Atmosphere, Volume 8, Issue 8 (August 2017) – 23 articles

Land surface reflectance (LSR) and aerosol types are the two main factors that affect aerosol inversions over land. According to LSR determination methods, Moderate resolution Imaging Spectroradiometer (MODIS) aerosol products are produced using the Deep Blue (DB) and Dark Target (DT) algorithms. Five aerosol types that are determined from Aerosol Robotic Network (AERONET) ground measurements are used to describe the global distribution of aerosol types in each algorithm. To assess the influence of LSR and the method used to determine aerosol type from aerosol retrievals, 10-km global aerosol products that cover 2013 are selected for validation using Level 2.0 aerosol observations from 175 AERONET sites. The variations in the retrieval accuracy of the DB and DT algorithms for different LSR values are analyzed by combining them with a global 10-km LSR database. Meanwhile, the adaptability of the MODIS products over areas covered with different aerosols is also explored. The results are as follows. (1) Compared with DT retrievals, the DB algorithm yields lower root mean squared error (RMSE) and mean absolut error (MAE) values, and a greater number of appropriate sample points fall within the expected error (EE). The DB algorithm shows higher overall reliability; (2) The aerosol retrieval accuracy of the DB and DT algorithms decline irregularly as the surface reflectance increases; the DB algorithm displays relatively high accuracy; (3) Both algorithms have a high retrieval accuracy over areas covered by weak absorbing aerosols, whereas dust aerosols and continental aerosols produce a low retrieval accuracy. The DB algorithm shows good retrieval results for most aerosols, but a lower accuracy for strong absorbing aerosols. Full article

Using the CMIP5 model outputs, a few characteristics of Indian Summer Monsoon (ISM) rainfall and Niño 3.4 temperature are analysed during June–July–August–September (JJAS). Focusing on specified regions around central-northeast India, some general characteristic features of ISM precipitation are studied, which are shown to be varying among models. The trend of decreasing rainfall in that region as noticed in observations suggests an inconsistency among models. The ENSO also shows variation, and its phasing indicates disagreement. Unlike other models, FGOALS-g2 is identified as not suggesting any trend in Niño 3.4 temperature and needs attention for model evaluation purposes. ISM and ENSO correlation in either historical or the RCP 8.5 scenario confirm a negative signature, agreeing with the usual ISM, ENSO connection. Precipitation over the globe shows a rising trend in an ensemble of CMIP5 model outputs for the RCP 8.5 scenario, though no consensus is reached for the Indian region. Precipitation time series around the Indian subcontinent vary widely among models. Analyses with various future scenarios indicate that the Indian subcontinent shows much larger uncertainty, in terms of precipitation, compared to that from the whole world. This study identifies a few areas where CMIP 5 models are in agreement or disagreement with each other. Such an analysis could be useful for understanding various processes in CMIP 5 models that involve ISM precipitation and can lead to improving the representation of processes in models. Full article

A generally accepted value for the Radiation Amplification Factor (RAF), with respect to the erythemal action spectrum for sunburn of human skin, is −1.1, indicating that a 1.0% increase in stratospheric ozone leads to a 1.1% decrease in the biologically damaging UV radiation in the erythemal action spectrum reaching the Earth. The RAF is used to quantify the non-linear change in the biologically damaging UV radiation in the erythemal action spectrum as a function of total column ozone (O₃). Spectrophotometer measurements recorded at ten US monitoring sites were used in this analysis, and over 71,000 total UVR measurement scans of the sky were collected at those 10 sites between 1998 and 2000 to assess the RAF value. This UVR dataset was examined to determine the specific impact of clouds on the RAF. Five de novo modeling approaches were used on the dataset, and the calculated RAF values ranged from a low of −0.80 to a high of −1.38. Full article

