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Air pollution can be produced from anthropogenic or natural sources. Most of the policies enacted to improve air quality focus on reducing anthropogenic sources of pollution, but if natural sources increase, then air quality does not improve with these policies. In this chapter, we first define the diurnal and monthly cycle of particulate matter and ozone concentration, depending on the weather, using data from air quality monitoring stations from Greater Mexico City. We then look at a mayor public policy intervention during the COVID-19 pandemic that drastically reduced anthropogenic sources of PM but did not reduce natural sources by doing robust trend analysis on air quality station data. We evaluate the effect of these interventions by looking at national air quality standards and the number of days air pollutants have been within recommended levels. The results show that during lockdown, air quality improved because less anthropogenic sources of PM were active. However, natural sources contributed to air pollution during that time.
Institute of Economic Research, National Autonomous University of Mexico, Mexico City, Mexico
Ernesto Caetano
Institute of Geography, National Autonomous University of Mexico, Mexico City, Mexico
Karla Pereyra-Castro
Faculty of Electronic Instrumentation, Universidad Veracruzana, Xalapa, Mexico
*Address all correspondence to: jmendez.astu@comunidad.unam.mx
1. Introduction
Greater Mexico City (GMC) is one of the largest cities in the world, with more than 22 million inhabitants [1]. The population density in the capital of the country is 6163.3 inhabitants per square kilometer. Municipalities located to the east and north of GMC (e.g., Iztapalapa and Gustavo A. Madero) are more populated than those in the south (e.g., Milpa Alta). The combined activity of vehicles and industries consumes more than 45 million liters of petroleum fuel per day, generating thousands of tons of pollutants.
The labor dynamics of the Mexico City metropolitan area include long daily commutes in search of better salaries and working conditions. The cause of the mobility toward the central metropolis is explained by the diversity and specialization of the labor market, as well as the better salaries offered there. Mobility requires the use of public or private transport, whose emissions are difficult to control. Emissions from industry or vehicular traffic, when combined with the climatic and topographic characteristics of the region, lead to the production of pollutants [2]. Ozone (O3) and particulate matter (PM10 and PM2.5) are two criteria pollutants whose concentrations have remained elevated over time, creating environmental contingencies in GMC.
The formation of O3 from pollutants has been studied, based on physics, chemistry, and statistics [3, 4, 5]. Ozone is formed by the photochemical reaction of volatile organic compounds (VOCs) and nitrogen oxides (NOx). Previous studies have reported that the formation of O3 is sensitive to VOCs in the GMC, explaining the decrease in the rate of titration of O3 by NO and the decrease in NOx in a VOC-limited environment. Control strategies to reduce VOC emissions will decrease ozone concentrations in VOC-limited regimes but increase their formation and concentration in NOx-limited areas [6].
Velasco et al. [7] found that in GMC, the release of heat stored in the urban surface forms a shallow stable layer (~200 m) near the ground, which favors the stagnation of nocturnal emissions. Strong inversion layers occur in the atmosphere of GMC during the night and early morning hours. After sunrise, surface heating favors convection and layer mixing, then the O3 balance depends on the photochemical production of the pollutant, the entrainment from the upper residual layer, and the destruction by titration with nitric oxide.
No reduction in ozone concentrations above GMC is observed on weekends when the number of cars on the roads is lower than during the week. Enforcement of traffic rules that restrict car circulation (with the goal of NOx reduction) during environmental contingencies does not necessarily reduce ozone production [8]. Improving air quality in the GMC requires the implementation of comprehensive measurements at the regional scale.
Lei et al. [5] explain that the O3 formation characteristics and sensitivity to emission changes were found to be weakly dependent on the meteorological conditions for GMC. The O3 formation is sensitive to NOx and VOC levels and to the photochemical plume transport pathway.
During the COVID-19 pandemic shutdown, emissions of primary criteria pollutants at GMC were significantly reduced; however, the daily mean ozone concentration profile was similar to that of previous non-pandemic years. The reductions in NOx were so drastic that ozone formation quickly shifted from a VOC-sensitive regime to a NOx-sensitive regime. A VOC-sensitive regime means that an increase in VOC leads to an increase in O3, while an increase in NOx leads to a decrease of O3; this regime is typical of densely populated urban atmospheres such as GMC [9].
