Atmosphere — Open Access Journal

Ozone absorbs harmful UV rays at high elevations but acts as a pollutant gas in the lower atmosphere. It is necessary to monitor both the vertical profile and the total column ozone. In this study, variations in the ozone concentration of Pohang were divided into three vertical layers: the stratospheric layer (STL), the second ozone peak layer (SOPL), and the tropospheric layer (TRL). Our results indicated that the ozone concentration in the STL, SOPL, TRL, and total column ozone increased by 0.45%, 2.64%, 5.26%, and 1.07% decade⁻¹, respectively. The increase in the SOPL during springtime indicates that stratosphere–troposphere exchange is accelerating, while the increase during summertime appears to have been influenced by the lower layers. The growth of tropospheric ozone concentration is the result of both increased ozone precursors from industrialization in East Asia and the influx of stratospheric ozone. Our results reaffirmed the trend of ozone concentration in mid-latitudes of the northern hemisphere from vertical profiles in Pohang and, in particular, suggests that the recent changes of ozone in this region need to be carefully monitored. Full article

In this study, twenty large-scale circulation patterns are identified to generate a synoptic classification of Weather Types (WT) over a region that comprises Mexico, the Intra-Americas Seas, Central America, and northern South America. This classification is performed using Self-Organizing Maps (SOMs) with mean sea-level pressure standardized anomalies from reanalysis. The influence of quasi-permanent pressure centers over the region, such as North Atlantic Subtropical High (NASH) and North Pacific High (NPH) are well captured. Seasonal variability of high-pressure centers for dry (November–April) and wet (May–October) periods over the entire region are also well represented in amplitude and pattern among the WTs. The NASH influence and intensification of the Caribbean low-level jet and the North American monsoon system is well captured. During the dry period, a strong trough wind advects cold air masses from mid-latitudes to the subtropics over the western Atlantic Ocean. High-frequency transitions among WTs tend to cluster around the nearest neighbors in SOM space, while low-frequency transitions occur along columns instead of rows in the SOM matrix. Low-frequency transitions are related to intraseasonal and seasonal scales. The constructed catalog can identify near-surface atmospheric circulation patterns from a unified perspective of synoptic climate variability, and it is in high agreement with previous studies for the region. Full article

The performance limits of electronic ultraviolet (EUV) dosimeters, which use AlGaN Schottky photodiodes as the ultraviolet radiation (UVR) sensing element to measure personal erythemally weighted UVR exposures, were investigated via a direct comparison with meteorological-grade reference instruments. EUV dosimeters with two types of AlGaN Schottky photodiode were compared to second-generation ‘Robertson–Berger type’ broadband erythemal radiometers. This comparison was done by calculating correction factors for the deviations of the spectral responsivity of each instrument from the CIE erythemal action spectrum and for deviations in their angular response from the ideal cosine response of flat surfaces and human skin. Correction factors were also calculated to convert the output of these instruments to vitamin D-weighted UV irradiances. These comparisons showed that EUV dosimeters can be engineered with spectral responsivities and cosine response errors approaching those of Robertson–Berger type radiometers, making them very acceptable for use in human UVR exposure and sun safety behaviour studies, provided appropriate side-by-side calibrations are performed. Examples of these calibrations and the effect of EUV dosimeter sampling rates on the calculation of received erythemal UVR doses and erythemal UVR dose rates are provided, as well as brief descriptions of their use in primary skin cancer prevention programmes, handheld meters, and public health displays. Full article

