Search Results (82)

Heavy metal contamination in urban street dust is often highly heterogeneous, limiting the effectiveness of conventional geostatistical mapping approaches. In Mexico City, previous studies have reported very low spatial autocorrelation for key elements, making interpolation-based methods unsuitable for representing contamination patterns. This study proposes a multiscale cartographic framework to analyze and visualize heavy metal contamination in street dust from 482 sampling sites using the contamination factor (CF) and pollution load index (PLI) at three levels of spatial analysis: (i) city-scale patterns identified through hierarchical clustering of districts based on median CF values, (ii) district-scale variability assessed through statistical comparisons of PLI distributions, and (iii) site-scale identification of contamination hotspots using observed PLI values. Results revealed five contamination clusters and significant differences in pollution load among districts (Kruskal–Wallis, p < 0.05); PLI values in Xochimilco and Tláhuac are significantly lower than in Cuauhtémoc, Gustavo A. Madero, and Magdalena Contreras. Higher contamination levels were concentrated in northern and central districts, whereas lower levels predominated in the south. Site-scale analysis identified localized hotspots associated with transportation infrastructure, industrial areas, and commercial corridors, reflecting the influence of local emission sources. The results demonstrate that contamination patterns operate simultaneously at city, district, and site scales and cannot be adequately represented through interpolation alone. The proposed framework provides a practical approach for visualizing heterogeneous contamination datasets, supporting environmental decision-making, and may apply to other metropolitan regions characterized by weak spatial autocorrelation and localized pollution processes. Full article

Cross-state air pollution can degrade air quality in regions surrounding urban areas. However, assessing health impacts and economic costs requires quantifying annual pollutant transport. This type of annual assessment poses significant technical challenges, including the need to differentiate between external and local pollution, which have different spatial distributions; the large number of required air quality simulations; and the extensive post-processing of results. The Mexico City Metropolitan Area is located within the most polluted airshed in the country and exports large quantities of atmospheric pollutants to neighboring airsheds; however, until now, the annual magnitude of the impact on the population and its associated costs had not been quantified. This paper presents the estimation of potentially avoidable premature mortality and the annual economic cost associated with importing PM_2.5 from the Mexico City Metropolitan Area into the metropolitan areas of Toluca and Cuernavaca. These estimates are based on 8640 simulations of air quality using the Hysplit 5.3.0 model, corresponding to each hour of 360 days in one year. Geoprocessing tools specifically designed in ArcGIS 10.4 were used to automate the calculations of PM_2.5 mass exchange, and the premature mortality and health costs were calculated using the BenMap-CE 1.5.0.4 program. Results indicate that the export of PM_2.5 from the Mexico City Metropolitan Area in 2018 may have resulted in 19,473 potentially avoidable premature deaths in the two recipient metropolitan areas. This impact could represent an annual economic cost of $12,197 million for the Toluca Valley Metropolitan Area and $4140 million for the Cuernavaca Metropolitan Area. Full article

Ecological risk assessment of ecosystems facing anthropogenic pressures informs coastal management. This study evaluated the ecological risk of ecosystems in two coastal municipalities in the Gulf of California, Mexico. The coastal area under study spans 175 km of coastline and includes various ecosystems, as well as the cities of Guaymas and Empalme (~160,000 inhabitants). Ecological risk was assessed by surveying the opinions of experts on local and global activities and influences (climate change), the ecological consequences of hazards, and the resilience (fragmentation) and natural recovery of ecosystems. In addition, potential synergies between human activities and the effects of climate change were identified. The results showed that the main threats are discharges of raw or poorly treated wastewater into the sea, the generation and dumping of garbage, and illegal fishing. Wastewater discharges represent the local threat that interacts most intensively with the effects of climate change. Mangroves, coastal water bodies, and rocky shores face the greatest ecological risk due to continuous exposure to anthropogenic threats, poorly planned urban growth, and industrial development. Approximately 20% of the coastal zone is estimated to correspond to the metropolitan areas of Guaymas and Empalme, where the greatest ecological risk occurs, and these represent opportunities to promote coastal management processes aimed at ecosystem restoration and planned urban development to prevent the loss of coastal ecosystem functions and the services they provide to society. Full article