Dust particles contribute to PM10 concentrations in GMC, particularly in the northeastern part of the city, where geologic material from the dry Lake of Texcoco is a dust source [10, 11]. This source alone generated about 80% of the total coarse particles measured in the northeastern GMC during exceptional dust events [12]. Another source of dust is the agricultural areas of Tenango del Aire and Chalco. These regions affected the central, southern, and southeastern parts of the city and contributed about 75% of the total coarse particles [12]. PM2.5 and PM10 affect the air quality during the rainy season when rain removes pollutants from the air. Bare soil and traffic-related conditions from February to May also contribute to the increasing concentration of PM2.5 and PM10 [13].
In this chapter, we evaluate the impact of policy interventions on anthropogenic pollution sources in the air quality of Mexico City. First, ground-based particulate matter (PM10, PM2.5) and ozone (O3) concentration data are used to define the diurnal and annual cycle of air pollution concentrations in Mexico City. Then, the effect of the mobility restrictions enacted in March 2020 to stop the spread of the COVID-19 pandemic is evaluated through trend analysis of the time series of the pollutants from 2012 to 2022. Finally, the number of days exceeding the National Air Quality Standard is presented to evaluate the effect of reducing anthropogenic sources of pollution during March-April 2020.
According to the latest census in 2020, Mexico City (19.43°N, 99.13°W) is home to 9,210,000 inhabitants [1]. Greater Mexico City (GMC) includes some municipalities of the State of Mexico that are attached to Mexico City, with a total population of about 22 million inhabitants in 2020 [1]. In terms of weather, the GMC experiences middle-latitude systems during the dry season and is exposed to tropical interactions during the rainy season. The wet season lasts from June to November and the dry season is defined from December to May [13].
In terms of urbanization, the layout of the GMC is heterogeneous, meaning that areas depending on land use or land cover are not well defined and they are rather mixed. In this study, we defined urban areas as areas with many built structures such as buildings and roads, rural areas are those with a few built structures and mostly vegetation cover, and semi-urban areas are defined as large green areas within an urban area, such as a large park near the city center [13].
In terms of mobility, in the morning at around 08:00 h local time (LT), most people travel from the municipalities further away from the city to the city center and the eastern business district. Moreover, from 17:00 h LT the flow population is in the opposite direction, from downtown and business district to the outskirts of Mexico City, as reported in the official 2019 mobility report [14].
Figure 1 shows a satellite image of the GMC and the location of the air quality monitoring stations used in this study.
Figure 1.
Location of stations used in this study.
2.2 Ground-based data
The Government of Mexico City, through the Secretary of the Environment, operates the Meteorology and Solar Radiation Monitoring Network (REDMET) [15] and the Automatic Ambient Monitoring Network (RAMA) [16], which are used to monitor the weather and air pollution concentrations, respectively.
2.2.1 Air temperature data
The REDMET measures surface temperature (2 m), relative humidity (2 m), wind direction, and wind speed (10 m) every hour at 26 locations in GMC [15]. REDMET has been in operation since 1986; in 2015, some stations were decommissioned, while new stations were commissioned to cover the entire metropolitan area. To evaluate the effect of meteorological conditions on PM concentrations, 5 REDMET weather stations that report meteorological and pollution concentration data at the same location were selected. The study period was 2012–2022. In some cases, the selected stations report only one type of pollutant, such as PM10, PM2.5, or O3.
2.2.2 PM and O3 concentration data
The RAMA network includes 44 air quality monitoring stations covering the entire Mexico City metropolitan area. It reports hourly concentrations of O3, NO2, NOx, NO, SO2, CO, PM10, and PM2.5 [16]. We selected eight stations (Figure 1) with data for the period 2012–2022. Two stations (MER and HGM) are in urban areas and report PM10, PM2.5, and O3 concentrations. We selected two stations located in rural areas (ACO and UAX), which report PM10 and O3 and PM2.5 and O3 concentrations, respectively. Stations CHO, CUA, CCA, and SFE are in semi-urban areas, and the first two report PM10 and O3 concentrations, while the other two reports PM2.5 and O3 concentrations.
