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Verifying Traffic Ban Effects on Air Pollution

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Air pollution started to become a problem for human beings with the industrial revolution, but nowadays, with the introduction of laws against emissions (e.g., the EuroX normative), the situation is getting better. Moreover, governments must constantly monitor pollution levels to check policies effects. This article describes a method to verify traffic ban effect claims on air pollution using monitored data. In Lombardia (our region), ARPA (the local EPA) maintains pollution monitoring stations from downtown Milano to remote places near the mountains since 1999. Measured data are " somewhat " available through ARPA's website. " Somewhat " because a CAPTCHA protected download request form must be filled up for every combination of (station, pollutant, time-frame < 1 yr). In 2003 the Lombardia government introduced a vehicle ban to reduce air pollution. Then, more recently (in 2008 and 2012) the Milano City Council introduced a stricter ban. The author implemented an automated (in place since 2004) data collecting " web gatherer " to overcome ARPA's overcomplicated download procedure and, above all, to verify air pollution reduction claims. Data are published on the author's website and this paper presents a method to analyse effects on air pollution and to verify policies claims.
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Journal of Atmospheric Pollution, 2015, Vol. 3, No. 1, 9-14
Available online at http://pubs.sciepub.com/jap/3/1/2
© Science and Education Publishing
DOI:10.12691/jap-3-1-2
Verifying Traffic Ban Effects on Air Pollution
Andrea Trentini*
Dipartimento di Informatica, Università degli Studi di Milano, Milano, Italy
*Corresponding author: andrea.trentini@unimi.it
Received February 22, 2015; Revised March 04, 2015; Accepted March 13, 2015
Abstract Air pollution started to become a problem for human beings with the industrial revolution, but
nowadays, with the introduction of laws against emissions (e.g., the EuroX normative), the situa- tion is getting
better. Moreover, governments must constantly monitor pollution levels to check policies effects. This article
describes a method to verify traffic ban effect claims on air pollution using monitored data. In Lombardia (our
region), ARPA (the local EPA) maintains pollution monitoring stations from down- town Milano to remote places
near the mountains since 1999. Measured data are “somewhat” available through ARPA’s website. “Somewhat”
because a CAPTCHA protected download request form must be filled up for every combination of (station, pollutant,
time-frame < 1 yr). In 2003 the Lombardia government introduced a vehicle ban to reduce air pollution. Then, more
recently (in 2008 and 2012) the Milano City Council introduced a stricter ban. The author implemented an
automated (in place since 2004) data collecting “web gatherer” to overcome ARPA’s overcomplicated download
procedure and, above all, to verify air pollution reduction claims. Data are published on the author’s website and
this paper presents a method to analyse effects on air pollution and to verify policies claims.
Keywords: open data, public accountancy, pollution, particulate matter, anti-pollution policies, web scraping,
vehicle banning
Cite This Article: Andrea Trentini, “Verifying Traffic Ban Effects on Air Pollution.” Journal of Atmospheric
Pollution, vol. 3, no. 1 (2015): 9-14. doi: 10.12691/jap-3-1-2.
1. Introduction
Air pollution started to become a problem for human
beings with the industrial revolution [14,19,20]. During
the second half of the twentieth century pollution
skyrocketed to the extension that some notable high peaks
were even given a name such as the Great Smog of '52"
[7]. After the seventies many governments started to
legislate [23] to try to reduce industrial (plants, materials,
transportation, power generation, etc.) emissions. From
then on, air pollution slowly began to decrease as new
generations of technologies replaced older ones (see
Figure 1 and all the graphs retrievable from ARPA:
http://ita.arpalombardia.it/ITA/qaria/img/qaria/graficiInqN
ew/<province>_<pollutant>.png such as
http://ita.arpalombardia.it/ITA/qaria/img/qaria/graficiInqN
ew/MI_PM10.png). A typical example context is the set
of land transportation technologies (i.e., vehicles) we use
every day to commute, to travel, to have fun, etc. Since
the original Clean Air Act" [23], cars, motorbikes, buses,
etc. makers have been compelled to fulfill ever updated
emission requirements. Stricter rules substitute older ones
as technologies progress. Europe has followed this trend
with the so-called EuroX legislation [10] to impose
maximum emission limits for every type of car+engine
(gasoline, diesel, 4-stroke, 2-stroke, hybrid-electric, etc.)
