Dataset Information

Long-Term Trend of Gaseous Ammonia Over the United States: Modeling and Comparison With Observations.

ABSTRACT: The concentrations of atmospheric ammonia ([NH₃]) have been observed to be increasing over the United States in the last decade, especially in Eastern United States. It is important to understand this temporal trend and variation due to the role of NH₃ in particle formation and its ecological effects. Here the long-term trend of [NH₃] over the United States is investigated using GEOS-Chem, a global 3-D tropospheric chemistry model, and is corroborated with empirical evidence from the Ammonia Monitoring Network. Model simulations, consistent with observations, show increase in [NH₃] over the United States from 2001 to 2016, with magnitude largest in the East (~5% to 12%/year) and smallest in the West (~0% to 5%/year). Reasons for this are examined, and evidence for the role of decreasing SO₂ and NO_x emissions in increasing [NH₃] is provided. The contributions of meteorology and NH₃ emission changes to the [NH₃] increase appear to be small during the period. Our sensitivity study suggests that decreasing SO₂ and NO_x emissions over the United States owing to stringent regulations explain about 2/3 and 1/3 of the increase in [NH₃], respectively. This effect is different for various NH₃ and SO₂ and NO_x regimes. Given the continued reduction of SO₂ and NO_x emissions due to U.S. regulations mainly aimed at PM_2.5 reduction, the present results are important towards better assessing the environmental impact of emission controlling policies.

SUBMITTER: Yu F

PROVIDER: S-EPMC6473605 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Long-Term Trend of Gaseous Ammonia Over the United States: Modeling and Comparison With Observations.

Yu Fangqun F Nair Arshad Arjunan AA Luo Gan G

Journal of geophysical research. Atmospheres : JGR 20180812 15

The concentrations of atmospheric ammonia ([NH3]) have been observed to be increasing over the United States in the last decade, especially in Eastern United States. It is important to understand this temporal trend and variation due to the role of NH3 in particle formation and its ecological effects. Here the long-term trend of [NH3] over the United States is investigated using GEOS-Chem, a global 3-D tropospheric chemistry model, and is corroborated with empiri ...[more]

PMID: 31032164

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Project description:BackgroundThe COVID-19 pandemic has had profound and differential impacts on metropolitan areas across the United States and around the world. Within the United States, metropolitan areas that were hit earliest with the pandemic and reacted with scientifically based health policy were able to contain the virus by late spring. For other areas that kept businesses open, the first wave in the United States hit in mid-summer. As the weather turns colder, universities resume classes, and people tire of lockdowns, a second wave is ascending in both metropolitan and rural areas. It becomes more obvious that additional SARS-CoV-2 surveillance is needed at the local level to track recent shifts in the pandemic, rates of increase, and persistence.ObjectiveThe goal of this study is to provide advanced surveillance metrics for COVID-19 transmission that account for speed, acceleration, jerk and persistence, and weekly shifts, to better understand and manage risk in metropolitan areas. Existing surveillance measures coupled with our dynamic metrics of transmission will inform health policy to control the COVID-19 pandemic until, and after, an effective vaccine is developed. Here, we provide values for novel indicators to measure COVID-19 transmission at the metropolitan area level.MethodsUsing a longitudinal trend analysis study design, we extracted 260 days of COVID-19 data from public health registries. We used an empirical difference equation to measure the daily number of cases in the 25 largest US metropolitan areas as a function of the prior number of cases and weekly shift variables based on a dynamic panel data model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R.ResultsMinneapolis and Chicago have the greatest average number of daily new positive results per standardized 100,000 population (which we refer to as speed). Extreme behavior in Minneapolis showed an increase in speed from 17 to 30 (67%) in 1 week. The jerk and acceleration calculated for these areas also showed extreme behavior. The dynamic panel data model shows that Minneapolis, Chicago, and Detroit have the largest persistence effects, meaning that new cases pertaining to a specific week are statistically attributable to new cases from the prior week.ConclusionsThree of the metropolitan areas with historically early and harsh winters have the highest persistence effects out of the top 25 most populous metropolitan areas in the United States at the beginning of their cold weather season. With these persistence effects, and with indoor activities becoming more popular as the weather gets colder, stringent COVID-19 regulations will be more important than ever to flatten the second wave of the pandemic. As colder weather grips more of the nation, southern metropolitan areas may also see large spikes in the number of cases.

Project description:BackgroundKnowing the geographical and temporal variation in radon concentrations is essential for assessing residential exposure to radon, the leading cause of lung cancer in never-smokers in the United States. Tens of millions of short-term radon measurements, which normally last 2 to 4 days, have been conducted during the past decades. However, these massive short-term measurements have not been commonly used in exposure assessment because of the conflicting evidence regarding their correlation with long-term measurements, the gold standard of assessing long-term radon exposure.ObjectiveWe aim to evaluate the extent to which a long-term radon measurement can be predicted by a collocated short-term radon measurement under different conditions.MethodsWe compiled a national dataset of 2245 pairs of collocated short- and long-term measurements, analyzed the predictability of long-term measurements with stratified linear regression and bootstrapping resampling.ResultsWe found that the extent to which a long-term measurement can be predicted by the collocated short-term measurement was a joint function of two factors: the temporal difference in starting dates between two measurements and the length of the long-term measurement. Short-term measurements, jointly with other factors, could explain up to 79% (0.95 Confidence Interval [CI]: 0.73-0.84) of the variance in seasonal radon concentrations and could explain up to 67% (0.95 CI: 0.52-0.81) of the variance in annual radon concentrations. The large proportions of variance explained suggest that short-term measurement can be used as convenient proxy for seasonal radon concentrations. Accurate annual radon estimation entails averaging multiple short-term measurements in different seasons.SignificanceOur findings will facilitate the usage of abundant short-term radon measurements, which have been obtained but was previously underutilized in assessing residential radon exposure.Impact statementTens of millions of short-term radon measurements have been conducted but underutilized in assessing residential exposure to radon, the greatest cause of lung cancer in non-smokers. We investigate the correlations between collocated short- and long-term measurements in 2245 U.S. buildings and find that short-term measurements can explain ~75% of the variance in subsequent long-term measurements in the same buildings. Our results can facilitate the usage of massive short-term radon measurements that have been conducted to estimate the spatial and longitudinal distribution of radon concentrations, which can be used in epidemiological studies to quantify the health effects of radon.

Dataset Information

Long-Term Trend of Gaseous Ammonia Over the United States: Modeling and Comparison With Observations.

Publications

Long-Term Trend of Gaseous Ammonia Over the United States: Modeling and Comparison With Observations.

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