scholarly journals Long-term changes of tropospheric NO<sub>2</sub> over megacities derived from multiple satellite instruments

2012 ◽  
Vol 12 (12) ◽  
pp. 31767-31828 ◽  
Author(s):  
A. Hilboll ◽  
A. Richter ◽  
J. P. Burrows
Keyword(s):  
Time Series ◽  
Middle East ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
The United States ◽  
Central China ◽  
Full Time ◽  
Data Set ◽  
Tropospheric No2

Abstract. Tropospheric NO2, a key pollutant in particular in cities, has been measured from space since the mid-1990s by the GOME, SCIAMACHY, OMI, and GOME-2 instruments. These data provide a unique global long-term data set of tropospheric pollution. However, the measurements differ in spatial resolution, local time of measurement, and measurement geometry. All these factors can severely impact the retrieved NO2 columns, which is why they need to be taken into account when analysing time series spanning more than one instrument. In this study, we present several ways to explicitly account for the instrumental differences in trend analyses of the NO2 columns derived from satellite measurements, while preserving their high spatial resolution. Both a physical method, based on spatial averaging of the measured earthshine spectra and extraction of a resolution pattern, and statistical methods, including instrument-dependent offsets in the fitted trend function, are developed. These methods are applied to data from GOME and SCIAMACHY separately, to the combined time series and to an extended data set comprising also GOME-2 and OMI measurements. All approaches show consistent trends of tropospheric NO2 for a selection of areas on both regional and city scales, for the first time allowing consistent trend analysis of the full time series at high spatial resolution and significantly reducing the uncertainties of the retrieved trend estimates compared to previous studies. We show that measured tropospheric NO2 columns have been strongly increasing over China, the Middle East, and India, with values over East Central China triplicating from 1996 to 2011. All parts of the developed world, including Western Europe, the United States, and Japan, show significantly decreasing NO2 amounts in the same time period. On a megacity level, individual trends can be as large as +27 ± 3.7% yr−1 and +20 ± 1.9% yr−1 in Dhaka and Baghdad, respectively, while Los Angeles shows a very strong decrease of −6.0 ± 0.37% yr−1. Most megacities in China, India, and the Middle East show increasing NO2 columns of +5–10% yr−1, leading to a doubling to triplication within the observed period. While linear trends derived with the different methods are consistent, comparison of the GOME and SCIAMACHY time series as well as inspection of time series over individual areas shows clear indication of non-linear changes in NO2 columns in response to rapid changes in technology used and the economical situation.

scholarly journals Long-term changes of tropospheric NO<sub>2</sub> over megacities derived from multiple satellite instruments

2013 ◽  
Vol 13 (8) ◽  
pp. 4145-4169 ◽  
Author(s):  
A. Hilboll ◽  
A. Richter ◽  
J. P. Burrows
Keyword(s):  
Time Series ◽  
Los Angeles ◽  
Middle East ◽  
Spatial Resolution ◽  
Western Europe ◽  
The United States ◽  
Central China ◽  
Full Time ◽  
Tropospheric No2

Abstract. Tropospheric NO2, a key pollutant in particular in cities, has been measured from space since the mid-1990s by the GOME, SCIAMACHY, OMI, and GOME-2 instruments. These data provide a unique global long-term dataset of tropospheric pollution. However, the observations differ in spatial resolution, local time of measurement, viewing geometry, and other details. All these factors can severely impact the retrieved NO2 columns. In this study, we present three ways to account for instrumental differences in trend analyses of the NO2 columns derived from satellite measurements, while preserving the individual instruments' spatial resolutions. For combining measurements from GOME and SCIAMACHY into one consistent time series, we develop a method to explicitly account for the instruments' difference in ground pixel size (40 × 320 km2 vs. 30 × 60 km2). This is especially important when analysing NO2 changes over small, localised sources like, e.g. megacities. The method is based on spatial averaging of the measured earthshine spectra and extraction of a spatial pattern of the resolution effect. Furthermore, two empirical corrections, which summarise all instrumental differences by including instrument-dependent offsets in a fitted trend function, are developed. These methods are applied to data from GOME and SCIAMACHY separately, to the combined time series, and to an extended dataset comprising also GOME-2 and OMI measurements. All approaches show consistent trends of tropospheric NO2 for a selection of areas on both regional and city scales, for the first time allowing consistent trend analysis of the full time series at high spatial resolution. Compared to previous studies, the longer study period leads to significantly reduced uncertainties. We show that measured tropospheric NO2 columns have been strongly increasing over China, the Middle East, and India, with values over east-central China tripling from 1996 to 2011. All parts of the developed world, including Western Europe, the United States, and Japan, show significantly decreasing NO2 amounts in the same time period. On a megacity level, individual trends can be as large as +27.2 ± 3.9% yr−1 and +20.7 ± 1.9% yr−1 in Dhaka and Baghdad, respectively, while Los Angeles shows a very strong decrease of −6.00 ± 0.72% yr−1. Most megacities in China, India, and the Middle East show increasing NO2 columns of +5 to 10% yr−1, leading to a doubling to tripling within the study period.

