Satellite-based Model Research Articles

Low ambient temperature and incident myocardial infarction with or without obstructive coronary arteries: a Chinese nationwide study.

Although non-optimum ambient temperature is a major non-traditional risk factor for acute myocardial infarction, there is no prior knowledge on whether non-optimum ambient temperature could differentially affect myocardial infarction with obstructive coronary artery disease (MI-CAD) and myocardial infarction with non-obstructive coronary arteries (MINOCA). Using the Chinese Cardiovascular Association database-Chest Pain Center Registry, a nationwide, time-stratified, case-crossover investigation was conducted from 2015 to 2021. Meteorological data were obtained from an established satellite-based model, and daily exposures were assigned according to the onset of myocardial infarction in each patient. A conditional logistic regression model combined with distributed lag non-linear models (10 days) was used to estimate the exposure-response relationships. A total of 83 784 MINOCA patients and 918 730 MI-CAD patients were included. The risk of MINOCA and MI-CAD associated with low temperature occurred at lag 2 day and lasted to 1 week. Extremely low temperature was associated with a substantially greater odds ratio (OR) of MINOCA [OR 1.58, 95% confidence interval (CI) 1.31-1.90] than MI-CAD (unmatched: OR 1.32, 95% CI 1.23-1.43; equally matched by age and sex: OR 1.25, 95% CI 1.04-1.50), compared with the corresponding reference temperatures (30°C, 35°C, and 30°C). Stronger associations were observed for patients who were aged ≥65 years, female, or resided in the south. There was no significant difference for the impacts of high temperature on MINOCA and MI-CAD. This nationwide study highlights the particular susceptibility of MINOCA patients to ambient low temperature compared with that of MI-CAD patients.

Ambient particulate matter and chronic obstructive pulmonary disease mortality: a nationwide, individual-level, case-crossover study in China

Short-term exposure to particulate matter air pollution has been associated with the exacerbations of COPD, but its association with COPD mortality was not fully elucidated. We aimed to assess the association between short-term particulate matter exposure and the risk of COPD mortality in China using individual-level data. We derived 2.26 million COPD deaths from a national death registry database in Chinese mainland between 2013 and 2019. Exposures to fine particulate matter (PM2.5) and coarse particulate matter (PM2.5-10) were assessed by satellite-based models of a 1×1km resolution and assigned to each individual based on residential address. The associations of PM2.5 and PM2.5-10 with COPD mortality were examined using a time-stratified case-crossover design and conditional logistic regressions with distributed lag models. We further conducted stratified analyses by age, sex, education level, and season. Short-term exposures to both PM2.5 and PM2.5-10 were associated with increased risks of COPD mortality. These associations appeared and peaked on the concurrent day, attenuated and became nonsignificant after 5 or 7 days, respectively. The exposure-response curves were approximately linear without discernible thresholds. An interquartile range increase in PM2.5 and PM2.5-10 concentrations was associated with 4.23% (95% CI: 3.75%, 4.72%) and 2.67% (95% CI: 2.18%, 3.16%) higher risks of COPD mortality over lag 0-7d, respectively. The associations of PM2.5 andPM2.5-10 attenuated slightly but were still significant in the mutual-adjustment models. A larger association of PM2.5-10 was observed in the warm season. This individual-level, nationwide, case-crossover study suggests that short-term exposure to PM2.5 and PM2.5-10 might act as one of the environmental risk factors for COPD mortality. This study is supported by the National Key Research and Development Program of China (2023YFC3708304 and 2022YFC3702701), the National Natural Science Foundation of China (82304090 and 82030103), the 3-year Action Plan for Strengthening the Construction of the Public Health System in Shanghai (GWVI-11.2-YQ31), and the Science and Technology Commission of Shanghai Municipality (21TQ015).

Long-Term Exposure to Fine Particulate Matter and Fasting Blood Glucose and Diabetes in 20 Million Chinese Women of Reproductive Age.

