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Atmosphere, Volume 7, Issue 12 (December 2016)

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Research

Jump to: Review

Open AccessArticle Cloud Properties under Different Synoptic Circulations: Comparison of Radiosonde and Ground-Based Active Remote Sensing Measurements
Atmosphere 2016, 7(12), 154; doi:10.3390/atmos7120154
Received: 30 August 2016 / Revised: 14 November 2016 / Accepted: 22 November 2016 / Published: 28 November 2016
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Abstract
In this study, long-term (10 years) radiosonde-based cloud data are compared with the ground-based active remote sensing product under six prevailing large-scale synoptic patterns, i.e., cyclonic center (CC), weak pressure pattern (WP), the southeast bottom of cyclonic center (CB), cold front (CF), anticyclone
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In this study, long-term (10 years) radiosonde-based cloud data are compared with the ground-based active remote sensing product under six prevailing large-scale synoptic patterns, i.e., cyclonic center (CC), weak pressure pattern (WP), the southeast bottom of cyclonic center (CB), cold front (CF), anticyclone edge (AE) and anticyclone center (AC) over the Southern Great Plains (SGP) site. The synoptic patterns are generated by applying the self-organizing map weather classification method to the daily National Centers for Environmental Protection mean sea level pressure records from the North American Regional Reanalysis. It reveals that the large-scale synoptic circulations can strongly influence the regional cloud formation, and thereby have impact on the consistency of cloud retrievals from the radiosonde and ground-based cloud product. The total cloud cover at the SGP site is characterized by the least in AC and the most in CF. The minimum and maximum differences between the two cloud methods are 10.3% for CC and 13.3% for WP. Compared to the synoptic patterns characterized by scattered cloudy and clear skies (AE and AC), the agreement of collocated cloud boundaries between the two cloud approaches tends to be better under the synoptic patterns dominated by overcast and cloudy skies (CC, WP and CB). The rainy and windy weather conditions in CF synoptic pattern influence the consistency of the two cloud retrieval methods associated with the limited capabilities inherent to the instruments. The cloud thickness distribution from the two cloud datasets compares favorably with each other in all synoptic patterns, with relative discrepancy of ≤0.3 km. Full article
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Open AccessArticle Bidimensional and Multidimensional Principal Component Analysis in Long Term Atmospheric Monitoring
Atmosphere 2016, 7(12), 155; doi:10.3390/atmos7120155
Received: 27 October 2016 / Revised: 28 November 2016 / Accepted: 30 November 2016 / Published: 2 December 2016
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Abstract
Atmospheric monitoring produces huge amounts of data. Univariate and bivariate statistics are widely used to investigate variations in the parameters. To summarize information graphs are usually used in the form of histograms or tendency profiles (e.g., variable concentration vs. time), as well as
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Atmospheric monitoring produces huge amounts of data. Univariate and bivariate statistics are widely used to investigate variations in the parameters. To summarize information graphs are usually used in the form of histograms or tendency profiles (e.g., variable concentration vs. time), as well as bidimensional plots where two-variable correlations are considered. However, when dealing with big data sets at least two problems arise: a great quantity of numbers (statistics) and graphs are produced, and only two-variable interactions are often considered. The aim of this article is to show how the use of multivariate statistics helps in handling atmospheric data sets. Multivariate modeling considers all the variables simultaneously and returns the main results as bidimensional graphs that are easy-to-read. Principal Component Analysis (PCA; the most known multivariate method) and multiway-PCA (Tucker3) are compared from methodological and interpretative points of view. The article demonstrates the ability to emphasize different information depending on the data handling performed. The results and benefits achieved using a more complex model that allows for the simultaneous consideration of the entire variability of the system are compared with the results provided by the simpler but better-known model. Atmospheric monitoring (SO2, NOx, NO2, NO, and O3) data from the Lake Como Area (Italy) since 1992 to 2007 were chosen for consideration for the case study. Full article
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Open AccessArticle Bias Correction for Retrieval of Atmospheric Parameters from the Microwave Humidity and Temperature Sounder Onboard the Fengyun-3C Satellite
Atmosphere 2016, 7(12), 156; doi:10.3390/atmos7120156
Received: 18 August 2016 / Revised: 21 November 2016 / Accepted: 30 November 2016 / Published: 3 December 2016