scholarly journals A new global gridded anthropogenic heat flux dataset with high spatial resolution and long-term time series

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Kai Jin ◽  
Fei Wang ◽  
Deliang Chen ◽  
Huanhuan Liu ◽  
Wenbin Ding ◽  
...  
Keyword(s):  
Heat Flux ◽  
Time Series ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Anthropogenic Heat ◽  
Anthropogenic Heat Flux

Italy's politics without policy: Balancing Atlanticism and Europeanism in the Middle East

Modern Italy ◽  
2008 ◽  
Vol 13 (2) ◽  
pp. 135-153 ◽  
Author(s):  
Raffaella A. Del Sarto ◽  
Nathalie Tocci
Keyword(s):  
Foreign Policy ◽  
Middle East ◽  
The United States ◽  
The European Union ◽  
Short Term ◽  
The Government ◽  
Specific Interests ◽  
Policy Shifts ◽  
The Eu

Focusing on Italy's Middle East policies under the second Berlusconi (2001–2006) and the second Prodi (2006–2008) governments, this article assesses the manner and extent to which the observed foreign policy shifts between the two governments can be explained in terms of the rebalancing between a ‘Europeanist’ and a transatlantic orientation. Arguing that Rome's policy towards the Middle East hinges less on Italy's specific interests and objectives in the region and more on whether the preference of the government in power is to foster closer ties to the United States or concentrate on the European Union, the analysis highlights how these swings of the pendulum along the EU–US axis are inextricably linked to a number of underlying structural weaknesses of Rome's foreign policy. In particular, the oscillations can be explained by the prevalence of short-term political (and domestic) considerations and the absence of long-term, substantive political strategies, or, in short, by the phenomenon of ‘politics without policy’ that often characterises Italy's foreign policy.

The Effects of the Opioid Epidemic on Prescribing Practices in Long-Term Care Residents

2019 ◽  
Vol 34 (4) ◽  
pp. 258-267
Author(s):  
Lisa Yamagishi ◽  
Olivia Erickson ◽  
Kelly Mazzei ◽  
Christine O'Neil ◽  
Khalid M. Kamal
Keyword(s):  
Severe Pain ◽  
Long Term Care ◽  
Care Facility ◽  
The United States ◽  
Self Report ◽  
Prescribing Practices ◽  
Term Care ◽  
Data Set ◽  
Opioid Prescribing

OBJECTIVE: Evaluate opioid prescribing practices for older adults since the opioid crisis in the United States.<br/> DESIGN: Interrupted time-series analysis on retrospective observational cohort study.<br/> SETTING: 176-bed skilled-nursing facility (SNF).<br/> PARTICIPANTS: Patients admitted to a long-term care facility with pain-related diagnoses between October 1, 2015, and March 31, 2017, were included. Residents discharged prior to 14 days were excluded. Of 392 residents, 258 met inclusion criteria with 313 admissions.<br/> MAIN OUTCOME MEASURE: Changes in opioid prescribing frequency between two periods: Q1 to Q3 (Spring 2016) and Q4 to Q6 for pre- and postgovernment countermeasure, respectively.<br/> RESULTS: Opioid prescriptions for patients with pain-related diagnoses decreased during period one at -0.10% per quarter (95% confidence interval [CI] -0.85-0.85; P = 0.99), with the rate of decline increasing at -3.8% per quarter from period 1 and 2 (95% CI -0.23-0.15; P = 0.64). Opioid prescribing from top International Classification of Diseases, Ninth Revision category, "Injury and Poisoning" decreased in prescribing frequency by -3.0% per quarter from Q1 to Q6 (95% CI -0.16-0.10; P = 0.54). Appropriateness of pain-control was obtained from the Minimum Data Set version 3.0 "Percent of Residents Who Self-Report Moderate to Severe Pain (Short Stay)" measure; these results showed a significant increase in inadequacy of pain relief by 0.28% per quarter (95% CI 0.12-0.44; P = 0.009).<br/> CONCLUSION: Residents who self-report moderate- to severe pain have significantly increased since October 2015. Opioid prescriptions may have decreased for elderly patients in SNFs since Spring 2016. Further investigation with a larger population and wider time frame is warranted to further evaluate significance.