In our previous in vitro research and also in laying hen production, attempts were made to minimise ammonia emissions in poultry houses with the use of Deodoric^® biopreparation. The objective of the present research was to evaluate the influence of the Deodoric^® on ammonia (NH₃) emission and turkey growth performance in a semi-industrial production system. Significant differences in NH₃ emission (p-value < 0.001), body weight (p-value < 0.001) and relative humidity (p-value < 0.001) were observed between the control group (C) and the experimental group (E) where Deodoric® was applied. In group C, an increase in ammonia concentration in air could have contributed to a decrease in the body weight of turkeys, but the above correlation was not observed in group E. In the control group, a relatively strong correlation between NH₃ emission and temperature (p-value = 0.0009; r = 0.74) and moderate correlations between NH₃ emission vs. relative humidity (p-value = 0.01; r = 0.59), air speed (p-value = 0.015; r = 0.60) and cooling (p-value = 0.005; r = 0.66) were noted. Studied correlations were not observed in group E. The preparation did not affect microbial levels in manure or body samples. Throughout the experiment, significant differences in the number of mesophilic bacteria (for the model: F = 46.14, p-value = 0.09; for mesophilic microorganisms: F = 3.29, p-value = 0.045) and Campylobacter spp. (for the model: F = 24.96, p-value = 0.008; for Campylobacter spp.: F = 0.25, p-value = 0.64) were not observed between group C and group E. The administration of Deodoric^® to manure decreased NH₃ concentration in the air and increased weight gains in the experimental group of turkeys relative to group C. Preparation may be applied in poultry farms to improve poultry farming conditions. Full article

Energy-efficient retrofitting of historic housing stock requires methodical approach, in-depth analysis and case-specific regulatory system, yet only limited efforts have been realized. In large scale rehabilitation projects, it is essential to develop a retrofit strategy on how to decide energy-efficient solutions for buildings providing the most energy saving in a short time. This paper presents a pilot study conducted at a neighborhood scale, consisting of 22 pre-, early-republican and contemporary residential buildings in a historic urban fabric in the Mediterranean climate. This study aims to develop an integrated approach to describe case-specific solutions for larger scale historic urban fabric. It covers the building performance simulation (BPS) model and numerical analysis to determine the most related design parameters affecting annual energy consumption. All the case buildings were classified into three main groups to propose appropriate retrofit solutions in different impact categories. Retrofit solutions were gathered into two retrofit packages, Package 1 and 2, and separately, three individual operational solutions were determined, considering a five-levelled assessment criteria of EN 16883:2017 Standard. Energy classes of case buildings were calculated based on National Building Energy Regulations. Changes in building classes were evaluated considering pre- and post-retrofit status of the buildings. For the integrated approach, the most related design parameters on annual energy consumption were specified through Pearson correlation analysis. The approach indicated that three buildings, representing each building group, can initially be retrofitted. For all buildings, while maximum energy saving was provided by Package 2 with 48.57%, minimum energy saving was obtained from Package 1 with 19.8%. Full article

Biological risks potentially affect workers in multiple occupational sectors through their exposure to pathogenic agents. These risks must be carefully assessed to prevent adverse health effects. This article identifies and critically analyzes approaches that manage the qualitative evaluation of biological risk (EvBR) as part of occupational health and safety prevention, for which no standard method yet exists. Bibliographic and computing references were searched to identify qualitative EvBR approaches, which were then analyzed based on defined criteria, such as the risks studied and the type of assessment. Approaches proposing the most representative types of assessment were analyzed. EvBR approaches in an occupational setting were identified in 32 sources. “Workstation analysis” combined with “assessment by risk level” were the most common approaches. The predominant risk descriptors (RDs) were defined in a characterized and quantifiable way, and a variety of hazard levels and exposure indices were created. Overall, the risk was determined by summing or multiplying the hazard level and exposure indicators. The results confirmed that no methodological consensus currently exists regarding the EvBR and no approach has yet been described that integrates all the parameters to allow for a full assessment of biological risk. Based on the detailed analysis of the existing data, the present paper proposes a general approach. Full article

