Distribution of PM2.5 Air Pollution in Mexico City: Spatial Analysis with Land-Use Regression Model

Hinojosa-Baliño, Israel; Infante, Oscar; Vallejo, Maité

doi:10.3390/app9142936

Cited by 19 publications

(8 citation statements)

References 48 publications

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“…Wang et al also found that PD exerts a remarkable effect on PM 2.5 concentrations [ 18 ]. Hinojosa et al indicated that population was correlated with PM 2.5 concentration using data from Mexico City [ 47 ]. The results of the current study demonstrate that anthropogenic emissions are the key factors leading to significant increases in PM 2.5 concentrations in North China between 2000 and 2017.…”

Section: Discussionmentioning

confidence: 99%

Determinant Powers of Socioeconomic Factors and Their Interactive Impacts on Particulate Matter Pollution in North China

Zhang

Lin

Cheng

et al. 2021

IJERPH

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Severe air pollution has significantly impacted climate and human health worldwide. In this study, global and local Moran’s I was used to examine the spatial autocorrelation of PM2.5 pollution in North China from 2000–2017, using data obtained from Atmospheric Composition Analysis Group of Dalhousie University. The determinant powers and their interactive effects of socioeconomic factors on this pollutant are then quantified using a non-linear model, GeoDetector. Our experiments show that between 2000 and 2017, PM2.5 pollution globally increased and exhibited a significant positive global and local autocorrelation. The greatest factor affecting PM2.5 pollution was population density. Population density, road density, and urbanization showed a tendency to first increase and then decrease, while the number of industries and industrial output revealed a tendency to increase continuously. From a long-term perspective, the interactive effects of road density and industrial output, road density, and the number of industries were amongst the highest. These findings can be used to develop the effective policy to reduce PM2.5 pollution, such as, due to the significant spatial autocorrelation between regions, the government should pay attention to the importance of regional joint management of PM2.5 pollution.

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Section: Discussionmentioning

confidence: 99%

Determinant Powers of Socioeconomic Factors and Their Interactive Impacts on Particulate Matter Pollution in North China

Zhang

Lin

Cheng

et al. 2021

IJERPH

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“…De igual forma nuestros hallazgos coinciden con lo descrito por Hinojosa et al, (2019), en un estudio realizado en la ciudad de México a partir de 37 exposiciones personales. Por lo contrario, en la estación RF1 se logró identificar uno de los valores medios más altos (10.82 µg/m 3 ), lo que indica que existe contaminación a causa de estas partículas por lo que son motivo de preocupación debido a su alta dimensión, así como a sus características fisicoquímicas.…”

Section: Discussionunclassified

“…Cabe indicar también que el PM 2.5 µm y 10 µm son partículas que al ingresar a la caja torácica causan efectos agudos y crónicos en la salud de las personas (Salinas, 2012). Por ello vale la pena mostrar que más del 80% de la población en países en vías de desarrollo están expuestos a niveles peligrosos de calidad del aire, que con frecuencia los contaminantes exceden los límites máximos permisibles (Hinojosa et al, 2019).…”

Section: Introductionunclassified

Evaluación de la concentración y distribución espacial de material particulado en los campus de la UNACH - Riobamba.

et al. 2021

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El objetivo de la presente investigación fue realizar un análisis comparativo de los niveles de concentraciones de Material Particulado Volátil y Sedimentable (PMV y PMS), en los tres campus de la Universidad Nacional de Chimborazo. El PMV se midió con el equipo DustTrakTM II por 24 días y el PMS se determinó por medio de un sistema de monitoreo pasivo. La caracterización de metales asociados al PM fue mediante espectroscopia de fotones rayos X dispersados (EDX). De acuerdo a la legislación ecuatoriana y la OMS, las concentraciones de PM2.5 en las estaciones de monitoreo no exceden los limites máximo permisibles. El PM10 presento un valor medio de 10 μg/m3, máximo de 15 μg/m3 y un mínimo de 6 μg/m3. Los metales asociados al PM son 11 elementos de origen antropogénico y natural, donde la principal fuente responsable es el tráfico, industria, construcción, partículas de polvo y ceniza. La distribución espacial del PM, determinó que los focos de emisión son de escala local; con un radio crítico de arrastre y deposición de 50 m. Con ello se demuestra que la modelación de PM, a través de herramientas SIG, brinda resultados favorables para estudiar el comportamiento de las partículas.

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“…The development of analytical approaches to determine and assess environmental pollution data with the best spatiotemporal granularity is key in the design and implementation of proper intervention policy, for example, regarding urbanization process, over-population, personal monitors, indoor environments, vehicle fleet, peak hours, and green areas in the city, among others (33). The PAHO Regional Plan on Urban Air Quality and Health 2000-2009 has proposed efficient systems for air pollution health impact monitoring.…”

Section: Intervention Policy and Assessmentmentioning

confidence: 99%

Spatio-Temporal Representativeness of Air Quality Monitoring Stations in Mexico City: Implications for Public Health

Baca-Lopez

Fresno

Espinal-Enríquez

et al. 2021

Front. Public Health

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Assessment of the air quality in metropolitan areas is a major challenge in environmental sciences. Issues related include the distribution of monitoring stations, their spatial range, or missing information. In Mexico City, stations have been located spanning the entire Metropolitan zone for pollutants, such as CO, NO2, O3, SO2, PM2.5, PM10, NO, NOx, and PMCO. A fundamental question is whether the number and location of such stations are adequate to optimally cover the city. By analyzing spatio-temporal correlations for pollutant measurements, we evaluated the distribution and performance of monitoring stations in Mexico City from 2009 to 2018. Based on our analysis, air quality evaluation of those contaminants is adequate to cover the 16 boroughs of Mexico City, with the exception of SO2, since its spatial range is shorter than the one needed to cover the whole surface of the city. We observed that NO and NOx concentrations must be taken into account since their long-range dispersion may have relevant consequences for public health. With this approach, we may be able to propose policy based on systematic criteria to locate new monitoring stations.

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Distribution of PM2.5 Air Pollution in Mexico City: Spatial Analysis with Land-Use Regression Model

Cited by 19 publications

References 48 publications

Determinant Powers of Socioeconomic Factors and Their Interactive Impacts on Particulate Matter Pollution in North China

Determinant Powers of Socioeconomic Factors and Their Interactive Impacts on Particulate Matter Pollution in North China

Evaluación de la concentración y distribución espacial de material particulado en los campus de la UNACH - Riobamba.

Spatio-Temporal Representativeness of Air Quality Monitoring Stations in Mexico City: Implications for Public Health

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