City Tech and Intelligence for Smart and Healthy Communities
/My work takes a systems approach to solving some of the world’s most complex problems, such as transportation planning and alternate transportation technologies, air quality and climate change, energy, health impacts, and sustainable food systems. I develop and apply data analytics, networks/complex systems models, engineering computing techniques, statistics/econometrics methods, mathematical optimization and operations research, and real-world experiments to achieve sustainable infrastructure systems, healthy communities, and smart cities.
We study two major challenges that face researchers and policy makers when it comes to balancing our transportation demands with air quality goals: 1) modeling microenvironment air quality, especially near roadways, and 2) clean diesel strategies.
Our research in this area is designed to examine the relationship between infrastructure, users, air quality, climate change, and health costs, particularly for at-risk populations that are disproportionately impacted by pollution.
In urban areas, transportation emissions are a major source of ozone precursors. To find strategies that will effectively reduce these emissions, we need to assess and analyze transportation activity and emission patterns in a statistically robust way. Using a chemical transport model (CTM) to simulate air quality, we have modeled atmospheric nanoparticulate matter and its precursor gaseous emission, and then assessed the public health effects of these pollutants.
We have also developed tools to help evaluate the effectiveness of environmental policies and regulation related to air quality and health. One is called the Estimating Air pollution Social Impact Using Regression (EASIUR) model. This model estimates the public health effects of major air pollutants anywhere in the United States at trivial computational costs without compromising the technical rigor and precision of CTMs. Another is called the Air Pollution Social Cost Accounting (APSCA) model, which estimates the health effects of air pollution at any downwind (receptor) location in unprecedented detail. The results can help policy makers determine which emission sources to target to improve air quality and help researchers develop new methods for designing emission control strategies at municipal, state, and federal levels. The “Cyber-Physical Infrastructure and Informatics for Healthy Living in Smart Cities” project, sponsored by the Atkinson Center for a Sustainable Future at Cornell, is working to establish urban sensor networks that will help decision makers monitor and reduce congestion and harmful emissions and improve environmental quality at the local level.
Monitoring atmospheric conditions accurately in real-time has a number of vital applications. Our work has shown that cellular communication networks can potentially be used to detect temperature inversions that trap pollutants at ground level and lead to adverse atmospheric conditions. In an ongoing research project, we are looking into the possibility of using existing telecommunications infrastructure in Africa to monitor rainfall and provide early warnings to protect against malaria.
We are also working on improving data and knowledge about the relationship between particulate matter (PM) emissions and vehicle operating mode (e.g., cruise, idle, acceleration, deceleration), especially for diesel vehicles, new transportation technologies, and alternative fuels that will be used for upgrading existing fleets. The goal is to develop and evaluate novel and robust models to predict PM emissions from vehicles, and our work on diesel particle number emissions provides a scientific and methodological base for PM number emissions quantification and regulation.