Evidence of the associations between fine particulate matter (PM2.5) and diabetes risk from women of reproductive age, in whom diabetes may have adverse long-term health effects for both themselves and future generations, remains scarce. We therefore examined the associations of long-term PM2.5 exposure with fasting blood glucose (FBG) level and diabetes risk in women of reproductive age in China. This study included 20,076,032 women age 20-49 years participating in the National Free Preconception Health Examination Project in China between 2010 and 2015. PM2.5 was estimated using a satellite-based model. Multivariate linear and logistic regression models were used to examine the associations of PM2.5 exposure with FBG level and diabetes risk, respectively. Diabetes burden attributable to PM2.5 was estimated using attributable fraction (AF) and attributable number. PM2.5 showed monotonic relationships with elevated FBG level and diabetes risk. Each interquartile range (27 μg/m3) increase in 3-year average PM2.5 concentration was associated with a 0.078 mmol/L (95% CI 0.077, 0.079) increase in FBG and 18% (95% CI 16%, 19%) higher risk of diabetes. The AF attributed to PM2.5 exposure exceeding 5 μg/m3 was 29.0% (95% CI 27.5%, 30.5%), corresponding to an additional 78.6 thousand (95% CI 74.5, 82.6) diabetes cases. Subgroup analyses showed more pronounced diabetes risks in those who were overweight or obese, age >35 years, less educated, of minority ethnicity, registered as a rural household, and residing in western China. We found long-term PM2.5 exposure was associated with higher diabetes risk in women of reproductive age in China.

Validation of satellite-derived solar irradiance datasets: a case study in Saudi Arabia

A robust dataset of Surface Solar Irradiance is essential for secure competitive financing for solar energy projects. Rating agencies and lenders alike require verification of the solar-resource dataset for utilizing each solar energy project, as this can be translated directly into expected electrical energy and revenues. The accuracy of the dataset and the variability of solar radiation, as recorded by historical solar data, play a significant role in estimating the future performance of the project and its budget. The historical observed solar irradiance datasets by local stations are the best and most reliable for a specific site, but they are not always available for long and continuous periods in any location, especially in arid areas. So, the importance of historical solar radiation datasets derived from satellite-based models arises here. This paper validates the historical modeled datasets of the three most famous satellite-based commercial prediction models (SolarGIS, SUNY, and Solcast) against the observed dataset by six ground stations in Saudi Arabia under different climatic zones. The validation method has been implemented using the standard error metrics: Maximum Absolute Error (MAE) and relative Maximum Bias Error (rMBE). The validation process showed that, in the case of GHI, the discrepancy between observed and predicted values is narrow, while in the case of DNI, the discrepancy is wide. Also, the predicted GHI values are more accurate than predicted DNI values, and -in general- the values predicted by the SUNY model are less accurate than those predicted by SolarGIS and Solcast models for both GHI and DNI. The resultant of this validation process could be accepted not for the six locations under study only but, also for deserts and arid areas across Saudi Arabia and might be extended to similar arid areas around the world.

Differential associations of fine and coarse particulate air pollution with cause-specific pneumonia mortality: A nationwide, individual-level, case-crossover study

BackgroundThe connections between fine particulate matter (PM2.5) and coarse particulate matter (PM2.5-10) and daily mortality of viral pneumonia and bacterial pneumonia were unclear. ObjectivesTo distinguish the connections between PM2.5 and PM2.5-10 and daily mortality due to viral pneumonia and bacterial pneumonia. MethodsUsing a comprehensive national death registry encompassing all areas of mainland China, we conducted a case-crossover investigation from 2013 to 2019 at an individual level. Residential daily particle concentrations were evaluated using satellite-based models with a spatial resolution of 1 km. To analyze the data, we employed the conditional logistic regression model in conjunction with polynomial distributed lag models. ResultsWe included 221,507 pneumonia deaths in China. Every interquartile range (IQR) elevation in concentrations of PM2.5 (lag 0–2 d, 37.6 μg/m3) was associated with higher magnitude of mortality for viral pneumonia (3.03%) than bacterial pneumonia (2.14%), whereas the difference was not significant (p-value for difference = 0.38). An IQR increase in concentrations of PM2.5-10 (lag 0–2 d, 28.4 μg/m3) was also linked to higher magnitude of mortality from viral pneumonia (3.06%) compared to bacterial pneumonia (2.31%), whereas the difference was not significant (p-value for difference = 0.52). After controlling for gaseous pollutants, their effects were all stable; however, with mutual adjustment, the associations of PM2.5 remained, and those of PM2.5-10 were no longer statistically significant. Greater magnitude of associations was noted in individuals aged 75 years and above, as well as during the cold season. ConclusionThis nationwide study presents compelling evidence that both PM2.5 and PM2.5-10 exposures could increase pneumonia mortality of viral and bacterial causes, highlighting the more robust effects of PM2.5 and somewhat higher sensitivity of viral pneumonia.