Long term variations of the hydrochemical composition of deep thermal ground water in the Lower Bavarian Molasse Basin – Causes and Perspectives

2021 ◽  
Author(s):  
Annette Dietmaier ◽  
Thomas Baumann
Keyword(s):  
Time Series ◽  
Real Life ◽  
Data Series ◽  
Fluid Composition ◽  
Molasse Basin ◽  
Deep Groundwater ◽  
Data Set ◽  
Groundwater Exploitation ◽  
Groundwater Aquifers

&lt;p&gt;The European Water Framework Directive (WFD) commits EU member states to achieve a good qualitative and quantitative status of all their water bodies.&amp;#160; WFD provides a list of actions to be taken to achieve the goal of good status.&amp;#160; However, this list disregards the specific conditions under which deep (&gt; 400 m b.g.l.) groundwater aquifers form and exist.&amp;#160; In particular, deep groundwater fluid composition is influenced by interaction with the rock matrix and other geofluids, and may assume a bad status without anthropogenic influences. Thus, a new concept with directions of monitoring and modelling this specific kind of aquifers is needed. Their status evaluation must be based on the effects induced by their exploitation. Here, we analyze long-term real-life production data series to detect changes in the hydrochemical deep groundwater characteristics which might be triggered by balneological and geothermal exploitation. We aim to use these insights to design a set of criteria with which the status of deep groundwater aquifers can be quantitatively and qualitatively determined. Our analysis is based on a unique long-term hydrochemical data set, taken from 8 balneological and geothermal sites in the molasse basin of Lower Bavaria, Germany, and Upper Austria. It is focused on a predefined set of annual hydrochemical concentration values. The data range dates back to 1937. Our methods include developing threshold corridors, within which a good status can be assumed, and developing cluster analyses, correlation, and piper diagram analyses. We observed strong fluctuations in the hydrochemical characteristics of the molasse basin deep groundwater during the last decades. Special interest is put on fluctuations that seem to have a clear start and end date, and to be correlated with other exploitation activities in the region. For example, during the period between 1990 and 2020, bicarbonate and sodium values displayed a clear increase, followed by a distinct dip to below-average values and a subsequent return to average values at site F. During the same time, these values showed striking irregularities at site B. Furthermore, we observed fluctuations in several locations, which come close to disqualifying quality thresholds, commonly used in German balneology. Our preliminary results prove the importance of using long-term (multiple decades) time series analysis to better inform quality and quantity assessments for deep groundwater bodies: most fluctuations would stay undetected within a &lt; 5 year time series window, but become a distinct irregularity when viewed in the context of multiple decades. In the next steps, a quality assessment matrix and threshold corridors will be developed, which take into account methods to identify these fluctuations. This will ultimately aid in assessing the sustainability of deep groundwater exploitation and reservoir management for balneological and geothermal uses.&lt;/p&gt;

scholarly journals The sdB pulsating star V391 Peg and its putative giant planet revisited after 13 years of time-series photometric data

2018 ◽  
Vol 611 ◽  
pp. A85 ◽  
Author(s):  
R. Silvotti ◽  
S. Schuh ◽  
S.-L. Kim ◽  
R. Lutz ◽  
M. Reed ◽  
...  
Keyword(s):  
Time Series ◽  
Previous Analysis ◽  
Time Derivative ◽  
Giant Planet ◽  
Sine Wave ◽  
Pulsation Period ◽  
Pulsation Frequency ◽  
Data Set ◽  
Term Variation

