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Article

Spatial Variation of PM10 and PM2.5 in Residential Indoor Environments in Municipalities Across Mexico City

by
Elizabeth Vega
1,*,
Ann Wellens
2,
Anil Namdeo
3,
Diana Meza-Figueroa
4,
Octavio Ornelas
1,
Jane Entwistle
3 and
Lindsay Bramwell
3
1
Instituto de Ciencias de la Atmósfera y Cambio Climático, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
2
Facultad de Ingeniería, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
3
Geography and Environmental Sciences Department, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
4
Department of Geology, Universidad de Sonora (Unison), Hermosillo 83000, Mexico
*
Author to whom correspondence should be addressed.
Atmosphere 2025, 16(9), 1039; https://doi.org/10.3390/atmos16091039
Submission received: 6 August 2025 / Revised: 26 August 2025 / Accepted: 29 August 2025 / Published: 31 August 2025
(This article belongs to the Section Air Quality)
Browse Figures
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Abstract

Despite significant progress in controlling outdoor air pollution in Mexico City over the past three decades, indoor air pollution remains largely unaddressed. This is particularly concerning because health authorities advise people to stay indoors when outdoor pollution exceeds safe limits, yet indoor concentrations can be higher. Two optical particle counters were deployed simultaneously indoors and outdoors in 38 homes across all municipalities in Mexico City. The average indoor 24 h PM2.5 concentration was 24.5 µg m−3, while PM10 concentration averaged 78.6 µg m−3 compared to outdoor averages of 20.5 µg m−3 and 72.0 µg m−3. The PM2.5/PM10 ratio was 0.3 both indoors and outdoors. Only 20% of the homes exhibited maximum outdoor PM2.5 concentrations 3.6 times higher than indoor; in 18%, indoor and outdoor levels were similar (0.8–1.2); and 60% of homes recorded indoor maxima up to nine times the outdoor peaks. Elevated indoor PM2.5 was primarily linked with cooking and, to a lesser extent, cleaning activities. Peaks in PM2.5 persisted for 4–8 h before returning to baseline. Ensuring adequate indoor ventilation is critical to maintain indoor air quality below outdoor levels and comply with WHO guidelines, highlighting the need for targeted strategies to reduce indoor exposure in urban homes.

1. Introduction

Air pollution is one of the most crucial public health problems of this century and can lead to premature deaths and other respiratory and cardiovascular diseases, lung cancer, and asthma [1,2], causing 6.7 million deaths globally yearly [3]. Particulate matter (PM) is especially harmful as it poses a significant risk to human health and well-being [4,5], and short-term exposure to PM2.5 (PM with a diameter of 2.5 μm or less) has been linked to cognitive decline, cardiometabolic syndrome, and other health issues [6,7,8]. In urban environments, air pollutants originate from both outdoor and indoor sources. Outdoor pollutants primarily stem from traffic emissions and industrial activities, while indoor pollutants arise from sources such as tobacco smoke, cooking, cleaning agents, and various household products [9]. PM and gaseous pollutants emitted by fossil fuel combustion sources, both mobile and stationary, are considered the most influential outdoor sources. In contrast, many sources related to daily indoor activities significantly contribute to the exposure to multiple components of PM, such as building materials, furniture, personal care products, heaters, household cleaning agents, and various indoor human activities, such as tobacco smoke, cooking, pet dander, burning of candles and incense sticks [10,11,12]. Fine particles in the smoke of one cigarette remain for up to ten hours and are equivalent to those produced during half an hour of cooking [13].
Indoor air quality (IAQ) has garnered significant attention in recent years, particularly as individuals spend a substantial portion of their time indoors—a trend exacerbated by the COVID-19 pandemic. Studies have shown that indoor pollutant concentrations can exceed outdoor levels, posing heightened health risks [14,15]. Susceptible groups are at a greater risk, as they spend a longer time indoors, and children and the elderly have been highlighted as groups disproportionately affected [16,17,18]. According to the WHO, 3.2 million premature deaths are attributed to indoor air pollution [3].
Indoor exposure may be attributed to the generation of indoor particles and the infiltration of outdoor particles [19]. This is of great relevance since, when environmental air quality standards are exceeded, the population is asked to stay indoors, and studies have shown that indoor air can be more polluted than ambient air in some cases [20]. Hence, real-time simultaneous monitoring of indoor and outdoor air quality in home environments is essential to developing strategies to prevent adverse health effects caused by indoor pollutants.
Optical particle counting (OPC) PM instruments, which are generally more affordable than reference/precision monitors, have enabled real-time PM monitoring with high spatiotemporal resolution in both indoor and outdoor environments. However, few studies have utilized OPCs to monitor both indoor and outdoor environments simultaneously, highlighting a gap in current research [21]. This article presents the results of a study conducted in Mexico City (CDMX) that investigates the variability in indoor and outdoor air quality in residential homes across the city. The main objective of this research was to use a relatively low-cost OPC monitor to determine various PM size fractions and concentrations in residential indoor and outdoor locations across the 16 municipalities of Mexico City.