Comparison of Actual Evapotranspiration assessment by satellite-based model SEBS and hydrological model BBH in northern Tunisia

Comparison of Actual Evapotranspiration assessment by satellite-based model SEBS and hydrological model BBH in northern Tunisia

Assessing Satellite-Derived OpenET Platform Evapotranspiration of Mature Pecan Orchard in the Mesilla Valley, New Mexico

Pecan is a major crop in the Mesilla Valley, New Mexico. Due to prolonged droughts, growers face challenges related to water shortages. Therefore, irrigation management is crucial for farmers. Advancements in satellite-derived evapotranspiration (ET) models and accessibility to data from web-based platforms like OpenET provide farmers with new tools to improve crop irrigation management. This study evaluates the evapotranspiration (ET) of a mature pecan orchard using OpenET platform data generated by six satellite-based models and their ensemble. The ET values obtained from the platform were compared with the ET values obtained from the eddy covariance (ETec) method from 2017 to 2021. The six models assessed included Google Earth Engine implementation of the Surface Energy Balance Algorithm for Land (geeSEBAL), Google Earth Engine implemonthsmentation of the Mapping Evapotranspiration at High Resolution with Internalized Calibration (eeMETRIC) model, Operational Simplified Surface Energy Balance (SSEBop), Satellite Irrigation Management Support (SIMS), Priestley–Taylor Jet Propulsion Laboratory (PT-JPL), and Atmosphere–Land Exchange Inverse and associated flux disaggregation technique (ALEXI/DisALEXI). The average growing season ET of mature pecan estimated from April to October of 2017 to 2021 by geeSEBAL, eeMETRIC, SSEBop, SIMS, PT-JPL, ALEXI/DisALEXI, and the ensemble were 1061, 1230, 1232, 1176, 1040, 1016, and 1130 mm, respectively, and 1108 mm by ETec. Overall, the ensemble model-based monthly ET of mature pecan during the growing season was relatively close to the ETec (R2 of 0.9477) with a 2% mean relative difference (MRD) and standard error of estimate (SEE) of 15 mm/month for the five years (N = 60 months). The high agreement of the OpenET ensemble of the six satellite-derived models’ estimates of mature pecan ET with the ETec demonstrates the utility of this promising approach to enhance the reliability of remote sensing-based ET data for agricultural and water resource management.

Bi-directional association between outdoor or social activities and cognitive function: do the PM2.5 exposure catalyze the detrimental inactivity-poor cognition cycle?