V391 Peg (alias HS 2201+2610) is a subdwarf B (sdB) pulsating star that shows both p- and g-modes. By studying the arrival times of the p-mode maxima and minima through the O–C method, in a previous article the presence of a planet was inferred with an orbital period of 3.2 years and a minimum mass of 3.2 MJup. Here we present an updated O–C analysis using a larger data set of 1066 h of photometric time series (~2.5× larger in terms of the number of data points), which covers the period between 1999 and 2012 (compared with 1999–2006 of the previous analysis). Up to the end of 2008, the new O–C diagram of the main pulsation frequency (f1) is compatible with (and improves) the previous two-component solution representing the long-term variation of the pulsation period (parabolic component) and the giant planet (sine wave component). Since 2009, the O–C trend of f1 changes, and the time derivative of the pulsation period (p.) passes from positive to negative; the reason of this change of regime is not clear and could be related to nonlinear interactions between different pulsation modes. With the new data, the O–C diagram of the secondary pulsation frequency (f2) continues to show two components (parabola and sine wave), like in the previous analysis. Various solutions are proposed to fit the O–C diagrams of f1 and f2, but in all of them, the sinusoidal components of f1 and f2 differ or at least agree less well than before. The nice agreement found previously was a coincidence due to various small effects that are carefully analyzed. Now, with a larger dataset, the presence of a planet is more uncertain and would require confirmation with an independent method. The new data allow us to improve the measurement of p. for f1 and f2: using only the data up to the end of 2008, we obtain p.1 = (1.34 ± 0.04) × 10−12 and p.2 = (1.62 ± 0.22) × 10−12. The long-term variation of the two main pulsation periods (and the change of sign of p.1) is visible also in direct measurements made over several years. The absence of peaks near f1 in the Fourier transform and the secondary peak close to f2 confirm a previous identification as l = 0 and l = 1, respectively, and suggest a stellar rotation period of about 40 days. The new data allow constraining the main g-mode pulsation periods of the star.

A 10-year regional reanalysis of desert dust aerosol at high spatial resolution

2021 ◽  
Author(s):  
Enza Di Tomaso ◽  
Jerónimo Escribano ◽  
Paul Ginoux ◽  
Sara Basart ◽  
Francesca Macchia ◽  
...  
Keyword(s):  
Middle East ◽  
Data Assimilation ◽  
Spatial Resolution ◽  
Weather Prediction ◽  
Reanalysis Data ◽  
Northern Africa ◽  
Dust Aerosol ◽  
Data Set ◽  
Desert Dust ◽  
Deep Blue

&lt;p&gt;Desert dust is the most abundant aerosol by mass residing in the atmosphere. It plays a key role in the Earth&amp;#8217;s system by influencing the radiation balance, by affecting cloud formation and cloud chemistry, and by acting as a fertilizer for the growth of phytoplankton and for soil through its deposition over the ocean and land.&lt;/p&gt;&lt;p&gt;Due to the nature of its emission and transport, atmospheric dust concentrations are highly variable in space and time and, therefore, require a continuous monitoring by measurements. Dust observations are best exploited by being combined with model simulations for the production of analyses and reanalyses, i.e., complete and consistent four dimensional reconstructions of the atmosphere. Existing aerosol (and dust) reanalyses for the global domain have been produced by total aerosol constraint and at relatively coarse spatial resolution, while regional reanalyses exclude some of the regions containing the major sources of desert dust in Northern Africa and the Middle East.&lt;/p&gt;&lt;p&gt;We present here a 10-year reanalysis data set of desert dust at a horizontal resolution of 0.1&amp;#176;, and which covers the domain of Northern Africa, the Middle East and Europe. The reanalysis has been produced by assimilating in the MONARCH chemical weather prediction system (Di Tomaso et al., 2017) satellite retrievals over dust source regions with specific dust observational constraint (Ginoux et al., 2012; Pu and Ginoux, 2016).&lt;/p&gt;&lt;p&gt;Furthermore, we describe its evaluation in terms of data assimilation diagnostics and comparison against independent observations. Statistics of analysis departures from assimilated observations prove the consistency of the data assimilation system showing that the analysis is closer to the observations than the first-guess. Temporal mean of analysis increments show that the assimilation led to an overall reduction of dust with pattern of systematic corrections that vary with the seasons, and can be linked primarily to misrepresentation of source strength.&lt;/p&gt;&lt;p&gt;Independent evaluation of the analysis with AERONET observations indicates that the reanalysis data set is highly accurate, and provides therefore a reliable historical record of atmospheric desert dust concentrations in a recent decade.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;References&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;Di Tomaso, E., Schutgens, N. A. J., Jorba, O., and P&amp;#233;rez Garc&amp;#237;a-Pando, C. (2017): Assimilation of MODIS Dark Target and Deep Blue observations in the dust aerosol component of NMMB-MONARCH version 1.0, Geosci. Model Dev., 10, 1107-1129.&lt;/p&gt;&lt;p&gt;Ginoux, P., Prospero, J. M., Gill, T. E., Hsu, N. C. and Zhao, M. (2012): Global-Scale Attribution of Anthropogenic and Natural Dust Sources and Their Emission Rates Based on Modis Deep Blue Aerosol Products. Rev Geophys 50.&lt;/p&gt;&lt;p&gt;Pu, B., and Ginoux, P. (2016). The impact of the Pacific Decadal Oscillation on springtime dust activity in Syria. Atmospheric Chemistry and Physics, 16(21), 13431-13448.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Acknowledgements &lt;/strong&gt;&lt;/p&gt;&lt;p&gt;The authors acknowledge the DustClim project which is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (435690462); PRACE (eDUST/eFRAGMENT1/eFRAGMENT2), RES (AECT-2020-3-0013/AECT-2019-3-0001/AECT-2020-1-0007) for awarding access to MareNostrum at BSC and for technical support.&lt;/p&gt;

scholarly journals Harmonizing Multi-Source Remote Sensing Images for Summer Corn Growth Monitoring