2.3 Methodology
Hourly and monthly averages of PM (10 and 2.5) and ozone concentrations in urban, rural, and semi-urban areas were obtained to define the diurnal and monthly cycles of these pollutants. Similarly, hourly and monthly averages of meteorological data from REDMET were obtained to study the effect of meteorological conditions on PM and ozone concentrations.
The effect of mobility restrictions in 2020 on PM and O3 concentrations, which can be considered as a major public policy intervention, was evaluated using the Mann-Kendall trend analysis with Sen’s slope [17]. In addition, the Chow [18] test, which is used to test whether a break occurs at a given time in a time series, was used to assess the effect of the 2020 mobility restrictions. Statistical significance of PM concentration differences was assessed using the nonparametric Mann-Whitney U test, which is commonly used in air quality studies to compare differences in pollutant levels [19, 20]. A 95% significance level (p < 0.05) was used for all tests. Finally, the number of days exceeding the national air quality standard is used to determine the effect of public policy intervention on PM concentrations in GMC.
3.1 Effect of meteorological variables on particulate matter and ozone concentrations
Multiple linear regression between meteorological variables (temperature, relative humidity RH, wind speed, and wind direction) and PM (10 and 2.5) and ozone was performed to assess the effect of meteorological variables on PM and O3 concentrations. In 90% of the linear regression models, wind speed and wind direction were statistically significant variables, whereas temperature and RH were not always statistically significant. The effect of meteorological variables on PM and on ozone concentrations was analyzed using the quantile regression at 75% between wind speed and pollutant concentration [21].
Table 1 indicates that there is a negative relation between PM10 in urban areas and wind speed. Similarly, a negative relation was found between PM2.5 in urban areas and wind speed. In semi-urban areas, a positive relation between PM2.5 and wind speed was found. Moreover, in rural and semi-urban areas, O3 was positively related to wind speed. Higher wind speeds in urban areas cause a higher dilution of pollutants in the Planetary Boundary Layer (PBL) decreasing PM concentrations. However, higher winds in rural and semi-urban areas enhance soil erosion, producing dust and increasing PM concentrations.
Station
Coefficient
p-value
PM10
Urban
Neg
p < 2e-6
Rural
NS
p = 0.861
Semi-urban
NS
p = 0.412
PM2.5
Urban
Neg
p < 2e-6
Rural
NS
p = 0.426
Semi-urban
Pos
p = 0.01
O3
Urban
NS
p = 0.91
Rural
Pos
p < 2e-6
Semi-urban
Pos
p < 2e-6
Table 1.
Results of quartile regression between pollutants (PM10, PM2.5, and O3) and wind speed.
Hourly mean pollution concentrations for stations in urban, semi-urban, and rural stations are shown in Figure 2 for June to January and Figure 3 for February to May.
Figure 2.
Diurnal cycle of PM10, PM2.5, and O3 concentrations in urban, semi-urban, and rural areas in GMC during the dry season (February-May). Data from 2012 to 2022. O3 concentrations in ppb and PM (10 and 2.5) in μg/m3.
Figure 3.
Diurnal cycle of PM10, PM2.5, and O3 concentrations in urban, semi-urban, and rural areas in GMC during the wet season (June-January) for the period 2012–2022. O3 concentrations in ppb and PM (10 and 2.5) in μg/m3.
During the first hours of the day (0:00 to 7:00 LT), the median PM10 concentration in the semi-urban area is lower than that recorded in the rural and urban areas. Between 9:00 h and 15:00 LT, the median PM10 concentration in the urban area increases and exceeds that of PM10 in other regions. At night (18:00–22:00 h LT), the PM10 concentration in the city becomes homogenous. This variability occurs during the dry and wet seasons and is more pronounced during the dry season (Figure 2). The hourly behavior of PM2.5 is similar to that of PM10. However, the difference in PM2.5 concentration between the urban area and the semi-urban and rural areas is more pronounced from 9:00 to 12:00 LT (Figure 3).
The diurnal variation of ozone is mainly due to photochemical reactions due to solar radiation. Therefore, the ozone concentration increases from 9:00 local time to a maximum of around 15:00 local time. At night (20:00 to 7:00 LT), the ozone concentration is the lowest in the urban area compared to other regions (Figures 2 and 3).