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Abstract
The microwave humidity and temperature sounder (MWHTS) on the Fengyun (FY)-3C satellite measures the outgoing radiance from the Earth’s surface and atmospheric constituents. MWHTS, which makes measurements in the isolated oxygen absorption line near 118 GHz and the vicinity of the strong water
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The microwave humidity and temperature sounder (MWHTS) on the Fengyun (FY)-3C satellite measures the outgoing radiance from the Earth’s surface and atmospheric constituents. MWHTS, which makes measurements in the isolated oxygen absorption line near 118 GHz and the vicinity of the strong water vapor absorption line around 183 GHz, can provide fine vertical distribution structures of both atmospheric humidity and temperature. However, in order to obtain the accurate soundings of humidity and temperature by physical retrieval methods, the bias between the observed and simulated radiance calculated by the radiative transfer model from the background or first guess profiles must be corrected. In this study, two bias correction methods are developed through the correlation analysis between MWHTS measurements and air mass identified by the first guess profiles of the physical inversion; one is the linear regression correction (LRC), and the other is the neural network correction (NNC), representing the linear and nonlinear relationships between MWHTS measurements and air mass, respectively. The correction methods have been applied to MWHTS observed brightness temperatures over the geographic area (180° W–180° E, 60° S–60° N). The corrected results are evaluated by the probability density function of the differences between corrected observations and simulated values and the root mean square errors (RMSE) with respect to simulated observations. The numerical results show that the NNC method has better performance, especially in MWHTS Channels 1 and 7–9, whose peak weight function heights are close to the surface. In order to assess the effects of bias correction methods proposed in this study on the retrieval accuracy, a one-dimensional variational system was built and applied to the MWHTS brightness temperatures to estimate the atmospheric temperature and humidity profiles. The retrieval results also show that NNC has better performance. An indication of the stability and robustness of the NNC method is given, which suggests that the NNC method has promising application perspectives in the physical retrieval. Full article
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Open AccessArticle Sensitive versus Rough Dependence under Initial Conditions in Atmospheric Flow Regimes
Atmosphere 2016, 7(12), 157; doi:10.3390/atmos7120157
Received: 5 October 2016 / Revised: 28 November 2016 / Accepted: 30 November 2016 / Published: 5 December 2016
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Abstract
In this work, we will identify the existence of “rough dependence on initial conditions” in atmospheric phenomena, a concept which is a problem for weather analysis and forecasting. Typically, two initially similar atmospheric states will diverge slowly over time such that forecasting the
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In this work, we will identify the existence of “rough dependence on initial conditions” in atmospheric phenomena, a concept which is a problem for weather analysis and forecasting. Typically, two initially similar atmospheric states will diverge slowly over time such that forecasting the weather using the Navier-Stokes equations is useless after some characteristic time scale. With rough dependence, two initial states diverge very quickly, implying forecasting may be impossible. Using previous research in atmospheric science, rough dependence is characterized by using quantities that can be calculated using atmospheric data and quantities. Rough dependence will be tested for and identified in atmospheric phenomena at different time scales using case studies. Data were provided for this project by archives outside the University of Missouri (MU) and by using the MU RADAR at the South Farm experiment station. Full article
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Open AccessArticle Feasibility Study of Rain Rate Monitoring from Polarimetric GNSS Propagation Parameters
Atmosphere 2016, 7(12), 159; doi:10.3390/atmos7120159
Received: 15 October 2016 / Revised: 13 November 2016 / Accepted: 30 November 2016 / Published: 6 December 2016
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Abstract
In this work, the feasibility of estimating rain rate based on polarimetric Global Navigation Satellite Systems (GNSS) signals is explored in theory. After analyzing the cause of polarimetric signals, three physical-mathematical relation models between co-polar phase shift (KHH, KVV
