A spatiotemporal reconstruction of daily ambient temperature using satellite data in the Megalopolis of Central Mexico from 2003 to 2019

Abstract

While weather stations generally capture near‐surface ambient air temperature (Ta) at a high temporal resolution to calculate daily values (i.e., daily minimum, mean, and maximum Ta), their fixed locations can limit their spatial coverage and resolution even in densely populated urban areas. As a result, data from weather stations alone may be inadequate for Ta‐related epidemiology particularly when the stations are not located in the areas of interest for human exposure assessment. To address this limitation in the Megalopolis of Central Mexico (MCM), we developed the first spatiotemporally resolved hybrid satellite‐based land use regression Ta model for the region, home to nearly 30 million people and includes Mexico City and seven more metropolitan areas. Our model predicted daily minimum, mean, and maximum Ta for the years 2003–2019. We used data from 120 weather stations and Land Surface Temperature (LST) data from NASA's MODIS instruments on the Aqua and Terra satellites on a 1 × 1 km grid. We generated a satellite‐hybrid mixed‐effects model for each year, regressing Ta measurements against land use terms, day‐specific random intercepts, and fixed and random LST slopes. We assessed model performance using 10‐fold cross‐validation at withheld stations. Across all years, the root‐mean‐square error ranged from 0.92 to 1.92 K and the R2 ranged from .78 to .95. To demonstrate the utility of our model for health research, we evaluated the total number of days in the year 2010 when residents ≥65 years old were exposed to Ta extremes (above 30°C or below 5°C). Our model provides much needed high‐quality Ta estimates for epidemiology studies in the MCM region.

Intermediate- and long-term associations between air pollution and ambient temperature and glycated hemoglobin levels in women of child bearing age

“…The ambient temperature model also performed very well, with annual cross-validated R 2 ′s ranging from 0.78 to 0.95. More in depth information about the approach of the models can be found in existing publications ( Gutíerrez-Avila et al, 2021 ; Just et al, 2015 ). Exposure estimates were assigned to study participants based on their geocoded home addresses and the corresponding PM 2.5 and ambient temperature 1-km 2 exposure grids from our satellite-based models.…”

“…Although some existing studies have investigated the relationship between PM 2.5 and glucose metabolism ( Moody et al, 2019 ; Yitshak-Sade et al, 2016 ), few studies have explored the association between ambient temperature and glucose metabolism ( Tien et al, 2016 ; Tseng et al, 2005 ), and even fewer have studied these two exposures simultaneously with formally defined time-metrics of exposure. We were able to address this gap with satellite-based models our group has recently developed over the study area ( Gutíerrez-Avila et al, 2021 ; Just et al, 2015 ), which provides detailed spatiotemporal resolved PM 2.5 and ambient temperature levels that can used to calculate discrete exposure windows of interest.…”

Prediction of daily mean and one-hour maximum PM2.5 concentrations and applications in Central Mexico using satellite-based machine-learning models

“…The model used a grid of 7745 square cells, 927 m on a side, in a global sinusoidal projection (the same one used for NASA's MODIS products). This study area and its grid was a subset of that considered in our ambient temperature model for Central Mexico [20]. We built the subset by finding the largest connected set of cells in the Valley of Mexico with all cells ≤3 km above sea level (Fig.…”

Research on Innovation Non-Equilibrium of Chinese Urban Agglomeration Based on SOM Neural Network

“…The concept of urban agglomerations originated from Western urban research and was proposed by the French geographer Gottman, known as the "father of urban agglomerations", and the word is "Megalopolis" (Gottmann (1964) [24]). The field of urban agglomeration research is wide, and includes studying the impact of urban agglomerations on the environment (Spivak, Loktev et al (2016) [25]; Gutiérrez-Avila, Arfer et al (2021) [26]; Ebel (2020) [27]; Terando, Costanza et al (2014) [28]); the spatial relevance of urban agglomerations (Lang and Knox (2009) [29]; Berry and Okulicz-Kozaryn (2012) [20]); and the definition of urban agglomeration size (Porfiryev and Bobylev (2018) [30]; Fang and Yu (2017) [31]). Studies in Russia, Mexico, and the United States all proved the effectiveness and practicality of urban agglomeration research.…”