Background
Short-lived climate pollutants (SLCP) are those gases and particles whose
atmospheric lifetimes range from less than a day to a few years (Shindell et al., 2012; Bowerman et al., 2013) and exert a positive radiative forcing to
the global climate. These pollutants, black carbon (BC), methane (CH4),
tropospheric ozone (O3) and selected hydrofluorocarbons (HFCs), also
impact the environment and have negative effects on human health. Although
the SLCPs play a role in the radiative balance of the earth and have global
impacts, their more immediate effects are observed locally and regionally.
Black carbon has been identified as the atmospheric component whose regional
warming effect on climate is second only to carbon dioxide (Bond et al.,
2013). Unlike CO2 whose increasing global concentration and long
lifetime require an international-scale effort to reduce concentrations, BC
is a contaminant whose negative effects locally can be significantly
decreased through local and regional mitigation (Shindell et al., 2012;
Bowerman et al., 2013). This is an optimistic possibility since BC is a
product of inefficient combustion from motor vehicles (cars, buses and
trucks), commercial processes (e.g., brick making, cooking, oil and gas
production), residential heating and cooking and open biomass burning (e.g.,
agricultural clearing and inadvertent forest fires). All of these emission
sources lend themselves to a greater level of control through regulatory
actions and with technologies that are already available. Likewise, the
other co-pollutant SLCPs may be similarly reduced through the measures taken
to reduce BC.
The Mexico City metropolitan area (MCMA), referred to as Greater Mexico
City, has a population that now exceeds 22 million people, according to the
2014 census, making it the largest urban area in North America. The MCMA is
also one of the most polluted megacities in the western hemisphere and a
major source of SLCPs that not only affect the local and regional
environment but can also contribute to global climate change (Barth and
Church,1999; Singh et al., 2009). According to the most recent assessment by
the city government (SMA-GDF, 2012) annual emissions of BC, PM2.5, CO
and NOx are 2 kilotons (Kt), 9.4, 1606 and 239 Kt, respectively.
The Mexico City government established a network of automatic air quality
stations in 1986 (Red Automática de Monitoreo Atmosférico – RAMA,
http://www.aire.df.gob.mx/) to monitor O3, CO, NOx
and SO2, adding mass concentration of particles with aerodynamic
diameter less than 10 µm (PM10) in 1991 and less than 2.5 µm
(PM2.5) in 2003. RAMA currently has 29 stations located in the MCMA
(Fig. 1, courtesy of the city government's environmental agency, http://www.aire.df.gob.mx/default.php). Until recently, however, there
were no measurements of BC at any of the RAMA stations. The few BC
measurements previously available had been taken during short field
campaigns. The first published data on BC were by Baumgardner et al. (2000)
who analyzed measurements with a particle soot aerosol photometer (PSAP) to
derive equivalent black carbon (eBC), the name that has been given to BC
derived from measurements of light absorption (Petzold et al., 2013). These
measurements were made during a 2 week period in November, 1997 at an
elevated site (400 m above the city) in the southwest sector of Mexico City.
Additional measurements were made, also with a PSAP, at three RAMA sites in
2000 over a 3 week period in January and February (Baumgardner et al.,
2002). In the spring of 2003 and again in 2005, measurements were made with
a co-located PSAP and a single particle soot photometer (Baumgardner et al.,
2007). These were very short measurement campaigns of 1 week each. In
2003, as part of the MCMA-2003 campaign (Molina et al., 2007), a mobile
laboratory was deployed at various locations around the city. Filter samples
were evaluated with electron microscopy and aerosol mass spectrometry
(Johnson et al., 2005) and eBC was derived from an aethalometer, a filter-based
instrument like the PSAP (Jiang et al., 2005). Marley et al. (2007)
also made measurements of eBC during MCMA-2003 with an aethalometer and
compared them with measurements that they had made in 1997.
During the MILAGRO campaign in March 2006 (Molina et al., 2010),
measurements of eBC and elemental carbon (EC) were made at various locations
within the city (Marley, 2009a, b) and in the regions around the urban area
(Baumgardner et al., 2009; Subramanian, 2010).
In 2011, a new instrument, the Photoacoustic Extinctiometer (PAX) was
installed at the RAMA supersite located in the north central section of the
city (Fig. 1). From 2011 to 2013 the instrument was being evaluated as a
possible addition to the RAMA air quality network. In March 2013, the PAX
was certified by RAMA as an operational instrument for measuring the optical
properties of BC relevant for climate, i.e., the light-scattering and
absorption coefficients, Bscat and Babs and the derived eBC. We
report in this paper the results from a year of continuous observations made
by this instrument since March 2013.
Numerous studies have focused on a better understanding of the
meteorological processes that transport pollutants from Mexico City and the
chemical processes that lead to the production of ozone and aerosol particle
mass (e.g., Stephens et al., 2008; Molina et al., 2010 and references therein).
There have been no studies, however, that look at how the chemical processes
are linked to the seasonal meteorology. The objective of this study is to
evaluate the temporal trends in some of the SLCPs (BC and O3) and
related co-pollutants (CO, NOx and PM2.5) and show how they are
modulated by seasonal variations in temperature, humidity, precipitation and
radiation.
This study is an extension of the evaluation of Mexico City pollutants by
Stephens et al. (2008) who analyzed daily trends but did not have
information on BC. In the following analysis, the diurnal trends in the
concentrations of SLCPs over a 13 month period are compared with respect to
seasonal climate changes and linked to the underlying physical processes
that lead to seasonal differences.
This map of the metropolitan area of Mexico City shows the locations
of the RAMA air quality monitoring stations and the location of the supersite
where the measurements were made for this paper. Map courtesy of the Mexico
City government (http://www.aire.df.gob.mx/default.php).
Measurement methodology
The RAMA “supersite” (Fig. 1) is located in an area of the city that is a
mixture of private residences and industry (19∘29.0′ N,
99∘ 9.85′ W; 2243 m a.s.l.). The site is approximately 300 m from
a major highway that is a conduit for north–south traffic. Automobiles,
local and regional buses and heavy trucks are responsible for most of the
primary pollutants (CO, NOx, BC and primary PM2.5). The cars and buses
run primarily on unleaded gasoline, whereas the trucks mostly consume diesel
that is the major producer of BC. Hence, although the majority of the BC
comes from the trucks, by the time the air reaches the measurement site, the
BC is well-mixed with the CO, NOx and other particles that are produced by
the cars and buses as well as by the trucks.