produced and sold. As an example, for passenger vehicles
(cars), EuroX rules define the following pollutants that
should be regulated: CO (Car-bon Monoxide), THC
(Hydrocarbon), NMHC (Non-methane hydrocarbons),
NOx (Nitrogen oxides), HC + NOx, PM (Particulate
Matter), Px (Particle number, this last one is still in the
process of being detailed, it is not part of the rules). It is
also important to note (it will be useful later) that EuroX1
rules do not impose limits to PM for gasoline vehicles,
since gasoline (4-stroke) engines do not produce any PM
[17] for practical purposes. Moreover, many countries
declared laws- e.g. Europe [9] - to limit pollution
concentration in the air. Europe legislation was adopted in
Italy by defining the following upper bounds, here
presented with the measured (see tables in section 2)
averages in 2013:
SO2 < 125µg/m3, 2013 average: < 52 (very low)
P M 10 < 50µg/m3, 2013 average: 38 (almost low,
see next paragraph)
P M 2.5 (it is not yet specified but already mon-
itored since EU commission is still debating about it,
the proposed limit is 20), average in 2013: 29 (high)
NO2 < 200µg/m3, 2013 average: 86 (low)
C O < 10µg/m3, 2013 average: 1.5 (very low)
O3 < 180 240µg/m3, 2013 average: 30 (very low)
Benzene (no bounds), 2013 average: 1.4
P M 10 is also monitored in terms of the number of limit
excesses during the year, and the number of excesses
should remain under a fixed number (usu- ally 35). This is
1 Except for Euro5 that speci_es a PM upper bound emission ony for
direct injection gasoline engines.
2 Monitoring stations print this value under low concen- tration
conditions.
10 Journal of Atmospheric Pollution
because P M 10 is a very cyclical pollutant: it raises
during the winter and it de- creases during the summer.
Currently, in Lombar- dia, even if the P M 10 yearly
average is below the limit, the number of excesses still
exceeds the EU prescription.
Summing up: almost every monitored pollutant is below
the upper limit and the only one that should be taken into
account is Particulate Mat- ter (P M 10 and P M 2.5).
Around the year 2000, despite the downward trends in
air pollutants, some Italian local govern- ments started
introducing legislation restricting the use of private
vehicles due to a supposed “upward trend” in pollutants.
Many vehicle owners could no longer drive their cars,
motorbikes, etc. while still paying ownership taxes and
mandatory insur- ance. Even in case of gasoline vehicles
that do not produce P M 10 and P M 2.5. While public
trans- port buses with very old (average public vehicles
age is about 20 yrs in Italy) diesel engines could pollute
without any limitation. The difficulty to understand
(engine, pollution, etc.) technologies [18] is probably the
source of this “unreasonable” if proven unsuccessful/useless
(see sections 2.2 and 3) - ban. In Italy the “principle of law
reasonabil- ity and proportionality” [6,8] is one of the
most important principles used to (in)validate laws, it
states that: The reasonability principle (RP) is a
corollary of the principle of equality, drafted by the
Constitutional Court, inspired by a similar prin- ciple
identified by the Anglo-Saxon jurisprudence. The RP
requires that the provisions contained in acts having the
force of law must be appropriate or congruent to the
objective pursued. It is therefore a breach of RP when
there is a major contradiction within a law, or between it
and the public interest pursued. The RP is therefore a
limit to the discre- tion of the legislature, which prevents
arbitrary ex- ercise. The verification of RP of a law
involves the investigation of its assumptions of fact, the
assess- ment of the congruence between means and
goals ... In case it is established the irrationality of the
law, it will be affected by the vice of excessive legislative
power, and, as such, can be held to be unconstitu- tional
by the Constitutional Court ...
Figure 1. N O2 (since 1990) and SO2 (since 1970) trends with upper bound (red line), source: ARPA
Of course the ban proposal worried many citi- zens to
no small end. In fact, the author of this paper, when
reading the proposed banning rules and, above all, the
motivations (the aforementioned supposed air pollution
increase), began searching for information about air
pollution and technical data to write an “open letter to the
administra- tion” that circulated on media and among
citizens. He found out about EuroX legislation, about
what other countries did (almost never permanent bans,
and banned vehicles could always be upgraded to cleaner
ones with aftermarket components, some- thing not
allowed by present Italian legislation) and about ARPA air
monitoring. The letter disclosed the many
incongruosnesses in the proposed ban and brought some
technical awareness in politicians and citizens: eventually
the Lombardia ban was soft- ened, i.e., applied to very old
vehicles and to 2- stroke motorbikes only.
