11 research outputs found
Predicting fine particulate matter (PM2.5) in the Greater London area: an ensemble approach using machine learning methods
Get PDFEstimating air pollution exposure has long been a challenge for environmental health researchers. Technological advances and novel machine learning methods have allowed us to increase the geographic range and accuracy of exposure models, making them a valuable tool in conducting health studies and identifying hotspots of pollution. Here, we have created a prediction model for daily PM2.5 levels in the Greater London area from 1st January 2005 to 31st December 2013 using an ensemble machine learning approach incorporating satellite aerosol optical depth (AOD), land use, and meteorological data. The predictions were made on a 1 km × 1 km scale over 3960 grid cells. The ensemble included predictions from three different machine learners: a random forest (RF), a gradient boosting machine (GBM), and a k-nearest neighbor (KNN) approach. Our ensemble model performed very well, with a ten-fold cross-validated R2 of 0.828. Of the three machine learners, the random forest outperformed the GBM and KNN. Our model was particularly adept at predicting day-to-day changes in PM2.5 levels with an out-of-sample temporal R2 of 0.882. However, its ability to predict spatial variability was weaker, with a R2 of 0.396. We believe this to be due to the smaller spatial variation in pollutant levels in this area
Stringency of COVID-19 containment response policies and air quality changes: a global analysis across 1851 cities
Get PDFThe COVID-19 containment response policies (CRPs) had a major impact on air quality (AQ). These CRPs have been time-varying and location-specific. So far, despite having numerous studies on the effect of COVID-19 lockdown on AQ, a knowledge gap remains on the association between stringency of CRPs and AQ changes across the world, regions, nations, and cities. Here, we show that globally across 1851 cities (each more than 300000 people) in 149 countries, after controlling for the impacts of relevant covariates (e.g., meteorology), Sentinel-5P satellite-observed nitrogen dioxide (NO2) levels decreased by 4.9% (95% CI: 2.2, 7.6%) during lockdowns following stringent CRPs compared to pre-CRPs. The NO2 levels did not change significantly during moderate CRPs and even increased during mild CRPs by 2.3% (95% CI: 0.7, 4.0%), which was 6.8% (95% CI: 2.0, 12.0%) across Europe and Central Asia, possibly due to population avoidance of public transportation in favor of private transportation. Among 1768 cities implementing stringent CRPs, we observed the most NO2 reduction in more populated and polluted cities. Our results demonstrate that AQ improved when and where stringent COVID-19 CRPs were implemented, changed less under moderate CRPs, and even deteriorated under mild CRPs. These changes were location-, region-, and CRP-specific
The Power of Environmental Observatories for Advancing Multidisciplinary Research, Outreach, and Decision Support: The Case of the Minnesota River Basin
Get PDFAn edited version of this paper was published by AGU. Copyright 2019 American Geophysical Union.Observatory‐scale data collection efforts allow unprecedented opportunities for integrative, multidisciplinary investigations in large, complex watersheds, which can affect management decisions and policy. Through the National Science Foundation‐funded REACH (REsilience under Accelerated CHange) project, in collaboration with the Intensively Managed Landscapes‐Critical Zone Observatory, we have collected a series of multidisciplinary data sets throughout the Minnesota River Basin in south‐central Minnesota, USA, a 43,400‐km2 tributary to the Upper Mississippi River. Postglacial incision within the Minnesota River valley created an erosional landscape highly responsive to hydrologic change, allowing for transdisciplinary research into the complex cascade of environmental changes that occur due to hydrology and land use alterations from intensive agricultural management and climate change. Data sets collected include water chemistry and biogeochemical data, geochemical fingerprinting of major sediment sources, high‐resolution monitoring of river bluff erosion, and repeat channel cross‐sectional and bathymetry data following major floods. The data collection efforts led to development of a series of integrative reduced complexity models that provide deeper insight into how water, sediment, and nutrients route and transform through a large channel network and respond to change. These models represent the culmination of efforts to integrate interdisciplinary data sets and science to gain new insights into watershed‐scale processes in order to advance management and decision making. The purpose of this paper is to present a synthesis of the data sets and models, disseminate them to the community for further research, and identify mechanisms used to expand the temporal and spatial extent of short‐term observatory‐scale data collection efforts
The Power of Environmental Observatories for Advancing Multidisciplinary Research, Outreach, and Decision Support: The Case of the Minnesota River Basin