Meteorology, radiation, gas and PM2.5 measurements
Temperature and relative humidity are measured using a Met One (Met One
Instrument, Grants Pass, OR) model 083E sensor located inside an aspirated
radiation shield. Wind speed and direction are measured with a Met One model
010C lightweight three-cup anemometer, and a Met One model 020C lightweight
airfoil vane. The temperature and relative humidity sensors are positioned
in a meteorological tower 4 m above the ground. The anemometer and vane
are located at the top of the 10 m meteorological tower. All sensors are
calibrated annually. The ultraviolet radiation, over the wavelength range
from 320 to 400 nm (UV-A), is measured with a Solar Light UVA model 501
radiometer that outputs its data in mW cm-2. The UV-A measurements used
in this paper are not made at the “supersite”, but are an average from six
stations across the city (see Fig. 1): Merced (MER), Montecillo (MON),
Pedregal (PED), San Agustin, (SAG), Santa Fe (SFE) and Tlalnepantla (TLA).
Ozone is measured by ultraviolet photometry (Teledyne API model 400E, San
Diego CA). The NOx, defined operationally, is measured by chemiluminescence
following conversion in a heated molybdenum NO2-to-NO converter
(Teledyne-API model 200E, San Diego, CA). The CO is measured by a gas filter
correlation, non-dispersive infrared analyzer (Teledyne API model 300E, San
Diego, CA). The gas analyzers are tested every 2 weeks with a known
standard and calibrated quarterly.
PM2.5 is measured with a TEOM 1400A ambient particulate monitor 8500C
FDMS (Thermo Scientific, Franklin, MA), with a BGI VSCC PM2.5 cyclone
inlet for size selection. PM2.5 hourly concentrations are reported at
local temperature and pressure conditions. The TEOM is operated at a
temperature of 35 ∘C in order to measure dry particle mass.
The Photoacoustic Extinctiometer
The eBC, Bscat and Babs are derived from the PAX, a sensor that
evolved from the Photoacoustic Soot Spectrometer (PASS) developed at the
University of Nevada (e.g., Arnott et al., 2005, 2006; Moosmüller et
al., 2009) and commercialized by Droplet Measurement Technologies (Chan et
al., 2011; Holder et al., 2014; Liu et al., 2014; Nakayama et al., 2015).
The PAX performs simultaneous measurements of aerosol scattering and
absorption coefficients using a single diode laser modulated at
approximately 1500 Hz. The 375, 405, 532 and 870 nm single wavelength
versions of the instrument have identical measurement cells and differ only
in a few optical components. The PAX whose measurements are reported in the
current study uses the 870 nm laser.
Figure 2 shows a diagram of the PAX measurement cells. The nominal 1 LPM
sample flow first passes a solenoid pinch valve (not shown) that allows for
periodic filter sampling to determine the instrument background signals. The
lengths of sample tubing between the filter line and sample line are the
same to provide the same acoustic properties along both channels. The sample
flow passes into an acoustically insulated enclosure containing the sample
cells, where it is then split, making opposing 90 degree turns into the
scattering and absorption measurement regions. The sample is exhausted
through two ports at the ends of the absorption cells before being filtered
and pumped out of the instrument. The flows are controlled using critical
orifices and do not affect the calculated absorption and scattering
coefficients, which depend only on the properties of the sampled particles
and geometry of the measurement region. Sample flow does affect the
residence time of particles in the measurement cells, which is approximately
7 s.
The absorption cell includes a 0.635 cm diameter tube 10.8 cm long, called
the resonator. A small microphone located at the top of the resonator
detects the pressure perturbations induced by the heating of the light-absorbing
particles. The microphone signal is passed through a fast Fourier
transform circuit that gives the peak power at the resonator frequency,
which is calculated based on the resonator geometry, air pressure,
temperature and dew point temperature measured in the cell. The microphone
raw pressure signal (pmic) is converted to the raw light absorption
coefficient using the equation
babs,raw=pmicAπ2fPLγ-1Qcosϕraw,
where A is the resonator cross section, f is the calculated resonator
frequency, PL is the laser power, γ is the ratio of isobaric and
isochoric specific heat for air, Q is the calculated resonator quality factor
(also calculated from measured cell pressure, temperature and dew point
temperature) and ϕraw is the phase raw absorption signal
relative to the phase of the laser power signal plus the phase correction.
The phase correction accounts for the difference between the speed of light
and sound that translates to a phase shift in the acoustic pressure measured
by the microphone relative to the modulated laser power incident on the
absorbing material. See Arnott et al. (2005) for more details on applying
phase corrections to photoacoustic measurements of light absorption
coefficients.
Diagram showing the scattering (blue dashed region) and absorption
(green dashed region) cells in the Photoacoustic Extinctiometer (PAX).
The raw absorption and scattering signals must be corrected for background
absorption and scattering signal in the cell to give the desired light
absorption and scattering coefficients due to the sampled aerosol particles.
Background absorption arises from absorbing gases (not important for the 870 nm version of the PAX but relevant for shorter wavelength versions),
absorption by the cell and particles deposited to the cell surfaces
(especially the cell windows) and acoustic and electrical noise that
contributes to signal at the resonator frequency. The background scattering
is not sensitive to acoustical noise, but is affected by electrical noise
and light scattering from the cell surfaces and particles deposited to the
cell surfaces and windows. The background signals are measured
simultaneously at a user-specified interval, typically once every 10 min, by passing the sample air through a filter before it enters the
measurement cell. The background measurement period occurs after a 20 s
flush with particle-free air to mix sampled particles out of the measurement
cells and lasts 30 s, followed by another 20 s flush before
regular measurements resume. Background absorption and scattering values are
saved in the data record and automatically subtracted from the measured raw
signals to give the absorption and scattering coefficients in real time.
Typically the background drift is small (< 3 Mm-1) compared to
the observed scattering and absorption coefficients, especially in more
polluted environments like Mexico City. Background drift occurs due to small
changes in the sampling environment, such as temperature and relative humidity.
Over longer periods of time, backgrounds are affected by contamination on the
cell windows and minor changes in the optical alignment. Cleaning and
re-alignment of the system can be necessary when the magnitude of the
background drift becomes comparable to changes in the scattering and
absorption values on the timescales of the filtered background measurements.
Post-processing of background values to apply a linearly interpolated
correction (using background measurements before and after a sample point)
provides a better correction for background drift compared to the standard
background correction performed in real time by the instrument.