ARPA Lombardia EPA (Environment Protection
Agency) maintains a network of pollution moni- toring
stations from downtown Milano to remote places near the
mountains since 1999. The author accessed the ARPA
website to learn if historical data were available as a
download, with the intent of seeing for himself what the
trends observed by ARPA really were without having to
rely on the mass media and other secondary sources. And
data were there, available for download and in proper
format: CSV (Comma Separated Values) files, i.e., 3 stars
[4] Open Data graded. But with some web- stacle
(web+obstacle crasis) to impede full data ex- ploitation.
If a citizen wanted to collect one data subset he should
fill and submit a web form, then data are sent by email (i.e.
the procedure is not anony- mous). Moreover, the citizen
can only request a data subset for each form submission,
i.e.: one sin- gle station, one single pollutant, one single
time- frame smaller than one year. Summing up, if a
citizen needs the whole dataset he/she should pre- pare
him/herself to complete about 80 (stations) x 7 (pollutants
per station) x 15 (years of monitoring) requests, by hand.
Yes, by hand since the download request web form is
CAPTCHA3 protected. This overcomplicated procedure is
the motivation for the author’s decision to create an
automatic web grab- bing (see 2.1) system, it was then
developed and it is working almost continuously since
2005.
In recent years (starting around 2008) the Mi- lan City
Council started using very similar meth- ods as those used
by the Lombardia local govern- ment in 2003 to justify
introducing a congestion charge, first called “Ecopass”
and then “Area C”, in an 8 km2 area roughly
corresponding to that inside the XVI century Spanish
Walls. The name change is very telling, as it was
introduced after its air-pollution reducing effectiveness
was debated by media and citizens. Still, the legal (and po-
litical) justification of “Area C” is heavily based to this
day on the idea it’s used to fight pollution
3 Completely Automated Public Turing test to tell Computers and
Humans Apart.
Journal of Atmospheric Pollution 11
(http://www.areac.it). This is due to the fact that Italian
laws only allow traffic restrictions of this kind in case of a
well documented and serious threat to the public health,
see [15] [comma b].
1.1. Open Data...
Open Data has become a worldwide movement
involving governmental and non-governmental ac- tors.
The Open Knowledge Foundation (OKF) was one of the
first organizations to define “open- ness” in this context
and it has recently given birth to [16] to formalise meta-
knowledge about open knowledge. The OKF definition of
“open- ness” can be quoted as: “A piece of data or con-
tent is open if anyone is free to use, reuse, and redistribute
it - only subject, at most, to the re- quirement to attribute
and/or share-alike”. More- over, Tim Berners-Lee [4]
defined a five star rat- ing for Open Data to highlight the
importance of not just legal but also technical aspects of
open- ness, for example through the use of open standards
and non-proprietary file formats for Open Data publishing.
More broadly, Berners-Lee and others [3,5] promoted the
concept of Linked Open Data to transform “data on the
web” into “the web of data” by encouraging the linking of
one’s own data with other datasets. HM Government’s
Open Data White Paper [11] states that Open Government
Data is “Public Sector Information that has been made
available to the public as Open Data” and de- fines Public
Sector Information (PSI) as “data and information
produced, collected or held by public authorities, as part
of their public task”, data that should be accessible
(ideally via the internet) at marginal cost and without
discrimination, available in digital and machine-readable
format, and provided free of restrictions on use or
redistribution.
Well, ARPA Lombardia is government, it is a public
agency owned by Regione Lombardia. The author
believes that ARPA should not force citizens to resort to
time consuming data gathering methods such as web
scraping [24] as he did, but it should publish all the data
without any “websta-cle”.
2.
Metho
dology
This section describes the methodology applied to web
scrape (collect from web), store and analyse ARPA data.
The whole system is developed as a set of
bash+sqlite3+gnuplot scripts running under Debian
GNU/Linux, its main (macro) components are the
following:
ARPA page gathering (download and store), based
on wget and crontab
data extraction (from stored pages), based on a
combination of Unix filters (such as grep, sed, tr,
etc.), to generate parsable data (i.e., CSV files)
data analysis, based on sqlite3 and gnuplot
2.1. Data Scraping, Cleaning and Verification
The author took inspiration from http:// archive.org
WayBackMachine for this module. The WayBackMachine
is a freely available service that saves snapshots of web
pages for “trusted ci- tation in the future”. URLs of
interesting (to be saved) web pages can be submitted by
users. In- stead of relying on the WayBackMachine, the
au- thor preferred to write a simple script to get peri- odic
snapshots of a subset of ARPA website.
