The impacts of transported wildfire smoke aerosols on surface air quality in New York State: A multi-year study using machine learning

AbstractVertical profiles of atmospheric temperature, moisture, wind, and aerosols are essential information for weather monitoring and prediction. Their availability, however, is limited in space and time due to the significant resources required to observe them. To fill this gap, the New York State Mesonet (NYSM) Profiler Network has been deployed as a national testbed to facilitate the research, development and evaluation of ground-based profiling technologies and applications. The testbed comprises 17 profiler stations across the state, forming a long-term regional observational network. Each Profiler station is comprised of a ground-based Doppler lidar, a microwave radiometer (MWR) and an environmental Sky Imaging Radiometer (eSIR). Thermodynamic profiles (temperature and humidity) from the MWR; wind and aerosol profiles from the Doppler lidar; and solar radiance and optical depth parameters from the eSIR are collected, processed, disseminated, and archived every 10 minutes. This paper introduces the NYSM Profiler Network and reviews the network design and siting, instrumentation, network operations and maintenance, data and products, and some example applications highlighting the benefits of the network. Some sample applications include improved situational awareness and monitoring of the sea/land breeze, long-range wildfire smoke transport, air quality (PM2.5 and AOD) and boundary layer height. Ground-based profiling systems promise a path forward for filling a critical gap in the nation’s observing system with the potential to improve analysis and prediction for many weather-sensitive sectors, such as aviation, ground transportation, health, and wind energy.

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Toward Carbon-Neutral Electric Power Systems in the New York State: a Novel Multi-Scale Bottom-Up Optimization Framework Coupled with Machine Learning for Capacity Planning at Hourly Resolution

ACS Sustainable Chemistry & Engineering ◽

10.1021/acssuschemeng.1c06612 ◽

2021 ◽

Author(s):

Ning Zhao ◽

Fengqi You

Keyword(s):

Machine Learning ◽

New York ◽

Power Systems ◽

Electric Power ◽

Capacity Planning ◽

New York State ◽

Electric Power Systems ◽

York State ◽

Multi Scale ◽

Optimization Framework

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Estimation of PM2.5 Concentrations in New York State: Understanding the Influence of Vertical Mixing on Surface PM2.5 Using Machine Learning

Atmosphere ◽

10.3390/atmos11121303 ◽

2020 ◽

Vol 11 (12) ◽

pp. 1303

Author(s):

Wei-Ting Hung ◽

Cheng-Hsuan (Sarah) Lu ◽

Stefano Alessandrini ◽

Rajesh Kumar ◽

Chin-An Lin

Keyword(s):

Machine Learning ◽

New York ◽

New York State ◽

Vertical Mixing ◽

Model Performance ◽

Machine Learning Techniques ◽

Joint Analysis ◽

Ann Model ◽

York State ◽

Ann Models

In New York State (NYS), episodic high fine particulate matter (PM2.5) concentrations associated with aerosols originated from the Midwest, Mid-Atlantic, and Pacific Northwest states have been reported. In this study, machine learning techniques, including multiple linear regression (MLR) and artificial neural network (ANN), were used to estimate surface PM2.5 mass concentrations at air quality monitoring sites in NYS during the summers of 2016–2019. Various predictors were considered, including meteorological, aerosol, and geographic predictors. Vertical predictors, designed as the indicators of vertical mixing and aloft aerosols, were also applied. Overall, the ANN models performed better than the MLR models, and the application of vertical predictors generally improved the accuracy of PM2.5 estimation of the ANN models. The leave-one-out cross-validation results showed significant cross-site variations and were able to present the different predictor-PM2.5 correlations at the sites with different PM2.5 characteristics. In addition, a joint analysis of regression coefficients from the MLR model and variable importance from the ANN model provided insights into the contributions of selected predictors to PM2.5 concentrations. The improvements in model performance due to aloft aerosols were relatively minor, probably due to the limited cases of aloft aerosols in current datasets.