The PAX absorption and scattering sensitivity depends on the sampling
conditions and laser power in the cell. The laser power in turn depends on
the quality of the alignment and level of contamination on the cell windows.
The scattering sensitivity is mainly limited by the small measurement volume
and variability in the particle sample stream. The absorption sensitivity is
mainly limited by electrical and acoustical noise that cannot be
distinguished from the true particle signal. While noise in the sampling
environment can reach the acoustic cell through vibrations in the case, a
bigger factor is acoustical noise transmitted through the sampling line
directly into the resonator. Helmholtz notch filters can be inserted into the
sampling line to filter noise at the measurement frequency in environments
with high acoustic noise and relatively low aerosol loadings. In Mexico
City, however, these were not necessary because the PAX was installed in a
temperature- and noise-controlled environment with minimal acoustic noise.
Background-corrected absorption coefficients (a) and Allan
variance (b) measured by an 870 nm PAX sampling filtered laboratory
air overnight in a temperature-controlled environment. The dashed line in
(b) gives the variance for white noise.
Figure 3 shows the corrected absorption coefficients and Allan variance plot
for the absorption signal measured during overnight sampling of filtered air
with an 870 nm PAX. The instrument performed a background measurement
approximately every 10 min, which corrected for the instrument drift on
timescales longer than 10 min, so the variance is only shown up to a
5 min averaging interval. At the highest temporal resolution of 1 s, the
variance was 0.14 Mm-1, equivalent to about 0.03 µg m-3 black
carbon using the factory-applied mass-specific absorption
cross section (MAC) of 4.74 m2 g-1. This is the value at 870 nm,
derived using the λ-1 correction to the 7.5 m2 g-1
recommended by Bond and Bergstrom (2006). The three-sigma sensitivity for
1 s averaging was 1.1 Mm-1 (0.23 µg m-3 BC). The
values are a “best case” scenario where the environmental conditions were
very stable (running overnight in an empty laboratory) and because the
particle filter also acted to block acoustical noise from the sampling
environment from reaching the cell. We have observed 1 s variance in
PAX absorption signals ranging from ∼ 1–5 Mm-1 depending
on the sample environment.
The PAX uses a wide-angle (6–174∘) integrating
reciprocal nephelometer to measure the light-scattering coefficient. The
scattering detector consists of a Teflon diffuser placed in front of a
photodiode (Fig. 2). The scattering measurement responds to all particle
types regardless of chemical makeup, mixing state, or morphology.
The PAX is calibrated through a two-step process by first introducing high
concentrations of purely light-scattering particles followed by high
concentrations of partially absorbing particles to the measurement cells
(Arnott et al., 2000; Nakayama et al., 2015). The concentrations must be
high enough to allow direct measurement of the extinction coefficient
(bext) from the reduction in laser power measured at the laser power
monitor (Fig. 2) using Beer's law:
bext=-1llnII0 106[Mm-1],
where I is the average laser power measured when the high concentration of
particles is being sampled, I0 is the average laser power immediately
before and after the high concentrations are introduced to the cell and l is
the path length of the laser beam through the entire optical cavity
(scattering region and absorption region) between the two windows, which for
the PAX is 0.354 m.
Ignoring truncation in the nephelometer cell, an error that is of order
6 % (Nakayama et al., 2015), the measured extinction coefficient equals
the measured scattering coefficient for purely scattering particles (e.g,
nebulized, dry ammonium sulfate). The relationship between the measured
extinction and scattering coefficients are fit using a linear regression
giving a calibration factor for the scattering measurement. Introducing
calibration particles that have non-zero absorption means the measured
extinction equals the now-calibrated scattering coefficient plus the
absorption coefficient. Subtracting the measured scattering coefficient from
the extinction coefficient determined from Beer's law gives the absorption
coefficient, which can be regressed against the measured absorption
coefficient to obtain a second calibration factor for the absorption cell.
One major uncertainty introduced by this method of calibration is it assumes
the measured particles have similar scattering-phase functions so the
truncation error in the scattering cell cancels.
The PAX measures the light-scattering and absorption coefficients,
Bscat and Babs, directly using the in-line nephelometer and
photoacoustic technique, respectively. The single scattering albedo (SSA),
defined as the ratio of Bscat to the sum of Bscat and Babs
(extinction coefficient) is also derived and recorded, along with the eBC
that is derived from the Babs as described previously.
The PAX was operated with a PM2.5 cyclone particle separator on the
inlet in order to remove larger particles and the particle stream was also
dried using a diffusion drier in order to minimize measurement effects at
high relative humidity (e.g., Lewis et al., 2009; Murphy, 2009) and
also to provide the scattering coefficients of dry particles only. The PAX
was connected to the cyclone by 3/8” conductive tubing. Transmission losses
are insignificant at the flow rate of 1 Lpm, based on calculations made using
the Aerocalc program developed by Baron and Willeke (2001).
The uncertainty in the measured Bscat is ca. ±10 % due to the
accuracy with which the instrument can be calibrated, the truncation error
and aerodynamic losses in the inlet system that brings the ambient air into
the sample cavity. The uncertainty in the measured Babs is ca. ±20 %. This uncertainty stems primarily from the accuracy of the
calibration but the losses in the inlet system also contribute to the
overall accuracy. Given the range of possible MAC values that depend on the
composition and size distribution of the black carbon (Bond and Bergstrom,
2006), the uncertainty in the MAC is of order 50 % and hence the derived
eBC from Babs is approximately 55–60 %. In addition, Bond et al. (2006) show that BC coated by non-light-absorbing material can cause a
lensing effect that leads to enhanced absorption, perhaps as much as a
factor of 1.5; hence, the derived eBC mass concentrations may be as much as
50 % higher than the actual concentration of BC due to this coating
effect.
Measurements and analysis
The meteorology, gas and particle measurements were made at the RAMA
supersite from 6 March 2013 to 31 March 2014. Mexico City is located in a
sub-tropical zone where the seasons can be generally separated into three
periods: (1) the rainy season extends from approximately June until October
with an average annual rainfall of more than 1000 mm, (2) the cool, dry
season, from November to March and (3) the warm, dry period from April
through May. The starting and ending dates for these seasons will vary from
year to year but these three seasonal periods will be used in the analysis
as an operational definition, abbreviated from hereon as “rainy season”
(RS), cold and dry season (CDS) and warm and dry season (WDS). During the
dry months, clear sky conditions lead to a strong thermal inversion at night
(Collins and Scott, 1993). This persists until several hours after sunrise
when it is eroded by turbulent mixing, generated by strong solar heating of
the surface.
