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

XIS-temperature: A daily spatiotemporal machine-learning model for air temperature in the contiguous United States

The challenge of reconstructing air temperature for environmental applications is to accurately estimate past exposures even where monitoring is sparse. We present XGBoost-IDW Synthesis for air temperature (XIS-Temperature), a high-resolution machine-learning model for daily minimum, mean, and maximum air temperature, covering the contiguous US from 2003 through 2023. XIS uses remote sensing (land surface temperature and vegetation) along with a parsimonious set of additional predictors to make predictions at arbitrary points, allowing the estimation of address-level exposures. We built XIS with a computationally tractable workflow for extensibility to future years, and we used weighted evaluation to fairly assess performance in sparsely monitored regions. The weighted root mean square error (RMSE) of predictions in site-level cross-validation for 2023 was 1.78 K for the minimum daily temperature, 1.19 K for the mean, and 1.48 K for the maximum. We obtained higher RMSEs in earlier years with fewer ground monitors. Comparing to three leading gridded temperature models in 2021 at thousands of private weather stations not used in model training, XIS had at most 60% of the mean square error for the minimum temperature and 93% for the maximum. In a national application, we report a stronger relationship between summertime minimum temperature and social vulnerability with XIS than with the other models. Thus, XIS-Temperature has potential for reconstructing important environmental exposures, and its predictions have applications in environmental justice and human health.

Prenatal and early life exposure to fine particulate matter and telomere length in early childhood

BACKGROUND

Telomere length is a biomarker of molecular aging that may be impacted by air pollution exposure starting in utero. We aimed to examine the association between prenatal and early life exposure to fine particulate matter (PM2.5) and leukocyte telomere length (LTL) in children and explore sex differences.

METHODS

Analyses included 384 mother-child pairs enrolled in the Programming Research in Obesity, Growth, and Environmental Stressors (PROGRESS) birth cohort in Mexico City. Exposure to PM2.5 was estimated at the residential level using a satellite based spatio-temporally resolved prediction model. Average relative LTL was measured in DNA isolated from blood collected at age 4-6 years using quantitative real-time polymerase chain reaction. Linear regression models were used to examine the association between average PM2.5 across pregnancy, individual trimesters, first postnatal year, and LTL. Models were adjusted for maternal age and education at enrollment, prenatal environmental tobacco smoke exposure, child sex, age, and body mass index z-score at LTL measurement. Effect modification by sex was investigated with interaction terms and stratification.

RESULTS

In trimester specific models, we found an association between 2nd trimester PM2.5 and elongated LTL (β: 4.34, 95%CI [0.42, 8.42], per 5 μg/m3 increase). There was suggestive effect modification by sex on average 2nd trimester PM2.5 with stronger associations seen in females compared to males (β: 7.12, [95%CI: 0.98, 13.6] and β: 1.43 [95%CI: -3.46, 6.57]) per 5 μg/m3 increase respectively.

CONCLUSION

Second trimester PM2.5 levels were associated with changes in LTL in early childhood. Understanding temporal and sex differences in PM2.5 exposure may provide insights into telomere dynamics over early life.

Windows of susceptibility and joint effects of prenatal and postnatal ambient air pollution and temperature exposure on asthma and wheeze in Mexican children

INTRODUCTION

Prenatal and early-life exposure to air pollution and extreme temperatures are associated with childhood asthma and wheeze. However, potential windows of susceptibility and their sex-specific and interactive effects have not been fully elucidated. We aimed to identify critical windows of susceptibility and evaluate sex-specific effects in these associations, and evaluate exposure interactions.

METHODS

We analyzed data from 468 mother-child pairs enrolled in the PROGRESS birth cohort in Mexico City. Daily residential levels of PM2.5, NO2, and temperature were generated from our validated spatiotemporally resolved models from conception to age 4 years. Childhood asthma and wheeze outcomes were collected at 4-6 and 7-8 years. Distributed lag nonlinear models (DLNMs) were used to identify susceptible windows for prenatal weekly-specific and postnatal monthly-specific associations of air pollution and temperature with respiratory outcomes adjusting for covariates. To evaluate sex-specific effects, DLNMs were stratified. Joint effects were assessed using relative excess risk due to interaction and attributable proportion.

