Ozone response modeling to NOx and VOC emissions: Examining machine learning models.

Current machine learning (ML) applications in atmospheric science focus on forecasting and bias correction for numerical modeling estimations, but few studies examined the nonlinear response of their predictions to precursor emissions. This study uses ground-level maximum daily 8-hour ozone average (MDA8 O) as an example to examine O responses to local anthropogenic NOx and VOC emissions in Taiwan by Response Surface Modeling (RSM). Three different datasets for RSM were examined, including the Community Multiscale Air Quality (CMAQ) model data, ML-measurement-model fusion (ML-MMF) data, and ML data, which respectively represent direct numerical model predictions, numerical predictions adjusted by observations and other auxiliary data, and ML predictions based on observations and other auxiliary data.

Localized energy burden, concentrated disadvantage, and the feminization of energy poverty.

Energy burden directly influences households' health and safety. Amid a growing literature on energy, poverty and gender remains relatively understudied. We evaluate socioeconomic, geographic, and health factors as multidimensions of concentrated disadvantage that magnify energy burden in the United States over time.

How limitations in energy access, poverty, and socioeconomic disparities compromise health interventions for outbreaks in urban settings.

Low-income households (LIHs) have experienced increased poverty and inaccess to healthcare services during the COVID-19 pandemic, limiting their ability to adhere to health-protective behaviors. We use an epidemiological model to show how a households' inability to adopt social distancing, owing to constraints in utility and healthcare expenditure, can drastically impact the course of disease outbreaks in five urban U.S.

Quantifying spatial heterogeneity of vulnerability to short-term PM exposure with data fusion framework.

The current estimations of the burden of disease (BD) of PM exposure is still potentially biased by two factors: ignorance of heterogeneous vulnerabilities at diverse urbanization levels and reliance on the risk estimates from existing literature, usually from different locations. Our objectives are (1) to build up a data fusion framework to estimate the burden of PM exposure while evaluating local risks simultaneously and (2) to quantify their spatial heterogeneity, relationship to land-use characteristics, and derived uncertainties when calculating the disease burdens. The feature of this study is applying six local databases to extract PM exposure risk and the BD information, including the risks of death, cardiovascular disease (CVD), and respiratory disease (RD), and their spatial heterogeneities through our data fusion framework.

Evaluating the impact of mobility on COVID-19 pandemic with machine learning hybrid predictions.

COVID-19 pandemic had expanded to the US since early 2020 and has caused nationwide economic loss and public health crisis. Until now, although the US has the most confirmed cases in the world and are still experiencing an increasing pandemic, several states insisted to re-open business activities and colleges while announced strict control measures. To provide a quantitative reference for official strategies, predicting the near future trend based on finer spatial resolution data and presumed scenarios are urgently needed.

Transient risk of ambient fine particulate matter on hourly cardiovascular events in Tainan City, Taiwan.

Background: The association between daily changes in ambient fine particulate matter (PM2.5) and cardiovascular diseases have been well established in mechanistic, epidemiologic and exposure studies. Only a few studies examined the effect of hourly variations in air pollution on triggering cardiovascular events.

Source Apportionment of Volatile Organic Compounds (VOCs) by Positive Matrix Factorization (PMF) supported by Model Simulation and Source Markers - Using Petrochemical Emissions as a Showcase.

This study demonstrates the use of positive matrix factorization (PMF) in a region with a major Petrochemical Complex, a prominent source of volatile organic compounds (VOCs), as a showcase of PMF applications. The PMF analysis fully exploited the quality and quantity of the observation data, sufficed by a cluster of 9 monitoring sites within a 20 km radius of the petro-complex. Each site provided continuous data of 54 speciated VOCs and meteorological variables.

Source apportionment of particulate matter and selected volatile organic compounds with multiple time resolution data.

Fine particulate matter (PM2.5) and volatile organic compounds (VOCs) co-exist in ambient air and contribute to adverse health effects in human populations. This study was conducted to demonstrate the feasibility of utilizing a composite data set which included PM2.