3.2.2 Monthly averages of pollution concentrations in GMC
Figure 4 shows the monthly average concentrations of pollutants in urban, semi-urban, and rural areas of the GMC.
Figure 4.
Monthly ozone (O3), PM10, and PM2.5 concentrations in urban, semi-urban, and rural areas of Mexico City for the period 2012—2022. O3 concentrations in ppb and PM (10 and 2.5) in μg/m3.
Monthly ozone concentrations increased during the dry season (February-May) in urban, semi-urban, and rural areas during 2012–2022. The rainy season begins in late May in central and southern Mexico. The rain removes pollutants from the atmosphere, resulting in a decrease in tropospheric ozone concentration beginning in June. Monthly ozone concentrations are higher in rural and semi-urban areas than in urban areas (Figure 4). This urban-rural gradient has also been observed in other cities around the world and originates from the ratio of NOx to VOCs [22, 23].
Similarly, PM10 concentrations increase during the dry season. However, during this period, PM10 concentrations are higher in semi-urban areas than in rural areas. Furthermore, during winter (October-January), PM10 concentrations do not differ significantly between rural, urban, and semi-urban areas. The seasonal behavior of PM2.5 is similar to that of PM10. The median PM2.5 concentration is 25 μg/m3 from November to May.
3.3 Trend analysis
The trend analysis shows that O3 in urban areas in the wet season has no trend change in the period 2012–2022. Also, PM2.5 concentrations in the dry season in rural and semi-urban areas have the same trend in the period 2012–2022. In contrast, PM2.5 concentrations in urban areas in dry and wet seasons changed six and four times in the period 2012–2022, respectively. O3 concentrations in rural areas in dry and wet seasons changed five times in the period 2012–2022.
In 2020, there are significant (p < 0.05) trend changes in PM2.5 concentrations in urban areas during the dry season and O3 concentrations in rural and semi-urban areas during the wet season.
3.4 Concentration difference
According to the boxplots shown in Figure 5, the concentration of PM10 is decreasing in urban, semi-urban, and rural areas compared to previous years. However, the largest decrease was found in rural areas in 2020. A decrease of 38.87% (U = 17,156; p < 5.02e-68) was found in 2020 compared to 2019. PM2.5 concentrations in urban areas decreased on average by 9.7% compared to 2019. PM2.5 in rural and semi-urban areas decreased on average by 11.68% and 14.84%, respectively (U = 17,156; p > 5.02e-68 and U = 58,648; p = 0.002). In contrast, ozone concentrations increased on average by 3% in urban, 14% in rural, and 16% in semi-urban stations in 2020 compared to 2019, due to the chemical reaction explained in detail by Peralta et al. [9].
Figure 5.
Daily PM10 and PM2.5 concentrations and hourly O3 concentrations in urban, rural, and semi-urban areas of Mexico City.
Average increases or changes of PM (10 and 2.5) and O3 were calculated using data from the whole year 2020. In order to assess the immediate effect of a major intervention on PM and ozone concentrations in urban, rural, and semi-urban areas of the GMC, the Chow test is performed. This test allows us to detect a structural change at a given time. In this case, the mobility restrictions started on March 23rd, 2020, or day of the year (DOY) 83. Therefore, the test is performed for this day. The results of the Chow test for DOY 83 are shown in Table 2. The PM2.5 diurnal cycle is also influenced by working hours since many PM2.5 anthropogenic sources are related to transportation and industrial activities. Finally, the ozone diurnal cycle is also influenced by the diurnal cycle of solar radiation.
At day of the year (DOY) 83, March 23rd, 2020
PM10
PM2.5
O3
Urban
F = 2.5954, p-value = 0.076
F = 1.3796, p-value = 0.253
F = 151.42, p-value <2.2e-16
Rural
F = 0.7036, p-value = 0.4955
F = 2.3805, p-value = 0.09396
F = 93.78, p-value <2.2e-16
Semi-urban
F = 1.7961, p-value = 0.1675
F = 4.3136, p-value = 0.01407
F = 115.11, p-value <2.2e-16
Table 2.
Results of Chow test (p < 0.05).
