Spatio-Temporal Analysis of Roadside Transportation-Related Air Quality (StarTraq 2021): A Characterization of Bike Trails and Highways in the Fresno/Clovis Area

The San Joaquin Valley is identified as an area with a high level of particulate matter (PM) in the air, reaching above the federal and state clean air standards (EPA 2019). Many of the cities in the valley are classified as the most polluted cities in the United States for both particulate matter and ozone pollution (American Lung Association, 2021). To resolve this issue, alternative forms of transportation have been considered in transportation planning. In this study, active transportation mode air quality was monitored on selected Woodward Park and Old Clovis trails and urban bike lanes. Real-time aerosol monitors, and low-cost sensors were carried in a backpack on bicycles during the sampling. Researchers collected GPS data via a portable GPS technology called Tracksticks. Driving transportation mode air quality data was acquired from the roadways within the Fresno/Clovis area, spanning six sampling routes, and during intercity trips between Fresno, Berkeley, and Los Angeles, for a total of five sampling routes. ‘On-Road\u27 (outside vehicle) monitors were installed on the roof of a vehicle while ‘In-Vehicle’ monitors were installed inside the vehicle for comparison with the particulate pollution levels in the two contrasting microenvironments. The results showed the following three main outcomes: (1) clear relationships exist among PMs of different sizes; (2) there were greater variations in air quality of bike trails and On-Road samples than backyard and In-Vehicle samples; (3) we observed significant differences in air quality inside and outside the vehicle while driving local and intercity roadways; and (4) the road trip to the Bay area revealed that San Joaquin Valley has increased ambient PM2.5 and black carbon (BC) levels compared to those in the Bay Area on every trip, regardless of the daily change of the air quality

Spatio-Temporal Analysis of the Roadside Transportation Related Air Quality (STARTRAQ) and Neighborhood Characterization

To promote active transportation modes (such as bike ride and walking), and to create safer communities for easier access to transit, it is essential to provide consolidated data-driven transportation information to the public. The relevant and timely information from data facilitates the improvement of decision-making processes for the establishment of public policy and urban planning for sustainable growth, and for promoting public health in the region. For the characterization of the spatial variation of transportation-emitted air pollution in the Fresno/Clovis neighborhood in California, various species of particulate matters emitted from traffic sources were measured using real-time monitors and GPS loggers at over 100 neighborhood walking routes within 58 census tracts from the previous research, Children’s Health to Air Pollution Study - San Joaquin Valley (CHAPS-SJV). Roadside air pollution data show that PM2.5, black carbon, and PAHs were significantly elevated in the neighborhood walking air samples compared to indoor air or the ambient monitoring station in the Central Fresno area due to the immediate source proximity. The simultaneous parallel measurements in two neighborhoods which are distinctively different areas (High diesel High poverty vs. Low diesel Low poverty) showed that the higher pollution levels were observed when more frequent vehicular activities were occurring around the neighborhoods. Elevated PM2.5 concentrations near the roadways were evident with a high volume of traffic and in regions with more unpaved areas. Neighborhood walking air samples were influenced by immediate roadway traffic conditions, such as encounters with diesel trucks, approaching in close proximity to freeways and/or busy roadways, passing cigarette smokers, and gardening activity. The elevated black carbon concentrations occur near the highway corridors and regions with high diesel traffic and high industry. This project provides consolidated data-driven transportation information to the public including: 1. Transportation-related particle pollution data 2. Spatial analyses of geocoded vehicle emissions 3. Neighborhood characterization for the built environment such as cities, buildings, roads, parks, walkways, etc

Spatio-Temporal Analysis of Roadside Transportation-Related Air Quality (StarTraq 2021): A Characterization of Bike Trails and Highways in the Fresno/Clovis Area