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In this work, the feasibility of estimating rain rate based on polarimetric Global Navigation Satellite Systems (GNSS) signals is explored in theory. After analyzing the cause of polarimetric signals, three physical-mathematical relation models between co-polar phase shift (KHH, KVV), specific differential phase shift (KDP), and rain rate (R) are respectively investigated. These relation models are simulated based on four different empirical equations of nonspherical raindrops and simulated Gamma raindrop size distribution. They are also respectively analyzed based on realistic Gamma raindrop size distribution and maximum diameter of raindrops under three different rain types: stratiform rain, cumuliform rain, and mixed clouds rain. The sensitivity of phase shift with respect to some main influencing factors, such as shape of raindrops, frequency, as well as elevation angle, is also discussed, respectively. The numerical results in this study show that the results by scattering algorithms T-matrix are consistent with those from Rayleigh Scattering Approximation. It reveals that they all have the possibility to estimate rain rate using the KHH-R, KVV-R or KDP-R relation. It can also be found that the three models are all affected by shape of raindrops and frequency, while the elevation angle has no effect on KHH-R. Finally, higher frequency L1 or B1 and lower elevation angle are recommended and microscopic characteristics of raindrops, such as shape and size distribution, are deemed to be important and required for further consideration in future experiments. Since phase shift is not affected by attenuation and not biased by ground clutter cancellers, this method has considerable potential in precipitation monitoring, which provides new opportunities for atmospheric research. Full article
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Open AccessArticle Tracing the Source of the Errors in Hourly IMERG Using a Decomposition Evaluation Scheme
Atmosphere 2016, 7(12), 161; doi:10.3390/atmos7120161
Received: 27 October 2016 / Revised: 3 December 2016 / Accepted: 8 December 2016 / Published: 13 December 2016
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Abstract
Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) is an important satellite precipitation product of Global Precipitation Measurement (GPM) mission. Quantitative information about the errors of IMERG has great significance for the data developers and end users. In order to investigate the characteristics
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Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) is an important satellite precipitation product of Global Precipitation Measurement (GPM) mission. Quantitative information about the errors of IMERG has great significance for the data developers and end users. In order to investigate the characteristics and the source of the errors contained in IMERG, a bias-decomposition scheme was employed to evaluate the hourly IMERG over the eastern part of Mainland China during the warm season. First, the total bias of IMERG before and after calibration (termed as precipitationUncal and precipitationCal) was calculated using rain gauge measurements as reference. Then the bias was decomposed into three independent components including false bias, missed bias, and hit bias. Finally, the hit bias was further decomposed according to the rainfall intensity measured by rain gauges. The results indicate that (1) the bias of precipitationUncal over the north part is dominated by hit bias and false bias, leading to the serious overestimation for the precipitation over this area, but it underestimates the precipitation over the south part with the false bias and missed bias acting as major contributors; (2) the precipitationCal overestimates the precipitation over more than 80% of the study areas mainly as a result of a large amplitude of false bias; (3) the calibration algorithm used by IMERG could not reduce the missed bias and enlarges the false bias over some regions, revealing a shortcoming of this algorithm in that it could not effectively alleviate the bias resulting from the rain areas delineation; (4) the hit bias of IMERG is strongly related with the rainfall intensity of rain gauge measurements, which should be beneficial for reducing the errors of IMERG. This study provides a deep insight into the characteristics and sources of the biases inherent in IMERG, which is significant for its utilization and possible correction in future. Full article
(This article belongs to the Special Issue Global Precipitation with Climate Change)
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Open AccessArticle Tracking the Origin of Moisture over the Danube River Basin Using a Lagrangian Approach
Atmosphere 2016, 7(12), 162; doi:10.3390/atmos7120162
Received: 16 November 2016 / Revised: 7 December 2016 / Accepted: 8 December 2016 / Published: 14 December 2016
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Abstract
In this study, we investigate the sources of moisture (and moisture for precipitation) over the Danube River Basin (DRB) by means of a Lagrangian approach using the FLEXPART V9.0 particle dispersion model together with ERA-Interim reanalysis data to track changes in atmospheric moisture