During the RS, the interaction of the predominant easterly winds with the
mountains that surround the Mexico City basin force upward motions and
convection. Early mornings are typically clear during this season, but
thermal inversions are infrequent due to the high moisture content in the
atmosphere. Solar heating during the mornings leads to the development of
turbulent eddies that vertically mix the pollutants emitted at the surface.
Convection develops over the mountains, resulting in further dilution of
pollutants due to the vertical updrafts within clouds. The precipitation
that develops from the convective clouds has a large gradient within the
basin (Jáuregui, 1971), from around 400 mm yr-1 in the northeast to
almost 1200 mm yr-1 in the southwestern part of the city. Figure 4
shows the daily precipitation during the period of the research study,
showing that in 2013 the rain began in the middle of May and ended in the first
week of November. It rained almost every day, some days more heavily than
others. In this study, the RS extends from 11 May to 7 November 2013. Only
days when the rain exceeded 2 mm are included in the present study.
In this time series, the daily accumulated rain is shown, along with
the accumulated precipitation beginning on 1 May.
The box and whisker plots show median (horizontal line within the
box), 25 % quantile (bottom of box), 75 % quantile (top of box),
5 % quantile (bottom whisker) and 95 % quantile (top whisker). In
addition the average of the daily maxima are shown with the red filled
circles. The trends in these statistics as a function of season, workdays and
Sundays are plotted for (a) CO, (b) NOx and
(c) O3.
The same as Fig. 5 but for (a) PM2.5,
(b) eBC, (c) Bscat and (d) SSA.
The concentration of gases and aerosols within the boundary layer is
controlled by the balance between the emission rates of primary pollutants
(i.e., CO, NOx, volatile organic compounds, VOC and eBC), the rate of
secondary production due to photochemical reactions of O3 and
PM2.5 and the rate of dilution by mixing with tropospheric air. Not
only does this balance vary with the season, but because the emissions
patterns change from workdays (Monday through Saturday) to Sundays, we
would expect to observe differences in the trends of SLCPs when comparing
these two seasons.
Seasonal trends
The average, median, daily average maxima and the quantiles values at 5,
25, 75 and 95 % were calculated for CO, NOx and O3 and are
shown in Fig. 5a–c by season, workdays and Sundays. The box and whisker
plots show median (horizontal line within the box), 25 % quantile (bottom
of box), 75 % quantile (top of box), 5 % quantile (bottom whisker) and
95 % quantile (top whisker). In addition the average of the daily maxima
are shown with the red filled circles. The same information for eBC,
PM2.5, Bscat and SSA is shown in Fig. 6a–d, respectively. The
values used to generate these graphs are tabulated in Table 1.
Daily and seasonal statistics.
Parameter
Statistic
Rainy
Rainy
Cold-dry
Cold-dry
Warm-dry
Warm-dry
workdays
Sundays
workdays
Sundays
workdays
Sundays
CO (ppm)
Average
0.64
0.43
0.83
0.69
0.81
0.68
Median
0.50
0.4
0.60
0.50
0.53
0.48
Q75
0.83
0.6
1.06
0.94
0.94
0.9
Q95
1.6
0.97
2.30
1.70
2.52
1.8
Maximum
2.69
0.43
3.99
3.52
4.01
2.90
O3 (ppb)
Average
19
19
22
25
41
45
Median
9
11
8
12
29
39
Q75
28
27
35
39
69
76
Q95
73
64
86
87
127
118
Maximum
74
68
83
89
125
124
NOx (ppb)
Average
58
40
82
67
80
61
Median
48
35
88
69
48
41
Q75
73
50
61
52
102
85
Q95
138
85
112
92
268
173
Maximum
170
99
252
179
330
226
PM2.5 µg m-3
Average
25
20
38
31
48
45
Median
21
17
35
29
46
44
Q75
31
26
48
39
61
52
Q95
51
46
70
60
83
71
Maximum
48
41
67
56
84
72
eBC µg m-3
Average
2.3
0.9
3.1
1.7
2.8
2.1
Median
1.8
0.8
2.2
1.3
1.8
1.9
Q75
3.0
1.3
4.1
2.7
3.2
2.9
Q95
5.8
2.4
9.2
4.5
8.9
4.6
Maximum
9.3
6.4
13.7
8.7
15.1
11.1
SSA
Average
0.76
0.84
0.72
0.78
0.75
0.81
Median
0.78
0.90
0.74
0.81
0.79
0.82
Q75
0.86
0.96
0.83
0.89
0.86
0.90
Q95
0.97
1.0
0.93
1.00
0.91
0.93
Bscat
Average
35
28
37
32
41
40
Median
29
22
31
24
29
39
Q75
45
36
48
37
56
46
Q95
82
78
84
71
93
59
Maximum
104
75
98
102
95
72
Table 2 summarizes the average and standard deviations about the average
maximum daily values tabulated by season, workdays and Sundays. A Student's
t test was applied to the differences and the significance was evaluated at a
confidence level of P < 0.10, i.e., that there is less than a 10 %
probability that the observed differences are due to chance. For each season
and each measured parameter, the significance of the differences between the
workday and Sunday values was tested using the hypothesis that the average
values were from the same population. This hypothesis was rejected when the
value of T>|1.7|, the critical value for
P < 0.10 with the pooled degrees of freedom of the two samples in
each comparison. In the Table, underlined numbers i had Sunday values
significantly smaller than for the workdays, i.e., the null hypothesis that they
were from the same population was rejected. Likewise, the numbers in bold
were the cases when the Sunday values exceeded the workday values.
Daily and seasonal maxima. Underlining indicates significant decrease at P < 0.10. Bold
indicates significant increase.
Parameter
Day of
Rainy
Cold-dry
Warm-dry
Rainy to
Rainy to
Cold to
the week
maximum
maximum
maximum
cold
warm
warm
(SD – %)
(SD – %)
(SD – %)
% difference (t test Values)
CO (ppm)
Mon–Sat.
2.6 (61)
4.0 (64)
4.0 (50)
41 (3.9)
43 (3.3)
0 (0.3)
Sunday
1.3 (30)
3.5 (68)
2.8 (32)
92 (4.0)
74 (3.1)
-21 (-1.0)
O3 (ppb)
Mon–Sat.