Tree rings record environmental conditions and can serve as long-term biomonitors of urban pollution. This study evaluated the radial growth and chemical composition of Pinus greggii wood in three urban green areas of Mexico City: San Juan de Aragón Park (SJA), Sierra de Guadalupe State Park (GUAD), and Vivero Coyoacán National Park (COY). Tree ring chemical elements were analyzed at annual resolution for the period 2002 to 2022, and their relationships with atmospheric pollutant concentrations, including nitrogen oxides (NO_x), carbon monoxide (CO), ozone (O₃), and particulate matter (PM), of medium size or smaller than 10 µm, including the fractions PM_2.5 and PM₁₀, were assessed using a spatial scaling approach. Elemental concentrations were determined using X-ray fluorescence (XRF). Statistical analyses included analysis of variance (ANOVA), Theil–Sen trend estimation, and Pearson correlation with lag analysis (up to 3 years). The oldest trees were recorded in COY (52 years), while the youngest were recorded in GUAD (13 years). Distinct temporal patterns in elemental concentrations were detected among sites; for instance, peak concentrations of Fe (307 ppm), Cu (11 ppm), and Zn (51 ppm) occurred in GUAD in 2021, while Pb concentrations declined during 2019–2020 across all three sites. Significant correlations (p < 0.05) were identified between Cu, Fe, Zn, and Pb and the atmospheric pollutants (NO_x, PM_2.5, PM₁₀, O₃). Notably, O₃ showed significant positive correlations with Fe at SJA (up to r = 0.80) and GUAD (up to r = 0.46) with lags ranging from 0 to 3 years, suggesting delayed responses between atmospheric pollution and elemental deposition in tree rings. These findings highlight the sensitivity of P. greggii to urban atmospheric pollution and support its potential as a long-term biomonitoring tool, as well as its importance for informing policies aimed at improving air quality and promoting the sustainable management of urban green spaces. Full article

Urban air quality is a key component of environmental sustainability and public health in large metropolitan areas. Following the substantial but temporary improvements in air quality observed during the COVID-19 lockdowns, it remains unclear whether structural changes in urban air pollution have persisted in the post-pandemic period. This study analyzes the temporal dynamics of major atmospheric pollutants in Mexico City between 2021 and 2024, including CO, NO₂, NO_x, O₃, PM₁₀, PM_2.5, and SO₂, using hourly data from the Mexico City Atmospheric Monitoring System (SIMAT). Annual and monthly median concentrations were computed to reduce the influence of extreme values and short-term pollution episodes. Station-level monotonic trends were evaluated using the non-parametric Mann–Kendall test, complemented by the use of Sen’s slope estimator to quantify the magnitude and direction of change. Absolute and relative changes between 2021 and 2024 were also analyzed to capture incremental variations not reflected by trend significance tests and performed together with hourly monthly analyses to characterize diurnal and seasonal patterns. Results indicate that no statistically significant monotonic trends were detected for any pollutant across the analyzed stations (p > 0.05), suggesting an overall stabilization of air quality levels during the post-pandemic period. Nevertheless, moderate increases in annual median concentrations were observed at specific locations, particularly for PM₁₀, PM_2.5, NO₂, and NO_x, with relative changes ranging from approximately 5% to 35%. Persistent diurnal and seasonal patterns were identified, closely associated with traffic activity, photochemical processes, and meteorological conditions. These findings suggest that, although no robust long-term trends are evident, incremental increases and stable temporal structures remain relevant from a sustainability perspective. Continued monitoring and targeted air quality management strategies are therefore necessary to support long-term urban environmental sustainability. Full article

Urban air quality assessment increasingly relies on spatial interpolation to complement fixed monitoring networks; however, the reliability of geostatistical methods depends strongly on temporal conditions and pollutant characteristics. Despite extensive application, limited attention has been paid to how kriging performance varies across hours of the day and months of the year, particularly when contrasting primary pollutants driven by local emissions with secondary pollutants formed through atmospheric chemistry. This study evaluates the temporal sensitivity of Ordinary Kriging (OK) for mapping urban air pollutants in the Mexico City Metropolitan Area. Using hourly observations from the official air quality monitoring network (2021), we analyze ozone (O₃), a secondary pollutant, and sulfur dioxide (SO₂), a primary pollutant, under representative diurnal and monthly scenarios. Variogram model selection and predictive performance are assessed through leave-one-out cross-validation and external hold-out validation across multiple temporal blocks and months. Results indicate that kriging performance is highly sensitive to both hour of day and month. For O₃, smoother Gaussian variogram structures perform best during peak photochemical conditions, producing coherent regional concentration fields with gradual spatial gradients. In contrast, SO₂ exhibits stronger local variability and sharper spatial gradients, favoring exponential variogram models, particularly under stable morning atmospheric conditions associated with primary emission accumulation. Sensitivity analyses further reveal that no single variogram model is universally optimal and that interpolation accuracy depends more on temporal stratification and pollutant behavior than on variogram form alone. These findings demonstrate that geostatistical interpolation is a valuable tool for urban air quality assessment only when temporal sensitivity and pollutant-specific dynamics are explicitly incorporated. The proposed framework provides practical guidance for the responsible use of interpolated air quality maps, supports sustainable urban monitoring strategies, and contributes to more reliable exposure assessment in megacities with limited sensor coverage. Full article