At the 95% confidence level, a significant change in PM2.5 concentrations was found in the semi-urban areas. Ozone concentrations also changed significantly in the three areas on that day.
3.5 National standard exceedances
The final part of the assessment of the effect of lockdown on air quality is done by looking at the number of exceedances of the National Standard (NOM) for air quality (NOM-025-SSA1-2014 until 2022 and NOM-025-SSA1-2021 after 2022). The NOM-025-SSA1-2014 stipulates that daily PM2.5 concentrations should not exceed 45 μg/m3 and PM10 concentrations should not exceed 75 μg/m3. In addition, the hourly average of O3 should not be greater than 0.090 parts per million (ppm) in order to be considered “good” air quality.
The number of exceedances of the NOM for PM and ozone concentrations during the wet and dry seasons from 2012 to 2022 at urban, rural, and semi-urban stations in Mexico City is shown in Figure 6.
Figure 6.
(a) Number of exceedances of NOM for PM10, (b) number of exceedances of NOM’s limits for PM2.5, and (c) number of exceedances of NOM for O3. In all cases, the wet and dry seasons are shown for urban, rural, and semi-urban areas in GMC.
In the 2020 dry season (when the mobility restrictions were enacted), the recommended PM10 concentration levels were exceeded five times in urban areas and once in rural areas. Fewer NOM exceedances were reported that year than in previous years. The recommended levels of PM2.5 concentration were exceeded only once in semi-urban areas during the dry season. In terms of PM2.5 concentrations during the dry season, air quality improved in urban and rural areas due to major policy intervention. In contrast, the recommended levels of ozone were exceeded more often than in previous years, especially in semi-urban areas.
The results of the analysis show that wind is the main environmental variable affecting PM and O3 concentrations; therefore, long-range transport of pollutants can be a limitation of policies reducing anthropogenic sources of pollutants. A refinery complex is located around 80 km to the northeast of the city center in Tula, Hidalgo. It contributes to air pollution in GMC via long-range transport of pollutants, mainly PM10 [13, 24]. Also, high winds cause erosion of dry soils, especially in rural areas during the dry season [12]. Therefore, there is a positive significant relation between PM concentrations and wind speed in rural areas.
The diurnal cycles of PM10 concentration show that in the early morning, concentrations in semi-urban areas are higher than in rural and urban areas. As the working hours begin, PM10 concentrations increase in urban and rural areas. This has to do with transportation, industrial production, long-range transport of pollutants, soil erosion during the morning hours, and the boundary layer height cycle [25].
The ozone diurnal cycle is affected by solar radiation and economic activity since ozone precursors are related to vehicle emissions and solar radiation contributes to the formation of surface ozone [26].
The monthly averages show that the rainy season washes out pollutants, thus causing an improvement in air quality. Also, wet soils are less likely to be affected by wind erosion. Thus, some natural sources of PM and O3 are reduced.
The trend analysis shows that in the long run, there was an effect of mobility restrictions (and halting or reduction of nonessential businesses and industries) in PM concentrations. However, ozone increased during this period as previous studies in the same region have shown [9]. The short-term analysis done with the Chow test showed that at the start of the restrictions, there was a decrease in PM2.5 concentrations in semi-urban areas and ozone in the whole GMC. The effect on the other areas was not statistically significant. Finally, in 2020, the NOM of air quality was exceeded a few times in PM2.5 and PM10 during the lockdown; however, the limits of ozone were exceeded more times than in the 10 previous years.
To analyze the effect of the reduction of anthropogenic sources on PM and O3 concentrations, it is also necessary to analyze the effect of winds and other meteorological variables. Furthermore, long-range transport of pollutants and natural sources of pollutants such as wildfires or dry soils caused by drought need to be taken into account to assess the effect of policy interventions aimed at reducing anthropogenic sources of air pollutants in Greater Mexico City. According to the results of this study, during the 2020 dry season in Mexico City, there was a statistically significant reduction of PM2.5 and PM10 concentrations, but there was an increase in ozone.
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Written By
Jorge Méndez-Astudillo, Ernesto Caetano and Karla Pereyra-Castro
Submitted: 25 February 2023Reviewed: 07 April 2023Published: 02 May 2023
Open access peer-reviewed chapter