Since ARPA data download form cannot be sub- mitted
automatically (see section 1), the author used a different
page, the one that displays cur- rent data day by day, for
any given Lombardy sub- region. The only “problemwas
to reverse engi- neer the correct URL of the page to
parametrize it, and to update that URL in the script when
ARPA changed (twice since 2003) their website structure
The (indeed small) script is based on wget
(http://gnu.org/software/wget) and it has run under crontab
every night for about ten years. The URL of the status page is
http://ita.arpalombardia.it/ITA/qaria/lista$<01to10>$.asp,
the numbers represent different areas in our region. That
page contains everything needed to create a complete air
pollution database: date (top of page), types of monitored
pollutants (middle), values read from stations (table). The
page of an area is just saved as a compressed .html.gz file
with a full date (YYYYMMDDHHMM) in the filename.
Author’s goal was to create CSV files, aggre- gated by
monitoring station, from the set of HTML pages (one for
each day). The process of extracting and cleaning raw data
from the HTML pages is a bit more complex since ARPA
web pages are not even W3C valid. Here tools like tidy (a
valida- tor/indenter/cleaner), html2 (a tool from the xml2
package, http://ofb.net/~egnor/xml2) or other well-formed
HTML expecting tools fail or behave erratically. Thus the
author had to combine some Unix filters by trial and error
to achieve acceptable data extraction, such as:
grep, to select lines in a _le based on pattern
sed, to substitute strings
tr, to substitute chars
vilistextum http://bhaak.dyndns.org/vilistextum/ (less
common than the others)\Vilistextum is a HTML to
text / ascii converter speci_cally programmed to get
the best out of incorrect HTML. It is released as free
software under the terms of the GNU GPL Version
2."
The complete GPL licensed sources are available on the
author's web site http://arcipelagoareac. it. The procedure
generates a set of CSV files containing chronological data.
2.2. Analysis
The Comune di Milano, through its division AMAT
(Agenzia Mobilità Ambiente Territo-rio) claims [1,2] (and
other documents present on http://www.amat-
mi.it/it/documenti/monitoraggio-area-c/, in italian) the
following “measured" effects:
exhaust PM10 = -58% (wrt 2010)
total PM10 = -40% (wrt 2010)
Elemental Carbon = -61% (wrt 2010)
Organic Carbon = -33% (wrt 2010)
Ammonia = -48% (wrt 2010)
NOx, volatile organic matter, benzopirene =
unquantified decrease (wrt 2010)
CO2 = -29% (wrt 2010)
Methane = -19% (wrt 2010)
NO2 = -24% (wrt 2010)
unspecified decrease of air pollutants inside. “Area
C” compared to the area outside.
12 Journal of Atmospheric Pollution
Figure 2. Values and di_erence season averages
Please bear in mind that the above claims are not based
on air pollution measurement but on computed figures
only, AMAT declares4 to use the COPERT [13] method to
associate an “emis- sive weight” to every vehicle, then it
multiplies that weight by the number of vehicles entering
“Area C” (!). AMAT and the Comune di Milano do not
own/maintain permanent and EU certified air pol- lution
measuring stations. While ARPA does, of course.
Moreover, AMAT lists a non-standard - accord- ing to
EU norms [9] - set of to-be-evaluated air elements:
one item is not considered a pollutant", CO2 is a
greenhouse gas" and it is neither monitored nor
limited
some elements (Carbon, Ammonia, Methane) are not
EU regulated so there are no specified upper bounds
to comply with and they are not continuously
monitored, i.e., there is no publicly available
downloadable historical data5
“exhaust" and “total" PM10 cannot be discerned
easily/directly [21] and, again there is no separate EU
prescription
4 http://areac.amat-mi.it/it/areac/emissioni-da-traffico/: Le emissioni
atmosferiche sono calcolate sulla base degli accessi in Area C, rilevati ai
varchi di controllo e distinti per tipologia, e della metodologia europea
COPERT4." - Atmospheric emissions are calculated on the basis of Area
C accesses, detected at checkpoints, by vehicle type...
5 Periodical reports are available, in textual form,
e.g.,http://ita.arpalombardia.it/ITA/qaria/pdf/Parfil/UO1/Ammoniaca/Am
moniaca%20UO_1.pdf
they do not take into account EU prescribed
pollutants such as CO and Benzene (!)
