Short-term changes in the base neutralizing capacity of an acid Adirondack lake, New York

Nature ◽  
1984 ◽  
Vol 310 (5975) ◽  
pp. 308-310 ◽  
Author(s):  
C. T. Driscoll ◽  
G. C. Schafran
Keyword(s):  
New York ◽  
Short Term ◽  
Neutralizing Capacity

Short-Term Changes in the Acid/Base Chemistry of Two Acidic Lakes Following Calcium Carbonate Treatment

1989 ◽  
Vol 46 (2) ◽  
pp. 306-314 ◽  
Author(s):  
G. F. Fordham ◽  
C. T. Driscoll
Keyword(s):  
New York ◽  
Dissolved Inorganic Carbon ◽  
Initial Perturbation ◽  
Acid Neutralizing Capacity ◽  
Short Term ◽  
Acidic Lakes ◽  
Neutralizing Capacity ◽  
Rapid Dissolution ◽  
Additional Release ◽  
Woods Lake

Woods Lake and Cranberry Pond, two chronically acidic lakes located in the Adirondack region of New York, USA, were intensively monitored following CaCO3 treatment in May 1985 to evaluate the mechanisms controlling short-term changes in water column chemistry. Immediately following base application (24 h), both lakes responded like systems closed to atmospheric CO2, because the dissolution of very small CaCO3 particles (median diameter 2 μm) exceeded the rate of atmospheric CO2 intrusion. Rapid dissolution of CaCO3 coupled with very low concentrations of dissolved inorganic carbon (DIC) prior to treatment, resulted in pH increases in the upper mixed waters from 4.9 to 9.4 in Woods Lake and from 4.6 to 9.1 in Cranberry Pond, as waters readily became saturated with CaCO3. pH increases were accompanied by stoichiometric increases in dissolved Ca2+, acid neutralizing capacity (ANC), and DIC. Following this initial perturbation, the upper mixed waters equilibrated with atmospheric CO2 over a 4 wk period, facilitating additional release of dissolved Ca2+ and ANC due to dissolution of suspended CaCO3. The amount of CaCO3 that dissolved during the 4 wk immediately following treatment, calculated from Ca2+ budgets, was very high; 86% in Woods Lake and 79% in Cranberry Pond.

Short-term forecast model of taxi demand based on time and space heterogeneity

2021 ◽  
pp. 1-12
Author(s):  
Zhiyu Yan ◽  
Shuang Lv
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Urban Traffic ◽  
Percentage Error ◽  
Density Level ◽  
Traffic Data ◽  
Short Term ◽  
Time And Space ◽  
Density Data

Accurate prediction of traffic flow is of great significance for alleviating urban traffic congestions. Most previous studies used historical traffic data, in which only one model or algorithm was adopted by the whole prediction space and the differences in various regions were ignored. In this context, based on time and space heterogeneity, a Classification and Regression Trees-K-Nearest Neighbor (CART-KNN) Hybrid Prediction model was proposed to predict short-term taxi demand. Firstly, a concentric partitioning method was applied to divide the test area into discrete small areas according to its boarding density level. Then the CART model was used to divide the dataset of each area according to its temporal characteristics, and KNN was established for each subset by using the corresponding boarding density data to estimate the parameters of the KNN model. Finally, the proposed method was tested on the New York City Taxi and Limousine Commission (TLC) data, and the traditional KNN model, backpropagation (BP) neural network, long-short term memory model (LSTM) were used to compare with the proposed CART-KNN model. The selected models were used to predict the demand for taxis in New York City, and the Kriging Interpolation was used to obtain all the regional predictions. From the results, it can be suggested that the proposed CART-KNN model performed better than other general models by showing smaller mean absolute percentage error (MAPE) and root mean square error (RMSE) value. The improvement of prediction accuracy of CART-KNN model is helpful to understand the regional demand pattern to partition the boarding density data from the time and space dimensions. The partition method can be extended into many models using traffic data.