2. Materials and Methods

Mexico City is part of the Mexico City Metropolitan Area (MCMA), a megalopolis with over 20 million inhabitants and a vehicular fleet of 6.5 million [22]. The altitude of Mexico City is 2240 m above MSL; its climate is predominantly sub-humid (87% of the territory), dry and semi-dry (7%), and humid (6%). Meteorology in Mexico City is strongly influenced by its high altitude. The City is a dry region with an average temperature of 18 °C. The lowest temperatures occur during the cold, dry season from November to February, and the highest during the warm, dry season from April to May. Lower particle levels are recorded during the rainy season from June to October [23]. Prevailing winds are from the northwest (NW) and northeast (NE) directions, with a daily pattern. The population is exposed to poorer air quality during the cold-dry season (November to February) with higher O3 and suspended particle concentrations [23]. High O3 and secondary aerosol levels are observed due to high solar radiation and high-pressure systems during the warm, dry season (April to May). Agricultural activities affect air quality during post-harvesting burning (late January to early March), producing large amounts of O3 precursors and particulate matter emissions.
The Mexico City Metropolitan Area has 11 underground lines (metro), seven Metrobus lines (confined buses in specific areas), eight trolleybus lines (buses that use electricity), 103 bus routes (regular and eco buses), and one light train route. In Mexico City, NO2 concentrations have decreased by replacing public transport with electric buses, with Euro VI technology implementing low and ultra-low emissions standards, and restricting vehicular traffic in the city center. Previous studies suggest that vehicular emissions remain a significant contributor to PM2.5 and other pollutants [24].

2.1. Method

Particles Plus model 8301-AQM and 8302-AQM optical particle counters (OPCs) were used to measure concentrations of five particulate matter fractions: PM0.5, PM1, PM2.5, PM5, and PM10. The monitors were programmed for continuous measurements indoors and outdoors in each home, 24 h a day, seven days a week. The devices operate using a light-scattering detection system capable of sizing particles from 0.3 to 25 μm. In addition, they feature sensors for ambient temperature and relative humidity, as well as long-term data storage.
The Particles Plus OPCs were selected due to their combination of portability, affordability, and high temporal resolution, which allows simultaneous measurement of air quality in multiple locations. Their ability to record measurements at minute- or second-level intervals enables the capture of rapid fluctuations in PM concentrations throughout the day, providing insights into short-term exposure peaks that fixed-site monitors may miss [25,26]. Furthermore, these instruments facilitate the identification of indoor pollution hotspots, characterization of pollutant sources, complement fixed-site monitoring networks, and enable personal exposure assessments. Their ease of use also allowed active citizen participation in air quality monitoring in Mexico City, fostering environmental awareness and engagement [27].
The Particles Plus monitors were installed in 38 homes for seven consecutive days, primarily in living rooms and kitchens, covering all 16 municipalities of Mexico City. A total of 81,867 indoor records and 79,087 outdoor records were available after assessing the quality and validity of the monitoring information. Participants maintained a diary of daily activities and completed a questionnaire documenting home characteristics, including flooring type, wall materials, proximity of windows to busy streets, number of inhabitants, and the presence of pets.
Given that low-cost OPCs may exhibit specific limitations in particle detection, performance comparisons were conducted to ensure data reliability [28]. Two levels of calibration were applied [29]: (1) direct comparison of the OPCs with reference-grade monitors and (2) co-location of the OPCs with reference equipment. The reference system was a beta attenuation monitor (BAM), which collects particles on a filter via a controlled airflow over a defined period for precise mass determination.

2.2. Analysis of Particle Concentration Results

Data analysis and visualization were performed using R software version 2023.06.1 [30], with the packages readxl version 1.4.5.9000 [31], lubridate version 1.9.4 [32], dplyr version 1.1.4.9000 [33], latex2exp version 0.9.6 [34], cowplot version 1.1.3 [35], and effsize version 0.8.1 [36].
Hourly and daily PM concentrations were calculated, and correlations between OPC measurements and the reference BAM were assessed. The influence of temperature and relative humidity on PM2.5 concentrations was also evaluated.
For each home, the minimum, maximum, and arithmetic mean concentrations of PM2.5 and PM10 were determined, as well as ratios with respect to PM10, PM2.5, and PM1. Paired t-tests and Cohen’s d analysis [37] were performed to determine if the effect size of the PM fraction ratio and concentration differences is significant and small (0.2 ≤ |d| < 0.5), medium (0.5 ≤ |d| < 0.8), or large (|d| ≥ 0.8). Differences between homes were analyzed with ANOVA and k-means clustering, considering smoking status, cooking habits, ventilation practices, and other household characteristics. This methodology allowed a comprehensive evaluation of indoor and outdoor PM variability and identification of factors contributing to elevated indoor particle levels [38,39,40].