RESULTS

Mid-gestation was a critical window for both PM2.5 (weeks 20-28, cumulative OR: 1.18 [95% CI: 1.01, 1.37]; weeks 19-26, cumulative OR: 1.18 [95% CI: 1.02, 1.36]) and NO2 (weeks 18-25, cumulative OR: 1.16 [95% CI: 1.02, 1.31]) exposure, associated with higher odds of wheeze. Postnatal exposure to PM2.5 and NO2 during the first year of life was also linked to higher odds of wheeze. The warmer and colder temperatures showed mixed effects on respiratory outcomes. We observed a synergistic interaction between high PM2.5 and high temperature exposure during the first year of life, associated with higher odds of current wheeze. The associations of prenatal air pollution and temperature exposure with respiratory outcomes were more pronounced in males.

CONCLUSIONS

Early-life air pollution exposure contributes to the development of childhood asthma and wheeze, while exposure to temperature showed mixed associations with respiratory outcomes.

Association between prenatal and childhood PM2.5 exposure and preadolescent anxiety and depressive symptoms

Background

Fine particulate matter (PM2.5) exposure has been linked to anxiety and depression in adults; however, there is limited research in the younger populations, in which symptoms often first arise.

Methods

We examined the association between early-life PM2.5 exposure and symptoms of anxiety and depression in a cohort of 8-11-year-olds in Mexico City. Anxiety and depressive symptoms were assessed using the Spanish versions of the Revised Children's Manifest Anxiety Scale and Children's Depression Inventory. Daily PM2.5 was estimated using a satellite-based exposure model and averaged over several early and recent exposure windows. Linear and logistic regression models were used to estimate the change in symptoms with each 5-µg/m3 increase in PM2.5. Models were adjusted for child's age, child's sex, maternal age, maternal socioeconomic status, season of conception, and temperature.

Results

Average anxiety and depressive symptom T-scores were 51.0 (range 33-73) and 53.4 (range 44-90), respectively. We observed consistent findings for exposures around the fourth year of life, as this was present for both continuous and dichotomized anxiety symptoms, in both independent exposure models and distributed lag modeling approaches. This window was also observed for elevated depressive symptoms. An additional consistent finding was for PM2.5 exposure during early pregnancy in relation to both clinically elevated anxiety and depressive symptoms, this was seen in both traditional and distributed lag modeling approaches.

Conclusion

Both early life and recent PM2.5 exposure were associated with higher mental health symptoms in the child highlighting the role of PM2.5 in the etiology of these conditions.

Recent ambient temperature and fine particulate matter (PM2.5) exposure is associated with urinary kidney injury biomarkers in children

BACKGROUND

Limited research has examined associations between exposure to ambient temperature, air pollution, and kidney function or injury during the preadolescent period. We examined associations between exposure to ambient temperature and particulate matter with aerodynamic diameter ≤ 2.5 μm (PM2.5) with preadolescent estimated glomerular filtration rate (eGFR) and urinary kidney injury biomarkers.

METHODS

Participants included 437 children without cardiovascular or kidney disease enrolled in the Programming Research in Obesity, Growth, Environment and Social Stressors birth cohort study in Mexico City. eGFR and urinary kidney injury biomarkers were assessed at 8-12 years. Validated satellite-based spatio-temporal models were used to estimate mean daily temperature and PM2.5 levels at each participant's residence 7- and 30-days prior to the date of visit. Linear regression and distributed lag nonlinear models (DLNM) were used to examine associations between daily mean temperature and PM2.5 exposure and kidney outcomes, adjusted for covariates.

RESULTS

In single linear regressions, higher seven-day average PM2.5 was associated with higher urinary alpha-1-microglobulin and eGFR. In DLNM analyses, higher temperature exposure in the seven days prior to date of visit was associated with a decrease in urinary cystatin C of -0.56 ng/mL (95 % confidence interval (CI): -1.08, -0.04) and in osteopontin of -0.08 ng/mL (95 % CI: -0.15, -0.001). PM2.5 exposure over the seven days prior to date of visit was associated with an increase in eGFR of 1.77 mL/min/1.73m2 (95 % CI: 0.55, 2.99) and urinary cystatin C of 0.19 ng/mL (95 % CI: 0.03, 0.35).

CONCLUSIONS

Recent exposure to ambient temperature and PM2.5 were associated with increased and decreased urinary kidney injury biomarkers that may reflect subclinical glomerular or tubular injury in children. Further research is required to assess environmental exposures and worsening subclinical kidney injury across development.

