419 research outputs found

    Spatial Representativeness of PM_(2.5) Concentrations Obtained Using Reduced Number of Network Stations

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    Haze has been a focused air pollution phenomenon in China, and its characterization is highly desired. Aerosol properties obtained from a single station are frequently used to represent the haze condition over a large domain, such as tens of kilometers, which could result in high uncertainties due to their spatial variation. Using a high resolution network observation over an urban city in North China from November 2015 to February 2016, this study examines the spatial representativeness of ground station observations of particulate matter with diameters less than 2.5 μm (PM_(2.5)). We developed a new method to determine the representative area of PM_(2.5) measurements from limited stations. The key idea is to determine the PM_(2.5) spatial representative area using its spatial variability and temporal correlation. We also determine stations with large representative area using two grid networks with different resolutions. Based on the high spatial resolution measurements, the representative area of PM_(2.5) at one station can be determined from the grids with high correlations and small differences of PM_(2.5). The representative area for a single station in the study period ranges from 0.25 to 16.25 km^2, but is less than 3 km^2 for more than half of the stations. The representative area varies with locations, and observation at 10 optimal stations would have a good representativeness of those obtained from 169 stations for the four-month time scale studied. Both evaluations with an empirical orthogonal function (EOF) analysis and with independent dataset corroborate the validity of the results found in this study

    Application of Spectral Analysis Techniques in the Intercomparison of Aerosol Data. Part II: Using Maximum Covariance Analysis to Effectively Compare Spatiotemporal Variability of Satellite and AERONET Measured Aerosol Optical Depth

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    Moderate Resolution Imaging SpectroRadiometer (MODIS) and Multi-angle Imaging Spectroradiomater (MISR) provide regular aerosol observations with global coverage. It is essential to examine the coherency between space- and ground-measured aerosol parameters in representing aerosol spatial and temporal variability, especially in the climate forcing and model validation context. In this paper, we introduce Maximum Covariance Analysis (MCA), also known as Singular Value Decomposition analysis as an effective way to compare correlated aerosol spatial and temporal patterns between satellite measurements and AERONET data. This technique not only successfully extracts the variability of major aerosol regimes but also allows the simultaneous examination of the aerosol variability both spatially and temporally. More importantly, it well accommodates the sparsely distributed AERONET data, for which other spectral decomposition methods, such as Principal Component Analysis, do not yield satisfactory results. The comparison shows overall good agreement between MODIS/MISR and AERONET AOD variability. The correlations between the first three modes of MCA results for both MODIS/AERONET and MISR/ AERONET are above 0.8 for the full data set and above 0.75 for the AOD anomaly data. The correlations between MODIS and MISR modes are also quite high (greater than 0.9). We also examine the extent of spatial agreement between satellite and AERONET AOD data at the selected stations. Some sites with disagreements in the MCA results, such as Kanpur, also have low spatial coherency. This should be associated partly with high AOD spatial variability and partly with uncertainties in satellite retrievals due to the seasonally varying aerosol types and surface properties

    Spatial Representativeness of PM_(2.5) Concentrations Obtained Using Reduced Number of Network Stations

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    Haze has been a focused air pollution phenomenon in China, and its characterization is highly desired. Aerosol properties obtained from a single station are frequently used to represent the haze condition over a large domain, such as tens of kilometers, which could result in high uncertainties due to their spatial variation. Using a high resolution network observation over an urban city in North China from November 2015 to February 2016, this study examines the spatial representativeness of ground station observations of particulate matter with diameters less than 2.5 μm (PM_(2.5)). We developed a new method to determine the representative area of PM_(2.5) measurements from limited stations. The key idea is to determine the PM_(2.5) spatial representative area using its spatial variability and temporal correlation. We also determine stations with large representative area using two grid networks with different resolutions. Based on the high spatial resolution measurements, the representative area of PM_(2.5) at one station can be determined from the grids with high correlations and small differences of PM_(2.5). The representative area for a single station in the study period ranges from 0.25 to 16.25 km^2, but is less than 3 km^2 for more than half of the stations. The representative area varies with locations, and observation at 10 optimal stations would have a good representativeness of those obtained from 169 stations for the four-month time scale studied. Both evaluations with an empirical orthogonal function (EOF) analysis and with independent dataset corroborate the validity of the results found in this study

    Spatial Representativeness of PM_(2.5) Concentrations Obtained Using Observations From Network Stations