Get PDFObservatory‐scale data collection efforts allow unprecedented opportunities for integrative, multidisciplinary investigations in large, complex watersheds, which can affect management decisions and policy. Through the National Science Foundation‐funded REACH (REsilience under Accelerated CHange) project, in collaboration with the Intensively Managed Landscapes‐Critical Zone Observatory, we have collected a series of multidisciplinary data sets throughout the Minnesota River Basin in south‐central Minnesota, USA, a 43,400‐km2 tributary to the Upper Mississippi River. Postglacial incision within the Minnesota River valley created an erosional landscape highly responsive to hydrologic change, allowing for transdisciplinary research into the complex cascade of environmental changes that occur due to hydrology and land use alterations from intensive agricultural management and climate change. Data sets collected include water chemistry and biogeochemical data, geochemical fingerprinting of major sediment sources, high‐resolution monitoring of river bluff erosion, and repeat channel cross‐sectional and bathymetry data following major floods. The data collection efforts led to development of a series of integrative reduced complexity models that provide deeper insight into how water, sediment, and nutrients route and transform through a large channel network and respond to change. These models represent the culmination of efforts to integrate interdisciplinary data sets and science to gain new insights into watershed‐scale processes in order to advance management and decision making. The purpose of this paper is to present a synthesis of the data sets and models, disseminate them to the community for further research, and identify mechanisms used to expand the temporal and spatial extent of short‐term observatory‐scale data collection efforts
A REVIEW OF THE IMPACT OF AIR POLLUTION ON THE INFECTION AND MORTALITY RATES DUE TO COVID-19
No full textIn the late February 2020, the first positive cases of the novel coronavirus, COVID-19, were confirmed in Iran, and the World Health Organization updated the
status of the global outbreak from epidemic to pandemic in mid-March 2020. The rapid outbreak of the virus intervened a significant portion of socioeconomic activities, leaving behind some serious questions on the main factors intensifying the infection and the morality rates. Although the primary impacts of the outbreak have been extensively explored at the global and regional scales since its emergence, the impacts of the environment on the viral spread are still poorly understood. The goal of this paper is to review the most recent scientific findings on the spatiotemporal correlation between the air pollution and the mortality rate due to COVID-19. These researches are based on statistical analysis of the ground and satellite-based recorded data on PM2.5,PM10, and NOx across the United States, China, Italy, and England. The majority of these studies also consider data on population intensity, meteorological variables, migration rate, age, and health service quality to guarantee the validity of the findings by excluding the possible impacts imposed by these stressors. The results suggest that there exists a significant positive correlation between the concentration of the aforementioned air pollutants and the infection and mortality rates due to COVID-19. While long-term exposure to NOx has been associated with hypertension, heart and cardiovascular diseases, and chronic obstructive pulmonary disease, high concentration of PM2.5 and PM10 pollutants additionally enhances the mortality rate by facilitating the transmit of pathogenic agents through the fine particulate matters in the air. Regarding the drastic air pollution condition during the cold seasons in the most populated cities across Iran, the conclusions of this study can guide policy makers towards an effective planning and management of the COVID-19 crisis in such seasons
Long‐Term Exposure to Particulate Air Pollution Is Associated With 30‐Day Readmissions and Hospital Visits Among Patients With Heart Failure
No full textBackground Long‐term air pollution exposure is a significant risk factor for inpatient hospital admissions in the general population. However, we lack information on whether long‐term air pollution exposure is a risk factor for hospital readmissions, particularly in individuals with elevated readmission rates. Methods and Results We determined the number of readmissions and total hospital visits (outpatient visits+emergency room visits+inpatient admissions) for 20 920 individuals with heart failure. We used quasi‐Poisson regression models to associate annual average fine particulate matter at the date of heart failure diagnosis with the number of hospital visits and 30‐day readmissions. We used inverse probability weights to balance the distribution of confounders and adjust for the competing risk of death. Models were adjusted for age, race, sex, smoking status, urbanicity, year of diagnosis, short‐term fine particulate matter exposure, comorbid disease, and socioeconomic status. A 1‐µg/m3 increase in fine particulate matter was associated with a 9.31% increase (95% CI, 7.85%–10.8%) in total hospital visits, a 4.35% increase (95% CI, 1.12%–7.68%) in inpatient admissions, and a 14.2% increase (95% CI, 8.41%–20.2%) in 30‐day readmissions. Associations were robust to different modeling approaches. Conclusions These results highlight the potential for air pollution to play a role in hospital use, particularly hospital visits and readmissions. Given the elevated frequency of hospitalizations and readmissions among patients with heart failure, these results also represent an important insight into modifiable environmental risk factors that may improve outcomes and reduce hospital use among patients with heart failure
The Use of Active Grids in Experimental Facilities
No full textActive grids allow for the turbulence in experimental facilities to be tailored through a broad range of turbulence intensities and Reynolds numbers. This work provides an overview of the active grids that presently exist around the globe as well as advances in turbulence research that are a result of their use. Focus is placed on homogeneous turbulent flows, turbulent boundary layers, and model testing
Fertilizer, landscape features and climate regulate phosphorus retention and river export in diverse Midwestern watersheds
No full textBenchmark analysis of piezoelectric bimorph energy harvesters composed of laminated composite beam substrates
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