3. Results and Discussion

3.1. Performance of the OPC—Co-Location with a BAM Reference Monitor

The performance comparisons were based on 24 h average PM2.5 concentrations from 19 August to 6 September 2022. Both the OPC monitor and BAM reference data were used to calculate these averages. The results (Figure 1) indicate satisfactory overall performance. The hourly BAM and OPC measurements showed similar diurnal variations at the Instituto de Ciencias de la Atmósfera station, located south of Mexico City. Peaks were observed primarily between 12:00 and 14:00, with additional peaks at 17:00 and 18:00 in some cases.
The hourly PM2.5 average concentration for BAM measurements ranged from 1.0 to 50 µg m−3, with a mean of 13.2 µg m−3, while the OPC ranged from 0.8 to 54.1 µg m−3, averaging 11.4 µg m−3. As shown in Figure 1, the OPC captures the particle concentration peaks observed by BAM; however, it generally underestimates concentrations in 87% of the measurements, with an average difference of 1.8 µg m−3.
Figure 2a shows the scatter plot of the OPC and BAM reference equipment, showing an R2 value of 0.96 for the hourly average PM2.5 concentration. The corresponding RMSE, MAE, and bias were, respectively, 2.7 µg m−3, 2.2 µg m−3, and −1.8 µg m−3, showing a good agreement and a slight underestimation of the OPC monitor (<2 µg m−3). Figure 2b shows the Bland–Altman analysis, where the difference between the OPC and BAM PM2.5 measurements are plotted against their averages. The previous bias value can be observed, as well as the fact that OPC-BAM PM2.5 concentration differences increase with increasing concentrations. The figure shows the 2.5 and 97.5 percentiles, as the differences between the two equipment were found to fail the Kolmogorov–Smirnov and Shapiro–Wilk goodness-of-fit tests for normality.

3.2. Hourly Concentration Results

Figure 3 presents hourly boxplots for both monitoring devices. Hourly measurements for the reference and OPC instruments showed similar diurnal patterns, with geometric mean PM2.5 concentrations of 18.6 and 19.0 µg m−3, respectively. A clear diurnal peak occurred from 10:00 to 17:00, with the highest concentration recorded at midnight. PM2.5 concentrations were generally below the Mexico National Ambient Quality Standard (NAAQS).
BAM measurements exhibited slightly higher mean and median values than the OPC. The frequency distribution of concentrations was similar, though the OPC had a higher percentage of lower concentrations. Specifically, 51–58% of OPC measurements were below 10.0 µg m−3, 29–31% were between 10.1 and 20.0 µg m−3, 3–6% were between 30.0 and 39.9 µg m−3, and less than 3% exceeded 40 µg m−3.
Figure 4 shows histograms of hourly measurements for BAM and OPC, and Figure 5 presents the histogram of differences between the two instruments. Analysis revealed that 56.6% of differences fell between −2.0 and 2.0 µg m−3, indicating good agreement. OPC overestimated concentrations by 2.0–5.0 µg m−3 in 2.6% of cases and by more than 5.0 µg m−3 in 0.5% of measurements. Underestimations were more frequent, with 33.4% of measurements between 2.0 and 5.0 µg m−3 and 6.9% exceeding 5.0 µg m−3.
An hourly analysis was conducted to examine the underestimation and overestimation of OPC in more detail. The hourly variation between the two instruments (Figure 6) shows that the OPC registers lower values than BAM at concentrations below 10 µg m−3. These PM levels were most frequently between 00:00 and 07:00 h and from 18:00 to 23:00 h, which coincides with the highest relative humidity (57.3%) and lowest temperatures (19.8 °C), as shown in Figure 7. Between 19:00 and 15:00, when higher PM2.5 concentrations are observed, the mean difference corresponded to 1.2 µg m−3 (an underestimation by the OPC), with a standard deviation of 1.0 µg m−3, indicating good agreement.
Hayward et al. [41] reported that temperature bias is insignificant in optical counters. In contrast, the RH can cause substantial bias at high levels; secondary ions may exhibit considerable hygroscopic growth, and therefore, their refractive index can lead to a significant overestimation. The OPC has a heated inlet to reduce the influence of RH on the measurements.
Recent evidence further validates this approach, Dinh et al., (2023) [42] demonstrated that cost-effective OPCs coupled with a heated inlet tube maintained comparable PM2.5 measurements to reference-grade instruments, even under high-humidity conditions, except during rain periods, showing low relative errors (<7%) and strong correlation with BAM data.