BackgroundPrevious research has shown that lack of leisure activities, either outdoor or social activities, impedes cognitive function. However, the interrelationship between poor cognition and deficient activities is understudied. In addition, whether exposure to air pollution, such as PM2.5, can accelerate the detrimental ‘inactivity-poor cognition’ cycle, is worthy of investigation. MethodsWe used data from the 2008, 2011, 2014, and 2018 waves of the Chinese Longitudinal Healthy Longevity Survey (CLHLS). We assessed the frequency of outdoor or social activities at each wave. The cognitive function was examined using a China-Modified Mini-mental State Examination. We estimated the residential exposure to fine particular matter (PM2.5) via a satellite-based model. We applied cross-lagged panel (CLP) model to examine the bi-directional relationship between outdoor or social activities and cognitive function. We then examined the effect of PM2.5 exposure with sequent cognitive function and activities using generalized estimation equation (GEE) model. FindingsOverall, we observed significant bi-directional associations between outdoor or social activities and cognitive function. Participants with better cognitive function in the last wave were more likely to engage in outdoor or social activities in the following wave (outdoor activities: β = 0.37, 95% CI [0.27,0.48], P < 0.01; social activities: β = 0.05, 95% CI [0.02,0.09] P < 0.01). Meanwhile, higher engagement in outdoor or social activities in the last wave was associated with more favorable cognitive function in the following wave (outdoor activities: β = 0.06, 95% CI [0.03,0.09], P < 0.01; social activities: β = 0.10, 95% CI [0.03,0.18], P < 0.01). Notably, an increase in PM2.5 exposure during the preceding year was significantly associated with a declining cognitive function (β = −0.05, 95% CI [−0.08,−0.03], P < 0.01), outdoor activities (β = −0.02, 95% CI [−0.04, −0.01], P < 0.01) and social activities (β = −0.02, 95% CI [−0.02, −0.01], P < 0.01) in the current year; the lagged effects of the PM2.5 exposure in the past year of the last wave on activities and cognitive function of the following wave were also observed. InterpretationOur findings not only indicate the bi-directional links between the frequency of outdoor or social activities and cognitive function, but also report that PM2.5 exposure plays a role in catalyzing the detrimental inactivity-poor cognition cycle. Future research should investigate whether the policy-driven interventions, such as clean air policies, can break the unfavorable activity-cognition cycle, and thereby promoting health from the dual gains in leisure activities and cognition.

Irrigation expansion has kept pace with the CO2 fertilization effect on vegetation growth in a typical arid region

Dynamics of vegetation in arid areas have drawn worldwide attention. The expansion of irrigated cropland (ICE) in arid regions contributes to increased food security and impacts on the extent and development of regional vegetation. However, the quantitative attribution of vegetation growth variation from ICE and biogeochemical factors (e.g., atmospheric CO2 concentration, climatic factors) is still lacking. Here, we assessed key drivers of vegetation growth in the inland arid region of Northwest China (IANC) from 1982 to 2018, including ICE, increased nitrogen rates, elevated atmospheric CO2 concentration (eCO2) and climate drivers, using normalized difference vegetation index (NDVI) and ecosystem gross primary productivity (GPP) as measures. These variables were quantified through trend decomposition, machine learning algorithms, and a satellite-based model. The results show that vegetation growth was increased in IANC mainly due to eCO2 and ICE. After 1995, as the regional climatic aridity intensified, the CO2 fertilization effect on vegetation growth decreased, as the atmospheric CO2 concentration continued to increase. Meanwhile, irrigated cropland area increased sharply, and ICE-driven GPP variation exceeded that driven by eCO2 in the whole region, while the ICE-driven NDVI variation exceeded that due to eCO2 when the ICE reached 6.38%. The ICE effect on regional vegetation growth rather than the CO2 fertilization effect has mitigated the slowdown of the rate of vegetation growth caused by climate changes. Although the ICE is conducive to food security and continuous greening of arid areas, further reclamation will exacerbate water scarcity. Our results provide research base for identifying the scale of sustainable agricultural development.

A Machine Learning-Based High-Resolution Soil Moisture Mapping and Spatial–Temporal Analysis: The mlhrsm Package

Soil moisture is a key environmental variable. There is a lack of software to facilitate non-specialists in estimating and analyzing soil moisture at the field scale. This study presents a new open-sourced R package mlhrsm, which can be used to generate Machine Learning-based high-resolution (30 to 500 m, daily to monthly) soil moisture maps and uncertainty estimates at selected sites across the contiguous USA at 0–5 cm and 0–1 m. The model is based on the quantile random forest algorithm, integrating in situ soil sensors, satellite-derived land surface parameters (vegetation, terrain, and soil), and satellite-based models of surface and rootzone soil moisture. It also provides functions for spatial and temporal analysis of the produced soil moisture maps. A case study is provided to demonstrate the functionality to generate 30 m daily to weekly soil moisture maps across a 70-ha crop field, followed by a spatial–temporal analysis.