ZSB12017-SJAUXThe San Joaquin Valley is identified as an area with a high level of particulate matter (PM) in the air, reaching above the federal and state clean air standards (EPA 2019). Many of the cities in the valley are classified as the most polluted cities in the United States for both particulate matter and ozone pollution (American Lung Association, 2021). To resolve this issue, alternative forms of transportation have been considered in transportation planning. In this study, active transportation mode air quality was monitored on selected Woodward Park and Old Clovis trails and urban bike lanes. Real-time aerosol monitors, and lowcost sensors were carried in a backpack on bicycles during the sampling. Researchers collected GPS data via a portable GPS technology called Tracksticks. Driving transportation mode air quality data was acquired from the roadways within the Fresno/Clovis area, spanning six sampling routes, and during intercity trips between Fresno, Berkeley, and Los Angeles, for a total of five sampling routes. \u2018On-Road' (outside vehicle) monitors were installed on the roof of a vehicle while \u2018In-Vehicle\u2019 monitors were installed inside the vehicle for comparison with the particulate pollution levels in the two contrasting microenvironments. The results showed the following three main outcomes: (1) clear relationships exist among PMs of different sizes; (2) there were greater variations in air quality of bike trails and On-Road samples than backyard and In-Vehicle samples; (3) we observed significant differences in air quality inside and outside the vehicle while driving local and intercity roadways; and (4) the road trip to the Bay area revealed that San Joaquin Valley has increased ambient PM2.5 and black carbon (BC) levels compared to those in the Bay Area on every trip, regardless of the daily change of the air quality

Clinical Characteristics, Racial Inequities, and Outcomes in Patients with Breast Cancer and COVID-19: A COVID-19 and Cancer Consortium (CCC19) Cohort Study

BACKGROUND: Limited information is available for patients with breast cancer (BC) and coronavirus disease 2019 (COVID-19), especially among underrepresented racial/ethnic populations. METHODS: This is a COVID-19 and Cancer Consortium (CCC19) registry-based retrospective cohort study of females with active or history of BC and laboratory-confirmed severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection diagnosed between March 2020 and June 2021 in the US. Primary outcome was COVID-19 severity measured on a five-level ordinal scale, including none of the following complications, hospitalization, intensive care unit admission, mechanical ventilation, and all-cause mortality. Multivariable ordinal logistic regression model identified characteristics associated with COVID-19 severity. RESULTS: 1383 female patient records with BC and COVID-19 were included in the analysis, the median age was 61 years, and median follow-up was 90 days. Multivariable analysis revealed higher odds of COVID-19 severity for older age (aOR per decade, 1.48 [95% CI, 1.32-1.67]); Black patients (aOR 1.74; 95 CI 1.24-2.45), Asian Americans and Pacific Islander patients (aOR 3.40; 95 CI 1.70-6.79) and Other (aOR 2.97; 95 CI 1.71-5.17) racial/ethnic groups; worse ECOG performance status (ECOG PS ≥2: aOR, 7.78 [95% CI, 4.83-12.5]); pre-existing cardiovascular (aOR, 2.26 [95% CI, 1.63-3.15])/pulmonary comorbidities (aOR, 1.65 [95% CI, 1.20-2.29]); diabetes mellitus (aOR, 2.25 [95% CI, 1.66-3.04]); and active and progressing cancer (aOR, 12.5 [95% CI, 6.89-22.6]). Hispanic ethnicity, timing, and type of anti-cancer therapy modalities were not significantly associated with worse COVID-19 outcomes. The total all-cause mortality and hospitalization rate for the entire cohort was 9% and 37%, respectively however, it varied according to the BC disease status. CONCLUSIONS: Using one of the largest registries on cancer and COVID-19, we identified patient and BC-related factors associated with worse COVID-19 outcomes. After adjusting for baseline characteristics, underrepresented racial/ethnic patients experienced worse outcomes compared to non-Hispanic White patients. FUNDING: This study was partly supported by National Cancer Institute grant number P30 CA068485 to Tianyi Sun, Sanjay Mishra, Benjamin French, Jeremy L Warner; P30-CA046592 to Christopher R Friese; P30 CA023100 for Rana R McKay; P30-CA054174 for Pankil K Shah and Dimpy P Shah; KL2 TR002646 for Pankil Shah and the American Cancer Society and Hope Foundation for Cancer Research (MRSG-16-152-01-CCE) and P30-CA054174 for Dimpy P Shah. REDCap is developed and supported by Vanderbilt Institute for Clinical and Translational Research grant support (UL1 TR000445 from NCATS/NIH). The funding sources had no role in the writing of the manuscript or the decision to submit it for publication. CLINICAL TRIAL NUMBER: CCC19 registry is registered on ClinicalTrials.gov, NCT04354701