2019 ◽  
Vol 11 (11) ◽  
pp. 1266 ◽  
Author(s):  
Mingzheng Zhang ◽  
Dehai Zhu ◽  
Wei Su ◽  
Jianxi Huang ◽  
Xiaodong Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Kalman Filter ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Meteorological Data ◽  
High Temporal Resolution ◽  
Growth Monitoring ◽  
Landsat 8 ◽  
Data Set

Continuous monitoring of crop growth status using time-series remote sensing image is essential for crop management and yield prediction. The growing season of summer corn in the North China Plain with the period of rain and hot, which makes the acquisition of cloud-free satellite imagery very difficult. Therefore, we focused on developing image datasets with both a high temporal resolution and medium spatial resolution by harmonizing the time-series of MOD09GA Normalized Difference Vegetation Index (NDVI) images and 30-m-resolution GF-1 WFV images using the improved Kalman filter model. The harmonized images, GF-1 images, and Landsat 8 images were then combined and used to monitor the summer corn growth from 5th June to 6th October, 2014, in three counties of Hebei Province, China, in conjunction with meteorological data and MODIS Evapotranspiration Data Set. The prediction residuals ( Δ P R K ) in NDVI between the GF-1 observations and the harmonized images was in the range of −0.2 to 0.2 with Gauss distribution. Moreover, the obtained phenological curves manifested distinctive growth features for summer corn at field scales. Changes in NDVI over time were more effectively evaluated and represented corn growth trends, when considered in conjunction with meteorological data and MODIS Evapotranspiration Data Set. We observed that the NDVI of summer corn showed a process of first decreasing and then rising in the early growing stage and discuss how the temperature and moisture of the environment changed with the growth stage. The study demonstrated that the synthesized dataset constructed using this methodology was highly accurate, with high temporal resolution and medium spatial resolution and it was possible to harmonize multi-source remote sensing imagery by the improved Kalman filter for long-term field monitoring.

scholarly journals Geo-Object-Based Land Cover Map Update for High-Spatial-Resolution Remote Sensing Images via Change Detection and Label Transfer

2020 ◽  
Vol 12 (1) ◽  
pp. 174
Author(s):  
Tianjun Wu ◽  
Jiancheng Luo ◽  
Ya’nan Zhou ◽  
Changpeng Wang ◽  
Jiangbo Xi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Change Detection ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Data Set ◽  
Label Transfer ◽  
Object Based ◽  
Training Samples ◽  
Map Updating

Land cover (LC) information plays an important role in different geoscience applications such as land resources and ecological environment monitoring. Enhancing the automation degree of LC classification and updating at a fine scale by remote sensing has become a key problem, as the capability of remote sensing data acquisition is constantly being improved in terms of spatial and temporal resolution. However, the present methods of generating LC information are relatively inefficient, in terms of manually selecting training samples among multitemporal observations, which is becoming the bottleneck of application-oriented LC mapping. Thus, the objectives of this study are to speed up the efficiency of LC information acquisition and update. This study proposes a rapid LC map updating approach at a geo-object scale for high-spatial-resolution (HSR) remote sensing. The challenge is to develop methodologies for quickly sampling. Hence, the core step of our proposed methodology is an automatic method of collecting samples from historical LC maps through combining change detection and label transfer. A data set with Chinese Gaofen-2 (GF-2) HSR satellite images is utilized to evaluate the effectiveness of our method for multitemporal updating of LC maps. Prior labels in a historical LC map are certified to be effective in a LC updating task, which contributes to improve the effectiveness of the LC map update by automatically generating a number of training samples for supervised classification. The experimental outcomes demonstrate that the proposed method enhances the automation degree of LC map updating and allows for geo-object-based up-to-date LC mapping with high accuracy. The results indicate that the proposed method boosts the ability of automatic update of LC map, and greatly reduces the complexity of visual sample acquisition. Furthermore, the accuracy of LC type and the fineness of polygon boundaries in the updated LC maps effectively reflect the characteristics of geo-object changes on the ground surface, which makes the proposed method suitable for many applications requiring refined LC maps.

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