Associations between fine particulate air pollution with small-airway inflammation: A nationwide analysis in 122 Chinese cities

Alveolar nitric oxide is a non-invasive indicator of small-airway inflammation, a key pathophysiologic mechanism underlying lower respiratory diseases. However, no epidemiological studies have investigated the impact of fine particulate matter (PM2.5) exposure on the concentration of alveolar nitric oxide (CANO). To explore the associations between PM2.5 exposure in multiple periods and CANO, we conducted a nationwide cross-sectional study in 122 Chinese cities between 2019 and 2021. Utilizing a satellite-based model with a spatial resolution of 1 × 1 km, we matched long-term, mid-term, and short-term PM2.5 exposure for 28,399 individuals based on their home addresses. Multivariable linear regression models were applied to estimate the associations between PM2.5 at multiple exposure windows and CANO. Stratified analyses were also performed to identify potentially vulnerable subgroups. We found that per interquartile range (IQR) unit higher in 1-year average, 1-month average, and 7-day average PM2.5 concentration was significantly associated with increments of 17.78% [95% confidence interval (95%CI): 12.54%, 23.26%], 8.76% (95%CI: 7.35%, 10.19%), and 4.00% (95%CI: 2.81%, 5.20%) increment in CANO, respectively. The exposure-response relationship curves consistently increased with the slope becoming statistically significant beyond 20 μg/m3. Males, children, smokers, individuals with respiratory symptoms or using inhaled corticosteroids, and those living in Southern China were more vulnerable to PM2.5 exposure. In conclusion, our study provided novel evidence that PM2.5 exposure in long-term, mid-term, and short-term periods could significantly elevate small-airway inflammation represented by CANO. Our results highlight the significance of CANO measurement as a non-invasive tool for early screening in the management of PM2.5-related inflammatory respiratory diseases.

Comparison of Differences in Actual Cropland Evapotranspiration under Two Irrigation Methods Using Satellite-Based Model

Remote sensing technology is widely used to obtain evapotranspiration (ETa), but whether it can distinguish the differences in farmland energy balance components and ETa under different irrigation methods has not been studied. We used Landsat 8 data as the primary dataset to drive the METRIC model and inverted the surface parameters and ETa of the Shiyang River Basin from 2014 to 2018. After improving the METRIC model using Ta obtained by the regression method instead of interpolation to calculate the net radiation flux (Rn), R2 was improved from 0.45 to 0.53, and the RMSE was reduced from 61 W/m2 to 51 W/m2. The ETa estimation results on satellite overpass days performed well, with R2 equal to 0.93 and RMSE equal to 0.48 mm when compared with the Eddy covariance method (EC) observations. Subsequently, the different growth stages and daily average ETa estimates of maize were compared with three observations (water balance, WB; Bowen ratio and energy balance method, BREB; and EC). The daily estimates of ETa correlate well with the observations of BREB (R2BI = 0.82, R2DI = 0.92; RMSEBI = 0.46 mm/day, RMSEDI = 0.32 mm/day) and EC (R2BI = 0.85, R2DI = 0.92; RMSEBI = 0.45 mm/day, RMSEDI = 0.34 mm/day), and the estimation for drip irrigation was found to be better than for border irrigation. The total accuracy of the ETa estimation on the five-year overpass day of maize farmland reached R2 = 0.93 and RMSE = 0.48 mm. With sufficient remote sensing data, the 4-year average ETa of maize was 31 mm lower for DI than for BI, and the mean value of ETa obtained from the three observation methods was 40 mm. The METRIC model can be used to distinguish ETa differences between the two irrigation methods in maize farmlands.