The present study examines the Kerala Flood Event (KFE, 15–16 August 2018, in India) that occurred along the west coast of India and resulted in ~400 mm of rainfall in one day. The KFE was unique in comparison to previous floods in India, not only due to the rainfall duration and amount, but also due to the fact that the dams failed to mitigate the flood, which made it the worst in history. The main goal of this study is to analyze and elucidate the KFE based on meteorological and hydrological parameters. A propagating low-pressure system (LPS) from the Bay of Bengal (BoB) caused the streak of plenty of rainfall over Kerala, the west coast, central India, and the BoB. Additionally, the upper-tropospheric anti-cyclonic system over the Middle East region inhibited a northward advancement of LPS. On the western coast of India, a non-propagating (with diurnal fluctuations) offshore trough was observed over the west coast (from Kerala to Gujarat state). Therefore, a synergic interaction between LPS, an intrusion of dry air in the middle-troposphere, and the offshore trough was the main reason for KFE. However, after around ten days, rainfall saturated the dam capacities; thus, the released water, along with the amount of precipitation on the day of the event, was one of the other possible reasons which worsened the flood over Kerala. Full article

As one of China’s emerging urban agglomerations, the Changzhutan urban area is suffering from regional composite air pollution. Previous studies mainly focus on single cities or world-class urban agglomerations, which cannot provide a scientific basis for air pollution in emerging urban agglomerations. This paper proposes the latest high-resolution emission inventory through the emission factor method and compares the results with the rest of the urban agglomeration. The emission inventory shows that the estimates for sulfur dioxide (SO₂), nitrogen oxides (NO_X), particulate matter 10 (PM₁₀), particulate matter 2.5 (PM_2.5), volatile organic compounds (VOCs), and ammonia (NH₃) emission are 132.5, 148.9, 111.6, 56.5, 119.0, and 72.0 kt, respectively. From the 3 × 3 km emission grid, the spatial difference of air pollutant emissions in the Changzhutan urban agglomeration was more obvious, but the overall trend of monthly pollutant discharge was relatively stable. Depending on the source apportionment, SO₄²⁻, OC, and NO₃⁻ are the main chemical constituents of PM_2.5, accounting for 13.06, 8.24, and 4.84 μg/m³, respectively. Simultaneously, industrial emissions, vehicle exhaust, and dust are still three main sources that cannot be ignored. With the support of these data, the results of this study may provide a reference for other emerging urban agglomerations in air quality. Full article

With the large-scale development of wind energy, wind power forecasting plays a key role in power dispatching in the electric power grid, as well as in the operation and maintenance of wind farms. The most important technology for wind power forecasting is forecasting wind speed. The current mainstream methods for wind speed forecasting involve the combination of mesoscale numerical meteorological models with a post-processing system. Our work uses the WRF model to obtain the numerical weather forecast and the gradient boosting decision tree (GBDT) algorithm to improve the near-surface wind speed post-processing results of the numerical weather model. We calculate the feature importance of GBDT in order to find out which feature most affects the post-processing wind speed results. The results show that, after using about 300 features at different height and pressure layers, the GBDT algorithm can output more accurate wind speed forecasts than the original WRF results and other post-processing models like decision tree regression (DTR) and multi-layer perceptron regression (MLPR). Using GBDT, the root mean square error (RMSE) of wind speed can be reduced from 2.7–3.5 m/s in the original WRF result by 1–1.5 m/s, which is better than DTR and MLPR. While the index of agreement (IA) can be improved by 0.10–0.20, correlation coefficient be improved by 0.10–0.18, Nash–Sutcliffe efficiency coefficient (NSE) be improved by −0.06–0.6. It also can be found that the feature which most affects the GBDT results is the near-surface wind speed. Other variables, such as forecast month, forecast time, and temperature, also affect the GBDT results. Full article

An intensity modulated, continuous-wave (IM-CW) integrated path differential absorption (IPDA) fiber-based lidar is developed herein for measuring atmospheric carbon dioxide (CO₂). There are two main challenges in improving measurement accuracy, which have not been given enough attention in the previous research: one is that temperature sensitivity in optical components causes biases, due to the drift of component characteristic, and the other is that speckle noise deteriorates the signal-to-noise ratio. With the components thermally controlled, a target calibration accuracy of 0.003 dB is realized, corresponding to a CO₂ concentration precision of better than 1 ppm for a 1 km path. A moving diffuser can reduce speckle noise by time averaging. In this paper, movement of the diffuser is substituted by the perturbation of the emitted laser beam by using a vibrating motor mounted on the optical antenna. Selecting on and off wavelengths with a small wavelength separation can improve the correlation between two laser speckle fields. These improvements result in the improved accuracy of the IPDA lidar system. Finally, the lidar performance was analyzed after the improvements described above were implemented. The diurnal variations of the atmospheric CO₂ concentration using a topographic target were performed, and the results showed good agreement with the data measured by an in situ sensor. The root mean square (rms) of the deviation between the IPDA lidar and the in situ sensor was less than 1.4%. Full article