73 (38)
82 (37)
124 (25)
12 (2.2)
52 (8.1)
41 (6.9)
Sunday
67 (43)
88 (31)
123 (13)
28 (2.1)
59 (5.0)
34 (3.8)
NOx (ppb)
Mon–Sat.
169 (42)
304(41)
330(37)
58 (10.3)
65 (7.2)
9 (1.2)
Sunday
98 (32)
246 (45)
226 (26)
87 (6.1)
80 (4.5)
-8 (-0.5)
PM2.5 µg m-3
Mon–Sat.
47 (36)
67 (31)
83 (30)
36 (7.7)
56 (7.8)
22 (3.5)
Sunday
41 (39)
55 (34)
71 (21)
30 (2.3)
54 (3.7)
26 (2.1)
eBC µg m-3
Mon–Sat.
9.3 (46)
13.6 (47)
15.1 (59)
38 (6.1)
48 (2.5)
-11 (-0.7)
Sunday
6.3 (114)
8.7 (70)
11.1 (59)
32 (1.1)
66(2.5)
-25 (-2.0)
SSA
Mon–Sat.
0.75 (14)
0.71 (15)
0.75 (14)
-5 (-2.5)
0 (0)
0 (0)
Sunday
0.84 (11)
0.77 (15)
0.81 (11)
-8 (-1.8)
0.3 (0.3)
-3 (-0.3)
Bscat Mm-1
Mon–Sat.
103 (43)
98 (45)
95 (51)
-6 (-1.0)
8 (0.5)
2 (0.1)
Sunday
74 (48)
102 (68)
71 (22)
32 (1.7)
4 (0.3)
-35 (-2.2)
Using the same approach the difference in the average maximum values between
each of the three seasons was tested (last three columns in Table 1). The
underlined numbers indicate a significant decrease in a parameter value from
one season to the next and the numbers in bold highlight significant
increases. The value in parentheses in each of these cells is the computed
value of the t statistic.
Median concentrations of CO and NOx (Fig. 5a and b, respectively) do
not significantly change with season, whereas there is a clear increase in
O3 (Fig. 5c) during the WDS compared to either the RS or CDS. The
maximum concentrations for all three of the gas species increase
significantly (Table 2) in the dry seasons (cold and warm) compared to the
RS. This increase is seen for workdays as well as Sundays. A significant
increase from the CDS to the WDS is only observed in the O3
concentrations, due to the larger actinic flux late in the spring. Of note
is that the maximum CO concentrations on Sundays actually decrease between
the CDS and WDS (numbers in underlined italics in Table 2).
A “weekend effect” is observed for CO, NOx and O3. The maximum
CO and NOx concentrations decrease between workdays and Sundays by more
than 30 % during all three seasons (Table 2). As discussed by Stephens et al. (2008)
the lack of a decrease in the O3 concentrations on Sundays is
a “weekend effect” because with the decrease in VOCs (CO is a surrogate
for VOCs), the O3 was expected to decrease as well. As detailed by
Stephens et al. (2008) for
Mexico City in particular, there are several possible mechanisms responsible
for this behavior; however, the one that was discussed as most plausible is
related to the inhibition of O3 by NOx under VOC-limited
conditions. The production of O3 is positively correlated with VOC
emissions and negatively correlated with NOx. Since the CO (VOC) and
NOx are both decreasing, there is a balancing effect on O3
production, i.e., less production from VOC reactions but less inhibition from
NOx
Figure 6a and b display the same grouping of comparisons as Fig. 5 but for
the mass concentrations of PM2.5 and eBC. The median PM2.5
concentrations increase between RS and CDS and between CDS and WDS. In
contrast, the median concentration of eBC does not show a seasonal trend.
When evaluating the seasonal trends of the maximum concentrations, both the
PM2.5 and eBC have much larger values in the dry seasons than during
the rainy season; however, unlike the PM2.5 that also shows a
significant increase between the CDS and WDS, the eBC actually decreases
slightly between these two seasons, but not significantly (Table 2). There
is a clear “weekend effect” that is seen in the maximum concentrations
drawn in Fig. 6, and highlighted in Table 2. The concentrations decrease
from workdays to Sundays over all seasons; however, unlike the eBC whose
decreases are statistically significant over all seasons, the PM2.5
decreases are only significant during the RS and CDS.
The seasonal impacts on the optical properties of the aerosol particles are
illustrated in Fig. 6c and d that show Bscat, and SSA, respectively.
The figures suggest that the differences between seasons of Bscat and
SSA are small; however, as seen in Table 2, there are statistically
quantifiable differences, although for Bscat, only the increase from RS
to WDS is significant. The SSA values are much more sensitive to season,
even though the median values do not appear that different. The SSA
decreases from the RS to CDS but increases from the CDS to WDS. A decrease
in the SSA can be a result of decreases in Bscat or from increases in
Babs, since SSA is directly proportional to Bscat and inversely
proportional to Babs; however, since Bscat is seen to be
relatively insensitive to seasonal changes, the changes with SSA are
primarily a result of the seasonal sensitivity of Babs, i.e., changes in
the eBC. It should be noted that other aerosols like certain organics, as
well as dust, will also absorb light, but at shorter wavelengths than the
870 nm used by the PAX. Hence, the majority of the absorption measured in
this study is by BC.
Daily trends
Figure 7a–c show the differences in the average daily UV-A radiation (Fig. 7a), temperature (Fig. 7b) and
relative humidity (RH, Fig. 7c). The UV radiation is averaged over the daylight hours from 10:00 to 16:00 local
standard time (LST), whereas the temperature and RH are hourly averages. In
Fig. 7b and c the red, green and blue curves differentiate the trends by
season: WDS, RS and CDS, respectively. There is on average 30 % more UV
radiation in the CDS and WDS than during the RS. The WDS temperature (Fig. 7b) is
generally about 5∘ warmer than either the RS or CDS, except
in the early mornings just before sunrise when the RS and WDS are the same.
The WDS is always much drier throughout the day (Fig. 7c) than during the
other seasons while the humidity during the rainy season is 30–50 % higher
than the dry seasons. As will be further discussed below, the seasonal
radiation, temperature and humidity difference are principal factors that
underlie the associated seasonal differences in gas and particle properties.