3.3. Analysis of Size Fractions

Average indoor concentrations of PM0.5, PM1, PM2.5, PM5, and PM10 were, respectively, 10.1, 15.7, 24.5, 48.4, and 78.6 µg m−3, while average outdoor concentrations corresponded to 9.5, 13.9, 20.5, 40.5, and 72.0 µg m−3. Maximum concentrations for each fraction were 86.8, 304.4, 1372.6, 4343.3, and 12,457.3 µg m−3 indoors and 75.9, 220.36, 1046.7, 4386.2 and 13,997.9 µg m−3 outdoors Average concentration ratios with respect to PM10, PM2.5, and PM1 are presented in Figure 8 for indoor and outdoor measurements across homes in Mexico City, showing important standard deviations. PM0.5/PM10, PM1/PM10, PM2.5/PM10, and PM5/PM10 indoors ratios were 19.7%, 28.5%, 40.6%, and 69.3%, respectively, and outdoors ratios were 17.1%, 24.3%, 33.9%, and 62.2%, respectively. PM0.5/PM2.5 and PM1/PM2.5 indoors ratios were 46.1% and 66.5%, respectively, and outdoors 48% and 68%, respectively. Finally, the observed PM0.5/PM1 ratio was 68.3% indoors and 69.9% outdoors.
The observed differences between indoor and outdoor ratios were assessed with a t-test paired by homes, confidence intervals, and a Cohen’s d effect size analysis. The corresponding results are presented in Table 1. All ratios with respect to PM10 were significantly higher indoors than outdoors; they were also observed to be more variable. On the other hand, the ratios with respect to the PM2.5 fraction were significantly lower indoors. The PM0.5/PM1 ratio showed more variation indoors, but the difference was not statistically relevant. Effect size was medium for the PM2.5/PM10 ratio, large for the PM5/PM10 ratio, and small for all other ratios, which may indicate the importance of dust resuspension.
An equivalent analysis on PM fraction concentrations showed that PM2.5 and PM5 are also significantly higher indoors than outdoors, with a small effect size. This fact should be considered when there are exceedances of the air quality standard to prevent high exposure of the residents. For the other concentrations, no significant differences were found between indoor and outdoor concentrations.

3.4. Variations in Indoor and Outdoor PM2.5 Concentrations Across Mexico City Municipalities

In most municipalities, the maximum concentration of PM2.5 was observed to be higher indoors than outdoors, indicating various sources within the home that contribute to this fraction of particles in addition to the ingress of outdoor air. Indoor particles and the variation between indoor samples may be attributed to fluctuations in the type and intensity of indoor activities.
Figure 9 compares maximum indoor and outdoor PM2.5 concentrations in Mexico City. The municipalities in central Mexico City—Benito Juarez, BJ, and Coyoacan, COY, generally exhibited the lowest maximum indoor and outdoor PM2.5 concentrations.
In contrast, municipalities located in the eastern and southern regions of the city—Tlalpan (TLAL), Tláhuac (TLAH), Xochimilco (XOCH), Iztapalapa (IZTAP), and Azcapotzalco (AZC)—recorded maximum indoor levels two to three times higher than outdoor levels. This pattern was consistent with activity logbooks, which documented frequent indoor activities such as cooking, cleaning, and the use of combustion-based heating throughout the day. Only a few houses in Álvaro Obregón (AO), Tláhuac (TLAH), and Cuajimalpa (CUAJ) registered higher maximum concentrations outdoors than indoors. These exceptions may be linked to proximity to industrial zones, construction sites, and major roads with heavy vehicular traffic, which elevate outdoor PM2.5 levels.
The influence of prevailing meteorological conditions and variations in outdoor sources was evident. For most residences, outdoor PM2.5 concentrations were below the Mexican National Ambient Air Quality Standard (NAAQS) of 41.0 µg m−3 [43] for 24 h averages, although exceedances occurred in a few locations. The highest outdoor concentrations were recorded in Xochimilco, Tláhuac, Iztapalapa, and Venustiano Carranza (VC). These elevated values may be attributed to dust emissions from bare soils, unpaved roads, and resuspension from traffic, consistent with findings from other urban environments [44,45]. In Xochimilco and Tláhuac, ongoing building construction and the presence of stored construction materials likely contributed to dust generation. Additionally, poorly maintained structures with deteriorating paint and the presence of livestock in rural household areas may further increase PM2.5 emissions [46].
While the NAAQS value applies to ambient air, it is used here as a benchmark to assess household air quality. The World Health Organization [47] recommends an annual 24 h PM2.5 limit of 15.0 µg m−3 for ambient air and a 25.0 µg m−3 24 h value for combustion-derived indoor pollutants [48]. Indoor PM2.5 concentrations are strongly influenced by the use of solid fuels and low-efficiency stoves for cooking, heating, and lighting, which can produce high emissions from incomplete combustion [49,50].
In our study, 40% of sampled homes had at least one day where the 24 h average indoor PM2.5 concentration exceeded the Mexican NAAQS. Given that the standard will become increasingly stringent in the coming years, targeted interventions, such as improving kitchen ventilation, switching to cleaner fuels, and minimizing high-emission indoor activities, are necessary to prevent exceedances and protect public health.