Deforestation, followed by abandonment and forest regeneration, has become one of the dominant types of land cover changes in the tropics. This study applied the eddy covariance (EC) technique to quantify the energy budget and evapotranspiration in a regenerated secondary dry dipterocarp forest in Western Thailand. The mean annual net radiation was 126.69, 129.61, and 125.65 W m⁻² day⁻¹ in 2009, 2010, and 2011, respectively. On average, fluxes of this energy were disaggregated into latent heat (61%), sensible heat (27%), and soil heat flux (1%). While the number of energy exchanges was not significantly different between these years, there were distinct seasonal patterns within a year. In the wet season, more than 79% of energy fluxes were in the form of latent heat, while during the dry season, this was in the form of sensible heat. The energy closure in this forest ecosystem was 86% and 85% in 2010 and 2011, respectively, and varied between 84–87% in the dry season and 83–84% in the wet season. The seasonality of these energy fluxes and energy closure can be explained by rainfall, soil moisture, and water vapor deficit. The rates of evapotranspiration also significantly varied between the wet (average 6.40 mm day⁻¹) and dry seasons (3.26 mm day⁻¹). Full article

The meteorological influences on Caribbean air chemistry are studied using in-situ, satellite and model data. Although African dust plumes join locally generated pollutants, concentrations are relatively low in the eastern Caribbean due to geographic remoteness and steady oceanic trade winds. Urban-industrial emissions from big cities (e.g., Kingston, Santo Domingo, San Juan), agricultural emissions from the south, and volcanic emissions from Montserrat contribute a noticeable burden. Conditions over Puerto Rico in the dry season (December–May) provide a focus for statistical analysis of air chemistry constituents and weather variables that describe dispersion conditions. Monthly and daily air indices are formed by summing the normalized values of fine aerosols and particulates, long- and short-lived trace gases from in-situ, satellite and model sources. The spatial correlation of a daily Puerto Rico air index onto regional dewpoint temperature, air pressure and outgoing longwave radiation fields in December–May 2005–2015 reveals the northward movement of a dry tongue and trough. At the climate timescale, Pacific El Nino conditions favor an increase of spring-time air pollution corresponding to anomalous inflow from Africa and the southern Caribbean coast. Composite weather patterns for a group of high air index values reflect divergent trade winds and a strong jet stream that imparts anticyclonic vorticity, subsidence and low humidity. This new understanding will underpin better air quality forecasts for Puerto Rico and the wider Caribbean. Full article

This study proposes an alternative method to estimate the potential predictability without assuming the perfect model. A theoretical consideration relates a maximum possible value of the initial-value error to the covariance between analysis and bias-corrected ensemble-mean forecast. To test the method, the prediction limit of the Madden–Julian Oscillation (MJO) was evaluated, based on three pairs of reanalysis and forecast datasets provided by the European Centre for Medium-Range Weather Forecasting, the Japan Meteorological Agency and the National Centers for Environmental Prediction, participating in the subseasonal-to-seasonal prediction project. The results showed that the predictability was higher when MJO amplitude exceeded unity, consistent with the conventional method in which the error is evaluated as the ensemble-forecast spread. Moreover, the multimodel analysis was also conducted because the proposed method is readily applicable to the multimodel average of ensemble-mean forecasts. The phase dependency of the MJO’s potential predictability is also discussed. Full article

This work reports the chemical and light extinction characteristics of the atmospheric particles collected from January to November 2014 in suburban Nanjing. Size-segregated measurement results showed that more than 80% of the major aerosol components were concentrated in PM_2.5. The concentration of PM_2.5 was highest in winter and lowest in autumn. Specifically, K⁺ concentration peaked in late spring indicating heavy influences from straw burning, while sulfate concentration was highest in summer and its daytime concentration was also higher than its nighttime concentration, both reflecting a significant role of photochemical production. Nevertheless, except for sulfate, all other components had higher concentrations during nighttime, signifying the role of unfavorable meteorological conditions in exacerbating the air pollution. The IMPROVE formula was employed, which can reconstruct the PM_2.5 mass and light extinction well. The light extinction was mainly contributed by (NH₄)₂SO₄ and NH₄NO₃ (together 58.3%). Mass concentrations of all PM_2.5 components increased significantly with the increase of pollution levels, but nitrate increased most rapidly; correspondingly, the contribution of nitrate to light extinction also increased quickly when pollution became heavy. Such results were different from those observed in Beijing-Tianjin-Hebei where sulfate increased most quickly. Our results thus highlight that reduction of vehicular NO₂ is likely a priority for air quality improvement in Nanjing. Back trajectory analysis showed the dominance of the local air mass and the one from Huanghai, yet the air mass originated from Bohai, and passed though Shandong and north of Jiangsu province could deliver highly-polluted air to Nanjing, as well. Full article