I.e., AMAT uses a non-standard model.
In general, ARPA stations can monitor the fol- lowing
EU specified pollutants: SO2, PM 10, PM 2.5, NO2, CO,
O3, Benzene, but not every sta- tion can sense all the
pollutants, e.g. station nr. 548 (see below) can measure
just PM 10, PM 2.5, NO2, C O and Benzene.
Since many elements in the original AMAT list are not
EU regulated and no downloadable data are available, the
author will only discuss items for which he has data, i.e.,
Elemental Carbon, Or- ganic Carbon, Ammonia, Methane
and CO2 will be dropped from discussion.
For the remaining pollutants we will focus on the ones
still exceeding the limits (see list in 1), i.e., P M 10, P M
2.5. We will use data from one station inside “Area C” to
verify trends claims and we will compare data from two
stations, one inside and one outside, to verify the decrease
claim inside “Area C”:
Milano Senato, nr.548, inside Area C", 1km from
the city centre, halfway to theArea C" boundary
PM10, PM2.5, NO2, CO and Benzene
http://ita.arpalombardia.it/ITA/qaria/stazione_548.asp
Limito Pioltello, nr.531, outside Area C" (no ban on
any vehicle), 12km from the city centre
SO2, PM10, NO2, CO, O3
http://ita.arpalombardia.it/ITA/qaria/stazione_531.asp
Journal of Atmospheric Pollution 13
Table 1. Milano Senato yearly averages (with bounds)
Year
PM10 < 50
PM2.5 (proposed < 20)
NO
2
< 200
CO < 10
2008
39.39
NA
93.50
0.86
2009 44.39 NA 113.66 1.40 2.10
2010
40.10
NA
100.71
1.51
2011
48.92
NA
100.01
1.61
2012
42.25
36.56
82.96
1.32
2013
38.03
29.28
86.27
1.49
2014
44.96
37.34
91.39
1.70
2.2.1. Claims verification
Data presented in Table 1 disprove the trends claims
presented by the Milan City Council as a justification for
“Area C”. Percent values in the following list are
calculated against the raw mean value, i.e., they are not
standardized against σ, the σ value is indicated in
parentheses to give an idea of the standardization factor
that should be applied, often reducing the
decrease/increase to nothing.
PM10 in 2010 was 40.10 while in 2013 was 38.03, a
5% decrease: definitely not a 58% (“exhaust")
neither a 40% (\total") decrease and, above all, this
difference is well within (0.07σ) observed yearly
uctuations (see Figure 2) due to a large array of
meteorological and climatic variables AMAT
wrong
PM2.5 measurement began in 2012 and values
remained stable since then, thus there is no reference
for 2010 no reference, ignored by AMAT
NO2 was 100.71 in 2010 and 86.27 in 2013, a 15%
(0.37σ) decrease unquantified by AMAT
CO was 1.51 in 2010 and 1.49 in 2013, a 2% (0.03σ)
decrease ignored by AMAT
Benzene was 1.20 in 2010 and 1.40 in 2013 a 15%
(0.13σ) increase ignored by AMAT
Claims of a reduction in pollutants inside the
congestion charge area can be disproven by comparing
data from monitoring stations inside (with ban) and
outside (without ban) of this area. Monthly differences
(plotted in Figure 2 with the difference averages) show
that the “effect” of ban- ning vehicles in Milano city
centre may weigh be- tween a good -0.5σ (2008) and a bad
+0.5σ (2011) but a question arises: from 2008 to 2013
forms of vehicle banning were always in place, so why the
difference is so low and rippling? Please also note that the
Comune di Milano declares [1] a 30% traffic decrease due
to banning. The average of monthly differences is -1.60,
against a standard de- viation of about 28 in the measured
P M 10 values (in & out), i.e., 0.06σ only. To thoroughly
and formally test the effectiveness of “Area C”, the au-
thor has applied an hypothesis test using Pioltello station
as a control group and testing for the hypothesis H0 : µin
µout, i.e., whether the area inside “Area C” is cleaner
then outside. Results are listed in Table 2. The hypothesis
can be ac- cepted for only 7 cases out of 24 and using
values of α quite generous for today standards [12]. More-
over in 14 cases out of 24 the hypothesis is badly rejected.