3.5. Variations in Indoor and Outdoor PM10 Concentrations Across Mexico City Municipalities

Figure 10 compares the maximum indoor and outdoor PM10 concentrations recorded across Mexico City. The results indicate that Álvaro Obregón registered the highest outdoor PM10 levels. In contrast, municipalities such as Azcapotzalco, Coyoacán, Cuauhtémoc, Milpa Alta (MA), and Tlalpan reported continuous indoor activities combined with poor ventilation, often with all windows closed, which resulted in substantially higher maximum indoor concentrations compared to outdoor measurements.
Coyoacán and Benito Juárez, located in the city center, recorded the lowest maximum PM10 concentrations both indoors and outdoors. Conversely, Tlalpan, Tláhuac, and Venustiano Carranza, situated in the southern and eastern parts of the city, showed the highest PM10 concentrations, likely influenced by a combination of outdoor sources (e.g., traffic, unpaved roads, soil erosion) and indoor sources (e.g., cooking, dust resuspension).
Overall, more than 70% of the sampled homes registered high indoor PM10 levels. The lowest concentrations were found in Tlalpan, Coyoacán, Iztacalco (IZTAC), and Cuajimalpa, where residents spent less time indoors and reported fewer indoor activities in their logbooks. This finding highlights the strong influence of human activities and ventilation practices on indoor particulate matter levels, consistent with previous research on indoor–outdoor PM dynamics in urban environments [51].

3.6. Ratio of PM2.5/PM10 Indoors

The PM2.5/PM10 ratio was calculated to evaluate which household activities were predominant. The results showed that 67% of the ratios were ≤0.40, 27% were between 0.41 and 0.59, and 6% were ≥0.6, indicating a higher proportion of larger particles (2.5–10 µm) compared to fine particles.
In the first case (ratio ≤ 0.40), resuspension processes dominate; in the second case (ratio 0.41–0.59), both resuspension and combustion processes contribute; and in the third case (ratio ≥ 0.6), combustion processes dominate. The indoor PM2.5/PM10 ratio suggests that, for most days, dust resuspension is the main contributor, primarily associated with cleaning activities, while combustion processes contribute to a smaller extent.
Indoor concentrations can also be influenced by the house’s location, due to external sources such as vehicular emissions from high-traffic roads, proximity to construction sites, unpaved streets, and street maintenance. Activities such as cooking and cleaning significantly contribute to particle emissions, and the extent of ventilation determines how long elevated concentrations persist indoors.

3.7. Ratio of PM2.5/PM10 Outdoors

The PM2.5/PM10 ratio results showed that dust resuspension dominates most days, primarily associated with unpaved roads with intense vehicular traffic, areas with eroded soils, and construction processes near houses.
The results indicated that 70% of the ratios were ≤0.40, 19% were 0.41–0.59, and 1% were ≥0.6, confirming that resuspension processes dominate most areas [52]. Significant activities impacting the PM2.5/PM10 ratio outdoors include woodworking or painting services, cement industries, paved and unpaved roads with high traffic, construction activities, and landfills.
The findings of this study are consistent with previously reported PM2.5/PM10 ratios: 0.5–0.9 across more than 60 European cities [53], 0.58 indoors and 0.49 outdoors in Nicosia, Cyprus [54], and 0.6–0.66 in Brazilian cities [55].

3.8. Basic Statistics of PM2.5 and PM10 Concentrations Indoors

In Mexico, there are no established domestic indoor NAAQS; however, ambient air quality standards exist for PM10 (annual average of 60.0 µg m−3 and 24 h average of 33.0 µg m−3) and PM2.5 (annual average of 10.0 µg m−3 and 24 h average of 28.0 µg m−3) [43].
Figure 11 presents a box-and-whisker plot illustrating the range of PM10 measurements inside the homes, primarily in kitchens or living rooms. PM10 concentrations were generally similar between kitchens and living rooms, with an average of 78.6 µg m−3, while PM2.5 averaged 24.5 µg m−3. Maximum concentrations were recorded in homes with smokers; for example, house 22 exhibited a peak PM10 concentration of 9031.0 µg m−3 and 1062.1 µg m−3 for PM2.5.
These results suggest that household activities, including cooking, cleaning, and smoking, significantly elevate indoor particulate levels, often exceeding both ambient standards and WHO guidelines [48]. The high variability observed between homes highlights the influence of occupant behavior, ventilation, and proximity to outdoor pollution sources on indoor air quality.