Background/Objectives: Pediatric palliative care seeks to relieve suffering and improve the quality of life of children with severe conditions and their families. This prospective cohort study assessed changes in quality of life following enrollment in a pediatric palliative care program at a tertiary care center in Mexico and explored factors associated with these changes. Methods: Children with life-limiting or severe disabling conditions were followed at baseline, 3 months, and 6 months. Quality of life was measured using the Pediatric Quality of Life Inventory (PedsQL™) Cancer Module for oncologic patients and the PedsQL™ Family Impact Module for all families. Results: A total of 166 families completed the Family Impact Module questionnaires, and 116 oncologic patients completed the Cancer Module. Mean children’s PedsQL Cancer Module scores improved from 58.9 to 77.9, and family scores improved from 60.1 to 78.8 over six months (both p < 0.001). Families of oncologic patients and those residing outside the Mexico City metropolitan area had lower baseline scores (adjusted differences −9.84, 95% CI: −15.9 to −3.77; and −6.9, 95% CI: −12.38 to −1.44, respectively); however, the latter group showed a greater rate of improvement over time, contrary to our initial hypothesis—survival varied by diagnosis, with longer survival observed in children with neurologic or intracranial conditions. Conclusions: The quality of life of families and pediatric oncologic patients showed improvement over time following enrollment in a specialist pediatric palliative care program in a middle-income setting. Equitable access should be ensured for families affected by chronic conditions, particularly those living beyond major urban areas. Full article

Atmospheric contamination and thermal pollution are two phenomena that impact negatively on the quality of life of residents in a city. Furthermore, exposure to these phenomena has a differential impact on socioeconomic strata, with the poorest being the most affected. Spatial and temporal analyses were conducted of PM_2.5 pollution and air temperature, as well as socioeconomic variable. This characterization revealed that low-income people are more exposed to thermal pollution during the warm season with temperatures up to 32.6 °C, and to PM_2.5 pollution with concentrations up to 205 µgm⁻³ during the cold season, focusing on the eastern part of the Metropolitan Area of Mexico City (MAMC). High temperatures can persist for up to 6 h, while PM_2.5 concentrations can persist for up to 5 h. The social backwardness index is a fixed variable that can change in the long term and is related to thermal pollution. This study will allow us to understand social and environmental vulnerability and, thus, to develop an appropriate mitigation methodology for these two phenomena and their impact on human health, with special attention to environmental justice issues. Full article

This study presents a methodological framework for estimating surface nitrogen dioxide (NO₂) concentrations in Mexico City during 2024. Sentinel-5P satellite observations, ERA5 meteorological variables, and ground measurements from the RAMA were integrated to generate high-resolution estimates through a Random Forest model combined with statistical downscaling. After data cleaning, 3246 aligned records were retained. The model achieved robust performance (R² = 0.9196; RMSE = 6.80 µg/m³; MAE = 4.55 µg/m³), demonstrating its ability to reproduce both spatial and temporal variations in NO₂ across the metropolitan area. These results confirm that machine-learning-based downscaling effectively enhances satellite-derived pollution estimates and provides a reliable tool for urban air quality assessment. Full article