The four-dimensional variational data assimilation (4DVar) method is one of the most popular techniques used in numerical weather prediction. Nevertheless, the needs of the adjoint model and the linearization of the forecast model largely limit the wider applications of 4DVar. 4D ensemble-variational data assimilation methods (4DEnVars) exploit the strengths of the Ensemble Kalman Filter and 4DVar, and use the ensemble trajectories to directly estimate four-dimensional background error covariance. This study evaluates the role of the empirical orthogonal function (EOF) analysis in 4DEnVars. The widely-recognized 4DEnVar method DRP-4DVar (the Dimension-reduced projection 4DVar) is adopted as the representation of EOF analyses in this study. The performance of the Dimension-reduced projection 4DVar (DRP-4DVar), 4DEnVar (i.e., another traditional 4DEnVar scheme without EOF transformation), and the Ensemble Transform Kalman Filter (ETKF) was compared to demonstrate the effect of the EOF analysis in DRP-4DVar. Sensitivity experiments indicate that EOF analyses construct basis vectors in eigenvalue space and the dimension reduction in the DRP-4DVar approach helps improve computational efficiency and analysis accuracy. When compared with 4DEnVar and the ETKF, the DRP-4DVar demonstrates similar analysis root-mean-square error (RMSE) to 4DEnVar, whereas it surpasses the ETKF by 22.3%. In addition, sensitivity experiments of DRP-4DVar on the ensemble size, the assimilation window length, and the standard deviation of the initial perturbation imply that the DRP-4DVar with the optimized EOF truncation number is robust to a wide range of the parameters, but extremely low values should be avoided. The results presented here suggest the potential wide application of EOF analysis in the hybrid 4DEnVar methods. Full article

In recent years, main cities in China have been suffering from hazy weather, which is gaining great attention among the public, government managers and researchers in different areas. Many studies have been conducted on the topic of urban air quality to reveal different aspects of the air quality problem in China. This paper focuses on the visualization problem of the big air quality monitoring data of all main cities on a nationwide scale. To achieve the intuitive visualization of this data set, this study develops two novel visualization tools for multi-granularity time series visualization (timezoom.js) and a dynamic symbol declutter map mashup layer for thematic mapping (symadpative.js). With the two invented tools, we develops an interactive web map visualization application of urban air quality data of all main cities in China. This application shows us significant air pollution findings at the nationwide scale. These results give us clues for further studies on air pollutant characteristics, forecasting and control in China. As the tools are invented for general visualization purposes of geo-referenced time series data, they can be applied to other environmental monitoring data (temperature, precipitation, etc.) through some configurations. Full article

Chinese cities are experiencing severe air pollution in particular, with extremely high PM_2.5 levels observed in cold seasons. Accurate forecasting of occurrence of such air pollution events in advance can help the community to take action to abate emissions and would ultimately benefit the citizens. To improve the PM_2.5 air quality model forecasts in China, we proposed a bias-correction framework that utilized the historic relationship between the model biases and forecasted and observational variables to post-process the current forecasts. The framework consists of four components: (1) a feature selector that chooses the variables that are informative to model forecast bias based on historic data; (2) a classifier trained to efficiently determine the forecast analogs (clusters) based on clustering analysis, such as the distance-based method and the classification tree, etc.; (3) an error estimator, such as the Kalman filter, to predict model forecast errors at monitoring sites based on forecast analogs; and (4) a spatial interpolator to estimate the bias correction over the entire modeling domain. One or more methods were tested for each step. We applied five combinations of these methods to PM_2.5 forecasts in 2014–2016 over China from the operational AiMa air quality forecasting system using the Community Multiscale Air Quality (CMAQ) model. All five methods were able to improve forecast performance in terms of normalized mean error (NME) and root mean square error (RMSE), though to a relatively limited degree due to the rapid changing of emission rates in China. Among the five methods, the CART-LM-KF-AN (a Classification And Regression Trees-Linear Model-Kalman Filter-Analog combination) method appears to have the best overall performance for varied lead times. While the details of our study are specific to the forecast system, the bias-correction framework is likely applicable to the other air quality model forecast as well. Full article