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    Haze has been a focused air pollution phenomenon in China, and its characterization is highly desired. Aerosol properties obtained from a single station are frequently used to represent the haze condition over a large domain, such as tens of kilometers, which could result in high uncertainties due to their spatial variation. Using a high‐resolution network observation over an urban city in North China from November 2015 to February 2016, this study examines the spatial representativeness of ground station observations of particulate matter with diameters less than 2.5 μm (PM_(2.5)). We developed a new method to determine the representative area of PM_(2.5) measurements from limited stations. The key idea is to determine the PM_(2.5) spatial representative area using its spatial variability and temporal correlation. We also determine stations with large representative area using two grid networks with different resolutions. Based on the high spatial resolution measurements, the representative area of PM_(2.5) at one station can be determined from the grids with high correlations and small differences of PM_(2.5). The representative area for a single station in the study period ranges from 0.25 to 16.25 km^2 but is less than 3 km^2 for more than half of the stations. The representative area varies with locations, and observation at 10 optimal stations would have a good representativeness of those obtained from 169 stations for the 4 month time scale studied. Both evaluations with an empirical orthogonal function analysis and with independent data set corroborate the validity of the results found in this study

    Aeolian dust deposition rates in south-western Iran

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    The annual atmospheric dust-load originating in the so-called Dust Belt ‎, which ranges from the ‎Sahara desert and the Arabian peninsula to the arid lowlands of Central Asia and the deserts of ‎northern China, impacts the air quality and the climate worldwide. Iran as a whole, and especially the ‎southwestern regions of the country, most affected by dust, with frequent dust storms characterized ‎by annual mean concentrations of more than 100 µg/m³ of suspended dust. Although aeolian dust is a ‎highly relevant problem in Iran, there is a lack of comprehensive regional studies on this topic. The ‎central aim of the study presented here is therefore the spatiotemporal analyses and classification of ‎dust events, the chemical composition of the dust, and the connections between regional and seasonal ‎climate variation and dust deposition rates in four sub-regions of Iran. This comprehensive approach is ‎based on the maximum mean dust concentration and the seasonality of dust events. The results are ‎provided new and valuable insights into the dust deposition and its related processes in the study area.‎ The study area covers 8.43% of Iran (about 117,000 km2), located between 45°30′00″ E 35°00′00″ N ‎and 49°30′00″ E 30°00′00″ N including Kermanshah, Lorestan and Khuzestan. The fieldwork area is ‎characterized by the rolling mountainous terrain about 4000 m above sea level (a.s.l) in the north and ‎east, plains and marshlands in the south. Study area has also located in dry climate and hot summer ‎conditions in the south, cold and hot desert climates in the west. The studies on aeolian dust in ‎southwestern Iran are based solely on ground deposition rates from 2014 to 2017‎‏.‏ To address the connections between the Ground observation of dust Deposition Rates (GDR), climate ‎zones, and weather patterns, a comparative analysis with various data sets was conducted. Both ‎gravimetric and directional dust samplers (10 each) were installed to record the monthly GDR between ‎‎2014 and 2017. The sampler design was deliberately kept simple to ensure long-term durability and ‎easy maintenance. The collected dust samples were analyzed for their chemical composition using ‎Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The ten sampling sites were also classified ‎by their land use / land cover (LULC) for a more detailed data interpretation. The observation data ‎during two typical dust cases (spring 2014 and winter 2015), have furthermore been compared with ‎the spatiotemporal dust concentration and dust load over the study area. Comparing the results of the ‎monthly mean Aerosol Optical Thickness (AOT) derived from the Moderate Resolution Imaging ‎Spectroradiometer (MODIS) and GDR data, using enhancement algorithms were applied in order to ‎investigate the spatiotemporal distribution of dust events. To demonstrate the aerosol movement, a ‎HYbrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used for tracing the ‎investigated dust events. The time-space consistency between AOT and GDR, in agreement with the ‎HYSPLIT model output was the basis for an improved estimation of the dust deposition rate from ‎separate thickness layers. Finally, by comparing the high temporal and maximum seasonal deposition ‎rates, using MODIS and GDR data, the impact of the regional climate on the deposition rates of ‎aeolian dust was assessed, which allows insights in potential future dust emission scenarios in times of ‎climate change. ‎ A major finding shows the impact of dust events on the environment and considers the influence of ‎geographical factors, such as weathering, and climate pattern over aeolian dust deposition rates. In ‎more detail, finding to address the first objective suggested that contributors of the elemental ‎concentrations are associated with elements emanating from local industrial and commercial activities ‎‎(Cr, V, and Cd). The dominant variables (K, Zn) strongly influence the aerosol composition values and ‎represent the dust transport route. Inter –element relationships shows that the highest proportion (80%) ‎of dust samples subjected to Airborne Metals Regulations are formed under local and regional ‎conditions. Besides, the analyses indicate that the WRF-Chem model adequately simulates the ‎evolution, spatial distribution and load of dust over the study area. Hence, the model performance has ‎been evaluated by GDR. It showed different values of GDR highly depending on LULC pattern. Due to ‎the fact, that there is no way to isolate each individual area from the effects of either anthropogenic ‎sources or natural weathering processes, developing guidance on the priorities of expanding projects ‎and preventative actions towards potential dust deposition from natural and dominant sources may be ‎a subject of institutional interest. ‎ The results of direct measurements of dust deposition, which are typically made by passive sampling ‎techniques (ground-based observations), along with analyzed data from AOT, represent the second ‎objective to understand the spatiotemporal pattern of the points with the same variation. The ‎corresponding points headed to find moving air mass trajectories, using HYSPLIT were proven to be a ‎discriminator of their local and regional origin of aeolian dust. Furthermore, the seasonal deposition rate ‎varied from 8.4 g/m2/month in the summer to 3.5 g/m2/month in the spring. Despite all the advances ‎of AOT, under certain circumstances, the ground-based solutions were able to represent aerosol ‎conditions over the research area, tested in the southwestern regions of Iran. And that is when the low ‎number of observations is a commonly acknowledged drawback of GDR.‎ In addition, the peak of the seasonal deposition rates (t/km2/month) occurred in [arid desert hot-BWh, ‎‎8.4], [arid steppe hot-BSh, 6.6], and [hot and dry summer-Csa, 3.5] climate regions. Thus, the third ‎objective response was‏ ‏detected as the highest deposition rates of dust BWh >BSh >Csa throughout ‎the year, once the annual mean deposition rates (t/km2/year) are 100.80 for [BWh], 79.27 for [BSh], ‎and 39.60 for [Csa]. The knowledge gained on the dust deposition processes, together with the ‎feedback from the climate pattern, will provide insights into the records of data for developing new ‎sources, deposition rates and their climate offsets. Taking this in mind, having information about the ‎ground deposition rates in the study region could make the estimations more accurate, while finding an ‎appropriate algorithm is necessary to enhance the affected areas exposed to the dust. In order to ‎assess the impact of dust events on human health, environment and the damage to the various ‎business sectors of the country’s economy, additional studies with adequate modelling tools are ‎needed. ‎ Due to this date, the data holding organizations are somewhat reluctant to make their data available to ‎other parties. This work is also a step toward an institutional suggestion to gain benefit from information ‎exchange amongst data holding organizations, providers and users. The need for capacity building and ‎strong policy for implementing user-friendly geo information portal‏ ‏is essential.

