Predicting fine-scale daily NO2 over Mexico city using an ensemble modeling approach

In recent years, there has been growing interest in developing air pollution prediction models to reduce exposure measurement error in epidemiologic studies. However, efforts for localized, fine-scale prediction models have been predominantly focused in the United States and Europe. Furthermore, the availability of new satellite instruments such as the TROPOsopheric Monitoring Instrument (TROPOMI) provides novel opportunities for modeling efforts. We estimated daily ground-level nitrogen dioxide (NO2) concentrations in the Mexico City Metropolitan Area at 1-km2 grids from 2005 to 2019 using a four-stage approach. In stage 1 (imputation stage), we imputed missing satellite NO2 column measurements from the Ozone Monitoring Instrument (OMI) and TROPOMI using the random forest (RF) approach. In stage 2 (calibration stage), we calibrated the association of column NO2 to ground-level NO2 using ground monitors and meteorological features using RF and extreme gradient boosting (XGBoost) models. In stage 3 (prediction stage), we predicted the stage 2 model over each 1-km2 grid in our study area, then ensembled the results using a generalized additive model (GAM). In stage 4 (residual stage), we used XGBoost to model the local component at the 200-m2 scale. The cross-validated R2 of the RF and XGBoost models in stage 2 were 0.75 and 0.86 respectively, and 0.87 for the ensembled GAM. Cross-validated root-mean-squared error (RMSE) of the GAM was 3.95 μg/m3. Using novel approaches and newly available remote sensing data, our multi-stage model presented high cross-validated fits and reconstructs fine-scale NO2 estimates for further epidemiologic studies in Mexico City

Predicting fine-scale daily NO₂ over Mexico city using an ensemble modeling approach

In recent years, there has been growing interest in developing air pollution prediction models to reduce exposure measurement error in epidemiologic studies. However, efforts for localized, fine-scale prediction models have been predominantly focused in the United States and Europe. Furthermore, the availability of new satellite instruments such as the TROPOsopheric Monitoring Instrument (TROPOMI) provides novel opportunities for modeling efforts. We estimated daily ground-level nitrogen dioxide (NO₂) concentrations in the Mexico City Metropolitan Area at 1-km2 grids from 2005 to 2019 using a four-stage approach. In stage 1 (imputation stage), we imputed missing satellite NO₂ column measurements from the Ozone Monitoring Instrument (OMI) and TROPOMI using the random forest (RF) approach. In stage 2 (calibration stage), we calibrated the association of column NO₂ to ground-level NO₂ using ground monitors and meteorological features using RF and extreme gradient boosting (XGBoost) models. In stage 3 (prediction stage), we predicted the stage 2 model over each 1-km2 grid in our study area, then ensembled the results using a generalized additive model (GAM). In stage 4 (residual stage), we used XGBoost to model the local component at the 200-m2 scale. The cross-validated R2 of the RF and XGBoost models in stage 2 were 0.75 and 0.86 respectively, and 0.87 for the ensembled GAM. Cross-validated root-mean-squared error (RMSE) of the GAM was 3.95 μg/m3. Using novel approaches and newly available remote sensing data, our multi-stage model presented high cross-validated fits and reconstructs fine-scale NO₂ estimates for further epidemiologic studies in Mexico City

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

Exploring links between greenspace and sudden unexpected death: A spatial analysis

Greenspace has been increasingly recognized as having numerous health benefits. However, its effects are unknown concerning sudden unexpected death (SUD), commonly referred to as sudden cardiac death, which constitutes a large proportion of mortality in the United States. Because greenspace can promote physical activity, reduce stress and buffer air pollutants, it may have beneficial effects for people at risk of SUD, such as those with heart disease, hypertension, and diabetes mellitus. Using several spatial techniques, this study explored the relationship between SUD and greenspace. We adjudicated 396 SUD cases that occurred from March 2013 to February 2015 among reports from emergency medical services (EMS) that attended out-of-hospital deaths in Wake County (central North Carolina, USA). We measured multiple greenspace metrics in each census tract, including the percentages of forest, grassland, average tree canopy, tree canopy diversity, near-road tree canopy and greenway density. The associations between SUD incidence and these greenspace metrics were examined using Poisson regression (non-spatial) and Bayesian spatial models. The results from both models indicated that SUD incidence was inversely associated with both greenway density (adjusted risk ratio [RR] = 0.82, 95% credible/ confidence interval [CI]: 0.69–0.97) and the percentage of forest (adjusted RR = 0.90, 95% CI: 0.81–0.99). These results suggest that increases in greenway density by 1 km/km2 and in forest by 10% were associated with a decrease in SUD risk of 18% and 10%, respectively. The inverse relationship was not observed between SUD incidence and other metrics, including grassland, average tree canopy, near-road tree canopy and tree canopy diversity. This study implies that greenspace, specifically greenways and forest, may have beneficial effects for people at risk of SUD. Further studies are needed to investigate potential causal relationships between greenspace and SUD, and potential mechanisms such as promoting physical activity and reducing stress