Land Use Regression (LUR) based on multiple linear regression model is one of the techniques used most frequently for modelling the spatial variability of air pollution and assessing exposure in urban areas. In this paper, a nonlinear generalised additive model is proposed for LUR and its performance is compared to a linear model in Sheffield, UK for the year 2019. Pollution models were estimated using NO₂ measurements obtained from 188 diffusion tubes and 40 low-cost sensors. Performance of the models was assessed by calculating several statistical metrics including correlation coefficient (R) and root mean square error (RMSE). High resolution (100 m × 100 m) maps demonstrated higher levels of NO₂ in the city centre, eastern side of the city and on major roads. The results showed that the nonlinear model outperformed the linear counterpart and that the model estimated using NO₂ data from diffusion tubes outperformed the models using data from low-cost sensors or both low-cost sensors and diffusion tubes. The proposed method provides a basis for further application of advanced nonlinear modelling approaches to constructing LUR models in urban areas which enable quantifying small scale variability in pollution levels. Full article

In this work, we applied X-ray Absorption Spectroscopy (XAS) and selective leaching experiments for investigating iron speciation in different dust advections collected on different unwashed quartz fiber filters. XAS analysis evidenced a predominance of Fe(III) in 6-fold coordination for Saharan dust and a trend towards Fe(II) and 4-fold coordination in the order: Saharan dust, mixed Saharan, and non-Saharan aerosol samples. The role of the sampling substrate was evaluated explicitly, including in the analysis a set of blank filters. We were able to pinpoint the possible contribution to the overall XAS spectrum of the residual Fe on quartz as the concentration decrease towards the blank value. In particular, the filter substrate showed a negligible effect on the structural trend mentioned above. Furthermore, selective leaching experiments evidenced a predominance of the residual fraction on Fe speciation and indicated the lowest Fe concentrations for which the blank contribution is <20% are 1 $\mathsf{\mu }$ g for the first three steps of the procedure (releasing the acid-labile, reducible and oxidizable phases, respectively) and 10 $\mathsf{\mu }$ g for the last step (dissolving the insoluble residuals). Full article

The large amount of carbon stored in trees and soils of the Amazon rain forest is under pressure from land use as well as climate change. Therefore, various efforts to monitor greenhouse gas exchange between the Amazon forest and the atmosphere are now ongoing, including regular vertical profile (surface to 4.5 km) greenhouse gas measurements across the Amazon. These profile measurements can be used to calculate fluxes to and from the rain forest to the atmosphere at large spatial scales by considering the enhancement or depletion relative to the mole fraction of air entering the Amazon basin from the Atlantic, providing an important diagnostic of the state, changes and sensitivities of the forests. Previous studies have estimated greenhouse gas mole fractions of incoming air (‘background’) as a weighted mean of mole fractions measured at two background sites, Barbados (Northern Hemisphere) and Ascension (Southern hemisphere) in the Tropical Atlantic, where the weights were based on sulphur hexafluoride (SF₆) measured locally (in the Amazon vertical profiles) and at the two background sites. However, this method requires the accuracy and precision of SF₆ measurements to be significantly better than 0.1 parts per trillion (picomole mole⁻¹), which is near the limit for the best SF₆ measurements and assumes that there are no SF₆ sources in the Amazon basin. We therefore present here an alternative method. Instead of using SF₆, we use the geographical position of each air-mass back-trajectory when it intersects the limit connecting these two sites to estimate contributions from Barbados versus Ascension. We furthermore extend the approach to include an observation site further south, Cape Point, South Africa. We evaluate our method using CO₂ vertical profile measurements at a coastal site in Brazil comparing with values obtained using this method where we find a high correlation (r² = 0.77). Similarly, we obtain good agreement for CO₂ background when comparing our results with those based on SF₆, for the period 2010–2011 when the SF₆ measurements had excellent precision and accuracy. We also found high correspondence between the methods for background values of CO, N₂O and CH₄. Finally, flux estimates based on our new method agree well with the CO₂ flux estimates for 2010 and 2011 estimated using the SF₆-based method. Together, our findings suggest that our trajectory-based method is a robust new way to derive background air concentrations for the purpose of greenhouse gas flux estimation using vertical profile data. Full article