This paper describes a study to evaluate the capability of a photochemical grid modeling system to predict changes in ozone concentrations in response to emission changes over a period of several years. The development of regulatory emission control plans to meet air quality standards primarily relies on modeled projections of future-year air quality, although a weight of evidence approach (which takes into account a number of factors including modeling results, model evaluation and other pertinent information such as ambient trends) is recommended and is also typically used as part of the attainment demonstration. Thus, it is important to determine if the modeling system used to project future-year quality can correctly simulate ozone responses to the projected emissions reductions. Uncertainties and errors in modeled projections can lead to erroneous estimates of emissions controls required to attain the standards. We use two existing regulatory modeling databases, employed for forecasting future-year air quality in the South Coast Air Basin (SoCAB) of California, for a number of historical years to evaluate the ability of the system to accurately simulate the observed changes in air quality over a multi-year period. The evaluation results with the older (2012) database show that the modeling system consistently under-predicts the reductions in ozone in response to emission reductions over the years. Model response improves with the newer (2016) database with good agreement at some locations, but the system still tends to under-predict ozone responses by as much as a factor of 2 in recent years for the Basin maximum ozone design value. This suggests that future-year estimates of ozone design values may be overly conservative, resulting in emission controls that are technologically challenging or very expensive to implement. The development of better emission inventories and model inputs is recommended to develop a modeling system that more accurately responds to emission changes. Future regulatory planning should include dynamic evaluation in addition to the traditional operational evaluation of the model to provide more confidence to all stakeholders that the resulting policy decisions are necessary to attain the air quality standards and to protect public health. Full article

The Metropolitan Area of São Paulo (MASP) is the largest megacity in South America, with 21 million inhabitants and more than 8 million vehicles. Those vehicles run on a complex fuel mix, with ethanol accounting for nearly 50% of all fuel sold. That has made the MASP a unique case study to assess the impact of biofuel use on air quality. Currently, the greatest challenge in terms of improving air quality is controlling the formation of secondary pollutants such as ozone, which represents the main air pollution problem in the MASP. We evaluated the temporal trends in the concentrations of ozone, its precursors (formaldehyde, acetaldehyde, and NO₂), CO, and NO, from 2012 to 2016. Formaldehyde and acetaldehyde concentrations were frequently higher in winter than in other seasons, showing the importance of meteorological conditions to the distribution of atmospheric pollutants in the MASP. We found no clear evidence that the recent growth in ethanol consumption in Brazil has affected acetaldehyde concentrations, which are associated with emissions from ethanol combustion. In fact, the formaldehyde/acetaldehyde ratio remained relatively constant over the period studied, despite the change in the fuel consumption profile in the MASP. Full article

This study focuses on the impacts of climate change on rainfall erosivity in the Huai Luang watershed, Thailand. The multivariate climate models (IPCC AR5) consisting of CCSM4, CSIRO-MK3.6.0 and MRI-CGCM3 under RCP4.5 and RCP8.5 emission scenarios are analyzed. The Quantile mapping method is used as a downscaling technique to generate future precipitation scenarios which enable the estimation of future rainfall erosivity under possible changes in climatic conditions. The relationship between monthly precipitation and rainfall erosivity is used to estimate monthly rainfall erosivity under future climate scenarios. The assessment compared values of rainfall erosivity during 1982–2005 with future timescales (i.e., the 2030s, 2050s, 2070s and 2090s). The results indicate that the average of each General Circulation Model (GCM) combination shows a rise in the average annual rainfall erosivity for all four future time scales, as compared to the baseline of 8302 MJ mm ha−1 h−1 year−1, by 12% in 2030s, 24% in 2050s, 43% in 2070s and 41% in 2090s. The magnitude of change varies, depending on the GCMs (CCSM4, CSIRO-MK3.6.0, and MRI-CGCM3) and RCPs with the largest change being 82.6% (15,159 MJ mm ha−1 h−1 year−1) occurring under the MRI-CGCM3 RCP8.5 scenario in 2090s. A decrease in rainfall erosivity has been found, in comparison to the baseline by 2.3% (8114 MJ mm ha−1 h−1 year−1) for the CCSM4 RCP4.5 scenario in 2030s and 2.6% (8088 MJ mm ha−1 h−1 year−1) for the 2050s period. However, this could be considered uncertain for future rainfall erosivity estimation due to different GCMs. The results of this study are expected to help development planners and decision makers while planning and implementing suitable soil erosion and deposition control plans to adapt climate change in the Huai Luang watershed. Full article

