A5005586757- Air Quality and Health Impacts
- Climate Change and Health Impacts
- Air Quality Monitoring and Forecasting
- Energy and Environment Impacts
- Atmospheric chemistry and aerosols
- Birth, Development, and Health
- Health, Environment, Cognitive Aging
- Noise Effects and Management
- Fire effects on ecosystems
- Atmospheric and Environmental Gas Dynamics
- Urban Transport and Accessibility
- Plant Water Relations and Carbon Dynamics
- Vehicle emissions and performance
- Impact of Light on Environment and Health
- Global Health Care Issues
- COVID-19 impact on air quality
- Forest ecology and management
- Urban Heat Island Mitigation
- Video Surveillance and Tracking Methods
- Injury Epidemiology and Prevention
- Forecasting Techniques and Applications
- Tree-ring climate responses
- Dam Engineering and Safety
- Environmental Justice and Health Disparities
- Remote Sensing and LiDAR Applications
Sonoma Technology (United States)
2019-2025
Universities Space Research Association
2024
Goddard Space Flight Center
2024
University of Illinois Urbana-Champaign
2014-2015
De La Salle University
2006
Estimating PM2.5 concentrations and their prediction uncertainties at a high spatiotemporal resolution is important for air pollution health effect studies. This particularly challenging California, which has variability in natural (e.g, wildfires, dust) anthropogenic emissions, meteorology, topography (e.g. desert surfaces, mountains, snow cover) land use. Using ensemble-based deep learning with big data fused from multiple sources we developed model uncertainty estimates spatial (1 km × 1...
Wildfires in the Western United States are a growing and significant source of air pollution that is eroding decades progress reduction. The effects on preterm birth during critical periods pregnancy unknown.
Prenatal exposure to air pollution has been associated with an increased risk of low birth weight. Disrupted metabolism may serve as underlying mechanism, but the specific metabolic pathways involved remain unclear. In Maternal and Developmental Risks from Environmental Social Stressors (MADRES) study, 382 third-trimester maternal serum samples were analyzed for untargeted metabolomics using liquid chromatography Fourier transform high-resolution mass spectrometry. Ambient concentrations...
Importance Fetal growth is precisely programmed and could be interrupted by environmental exposures during specific times pregnancy. Insights on potential sensitive windows of air pollution exposure in association with birth weight are needed. Objective To examine the ambient heterogeneity individual- neighborhood-level stressors. Design, Setting, Participants Data a cohort low-income Hispanic women singleton term pregnancy were collected from 2015 to 2021 ongoing Maternal Developmental...
Previous studies have reported associations between in utero exposure to regional air pollution and autism spectrum disorders (ASD). In components of near-roadway (NRAP) has been linked adverse neurodevelopment animal models, but few investigated NRAP association with ASD risk. To identify risk associated a large, representative birth cohort. This retrospective pregnancy cohort study included 314,391 mother–child pairs singletons born 2001 2014 at Kaiser Permanente Southern California (KPSC)...
Air pollution has been associated with gestational diabetes mellitus (GDM). We aim to investigate susceptible windows of air exposure and factors determining population vulnerability. ascertained GDM status in the prospective Maternal Developmental Risks from Environmental Social Stressors (MADRES) pregnancy cohort Los Angeles, California, USA. calculated relative risk by ambient particulate matter (PM10; PM2.5), nitrogen dioxide (NO2), ozone (O3) each week 12 weeks before 24 after...
Prenatal air pollution exposure may increase risk for childhood obesity. However, few studies have evaluated in utero growth measures and infant weight trajectories. This study will evaluate the associations of prenatal to ambient pollutants with trajectories from 3rd trimester through age 2 years.
Low-cost sensors can provide insight on the spatio-temporal variability of air pollution, provided that sufficient efforts are made to ensure data quality. Here, 19 AirBeam particulate matter (PM) were deployed from December 2016 January 2017 determine spatial PM2.5 in Sacramento, California. Prior to, and after, study, collocated at a regulatory monitoring site. The demonstrated high degree precision during all measurement periods (Pearson R2 = 0.98 − 0.99 across sensors), with little...
Wildland fire emissions from both wildfires and prescribed fires represent a major component of overall U.S. emissions. Obtaining an accurate, time-resolved inventory these is important for many purposes, including to account greenhouse gases short-lived climate forcers, as well model air quality health, regulatory, planning purposes. For the Environmental Protection Agency's 2011 2014 National Emissions Inventories, new methodology was developed reconcile wide range available information...
Background Fire research and management applications, such as fire behaviour analysis emissions modelling, require consistent, highly resolved spatiotemporal information on wildfire growth progression. Aims We developed a new mapping method that uses quality-assured sub-daily active fire/thermal anomaly satellite retrievals (2003–2020 MODIS 2012–2020 VIIRS data) to develop high-resolution dataset, including areas, perimeters, cross-referenced from agency reports. Methods Satellite detections...
Air pollution exposure has been associated with increased risk of COVID-19 incidence and mortality by ecological analyses. Few studies have investigated the specific effect traffic-related air on severity.To investigate associations near-roadway (NRAP) severity using individual-level outcome data.The retrospective cohort includes 75,010 individuals (mean age 42.5 years, 54% female, 66% Hispanic) diagnosed at Kaiser Permanente Southern California between 3/1/2020-8/31/2020. NRAP exposures...
Few studies have examined the effects of long-term childhood air pollution exposure on adult respiratory health, including whether underlie this relation.
Abstract Critical loads (CLs) are frequently used to quantify terrestrial ecosystem impacts from nitrogen (N) deposition using ecological responses such as the growth and mortality of tree species. Typically, CLs reported a single value, with uncertainty, for an indicator across species' entire range. Mediating factors climate soil conditions can influence sensitivity N, but magnitudes these effects rarely calculated explicitly. Here, we spatial variability estimation error in N survival 10...
Importance Emerging evidence suggests that exposure to air pollution affects children’s glucose metabolism. However, the underlying mechanisms are not fully understood. Objective To investigate whether body mass index (BMI; calculated as weight in kilograms divided by height meters squared) growth trajectories mediate association between traffic-related (TRAP) and insulin resistance. Design, Setting, Participants As part of Southern California Children’s Health Study, ongoing Meta-Air2...
Air pollution has been associated with gestational hypertension (GH) and preeclampsia, but susceptible windows of exposure potential vulnerability by comorbidities, such as prenatal depression, remain unclear.
ABSTRACT Development organizations are increasingly adopting market‐based approaches to reducing rural poverty and food insecurity in the global South. The value chain approach is particularly popular. Aid donors, governments non‐governmental applying concepts originally designed for promoting industrial production smallholder agricultural production. Cashew development Côte d'Ivoire illustrates this new which ‘upgrading’ processing links top priorities. A core assumption informing that...
Critical loads (CLs) of atmospheric deposition for nitrogen (N) and sulfur (S) are used to support decision making related air regulation land management. Frequently, CLs calculated using empirical methods, the certainty results depends on accurate representation underlying ecological processes. Machine learning (ML) models perform well in modeling processes with non-linear characteristics significant variable interactions. We bootstrap ensemble ML methods develop CL estimates assess...