scholarly journals Asthma Hospital Admissions and Ambient Air Pollutant Concentrations in New York City

2012 ◽  
Vol 03 (09) ◽  
pp. 1102-1116 ◽  
Author(s):  
Carlos E. Restrepo ◽  
Jeffrey S. Simonoff ◽  
George D. Thurston ◽  
Rae Zimmerman
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Hospital Admissions ◽  
Ambient Air ◽  
Air Pollutant ◽  
Pollutant Concentrations ◽  
Air Pollutant Concentrations

Asthma Hospital Admissions and Ambient Air Pollution Concentrations in New York City

Epidemiology ◽  
2006 ◽  
Vol 17 (Suppl) ◽  
pp. S266 ◽  
Author(s):  
C Restrepo ◽  
J Simonoff ◽  
G Thurston ◽  
R Zimmerman
Keyword(s):  
New York ◽  
Air Pollution ◽  
New York City ◽  
York City ◽  
Hospital Admissions ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Pollution Concentrations

scholarly journals The causal effects of chronic air pollution on the intensity of COVID-19 disease: Some answers are blowing in the wind

2021 ◽  
Author(s):  
Marc N Conte ◽  
Matthew Gordon ◽  
Nicole Swartwood ◽  
Rachel Wilwerding ◽  
Chu A. (Alex) Yu
Keyword(s):  
New York ◽  
Ambient Air ◽  
Air Pollutant ◽  
Pollution Monitoring ◽  
Causal Relationships ◽  
Monitoring Networks ◽  
Pollutant Concentrations ◽  
Potential Benefits ◽  
Clean Air ◽  
Air Pollutant Concentrations

The threats posed by COVID-19 have catalyzed a search by researchers across multiple disciplines for policy-relevant findings about critical risk factors. We contribute to this effort by providing causal estimates of the link between increased chronic ambient pollutant concentrations and the intensity of COVID-19 disease, as measured by deaths and hospitalizations in New York City from March through August, 2020. Given concerns about unobservable characteristics that contribute to both ambient air pollutant concentrations and the impacts of COVID-19 disease, we instrument for pollutant concentrations using the time spent downwind of nearby highways and estimate key causal relationships using two-stage least squares models. The causal links between increases in concentrations of our traffic-related air pollutants (PM2.5, NO2, and NO) and COVID-19 deaths are much larger than the correlations presented in recent observational studies. We find that a 0.16 μg/m3 increase in average ambient PM2.5 concentration leads to an approximate 30% increase in COVID-19 deaths. This is the change in concentration associated with being downwind of a nearby highway. We see that this effect is mostly driven by residents with at least 75 years of age. In addition to emphasizing the importance of searching for causal relationships, our analysis highlights the value of increasing the density of pollution-monitoring networks and suggests potential benefits of further tightening of Clean Air Act amendments, as our estimated effects occur at concentrations well below thresholds set by the National Ambient Air Quality Standards.

scholarly journals The New York City COVID-19 Spread in the 2020 Spring: A Study on the Potential Role of Particulate Using Time Series Analysis and Machine Learning

2021 ◽  
Vol 11 (3) ◽  
pp. 1177
Author(s):  
Silvia Mirri ◽  
Marco Roccetti ◽  
Giovanni Delnevo
Keyword(s):  
Machine Learning ◽  
New York ◽  
Time Series ◽  
New York City ◽  
York City ◽  
Statistical Tests ◽  
Ambient Air ◽  
Air Pollutant ◽  
Machine Learning Algorithms ◽  
P Value

This study investigates the potential association between the daily distribution of the PM2,5 air pollutant and the initial spreading of COVID-19 in New York City. We study the period from 4 March to 22 March 2020, and apply our analysis to all five counties, including the city, plus seven neighboring counties, including both urban and peripheral districts. Using the Granger causality methodology, and considering the maximum lag period (14 days) between infection and the correspondent diagnosis, we found that the time series of the new daily infections registered in those 12 counties appear to correlate to the time series of the concentrations of the PM2.5 particulate circulating in the air, with 33 over 36 statistical tests with a p-value less than 0.005, thus confirming such a hypothesis. Moreover, looking for further confirmation of this association, we train four different machine learning algorithms on a portion of those time series. These are able to predict that the number of the new daily infections would have surpassed a given infections threshold for the remaining portion of the series, with an average accuracy ranging from 84% to 95%, depending on the algorithm and/or on the specific county under observation. This is similar to other results obtained from several polluted urban areas, e.g., Wuhan, Xiaogan, and Huanggang in China, and Northern Italy. Our study provides further evidence that ambient air pollutants can be associated with a daily COVID-19 infection incidence.

scholarly journals Term birth weight and ambient air pollutant concentrations during pregnancy, among women living in Monroe County, New York

