Benthic Data Collection in Lake Ontario: Continuation of a Long‐term Data Series

Abstract. Long-term data series (1996–2009) for eleven acidic headwater streams (< 10 km2) in Sweden were analyzed to determine factors controlling concentrations of trace metals. In-stream chemical data as well climatic, flow, and deposition chemistry data were used to develop models predicting concentrations of chromium (Cr), lead (Pb), and zinc (Zn). Data were initially analyzed using partial least squares to determine a set of variables that could predict metal concentrations across all sites. Organic matter (as absorbance) and iron related positively to Pb and Cr, while pH related negatively to Pb and Zn. Other variables such as conductivity, manganese, and temperature were important as well. Multiple linear regression was then used to determine minimally adequate prediction models which explained an average of 35% (Cr), 52% (Zn), and 72% (Pb) of metal variation across all sites. While models explained at least 50% of variation in the majority of sites for Pb (10) and Zn (8), only three sites met this criterion for Cr. Investigation of variation between site models for each metal revealed geographical (altitude), chemical (sulfate), and land-use (silvaculture) influences on predictive power of the models. Residual analysis revealed seasonal differences in the ability of the models to predict metal concentrations as well. Expected future changes in model variables were applied and results showed the potential for long-term increases (Pb) or decreases (Zn) for trace metal concentrations at these sites.

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Developing Models for Matching of Short-term and Long-term Data Collection Sites to Improve the Estimation of Average Annual Daily Bicyclists

2018 21st International Conference on Intelligent Transportation Systems (ITSC) ◽

10.1109/itsc.2018.8569815 ◽

2018 ◽

Author(s):

Mahdie Hasani ◽

Arash Jahangiri ◽

Sahar Ghanipoor Machiani

Keyword(s):

Data Collection ◽

Short Term ◽

Term Data ◽

Collection Sites

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Analysis of Trends in the FireCCI Global Long Term Burned Area Product (1982–2018)

Fire ◽

10.3390/fire4040074 ◽

2021 ◽

Vol 4 (4) ◽

pp. 74

Author(s):

Gonzalo Otón ◽

José Miguel C. Pereira ◽

João M. N. Silva ◽

Emilio Chuvieco

Keyword(s):

Time Series ◽

Temporal Trends ◽

European Space Agency ◽

Eastern Africa ◽

Burned Area ◽

Data Series ◽

Short Term ◽

The Impact ◽

Term Data

We present an analysis of the spatio-temporal trends derived from long-term burned area (BA) data series. Two global BA products were included in our analysis, the FireCCI51 (2001–2019) and the FireCCILT11 (1982–2018) datasets. The former was generated from Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m reflectance data, guided by 1 km active fires. The FireCCILT11 dataset was generated from Land Long-Term Data Record data (0.05°), which provides a consistent time series for Advanced Very High Resolution Radiometer images, acquired from the NOAA satellite series. FireCCILT11 is the longest time series of a BA product currently available, making it possible to carry out temporal analysis of long-term trends. Both products were developed under the FireCCI project of the European Space Agency. The two datasets were pre-processed to correct for temporal autocorrelation. Unburnable areas were removed and the lack of the FireCCILT11 data in 1994 was examined to evaluate the impact of this gap on the BA trends. An analysis and comparison between the two BA products was performed using a contextual approach. Results of the contextual Mann-Kendall analysis identified significant trends in both datasets, with very different regional values. The long-term series presented larger clusters than the short-term ones. Africa displayed significant decreasing trends in the short-term, and increasing trends in the long-term data series, except in the east. In the long-term series, Eastern Africa, boreal regions, Central Asia and South Australia showed large BA decrease clusters, and Western and Central Africa, South America, USA and North Australia presented BA increase clusters.

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Government expenditure in New Zealand since 1935: a preliminary reassessment

Policy Quarterly ◽

10.26686/pq.v13i2.4664 ◽

2017 ◽

Vol 13 (2) ◽

Author(s):

Matthew Gibbons

Keyword(s):

New Zealand ◽

Comparative Studies ◽

Government Expenditure ◽

Data Series ◽

Optimal Size ◽

Economic Issue ◽

Size Of Government ◽

Expenditure Data ◽

Term Data

The optimal size of government is an important political and economic issue. However, because no long-term government expenditure series has official standing, New Zealand is often a missing case in comparative studies of government expenditure (Castles, 1998). Although government expenditure data is available from 1972 on Treasury’s website (New Zealand Treasury, 2016), the most widely used data before 1972 is a ‘consolidated’ long-term data series, on Statistics New Zealand’s website, which uses data from a number of sources and is published with strong disclaimers.

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Plastic Hotspot Mapping in Urban Water Systems

Geosciences ◽

10.3390/geosciences10090342 ◽

2020 ◽

Vol 10 (9) ◽

pp. 342

Author(s):

Paolo Tasseron ◽

Hestia Zinsmeister ◽

Liselotte Rambonnet ◽

Auke-Florian Hiemstra ◽

Daniël Siepman ◽

...

Keyword(s):

Data Collection ◽

Urban Areas ◽

Water Systems ◽

Urban Water ◽

Plastic Pollution ◽

Science Data ◽

Simple Method ◽

Urban Water Systems ◽

Term Data

Reducing plastic pollution in rivers, lakes, and oceans is beneficial to aquatic animals and human livelihood. To achieve this, reliable observations of the abundance, spatiotemporal variation, and composition of plastics in aquatic ecosystems are crucial. Current efforts mainly focus on collecting data on the open ocean, on beaches and coastlines, and in river systems. Urban areas are the main source of plastic leakage into the natural environment, yet data on plastic pollution in urban water systems are scarce. In this paper, we present a simple method for plastic hotspot mapping in urban water systems. Through visual observations, macroplastic abundance and polymer categories are determined. Due to its simplicity, this method is suitable for citizen science data collection. A first application in the Dutch cities of Leiden and Wageningen showed similar mean plastic densities (111–133 items/km canal) and composition (75–80% soft plastics), but different spatial distributions. These observations emphasize the importance of long-term data collection to further understand and quantify spatiotemporal variations of plastics in urban water systems. In turn, this will support improved estimates of the contribution of urban areas to the plastic pollution of rivers and oceans.

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