3.9. Effect of Residential Location on Indoor and Outdoor PM

House 18, situated on a busy road in the city center, showed similar PM2.5 concentrations indoors and outdoors, at 30.1 and 30.6 µg m−3, respectively. Similarly to house 21, where the influence of the busy roads affected indoor concentrations.
During the sampling period, two days with high outdoor PM2.5 concentrations were recorded, with indoor and outdoor averages of 36.6 µg m−3 and 38.8 µg m−3, respectively, indicating a significant influence of outdoor pollution on the indoor environment. Similar results were obtained for houses 1, 2, 3, 4, and 6 in Alvaro Obregon, house 7 in Miguel Hidalgo; Houses 9 and 10 in Benito Juarez; Houses 12 and 13 in Coyoacan; House 15 in Gustavo A Madero; House 17 in Iztapalapa; Houses 18 and 19 in Iztapalapa; House 23 in MC; House 25 in Azcapotzalco; Houses 26 and 28 in Tlalpan; and Houses 29, 30 and 31 in Cuajimalpa.
The transport factor (Ft), which accounts for different ventilation modes, was determined to assess the steady-state fraction of outdoor particles that penetrate indoors. The infiltration factor defines the contribution of outdoor particles to indoor concentrations and their persistence in suspension [15]:
Ft = Cin/Cout
Cin (µg m−3) is the indoor PM concentration from outdoors, and Cout is the outdoor PM concentration. Assuming no clean air is present, and infiltration is the only form of ventilation, the transport factor is equivalent to the infiltration factor [10].
The average transport factor for the houses mentioned above was 0.98, showing a ratio similar to that reported in other studies. These values are generally close to unity in residences when there is an impact from outdoors [56]. A higher ratio of 1.59 was determined when activities such as cooking or cleaning were recorded in the houses (3, 4, 5, 8, 16, 20, 24, 27, 32, and 34), with the highest ratio of 3.16 in house 27, located in Tlalpan. This may be due to the complex compounds in the organic fraction of the PM that are emitted from sources such as the fuel used for cooking, the emissions from food, cleaning, and personal care products, among others, compared to oxidation of volatile compounds and their partition to particles [57,58]. Our results compare with other studies. Sonntag et al. [59] found infiltration factors as high as 0.96 in homes using evaporative coolers during wildfire smoke events, reflecting similar indoor penetration under specific pollutant episodes. Su et al. [60] reported daytime infiltration factors around 0.70 ± 0.23 in children’s bedrooms, higher than nighttime, underscoring diurnal variability. Lunderberg et al. [61] demonstrated decreasing infiltration factors during wildfire events and variable seasonal infiltration across climate zones, indicating the impact of occupant behavior and building operation on indoor air quality.
Overall, results indicated that average PM2.5 indoor concentration were two to five times higher than outdoor levels. This finding suggests that indoor activities such as cooking and smoking, combined with the number of occupants, are the primary contributors to poor indoor air quality. This highlights the significance of both residential location and indoor behavior in determining exposure risk.

3.10. Specific Cases

There were specific cases where high PM2.5 concentrations were recorded, such as in the smoker’s house 22 (Figure 12), which had eight family members. Concentrations close to 900.0 µg m−3 were associated with cooking activities and smoking cigarettes with the door closed while sanding wood.
The first event is unusual, as it corresponded to the accidental carbonization of a Mexican tortilla reheated directly on a stove burner without supervision. Such combustion events can release substantial amounts of fine particles within a short time, contributing to extreme indoor PM peaks.
Although the 24 h air quality standard was not exceeded, the inhabitants were exposed to very high short-term concentrations of PM2.5. Acute exposure of this nature, even for short periods, has been linked to increased risks of respiratory symptoms, cardiovascular stress, and exacerbation of asthma. This underscores the importance of considering not only compliance with daily or annual standards but also short-term peak events, which may disproportionately affect vulnerable populations such as children, older adults, and individuals with pre-existing health conditions.
Another noteworthy case is house 27, located in the south of the city. Over three days, elevated PM concentrations were recorded, reaching up to 70.5 µg m−3. These peaks were primarily associated with cooking and cleaning activities and exceeded the standard when compared to the average outdoor concentrations of 16.8 µg m−3.

3.11. Summary of Participating Household (n = 27) Characteristics

The contribution of indoor sources to PM2.5 appears to be more significant than that from outdoor concentrations. This is likely related to a range of indoor activities, as well as factors such as building construction type, type of cooking appliance, number of inhabitants, presence of smokers and pets, among others. Figure 13 summarizes the characteristics of the homes that participated in this study and filled in the questionnaire, highlighting factors that can impact indoor air quality. On average, the houses had three occupants, although a few had five to six occupants. Higher PM2.5 concentrations were observed in homes with more inhabitants, although the difference was not significant.

3.12. Type of Cooking

The type of cooking appliance, such as LPG gas or electricity used in stoves, plays an essential role as it is one of the major sources of indoor PM. In this study, 83% of the houses used LPG gas, and 34% used electricity. Lower PM2.5 concentrations were generally registered in houses using electricity, with an average of 18.6 µg m−3, compared to 24.8 µg m−3 in those using LPG. It has been reported that individuals using gas stoves rather than electric stoves experience more respiratory-related health problems [62].
Long et al. [63] have also reported increased bioactivity of indoor particles and significant variation between indoor samples, which may be attributed to fluctuations in the type and intensity of indoor activities. Air exchange rate may also play an important role, promoting ambient particle infiltration or favoring the accumulation of indoor-generated particles.
Recent studies have further highlighted the impact of cooking appliances on indoor air quality. A study conducted in 2022 found that cooking with gas stoves significantly increased indoor concentrations of nitrogen dioxide (NO2) and fine particulate matter (PM2.5), both of which are associated with respiratory issues [64]. Conversely, transitioning to electric stoves, particularly induction models, has been shown to reduce these pollutants, thereby improving indoor air quality and potentially reducing health risks.
Observed PM fraction concentration and ratios were analyzed with ANOVA and k-means cluster analysis. The presence/absence of each of the characteristics in the questionnaire was codified by 1/0 values for the k-means analysis. No significant differences were found between fraction concentrations or ratios for individual domestic habits and characteristics. Clustering suggested that newer homes may have higher ratios of fine fractions, possibly due to the use of more synthetic materials and greater airtightness. However, due to the small quantity of houses that filled in the questionnaire, as well as the high variability of habit combinations, no firm conclusions could be drawn in this regard. The only significant factor was found to be the geographical location of the house, being more significant for the outdoor concentrations than for indoor concentrations, and more significant for PM fraction ratios than for PM fraction concentrations.