This study focused on decadalvariations of extreme precipitation thresholds within a 50-year period (1961–2010) for 250 stations of Iran’s northwest. The 99th percentile was used as the threshold of extreme precipitation. In order to analyze threshold cycles and spatial autocorrelation pattern dominating extreme precipitation thresholds, spectral analysis and Gi (known as HOTSPOT) were used respectively. The results revealed that the highest threshold of extreme precipitation occurred along the Ghoosheh Dagh mountain range. Additionally, in all the five studied decades, the highest positive anomalies were observed in the same region (i.e., the Ghoosheh Dagh). The findings also showed that the intensity of positive spatial autocorrelation pattern of extreme precipitation thresholds experienced a declining trend in recent decades. At the same time, extreme precipitation weighted mean center indicated that they followed an ordered pattern during the studied period. The results of harmonic analysis demonstrated that, in all decades, short-term (2–4 years) and mid-term (4–8 years) cycles of extreme precipitation thresholds were dominated. However, especially the southwest of the studied area, the return period of extreme precipitation thresholds was as long as the studied period, a phenomenon that indicates the existence of a trend in extreme precipitation thresholds of these regions. Full article

This study investigates the temporal dynamics of the drop size distribution (DSD) and its influence on the relationship between the liquid water content (LWC) and the radar reflectivity (Z) in fogs. Data measured during three radiation fog events at the Marburg Ground Truth and Profiling Station in Linden-Leihgestern, Germany, form the basis of this analysis. Specifically, we investigated the following questions: (1) Do the different fog life cycle stages exhibit significantly different DSDs? (2) Is it possible to identify characteristic DSDs for each life cycle stage? (3) Is it possible to derive reliable Z-LWC relationships by means of a characteristic DSD? The results showed that there were stage-dependent differences in the fog life cycles, although each fog event was marked by unique characteristics, and a general conclusion about the DSD during the different stages could not be made. A large degree of variation within each stage also precludes the establishment of a representative average spectrum. Full article

Land surface models (LSMs) are important tools for simulating energy, water and momentum transfer across the land–atmosphere interface. Many LSMs have been developed over the past 50 years, including the Common Land Model (CoLM), a LSM that has primarily been developed and maintained by Chinese researchers. CoLM has been adopted by several Chinese Earth System Models (GCMs) that will participate in the Coupled Model Intercomparison Project Phase 6 (CMIP6). In this study, we evaluate the performance of CoLM with respect to simulating the water and energy budgets. We compare simulations using the latest version of CoLM (CoLM2014), the previous version of CoLM (CoLM2005) that was used in the Beijing Normal University Earth System Model (BNU-GCM) for CMIP5, and the Community Land Model version 4.5 (CLM4.5) against global diagnostic data and observations. Our results demonstrate that CLM4.5 outperforms CoLM2005 and CoLM2014 in simulating runoff (R), although all three models overestimate runoff in northern Europe and underestimate runoff in North America and East Asia. Simulations of runoff and snow depth (SNDP) are substantially improved in CoLM2014 relative to CoLM2005, particularly in the Northern Hemisphere. The simulated global energy budget is also substantially improved in CoLM2014 relative to CoLM2005. Simulations of sensible heat (SH) based on CoLM2014 compare favorably to those based on CLM4.5, while root-mean-square errors (RMSEs) in net surface radiation indicate that CoLM2014 (RMSE = 16.02 W m⁻²) outperforms both CoLM2005 (17.41 W m⁻²) and CLM4.5 (23.73 W m⁻²). Comparisons at regional scales show that all three models perform poorly in the Amazon region but perform relatively well over the central United States, Siberia and the Tibetan Plateau. Overall, CoLM2014 is improved relative to CoLM2005, and is comparable to CLM4.5 with respect to many aspects of the energy and water budgets. Our evaluation confirms CoLM2014 is suitable for inclusion in Chinese GCMs, which will increase the diversity of LSMs considered during CMIP6. Full article