The same test was iterated over every sta- tion pair in
Milan and outside with similar results. The conclusion is
that “Area C” (and the previ- ous “Ecopass”) has no
positive effect on Milano air pollution
.
Table 2. PM10 seasons, in&out and hypothesis test results
Season
µin
σin
µout
σout
Z
P-value
H0
2008-03-21-2008-06-20
36.2
9.855
27.94
11.21
17.28
1
badly rejected
2008-06-21-2008-09-22
35.75
12.37
25.63
10.3
28.89
1
badly rejected
2008-09-23-2008-12-21
42.63
21.91
41.97
24.83
1.15
0.87
badly rejected
2008-12-22-2009-03-20
70.53
30.9
82.02
39.94
-13.73
3.5e-43 < 0.5%
accepted
2009-03-21-2009-06-20
32.25
14.42
32.28
15.18
-0.064
0.475
rejected
2009-06-21-2009-09-22
27.67
9.82
24.96
9.428
7.33
1
badly rejected
2009-09-23-2009-12-21
55.53
28.37
57.07
29.59
-2.441
0.007 < 1%
accepted
2009-12-22-2010-03-20
61.71
28.19
66.13
27.77
-7.681
7.9e-15 < 0.5%
accepted
2010-03-21-2010-06-20
28.07
12.56
26.41
12.74
4.011
1
badly rejected
2010-06-21-2010-09-22
24.66
11.17
22
10.8
7.189
1
badly rejected
2010-09-23-2010-12-21
48.93
38.6
40.88
24.26
15.34
1
badly rejected
2010-12-22-2011-03-20
79.7
43.51
66.87
35.88
19.03
1
badly rejected
2011-03-21-2011-06-20
31.81
15.61
34.98
16.79
-7.121
5.35e-13 < 0.5%
accepted
2011-06-21-2011-09-22
23.83
10.59
25.28
8.629
-4.463
4.0e-06 < 0.5%
accepted
2011-09-23-2011-12-21
58.47
26.17
48.13
21.31
20.65
1
badly rejected
2011-12-22-2012-03-20
79.95
36.33
65.21
28.41
23.94
1
badly rejected
2012-03-21-2012-06-20
27.26
15.68
19.28
8.81
24.78
1
badly rejected
2012-06-21-2012-09-22
23.49
11.58
18.66
5.677
17.08
1
badly rejected
2012-09-23-2012-12-21
47.58
25.03
54.23
27.37
NA
NA
NA
2012-12-22-2013-03-20
55.1
23.14
59.18
28.25
-6.96
1.7e-12 < 0.5%
accepted
2013-03-21-2013-06-20
30.23
16.88
30.46
18.48
-0.4711
0.32
rejected
2013-06-21-2013-09-22
24.12
8.645
19.74
10.23
11.78
1
badly rejected
2013-09-23-2013-12-21
47.55
27.38
46.76
31.6
1.212
0.89
badly rejected
2013-12-22-2014-03-20
46.14
24.93
49.37
27.48
-5.185
1.08e-07 < 0.5%
accepted
2014-03-21-2014-06-20
39
16.13
39.68
20.3
-0.7099
0.239
rejected
14 Journal of Atmospheric Pollution
Table 1, Table 2 and Figure 2 were created us- ing
ARPA scraped data and generated by a
sqlite3+gnuplot+bash script.
3. Conclusion
The method presented in this article aims at verifying
the claims on traffic ban effects on air pollution, it is based
on data gathered by monitoring stations spread throughout
a metropolitan region. The method can be summarized as
follows:
1. gather (by web scraping or just download if available)
chronological data related to monitoring stations
inside and outside the supposedly affected area
2. compute averages (by periods such as seasons) on
every pollutant
3. apply hypothesis tests using outside stations, as a
control group
This article also describes the author’s data col- lecting
and analysis work to overcome the artifi- cial barriers
raised by ARPA Lombardia to “pro- tect indiscriminate
download” of their air pollu- tion monitoring data. The
automated web scraping system was built to avoid
submitting thousands of CAPTCHA protected forms by
hand to get data.
Data gathered during almost ten years (2005 through
2014) were useful to verify and, alas, prove traffic
restrictions adopted in the Milan area to decrease air
pollution ineffective. Data show that the two main
Milano (Italy) banning laws, “Ecopass” (2008) and “Area
C” (2012), had almost undetectable effects on air
pollution. This conclusion is in fact consistent with the
Lon- don Transport technical report [22] about the Lon-
don congestion charge, similar to “Area C”: Even so,
trends in actual measured air quality continued to
primarily reflect the diversity and dominance of external
factors in determining pollution concentra- tions and, as
such, did not allow the identification of a
clear ’congestion charging effect’. ... Despite substantial
reductions to road traffic emissions in London, trends in
measured air pollution remain broadly static.
