse health outcome such as asthma, cardiovascular disease and increase
linked to diverse health outcomes such as asthma, cardiovascular disease and increase
cancer risk. However, epidemiological studies focus only on PM mass despite the fact that
the PM present a wide span of size, shape, chemical composition and so reactivity. The
the PM presents a wide span of size, shape, chemical composition and so reactivity. The
oxidative potential of PM has been proposed as a new proxy for air quality in order to
better estimate the population exposition. Indeed, OP integrates the different PM
characteristic and is more closely linked to the inflammatory response of the body to the
oxidative stress induced by PM, and so to different health outcomes. However, long time
series of OP measurement are still poorly documented in the literature and no standardized
assays has emerged so far. Moreover, very scars source apportionment of OP has been
assays has emerged so far. Moreover, very scarce source apportionment of OP has been
conducted yet (i.e. which PM sources contribute to the OP?).
In this study, we sampled aerosols one every third day at 15 different sites in France for
at least one year between 2013 and 2018. We measured OP thanks to 2 different assays (OP
measured by the ascorbic acid, OP<sup>AA</sup>, and by the dithiotreitol,
OP<sup>DTT</sup>) together with a advanced chemical speciation (ions, EC/OC, metals,
organics, etc) on the very same PM filters. It results in the biggest database of OP
organics, etc) on the very sames PM filters. It results in the biggest database of OP
measurements with concomitant observation of chemistry of PM available in the literature,
with more than 1700 samples analyzed with a standardized procedure
with more than 1700 samples analyzed with a standardized procedure.
Thanks to PM source apportionment through the use of PMF and a multiple linear-regression
(see [Weber et al, 2018](https://www.atmos-chem-phys.net/18/9617/2018/)), we then
expressed the intrinsic OP (OP per μg PM) for each identified source. We also compare the
similarity between the different PMF factor at each site (thanks to tools developed in the
expressed the intrinsic OP (OP per μg PM) for each identified sources. We also compare the
similarity between the different PMF factors at each site (thanks to tools developed in the
FAIRMODE group ([Belis et al,
2018](http://fairmode.jrc.ec.europa.eu/document/fairmode/WG3/DeltaSA_tool_for_source_apportionment.pdf)))
in order to assess the geochemical stability of the intrinsic OP of the factors at the
......@@ -59,15 +60,27 @@ regional level.
### Main highlights
* Both OP present clear seasonal pattern, notably for alpine cities, with high
value during winter and relatively lower during summer.
* In addition to the seasonal cycle, rapid day-to-day variation are also
observed.
* The 2 different OP assays do not present the same reactivity toward the
sampled aerosols.
values during winter and relatively lower values during summer.
* In addition to the seasonal cycle, rapid day-to-day variation are also observed.
* The 2 different OP assays do not present the same reactivity toward the sampled aerosols.
* Comparing to the PM mass source apportionment, we clearly observe a redistribution of
the different sources contribution when considering the OP instead of the mass.
* We also show that the primary road traffic source has an intrinsic OP (OP per μg of
PM) significantly higher than the other PM sources, notably when considering the AA
assay;
* As a consequence of the different intrinsic OP, the contribution of the
different sources highly depends on the metric we use (ie. mass or OP).
* As a consequence of the different intrinsic OP, the contribution of the different
* sources highly depends on the metric we use (ie. mass or OP).
### Reference
<div class="csl-entry">
Weber, S., Uzu, G., Favez, O., Borlaza, L. J. S., Calas, A.,
Salameh, D., Chevrier, F., Allard, J., Besombes, J.-L., Albinet, A., Pontet, S., Mesbah,
B., Gille, G., Zhang, S., Pallares, C., Leoz-Garziandia, E., and Jaffrezo, J.-L.:
Source apportionment of atmospheric PM<sub>10</sub> oxidative potential: synthesis of 15
year-round urban datasets in France, 21, 11353–11378,
<a href="https://doi.org/10.5194/acp-21-11353-2021">https://doi.org/10.5194/acp-21-11353-2021</a>,
2021.
</div>
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