Proposal of a methodology for prediction of heavy metals concentration based on PM2.5 concentration and meteorological variables using machine learning

Abstract In this study, we developed a prediction model for heavy metal concentrations using PM2.5 concentrations and meteorological variables. Data was collected from five sites, encompassing meteorological factors, PM2.5, and 18 metals over 2 years. The study employed four analytical methods: multiple linear regression (MLR), random forest regression (RFR), gradient boosting, and artificial neural networks (ANN). RFR was the best predictor for most metals, and gradient boosting and ANN were optimal for certain metals like Al, Cu, As, Mo, Zn, and Cd. Upon evaluating the final model’s predicted values against the actual measurements, differences in the concentration distribution between measurement locations were observed for Mn, Fe, Cu, Ba, and Pb, indicating varying prediction performances among sites. Additionally, Al, As, Cd, and Ba showed significant differences in prediction performance across seasons. The developed model is expected to overcome the technical limitations involved in measuring and analyzing heavy metal concentrations. It could further be utilized to obtain fundamental data for studying the health effects of exposure to hazardous substances such as heavy metals

Personal Exposure to PM2.5 Black Carbon and Aerosol Oxidative Potential using an Automated Microenvironmental Aerosol Sampler (AMAS).

Traditional methods for measuring personal exposure to fine particulate matter (PM2.5) are cumbersome and lack spatiotemporal resolution; methods that are time-resolved are limited to a single species/component of PM. To address these limitations, we developed an automated microenvironmental aerosol sampler (AMAS), capable of resolving personal exposure by microenvironment. The AMAS is a wearable device that uses a GPS sensor algorithm in conjunction with a custom valve manifold to sample PM2.5 onto distinct filter channels to evaluate home, school, and other (e.g., outdoors, in transit, etc.) exposures. Pilot testing was conducted in Fresno, CA where 25 high-school participants ( n = 37 sampling events) wore an AMAS for 48-h periods in November 2016. Data from 20 (54%) of the 48-h samples collected by participants were deemed valid and the filters were analyzed for PM2.5 black carbon (BC) using light transmissometry and aerosol oxidative potential (OP) using the dithiothreitol (DTT) assay. The amount of inhaled PM2.5 was calculated for each microenvironment to evaluate the health risks associated with exposure. On average, the estimated amount of inhaled PM2.5 BC (μg day-1) and OP [(μM min-1) day-1] was greatest at home, owing to the proportion of time spent within that microenvironment. Validation of the AMAS demonstrated good relative precision (8.7% among collocated instruments) and a mean absolute error of 22% for BC and 33% for OP when compared to a traditional personal sampling instrument. This work demonstrates the feasibility of new technology designed to quantify personal exposure to PM2.5 species within distinct microenvironments

Proposal of a Methodology for Prediction of Indoor PM_2.5 Concentration Using Sensor-Based Residential Environments Monitoring Data and Time-Divided Multiple Linear Regression Model

This study aims to propose an indoor air quality prediction method that can be easily utilized and reflects temporal characteristics using indoor and outdoor input data measured near the indoor target point as input to calculate indoor PM2.5 concentration through a multiple linear regression model. The atmospheric conditions and air pollution detected in one-minute intervals using sensor-based monitoring equipment (Dust Mon, Sentry Co Ltd., Seoul, Korea) inside and outside houses from May 2019 to April 2021 were used to develop the prediction model. By dividing the multiple linear regression model into one-hour increments, we attempted to overcome the limitation of not representing the multiple linear regression model’s characteristics over time and limited input variables. The multiple linear regression (MLR) model classified by time unit showed an improvement in explanatory power by up to 9% compared to the existing model, and some hourly models had an explanatory power of 0.30. These results indicated that the model needs to be subdivided by time period to more accurately predict indoor PM2.5 concentrations