This study aims to establish the influence of primary emission sources and atmospheric transformation process contributing to the concentrations of quinones associated to particulate matter of less than 2.5 µm (PM_2.5) in three sites within the Metropolitan Area of Guadalajara (MAG), namely Centro (CEN), Tlaquepaque (TLA) and Las Águilas (AGU). Environmental levels of quinones extracted from PM_2.5 filters were analyzed using Gas Chromatography coupled to Mass Spectrometry (GC-MS). Overall, primary emissions in combination with photochemical and oxidation reactions contribute to the presence of quinones in the urban atmosphere of MAG. It was found that quinones in PM_2.5 result from the contributions from direct emission sources by incomplete combustion of fossil fuels such as diesel and gasoline that relate mainly to vehicular activity intensity in the three sampling sites selected. However, this also suggests that the occurrence of quinones in MAG can be related to photochemical transformation of the parent Polycyclic Aromatic Hydrocarbons (PAHs), to chemical reactions with oxygenated species, or a combination of both routes. The higher concentration of 1,4-Chrysenequinone during the rainy season compared to the warm-dry season indicates chemical oxidation of chrysene, since the humidity could favor singlet oxygen collision with parent PAH present in the particle phase. On the contrary, 9,10-Anthraquinone/Anthracene and 1,4-Naftoquinone/Naphthalene ratios were higher during the warm-dry season compared to the rainy season, which might indicate a prevalence of the photochemical formation during the warm-dry season favored by the large solar radiation typical of the season. In addition, the estimated percentage of photochemical formation of 9,10-Phenanthrenequinone showed that the occurrence of this compound in Tlaquepaque (TLA) and Las Águilas (AGU) sites is mainly propagated by conditions of high solar radiation such as in the warm-dry season and during long periods of advection of air masses from emission to the reception areas. This was shown by the direct association between the number hourly back trajectories arriving in the TLA and AGU from Centro and other areas in MAG and the highest photochemical formation percentage. Full article

This work made an improvement upon and a further evaluation of previous work for estimating water vapor content from near-infrared around 1 μm from MODIS data. The accuracy of RM-NN is determined by the complicated relationship of the geophysical parameters. An advanced scheme is proposed for building different training databases for different seasons in different regions to reduce the complexity. The training database includes three parts. The first part is a simulation database by MODTRAN for different weather conditions, which is made as a basic database; the second part is reliable field measurement data in observation stations; and the third part is the MYD05_L2 product on clear days, which is produced by the standard product algorithm for water vapor content. The comparative analyses based on simulation data indicate that maximum accuracy of single condition could be improved by about 34% relative to the “all conditions” results. Two study regions in China and America are selected as test areas, and the evaluation shows that the mean and the standard deviation of estimation error are about 0.08 g cm⁻² and 0.09 g cm⁻², respectively. All the analysis indicates that the advanced scheme can improve the retrieval accuracy of water vapor content, which can make full use of the advantages of previous methods. Full article

There has been increasing interest in ice nucleation research in the last decade. To identify important gaps in our knowledge of ice nucleation processes and their impacts, two international workshops on ice nucleation were held in Vienna, Austria in 2015 and 2016. Experts from these workshops identified the following research needs: (1) uncovering the molecular identity of active sites for ice nucleation; (2) the importance of modeling for the understanding of heterogeneous ice nucleation; (3) identifying and quantifying contributions of biological ice nuclei from natural and managed environments; (4) examining the role of aging in ice nuclei; (5) conducting targeted sampling campaigns in clouds; and (6) designing lab and field experiments to increase our understanding of the role of ice-nucleating particles in the atmosphere. Interdisciplinary teams of scientists should work together to establish and maintain a common, unified language for ice nucleation research. A number of commercial applications benefit from ice nucleation research, including the production of artificial snow, the freezing and preservation of water-containing food products, and the potential modulation of weather. Additional work is needed to increase our understanding of ice nucleation processes and potential impacts on precipitation, water availability, climate change, crop health, and feedback cycles. Full article