Association of long-term ozone air pollution and age-related macular degeneration in older Chinese population

BackgroundAge-related macular degeneration (AMD) is the leading cause of legal blindness. It remains unclear whether and to what extent the ambient ozone pollution could increase the risk of AMD. MethodsA nationwide cross-sectional survey was conducted in 129 major cities in 27 of 31 provincial regions across China from 2018 to 2021. Data in relation to demographics, residential address, and medical histories were collected. The exposure-response relationship between ozone exposure and AMD was explored using the restricted cubic splines. A piecewise logistic regression model was used to examine the magnitudes of the association, after adjusting demographic, social-economic and co-pollutants. Residential ozone exposures were estimated using a satellite-based model. ResultsA total of 624,167 middle-aged and older participants were included in the final analyses, the overall prevalence of AMD was 16.76 %. The risk of AMD was consistently increasing with higher warm-season ozone concentration, and the risk became much larger after the cut-off of 110 μg/m3 (approximately 50 ppb). Every 10 μg/m3 increment in warm-season ozone concentration, the adjusted odds ratio (OR) for AMD were 1.15 (1.13, 1.16) and 1.66 (1.63, 1.69) when the warm-season ozone concentration was below or above 110 μg/m3, respectively. ConclusionThis large-scale nationwide study provides the first epidemiological evidence demonstrating significant associations between long-term residential ozone exposure and AMD prevalence. Based on our findings, in conjunction with WHO global air quality guidelines, we suggest that a warm-season ozone of 110 μg/m3 should be adopted for middle-aged and older populations to reduce the risk of AMD. Ongoing efforts to reduce ozone exposure in communities through improved air quality regulations and public education are essential for the improvement of public health.

Short-term exposure to ozone may trigger the onset of Kawasaki disease: An individual-level, case-crossover study in East China

Kawasaki disease (KD) is an acute, systemic vasculitis that primarily affects children aged under the age of 5. While environmental factors have been linked to the development of KD, the specific role of ozone (O3) pollution in triggering the disease onset remains uncertain. This study aimed to examine the associations between short-term O3 exposure and KD onset in children. Utilizing a satellite-based model with a spatial resolution of 1 × 1 km, we matched 1808 KD patients (out of a total of 6115 eligible individuals) to pre-onset ozone exposures based on their home addresses in East China between 2013 and 2020. Our findings revealed a significant association of O3 exposure with KD onset on the day of onset (lag 0 day). However, this association attenuated and became statistically insignificant on lag 1 and lag 2 days. Each interquartile range (52.32 μg/m3) increase in O3 concentration at lag 0 day was associated with a 16.2% (95% CI: 3.6%, 30.3%) increased risk of KD onset. The E-R curve for O3 exhibited a plateau at low concentrations and then increased rapidly at concentrations ≥75 μg/m3. Notably, these associations were stronger in male children, younger children (<2 years of age) and patients experiencing KD onset during the warm season. This study provides novel epidemiological evidence indicating that short-term O3 exposure is associated with an increased risk of childhood KD onset. These findings emphasized the importance of considering this environmental risk factor in KD prevention strategies.

Soil moisture-based global liquefaction model (SMGLM) using soil moisture active passive (SMAP) satellite data

The role of soil saturation condition on the liquefaction occurrence highlights the need for a tool to track the ground-truth soil moisture content involved in this seismic phenomenon. Soil Moisture Active Passive (SMAP) satellite estimates near-real time surface and root zone soil moisture measurements with global coverage. Typical proxies for soil saturation in liquefaction analysis include average water table depth patterns, mean annual precipitation measurements, and topographic conditions. As an alternative to these proxies, this paper incorporates satellite-based soil moisture data to enhance the understanding of the interrelation between saturation conditions and liquefaction events. Proposing a new approach for sampling non-liquefaction cases, a liquefaction/non-liquefaction database was developed in this paper based on reconnaissance reports of eleven target earthquakes. Well-known geospatial explanatory variables as well as new SMAP-based soil moisture parameters, affecting soil liquefaction, are used to develop a new soil moisture-based global liquefaction model (SMGLM), which is compared with an existing global liquefaction model. Considering the ongoing advancement of earth observing satellites, the results of this paper can build a basis for developing fully satellite-based models that could identify liquefied sites using high resolution near-real time soil moisture data.

