scholarly journals The transition process of aerosol distribution state with spatio-temporal analysis based on MODIS product

2019 ◽  
Vol 1 ◽  
pp. 1-2
Author(s):  
Hong Zhang ◽  
Weiqing Su ◽  
Li Long Chen
Keyword(s):  
Time Series ◽  
Spatial Statistics ◽  
Transfer Process ◽  
Urban Areas ◽  
Meteorological Factors ◽  
Aerosol Concentration ◽  
Distribution Area ◽  
State Transfer ◽  
Aerosol State ◽  
Fixed Region

<p><strong>Abstract.</strong> Atmospheric aerosol can have an impact on the climate through direct and indirect radiative forcing and cause air quality problems to harm human health [Charlson, R.J., et al. 1992]. Many scholars have made a lot of contributions to aerosol concentration analysis, but the analysis of chaotic aerosol concentration time series is always unable to perfectly solve the inherent randomness brought by complicated dynamic behaviors [Abarbanel, H.D.I, 1996]. This research has established the aggregation and dispersion states of aerosol optical thickness in fixed region and fixed time period by calculating spatial statistics of value-containing pixels in remote sensing images. And it has also analyzed the process of this state which is influenced by several meteorological factors. This kind of aggregation and dispersion is a conceptual method of spatial statistics in geography. The research compares the aerosol AOD value and its distribution area in the atmosphere from the signal frequency and utilizes the methods of nonlinear analysis and wavelet analysis to solve for its state transition characteristics at a regular point in time. Then the aerosol state transfer process is unfolded in space to analyze the interaction between the sequential meteorological factors and the aerosol state transfer process, as well as the comparison of the differences and similarities between the aerosol state transfer process in urban areas and non-urban areas. The research can not only provide theoretical foundation for pollution warning on the basis of long time series but also provide a new idea for analyzing the nonlinear dynamic process of the atmospheric system, which is the analysis of the change process of the chaotic system by spatial deploy at a specific time point.</p>

Analysing urban floods and combined sewer overflows in a changing climate

2011 ◽  
Vol 2 (4) ◽  
pp. 260-271 ◽  
Author(s):  
V. Nilsen ◽  
J. A. Lier ◽  
J. T. Bjerkholt ◽  
O. G. Lindholm
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Urban Areas ◽  
Convective Precipitation ◽  
Urban Drainage ◽  
Combined Sewer Overflows ◽  
Extreme Precipitation Events ◽  
Combined Sewer ◽  
Sewer Overflows ◽  
Precipitation Events

Climate change is expected to lead to an increased frequency and intensity of extreme precipitation events. For urban drainage, the primary adverse effects are more frequent and severe sewer overloading and flooding in urban areas, and higher discharges through combined sewer overflows (CSO). For assessing the possible effects of climate change, urban drainage models are run with climate-change-adjusted input data. However, current climate models are run on a spatial–temporal scale that is too coarse to resolve processes relevant to urban drainage modelling, in particular convective precipitation events. In the work reported here the delta-change method was used to develop a high-resolution time series of precipitation for the period 2071–2100 based on a recently produced climate model precipitation time series for Oslo. The present and future performance of the sewer networks was determined using MOUSE software. The simulations indicated future increases in annual CSO discharge of 33% when comparing years of maximum annual runoff. There is also an 83% increase in annual CSO discharge when comparing years of maximum annual precipitation. In addition, there are increases in the flooding of manholes and increased levels of backwater in pipes, which translates into more flooding of basements.

The Use of Satellite TIR Time Series for Thermal Anomalies’ Detection on Natural and Urban Areas

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Malvina Silvestri ◽  
Federico Rabuffi ◽  
Massimo Musacchio ◽  
Sergio Teggi ◽  
Maria Fabrizia Buongiorno
Keyword(s):  
Time Series ◽  
Land Surface Temperature ◽  
Satellite Data ◽  
Land Surface ◽  
Urban Areas ◽  
Temperature Time Series ◽  
Campi Flegrei ◽  
Thermal Anomalies ◽  
Pca Method ◽  
Temperature Time

In this work, the land surface temperature time series derived using Thermal InfraRed (TIR) satellite data offers the possibility to detect thermal anomalies by using the PCA method. This approach produces very detailed maps of thermal anomalies, both in geothermal areas and in urban areas. Tests were conducted on the following three Italian sites: Solfatara-Campi Flegrei (Naples), Parco delle Biancane (Grosseto) and Modena city.