Short-term exposure to PM2.5 and 1.5 million deaths: a time-stratified case-crossover analysis in the Mexico City Metropolitan Area

BACKGROUND

Satellite-based PM2.5 predictions are being used to advance exposure science and air-pollution epidemiology in developed countries; including emerging evidence about the impacts of PM2.5 on acute health outcomes beyond the cardiovascular and respiratory systems, and the potential modifying effects from individual-level factors in these associations. Research on these topics is lacking in low and middle income countries. We aimed to explore the association between short-term exposure to PM2.5 with broad-category and cause-specific mortality outcomes in the Mexico City Metropolitan Area (MCMA), and potential effect modification by age, sex, and SES characteristics in such associations.

METHODS

We used a time-stratified case-crossover study design with 1,479,950 non-accidental deaths from the MCMA for the period of 2004-2019. Daily 1 × 1 km PM2.5 (median = 23.4 μg/m3; IQR = 13.6 μg/m3) estimates from our satellite-based regional model were employed for exposure assessment at the sub-municipality level. Associations between PM2.5 with broad-category (organ-system) and cause-specific mortality outcomes were estimated with distributed lag conditional logistic models. We also fit models stratifying by potential individual-level effect modifiers including; age, sex, and individual SES-related characteristics namely: education, health insurance coverage, and job categories. Odds ratios were converted into percent increase for ease of interpretation.

RESULTS

PM2.5 exposure was associated with broad-category mortality outcomes, including all non-accidental, cardiovascular, cerebrovascular, respiratory, and digestive mortality. A 10-μg/m3 PM2.5 higher cumulative exposure over one week (lag06) was associated with higher cause-specific mortality outcomes including hypertensive disease [2.28% (95%CI: 0.26%-4.33%)], acute ischemic heart disease [1.61% (95%CI: 0.59%-2.64%)], other forms of heart disease [2.39% (95%CI: -0.35%-5.20%)], hemorrhagic stroke [3.63% (95%CI: 0.79%-6.55%)], influenza and pneumonia [4.91% (95%CI: 2.84%-7.02%)], chronic respiratory disease [2.49% (95%CI: 0.71%-4.31%)], diseases of the liver [1.85% (95%CI: 0.31%-3.41%)], and renal failure [3.48% (95%CI: 0.79%-6.24%)]. No differences in effect size of associations were observed between age, sex and SES strata.

CONCLUSIONS

Exposure to PM2.5 was associated with non-accidental, broad-category and cause-specific mortality outcomes beyond the cardiovascular and respiratory systems, including specific death-causes from the digestive and genitourinary systems, with no indication of effect modification by individual-level characteristics.

Urban Stress and its Association with Symptoms of Depression, Fatigue, and Sleep Disruption in Women in Mexico City

Women in urban neighborhoods often face disproportionately higher levels of environmental and social stressors; however, the health effects from urban stressors remains poorly understood. We aimed to evaluate the association between urban stress and symptoms of depression, fatigue, and sleep disruption in a cohort of 460 women in Mexico City. To assess urban stress, women were administered the Urban Annoyances (Nuisances Environnementales) scale. Six constructs were summarized to create an overall index. Depressive symptoms were assessed using the Edinburgh Depression Scale; the Patient-Reported Outcomes Information System scales were used to assess sleep disruption and fatigue. Linear regression models were used to estimate the association with continuous symptoms comparing women with high urban stress to those with lower levels. Models were adjusted for socioeconomic status, education, age, social support, and previous depressive symptoms. High urban stress was associated with greater depressive symptoms (β: 1.77; 95%CI: 0.83, 2.71), fatigue (β: 2.47; 95%CI: 0.87, 4.07), and sleep disruption (β: 2.14; 95%CI: 0.54, 3.73). Urban stress plays an important role in women's psychological and physical health, highlighting the importance of including these measures in environmental health studies. Urban interventions, such as promoting alternative transport options, should additionally be addressed to improve health of urban populations.

Associations between early-life exposure to PM2.5 and reductions in childhood lung function in two North American longitudinal pregnancy cohort studies

Data integration of epidemiologic studies across different geographic regions can provide enhanced exposure contrast and statistical power to examine adverse respiratory effects of early-life exposure to particulate matter <2.5 microns in diameter (PM2.5). Methodological tools improve our ability to combine data while more fully accounting for study heterogeneity.