4. Conclusions

Monitoring indoor particulate matter using real-time instruments is essential for assessing health risks and designing effective prevention strategies. Optical particle counters (OPCs) provide a practical solution for continuous indoor air quality monitoring, enabling the evaluation of population exposure in spatial–temporal studies across urban areas.
This study examined the relationship and spatial variation of indoor and outdoor PM of different sizes in 38 houses across 16 municipalities in Mexico City. The performance of the OPC was evaluated against a beta attenuation monitor (BAM), showing a slight underestimation by the OPC compared to the reference instrument.
Indoor/outdoor PM relationships were analyzed to determine the influence of outdoor air on indoor air quality. The results indicate that outdoor PM levels significantly affect indoor concentrations, particularly in homes located near busy roads. On average, indoor PM2.5 concentrations were 2 to 5 times higher than outdoor levels, primarily due to cooking, cleaning activities, smoking, and the number of occupants. Maintaining proper ventilation during cooking is recommended to reduce exposure to cooking-related aerosols.
Furthermore, the PM1 fraction represented more than 60% of PM2.5, suggesting that this fine fraction could be considered for inclusion in the National Ambient Air Quality Standards in Mexico.
Addressing indoor air pollution requires the active involvement of communities and individuals, supported by public education and behavior change initiatives. Understanding the sources of indoor pollutants is essential for developing effective control strategies. Engaging residents in reporting their household activities can help link behaviors with indoor air quality, promoting awareness and encouraging proactive measures. The collaborative use of OPCs can empower households to reduce exposure, thereby improving both individual and collective knowledge of indoor air pollution.

Author Contributions

Conceptualization, E.V., A.W., A.N. and D.M.-F.; methodology, E.V., O.O. and J.E.; software, A.W.; validation, E.V. and A.W.; visualization, A.W.; formal analysis, E.V., A.W. and A.N.; investigation, E.V., A.W., A.N., D.M.-F., O.O., L.B. and J.E.; resources, E.V.; data curation, A.W. and O.O.; writing—original draft preparation, E.V.; writing—review and editing, E.V., A.W., A.N., D.M.-F., L.B. and J.E.; supervision, E.V.; project administration, E.V.; funding acquisition, E.V. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Programa de Apoyo a Proyectos de Investigación e Innovación Tecnológica PAPIIT IA104723, UNAM.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; sample collection, analyses, or data interpretation.