Patterns of waste generation: A gradient boosting model for short-term waste prediction in New York City

2017 ◽  
Vol 62 ◽  
pp. 3-11 ◽  
Author(s):  
Nicholas E. Johnson ◽  
Olga Ianiuk ◽  
Daniel Cazap ◽  
Linglan Liu ◽  
Daniel Starobin ◽  
...  
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Gradient Boosting ◽  
Waste Generation ◽  
Short Term

scholarly journals An Efficient Prediction Model for Water Discharge in Schoharie Creek, NY

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Katerina G. Tsakiri ◽  
Antonios E. Marsellos ◽  
Igor G. Zurbenko
Keyword(s):  
New York ◽  
Time Series ◽  
Water Discharge ◽  
Winter Period ◽  
Statistical Methodology ◽  
Summer Period ◽  
Short Term ◽  
Efficient Prediction ◽  
Climatic Time Series

Flooding normally occurs during periods of excessive precipitation or thawing in the winter period (ice jam). Flooding is typically accompanied by an increase in river discharge. This paper presents a statistical model for the prediction and explanation of the water discharge time series using an example from the Schoharie Creek, New York (one of the principal tributaries of the Mohawk River). It is developed with a view to wider application in similar water basins. In this study a statistical methodology for the decomposition of the time series is used. The Kolmogorov-Zurbenko filter is used for the decomposition of the hydrological and climatic time series into the seasonal and the long and the short term component. We analyze the time series of the water discharge by using a summer and a winter model. The explanation of the water discharge has been improved up to 81%. The results show that as water discharge increases in the long term then the water table replenishes, and in the seasonal term it depletes. In the short term, the groundwater drops during the winter period, and it rises during the summer period. This methodology can be applied for the prediction of the water discharge at multiple sites.

scholarly journals Airbnb Short-term Housing Rental Status Prediction Model Under the Impact of the COVID-19 Pandemic

2021 ◽  
Vol 251 ◽  
pp. 01017
Author(s):  
Zhixiang Lu
Keyword(s):  
Machine Learning ◽  
New York ◽  
Regional Differences ◽  
Prediction Accuracy ◽  
Visual Analysis ◽  
Sharing Economy ◽  
Short Term ◽  
Emerging Industries ◽  
Machine Learning Model ◽  
The Impact

With the vigorous development of the sharing economy, the short-term rental industry has also spawned many emerging industries that belong to the sharing economy. However, due to the impact of the COVID-19 pandemic in 2020, many sharing economy industries, including the short-term housing leasing industry, have been affected. This study takes the rental information of 1,004 short-term rental houses in New York in April 2020 as an example, through machine learning and quantitative analysis, we conducted statistical and visual analysis on the impact of different factors on the housing rental status. This project is based on the machine learning model to predict the changes in the rental status of the house on the time series. The results show that the prediction accuracy of the random forest model has reached more than 94%, and the prediction accuracy of the logistic model has reached more than 74%. At the same time, we have further explored the impact of time span differences and regional differences on the housing rental status.

29 SHORT-TERM v. LONG-TERM PROGESTERONE PROTOCOL USING CERVICAL OR INTRAUTERINE FIXED-TIME INSEMINATION IN SHEEP

2010 ◽  
Vol 22 (1) ◽  
pp. 172
Author(s):  
A. Menchaca ◽  
M. Vilariño ◽  
E. Rubianes
Keyword(s):  
New York ◽  
Pregnancy Rate ◽  
Body Condition Score ◽  
Skim Milk ◽  
Prostaglandin F2α ◽  
Fixed Time ◽  
Short Term ◽  
Condition Score ◽  
N 93