Methods

Analyses included children enrolled in two longitudinal birth cohorts in Boston, Massachusetts, and Mexico City. Propensity score matching using the 1:3 nearest neighbor with caliper method was used. Residential PM2.5 exposure was estimated from 2 months before birth to age 6 years using a validated satellite-based spatiotemporal model. Lung function was tested at ages 6-11 years and age, height, race, and sex adjusted z scores were estimated for FEV1, FVC, FEF25-75%, and FEV1/FVC. Using distributed lag nonlinear models, we examined associations between monthly averaged PM2.5 levels and lung function outcomes adjusted for covariates, in unmatched and matched pooled samples.

Results

In the matched pooled sample, PM2.5 exposure between postnatal months 35-44 and 35-52 was associated with lower FEV1 and FVC z scores, respectively. A 5 µg/m3 increase in PM2.5 was associated with a reduction in FEV1 z score of 0.13 (95% CI = -0.26, -0.01) and a reduction in FVC z score of 0.13 (95% CI = -0.25, -0.01). Additionally PM2.5 during postnatal months 23-39 was associated with a reduction in FEF25-75% z score of 0.31 (95% CI = -0.57, -0.05).

Conclusions

Methodological tools enhanced our ability to combine multisite data while accounting for study heterogeneity. Ambient PM2.5 exposure in early childhood was associated with lung function reductions in middle childhood.

Daily exposure to PM2.5 and 1.5 million deaths: A time-stratified case-crossover analysis in the Mexico City Metropolitan Area

Background

Satellite-based PM2.5 predictions are being used to advance exposure science and air-pollution epidemiology in developed countries; including emerging evidence about the impacts of PM2.5 on acute health outcomes beyond the cardiovascular and respiratory systems, and the potential modifying effects from individual-level factors in these associations. Research on these topics is lacking in Latin America.

Methods

We used a time-stratified case-crossover study design with 1,479,950 non-accidental deaths from Mexico City Metropolitan Area for the period of 2004-2019. Daily 1×1 km PM2.5 (median=23.4 μg/m3; IQR=13.6 μg/m3) estimates from our satellite-based regional model were employed for exposure assessment at the sub-municipality level. Associations between PM2.5 with broad-category (organ-system) and cause-specific mortality outcomes were estimated with distributed lag conditional logistic models. We also fit models stratifying by potential individual-level effect modifiers including; age, sex, and individual SES-related characteristics namely: education, health insurance coverage, and job categories.

Results

PM2.5 exposure was associated with higher total non-accidental, cardiovascular, cerebrovascular, respiratory, and digestive mortality. A 10-μg/m3 PM2.5 higher cumulative exposure over one week (lag06) was associated with higher cause-specific mortality outcomes including hypertensive disease [2.28% (95%CI: 0.26%-4.33%)], acute ischemic heart disease [1.61% (95%CI: 0.59%-2.64%)], other forms of heart disease [2.39% (95%CI: -0.35%-5.20%)], hemorrhagic stroke [3.63% (95%CI: 0.79%-6.55%)], influenza and pneumonia [4.91% (95%CI: 2.84%-7.02%)], chronic respiratory disease [2.49% (95%CI: 0.71%-4.31%)], diseases of the liver [1.85% (95%CI: 0.31%-3.41%)], and renal failure [3.48% (95%CI: 0.79%-6.24%)]. No differences in effect size of associations were observed between SES strata.

Conclusions

Exposure to PM2.5 was associated with mortality outcomes beyond the cardiovascular and respiratory systems, including specific death-causes from the digestive and genitourinary systems, with no indications of effect modification by individual SES-related characteristics.

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

BACKGROUND

Machine-learning algorithms are becoming popular techniques to predict ambient air PM_2.5 concentrations at high spatial resolutions (1 × 1 km) using satellite-based aerosol optical depth (AOD). Most machine-learning models have aimed to predict 24 h-averaged PM_2.5 concentrations (mean PM_2.5) in high-income regions. Over Mexico, none have been developed to predict subdaily peak levels, such as the maximum daily 1-h concentration (max PM_2.5).

OBJECTIVE

Our goal was to develop a machine-learning model to predict mean PM_2.5 and max PM_2.5 concentrations in the Mexico City Metropolitan Area from 2004 through 2019.

METHODS

We present a new modeling approach based on extreme gradient boosting (XGBoost) and inverse-distance weighting that uses AOD, meteorology, and land-use variables. We also investigated applications of our mean PM_2.5 predictions that can aid local authorities in air-quality management and public-health surveillance, such as the co-occurrence of high PM_2.5 and heat, compliance with local air-quality standards, and the relationship of PM_2.5 exposure with social marginalization.