(a) The average daily accumulated UV-A is shown in the bar
chart, illustrating the impact of clouds during the rainy season when the sun
reaches its maximum elevation angle at the Mexico City latitude. The
(b) average hourly and seasonal temperature and (c)
relative humidity are separated by season: warm-dry (red), rainy (green) and
cold-dry (blue).
The average hourly values are shown, separated by workday (solid) and
weekend (dashed) and by season: warm-dry (red), rainy (green) and cold-dry
(blue) for (a) CO, (b) NOx
and (c) O3 concentrations.
An examination of the average hourly properties of the pollutants,
differentiated by season and by workday/Sunday, provides a different
perspective than the comparison of just the daily quantiles and maxima that
are shown in Figs. 5 and 6. All of the pollutants have daily cycles that are
linked to the traffic patterns, photochemical processes and boundary layer
depth (vertical mixing with clean air). Figure 7a–c illustrate these cycles
for CO, NOx and O3, respectively. The solid and dashed lines separate
the measurements into the workdays (Monday–Saturday) and Sunday. The red,
green and blue curves represent the seasons as described for Fig. 7b and c.
The average hourly CO and NOx (Fig. 8a, b) have very similar patterns over
all three seasons. The maximum concentrations are reached between 07:00 and
08:00 LST during the RS and WDS and between 08:00 and 09:00 in the CDS,
respectively. A secondary, broader maximum is seen near midnight, formed
after the concentration begins increasing after the minimum at 16:00. As was
illustrated in Fig. 5a, b, the maximum CO and NOx values during the RS are
ca. 40 % lower than the dry seasons; however, when comparing the rRS with
the WDS, this difference appears to be dominant only from about 04:00 to
11:00 LST. Outside of this time period the concentrations are nearly the
same. Likewise, after 13:00 LST there is no longer a significant difference
in the CO and NOx concentrations in the RS and CDS. This is a result of the
manner in which the vertical mixing progresses and the boundary layer depth
increases during the three seasons. The shift in the time of the peaks
between the CD and other seasons is due to the shift in Mexico from daylight
savings time (DST) the first Sunday in November to standard time then back
to DST the first Sunday in April; however, the measurement time base does
not shift with changes in DST.
In the RS, as discussed previously, the boundary layer growth is accelerated
by the vertical motions as convective clouds develop, resulting in much
larger updrafts that reach higher in the troposphere than during the dry
season. This leads to more rapid dilution of the primary emissions. At the
same time, the clouds reduce the solar radiation, hence limiting photolysis
and the photochemical reactions that produce O3 (Fig. 8c). In the dry
seasons there is also vertical mixing but not as intense as during the rainy
season, and it is only due to dry turbulent eddies within the boundary layer
that slowly erode the inversion at the top by mixing with the free
tropospheric air. There is less O3 during the CDS because the solar
zenith angle is larger with subsequent decreased photochemical activity.
The minimum in concentrations at 16:00 LST, and steady increase thereafter,
is due to the maximum boundary layer depth in late afternoon (Stull, 1988;
Pérez-Vidal and Raga, 1998), followed by its gradual collapse as the solar
insolation diminishes and the dilution decreases, while the daytime primary
emissions remain more or less constant. There is actually an increase in
emissions from traffic during the evening rush hour in Mexico City that
contributes to the increase in CO and NOx at night (Edgerton et al., 1999;
Schifter et al., 2003)
A “weekend effect” (only Sunday) is also evident during all seasons
whereby the maximum concentrations on Sundays are 50–70 % lower than on
workdays (See Table 2) regardless of the time of day. Given that the
meteorology on Sundays is not expected to differ from workdays, i.e., the
boundary layer depth and solar radiation should not be dependent on the day
of the week, this decrease over all seasons must be due to decreased,
work-related traffic on Sundays.
The hourly average O3 (Fig. 8c) reaches the maximum concentrations
between 13:00 and 14:00 LST during the rainy season and between 14:00 and
15:00 LST during the dry periods. Differences in the O3 concentrations
between the rainy and cold-dry seasons are only obvious between the hours of
approximately 14:00 and 20:00 LST and between 10:00 and midnight LST, when
comparing the RS and WDS. The differences in O3 are primarily being
driven by the photochemical production of O3. As summarized in Table 1,
the CDS maximum O3 is 13 % larger than the RS, whereas the maximum
during the warm-dry season is 47 % larger. The impact of available solar
radiation on the production of O3 from the precursor gases (NOx
and VOCs) can be better understood by comparing measurements of the UV-A
that are shown in Fig. 7a. The summer months of June–August are a period
with minimum solar zenith angle; yet, as this figure shows, the daily
averaged UV-A in the RS is about 20 % less than the CDS and 30 %
less than the WDS. Hence, a major fraction of the difference between RS and
WDS O3 can be explained by the decreased actinic flux in the RS with
the remaining difference due to the increased vertical mixing produced by
the cloud formation.
Figure 9a and b display the hourly trends in PM2.5 and eBC where it is
seen that there are large differences, not only in the hourly concentrations
that vary with season, but the hours during which these PM2.5 mass
concentrations peak. These peak hours change with season, although the eBC
to a lesser extent. The differences between the rainy and dry season
PM2.5 maximum average mass concentrations are 36 and 56 %,
respectively, when compared to the CDS and WDS (Table 2). For the eBC,
these differences are 38 and 48 %. Whereas the PM2.5
concentrations during the CDS and WDS are always larger than those in the
RS, regardless of the time of day, the eBC concentrations in the dry seasons
only exceed those during the rainy season from approximately midnight until
12:00 LST. After this time the concentrations are the same on average. This
is a reflection of the same atmospheric process that was discussed for CO
and NOx, i.e., the difference in vertical mixing between the rainy and dry
seasons. There is much greater dilution of the eBC during the rainy season,
since clouds are responsible for the vertical mixing.
As in Fig. 8, the average hourly (a) PM2.5 mass and
(b)
equivalent black carbon concentrations are shown, differentiated by seasons
and day of the week.
The daily cycles in PM2.5 have seasonal patterns that are much more
distinctive than either the gases (Fig. 8a–c) or the eBC. There is a slight
shift in the hour of maximum concentrations for the eBC, between 06:00 and
07:00 for the RS and WDS, and between 07:00 and 08:00 for the CDS. This shift
is only a result of the change from daylight savings time (DST) in November
and April, so that in the CDS the major commuter traffic begins an hour
later relative to the clock used on the RAMA data that doesn't change to
DST. The PM2.5 reaches maximum concentrations between 07:00 and 08:00,
09:00 and 10:00 and 10:00 and 11:00, respectively, for the WDS, CS and RS.