    XBAER-derived aerosol optical thickness from OLCI/Sentinel-3 observation

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    Estimating PM 2.5 concentrations in Xi'an City using a generalized additive model with multi-source monitoring data

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    © 2015 Song et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Particulate matter with an aerodynamic diameter <2.5 μm (PM2.5) represents a severe environmental problem and is of negative impact on human health. Xi'an City, with a population of 6.5 million, is among the highest concentrations of PM2.5 in China. In 2013, in total, there were 191 days in Xi'an City on which PM2.5 concentrations were greater than 100 μg/m3. Recently, a few studies have explored the potential causes of high PM2.5 concentration using remote sensing data such as the MODIS aerosol optical thickness (AOT) product. Linear regression is a commonly used method to find statistical relationships among PM2.5 concentrations and other pollutants, including CO, NO2, SO2, and O3, which can be indicative of emission sources. The relationships of these variables, however, are usually complicated and non-linear. Therefore, a generalized additive model (GAM) is used to estimate the statistical relationships between potential variables and PM2.5 concentrations. This model contains linear functions of SO2 and CO, univariate smoothing non-linear functions of NO2, O3, AOT and temperature, and bivariate smoothing non-linear functions of location and wind variables. The model can explain 69.50% of PM2.5 concentrations, with R2 = 0.691, which improves the result of a stepwise linear regression (R2 = 0.582) by 18.73%. The two most significant variables, CO concentration and AOT, represent 20.65% and 19.54% of the deviance, respectively, while the three other gas-phase concentrations, SO2, NO2, and O3 account for 10.88% of the total deviance. These results show that in Xi'an City, the traffic and other industrial emissions are the primary source of PM2.5. Temperature, location, and wind variables also non-linearly related with PM2.5