Associations of Long-Term Exposure to PM2.5 and Its Constituents with Erythrocytosis and Thrombocytosis in Rural Populations.

Evidence on the effect of long-term exposure to fine particulate matter (PM2.5) on erythrocytosis and thrombocytosis prevalence was limited. We aimed to investigate the association of PM2.5 and its constituents with the risks of erythrocytosis and thrombocytosis. The present study included a total of 33,585 participants from the Henan Rural Cohort at baseline between 2015 and 2017. A hybrid satellite-based model was employed to estimate the concentrations of PM2.5 mass and its constituents (including black carbon [BC], nitrate [NO3-], ammonium [NH4+], inorganic sulfate [SO42-], organic matter [OM], and soil particles [SOIL]). The logistic regression model was used to assess the associations of single exposure to PM2.5 and its constituents with the risks of erythrocytosis and thrombocytosis, and the quantile G-computation method was applied to evaluate their joint exposure risk. For the independent association, the odds ratios for erythrocytosis/thrombocytosis with 1 μg/m3 increase was 1.049/1.043 for PM2.5 mass, 1.596/1.610 for BC, 1.410/1.231 for NH4+, 1.205/1.139 for NO3-, 1.221/1.359 for OM, 1.300/1.143 for SO42-, and 1.197/1.313 for SOIL. Joint exposure to PM2.5 and its components was also positively associated with erythrocytosis and thrombocytosis. The estimated weight of NH4+ was found to be the largest for erythrocytosis, while OM had the largest weight for thrombocytosis. PM2.5 mass and its constituents were positively linked to prevalent erythrocytosis and thrombocytosis, both in single-exposure and joint-exposure models. Additionally, NH4+/OM was identified as a potentially responsible component for the association between PM2.5 and erythrocytosis/thrombocytosis.

Assessing household fine particulate matter (PM2.5) through measurement and modeling in the Bangladesh cook stove pregnancy cohort study (CSPCS)

Biomass fuel burning is a significant contributor of household fine particulate matter (PM2.5) in the low to middle income countries (LMIC) and assessing PM2.5 levels is essential to investigate exposure-related health effects such as pregnancy outcomes and acute lower respiratory infection in infants. However, measuring household PM2.5 requires significant investments of labor, resources, and time, which limits the ability to conduct health effects studies. It is therefore imperative to leverage lower-cost measurement techniques to develop exposure models coupled with survey information about housing characteristics. Between April 2017 and March 2018, we continuously sampled PM2.5 in three seasonal waves for approximately 48-h (range 46 to 52-h) in 74 rural and semi-urban households among the participants of the Bangladesh Cook Stove Pregnancy Cohort Study (CSPCS). Measurements were taken simultaneously in the kitchen, bedroom, and open space within the household. Structured questionnaires captured household-level information related to the sources of air pollution. With data from two waves, we fit multivariate mixed effect models to estimate 24-h average, cooking time average, daytime and nighttime average PM2.5 in each of the household locations. Households using biomass cookstoves had significantly higher PM2.5 concentrations than those using electricity/liquefied petroleum gas (626 μg/m3 vs. 213 μg/m3). Exposure model performances showed 10-fold cross validated R2 ranging from 0.52 to 0.76 with excellent agreement in independent tests against measured PM2.5 from the third wave of monitoring and ambient PM2.5 from a separate satellite-based model (correlation coefficient, r = 0.82). Significant predictors of household PM2.5 included ambient PM2.5, season, and types of fuel used for cooking. This study demonstrates that we can predict household PM2.5 with moderate to high confidence using ambient PM2.5 and household characteristics. Our results present a framework for estimating household PM2.5 exposures in LMICs, which are often understudied and underrepresented due to resource limitations.