RESULTS

Our models for mean and max PM_2.5 exhibited good performance, with overall cross-validated mean absolute errors (MAE) of 3.68 and 9.20 μg/m³, respectively, compared to mean absolute deviations from the median (MAD) of 8.55 and 15.64 μg/m³. In 2010, everybody in the study region was exposed to unhealthy levels of PM_2.5. Hotter days had greater PM_2.5 concentrations. Finally, we found similar exposure to PM_2.5 across levels of social marginalization.

SIGNIFICANCE

Machine learning algorithms can be used to predict highly spatiotemporally resolved PM_2.5 concentrations even in regions with sparse monitoring.

IMPACT

Our PM_2.5 predictions can aid local authorities in air-quality management and public-health surveillance, and they can advance epidemiological research in Central Mexico with state-of-the-art exposure assessment methods.

Persistently high incidence rates of childhood acute leukemias from 2010 to 2017 in Mexico City: A population study from the MIGICCL

Introduction

Over the years, the Hispanic population living in the United States has consistently shown high incidence rates of childhood acute leukemias (AL). Similarly, high AL incidence was previously observed in Mexico City (MC). Here, we estimated the AL incidence rates among children under 15 years of age in MC during the period 2010-2017.

Methods

The Mexican Interinstitutional Group for the Identification of the Causes of Childhood Leukemia conducted a study gathering clinical and epidemiological information regarding children newly diagnosed with AL at public health institutions of MC. Crude age incidence rates (cAIR) were obtained. Age-standardized incidence rates worldwide (ASIRw) and by municipalities (ASIRm) were calculated by the direct and indirect methods, respectively. These were reported per million population <15 years of age; stratified by age group, sex, AL subtypes, immunophenotype and gene rearrangements.

Results

A total of 903 AL cases were registered. The ASIRw was 63.3 (cases per million) for AL, 53.1 for acute lymphoblastic leukemia (ALL), and 9.4 for acute myeloblastic leukemia. The highest cAIR for AL was observed in the age group between 1 and 4 years (male: 102.34 and female: 82.73). By immunophenotype, the ASIRw was 47.3 for B-cell and 3.7 for T-cell. The incidence did not show any significant trends during the study period. The ASIRm for ALL were 68.6, 66.6 and 62.8 at Iztacalco, Venustiano Carranza and Benito Juárez, respectively, whereas, other municipalities exhibited null values mainly for AML.

Conclusion

The ASIRw for childhood AL in MC is among the highest reported worldwide. We observed spatial heterogeneity of rates by municipalities. The elevated AL incidence observed in Mexican children may be explained by a combination of genetic background and exposure to environmental risk factors.

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

BACKGROUND

Air pollution has been linked to obesity while higher ambient temperatures typically reduce metabolic demand in a compensatory manner. Both relationships may impact glucose metabolism, thus we examined the association between intermediate- and long-term exposure to fine particulate matter (PM2.5) and ambient temperature and glycated hemoglobin(HbA1c), a longer-term marker of glucose control.

METHODS

We assessed 3-month, 6-month, and 12-month average air pollution and ambient temperature at 1-km2 spatial resolution via satellite remote sensing models (2013-2019), and assessed HbA1c at four, six, and eight years postpartum in women enrolled in the Programming Research in Obesity, Growth, Environment and Social Stressors (PROGRESS) cohort based in Mexico City. PM2.5 and ambient temperature were matched to participants' addresses and confirmed by GPS tracker. Using linear mixed-effects models, we examined the association between 3-month, 6-month, and 12-month average PM2.5 and ambient temperature with repeated log-transformed HbA1c values. All models included a random intercept for each woman and were adjusted for calendar year, season, and individual-level confounders (age, marital status, smoking, alcohol consumption level, and education level).

RESULTS

We analyzed 1,265 HbA1c measurements of 484 women. Per 1 µg/m3 increase in 3-month and 6-month PM2.5, HbA1c levels increased by 0.28% (95% confidence interval (95 %CI): 0.14, 0.42%) and 0.28% (95 %CI: 0.04, 0.52%) respectively. No association was seen for 12-month average PM2.5. Per 1 °C increase in ambient temperature, HbA1c levels decreased by 0.63% (95 %CI: -1.06, -0.21%) and 0.61% (95 %CI: -1.08, -0.13%), while the 12-month average again is not associated with HbA1c.

CONCLUSIONS

Intermediate-term exposure to PM2.5 and ambient temperature are associated with opposing changes in HbA1c levels, in this region of high PM2.5 and moderate temperature fluctuation. These effects, measurable in mid-adult life, may portend future risk of type 2 diabetes and possible heart disease.

