Gender Gaps in Birth Weight Across Latin America: Evidence on the Role of Air Pollution

Aparicio, Gabriela; Gerardino, Maria Paula; Rangel, Marcos A.

doi:10.1007/s41996-019-00043-z

Cited by 4 publications

(4 citation statements)

References 22 publications

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“…While air quality has improved in some large cities in Latin America and the Caribbean (LAC), it remains a serious threat to the health of citizens in LAC (Jorquera et al, 2019). Over 100 million people in LAC are exposed to air quality that does not meet the World Health Organization's Air Quality Guidelines (Aparicio et al, 2019)geographically concentrated industrialization, and biomass burning. This paper focuses on a relatively understudied consequence of this pollution-intensive development process: its gender impact.…”

Section: Trends and Levels Of Ambient Air And Noise Pollution In Lati...mentioning

confidence: 99%

Inclusive Cities: Healthy Cities for All

Duren¹,

Benítez²,

Bonilla³

et al. 2022

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Inter-American Development Bank Felipe Herrera Library Inclusive cities: healthy cities for all / edited by Nora Libertun. p. cm. -(IDB Monograph ; 984) Includes bibliographic references. 1. Environmental health-Latin America. 2. Housing and health-Latin America. 3. Health risk assessment-Latin America. 4. Urban policy-Latin America. I. Libertun, Nora. II.Inter-American Development Bank. Housing and Urban Development Division. III. Series. IDB-MG-984This publication reports on some of the health challenges facing cities. It aims to serve as a guide for public managers and decision makers optimize the great potential of cities to improve the well-being of those who reside in the cities of Latin America and the Caribbean. It is organized in two parts. The first part, Health Inequalities in Latin American Cities, focuses on identifying the ways in which social inequality has led to negative health outcomes, in order to make visible the relevance of the challenge of inequality and the urgency to grapple with it. The second part, Urban Policies for Healthy Cities, focuses on how cities can contribute to improving the health standards in their population.The publication addresses critical issues for urban health, such as the interdependence between physical-social factors and health, the relationship between urban characteristics and the incidence of COVID-19, the connections between social inequality and exposure to pollution environment, the relationship between urban planning and gender violence, the power of urban interventions -such as public transport and social housing-to improve health indicators, and the relevance of having good data to improve the accessibility of health systems. All the contributions in this book are based on data and rigorous research, and present real cases of the cities of the region.

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Section: Trends and Levels Of Ambient Air And Noise Pollution In Lati...mentioning

confidence: 99%

Inclusive Cities: Healthy Cities for All

Duren¹,

Benítez²,

Bonilla³

et al. 2022

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“…GDAS data were used to support the HYSPLIT model [33,34]. GDAS data include temperature, air pressure, relative humidity, and horizontal and vertical wind speeds.…”

Section: Global Data Assimilation System (Gdas) Datamentioning

confidence: 99%

Aerosol Evolution and Influencing Factor Analysis during Haze Periods in the Guanzhong Area of China Based on Multi-Source Data

et al. 2022

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Aerosols suspended in the atmosphere negatively affect air quality and public health and promote global climate change. The Guanzhong area in China was selected as the study area. Air quality data from July 2018 to June 2021 were recorded daily, and 19 haze periods were selected for this study. The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used to simulate the air mass transport trajectory during this haze period to classify the formation process. The spatial distribution of the aerosol optical depth (AOD) was obtained by processing Moderate-resolution Imaging Spectroradiometer (MODIS) data using the dark target (DT) method. Three factors were used to analyze the AOD spatial distribution characteristics based on the perceptual hashing algorithm (PHA): GDP, population density, and topography. Correlations between aerosols and the wind direction, wind speed, and precipitation were analyzed using weather station data. The research results showed that the haze period in Guanzhong was mainly due to locally generated haze (94.7%). The spatial distribution factors are GDP, population density, and topography. The statistical results showed that wind direction mainly affected aerosol diffusion in Guanzhong, while wind speed (r = −0.63) and precipitation (r = −0.66) had a significant influence on aerosol accumulation and diffusion.

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“…For example, Guevara-Luna et al ( 2018) [18] correlated MODIS AOD at 1 • resolution (111 km) downloaded from the GIOVANNI online interface and the monthly average surface measurements (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015) for three Colombian cities (Bogota, Medellin, and Bucaramanga) and obtained correlation coefficient R values of over 0.4. Aparicio et al (2019) [16] used MODIS MOD09CMA AOD data with a spatial resolution of 0.05 • as a pollution proxy in La Paz, Lima, and Bogota, by qualitatively comparing AOD and PM 10 data.…”

Section: Contributions In the Context Of Latin Americamentioning

confidence: 99%

“…The AOD-PM 2.5 relationship, however, varies in space and time and needs to be specifically assessed for different regions and seasons [15]. Despite Latin America being one of the most urbanized, populated, and polluted regions in the world, where most cities lack robust ground-based PM 2.5 monitoring networks, few studies have examined the relationship between AOD and PM 2.5 in this region (e.g., [16][17][18][19][20]).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of MODIS Aerosol Optical Depth and Surface Data Using an Ensemble Modeling Approach to Assess PM2.5 Temporal and Spatial Distributions

et al. 2021

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The use of statistical models and machine-learning techniques along satellite-derived aerosol optical depth (AOD) is a promising method to estimate ground-level particulate matter with an aerodynamic diameter of ≤2.5 μm (PM2.5), mainly in urban areas with low air quality monitor density. Nevertheless, the relationship between AOD and ground-level PM2.5 varies spatiotemporally and differences related to spatial domains, temporal schemes, and seasonal variations must be assessed. Here, an ensemble multiple linear regression (EMLR) model and an ensemble neural network (ENN) model were developed to estimate PM2.5 levels in the Monterrey Metropolitan Area (MMA), the second largest urban center in Mexico. Four AOD-SDSs (Scientific Datasets) from MODIS Collection 6 were tested using three spatial domains and two temporal schemes. The best model performance was obtained using AOD at 0.55 µm from MODIS-Aqua at a spatial resolution of 3 km, along meteorological parameters and daily scheme. EMLR yielded a correlation coefficient (R) of ~0.57 and a root mean square error (RMSE) of ~7.00 μg m−3. ENN performed better than EMLR, with an R of ~0.78 and RMSE of ~5.43 μg m−3. Satellite-derived AOD in combination with meteorology data allowed for the estimation of PM2.5 distributions in an urban area with low air quality monitor density.

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Gender Gaps in Birth Weight Across Latin America: Evidence on the Role of Air Pollution

Cited by 4 publications

References 22 publications

Inclusive Cities: Healthy Cities for All

Inclusive Cities: Healthy Cities for All

Aerosol Evolution and Influencing Factor Analysis during Haze Periods in the Guanzhong Area of China Based on Multi-Source Data

Evaluation of MODIS Aerosol Optical Depth and Surface Data Using an Ensemble Modeling Approach to Assess PM2.5 Temporal and Spatial Distributions

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