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In this study, we investigate the sources of moisture (and moisture for precipitation) over the Danube River Basin (DRB) by means of a Lagrangian approach using the FLEXPART V9.0 particle dispersion model together with ERA-Interim reanalysis data to track changes in atmospheric moisture over 10-day trajectories. This approach computes the budget of evaporation-minus-precipitation by calculating changes in specific humidity along forward and backward trajectories. We considered a time period of 34 years, from 1980 to 2014, which allowed for the identification of climatological sources and moisture transport towards the basin. Results show that the DRB mainly receives moisture from seven different oceanic, maritime, and terrestrial moisture source regions: North Atlantic Ocean, North Africa, the Mediterranean Sea, Black Sea, Caspian Sea, the Danube River Basin, and Central and Eastern Europe. The contribution of these sources varies by season. During winter (October–March) the main moisture source for the DRB is the Mediterranean Sea, while during summer (April–September) the dominant source of moisture is the DRB itself. Moisture from each source has a different contribution to precipitation in the DRB. Among the sources studied, results show that the moisture from the Mediterranean Sea provides the greatest contribution to precipitation in the basin in both seasons, extending to the whole basin for the winter, but being more confined to the western side during the summer. Moisture from the Caspian and Black Seas contributes to precipitation rather less. Full article
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Open AccessArticle Chemical Composition of PM10 at Urban Sites in Naples (Italy)
Atmosphere 2016, 7(12), 163; doi:10.3390/atmos7120163
Received: 28 October 2016 / Revised: 6 December 2016 / Accepted: 9 December 2016 / Published: 16 December 2016
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Abstract
Here, we report the chemical characterization and identification of the possible sources of particulate matter (fraction PM10) at two different sites in Naples. PM10 concentration and its chemical composition were studied using the crustal enrichment factor (EF) and principal component
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Here, we report the chemical characterization and identification of the possible sources of particulate matter (fraction PM10) at two different sites in Naples. PM10 concentration and its chemical composition were studied using the crustal enrichment factor (EF) and principal component analysis (PCA). In all of the seasons, the PM10 levels, were significantly higher (p < 0.01) in the urban-traffic site (denominated NA02) than in the urban-background site (denominated NA01). In order to reconstruct the particle mass, the components were classified into seven classes as follows: mineral dust (MD), trace elements (TE), organic matter (OM), elemental carbon (EC), sea salt (SS), secondary inorganic aerosol (SIA) and undetermined parts (unknown (UNK)). According to the chemical mass closure obtained, the major contribution was OM, which was higher (p < 0.01) during summer than in other seasons. In both sites, a good correlation (R2 > 0.8) was obtained between reconstructed mass and gravimetric mass. PCA analysis explained 76% and 79% of the variance in NA01 and NA02, respectively. The emission sources were the same for both sites; but, the location of the site, the different distances from the sources and the presence and absence of vegetation proved the different concentrations and compositions of PM10. Full article
(This article belongs to the Special Issue Urban Air Pollution)
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Open AccessArticle Rain Attenuation Correction of Reflectivity for X-Band Dual-Polarization Radar
Atmosphere 2016, 7(12), 164; doi:10.3390/atmos7120164
Received: 19 September 2016 / Revised: 6 December 2016 / Accepted: 8 December 2016 / Published: 17 December 2016
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Abstract
In order to improve the performance of X-band dual-polarization radars, it is necessary to conduct attenuation correction before using the X-band radar data. This study analyzes a variety of attenuation correction methods for the X-band radar reflectivity, and proposes a high-resolution slide self-consistency
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In order to improve the performance of X-band dual-polarization radars, it is necessary to conduct attenuation correction before using the X-band radar data. This study analyzes a variety of attenuation correction methods for the X-band radar reflectivity, and proposes a high-resolution slide self-consistency correction (SSCC) method, which is an improvement of Kim et al.’s method based on Bringi et al.’s original method. The new method is comprehensively evaluated with the observational data of convective cloud, stratiform cloud, and the stratiform cloud with embedded convection. Comparing with the intrinsic reflectivity at X-band calculated from the reflectivity at S-band, it is found that the new method can effectively reduce the correction errors when calculating differential propagation shift increments using the conventional self-consistency attenuation correction method. This method can efficiently correct the X-band dual-polarization radar reflectivity, in particular, for the echoes with reflectivity greater than 35 dBZ. Full article