Assessing the Performance of Satellite-Based Models for Crop Yield Estimation in the Canadian Prairies

Timely monitoring of crop production using a remote sensing-based approach offers promise toward enhancing food security. Statistical models developed using satellite data typically employ a single vegetation index from a single sensor for yield estimation. With the increasing availability of satellite datasets, there is now an opportunity to investigate the potential of available vegetation indices from different sensors in estimating yields. The key objective of this study was to develop a best-performing satellite-based yield model for the Canadian Prairies for wheat, barley, and canola, trained using municipality-level data from 2009 to 2019. We tested the statistical performance of models built using (a) indices from different sensors (Landsat and Sentinel-2), (b) indices sensitive to different yield properties, and (c) single versus multiple vegetation indices. Results showed Landsat-NDWI as the best performing single-index across all indices and sensors for each crop. Sentinel-2-EVI performed best for wheat and canola and Sentinel-2-SR for barley; but these models were built using only 4 years of data. We found that best-performing single-index models recorded similar predictive accuracy as multi-index models during model validation. The results from this work suggest that satellite-based yield estimation can be improved by selecting the right index related to different crop properties.

Estimating Maize Yield from 2001 to 2019 in the North China Plain Using a Satellite-Based Method

Maize is one of the main food crops and is widely planted in China; however, it is difficult to get timely and precise information on yields. Because of the benefits of remote sensing technology, satellite-based models (e.g., eddy covariance light use efficiency, EC-LUE) have a lot of potential for monitoring crop productivity. In this study, the gross primary productivity (GPP) of maize in the NCP was estimated using the EC-LUE model, and the GPP was subsequently transformed into yield using the harvest index. Specifically accounting for the spatiotemporal variation in the harvest index, the statistical yield and estimated GPP from the previous year were used to generate region-specific harvest indexes at the county scale. The model’s performance was assessed using statistical yield data. The results demonstrate that the increase in the total GPP in the summer maize-growing season in the NCP is directly related to the increase in the planting area, and the harvest index has significant heterogeneity in space, and the fluctuation in time is small, and the estimated yield can simulate 64% and 55%, respectively, of the variability in the yield at the county and city scales. The model also accurately captures the inter-annual changes in yield (the average absolute percentage errors are less than 20% for almost all years), but model performance varies by region. It performs better in continuous areas of maize-growing. The results from this study demonstrate that the EC-LUE model can be applied to estimate the yield from a variety of crops (other than winter wheat) and that it can be used in conjunction with a region-specific harvest index to track the production of large-scale crops.

The Association of Air Pollution Exposure With Glucose and Lipid Levels: The Role of an Extreme Air Pollution Event Alongside 2 Decades of Moderate Exposure.

Extreme air pollution events and moderate exposure to fine particulate matter (PM2.5) are associated with increased cardiometabolic risk. The World Trade Center (WTC) Health Program general responder cohort includes responders to the WTC disaster. We investigated whether their exposure to this extreme air pollution event (2001) was associated with long-term metabolic outcomes, independently from the associations of intermediate-term PM2.5 exposure later in life (2004-2019). We included 22,447 cohort members with cholesterol (n= 96,155) and glucose (n= 81,599) laboratory results. Self-reported WTC exposure was derived from a questionnaire. PM2.5 exposure was derived from a satellite-based model. We observed an increase of 0.78 mg/dL (95% confidence interval (CI): 0.30, 1.26) in glucose and 0.67 mg/dL (95% CI: 1.00, 2.35) in cholesterol levels associated with an interquartile range increase in PM2.5 averaged 6 months before the study visit. Higher WTC-exposure categories were also associated with higher cholesterol (0.99 mg/dL, 95% CI: 0.30, 1.67, for intermediate exposure) and glucose (0.82 mg/dL, 95% CI: 0.22, 1.43, for high exposure) levels. Most associations were larger among people with diabetes. Extreme air pollution events and intermediate PM2.5 exposure have independent metabolic consequences. These exposures contributed to higher glucose and lipids levels among WTC responders, which may be translated into increased cardiovascular risk.