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Improvements in Urban Air Quality: Case Studies from New York State, USA

Water Air & Soil Pollution ◽

10.1007/s11270-010-0407-z ◽

2010 ◽

Vol 214 (1-4) ◽

pp. 93-106 ◽

Cited By ~ 12

Author(s):

Shannon M. Buckley ◽

Myron J. Mitchell

Keyword(s):

New York ◽

Air Quality ◽

Case Studies ◽

New York State ◽

Urban Air Quality ◽

Urban Air ◽

York State

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COVID-19 in New York state: Effects of demographics and air quality on infection and fatality

10.1101/2021.02.22.21252262 ◽

2021 ◽

Author(s):

Sumona Mondal ◽

Chaya Chaipitakporn ◽

Vijay Kumar ◽

Bridget Wangler ◽

Supraja Gurajala ◽

...

Keyword(s):

New York ◽

Air Quality ◽

Infection Rate ◽

Environmental Variables ◽

Strong Association ◽

New York State ◽

Significant Risk ◽

Mitigation Strategies ◽

York State ◽

Direct Location

ABSTRACTThe coronavirus disease 2019 (COVID-19) has had a global impact that has been unevenly distributed amongst and, even within countries. Multiple demographic and environmental factors have been associated with the risk of COVID-19 spread and fatality, including age, gender, ethnicity, poverty, and air quality among others. However, specific contributions of these factors are yet to be understood. Here, we attempted to explain the variability in infection, death, and fatality rates by understanding the contributions of a few selected factors. We compared the incidence of COVID-19 in New York State (NYS) counties during the first wave of infection and analyzed how different demographic and environmental variables associate with the variation observed across the counties. We observed that the two important COVID-19 metrics of infection rates and death rates to be well correlated, and both metrics being highest in counties located near New York City, considered one of the epicenters of the infection in the US. In contrast, disease fatality was found to be highest in a different set of counties despite registering a low infection rate. To investigate this apparent discrepancy, we divided the counties into three clusters based on COVID-19 infection, death rate, or fatality, and compared the differences in the demographic and environmental variables such as ethnicity, age, population density, poverty, temperature, and air quality in each of these clusters. Furthermore, a regression model built on this data reveals PM2.5 and distance from the epicenter are significant risk factors for high infection rate, while disease fatality has a strong association with age and PM2.5. Our results demonstrate, for the NYS, distinct contributions of old age, PM2.5, ethnicity these factors to the overall COVID-19 burden and highlight the detrimental impact of poor air quality. These results could help design and direct location-specific control and mitigation strategies.

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Asthma Prevalence, Management, and Education in New York State Elementary Schools: A Survey of School Nurses

The Journal of School Nursing ◽

10.1177/10598405070230050501 ◽

2007 ◽

Vol 23 (5) ◽

pp. 267-275 ◽

Cited By ~ 13

Author(s):

Christine Kielb ◽

Shao Lin ◽

Syni-an Hwang

Keyword(s):

New York ◽

Elementary Schools ◽

Air Quality ◽

Indoor Air Quality ◽

Indoor Air ◽

New York State ◽

Asthma Management ◽

School Nurses ◽

Urgent Care ◽

York State

A survey of school nurses was conducted in New York State elementary schools to assess asthma and asthma management in students. The survey contained questions about asthma morbidity, management and education, obstacles to management, and school indoor air quality. The reported prevalence of asthma among students was 8.5%. Of the students with asthma, 64% visited the health office, 26% were absent from school, 20% had physical limitations, and 7% needed urgent care. Only 28% had a written management plan at school, less than 25% of schools used asthma self-management programs, and obstacles to management included lack of time and funding. More than 25% rated school indoor air quality as “fair” or “poor.” Schools need to adopt key components of asthma management, and school nurses should be encouraged to work with others in the school setting to address indoor air quality problems that might be affecting health.

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