2019 ◽  
Vol 29 (4) ◽  
pp. 500-509 ◽  
Author(s):  
Rui Li ◽  
Philip K. Hopke ◽  
Ann Dozier ◽  
Sally W. Thurston ◽  
Kelly Thevenet-Morrison ◽  
...  
Keyword(s):  
New York ◽  
Birth Weight ◽  
Ambient Air ◽  
Air Pollutant ◽  
Monroe County ◽  
Term Birth ◽  
Pollutant Concentrations ◽  
Air Pollutant Concentrations

scholarly journals Associations of Ambient Air Pollutants and Meteorological Factors With COVID-19 Transmission in 31 Chinese Provinces: A Time Series Study

2021 ◽  
Vol 58 ◽  
pp. 004695802110602
Author(s):  
Han Cao ◽  
Bingxiao Li ◽  
Tianlun Gu ◽  
Xiaohui Liu ◽  
Kai Meng ◽  
...  
Keyword(s):  
Air Pollution ◽  
Relative Humidity ◽  
Wind Velocity ◽  
Air Pollutants ◽  
Meteorological Factors ◽  
Meta Analysis ◽  
Ambient Air ◽  
Air Pollutant ◽  
Pollutant Concentrations ◽  
Air Pollutant Concentrations

Evidence regarding the effects of environmental factors on COVID-19 transmission is mixed. We aimed to explore the associations of air pollutants and meteorological factors with COVID-19 confirmed cases during the outbreak period throughout China. The number of COVID-19 confirmed cases, air pollutant concentrations, and meteorological factors in China from January 25 to February 29, 2020, (36 days) were extracted from authoritative electronic databases. The associations were estimated for a single-day lag as well as moving averages lag using generalized additive mixed models. Region-specific analyses and meta-analysis were conducted in 5 selected regions from the north to south of China with diverse air pollution levels and weather conditions and sufficient sample size. Nonlinear concentration–response analyses were performed. An increase of each interquartile range in PM2.5, PM10, SO2, NO2, O3, and CO at lag4 corresponded to 1.40 (1.37–1.43), 1.35 (1.32–1.37), 1.01 (1.00–1.02), 1.08 (1.07–1.10), 1.28 (1.27–1.29), and 1.26 (1.24–1.28) ORs of daily new cases, respectively. For 1°C, 1%, and 1 m/s increase in temperature, relative humidity, and wind velocity, the ORs were 0.97 (0.97–0.98), 0.96 (0.96–0.97), and 0.94 (0.92–0.95), respectively. The estimates of PM2.5, PM10, NO2, and all meteorological factors remained significantly after meta-analysis for the five selected regions. The concentration–response relationships showed that higher concentrations of air pollutants and lower meteorological factors were associated with daily new cases increasing. Higher air pollutant concentrations and lower temperature, relative humidity and wind velocity may favor COVID-19 transmission. Controlling ambient air pollution, especially for PM2.5, PM10, NO2, may be an important component of reducing risk of COVID-19 infection. In addition, as winter months are arriving in China, the meteorological factors may play a negative role in prevention. Therefore, it is significant to implement the public health control measures persistently in case another possible pandemic.

scholarly journals Associations Between Ambient Air Pollutant Concentrations and Birth Weight

Epidemiology ◽  
2019 ◽  
Vol 30 (5) ◽  
pp. 624-632 ◽  
Author(s):  
Matthew J. Strickland ◽  
Ying Lin ◽  
Lyndsey A. Darrow ◽  
Joshua L. Warren ◽  
James A. Mulholland ◽  
...  
Keyword(s):  
Birth Weight ◽  
Ambient Air ◽  
Air Pollutant ◽  
Pollutant Concentrations ◽  
Air Pollutant Concentrations

Land use regression modelling of ambient air pollutants in Hong Kong

2020 ◽  
Author(s):  
Zhiyuan Li ◽  
Steve Hung Lam Yim ◽  
Kin-Fai Ho
Keyword(s):  
Land Use ◽  
Hong Kong ◽  
Air Quality ◽  
Air Pollutants ◽  
Ambient Air ◽  
Air Pollutant ◽  
High Rise ◽  
Pollutant Concentrations ◽  
Air Quality Measurements ◽  
Air Pollutant Concentrations

<p>Land use regression (LUR) models estimate air pollutant concentrations for areas without air quality measurements, which provides valuable information for exposure assessment and epidemiological studies. In the present study, we developed LUR models for ambient air pollutants in Hong Kong, China, a typical high-density and high-rise city. Air quality measurements at sixteen air quality monitoring stations, operated by the Hong Kong Environmental Protection Department, were collected. Moreover, five categories of predictor variables, including population distribution, traffic emissions, land use variables, urban/building morphology, and meteorological parameters, were employed to establish the LUR models of various air pollutants. Then the spatial distribution of air pollutant concentrations at 1 km × 1 km grid cells were plotted. Taking fine particle (PM2.5) as an example, the developed LUR model explained 89% of variability of PM2.5 concentrations, with a leave-one-out-cross-validation R2 of 0.64. LUR modelling results for other air pollutants will be presented. In addition, further improvements on the development of LUR models will be discussed. This study can help to assess long-term exposures to air pollutants for high-density and high-rise urban areas like Hong Kong.</p>

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