    Lidar Measurements for Desert Dust Characterization: An Overview

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    We provide an overview of light detection and ranging (lidar) capability for describing and characterizing desert dust. This paper summarizes lidar techniques, observations, and fallouts of desert dust lidar measurements. The main objective is to provide the scientific community, including non-practitioners of lidar observations with a reference paper on dust lidar measurements. In particular, it will fill the current gap of communication between research-oriented lidar community and potential desert dust data users, such as air quality monitoring agencies and aviation advisory centers. The current capability of the different lidar techniques for the characterization of aerosol in general and desert dust in particular is presented. Technical aspects and required assumptions of these techniques are discussed, providing readers with the pros and cons of each technique. Information about desert dust collected up to date using lidar techniques is reviewed. Lidar techniques for aerosol characterization have a maturity level appropriate for addressing air quality and transportation issues, as demonstrated by some first results reported in this pape

    Investigating Elevated Aqua Modis Aerosol Optical Depth Retrievals Over The Mid-Latitude Southern Oceans

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    A band of elevated aerosol optical depth (AOD) over the mid-latitude Southern Oceans has been identified in some passive satellite-based aerosol datasets such as Moderate Resolution Imaging SpectroRadiometer (MODIS) and Multi-angle Imaging SpectroRadiometer (MISR) products. In this study, Aqua MODIS (AM) aerosol products in this zonal region are investigated in detail to assess retrieval accuracy. This is done through multiple data sets, including spatially and temporally collocated cloud and aerosol products produced by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) project for investigating AM AOD in this region with respect to lidar profiling of cloud presence. Maritime Aerosol Network (MAN) and Aerosol Robotic Network (AERONET) AOD data are also collocated with AM for surface context. The results of this study suggest that the apparent high AOD belt, seen in some satellite aerosol products based on passive remote sensing methods, is not seen in the CALIOP aerosol product based on an active remote sensing technique with an enhanced cloud detection capability and is not detected from ground-based observations such as MAN and AERONET data. The apparent high AOD belt, although largely attributed to stratocumulus and low broken cumulus cloud contamination as suggested by CALIOP products, could not be fully credited to cloud contamination. Collocated CALIOP data also suggest that the current cloud screening methods implemented in the over ocean AM aerosol products are ineffective in identifying cirrus clouds. Cloud residuals still exist in the AM AOD products even with the use of the most stringent cloud screening settings

    Using measurements for evaluation of black carbon modeling

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    The ever increasing use of air quality and climate model assessments to underpin economic, public health, and environmental policy decisions makes effective model evaluation critical. This paper discusses the properties of black carbon and light attenuation and absorption observations that are the key to a reliable evaluation of black carbon model and compares parametric and nonparametric statistical tools for the quantification of the agreement between models and observations. Black carbon concentrations are simulated with TM5/M7 global model from July 2002 to June 2003 at four remote sites (Alert, Jungfraujoch, Mace Head, and Trinidad Head) and two regional background sites (Bondville and Ispra). Equivalent black carbon (EBC) concentrations are calculated using light attenuation measurements from January 2000 to December 2005. Seasonal trends in the measurements are determined by fitting sinusoidal functions and the representativeness of the period simulated by the model is verified based on the scatter of the experimental values relative to the fit curves. When the resolution of the model grid is larger than 1&amp;deg; &amp;times; 1&amp;deg;, it is recommended to verify that the measurement site is representative of the grid cell. For this purpose, equivalent black carbon measurements at Alert, Bondville and Trinidad Head are compared to light absorption and elemental carbon measurements performed at different sites inside the same model grid cells. Comparison of these equivalent black carbon and elemental carbon measurements indicates that uncertainties in black carbon optical properties can compromise the comparison between model and observations. During model evaluation it is important to examine the extent to which a model is able to simulate the variability in the observations over different integration periods as this will help to identify the most appropriate timescales. The agreement between model and observation is accurately described by the overlap of probability distribution (PD) curves. Simple monthly median comparisons, the Student's t-test, and the Mann-Whitney test are discussed as alternative statistical tools to evaluate the model performance. The agreement measured by the Student's t-test, when applied to the logarithm of EBC concentrations, overestimates the higher PD agreements and underestimates the lower PD agreements; the Mann-Whitney test can be employed to evaluate model performance on a relative scale when the shape of model and experimental distributions are similar
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