Meteorology and emission sources are the two main factors determining concentrations of air pollutants, including fine particulate matter. A regional air quality modeling system was used to analyze the sources of fine-particulate air pollution in Hanoi, Vietnam, in December 2010. The impacts of precipitation and winds on PM_2.5 concentrations was investigated. Precipitation was negatively correlated with PM_2.5 concentrations. However, winds showed both positive and negative correlations with PM_2.5 concentrations, depending on wind direction (WD) and the level of upwind concentrations. Sensitivity simulations were conducted to investigate the contribution of local and non-local emissions sources on total PM_2.5 by perturbing the emission inputs of the model. Overall, local and non-local sources contributed equally to the total PM_2.5 in Hanoi. Local emission sources comprised 57% of the total PM_2.5 concentrations for the high PM_2.5 pollution levels, while only comprising 42% of the total PM_2.5 for low levels of PM_2.5 concentrations. In Hanoi’s urban areas, local sources contributed more to the total PM_2.5 than non-local sources. In contrast, non-local sources were the main contributors to the PM_2.5 in Hanoi’s rural areas. Additional sensitivity simulations were conducted to identify the main local emission sources of PM_2.5 concentrations in December 2010. The industrial and residential sectors collectively comprised 79% of the total PM_2.5 concentrations while the transport and power sectors comprised only 2% and 3%, respectively. This is the first case study which used a regional air quality modeling system to provide new and informative insights into PM_2.5 air pollution in Hanoi by estimating the contributions of local and non-local emissions sources, as well as the contribution of local emission sectors to PM_2.5 concentrations in Hanoi. Full article

A statistical analysis of 295 cloud samples collected at the Puy de Dôme station in France (PUY), covering the period 2001–2018, was conducted using principal component analysis (PCA), agglomerative hierarchical clustering (AHC), and partial least squares (PLS) regression. Our model classified the cloud water samples on the basis of their chemical concentrations and of the dynamical history of their air masses estimated with back-trajectory calculations. The statistical analysis split our dataset into two sets, i.e., the first set characterized by westerly air masses and marine characteristics, with high concentrations of sea salts and the second set having air masses originating from the northeastern sector and the “continental” zone, with high concentrations of potentially anthropogenic ions. It appears from our dataset that the influence of cloud microphysics remains minor at PUY as compared with the impact of the air mass history, i.e., physicochemical processes, such as multiphase reactivity. Full article

The aim of this study is to estimate wintertime emissions of greenhouse gases CO₂, N₂O and CH₄ in two abandoned peat extraction areas (APEA), Ess-soo and Laiuse, and in two Oxalis site-type drained peatland forests (DPF) on nitrogen-rich sapric histosol, a Norway spruce and a Downy birch forest, located in eastern Estonia. According to the long-term study using a closed chamber method, the APEAs emitted less CO₂ and N₂O, and more CH₄ than the DPFs. Across the study sites, CO₂ flux correlated positively with soil, ground and air temperatures. Continuous snow depth > 5 cm did not influence CO₂, but at no snow or a thin snow layer the fluxes varied on a large scale (from −1.1 to 106 mg C m⁻² h⁻¹). In all sites, the highest N₂O fluxes were observed at a water table depth of −30 to −40 cm. CH₄ was consumed in the DPFs and was always emitted from the APEAs, whereas the highest flux appeared at a water table >20 cm above the surface. Considering the global warming potential (GWP) of the greenhouse gas emissions from the DPFs in the wintertime, the flux of N₂O was the main component of warming, showing 3–6 times higher radiative forcing values than that of CO₂ flux, while the role of CH₄ was unimportant. In the APEAs, CO₂ and CH₄ made up almost equal parts, whereas the impact of N₂O on GWP was minor. Full article

