The following projects include active grants with open positions. For more information, please contact us.
Beyond wildfire fine particles and average effects, modelling wildfire-specific PM10 and Ozone and vulnerable subgroups using modern machine learning algorithms
Funding institution: CalEPA
PI: Tarik Benmarhnia
The aim of this project is to apply recent techniques based on machine learning algorithms to model wildfire specific PM10 and O3 and then identify subgroups of the population that are particularly vulnerable to such wildfires’ mixtures. Fires contribute to enhanced surface concentrations of various air pollutants. Fine particulate matter (PM2.5) is by far the most investigated air pollutant generated by wildfires in California and globally. However, wildfire can also contribute to the generation of other pollutants that are monitored and regulated including coarse PM (PM10) and tropospheric Ozone (O3). Recent studies have shown that wildfires generate increases in tropospheric O3 levels through processes distinct from PM2.5. The epidemiological evidence regarding acute exposure to PM10 and O3 is vast. Yet, very few studies have been aimed at quantifying the impacts of such pollutants in relation to wildfires emissions. In this project, we will expand our recently developed approach to model wildfire specific PM10 and O3 with fine spatial and temporal resolutions that could be directly merged with health data such hospital admissions. We will also leverage modern mixtures methods based on quantile g-computation to study the synergetic impacts of these pollutants (PM2.5, PM10, O3) specifically attributable to wildfires. Furthermore, though the environmental justice literature has found that socioeconomic and racial and ethnic minorities suffer from a disproportionate burden of air pollution exposure in general, and PM2.5 in particular, studies assessing the extent to which certain socio-demographic characteristics modify the smoke pollution-health risk remain limited. We will adopt a data-driven approach based on recursive partitioning methods to comprehensively characterize heterogeneous effects and constitute susceptibility profiles regarding wildfire health impacts considering a large set of individual and neighborhood-level social and environmental characteristics.
Extreme weather, air pollution, and stroke among an aging female population
Funding institution: National Institutes of Health
PI: Tarik Benmarhnia
Wildfires adversely affect air quality through smoke and winds, and in California, the occurrence of and effects from these wildfires have been exacerbated by an extended drought. Despite environmental policies that have curtailed air pollution in the state, a number of metropolitan California counties have particulate pollution levels above federal and state ambient standards. During extreme weather events including heat waves and/or active wildfires, these levels rival that of the worst cities in the world. Aging populations are of particular concern during extreme weather events as air pollution can exacerbate underlying risk factors for cardiovascular diseases (CVD). The prevalence of both stroke and CVD risk factors also markedly increases with age, making older adults and, specifically, post-menopausal women particularly susceptible to environmental impacts. This study aims to uncover the intersection between air pollution and extreme weather events (wildfire, heat index) in the context of the underlying environment (drought, heat/temperature) and the fluidity of daily weather (wind), and simultaneously consider personal risk factors that contribute to one’s susceptibility to environmental stressors. In this project, we will: evaluate acute effects from wildfire events by ascertaining stroke events within geographically affected areas based on satellite imagery, evaluate the effects of drought and wildfire events on specific sources and components of air pollution and determine the role of specific PM2.5 components in stroke risk and mortality, and evaluate the association between PM2.5 and immune markers.
Susceptibility and adverse health outcomes related to climate-sensitive events among older Medicare beneficiaries with Alzheimer and Dementia
Funding institution: National Institute on Aging
PI: Tarik Benmarhnia
The burden of Alzheimer’s disease (AD) and related dementias (ADRD) is expected to double by 2060 with almost 14 million people affected. Simultaneously, air pollution remains a health burden, with >100 million persons in the US living in areas exceeding health-based regulations for fine particles and ozone. Exposure to wildfire smoke is also increasing. Many studies have reported that exposure to air pollution can increase the risk of AD/ADRD, suggesting that the environmental impact of climate change could, in fact, become a brain health emergency that we are unprepared to tackle. This work capitalizes on a large and validated cohort of US Medicare beneficiaries (>65y) with AD/ADRD (approx. 10 million beneficiaries for the period 2000-2019) and spatially resolved weather data combined with state-of-the-science machine learning for estimating exposure to air pollution including wildfire smoke, leveraging satellite imagery, land use data, and monitors. Our long-term goals are to characterize the vulnerability and health impacts of climate change-related exposures within a large cohort of older adults with AD/ADRD. Results from this work will advance evidence-based policies and action to protect older populations with AD/ADRD under several converging trends: the co-occurrence of multiple exposures of extreme weather and air pollution; the rapidly aging US population; increasing extreme weather events; increasing wildfires and subsequent smoke; and health inequities.