This study aimed to determine carbonyl compound concentrations and assess their potential health risk in indoor air at three homes in different areas of the Mexico City Metropolitan Area (MCMA) during the COVID-19 lockdown. Sampling was conducted from March to April 2021, during the home office confinement period. Average concentrations of formaldehyde, acetaldehyde, acrolein, acetone, propionaldehyde, and butyraldehyde in living rooms ranged from 84.15 to 74.93 μg m⁻³, 66.49 to 50.20 μg m⁻³, 60.01 to 41.35 μg m⁻³, 74.58 to 63.02 μg m⁻³, 10.90 to 6.21 μg m⁻³, and 12.45 to 9.91 μg m⁻³, respectively. In bedrooms, concentrations ranged from 84.76 to 59.70 μg m⁻³, 50.12 to 51.73 μg m⁻³, 59.74 to 37.25 μg m⁻³, 76.62 to 59.72 μg m⁻³, 14.45 to 8.40 μg m⁻³, and 10.72 to 8.82 μg m⁻³, respectively. All measured carbonyls had significant indoor concentrations, exceeding those reported in other studies worldwide. From the statistical analysis, it was found that there were significant differences in carbonyl levels between the studied homes. This suggests diverse and prevalent sources in these environments. E-cigarette vapors clearly increased acrolein levels, and the use of personal care and household products (PCHPs) also contributed to higher carbonyl concentrations indoors. The lifetime cancer risk coefficient (LTCR) and hazard quotient (HQ) values for formaldehyde and acetaldehyde exceeded WHO and US EPA recommendations, indicating increased risks of both carcinogenic and non-carcinogenic effects. Full article

Landfill disposal continues to be the most economically viable municipal solid waste (MSW) management practice in many countries, including Mexico. Landfills are the third-largest source of methane emissions from human activity, a fact that has significant implications for the environment and human health. Due to the difficulty in experimentally quantifying methane emissions, mathematical models have been employed to predict gas emissions. In this work, three first-order decay models were implemented to estimate methane emissions in a landfill located in the metropolitan area of Oaxaca City, Mexico. Each model incorporated a Van’t Hoff–Arrhenius-type approach for calculating the reaction rate constant. Additionally, an uncertainty analysis of the models was presented, applying Monte Carlo simulations with triangular and log-normal distributions. The results show that the simple model exhibited the best predictive performance. For 2020, the simple model estimated 3,488,392.1 m³ of methane at a temperature of 46 °C, 3,509,625.1 m³ of methane at 47 °C, and 3,530,850.2 m³ of methane at 48 °C. The Monte Carlo simulation with a log-normal distribution exhibited more robust and natural process behavior. For the simple model, the mean was 3,486,946.03, the median was 3,487,154.73, and the standard deviation was 212,095.95. The LandGEM model exhibited more linear methane generation behavior, and the uncertainty analysis confirmed that this model had the lowest predictive capability of the three proposed models. Full article

Although access to quality public spaces encourages physical activity (PA), their unequal distribution can exacerbate social inequalities. This study examined the relationship between the availability of spaces for PA and social marginalization in urban Basic Geostatistical Areas (Spanish acronym AGEB) in Mexico, using national databases on urban facilities and demographics. AGEB calculated space densities for PA, and the bivariate Moran’s I and LISA methodology were followed to identify global and local patterns. A weak negative spatial correlation was detected (I = −0.006) at the national level, with clusters of AGEBs with low marginalization and low density of spaces for PA. Contrasts were observed among the three most populous metropolitan areas: Mexico City and Guadalajara showed significant positive correlations, while Monterrey exhibited a different pattern. The urban furniture earmarked for PA is insufficient and its distribution reproduces socio-spatial inequalities. The dynamics differ across metropolises, underscoring the need for localized policies that will prioritize the provision of public spaces in marginalized communities. Full article

The petrochemical industry emits large amounts of nitrogen oxides (NOx). It is the second source of volatile organic compounds (VOCs), which, through photochemical reactions, can form tropospheric ozone (O₃) and, together with geographic and meteorological conditions, influence the spatial and temporal behavior of pollution. The objective of this study is to assess the influence of air pollutants NOx, NO₂, and NO, as well as meteorological factors on O₃ concentration levels in the city of Cadereyta, Nuevo Leon, which is characterized by its petrochemical industry as part of the metropolitan area of Monterrey, Mexico. The data were analyzed using the Spearman’s correlation coefficient, identifying a weak-to-moderate negative association between NOx and NO₂ with O₃ in the spring season and a null relationship in the summer. However, the autumn and winter seasons observed a moderate to strong negative relationship. Subsequently, a multiple linear regression analysis determined the influence of air pollutants NOx, NO₂, and NO, as well as meteorological factors on O₃ concentration levels. In this sense, when the concentration levels of NOx and NO₂ decrease, the concentration of O₃ will increase proportionally according to the season of the year. The prediction model obtains a coefficient of determination (R²) of 0.60 and a root-mean-square error (RMSE) value of 0.0096 ppm. In the prediction model, all variables presented a significant effect on the interpretation of the dependent variable. The independent variables that provided the most significant variation in the concentration levels of O₃ were NOx and NO₂. Full article