The large shift in the peaks of the daily cycles is the result of both the
atmospheric dynamics, i.e., boundary layer growth, and the chemical processes
behind the formation and growth of the particles that make up the
PM2.5.
There is a very distinctive “weekend effect” displayed in the eBC and
PM2.5 concentrations. As summarized in Table 2, the maximum eBC
concentrations decrease by more than 50 % from workdays to Sundays during
all seasons. As can be observed in Fig. 9b, the difference is distinct over
all hours of the day but is the most predominant between around 05:00 to
18:00. One of the major differences in primary emissions of BC between
workdays and Sundays is that the use of large, diesel-burning trucks and
machinery is much less on Sundays. Although vehicular traffic in general is
much less on Sundays, since combustion of diesel fuel is the major
contributor of BC emissions in the city, the large decrease in BC is linked
more to the decrease in diesel combustion than gasoline. To support this
assertion, looking only at the dry season to remove any effects of
precipitation, the average eBC to CO ratio on workdays was 3.6 µg m-3 of eBC to 1.0 ppm of CO. This compared to the Sunday ratio that is
2.1 µg m-3 of eBC to 1.0 ppm of CO; hence, the eBC decreases by
a much larger percentage than the CO.
The hourly trends in the optical properties of the aerosol particles,
Bscat and SSA, are shown in Fig. 10a and b, respectively. The maximum
Bscat falls between 09:00 and 10:00 LST during the WDS and between 10:00
and 11:00 LST in the RS and CDS. Unlike the particle mass concentrations of
PM2.5 and eBC, there are no distinct differences from season to season
in the maximum values. The differences are instead seen in the morning hours
before reaching the maximum then again in the afternoon. During both of
these periods the dry season values are about 30 % larger than the rainy
period. In the same way that the trends in PM2.5 are a balance between
the processes that drive particle growth and the dynamic processes that
dilute the number concentration, likewise the trends in the scattering are
driven by complex interactions. The intensity of light scattering and the
PM2.5 mass concentrations are strongly correlated because both are
proportional to particle concentration and size. The Bscat and
PM2.5 values peak at the same time periods during the three seasons
(Figs. 9a and 10a).
The SSA reaches its minimum value in the morning between 06:00 and 07:00 LST
in the WDS and RS and between 07:00 and 08:00 LST in the CDS. As mentioned
previously, the SSA appears to be much more sensitive to changes in eBC,
i.e., light absorption, than in Bscat. Since the minimum in the SSA
corresponds to the maximum in the eBC this further reinforces the
sensitivity of SSA to changes in the eBC concentration in this environment.
The changes with season of the SSA do not mirror those of eBC. Whereas the
maximum eBC in the dry seasons was significantly larger than the rainy
season (Table 2), the trends in the SSA are more complicated with the RS
and WDS values being nearly equal until they separate after midday at 13:00 LST, with the RS SSA decreasing while the WDS maintains an almost constant
value. These trends can be best understood by comparing the trends in
PM2.5, the parameter that is correlated with Bscat, and eBC and
observing that while the eBC decreases rapidly after its morning maximum
during all seasons, the PM2.5 is decreasing much more slowly, due to
photochemical reactions leading to the growth of pre-existing particles
(Baumgardner et al., 2004) and to the secondary production of particles
(e.g., de Gouw et al., 2009; Herndon et al., 2008). This illustrates that whereas
the minimum in SSA observed during the morning is being driven by the
presence of particles with eBC, the afternoon SSA is being driven mainly by
changes in non-absorbing particles represented by Bscat.
As in Figs. 8 and 9 but for (a) the light-scattering
coefficient, Bscat, and (b) the single scattering albedo, SSA.
These plots illustrate the correlations between (a) CO and
NOx, (b), O3 and NOx, as a function of the lag time
and differentiated by season. The vertical dashed line shows the correlation
of the parameters at no lag time. The horizontal dashed line demarks the
crossover from positive to negative correlation. The lag time was varied by
10 min intervals.
As in Fig. 11, these plots illustrate the correlations between
(a) eBC and CO, and (b) eBC and PM2.5, as a function of
the lag time and differentiated by season.
As in Fig. 12, these plots illustrate the correlations between
(a) Bscat and PM2.5 and (b) eBC and SSA, as a function of
the lag time and differentiated by season.
Correlations and causal links between co-pollutants
In addition to deducing the potential relationships between co-pollutants by
comparing the hourly trends in Figs. 8, 9 and 10, these links can be further
investigated by calculating the cross correlations to highlight the temporal
shifts that may change with season and might offer additional evidence of
how photochemical and thermodynamic processes drive the daily trends in gas
concentrations and particle properties.
Figures 11–13 are correlations between pairs of pollutants, as a function of
the lag time that is varied from -10 to +10 h, in 10 min
increments. The vertical black dashed line is drawn at the zero lag time and
the horizontal black dashed line is drawn at the crossover point from
positive to negative correlation. The colors of the lines denote the season
using the same coding as previous figures. Figure 11a illustrates the
relationship between CO and NOx and indicates that these two pollutants are
highly correlated (R=0.9) with no lag time, i.e., they are being produced
at the same time and likely by the same sources. The correlation between
O3 and NOx (Fig. 11b), shows that their highest correlations are at lag
times of -8, -7.5 and -7 h, for the WDS, CDS and RS, respectively. The
maximum correlation at negative lag indicates that O3 is linked to the
NOx that was formed approximately 8 h earlier, an expected
correlation. These lag times are just another way of quantifying the time
differences between the peaks in NOx and O3 that were seen in Fig. 8b
and c. The 1 h difference between the CDS and WDS is caused by the
change in DST as was explained earlier for the daily trends.
There is additional information contained in these cross correlations by
evaluating the time from maximum to zero correlation. The time at which the
correlation coefficient crosses the zero point is a measure of a
characteristic timescale, referred to as the integral timescale, that is
related to the rate at which atmospheric processes produce, dilute or remove
the pollutants. This integral timescale indicates how quickly two
pollutants are being de-correlated by dilution and mixing with other
atmospheric components. For example, there is a single, season-independent
integral timescale for the CO and NOx correlation, 4–5 h, whereas for
the O3 and NOx there are three timescales: 5, 4.5 and 4 h,
respectively, for the CDS, WDS and RS.