Heat waves in the Southern California coastal zone: Their oceanic and atmospheric drivers, human health impacts, and sustainable adaptation
Funding institution: National Science Foundation
PI: Mark Merrifield
Extreme heat is the number one weather-related cause of death in the US, and its impacts are felt especially strongly in the diverse, densely populated coastal zone of Southern California, where heat exposure has been shown to dramatically affect population mortality and morbidity. Furthermore, spatial heterogeneity in the impacts of heat due to factors such as the urban heat-island effect, which disproportionately affects disadvantaged populations, has made this an issue of environmental justice. Equitable adaptation strategies are needed. One oft-suggested heat adaptation approach is urban greening. However, there is scant research addressing the climate suitability of and fresh water constraints on urban greening potential – a possible limiting factor that could make or break long-term greening efforts, especially in arid and drought-prone regions like Southern California. This research hub will address the issue of extreme heat amidst climate change, and sustainable and equitable long-term solutions to heat in Southern California’s coastal zone. We will answer critical convergent questions concerning: 1) the atmosphere-land-ocean dynamics that drive and modulate patterns of extreme heat; 2) localized health impacts of extreme heat, with an emphasis on how the built environment and socioeconomic factors determine who faces the brunt of heat-health impacts; and 3) regionally-specific vegetation-temperature relationships, and the climate suitability and water needs of urban greening efforts. Central to the Hub is the co-production of sustainable intervention strategies that consider climate dynamics, public health, and ecohydrological constraints along with the goals and needs of local communities. Informing this research at every step will be input from a collective of local and regional government agencies, NGOs, and community groups that are already invested and engaged in climate change adaptation.
Integrating novel GIS and GPS data to assess the impact of built environments on changes in BMI, physical activity and cancer-related biomarkers
Funding institution: National Cancer Institute
PIs: Tarik Benmarhnia and Marta Jankowska
Just in Time Adaptive Interventions (JITAIs) employ temporal and spatial cues to prompt behavior change, but little is known about spatial predictors of behaviors at the minute level and beyond home neighborhoods. We investigate the relationship between minute level objective GIS measured walkability, green space, pollution and food environments and changes in BMI, physical activity (PA), and cancer related biomarkers in the context of 2 existing weight loss trials in women at risk for breast cancer. We focus on the development of advanced methods for dynamic environmental exposure estimation using GPS and accelerometer sensors, development of novel environmental measures through use of machine learning as applied to satellite imagery. Our results will ultimately inform JITAIs by developing and testing minute level spatial, temporal and behavioral interventions.
Characterizing air pollution episodes and evaluation of implemented policies
Funding institution: French Environment and Energy Management Agency, ADEME
PI: Tarik Benmarhnia
Air pollution is one of the most documented environmental determinants of health. When emission levels are particularly high, pollution episodes or peaks appear. These days are defined based on concentrations exceeding a certain threshold for one or more air pollutants. In this project we first explore the dose-response relationships between different regulated pollutants and several health indicators in France and the links between extreme weather events. We then evaluate the effectiveness of measures to combat pollution peaks capitalizing on natural experiments and proposing novel quasi-experimental methods to evaluate these types of policies. Finally, we will examine the potential health benefits of sustainable urban modification interventions carried out by municipalities in France aimed at reducing emissions linked to vehicular traffic. We will start by carrying out a survey consisting of measures aimed at reducing air pollution linked to traffic in the medium and long term, building on existing work. We will then evaluate the health benefits associated with the implementation of the Paris low emission zone.
Chronic disease and air pollution: disease trajectory and intervention
Funding institution: Health Canada
PI: Hong Chen
Exposure to low levels of air pollution is clearly linked to survival, but important questions remain. The precise mechanism and pathways, whereby the accumulation of exposures elicits premature death, requires more precise elucidation. This information is crucial for supporting health guidance and preventive interventions, as well as for estimating the burden of air pollution. It has been hypothesized that repetitive exposures to air pollution may promote chronic disease states and/or enhance the progression of diseases by worsening severity and complication, which in turn could magnify health risks by exacerbating susceptibility for future events. The first objective of this project is to proceed from the question "does air pollution affect health?" to the question "how does it affect health?". We will comprehensively investigate the role of chronic exposure to air pollution (months to years) in affecting individuals’ trajectories over different health states, and how this unfolds along different physiological pathways. This information is crucial for health guidance and timely interventions, as well as for estimating the burden of exposure on various aspects of the disease trajectory, not only on death. The second objective of our project is to further evaluate the effectiveness of some widely-implemented or potential individual- and policy-level interventions in reducing air health effects. Air pollution has major public health and economic consequences, but considerable uncertainty exists concerning which actions can be taken to reduce its effects. To achieve the two objectives, we will draw on Big Data sources (i.e., CanCHEC and ONPHEC cohorts), and use various causal inference methods (e.g., causal mediation, g-formula, marginal structural modeling).
Using Machine learning techniques to characterize wildfire-specific PM2.5 and associated impacts on respiratory health in California
Funding institution: CalEPA
PI: Tarik Benmarhnia
The Western United States (US) and other locations worldwide are experiencing more frequent and destructive wildfires in a changing climate. Fine particulate matter, PM2.5, is the main component of wildfire smoke adversely impacting respiratory health. PM2.5 can be inhaled into the deepest recesses of the lung and may enter the bloodstream and impair vital organs including the lungs. Furthermore, wildfires account for 29% of total PM2.5 emissions in the US, 50% in California (CA), and this proportion is expected to increase in the next years in the context of climate change. The aim of this project is to apply recent techniques based on machine learning algorithms to quantify the specific impacts of wildfire-specific PM2.5 on daily hospital admissions for respiratory diseases and analyze spatial variations in the estimated health burden to identify most vulnerable communities regarding the impact of wildfire smoke.