Merging framework for estimating daily surface air temperature by integrating observations from multiple polar-orbiting satellites

Reconstructing spatially continuous surface air temperature (SAT) is of great significance to climate and environmental studies. Substantial efforts have been made to estimate daily SAT based on land surface temperature (LST) derived from polar-orbiting satellites. However, previous studies are nearly all limited to estimating daily SAT based on MODIS LST from NASA's Terra or Aqua by applying different statistical learning methods. Various satellites from earth observation missions, particularly the missions for meteorological satellites, are capable of acquiring thermal infrared observations, but its implications for SAT estimation are significantly ignored. In this study, for the first time, we proposed a merging framework for estimating daily mean SAT by integrating LST datasets from multiple polar-orbiting satellites, including Metop-B from EUMETSAT's Polar System (EPS), SNPP and JPSS-1 from NOAA's Joint Polar Satellites System (JPSS), and Terra and Aqua from NASA's EOS. This study is also the first to explore the estimating of daily SAT based on LST derived from the meteorological satellites in EPS and JPSS. The framework integrates 10 estimation models based on different LST from the five satellites and generates daily merged SAT by averaging the daily SAT estimates from the models. Here we show that the framework significantly improves the spatial coverage of daily SAT estimates for cloud-free areas by an overall increase of 39% with respect to the mean coverage of the LST datasets from the five satellites. Daily coverage of the merged SAT from the framework is nearly all above 75% with an average of 91%. Compared to the SAT estimated from MODIS LST, overall increases in the coverage of daily SAT are 37%-51%. Estimation models in the framework all achieved comparable and satisfactory predicative performances with an average RMSE of 1.7-1.9 K for sample-based cross-validation, and 1.9-2.2 K for site-based cross-validation.

A 1-km hourly air-temperature model for 13 northeastern U.S. states using remotely sensed and ground-based measurements

BACKGROUND

Accurate and precise estimates of ambient air temperatures that can capture fine-scale within-day variability are necessary for studies of air temperature and health.

METHOD

We developed statistical models to predict temperature at each hour in each cell of a 927-m square grid across the Northeast and Mid-Atlantic United States from 2003 to 2019, across ~4000 meteorological stations from the Integrated Mesonet, using inputs such as elevation, an inverse-distance-weighted interpolation of temperature, and satellite-based vegetation and land surface temperature. We used a rigorous spatial cross-validation scheme and spatially weighted the errors to estimate how well model predictions would generalize to new cell-days. We assess the within-county association of temperature and social vulnerability in a heat wave as an example application.

RESULTS

We found that a model based on the XGBoost machine-learning algorithm was fast and accurate, obtaining weighted root mean square errors (RMSEs) around 1.6 K, compared to standard deviations around 11.0 K. We found similar accuracy when validating our model on an external dataset from Weather Underground. Assessing predictions from the North American Land Data Assimilation System-2 (NLDAS-2), another hourly model, in the same way, we found it was much less accurate, with RMSEs around 2.5 K. This is likely due to the NLDAS-2 model's coarser spatial resolution, and the dynamic variability of temperature within its grid cells. Finally, we demonstrated the health relevance of our model by showing that our temperature estimates were associated with social vulnerability across the region during a heat wave, whereas the NLDAS-2 showed a much weaker association.

CONCLUSION

Our high spatiotemporal resolution air temperature model provides a strong contribution for future health studies in this region.

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

Different indicators, such as the number of patent applications, the number of grants, and the patent conversion rate, were proposed in this study to analyze the issue of innovation imbalance within and between urban agglomerations from a new perspective. First, a preliminary analysis of the current state of innovation and development of China’s nine urban agglomerations was conducted. Then the Theil index, widely used in equilibrium research, was employed to measure the overall innovation gap of China’s urban agglomerations. The study innovatively used the self-organizing feature map to identify the correlation characteristics of the innovation and development within China’s urban agglomerations and visualize them through Geographic Information Science. The research findings show that the hierarchical differentiation of the innovation and development of China’s urban agglomerations is becoming increasingly clear, and that the imbalance in regional innovation development is pronounced. The imbalance in innovation development within urban agglomerations is more significant than the imbalance in innovation development among urban agglomerations. The analysis indicated that a possible cause is the crowding effect and administrative standard effect of the central city. The key to addressing this problem is promoting innovative and coordinated development between regions.