Neuronal hemoglobin affects dopaminergic cells' response to stress

Hemoglobin (Hb) is the major protein in erythrocytes and carries oxygen (O2) throughout the body. Recently, Hb has been found synthesized in atypical sites, including the brain. Hb is highly expressed in A9 dopaminergic (DA) neurons of the substantia nigra (SN), whose selective degeneration leads to Parkinson's disease (PD). Here we show that Hb confers DA cells' susceptibility to 1-methyl-4-phenylpyridinium (MPP(+)) and rotenone, neurochemical cellular models of PD. The toxic property of Hb does not depend on O2 binding and is associated with insoluble aggregate formation in the nucleolus. Neurochemical stress induces epigenetic modifications, nucleolar alterations and autophagy inhibition that depend on Hb expression. When adeno-associated viruses carrying \u3b1- and \u3b2-chains of Hb are stereotaxically injected into mouse SN, Hb forms aggregates and causes motor learning impairment. These results position Hb as a potential player in DA cells' homeostasis and dysfunction in PD. Copyright The Author(s) 201

Parkinson’s Disease Prevalence and Proximity to Agricultural Cultivated Fields

The risk for developing Parkinson’s disease (PD) is a combination of multiple environmental and genetic factors. The Negev (Southern Israel) contains approximately 252.5 km2 of agricultural cultivated fields (ACF). We aimed to estimate the prevalence and incidence of PD and to examine possible geographical clustering and associations with agricultural exposures. We screened all “Clalit” Health Services members in the Negev (70% of the population) between the years 2000 and 2012. Individual demographic, clinical, and medication prescription data were available. We used a refined medication tracer algorithm to identify PD patients. We used mixed Poisson models to calculate the smoothed standardized incidence rates (SIRs) for each locality. We identified ACF and calculate the size and distance of the fields from each locality. We identified 3,792 cases of PD. SIRs were higher than expected in Jewish rural localities (median SIR [95% CI]: 1.41 [1.28; 1.53] in 2001–2004, 1.62 [1.48; 1.76] in 2005–2008, and 1.57 [1.44; 1.80] in 2009–2012). Highest SIR was observed in localities located in proximity to large ACF (SIR 1.54, 95% CI 1.32; 1.79). In conclusion, in this population based study we found that PD SIRs were higher than expected in rural localities. Furthermore, it appears that proximity to ACF and the field size contribute to PD risk

The effect of prenatal temperature and PM2.5 exposure on birthweight: Weekly windows of exposure throughout the pregnancy

Background: Birthweight is a strong predictor of normal growth, healthy development, and survival. Several studies have found associations between temperature, fine particulate matter (PM2.5), and birth weight. However, the relevant timing of exposures varies between studies and is yet unclear. Therefore, we assessed the difference in term birthweight (TBW) associated with weekly exposure to temperature and PM2.5 throughout 37 weeks of gestation. Methods: We included all singleton live term births in Massachusetts, U.S between 2004 and 2015 (n = 712,438). Weekly PM2.5 and temperature predictions were estimated on a 1 km grid from satellite-based models. We utilized a distributed lag nonlinear model (DLNM) to estimate the difference in TBW associated with weekly exposures from the last menstrual period to 37 weeks of gestation. Results: We found a nonlinear association with prenatal temperature exposure. Larger effects were observed in warmer temperatures, where higher temperatures were negatively associated with TBW. Temperature effects were larger in the first and final weeks of gestation. We observed a negative difference in TBW associated with PM2.5 exposure. Overall, a 1 µg/m3 increase in prenatal exposure was associated with 3.9 g lower TBW (95% CI −5.0 g; −2.9 g). PM2.5 effects were larger in the final weeks of gestation. Conclusion: We found heat and PM2.5 exposure to be related to lower TBW. Our findings suggest that women are more susceptible to both exposures towards the end of pregnancy. Susceptibility to heat was higher in the initial weeks of pregnancy as well. These critical windows of susceptibility can be communicated to pregnant women during routine prenatal visits to increase awareness and target interventions to reduce exposures