(This article belongs to the Special Issue Radar Meteorology)
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Open AccessArticle Applications of Cell-Ratio Constant False-Alarm Rate Method in Coherent Doppler Wind Lidar
Atmosphere 2016, 7(12), 165; doi:10.3390/atmos7120165
Received: 8 October 2016 / Revised: 28 November 2016 / Accepted: 13 December 2016 / Published: 17 December 2016
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Abstract
A cell-ratio constant false-alarm rate (CR-CFAR) method for detecting the Doppler frequency shift is proposed to improve the accuracy of velocity measured by coherent Doppler wind lidar (CWL) in low signal-to-noise ratio (SNR) environments. The method analyzes the spectrum to solve issues of
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A cell-ratio constant false-alarm rate (CR-CFAR) method for detecting the Doppler frequency shift is proposed to improve the accuracy of velocity measured by coherent Doppler wind lidar (CWL) in low signal-to-noise ratio (SNR) environments. The method analyzes the spectrum to solve issues of weak signal submergence in noise encountered in the widely used periodogram method. This characteristic is that the signal region slope is larger than the noise region slope in the frequency spectrum. We combined the ratio and CFAR to propose the CR-CFAR method. The peak area is discriminated from the spectrum using this method. By removing background noise, the peak signal is obtained along with the Doppler shift. To verify the CR-CFAR method, a campaign experiment using both CWL and a commercial Doppler lidar was performed in Hami, China (42°32′ N, 94°03′ E) during 1–7 June 2016. The results showed that the proposed method significantly improved the reliability of CWL data under low SNR conditions. The height—at which both horizontal wind speed correlativity and horizontal wind direction correlativity exceeded 0.99—increased by 65 m. The relative deviation of the horizontal wind speed at 120 m decreased from 40.37% to 11.04%. We used the CR-CFAR method to analyze continuous data. A greater number of wind field characteristics were obtained during observation compared to those obtained using the common wind field inversion method. Full article
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Review

Jump to: Research

Open AccessReview Seasonal Variations and Sources of 17 Aerosol Metal Elements in Suburban Nanjing, China
Atmosphere 2016, 7(12), 153; doi:10.3390/atmos7120153
Received: 16 October 2016 / Revised: 18 November 2016 / Accepted: 22 November 2016 / Published: 25 November 2016
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Abstract
In this work, the seasonal variations and sources of trace metal elements in atmospheric fine aerosols (PM2.5) were investigated for a year-long field campaign from July 2012 to June 2013, conducted in suburban Nanjing, eastern China, at a site adjacent to
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In this work, the seasonal variations and sources of trace metal elements in atmospheric fine aerosols (PM2.5) were investigated for a year-long field campaign from July 2012 to June 2013, conducted in suburban Nanjing, eastern China, at a site adjacent to an industry zone. The PM2.5 samples collected across four seasons were analyzed for 17 metal elements, namely, Sodium (Na), Magnesium (Mg), Aluminum (Al), Vanadium (V), Chromium (Cr), Manganese (Mn), Nickel (Ni), Copper (Cu), Zinc (Zn), Arsenic (As), Selenium (Se), Strontium (Sr), Cadmium (Cd), Barium (Ba), Lead (Pb), Molybdenum (Mo), and Antimony (Sb) using an inductively coupled plasma mass spectrometry (ICP-MS). We found that the total concentration of all 17 metal elements was 1.23 μg/m3, on average accounting for 1.0% of the total PM2.5 mass. For our data, mass concentrations of Al, Cd, Ba were highest in summer, Mg, Cu, Zn, Se, Pb peaked in autumn, Cr, Mn, Ni, As, Sr, Sb increased significantly in winter, while the concentrations of Na, V, Mo were at their highest levels in spring. Air mass back trajectory analysis suggested that air parcels that arrived at the site originated from four dominant regions (Japan, yellow sea and bohai; Southeast of China, the Pacific Ocean; Southwest of Jiangsu and Anhui province; Northern Asia inland and Mongolia region), in particular, the one from Northern Asia inland and Mongolia contained the highest concentrations of As, Sb, Sr, and was predominant in winter. Positive matrix factorization (PMF) analyses revealed that the industrial emission is the largest contributor (34%) of the observed metal elements, followed by traffic (25%), soil dust (19%), coal combustion (10%), incineration of electronic waste (9%), and a minor unknown source (3%). In addition, we have also investigated the morphology and composition of particles by using the scanning electron microscopy (SEM)/energy-dispersive spectrometry (EDS) techniques, and identified particles from coal burning sources, etc., similar to the PMF results. Full article