Stratocumulus clouds have a distinctive structure composed of a combination of lumpy cellular structures and thin elongated regions, resembling canyons or slits. The elongated slits are referred to as “spiderweb” structure to emphasize their interconnected nature. Using very high resolution large-eddy simulations (LES), it is shown that the spiderweb structure is generated by cloud-top evaporative cooling. Analysis of liquid water path (LWP) and cloud liquid water content shows that cloud-top evaporative cooling generates relatively shallow slits near the cloud top. Most of liquid water mass is concentrated near the cloud top, thus cloud-top slits of clear air have a large impact on the entire-column LWP. When evaporative cooling is suppressed in the LES, LWP exhibits cellular lumpy structure without the elongated low-LWP regions. Even though the spiderweb signature on the LWP distribution is negligible, the cloud-top evaporative cooling process significantly affects integral boundary layer quantities, such as the vertically integrated turbulent kinetic energy, mean liquid water path, and entrainment rate. In a pair of simulations driven only by cloud-top radiative cooling, evaporative cooling nearly doubles the entrainment rate. Full article

In this study, bias-corrected temperature and moisture retrievals from the Atmospheric Emitted Radiance Interferometer (AERI) were assimilated using the Data Assimilation Research Testbed ensemble adjustment Kalman filter to assess their impact on Weather Research and Forecasting model analyses and forecasts of a severe convective weather (SCW) event that occurred on 18–19 May 2017. Relative to a control experiment that assimilated conventional observations only, the AERI assimilation experiment produced analyses that were better fit to surface temperature and moisture observations and which displayed sharper depiction of surface boundaries (cold front, dry line) known to be important in the initiation and development of SCW. Forecasts initiated from the AERI analyses also exhibited improved performance compared to the control forecasts using several metrics, including neighborhood maximum ensemble probabilities (NMEP) and fractions skill scores (FSS) computed using simulated and observed radar reflectivity factor. Though model analyses were impacted in a broader area around the AERI network, forecast improvements were generally confined to the relatively small area of the computational domain located downwind of the small cluster of AERI observing sites. A larger network would increase the spatial coverage of “downwind areas” and provide increased sampling of the lower atmosphere during both active and quiescent periods. This would in turn offer the potential for larger and more consistent improvements in model analyses and, in turn, improved short-range ensemble forecasts. Forecast improvements found during this and other recent studies provide motivation to develop a nationwide network of boundary layer profiling sensors. Full article

With snow cover changing worldwide in several worrisome ways, it is imperative to determine both the variability in snow cover in greater detail and its relationship with ongoing climate change. Here, we used the satellite-based snow cover extent (SCE) dataset of National Oceanic and Atmospheric Administration (NOAA) to detect SCE variability and its linkages to climate over the 1967–2018 periods across the Northern Hemisphere (NH). Interannually, the time series of SCE across the NH reveal a substantial decline in both spring and summer (−0.54 and −0.71 million km²/decade, respectively), and this decreasing trend corresponded with rising spring and summer temperatures over high-latitude NH regions. Among the four seasons, the temperature rise over the NH was the highest in winter (0.39 °C/decade, p < 0.01). More precipitation in winter was closely related to an increase of winter SCE in mid-latitude areas of NH. Summer precipitation over the NH increased at a significant rate (1.1 mm/decade, p < 0.01), which likely contribute to the accelerated reduction of summer’s SCE across the NH. However, seasonal sensitivity of SCE to temperature changes differed between the Eurasian and North American continents. Thus, this study provides a better understanding of seasonal SCE variability and climatic changes that occurred at regional and hemispheric spatial scales in the past 52 years. Full article