The whole set of collected data has been made freely
available on the web as a set of CSV files on the author’s
website: http://arcipelagoareac. it/CSV, updated daily.
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... 7 Another solution which has been occasionally sought is to ban traffic temporarily when pollution overcomes specific thresholds. 8 Despite being questioned in terms of efficacy, these temporary traffic bans represent an ideal experimental tool to test the shared hypothesis that environment pollution directly increases the risk of cardiovascular events. Yet, to date no study has ever appraised in any fashion, qualitatively or quantitatively, the impact of temporary traffic bans on the risk of cardiovascular morbidity. ...
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Background: Strong epidemiologic evidence has highlighted the role of pollution, on top of adverse climate features, as a novel cardiovascular risk factor. However, mechanistic proof that reducing pollution may be beneficial to prevent atherothrombotic events is limited. We aimed at appraising the impact of temporary traffic bans in a large metropolitan area on the risk of acute coronary syndromes. Methods: Aggregate and anonymized data from 15 tertiary cardiac care centers were obtained detailing pre-coronarivus disease 2019 (COVID-19) daily cases of ST-elevation myocardial infarction (STEMI) and non-ST-elevation myocardial infarction (NSTEMI), including those treated with percutaneous coronary intervention (PCI). Data on pollutants and climate were sought for the same days. Mixed level regression was used to compare the week before vs after the traffic ban (Fortnight analysis), the 3 days before vs after (Weekly analysis) and the Sunday before vs after (Sunday analysis). Results: A total of 8 days of temporary traffic bans were included, occurring between 2017 and 2020, totaling 802 STEMI and 1196 NSTEMI in the Fortnight analysis, 382 STEMI and 585 in the Weekly analysis, and 148 STEMI and 210 NSTEMI in the Sunday analysis. Fortnight and Sunday analysis did not disclose a significant impact of traffic ban on STEMI or NSTEMI (all p>0.05). Conversely, Weekly analysis showed non-significant changes for STEMI but a significant decrease in daily NSTEMI when comparing the 3 days before the traffic ban with the ban day (p=0.043), as well as the 3 days before vs the 3 days after the ban (p=0.025). No statistically significant effect of traffic ban was found at Fortnight, Weekly or Sunday analyses for daily mean concentrations of benzene, carbon monoxide, nitric oxide, nitrogen dioxide, ozone, sulfur dioxide, particulate matter (PM) <2.5 μm or PM <10 μm (all p>0.05). However, minimum daily concentrations showed a significant reduction of ozone during the ban in comparison to the week preceding it (p=0.034), nitric oxide during the ban in comparison to the 3 days preceding it (p=0.046), and an increase in benzene during the ban in comparison to the Sunday before (p=0.039). Conclusions: Temporary traffic ban may favorably reduce coronary atherothrombotic events, and in particular NSTEMI, even if not globally and immediately impacting on environmental pollution. Further controlled studies are required to confirm and expand this hypothesis-generating results.
... Os outros cenários, em especial C1 e C3, demonstram também redução local de poluentes primários, ainda que em áreas menores. No entanto, o custo financeiro e social deste tipo de política de controle é alto(Giugliano et al., 2005; OMS, 2006;2015, Trentini) e, por esta razão, estas políticas tendem a ser impopulares, mesmo em vista dos benefícios à saúde que promovem em curto prazo.Neste âmbito, a implementação de parques urbanos tende a ser uma política pública mais popular para o melhoramento da qualidade ambiental e de vida nos grandes centros urbanos, como é de fato verificado pela atenuação da ilha de calor urbana (LOMBARDO, 1985, BARROS e LOMBARDO, 2016) e por propiciarem espaços de convivência e lazer na metrópole. Em especial, isto é bem visto quando essa implementação ocorre em áreas anteriormente ocupadas por uso do solo veicular ou comercial, pois a redução dos poluentes primários é mais clara e facilmente perceptível, conforme foi avaliado nas simulações dos cenários C3 e E, com retirada das emissões nas áreas alteradas e substituição do uso do solo urbano por floresta mista e floresta tropical latifoliada, respectivamente. ...
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