A5083580711- Air Quality and Health Impacts
- Air Quality Monitoring and Forecasting
- Vehicle emissions and performance
- Indoor Air Quality and Microbial Exposure
- Atmospheric chemistry and aerosols
- Noise Effects and Management
- Toxic Organic Pollutants Impact
- Climate Change and Health Impacts
- Odor and Emission Control Technologies
- Asthma and respiratory diseases
- Water Quality Monitoring and Analysis
- Traffic Prediction and Management Techniques
- Impact of Light on Environment and Health
- Heavy metals in environment
- Water Quality Monitoring Technologies
- Radioactivity and Radon Measurements
- Transportation Planning and Optimization
- Pediatric health and respiratory diseases
California State University, Fresno
2016-2025
San Jose State University
2021-2022
California State University System
2021-2022
Seoul National University Bundang Hospital
2012
The University of Texas Health Science Center at Houston
2009
Environmental and Occupational Health Sciences Institute
2002-2006
Rutgers, The State University of New Jersey
2002-2006
New Jersey Department of Environmental Protection
2006
The indoor and outdoor concentrations of 30 polycyclic aromatic hydrocarbons (PAHs) were measured in 55 nonsmoking residences three urban areas during June 1999−May 2000. data represent the subset samples collected within Relationship Indoor, Outdoor, Personal Air study (RIOPA). from homes Los Angeles, CA, Houston, TX, Elizabeth, NJ. In samples, total PAH (∑PAH) 4.2−64 ng m-3 10−160 12−110 Elizabeth. ∑PAH 16−220 21−310 22−350 profiles low molecular weight PAHs (3−4 rings) cities not...
Previous studies have consistently shown a significant correlation between air pollution, particularly PM 2.5 , and various diseases, as well increased mortality rates. This study introduces novel approach for predicting time‐specific indoor exposure by incorporating individual movement routes activity spaces using GPS tracking data time–activity diary. The models were trained separately each hour of the day (e.g., 0:00–0:59, 1:00–1:59) with total 24 models. Their applicability was...
This study analyzed the factors influencing personal PM2.5 exposure levels among adults with allergic diseases in Seoul using a linear mixed-effects (LMEs) model. The average concentration of participants was 17.38 μg/m3, exceeding World Health Organization (WHO) daily recommended guideline (15.00 μg/m3), though it relatively low compared to global levels. Inter-individual variability approximately 43.5%, varying significantly depending on microenvironments. Notably, 58% exhibited higher...
To investigate the place-based association between BCM and air pollution in middle-aged (45–64) older-aged women (65+) California at zip code level, secondary data were collected from Department of Public Health (CDPH) Data Vital Statistics, CalEnviroScreen 4.0, American Community Survey (ACS) Census. Multiple linear regression was used to test significance age-standardized rates. The results indicate a significant PM2.5 rates for both groups (β = 3.73, 95% CI [2.89, 4.58]; β 5.33, [2.75,...
Abstract The study investigated PM 2.5 and heavy metal pollutant concentrations in Seoul Wonju, South Korea, emphasizing the importance of considering chemical constituents for health impact assessments. While were similar between two cities with slight variations, differed significantly. Regional sources, composition, meteorological conditions influenced these variations. Exposure to Fe was highest all areas, some metals exceeding permissible levels, stressing need consider regional...
Ambient volatile organic compound concentrations outside residences were measured in Elizabeth, New Jersey as part of the Relationship Indoor, Outdoor, and Personal Air (RIOPA) study to assess influence proximity known ambient emissions sources. The closest distances between outdoor samplers emission sources determined using Geographic Information Systems (GIS) techniques. Multiple regression models developed for residential aromatic hydrocarbons (BTEX), methyl tert butyl ether (MTBE),...
Fourier transform infrared (FTIR) spectra of outdoor, indoor, and personal fine particulate matter (PM(2.5)) samples were collected during the Relationship Indoor, Outdoor, Personal Air (RIOPA) study. FTIR spectroscopy provides functional group information about entire PM(2.5) sample without any chemical preparation. It is particularly important to characterizing poorly understood organic fraction PM(2.5). To our knowledge this first time that has been applied a exposure The results used...
Traditional methods for measuring personal exposure to fine particulate matter (PM2.5) are cumbersome and lack spatiotemporal resolution; that time-resolved limited a single species/component of PM. To address these limitations, we developed an automated microenvironmental aerosol sampler (AMAS), capable resolving by microenvironment. The AMAS is wearable device uses GPS sensor algorithm in conjunction with custom valve manifold sample PM2.5 onto distinct filter channels evaluate home,...
Abstract This paper presents the analysis of ambient air concentrations 10 carbonyl compounds (aldehydes and ketones) measured in yards 87 residences city Elizabeth, NJ, throughout 1999–2001. Most these were twice different seasons; sampling duration was 48 hr each time. The authors observed higher for most on warmer days, reflecting larger contributions photochemical reactions days. estimated production varied substantially across could be as high 60%. Photochemical activity, however,...
This study aims to propose an indoor air quality prediction method that can be easily utilized and reflects temporal characteristics using outdoor input data measured near the target point as calculate PM2.5 concentration through a multiple linear regression model. The atmospheric conditions pollution detected in one-minute intervals sensor-based monitoring equipment (Dust Mon, Sentry Co Ltd., Seoul, Korea) inside outside houses from May 2019 April 2021 were used develop By dividing model...
Abstract In this study, we developed a prediction model for heavy metal concentrations using PM 2.5 and meteorological variables. Data was collected from five sites, encompassing factors, , 18 metals over 2 years. The study employed four analytical methods: multiple linear regression (MLR), random forest (RFR), gradient boosting, artificial neural networks (ANN). RFR the best predictor most metals, boosting ANN were optimal certain like Al, Cu, As, Mo, Zn, Cd. Upon evaluating final model’s...