The short-term protocol with progesterone, prostaglandin F2α (PGF2α), and eCG is used to control follicular dynamics and luteal activity synchronizing the ovulation for fixed-time AI in sheep. The objective of this experiment was to compare the pregnancy rate obtained with short-term protocol (6 d) and long-term protocol (14 d) using cervical or intrauterine fixed-time AI in sheep. Three hundred fifty-two Merino ewes with a body condition score of 2.9 ± 0.3 (mean ± SD; scale 0 to 5) were used during the breeding season (April, 33S, Uruguay). All the females received a CIDR-G (0.3 g of progesterone, InterAg, Hamilton, New Zealand) for 6 d (short-term protocol; n = 178) or 14 d (long-term protocol, n = 174). One imdose of eCG (300 IU, Novormon, Syntex, BA, Argentina) was given at the moment of device withdrawal for the both protocols, and one imdose of PGF2α (10 mg of dinoprost, Lutalyse, Pfizer, New York, NY, USA) was given at the end of the short-term protocol to ensure luteolysis. Cervical AI (short-term protocol, n = 85; long-term protocol, n = 104) or intrauterine AI (short-term protocol, n = 93; long-term protocol, n = 70) was performed 48 or 54 h after device withdrawal, using 200 × 106 or 100 × 106 spermatozoa per ewe, respectively. Fresh semen was extended in UHT skim milk (1000 × 106 spermatozoa mL-1) and used within 1 h of collection. Estrus was recorded twice a day for 4 days after device withdrawal using vasectomized males. Pregnancy diagnosis was performed by transrectal ultrasonography 40 d after AI (5.0 MHz, Aloka, Tokyo, Japan). Logistic regression was used to analyze the effect of the treatment (P < 0.05), the AI technique (P < 0.05), and their interaction (P = NS). Pregnancy rate was higher for the short-term than for the long-term protocol, and for intrauterine than for cervical AI (Table 1). The highest pregnancy rate was achieved with short-term protocol using intrauterine AI (54.8%, 51/93), and the lowest response was obtained with long-term protocol using cervical AI (33.7%, 35/104; P < 0.05). These data were not different from data of short-term protocol using cervical AI or long-term protocol using intrauterine AI (42.4%, 36/85; and 44.3% 31/70, respectively). Ewes in estrus/treated ewes was not different among short-term and long-term protocols (83.7%, 149/178; and 82.8%, 144/174, respectively; P = NS). In summary, regardless of insemination technique, short-term protocol of 6 d enhances pregnancy rate in fixed-time AI programs in sheep. Table 1.Main effects of short-term (6 d) v. long-term (14 d) protocol using cervical or intrauterine fixed-time AI on pregnancy rate in sheep Financially supported by Pfizer, SP, Brazil.

Common loon reproduction and chick feeding on acidified lakes in the Adirondack Park, New York

1988 ◽  
Vol 66 (4) ◽  
pp. 804-810 ◽  
Author(s):  
Karl E. Parker
Keyword(s):  
New York ◽  
Food Resource ◽  
Resource Depletion ◽  
Low Ph ◽  
Lake Acidification ◽  
Common Loon ◽  
Feeding Problems ◽  
Short Term ◽  
Lake Acidity ◽  
Adirondack Lakes

The effects of lake acidification on common loon reproduction were studied on a total of 24 Adirondack lakes from May through August in 1983 and 1984. The lakes ranged in size from 10.5 to 179 ha; pH ranged from 4.65 to 6.77 and alkalinity from −66 to 111 μequiv./L. Although loons nesting on small, low-pH lakes had a high fledging rate, possibly because of reduced disturbance or predation, no significant relationship (P > 0.10) was found between lake acidity status and loon reproductive success. No chick mortality could be attributed to lake acidification, but chicks on low-pH lakes were generally fed prey much smaller or much larger than those normally preferred. A pair nesting on a fishless lake fed aquatic insects to their constantly begging chick, spending two to four times longer feeding the chick compared with loons on lakes with fish. This pair, alternating absences, flew to another lake to feed, and on three occasions returned to the nesting lake carrying a fish. Loons on the low-pH study lakes apparently adapted, at least in the short term, to food resource depletion associated with acidification. Despite this, acidification creates potentially severe feeding problems for chicks by reducing prey diversity and quantity.

Sign in / Sign up

Export Citation Format

Share Document