scholarly journals Time Series Analysis of the Pollutants Imissions in Urban Areas

1970 ◽  
Vol 66 (2) ◽  
Author(s):  
Carmen Leane NICOLESCU ◽  
Daniel DUNEA ◽  
Virgil MOISE ◽  
Gabriel GORGHIU
Keyword(s):  
Time Series ◽  
Air Quality ◽  
Urban Areas ◽  
Linear Trend ◽  
Moving Average ◽  
Quality Parameters ◽  
Temporal And Spatial Distribution ◽  
Simple Exponential Smoothing ◽  
Environmental Air ◽  
Local Agencies

Environmental pollution of urban areas is one of the key factors that local agencies and authorities have to consider in the decision-making process. To succeed a sustainable management of the environment, there is necessary to use different kinds of instruments in order to evaluate and forecast the evolution of the environmental state. Understanding temporal and spatial distribution of air quality is essential in making decisions for regional management. In this paper a model for urban air quality forecasting using time series of monthly averages concentrations is presented. Sedimentable dusts (SD), total suspended particulates (TSP), nitrogen dioxide (NO2), and sulfur dioxide (SO2), imissions, recorded between 1995 and 2008 in the urban area of Târgovişte city are used as inputs in the model. The measured pollutant data from the local Environmental Agency database were statistically analyzed in time series including monthly patterns using the auto-regressive integrated moving average (ARIMA) method, linear trend, simple moving average of three terms and simple exponential smoothing. There was discussed the efficiency of using this method in forecasting the environmental air quality. In general, ARIMA technique scores well in predicting the analysed environmental air quality parameters.

scholarly journals Recent changes and drivers of the atmospheric evaporative demand in the Canary Islands

2016 ◽  
Author(s):  
S. M. Vicente-Serrano ◽  
C. Azorin-Molina ◽  
A. Sanchez-Lorenzo ◽  
A. El Kenawy ◽  
N. Martín-Hernández ◽  
...  
Keyword(s):  
Time Series ◽  
Relative Humidity ◽  
Canary Islands ◽  
Temporal Variability ◽  
Meteorological Factors ◽  
Negative Consequences ◽  
Evaporative Demand ◽  
High Quality ◽  
Radiative Component ◽  
Main Driver

Abstract. We analysed recent evolution and meteorological drivers of the atmospheric evaporative demand (AED) in the Canary Islands for the period 1961–2013. We employed long and high quality time series of meteorological variables to analyze current AED changes in this region and found that AED has increased during the investigated period. Overall, the annual ETo increased significantly by 18.2 mm decade−1 on average, with a stronger trend in summer (6.7 mm decade−1). The radiative component showed much lower temporal variability than the aerodynamic component did. Thus, more than 90 % of the observed ETo variability at the seasonal and annual scales can be associated with the variability of the aerodynamic component. The variable that recorded more significant changes in the Canary Islands was relative humidity, and among the different meteorological factors used to calculate ETo, relative humidity was the main driver of the observed ETo trends. The observed trend could have negative consequences in a number of water-depending sectors if it continues in the future.

scholarly journals Imprint of Climate Change on Pan-Arctic Marine Vegetation

2020 ◽  
Vol 7 ◽  
Author(s):  
Dorte Krause-Jensen ◽  
Philippe Archambault ◽  
Jorge Assis ◽  
Inka Bartsch ◽  
Kai Bischof ◽  
...  
Keyword(s):  
Time Series ◽  
Management Strategies ◽  
Open Water ◽  
The Arctic ◽  
Distribution Area ◽  
Arctic Ecosystems ◽  
Migration Rates ◽  
Lack Of Information ◽  
Distribution Limits ◽  
And Storage

