Long-Term Variation in Mesohaline Chesapeake Bay Macrobenthos: Spatial and Temporal Patterns

Estuaries ◽  
1987 ◽  
Vol 10 (3) ◽  
pp. 227 ◽  
Author(s):  
A. F. Holland ◽  
Anna T. Shaughnessy ◽  
Martha H. Hiegel
Keyword(s):  
Chesapeake Bay ◽  
Temporal Patterns ◽  
Spatial And Temporal Patterns ◽  
Term Variation

Capturing Spatial and Temporal Patterns of NO2 in Cities using mobile and stationary DOAS measurements

2021 ◽  
Author(s):  
Mark Wenig ◽  
Sheng Ye ◽  
Ying Zhu ◽  
Hanlin Zhang
Keyword(s):  
Air Quality ◽  
Hot Spots ◽  
Temporal Patterns ◽  
Optical Absorption Spectroscopy ◽  
Spatial And Temporal Patterns ◽  
The Public ◽  
Measurement Height ◽  
Weekly Cycles

<p>The problem of elevated NO<sub>2</sub> levels in cities has gained some attention in the public in recent years and has given rise to questions about the plausibility of banning diesel engines in cities, the meaning of exceedances of air quality limits and the effects of corona lock-downs on air quality to name a few. Urban air quality is typically monitored using a relatively small number of monitoring stations. Those in-situ measurements follow certain guidelines in terms of inlet height and location relative to streets, but the question remains how a limited number of point measurements can capture the spatial variability in cities. In this talk we present two measurement campaigns in Hong Kong and Munich where we utilized a combination of mobile in-situ and stationary remote sensing differential optical absorption spectroscopy (DOAS) instruments. We developed an algorithm to separate spatial and temporal patterns in order to generate pollution maps that represent average NO<sub>2</sub> exposure. </p> <p>We use those maps to identify pollution hot spots and capture the weekly cycles of on-road NO2 levels and spatial dependency of long-term changes and we analyze how on-road measurements compare to monitoring station data and how the measurement height and distance to traffic emissions have to be considered when interpreting observed concentration patterns.</p>

scholarly journals Long-term surveillance defines spatial and temporal patterns implicating Culex tarsalis as the primary vector of West Nile virus

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Brendan M. Dunphy ◽  
Kristofer B. Kovach ◽  
Ella J. Gehrke ◽  
Eleanor N. Field ◽  
Wayne A. Rowley ◽  
...  
Keyword(s):  
West Nile Virus ◽  
Temporal Patterns ◽  
Culex Tarsalis ◽  
Spatial And Temporal Patterns ◽  
West Nile ◽  
Primary Vector

Long-term spatial and temporal patterns of sea turtle strandings in southern Brazil

2016 ◽  
Vol 163 (12) ◽  
Author(s):  
Danielle S. Monteiro ◽  
Sérgio C. Estima ◽  
Tiago B. R. Gandra ◽  
Andrine P. Silva ◽  
Leandro Bugoni ◽  
...  
Keyword(s):  
Temporal Patterns ◽  
Sea Turtle ◽  
Southern Brazil ◽  
Spatial And Temporal Patterns

The macroinvertebrate fauna of an Australian dryland river: spatial and temporal patterns and environmental relationships

2006 ◽  
Vol 57 (1) ◽  
pp. 61 ◽  
Author(s):  
Jonathan C. Marshall ◽  
Fran Sheldon ◽  
Martin Thoms ◽  
Satish Choy
Keyword(s):  
Spatial Patterns ◽  
Spatial Scales ◽  
Temporal Patterns ◽  
Environmental Aspects ◽  
Spatial And Temporal Patterns ◽  
Channel Network ◽  
Spatial Changes ◽  
Dryland River ◽  
Macroinvertebrate Fauna

Waterholes within the dryland Cooper Creek, Lake Eyre Basin, Australia, are connected only during floods and are typically isolated for long periods. Spatial changes in the macroinvertebrate assemblages of 15 of these waterholes belonging to four regions were explored and these changes were related to environmental aspects of the waterholes measured at four spatial scales: floodplain, waterhole, within waterhole and sample habitat. To explore temporal patterns, one region was sampled on four occasions differing in time since connection. Spatial patterns were characterised by ‘differentiation by distance’ whereby samples collected closer to each other in the landscape were more similar in assemblage composition than those collected further apart. Thus, there were significant differences between the assemblages of the four regions. Although there was a correlation between macroinvertebrate spatial patterns and a combination of local habitat, geomorphology and water chemistry attributes, it appears unlikely that these variables were responsible for the faunal differentiation by distance. Temporal variability was larger than spatial variability and temporal assemblage patterns were best explained by the ‘connectivity potential’ of waterholes, reflecting the position of individual waterholes within the broader channel network and long-term connectivity relationships, rather than the actual time since hydrological connection.

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