China faces unprecedented air pollution today. In this study, a database (SO₂, NO₂, CO, O₃, PM_2.5 (particulate matter with aerodynamic diameter less than 2.5 μm), and PM₁₀ (particulate matter with aerodynamic diameter less than 10 μm) was developed from recordings in 188 cities across China in 2014 and 2015 to explore the spatial-temporal characteristics, relationships among atmospheric contaminations, and variations in these contaminants. Across China, the results indicated that the average monthly concentrations of air pollutants were higher from November to February than in other months. Further, the spatial patterns of air pollutants showed that the most polluted areas were located in Shandong, Henan, and Shanxi provinces, and the Beijing-Tianjin-Hebei region. In addition, the average daily concentrations of air pollutants were also higher in spring and winter, and significant relationships between the principal air pollutants (negative for O₃ and positive for the others) were found. Finally, the results of a generalized additive model (GAM) indicated that the concentrations of PM₁₀ and O₃ fluctuate dynamically; there was a consistent increase in CO and NO₂, and PM_2.5 and SO₂ showed a sharply decreasing trend. To minimize air pollution, open biomass burning should be prohibited, the energy efficiency of coal should be improved, and the full use of clean fuels (nuclear, wind, and solar energy) for municipal heating should be encouraged from November to February. Consequently, an optimized program of urban development should be highlighted. Full article

Background: Air pollution is a major global environmental risk factor. Since people spend most of their time indoors, the sole measure of outdoor concentrations is not sufficient to assess total exposure to air pollution. Therefore, the arising interest by the international community to indoor-outdoor relationships has led to the development of various techniques for the study of emission and exchange parameters among ambient and non-ambient pollutants. However, a standardised method is still lacking due to the complex release and dispersion of pollutants and the site conditions among studies. Methods: This review attempts to fill this gap to some extent by focusing on the analysis of the variety of site-specific approaches for the assessment of particulate matter in work and life environments. Results: First, the main analogies and differences between indoor and outdoor particles emerging from several studies are briefly described. Commonly-used indicators, sampling methods, and other approaches are compared. Second, recommendations for further studies based on recent results in order to improve the assessment and management of those issues are provided. Conclusions: This review is a step towards a comprehensive understanding of indoor and outdoor exposures which may stimulate the development of innovative tools for further epidemiological and multidisciplinary research. Full article

An algorithm based on a support vector machine (SVM) is proposed for hydrometeor classification. The training phase is driven by the output of a fuzzy logic hydrometeor classification algorithm, i.e., the most popular approach for hydrometer classification algorithms used for ground-based weather radar. The performance of SVM is evaluated by resorting to a weather scenario, generated by a weather model; the corresponding radar measurements are obtained by simulation and by comparing results of SVM classification with those obtained by a fuzzy logic classifier. Results based on the weather model and simulations show a higher accuracy of the SVM classification. Objective comparison of the two classifiers applied to real radar data shows that SVM classification maps are spatially more homogenous (textural indices, energy, and homogeneity increases by 21% and 12% respectively) and do not present non-classified data. The improvements found by SVM classifier, even though it is applied pixel-by-pixel, can be attributed to its ability to learn from the entire hyperspace of radar measurements and to the accurate training. The reliability of results and higher computing performance make SVM attractive for some challenging tasks such as its implementation in Decision Support Systems for helping pilots to make optimal decisions about changes inthe flight route caused by unexpected adverse weather. Full article

Simple analytical approximations for aerosol radiative forcing generally contain the aerosol upscatter fraction (the fraction of scattered light that is scattered into the upper hemisphere), while ambient measurements generally yield the backscatter fraction, and theoretical calculations of scattering phase functions often yield the asymmetry parameter. Therefore, simple analytical relationships and parameterizations relating these three parameters are very valuable for radiative transfer calculations. Here, we review published parameterizations, mostly based on the Henyey-Greenstein phase function, and evaluate their goodness and range of validity. In addition, we give new parameterizations that are valid over the full range of backscatter fractions that are possibly encountered in the ambient atmosphere (i.e., 0 to 0.5). Full article