The Arctic climate is changing rapidly. The warming and resultant longer open water periods suggest a potential for expansion of marine vegetation along the vast Arctic coastline. We compiled and reviewed the scattered time series on Arctic marine vegetation and explored trends for macroalgae and eelgrass (Zostera marina). We identified a total of 38 sites, distributed between Arctic coastal regions in Alaska, Canada, Greenland, Iceland, Norway/Svalbard, and Russia, having time series extending into the 21st Century. The majority of these exhibited increase in abundance, productivity or species richness, and/or expansion of geographical distribution limits, several time series showed no significant trend. Only four time series displayed a negative trend, largely due to urchin grazing or increased turbidity. Overall, the observations support with medium confidence (i.e., 5–8 in 10 chance of being correct, adopting the IPCC confidence scale) the prediction that macrophytes are expanding in the Arctic. Species distribution modeling was challenged by limited observations and lack of information on substrate, but suggested a current (2000–2017) potential pan-Arctic macroalgal distribution area of 820.000 km2 (145.000 km2 intertidal, 675.000 km2 subtidal), representing an increase of about 30% for subtidal- and 6% for intertidal macroalgae since 1940–1950, and associated polar migration rates averaging 18–23 km decade–1. Adjusting the potential macroalgal distribution area by the fraction of shores represented by cliffs halves the estimate (412,634 km2). Warming and reduced sea ice cover along the Arctic coastlines are expected to stimulate further expansion of marine vegetation from boreal latitudes. The changes likely affect the functioning of coastal Arctic ecosystems because of the vegetation’s roles as habitat, and for carbon and nutrient cycling and storage. We encourage a pan-Arctic science- and management agenda to incorporate marine vegetation into a coherent understanding of Arctic changes by quantifying distribution and status beyond the scattered studies now available to develop sustainable management strategies for these important ecosystems.

scholarly journals Explicit Modeling of Meteorological Explanatory Variables in Short-Term Forecasting of Maximum Ozone Concentrations via a Multiple Regression Time Series Framework

Atmosphere ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1304
Author(s):  
Sigfrido Iglesias-Gonzalez ◽  
Maria E. Huertas-Bolanos ◽  
Ivan Y. Hernandez-Paniagua ◽  
Alberto Mendoza
Keyword(s):  
Time Series ◽  
Air Quality ◽  
Multiple Regression ◽  
Urban Areas ◽  
Moving Average ◽  
Time Series Forecasting ◽  
Air Pollutant ◽  
Short Term ◽  
Ozone Level ◽  
Ozone Concentrations

Statistical time series forecasting is a useful tool for predicting air pollutant concentrations in urban areas, especially in emerging economies, where the capacity to implement comprehensive air quality models is limited. In this study, a general multiple regression with seasonal autoregressive moving average errors model was estimated and implemented to forecast maximum ozone concentrations with a short time resolution: overnight, morning, afternoon and evening. In contrast to a number of short-term air quality time series forecasting applications, the model was designed to explicitly include the effects of meteorological variables on the ozone level as exogenous variables. As the application location, the model was constructed with data from five monitoring stations in the Monterrey Metropolitan Area of Mexico. The results show that, together with structural stochastic components, meteorological parameters have a significant contribution for obtaining reliable forecasts. The resulting model is an interpretable, useful and easily implementable model for forecasting ozone maxima. Moreover, it proved to be consistent with the general dynamics of ozone formation and provides a suitable platform for forecasting, showing similar or better performance compared to models in other existing studies.

scholarly journals GNSS Multipath Detection Using Continuous Time-Series C/N0

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 4059
Author(s):  
Nobuaki Kubo ◽  
Kaito Kobayashi ◽  
Rei Furukawa
Keyword(s):  
Time Series ◽  
Continuous Time ◽  
Urban Areas ◽  
Satellite System ◽  
Line Of Sight ◽  
Static Tests ◽  
Gnss Receivers ◽  
Multipath Detection ◽  
Global Navigation Satellite ◽  
Multipath Errors

The reduction of multipath errors is a significant challenge in the Global Navigation Satellite System (GNSS), especially when receiving non-line-of-sight (NLOS) signals. However, selecting line-of-sight (LOS) satellites correctly is still a difficult task in dense urban areas, even with the latest GNSS receivers. This study demonstrates a new method of utilization of C/N0 of the GNSS to detect NLOS signals. The elevation-dependent threshold of the C/N0 setting may be effective in mitigating multipath errors. However, the C/N0 fluctuation affected by NLOS signals is quite large. If the C/N0 is over the threshold, the satellite is used for positioning even if it is still affected by the NLOS signal, which causes the positioning error to jump easily. To overcome this issue, we focused on the value of continuous time-series C/N0 for a certain period. If the C/N0 of the satellite was less than the determined threshold, the satellite was not used for positioning for a certain period, even if the C/N0 recovered over the threshold. Three static tests were conducted at challenging locations near high-rise buildings in Tokyo. The results proved that our method could substantially mitigate multipath errors in differential GNSS by appropriately removing the NLOS signals. Therefore, the performance of real-time kinematic GNSS was significantly improved.

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