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Open AccessReview A Systematic Review of Global Desert Dust and Associated Human Health Effects
Atmosphere 2016, 7(12), 158; doi:10.3390/atmos7120158
Received: 28 July 2016 / Revised: 20 October 2016 / Accepted: 15 November 2016 / Published: 6 December 2016
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Abstract
Dust storms and sandy dust events originating in arid and semi-arid areas can transport particulate material, pollutants, and potential transport long distances from their sources. Exposure to desert dust particles is generally acknowledged to endanger human health. However, most studies have examined anthropogenic
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Dust storms and sandy dust events originating in arid and semi-arid areas can transport particulate material, pollutants, and potential transport long distances from their sources. Exposure to desert dust particles is generally acknowledged to endanger human health. However, most studies have examined anthropogenic particulate sources, with few studies considering contributions from natural desert dust. A systematic literature review was undertaken using the ISI Web of Knowledge and PubMed databases with the objective of identifying all studies presenting results on the potential health impact from desert dust particles across the world. This review reveals an imbalance between the areas most exposed to dust and the areas most studied in terms of health effects. Among the human health effects of dust storms are mortality and morbidity, arising from respiratory system, circulatory system, and other diseases. We summarize the quantitative results of current scientific health research and possible pathological mechanisms, and describe some of the many challenges related to understanding health effects from exposures to desert dust particles. Overall, for respiratory and circulatory mortality, both positive and negative associations have been reported for PM10 of desert dust, but only a positive relationship was reported between PM2.5–10 and mortality, and a positive relationship was also reported between PM2.5 and human mortality. Future pathological studies should continue to focus on those mechanisms causing the most harmful effect of desert dust on respiratory and cardiovascular diseases. More attention should also be paid to the association between desert dust and the morbidity of other diseases, such as those affecting the reproductive system and nervous system. Full article
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Open AccessReview Insights from a Chronology of the Development of Atmospheric Composition Monitoring Networks Since the 1800s
Atmosphere 2016, 7(12), 160; doi:10.3390/atmos7120160
Received: 9 November 2016 / Revised: 1 December 2016 / Accepted: 5 December 2016 / Published: 8 December 2016
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Abstract
Ground-based monitoring networks for evaluating atmospheric composition relevant to impacts on human health and the environment now exist worldwide (according to the United Nations Environment Programme, 48% of countries have an air quality monitoring system). Of course, this has not always been the
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Ground-based monitoring networks for evaluating atmospheric composition relevant to impacts on human health and the environment now exist worldwide (according to the United Nations Environment Programme, 48% of countries have an air quality monitoring system). Of course, this has not always been the case. Here, we analyse for the first time the key developments in network coordination and standardisation over the last 150 years that underpin the current implementations of city-scale to global monitoring networks for atmospheric composition. Examples include improvements in respect of site type and site representativeness, measurement methods, quality assurance, and data archiving. From the 1950s, these developments have progressed through two distinct types of network: those designed for the protection of human health, and those designed to increase scientific understanding of atmospheric composition and its interaction with the terrestrial environment. The step changes in network coordination and standardisation have increased confidence in the comparability of measurements made at different sites. Acknowledged limitations in the current state of monitoring networks include a sole focus on compliance monitoring. In the context of the unprecedented volumes of atmospheric composition data now being collected, we suggest the next developments in network standardisation should include more integrated analyses of monitor and other relevant data within “chemical climatology” frameworks that seek to more directly link the impacts, state and drivers of atmospheric composition. These approaches would also enhance the role of monitoring networks in the development and evaluation of air pollution mitigation strategies. Full article
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