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Figure 1. Comparison of PM2.5 time series for BAM (blue) and OPC (orange) measurements at the Instituto de Ciencias de la Atmósfera. 19 August 2022–5 September 2022.
Figure 1. Comparison of PM2.5 time series for BAM (blue) and OPC (orange) measurements at the Instituto de Ciencias de la Atmósfera. 19 August 2022–5 September 2022.
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Atmosphere 16 01039 g002
Figure 2. Comparison of BAM and OPC PM2.5 measurements at the Instituto de Ciencias de la Atmósfera. 19 August 2022–5 September 2022. (a) Scatterplot, (b) Bland–Altman plot. Green dots correspond to [PM2.5].
Figure 2. Comparison of BAM and OPC PM2.5 measurements at the Instituto de Ciencias de la Atmósfera. 19 August 2022–5 September 2022. (a) Scatterplot, (b) Bland–Altman plot. Green dots correspond to [PM2.5].
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Atmosphere 16 01039 g003
Figure 3. Box and whisker plots for hourly BAM (a) and OPC (b) PM2.5 measurements at the Instituto de Ciencias de la Atmósfera. 19 August 2022–5 September 2022.
Figure 3. Box and whisker plots for hourly BAM (a) and OPC (b) PM2.5 measurements at the Instituto de Ciencias de la Atmósfera. 19 August 2022–5 September 2022.
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Figure 4. Histograms and boxplots for hourly BAM (a) and OPC (b) PM2.5 measurements at the Instituto de Ciencias de la Atmósfera. 19 August 2022–5 September 2022.
Figure 4. Histograms and boxplots for hourly BAM (a) and OPC (b) PM2.5 measurements at the Instituto de Ciencias de la Atmósfera. 19 August 2022–5 September 2022.
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Atmosphere 16 01039 g005
Figure 5. Histogram of differences between hourly BAM and OPC PM2.5 measurements at the Instituto de Ciencias de la Atmósfera. 19 August 2022–5 September 2022.
Figure 5. Histogram of differences between hourly BAM and OPC PM2.5 measurements at the Instituto de Ciencias de la Atmósfera. 19 August 2022–5 September 2022.
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Atmosphere 16 01039 g006
Figure 6. Hourly mean BAM and OPC PM2.5 measurements at the Instituto de Ciencias de la Atmósfera. 19 August 2022–5 September 2022.
Figure 6. Hourly mean BAM and OPC PM2.5 measurements at the Instituto de Ciencias de la Atmósfera. 19 August 2022–5 September 2022.
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Figure 7. Hourly mean temperature and relative humidity measurements at the Instituto de Ciencias de la Atmósfera. 19 August 2022–20 September 2022.
Figure 7. Hourly mean temperature and relative humidity measurements at the Instituto de Ciencias de la Atmósfera. 19 August 2022–20 September 2022.
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Atmosphere 16 01039 g008
Figure 8. Optical Particle Counter particle size distribution profile in residential homes across Mexico City.
Figure 8. Optical Particle Counter particle size distribution profile in residential homes across Mexico City.
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Figure 9. Comparison of maximum indoor and outdoor PM2.5 concentrations in Mexico City.
Figure 9. Comparison of maximum indoor and outdoor PM2.5 concentrations in Mexico City.
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Figure 10. Comparison of maximum indoor and outdoor PM10 concentrations in Mexico City.
Figure 10. Comparison of maximum indoor and outdoor PM10 concentrations in Mexico City.
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Figure 11. Box and whisker plots summarizing PM2.5 and PM10 concentrations for each home. Red bars and house numbers correspond to smoker’s homes.
Figure 11. Box and whisker plots summarizing PM2.5 and PM10 concentrations for each home. Red bars and house numbers correspond to smoker’s homes.
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Atmosphere 16 01039 g012
Figure 12. Time series of PM2.5 concentrations for home 22. Registered particle-releasing activities included smoking cigarettes in the living room with the door closed (events 2, 3, 4, 6, 7), cleaning and cooking on a stove (events 1 and 5), and sanding wood while smoking (event 7).
Figure 12. Time series of PM2.5 concentrations for home 22. Registered particle-releasing activities included smoking cigarettes in the living room with the door closed (events 2, 3, 4, 6, 7), cleaning and cooking on a stove (events 1 and 5), and sanding wood while smoking (event 7).
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Atmosphere 16 01039 g013
Figure 13. Summary of participating household characteristics (n = 27).
Figure 13. Summary of participating household characteristics (n = 27).
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Table 1. Statistical analysis for PM ratio differences across homes in Mexico City.
Table 1. Statistical analysis for PM ratio differences across homes in Mexico City.
DifferenceConfidence IntervalHypothesis TestCohen’s d
MeanSDCIinfCIsupttestp-ValueValueEffect
PM0.5/PM100.02590.05210.00860.04333.0290.0050.39small
PM1/PM100.04260.07200.01860.06663.5960.0010.45small
PM2.5/PM100.06640.09930.03330.09954.0670.0000.63medium
PM5/PM100.07590.09820.04320.10874.7040.0000.81large
PM0.5/PM2.5−0.01930.0476−0.0351−0.0034−2.4640.019−0.33small
PM1/PM2.5−0.01520.0338−0.0265−0.0039−2.7320.010−0.21small
PM0.5/PM1−0.01580.0573−0.03490.0033−1.6800.102−0.39small
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Vega, E.; Wellens, A.; Namdeo, A.; Meza-Figueroa, D.; Ornelas, O.; Entwistle, J.; Bramwell, L. Spatial Variation of PM10 and PM2.5 in Residential Indoor Environments in Municipalities Across Mexico City. Atmosphere 2025, 16, 1039. https://doi.org/10.3390/atmos16091039

AMA Style

Vega E, Wellens A, Namdeo A, Meza-Figueroa D, Ornelas O, Entwistle J, Bramwell L. Spatial Variation of PM10 and PM2.5 in Residential Indoor Environments in Municipalities Across Mexico City. Atmosphere. 2025; 16(9):1039. https://doi.org/10.3390/atmos16091039

Chicago/Turabian Style

Vega, Elizabeth, Ann Wellens, Anil Namdeo, Diana Meza-Figueroa, Octavio Ornelas, Jane Entwistle, and Lindsay Bramwell. 2025. "Spatial Variation of PM10 and PM2.5 in Residential Indoor Environments in Municipalities Across Mexico City" Atmosphere 16, no. 9: 1039. https://doi.org/10.3390/atmos16091039

APA Style

Vega, E., Wellens, A., Namdeo, A., Meza-Figueroa, D., Ornelas, O., Entwistle, J., & Bramwell, L. (2025). Spatial Variation of PM10 and PM2.5 in Residential Indoor Environments in Municipalities Across Mexico City. Atmosphere, 16(9), 1039. https://doi.org/10.3390/atmos16091039

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