The main anthropogenic sources of air pollution in big cities are vehicular traffic and industrial activities. The emissions of primary pollutants are produced directly from the combustion of fossil fuels of vehicles and industry, whilst the secondary pollutants, such as tropospheric ozone ($O_3$), are produced from precursors like Carbon monoxide ($CO$), among others, and meteorological factors such as radiation. In this study, we analyze the time series of $\mathrm{CO}$ and ${\mathrm{O}}_3$ concentrations monitored by the RAMA program between 2019 and 2023 in the southwest of the Mexico City Metropolitan Area, encompassing the COVID-19 lockdown period declared from March to September–October 2020. After removing cyclic patterns and normalizing the data, we applied informational and topological methods to investigate variability changes in the concentration time series, particularly in response to the lockdown. Following the onset of lockdown measures in March 2020—which led to a significant reduction in industrial activity and vehicular traffic—the informational quantities $N_X$ and Fisher Information Measure (FIM) for $\mathrm{CO}$ revealed significant shifts during the lockdown, while these metrics remained stable for ${\mathrm{O}}_3$. Also, the coefficient of variation of the degree $C{V}_k$, which was defined for the network constructed for each series by the Visibility Graph, showed marked changes for $\mathrm{CO}$ but not for ${\mathrm{O}}_3$. The combined informational and topological analysis highlighted distinct underlying structures: $\mathrm{CO}$ exhibited localized, intermittent emission patterns leading to greater structural complexity, while ${\mathrm{O}}_3$ displayed smoother, less organized variability. Also, the temporal variation of the FIM and $N_X$ provides a means to monitor the evolving statistical behavior of the $CO$ and $O_3$ time series over time. Finally, the Visibility Graph (VG) method shows a behavioral trend similar to that shown by the informational quantifiers, revealing a significant change during the lockdown for $CO$, although remaining almost stable for $O_3$. Full article

Background: Non-communicable diseases (NCDs) are a leading cause of mortality globally, contributing significantly to the burden on healthcare systems. Understanding the spatiotemporal patterns of NCD mortality is crucial for identifying vulnerable populations and regions at high risk. Objectives: Here, we evaluated the spatiotemporal patterns of NCD mortality in the Metropolitan Area of the Valley of Mexico (MAVM) from 2000 to 2019 for five International Classification of Diseases chapters (4, 5, 6, 9, and 10) at two spatial scales: the municipal level and metropolitan region. Methods: Mortality rates were calculated for the total population and stratified by sex and age groups at both spatial scales. In addition, the relative risk (RR) of mortality was estimated to identify vulnerable population groups and regions with a high risk of mortality, using women and the 25–34 age group as reference categories for population-level analysis, and the overall MAVM mortality rate as the reference for municipal-level analysis. Results: Mortality trends showed that circulatory-system diseases (Chapter 9) are emerging as a concerning health issue, with 45 municipalities showing increasing mortality trends, especially among older adults. Respiratory-system diseases (Chapter 10), mental and behavioral disorders (Chapter 5) and nervous-system diseases (Chapter 6) predominantly did not exhibit a consistent general mortality trend. However, upon disaggregating by sex and age groups, specific negative or positive trends emerged at the municipal level for some of these chapters or subgroups. Endocrine, nutritional, and metabolic diseases (Chapter 4) showed a complex pattern, with some age groups presenting increasing mortality trends, and 52 municipalities showing increasing trends overall. The RR showed men and older age groups (≥35 years) exhibiting higher mortality risks. The temporal trend of RR allowed us to identify spatial mortality hotspots mainly in chapters related to circulatory, endocrine, and respiratory diseases, forming four geographical clusters in Mexico City that show persistent high risk of mortality. Conclusions: The spatiotemporal analysis highlights municipalities and vulnerable populations with a consistently elevated mortality risk. These findings emphasize the need for monitoring NCD mortality patterns at both the municipal and metropolitan levels to address disparities and guide the implementation of health policies aimed at reducing mortality risk in vulnerable populations. Full article