The eBC and CO concentrations are highly correlated (R>0.8) with
no lag as seen in Fig. 12a, indicating that their sources are located in the
same region. The high correlation between CO and BC mass (derived by an
alternative method) in Mexico City was already reported by Baumgardner et al. (2000, 2002). The integral timescales are 6, 7 and 8.5 h during the
WDS, RS and CDS, respectively.
The cross-correlation between the eBC and PM2.5 (Fig. 12b) presents a
more complicated picture that is related to the complex relationship between
the black carbon that is produced by primary emissions and particle mass
that is a mixture of primary and secondary processes. Looking at the average
maximum values in Table 2, we see that eBC is approximately 20 % of the
mass of PM2.5. Hence, the good correlation at zero lag for all seasons
represents the fraction of the PM2.5 that is made up of BC. The trends
in the correlation coefficients rapidly diverge as the lag time increases.
This reflects the secondary processes that either produce new particles or
lead to the increase in mass of existing particles. These secondary
processes can be aqueous, photochemical or a combination of both, so the rate
at which particles grow will depend on the temperature, relative humidity,
pH and UV radiation flux. Since BC is a primary particle onto which
organics, sulfates or nitrates can condense, a correlation
between BC and PM2.5 will remain. As the BC particle evolves and takes on a coating
or mixture of other substances, when it is measured by the PAX it will
continue to absorb energy and be identified as eBC, i.e., there is still a
“memory” of the original particles that are being emitted even over very
long lag times. As Fig. 12b illustrates, the integral timescales exceed 10 h during
all seasons. The much larger integral timescale of the rainy
season is most likely due to the higher humidity during this period that
promotes aqueous-phase reactions and particle growth. During the dry
seasons, the secondary processes are only related to photochemical
reactions. These interpretations are purely speculative without
corroboration using a chemical growth model, which is beyond the scope of
this current study.
The correlations between the particle optical properties (Bscat and
SSA) and the eBC and PM2.5 are displayed in Fig. 13a, b. The
relationship between Bscat and PM2.5 that was discussed previously
is highlighted in Fig. 13a, showing a correlation coefficient > 0.8 at zero lag time. The very long integral timescale is a result of the
same complex processes that led to the greater than 10 h timescales for
the eBC and PM2.5 (Fig. 12b). Figure 13b underscores the earlier
discussion about the sensitivity of the SSA to eBC. The negative correlation
is due to the inverse relationship between SSA and eBC. The correlations are
higher in the dry months than in the rainy season because of the additional
contribution to light scattering by the particles that are formed under
conditions of high humidity in the RS. The separation in the integral timescales is also linked to the relative rate by which the Bscat increases
as secondary processes promote particle growth in the different seasons.
Summary and conclusions
Measurements of SLCPs and precursor gases, made over a 13 month period from
6 March 2013 to 31 March 2014, have been evaluated to document and explain
the seasonal trends related to changes in meteorology and radiative fluxes.
The SLCPs that were analyzed are the eBC, O3 and PM2.5 along with
the co-pollutant gases of CO and NOx. The eBC data are the longest,
continuous measurements that have ever been conducted in Mexico City. These
data extend over the three primary seasons in Mexico City, i.e., rainy, cold
and dry and warm and dry, and provide the basis for linking daily trends to
the underlying physicochemical processes that drive them.
The maximum concentrations of the gases and particles were significantly
less in the rainy season compared to the dry seasons. The maximum
concentration changes from the rainy to dry seasons were: eBC 8.8 to 13.1 µg m-3 (44 %), PM2.5 from 49 to 73 µg m-3
(61 %), NOx from 144 to 252 ppm (78 %), O3 from 73 to 100 ppb
(51 %) and CO from 2.5 to 3.8 ppm (75 %). The primary factor that leads
to lower concentrations in the rainy season is morning cloud formation that
produces vigorous vertical motions and results in the dilution of all the
pollutants. The clouds also reduce solar energy (UV radiation) and decrease
the photochemical reactions that produce ozone and secondary aerosol
particles. The other factor that will primarily impact the particle
population is the precipitation during the rainy season that can remove the
aerosol particle by inertial scavenging. The magnitude of this effect
requires an analysis beyond the scope of the current study but given the
almost daily rain rates of more than 2 mm, this source of particle removal
could be of a similar magnitude as vertical mixing as the cause for
decreased particle concentrations in the rainy season.
A “weekend effect” is observed that is linked to changes in traffic
patterns and the type of fuel burned. Significant decreases (t test) are
observed between workdays (Monday through Saturday) and Sundays in the
concentrations of CO, NOx, eBC and PM2.5. This decrease is most likely
a result of reduced emissions by diesel-burning vehicles. A significant
increase is seen in the SSA because of its sensitivity and inverse
relationship to changes in the light-absorbing eBC. A significant effect was
also seen in O3 concentrations that did not change from workdays to
Sundays because of decreased production from VOCs that was balanced by decreased
inhibition by NOx.
Cross correlations and the derived integral timescales were calculated
between the co-pollutants in order to establish the temporal links and
de-correlation times. Shorter integral timescales are related to rapid
vertical mixing that erodes the “memory” of the pollutants that are
originally coupled to the same source or location where they are produced.
Cross correlations between pollutants where one or both can be affected by
secondary chemical reactions typically have much longer integral timescales
since these secondary reactions extend the memory of the coupled
co-pollutants.
The maximum concentrations of eBC measured in 2000 (Baumgardner et al.,
2002) and 2006 (Marley et al., 2009a, b), were 9.1 and 9.4 µg m-3, respectively, compared to 8.8 and 13.1 µg m-3 in the
wet and dry seasons between March 2013 and March 2014. Not only are these
exceptionally large concentrations of a type of particle that has been
specifically identified by the World Health Organization as being hazardous to
health, there has been no significant change in the eBC emissions, within
the estimated uncertainties of more than 60 %, over a 15 year period. The
efforts of the city government to reduce some of the pollutants have been
highly successful in reducing CO and O3; however, the mitigation
strategies target gases more than particles. Given that the fleet of
vehicles that emit BC has grown from 210 000 trucks and buses in 2000 to
almost 350 000 in 2014, it is encouraging that the BC has remained more or
less constant for this same time period. This would suggest that some of the
strategies to reduce emissions are partially mitigating the effects of BC
despite the increase in vehicles. As the time of writing, Mexico is planning to
adopt the European standard for diesel fuel that will decrease the emissions
of BC and possibly counter the impact of a higher density of vehicles.